2006 Model Submission - Florida State Board of Administration

FLORIDA COMMISSION ON 

HURRICANE LOSS PROJECTION 

METHODOLOGY 

February 2007 Submission 

Prepared by: 

This document contains information proprietary to 

EQECAT, which should be used for the purpose of the 

evaluation of EQECAT hurricane modeling technology 

by the Florida Commission on Hurricane Loss 

Projection Methodology. This document is not to be 

used by any other party nor for any other purpose 

without prior written authorization from EQECAT. 

1

February 23, 2007 

Larry Johnson, Vice Chair 

Florida Commission on Hurricane Loss Projection Methodology 

c/o Donna Sirmons 

Florida State Board of Administration 

1801 Hermitage Boulevard, Suite 100 

Tallahassee, FL 32308 

Frankfurt 

Irvine 

London 

Oakland 

Paris 

Tokyo 

Warrington 

Wilmington 

Dear Mr. Johnson, 

I am pleased to inform you that EQECAT, Inc. is ready for the Commission’s review and recertification 

of its WORLDCATenterprise TM / USWIND ® hurricane model for use in Florida. As 

required by the Commission, enclosed are the data and analyses for the General, 

Meteorological, Vulnerability, Actuarial, Statistical, and Computer Standards, updated to reflect 

compliance with the Standards set forth in the Commission’s Report of Activities as of 

November 1, 2006. In addition, WORLDCATenterprise / USWIND has been reviewed by 

professionals having credentials and/or experience in the areas of meteorology, engineering, 

actuarial science, statistics, and computer science, as documented in the signed Expert 

Certification (Forms G-1 to G-6). 

Note that we are seeking certification for both our standalone software USWIND and our clientserver 

software WORLDCATenterprise, as the hurricane loss model is the same in the two 

platforms. In our submission, for simplicity we refer to the hurricane loss model common to both 

platforms as USWIND, and only refer to specific platform or version numbers as relevant. 

The following change was made to WORLDCATenterprise / USWIND between the submission 

last year (USWIND Version 5.10 / WORLDCATenterprise Version 3.8) and the current 

submission (USWIND Version 5.11 / WORLDCATenterprise Version 3.9): 

1. The probabilistic hurricane database was regenerated to be consistent with the 

Commission’s November 1, 2006 storm set, and to additionally include the 2006 

hurricane season. 

EQECAT is confident that WORLDCATenterprise / USWIND is in compliance with the 

Commission’s standards and is ready to be reviewed by the Professional Team. 

Sincerely, 

EQECAT, Inc. 

David F. Smith 

Director, Technology Development and Consulting 

■ EQECAT, INC., An ABS Group Company • 475 14th Street, 5th Floor, Suite 550 • Oakland, California 94612-1900 USA • Phone 510.817.3100 • Fax 510.663.1048 

2

Enclosures: 

1. 20 bound copies of EQECAT Submission 

2. 20 CDs (labelled ‘FCHLPM – EQECAT 2006’) containing electronic copy of 

EQECAT Submission (FCHLPM_EQECAT2006.pdf) and the following files: 

• 2006FormV2_EQECAT.xls 

• 2006FormA1_EQECAT.xls 






• 2006FormV2_EQECAT.pdf 

• 2006FormA1_EQECAT.pdf 






■ EQECAT, INC., An ABS Group Company • 475 14th Street, 5th Floor, Suite 550 • Oakland, California 94612-1900 USA • Phone 510.817.3100 • Fax 510.663.1048 

3

The Florida Commission on Hurricane Loss Projection Methodology 

Model Submission Checklist 

1. Please indicate by checking below that the following has been included in your 

submission to the Florida Commission on Hurricane Loss Projection Methodology. 

Yes No Item 

X 1. Letter to the Commission 

X 

a. Refers to the Expert Certification Forms and states that professionals 

having credentials and/or experience in the areas of meteorology, 

engineering, actuarial science, statistics, and computer science have 

reviewed the model for compliance with the Standards 

X 

b. States model is ready to be reviewed by the Professional Team 

X 

c. Any caveats to the above statements noted with a complete explanation 

X 2. Summary statement of compliance with each individual Standard and the data 

and analyses required in the Disclosures and Forms 

X 3. General description of any trade secrets the modeler intends to present to the 

Professional Team 

X 4. Model Identification 

X 5. 20 Bound Copies 

X 6. 20 CD ROMs containing: 

X 

a. Submission text in PDF format 

X 

b. PDF file highlightable and bookmarked by Standard, Form, and section 

X 

c. Data file names include abbreviated name of modeler, Standards year, 

and Form name (when applicable) 

X 

d. Forms V-2, A-1, A-3, A-4, A-5, A-6, A-7, and S-5 (for models submitted 

by organizations which have not previously provided the Commission 

with this analysis) in PDF format 

X 

e. Forms V-2, A-1, A-3, A-4, A-5, A-6, and A-7 in Excel format 

X 

f. Form S-5 (for models submitted by organizations which have not 

previously provided the Commission with this analysis) in ASCII format 

X 7. Table of Contents 

X 8. Materials consecutively numbered from beginning to end starting with the first 

page (including cover) using a single numbering system 

X 9. All tables, graphs, and other non-text items specifically listed in Table of 

Contents 

X 10. All tables, graphs, and other non-text items clearly labeled with abbreviations 

defined 

X 11. Standards, Disclosures, and Forms in italics, modeler responses in non-italics 

X 12. Graphs accompanied by legends and labels for all elements 

X 13. All units of measurement clearly identified with appropriate units used 

X 14. Hard copy of all Forms included except Forms A-1 and S-5 

2. Explanation of “No” responses indicated above. (Attach additional pages if needed.) 

USWIND ® 5.11 / 

WORLDCATenterprise TM 3.9 Feb. 23, 2007 

Model Name Modeler Signature Date 

4


Model Identification 

Name of Model and Version: USWIND ® 5.11 / WORLDCATenterprise TM 3.9 

Name of Modeling Organizarion: EQECAT, INC. 

Street Address: 475 14 th Street, Suite 550 

City, State, ZIP Code: Oakland, CA 94612-1900 

Mailing Address, if different from above: 

______________________________________________________________________ 

Contact Person: David F. Smith 

Phone Number: (510) 817-3100 Fax Number: (510) 663-1048 

E-mail Address: dfsmith@eqecat.com 

Date: February 23, 2007 

5


Licenses and Trademarks 

A number of trademarks and registered trademarks appear in this document. EQECAT, 

Inc. acknowledges all trademarks and the rights in the trademarks owned by the 

companies referred to herein. 

• EQECAT ® , USWIND ® , USQUAKE ® , WORLDCATenterprise are 

trademarks of EQECAT, Inc. 

• Windows is a trademark of Microsoft Corporation. 

• MapInfo ® is a trademark of the MapInfo Corporation. MapInfo ® 

contains data which is sublicensed from MapInfo Corporation. MapInfo 

has obtained this data under license from other third party vendors as 

noted below. 

5-Digit ZIP Code data for the United States. Copyright © 1993-2005 

Geographic Data Technologies, Inc. All Rights Reserved. 

5-Digit ZIP Code Data for Puerto Rico. Copyright © 1994-2005 

Geodata Consultants, Inc. All Rights Reserved. 

6


TABLE OF CONTENTS 

2006 STANDARDS 

Page 

GENERAL STANDARDS ..........................................................................10 

G-1 Scope of the Computer Model and Its Implementation............................................................10 

G-2 Qualifications of Modeler Personnel and Consultants ............................................................23 

G-3 Risk Location................................................................................................................................32 

G-4 Independence of Model Components ........................................................................................34 

Form G-1: General Standards Expert Certification ..............................................................................35 

Form G-2: Meteorological Standards Expert Certification ..................................................................36 

Form G-3: Vulnerability Standards Expert Certification ......................................................................37 

Form G-4: Actuarial Standards Expert Certification ............................................................................38 

Form G-5: Statistical Standards Expert Certification...........................................................................39 

Form G-6: Computer Standards Expert Certification...........................................................................40 

METEOROLOGICAL STANDARDS .........................................................41 

M-1 Base Hurricane Storm Set...........................................................................................................41 

M-2 Hurricane Characteristics ...........................................................................................................42 

M-3 Landfall Intensity..........................................................................................................................47 

M-4 Hurricane Probabilities................................................................................................................49 

M-5 Land Friction and Weakening.....................................................................................................51 

M-6 Logical Relationships of Hurricane Characteristics ................................................................58 

Form M-1: Annual Occurrence Rates.....................................................................................................59 

Form M-2: Maps of Maximum Winds......................................................................................................62 

Form M-3: Radius of Maximum Winds...................................................................................................65 

VULNERABILITY STANDARDS...............................................................67 

V-1 Derivation of Vulnerability Functions ........................................................................................67 

V-2 Mitigation Measures.....................................................................................................................73 

Form V-1: One Hypothetical Event.........................................................................................................74 

Form V-2: Mitigation Measures – Range of Changes in Damage .......................................................78 

ACTUARIAL STANDARDS.......................................................................80 

A-1 Modeled Loss Costs ....................................................................................................................80 

A-2 Underwriting Assumptions .........................................................................................................81 

A-3 Loss Cost Projections .................................................................................................................84 

A-4 Demand Surge..............................................................................................................................86 

A-5 User Inputs ...................................................................................................................................87 

A-6 Logical Relationship to Risk.......................................................................................................96 

A-7 Deductibles and Policy Limits ..................................................................................................101 

A-8 Contents......................................................................................................................................106 

A-9 Additional Living Expense (ALE) .............................................................................................108 

A-10 Output Ranges ...........................................................................................................................110 

7


Form A-1: Loss Costs............................................................................................................................112 

Form A-2: Zero Deductible Loss Costs by ZIP Code .........................................................................115 

Form A-3: Base Hurricane Storm Set Average Annual Zero Deductible Statewide Loss Costs ...118 

Form A-4: Hurricane Andrew Percent of Losses................................................................................119 

Form A-5: Distribution of Hurricanes by Size of Loss .......................................................................127 

Form A-6: Output Ranges ......................................................................................................................131 

Form A-7: Percentage Change In Output Ranges ...............................................................................172 

Form A-8: Percentage Change in Output Ranges by County.............................................................175 

STATISTICAL STANDARDS ..................................................................179 

S-1 Modeled Results and Goodness-of-Fit ....................................................................................179 

S-2 Sensitivity Analysis for Model Output .....................................................................................184 

S-3 Uncertainty Analysis for Model Output ...................................................................................187 

S-4 County Level Aggregation ........................................................................................................189 

S-5 Replication of Known Hurricane Losses.................................................................................190 

S-6 Comparison of Projected Hurricane Loss Costs....................................................................191 

Form S-1: Probability of Florida Landfalling Hurricanes per Year.....................................................193 

Form S-2: Probable Maximum Loss (PML)...........................................................................................194 

Form S-3: Five Validation Comparisons...............................................................................................195 

Form S-4: Average Annual Zero Deductible Statewide Loss Costs – Historical versus Modeled .200 

COMPUTER STANDARDS .....................................................................202 

C-1 Documentation ...........................................................................................................................202 

C-2 Requirements .............................................................................................................................203 

C-3 Model Architecture and Component Design...........................................................................204 

C-4 Implementation...........................................................................................................................205 

C-5 Verification..................................................................................................................................207 

C-6 Model Maintenance and Revision ............................................................................................209 

C-7 Security .......................................................................................................................................210 

Appendix 1 - Credentials of Selected Personnel .................................................................................211 

Appendix 2 - Independent Review.........................................................................................................214 

TABLES 

Table 1. Key classes of the USWIND wind speed and damage calculation........................................19 

Table 2. Example damage to loss simulation.......................................................................................104 

Table 3. Comparison of point location observations with model-generated winds .......................180 

FIGURES 

Figure 1. Flowchart – USWIND Probabilistic Analysis..........................................................................17 

Figure 2. Flowchart – USWIND Hazard and Damage Calculation Procedure .....................................18 

Figure 3. Flowchart - Object Deployment for USWIND Hazard and Damage Calculations................20 

Figure 4. Business Workflow Diagram ...................................................................................................28 

Figure 5. USWIND vs. Kaplan-DeMaria Inland Decay Rates.................................................................55 

Figure 6a. 1916-13 Hurricane ...................................................................................................................56 

Figure 6b. Hurricane Charley (2004) .......................................................................................................56 

8


Figure 6c. Wind field for Hurricane Wilma (2005) ..................................................................................57 

Figure 7. State of Florida and Neighboring States by Region..............................................................60 

Figure 8. Hurricane Frequencies by Category by Region.....................................................................61 

Figure 9. Contour Map - Maximum Winds For Modeled Version Of Official Hurricane Set ..............63 

Figure 10. Contour Map - Maximum Winds For 100-Year Return Period From Stochastic Storm Set. 

Wind Speeds Are One-Minute Sustained Mph. .............................................................................64 

Figure 11. Rmax vs. Central Pressure ....................................................................................................66 

Figure 12. Flowchart – Vulnerability Development................................................................................69 

Figure 13. Loss Cost Relationships by Coverage .................................................................................97 

Figure 14. Loss Costs for Coastal Counties ..........................................................................................99 

Figure 15. Integration of Uncertainty on Hazard and Damage ...........................................................101 

Figure 16. Integration of Damage Distribution to Calculate Loss......................................................102 

Figure 17. Relationship Between Building and Contents Losses......................................................107 

Figure 18. Ground-up Loss Costs for Frame Structures ....................................................................115 

Figure 19. Ground-up Loss Costs for Masonry Structures ................................................................116 

Figure 20. Ground-up Loss Costs for Mobile Home Structures.........................................................117 

Figure 21. State of Florida by North/Central/South Regions..............................................................172 

Figure 22. State of Florida by Coastal/Inland Counties ......................................................................173 

Figure 23. Uncertainty Analysis for Frequency ...................................................................................181 

Figure 24. Goodness-of-fit for Translational Speed ............................................................................182 

Figure 25. Goodness-of-fit for Hurricane Frequency in Florida.........................................................182 

Figure 27. Historical vs. Modeled Losses for Companies A to F.......................................................196 

Figure 28. Historical vs. Modeled Losses by LOB for Company C....................................................197 

Figure 29. Historical vs. Modeled Losses by County for Company D...............................................198 

Figure 30. Historical vs. Modeled Losses by LOB for Company E....................................................199 

9


General Standards 

GENERAL STANDARDS 

G-1 Scope of the Computer Model and Its Implementation 

The computer model shall project loss costs for personal lines residential 

property from hurricane events. 

USWIND projects loss costs for personal lines residential property from hurricane 

events. 

For purposes of the Commission’s review and determination of acceptability, the 

loss costs submitted for this review are expected losses resulting from 

hurricanes. Wind losses resulting from a hurricane are included even if wind 

speeds fall below hurricane force. The vulnerability functions are based to a large 

degree on hurricane claims data, which includes wind speeds above and below 

the hurricane threshold of 74 mph. 

Expected loss costs include primary structure, appurtenant structures, contents, 

other covered personal property, and additional living expenses. 

Disclosures 

1. Specify the model and program version number. 

USWIND Version 5.11 / WORLDCATenterprise Version 3.9. 

2. Provide a concise, technical description of the model including each major 

component of the model used to produce personal lines residential loss costs in the 

State of Florida. Describe the theoretical basis of the model and include a 

description of the methodology, particularly the wind components, the damage 

components, and the insured loss components used in the model. The description 

should be complete and not reference unpublished work. 

General description of WORLDCATenterprise / USWIND 

WORLDCATenterprise is EQECAT’s global catastrophe management 

software, covering over 80 countries and the perils of hurricane / typhoon / 

cyclone, windstorm, winterstorm, tornado, hail, wildfire, earthquake (ground 

shaking, fire following, sprinkler leakage, workers comp), flood and terrorism. 

The WORLDCATenterprise platform is a networked, multi-user, client server 

architecture enabling enterprise-wide analysis using centralized and sharable 

databases. WORLDCATenterprise uses a cost efficient industry standard 

computer infrastructure that can easily expand to meet growing user demand. 

WORLDCATenterprise uses standard PCs for end user ‘clients’ running 

ordinary internet browsers. All users are networked to standard Windows 

10



based servers which can be configured in scalable clusters to provide higher 

performance and capacity. 

WORLDCATenterprise enables insurer and reinsurer analysis of multiple 

perils for over 80 countries. A single product platform and user interface 

provides primary, facultative, treaty underwriting and accumulation 

management capability across all lines of business with aggregation up to the 

corporate level. WORLDCATenterprise also provides underwriters with 

important information about risk volatility and the impact of writing a new 

program on available capacity to enable real-time portfolio optimization. 

One of the components of WORLDCATenterprise, and also available as 

standalone software, is USWIND, a probabilistic model designed to estimate 

damage and insured losses due to the occurrence of hurricanes along the 

3100 miles of US coastline from Texas to Maine. USWIND estimates the full 

probabilistic distribution of damage and loss for any scenario storm event. 

USWIND also calculates Average Annual Damage and Loss estimates, as 

well as annual probability exceedances using a database of 511,500 

stochastic storm simulation results to develop average annual loss rates for 

each property site. Scenario and average annual damage and losses can be 

calculated for individual property sites or for entire portfolios of residential and 

commercial properties. 

Scenario storms are used to estimate expected and probable maximum 

damage and loss due to a single event. USWIND models damage and loss 

due to scenario storms in several ways. Any of the approximately 100 years 

of historical storms contained in the storm database can be selected by users 

to calculate damage and loss. In addition, the USWIND Landfall Series 

enables users to select scenario storms by stating either the return period or 

the SSI intensity level desired and choosing from any or all of 3,100 individual 

landfalls from Texas to Maine. This enables users to define from 16 different 

storm types at each of 3,100 landfalls (or 49,600 different scenario events). A 

third scenario storm option enables users to define a virtually unlimited variety 

of user defined storm events. User-defined Storms can be created by 

modifying historical tracks or drawing entirely new tracks and then specifying 

the storm parameter values to be utilized. User-defined storms are often used 

to model incoming storms at a variety of potential landfall locations. 

Probabilistic Annual Damage & Loss is computed using the results of 511,500 

stochastic storm simulation results. Annual damage and loss estimates are 

developed for each individual site and aggregated, if desired, to overall 

portfolio damage and loss amounts. USWIND’s climatological models are 

based on NOAA (National Oceanic & Atmospheric Administration)/NWS 

(National Weather Service) Technical Reports. Climatological probability 

distributions (i.e., for storm parameters) were developed using an Adaptive 

Kernel Smoothing technique applied to the historical hurricane record 

11



published by NOAA and by the Commission’s November 1, 2006 Florida 

Storm Set. 

Overall Model Methodology 

USWIND modeling methodology can be segmented into four components: 1) 

the Hazard definition, 2) Propagation of the hazard to a site, 3) Damage 

estimate, and 4) Loss estimation. 

1. Hazard Definition 

The storm database used by USWIND is a combination of historical and 

stochastic storms. Wind speed probabilistic distributions are calculated using 

the probabilistic distributions of all important storm parameters. The 

proprietary wind speed equation was based upon the NOAA model as 

published in NWS 23 and NWS 38. The NOAA model was modified and 

generalized to properly simulate wind speeds for all categories of storms, 

from a weak SSI 1 through a strong SSI 5. The NOAA model was further 

modified to properly simulate storm decay and wind attenuation due to friction 

and filling as a storm moves inland from landfall point. The equation 

computes wind speeds using pressure, the filling rate, radius to maximum 

winds, the angle of attack, translation speed, the gradient to sustained winds, 

the gust factor, the storm profile (attenuation of wind speed outward from the 

center), and the friction caused by local terrain and man-made structures. 

2. Propagation of the Hazard to the Site 

USWIND utilizes an embedded commercial GIS (Geographic Information 

System), MapInfo, to compute the latitude and longitude of each site 

analyzed. The street address level, where such data is available, is used to 

geocode to the lat./long. coordinates. Failing the presence of a street 

address, the geocoding can be done at a ZIP Code, City or County centroid 

basis. Wind speed distributions at the site locations are computed taking local 

friction and distance to coast into account. 

3. Estimation of Damage 

USWIND provides the facility to define each of the property assets being 

analyzed in order to compute resulting damage. Damage can be calculated 

for Structure, Contents, Time Element (such as Additional Living Expense 

(ALE) or Business Interruption (BI)) and up to three additional user defined 

coverage types. Site information includes the latitude and longitude of the 

locations, the structure types (96 types), structure details such as number of 

stories, insured value, cladding type and a class of occupancy type (12 

types). Damage is estimated using vulnerability functions associated with the 

12



structure definition and occupancy type and the distribution of peak gust wind 

speeds at each site. The vulnerability functions used by USWIND have been 

derived through three methods: empirical data, expert opinion, and 

engineering analysis. 

The probabilistic distribution of damage (for each coverage and site) is 

derived through the integration of the probabilistic distribution of wind speeds 

for the site with the probabilistic distributions of damage for given wind 

speeds. Damage distributions for each of the sites are aggregated into an 

overall portfolio distribution of damage. 

Since there can be a high degree of damage correlation for similar structure 

types within a geographic area, USWIND properly takes into account site and 

coverage level correlations when aggregating individual site damage into an 

overall portfolio damage amount. 

4. Estimation of Loss 

Insurance information in the form of insured values, limits, deductibles and 

facultative and/or treaty reinsurance are then integrated with the probabilistic 

distribution of computed damage for each site to determine the probabilistic 

distribution of “insured loss” amount. Correlation is properly taken into 

account when aggregating individual site loss into an overall portfolio loss 

amount. 

Reports 

USWIND produces a vast array of management information, more than 200 

reports in all. Report categories include: 

Underwriting. TIV and premium can be mapped by geographical 

segmentation (state, county or ZIP Code) or reported by corporate 

segmentation (company, division, branch, line of business, policy type, 

producer, account, policy or site). Profiles of the deductibles and limits in the 

portfolio can also be displayed. 

Scenario Storms. Damage (ground-up effects), gross loss (including 

deductibles and limits), net loss (including facultative reinsurance) can be 

reported at all of the levels noted in the underwriting reports. Mean values 

and an upper bound corresponding to a prescribed non-exceedance level are 

provided. 

Probabilistic. In a manner similar to Scenario Storms, the damage, gross loss, 

and net loss can be reported, including non-exceedances. Additional reports 

displaying portfolio damage and loss for different non-exceedance levels, for 

either annual aggregate or per occurrence analysis methods, are available. 

13



Reinsurance. Scenario and probabilistic results are displayed by reinsurer 

(including facultative reinsurance) or by treaty. Probabilistic results include the 

probability of penetrating and exceeding treaty layers. 

Landfall Series. An abbreviated set of reports is available from running a 

series of storms against the portfolio. The series of storms can be either of 

uniform intensity (as denoted by the SSI scale) or uniform recurrence levels. 

The storm series can have landfalls at 1, 10 or 35 mile intervals. 

Probability Distributions 

In many instances, probability distributions have been developed from 

historical data, e.g., storm parameters such as radius to maximum winds, 

forward speed, etc.; and vulnerability functions. The goodness-of-fit tests 

used to compare modeled distributions of various parameters with the 

underlying historical data will be presented to the professional team during 

the on-site review. 

Sensitivity and Uncertainty Analyses 

Many sensitivity and uncertainty analyses have been performed in the 

development of USWIND. For example, sensitivity analyses have been 

performed on track spacing; on the number of attack angles given landfall; on 

the number of wind speed class intervals given landfall and attack angle; and 

on the number of other storm parameter samples used in the stochastic 

hurricane database. A number of uncertainty analyses have been performed 

as well, including studies on the impact of vulnerability uncertainty on the loss 

exceedance curve. The sensitivity and uncertainty analyses EQECAT has 

performed will be presented to the professional team during the on-site 

review. 

Software/Hardware 

WORLDCATenterprise 

The requirements for the WORLDCATenterprise (WCe) hardware 

configuration consist of a Master Server and one or more Analysis Servers. 

Applications running on the Master Server include the Master database, the 

Web Server, and the Java Server. EQECAT processes, including the 

importing of portfolio data and some analyses also run on the Master Server. 

Master database: Contains WCe System tables, customer portfolio data, and 

final analysis results. 

Web Server: Handles communications between the remote Client PCs and 

communicates with the Java Server. 

14



Java Server: Manages the activities performed on the Master and Analysis 

Server(s) and the Master and Results Databases. 

The Analysis Server houses the Results Database, containing the 

intermediate results tables, and runs most of the analysis calculations. WCe 

users access the Master Server via Internet Explorer web browsers 

commonly installed on the Client PCs. The Client PC may access the system 

via the LAN or via a WAN/Internet. 

Minimum Client Requirements: 

• Operating System: Windows 2000 Professional or Windows XP. 

• Processor: 1.5 GHz or higher. 

• RAM: 256 MB minimum (512 MB is recommended). 

• Microsoft Office 97 or later (Office is only required if using the spreadsheet 

import option in WCe). 

• Browser: Microsoft Internet Explorer Version 5.5 or later 

• Monitor: Screen resolution of 1024 by 768 or greater; screen color depth 

of 256 colors or greater. 

Minimum Server Hardware Requirements: 

(Master Server and Analysis Server(s)) 

• Operating System: Windows 2000 Advanced Server (SP3) or Windows 

2003 Server 

• Processors: Dual (2), Pentium 4, 2.4 GHz or higher CPUs. 

• RAM: 4 GB. 

• Hard Drives: Capacity to house five or six 70 Gigabyte drives. 

• NTFS File System 

• CDROM: 24 X internal CDROM for installation. 

• Network Card: Compatible with network, 1.0 GHz. 



USWIND 

• Operating System: Windows 2000 or Windows 2003 Server 

• CPU: One Pentium 4 Processor, 2.0 GHz or higher. 

• RAM: 1 GB. 

• Hard Drive: 100 GB. 

• NTFS File System 

• CDROM: 24 X internal CDROM for installation. 

• Network Card: Compatible with network, 1.0 GHz. 



15



The model structure is translated to the program structure using Object 

Oriented Design and Analysis methodology. Physical and abstract entities in 

the model structure are mapped to objects of the program structure. The 

interactions between objects are captured using Flowcharts and Event 

diagrams. Object oriented practices (data encapsulation, abstraction, 

inheritance and polymorphism) are extensively used to derive the benefits of 

Object Oriented approach. 

USWIND’s climatological models are based on NOAA/NWS Technical 

Reports [Schwerdt, et. al.; Ho, et. al.]. Climatological probability distributions 

(i.e., for storm parameters) were developed using Adaptive Kernel Smoothing 

[Scott] applied to the historical hurricane record published by NOAA 

[Jarvinen, et. al.; Cry] and by the Commission’s November 1, 2006 Florida 

Storm Set. The maximum wind speed and overwater wind field modeling was 

developed from NOAA/NWS equations [Schwerdt, et. al.], with some 

empirical adjustment to the “Observed/Gradient Wind Ratio” in order to 

generalize the equations for lower intensity storms. USWIND uses 

NOAA/NWS methods [Schwerdt, et. al.] as a starting point for calculating 

local overland frictional wind speed reduction, but the specific categories of 

surface roughness were augmented with additional information [Simiu and 

Scanlan]. Vulnerability relationships were developed from several sources, 

including observed damage relationships in historical storms [Friedman 1972, 

1984; numerous Travelers Insurance Company internal memoranda] and 

engineering studies [McDonald-Mehta]. The simulation methodology 

combines several standard techniques including physical modeling [Friedman 

1975], Monte Carlo simulation [Metropolis and Ulam] and Variance Reduction 

Techniques [Kahn; Rubinstein]. The evaluation of loss costs and other risk 

measures is based on standard actuarial theory [Beard, et. al.]. 

3. Provide a flow diagram that illustrates interactions among major model 

components. 

USWIND is a complex system made up of many components, databases, and 

data files. The flowcharts, class diagrams, and tables on the following pages 

summarize the key aspects of the system. These aspects include the 

representation of physical entities of the hurricane catastrophe domain (e.g. 

storm, site, portfolio, etc.) as classes and objects within the program (Figure 

1); the procedural flow of information and steps within the program (Figure 2); 

and the exchange of information among various components of the system 

(e.g. portfolio tables, storm database, results tables, etc.) (Table 1 and Figure 

3). 

16



FLOWCHART - USWIND PROBABILISTIC ANALYSIS 

Climatology 

Database 

Track/Pressure 

Joint Distribution 

Probabilistic 

Storm Data Base 

(511,500 storms) 

Wind Speed Joint 

Distribution 

Other Storm 

Parameter Joint 

Distribution 

Site Specific 

Parameters 

Joint Distribution 

Land Use 

Database 

Wind Field 

Samples 

Portfolio 

Description 

Damage 

Calculation 

Loss 

Calculation 

Vulnerability 

Functions 

Insurance 

Structures 

Risk Curve 

Output Reports 

WSJD_2_07.ppt 

Figure 1. Flowchart – USWIND Probabilistic Analysis 

17



Begin 

Portfolio 

Tables 

Read Site's Information 

from Database 

Compute Hazard at the 

Site 

Compute Damage to 

the Site due to 

calculated hazard 

Results 

Tables 

Output hazard and 

Damage Results 

End of Portfolio 

No 

Yes 

End 

Figure 2. Flowchart – USWIND Hazard and Damage Calculation Procedure 

18



TABLE 1. 

KEY CLASSES OF THE USWIND WIND SPEED AND DAMAGE CALCULATION 

Class 

Cportfolio 

No. of 

Instances 

Once 

Owner(s) 

Main() 

Csite Multiple CPortfolio 

CstormPeril Multiple CPortfolio 

Cstorm Multiple CStormPeril 

CsiteWindHa 

zard 

CsiteWindDa 

mage 

Once 

Once 

CStormPeril 

CSiteWindH 

azard, 

CStormPeril 

Responsibilities 

• Principal object that serves as starting 

point. 

• Connects to Database. 

• Opens Input and Output tables. 

• Performs static initializations (like 

loading binary files into memory) 

• Creates CSite objects (one at time) 

• Creates the Peril objects 

(CStormPeril) 

• Analyzes the portfolio using the Peril 

objects 

• Holds site specific information 

• Calculates information necessary for 

performing hazard and damage 

computations. 

• Represents the Peril 

• Loads storm information from 

Database and prepares the storm 

• Uses CSiteWindHazard object to 

perform hazard calculations 

• Holds the storm information read from 

Database. 

• Calculates storm parameters 

necessary for subsequent 

computations. 

• Calculates hazard from a given Storm 

to a given Site. 

• Uses CStormPeril, CStorm, CSite 

objects to perform hazard calculations 

• Calculates damage to a site from a 

given hazard. 

• Uses CSite, CSiteWindHazard and 

other objects (e.g. CCoverage for 

coverage information, CDamage for 

damage curves, CResult for storing 

results information etc.) 

19



CPortfolio 

object 

1. Create a CSite object 

with information read about 

the site from Portfolio 

Tables 

CSite Object 

(sitepar.cpp) 

2. Ask CStormPeril objects 

to analyze this site 

uses 

uses 

CStormPeril 

Object 

(storm.cpp) 

Creates CSiteWindHazard 

object to perform Hazard 

Calculations 

uses 

CSiteWindHazard 

object 

(sitewind.cpp) 

uses 

CSiteWindDamage 

object 

(sitewdmg.cpp) 

Creates a 

CSiteWindDamage object 

to perform Damage 

Calculations 

Figure 3. Flowchart - Object Deployment for USWIND Hazard and Damage Calculations 

4. Provide a comprehensive list of complete references pertinent to the submission by 

Standard grouping, according to professional citation standards. 

List of References: 

Meteorology Standards 

Cry, G. W. (1965). Tropical Cyclones of the North Atlantic Ocean, Technical 

Paper No. 55, U.S. Department of Commerce, Weather Bureau, 

Washington, DC. 

Franklin, J.L., M.L. Black, and K. Valde (2003). “GPS dropwindsonde wind 

profiles in hurricanes and their operational implications”, Weather and 

Forecasting, Vol. 18, No. 1, pp. 32-44. 

20



Ho, F. P., Su, J. C., Hanevich, K. L., Smith, R. J., and Richards, F. P. (1987). 

Hurricane Climatology for the Atlantic and Gulf Coasts of the United 

States, NOAA Technical Report NWS 38, U.S. Department of Commerce, 

National Oceanographic and Atmospheric Administration, National 

Weather Service, Washington, DC. 

Houston, S.H., and M.D. Powell (2003). “Surface wind fields for Florida Bay 

Hurricanes”, Journal of Coastal Research, Vol. 19, pp. 503-513. 

Jarvinen, B. R., Neumann, C. J., and Davis, M. A. S. (1984). A Tropical 

Cyclone Data Tape for the North Atlantic Basin, Technical Memorandum 

NWS NHC 22, National Oceanic and Atmospheric Administration and 

National Weather Service, Washington, DC. 

Krayer, W.R., and Marshall, R.D., (1992). “Gust factors applied to hurricane 

winds,” Bulletin of the American Meteorological Society, Vol. 73, No. 5, pp. 

613-617. 

Landsea, C. W. et al (2004). “A Reanalysis of Hurricane Andrew’s Intensity,” 

Bulletin of the American Meteorological Society, Vol. 85, No. 11, pp. 1699- 

1712. 

Powell, M.D., D. Bowman, D. Gilhousen, S. Murillo, N. Carrasco, and R. St. 

Fluer (2004). “Tropical Cyclone Winds at Landfall”, Bulletin of the 

American Meteorological Society, Vol. 85, No. 6, pp. 845-851. 

Schwerdt, R. W., Ho, F. P., and Watkins, R. R. (1979). Meteorological Criteria 

for Standard Project Hurricane and Maximum Probable Hurricane Wind 

Fields, Gulf and East Coasts of the United States, NOAA Technical Report 

NWS 23, U.S. Department of Commerce, National Oceanographic and 

Atmospheric Administration, National Weather Service, Washington, DC. 

Vulnerability Standards 

Fujita, T. T. (1992). “Damage survey of Hurricane Andrew in south Florida,” 

Storm Data, Vol. 34, pp. 25–30. 

McDonald-Mehta Engineers (1993). Vulnerability Functions for Estimating 

Wind Damage to Buildings, for EQE Engineering and Design, Texas Tech 

University, Lubbock, TX. (Available on-site, only) 

Simiu, E. and Scanlan, R. H. (1996). Wind Effects on Structures, John Wiley 

and Sons, New York, NY. 

Actuarial Standards 

Friedman, D. G. (1972). "Insurance and the natural hazards," 9th ASTIN 

Colloquium, International Congress of Actuaries, Randers, Denmark, 

International Journal for Actuarial Studies in Non-Life Insurance and Risk 

Theory, Amsterdam, The Netherlands, Vol. VII, Part 1, pp. 4-58. 

Friedman, D. G. (1984). "Natural hazard risk assessment for an insurance 

program," The Geneva Papers on Risk and Insurance, Vol. 9, pp. 57-128. 

21



Statistical Standards 

Beard, R. E., Pentikäinen, T., and Pesonen, E. (1984). Risk Theory: The 

Stochastic Basis of Insurance, London: Chapman and Hall. 

Kahn, H. (1950). “Modifications of the Monte Carlo method,” in Proceedings, 

Seminar on Scientific Computations, November 16-18, 1949, Hurd, C. C., 

ed., pp. 20-27, International Business Machines, New York, NY. 

Metropolis, N., and Ulam, S. (1949). “The Monte Carlo method,” Journal of 

the American Statistical Association, Volume 44, page 335. 

Rubinstein, R. Y. (1981). Simulation and the Monte Carlo Method, John Wiley 

and Sons, New York, NY. 

Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and 

Visualization, John Wiley and Sons, New York, NY. 

Computer Standards 

Friedman, D. G. (1975). Computer Simulation in Natural Hazard Assessment, 

Monograph NSF-RA-E-75-002. Institute of Behavioral Sciences, University 

of Colorado, Boulder, CO. 

5. Provide a detailed description of all changes in the model from the prior year’s 

submission. 

The following change was made to WORLDCATenterprise / USWIND between 

the submission last year (USWIND Version 5.10 / WORLDCATenterprise Version 

3.8) and the current submission (USWIND Version 5.11 / WORLDCATenterprise 

Version 3.9): 

1. The probabilistic hurricane database was regenerated to be consistent with the 

Commission’s November 1, 2006 storm set, and to additionally include the 2006 

hurricane season. 

22



G-2 Qualifications of Modeler Personnel and Consultants 

A. Model construction, testing, and evaluation shall be performed by 

modeler personnel or consultants who possess the necessary skills, 

formal education, or experience to develop the relevant components 

for hurricane loss projection methodologies. 

The model construction, testing, and evaluation was performed by a team of 

individuals who possess the necessary skills, formal education, and experience 

to develop hurricane loss projection methodologies, and who abide by the 

standards of professional conduct adopted by their profession. 

B. The model or any modifications to an accepted model shall be 

reviewed by either modeler personnel or consultants in the following 

professional disciplines: structural/wind engineering (licensed 

Professional Engineer), statistics (advanced degree), actuarial science 

(Associate or Fellow of Casualty Actuarial Society), meteorology 

(advanced degree), and computer/information science (advanced 

degree). These individuals shall be signatories on Forms G-1 through 

G-6 as applicable and shall abide by the standards of professional 

conduct if adopted by their profession. 

The model and all modifications to it have been reviewed by modeler personnel 

or consultants in the following professional disciplines, if relevant: structural/wind 

engineering (licensed Professional Engineer), statistics (advanced degree), 

actuarial science (Associate or Fellow of Casualty Actuarial Society), 

meteorology (advanced degree), and computer/information science (advanced 

degree). These individuals are signatories on Forms G-1 through G-6 as 

applicable and abide by the standards of professional conduct if adopted by their 

profession. 

Disclosures 

1. Organization Background 

A. Describe the ownership structure of the modeling organization. Describe 

affiliations with other companies and the nature of the relationship, if any. 

Indicate if your organization has changed its name and explain the 

circumstances. 

EQECAT, Inc. is a wholly owned subsidiary of ABS Group, Inc. 

B. If the model is developed by an entity other than a modeling company, describe its 

organizational structure and indicate how proprietary rights and control over the 

model and its critical components is exercised. If more than one entity is involved 

in the development of the model, describe all involved. 

23



USWIND is developed by EQECAT, Inc., a modeling company. 

C. If the model is developed by an entity other than a modeling company, describe 

the funding source for the model. 

USWIND is developed by EQECAT, Inc., a modeling company. 

D. Describe the modeler’s services. 

EQECAT, Inc. provides a complete range of catastrophe management 

services: portfolio analysis; consulting on product pricing, structure, and 

underwriting guidelines; training for underwriters and loss control staff on 

critical structural details; securitization; information on scientific 

developments and hazard investigations from its own research and via 

links to key sites on the World Wide Web from the EQECAT home page; 

engineering evaluations of major individual risks; assistance with large 

claims settlements; and hazard modeling software. 

E. Indicate how long the model has been used for analyzing insurance company 

exposures or other such uses. Describe these uses. 

USWIND has been used since 1995 to assist companies in establishing 

their Probable Maximum Loss (PML), managing claim response after 

hurricanes, and structuring reinsurance treaties. 

F. Indicate if the modeling organization has ever been involved in litigation or 

challenged by a statutory authority where the credibility of one of its U. S. 

hurricane model versions was disputed. Describe the nature of the case and the 

conclusion. 

EQECAT has engaged in a review of the model with the Hawaii Insurance 

Division, which is a requirement for all hurricane loss models to be used in 

residential rate filings in Hawaii. 

In December 2005, EQECAT sent an addendum to the Hawaii Insurance 

Division Questionnaire (Memorandum 2003-3R). EQECAT subsequently 

received a reply correspondence from the Hawaii Insurance Division, 

containing a series of follow-up questions. EQECAT submitted its 

response to the Division on February 14, 2007. These follow-up questions 

are of clarification nature and have no bearing on any aspect of the model 

applicable to Florida. 

24



2. Professional Credentials 

A. Provide in a chart format (a) the highest degree obtained (discipline and 

University), (b) employment or consultant status and tenure in years, and (c) 

relevant experience and responsibilities of individuals involved in the primary 

development of or revisions to the following aspects of the model: 

1. Meteorology 

2. Vulnerability 

3. Actuarial Science 

4. Statistics 

5. Computer Science 

The tables below summarize the credentials for the individuals involved in 

the development and maintenance of USWIND. More detailed credentials 

for selected personnel are provided in Appendix 1. 


Name 

Apoorv Dabral 

Mahmoud 

Khater 

Andreas Mueller 

David Smith 

Qing Xia 

Krishnaraj 

Santhanam 

Highest Degree 

Ph.D. Wind Science and 

Engineering, Texas Tech 

University 

Ph.D. Structural Engineering 

Cornell University 

Ph.D. Physics 

Univeristy of Karlsruhe, 

Germany 

M.S. Geophysics 

Yale University 

Ph.D. Meteorology 

University of Chicago 

Ph.D Meteorology 

Saint Louis University 

Employee 

Since 

Relevant Experience 

2007 Wind engineering 

1988 

Model design, probabilistic 

analysis 

2002 Meteorology 

1994 

Meteorology, hurricane 

analysis 



25




Name 

James R. (Bob) 

Bailey 

Apoorv Dabral 

Jun-Rong Huo 

Omar Khemici 


Ph.D. Civil Engineering 

Texas Tech University 

Ph.D. Wind Science and 

Engineering, Texas Tech 

University 


Institute of Engineering 

Mechanics, State 

Seismological Bureau, 

China 


Stanford University 

Employee 

Since 

Consultant 


Wind engineering 

2007 Wind engineering 

1998 Structural engineering 

1990 Structural engineering 


Name 

Shawna 

Ackerman, 

FCAS, MAAA 


B.A. Mathematics 

Oregon State University 

Employee 

Since 

Consultant 


Actuarial science 

4. Statistics 

Name 

James Johnson 

Petros 

Keshishian 

Mahmoud 

Khater 

Nilesh Shome 

David Smith 



University of Illinois 


University of California, 

Berkeley 


Cornell University 


Stanford University 



Employee 

Since 

Consultant 


Probabilistic analysis 

1999 Probabilistic analysis 

1988 


analysis 

1999 Probabilistic analysis 

1994 


analysis 

26




Name 

Branimir Betov 

Phil Burtis 

Richard Clinton, 

CPCU 

Kent David 

Ray Kincaid 

Tom Larsen 

Jason Mok 

Sergey 

Pasternak 

Anu Sandhu 

David Smith 

Kerry 

Zimmerman 


M.S. Electrical Engineering 

Technical University of 

Sofia, Bulgaria 

B.S. Electrical Engineering 

Iowa State University 

B.A. Mathematics 

California State University, 

Long Beach 

M.S. Structural Analysis and 

Design 


Berkeley 

M.B.A. 

Pepperdine University 

M. Eng. Structural 

Engineering 


Berkeley 

B.S. Computer Engineering 

San Jose State University 

B.S. Electrical Engineering 

Petrochemical and Gas 

Industry Institute, Moscow, 

Russia 

M.B.A. Marketing and 

Finance 

Symbiosis Institute of 

Management Studies, Pune, 

India 



B.S. Computer Science 

California State University, 

San Luis Obispo 

Employee 

Since 


1998 Software development 


1993 

1987 

Insurance underwriting, 

software product 

management 

Software quality 

assurance 


1989 

Model design, software 

development, software 

product management 

2006 Software development 


2004 

1994 

Software product 

management 

Model design, software 

development 


B. Identify any new employees or consultants (since the previous submission) 

working on the model. 

Jason Mok and Apoorv Dabral joined the EQECAT model development 

team in August 2006 and January 2007, respectively. Bob Bailey has 

served as a consultant since leaving ABS Group in March 2006. 

C. Provide visual business workflow documentation connecting all personnel related 

to model design, testing, execution, maintenance, and decision-making. 

27



See Figure 4 below. 

Product 

Manage 

ment 

(Tom 

Larsen, 

Anu 

Sandhu) 

Methodology Development 

(Mahmoud Khater, 

David Smith) 

GUI Development 

(Ray Kincaid, 

Phil Burtis) 

Model Development 


David Smith) 

Configuration Managemnt 

(Ray K ncaid, 

Phil Burtis) 

Documentation/Publications 


David Smith, 

Ray Kincaid, 

Phil Burtis) 

Quality Assurance 

(Kent David) 

Shipping 

(Ray Kincaid, 

Phil Burtis) 

Customer 

Customer Service 

(Tom Larsen) 

Figure 4. Business Workflow Diagram 

D. Indicate specifically whether individuals listed in A. and B. are associated with 

the insurance industry, consumer advocacy group, or a government entity as well 

as their involvement with consulting activities. 

None of the individuals listed in A. and B. including the consultants 

Shawna Ackerman (credentials above); Dr. James Johnson (credentials 

above and in Appendix 1); and Dr. James R. (Bob) Bailey are associated 

28



with the insurance industry, a consumer advocacy group, or a government 

entity. 

Shawna Ackerman a principal and consulting actuary with a private 

actuarial firm, provides advice in the area of actuarial science. 

Dr. James Johnson provides advice in the area of probabilistic analysis. 

Dr. James R. (Bob) Bailey provides support in the area of mitigation 

measures within vulnerability. 

3. Independent Peer Review 

A. Provide dates of external independent peer reviews that have been performed on 

the following components as currently functioning in the model: 




4. Statistics 



Dr. Don Friedman performed a review of the meteorological aspects of the 

model in 1995. His concerns were as follows: (1) the method used to 

generate a visual representation of the wind patterns (a “friction index”) 

over a geographic area produces some discontinuities which might make 

a user suspicious of the credibility of the underlying analysis. (2) The 

degree of variation between predicted wind speeds versus measured wind 

speeds in several test cases indicates that there are varying degrees of 

uncertainty in the various estimated wind speed patterns. This is not 

surprising, because Dr. Friedman believes “that there are inherent 

deficiencies in the sole use of information from National Weather Service 

Reports, and that all models suffer from the same inadequacies.” He 

stated that it is up to the modeler to decide whether that output is of 

sufficient accuracy to provide a realistic basis for estimating the loss 

producing potential of past and future hurricanes for the purposes for 

which the results are to be used. We feel that the accuracy is sufficient for 

the purpose. We have addressed the concern about the visual 

representation of the wind patterns and related suggestions for the 

improvements made by Dr. Friedman. 


Dr. Kishor Mehta, Dr. James McDonald, and Dr. C. Allin Cornell performed 

independent reviews of the vulnerability model in 1995. 

29




Discussed in conjunction with Statistics below. 

4. Statistics 

Dr. C. Allin Cornell and Dr. Richard Mensing reviewed the overall 

methodology and technical approach in 1995. Their comments were as 

follows: Cornell - suggested we make the procedure more transparent in 

order to facilitate communication and learning by the users - “simple, brute 

force Monte Carlo simulation is about as straight-forward as you can be... 

but you are doing something smarter and hence more difficult to grasp.” 

Further suggestions were for a thorough sensitivity study and ideas for the 

treatment of uncertainty. Mensing - “Overall, I believe the methodology 

represents a very good approach to a probabilistic analysis of the 

damages and losses associated with hurricanes.” His suggestions were to 

review the treatment of uncertainty and verify the adequacy of the portfolio 

input data. Additional studies were done to address these issues prior to 

the release of USWIND. 

Mr. Peter Kelly and Dr. Lixin Zeng of Arkwright Mutual Insurance 

Company reviewed all aspects of the USWIND model in their paper ‘The 

Engineering, Statistical, and Scientific Validity of EQECAT USWIND 

Modeling Software’ in 1996. They stated the following in their review: 

“The validity of EQECAT USWIND modeling software is reviewed 

from several perspectives. Using several external sources for 

hurricane data, it is found that the storm data set represents the 

historical and expected long term storm patterns well and generally 

without bias. By reviewing storm damage estimates against a 

theoretical understanding of the wind effects on structures as well as 

actual experience, it was found that the model’s damage estimates 

reasonably reflect the physical properties of force and damage and 

that the system has no systematic bias in its damage estimation 

logic.” 

A copy of their review is provided in Appendix 2. 


Daryl Orts and Chuck Walrad performed independent reviews of the 

computer science aspects of the model in 1998. 

B. Provide documentation of independent peer reviews directly relevant to the 

modeler’s responses to the current Standards, Disclosures, or Forms. Identify 

any unresolved or outstanding issues as a result of these reviews. 

30



Refer to the Appendix for documentation. There are no unresolved or 

outstanding issues resulting from the reviews. 

C. Describe the nature of any on-going or functional relationship the organization 

has with any of the persons performing the independent peer reviews. 

Dr. Cornell has also done a peer review on our USQUAKE model. Dr. 

Mensing was a full-time employee of EQECAT for several years and 

continues as a consultant to EQECAT, although he was an independent 

consultant at the time he performed the review described above. Drs. 

Cornell, Mensing, and Friedman were compensated for their time by 

EQECAT. 

4. Provide a completed Form G-1, General Standards Expert Certification. 

5. Provide a completed Form G-2, Meteorological Standards Expert Certification. 

6. Provide a completed Form G-3, Vulnerability Standards Expert Certification. 

7. Provide a completed Form G-4, Actuarial Standards Expert Certification. 

8. Provide a completed Form G-5, Statistical Standards Expert Certification. 

9. Provide a completed Form G-6, Computer Standards Expert Certification. 

See Forms G-1 to G-6 at the end of this section. 

31



G-3 Risk Location 

A. ZIP Codes used in the model shall be updated at least every 24 months 

using information originating from the United States Postal Service. 

The United States Postal Service issue date of the updated information 

shall be reasonable. 

The USWIND ZIP Code database was updated in February 2006, based on 

information originating from the United States Postal Service current as of August 

2005. EQECAT will update the ZIP Code database in the USWIND model at 

least every 24 months. 

B. ZIP Code centroids, when used in the model, shall be based on 

population data. 

The ZIP Code centroids used in USWIND are derived using population. 

C. ZIP Code information purchased by the modeler shall be verified by the 

modeler for accuracy and appropriateness. 

EQECAT verifies each new ZIP Code database through a suite of procedures, 

including automated numeric tests and visual tests. 

Disclosures 

1. List the current ZIP Code databases used by the model and the components of the 

model to which they relate. Provide the effective (official United States Postal 

Service) date corresponding to the ZIP Code databases. 

USWIND uses Dynamap 5-Digit ZIP Codes distributed by MapInfo. The 

source of the data is Geographic Data Technology, Inc. (GDT). GDT created 

the data using a combination of its DYNAMAP/2000 data, the United States 

Postal Service (USPS) ZIP+4 Data File, the USPS National 5-Digit ZIP Code 

and Post Office Directory, USPS ZIP+4 State Directories, and the USPS City 

State File. 

The ZIP Code data is used in the import component of the model. 

The effective date of the ZIP Code data is August 2005. 

2. Describe in detail how invalid ZIP Codes are handled. 

Invalid ZIP Codes in input data are generated from many sources, including 

(a) typographical errors in the insurers’ data, (b) usage of mailing address 

instead of site address, or (c) usage of an out of date ZIP Code. The 

32



USWIND program attempts to locate any invalid sites to the most refined level 

possible, the data quality permitting. At the end of the ‘geocoding’ process, 

USWIND echoes the status of the quality of the data, indicating how many 

locations were mapped to the street address level, to ZIP Code centroids, city 

centroids, and to county centroids. 

In addition, if users are uncertain of the quality of street address information, 

they can enter latitude and longitude coordinates. 

The steps in the geocoding process are as follows: 

1. If the street address is available, the program attempts to geocode the 

location to its exact location, to within approximately 400 feet in most 

urban areas. 

2. If the program was unable to calculate the exact street location, the 

program looks at the site ZIP Code. If the input ZIP Code exactly matches 

a ZIP Code in our database, the geocoding stops. 

3. If the exact ZIP Code was not matched, the program then looks through 

the database of ‘point’ ZIP Codes. Point ZIP Codes indicate Post Office 

boxes or private entities who desire their own ZIP Code. The location of 

these point ZIP Codes is provided by the US Government. For displaying 

maps of exposure and losses, these ZIP Codes are also ‘mapped’ to 

regional ZIP Codes which correspond to the ZIP Code area which the 

point ZIP Code is in. 

4. If the location is still not found, the program next looks at the city name in 

the input data. If the city name was included in the input data, and the city 

name is in the USWIND databases, then the location is geocoded to a city 

centroid, and the geocoding summary is updated to indicate this. 

5. If the location is still not found, the program next looks at the county name 

in the input data. If the county name was included in the input data, and 

the county name is in the USWIND databases, then the location is 

geocoded to a county centroid, and the geocoding summary is updated to 

indicate this. 

6. If the data provided fails these steps then the risk is removed from the 

database. 

33



G-4 Independence of Model Components 

The meteorological, vulnerability, and actuarial components of the model 

shall each be theoretically sound without compensation for potential bias 

from the other two components. 

The meteorology, vulnerability, and actuarial components of USWIND have been 

independently developed, verified, and validated. The meteorology component, 

completely independent of the other components, calculates wind speed at each 

site. 

The vulnerability component is entirely independent of all other calculations, e.g. 

meteorological, loss, etc. Validation of the vulnerability functions has been 

performed independently from other validation tests, e.g. whenever the 

vulnerability functions have been validated using claims data from a historical 

storm, the wind field for that storm has first been validated independently. If any 

of the other calculation modules were changed, no changes would be necessary 

to the vulnerability functions. 

The loss distributions are calculated using the damage distribution at each site 

and the policy structure. Finally, the site distributions (damage and loss) are 

combined statistically to estimate the expected annual loss and the loss 

exceedance curve for the portfolio. All components together have been validated 

and verified to produce reasonable and consistent results. 

34



Form G-1: General Standards Expert Certification 

I hereby certify that I have personally reviewed the submission of USWIND Version 5.11 / 

WORLDCATenterprise Version 3.9 for compliance with the 2006 Standards adopted by the 

Florida Commission on Hurricane Loss Projection Methodology and hereby certify: 

1) that the model meets the General Standards (G1 – G4), 

2) that the Disclosures and Forms related to the General Standards section contain accurate, 

reliable, unbiased, and complete information, 

3) that my review was completed in accordance with the professional standards and code of 

ethical conduct for my profession, and 

4) that in expressing my opinion I have not been influenced by any other party in order to 

bias or prejudice my opinion. 

Mahmoud Khater, Chief Technical Officer 

Name 


Professional Credentials (Area of Expertise) 

Signature (original submission) 

Date 

Signature (response to Deficiencies, if any) 

Date 

Signature (final submission) 

Date 

An updated signature is required following modifications to the model and any revisions to the 

original submission. 

NOTE: A facsimile or any properly reproduced signature will be acceptable to meet this 

requirement. 

35



Form G-2: Meteorological Standards Expert Certification 




1) that the model meets the Meteorological Standards (M1 – M6), 

2) that the Disclosures and Forms related to the Meteorological Standards section contain 

accurate, reliable, unbiased, and complete information, 





David Smith, Director 

Name 

MS Geophysics 



Date 


Date 


Date 




requirement. 

36



Form G-3: Vulnerability Standards Expert Certification 




1) that the model meets the Vulnerability Standards (V1 – V2), 

2) that the Disclosures and Forms related to the Vulnerability Standards section contain 






Omar Khemici, Director 

Name 

Ph.D., P.E Civil Engineering 



Date 


Date 


Date 




requirement. 

37



Form G-4: Actuarial Standards Expert Certification 




1) that the model meets the Actuarial Standards (A1 – A10), 

2) that the Disclosures and Forms related to the Actuarial Standards section contain 






Shawna Ackerman, Consulting Actuary 

Name 

FCAS, MAAA 



Date 


Date 


Date 




requirement. 

38



Form G-5: Statistical Standards Expert Certification 




1) that the model meets the Statistical Standards (S1 – S6), 

2) that the Disclosures and Forms related to the Statistical Standards section contain 






Mahmoud Khater, Chief Technical Officer 

Name 




Date 


Date 


Date 




requirement. 

39



Form G-6: Computer Standards Expert Certification 




1) that the model meets the Computer Standards (C1 – C7), 

2) that the Disclosures and Forms related to the Computer Standards section contain 






Branimir Betov, Senior Software Engineer 

Name 

M.S. Electrical Engineering 



Date 


Date 


Date 




requirement. 

40


Meteorological Standards 


M-1 Base Hurricane Storm Set 

For validation of landfall and by-passing storm frequency in the stochastic 

storm set, the modeler shall use the latest updated Official Hurricane Set or 

the National Hurricane Center HURDAT as of June 1, 2006 or later. 

Complete additional season increments based on updates to HURDAT 

approved by the Tropical Prediction Center/National Hurricane Center are 

acceptable modifications to these storm sets. Peer reviewed atmospheric 

science literature can be used to justify modifications to the Base 

Hurricane Storm Set. 

The storm set used is the Official Hurricane Set provided by the Commission on 

November 1, 2006, with the 2006 hurricane season additionally included. 

Disclosures 

1. Identify the Base Hurricane Storm Set, the release date, and the time period 

included for landfall and by-passing storm frequencies. 

The storm set used is the Official Hurricane Set provided by the Commission 

on November 1, 2006, with the 2006 hurricane season additionally included. 

2. If the modeler has modified the Base Hurricane Storm Set, provide justification for 

such modifications. 

EQECAT has not modified the Base Hurricane Storm Set. In updating the 

model, EQECAT has included the 2006 hurricane season in addition to the 

period 1900 through 2005. No hurricanes made landfall in or close bypass to 

the mainland United States during 2006. 

41



M-2 Hurricane Characteristics 

Methods for depicting all modeled hurricane characteristics, including but 

not limited to wind speed, radial distributions of wind and pressure, 

minimum central pressure, radius of maximum winds, strike probabilities, 

tracks, the spatial and time variant wind fields, and conversion factors, 

shall be based on information documented by currently accepted scientific 

literature. 

The modeling of hurricane characteristics is based on information documented 

by currently accepted scientific literature or on EQECAT analyses of 

meteorological data. 

Disclosures 

1. Identify the hurricane characteristics (e.g., central pressure or radius of maximum 

winds) that are used in the model. Describe the historical data used for each of these 

characteristics identifying all storms used. 

The following parameter descriptions all pertain specifically to the USWIND 

probabilistic analysis. Use of USWIND in a ‘user storm’ scenario mode may 

allow much greater flexibility in some parameters (i.e., landfall location, track 

direction, etc.) than the discrete, categorized values used in the probabilistic 

database. 

Hurricane Parameters in the Model: 

1. Landfall Location: Landfall points run along the coastline every 10 nautical 

miles from south of the Texas-Mexico border through Maine, and 

generally follow the NWS-38 smoothed coastal mileposts. There are 310 

discrete landfall points used to develop the probabilistic hurricane data 

set. The Florida coast runs from landfall #84 (Escambia county, FL- 

Alabama border), through #180 (Nassau county, FL-Georgia border). That 

is, from coastal milepost 840 through 1800. The historical data used is the 

Commission's Official Hurricane Set of November 1, 2006, and HURDAT. 

All hurricanes in the Official Hurricane Set were used, and the Official 

Hurricane Set takes precedence over HURDAT with regard to information 

contained in both data sets. 

2. Track Direction: Each landfall location will have different bounds on the 

limits of the storm direction depending on the regional coastline 

orientation. At each landfall point, the probabilistic data set scenarios use 

three equiprobable track directions, represented by the 16 2/3 rd , 50 th , and 

83 1/3 rd percentiles on the coastline-dependent smoothed direction 

distributions. The historical data used is information contained in NOAA 

Technical Report NWS 38, updated through the 1998 hurricane season 

with information from HURDAT. All hurricanes in the Official Hurricane Set 

from 1900 through 1998 were used. 

42



3. Maximum One-Minute Sustained Wind Speed: The maximum one-minute 

sustained wind speed is the main parameter used to define hurricane 

intensity, and is one of the most critical items when considering loss 

sensitivity. The possible range in landfall values is from 74 mph to 180 

mph, although the model will run at lower values (weaker storms) to 

accommodate inland filling. The storm strength is driven directly from the 

coastline-dependent smoothed wind speed distributions generated from 

the information in the Commission’s November 1, 2006 Storm Set. All 

hurricanes in the Official Hurricane Set were used. 

4. Radius of Maximum Winds: This is the distance from the geometric center 

of the storm to the region of highest winds, typically within the eye wall of 

a well-developed hurricane. This parameter, after landfall location and 

central pressure (storm strength), is the next most critical in terms of loss 

sensitivity. It can range from 4 to 60 miles, and is statistically dependent 

on coastline location and storm strength. The high end of this range, 

however, would only be applicable in the Northeastern U.S. / New 

England area. The historical data used is information contained in NOAA 

Technical Report NWS 38, updated through the 2004 hurricane season 

with information from the National Hurricane Center's Tropical Cyclone 

Reports and Advisories. All hurricanes in the Official Hurricane Set were 

used. 

5. Translational Speed: This is the speed of the movement of the entire 

storm system itself. It is generally responsible for the asymmetry of a 

hurricane’s wind field. It also has an effect on the distance which the 

highest winds are carried inland as the time-dependent filling weakens the 

storm. This parameter can range from about 4 mph to 50 mph, though the 

high end of this range would only apply in the Northeastern / New England 

region. The parameter is statistically dependent on coastline location and 

storm strength, and in Florida, averages about 12-14 mph. The historical 

data used is information contained in NOAA Technical Report NWS 38, 

updated through the 2004 hurricane season with information from the 

National Hurricane Center's Tropical Cyclone Reports. All hurricanes in 

the Official Hurricane Set were used. 

6. Filling Rate (inland decay rate): Overland attenuation (filling) is described 

by exponential decay of the hurricane central pressure deficit (difference 

between the background pressure and the storm central pressure). The 

filling rate is the parameter specifying the rate of this exponential decay. 

The historical data used is information contained in NOAA Technical 

Report NWS 38. 

7. Profile Factor: This is a dimensionless shape parameter that varies the 

drop-off of winds outward from the hurricane’s eye. Since an individual 

hurricane’s profile may differ from the average, this parameter allows the 

user to best fit an actual storm’s profile when modeling the specific event. 

43



In the probabilistic data set, however, its default value of ‘1’ is used to 

generate an average profile that is dependent on the radius of maximum 

winds, as per NWS-23. 

8. The model also considers air density, Coriolis, and gradient to sustained 

wind speed, among other variables. 

2. Describe the dependencies among variables in the wind field component and how 

they are represented in the model. 

The model considers the radius of maximum winds to be dependent on 

central pressure for hurricanes with central pressure < 930 mb. 

We have analyzed the dependence of the radius of maximum winds (Rmax) 

on central pressure (P0) using the empirical data taken from NWS 38 Tables 

1 and 2. For storms with P0 greater than 930 mbar, we have not found any 

statistically significant correlation between Rmax and P0. This result is 

consistent with the findings of NWS 38. Therefore, for storms with P0 greater 

than 930 mbar, we use Rmax as a function of landfall only, as given by NWS 

38 Figures 37 and 38. 

For stronger storms with P0 less than 930 mbar, we have found a statistically 

significant correlation between P0 and Rmax. This is consistent with the 

results of NWS 38. Therefore, below 930 mbar, we use a piecewise linear 

relationship to model the dependence of Rmax on P0. This information is 

reflected in Form M-2. 

Aside from this dependency, all variables in the wind field component of the 

model are considered to be independent. 

3. Describe the process for converting gradient winds to surface winds including the 

treatment of the inherent uncertainties in the conversion factor with respect to 

location of the site compared to the radius of maximum winds over time. Justify the 

variation of the gradient to surface winds conversion factor relative to hurricane 

intensity. 

The maximum one-minute sustained 10-meter wind speed, not the central 

pressure or the gradient wind speed, is used as input into the wind field 

model. The spatial and temporal variations in the wind field, including their 

uncertainties, are modeled at the surface (10-meter) level, based on historical 

data. 

Gradient winds are considered in the model only in terms of a gradient-level 

force balance that determines the relationship between central pressure and 

wind speed. This force balance includes the maximum rotational gradientlevel 

10-minute sustained wind speed. This is the only gradient-level wind 

44



speed in the model, so spatial variation of the relationship between gradientlevel 

and surface (10-meter) level wind speed is not relevant. 

USWIND converts 10-minute sustained gradient-level wind speeds 

(considered to be at the 700 mb level, or approximately 10,000 feet) to 10- 

minute sustained 10-meter wind speeds by multiplying by 0.9. 

USWIND converts 10-minute sustained 10-meter wind speeds to one-minute 

sustained 10-meter wind speeds by dividing by 0.863, according to Simiu and 

Scanlan (1996), Figure 2.3.10. 

4. Describe how the wind speeds generated in the wind field model were converted 

from sustained to gust and identify the average time. 

USWIND converts one-minute sustained 10-meter wind speeds to peak gust 

10-meter wind speeds using a gust factor function that takes surface friction 

from land use and land cover into account (rougher terrain has a higher gust 

factor). The uncertainty on the gust factor depends on the input one-minute 

sustained wind speed (higher wind speeds have less uncertainty on the gust 

factor). The averaging interval for gust wind speeds is defined as 2 seconds. 

5. Describe how the asymmetric nature of hurricanes is considered in the model. 

The asymmetric nature of hurricanes is accommodated within the model’s 

wind speed equations, following NWS 23. The equations include a term 

corresponding to the translational speed of the hurricane, which depends on 

the geometrical relationship between the location analyzed and the hurricane 

track. 

6. Describe the stochastic hurricane tracks and discuss their appropriateness. 

Describe the historical data used as the basis for the model’s hurricane tracks. 

In the probabilistic database, three equally weighted track directions are 

simulated at each of the 10 nmi-spaced landfall segments used in the 

analysis. These three directions correspond to the 16 2/3rd, 50th, and 83 

1/3rd percentiles of the smoothed coastline-dependent track direction 

distributions. The direction distributions were developed from information 

available in the NOAA Technical Report NWS 38, updated through the 1998 

hurricane season with information on the HURDAT (storm track) file or other 

scientifically accepted publications. 

7. Describe how the coastline is segmented (or partitioned) in determining the 

parameters for hurricane frequency used in the model. Provide the hurricane 

frequency distribution by intensity for each segment. 

In the probabilistic analysis, the coast is divided into a series of 10 nautical 

mile (nmi) segments. The landfall frequency is a smooth curve developed 

along the entire coast using an adaptive smoothing procedure on the milepost 

45



locations of the historic storm set landfalls. Distributions of the other modeling 

parameters were similarly developed. Frequencies, parameters, and 

distributions thus change smoothly from one segment to the next. For 

hurricane frequency distributions by intensity, see Form M-1. 

8. For hurricane characteristics modeled as random variables, describe the 

probability distributions. 

The joint probability distribution for landfall location, track direction, and 

maximum one-minute sustained wind speed is obtained from a Maximum 

Likelihood Estimation kernel smoothing technique applied to the historical 

data. Radius to maximum winds and translational speed are modeled using 

lognormal distributions, the parameters of which vary smoothly along the 

coast. Filling rate is modeled using a normal distribution. 

9. Identify any changes in the functional representation of hurricane characteristics 

during an individual storm event life cycle (e.g., discontinuous intensity reduction at 

the coastal boundary and inland). 

The EQECAT model has no changes in the functional representation of 

hurricane characteristics during an individual storm event life cycle, although 

local wind speeds are modified according to frictional effects, often resulting 

in substantial changes in wind speeds over short distances, particularly near 

the coast. 

10. Describe how your model’s wind field is consistent with the inherent differences in 

wind fields for such diverse storms as Hurricane Charley, Hurricane Katrina, and 

Hurricane Wilma, for example. 

The parameters used to define a hurricane in the EQECAT wind field model 

provide enough control to capture a wide variety of storm characteristics. 

Obvious features such as the landfall location, storm track, and intensity of 

the storm in terms of one-minute sustained winds are included, and further 

definition of the event is provided by the radius to maximum winds and profile 

factor to describe the ‘width’ of the storm, and by the translational speed to 

describe the asymmetry between the right and left sides of the storm. All of 

these parameters can vary widely from event to event. 

46



M-3 Landfall Intensity 

Models shall use maximum one-minute sustained 10-meter wind speed 

when defining hurricane landfall intensity. This applies both to the Base 

Hurricane Storm Set used to develop landfall strike probabilities as a 

function of coastal location and to the modeled winds in each hurricane 

which causes damage. The associated maximum one-minute sustained 10- 

meter wind speed shall be within the range of wind speeds (in statute miles 

per hour) categorized by the Saffir-Simpson scale. 

Saffir-Simpson Hurricane Scale: 

Category Winds (mph) Damage 

1 74 - 95 Minimal 

2 96 - 110 Moderate 

3 111 - 130 Extensive 

4 131 - 155 Extreme 

5 Over 155 Catastrophic 

USWIND uses maximum one-minute sustained 10-meter wind speed when 

defining hurricane landfall intensity. 

The USWIND pressure-wind speed relationship generates wind speeds which 

are in agreement with the Saffir-Simpson category definition. Wind speeds 

developed for historical hurricanes are also consistent with the observed values. 

Disclosures 

1. Define an “event” in the model. Discuss how storms that intensify or decay at or 

below the Category 1 level are accounted for in the model. 

The USWIND probabilistic database consists of hypothetical hurricane tracks 

modeled to accurately represent hurricane climatology in the North Atlantic 

basin. The database includes both hurricanes that make landfall and 

hurricanes that do not make landfall but pass close enough to the coast to 

generate damaging winds on land. Each event in the model is one complete 

hurricane track from genesis to decay. 

For any storm that makes landfall or close bypass at hurricane status 

(Category 1 or above), the entire track from genesis to decay is included in 

the model, including portions below Category 1 strength. 

47



2. Describe how the model handles events with multiple landfalls and by-passing 

storms. Be specific with respect to how by-passing storms are handled in the model 

when the wind speeds are less than hurricane force winds. 

A hurricane with multiple landfalls is considered to be a single event. For any 

storm that makes landfall or close bypass at hurricane status (Category 1 or 

above), the entire track from genesis to decay is included in the model, 

including portions below Category 1 strength. 

3. Provide all model derived characteristics of the Florida hurricane in the stochastic 

storm set with the greatest over water intensity at the time of landfall. 

The Florida hurricane in the stochastic storm set with the greatest over water 

intensity at the time of landfall has a maximum one-minute sustained 

windspeed of 183 mph, a radius to maximum winds of 5.5 miles, a 

translational speed of 6.8 mph, and a filling rate parameter of 0.093. 

48



M-4 Hurricane Probabilities 

A. Modeled probability distributions for hurricane intensity, forward speed, 

radii for maximum winds, and storm heading shall be consistent with 

historical hurricanes in the Atlantic basin. 

The modeled probability distributions for hurricane intensity, forward speed, radii 

for maximum winds, and landfall angle are consistent with historical hurricanes in 

the Atlantic basin. 

B. Modeled hurricane probabilities shall reflect the Base Hurricane Storm 

Set used for category 1 to 5 hurricanes and shall be consistent with 

those observed for each coastal segment of Florida and neighboring 

states (Alabama, Georgia, and Mississippi). 

Modeled hurricane probabilities reasonably reflect the Official Hurricane Set and 

the period 1900 through 2006 for category 1 to 5 hurricanes and are consistent 

with those observed for each coastal segment of Florida, Alabama, Georgia, and 

Mississippi. Probabilities were developed along the Florida coast and adjacent 

areas using smoothed distributions of hurricane frequency and wind speeddefined 

intensity, fit to the Commission’s Official Hurricane Set using scientific 

methods. 

Disclosures 

1. List assumptions used in creating the hurricane characteristic databases. 

NOAA Publication NWS-38 covers the period 1900-1984, and was the main 

source for compiling information on hurricane modeling parameters, (radius of 

maximum winds, direction of motion, translation speed, etc.) Data for later 

storms (1985-2004) was obtained in specific reports or publications from the 

National Hurricane Center (including Tropical Cyclone Reports and 

Advisories), analyses from the Hurricane Research Division, or from other 

scientifically accepted publications. These publications include Powell, M.D., 

D. Bowman, D. Gilhousen, S. Murillo, N. Carrasco, and R. St. Fluer, “Tropical 

Cyclone Winds at Landfall”, Bulletin of the American Meteorological Society 

85(6): 845-851 (2004); Franklin, J.L., M.L. Black, and K. Valde, “GPS 

dropwindsonde wind profiles in hurricanes and their operational implications”, 

Weather and Forecasting, 18(1): 32-44 (2003); and Houston, S.H., and M.D. 

Powell, “Surface wind fields for Florida Bay Hurricanes”, Journal of Coastal 

Research, 19: 503-513 (2003). Coastline-dependent landfall frequency and 

severity distributions for the state of Florida were developed from the 

Commission’s November 1, 2006 Florida storm set. 

Standard statistical techniques were used to develop the hurricane parameter 

and frequency distributions. The underlying assumption is that the period 

1900 through 2006 is representative in terms of hurricane climatology in 

Florida and adjacent areas. 

49



2. If the model incorporates short term and long term variations in annual storm 

frequencies, describe how this is incorporated. 

The model considers only the long term view of storm frequencies. 

3. Provide a completed Form M-1, Annual Occurrence Rates. 

See Form M-1 at the end of this section. 

50



M-5 Land Friction and Weakening 

A. The magnitude of land friction coefficients shall be consistent with 

currently accepted scientific literature relevant to current geographic 

surface roughness distributions and shall be implemented with 

appropriate geographic information system data. 

USWIND uses land friction to produce a reduction of wind speeds over 

land which are consistent with the accepted scientific literature and with 

geographic surface roughness. A smooth transition is used to reduce onshore 

marine-exposure wind speeds to inland frictionally reduced wind speeds. Wind 

speeds between adjacent ZIP Codes, counties, or territories, also exhibit a 

smooth transition. Appropriate geographic information system data have been 

used to develop the USWIND land friction database. 

The USWIND land friction database is derived from land use / land cover data 

from the five Florida water management districts at a resolution of approximately 

200 meters or finer. The list below shows the different classifications used: 

11 Residential, low density 

12 Residential, medium density 

13 Residential, high density 

14 Commercial and services 

1 URBAN AND BUILT-UP 15 Industrial 

16 Extractive 

17 Institutional 

18 Recreational 

19 Open land (Urban) 

21 Cropland and pastureland 

22 Tree crops 

2 

23 Feeding operations 

24 Nurseries and vineyards 

25 Specialty farms 

AGRICULTURE 26 Other open land (Rural) 

31 Herbaceous Rangeland 

3 RANGELAND 32 Shrub and Brush Rangeland 

33 Mixed Rangeland 

41 Upland coniferous forests 

4 UPLAND FORESTS 

42 Upland hardwood forests 

43 Upland hardwood forests 

44 Tree plantations 

51 Streams and waterways 

52 Lakes 

53 Reservoirs 

5 WATER 

54 Bays and estuaries 

55 Major springs 

56 Slough waters 

51



6 WETLANDS 

7 BARREN LAND 

61 Wetland hardwood forests 

62 Wetland coniferous forests 

63 Wetland forested mixed 

64 Vegetated non-forested wetlands 

65 Non-vegetated wetland 

66 Cut over Wetlands 

69 Wetland shrub 

71 Beaches other than swimming beaches 

72 Sand other than beaches 

73 Exposed rocks 

74 Disturbed land 

75 Riverine sandbars 

81 Transportation 

8 TRANSPORTATION, 

COMMUNICATIONS AND 

82 Communications 

UTILITIES 83 Utilities 

9 SPECIAL 

CLASSIFICATIONS 91 Vegetation, special classification 

B. The hurricane overland weakening rate methodology used by the model 

shall be consistent with historical records. 

The hurricane overland weakening rate methodology used by USWIND for 

hurricanes in Florida is reasonable in comparison to historical records, and 

produces a result within twenty percent of the Kaplan-DeMaria filling rate. 

Disclosures 

1. Describe and justify the functional form of hurricane decay rates used by the model. 

Overland attenuation (filling) is handled by exponential decay formulas fit to 

data provided in NOAA Technical Report NWS 38, Chapter 10, Table 20. The 

basic form of this equation is: 

DP(t) = DP(0) exp [ -m * t ] 

where DP(0) is the hurricane central pressure deficit (difference in the 

ambient pressure of 1013 mb and the storm central pressure) at landfall; t is 

the time after landfall; and m is the decay rate parameter. The formula 

estimates the pressure deficit at any time t after landfall. 

52



The decay parameter, m, is a function of the initial pressure deficit, and is 

independent of geographical region in Florida. The pressure dependence is 

caused by the observation that stronger storms tend to have a faster initial 

decay than weaker storms. The dependence of the decay parameter m on the 

pressure deficit DP(0) was statistically determined so that, for a given initial 

pressure deficit, the time dependence of hurricane wind speeds produced by 

the EQECAT model are within +/- 20% of the wind speeds determined from 

the Inland Wind Decay Model (IWDM). (The IWDM is described in the paper 

by Kaplan and DeMaria, Journal of Applied Meteorology, Volume 34, pp. 

2499 - 2512 (1995)). 

2. Describe the relevance of the gust factor used in the model. 

USWIND converts 10-minute sustained 10-meter wind speeds to one-minute 

sustained 10-meter wind speeds by dividing by 0.863. 

USWIND converts one-minute sustained 10-meter wind speeds to peak gust 

10-meter wind speeds using a gust factor function that takes surface friction 

from land use and land cover into account (rougher terrain has a higher gust 

factor). The uncertainty on the gust factor depends on the input one-minute 

sustained wind speed (higher wind speeds have less uncertainty on the gust 

factor). The gust factor is based on information in Krayer and Marshall, 1992: 

Gust factors applied to hurricane winds, Bulletin of the American 

Meteorological Society, Volume 73, pp. 613-617, and other scientifically 

accepted studies. 

As discussed in the vulnerability standards, the EQECAT model uses peak 

gust wind speed because damage is believed to be better correlated with 

peak gusts than with long-term sustained wind speeds. 

3. Identify all non-meteorological variables that affect the wind speed estimation (e.g., 

surface roughness, topography, etc.) 

Surface roughness, as determined by land use and land cover data, affects 

the local wind speeds in the model. 

4. Provide the collection and publication dates of the land use and land cover data 

used in the model and justify their timeliness for Florida. 

In Florida, USWIND uses land use and land cover data distributed by the five 

Florida water management districts. The collection dates for this data are 

2000 for the South Florida district, 1999 for the Southwest Florida district, and 

1995 for the St. Johns River, Suwannee River, and Northwest Florida 

districts. Publication dates are 2002 for the South and Southwest Florida 

districts, 1999 for the Suwannee River district, and 1997 for the St. Johns 

River and Northwest Florida districts. 

53



5. Provide a graphical representation of the modeled degradation rates for Florida 

hurricanes over time compared to published wind observations. The model 

generated winds should be demonstrated to be consistent with the observed winds. 

Reference to the Kaplan-DeMaria decay rates alone is not acceptable. 

This Kaplan-DeMaria decay representation is provided in Figure 5 below. 

The degradation rates for two Florida hurricanes are shown in Figures 6a and 

6b. 

54



990 985 980 975 

970 965 960 955 

950 945 940 935 

930 925 920 915 

910 905 900 895 

Figure 5. USWIND vs. Kaplan-DeMaria Inland Decay Rates 

The plots in the figure compare the USWIND decay rate (bold) with the Kaplan-DeMaria decay rate (thin) and Kapland-DeMaria +/- 

20% (dashed). Each plot is for a different central pressure at landfall (indicated in mb). In each plot the horizontal axis is time (first 

25 hours after landfall, with a tick mark every 2 hours); and the vertical axis is maximum one-minute sustained wind speed (ranging 

from 20 to 170 mph, with a tick mark every 10 mph). The large black diamonds indicate the large initial drop in wind speed due to 

frictional effects in the first 10 nautical miles after landfall. 

55



Figure 6a. 1916-13 Hurricane 

Figure 6b. Hurricane Charley (2004) 

56



6. The spatial distribution of model- generated winds should be demonstrated to be 

consistent with the observed winds. 

EQECAT regularly reviews modeled versus observed hurricane wind fields. 

Figure 6c below is a comparison of modeled (shading) and observed 

(numbers) surface winds in mph gust for Hurricane Wilma (2005). 

Figure 6c. Wind field for Hurricane Wilma (2005) 

7. Document any differences between the treatment in the model of decay rates for 

stochastic hurricanes compared to historical hurricanes affecting Florida. 

The treatment of decay rates for stochastic and historical hurricanes in the 

EQECAT model is the same, except that for historical hurricanes the storm 

intensity is fixed every six hours with the observed storm intensities. 

Specifically, the decay rate is a regionally-dependent parameter for stochastic 

hurricanes, whereas for historical hurricanes a decay rate is fitted for each 

six-hourly track segment and used to interpolate the intensity between the sixhourly 

observations. 

8. Provide a completed Form M-2, Maps of Maximum Winds. 

See Form M-2 at the end of this Section. 

57



M-6 Logical Relationships of Hurricane Characteristics 

Disclosure 

A. The magnitude of asymmetry shall increase as the translation speed 

increases, all other factors held constant. 

The magnitude of asymmetry in USWIND increases as the translation speed 


B. The mean wind speed shall decrease with increasing surface roughness 

(friction), all other factors held constant. 

The mean wind speed in USWIND decreases with increasing surface roughness 

(friction), all other factors held constant. 

1. Provide a completed Form M-3, Radius of Maximum Winds. 

See Form M-3 at the end of this section. 

58



Form M-1: Annual Occurrence Rates 

A. Provide annual occurrence rates for landfall from the data set that the model generates by 

hurricane category (defined by wind speed in the Saffir-Simpson scale) for the entire state of 

Florida and selected regions as defined in Figure 7 below. List the annual occurrence rate 

(probability of an event in a given year) per hurricane category. Annual occurrence rates 

should be rounded to two decimal places. 

See the tables below. 

B. The historical frequencies below have been derived from the Commission’s Official 

Hurricane Set. If hurricanes are used in addition to the Official Hurricane Set as specified 

in Standard M-1, then the historical frequencies should be modified. 

C. Describe model variations from the historical frequencies. 

Model variations from the historical frequencies are primarily due to the sparseness 

in the historical data. The development of the stochastic event set has included 

smoothing this data, resulting in what we believe is the best estimate of hurricane 

frequencies by location and intensity. 

D. Provide vertical bar graphs depicting distributions of hurricane frequencies by category by 

region of Florida (Figure 7 below) and for the neighboring states of Alabama/Mississippi 

and Georgia. For the neighboring states, statistics based on the closest milepost to the state 

boundaries used in the model are adequate. 


Modeled Annual Occurrence Rates 

Entire State Region A – NW Florida Region B – SW Florida 

Category Historical Modeled Historical Modeled Historical Modeled 

1 0.28 + 0.27 0.14 + 0.14 0.07 + 0.06 

2 0.13 0.15 0.05 + 0.06 0.02 0.03 

3 0.17 + 0.19 0.04 + 0.05 0.05 0.06 

4 0.04 0.03 0.00 0.00 0.02 0.01 

5 0.02 0.01 0.00 0.00 0.01 0.00 

Region C – SE Florida Region D – NE Florida 

Florida By-Passing 

Hurricanes 

Category Historical Modeled Historical Modeled Historical Modeled 

1 0.07 0.06 0.00 0.01 0.00 0.02 

2 0.05 0.05 0.02 0.01 0.04 0.01 

3 0.08 0.08 0.00 0.00 0.02 0.01 

4 0.02 0.02 0.00 0.00 0.01 0.00 

5 0.01 0.00 0.00 0.00 0.00 0.00 

59



(FORM M-1 CONTINUED) 

Region E – Georgia Region F – Alabama/Mississippi 

Category Historical Modeled Historical Modeled 

1 0.00 0.02 0.06 + 0.05 

2 0.04 0.01 0.03 0.03 

3 0.00 0.00 0.05 + 0.04 

4 0.00 0.00 0.00 0.01 

5 0.00 0.00 0.01 0.00 

Note: Except where specified, Number of Hurricanes does not include By-Passing Storms 

Note that in the above tables, entries marked with a (+) were modified to be consistent 

with the landfall counts in the Commission’s Official Hurricane Set. 

81.45 W 30.71 N 

87.55 W 30.27 N 

Figure 7. State of Florida and Neighboring States by Region 

60



0.30 

Annual Landfall/By-pass Frequency 

0.25 

0.20 

0.15 

0.10 

SSI 1 - Historical 

SSI 1 - Modeled 









0.05 

- 

Entire FL 

(Excluding By- 

Passing 

Storms) 

Region A - NW 

Florida 

Region B - SW 


Region C - SE 


Region D - NE 


By-Passing 

Storms 

GA 

AL/MS 

Figure 8. Hurricane Frequencies by Category by Region 

61



Form M-2: Maps of Maximum Winds 

A. Provide a color contour map of the maximum winds for the modeled version of the Base 

Hurricane Storm Set. 


B. Provide a color contour map of the maximum winds for a 100-year return period from the 

stochastic storm set. 


C. Provide the maximum winds plotted on each contour map. 

Maximum winds in these maps are defined as the maximum one-minute sustained winds over the 

terrain as modeled and recorded at each location. 

The same color contours and increments should be used for both maps. 

Use the following seven isotach values: 

(1) 40 mph 

(2) 75 mph 

(3) 95 mph 

(4) 110 mph 

(5) 130 mph 

(6) 140 mph 

(7) 155 mph 

The maximum historical windspeed plotted is 155 mph; the maximum stochastic 

windspeed plotted is 127 mph. 

62




Figure 9. Contour Map - Maximum Winds For Modeled Version Of Official Hurricane Set 

Wind Speeds Are One-Minute Sustained Mph. 

63




Figure 10. Contour Map - Maximum Winds For 100-Year Return Period From Stochastic 

Storm Set. Wind Speeds Are One-Minute Sustained Mph. 

64



Form M-3: Radius of Maximum Winds 

A. For the central pressures in the table below, provide ranges for radius of maximum winds 

used by the model to create the stochastic storm set. 

Central Pressure (mb) 

Range of Rmax (mi) 

900 4-24 

910 4-33 

920 5-42 

930 6-60 

940 6-60 

950 6-60 

955 6-60 

960 6-60 

965 6-60 

970 6-60 

975 6-60 

980 6-60 

985 6-60 

990 6-60 

B. Identify the other variables that influence Rmax. 

For a given hurricane, central pressure is the only variable that influences Rmax. 

C. Provide a representative scatter plot of Central Pressure (x-axis) versus Rmax (y-axis) to 

demonstrate relative populations and continuity of sampled hurricanes in the stochastic 

storm set. “Representative” means that the relative distribution of hurricane frequencies 

across both Central Pressure and Rmax ranges should be evident. 

A graphical representation of Rmax vs. Central Pressure is provided in Figure 11 

below. 

65



Figure 11. Rmax vs. Central Pressure 

66




V-1 Derivation of Vulnerability Functions 

A. Development of the vulnerability functions is to be based on a 

combination of the following: (1) historical data, (2) tests, (3) structural 

calculations, (4) expert opinion, or (5) site inspections. Any 

development of the vulnerability functions based on structural 

calculations or expert opinion shall be supported by tests, site 

inspections, or historical data. 

USWIND vulnerability functions are based on historically observed damage (in 

terms of both claims data and post-hurricane field surveys), experimental 

research conducted by Professors Kishor Mehta and James McDonald at Texas 

Tech, and structural calculations performed by EQE / EQECAT engineers. 

The claims data analyzed is from two basic sources: (1) claims data from all 

major storms during the period 1954 - 1994 analyzed by Dr. Don Friedman and 

John Mangano while managing the Natural Hazard Research Service (NHRS) 

effort for The Travelers Insurance Company; and (2) claims data from Hurricanes 

Alicia (1983), Elena (1985), Gloria (1985), Juan (1985), Kate (1985), Hugo 

(1989), Bob (1991), Andrew (1992), Iniki (1992), Erin (1995), and Opal (1995) 

provided to EQECAT by the insurance companies assisting with the development 

of USWIND. 

In addition, EQECAT is currently analyzing claims data from Hurricanes Charley 

(2004), Frances (2004), Ivan (2004), Jeanne (2004), Katrina (2005), Rita (2005), 

and Wilma (2005). 

EQE / EQECAT teams have conducted post-disaster field surveys for several 

storms in the past few years, including Hurricanes Andrew (1992), Iniki (1992), 

Luis (1995), Marilyn (1995), Opal (1995), Georges (1998), Irene (1999), Lili 

(2002), Fabian (2003), Isabel (2003), Charley (2004), Frances (2004), Ivan 

(2004), Jeanne (2004), Katrina (2005), and Rita (2005); Typhoon Paka (1997); 

and the Oklahoma City (1999), Fort Worth (2000), and Midwest (2003) tornado 

outbreaks. In addition, the research of Professors Mehta and McDonald 

incorporates a large amount of investigation into the effects of all major storms in 

roughly the last 25 years. 

B. The method of derivation of the vulnerability functions shall be 

theoretically sound. 

The method of derivation of the USWIND vulnerability functions is theoretically 

sound. 

67



C. Any modification factors/functions to the vulnerability functions or 

structural characteristics and their corresponding effects shall be 

clearly defined and be theoretically sound. 

Modification factors/functions to the vulnerability functions are applied in the 

secondary structural component of the model. The effects of particular structural 

characteristics are clearly defined and theoretically sound. 

D. Construction type and construction characteristics shall be used in the 

derivation and application of vulnerability functions. 

USWIND allows a user to account for the unique features of individual buildings, 

including construction type and construction characteristics. Such features 

modify the vulnerability functions. 

E. In the derivation and application of vulnerability functions, assumptions 

concerning building code revisions and building code enforcement 

shall be justified. 

When the user provides the year of construction for a building, USWIND takes 

into account the impact of design codes as applied during a particular period (or 

era) of construction. In addition, the Secondary Structural Modifiers also allow the 

user to input additional, more detailed information to account for the unique 

characteristics of individual buildings. Many of these features are often related to 

code enforcement: roof-to-wall anchorage, foundation anchorage, and debris 

potential from nearby buildings, for example. 

USWIND’s capability to handle site-specific features also extends beyond those 

factors affected by codes or code enforcement. This capability acknowledges the 

fact that, among structural engineers trained in the analysis and design of 

buildings, it is generally accepted that code enforcement is only one variable 

among many which affect building performance. Most of these features vary 

structure by structure, while code enforcement tends to vary more by region. 

How a building will respond in a windstorm, or for that matter in an earthquake or 

under ordinary conditions, will depend not just on the building code and how well 

it was enforced, but also on the building’s materials, configuration, and detailing; 

the quality of structural and architectural design; the skill, experience, and care 

exercised by the construction contractors; the degree to which the building’s 

structural and architectural systems have been maintained or modified over time; 

and the location and features of the local environment surrounding the building. 

It is easy to see that code enforcement is, in fact, only indirectly related to 

expected building performance. Professional, conscientious building designers 

and contractors can construct a well designed, well built home regardless of how 

frequently a local building official inspects the construction site. 

68



F. Vulnerability functions shall be separately derived for building 

structures, mobile homes, appurtenant structures, contents, and 

additional living expense. 

The USWIND vulnerability functions separately compute damages for building 

structures, mobile homes, appurtenant structures, contents, and additional living 

expense. 

G. The minimum wind speed that generates damage shall be reasonable. 

The USWIND vulnerability functions calculate damage for all peak gust wind 

speeds greater than or equal to 40 miles per hour. 

Disclosures 

1. Provide a flow chart documenting the process by which the vulnerability functions 

are derived and implemented. 

Figure 12 below summarizes the process by which EQECAT develops its 

vulnerability functions. 

Review claims data for consistency, errors 

Group data into construction classes 

Correct for under-insurance 

Calculate ground up loss for each claim 

Apply corrections for unreported data 

Associate a wind speed with each claim, 

from the best available information 

Perform regression analysis, sometimes 

involving merging with previously 

analyzed claims 

Validate vulnerability curves with actual 

insured losses 

Figure 12. Flowchart – Vulnerability Development 

2. Describe the nature and extent of actual insurance claims data used to develop the 

model’s vulnerability functions. Describe in detail what is included, such as, 

69



number of policies, number of insurers, date of loss, and number of units of dollar 

exposure, separated into personal lines, commercial, and mobile home. 

The primary set of claims data used to develop the vulnerability functions 

contains over 13 million policies from 6 insurers, with a total exposure of 

about $2.2 trillion. By far the majority of this data is from personal lines, but 

about 175,000 of the policies are from commercial lines, and about 125,000 

of the policies are from mobile homes. The corresponding exposures are 

about $55 billion for commercial lines and about $8 billion for mobile homes. 

The data set includes claims from 18 hurricanes since 1983. 

In addition, the EQECAT vulnerability functions are based on a large body of 

claims data from the Natural Hazard Research Service (NHRS), covering all 

major storms during the period 1954 – 1994. 

3. Summarize site inspections, including the source, and a brief description of the 

resulting use of these data in development, validation, or verification of 


Site inspections are summarized in the EQECAT document ‘Secondary 

Structural Modifiers: Features and Model Description’, July 2003. The primary 

use of these site inspections was to calibrate and validate the secondary 

structural module of the software. 

Following major windstorms, ABS Consulting/EQECAT engineers conduct 

reconnaissance field surveys of the affected areas to collect data. This 

information enable us to verify that the overall building performance of 

different structures matches the damage functions in our model. In addition, 

these events offer us the unique opportunity to gather evidence on failure 

modes of secondary features, which allows us to constantly enhance the 

mitigation measures component of the model. 

4. Describe research used in the development of the model’s vulnerability functions. 

Claims data from all major storms during the period 1954 – 1994 analyzed by 

Dr. Friedman and John Mangano while managing the Natural Hazard 

Research Service (NHRS) effort for the Travelers Insurance Company. 

Research conducted by Professors Mehta and McDonald at Texas Tech over 

a 25-year period by investigating damage in all major hurricanes and 

tornadoes. Investigations by EQE / EQECAT of Hurricanes Andrew, Iniki, 

Luis, Marilyn, Opal, Charley, Frances, Ivan, Jeanne, Katrina, and Rita; and 

Typhoon Paka (investigations of Typhoon Paka and the 2004 and 2005 

hurricanes were used to validate rather than modify the vulnerability 

functions). Analysis of claims from Hurricanes Alicia, Elena, Gloria, Juan, 

Kate, Hugo, Bob, Andrew, Iniki, Erin, Opal, Charley, Frances, Ivan, Jeanne, 

Katrina, Rita, and Wilma provided by companies assisting with the 

development of USWIND (the 2004 and 2005 claims data were used to 

validate rather than modify the vulnerability functions in Florida). 

70



5. Describe the number of categories of the different vulnerability functions. 

Specifically, include descriptions of the structure types, lines of business, and 

coverages in which a unique vulnerability function is used. 

USWIND uses 96 basic construction types, 21 of which are residential types. 

They are characterized by four parameters: occupancy, number of stories, 

structural system, and exterior cladding strength. USWIND uses occupancies 

rather than line of business, because empirical evidence has shown that the 

former is more relevant to building performance. USWIND has distinct 

vulnerability functions for structure and contents, and describes time element 

losses as a function of direct damage and detailed occupancy type. 

For residential structures, the 21 structure types are as follows: 

9 residential ISO classes plus one curve for average residential ISO: 

• ISO Residential Class 1:Frame 

• ISO Residential Class 2:Joisted Masonry 

• ISO Residential Class 3:Non-combustible 

• ISO Residential Class 4:Masonry Non-combustible 

• ISO Residential Class 5:Modified Fire-resistive 

• ISO Residential Class 6:Fire-resistive 

• ISO Residential Class 7:Heavy Timber Joisted Masonry 

• ISO Residential Class 8:Super Non-combustible 

• ISO Residential Class 9:Super Masonry Non-combustible 

• ISO Residential Average 

9 engineering classifications: 

• Residential, Low-Rise, Reinforced-Masonry, Strong-Cladding 

• Residential, Low-Rise, Reinforced-Masonry, Weak-Cladding 

• Residential, Low-Rise, Reinforced-Masonry, Average-Cladding 

• Residential, Low-Rise, Timber, Strong-Cladding 

• Residential, Low-Rise, Timber, Weak-Cladding 

• Residential, Low-Rise, Timber, Average-Cladding 

• Residential, Low-Rise, Unreinforced-Masonry, Strong-Cladding 

• Residential, Low-Rise, Unreinforced-Masonry, Weak-Cladding 

• Residential, Low-Rise, Unreinforced-Masonry, Average-Cladding 

2 mobile home curves: 

• Residential, Low-Rise, Mobile Home - Tied Down 

• Residential, Low-Rise, Mobile Home - Not Tied Down 

71



6. Identify the one-minute average sustained wind speed at which the model begins to 

estimate damage. 

The model begins estimating damage at a peak gust wind speed of 40 mph. 

An equivalent one-minute average wind speed can be estimated, but will vary 

depending on terrain conditions and elevation. For open terrain and an 

elevation of 10 meters, 40 mph peak gusts equate approximately with a oneminute 

average wind speed of 35 mph. At other elevations and on different 

terrain, the one-minute average may be significantly different from this 

amount. For detail on this issue, the reader is referred to Simiu and Scanlan, 

1996, Wind Effects on Structures, 3rd ed., John Wiley and Sons, New York, 

section 2.3.6. Note that USWIND uses peak gust wind speed because 

damage is believed to be better correlated with peak gusts than with longterm 

sustained wind speeds. This approach is consistent with standard 

structural design philosophy: one designs for extreme, or peak, conditions, 

such as the momentary resting of a heavy piece of equipment on an 

inadequately strong patch of roof. It is the load at that moment that causes 

the equipment to punch through the roof, not the load averaged over the 

previous minute. 

Ted Fujita, of the University of Chicago, also pointed out (following Hurricanes 

Andrew and Iniki) that the gusts should be more important than the sustained 

wind when considering damage production. In his concluding remarks in an 

analysis of a videotape of a roof being blown from a house during Hurricane 

Iniki, he states: "It is important to realize that the roof can be blown away by 1 

to 2 sec winds rather than a sustained wind" (Storm Data, Sept. 1992, Vol. 

34, page 27). 

7. Describe how the duration of wind speeds at a particular location over the life of a 

hurricane is considered. 

The duration of wind speeds is not explicitly considered in the model, 

although duration effects are included in the claims data used to develop the 


8. Provide a completed Form V-1, One Hypothetical Event. 

Please see Form V-1 at the end of this section. 

72



V-2 Mitigation Measures 

A. Modeling of mitigation measures to improve a structure’s wind 

resistance and the corresponding effects on vulnerability shall be 

theoretically sound. These measures shall include fixtures or 

construction techniques that enhance: 

• Roof strength 

• Roof covering performance 

• Roof-to-wall strength 

• Wall-to-floor-to-foundation strength 

• Opening protection 

• Window, door, and skylight strength. 

The USWIND model allows for modifications to the vulnerability curves in the 

secondary structural component of the model if additional knowledge about 

the construction characteristics is available. Such construction characteristics 

include roof strength, roof covering performance, roof-to-wall strength, wall-tofloor-to-foundation 

strength, opening protection, and window, door, and 

skylight strength. 

Disclosures 

B. Application of mitigation measures shall be empirically justified both 

individually and in combination. 

The application of modifications to the vulnerability curves in the secondary 

structural component of USWIND is reasonable both individually and in 

combination. 

1. Provide a completed Form V-2, Mitigation Measures – Range of Changes in Damage. 

See Form V-2 at the end of this section. 

2. Provide a description of the mitigation measures used by the model that are not listed 

in Form V-2. 

A large number of mitigation measures relevant to residential structures 

including mobile homes are available in the model. These measures are 

provided as various options under about 30 different secondary structural 

features. There are both a number of features available that are not used in 

Form V-2, e.g. glazing extent, as well as a number of options within each 

feature, e.g. additional roof profile types beyond braced gable and hip. 

73



Form V-1: One Hypothetical Event 

A. Wind speeds for 336 ZIP Codes are provided in the file named “FormV1Input06.xls.” The 

wind speeds and ZIP Codes represent a hypothetical hurricane track. The modeler is 

instructed to model the sample exposure data provided in the file named 

“FormA2Input06.xls” against these wind speeds at the specified ZIP Codes and provide the 

damage ratios summarized by wind speed (mph) and construction type. 

The wind speeds provided are one-minute sustained 10-meter wind speeds. The sample 

exposure data provided consists of three structures (one of each construction type – wood 

frame, masonry, and mobile home) individually placed at the population centroid of each of 

the ZIP Codes provided. Each ZIP Code is subjected to a specific wind speed. For 

completing Part A, Estimated Damage for each individual wind speed range is the sum of 

loss to all structures in the ZIP Codes subjected to that individual wind speed range. Subject 

Exposure is all exposures in the ZIP Codes subjected to that individual wind speed range. 

For completing Part B, Estimated Damage is the sum of the loss to all structures of a specific 

type (wood frame, masonry, or mobile home) in all of the wind speed ranges. Subject 

Exposure is all exposures of that specific type in all of the ZIP Codes. 

One reference structure for each of the construction types should be placed at the population 

center of the ZIP Codes. 

Reference Frame Structure: 

One story 

Unbraced gable end roof 

Normal shingles (55mph) 

½” plywood deck 

6d nails, deck to roof members 

Toe nail truss to wall anchor 

Wood framed exterior walls 

5/8” diameter anchors at 48” centers for 

wall/floor/foundation connections 

No shutters 

Standard glass windows 

No door covers 

No skylight covers 

Constructed in 1980 

Reference Mobile Home Structure: 

Tie downs 

Single unit 

Manufactured in 1980 

Reference Masonry Structure: 

One story 






Masonry exterior walls 

No vertical wall reinforcing 

No shutters 





B. Confirm that the structures used in completing the Form are identical to those in the above 

table. If additional non-structural assumptions are necessary to complete this Form (for 

example, regarding duration or surface roughness), the modeler should provide the reasons 

74



why the assumptions were necessary as well as a detailed description of how they were 

included. 

The structures used in completing the Form are identical to those in the above table. 

C. Provide a plot of the Form V-1, Part A data. 

75



Form V-1: One Hypothetical Event 

Part A 

Wind Speed (mph) 

Estimated Damage/ 

Subject Exposure 

41 – 50 0.09% 

51 – 60 0.21% 

61 – 70 0.58% 

71 – 80 1.22% 

81 – 90 2.48% 

91 – 100 5.26% 

101 – 110 9.66% 

111 – 120 25.19% 

121 – 130 37.68% 

131 – 140 56.50% 

141 – 150 72.68% 

151 – 160 81.50% 

161 – 170 90.18% 

Part B 

Construction Type 

Estimated Damage/ 

Subject Exposure 

Wood Frame 6.78% 

Masonry 5.98% 

Mobile Home 9.47% 

76



A plot of the Form V-1 Part A data is provided below 

100 00% 

90 00% 

80 00% 

70 00% 

60 00% 

% Damage 

50 00% 

40 00% 

30 00% 

20 00% 

10 00% 

0 00% 

41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 141-150 151-160 161-170 

Wind Speed (1-minute MPH) 

77



Form V-2: Mitigation Measures – Range of Changes in Damage 

A. Provide the change in the zero deductible personal residential reference structure damage 

rate (not loss cost) for each individual mitigation measure listed in Form V-2 as well as for 

the combination of the four mitigation measures provided for the Mitigated Frame Structure 

and the Mitigated Masonry Structure below. 

B. If additional assumptions are necessary to complete this Form (for example, regarding 

duration or surface roughness), the modeler should provide the rationale for the assumptions 

as well as a detailed description of how they are included. 

C. Provide this Form on CD in both Excel and PDF format. The file name should include the 

abbreviated name of the modeler, the Standards year, and the Form name. A hard copy of 

Form V-2 should be included in the submission. 

Reference Frame Structure: 

One story 






Wood framed exterior walls 

5/8” diameter anchors at 48” centers for 

wall/floor/foundation connections 

No shutters 





Mitigated Frame Structure: 

Rated shingles (110mph) 


Truss straps at roof 

Plywood Shutters 

Reference Masonry Structure: 

One story 






Masonry exterior walls 

No vertical wall reinforcing 

No shutters 





Mitigated Masonry Structure: 

Rated shingles (110mph) 


Truss straps at roof 

Plywood Shutters 

Reference and mitigated structures are $100,000 fully insured structures with a zero deductible 

policy as indicated under “Owners” Policy Type for Form A-6. 

Place the reference structure at the population centroid for ZIP Code 33921 located in Lee 

County. 

78



Form V-2: Mitigation Measures – Range of Changes in Damage 

ROOF 

STRENGTH 

ROOF 

COVERING 

ROOF-WALL 

STRENGTH 

WALL-FLOOR 

STRENGTH 

WALL- 

FOUNDATION 

STRENGTH 

OPENING 

PROTECTION 

PERCENTAGE CHANGES IN DAMAGE* 

(REFERENCE DAMAGE RATE - MITIGATED DAMAGE RATE) / 

INDIVIDUAL 

REFERENCE DAMAGE RATE * 100 

MITIGATION MEASURES 

FRAME STRUCTURE 

MASONRY STRUCTURE 

WIND SPEED (MPH) 


60 85 110 135 160 60 85 110 135 160 

REFERENCE STRUCTURE - - - - - - - - - - 

BRACED GABLE ENDS 15.4% 15.1% 13.0% 10.5% 5.2% 13.1% 13.5% 11.8% 9.6% 6.1% 

HIP ROOF 19.5% 18.9% 16.2% 13.3% 6.7% 17.3% 17.4% 15.2% 12.5% 8.1% 

RATED SHINGLES (110 MPH) 8.0% 8.0% 6.8% 5.5% 2.6% 5.2% 5.4% 4.7% 3.8% 2.4% 

MEMBRANE 2.7% 2.7% 2.3% 1.8% 0.9% 0.0% 0.0% 0.0% 0.0% 0.0% 

NAILING OF DECK 8d 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

CLIPS 19.5% 18.9% 16.2% 13.3% 6.7% 15.2% 15.5% 13.5% 11.1% 7.1% 

STRAPS 19.5% 18.9% 16.2% 13.3% 6.7% 15.2% 15.5% 13.5% 11.1% 7.1% 

TIES OR CLIPS 8.0% 8.0% 6.8% 5.5% 2.6% 5.2% 5.4% 4.7% 3.8% 2.4% 

STRAPS 8.0% 8.0% 6.8% 5.5% 2.6% 5.2% 5.4% 4.7% 3.8% 2.4% 

LARGER ANCHORS 

OR CLOSER SPACING 

0.0% 0.0% 0.0% 0.0% 0.0% - - - - - 

STRAPS 8.0% 8.0% 6.8% 5.5% 2.6% - - - - - 

VERTICAL REINFORCING - - - - - - - - - - 

WINDOW PLYWOOD 10.7% 10.6% 9.1% 7.3% 3.5% 10.5% 10.8% 9.5% 7.7% 4.9% 

SHUTTERS STEEL 10.7% 10.6% 9.1% 7.3% 3.5% 10.5% 10.8% 9.5% 7.7% 4.9% 

ENGINEERED 17.4% 17.0% 14.6% 11.9% 5.9% 17.3% 17.4% 15.2% 12.5% 8.1% 

DOOR AND SKYLIGHT COVERS 15.4% 15.1% 13.0% 10.5% 5.2% 10.5% 10.8% 9.5% 7.7% 4.9% 

WINDOW, 

DOOR, 

WINDOWS LAMINATED 10.7% 10.6% 9.1% 7.3% 3.5% 7.9% 8.1% 7.1% 5.8% 3.6% 

SKYLIGHT 

STRENGTH IMPACT GLASS 10.7% 10.6% 9.1% 7.3% 3.5% 7.9% 8.1% 7.1% 5.8% 3.6% 

MITIGATION MEASURES IN 

COMBINATION 

PERCENTAGE CHANGES IN DAMAGE* 

(REFERENCE DAMAGE RATE - MITIGATED DAMAGE RATE) / 

REFERENCE DAMAGE RATE * 100 

FRAME STRUCTURE 

MASONRY STRUCTURE 



60 85 110 135 160 60 85 110 135 160 

STRUCTURE MITIGATED STRUCTURE 25.7% 24.4% 21.1% 17.5% 9.0% 23.5% 23.1% 20.3% 16.8% 11.1% 

* Note: 8d nails vs. 6d nails: not currently distinguished in the model, as other aspects are deemed more important; nail spacing and contact with 

rafter are also important. Larger or closer spaced anchor bolts: not currently distinguished in the model, as other aspects are deemed more 

important; also difficult to ascertain vertical reinforcing for masonry walls: this feature is accounted for through the selection of the base 

structure; vertically reinforced masonry walls are considered by the EQECAT model as Reinforced Masonry (RM). 

79




A-1 Modeled Loss Costs 

Modeled loss costs shall reflect all damages from storms that reach 

hurricane strength and produce minimum damaging wind speeds or 

greater on land in Florida. 

Disclosure 

Modeled loss costs reflect all damages starting when damage is first caused in 

Florida from an event modeled as a hurricane at that point in time and will include 

all subsequent damage in Florida from that event. 

1. Describe how damage from model generated storms (landfalling and by-passing) is 

excluded or included in the calculation of loss costs for the state of Florida. 

All damage for any storm that makes landfall or close bypass at hurricane 

status (Category 1 or above) is included in the calculation of loss costs, 

including portions below Category 1 strength. 

80



A-2 Underwriting Assumptions 

A. When used in the modeling process or for verification purposes, 

adjustments, edits, inclusions, or deletions to insurance company input 

data used by the modeler shall be based upon accepted actuarial, 

underwriting, and statistical procedures. 

When used in the modeling process or for verification purposes, adjustments, 

edits, inclusions, or deletions to insurance company input data used by the 

modeler are based upon accepted actuarial, underwriting, and statistical 

procedures. 

B. For loss cost estimates derived from or validated with historical insured 

hurricane losses, the assumptions in the derivations concerning (1) 

construction characteristics, (2) policy provisions, (3) claim payment 

practices, and (4) relevant underwriting practices underlying those 

losses, as well as any actuarial modifications, shall be appropriate. 

Vulnerability functions in USWIND are based on claims data obtained from 

insurance companies. For each data set obtained, the following process is used 

to incorporate the data into new or existing vulnerability functions: 

1. Review claims data to ensure consistency, correct any errors through 

interactions with the insurance company that provided the data and 

determine all of the elements included within the claims data (e.g., 

allocated loss adjustment expense, etc.). 

2. Group the data into appropriate construction classes, and ensure 

consistency between definitions of different insurers. This includes 

incorporating consideration of the relevant underwriting practices of the 

insurance company that provided the data. 

3. Correct insured values to include under-insurance, if any (e.g., 80% 

insured to value clause in many homeowner policies). This process in 

done by consulting with the insurance company that provided the data. 

4. Calculate ground up loss for each coverage, using the paid claim 

amount and the deductible. 

5. Apply corrections to account for unreported data, e.g. damage below 

the deductible. This correction is generally negligible for residential 

claims, which typically have low deductibles. 

6. Associate a wind speed to each location using the best available 

official historical information. 

7. Perform regression analysis to derive the vulnerability functions by 

construction class and coverage. This process may involve merging 

the new data set with previously analyzed claims. 

81



8. Validate curves against loss experience from various insurance 

portfolios. 

Disclosures 

1. Identify the assumptions used to develop loss costs for unknown residential 

construction types. 

Unknown residential structures are handled one of two ways. We prefer that 

our engineers and data specialists work with the insurer to determine the 

most likely mix of known types comprising the unknown sites. This mix is then 

entered into a simple lookup table, and USWIND automatically calculates the 

weighted-average vulnerability function for the unknown sites. 

The alternative is to use the default residential structure type - a built-in 

vulnerability function we developed by fitting a smooth curve to all residential 

claims and exposure data, regardless of structure type. These data include 

several hundred thousand residential structures throughout the eastern US 

that have been subjected to hurricanes over the last 40 years. 

2. Describe how the modeled loss costs take into consideration storm surge and flood 

damage to the infrastructure. 

Storm surge and flood damage are not modeled explicitly in USWIND. 

However, to the extent that such perils affect Additional Living Expense (ALE) 

claims through damage to the infrastructure, they are implicitly included in the 

USWIND ALE vulnerability functions. 

3. Describe the assumptions included in model development and validation concerning 

insurance company claim payment practices. 

The claim payment practices of the insurance companies providing the claims 

data used to develop the vulnerability functions are implicit in both model 

development and validation. 

4. Identify depreciation assumptions and describe the methods and assumptions used 

to reduce insured losses on account of depreciation. Provide a sample calculation 

for determining the amount of depreciation and the actual cash value (ACV) losses. 

USWIND does not calculate a depreciation factor. 

82



5. Identify property value assumptions and describe the methods and assumptions used 

to determine the true property value and associated losses. Provide a sample 

calculation for determining the property value and guaranteed replacement cost 

losses. 

USWIND does not include a factor for under insurance and the impact of full 

replacement value coverage. This was a significant cost in the Northridge 

Earthquake and EQECAT was able to account for its impact in studies done 

for several large insurers. This factor was developed after an analysis of 

actual claims. The assumption made is that the total insured value provided 

represents true property value, so no under insurance factor is necessary. 

6. Describe how loss adjustment expenses are considered within the loss cost 

estimates. 

Loss adjustment expenses are not considered. 

83



A-3 Loss Cost Projections 

A. Loss cost projections produced by hurricane loss projection models 

shall not include expenses, risk load, investment income, premium 

reserves, taxes, assessments, or profit margin. 

Loss cost projections produced do not include expenses, risk load, investment 

income, premium reserves, taxes, assessments or profit margin. 

B. Loss cost projections shall not make a prospective provision for 

economic inflation. 

The model does not make a prospective provision for economic inflation, with 

regard to either losses or policy limits. 

Disclosures 

1. Describe the method or methods used to estimate annual loss costs needed for 

ratemaking. Identify any source documents used and research performed. 

Overall Model Methodology 

USWIND modeling methodology can be segmented into four components: 1) 

definition of the hazard, 2) propagation of the hazard to the site, 3) damage 

estimation, and 4) loss estimation. 

1. Hazard definition 

The storm database used by USWIND is a combination of historical and 

stochastic storms. Wind speed probabilistic distributions are calculated using 

the probabilistic distributions of all important storm parameters. The 

proprietary wind speed equation was based upon the NOAA model as 

published in NWS 23 and NWS 38. The NOAA model was modified and 

generalized to properly simulate wind speeds for all categories of storms, 

from a weak SSI 1 through a strong SSI 5. The NOAA model was further 

modified to properly simulate storm decay and wind attenuation due to friction 

and filling as a storm moves inland from landfall point. The equation 

computes wind speeds using pressure, the filling rate, radius to maximum 

winds, the angle of attack, translation speed, the gradient to sustained winds, 

the gust factor, the storm profile (attenuation of wind speed outward from the 

center), and the friction caused by local terrain and man-made structures. 

2. Propagation of the hazard to the site 

USWIND utilizes an embedded commercial GIS (Geographic Information 

System), MapInfo to compute the latitude and longitude of each site analyzed. 

The street address level, where such data is available, is used to geocode to 

the lat./long. coordinates. Failing the presence of a street address, the 

geocoding can be done at a ZIP Code, City or County centroid basis. Wind 

84



speed distributions at the site locations are computed taking local friction and 

distance to coast into account. 

3. Estimation of Damage 

USWIND provides the facility to define each of the property assets being 

analyzed in order to compute resulting damage. Damage can be calculated 

for Structure, Contents, Time Element (such as Additional Living Expense 

(ALE) or Business Interruption (BI)) and up to three additional user defined 

coverage types. Site information includes the latitude and longitude of the 

locations, the structure types (96 types), structure details such as number of 

stories, insured value, cladding type and a class of occupancy type (12 

types). Damage is estimated using vulnerability functions associated with the 

structure definition and occupancy type and the distribution of peak gust wind 

speeds at each site. The vulnerability functions used by USWIND have been 

derived through three methods: empirical data, expert opinion, and 

engineering analysis. 

The probabilistic distribution of damage (for each coverage and site) is 

derived through the integration of the probabilistic distribution of wind speeds 

for the site with the probabilistic distributions of damage for given wind 

speeds. Damage distributions for each of the sites are aggregated into an 

overall portfolio distribution of damage. 

Since there can be a high degree of damage correlation for similar structure 

types within a geographic area, USWIND properly takes into account site and 

coverage level correlations when aggregating individual site damage into an 

overall portfolio damage amount. 

4. Estimation of Loss 

Insurance information in the form of insured values, limits, deductibles and 

facultative and/or treaty reinsurance are then integrated with the probabilistic 

distribution of computed damage for each site to determine the probabilistic 

distribution of “insured loss” amount. 

2. Identify the highest level of resolution for which loss costs can be provided. Identify 

the resolution used for the reported output ranges. 

Loss costs can be provided at many different geographical resolutions, 

including state, county, ZIP Code and site levels. The distance of a site to the 

closest coastline is used in the evaluation of wind speeds at the site level, 

according to NWS 23. Loss calculations for properties located in 

beach/coastal areas are handled accordingly. For the reported output ranges, 

all analyses were performed at the ZIP Code level. 

85



A-4 Demand Surge 

A. Demand surge shall be included in the model’s calculation of loss 

costs. 

Demand surge has been included in all analyses submitted for review by the 

Commission. 

B. The methods, data, and assumptions used in the estimation of demand 

surge shall be actuarially sound. 

The methods, data, and assumptions used in the estimation of demand surge are 

actuarially sound. 

Disclosures 

1. Describe how the model incorporates demand surge in the calculation of loss costs 

The USWIND model offers the option to either include or exclude the 

increased loss resulting from the effect of demand surge which is observed 

following major cat events. 

Two indices are calculated to determine the magnitude of the demand surge 

at any given location subjected to a windspeed V. The Cat Index is a function 

of the storm intensity and the landfall milepost. It is determined using the 

building materials inventory available in the affected region and the building 

materials demand resulting from the storm damage. The Cat Inflation Index 

represents the factor by which repair cost increases in a cat event as a 

function of V. 

2. Provide citations to published papers, if any, that were used to develop how the 

model estimates demand surge. 

The demand surge algorithm used in USWIND is strictly based on EQECAT 

research. 

86



A-5 User Inputs 

All modifications, adjustments, assumptions, and defaults necessary to 

use the inputs in the model shall be actuarially sound and included with the 

model output. Treatment of missing values for user inputs required to run 

the model shall be actuarially sound and described with the model output. 

Any assumption or method used by EQECAT’s hurricane loss projection model 

that relates to a specific insurer’s inputs to the model, if any, for the purposes of 

preparing the insurer’s rate filing shall be clearly identified. EQECAT will disclose 

any implicit assumptions relating to insurance to value, the prevalence of 

appurtenant structures, or demographic risk characteristics. 

Disclosures 

1. Describe the methods used to distinguish among policy form types (e.g., 

homeowners, dwelling property, mobile home, tenants, condo unit owners). 

USWIND has no pre-determined policy form types in it. The user must specify 

the format of the policy form in the input file. The model can accept a wide 

variety of combinations of deductible and limits. The primary assumption in 

the analysis of different policy forms is that the user has input the data 

correctly. USWIND has many reports which the user can use to validate the 

correctness of the data, but the responsibility for the correctness of the 

analysis resides with the user. The model can produce loss costs for different 

types of policies. All the elements of the loss are retained following an 

analysis. With a properly formatted input file, the user can produce reports 

which detail many breakdowns of the data, not just by Policy Type, but also 

by ZIP Code, county, state, line of business, branch, division, etc. The user 

has to select the correct identification codes for the various reports needed. 

2. Disclose, in a model output report, the specific type of input that is required to use 

the model or model output in a personal residential property insurance rate filing. 

Such input includes, but is not limited to, optional features of the model, type of data 

to be supplied by the model user and needed to derive loss projections from the 

model, and any variables that a model user is authorized to set in implementing the 

model. Include the model name and version number on the model output report. All 

items included in the output form submitted to the Commission should be clearly 

labeled and defined. 

The output reports on the next four pages provide an example of the 

information given. In the reports, ‘Multiple Layer Flag’, if ‘On’, indicates that 

policies having the same account number should be treated as layers of a 

single policy, and ‘Global Limits/Deductibles’, if other than ‘None Applied’, 

indicates that the limits and/or deductibles in the portfolio have been 

overridden with some user-specified global values. 

87



Geocode Statistics by State 

for Portfolio case1 

Number of Building Contents Total Property Time Element Total 

Geocode Statistics Locations TIV TIV TIV TIV TIV 

$(Thousands) $(Thousands) $(Thousands) $(Thousands) $(Thousands) 

State: Florida 

Postal Code 2 200 0 200 0 200 

Florida State Total 2 $200 $0 $200 $0 $200 

Total for All States 2 $200 $0 $200 $0 $200 

Factors Used in Analysis: 

Peril Type: 

Multiple Layer Flag: 

Hurricane 

Off 

User ID = 1, Window ID = 1 

EQECAT: USWIND 5.11 Page 1 of 1 February 9, 2007 10:57AM 

88



Quality Factor by State 



Quality Factor Locations TIV TIV TIV TIV TIV 



50 2 200 0 200 0 200 




Peril Type: 


Hurricane 

Off 



89



Structure Types by State 



Structure Types Locations TIV TIV TIV TIV TIV 



Commercial, Low-Rise, Unreinforced-Masonry, 1 100 0 100 0 100 

Average-Cladding 

Residential, Low-Rise, Timber, Average-Cladding 1 100 0 100 0 100 




Peril Type: 


Hurricane 

Off 



90



Hurricane - Expected Annual Damage and Loss by State 


Loss 

Total No. of Expected Annual Damage Expected Annual Gross Loss Expected Annual Expected Annual Net 

State TIV Bldgs. % Total % Total Fac. Cessions % Total 

$(Thousands) $(Thousands) TIV $(Thousands) TIV $(Thousands) $(Thousands) TIV 

Florida 200 2 0.99 0.4959 0.67 0.3374 0.00 0.67 0.3374 

Total for All States $200 2 $0.99 0.4959% $0.67 0.3374% $0.00 $0.67 0.3374% 


Demand Surge Factor: 

Region: 

Global Limits/Deductibles: 


Demand Surge Not Included 

U.S. Mainland 

None Applied 

Off 



91



3. Provide a copy of the input form used by a model user to provide input criteria to be 

used in the model. The modeler should demonstrate that the input form relates 

directly to the model output. Include the model name and version number on the 

input form. All items included in the input form submitted to the Commission should 

be clearly labeled and defined. 

An example USWIND input form is shown below. The field names are in the 

first column, and arranged into six groups (P for policy information, PC for 

policy coverage information, PF for policy facultative reinsurance, S for site 

information, and SC for site coverage information). The example below has 

five records of data (policy numbers FLP001 through FLP005). 

USWIND 5.11 

P PolNum FLP001 FLP002 FLP003 FLP004 FLP005 

P InsName 

P AcctNum 

P_AcctName 

P_Company C1 C1 C1 C1 C1 

P_Division NY NY FL FL NY 

P_Branch Mia Mia Mia Mia Mia 

P_LineBus HO HO MP MP HO 

P_PolTyp COMF COMF HO HO HO 

P PolStats IN IN IN IN IN 

P IncpDate 20020901 20021101 20021201 20020901 20021101 

P ExprDate 20030831 20031031 20031130 20030831 20031031 

P Producer 9912 4412 7413 1284 9912 

P TransID 99 99 99 99 99 

PC_PerlTyp Wind Wind Wind Wind Wind 

PC_CvgTyp Bldg Cont Time Time ALE 

PC_LmtAmt 500 333 111 222 67 

PC_LmtTyp CovSpec CovSpec CovSpec CovSpec CovSpec 

PC_DedAmt 1000 1000 1000 1000 1000 

PC_DedTyp CovSpec CovSpec CovSpec CovSpec CovSpec 

PC PolPrm 600 600 600 600 600 

PC AttcPnt 0 0 0 0 0 

PC ProRata 100 100 100 100 100 

PF CertNum 

PF_PerlTyp 

PF_CvgTyp 

PF_ReinApp 

PF_AttPnt 

PF_LayAmt 

PF CedPcnt 

PF ReinTyp 

PF AggLmt 

PF Reinsr 

PF Broker 

PF_CertSta 

PF_ReinPrm 

PF_TrtyNum 

S_Number 1 1 1 1 1 

S_Name 

S StrAddr 400 S Greenwood 2040 Whitfield 7400 Nw 19 2801 Rosselle 4586 Palm Ave 

S City Clearwater Sarasota Miami Jacksonville Hialeah 

92



S_County Pinellas Manatee Dade Duval Dade 

S_State FL FL FL FL FL 

S_Zip_5dg 34616 34243 33147 32205 33012 

S_WndStruc SC52 SC52 SC654 SC654 SC654 

S_WndOccpy OC1 OC1 OC1 OC1 OC1 

S YrBuilt 1968 1980 1934 1942 1960 

S NumStory 1 2 1 1 1 

S NumStruc 1 1 1 1 1 

SC PerlTyp Wind Wind Wind Wind Wind 

SC_CvgTyp Bldg Cont Time Time ALE 

SC_CovQual 50 50 50 50 50 

SC_TIV 600 350 150 250 75 

SC_LmtAmt 500 333 111 222 67 

SC_LmtTyp CovSpec CovSpec CovSpec CovSpec CovSpec 

SC_DedAmt 1000 1000 1000 1000 1000 

SC DedTyp CovSpec CovSpec CovSpec CovSpec CovSpec 

SC Prem 600 600 600 600 600 

SF CertNum 

SF PerlTyp 

SF_CvgTyp 

SF_ReinApp 

SF_AttPnt 

SF_LayAmt 

SF_CedPcnt 

SF_ReinTyp 

SF Reinsr 

SF Broker 

SF CertSta 

SF Prem 

SF TrtyNum 

The table below provides descriptions for each of the input data fields. 

Field Name Data Group Description 

P PolNum Policy Policy Number 

P InsName Policy Insured Name 

P AcctNum Policy Account Number 

P_AcctName Policy Account Name 

P_Company Policy Company 

P_Division Policy Division 

P_Branch Policy Branch 

P_LineBus Policy Line of Business 

P PolTyp Policy Policy Type 

P PolStats Policy Policy Status 

P IncpDate Policy Inception Date 

P ExprDate Policy Expiration Date 

P Producer Policy Producer 

P_TransID Policy Translation ID 

PC_PerlTyp Policy Coverage Peril Type 

PC_CvgTyp Policy Coverage Coverage Type 

PC_LmtAmt Policy Coverage Limit Amount 

PC_LmtTyp Policy Coverage Limit Type 

PC DedAmt Policy Coverage Deductible Amount 

PC DedTyp Policy Coverage Deductible Type 

PC PolPrm Policy Coverage Policy Premium 

PC AttcPnt Policy Coverage Attachment Point 

93



Field Name Data Group Description 

PC ProRata Policy Coverage Prorata 

PF_CertNum Policy Facultative Certificate Number 

PF_PerlTyp Policy Facultative Peril Type 

PF_CvgTyp Policy Facultative Coverage Type 

PF_ReinApp Policy Facultative Reinsurance Applies 

PF_AttPnt Policy Facultative Attachment Point 

PF LayAmt Policy Facultative Layer Amount 

PF CedPcnt Policy Facultative Ceded Percentage 

PF ReinTyp Policy Facultative Reinsurance Type 

PF AggLmt Policy Facultative Aggregate Limit 

PF Reinsr Policy Facultative Reinsurer 

PF_Broker Policy Facultative Broker 

PF_CertSta Policy Facultative Certificate Status 

PF_ReinPrm Policy Facultative Reinsurance Premium 

PF_TrtyNum Policy Facultative Treaty Number 

S_Number Site Site Number 

S Name Site Site Name 

S StrAddr Site Street Address 

S City Site City 

S County Site County 

S State Site State 

S_Zip_5dg Site ZIP Code 

S_WndStruc Site Wind Structure Type 

S_WndOccpy Site Wind Occupancy Type 

S_YrBuilt Site Year Built 

S_NumStory Site Number of Stories 

S NumStruc Site Number of Structures 

SC PerlTyp Site Coverage Peril Type 

SC CvgTyp Site Coverage Coverage Type 

SC CovQual Site Coverage Coverage Quality 

SC TIV Site Coverage Total Insured Value 

SC_LmtAmt Site Coverage Limit Amount 

SC_LmtTyp Site Coverage Limit Type 

SC_DedAmt Site Coverage Deductible Amount 

SC_DedTyp Site Coverage Deductible Type 

SC_Prem Site Coverage Premium 

SF CertNum Site Facultative Certificate Number 

SF PerlTyp Site Facultative Peril Type 

SF CvgTyp Site Facultative Coverage Type 

SF ReinApp Site Facultative Reinsurance Applies 

SF AttPnt Site Facultative Attachment Point 

SF_LayAmt Site Facultative Layer Amount 

SF_CedPcnt Site Facultative Ceded Percentage 

SF_ReinTyp Site Facultative Reinsurance Type 

SF_Reinsr Site Facultative Reinsurer 

SF_Broker Site Facultative Broker 

SF CertSta Site Facultative Certificate Status 

SF Prem Site Facultative Reinsurance Premium 

SF TrtyNum Site Facultative Treaty Number 

94



4. Describe actions performed to ensure the validity of insurer data used for model 

inputs or validation/verification. 

Client data is extensively tested during the import process into the EQECAT 

system to confirm its accuracy. Field level validation is performed to confirm 

that every data element within each record falls within known ranges. Data 

not falling within known ranges is marked as an error or a warning in a log 

depending upon the severity of the problem. Child/parent and other key 

relationships are also checked. A summary log is displayed at the end of 

import process denoting the number records which have warnings or errors. 

95



A-6 Logical Relationship to Risk 

A. Loss costs shall not exhibit an illogical relation to risk, nor shall loss 

costs exhibit a significant change when the underlying risk does not 

change significantly. 

EQECAT’s loss costs exhibit logical relation to risk. Loss costs produced by the 

model do not exhibit a significant change when the underlying risk does not 

change significantly. 

B. Loss costs produced by the model shall be positive and non-zero for all 

valid Florida ZIP Codes. 

Loss costs produced by the model are positive and non-zero for all valid Florida 

ZIP Codes. 

C. Loss costs cannot increase as the quality of construction type, 

materials and workmanship increases, all other factors held constant. 

Loss costs do not increase as the quality of construction type, materials and 

workmanship increases, all other factors held constant. 

D. Loss costs cannot increase as the presence of fixtures or construction 

techniques designed for hazard mitigation increases, all other factors 

held constant. 

Loss costs do not increase with the presence of fixtures or construction 

techniques designed for hazard mitigation, all other factors held constant. 

E. Loss costs cannot increase as the quality of building codes and 

enforcement increases, all other factors held constant. 

Loss costs do not increase as the quality of building codes and enforcement 


F. Loss costs shall decrease as deductibles increase, all other factors held 

constant. 

Loss costs decrease as deductibles increase, all other factors held constant. 

G. The relationship of loss costs for individual coverages, (e.g., structures 

and appurtenant structures, contents, and loss of use/additional living 

expense) shall be consistent with the coverages provided. 

Relationships among the loss costs for coverages A,B,C,D are consistent with 

the coverages provided. 

96



Disclosures 

1. Demonstrate that loss cost relationships by type of coverage (structures, 

appurtenant structures, contents, additional living expenses) are consistent with 

actual insurance data. 

Figure 13 below compares a representative sample of Hurricane Andrew 

claims data with the statewide weighted average zero deductible loss costs 

for wood frame and masonry. All values have been normalized to the 

corresponding coverage A value. 

1.0 

0.9 

Loss Relative to Cov A Loss 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

Representative Hurricane Andrew Claims 

Statewide Wtd Ave Loss Cost - Frame 

Statewide Wtd Ave Loss Cost - Masonry 

0.2 

0.1 

0.0 

Cov A Cov B Cov C Cov D 

Figure 13. Loss Cost Relationships by Coverage 

2. Demonstrate that loss cost relationships by construction type or vulnerability 

function (frame, masonry, and mobile home) are consistent with actual insurance 

data. 

Looking at a representative sample of Hurricane Andrew claims data, we 

found that among ZIP Codes having claims for wood frame, masonry, and 

mobile homes, the average ratio of the percentage loss for wood frame to the 

percentage loss for masonry was 2.2, and the average ratio of the percentage 

97



loss for mobile home to the percentage loss for masonry was 7.9. This 

relationship is consistent with the loss costs produced by the EQECAT model. 

3. Demonstrate that loss cost relationships among coverages, territories, and regions 

are consistent and reasonable. 

We have conducted several analyses to demonstrate how the loss cost 

(Average Annual Loss or AAL) relationships produced by USWIND are 

consistent with actual insurance data. Using our large empirical data set of 

exposure loss information from historical storms, we have calculated 

expected ratios of loss rates between coverages and between structure types 

by peak gust wind speeds. We compared these ratios at various wind speeds 

to corresponding ratios for AAL. We looked at various different AAL ratios 

including the average and 75th percentile over all ZIP Codes for each 

corresponding group. The results highlighted how the AAL relationships are 

within the expectations derived from actual insurance data. 

As an example of the consistency of the loss cost relationships between 

structure types, the ratio of expected loss between Construction Type #1 and 

Construction Type #2 varied between 56% and 93% for gust wind speeds 

between 70 and 200 mph. The average ratio of AALs between these two 

construction types over all ZIP Codes was 57%, and the 75th percentile was 

68%. 

Demonstrating the consistency of the relationships between loss costs and 

territories, Figure 14 displays USWIND’s loss cost estimates. Depicted here 

are the loss costs for all construction types and all coverages. The horizontal 

axis lists all coastal counties starting with the north-eastern most county of 

Nassau and ending with the western most county of Escambia. The chart 

below the graph in the figure illustrates the corresponding historical events by 

SSI category for these regions. 

The progression from north to south and then back up to the panhandle 

coincides with the historical storm activity. For example, the southeast region 

has historically seen the most frequent and most severe storms and therefore 

yields the highest loss costs. 

Please note that the erratic behavior of the graph in certain regions can be 

explained by the fact that counties have varying geographic area. 

USWIND produces loss costs that are highly correlated to historical data. 

98



Figure 14. Loss Costs for Coastal Counties 

4. Explain any anomalies or special circumstances that might preclude any of the 

above conditions from occurring. 

There are no such anomalies. 

5. Provide a completed Form A-1, Loss Costs. 

See Form A-1 at the end of this section. 

6. Provide a completed Form A-2, Zero Deductible Loss Costs by ZIP Code. 


7. Provide a completed Form A-3, Base Hurricane Storm Set Average Annual Zero 

Deductible Statewide Loss Costs. 


8. Provide a completed Form A-4, Hurricane Andrew Percent of Losses. 

99




9. Provide a completed Form A-5, Distribution of Hurricanes by Size of Loss. 


100



A-7 Deductibles and Policy Limits 

A. The methods used in the development of mathematical distributions to 

reflect the effects of deductibles and policy limits shall be actuarially 

sound. 

The methods used in the development of mathematical distributions to reflect the 

effects of deductibles and policy limits are actuarially sound. 

B. The relationship among the modeled deductible loss costs shall be 

reasonable. 

USWIND estimates the damage distribution for a given site through the 

integration of the site hazard distribution and the corresponding vulnerability 

function as shown in Figure 15 below. 

Figure 15. Integration of Uncertainty on Hazard and Damage 

The loss distribution is estimated through the integration of the site damage 

distribution, taking into account deductibles and limits, as shown in Figure 16 

below. 

101



Damage 

PDF 

Loss = f(Damage, Deductible, Limit,...) 

Deductible 

Limit 

$ Dmg 

Loss 

Figure 16. Integration of Damage Distribution to Calculate Loss 

C. Deductible loss costs shall be calculated in accordance with s. 

627.701(5)(a), F.S. 

All loss costs have been calculated in accordance with s.627.701(5)(a), F.S. 

Disclosures 

1. Describe the methods used in the model to treat deductibles (both flat and 

percentage), policy limits, replacement costs, and insurance-to-value when 

projecting loss costs. 

The model assumes that the user has correctly input the replacement cost of 

all coverages in the portfolio. The input replacement cost must include any 

adjustments for Insurance-to value, as the model does not make any 

corrections for this. The deductible is also a user input value. The user may 

input a flat deductible (i.e., a fixed dollar amount) or a percentage amount (a 

percentage of the TIV). Deductibles may be applied separately to each 

coverage, or applied to aggregated damage. The allowed aggregations are 

Blanket (i.e., all coverages subject to one deductible) or Property Damage / 

Business Interruption (PD/BI) (i.e. all real property coverages are subject to 

102



one deductible, and the Time Element coverage is subject to a different 

deductible). Limits are input by the user, in a manner similar to that for 

deductibles. Limits are input as a dollar amount, to be applied either to (a) all 

coverages separately, (b) all coverages in aggregate, or (c) two limits, one for 

real property, and one for Time Element. Internally, the program calculates 

the loss by integrating over the probability distribution function (PDF) of the 

damage. 

2. Provide an example of how insurer loss (loss net of deductibles) is calculated. 

Discuss data or documentation used to confirm or validate the method used by the 

model. 

Example: 

(A) (B) (C) (D)=(A)*(C) (E)=(D)-(B) 

Structure 

Value 

Policy 

Limit Deductible 

Damage 

Ratio 

Zero Deductible 

Loss 

Loss Net of 

Deductible 

100,000 90,000 500 2% 2,000 1,500 

Consider the property in the example above with given value, limit, and 

deductible, subject to a wind speed with average damage ratio as given. 

Assume further that the vulnerability functions specify the range of possible 

outcomes as follows: 

Probability of Zero Damage = 0.50 

Probability of Damage Greater 

than Zero = 

Probability Distribution of Positive 

Damages = 

0.50 

{Lognormal with mean=4% 

and standard deviation=6%} 

truncated at 100% 

(Note: this functional distribution is only used for illustrative purposes and 

does not necessarily reflect the method contained within USWIND.) 

Then the average damage rate (mathematical expectation) is 0.5 x 4% = 2%, 

as specified, providing an expected damage amount (ground up loss) of 

$100,000 x 2% = $2000. 

For any given property, the insurer loss is the greater of two quantities: (1) 

zero, and (2) the damage minus the deductible, but not greater than the policy 

limit. Because the damage is a random variable, i.e., it is associated with a 

probability distribution, so too is the insurer loss. However, we can calculate 

the average insurer loss (mathematical expectation) by the following 

expression: 

103



0.9+.005 1 

100,000 • [ ∫ (x-0.005) • f(x)dx + ∫ (0.9) • f(x)dx] 

0.005 0.9 + .005 

where f(x) is the probability density function defined above. In this case, the 

result comes out to be an expected insurer loss of $1752. This is substantially 

higher than $1500 because the expectation combines the probabilities of 

high-loss outcomes, where the deductible is fully applied, with low-loss 

outcomes, where the deductible does not fully apply. 

The foregoing example illustrates the actuarial theory behind the application 

of deductibles and limits. USWIND implements this theory in loss cost 

calculations by a Latin Hypercube Sampling. For each property, one thousand 

instances of the random damage ratio are drawn from the model's probability 

distribution for damage ratio. The deductibles and limits are applied to each 

outcome and the results are averaged. Table 2 illustrates this process. 

TABLE 2. 

EXAMPLE DAMAGE TO LOSS SIMULATION 

Instance # Damage Ratio Ground Up Loss Insurer Loss 

1 0.00 0.00 0.00 

2 2% 2,000.00 1,500.00 

... ... ... ... 

999 0.37% 370.00 0.00 

1000 10% 10,000.00 9,500.00 

Total 2,000,765 1,751,942.00 

Average 2.001% 2,001.00 1.752.00 

The theoretical calculation presented above is standard in the actuarial 

literature. See, for example, chapter 3 of R. E. Beard, T. Pentikainen, and E. 

Pesonen's Risk Theory: the Stochastic Basis of Insurance (3rd Edition, New 

York: Chapman and Hall) or chapter 5 of R. V. Hogg and S. A. Klugman's 

Loss Distributions (New York: John Wiley and Sons). 

The implementation by way of simulation is standard in the simulation 

literature. See, for example, chapter 4 of R. Y. Rubenstein's Simulation and 

the Monte Carlo Method (New York: John Wiley and Sons) or chapter 5 of J. 

M. Hammersley and D. C. Handscomb's Monte Carlo Methods (New York: 

104



Barnes & Noble), or M.P. Bohn, et. al., “Application of the SSMRP 

Methodology to the Seismic Risk at the Zion Nuclear Plant,” prepared for the 

U.S. Nuclear Regulatory Commission, Lawrence Livermore National 

Laboratory. 

The specifics of the distributional models are based both on engineering 

studies of the variability of damage from winds and on extensive historical 

datasets detailing losses risk-by-risk. 

3. Describe how the model calculates annual deductibles. 

All results in this submission, where annual deductibles are required, were 

compiled through the post-processing of intermediate results generated by 

the standard EQECAT model. The handling of the annual deductibles was 

done according to the 627.701(5)(a) Florida Statutes. 

Using stratified sampling, for each year, a number of events are simulated 

from the hurricane frequency distribution. As each simulated year 

progresses, losses from each hurricane during that year are kept track of by 

policy and the corresponding effect on the remaining amount of the hurricane 

deductible evaluated. The results are used to quantify the annual deductible 

effects. 

105



A-8 Contents 

Disclosure 

A. The methods used in the development of contents loss costs shall be 

actuarially sound. 

The methods used in the development of contents loss costs are actuarially 

sound. 

B. The relationship between the modeled structure and contents loss costs 

shall be reasonable, based on the relationship between historical 

structure and contents losses. 

EQECAT’s model calculates damage to contents separately from damage to 

buildings and appurtenant structures. Content vulnerability curves in USWIND 

are based on claims data. 

Information regarding relationships among loss costs by coverage has been 

submitted in Forms A-1 and A-2. 

1. Describe the methods used in the model to calculate loss costs for contents coverage 

associated with personal residential structures (including mobile homes), tenants, 

and condo unit owners.. 

Residential content vulnerability functions were developed by regressing 

historic content claims against peak gust wind speed, using claims data 

gathered and analyzed over the last 40 years. In USWIND, the user identifies 

the structure type containing the contents, choosing from one or a 

combination of the basic structure types available in USWIND. That is not to 

say that content vulnerability is the same as building vulnerability: there are 

two sets of vulnerability functions for each of the basic types, one set for 

contents and one set for buildings. The content vulnerability is a function of 

the structure type, but it is not a direct function of the building vulnerability 

function. At this point, it would be helpful to clarify the distinction between 

“content vulnerability,” “building vulnerability,” and “structure type.” Structure 

type refers to the building’s structural system: whether the building is woodframe, 

masonry, concrete, etc.; whether the exterior wall material is strong or 

not; whether the windows are large or small; and so on. When we say 

building vulnerability, we mean the degree to which a building of a given 

structure type is estimated to be damaged at a given wind speed. A building 

with a concrete structure type is likely to be less vulnerable than a building 

with a timber structure type. Similarly, content vulnerability refers to the 

degree to which contents within a building of a given structure type are 

estimated to be damaged at a given wind speed. Contents in a building with a 

concrete structure type are less vulnerable to wind damage than contents in a 

building with a timber structure type. Building vulnerability and content 

vulnerability are both functions of structure type, but content vulnerability is 

not a function of building vulnerability. 

106



To the extent that both building damage and content damage increase at 

higher wind speeds, and to the extent that both building and content damage 

are generally higher in more vulnerable structure types, the two are positively 

correlated, but there is no direct functional dependency defined in USWIND 

between the content vulnerability function and the building vulnerability for the 

same structure type -- there is no magic factor applied to building damage to 

get content damage, nor should there be in the best designed model. To 

impose such a direct dependency would produce poorer vulnerability 

functions than are incorporated in USWIND. Regarding a minimum threshold, 

see our answer to question III.1: Content damage, like building damage, is 

estimated when peak gust wind speed (2 second averaging time) exceeds 40 

mph. Loss is calculated based on damage, deductible, limits, etc. 

Figure 17 below demonstrates the relationship between building and contents 

losses exhibited in a series of hypothetical storms run in the model. 

16,000 

14,000 

12,000 

Contents Loss ($1,000s) 

10,000 

8,000 

6,000 

4,000 

2,000 

0 

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 

Building Loss ($1,000s) 

Figure 17. Relationship Between Building and Contents Losses 

107



A-9 Additional Living Expense (ALE) 

A. The methods used in the development of Additional Living Expense 

(ALE) loss costs shall be actuarially sound. 

The methods used in the development of Additional Living Expense (ALE) loss 

costs are actuarially sound. 

B. ALE loss cost derivations shall consider the estimated time required to 

repair or replace the property. 

ALE loss cost derivations consider the estimated time required to repair or 

replace the property. 

C. The relationship between the modeled structure and ALE loss costs 

shall be reasonable, based on the relationship between historical 

structure and ALE losses. 

EQECAT’s model calculates damage to additional living expenses (ALE) as a 

function of building and content damage. ALE vulnerability curves in USWIND 

are based on claims data. 

Information regarding relationships among loss costs by coverage has been 

submitted in Form A-1. 

D. ALE loss costs produced by the model shall appropriately consider ALE 

claims arising from damage to the infrastructure. 

Disclosures 

ALE vulnerability curves in USWIND are based on claims data. 

1. Describe the methods used to develop loss cost for additional living expense 

coverage. State whether the model considers both direct and indirect loss to the 

structure. For example, direct loss is for expenses paid to house policyholders in an 

apartment while their home is being repaired. Indirect loss is for expenses incurred 

for loss of power (e.g., food spoilage). 

USWIND estimates time element costs as a function of building damage, 

content damage, and occupancy. For additional living expense (ALE) costs, 

USWIND uses its residential occupancy time-element vulnerability function. 

The program first determines the greater of building or content damage (as 

percentages of the coverage value) and then evaluates the residential timeelement 

vulnerability function at this x-value. There is no minimum threshold 

at which ALE loss is calculated. That is to say, if a site experiences significant 

structure or content damage, some time-element cost is estimated to occur. 

The size of the storm, even if it is “merely” a category 1 event, is irrelevant to 

the policyholder and the insurer; all that matters is whether the home is 

occupiable under the terms of the policy. Nor would a threshold structure 

damage make sense: even if the structure experiences minimal damage, 

108



such as just a few broken windows, significant damage to contents can result 

in significant time-element costs. It is for this reason that USWIND uses both 

structure and content damage, as well as occupancy, in determining timeelement 

costs. (Occupancies other than residential only come into play in 

other insurance lines of business, and so are not discussed here, although 

they are incorporated in USWIND.) 

We recognize that the ideal model would also include explicit consideration of 

lifeline functionality, for example, whether electrical power and water are 

available at the insured’s home. Unfortunately, proper analysis of lifeline 

functionality is a complex issue. Merely to begin such an analysis requires 

detailed information on the lifeline facilities, such as the locations, structural 

characteristics, and links between utility elements like local water mains, 

pumping stations, power plants, etc. Suffice it to say this type of data is tightly 

controlled by the multitude of public utilities involved, and is generally 

unavailable at the local level. The reader should not infer from this that 

USWIND underestimates time-element costs by an amount equal to lifelinerelated 

effects. There is a strong positive correlation between local lifeline 

damage and damage to a policyholder’s home. That correlation results from a 

common cause: higher wind speeds generally result in higher damage to 

homes, power lines, and even underground water mains, which can 

experience damage from uprooted trees. The historical data that go into 

USWIND’s time-element vulnerability functions therefore account for lifeline 

damage, if only in an indirect, average way, because they are based on 

damage that is correlated with lifeline damage. 

2. State the minimum threshold at which ALE loss is calculated (e.g., loss is estimated 

for structure damage greater than 20% or only for category 3, 4, 5 events). Provide 

documentation of validation test results to verify the approach used. 

The minimum threshold at which ALE is calculated is the point at which either 

building or contents damage becomes nonzero (i.e., at 40 mph gust). 

A simple validation test looked at a representative sample of claims data from 

Hurricane Andrew, and found that only 0.21% of the total dollar value of ALE 

claims came from policies having no corresponding building or content claim. 

In many of these cases, building or content damage may have occurred 

below the level of the deductible. 

109



A-10 Output Ranges 

A. Output ranges shall be logical and any deviations supported. 

The output ranges produced by the model are logical and any deviations are 

supported. 

B. All other factors held constant, output ranges produced by the model 

shall reflect lower loss costs for: 

1. masonry construction versus frame construction, 

The output ranges produced by the model reflect lower loss costs for masonry 

construction versus frame construction, subject to the discussion in Disclosure 1 

below. 

2. residential risk exposure versus mobile home risk exposure, 

The output ranges produced by the model reflect lower loss costs for residential 

risk exposure versus mobile home risk exposure. 

3. in general, for inland counties versus coastal counties, and 

The output ranges produced by the model reflect lower loss costs, in general, for 

inland counties versus coastal counties. 

4. in general, for northern counties versus southern counties. 

The output ranges produced by the model reflect lower loss costs, in general, for 

northern counties versus southern counties. 

Disclosures 

1. Provide an explanation for all anomalies in the loss costs that are not consistent 

with the requirements of this Standard. 

All the loss costs shown in Form A-6 are consistent with the requirements of 

this standard, except: 

• Statewide weighted average loss costs for masonry are higher than the 

corresponding statewide weighted average loss costs for frame for all 

coverage types, policy types, and deductibles except appurtenant 

structures for owners. This is due to the masonry exposures generally 

being more heavily weighted than the frame exposures in areas having 

higher levels of hazard. 

• Weighted average loss costs for masonry renters are higher than the 

corresponding weighted average loss costs for frame renters for certain 

coverage types, policy types, and deductibles for Gulf and Walton 

counties. Weighted average loss costs for masonry condos are higher 

than the corresponding weighted average loss costs for frame condos for 

certain coverage types, policy types, and deductibles for Brevard, 

Madison, Miami-Dade, and Pasco counties. All of these situations are due 

110



to the masonry exposures in these counties being more heavily weighted 

than the frame exposures in areas having higher levels of hazard. 

2. Provide an explanation of the differences in the output ranges between the prior 

year and the current year submission. 

The following change was made to WORLDCATenterprise / USWIND 

between the submission last year (USWIND Version 5.10 / 

WORLDCATenterprise Version 3.8) and the current submission (USWIND 

Version 5.11 / WORLDCATenterprise Version 3.9): 

1. The probabilistic hurricane database was regenerated to be consistent 

with the Commission’s November 1, 2006 storm set, and to additionally 

include the 2006 hurricane season. 

3. Provide justification for changes from the prior submission of greater than ten 

percent in weighted average loss costs for any county, specifically by county. 

Weighted average loss costs for certain coverage types, policy types, and 

deductibles have changed by more than 10% in a number of counties, due to 

the regeneration of the probabilistic hurricane database and the inclusion of 

demand surge. 

4. Provide justification for changes from the prior submission of ten percent or less in 

the weighted average loss costs for any county, in the aggregate. 

Weighted average loss costs for certain coverage types, policy types, and 

deductibles in many counties have changed by less than 10%, attributable to 

the regeneration of the probabilistic hurricane database and the inclusion of 

demand surge. 

5. Provide a completed Form A-6, Output Ranges. 

See Form A-6. 

6. Provide a completed Form A-7, Percentage Change in Output Ranges. 

See Form A-7. 

7. Provide a completed Form A-8, Percentage Change in Output Ranges by County. 

See Form A-8. 

111



Form A-1: Loss Costs 

A. Provide the expected annual loss costs by construction type and coverage for each ZIP Code 

in the sample data set named “FormA2Input06.xls.” Loss costs should be rounded to six 

decimal places. There are 1,479 ZIP Codes and three construction types; therefore, the 

completed file should have 4,437 records in total. The following is a description of the 

requested file layout. Follow the instructions on Form A-1 below and in the Submission 

Data description. Note that fields 2-9 are the exposure fields from the sample data set. 

Fields 10-13 are for the loss costs (net of deductibles). 

B. If there are ZIP Codes in the sample data set that the model does not recognize as “valid,” 

provide a list in the submission document of such ZIP Codes and provide either a) the new 

ZIP Code to which the original one was mapped, or b) an indication that the insured values 

from this ZIP Code were not modeled. 

Loss cost data should be provided for all ZIP Codes given in the sample data set. That is, if 

no losses were modeled, the record should still be included in the completed file with loss 

cost of zero, and if a ZIP Code was mapped to a new one, the resulting loss costs should be 

reported with the original ZIP Code. 

C. Provide the results on CD in both Excel and PDF format using the following file layout. The 

file name should include the abbreviated name of the modeler, the Standards year, and the 

Form name. 

No. Field Name Description 

Exposure Fields from Sample Data Set 

1 Analysis Date Date of Analysis – YYYY/MM/DD 

2 County Code FIPS County Code 

3 ZIP Code 5-digit ZIP Code 

4 Construction Type Use the following: 1 = Wood Frame, 2 = Masonry, 3 = Mobile Home 

5 Annual Deductible 1% (of the Structure Value) policy deductible for each 

record (i.e., 0.01*$100,000) 

6 Structure Value $100,000 for each record 

7 Appurtenant Structures Value $10,000 for each record 

8 Contents Value $50,000 for each record 

9 Additional Living Expense Value $20,000 for each record 

Loss Costs (net of deductibles) 

10 Structure Loss Cost Projected expected annual loss cost for structure divided by the structure 

value modeled for each record ($100,000) 

11 Appurtenant Structures Loss Cost Projected expected annual loss cost for appurtenant structures divided by the 

appurtenant structures value modeled for each record ($10,000) 

12 Contents Loss Cost Projected expected annual loss cost for contents divided by the contents value 

modeled for each record ($50,000) 

13 Additional Living Expense Loss Cost Projected expected annual loss cost for additional living expense divided by 

the additional living expense value modeled for each record ($20,000) 

112



All deductibles are a percentage of the Structure Value and are policy-level deductibles; 

however, for reporting purposes, the policy deductible should be pro-rated to the individual 

coverage losses in proportion to the loss. The default all-other perils deductible is $500. 

Example 

Assume that a model analyzing Wood Frame properties in ZIP Code 33102 (Miami-Dade 

County) estimated the following: 

Field Name 

Value 

Analysis Date 1999/11/15 

County Code Miami-Dade County = 86 

ZIP Code 33102 

Construction Type Wood Frame = 1 

Annual Deductible 1% = 0.01*$100,000 = $1,000 

Structure Value $100,000 

Appurtenant Structures Value $10,000 

Contents Value $50,000 

Additional Living Expense Value $20,000 

Structure Loss Cost* $10,000 

Appurtenant Structures Loss Cost* $1,000 

Contents Loss Cost* $2,500 

Additional Living Expense Loss Cost* $500 

*Represents first dollar losses (i.e., prior to application of deductibles) 

The $1,000 hurricane deductible would be applied as follows: 

Annual Deductible 1% = 0.01*$100,000=$1,000 

Structure Loss Cost 

$10,000-[($10,000÷$14,000)x$1,000]=$9,285.71 

Appurtenant Structures Loss Cost $1,000-[($1,000÷$14,000)x$1,000]=$928.57 

Contents Loss Cost 

$2,500-[($2,500÷$14,000)x$1,000]=$2,321.43 

Additional Living Expense Loss Cost $500-[($500÷$14,000)x$1,000]=$464.29 

The reported Form A-1 data are shown below: 

Field Name 

Value 

Analysis Date 1999/11/15 

County Code Miami-Dade County = 86 

ZIP Code 33102 

Construction Type Wood Frame = 1 

Annual Deductible 1% = 0.01 

Structure Value $100,000 

Appurtenant Structures Value $10,000 

Contents Value $50,000 

Additional Living Expense Value $20,000 

Structure Loss Cost $9,285.71÷$100,000 = 0.092857 

Appurtenant Structures Loss Cost $928.57÷$10,000 = 0.092857 

Contents Loss Cost $2,321.43÷$50,000 = 0.046429 

Additional Living Expense Loss Cost $464.29÷$20,000 = 0.023214 

113



Based on the above information, the data should be reported in the following format: 

1999/11/15,86,33102,1,0.01,100000,10000,50000,20000,0.092857,0.092857,0.046429,0.023214 

This information is provided in the files 2006FormA1_EQECAT.xls and 

2006FormA1_EQECAT.pdf. 

114



Form A-2: Zero Deductible Loss Costs by ZIP Code 

Provide a map color-coded by ZIP Code (with a minimum of 6 value ranges) displaying zero 

deductible loss costs for frame, masonry, and mobile home. 

Thematic maps displaying zero deductible loss costs by 5-digit ZIP Code for 

frame, masonry, and mobile home are provided in Figures 18 to 20. 

Figure 18. Ground-up Loss Costs for Frame Structures 

115



(FORM A-2 CONTINUED) 

Figure 19. Ground-up Loss Costs for Masonry Structures 

116




Figure 20. Ground-up Loss Costs for Mobile Home Structures 

117



Form A-3: Base Hurricane Storm Set Average Annual Zero Deductible 

Statewide Loss Costs 

A. Provide the monetary contribution to the average annual personal residential zero 

deductible statewide loss costs from each specific hurricane in the Base Hurricane Storm Set 

for the 2002 Florida Hurricane Catastrophe Fund’s aggregate exposure data found in the 

file named “hlpm2002.exe”. Additional storms that are included in the model’s Base 

Hurricane Storm Set should be included. 

B. Provide this Form on CD in both an Excel and a PDF format. The file name should include 

e abbreviated name of the modeler, the Standards year, and the Form name. A hard copy of 

Form A-3 should be included in the submission. 

Date Year Name Contribution 

8/2/1901 1901 NoName 4 723 

9/9/1903 1903 NoName 3 19,783 

6/14/1906 1906 NoName 2 13,799 

9/19/1906 1906 NoName 6 8,403 

10/8/1906 1906 NoName 8 11,846 

9/13/1909 1909 NoName 8 3,064 

10/9/1910 1910 NoName 5 27,677 

8/9/1911 1911 NoName 1 3,537 

8/23/1911 1911 NoName 2 - 

9/11/1912 1912 NoName 3 310 

8/31/1915 1915 NoName 4 904 

6/29/1916 1916 NoName 1 2,099 

10/12/1916 1916 NoName 13 4,054 

11/11/1916 1916 NoName 14 393 

9/21/1917 1917 NoName 3 8,115 

9/2/1919 1919 NoName 2 32,302 

10/20/1921 1921 NoName 6 60,748 

9/13/1924 1924 NoName 4 787 

10/14/1924 1924 NoName 7 3,184 

11/29/1925 1925 NoName 2 3,908 

7/22/1926 1926 NoName 1 22,107 

9/11/1926 1926 NoName 6 259,706 

10/14/1926 1926 NoName 10 9,926 

8/3/1928 1928 NoName 1 12,183 

9/6/1928 1928 NoName 4 203,601 

9/22/1929 1929 NoName 2 40,968 

8/26/1932 1932 NoName 3 3,597 

7/25/1933 1933 NoName 5 2,917 

8/31/1933 1933 NoName 12 57,841 

8/29/1935 1935 NoName 2 58,372 

10/30/1935 1935 NoName 6 13,007 

7/27/1936 1936 NoName 5 9,101 

8/7/1939 1939 NoName 2 5,236 

8/5/1940 1940 NoName 3 - 

10/3/1941 1941 NoName 5 77,025 

10/12/1944 1944 NoName 11 68,931 

6/20/1945 1945 NoName 1 6,639 

9/12/1945 1945 NoName 9 41,455 

10/5/1946 1946 NoName 5 5,941 

9/4/1947 1947 NoName 4 171,262 

10/9/1947 1947 NoName 8 7,390 

118 

Date Year Name Contribution 

9/18/1948 1948 NoName 7 10,647 

10/3/1948 1948 NoName 8 3,193 

8/23/1949 1949 NoName 2 50,584 

8/20/1950 1950 Baker 1,201 

9/1/1950 1950 Easy 48,583 

10/13/1950 1950 King 36,259 

9/23/1953 1953 Florence 1,857 

9/21/1956 1956 Flossy 3,156 

8/29/1960 1960 Donna 74,719 

9/14/1960 1960 Ethel 12 

8/20/1964 1964 Cleo 22,264 

8/28/1964 1964 Dora 14,921 

10/8/1964 1964 Isbell 16,230 

8/27/1965 1965 Betsy 32,200 

6/4/1966 1966 Alma 4,923 

9/21/1966 1966 Inez 2,557 

10/13/1968 1968 Gladys 8,540 

8/14/1969 1969 Camille 242 

6/14/1972 1972 Agnes 464 

9/13/1975 1975 Eloise 5,469 

8/25/1979 1979 David 23,730 

8/29/1979 1979 Frederic 4,279 

8/28/1985 1985 Elena 15,354 

11/15/1985 1985 Kate 1,880 

10/9/1987 1987 Floyd 227 

8/16/1992 1992 Andrew 140,436 

7/31/1995 1995 Erin 7,045 

9/27/1995 1995 Opal 10,010 

7/16/1997 1997 Danny 1,028 

8/31/1998 1998 Earl 1,091 

9/15/1998 1998 Georges 3,126 

10/12/1999 1999 Irene 14,707 

8/9/2004 2004 Charley 65,929 

8/25/2004 2004 Frances 58,288 

9/2/2004 2004 Ivan 28,135 

9/13/2004 2004 Jeanne 52,402 

7/5/2005 2005 Dennis 9,163 

8/23/2005 2005 Katrina 9,816 

9/18/2005 2005 Rita 830 

10/15/2005 2005 Wilma 68,428



Form A-4: Hurricane Andrew Percent of Losses 

A. Provide the percentage of personal residential zero deductible losses, rounded to four 

decimal places, from Hurricane Andrew for each affected ZIP Code. Include all ZIP Codes 

where losses are equal to or greater than $500,000. 

See the table below. 

B. Provide a map color-coded by ZIP Code depicting the percentage of total losses from 

Hurricane Andrew below latitude 27 o N using the following interval coding: 

Red Over 5% 

Light Red 2% to 5% 

Pink 1% to 2% 

Light Pink 0.5% to 1% 

Light Blue 0.2% to 0.5% 

Medium Blue 0.1% to 0.2% 

Blue Below 0.1% 

See the figure below. 

C. Provide this Form on CD in both an Excel and a PDF format. The file name should include 

the abbreviated name of the modeler, the Standards year, and the Form name. A hard copy 

of Form A-4 should be included in the submission. 

Rather than using directly a published wind field for Hurricane Andrew, the winds underlying 

the loss cost calculations must be produced by the model being evaluated and should be the one 

most emulated by the model. Use the 2002 Florida Hurricane Catastrophe Fund’s aggregate 

exposure data found in the file named “hlpm2002.exe”. 

119




Zip Code 

Hurricane Andrew Losses by ZIP Code 

Zero Deductible Loss 

Monetary Contribution 

($1000s) 

120 

Percent of Zero 

Deductible Losses (%) 

33004 5,955 0.04% 

33009 19,837 0.13% 

33010 34,421 0.23% 

33012 44,264 0.29% 

33013 34,653 0.23% 

33014 27,610 0.18% 

33015 26,788 0.18% 

33016 25,833 0.17% 

33018 24,308 0.16% 

33019 20,027 0.13% 

33020 15,221 0.10% 

33021 28,845 0.19% 

33023 28,473 0.19% 

33024 25,386 0.17% 

33025 19,363 0.13% 

33026 17,989 0.12% 

33027 31,612 0.21% 

33028 17,214 0.11% 

33029 32,341 0.22% 

33030 71,225 0.47% 

33031 66,669 0.44% 

33032 124,140 0.83% 

33033 89,402 0.59% 

33034 25,399 0.17% 

33035 13,638 0.09% 

33036 572 0.00% 

33037 8,106 0.05% 

33039 1,772 0.01% 

33054 11,073 0.07% 

33055 22,005 0.15% 

33056 15,361 0.10% 

33060 5,215 0.03% 

33062 8,285 0.06% 

33063 7,181 0.05% 

33064 8,996 0.06% 

33065 5,945 0.04% 

33066 2,114 0.01% 

33067 7,463 0.05% 

33068 5,745 0.04% 

33069 1,880 0.01% 

33070 1,442 0.01% 

33071 9,249 0.06%



Zip Code 




($1000s) 

121 



33073 3,885 0.03% 

33076 6,823 0.05% 

33101 1,373 0.01% 

33109 27,245 0.18% 

33114 2,308 0.02% 

33116 719 0.00% 

33125 89,703 0.60% 

33126 61,434 0.41% 

33127 26,902 0.18% 

33128 3,791 0.03% 

33129 101,358 0.67% 

33130 17,588 0.12% 

33131 17,369 0.12% 

33132 4,877 0.03% 

33133 540,997 3.60% 

33134 356,220 2.37% 

33135 73,870 0.49% 

33136 8,035 0.05% 

33137 39,857 0.27% 

33138 66,060 0.44% 

33139 120,346 0.80% 

33140 132,389 0.88% 

33141 46,257 0.31% 

33142 47,076 0.31% 

33143 761,852 5.07% 

33144 99,405 0.66% 

33145 192,175 1.28% 

33146 337,006 2.24% 

33147 33,798 0.22% 

33148 541 0.00% 

33149 335,875 2.24% 

33150 20,808 0.14% 

33152 594 0.00% 

33154 38,674 0.26% 

33155 358,307 2.38% 

33156 1,507,696 10.03% 

33157 1,309,238 8.71% 

33158 374,677 2.49% 

33160 29,741 0.20% 

33161 40,680 0.27% 

33162 23,252 0.15% 

33165 384,280 2.56% 

33166 38,289 0.25%



Zip Code 




($1000s) 

122 



33167 12,968 0.09% 

33168 20,499 0.14% 

33169 16,624 0.11% 

33170 78,421 0.52% 

33172 30,440 0.20% 

33173 394,209 2.62% 

33174 86,297 0.57% 

33175 363,327 2.42% 

33176 1,203,642 8.01% 

33177 496,958 3.31% 

33178 58,410 0.39% 

33179 22,654 0.15% 

33180 26,342 0.18% 

33181 22,820 0.15% 

33182 51,502 0.34% 

33183 287,461 1.91% 

33184 85,840 0.57% 

33185 144,511 0.96% 

33186 863,199 5.74% 

33187 237,751 1.58% 

33189 256,642 1.71% 

33190 43,239 0.29% 

33193 286,142 1.90% 

33196 563,623 3.75% 

33301 10,563 0.07% 

33304 6,203 0.04% 

33305 6,015 0.04% 

33306 2,137 0.01% 

33308 15,633 0.10% 

33309 6,062 0.04% 

33311 7,266 0.05% 

33312 17,358 0.12% 

33313 5,464 0.04% 

33314 5,325 0.04% 

33315 4,579 0.03% 

33316 10,395 0.07% 

33317 14,185 0.09% 

33319 7,535 0.05% 

33321 7,242 0.05% 

33322 10,047 0.07% 

33323 5,900 0.04% 

33324 13,042 0.09% 

33325 10,767 0.07%



Zip Code 




($1000s) 

123 



33326 12,257 0.08% 

33327 14,373 0.10% 

33328 14,149 0.09% 

33330 10,960 0.07% 

33331 17,168 0.11% 

33332 6,225 0.04% 

33334 6,232 0.04% 

33351 4,699 0.03% 

33401 682 0.00% 

33404 781 0.01% 

33405 1,387 0.01% 

33406 1,056 0.01% 

33407 636 0.00% 

33408 1,564 0.01% 

33409 575 0.00% 

33410 1,554 0.01% 

33411 2,272 0.02% 

33412 700 0.00% 

33414 3,102 0.02% 

33415 1,000 0.01% 

33417 592 0.00% 

33418 2,262 0.02% 

33426 1,687 0.01% 

33428 6,302 0.04% 

33431 4,678 0.03% 

33432 6,890 0.05% 

33433 9,093 0.06% 

33434 4,848 0.03% 

33435 2,461 0.02% 

33436 4,214 0.03% 

33437 6,375 0.04% 

33441 3,752 0.02% 

33442 3,755 0.02% 

33444 2,054 0.01% 

33445 4,285 0.03% 

33446 4,195 0.03% 

33455 1,315 0.01% 

33458 1,871 0.01% 

33460 1,533 0.01% 

33461 1,080 0.01% 

33462 3,224 0.02% 

33463 2,225 0.01% 

33467 3,940 0.03%



Zip Code 




($1000s) 

124 



33469 1,209 0.01% 

33470 931 0.01% 

33477 1,289 0.01% 

33480 7,583 0.05% 

33483 3,508 0.02% 

33484 3,223 0.02% 

33486 5,141 0.03% 

33487 4,957 0.03% 

33496 8,869 0.06% 

33498 3,891 0.03% 

33901 826 0.01% 

33903 1,148 0.01% 

33904 3,218 0.02% 

33905 859 0.01% 

33907 843 0.01% 

33908 3,798 0.03% 

33909 638 0.00% 

33912 4,015 0.03% 

33913 605 0.00% 

33914 3,050 0.02% 

33917 1,229 0.01% 

33919 2,554 0.02% 

33921 1,083 0.01% 

33924 663 0.00% 

33928 2,157 0.01% 

33931 1,842 0.01% 

33936 1,368 0.01% 

33950 1,081 0.01% 

33952 825 0.01% 

33957 3,689 0.02% 

33971 584 0.00% 

33972 566 0.00% 

33990 1,680 0.01% 

33991 740 0.00% 

34102 19,545 0.13% 

34103 7,794 0.05% 

34104 8,552 0.06% 

34105 8,663 0.06% 

34108 13,106 0.09% 

34109 8,959 0.06% 

34110 6,437 0.04% 

34112 12,591 0.08% 

34113 10,316 0.07%



Zip Code 




($1000s) 



34114 10,070 0.07% 

34116 5,862 0.04% 

34117 3,115 0.02% 

34119 9,185 0.06% 

34120 4,057 0.03% 

34134 7,704 0.05% 

34135 5,912 0.04% 

34138 1,277 0.01% 

34139 2,238 0.01% 

34140 1,106 0.01% 

34145 91,024 0.61% 

34223 782 0.01% 

34224 597 0.00% 

34231 704 0.00% 

34238 538 0.00% 

34275 539 0.00% 

34287 622 0.00% 

34292 602 0.00% 

34293 1,025 0.01% 

34952 542 0.00% 

34957 504 0.00% 

34990 706 0.00% 

34996 512 0.00% 

34997 972 0.01% 

125




Hurricane Andrew % of Loss for FHCF2002 Portfolio by Zip Code 

126



Form A-5: Distribution of Hurricanes by Size of Loss 

A. Provide a detailed explanation of how the Expected Annual Hurricane Losses and Return 

Times are calculated. 

Due to the large number of stochastic storm simulations, the standard output from 

USWIND does not include event specific results. USWIND can produce event 

specific results using a ‘Reduced Set’ of approximately 50,000 stochastic events, 

and it is the results from this set that are presented. 

The table is based on the EQECAT ‘Reduced Set’ of approximately 50,000 

stochastic events, of which 29,804 affect the 2002 FHCF exposure data provided 

by the Commission. Each of the 29,804 hurricanes has an annual frequency 

defined in the model, and a modeled result for Personal Residential Zero 

Deductible statewide loss using the FHCF exposure data. When the 29,804 

hurricanes are sorted in descending order of loss, the exceedance frequency for 

each loss is given by the sum of all hurricane frequencies with losses at or above 

that level. 

Each row of the table represents a range of losses. We calculated the average loss 

for each range as the sum of all losses (from the 29,804 hurricanes) falling within 

the range divided by the number of such losses (the number of losses is provided 

in the ‘No. of storms’ column). 

We calculated the expected annual hurricane loss for each range by summing the 

product of loss and annual frequency over all hurricanes with losses falling within 

the range. 

We calculated the return time in years for each range by first interpolating the 

exceedance frequency to the value corresponding to the average loss for the range 

(this was done linearly between the adjacent hurricane losses, from among the 

29,804 hurricanes). Taking this exceedance frequency to be λ, we calculated the 

return time in years as 1/ (1 – exp(-λ)). 

B. Complete Form A-5 showing the Distribution of Hurricanes by Size of Loss. For the 

Expected Annual Hurricane Losses column, provide personal residential, zero deductible 

statewide loss costs based on the 2002 Florida Hurricane Catastrophe Fund’s aggregate 

exposure data found in the file named “hlpm2002.exe.” 

See the completed form below. 

127



C. In the column, Return Time (Years), provide the return time associated with the average loss 

within the ranges indicated on a cumulative basis. 

For example, if the average loss is $4,705 million for the range $4,501 million to $5,000 

million, provide the return time associated with a loss that is $4,705 million or greater. 

For each loss range in millions ($1,001-$1,500, $1,501-$2,000, $2,001-$2,500) the average 

loss within that range should be identified and then the return time associated with that loss 

calculated. The return time is then the reciprocal of the probability of the loss equaling or 

exceeding this average loss size. 

The probability of equaling or exceeding the average of each range should be smaller as the 

ranges increase (and the average losses within the ranges increase). Therefore, the return 

time associated with each range and average loss within that range should be larger as the 

ranges increase. Return times should be based on cumulative probabilities. 

A return time for an average loss of $4,705 million within the $4,501-$5,000 million range 

should be lower than the return time for an average loss of $5,455 million associated with a 

$5,001- $6,000 million range. 

D. Provide a graphical comparison of the current submission Return Times to the prior year’s 

submission Return Times. Return Time (Years) should be shown on the y-axis on a log 10 

sacle with Losses in Billions shown on the x-axis. The legend should indicate the 

corresponding submission with a solid line representing the current year and a dotted line 

representing the prior year. 

See the figure below. 

E. Provide this Form on CD in both an Excel and a PDF format. The file name should include 



128



Form A-5: Distribution of Hurricanes by Size of Loss 

LOSS RANGE 

(MILLIONS) 

TOTAL 

LOSS 

AVERAGE 

LOSS 

(MILLIONS) 

NUMBER OF 

HURRICANES 

EXPECTED 

ANNUAL 

HURRICANE 

LOSSES* 

RETURN 

TIME 

(YEARS) 

$ - to $ 500 $ 1,305,166 $ 129 10,095 $ 52.3 2 

$ 501 to $ 1,000 $ 2,075,212 $ 729 2,847 $ 76.9 3 

$ 1,001 to $ 1,500 $ 2,185,111 $ 1,235 1,769 $ 78.9 3 

$ 1,501 to $ 2,000 $ 2,175,661 $ 1,741 1,250 $ 79.2 4 

$ 2,001 to $ 2,500 $ 2,122,878 $ 2,242 947 $ 77.6 4 

$ 2,501 to $ 3,000 $ 2,141,499 $ 2,742 781 $ 70.4 5 

$ 3,001 to $ 3,500 $ 2,083,786 $ 3,241 643 $ 72.7 6 

$ 3,501 to $ 4,000 $ 2,213,475 $ 3,745 591 $ 78.6 6 

$ 4,001 to $ 4,500 $ 2,250,276 $ 4,246 530 $ 68.1 7 

$ 4,501 to $ 5,000 $ 2,188,623 $ 4,737 462 $ 63.2 7 

$ 5,001 to $ 6,000 $ 4,018,585 $ 5,482 733 $ 112.2 8 

$ 6,001 to $ 7,000 $ 3,986,777 $ 6,504 613 $ 107.0 10 

$ 7,001 to $ 8,000 $ 4,175,219 $ 7,482 558 $ 104.6 11 

$ 8,001 to $ 9,000 $ 3,682,583 $ 8,466 435 $ 98.7 13 

$ 9,001 to $ 10,000 $ 3,591,076 $ 9,500 378 $ 72.2 14 

$ 10,001 to $ 11,000 $ 3,660,349 $ 10,488 349 $ 76.8 16 

$ 11,001 to $ 12,000 $ 3,154,015 $ 11,469 275 $ 73.2 18 

$ 12,001 to $ 13,000 $ 3,664,286 $ 12,506 293 $ 75.2 20 

$ 13,001 to $ 14,000 $ 3,210,060 $ 13,488 238 $ 62.9 22 

$ 14,001 to $ 15,000 $ 3,155,983 $ 14,477 218 $ 59.3 24 

$ 15,001 to $ 16,000 $ 3,499,812 $ 15,486 226 $ 61.4 27 

$ 16,001 to $ 17,000 $ 2,934,177 $ 16,484 178 $ 52.6 30 

$ 17,001 to $ 18,000 $ 2,989,551 $ 17,483 171 $ 46.2 32 

$ 18,001 to $ 19,000 $ 2,740,575 $ 18,517 148 $ 46.0 35 

$ 19,001 to $ 20,000 $ 2,669,928 $ 19,489 137 $ 43.5 38 

$ 20,001 to $ 21,000 $ 2,598,515 $ 20,461 127 $ 33.0 41 

$ 21,001 to $ 22,000 $ 2,709,103 $ 21,501 126 $ 33.8 43 

$ 22,001 to $ 23,000 $ 2,651,566 $ 22,471 118 $ 28.7 46 

$ 23,001 to $ 24,000 $ 2,513,469 $ 23,490 107 $ 34.9 49 

$ 24,001 to $ 25,000 $ 2,371,765 $ 24,451 97 $ 38.0 53 

$ 25,001 to $ 26,000 $ 2,654,713 $ 25,526 104 $ 36.2 58 

$ 26,001 to $ 27,000 $ 2,570,007 $ 26,495 97 $ 30.3 63 

$ 27,001 to $ 28,000 $ 2,633,347 $ 27,431 96 $ 21.3 66 

$ 28,001 to $ 29,000 $ 2,591,733 $ 28,481 91 $ 31.6 71 

$ 29,001 to $ 30,000 $ 2,062,566 $ 29,465 70 $ 26.1 75 

$ 30,001 to $ 35,000 $ 10,825,066 $ 32,410 334 $ 113.4 92 

$ 35,001 to $ 40,000 $ 8,723,458 $ 37,280 234 $ 104.3 128 

$ 40,001 to $ 45,000 $ 6,928,395 $ 42,505 163 $ 71.5 181 

$ 45,001 to $ 50,000 $ 7,148,847 $ 47,343 151 $ 59.6 241 

$ 50,001 to $ 55,000 $ 5,286,962 $ 52,346 101 $ 53.2 339 

$ 55,001 to $ 60,000 $ 3,572,957 $ 57,628 62 $ 26.2 439 

$ 60,001 to $ 65,000 $ 3,434,256 $ 62,441 55 $ 28.8 561 

$ 65,001 to $ 70,000 $ 3,779,468 $ 67,490 56 $ 26.1 713 

$ 70,001 to $ 75,000 $ 2,980,639 $ 72,699 41 $ 20.8 931 

$ 75,001 to $ 80,000 $ 2,317,866 $ 77,262 30 $ 22.2 1,236 

$ 80,001 to $ 90,000 $ 3,737,164 $ 84,936 44 $ 21.0 1,984 

$ 90,001 to $ 100,000 $ 2,439,652 $ 93,833 26 $ 10.7 2,956 

$ 100,001 to $ Maximum $ 14,542,810 $ 149,926 97 $ 40.2 11,551 

TOTAL: 27,292 $ 2,721.4 

*Personal residential zero deductible statewide loss using 2002 FHCF exposure data – file name: hlpm2002.exe. 

129



(Form A-5 Continued) 

100,000 

10,000 

Current submission 

Previous submission 

Return TIme (Years) 

1,000 

100 

10 

1 

$- $20 $40 $60 $80 $100 $120 $140 $160 

2002 FHCF Loss ($Billion) 

Current Submission Return Times vs. Prior Year’s Submission Return Times 

130



Form A-6: Output Ranges 

A. Provide output ranges in the format shown in the file named “2006FormA6.xls” by using an 

automated program or script. A hard copy of the output range spreadsheets should be 

included in the submission. Provide the output ranges on CD in both Excel and PDF format 

as specified. The file name should include the abbreviated name of the modeler, the 

Standards year, and the Form name. 

B. Provide loss costs by county. Within each county, loss costs should be shown separately per 

$1,000 of exposure for personal residential, tenants, condo unit owners, and mobile home; 

for each major deductible option; and by construction type. For each of these categories 

using ZIP Code centroids, the output range should show the highest loss cost, the lowest loss 

cost, and the weighted average loss cost based on the 2002 Florida Hurricane Catastrophe 

Fund aggregate exposure data provided to each modeler in the file named “hlpm2002.exe”. 

A file named “02FHCFWts.xls” has also been provided for use in determining the weighted 

average loss costs. Include the statewide range of loss costs (i.e., low, high, and weighted 

average). For each of the loss costs provided, identify what that loss cost represents by line 

of business, deductible option, construction type, and coverages included, i.e., structure, 

contents, appurtenant structures, or additional living expenses as specified. 

C. If a modeler has loss costs for a ZIP Code for which there is no exposure, then the modeler 

should give the loss costs zero weight (i.e., assume the exposure in that ZIP Code is zero). 

Provide a list in the submission document of those ZIP Codes where this occurs. 

D. If the modeler does not have loss costs for a ZIP Code for which there is some exposure, the 

modeler should not assume such loss costs are zero, but should use only the exposures for 

which it has loss costs in calculating the weighted average loss costs. Provide a list in the 

submission document of the ZIP Codes where this occurs. 

E. All anomalies in loss costs that are not consistent with the requirements of Standard A-10 

and have been explained in Disclosure A-10.1 should be shaded. 

Output ranges should be computed assuming an average structure. 

Modelers should indicate if per diem is used in producing loss costs for Coverage D (ALE) in the 

output ranges. If a per diem rate is used in the submission, a rate of $150.00 per day per policy 

should be used. 

The following ZIP Codes have loss costs in USWIND but are not in the 2002 

FHCF exposure: 

3304400041, 00042, 00043, 00045, 00053, 00087, 00097, 00098, 32006, 32026, 

32723, 32745, 32896, 33336, 33542, 33563, 33575, 34637, 34638, 34692, 

34714, and 34715. 

131



The following ZIP Codes are in the 2002 FHCF exposure but do not have loss 

costs in USWIND: 

32151, 32230, 32335, 32573, 32574, 32575, 32576, 32581, 32582, 32589, 

32590, 32592, 32593, 32594, 32595, 32596, 32597, 32598, 32613, 33044, 

33188, 33192, and 33651 

The Form A-6 results appear in the files 2006FormA6_EQECAT.xls and 

2006FormA6_EQECAT.pdf. 

132



Output Range Specifications 

“Owners” Policy Type 

Coverage A: Structure 

• Amount of Insurance = $100,000 

• Replacement Cost Included Subject to Coverage “A” Limit 

• Ordinance or Law Not Included 

Coverage B: Appurtenant Structures 

• Amount of Insurance = 10% of Coverage “A” Amount 

• Replacement Cost Included Subject to Coverage “B” Limit 

• Ordinance or Law Not Included 

Coverage C: Contents 


• Replacement Cost Included Subject to Coverage “C” Limit 

Coverage D: Additional Living Expense 


• Time Limit = 12 Months 

• Per Diem = $150.00/day per policy, if used 

‣ Loss Costs per $1,000 should be related to the Coverage “A” Amount. 

‣ For weighting the Coverage “D” Loss Costs, use the file named “02FHCFWts.xls” 

for distribution for Coverage “D.” 

‣ Loss Costs for the various deductibles should be determined based on annual 

deductibles. 

‣ All-other perils deductible should be $500. 

‣ Explain any deviations and differences from the prescribed format above. 

‣ Specify the model name and version number reflecting the release date as a footnote 

on each page of the output. 

133




“Tenants” Policy Type 





• Amount of Insurance = 40% of Coverage “C” Amount 



‣ Loss Costs per $1,000 should be related to the Coverage “C” Amount. 




deductibles. 


‣ For weighting the Coverage “C” Loss Costs, use the file named “02FHCFWts.xls” 

for distribution for Coverage “C.” 




134




“Condo Unit Owners” Policy Type 











‣ Loss Costs per $1,000 should be related to the Coverage “C” Amount. 




deductibles. 


‣ For weighting the Coverage “C” Loss Costs, use the file named “02FHCFWts.xls” 

for distribution for Coverage “C.” 




135




“Mobile Home Owners” Policy Type 




Coverage B: Appurtenant Structures 


• Replacement Cost Included Subject to Coverage “B” Limit 








‣ Loss Costs per $1,000 should be related to the Coverage “A” Amount 




deductibles. 





136

Output Range Loss Costs 

Form A-6 (2002 FHCF Exposure) LOSS COSTS PER $1,000 

Personal Residential -- Owners -- FRAME 

$0 $0 $0 DEDUCTIBLE $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 

DEDUCTIBLE DEDUCTIBLE APPURTENANT ADDITIONAL DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE 

COUNTY LOSS COSTS STRUCTURE CONTENTS STRUCTURE LIVING EXPENSE TOTAL* TOTAL* TOTAL* TOTAL* TOTAL* TOTAL* 

ALACHUA LOW 0 514 0.059 0.051 0.064 0.478 0.384 0.220 0 381 0 259 0.102 

HIGH 0.711 0.090 0.071 0.075 0.684 0.559 0.348 0 557 0 398 0.178 

WGHTD AVE 0 578 0.069 0.058 0.068 0.550 0.447 0.261 0.445 0 310 0.130 

BAKER LOW 0 333 0.033 0.033 0.050 0.294 0.224 0.118 0 224 0.143 0 049 

HIGH 0.497 0.056 0.050 0.062 0.463 0.367 0.210 0 367 0 251 0 098 

WGHTD AVE 0.477 0.053 0.048 0.061 0.444 0.352 0.198 0 350 0 236 0 092 

BAY LOW 1 364 0.211 0.136 0.095 1.436 1.230 0.832 1 230 0 940 0.484 

HIGH 5 844 1.866 0.584 0.424 8.092 7.704 6.804 7.704 7 069 5.792 

WGHTD AVE 4 555 1.294 0.429 0.308 6.004 5.653 4.865 5 653 5 094 4 015 

BRADFORD LOW 0 513 0.057 0.051 0.062 0.478 0.374 0.217 0 378 0 256 0.100 

HIGH 0 535 0.060 0.053 0.066 0.497 0.394 0.226 0 394 0 268 0.107 

WGHTD AVE 0 524 0.059 0.053 0.064 0.490 0.385 0.224 0 389 0 263 0.103 

BREVARD LOW 1.472 0.281 0.147 0.111 1.661 1.466 1.082 1.466 1.188 0.730 

HIGH 5 631 1.776 0.563 0.423 7.669 7.286 6.458 7 286 6.700 5 534 

WGHTD AVE 3 296 0.870 0.327 0.239 4.235 3.946 3.326 3 946 3 504 2 683 

BROWARD LOW 4.705 0.943 0.471 0.267 5.620 5.164 4.116 5.164 4.420 3 017 

HIGH 12.448 3.973 1.245 1.015 17.656 17.083 15.663 17 083 16 096 13 934 

WGHTD AVE 6 962 1.758 0.692 0.466 9.011 8.511 7.333 8 511 7 681 6 015 

CALHOUN LOW 1 206 0.173 0.121 0.090 1.238 1.046 0.684 1 046 0.780 0 380 

HIGH 1.462 0.232 0.146 0.100 1.552 1.335 0.915 1 335 1 029 0 543 

WGHTD AVE 1 243 0.182 0.125 0.091 1.285 1.090 0.718 1 090 0 818 0.404 

CHARLOTTE LOW 2 213 0.475 0.221 0.151 2.611 2.350 1.813 2 350 1 964 1 287 

HIGH 5 508 1.815 0.551 0.451 7.763 7.436 6.705 7.436 6 919 5 884 

WGHTD AVE 4 212 1.206 0.405 0.326 5.618 5.315 4.655 5 315 4 846 3 946 

CITRUS LOW 0 922 0.139 0.092 0.083 0.958 0.811 0.541 0 811 0 613 0 311 

HIGH 1.465 0.282 0.147 0.117 1.670 1.483 1.120 1.483 1 220 0.782 

WGHTD AVE 1.110 0.183 0.110 0.095 1.190 1.027 0.721 1 027 0 804 0.453 

CLAY LOW 0.440 0.048 0.044 0.057 0.407 0.320 0.180 0 320 0 215 0 082 

HIGH 0 568 0.065 0.057 0.066 0.533 0.425 0.245 0.425 0 291 0.115 

WGHTD AVE 0.488 0.055 0.049 0.060 0.458 0.361 0.206 0 361 0 247 0 096 

COLL ER LOW 2.709 0.521 0.271 0.165 3.107 2.781 2.091 2.781 2 286 1.421 

HIGH 7 841 2.685 0.784 0.604 11.184 10.738 9.689 10.738 10 001 8.470 

WGHTD AVE 5 372 1.604 0.533 0.388 7.259 6.880 6.021 6 880 6 272 5 073 

COLUMBIA LOW 0 384 0.040 0.038 0.059 0.343 0.263 0.142 0 265 0.173 0 060 

HIGH 0.480 0.053 0.048 0.064 0.442 0.347 0.197 0 348 0 234 0 090 

WGHTD AVE 0.402 0.042 0.041 0.060 0.366 0.281 0.154 0 280 0.185 0 067 

DESOTO LOW 2 031 0.418 0.203 0.142 2.357 2.106 1.598 2.106 1.740 1.110 

HIGH 2.101 0.430 0.210 0.145 2.449 2.202 1.695 2 202 1 837 1.199 

WGHTD AVE 2 035 0.418 0.203 0.142 2.361 2.111 1.603 2.111 1.744 1.115 

DIXIE LOW 0 653 0.081 0.065 0.071 0.631 0.512 0.310 0 512 0 362 0.156 

HIGH 1 281 0.234 0.128 0.109 1.400 1.225 0.880 1 228 0 979 0 584 

WGHTD AVE 0 865 0.131 0.087 0.082 0.887 0.746 0.497 0.746 0 560 0 291 

*Includes contents and A L.E. Model USWIND 5.11 / WORLDCATenterprise 3.9 137







DUVAL LOW 0.438 0.048 0.044 0.056 0.405 0.316 0.180 0 318 0 215 0 081 

HIGH 0 906 0.153 0.091 0.083 0.976 0.841 0.597 0 841 0 662 0 388 

WGHTD AVE 0 549 0.070 0.054 0.063 0.533 0.433 0.267 0.433 0 310 0.140 

ESCAMBIA LOW 1.198 0.176 0.120 0.084 1.246 1.058 0.700 1 058 0.797 0 392 

HIGH 7 667 2.582 0.767 0.578 10.905 10.473 9.450 10.473 9.757 8 246 

WGHTD AVE 4 664 1.311 0.447 0.308 6.156 5.802 5.002 5 802 5 236 4.117 

FLAGLER LOW 0.744 0.101 0.075 0.073 0.737 0.620 0.387 0 616 0.448 0 207 

HIGH 1 386 0.294 0.139 0.111 1.619 1.454 1.108 1.456 1 209 0 803 

WGHTD AVE 1 079 0.193 0.104 0.091 1.181 1.014 0.726 1 024 0 802 0.476 

FRANKLIN LOW 1 862 0.356 0.186 0.129 2.103 1.859 1.373 1 859 1 507 0 927 

HIGH 4 388 1.257 0.439 0.309 5.800 5.448 4.665 5.448 4 891 3 826 

WGHTD AVE 3 544 0.929 0.361 0.243 4.528 4.207 3.511 4 207 3.711 2.789 

GADSDEN LOW 0 606 0.066 0.061 0.069 0.557 0.439 0.245 0.439 0 293 0.109 

HIGH 0.761 0.090 0.076 0.075 0.727 0.587 0.346 0 587 0.408 0.166 

WGHTD AVE 0 677 0.076 0.068 0.072 0.632 0.504 0.289 0 504 0 343 0.133 

GILCHRIST LOW 0 553 0.064 0.055 0.067 0.513 0.415 0.242 0.413 0 286 0.114 

HIGH 0 639 0.079 0.064 0.071 0.614 0.497 0.300 0.497 0 350 0.150 

WGHTD AVE 0 611 0.075 0.061 0.070 0.583 0.473 0.283 0.475 0 332 0.139 

GLADES LOW 2.146 0.371 0.215 0.142 2.354 2.060 1.461 2 060 1 626 0 897 

HIGH 2.461 0.423 0.246 0.154 2.710 2.386 1.715 2 386 1 901 1 092 

WGHTD AVE 2.413 0.413 0.242 0.152 2.650 2.329 1.667 2 329 1 851 1 054 

GULF LOW 1 670 0.284 0.167 0.109 1.815 1.580 1.112 1 580 1 241 0 687 

HIGH 5 291 1.623 0.529 0.379 7.206 6.825 5.959 6 825 6 213 5 001 

WGHTD AVE 4.752 1.342 0.420 0.329 6.286 5.930 5.131 5 930 5 364 4 264 

HAM LTON LOW 0 337 0.033 0.034 0.055 0.293 0.221 0.115 0 221 0.140 0 047 

HIGH 0 371 0.038 0.037 0.056 0.330 0.254 0.137 0 254 0.166 0 059 

WGHTD AVE 0 346 0.035 0.035 0.056 0.304 0.230 0.121 0 230 0.148 0 050 

HARDEE LOW 1 666 0.282 0.167 0.122 1.826 1.597 1.149 1 597 1 272 0.741 

HIGH 1.741 0.305 0.174 0.127 1.911 1.672 1.200 1 672 1 329 0.775 

WGHTD AVE 1 681 0.286 0.168 0.124 1.844 1.614 1.161 1 614 1 285 0.749 

HENDRY LOW 2 234 0.403 0.223 0.145 2.479 2.183 1.576 2.183 1.744 1 014 

HIGH 2 679 0.473 0.268 0.162 2.949 2.600 1.913 2 600 2 097 1 266 

WGHTD AVE 2 382 0.421 0.237 0.151 2.634 2.321 1.674 2 321 1 853 1 071 

HERNANDO LOW 0 985 0.150 0.099 0.086 1.023 0.866 0.578 0 866 0 655 0 335 

HIGH 1 620 0.339 0.162 0.125 1.886 1.687 1.296 1 687 1.404 0 926 

WGHTD AVE 1 267 0.229 0.122 0.103 1.393 1.217 0.883 1 217 0 974 0 582 

HIGHLANDS LOW 1 541 0.239 0.154 0.115 1.622 1.390 0.939 1 390 1 061 0 551 

HIGH 1 964 0.327 0.196 0.137 2.144 1.877 1.338 1 877 1.486 0 852 

WGHTD AVE 1.741 0.281 0.175 0.125 1.869 1.619 1.126 1 619 1 260 0 691 

HILLSBOROUGH LOW 1 333 0.222 0.133 0.105 1.433 1.238 0.862 1 238 0 964 0 530 

HIGH 3.477 0.919 0.348 0.297 4.567 4.307 3.759 4 307 3 915 3.190 

WGHTD AVE 2 038 0.450 0.211 0.163 2.464 2.247 1.813 2 247 1 934 1 393 

HOLMES LOW 1 061 0.150 0.106 0.082 1.083 0.912 0.590 0 912 0 676 0 324 

HIGH 1 301 0.200 0.130 0.090 1.370 1.173 0.792 1.173 0 895 0.457 

WGHTD AVE 1 285 0.196 0.128 0.090 1.346 1.151 0.773 1.151 0 875 0.444 








INDIAN RIVER LOW 2 525 0.514 0.252 0.158 2.931 2.629 1.996 2 629 2.174 1 374 

HIGH 7.141 2.207 0.714 0.561 9.931 9.521 8.565 9 521 8 848 7.472 

WGHTD AVE 5 654 1.627 0.565 0.413 7.600 7.214 6.335 7 214 6 592 5 360 

JACKSON LOW 0 827 0.103 0.083 0.076 0.804 0.658 0.400 0 658 0.467 0 200 

HIGH 1 093 0.154 0.109 0.084 1.112 0.935 0.604 0 935 0 692 0 330 

WGHTD AVE 0 969 0.128 0.097 0.081 0.965 0.801 0.503 0 801 0 581 0 263 

JEFFERSON LOW 0 515 0.055 0.052 0.064 0.472 0.369 0.204 0 369 0 245 0 090 

HIGH 0 579 0.064 0.058 0.067 0.535 0.422 0.237 0.422 0 284 0.106 

WGHTD AVE 0 520 0.056 0.052 0.064 0.476 0.373 0.207 0 373 0 248 0 091 

LAFAYETTE LOW 0.474 0.052 0.047 0.062 0.436 0.343 0.192 0 339 0 230 0 089 

HIGH 0.493 0.054 0.049 0.064 0.457 0.358 0.204 0 356 0 243 0 094 

WGHTD AVE 0.485 0.054 0.049 0.064 0.447 0.354 0.204 0 352 0 243 0 094 

LAKE LOW 0.768 0.095 0.077 0.076 0.745 0.607 0.367 0 607 0.429 0.183 

HIGH 1 203 0.177 0.120 0.100 1.240 1.049 0.691 1 049 0.786 0 391 

WGHTD AVE 1 036 0.147 0.104 0.091 1.054 0.884 0.572 0 884 0 655 0 317 

LEE LOW 2 335 0.452 0.234 0.152 2.677 2.391 1.796 2 391 1 963 1 227 

HIGH 6 648 2.216 0.665 0.543 9.253 8.862 7.982 8 862 8 241 7 026 

WGHTD AVE 4 908 1.334 0.415 0.358 6.474 6.140 5.400 6.140 5 615 4 590 

LEON LOW 0.485 0.050 0.049 0.064 0.438 0.339 0.183 0 339 0 221 0 078 

HIGH 0.728 0.085 0.073 0.074 0.689 0.553 0.325 0 553 0 383 0.154 

WGHTD AVE 0 620 0.068 0.062 0.069 0.573 0.454 0.256 0.454 0 306 0.116 

LEVY LOW 0 683 0.088 0.068 0.072 0.668 0.549 0.334 0 546 0 393 0.172 

HIGH 1 685 0.372 0.169 0.132 1.983 1.775 1.385 1.767 1.493 0 999 

WGHTD AVE 0 896 0.133 0.086 0.084 0.919 0.778 0.525 0.778 0 592 0 314 

LIBERTY LOW 0 878 0.107 0.088 0.079 0.848 0.692 0.416 0 692 0.488 0 205 

HIGH 1.145 0.160 0.114 0.088 1.164 0.978 0.632 0 978 0.724 0 344 

WGHTD AVE 1.107 0.153 0.111 0.087 1.121 0.939 0.602 0 939 0 691 0 325 

MADISON LOW 0 391 0.040 0.039 0.057 0.346 0.271 0.145 0 269 0.176 0 063 

HIGH 0.463 0.052 0.046 0.062 0.430 0.338 0.190 0 341 0 226 0 088 

WGHTD AVE 0.430 0.045 0.043 0.059 0.390 0.302 0.164 0 302 0.198 0 072 

MANATEE LOW 1.473 0.250 0.147 0.112 1.597 1.384 0.973 1 384 1 086 0 603 

HIGH 4 398 1.252 0.440 0.375 5.937 5.658 5.057 5 658 5 231 4.405 

WGHTD AVE 3 236 0.804 0.315 0.257 4.147 3.895 3.363 3 895 3 515 2 809 

MARION LOW 0 650 0.079 0.065 0.071 0.625 0.503 0.297 0 504 0 353 0.146 

HIGH 0 997 0.151 0.100 0.089 1.029 0.869 0.581 0 869 0 656 0 342 

WGHTD AVE 0 833 0.113 0.082 0.081 0.834 0.692 0.442 0 692 0 507 0 241 

MARTIN LOW 3.169 0.635 0.317 0.185 3.668 3.294 2.490 3 294 2.718 1 697 

HIGH 8 681 2.723 0.868 0.647 12.064 11.560 10.361 11 560 10.719 8 949 

WGHTD AVE 6 610 1.940 0.673 0.471 8.943 8.492 7.449 8.492 7.756 6 276 

MIAMI-DADE LOW 4.745 0.996 0.474 0.263 5.701 5.237 4.172 5 237 4.482 3 042 

HIGH 13 848 4.483 1.385 1.161 19.835 19.249 17.791 19 249 18 234 15 970 

WGHTD AVE 8.776 2.419 0.881 0.628 11.791 11.260 9.981 11 260 10 363 8.486 

MONROE LOW 10 050 3.352 1.005 0.797 14.486 14.032 12.913 14 032 13 255 11 510 

HIGH 13 918 4.427 1.392 1.102 19.811 19.206 17.679 19 206 18.150 15.751 

WGHTD AVE 11.156 3.880 1.185 0.885 16.151 15.645 14.385 15 645 14.770 12 816 

NASSAU LOW 0.419 0.046 0.042 0.055 0.388 0.303 0.171 0 303 0 207 0 078 

HIGH 0.784 0.125 0.078 0.075 0.825 0.701 0.478 0.701 0 537 0 288 

WGHTD AVE 0.705 0.107 0.068 0.071 0.726 0.611 0.408 0 611 0.461 0 240 








OKALOOSA LOW 1.478 0.241 0.148 0.096 1.591 1.377 0.952 1 377 1 068 0 571 

HIGH 6.777 2.194 0.678 0.515 9.492 9.082 8.121 9 082 8.405 7 018 

WGHTD AVE 5.187 1.570 0.501 0.371 7.044 6.688 5.877 6 688 6.115 4 977 

OKEECHOBEE LOW 2.178 0.367 0.218 0.140 2.369 2.067 1.455 2 067 1 624 0 897 

HIGH 2.454 0.419 0.245 0.153 2.698 2.374 1.705 2 374 1 891 1 083 

WGHTD AVE 2.185 0.368 0.218 0.141 2.377 2.075 1.461 2 075 1 630 0 902 

ORANGE LOW 0 985 0.131 0.098 0.087 0.981 0.814 0.512 0 814 0 592 0 269 

HIGH 1 349 0.212 0.135 0.102 1.425 1.222 0.832 1 222 0 937 0.492 

WGHTD AVE 1.162 0.167 0.117 0.096 1.190 1.003 0.656 1 003 0.748 0 366 

OSCEOLA LOW 1 055 0.142 0.106 0.092 1.056 0.879 0.555 0 879 0 640 0 294 

HIGH 1.792 0.297 0.179 0.124 1.929 1.673 1.166 1 673 1 305 0.714 

WGHTD AVE 1 301 0.190 0.128 0.103 1.344 1.143 0.761 1.143 0 863 0.438 

PALM BEACH LOW 2 964 0.538 0.296 0.175 3.330 2.958 2.168 2 958 2 390 1.409 

HIGH 11 274 3.533 1.127 0.891 15.859 15.309 13.959 15 309 14 365 12 340 

WGHTD AVE 7.137 1.895 0.712 0.496 9.394 8.912 7.782 8 912 8.116 6 511 

PASCO LOW 1.113 0.174 0.111 0.094 1.167 0.995 0.673 0 995 0.759 0 399 

HIGH 2 632 0.654 0.263 0.211 3.317 3.078 2.590 3 078 2.728 2 094 

WGHTD AVE 1 637 0.327 0.158 0.128 1.882 1.683 1.293 1 683 1.400 0 929 

P NELLAS LOW 1 685 0.348 0.169 0.128 1.964 1.759 1.352 1.759 1.465 0 967 

HIGH 3 927 1.087 0.393 0.329 5.238 4.963 4.372 4 963 4 541 3.743 

WGHTD AVE 2 939 0.726 0.291 0.237 3.741 3.496 2.987 3.496 3.131 2.464 

POLK LOW 1 097 0.153 0.110 0.094 1.111 0.930 0.600 0 930 0 687 0 327 

HIGH 1.490 0.233 0.149 0.114 1.558 1.334 0.911 1 334 1 022 0 551 

WGHTD AVE 1 319 0.203 0.132 0.105 1.387 1.187 0.807 1.187 0 910 0.479 

PUTNAM LOW 0 600 0.070 0.060 0.066 0.565 0.452 0.264 0.452 0 312 0.126 

HIGH 0.798 0.102 0.080 0.077 0.786 0.647 0.404 0 647 0.467 0 211 

WGHTD AVE 0 653 0.080 0.065 0.070 0.626 0.507 0.303 0 507 0 356 0.149 

SAINT JOHNS LOW 0 513 0.060 0.051 0.061 0.485 0.388 0.227 0 388 0 267 0.110 

HIGH 1.173 0.228 0.117 0.100 1.319 1.159 0.858 1.159 0 940 0 587 

WGHTD AVE 0.777 0.122 0.077 0.076 0.807 0.697 0.467 0 684 0 524 0 288 

SA NT LUCIE LOW 2.712 0.511 0.271 0.163 3.069 2.730 2.014 2.730 2 216 1 324 

HIGH 7 620 2.544 0.762 0.577 10.724 10.274 9.218 10 274 9 531 8 005 

WGHTD AVE 4 651 1.204 0.465 0.303 5.956 5.558 4.666 5 558 4 924 3.710 

SANTA ROSA LOW 1.111 0.157 0.111 0.080 1.139 0.960 0.622 0 960 0.712 0 338 

HIGH 7 562 2.540 0.756 0.570 10.738 10.309 9.297 10 309 9 599 8.107 

WGHTD AVE 4 286 1.237 0.427 0.295 5.704 5.368 4.613 5 368 4 833 3.793 

SARASOTA LOW 2 529 0.556 0.253 0.187 3.114 2.878 2.384 2 878 2 525 1 881 

HIGH 5 098 1.593 0.510 0.433 6.967 6.670 6.012 6 670 6 204 5 284 

WGHTD AVE 3 846 1.045 0.364 0.306 5.076 4.806 4.224 4 806 4 392 3 598 

SEMINOLE LOW 1 020 0.138 0.102 0.088 1.024 0.854 0.542 0 854 0 624 0 290 

HIGH 1 278 0.195 0.128 0.099 1.347 1.154 0.785 1.154 0 884 0.466 

WGHTD AVE 1 094 0.156 0.109 0.091 1.118 0.941 0.613 0 941 0.700 0 340 

SUMTER LOW 0 926 0.128 0.093 0.086 0.932 0.777 0.497 0.777 0 571 0 271 

HIGH 1 011 0.148 0.101 0.089 1.038 0.874 0.574 0 874 0 654 0 324 

WGHTD AVE 0 950 0.134 0.095 0.086 0.963 0.805 0.520 0 805 0 596 0 287 








SUWANNEE LOW 0 354 0.036 0.036 0.057 0.316 0.240 0.126 0 237 0.153 0 052 

HIGH 0.481 0.054 0.048 0.064 0.444 0.351 0.200 0 350 0 237 0 093 

WGHTD AVE 0 379 0.039 0.038 0.059 0.342 0.261 0.139 0 258 0.170 0 061 

TAYLOR LOW 0 506 0.056 0.051 0.063 0.469 0.369 0.208 0 369 0 249 0 095 

HIGH 0 634 0.079 0.063 0.072 0.613 0.490 0.299 0.491 0 351 0.151 

WGHTD AVE 0 607 0.072 0.061 0.069 0.575 0.462 0.271 0.462 0 320 0.131 

UNION LOW 0.484 0.054 0.048 0.062 0.450 0.352 0.202 0 355 0 236 0 093 

HIGH 0 513 0.058 0.051 0.065 0.480 0.385 0.220 0 379 0 264 0.104 

WGHTD AVE 0.489 0.055 0.049 0.063 0.461 0.359 0.207 0 361 0 245 0 097 

VOLUSIA LOW 0 819 0.103 0.082 0.078 0.808 0.669 0.422 0 669 0.487 0 221 

HIGH 2 201 0.571 0.220 0.167 2.761 2.517 2.085 2 538 2 223 1 650 

WGHTD AVE 1 370 0.261 0.133 0.109 1.541 1.360 1.014 1 360 1.108 0.703 

WAKULLA LOW 0 918 0.115 0.092 0.082 0.896 0.736 0.449 0.736 0 524 0 226 

HIGH 1 085 0.160 0.109 0.092 1.116 0.942 0.622 0 942 0.707 0 354 

WGHTD AVE 0 951 0.123 0.095 0.084 0.939 0.776 0.482 0.776 0 559 0 248 

WALTON LOW 1.110 0.157 0.111 0.081 1.140 0.962 0.626 0 962 0.716 0 343 

HIGH 6 281 2.054 0.628 0.464 8.788 8.391 7.467 8 391 7.740 6.415 

WGHTD AVE 4 682 1.341 0.406 0.329 6.221 5.881 5.116 5 881 5 339 4 283 

WASH NGTON LOW 1 399 0.218 0.140 0.095 1.482 1.274 0.870 1 274 0 980 0 511 

HIGH 2 204 0.446 0.220 0.135 2.550 2.284 1.727 2 284 1 884 1.188 

WGHTD AVE 1.471 0.236 0.147 0.098 1.574 1.360 0.941 1 360 1 056 0 565 

Statewide LOW 0 333 0.033 0.033 0.050 0.293 0.220 0.113 0 220 0.139 0 047 

HIGH 13 918 4.483 1.392 1.161 19.835 19.249 17.791 19 249 18 234 15 970 

WGHTD AVE 2 855 0.671 0.269 0.206 3.538 3.296 2.784 3 296 2 930 2 260 




PERSONAL RESIDENTIAL -Owners -- MASONRY 

0% 0% $0 DEDUCTIBLE $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



ALACHUA LOW 0 346 0 044 0.035 0 062 0 313 0 242 0.129 0 240 0.155 0.054 

HIGH 0.479 0 067 0.048 0 071 0.453 0 359 0 209 0 357 0.244 0.097 

WGHTD AVE 0 391 0 051 0.039 0 065 0 365 0 286 0.156 0 285 0.189 0.070 

BAKER LOW 0 223 0 025 0.022 0 049 0.193 0.141 0 068 0.141 0.085 0.025 

HIGH 0 338 0 042 0.034 0 059 0 307 0 234 0.125 0 236 0.153 0.053 

WGHTD AVE 0 326 0 040 0.032 0 058 0 295 0 225 0.118 0 224 0.143 0.049 

BAY LOW 0 942 0.153 0.094 0 083 0 970 0 808 0 512 0 808 0.590 0.273 

HIGH 4 381 1 526 0.438 0 334 6.137 5 809 5 081 5 809 5.292 4.296 

WGHTD AVE 3 234 0 992 0.313 0 228 4 270 3 983 3 367 3 983 3.543 2.735 

BRADFORD LOW 0 348 0 043 0.034 0 060 0 318 0 240 0.130 0 242 0.157 0.054 

HIGH 0 362 0 045 0.036 0 063 0 329 0 251 0.134 0 251 0.163 0.057 

WGHTD AVE 0 354 0 044 0.036 0 062 0 324 0 245 0.133 0 247 0.159 0.054 

BREVARD LOW 1 033 0 214 0.103 0 096 1.150 0 995 0.707 0 995 0.784 0.455 

HIGH 4.194 1.469 0.419 0 334 5 810 5 501 4 830 5 501 5.025 4.109 

WGHTD AVE 2 676 0 803 0.265 0 212 3 523 3 279 2.769 3 279 2.914 2.254 

BROWARD LOW 3 322 0.702 0.332 0 201 3 901 3 522 2 697 3 522 2.930 1.894 

HIGH 9 630 3 369 0.963 0 806 13 897 13 398 12 200 13 398 12.560 10.800 

WGHTD AVE 4 974 1 287 0.433 0 318 6 282 5 864 4 923 5 864 5.195 3.934 

CALHOUN LOW 0 832 0.128 0.083 0 081 0 833 0 683 0.419 0 683 0.488 0.214 

HIGH 1 012 0.170 0.101 0 088 1 049 0 878 0 564 0 878 0.648 0.309 

WGHTD AVE 0 857 0.133 0.086 0 082 0 862 0.709 0.438 0.709 0.508 0.225 

CHARLOTTE LOW 1 561 0 362 0.156 0.126 1 823 1 613 1 201 1 613 1.314 0.820 

HIGH 4.186 1 513 0.419 0 364 6 007 5.732 5.137 5.732 5.309 4.486 

WGHTD AVE 3 084 0 971 0.308 0 256 4.166 3 916 3 392 3 916 3.542 2.847 

CITRUS LOW 0 626 0.100 0.063 0 075 0 632 0 519 0 323 0 519 0.374 0.170 

HIGH 1 023 0 215 0.102 0.100 1.158 1 011 0.740 1 011 0.813 0.499 

WGHTD AVE 0.733 0.126 0.073 0 082 0.764 0 639 0.420 0 639 0.478 0.241 

CLAY LOW 0 295 0 036 0.030 0 055 0 266 0 201 0.105 0 201 0.128 0.043 

HIGH 0 384 0 049 0.038 0 063 0 352 0 270 0.146 0 270 0.176 0.061 

WGHTD AVE 0 327 0 041 0.033 0 057 0 300 0 227 0.120 0 227 0.148 0.050 

COLLIER LOW 1 898 0 389 0.190 0.135 2.142 1 878 1 350 1 878 1.495 0.871 

HIGH 5 974 2 241 0.597 0.486 8 667 8 289 7.426 8 289 7.680 6.454 

WGHTD AVE 3 930 1 235 0.368 0 291 5 281 4 966 4 281 4 966 4.478 3.557 

COLUMBIA LOW 0 257 0 030 0.026 0 058 0 223 0.164 0 082 0.164 0.102 0.031 

HIGH 0 326 0 040 0.033 0 061 0 291 0 221 0.117 0 222 0.142 0.048 

WGHTD AVE 0 270 0 032 0.027 0 058 0 239 0.176 0 090 0.175 0.110 0.035 

DESOTO LOW 1.425 0 314 0.143 0.119 1 631 1.430 1 042 1.430 1.148 0.691 

HIGH 1.484 0 327 0.148 0.121 1.715 1 515 1.125 1 515 1.232 0.764 

WGHTD AVE 1.434 0 316 0.143 0.120 1 645 1.443 1 056 1.443 1.162 0.703 

DIXIE LOW 0.438 0 059 0.044 0 067 0.414 0 322 0.184 0 327 0.221 0.084 

HIGH 0 894 0.176 0.089 0 097 0 971 0 826 0 571 0 829 0.642 0.362 

WGHTD AVE 0 508 0 077 0.051 0 071 0.494 0.401 0 241 0 398 0.281 0.123 








DUVAL LOW 0 296 0 036 0.030 0 054 0 266 0 201 0.105 0 201 0.129 0.043 

HIGH 0 621 0.114 0.062 0 075 0 654 0 550 0 375 0 550 0.421 0.230 

WGHTD AVE 0 356 0 049 0.035 0 059 0 335 0 260 0.149 0 263 0.178 0.068 

ESCAMBIA LOW 0 824 0.128 0.082 0 074 0 831 0 684 0.420 0 684 0.489 0.213 

HIGH 5 808 2.147 0.581 0.457 8 398 8 030 7.183 8 030 7.433 6.221 

WGHTD AVE 3 601 1.119 0.348 0 240 4 803 4 502 3 844 4 502 4.034 3.145 

FLAGLER LOW 0 505 0 074 0.050 0 069 0.486 0 395 0 229 0 392 0.272 0.111 

HIGH 0 975 0 224 0.097 0 095 1.127 0 995 0.736 0 996 0.809 0.516 

WGHTD AVE 0.727 0.136 0.072 0 080 0.768 0 644 0.436 0 651 0.490 0.266 

FRANKL N LOW 1 303 0 270 0.130 0.110 1.450 1 257 0 894 1 257 0.991 0.579 

HIGH 3 217 0 997 0.322 0 235 4 269 3 976 3 351 3 976 3.529 2.709 

WGHTD AVE 2 347 0 654 0.235 0.178 2 973 2.722 2 207 2.722 2.351 1.709 

GADSDEN LOW 0.411 0 050 0.041 0 066 0 368 0 279 0.145 0 279 0.177 0.058 

HIGH 0 519 0 067 0.052 0 071 0.483 0 376 0 206 0 376 0.248 0.090 

WGHTD AVE 0.461 0 057 0.046 0 069 0.420 0 323 0.172 0 323 0.209 0.072 

GILCHRIST LOW 0 367 0 047 0.037 0 064 0 334 0 260 0.141 0 259 0.170 0.059 

HIGH 0.432 0 058 0.043 0 067 0.409 0 320 0.181 0 320 0.215 0.082 

WGHTD AVE 0.414 0 055 0.041 0 066 0 388 0 302 0.168 0 302 0.202 0.075 

GLADES LOW 1.486 0 272 0.149 0.121 1 590 1 355 0 909 1 355 1.028 0.526 

HIGH 1.722 0 316 0.172 0.130 1 860 1 599 1 093 1 599 1.230 0.654 

WGHTD AVE 1 699 0 310 0.170 0.129 1 831 1 572 1 071 1 572 1.206 0.637 

GULF LOW 1.163 0 210 0.116 0 094 1 242 1 055 0.704 1 055 0.798 0.407 

HIGH 3 933 1 319 0.393 0 296 5.412 5 092 4 395 5 092 4.596 3.658 

WGHTD AVE 3 243 1 014 0.314 0 236 4 320 4 035 3.429 4 035 3.602 2.807 

HAMILTON LOW 0 224 0 025 0.022 0 054 0.192 0.139 0 067 0.139 0.084 0.024 

HIGH 0 248 0 029 0.025 0 055 0 218 0.160 0 081 0.161 0.100 0.031 

WGHTD AVE 0 229 0 026 0.023 0 055 0.197 0.143 0 070 0.143 0.087 0.026 

HARDEE LOW 1.153 0 207 0.115 0.106 1 240 1 059 0.726 1 059 0.815 0.440 

HIGH 1 203 0 225 0.120 0.109 1 293 1.104 0.754 1.104 0.848 0.458 

WGHTD AVE 1.163 0 209 0.116 0.107 1 251 1 069 0.732 1 069 0.822 0.444 

HENDRY LOW 1 559 0 301 0.156 0.123 1.705 1.467 1 009 1.467 1.133 0.613 

HIGH 1 863 0 351 0.186 0.135 2 003 1.723 1 215 1.723 1.359 0.765 

WGHTD AVE 1.718 0 319 0.170 0.129 1 859 1 599 1 093 1 599 1.230 0.654 

HERNANDO LOW 0 676 0.109 0.068 0 078 0 687 0 565 0 355 0 565 0.410 0.190 

HIGH 1.134 0 259 0.113 0.107 1 309 1.151 0 856 1.151 0.936 0.592 

WGHTD AVE 0 922 0.187 0.092 0 094 1 015 0 873 0 615 0 873 0.684 0.394 

HIGHLANDS LOW 1 065 0.175 0.106 0.103 1 096 0 912 0 580 0 912 0.667 0.313 

HIGH 1 366 0 243 0.137 0.117 1.462 1 249 0 847 1 249 0.955 0.509 

WGHTD AVE 1 238 0 215 0.124 0.111 1 308 1.108 0.737 1.108 0.835 0.428 

H LLSBOROUGH LOW 0 920 0.163 0.092 0 093 0 967 0 813 0 536 0 813 0.609 0.306 

HIGH 2 579 0.737 0.258 0 236 3.405 3.194 2.767 3.194 2.887 2.337 

WGHTD AVE 1 337 0 304 0.135 0.124 1 574 1.405 1 086 1.405 1.173 0.796 

HOLMES LOW 0.728 0.109 0.073 0 074 0.721 0 587 0 354 0 587 0.414 0.176 

HIGH 0 897 0.146 0.090 0 079 0 920 0.765 0.484 0.765 0.559 0.256 

WGHTD AVE 0 890 0.143 0.089 0 079 0 909 0.755 0.475 0.755 0.549 0.251 








NDIAN RIVER LOW 1.767 0 384 0.177 0.129 2 021 1.777 1 291 1.777 1.425 0.845 

HIGH 5.407 1 824 0.541 0.447 7 617 7 270 6.492 7 270 6.719 5.631 

WGHTD AVE 4.443 1 388 0.416 0 336 6 014 5 687 4 966 5 687 5.175 4.193 

JACKSON LOW 0 563 0 076 0.056 0 071 0 535 0.423 0 239 0.423 0.286 0.108 

HIGH 0.754 0.114 0.075 0 077 0.749 0 611 0 370 0 611 0.432 0.185 

WGHTD AVE 0 663 0 094 0.066 0 074 0 643 0 516 0 301 0 516 0.356 0.143 

JEFFERSON LOW 0 346 0 041 0.035 0 062 0 309 0 232 0.119 0 232 0.146 0.047 

HIGH 0 394 0 048 0.039 0 065 0 356 0 271 0.141 0 271 0.173 0.057 

WGHTD AVE 0 350 0 042 0.035 0 062 0 313 0 236 0.121 0 236 0.149 0.048 

LAFAYETTE LOW 0 320 0 039 0.032 0 060 0 287 0 218 0.114 0 216 0.139 0.047 

HIGH 0 330 0 040 0.033 0 062 0 300 0 227 0.120 0 225 0.147 0.050 

WGHTD AVE 0 326 0 040 0.033 0 061 0 293 0 223 0.120 0 222 0.146 0.050 

LAKE LOW 0 522 0 070 0.052 0 071 0.494 0 389 0 219 0 389 0.261 0.099 

HIGH 0 820 0.128 0.082 0 090 0 822 0 673 0.414 0 673 0.481 0.213 

WGHTD AVE 0.742 0.114 0.074 0 085 0.739 0 602 0 368 0 602 0.429 0.188 

LEE LOW 1 636 0 338 0.164 0.126 1 844 1 613 1.161 1 613 1.285 0.753 

HIGH 5 060 1 857 0.506 0.436 7.166 6 837 6.138 6 837 6.336 5.380 

WGHTD AVE 2 888 0 836 0.281 0 220 3.764 3 501 2 946 3 501 3.104 2.378 

LEON LOW 0 326 0 038 0.033 0 062 0 287 0 213 0.107 0 213 0.132 0.041 

HIGH 0.493 0 063 0.049 0 070 0.458 0 356 0.194 0 356 0.234 0.083 

WGHTD AVE 0.425 0 052 0.042 0 067 0 383 0 292 0.153 0 292 0.187 0.063 

LEVY LOW 0.466 0 065 0.047 0 068 0.444 0 352 0.199 0 350 0.240 0.093 

HIGH 1.174 0 283 0.118 0.112 1 381 1 217 0 921 1 211 1.002 0.643 

WGHTD AVE 0 559 0 086 0.055 0 073 0 557 0.454 0 284 0.454 0.327 0.153 

L BERTY LOW 0 602 0 080 0.060 0 074 0 569 0.448 0 251 0.448 0.300 0.111 

HIGH 0.785 0.116 0.078 0 080 0.775 0 630 0 378 0 630 0.443 0.185 

WGHTD AVE 0.761 0.111 0.076 0 079 0.749 0 607 0 361 0 607 0.425 0.175 

MADISON LOW 0 264 0 031 0.026 0 056 0 232 0.173 0 086 0.171 0.107 0.034 

HIGH 0 312 0 039 0.031 0 060 0 283 0 214 0.113 0 217 0.137 0.047 

WGHTD AVE 0 290 0 034 0.029 0 058 0 257 0.191 0 097 0.191 0.120 0.038 

MANATEE LOW 1 017 0.182 0.102 0 098 1 077 0 909 0 604 0 909 0.685 0.347 

HIGH 3 277 1 010 0.328 0 290 4.468 4 234 3.748 4 234 3.887 3.239 

WGHTD AVE 2 217 0 580 0.221 0.189 2 827 2 626 2 220 2 626 2.334 1.814 

MARION LOW 0.440 0 058 0.044 0 068 0.411 0 319 0.174 0 319 0.213 0.077 

HIGH 0 680 0.109 0.068 0 081 0 684 0 560 0 353 0 560 0.405 0.193 

WGHTD AVE 0 558 0 081 0.056 0 074 0 544 0.437 0 261 0.437 0.306 0.129 

MART N LOW 2 222 0.477 0.222 0.150 2 536 2 233 1 614 2 233 1.785 1.048 

HIGH 6 572 2 255 0.657 0 509 9 264 8 829 7 841 8 829 8.131 6.734 

WGHTD AVE 4 947 1 568 0.498 0 366 6.728 6 349 5 505 6 349 5.750 4.596 

MIAMI-DADE LOW 3 349 0.747 0.335 0.198 3 959 3 574 2.736 3 574 2.974 1.910 

HIGH 10 669 3.766 1.067 0 917 15 526 15.018 13.781 15 018 14.153 12.303 

WGHTD AVE 6.195 1.777 0.548 0.425 8.189 7.744 6.714 7.744 7.016 5.584 

MONROE LOW 7 691 2.784 0.769 0 623 11 218 10.821 9 876 10 821 10.159 8.743 

HIGH 10 680 3.700 1.068 0 851 15 387 14.852 13 551 14 852 13.942 11.985 

WGHTD AVE 9 561 3 514 0.977 0.764 13 961 13.472 12 280 13.472 12.640 10.845 

NASSAU LOW 0 282 0 035 0.029 0 053 0 255 0.191 0.101 0.192 0.124 0.041 

HIGH 0 532 0 090 0.053 0 068 0 547 0.452 0 292 0.452 0.334 0.163 

WGHTD AVE 0.436 0 067 0.043 0 062 0.430 0 347 0 214 0 347 0.248 0.113 








OKALOOSA LOW 1 020 0.175 0.102 0 083 1 069 0 899 0 584 0 899 0.668 0.322 

HIGH 5.140 1 832 0.514 0.406 7 315 6 969 6.184 6 969 6.414 5.315 

WGHTD AVE 3 838 1 286 0.377 0 286 5 285 4 987 4 333 4 987 4.522 3.640 

OKEECHOBEE LOW 1 510 0 270 0.151 0.120 1 607 1 366 0 908 1 366 1.031 0.519 

HIGH 1.712 0 312 0.171 0.129 1 845 1 586 1 081 1 586 1.217 0.644 

WGHTD AVE 1 516 0 271 0.152 0.120 1 613 1 371 0 912 1 371 1.036 0.522 

ORANGE LOW 0 673 0 096 0.067 0 080 0 653 0 524 0 307 0 524 0.363 0.146 

HIGH 0 926 0.152 0.093 0 091 0 952 0.792 0 505 0.792 0.581 0.277 

WGHTD AVE 0.797 0.122 0.080 0 087 0.795 0 649 0 396 0 649 0.462 0.201 

OSCEOLA LOW 0.719 0.104 0.072 0 085 0 699 0 563 0 331 0 563 0.390 0.159 

HIGH 1 239 0 218 0.124 0.108 1 305 1.103 0.727 1.103 0.827 0.414 

WGHTD AVE 0 923 0.146 0.092 0 094 0 940 0.778 0.491 0.778 0.566 0.262 

PALM BEACH LOW 2 067 0 399 0.207 0.143 2 279 1 979 1 378 1 979 1.543 0.844 

HIGH 8 590 2 948 0.859 0 695 12 255 11.781 10 663 11.781 10.995 9.372 

WGHTD AVE 5 272 1.483 0.477 0 359 6 869 6.460 5 539 6.460 5.806 4.553 

PASCO LOW 0.759 0.124 0.076 0 084 0.775 0 641 0.406 0 641 0.468 0.220 

HIGH 1 960 0 520 0.196 0.178 2.478 2 285 1 910 2 285 2.012 1.554 

WGHTD AVE 1 284 0 290 0.126 0.117 1.499 1 335 1 025 1 335 1.110 0.745 

PINELLAS LOW 1.173 0 262 0.117 0.107 1 351 1.189 0 884 1.189 0.967 0.610 

HIGH 2 896 0 867 0.290 0 253 3 884 3 658 3.191 3 658 3.323 2.705 

WGHTD AVE 2.103 0 552 0.210 0.185 2 672 2.475 2 085 2.475 2.194 1.699 

POLK LOW 0.755 0.114 0.075 0 086 0.748 0 608 0 368 0 608 0.430 0.184 

HIGH 1 027 0.172 0.103 0.101 1 052 0 876 0 566 0 876 0.646 0.317 

WGHTD AVE 0 903 0.148 0.091 0 094 0 927 0.771 0.493 0.771 0.566 0.269 

PUTNAM LOW 0.408 0 052 0.041 0 063 0 377 0 291 0.158 0 291 0.191 0.067 

HIGH 0 544 0 076 0.054 0 071 0 524 0.417 0 242 0.417 0.287 0.115 

WGHTD AVE 0.443 0 060 0.044 0 066 0.416 0 329 0.182 0 323 0.219 0.081 

SAINT JOHNS LOW 0 350 0 045 0.035 0 059 0 324 0 250 0.137 0 250 0.165 0.059 

HIGH 0 825 0.173 0.082 0 088 0 918 0.792 0 567 0.792 0.627 0.373 

WGHTD AVE 0 569 0 099 0.056 0 072 0 588 0.488 0 319 0.488 0.362 0.189 

SAINT LUCIE LOW 1 897 0 380 0.190 0.134 2.103 1 829 1 283 1 829 1.433 0.795 

HIGH 5.759 2.120 0.576 0.461 8 231 7 848 6 981 7 848 7.235 6.020 

WGHTD AVE 3.447 0 971 0.346 0 241 4.432 4.102 3 393 4.102 3.595 2.672 

SANTA ROSA LOW 0.764 0.115 0.076 0 071 0.760 0 620 0 374 0 620 0.438 0.183 

HIGH 5.728 2.109 0.573 0.450 8 262 7 897 7 060 7 897 7.307 6.111 

WGHTD AVE 3.167 1 000 0.306 0 226 4 235 3 955 3 350 3 955 3.523 2.721 

SARASOTA LOW 1 802 0.426 0.180 0.147 2 208 2 016 1 632 2 016 1.739 1.256 

HIGH 3 828 1 324 0.383 0 330 5 327 5 070 4 517 5 070 4.676 3.939 

WGHTD AVE 2 811 0 838 0.272 0 236 3.751 3 528 3 064 3 528 3.197 2.581 

SEM NOLE LOW 0 699 0.102 0.070 0 081 0 683 0 551 0 327 0 551 0.384 0.158 

HIGH 0 882 0.143 0.088 0 088 0 908 0.756 0.485 0.756 0.556 0.266 

WGHTD AVE 0.756 0.115 0.076 0 083 0.753 0 614 0 374 0 614 0.436 0.189 

SUMTER LOW 0 631 0 094 0.063 0 079 0 620 0 500 0 299 0 500 0.351 0.148 

HIGH 0 692 0.108 0.069 0 081 0 690 0 563 0 346 0 563 0.402 0.178 

WGHTD AVE 0 656 0.100 0.066 0 080 0 650 0 528 0 320 0 528 0.373 0.161 








SUWANNEE LOW 0 233 0 027 0.023 0 056 0 203 0.147 0 072 0.146 0.089 0.027 

HIGH 0 322 0 040 0.032 0 062 0 289 0 221 0.117 0 221 0.142 0.049 

WGHTD AVE 0 250 0 030 0.025 0 057 0 222 0.163 0 081 0.161 0.101 0.032 

TAYLOR LOW 0 342 0 042 0.034 0 061 0 308 0 234 0.123 0 234 0.150 0.049 

HIGH 0.429 0 058 0.043 0 068 0.404 0 312 0.178 0 312 0.213 0.081 

WGHTD AVE 0.413 0 054 0.041 0 066 0 384 0 298 0.164 0 298 0.197 0.071 

UNION LOW 0 326 0 041 0.033 0 060 0 298 0 225 0.121 0 227 0.144 0.050 

HIGH 0 346 0 044 0.035 0 062 0 316 0 241 0.129 0 241 0.159 0.055 

WGHTD AVE 0 330 0 041 0.033 0 061 0 305 0 229 0.123 0 230 0.149 0.052 

VOLUSIA LOW 0 562 0 077 0.056 0 072 0 542 0.434 0 254 0.434 0.301 0.121 

HIGH 1 618 0.447 0.162 0.140 2 036 1 855 1 502 1 855 1.606 1.181 

WGHTD AVE 0 952 0.196 0.094 0 093 1 056 0 914 0 656 0 914 0.725 0.439 

WAKULLA LOW 0 628 0 085 0.063 0 076 0 595 0.472 0 267 0.472 0.319 0.121 

HIGH 0.744 0.117 0.074 0 083 0.748 0 614 0 381 0 614 0.441 0.200 

WGHTD AVE 0 649 0 090 0.065 0 078 0 622 0.496 0 285 0.496 0.339 0.132 

WALTON LOW 0.762 0.115 0.076 0 072 0.761 0 622 0 377 0 622 0.441 0.187 

HIGH 4.741 1.705 0.474 0 372 6.739 6.402 5 649 6.402 5.868 4.826 

WGHTD AVE 3 236 0 980 0.290 0 219 4 259 3 987 3.404 3 987 3.570 2.801 

WASH NGTON LOW 0 966 0.158 0.097 0 083 0 998 0 834 0 532 0 834 0.612 0.285 

HIGH 1 544 0 332 0.154 0.110 1.760 1 545 1.119 1 545 1.236 0.731 

WGHTD AVE 1 027 0.174 0.103 0 085 1 075 0 905 0 590 0 905 0.674 0.327 

Statewide LOW 0 223 0 025 0.022 0 049 0.192 0.139 0 067 0.139 0.084 0.024 

HIGH 10 680 3.766 1.068 0 917 15 526 15 018 13.781 15 018 14.153 12.303 

WGHTD AVE 3.123 0 809 0.272 0 222 3 916 3 646 3 072 3 646 3.236 2.485 




PERSONAL RESIDENTIAL - MOBILE HOMES 




ALACHUA LOW 0.871 0.092 0 087 0.077 0.815 0 683 0.455 0 816 0.683 0.455 

HIGH 1.280 0.161 0.128 0.099 1.278 1 099 0.797 1 274 1.095 0.791 

WGHTD AVE 1.030 0.119 0.104 0.086 1.005 0 857 0 581 1 010 0.853 0.591 

BAKER LOW 0.511 0.048 0 051 0.054 0.438 0 346 0 202 0.438 0.346 0.202 

HIGH 0.838 0.087 0 084 0.073 0.787 0 658 0.435 0.787 0.658 0.435 

WGHTD AVE 0.756 0.076 0 075 0.068 0.697 0 571 0 370 0 693 0.571 0.370 

BAY LOW 2.742 0.453 0 274 0.171 3.080 2.777 2.164 3 080 2.777 2.164 

HIGH 13.188 4.374 1 319 1.239 18.964 18.341 16 847 18 964 18.341 16.847 

WGHTD AVE 7.849 2.210 0.739 0.630 10.577 10.093 8 985 10 577 10.093 8.985 

BRADFORD LOW 0.860 0.088 0 085 0.074 0.807 0 660 0.439 0 805 0.665 0.439 

HIGH 0.897 0.096 0 090 0.079 0.842 0.706 0.472 0 842 0.706 0.472 

WGHTD AVE 0.877 0.091 0 088 0.075 0.826 0 682 0.450 0 824 0.682 0.450 

BREVARD LOW 3.240 0.712 0 324 0.235 3.945 3 656 3 058 3 945 3.656 3.058 

HIGH 12.693 4.105 1 269 1.223 18.054 17.455 16 029 18 054 17.455 16.029 

WGHTD AVE 9.080 2.746 0 838 0.858 12.661 12.170 11 032 12 661 12.170 11.032 

BROWARD LOW 11.203 2.459 1.120 0.840 14.303 13.586 11 888 14 303 13.586 11.888 

HIGH 27.336 9.040 2.734 3.118 40.164 39.242 36 833 40.164 39.242 36.833 

WGHTD AVE 16.144 4.433 1 556 1.496 22.126 21.334 19 383 22.126 21.334 19.383 

CALHOUN LOW 2.321 0.347 0 232 0.146 2.515 2 235 1 689 2 515 2.235 1.689 

HIGH 2.970 0.513 0 297 0.187 3.381 3 062 2.414 3 381 3.062 2.414 

WGHTD AVE 2.476 0.388 0 249 0.157 2.724 2.435 1 863 2.724 2.435 1.863 

CHARLOTTE LOW 5.111 1.264 0 511 0.373 6.549 6.151 5 283 6 549 6.151 5.283 

HIGH 12.209 4.153 1 221 1.302 17.832 17.325 16.129 17 832 17.325 16.129 

WGHTD AVE 8.844 2.798 0 907 0.853 12.496 12.031 10 955 12.496 12.031 10.955 

CITRUS LOW 1.825 0.301 0.182 0.132 2.013 1.802 1 393 2 013 1.802 1.393 

HIGH 3.263 0.721 0 326 0.255 4.029 3.755 3.190 4 029 3.755 3.190 

WGHTD AVE 2.316 0.436 0 231 0.171 2.683 2.445 1 970 2 683 2.445 1.970 

CLAY LOW 0.723 0.073 0 072 0.066 0.664 0.548 0 353 0 664 0.548 0.353 

HIGH 0.962 0.103 0 096 0.080 0.910 0.763 0 509 0 910 0.763 0.509 

WGHTD AVE 0.874 0.092 0 087 0.074 0.821 0.685 0.452 0 821 0.685 0.452 

COLLIER LOW 6.143 1.315 0 614 0.411 7.602 7.101 5 995 7 602 7.101 5.995 

HIGH 17.454 6.162 1.745 1.760 25.770 25.069 23 298 25.770 25.069 23.298 

WGHTD AVE 10.421 3.193 1 024 0.871 14.504 13.925 12 550 14 504 13.925 12.550 

COLUMBIA LOW 0.605 0.058 0 060 0.064 0.530 0.423 0 258 0 534 0.427 0.256 

HIGH 0.794 0.081 0 079 0.074 0.731 0.605 0 394 0.731 0.605 0.394 

WGHTD AVE 0.673 0.067 0 068 0.068 0.610 0.489 0 307 0 604 0.490 0.308 

DESOTO LOW 4.610 1.087 0.461 0.332 5.797 5.417 4 608 5.797 5.417 4.608 

HIGH 4.882 1.145 0.488 0.358 6.193 5.815 4 998 6.193 5.815 4.998 

WGHTD AVE 4.617 1.089 0.462 0.333 5.808 5.429 4 619 5 808 5.429 4.619 

DIX E LOW 1.153 0.136 0.115 0.091 1.138 0.975 0 684 1.138 0.975 0.684 

HIGH 2.742 0.576 0 272 0.205 3.247 2.980 2.437 3 265 2.987 2.469 

WGHTD AVE 1.462 0.225 0.146 0.114 1.549 1.367 1 026 1 552 1.367 1.026 








DUVAL LOW 0.720 0.073 0 072 0.065 0.665 0 545 0 351 0 658 0.545 0.351 

HIGH 1.920 0.374 0.192 0.149 2.235 2 042 1 676 2 235 2.042 1.676 

WGHTD AVE 0.918 0.112 0 091 0.078 0.908 0.771 0 542 0 913 0.771 0.542 

ESCAMBIA LOW 2.345 0.352 0 234 0.142 2.567 2 291 1.745 2 567 2.291 1.745 

HIGH 17.341 6.017 1.734 1.730 25.524 24 837 23.107 25 524 24.837 23.107 

WGHTD AVE 8.416 2.323 0 835 0.652 11.356 10 858 9.701 11 356 10.858 9.701 

FLAGLER LOW 1.391 0.186 0.140 0.103 1.434 1 266 0 917 1.446 1.256 0.918 

HIGH 3.073 0.767 0 307 0.244 3.912 3 663 3.111 3 938 3.667 3.135 

WGHTD AVE 2.160 0.486 0.182 0.172 2.624 2 365 1 934 2 618 2.357 1.937 

FRANKL N LOW 4.017 0.886 0.402 0.281 4.922 4 559 3.797 4 922 4.559 3.797 

HIGH 10.020 3.053 1 002 0.905 13.991 13.442 12.149 13 991 13.442 12.149 

WGHTD AVE 7.412 2.093 0.741 0.603 9.991 9 514 8.429 9 991 9.514 8.429 

GADSDEN LOW 0.991 0.099 0 099 0.081 0.913 0.752 0.479 0 913 0.752 0.479 

HIGH 1.308 0.149 0.131 0.096 1.273 1 079 0.733 1 273 1.079 0.733 

WGHTD AVE 1.134 0.121 0.113 0.087 1.072 0 896 0 589 1 072 0.896 0.589 

G LCHRIST LOW 0.945 0.100 0 094 0.080 0.883 0.747 0 500 0 890 0.746 0.494 

HIGH 1.123 0.132 0.112 0.090 1.106 0 946 0 661 1.106 0.946 0.661 

WGHTD AVE 1.066 0.122 0.105 0.087 1.032 0 875 0 604 1 041 0.875 0.604 

GLADES LOW 4.551 0.830 0.455 0.289 5.343 4 900 3 937 5 343 4.900 3.937 

HIGH 5.268 0.979 0 527 0.333 6.212 5.722 4 675 6 212 5.722 4.675 

WGHTD AVE 5.163 0.954 0 516 0.326 6.072 5 586 4 550 6 072 5.586 4.550 

GULF LOW 3.509 0.652 0 351 0.222 4.101 3.748 3 017 4.101 3.748 3.017 

HIGH 11.963 3.832 1.196 1.113 17.013 16.411 14 966 17 013 16.411 14.966 

WGHTD AVE 6.632 1.803 0 623 0.535 8.800 8.357 7 366 8 800 8.357 7.366 

HAM LTON LOW 0.509 0.047 0 051 0.059 0.429 0.335 0.195 0.431 0.335 0.192 

HIGH 0.584 0.056 0 058 0.061 0.512 0.409 0 249 0 513 0.412 0.247 

WGHTD AVE 0.536 0.050 0 054 0.060 0.456 0.360 0 212 0.454 0.360 0.212 

HARDEE LOW 3.602 0.670 0 360 0.251 4.248 3.908 3 209 4 248 3.908 3.209 

HIGH 3.747 0.744 0 375 0.259 4.419 4.063 3 331 4.419 4.063 3.331 

WGHTD AVE 3.677 0.688 0 368 0.255 4.340 3.993 3 277 4 340 3.993 3.277 

HENDRY LOW 4.841 0.974 0.484 0.315 5.795 5.345 4 388 5.795 5.345 4.388 

HIGH 5.750 1.174 0 575 0.376 6.958 6.486 5.422 6 958 6.486 5.422 

WGHTD AVE 5.310 1.011 0 534 0.336 6.293 5.803 4.751 6 293 5.803 4.751 

HERNANDO LOW 1.946 0.328 0.195 0.139 2.161 1.935 1 500 2.161 1.935 1.500 

HIGH 3.631 0.887 0 363 0.282 4.597 4.301 3 682 4 597 4.301 3.682 

WGHTD AVE 2.448 0.486 0 244 0.181 2.880 2.636 2.147 2 880 2.636 2.147 

HIGHLANDS LOW 3.112 0.522 0 311 0.204 3.499 3.157 2.467 3.499 3.157 2.467 

HIGH 4.178 0.760 0.418 0.280 4.890 4.491 3 657 4 890 4.491 3.657 

WGHTD AVE 3.650 0.643 0 364 0.243 4.202 3.832 3 073 4 202 3.832 3.073 

HILLSBOROUGH LOW 2.783 0.512 0 278 0.193 3.215 2.927 2 347 3 215 2.927 2.347 

HIGH 7.552 2.177 0.755 0.836 10.479 10.079 9 203 10.479 10.079 9.203 

WGHTD AVE 4.047 0.956 0.406 0.342 5.132 4.817 4.157 5.132 4.817 4.157 

HOLMES LOW 2.021 0.295 0 202 0.129 2.168 1.920 1.438 2.168 1.920 1.438 

HIGH 2.608 0.426 0 261 0.162 2.922 2.631 2 046 2 922 2.631 2.046 

WGHTD AVE 2.567 0.417 0 257 0.160 2.866 2.577 1 996 2 866 2.577 1.996 








INDIAN RIVER LOW 5.743 1.323 0 574 0.391 7.213 6.749 5.730 7 213 6.749 5.730 

HIGH 15.890 5.061 1 589 1.694 22.941 22 289 20.701 22 941 22.289 20.701 

WGHTD AVE 9.374 2.661 0 930 0.783 12.765 12 213 10 935 12.765 12.213 10.935 

JACKSON LOW 1.469 0.178 0.147 0.103 1.472 1 266 0 890 1.472 1.266 0.890 

HIGH 2.070 0.303 0 207 0.132 2.217 1 961 1.465 2 217 1.961 1.465 

WGHTD AVE 1.793 0.236 0.180 0.119 1.860 1 625 1.181 1 860 1.625 1.181 

JEFFERSON LOW 0.834 0.083 0 083 0.074 0.757 0 620 0 390 0.757 0.620 0.390 

HIGH 0.955 0.096 0 095 0.079 0.883 0.730 0.469 0 883 0.730 0.469 

WGHTD AVE 0.847 0.084 0 085 0.074 0.770 0 631 0 398 0.770 0.631 0.398 

LAFAYETTE LOW 0.776 0.079 0 078 0.071 0.718 0 584 0 373 0.706 0.583 0.377 

HIGH 0.813 0.083 0 081 0.074 0.753 0 616 0.403 0.744 0.618 0.402 

WGHTD AVE 0.805 0.083 0 081 0.074 0.741 0 611 0.401 0.740 0.619 0.402 

LAKE LOW 1.357 0.162 0.136 0.100 1.350 1.158 0 808 1 350 1.158 0.808 

HIGH 2.347 0.363 0 235 0.159 2.558 2 282 1.741 2 558 2.282 1.741 

WGHTD AVE 1.977 0.296 0.195 0.139 2.120 1 876 1.412 2.120 1.876 1.412 

LEE LOW 5.286 1.151 0 529 0.365 6.551 6.116 5.170 6 551 6.116 5.170 

HIGH 14.532 5.019 1.453 1.634 21.156 20.539 19 059 21.156 20.539 19.059 

WGHTD AVE 7.990 2.269 0.746 0.689 10.849 10.381 9 313 10 849 10.381 9.313 

LEON LOW 0.775 0.075 0 077 0.072 0.689 0 557 0 341 0 689 0.557 0.341 

HIGH 1.236 0.139 0.124 0.093 1.196 1 011 0 682 1.196 1.011 0.682 

WGHTD AVE 1.154 0.124 0.115 0.089 1.096 0 918 0 608 1 096 0.918 0.608 

LEVY LOW 1.238 0.153 0.124 0.095 1.245 1 073 0.754 1 243 1.073 0.757 

HIGH 3.790 0.980 0 381 0.299 4.865 4.529 3 913 4 826 4.544 3.876 

WGHTD AVE 1.461 0.198 0.145 0.112 1.525 1.342 1 002 1 525 1.342 1.002 

LIBERTY LOW 1.538 0.180 0.154 0.106 1.528 1.308 0 906 1 528 1.308 0.906 

HIGH 2.169 0.313 0 217 0.138 2.320 2.051 1 532 2 320 2.051 1.532 

WGHTD AVE 2.020 0.279 0 201 0.130 2.128 1.871 1 378 2.128 1.871 1.378 

MADISON LOW 0.617 0.059 0 062 0.063 0.538 0.438 0 263 0 542 0.435 0.264 

HIGH 0.765 0.078 0 076 0.071 0.708 0.580 0 374 0.714 0.576 0.378 

WGHTD AVE 0.695 0.068 0 069 0.067 0.621 0.504 0 314 0 621 0.504 0.314 

MANATEE LOW 3.116 0.586 0 312 0.213 3.634 3.320 2 679 3 634 3.320 2.679 

HIGH 9.641 2.911 0 964 1.108 13.662 13.226 12 243 13 662 13.226 12.243 

WGHTD AVE 7.178 2.030 0.729 0.740 9.881 9.496 8 637 9 881 9.496 8.637 

MARION LOW 1.127 0.130 0.112 0.089 1.102 0.926 0 630 1.100 0.932 0.641 

HIGH 1.962 0.332 0.196 0.142 2.178 1.958 1 532 2.178 1.958 1.532 

WGHTD AVE 1.528 0.205 0.153 0.114 1.587 1.389 1 024 1 587 1.389 1.024 

MARTIN LOW 7.183 1.600 0.718 0.471 8.963 8.385 7 087 8 963 8.385 7.087 

HIGH 19.811 6.480 1 981 2.012 28.648 27.847 25 840 28 648 27.847 25.840 

WGHTD AVE 15.298 4.621 1 501 1.436 21.518 20.801 19 063 21 518 20.801 19.063 

MIAMI-DADE LOW 11.366 2.633 1.137 0.832 14.686 13.950 12.196 14 686 13.950 12.196 

HIGH 30.224 10.038 3 022 3.558 44.752 43.789 41 299 44.752 43.789 41.299 

WGHTD AVE 15.722 4.342 1 553 1.333 21.520 20.704 18 687 21 520 20.704 18.687 

MONROE LOW 22.357 7.837 2 236 2.442 33.582 32.808 30 835 33 582 32.808 30.835 

HIGH 31.494 10.490 3.149 3.589 46.610 45.630 43 021 46 610 45.630 43.021 

WGHTD AVE 28.269 9.846 2.774 3.135 42.178 41.264 38 853 42.178 41.264 38.853 

NASSAU LOW 0.689 0.070 0 070 0.063 0.637 0.518 0 334 0 634 0.526 0.336 

HIGH 1.606 0.287 0.161 0.123 1.814 1.636 1 297 1 814 1.636 1.297 

WGHTD AVE 0.902 0.114 0 092 0.077 0.914 0.775 0 550 0 908 0.775 0.550 








OKALOOSA LOW 3.073 0.535 0 307 0.190 3.532 3 210 2 548 3 532 3.210 2.548 

HIGH 15.295 5.103 1 530 1.555 22.256 21 607 19 986 22 256 21.607 19.986 

WGHTD AVE 7.552 2.215 0.709 0.628 10.305 9 852 8 817 10 305 9.852 8.817 

OKEECHOBEE LOW 4.555 0.831 0.455 0.284 5.299 4 845 3 892 5 299 4.845 3.892 

HIGH 5.251 0.969 0 525 0.331 6.186 5 697 4 651 6.186 5.697 4.651 

WGHTD AVE 4.573 0.835 0.457 0.285 5.323 4 867 3 912 5 323 4.867 3.912 

ORANGE LOW 1.816 0.238 0.182 0.125 1.880 1 642 1.195 1 880 1.642 1.195 

HIGH 2.745 0.467 0 275 0.182 3.102 2 803 2 205 3.102 2.803 2.205 

WGHTD AVE 2.227 0.335 0 224 0.150 2.407 2.140 1 622 2.407 2.140 1.622 

OSCEOLA LOW 1.964 0.263 0.196 0.134 2.048 1.794 1 314 2 048 1.794 1.314 

HIGH 3.699 0.667 0 370 0.239 4.267 3 886 3.103 4 267 3.886 3.103 

WGHTD AVE 2.726 0.427 0 280 0.180 3.013 2.708 2.101 3 013 2.708 2.101 

PALM BEACH LOW 6.486 1.290 0 649 0.408 7.840 7 270 6 014 7 840 7.270 6.014 

HIGH 24.974 8.130 2.497 2.787 36.504 35.612 33 341 36 504 35.612 33.341 

WGHTD AVE 16.403 4.849 1 506 1.574 22.946 22.168 20 267 22 946 22.168 20.267 

PASCO LOW 2.239 0.388 0 224 0.159 2.522 2 272 1.782 2 522 2.272 1.782 

HIGH 5.961 1.640 0 596 0.595 8.047 7 686 6 898 8 047 7.686 6.898 

WGHTD AVE 3.255 0.701 0 318 0.249 3.967 3 679 3 089 3 967 3.679 3.089 

PINELLAS LOW 3.797 0.910 0 380 0.293 4.805 4 500 3 859 4 805 4.500 3.859 

HIGH 8.611 2.571 0 861 0.916 12.105 11.685 10.723 12.105 11.685 10.723 

WGHTD AVE 6.113 1.668 0 615 0.619 8.269 7 906 7.109 8 269 7.906 7.109 

POLK LOW 2.089 0.297 0 209 0.142 2.219 1 960 1.465 2 219 1.960 1.465 

HIGH 3.012 0.521 0 301 0.204 3.359 3.043 2.409 3 359 3.043 2.409 

WGHTD AVE 2.653 0.438 0 264 0.182 2.970 2.677 2 096 2 970 2.677 2.096 

PUTNAM LOW 1.029 0.113 0.103 0.083 0.987 0.832 0 560 0 987 0.832 0.560 

HIGH 1.462 0.184 0.146 0.107 1.490 1.296 0 938 1.490 1.296 0.938 

WGHTD AVE 1.185 0.142 0.118 0.092 1.173 1.004 0.700 1.173 1.004 0.700 

SA NT JOHNS LOW 0.886 0.097 0 089 0.075 0.849 0.718 0.488 0 849 0.718 0.488 

HIGH 2.555 0.587 0 256 0.198 3.133 2.902 2.437 3.133 2.902 2.437 

WGHTD AVE 1.373 0.214 0.138 0.107 1.469 1.318 0 984 1.467 1.294 1.000 

SA NT LUC E LOW 6.019 1.251 0 602 0.385 7.335 6.815 5 674 7 335 6.815 5.674 

HIGH 17.148 5.795 1.715 1.694 24.963 24.252 22 503 24 963 24.252 22.503 

WGHTD AVE 12.212 3.584 1 215 1.108 16.941 16.301 14.775 16 941 16.301 14.775 

SANTA ROSA LOW 2.129 0.296 0 213 0.129 2.282 2.020 1 510 2 282 2.020 1.510 

HIGH 17.112 5.912 1.711 1.708 25.155 24.473 22.758 25.155 24.473 22.758 

WGHTD AVE 9.033 2.735 0 912 0.789 12.581 12.069 10 874 12 581 12.069 10.874 

SARASOTA LOW 6.093 1.537 0 609 0.524 8.083 7.728 6 942 8 083 7.728 6.942 

HIGH 11.296 3.658 1.130 1.346 16.215 15.758 14 682 16 215 15.758 14.682 

WGHTD AVE 8.299 2.539 0 842 0.824 11.667 11.245 10 278 11 667 11.245 10.278 

SEM NOLE LOW 1.905 0.260 0.190 0.129 1.995 1.751 1 288 1 995 1.751 1.288 

HIGH 2.586 0.425 0 259 0.174 2.904 2.621 2 060 2 904 2.621 2.060 

WGHTD AVE 2.224 0.342 0 226 0.149 2.426 2.163 1 652 2.426 2.163 1.652 

SUMTER LOW 1.744 0.249 0.174 0.125 1.841 1.620 1 205 1 841 1.620 1.205 

HIGH 1.957 0.306 0.196 0.138 2.127 1.892 1.441 2.127 1.892 1.441 

WGHTD AVE 1.844 0.276 0.186 0.131 1.977 1.751 1 320 1 977 1.751 1.320 








SUWANNEE LOW 0.545 0.051 0 055 0.061 0.469 0 368 0 214 0.463 0.365 0.213 

HIGH 0.798 0.082 0 080 0.075 0.733 0 610 0 396 0.739 0.608 0.396 

WGHTD AVE 0.600 0.059 0 060 0.064 0.534 0.424 0 256 0 526 0.426 0.258 

TAYLOR LOW 0.837 0.085 0 084 0.074 0.772 0 639 0.414 0.772 0.639 0.414 

HIGH 1.111 0.133 0.111 0.091 1.114 0 935 0 658 1 099 0.945 0.658 

WGHTD AVE 1.042 0.115 0.104 0.085 1.002 0 847 0 575 1 002 0.847 0.575 

UNION LOW 0.801 0.083 0 080 0.072 0.750 0 612 0.403 0.751 0.608 0.402 

HIGH 0.875 0.091 0 088 0.077 0.824 0 685 0.458 0 818 0.685 0.458 

WGHTD AVE 0.822 0.085 0 082 0.074 0.777 0 633 0.419 0.769 0.634 0.420 

VOLUSIA LOW 1.515 0.183 0.152 0.109 1.557 1 361 0 988 1 557 1.361 0.988 

HIGH 4.935 1.430 0.494 0.408 6.614 6 284 5 548 6 630 6.284 5.548 

WGHTD AVE 3.060 0.720 0 317 0.235 3.808 3 538 2 988 3 808 3.538 2.988 

WAKULLA LOW 1.624 0.198 0.162 0.112 1.631 1.405 0 987 1 631 1.405 0.987 

HIGH 2.103 0.344 0 210 0.147 2.311 2 060 1 578 2 311 2.060 1.578 

WGHTD AVE 1.707 0.216 0.171 0.118 1.745 1 515 1 085 1.745 1.515 1.085 

WALTON LOW 2.131 0.304 0 213 0.131 2.292 2 032 1 524 2 292 2.032 1.524 

HIGH 14.087 4.729 1.409 1.345 20.443 19.810 18 243 20.443 19.810 18.243 

WGHTD AVE 6.225 1.621 0 576 0.510 8.194 7.774 6 839 8.194 7.774 6.839 

WASHINGTON LOW 2.850 0.469 0 285 0.177 3.218 2 911 2 286 3 218 2.911 2.286 

HIGH 4.980 1.143 0.498 0.336 6.244 5 836 4 942 6 244 5.836 4.942 

WGHTD AVE 3.148 0.560 0 313 0.197 3.633 3 310 2 644 3 633 3.310 2.644 

Statewide LOW 0.510 0.047 0 051 0.054 0.429 0 339 0.192 0.429 0.333 0.194 

HIGH 31.494 10.490 3.149 3.589 46.610 45.630 43 021 46 610 45.630 43.021 

WGHTD AVE 5.145 1.351 0.455 0.458 6.769 6.419 5 658 6.769 6.419 5.658 




PERSONAL RESIDENTIAL - Renters -- FRAME 

$0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 

DEDUCTIBLE ADDITIONAL DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE 

COUNTY LOSS COSTS CONTENTS LIVING EXPENSE TOTAL* TOTAL* TOTAL* TOTAL* TOTAL* TOTAL* 

ALACHUA LOW 0.117 0.129 0.040 0 019 0.004 0 046 0 040 0 014 

HIGH 0.181 0.150 0.078 0 043 0.013 0 081 0 078 0 034 

WGHTD AVE 0.140 0.136 0.054 0 027 0.007 0 059 0 054 0 021 

BAKER LOW 0 066 0.100 0.016 0 006 0.001 0 022 0 016 0 004 

HIGH 0.112 0.123 0.038 0 018 0.004 0 043 0 038 0 013 

WGHTD AVE 0.109 0.123 0.036 0 017 0.003 0 042 0 036 0 012 

BAY LOW 0.421 0.189 0.233 0.151 0.064 0 228 0 233 0.127 

HIGH 3.732 0 849 3.730 3 375 2.709 3 697 3.730 3 233 

WGHTD AVE 2 696 0 623 2.542 2 257 1.754 2 511 2 542 2.148 

BRADFORD LOW 0.115 0.125 0.038 0 017 0.003 0 043 0 039 0 013 

HIGH 0.121 0.131 0.042 0 020 0.004 0 050 0 042 0 015 

WGHTD AVE 0.118 0.127 0.040 0 018 0.004 0 046 0 040 0 013 

BREVARD LOW 0 563 0 223 0.399 0 300 0.175 0 397 0 398 0 273 

HIGH 3 552 0 847 3.572 3 248 2.628 3 559 3 572 3.118 

WGHTD AVE 1 917 0 510 1.761 1 545 1.176 1.755 1.761 1.463 

BROWARD LOW 1 886 0 534 1.495 1.174 0.708 1.451 1.495 1 061 

HIGH 7 946 2 030 8.553 7 897 6.557 8.472 8 553 7 623 

WGHTD AVE 3 821 0 878 3.625 3.181 2.405 3 576 3 625 3 010 

CALHOUN LOW 0 346 0.180 0.174 0.107 0.041 0.175 0.174 0 089 

HIGH 0.464 0 201 0.268 0.179 0.081 0 266 0 268 0.153 

WGHTD AVE 0 354 0.181 0.179 0.111 0.042 0.179 0.179 0 091 

CHARLOTTE LOW 0 949 0 303 0.729 0 578 0.359 0.724 0.729 0 525 

HIGH 3 630 0 903 3.770 3.465 2.854 3.747 3.770 3 342 

WGHTD AVE 2.437 0 637 2.390 2.146 1.697 2 372 2 390 2 050 

CITRUS LOW 0 278 0.166 0.151 0 098 0.042 0.157 0.152 0 083 

HIGH 0 563 0 234 0.415 0 323 0.198 0.418 0.415 0 292 

WGHTD AVE 0 371 0.191 0.221 0.155 0.077 0 224 0 221 0.134 

CLAY LOW 0 097 0.113 0.030 0 013 0.002 0 037 0 031 0 009 

HIGH 0.130 0.132 0.045 0 021 0.004 0 052 0 045 0 016 

WGHTD AVE 0.108 0.119 0.036 0 016 0.003 0 041 0 036 0 012 

COLL ER LOW 1 042 0 330 0.743 0 561 0.315 0.725 0.743 0 500 

HIGH 5 369 1 208 5.539 5 085 4.179 5.490 5 539 4 899 

WGHTD AVE 3 030 0.703 2.852 2 542 1.982 2 826 2 852 2.421 

COLUMBIA LOW 0 079 0.117 0.021 0 008 0.001 0 027 0 021 0 005 

HIGH 0.106 0.127 0.034 0 015 0.003 0 041 0 034 0 011 

WGHTD AVE 0 082 0.119 0.022 0 008 0.001 0 028 0 022 0 006 

DESOTO LOW 0 835 0 284 0.622 0.485 0.293 0 611 0 622 0.439 

HIGH 0 860 0 290 0.652 0 511 0.309 0 645 0 652 0.462 

WGHTD AVE 0 836 0 284 0.623 0.486 0.294 0 611 0 623 0.439 

DIXIE LOW 0.162 0.142 0.065 0 033 0.008 0 071 0 065 0 025 

HIGH 0.466 0 218 0.312 0 235 0.132 0 316 0 310 0 208 

WGHTD AVE 0 315 0.182 0.157 0.107 0.053 0.159 0.156 0 092 





$0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



DUVAL LOW 0 096 0.112 0.030 0 013 0.002 0 037 0 030 0 009 

HIGH 0 311 0.166 0.204 0.150 0.082 0 204 0 202 0.132 

WGHTD AVE 0.135 0.125 0.060 0 034 0.012 0 066 0 060 0 027 

ESCAMBIA LOW 0 352 0.167 0.181 0.111 0.041 0.184 0.181 0 091 

HIGH 5.163 1.156 5.320 4 873 3.979 5 267 5 320 4 690 

WGHTD AVE 2 873 0 649 2.725 2.413 1.853 2 697 2.725 2 292 

FLAGLER LOW 0 201 0.146 0.095 0 054 0.017 0 095 0 093 0 044 

HIGH 0 591 0 218 0.460 0 375 0.246 0.464 0.461 0 345 

WGHTD AVE 0 372 0.179 0.240 0.177 0.102 0 241 0 240 0.160 

FRANKLIN LOW 0.711 0 258 0.501 0 384 0.231 0 502 0 501 0 346 

HIGH 2 513 0 617 2.383 2.107 1.623 2 365 2 383 2 002 

WGHTD AVE 2 098 0 555 1.977 1.728 1.305 1 958 1 977 1 634 

GADSDEN LOW 0.131 0.138 0.040 0 017 0.003 0 046 0 040 0 012 

HIGH 0.180 0.150 0.067 0 034 0.009 0 073 0 067 0 026 

WGHTD AVE 0.156 0.144 0.052 0 025 0.006 0 059 0 052 0 018 

GILCHRIST LOW 0.128 0.134 0.045 0 021 0.004 0 050 0 045 0 015 

HIGH 0.157 0.141 0.062 0 032 0.008 0 068 0 062 0 024 

WGHTD AVE 0.152 0.140 0.057 0 029 0.007 0 064 0 058 0 022 

GLADES LOW 0.741 0 284 0.448 0 308 0.146 0.452 0.448 0 264 

HIGH 0 845 0 307 0.537 0 381 0.192 0 540 0 537 0 331 

WGHTD AVE 0 820 0 303 0.513 0 361 0.179 0 516 0 513 0 313 

GULF LOW 0 567 0 219 0.349 0 243 0.119 0 349 0 349 0 210 

HIGH 3 245 0.757 3.186 2 858 2.260 3.151 3.186 2.730 

WGHTD AVE 2 006 0.487 1.883 1 657 1.277 1 867 1 883 1 573 

HAM LTON LOW 0 066 0.111 0.016 0 005 0.000 0 022 0 016 0 003 

HIGH 0 075 0.112 0.021 0 008 0.001 0 027 0 021 0 005 

WGHTD AVE 0 068 0.111 0.017 0 006 0.001 0 024 0 017 0 004 

HARDEE LOW 0 563 0 245 0.363 0 259 0.131 0 363 0 363 0 226 

HIGH 0 609 0 254 0.405 0 295 0.155 0.402 0.405 0 260 

WGHTD AVE 0 574 0 246 0.372 0 267 0.136 0 371 0 372 0 233 

HENDRY LOW 0 807 0 291 0.531 0 388 0.195 0 534 0 531 0 341 

HIGH 0 946 0 323 0.654 0.484 0.263 0 642 0 654 0.428 

WGHTD AVE 0 847 0 301 0.546 0 392 0.203 0 545 0 546 0 343 

HERNANDO LOW 0 299 0.172 0.165 0.108 0.048 0.169 0.165 0 092 

HIGH 0 678 0 251 0.521 0.416 0.267 0 522 0 521 0 380 

WGHTD AVE 0.425 0.196 0.289 0 214 0.121 0 290 0 289 0.190 

HIGHLANDS LOW 0.478 0 230 0.267 0.176 0.076 0 269 0 267 0.148 

HIGH 0 654 0 273 0.413 0 292 0.146 0.406 0.413 0 254 

WGHTD AVE 0 559 0 250 0.333 0 228 0.107 0 333 0 333 0.195 

HILLSBOROUGH LOW 0.443 0 209 0.269 0.186 0.089 0 272 0 269 0.160 

HIGH 1 838 0 594 1.858 1 665 1.322 1 849 1 858 1 591 

WGHTD AVE 0 894 0 323 0.756 0 633 0.448 0.757 0.756 0 590 

HOLMES LOW 0 300 0.165 0.148 0 090 0.033 0.150 0.148 0 073 

HIGH 0 399 0.180 0.218 0.141 0.058 0 217 0 218 0.118 

WGHTD AVE 0 389 0.179 0.206 0.132 0.054 0 207 0 206 0.110 





$0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



INDIAN RIVER LOW 1 028 0 315 0.754 0 581 0.346 0.756 0.754 0 524 

HIGH 4.413 1.122 4.597 4.199 3.424 4 568 4 597 4 038 

WGHTD AVE 3 074 0.744 2.969 2 645 2.042 2 985 2 969 2 518 

JACKSON LOW 0 206 0.151 0.083 0 044 0.012 0 086 0 083 0 035 

HIGH 0 307 0.169 0.150 0 091 0.034 0.151 0.150 0 075 

WGHTD AVE 0 253 0.161 0.115 0 065 0.021 0.119 0.115 0 052 

JEFFERSON LOW 0.110 0.128 0.033 0 014 0.002 0 040 0 033 0 010 

HIGH 0.127 0.134 0.040 0 017 0.003 0 048 0 040 0 012 

WGHTD AVE 0.111 0.129 0.033 0 014 0.002 0 041 0 033 0 010 

LAFAYETTE LOW 0.104 0.124 0.033 0 014 0.002 0 038 0 033 0 010 

HIGH 0.109 0.128 0.036 0 016 0.003 0 044 0 036 0 011 

WGHTD AVE 0.108 0.127 0.035 0 015 0.003 0 042 0 035 0 011 

LAKE LOW 0.190 0.151 0.076 0 040 0.011 0 083 0 076 0 031 

HIGH 0 353 0 200 0.184 0.116 0.046 0.191 0.184 0 096 

WGHTD AVE 0 306 0.186 0.148 0 091 0.035 0.154 0.148 0 075 

LEE LOW 0 904 0 303 0.649 0.494 0.282 0 643 0 649 0.442 

HIGH 4.432 1 086 4.591 4 233 3.515 4 556 4 591 4 085 

WGHTD AVE 2.161 0 520 1.988 1.745 1.325 1 969 1 988 1 653 

LEON LOW 0.101 0.127 0.028 0 011 0.002 0 035 0 028 0 008 

HIGH 0.170 0.149 0.061 0 030 0.007 0 070 0 061 0 023 

WGHTD AVE 0.139 0.139 0.046 0 021 0.004 0 054 0 046 0 015 

LEVY LOW 0.176 0.145 0.074 0 039 0.011 0 079 0 073 0 031 

HIGH 0.738 0 266 0.589 0.473 0.316 0 583 0 586 0.435 

WGHTD AVE 0 229 0.159 0.098 0 059 0.022 0.105 0 099 0 048 

LIBERTY LOW 0 214 0.158 0.083 0 043 0.011 0 092 0 083 0 033 

HIGH 0 321 0.177 0.156 0 094 0.034 0.161 0.156 0 077 

WGHTD AVE 0 304 0.174 0.136 0 080 0.028 0.140 0.136 0 064 

MADISON LOW 0 081 0.114 0.022 0 008 0.001 0 029 0 022 0 006 

HIGH 0.102 0.123 0.033 0 015 0.003 0 040 0 033 0 011 

WGHTD AVE 0 092 0.120 0.027 0 011 0.002 0 034 0 027 0 008 

MANATEE LOW 0 500 0 223 0.309 0 216 0.104 0 307 0 309 0.186 

HIGH 2 504 0.750 2.610 2 370 1.920 2 593 2 610 2 276 

WGHTD AVE 1 682 0 528 1.721 1 528 1.187 1.709 1.721 1.454 

MARION LOW 0.156 0.142 0.059 0 030 0.007 0 060 0 059 0 023 

HIGH 0 301 0.179 0.170 0.112 0.049 0.174 0.170 0 095 

WGHTD AVE 0 211 0.157 0.099 0 058 0.020 0.106 0 099 0 047 

MARTIN LOW 1 271 0 370 0.917 0 699 0.402 0 918 0 917 0 625 

HIGH 5.446 1 293 5.589 5.108 4.164 5 552 5 589 4 913 

WGHTD AVE 3 806 0 899 3.707 3 314 2.594 3 680 3.707 3.159 

MIAMI-DADE LOW 1 993 0 527 1.581 1 249 0.760 1 529 1 581 1.130 

HIGH 8 965 2 323 9.797 9 087 7.604 9.700 9.797 8.787 

WGHTD AVE 5 580 1 363 5.740 5.188 4.137 5 672 5.740 4 966 

MONROE LOW 6.705 1 595 7.192 6 635 5.485 7.113 7.192 6.403 

HIGH 8 855 2 205 9.531 8.778 7.207 9.420 9 531 8.459 

WGHTD AVE 8.124 1 833 8.675 8 001 6.593 8 567 8 675 7.716 

NASSAU LOW 0 092 0.110 0.029 0 013 0.002 0 035 0 029 0 009 

HIGH 0 249 0.151 0.146 0 099 0.045 0.151 0.146 0 085 

WGHTD AVE 0 230 0.144 0.135 0 092 0.042 0.141 0.135 0 079 





$0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



OKALOOSA LOW 0.482 0.193 0.283 0.189 0.084 0 284 0 283 0.161 

HIGH 4 388 1 030 4.481 4 080 3.310 4.443 4.481 3 919 

WGHTD AVE 3 258 0.750 3.252 2 930 2.336 3 226 3 252 2 803 

OKEECHOBEE LOW 0.734 0 281 0.447 0 309 0.149 0.453 0.447 0 266 

HIGH 0 838 0 306 0.530 0 375 0.187 0 534 0 530 0 325 

WGHTD AVE 0.738 0 281 0.449 0 311 0.150 0.454 0.449 0 268 

ORANGE LOW 0 261 0.173 0.117 0 067 0.021 0.124 0.117 0 053 

HIGH 0.424 0 205 0.241 0.165 0.077 0 249 0 242 0.141 

WGHTD AVE 0 338 0.193 0.172 0.107 0.041 0.177 0.172 0 088 

OSCEOLA LOW 0 283 0.184 0.130 0 075 0.026 0.137 0.130 0 060 

HIGH 0 593 0 249 0.357 0 246 0.119 0 360 0 357 0 213 

WGHTD AVE 0 370 0 204 0.199 0.126 0.051 0 204 0.199 0.105 

PALM BEACH LOW 1 077 0 349 0.714 0 516 0.269 0.706 0.714 0.452 

HIGH 7 066 1.782 7.509 6 905 5.689 7.444 7 509 6 656 

WGHTD AVE 4 243 0 983 4.037 3 594 2.792 3 998 4 037 3.421 

PASCO LOW 0 348 0.188 0.197 0.131 0.058 0 202 0.197 0.111 

HIGH 1 324 0.422 1.225 1 073 0.821 1 221 1 225 1 017 

WGHTD AVE 0.736 0 272 0.566 0.459 0.307 0 569 0 566 0.423 

P NELLAS LOW 0 695 0 256 0.535 0.425 0.270 0 538 0 535 0 388 

HIGH 2.173 0 658 2.217 1 999 1.597 2 203 2 217 1 913 

WGHTD AVE 1.481 0.469 1.429 1 257 0.964 1.424 1.429 1.192 

POLK LOW 0 306 0.188 0.149 0 090 0.032 0.152 0.149 0 073 

HIGH 0.465 0 228 0.273 0.186 0.088 0 280 0 273 0.160 

WGHTD AVE 0.406 0 210 0.228 0.151 0.067 0 232 0 228 0.128 

PUTNAM LOW 0.140 0.133 0.050 0 024 0.005 0 056 0 050 0 018 

HIGH 0 207 0.153 0.092 0 052 0.017 0 097 0 092 0 042 

WGHTD AVE 0.156 0.139 0.062 0 032 0.009 0 068 0 062 0 025 

SA NT JOHNS LOW 0.120 0.122 0.044 0 022 0.005 0 050 0 044 0 016 

HIGH 0.455 0 200 0.328 0 254 0.157 0 327 0 328 0 230 

WGHTD AVE 0 275 0.158 0.161 0.112 0.056 0.161 0.159 0 097 

SA NT LUC E LOW 1 021 0 326 0.696 0 512 0.278 0 699 0 696 0.452 

HIGH 5 083 1.153 5.183 4.708 3.883 5.177 5.186 4 567 

WGHTD AVE 2 548 0 619 2.341 2 026 1.496 2 328 2 341 1 908 

SANTA ROSA LOW 0 313 0.160 0.152 0 089 0.029 0.154 0.152 0 072 

HIGH 5 080 1.140 5.231 4.789 3.907 5.176 5 231 4 608 

WGHTD AVE 2 639 0 645 2.636 2 344 1.824 2 612 2 636 2 231 

SARASOTA LOW 1.112 0 373 0.990 0 831 0.576 0 977 0 990 0.773 

HIGH 3.185 0 866 3.290 3 014 2.473 3 269 3 290 2 902 

WGHTD AVE 2.148 0 617 2.128 1 911 1.513 2.112 2.128 1 826 

SEMINOLE LOW 0 277 0.175 0.128 0 075 0.025 0.134 0.128 0 060 

HIGH 0 390 0.197 0.220 0.144 0.062 0 223 0 220 0.122 

WGHTD AVE 0 306 0.181 0.152 0 092 0.034 0.157 0.152 0 076 

SUMTER LOW 0 256 0.171 0.124 0 074 0.027 0.130 0.124 0 060 

HIGH 0 295 0.178 0.154 0 097 0.040 0.163 0.154 0 081 

WGHTD AVE 0 267 0.172 0.131 0 080 0.030 0.137 0.131 0 066 





$0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



SUWANNEE LOW 0 071 0.114 0.018 0 006 0.001 0 025 0 018 0 004 

HIGH 0.108 0.129 0.035 0 016 0.003 0 043 0 035 0 011 

WGHTD AVE 0 077 0.117 0.020 0 007 0.001 0 027 0 020 0 005 

TAYLOR LOW 0.111 0.126 0.036 0 015 0.003 0 043 0 036 0 011 

HIGH 0.156 0.144 0.063 0 033 0.008 0 069 0 063 0 025 

WGHTD AVE 0.144 0.137 0.053 0 025 0.006 0 060 0 052 0 019 

UNION LOW 0.107 0.124 0.035 0 016 0.003 0 043 0 035 0 011 

HIGH 0.118 0.130 0.041 0 019 0.004 0 049 0 040 0 014 

WGHTD AVE 0.110 0.126 0.037 0 017 0.003 0 044 0 037 0 012 

VOLUSIA LOW 0 206 0.155 0.092 0 052 0.016 0.100 0 092 0 041 

HIGH 1.138 0 335 1.015 0 882 0.651 1 011 1 016 0 828 

WGHTD AVE 0 544 0 220 0.402 0 318 0.205 0.408 0.402 0 290 

WAKULLA LOW 0 230 0.163 0.093 0 050 0.014 0.102 0 093 0 039 

HIGH 0 319 0.184 0.169 0.110 0.048 0.175 0.169 0 093 

WGHTD AVE 0 250 0.169 0.111 0 063 0.021 0.119 0.111 0 050 

WALTON LOW 0 315 0.162 0.155 0 092 0.032 0.157 0.155 0 075 

HIGH 4.107 0 928 4.155 3.781 3.066 4.120 4.155 3 632 

WGHTD AVE 2 960 0.704 2.795 2 509 1.992 2.775 2.795 2 398 

WASHINGTON LOW 0.435 0.190 0.244 0.160 0.067 0 238 0 244 0.134 

HIGH 0 891 0 270 0.651 0 501 0.297 0 641 0 651 0.451 

WGHTD AVE 0.458 0.191 0.278 0.187 0.085 0 272 0 278 0.159 

Statewide LOW 0 066 0.100 0.016 0 005 0.000 0 022 0 016 0 003 

HIGH 8 965 2 323 9.797 9 087 7.604 9.700 9.797 8.787 

WGHTD AVE 1 087 0 324 0.947 0 821 0.619 0 947 0 947 0.775 




PERSONAL RESIDENTIAL - Renters -- MASONRY 

0% $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



ALACHUA LOW 0.087 0.124 0.023 0.009 0.001 0.030 0 023 0 006 

HIGH 0.133 0.142 0.047 0.023 0.006 0.052 0 047 0 017 

WGHTD AVE 0.103 0.129 0.032 0.014 0.003 0.038 0 032 0 010 

BAKER LOW 0.050 0.099 0.010 0.003 0.000 0.015 0 010 0 002 

HIGH 0.084 0.118 0.023 0.009 0.001 0.027 0 023 0 006 

WGHTD AVE 0.082 0.117 0.021 0.008 0.001 0.026 0 021 0 005 

BAY LOW 0.307 0.166 0.143 0.085 0.031 0.144 0.143 0 069 

HIGH 3.053 0.669 2.976 2.689 2.159 2.955 2 976 2 576 

WGHTD AVE 2.529 0.497 2.250 2.007 1.576 2.227 2 250 1 913 

BRADFORD LOW 0.086 0.120 0.022 0.008 0.001 0.028 0 023 0 006 

HIGH 0.090 0.126 0.025 0.010 0.002 0.032 0 025 0 007 

WGHTD AVE 0.088 0.123 0.023 0.009 0.001 0.030 0 023 0 006 

BREVARD LOW 0.429 0.192 0.281 0.207 0.120 0.279 0 280 0.187 

HIGH 2.925 0.668 2.851 2.611 2.118 2.882 2 884 2 505 

WGHTD AVE 1.673 0.425 1.495 1.316 1.012 1.494 1.495 1 249 

BROWARD LOW 1.405 0.401 1.015 0.775 0.453 0.992 1 015 0 694 

HIGH 6.739 1.612 7.090 6.543 5.437 7.033 7 090 6 317 

WGHTD AVE 2.974 0.646 2.650 2.304 1.727 2.622 2 650 2.174 

CALHOUN LOW 0.256 0.161 0.108 0.061 0.020 0.112 0.108 0 049 

HIGH 0.340 0.176 0.168 0.105 0.043 0.171 0.168 0 087 

WGHTD AVE 0.262 0.163 0.110 0.062 0.021 0.114 0.110 0 050 

CHARLOTTE LOW 0.723 0.252 0.516 0.400 0.243 0.515 0 516 0 362 

HIGH 3.027 0.729 3.079 2.824 2.323 3.064 3 079 2.721 

WGHTD AVE 1.945 0.506 1.873 1.673 1.313 1.862 1 873 1 596 

CITRUS LOW 0.202 0.150 0.093 0.055 0.020 0.099 0 093 0 045 

HIGH 0.431 0.201 0.294 0.225 0.137 0.299 0 294 0 203 

WGHTD AVE 0.254 0.165 0.130 0.084 0.037 0.136 0.130 0 071 

CLAY LOW 0.072 0.110 0.018 0.006 0.001 0.024 0 018 0 004 

HIGH 0.097 0.127 0.027 0.011 0.002 0.034 0 027 0 007 

WGHTD AVE 0.082 0.116 0.021 0.008 0.001 0.027 0 021 0 005 

COLL ER LOW 0.778 0.269 0.502 0.368 0.199 0.495 0 502 0 324 

HIGH 4.482 0.971 4.540 4.162 3.419 4.507 4 540 4 009 

WGHTD AVE 2.643 0.593 2.395 2.134 1.667 2.380 2 395 2 033 

COLUMBIA LOW 0.059 0.115 0.013 0.004 0.000 0.019 0 013 0 002 

HIGH 0.080 0.123 0.021 0.007 0.001 0.027 0 021 0 005 

WGHTD AVE 0.060 0.116 0.013 0.004 0.000 0.020 0 014 0 002 

DESOTO LOW 0.627 0.238 0.429 0.325 0.190 0.424 0.429 0 292 

HIGH 0.653 0.242 0.456 0.349 0.204 0.455 0.456 0 314 

WGHTD AVE 0.632 0.239 0.432 0.328 0.192 0.428 0.432 0 295 

DIXIE LOW 0.119 0.134 0.038 0.016 0.003 0.044 0 038 0 012 

HIGH 0.352 0.194 0.215 0.157 0.087 0.223 0 214 0.139 

WGHTD AVE 0.171 0.151 0.076 0.046 0.020 0.082 0 076 0 038 





0% $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



DUVAL LOW 0.072 0.108 0.018 0.006 0.001 0.024 0 018 0 004 

HIGH 0.233 0.150 0.138 0.097 0.050 0.137 0.136 0 084 

WGHTD AVE 0.101 0.118 0.037 0.019 0.006 0.043 0 037 0 015 

ESCAMBIA LOW 0.256 0.147 0.108 0.060 0.018 0.114 0.108 0 047 

HIGH 4.293 0.914 4.340 3.966 3.237 4.301 4 340 3 814 

WGHTD AVE 2.746 0.520 2.468 2.195 1.709 2.448 2.468 2 090 

FLAGLER LOW 0.145 0.137 0.056 0.028 0.007 0.060 0 055 0 022 

HIGH 0.450 0.190 0.327 0.263 0.170 0.330 0 328 0 241 

WGHTD AVE 0.267 0.159 0.147 0.102 0.054 0.147 0.147 0 090 

FRANKLIN LOW 0.540 0.219 0.351 0.264 0.158 0.356 0 351 0 236 

HIGH 1.993 0.469 1.807 1.585 1.210 1.802 1 807 1 502 

WGHTD AVE 1.477 0.410 1.233 1.061 0.788 1.226 1 233 0 999 

GADSDEN LOW 0.099 0.133 0.024 0.008 0.001 0.030 0 024 0 005 

HIGH 0.134 0.143 0.040 0.018 0.003 0.047 0 040 0 013 

WGHTD AVE 0.119 0.138 0.031 0.012 0.002 0.038 0 031 0 009 

GILCHRIST LOW 0.094 0.128 0.026 0.010 0.001 0.033 0 026 0 006 

HIGH 0.116 0.134 0.037 0.016 0.003 0.044 0 037 0 012 

WGHTD AVE 0.114 0.133 0.037 0.016 0.003 0.043 0 037 0 011 

GLADES LOW 0.545 0.242 0.288 0.187 0.083 0.301 0 288 0.158 

HIGH 0.632 0.259 0.357 0.243 0.117 0.365 0 357 0 208 

WGHTD AVE 0.614 0.256 0.343 0.231 0.110 0.352 0 343 0.198 

GULF LOW 0.420 0.188 0.228 0.151 0.069 0.232 0 228 0.129 

HIGH 2.637 0.591 2.508 2.246 1.776 2.484 2 508 2.144 

WGHTD AVE 2.010 0.422 1.939 1.723 1.350 1.924 1 939 1 641 

HAM LTON LOW 0.051 0.109 0.010 0.002 0.000 0.016 0 010 0 001 

HIGH 0.057 0.110 0.013 0.004 0.000 0.019 0 013 0 002 

WGHTD AVE 0.051 0.109 0.010 0.003 0.000 0.016 0 010 0 002 

HARDEE LOW 0.414 0.212 0.236 0.160 0.076 0.240 0 236 0.137 

HIGH 0.449 0.218 0.267 0.186 0.092 0.270 0 267 0.161 

WGHTD AVE 0.419 0.214 0.239 0.162 0.077 0.242 0 239 0.139 

HENDRY LOW 0.601 0.247 0.355 0.248 0.113 0.361 0 355 0 211 

HIGH 0.701 0.269 0.435 0.310 0.161 0.432 0.435 0 271 

WGHTD AVE 0.637 0.257 0.364 0.250 0.122 0.369 0 364 0 215 

HERNANDO LOW 0.219 0.157 0.103 0.062 0.024 0.108 0.103 0 051 

HIGH 0.518 0.214 0.372 0.292 0.187 0.375 0 372 0 267 

WGHTD AVE 0.371 0.187 0.237 0.175 0.101 0.241 0 237 0.156 

HIGHLANDS LOW 0.350 0.205 0.167 0.101 0.038 0.172 0.167 0 083 

HIGH 0.485 0.235 0.270 0.182 0.086 0.270 0 270 0.156 

WGHTD AVE 0.420 0.220 0.221 0.144 0.063 0.225 0 221 0.121 

HILLSBOROUGH LOW 0.325 0.185 0.173 0.113 0.050 0.179 0.173 0 096 

HIGH 1.474 0.472 1.435 1.281 1.012 1.432 1.435 1 223 

WGHTD AVE 0.670 0.261 0.548 0.454 0.319 0.554 0 548 0.422 

HOLMES LOW 0.218 0.149 0.088 0.048 0.014 0.093 0 088 0 037 

HIGH 0.291 0.159 0.135 0.079 0.029 0.138 0.135 0 064 

WGHTD AVE 0.286 0.159 0.131 0.077 0.027 0.135 0.131 0 062 





0% $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



INDIAN RIVER LOW 0.767 0.257 0.513 0.383 0.221 0.523 0 513 0 343 

HIGH 3.647 0.894 3.717 3.388 2.761 3.700 3.717 3 256 

WGHTD AVE 2.102 0.510 1.989 1.748 1.329 1.986 1 989 1 657 

JACKSON LOW 0.152 0.141 0.051 0.024 0.005 0.055 0 051 0 017 

HIGH 0.227 0.153 0.092 0.051 0.016 0.096 0 092 0 040 

WGHTD AVE 0.191 0.149 0.069 0.034 0.009 0.076 0 069 0 026 

JEFFERSON LOW 0.082 0.124 0.019 0.006 0.001 0.026 0 019 0 004 

HIGH 0.095 0.130 0.023 0.008 0.001 0.031 0 023 0 005 

WGHTD AVE 0.083 0.125 0.019 0.006 0.001 0.027 0 019 0 004 

LAFAYETTE LOW 0.078 0.120 0.019 0.007 0.001 0.026 0 020 0 004 

HIGH 0.081 0.123 0.021 0.008 0.001 0.029 0 021 0 005 

WGHTD AVE 0.080 0.122 0.021 0.007 0.001 0.028 0 021 0 005 

LAKE LOW 0.140 0.142 0.046 0.021 0.004 0.053 0 046 0 015 

HIGH 0.255 0.180 0.111 0.065 0.023 0.121 0.111 0 053 

WGHTD AVE 0.224 0.169 0.097 0.055 0.019 0.105 0 097 0 044 

LEE LOW 0.675 0.251 0.440 0.324 0.178 0.442 0.440 0 286 

HIGH 3.714 0.871 3.800 3.499 2.897 3.772 3 800 3 377 

WGHTD AVE 1.629 0.415 1.417 1.227 0.911 1.407 1.417 1.156 

LEON LOW 0.076 0.123 0.017 0.005 0.000 0.023 0 017 0 003 

HIGH 0.126 0.141 0.037 0.015 0.003 0.045 0 037 0 011 

WGHTD AVE 0.108 0.135 0.028 0.011 0.002 0.036 0 028 0 007 

LEVY LOW 0.129 0.136 0.044 0.020 0.004 0.051 0 044 0 015 

HIGH 0.562 0.224 0.420 0.333 0.220 0.417 0.419 0 304 

WGHTD AVE 0.162 0.145 0.060 0.032 0.011 0.068 0 060 0 025 

LIBERTY LOW 0.159 0.148 0.050 0.022 0.005 0.059 0 050 0 016 

HIGH 0.232 0.159 0.091 0.048 0.014 0.100 0 091 0 038 

WGHTD AVE 0.222 0.158 0.090 0.047 0.014 0.098 0 090 0 037 

MADISON LOW 0.061 0.112 0.014 0.004 0.000 0.021 0 013 0 002 

HIGH 0.076 0.120 0.019 0.007 0.001 0.027 0 020 0 005 

WGHTD AVE 0.068 0.116 0.016 0.005 0.000 0.023 0 016 0 003 

MANATEE LOW 0.365 0.197 0.198 0.130 0.057 0.201 0.198 0.110 

HIGH 2.019 0.579 2.023 1.830 1.473 2.014 2 023 1.755 

WGHTD AVE 1.327 0.416 1.275 1.121 0.859 1.270 1 275 1 063 

MARION LOW 0.115 0.135 0.035 0.015 0.003 0.040 0 035 0 011 

HIGH 0.217 0.162 0.104 0.063 0.024 0.110 0.104 0 051 

WGHTD AVE 0.154 0.147 0.060 0.031 0.009 0.068 0 060 0 024 

MARTIN LOW 0.953 0.299 0.627 0.466 0.263 0.636 0 627 0.414 

HIGH 4.509 1.019 4.512 4.115 3.351 4.505 4 512 3 956 

WGHTD AVE 2.920 0.670 2.855 2.539 1.980 2.841 2 855 2.418 

MIAMI-DADE LOW 1.494 0.396 1.086 0.835 0.494 1.053 1 086 0.750 

HIGH 7.531 1.834 8.056 7.461 6.247 7.979 8 056 7 215 

WGHTD AVE 4.853 0.997 4.672 4.221 3.383 4.627 4 672 4 042 

MONROE LOW 5.569 1.246 5.823 5.364 4.421 5.760 5 823 5.174 

HIGH 7.399 1.703 7.730 7.102 5.825 7.649 7.730 6 841 

WGHTD AVE 6.967 1.583 7.259 6.678 5.492 7.177 7 259 6.436 

NASSAU LOW 0.068 0.107 0.017 0.006 0.001 0.023 0 017 0 004 

HIGH 0.180 0.137 0.091 0.057 0.023 0.097 0 091 0 048 

WGHTD AVE 0.170 0.127 0.080 0.049 0.020 0.086 0 080 0 041 





0% $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



OKALOOSA LOW 0.350 0.166 0.176 0.110 0.043 0.181 0.176 0 091 

HIGH 3.663 0.813 3.673 3.343 2.719 3.644 3 673 3 213 

WGHTD AVE 3.160 0.618 3.025 2.735 2.198 3.004 3 025 2 621 

OKEECHOBEE LOW 0.539 0.240 0.287 0.188 0.085 0.298 0 287 0.159 

HIGH 0.624 0.258 0.350 0.236 0.113 0.359 0 350 0 202 

WGHTD AVE 0.539 0.240 0.287 0.188 0.086 0.298 0 287 0.159 

ORANGE LOW 0.192 0.161 0.071 0.036 0.009 0.078 0 071 0 027 

HIGH 0.304 0.181 0.151 0.097 0.040 0.156 0.152 0 080 

WGHTD AVE 0.247 0.174 0.104 0.058 0.019 0.112 0.104 0 046 

OSCEOLA LOW 0.208 0.170 0.078 0.040 0.011 0.087 0 078 0 031 

HIGH 0.436 0.217 0.230 0.151 0.068 0.238 0 230 0.128 

WGHTD AVE 0.279 0.187 0.121 0.070 0.024 0.128 0.121 0 056 

PALM BEACH LOW 0.797 0.287 0.471 0.326 0.162 0.473 0.471 0 282 

HIGH 5.895 1.389 6.106 5.607 4.620 6.058 6.106 5.403 

WGHTD AVE 3.401 0.774 3.162 2.801 2.168 3.137 3.162 2 663 

PASCO LOW 0.248 0.168 0.119 0.072 0.028 0.126 0.119 0 059 

HIGH 1.039 0.356 0.950 0.828 0.630 0.950 0 950 0.784 

WGHTD AVE 0.639 0.246 0.505 0.415 0.286 0.512 0 505 0 384 

P NELLAS LOW 0.524 0.215 0.373 0.291 0.182 0.381 0 373 0 264 

HIGH 1.734 0.506 1.696 1.518 1.200 1.691 1 696 1.450 

WGHTD AVE 1.171 0.377 1.075 0.938 0.713 1.075 1 075 0 888 

POLK LOW 0.227 0.173 0.092 0.050 0.015 0.097 0 092 0 039 

HIGH 0.343 0.202 0.176 0.113 0.049 0.187 0.176 0 095 

WGHTD AVE 0.300 0.189 0.140 0.085 0.033 0.147 0.140 0 070 

PUTNAM LOW 0.104 0.126 0.029 0.012 0.002 0.036 0 029 0 008 

HIGH 0.153 0.143 0.056 0.028 0.007 0.062 0 056 0 021 

WGHTD AVE 0.118 0.132 0.038 0.017 0.003 0.045 0 038 0 012 

SA NT JOHNS LOW 0.091 0.117 0.027 0.011 0.002 0.033 0 027 0 008 

HIGH 0.346 0.176 0.230 0.174 0.104 0.232 0 230 0.156 

WGHTD AVE 0.205 0.145 0.108 0.072 0.034 0.112 0.107 0 061 

SA NT LUCIE LOW 0.759 0.269 0.465 0.334 0.175 0.476 0.465 0 290 

HIGH 4.237 0.922 4.239 3.846 3.174 4.235 4 241 3.732 

WGHTD AVE 2.186 0.505 2.129 1.864 1.415 2.125 2.129 1.764 

SANTA ROSA LOW 0.230 0.142 0.092 0.048 0.013 0.096 0 092 0 037 

HIGH 4.218 0.900 4.259 3.890 3.170 4.219 4 259 3.741 

WGHTD AVE 2.386 0.441 2.061 1.820 1.402 2.048 2 061 1.728 

SARASOTA LOW 0.852 0.294 0.709 0.586 0.397 0.704 0.709 0 542 

HIGH 2.647 0.660 2.665 2.438 1.995 2.652 2 665 2 346 

WGHTD AVE 1.827 0.493 1.743 1.562 1.235 1.734 1.743 1.492 

SEMINOLE LOW 0.204 0.161 0.078 0.040 0.011 0.084 0 078 0 031 

HIGH 0.286 0.175 0.137 0.082 0.031 0.143 0.137 0 068 

WGHTD AVE 0.229 0.166 0.094 0.052 0.016 0.101 0 094 0 041 

SUMTER LOW 0.187 0.158 0.074 0.040 0.012 0.082 0 074 0 031 

HIGH 0.215 0.162 0.094 0.054 0.019 0.104 0 094 0 044 

WGHTD AVE 0.203 0.160 0.085 0.047 0.016 0.092 0 085 0 038 





0% $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



SUWANNEE LOW 0.053 0.111 0.011 0.003 0.000 0.017 0 011 0 002 

HIGH 0.080 0.124 0.021 0.007 0.001 0.029 0 021 0 005 

WGHTD AVE 0.059 0.115 0.012 0.003 0.000 0.019 0 012 0 002 

TAYLOR LOW 0.083 0.122 0.021 0.007 0.001 0.029 0 021 0 005 

HIGH 0.115 0.136 0.038 0.017 0.003 0.045 0 038 0 012 

WGHTD AVE 0.107 0.131 0.031 0.013 0.002 0.040 0 031 0 009 

UNION LOW 0.080 0.120 0.021 0.008 0.001 0.028 0 021 0 005 

HIGH 0.087 0.125 0.024 0.009 0.001 0.032 0 024 0 006 

WGHTD AVE 0.081 0.121 0.022 0.008 0.001 0.029 0 022 0 005 

VOLUSIA LOW 0.153 0.144 0.055 0.027 0.007 0.065 0 055 0 020 

HIGH 0.891 0.280 0.767 0.662 0.488 0.763 0.767 0 621 

WGHTD AVE 0.454 0.186 0.301 0.236 0.155 0.309 0 301 0 216 

WAKULLA LOW 0.169 0.153 0.055 0.025 0.006 0.065 0 055 0 019 

HIGH 0.233 0.167 0.105 0.062 0.024 0.111 0.105 0 051 

WGHTD AVE 0.176 0.155 0.061 0.029 0.008 0.069 0 060 0 022 

WALTON LOW 0.230 0.144 0.093 0.050 0.014 0.098 0 093 0 038 

HIGH 3.410 0.743 3.387 3.078 2.501 3.365 3 387 2 956 

WGHTD AVE 2.993 0.554 2.760 2.492 2.004 2.746 2.760 2 388 

WASHINGTON LOW 0.315 0.166 0.148 0.088 0.032 0.148 0.148 0 072 

HIGH 0.664 0.219 0.443 0.332 0.191 0.441 0.443 0 296 

WGHTD AVE 0.370 0.171 0.164 0.101 0.040 0.164 0.164 0 083 

Statewide LOW 0.050 0.099 0.010 0.002 0.000 0.015 0 010 0 001 

HIGH 7.531 1.834 8.056 7.461 6.247 7.979 8 056 7 215 

WGHTD AVE 1.758 0.431 1.435 1.259 0.966 1.431 1.435 1.194 




PERSONAL RESIDENTIAL - Condo Owners -- FRAME 

$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 

DEDUCTIBLE DEDUCTIBLE ADDITIONAL DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE DEDUCTIBLE 

COUNTY LOSS COSTS STRUCTURE CONTENTS LIVING EXPENSE TOTAL* TOTAL* TOTAL* TOTAL* TOTAL* TOTAL* 

ALACHUA LOW 0.051 0.118 0.129 0.102 0.050 0.016 0.101 0.050 0 016 

HIGH 0.071 0.181 0.150 0.162 0.094 0.040 0.162 0.094 0 039 

WGHTD AVE 0.058 0.139 0.135 0.123 0.068 0.025 0.123 0.068 0 025 

BAKER LOW 0.033 0.066 0.100 0.055 0.021 0.005 0.055 0.021 0 005 

HIGH 0.049 0.113 0.123 0.097 0.048 0.016 0.097 0.048 0 016 

WGHTD AVE 

BAY LOW 0.136 0.421 0.189 0.400 0.282 0.148 0.400 0.282 0.148 

HIGH 0.584 3.732 0.849 4.485 4.169 3.582 4.485 4.169 3 582 

WGHTD AVE 0.553 3.535 0.800 4.205 3.895 3.331 4.205 3.895 3 331 

BRADFORD LOW 0.051 0.114 0.125 0.097 0.049 0.015 0.099 0.049 0 015 

HIGH 0.053 0.121 0.131 0.105 0.053 0.018 0.105 0.053 0 018 

WGHTD AVE 

BREVARD LOW 0.147 0.563 0.223 0.579 0.458 0.307 0.585 0.465 0 310 

HIGH 0.563 3.553 0.847 4.284 3.987 3.451 4.284 3.987 3.451 

WGHTD AVE 0.367 2.043 0.534 2.422 2.198 1.828 2.422 2.198 1 828 

BROWARD LOW 0.470 1.886 0.534 2.111 1.772 1.230 2.111 1.772 1 230 

HIGH 1.245 7.946 2.030 10.121 9.590 8.498 10.121 9.590 8.498 

WGHTD AVE 0.728 3.886 0.987 4.695 4.279 3.523 4.695 4.279 3 523 

CALHOUN LOW 0.120 0.346 0.180 0.319 0.213 0.103 0.319 0.213 0.103 

HIGH 0.146 0.464 0.201 0.446 0.321 0.176 0.446 0.321 0.176 

WGHTD AVE 0.120 0.346 0.319 0.213 0.103 0.319 0.213 0.103 

CHARLOTTE LOW 0.221 0.950 0.303 1.021 0.844 0.594 1.021 0.844 0 594 

HIGH 0.551 3.630 0.903 4.473 4.202 3.704 4.473 4.202 3.704 

WGHTD AVE 0.439 3.023 0.739 3.544 3.294 2.852 3.544 3.294 2 852 

CITRUS LOW 0.092 0.278 0.166 0.268 0.181 0.096 0.268 0.183 0 097 

HIGH 0.146 0.564 0.234 0.605 0.485 0.333 0.605 0.485 0 333 

WGHTD AVE 0.112 0.359 0.190 0.366 0.269 0.160 0.366 0.269 0.160 

CLAY LOW 0.044 0.097 0.113 0.081 0.039 0.011 0.082 0.039 0 011 

HIGH 0.057 0.130 0.132 0.111 0.057 0.019 0.111 0.057 0 019 

WGHTD AVE 0.047 0.105 0.117 0.090 0.044 0.013 0.089 0.044 0 013 

COLL ER LOW 0.271 1.042 0.330 1.092 0.876 0.573 1.092 0.876 0 573 

HIGH 0.784 5.370 1.208 6.546 6.164 5.416 6.546 6.164 5.416 

WGHTD AVE 0.567 3.553 0.828 4.264 3.951 3.381 4.264 3.951 3 381 

COLUMBIA LOW 0.038 0.080 0.117 0.067 0.027 0.006 0.067 0.028 0 006 

HIGH 0.048 0.106 0.127 0.091 0.043 0.013 0.091 0.043 0 013 

WGHTD AVE 0.038 0.081 0.118 0.068 0.028 0.007 0.068 0.028 0 007 

DESOTO LOW 0.203 0.836 0.284 0.887 0.722 0.496 0.887 0.722 0.496 

HIGH 0.210 0.860 0.290 0.927 0.761 0.527 0.927 0.761 0 527 

WGHTD AVE 0.204 0.840 0.285 0.893 0.728 0.501 0.893 0.728 0 501 

DIX E LOW 0.065 0.162 0.142 0.144 0.081 0.030 0.144 0.081 0 030 

HIGH 0.128 0.467 0.218 0.483 0.366 0.236 0.474 0.368 0 237 

WGHTD AVE 0.118 0.414 0.201 0.418 0.311 0.196 0.415 0.312 0.195 





$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



DUVAL LOW 0.044 0.096 0.112 0.081 0.039 0.011 0.081 0.038 0 011 

HIGH 0.090 0.305 0.166 0.318 0.238 0.151 0.315 0.237 0.152 

WGHTD AVE 0.052 0.134 0.124 0.120 0.069 0.029 0.120 0.070 0 029 

ESCAMBIA LOW 0.120 0.352 0.167 0.328 0.222 0.107 0.328 0.222 0.107 

HIGH 0.766 5.163 1.156 6.310 5.933 5.195 6.310 5.933 5.195 

WGHTD AVE 0.575 3.747 0.816 4.413 4.089 3.482 4.413 4.089 3.482 

FLAGLER LOW 0.074 0.203 0.146 0.184 0.114 0.051 0.184 0.115 0 051 

HIGH 0.138 0.588 0.219 0.647 0.531 0.388 0.643 0.535 0 389 

WGHTD AVE 0.109 0.428 0.185 0.421 0.333 0.216 0.422 0.332 0 216 

FRANKLIN LOW 0.186 0.711 0.258 0.738 0.587 0.393 0.738 0.587 0 393 

HIGH 0.439 2.514 0.617 2.953 2.685 2.229 2.953 2.685 2 229 

WGHTD AVE 0.303 1.341 0.394 1.573 1.369 1.060 1.573 1.369 1 060 

GADSDEN LOW 0.060 0.132 0.138 0.107 0.051 0.015 0.107 0.051 0 015 

HIGH 0.076 0.180 0.150 0.153 0.084 0.031 0.153 0.084 0 031 

WGHTD AVE 0.060 0.107 0.051 0.015 0.107 0.051 0 015 

G LCHRIST LOW 0.055 0.128 0.134 0.111 0.056 0.018 0.111 0.057 0 018 

HIGH 0.064 0.157 0.141 0.140 0.078 0.029 0.140 0.078 0 029 

WGHTD AVE 0.157 0.140 0.078 0.029 0.140 0.078 0 029 

GLADES LOW 0.214 0.741 0.284 0.728 0.539 0.306 0.728 0.539 0 306 

HIGH 0.246 0.845 0.307 0.847 0.644 0.382 0.847 0.644 0 382 

WGHTD AVE 0.237 0.836 0.307 0.825 0.625 0.368 0.825 0.625 0 368 

GULF LOW 0.167 0.567 0.219 0.559 0.417 0.243 0.559 0.417 0 243 

HIGH 0.529 3.245 0.757 3.872 3.570 3.029 3.872 3.570 3 029 

WGHTD AVE 0.529 3.245 0.757 3.872 3.570 3.029 3.872 3.570 3 029 

HAM LTON LOW 0.033 0.066 0.111 0.055 0.020 0.004 0.055 0.020 0 004 

HIGH 0.037 0.076 0.112 0.064 0.027 0.007 0.064 0.027 0 007 

WGHTD AVE 

HARDEE LOW 0.166 0.564 0.245 0.576 0.435 0.262 0.576 0.435 0 262 

HIGH 0.174 0.610 0.254 0.621 0.478 0.297 0.621 0.478 0 297 

WGHTD AVE 

HENDRY LOW 0.223 0.807 0.291 0.817 0.631 0.392 0.817 0.631 0 392 

HIGH 0.268 0.946 0.323 0.981 0.775 0.492 0.981 0.775 0.492 

WGHTD AVE 0.248 0.872 0.307 0.870 0.664 0.396 0.870 0.664 0 396 

HERNANDO LOW 0.098 0.300 0.172 0.288 0.199 0.106 0.288 0.199 0.106 

HIGH 0.162 0.679 0.251 0.734 0.603 0.431 0.734 0.603 0.431 

WGHTD AVE 0.133 0.482 0.212 0.516 0.405 0.267 0.515 0.405 0 271 

HIGHLANDS LOW 0.154 0.478 0.230 0.456 0.321 0.171 0.456 0.321 0.171 

HIGH 0.196 0.655 0.273 0.659 0.495 0.294 0.659 0.495 0 294 

WGHTD AVE 0.185 0.598 0.260 0.599 0.445 0.259 0.599 0.445 0 259 

HILLSBOROUGH LOW 0.133 0.443 0.209 0.436 0.319 0.184 0.436 0.319 0.184 

HIGH 0.347 1.838 0.594 2.299 2.105 1.790 2.299 2.105 1.790 

WGHTD AVE 0.208 0.923 0.326 1.062 0.914 0.706 1.062 0.914 0.706 

HOLMES LOW 0.106 0.300 0.165 0.274 0.180 0.086 0.274 0.180 0 086 

HIGH 0.130 0.399 0.180 0.376 0.263 0.136 0.376 0.263 0.136 

WGHTD AVE 





$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



INDIAN RIVER LOW 0.252 1.029 0.315 1.086 0.883 0.597 1.086 0.883 0 597 

HIGH 0.714 4.413 1.122 5.505 5.157 4.499 5.505 5.157 4.499 

WGHTD AVE 0.530 3.316 0.777 3.889 3.582 3.033 3.889 3.582 3 033 

JACKSON LOW 0.083 0.207 0.151 0.179 0.104 0.041 0.179 0.104 0 041 

HIGH 0.109 0.307 0.169 0.281 0.184 0.088 0.281 0.184 0 088 

WGHTD AVE 0.095 0.218 0.133 0.055 0.218 0.133 0 055 

JEFFERSON LOW 0.051 0.111 0.128 0.091 0.042 0.012 0.091 0.042 0 012 

HIGH 0.058 0.127 0.134 0.105 0.050 0.015 0.105 0.050 0 015 

WGHTD AVE 

LAFAYETTE LOW 0.047 0.105 0.124 0.088 0.042 0.012 0.087 0.041 0 012 

HIGH 0.049 0.109 0.128 0.093 0.046 0.014 0.093 0.045 0 014 

WGHTD AVE 0.048 0.109 0.128 0.092 0.045 0.013 0.093 0.044 0 013 

LAKE LOW 0.077 0.190 0.151 0.165 0.094 0.036 0.165 0.094 0 036 

HIGH 0.120 0.354 0.200 0.331 0.223 0.112 0.331 0.223 0.112 

WGHTD AVE 0.110 0.321 0.189 0.300 0.200 0.100 0.300 0.200 0.100 

LEE LOW 0.233 0.904 0.303 0.953 0.765 0.506 0.953 0.765 0 506 

HIGH 0.665 4.433 1.086 5.408 5.098 4.510 5.408 5.098 4 510 

WGHTD AVE 0.416 2.929 0.655 3.243 2.985 2.533 3.243 2.985 2 533 

LEON LOW 0.048 0.101 0.127 0.082 0.036 0.009 0.082 0.036 0 009 

HIGH 0.073 0.170 0.149 0.143 0.077 0.027 0.143 0.077 0 027 

WGHTD AVE 0.066 0.150 0.143 0.124 0.063 0.020 0.124 0.063 0 020 

LEVY LOW 0.068 0.177 0.145 0.156 0.092 0.036 0.155 0.092 0 036 

HIGH 0.168 0.743 0.266 0.803 0.674 0.493 0.808 0.668 0.494 

WGHTD AVE 0.119 0.436 0.210 0.449 0.345 0.217 0.448 0.339 0 219 

LIBERTY LOW 0.088 0.215 0.158 0.183 0.104 0.039 0.183 0.104 0 039 

HIGH 0.114 0.321 0.177 0.292 0.191 0.090 0.292 0.191 0 090 

WGHTD AVE 

MADISON LOW 0.039 0.080 0.114 0.067 0.029 0.007 0.067 0.029 0 007 

HIGH 0.046 0.103 0.123 0.089 0.042 0.012 0.088 0.042 0 012 

WGHTD AVE 0.043 0.091 0.119 0.077 0.034 0.009 0.076 0.034 0 009 

MANATEE LOW 0.147 0.500 0.223 0.497 0.368 0.215 0.497 0.368 0 215 

HIGH 0.439 2.504 0.750 3.161 2.938 2.547 3.161 2.938 2 547 

WGHTD AVE 0.357 1.965 0.589 2.411 2.211 1.877 2.411 2.211 1 877 

MARION LOW 0.065 0.157 0.142 0.135 0.074 0.027 0.134 0.074 0 027 

HIGH 0.099 0.301 0.179 0.294 0.205 0.110 0.294 0.205 0.110 

WGHTD AVE 0.083 0.221 0.161 0.206 0.130 0.060 0.206 0.130 0 060 

MARTIN LOW 0.317 1.271 0.370 1.330 1.077 0.714 1.330 1.077 0.714 

HIGH 0.868 5.447 1.293 6.670 6.258 5.459 6.670 6.258 5.459 

WGHTD AVE 0.725 4.406 1.066 5.356 4.970 4.249 5.356 4.970 4 249 

MIAMI-DADE LOW 0.474 1.993 0.527 2.211 1.864 1.302 2.211 1.864 1 302 

HIGH 1.385 8.965 2.323 11.538 10.974 9.796 11.538 10.974 9.796 

WGHTD AVE 1.032 5.956 1.623 7.662 7.177 6.223 7.662 7.177 6 223 

MONROE LOW 1.005 6.705 1.595 8.478 8.040 7.126 8.478 8.040 7.126 

HIGH 1.392 8.855 2.205 11.307 10.718 9.461 11.307 10.718 9.461 

WGHTD AVE 1.267 7.931 1.975 10.132 9.610 8.499 10.132 9.610 8.499 

NASSAU LOW 0.042 0.093 0.110 0.078 0.037 0.011 0.078 0.037 0 011 

HIGH 0.078 0.249 0.151 0.246 0.174 0.099 0.246 0.174 0 098 

WGHTD AVE 0.078 0.249 0.151 0.246 0.173 0.097 0.246 0.173 0 098 





$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



OKALOOSA LOW 0.148 0.482 0.193 0.465 0.338 0.185 0.465 0.338 0.185 

HIGH 0.678 4.389 1.030 5.355 5.009 4.346 5.355 5.009 4 346 

WGHTD AVE 0.627 4.127 0.946 4.994 4.664 4.043 4.994 4.664 4 043 

OKEECHOBEE LOW 0.218 0.734 0.281 0.721 0.537 0.307 0.721 0.537 0 307 

HIGH 0.245 0.839 0.306 0.839 0.637 0.376 0.839 0.637 0 376 

WGHTD AVE 

ORANGE LOW 0.098 0.261 0.173 0.234 0.145 0.062 0.234 0.145 0 062 

HIGH 0.135 0.425 0.205 0.411 0.291 0.164 0.411 0.291 0.163 

WGHTD AVE 0.116 0.334 0.191 0.310 0.206 0.100 0.310 0.206 0.100 

OSCEOLA LOW 0.105 0.284 0.184 0.255 0.159 0.071 0.255 0.159 0 070 

HIGH 0.179 0.594 0.249 0.580 0.427 0.245 0.580 0.427 0 245 

WGHTD AVE 0.132 0.390 0.210 0.369 0.253 0.129 0.369 0.253 0.129 

PALM BEACH LOW 0.296 1.077 0.349 1.096 0.854 0.521 1.096 0.854 0 521 

HIGH 1.127 7.067 1.782 8.935 8.435 7.428 8.935 8.435 7.428 

WGHTD AVE 0.733 4.286 1.041 5.115 4.704 3.947 5.115 4.704 3 947 

PASCO LOW 0.111 0.349 0.188 0.337 0.237 0.129 0.337 0.237 0.129 

HIGH 0.263 1.309 0.422 1.558 1.392 1.142 1.558 1.392 1.142 

WGHTD AVE 0.164 0.739 0.267 0.785 0.652 0.476 0.785 0.652 0.476 

P NELLAS LOW 0.168 0.696 0.256 0.754 0.620 0.439 0.754 0.620 0.439 

HIGH 0.392 2.173 0.658 2.714 2.503 2.143 2.714 2.503 2.143 

WGHTD AVE 0.286 1.530 0.473 1.812 1.631 1.347 1.812 1.631 1 347 

POLK LOW 0.109 0.306 0.188 0.282 0.183 0.086 0.282 0.183 0 086 

HIGH 0.149 0.466 0.228 0.452 0.327 0.184 0.452 0.327 0.184 

WGHTD AVE 0.133 0.412 0.212 0.399 0.282 0.153 0.399 0.282 0.153 

PUTNAM LOW 0.060 0.140 0.133 0.121 0.063 0.021 0.121 0.063 0 021 

HIGH 0.080 0.205 0.153 0.184 0.113 0.049 0.186 0.113 0 049 

WGHTD AVE 0.066 0.167 0.141 0.145 0.083 0.033 0.145 0.082 0 032 

SA NT JOHNS LOW 0.051 0.121 0.122 0.105 0.055 0.019 0.105 0.055 0 019 

HIGH 0.117 0.456 0.200 0.480 0.381 0.261 0.480 0.381 0 261 

WGHTD AVE 0.089 0.299 0.165 0.305 0.225 0.140 0.304 0.225 0.137 

SA NT LUC E LOW 0.271 1.021 0.326 1.046 0.824 0.518 1.046 0.824 0 518 

HIGH 0.762 5.087 1.153 6.081 5.730 5.059 6.146 5.756 5 062 

WGHTD AVE 0.556 3.459 0.819 3.992 3.659 3.064 3.992 3.659 3 064 

SANTA ROSA LOW 0.111 0.314 0.160 0.284 0.186 0.083 0.284 0.186 0 083 

HIGH 0.756 5.080 1.140 6.205 5.834 5.104 6.205 5.834 5.104 

WGHTD AVE 0.584 3.620 0.829 4.357 4.033 3.427 4.357 4.033 3.427 

SARASOTA LOW 0.253 1.112 0.373 1.320 1.149 0.886 1.320 1.149 0 886 

HIGH 0.510 3.186 0.866 3.925 3.673 3.219 3.925 3.673 3 219 

WGHTD AVE 0.390 2.362 0.651 2.863 2.647 2.275 2.863 2.647 2 275 

SEMINOLE LOW 0.102 0.277 0.175 0.249 0.157 0.070 0.249 0.157 0 070 

HIGH 0.128 0.390 0.197 0.377 0.265 0.141 0.377 0.265 0.141 

WGHTD AVE 0.107 0.299 0.180 0.274 0.178 0.083 0.274 0.178 0 083 

SUMTER LOW 0.092 0.257 0.171 0.237 0.152 0.071 0.237 0.152 0 071 

HIGH 0.101 0.295 0.178 0.279 0.186 0.094 0.279 0.186 0 094 

WGHTD AVE 0.093 0.262 0.171 0.242 0.159 0.078 0.245 0.160 0 078 





$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



SUWANNEE LOW 0.035 0.071 0.114 0.059 0.023 0.005 0.059 0.023 0 005 

HIGH 0.048 0.107 0.129 0.091 0.045 0.014 0.093 0.045 0 014 

WGHTD AVE 0.035 0.072 0.114 0.059 0.023 0.005 0.059 0.023 0 005 

TAYLOR LOW 0.050 0.112 0.126 0.094 0.045 0.013 0.094 0.045 0 013 

HIGH 0.063 0.158 0.144 0.139 0.079 0.030 0.139 0.078 0 030 

WGHTD AVE 

UNION LOW 0.048 0.108 0.124 0.092 0.044 0.013 0.092 0.045 0 013 

HIGH 0.051 0.116 0.130 0.102 0.052 0.017 0.101 0.051 0 017 

WGHTD AVE 

VOLUSIA LOW 0.082 0.207 0.155 0.190 0.116 0.050 0.190 0.116 0 050 

HIGH 0.220 1.141 0.335 1.308 1.149 0.916 1.300 1.152 0 926 

WGHTD AVE 0.151 0.717 0.241 0.751 0.628 0.464 0.751 0.628 0.464 

WAKULLA LOW 0.092 0.230 0.163 0.199 0.117 0.046 0.199 0.117 0 046 

HIGH 0.108 0.320 0.184 0.302 0.205 0.108 0.302 0.205 0.108 

WGHTD AVE 0.093 0.240 0.168 0.212 0.127 0.052 0.212 0.127 0 052 

WALTON LOW 0.111 0.315 0.162 0.289 0.190 0.088 0.289 0.190 0 088 

HIGH 0.628 4.107 0.928 4.962 4.634 4.018 4.962 4.634 4 018 

WGHTD AVE 0.600 3.621 0.867 4.443 4.130 3.553 4.443 4.130 3 553 

WASHINGTON LOW 0.140 0.436 0.190 0.417 0.296 0.156 0.417 0.296 0.156 

HIGH 0.220 0.892 0.270 0.938 0.763 0.513 0.938 0.763 0 513 

WGHTD AVE 0.184 0.671 0.228 0.678 0.525 0.323 0.678 0.525 0 323 

Statewide LOW 0.033 0.066 0.100 0.055 0.020 0.004 0.055 0.020 0 004 

HIGH 1.392 8.965 2.323 11.538 10.974 9.796 11.538 10.974 9.796 

WGHTD AVE 0.379 2.342 0.575 2.690 2.459 2.066 2.690 2.459 2 066 




PERSONAL RESIDENTIAL - Condo Owners -- MASONRY 

$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



ALACHUA LOW 0.034 0.087 0.124 0.071 0.029 0.007 0.071 0.029 0 007 

HIGH 0.048 0.133 0.142 0.111 0.057 0.020 0.112 0.056 0 020 

WGHTD AVE 0.039 0.103 0.129 0.086 0.040 0.012 0.086 0.040 0 012 

BAKER LOW 0.022 0.051 0.099 0.041 0.013 0.002 0.041 0.013 0 002 

HIGH 0.034 0.084 0.118 0.068 0.028 0.007 0.068 0.029 0 007 

WGHTD AVE 0.034 0.068 0.029 0.007 0.068 0.028 0 007 

BAY LOW 0.094 0.307 0.166 0.266 0.172 0.080 0.266 0.172 0 080 

HIGH 0.438 3.053 0.669 3.564 3.299 2.832 3.564 3.299 2 832 

WGHTD AVE 0.416 2.866 0.626 3.314 3.056 2.608 3.314 3.056 2 608 

BRADFORD LOW 0.035 0.085 0.120 0.068 0.029 0.007 0.070 0.028 0 007 

HIGH 0.036 0.090 0.126 0.074 0.031 0.008 0.073 0.031 0 008 

WGHTD AVE 0.035 0.089 0.124 0.071 0.030 0.007 0.071 0.030 0 007 

BREVARD LOW 0.103 0.429 0.192 0.417 0.319 0.209 0.422 0.324 0 211 

HIGH 0.419 2.938 0.668 3.420 3.178 2.756 3.432 3.174 2.757 

WGHTD AVE 0.319 2.073 0.506 2.403 2.200 1.865 2.403 2.200 1 865 

BROWARD LOW 0.332 1.405 0.401 1.466 1.194 0.799 1.466 1.194 0.799 

HIGH 0.963 6.739 1.612 8.339 7.882 6.983 8.339 7.882 6 983 

WGHTD AVE 0.597 3.554 0.808 4.127 3.768 3.139 4.127 3.768 3.139 

CALHOUN LOW 0.083 0.256 0.161 0.216 0.132 0.057 0.216 0.132 0 057 

HIGH 0.101 0.340 0.176 0.300 0.200 0.099 0.300 0.200 0 099 

WGHTD AVE 0.084 0.265 0.220 0.134 0.057 0.220 0.134 0 057 

CHARLOTTE LOW 0.156 0.724 0.252 0.738 0.594 0.406 0.738 0.594 0.406 

HIGH 0.418 3.027 0.729 3.634 3.402 2.992 3.634 3.402 2 992 

WGHTD AVE 0.317 1.978 0.516 2.347 2.147 1.821 2.347 2.147 1 821 

CITRUS LOW 0.062 0.201 0.150 0.177 0.110 0.052 0.178 0.111 0 052 

HIGH 0.102 0.430 0.201 0.436 0.339 0.228 0.436 0.339 0 228 

WGHTD AVE 0.079 0.279 0.174 0.267 0.187 0.107 0.267 0.187 0.107 

CLAY LOW 0.029 0.072 0.110 0.058 0.022 0.005 0.058 0.022 0 005 

HIGH 0.038 0.097 0.127 0.078 0.033 0.009 0.078 0.033 0 009 

WGHTD AVE 0.030 0.075 0.111 0.061 0.024 0.005 0.060 0.024 0 005 

COLL ER LOW 0.190 0.778 0.269 0.760 0.587 0.367 0.760 0.587 0 367 

HIGH 0.597 4.482 0.971 5.336 5.009 4.396 5.336 5.009 4 396 

WGHTD AVE 0.430 3.160 0.680 3.581 3.312 2.840 3.581 3.312 2 840 

COLUMBIA LOW 0.025 0.059 0.115 0.049 0.016 0.003 0.049 0.016 0 003 

HIGH 0.032 0.080 0.123 0.066 0.026 0.006 0.066 0.026 0 006 

WGHTD AVE 0.026 0.060 0.115 0.050 0.016 0.003 0.050 0.016 0 003 

DESOTO LOW 0.142 0.628 0.238 0.628 0.494 0.328 0.628 0.494 0 328 

HIGH 0.148 0.653 0.242 0.665 0.528 0.354 0.665 0.528 0 354 

WGHTD AVE 0.145 0.637 0.240 0.643 0.508 0.338 0.643 0.508 0 338 

DIX E LOW 0.044 0.118 0.134 0.097 0.048 0.014 0.098 0.048 0 014 

HIGH 0.089 0.352 0.194 0.345 0.250 0.157 0.339 0.251 0.157 

WGHTD AVE 0.082 0.312 0.180 0.298 0.211 0.128 0.295 0.211 0.127 





$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



DUVAL LOW 0.029 0.072 0.108 0.058 0.023 0.005 0.058 0.023 0 005 

HIGH 0.062 0.229 0.150 0.225 0.159 0.096 0.223 0.159 0 096 

WGHTD AVE 0.040 0.128 0.122 0.109 0.063 0.028 0.110 0.063 0 029 

ESCAMBIA LOW 0.082 0.256 0.147 0.215 0.132 0.055 0.215 0.132 0 055 

HIGH 0.581 4.294 0.914 5.113 4.796 4.188 5.113 4.796 4.188 

WGHTD AVE 0.470 3.220 0.672 3.754 3.470 2.953 3.754 3.470 2 953 

FLAGLER LOW 0.050 0.147 0.137 0.123 0.067 0.025 0.123 0.067 0 025 

HIGH 0.097 0.448 0.190 0.466 0.372 0.267 0.463 0.375 0 268 

WGHTD AVE 0.083 0.368 0.174 0.352 0.276 0.183 0.353 0.275 0.182 

FRANKLIN LOW 0.130 0.540 0.219 0.527 0.406 0.266 0.527 0.406 0 266 

HIGH 0.321 1.993 0.469 2.243 2.021 1.661 2.243 2.021 1 661 

WGHTD AVE 0.190 1.143 0.309 1.178 1.015 0.785 1.178 1.015 0.785 

GADSDEN LOW 0.041 0.099 0.133 0.076 0.030 0.006 0.076 0.030 0 006 

HIGH 0.052 0.135 0.143 0.106 0.050 0.015 0.106 0.050 0 015 

WGHTD AVE 0.048 0.122 0.139 0.095 0.042 0.011 0.095 0.042 0 011 

G LCHRIST LOW 0.036 0.094 0.128 0.076 0.032 0.008 0.076 0.032 0 008 

HIGH 0.043 0.117 0.134 0.097 0.047 0.014 0.097 0.047 0 014 

WGHTD AVE 0.036 0.094 0.128 0.075 0.032 0.007 0.076 0.032 0 008 

GLADES LOW 0.148 0.545 0.242 0.494 0.343 0.180 0.494 0.343 0.180 

HIGH 0.172 0.632 0.259 0.586 0.424 0.238 0.586 0.424 0 238 

WGHTD AVE 0.172 0.632 0.259 0.586 0.424 0.238 0.586 0.424 0 238 

GULF LOW 0.116 0.421 0.188 0.383 0.270 0.147 0.383 0.270 0.147 

HIGH 0.393 2.637 0.591 3.037 2.786 2.361 3.037 2.786 2 361 

WGHTD AVE 0.116 2.621 2.670 2.438 2.054 2.670 2.438 2 054 

HAM LTON LOW 0.022 0.051 0.109 0.043 0.013 0.002 0.043 0.013 0 002 

HIGH 0.024 0.058 0.110 0.048 0.016 0.003 0.048 0.016 0 003 

WGHTD AVE 

HARDEE LOW 0.115 0.415 0.212 0.393 0.280 0.158 0.393 0.280 0.158 

HIGH 0.120 0.450 0.218 0.427 0.311 0.182 0.427 0.311 0.182 

WGHTD AVE 0.115 0.393 0.280 0.158 0.393 0.280 0.158 

HENDRY LOW 0.156 0.601 0.247 0.565 0.416 0.241 0.565 0.416 0 241 

HIGH 0.186 0.701 0.269 0.676 0.511 0.308 0.676 0.511 0 308 

WGHTD AVE 0.177 0.653 0.261 0.603 0.435 0.243 0.603 0.435 0 243 

HERNANDO LOW 0.067 0.219 0.157 0.194 0.122 0.058 0.194 0.122 0 058 

HIGH 0.113 0.518 0.214 0.532 0.426 0.299 0.532 0.426 0 299 

WGHTD AVE 0.091 0.371 0.187 0.369 0.279 0.181 0.369 0.279 0.181 

HIGHLANDS LOW 0.106 0.351 0.205 0.308 0.200 0.095 0.308 0.200 0 095 

HIGH 0.136 0.486 0.235 0.453 0.322 0.179 0.453 0.322 0.179 

WGHTD AVE 0.125 0.427 0.223 0.394 0.272 0.145 0.394 0.272 0.145 

HILLSBOROUGH LOW 0.092 0.325 0.185 0.300 0.205 0.109 0.300 0.205 0.109 

HIGH 0.258 1.474 0.472 1.777 1.615 1.366 1.777 1.615 1 366 

WGHTD AVE 0.161 0.793 0.287 0.886 0.758 0.589 0.886 0.758 0 589 

HOLMES LOW 0.073 0.218 0.149 0.182 0.107 0.043 0.182 0.107 0 043 

HIGH 0.089 0.291 0.159 0.251 0.162 0.074 0.251 0.162 0 074 

WGHTD AVE 





$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



INDIAN RIVER LOW 0.176 0.767 0.257 0.757 0.593 0.385 0.757 0.593 0 385 

HIGH 0.540 3.648 0.894 4.427 4.135 3.600 4.427 4.135 3 600 

WGHTD AVE 0.455 3.050 0.712 3.577 3.305 2.827 3.577 3.305 2 827 

JACKSON LOW 0.056 0.153 0.141 0.122 0.062 0.020 0.122 0.062 0 020 

HIGH 0.075 0.227 0.153 0.189 0.112 0.047 0.189 0.112 0 047 

WGHTD AVE 0.066 0.192 0.149 0.154 0.084 0.030 0.154 0.084 0 030 

JEFFERSON LOW 0.034 0.082 0.124 0.065 0.024 0.005 0.065 0.024 0 005 

HIGH 0.039 0.096 0.130 0.075 0.029 0.006 0.075 0.029 0 006 

WGHTD AVE 

LAFAYETTE LOW 0.032 0.079 0.120 0.064 0.025 0.005 0.063 0.025 0 005 

HIGH 0.033 0.081 0.123 0.066 0.027 0.006 0.066 0.026 0 006 

WGHTD AVE 

LAKE LOW 0.052 0.141 0.142 0.114 0.057 0.018 0.114 0.057 0 018 

HIGH 0.082 0.255 0.180 0.219 0.133 0.060 0.219 0.133 0 060 

WGHTD AVE 0.074 0.231 0.171 0.199 0.120 0.053 0.199 0.120 0 053 

LEE LOW 0.163 0.675 0.251 0.665 0.513 0.324 0.665 0.513 0 324 

HIGH 0.506 3.715 0.871 4.447 4.183 3.697 4.447 4.183 3 697 

WGHTD AVE 0.327 2.141 0.521 2.439 2.222 1.866 2.439 2.222 1 866 

LEON LOW 0.032 0.076 0.123 0.060 0.021 0.004 0.060 0.021 0 004 

HIGH 0.049 0.127 0.141 0.100 0.046 0.013 0.100 0.046 0 013 

WGHTD AVE 0.047 0.117 0.138 0.092 0.040 0.010 0.092 0.040 0 010 

LEVY LOW 0.046 0.130 0.136 0.107 0.055 0.017 0.107 0.054 0 017 

HIGH 0.117 0.566 0.224 0.580 0.474 0.340 0.584 0.471 0 341 

WGHTD AVE 0.059 0.235 0.149 0.189 0.123 0.062 0.189 0.121 0 063 

LIBERTY LOW 0.060 0.160 0.148 0.125 0.062 0.019 0.125 0.062 0 019 

HIGH 0.078 0.232 0.159 0.191 0.111 0.044 0.191 0.111 0 044 

WGHTD AVE 

MADISON LOW 0.026 0.061 0.112 0.050 0.017 0.003 0.050 0.017 0 003 

HIGH 0.031 0.077 0.120 0.064 0.025 0.005 0.063 0.025 0 005 

WGHTD AVE 0.031 0.078 0.120 0.063 0.025 0.005 0.063 0.025 0 005 

MANATEE LOW 0.101 0.365 0.197 0.337 0.233 0.125 0.337 0.233 0.125 

HIGH 0.327 2.020 0.579 2.448 2.262 1.952 2.448 2.262 1 952 

WGHTD AVE 0.258 1.470 0.448 1.757 1.592 1.333 1.757 1.592 1 333 

MARION LOW 0.044 0.116 0.135 0.093 0.043 0.013 0.092 0.043 0 013 

HIGH 0.068 0.217 0.162 0.195 0.123 0.058 0.195 0.123 0 059 

WGHTD AVE 0.061 0.188 0.153 0.166 0.100 0.044 0.165 0.099 0 044 

MARTIN LOW 0.222 0.953 0.299 0.931 0.729 0.467 0.931 0.729 0.467 

HIGH 0.657 4.510 1.019 5.385 5.017 4.358 5.385 5.017 4 358 

WGHTD AVE 0.529 3.558 0.801 4.155 3.838 3.272 4.155 3.838 3 272 

MIAMI-DADE LOW 0.335 1.494 0.396 1.548 1.269 0.855 1.548 1.269 0 855 

HIGH 1.067 7.532 1.834 9.427 8.952 7.981 9.427 8.952 7 981 

WGHTD AVE 0.819 6.139 1.377 7.294 6.860 6.019 7.294 6.860 6 019 

MONROE LOW 0.769 5.569 1.246 6.833 6.462 5.714 6.833 6.462 5.714 

HIGH 1.068 7.400 1.703 9.128 8.625 7.590 9.128 8.625 7 590 

WGHTD AVE 0.985 7.045 1.561 8.619 8.147 7.180 8.619 8.147 7.180 

NASSAU LOW 0.028 0.069 0.107 0.056 0.021 0.005 0.056 0.021 0 005 

HIGH 0.053 0.180 0.137 0.168 0.109 0.055 0.168 0.109 0 055 

WGHTD AVE 0.053 0.180 0.137 0.168 0.108 0.055 0.168 0.108 0 055 





$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



OKALOOSA LOW 0.102 0.350 0.166 0.311 0.210 0.104 0.311 0.210 0.104 

HIGH 0.514 3.664 0.813 4.356 4.066 3.530 4.356 4.066 3 530 

WGHTD AVE 0.497 3.512 0.792 4.178 3.896 3.381 4.178 3.896 3 381 

OKEECHOBEE LOW 0.151 0.540 0.240 0.487 0.341 0.181 0.487 0.341 0.181 

HIGH 0.171 0.624 0.258 0.577 0.416 0.232 0.577 0.416 0 232 

WGHTD AVE 0.154 0.560 0.242 0.507 0.357 0.192 0.507 0.357 0.192 

ORANGE LOW 0.067 0.192 0.161 0.158 0.086 0.031 0.158 0.086 0 031 

HIGH 0.092 0.304 0.181 0.271 0.183 0.093 0.270 0.181 0 092 

WGHTD AVE 0.080 0.248 0.175 0.211 0.127 0.054 0.211 0.127 0 054 

OSCEOLA LOW 0.072 0.208 0.170 0.173 0.096 0.036 0.173 0.096 0 036 

HIGH 0.124 0.437 0.217 0.394 0.272 0.145 0.394 0.272 0.145 

WGHTD AVE 0.089 0.285 0.187 0.245 0.153 0.069 0.245 0.153 0 069 

PALM BEACH LOW 0.206 0.797 0.287 0.751 0.557 0.322 0.751 0.557 0 322 

HIGH 0.859 5.895 1.389 7.223 6.800 5.981 7.223 6.800 5 981 

WGHTD AVE 0.592 3.762 0.867 4.385 4.032 3.403 4.385 4.032 3.403 

PASCO LOW 0.076 0.249 0.168 0.222 0.142 0.068 0.222 0.142 0 068 

HIGH 0.196 1.039 0.356 1.211 1.072 0.878 1.211 1.072 0 878 

WGHTD AVE 0.142 0.720 0.261 0.771 0.649 0.492 0.771 0.649 0.492 

P NELLAS LOW 0.117 0.524 0.215 0.539 0.430 0.298 0.539 0.430 0 298 

HIGH 0.289 1.734 0.506 2.079 1.902 1.616 2.079 1.902 1 616 

WGHTD AVE 0.226 1.251 0.394 1.466 1.312 1.083 1.466 1.312 1 083 

POLK LOW 0.075 0.227 0.173 0.192 0.112 0.046 0.192 0.112 0 046 

HIGH 0.102 0.343 0.202 0.310 0.208 0.108 0.310 0.208 0.108 

WGHTD AVE 0.093 0.313 0.191 0.280 0.185 0.093 0.280 0.185 0 093 

PUTNAM LOW 0.041 0.104 0.126 0.084 0.037 0.010 0.084 0.037 0 010 

HIGH 0.054 0.151 0.143 0.126 0.068 0.025 0.127 0.068 0 025 

WGHTD AVE 0.044 0.115 0.133 0.094 0.044 0.013 0.094 0.044 0 013 

SA NT JOHNS LOW 0.035 0.091 0.117 0.075 0.033 0.009 0.075 0.033 0 009 

HIGH 0.082 0.347 0.176 0.346 0.264 0.175 0.346 0.264 0.175 

WGHTD AVE 0.063 0.229 0.153 0.222 0.155 0.093 0.221 0.155 0 091 

SA NT LUC E LOW 0.189 0.760 0.269 0.723 0.545 0.326 0.723 0.545 0 327 

HIGH 0.576 4.241 0.922 4.943 4.645 4.098 4.995 4.666 4.100 

WGHTD AVE 0.464 3.263 0.704 3.700 3.398 2.927 3.700 3.415 2 929 

SANTA ROSA LOW 0.076 0.230 0.142 0.190 0.112 0.043 0.190 0.112 0 043 

HIGH 0.573 4.218 0.900 5.022 4.707 4.108 5.022 4.707 4.108 

WGHTD AVE 0.428 2.889 0.618 3.329 3.058 2.577 3.329 3.058 2 577 

SARASOTA LOW 0.180 0.852 0.294 0.958 0.816 0.616 0.958 0.816 0 616 

HIGH 0.382 2.648 0.660 3.166 2.952 2.584 3.166 2.952 2 584 

WGHTD AVE 0.302 1.967 0.523 2.316 2.133 1.829 2.316 2.133 1 829 

SEMINOLE LOW 0.070 0.204 0.161 0.170 0.095 0.036 0.170 0.095 0 036 

HIGH 0.088 0.286 0.175 0.255 0.165 0.078 0.255 0.165 0 078 

WGHTD AVE 0.074 0.222 0.165 0.188 0.110 0.045 0.188 0.110 0 045 

SUMTER LOW 0.063 0.187 0.158 0.159 0.090 0.036 0.159 0.090 0 036 

HIGH 0.069 0.215 0.162 0.187 0.112 0.050 0.187 0.112 0 050 

WGHTD AVE 0.063 0.194 0.158 0.165 0.098 0.042 0.167 0.098 0 042 





$0 $0 $0 DEDUCTIBLE $500 $1,000 $2,500 1% 2% 5% 



SUWANNEE LOW 0.023 0.053 0.111 0.044 0.014 0.002 0.044 0.014 0 002 

HIGH 0.032 0.080 0.124 0.065 0.026 0.006 0.066 0.026 0 006 

WGHTD AVE 

TAYLOR LOW 0.034 0.083 0.122 0.067 0.026 0.006 0.067 0.026 0 006 

HIGH 0.043 0.116 0.136 0.096 0.047 0.014 0.096 0.047 0 014 

WGHTD AVE 

UNION LOW 0.032 0.081 0.120 0.066 0.026 0.006 0.066 0.026 0 006 

HIGH 0.034 0.087 0.125 0.072 0.030 0.007 0.072 0.030 0 007 

WGHTD AVE 0.032 0.081 0.120 0.066 0.026 0.006 0.065 0.026 0 006 

VOLUSIA LOW 0.056 0.153 0.144 0.129 0.069 0.025 0.129 0.069 0 025 

HIGH 0.162 0.894 0.280 0.994 0.863 0.685 0.988 0.865 0 692 

WGHTD AVE 0.127 0.652 0.229 0.691 0.581 0.441 0.691 0.581 0.441 

WAKULLA LOW 0.063 0.169 0.153 0.134 0.068 0.022 0.134 0.068 0 022 

HIGH 0.074 0.234 0.167 0.203 0.126 0.059 0.203 0.126 0 059 

WGHTD AVE 

WALTON LOW 0.076 0.230 0.144 0.191 0.113 0.045 0.191 0.113 0 045 

HIGH 0.474 3.411 0.743 4.017 3.740 3.241 4.017 3.740 3 241 

WGHTD AVE 0.456 3.149 0.698 3.730 3.461 2.984 3.730 3.461 2 984 

WASHINGTON LOW 0.096 0.315 0.166 0.275 0.179 0.083 0.275 0.179 0 083 

HIGH 0.154 0.665 0.219 0.653 0.513 0.333 0.653 0.513 0 333 

WGHTD AVE 

Statewide LOW 0.022 0.051 0.099 0.041 0.013 0.002 0.041 0.013 0 002 

HIGH 1.068 7.532 1.834 9.427 8.952 7.981 9.427 8.952 7 981 

WGHTD AVE 0.457 3.239 0.716 3.654 3.368 2.867 3.654 3.368 2 867 




Form A-7: Percentage Change In Output Ranges 

A. Provide the percentage change in the weighted average loss costs using the 2002 Florida 

Hurricane Catastrophe Fund’s aggregate personal residential exposure data found in the file 

named “hlpm2002.exe”, as of August 1, 2003, from the output ranges from the prior year 

submission for the following: 

• statewide (overall percentage change), 

• by region, as defined in Figure 21 – North, Central and South, 

• by coastal and inland counties, as defined in Figure 22. 

B. Provide this Form on CD in both an Excel and a PDF format. The file name should include 



North 

Central 

South 

Figure 21. State of Florida by North/Central/South Regions 

172



Inland 

Coastal 

Figure 22. State of Florida by Coastal/Inland Counties 

The results are shown on Form A-7. 

173



Form A-7: Percentage Change In Output Ranges 

Frame 

Owners 

Masonry 

Owners 

Mobile 

Homes 

Frame 

Renters 

Masonry 

Renters 

Frame 

Condos 

Masonry 

Condos 

Structure 

$0 Deductible 

Appurtenant 

Contents 

Structure 

Additional 

Living 

Expense 

$500 

Deductible 

Total 

$1,000 

Deductible 

Total 

$2,500 

Deductible 

Total 

1% 

Deductible 

Total 

2% 

Deductible 

Total 

5% 

Deductible 

Total 

Coastal 6.81% 3.14% 6.43% 8.91% 6.45% 6.80% 7.61% 6.79% 7.35% 8.73% 

Inland 1.54% 1.74% 1.67% 1.43% 1.72% 1.87% 2.19% 1.84% 2.15% 3.02% 

North 6.56% 6.65% 6.41% 6.41% 6.99% 7.29% 7.96% 7.28% 7.78% 8.74% 

Central 3.92% 0.05% 3.91% 5.91% 3.67% 4.08% 4.98% 4.03% 4.67% 6.49% 

South 7.97% 2.19% 7.26% 11.43% 7.14% 7.48% 8.33% 7.48% 8.06% 9.52% 

Statewide 6.34% 3.03% 5.95% 7.99% 6.09% 6.45% 7.33% 6.45% 7.06% 8.52% 

Coastal 8.02% 0.66% 7.64% 10.96% 7.00% 7.47% 8.71% 7.48% 8.32% 10.44% 

Inland 1.95% 1.81% 1.84% 1.47% 2.13% 2.30% 2.83% 2.29% 2.61% 3.68% 

North 5.93% 6.59% 5.80% 4.46% 6.57% 6.91% 7.81% 7.02% 7.57% 8.64% 

Central 4.86% 0.46% 4.86% 5.83% 4.63% 5.12% 6.45% 5.12% 6.02% 8.46% 

South 8.53% 0.57% 8.14% 12.19% 7.36% 7.82% 9.03% 7.82% 8.65% 10.75% 


Coastal 6.75% 2.96% 6.50% 14.37% 6.54% 7.24% 8.31% 6.54% 7.23% 8.31% 

Inland 3.19% 3.17% 3.19% 3.71% 3.62% 4.59% 5.63% 3.62% 4.59% 5.66% 

North 6.24% 6.87% 6.09% 6.77% 6.90% 7.64% 8.70% 6.88% 7.65% 8.71% 

Central 4.19% 0.91% 4.06% 10.73% 4.14% 4.89% 5.89% 4.15% 4.88% 5.90% 

South 8.26% 4.39% 8.04% 15.76% 7.99% 8.72% 9.85% 7.99% 8.72% 9.85% 


Coastal 3.51% 8.35% 5.52% 5.76% 6.19% 4.88% 5.50% 5.86% 

Inland 1.39% 1.47% 1.98% 2.60% 2.53% 6.92% 1.94% 2.37% 

North 6.58% 6.11% 7.81% 8.09% 8.46% 7.35% 7.80% 8.18% 

Central 0.50% 5.83% 2.68% 3.02% 3.54% 3.03% 2.65% 3.14% 

South 1.99% 12.11% 4.79% 4.96% 5.32% 3.78% 4.79% 5.03% 

Statewide 3.30% 6.94% 5.30% 5.62% 6.13% 5.34% 5.30% 5.73% 

Coastal 0.89% 11.26% 3.61% 3.79% 4.11% 2.99% 3.61% 3.86% 

Inland 1.72% 1.51% 2.53% 2.68% 3.80% 10.02% 2.51% 3.03% 

North 7.09% 4.78% 8.23% 8.44% 8.71% 7.95% 8.22% 8.50% 

Central 0.56% 6.75% 3.16% 3.54% 4.18% 4.02% 3.16% 3.71% 

South 0.58% 12.34% 3.41% 3.53% 3.79% 2.54% 3.41% 3.58% 

Statewide 0.93% 10.24% 3.59% 3.77% 4.12% 3.32% 3.59% 3.84% 

Coastal 7.29% 2.79% 10.73% 5.06% 5.28% 5.67% 5.06% 5.28% 5.66% 

Inland 1.30% 1.20% 1.39% 1.66% 2.15% 2.73% 1.62% 2.18% 2.70% 

North 7.31% 7.21% 7.86% 8.14% 8.41% 8.70% 8.15% 8.41% 8.70% 

Central 4.51% -0.19% 6.91% 2.45% 2.69% 3.13% 2.44% 2.68% 3.09% 

South 8.00% 2.09% 12.32% 4.87% 5.06% 5.43% 4.87% 5.06% 5.43% 

Statewide 6.98% 2.74% 10.18% 4.99% 5.23% 5.64% 4.99% 5.23% 5.64% 

Coastal 8.57% 0.54% 11.71% 3.69% 3.82% 4.09% 3.69% 3.82% 4.09% 

Inland 2.09% 1.88% 1.70% 2.41% 2.94% 3.92% 2.33% 2.83% 4.03% 

North 8.31% 7.44% 8.59% 8.47% 8.72% 8.95% 8.47% 8.72% 8.95% 

Central 6.56% -0.06% 8.81% 2.86% 3.04% 3.54% 2.85% 3.09% 3.55% 

South 8.77% 0.45% 12.07% 3.66% 3.78% 4.03% 3.66% 3.78% 4.03% 

Statewide 8.50% 0.54% 11.60% 3.68% 3.82% 4.08% 3.68% 3.82% 4.08% 

174



Form A-8: Percentage Change in Output Ranges by County 

Provide color-coded maps by county reflecting the percentage changes in the weighted average 

2% deductible loss costs for frame owners, masonry owners, mobile homes, frame renters, 

masonry renters, frame condos, and masonry condos from the output ranges using the 2002 

Florida Hurricane Catastrophe Fund’s aggregate personal residential exposure data found in 

the file named “hlpm2002.exe”, as of August 1, 2003. 

Counties with a negative percentage change (reduction in loss costs) would be indicated with 

shades of blue; counties with a positive percentage change (increase in loss costs) would be 

indicated with shades of red, and counties with no percentage change would be white. The larger 

the percentage change in the county, the more intense the color-shade. 

The percentage changes in the county level weighted average loss costs for a 

2% deductible for the seven policy types are shown in the maps below. 

175



176



177



178




S-1 Modeled Results and Goodness-of-Fit 

A. The use of historical data in developing the model shall be supported by 

rigorous methods published in currently accepted scientific literature. 

EQECAT’s use of historical data in developing USWIND is supported by rigorous 

methods published in currently accepted scientific literature. 

B. Modeled and historical results shall reflect agreement using currently 

accepted scientific and statistical methods. 

Modeled and historical results reflect agreement using currently accepted 

scientific and statistical methods. 

Disclosures 

1. Identify the form of the probability distributions used for each function or variable, 

if applicable. Identify statistical techniques used for the estimates and the specific 

goodness-of-fit tests applied. Describe whether the p-values associated with the 

fitted distributions provide a reasonable agreement with the historical data. 

Radius to maximum winds and translational speed are modeled using 

lognormal distributions, the parameters of which vary smoothly along the 

coast. Filling rate is modeled using a normal distribution. Friction and gust 

factor are modeled using lognormal distributions. Chi-squared and 

Kolmogorov-Smirnov tests have been performed to assess the goodness-offit, 

and reasonable aggreement with the historical data has been shown. 

2. Provide the source and the number of years of the historical data set used to develop 

probability distributions for specific hurricane characteristics. If any modifications 

have been made to the data set, describe them in detail and their appropriateness. 

NOAA Publication NWS-38 covers the period 1900-1984, and was the main 

source for compiling information on hurricane modeling parameters, (radius of 

maximum winds, direction of motion, translation speed, etc.) Data for later 

storms (1985-2004) was obtained in specific reports or publications from the 

National Hurricane Center (including Tropical Cyclone Reports and 

Advisories), analyses from the Hurricane Research Division, or from other 

scientifically accepted publications. Coastline-dependent landfall frequency 

and severity distributions for the state of Florida were developed from the 

Commission’s November 1, 2006 Florida storm set. 

179



3. Describe the nature and results of the tests performed to validate the wind speeds 

generated. 

Dr. Don Friedman, who has a Doctorate in meteorology and over 35 years 

analyzing hurricane wind patterns and their relation to insurance claims while 

at the Research Department of The Travelers Insurance Company, performed 

a study of USWIND-generated peak gust wind patterns with those of actual 

hurricanes: Actual peak gust observations were obtained for eleven landfalls 

of nine notable hurricanes since 1960. These observations were compared 

with model-generated peak gust wind speeds. Scatter plots were made of 

observed versus modeled. Table 3 below summarizes his results, and is 

limited to observations with gusts above 60 mph to avoid including areas 

where wind damage on the fringe of a storm would not be significant; (this 

level would roughly correspond to a 1-minute sustained of 45 mph). 

TABLE 3. 

COMPARISON OF POINT LOCATION OBSERVATIONS WITH 

MODEL-GENERATED WINDS 

(Peak Gust Observations 60 mph or more) 

Hurricane #Obs 

#Simulated +/- 10 #Simulated +/- 15 

mph 

mph 

Andrew 1992 10 9 90% 9 90% 

Bob 1991 16 12 75% 13 82% 

Hugo 1989 7 5 71% 6 86% 

Gloria 1985 11 7 64% 9 82% 

Elena 1985 6 4 67% 6 100% 

Alicia 1983 9 5 56% 5 56% 

Betsy 1965 24 13 54% 19 79% 

Carla 1961 17 12 71% 14 82% 

Donna 1960 26 15 58% 22 85% 

Total 126 82 65% 103 82% 

4. Provide the date of loss of the insurance company data available for validation and 

verification of the model. 

The primary information available for validation and verification of the model 

is claims data from Hurricanes Alicia (1983), Elena (1985), Gloria (1985), 

Juan (1985), Kate (1985), Hugo (1989), Bob (1991), Andrew (1992), Iniki 

(1992), Erin (1995), Opal (1995), Charley (2004), Frances (2004), Ivan 

(2004), Jeanne (2004), Katrina (2005), Rita (2005), and Wilma (2005). 

5. Provide an assessment of uncertainty in loss costs for output ranges using confidence 

intervals or other accepted scientific characterizations of uncertainty. 

Figure 23 below compares the loss exceedance curve presented in Form S-2 

with the curves that would result from adding or subtracting one standard 

deviation (sigma) to the total annual hurricane frequency in the model. 

180



100,000,000 

90,000,000 

80,000,000 

Frequency plus one sigma 

Mean frequency 

Frequency minus one sigma 

70,000,000 

60,000,000 

Loss ($) 

50,000,000 

40,000,000 

30,000,000 

20,000,000 

10,000,000 

- 

1 10 100 1,000 10,000 

Return Period (Years) 

Figure 23. Uncertainty Analysis for Frequency 

6. Provide graphical comparisons of modeled and historical data and goodness-of-fit 

tests. Examples include hurricane frequencies, tracks, intensities, and physical 

damage. 

Figures 24 and 25 are examples of graphical comparisons of modeled and 

historical data. 

Figure 24 compares the historical data for translational speed near Ft. Myers 

and Daytona Beach with the lognormal distribution used to model it. As an 

example of a more quantitative comparison, we performed a Kolmogorov- 

Smirnov test to assess the goodness-of-fit of our modeled distribution for 

translational speed at Ft. Myers to the historical data. The test statistic value 

is 0.116. The critical test value at a 5% level of significance is 0.21, for 41 

data points. Hence, the modeled distribution cannot be rejected at that level 

of significance. Similarly, for Daytona Beach the test statistic value is 0.208, 

and the critical test value at a 5% level of significance is 0.27, for 23 data 

points; hence the modeled distribution cannot be rejected at that level of 

significance. 

181



(a) 

(b) 

Figure 24. Goodness-of-fit for Translational Speed 

0.7 

0.6 

0.5 

historical 

negative binomial 

probability 

0.4 

0.3 

0.2 

0.1 

0.0 

0 1 2 3 4 5 6 7 8 9 10 or 

more 

# events per year 

Figure 25. Goodness-of-fit for Hurricane Frequency in Florida 

Figure 25 compares the historical data for hurricane frequency in Florida with 

the negative binomial distribution used to model it. We performed a 

Kolmogorov-Smirnov test to assess the goodness-of-fit of our modeled 

182



distribution for hurricane frequency in Florida to the historical data. The test 

statistic value is 0.0384. The critical test value at a 5% level of significance is 

0.130, hence the modeled distribution cannot be rejected at that level of 

significance. 

7. Provide a completed Form S-1, Probability of Florida Landfalling Hurricanes per 

Year. 

See Form S-1 at the end of this section. 

8. Provide a completed Form S-2, Probable Maximum Loss. 


183



S-2 Sensitivity Analysis for Model Output 

The modeler shall have assessed the sensitivity of temporal and spatial 

outputs with respect to the simultaneous variation of input variables using 

currently accepted scientific and statistical methods and have taken 

appropriate action. 

EQECAT has assessed the sensitivity of temporal and spatial outputs with 

respect to the simultaneous variation of input variables using currently accepted 

scientific and statistical methods, and has taken appropriate action. 

Disclosures 

1. Provide a detailed explanation of the sensitivity analyses that have been performed 

on the model above and beyond those completed for the original submission of Form 

S-5 and provide specific results. (Requirement for modeling organizations that have 

previously provided the Commission with Form S-5. This Disclosure can be satisfied 

with an updated Form S-5 that incorporates changes to the model since the previous 

submission of the Form.) 

Sensitivity analyses have been performed on track spacing; on the number of 

attack angles given landfall; on the number of wind speed class intervals 

given landfall and attack angle; and on the number of other storm parameter 

samples used in the stochastic hurricane database. Figure 26 presents an 

example of such an analysis. 

3,500 

3,000 

2,500 

expected annual loss 

2,000 

1,500 

1,000 

500 

0 

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 

number of events affecting Nassau county 

Figure 26. Sensitivity Analysis for Number of Events 

184



In the figure, the expected annual loss for Nassau county is shown as a 

function of the size of the stochastic event set, displayed in terms of the 

number of events affecting the county. The red lines indicate a 90% 

confidence interval on the expected annual loss. Note that this test is based 

on the reduced set of approximately 50,000 events (see Form A-5 for a 

description of this event set). 

2. Provide a description of the statistical methods used to perform the sensitivity 

analysis. 

The sensitivity analyses performed were primarily convergence tests. 

3. Identify the most sensitive aspect of the model and the basis for making this 

determination. Provide a full discussion of the degree to which these sensitivities 

affect output results and illustrate with an example. 

The most sensitive aspect of our model involves the conversion of wind 

speed to damage. This is due to the fact that the damage sustained by a 

particular structure type depends very sensitively on the wind speed 

experienced at the site. For example, the damage sustained by a given 

structure type depends approximately on the wind speed raised to some 

power. If the damage is proportional to the fifth power of the wind speed, then 

a 1% uncertainty in the wind speed will result in a 5% uncertainty in the 

damage calculated at that site. The origin of this uncertainty is the underlying 

non-linearity of the vulnerability relationship, and not in any assumptions, data 

or properties unique to our model. 

4. Describe how other aspects of the model may have a significant impact on the 

sensitivities in output results and the basis for making this determination. 

The results of any model depend sensitively on details of the structural 

characteristics and location of the insured sites. Often, this information is not 

provided by the insurance or underwriting agency for use by the model. Such 

details can potentially have a large impact on results due to the large variation 

in damageability among different structure classes and secondary structural 

configurations, and to the large variation in the wind hazard with respect to 

distance to coast and other factors. 

5. Describe actions taken in light of the sensitivity analyses performed. 

The sensitivity analyses performed during the initial development of the model 

were crucial in determining optimal sample sizes and the relative importance 

of parameters. Subsequent analyses have been used to verify that the 

decisions made continue to be valid. 

185



6. Provide a completed Form S-5, Hypothetical Events for Sensitivity and Uncertainty 

Analysis (requirement models submitted by modeling organizations which have not 

previously provided the Commission with this analysis). 

Not applicable. 

186



S-3 Uncertainty Analysis for Model Output 

The modeler shall have performed an uncertainty analysis on the temporal 

and spatial outputs of the model using currently accepted scientific and 

statistical methods and have taken appropriate action. The analysis shall 

identify and quantify the extent that input variables impact the uncertainty 

in model output as the input variables are simultaneously varied. 

EQECAT has performed uncertainty analysis on the temporal and spatial outputs 

of the model using currently accepted scientific and statistical methods and has 

taken appropriate action. The analysis has identified and quantified the extent 

that input variables impact the uncertainty in model output as the input variables 

are simultaneously varied. 

Disclosures 

1. Provide a detailed explanation of the uncertainty analyses that have been performed 

on the model above and beyond those completed for the original submission of Form 

S-5 and provide specific results. (Requirement for modeling organizations that have 

previously provided the Commission with Form S-5. This Disclosure can be satisfied 

with an updated Form S-5 that incorporates changes to the model since the previous 

submission of the Form.) 

Several uncertainty analyses were performed, including an analysis of the 

effect of uncertainty on hurricane frequency on the loss exceedance curve for 

the FHCF portfolio, and the effect of uncertainty in damage estimation 

(including both modeling uncertainty and uncertainty in physical properties) 

on expected annual losses for the FHCF portfolio. 

2. Provide a description of the statistical methods used to perform the uncertainty 

analysis. 

The uncertainty analyses performed were primarily confidence interval tests. 

3. Identify the major contributors to the uncertainty in model outputs and the basis for 

making this determination. Provide a full discussion of the degree to which these 

uncertainties affect output results and illustrate with an example. 

Major contributors to the uncertainty in model output include uncertainty on 

storm parameters, uncertainty on site parameters, and uncertainty on the 

vulnerability functions, as identified in our uncertainty analysis. 

One such contributor is the conversion of wind speed to damage. This is due 

to the fact that the damage sustained by a particular structure type depends 

very sensitively on the of wind speed experienced at the site. For example, 

the damage sustained by a given structure type depends approximately on 

the of wind speed raised to some power. If the damage is proportional to the 

fifth power of the of wind speed, then a 1% uncertainty in the wind speed will 

187



result in a 5% uncertainty in the damage calculated at that site. The origin of 

this uncertainty is the underlying non-linearity of the vulnerability relationship, 

and not in any assumptions, data or properties unique to our model. 

4. Describe how other aspects of the model may have a significant impact on the 

uncertainties in output results and the basis for making this determination. 

The results of any model depend sensitively on details of the structural 

characteristics and location of the insured sites. Often, this information is not 

provided by the insurance or underwriting agency for use by the model. Such 

details can potentially have a large impact on results due to the large variation 

in damageability among different structure classes and secondary structural 

configurations, and to the large variation in the wind hazard with respect to 

distance to coast and other factors. 

5. Describe actions taken in light of the uncertainty analyses performed. 

The uncertainty analyses performed during the initial development of the 

model were crucial in determining optimal sample sizes and the relative 

importance of parameters. Subsequent analyses have been used to verify 

that the decisions made continue to be valid. 

6. For models submitted by modeling organizations, which have not previously 

provided this analysis to the Commission, Form S-5 was disclosed under Standard 

S-2 and will be used in the verification of Standard S-3. 

Not applicable. 

188



S-4 County Level Aggregation 

Disclosure 

At the county level of aggregation, the contribution to the error in loss cost 

estimates attributable to the sampling process shall be negligible. 

USWIND estimates loss costs in the mainland United States from Texas to 

Maine on the basis of 511,500 stochastic storm simulation results. Of these, 

about 160,000 are Florida landfalling or bypassing events. Given the high 

resolution of the stochastic storm database, the contribution to the error in loss 

cost estimates induced by the sampling process is negligible. 

1. Describe the sampling plan used to obtain the average annual loss costs and output 

ranges. For a direct Monte Carlo simulation, indicate steps taken to determine sample 

size. For importance sampling design, describe the underpinnings of the design. 

USWIND estimates loss costs using a Latin Hypercube technique. The 

primary storm (e.g. radius, forward speed, filling rate) and site (e.g. friction, 

gust factor) parameters are all random variables in the model. 

189



S-5 Replication of Known Hurricane Losses 

The model shall estimate incurred losses in an unbiased manner on a 

sufficient body of past hurricane events from more than one company, 

including the most current data available to the modeler. This Standard 

applies separately to personal residential and, to the extent data are 

available, to mobile homes. Personal residential experience may be used to 

replicate structure-only and contents-only losses. The replications shall be 

produced on an objective body of loss data by county or an appropriate 

level of geographic detail. 

USWIND reasonably replicates incurred losses on a sufficient body of past 

hurricane events, including the most current data available to EQECAT. 

Disclosures 

1. Describe the nature and results of the analyses performed to validate the loss 

projections generated by the model. 

Overall reasonability/validity checks on historical storm estimates and 

expected annual loss estimates are continuously conducted on personal lines 

portfolios received from our clients. 

We are cautious about having false security with validations made against 

one or a few storms. We thus place limited value on Andrew loss 

comparisons. Unfortunately, portfolio exposures at the time of other historic 

storms are rare. However, the results USWIND has produced for Andrew 

simulations have been consistently within EQECAT’s and our client’s 

expectations. In addition to Andrew, we have done several studies comparing 

claims information from hurricane Opal, as well as for hurricane Alicia with 

losses produced by USWIND. 

Some of the validation comparisons performed are summarized in Form S-3. 

2. Provide a completed Form S-3, Five Validation Comparisons. 


190



S-6 Comparison of Projected Hurricane Loss Costs 

The difference, due to uncertainty, between historical and modeled annual 

average statewide loss costs shall be reasonable, given the body of data, 

by established statistical expectations and norms. 

The difference, due to uncertainty, between historical and modeled annual 

average statewide loss costs is statistically reasonable, as shown in the 

information provided below. 

Disclosures 

1. Describe the nature and results of the tests performed to validate the expected loss 

projections generated. If a set of simulated hurricanes or simulation trials was used 

to determine these loss projections, specify the convergence tests that were used and 

the results. Specify the number of hurricanes or trials that were used. 

The results of our model were validated by checking each component of the 

model separately. We took the following steps to validate the hazard 

component: 

a) Ensure that the frequency of the simulated storms matches against 

historical landfall frequency. 

b) Compare USWIND return period wind speed estimates by landfall 

location against other substantive research in this area. 

Steps a) and b) were used as the reasonability check for the hazard 

frequency (number of landfalls per year) and severity (expected wind speeds 

to be experienced every x years). 

Given reasonability of the hazard component of the model, loss estimates 

were compared to actual losses sustained by specific insurance companies. 

In addition, comparisons of statewide expected annual loss versus the 

average of all historical events impacting Florida in this century were 

compared in order to validate estimated losses. 

The expected annual loss estimates produced by USWIND are further 

checked for reasonability against alternative methods of obtaining the same 

results. Such methods include Monte Carlo simulations, analyses based 

solely on historical storms and actuarial techniques, and alternative methods 

using NHRS and historical frequency rates. 

Relativities of the expected annual loss estimates by geographic territory and 

by construction type have also been evaluated to ensure reasonableness. 

Convergence tests were also performed in order to ensure that USWIND 

produces stable results and that additional detail (i.e., simulated storms) 

191



would not significantly alter the result. The basis for our expected annual loss 

estimates is the modeling of 511,500 storms. 

2. Identify differences, if any, in how the model produces loss costs for specific 

historical events versus loss costs for events in the stochastic hurricane set. 

There are no differences in how the model produces loss costs for specific 

historical events versus loss costs for events in the stochastic hurricane set. 

3. Provide a completed Form S-4, Average Annual Zero Deductible Statewide Loss 

Costs – Historical versus Modeled. 


192



Form S-1: Probability of Florida Landfalling Hurricanes per Year 

Complete the table below showing the probability of landfalling Florida hurricanes per year. 

Modeled probability should be rounded to four decimal places. The historical probabilities 

below have been derived from the Commission’s Official Hurricane Set. If the National 

Hurricane Center’s HURDAT or other hurricanes in addition to the Official Hurricane Set as 

specified in Standard M-1 are used by the modeler, then the historical probabilities should be 

modified accordingly. If the National Hurricane Center’s HURDAT is used, provide the 

HURDAT revision date. 

Model Results 

Probability of Florida Landfalling Hurricanes per Year 

Number 

Of Hurricanes 

Per Year 

Historical 

Probability 

Modeled 

Probability 

0 0.5887 0.5687 

1 0.2523 0.2907 

2 0.1215 0.1015 

3 0.0280 0.0291 

4 0.0000 0.0076 

5 0.0000 0.0018 

6 0.0000 0.0004 

7 0.0000 0.0001 

8 0.0000 0.0000 

9 0.0000 0.0000 

10 or more 0.0000 0.0000 

193



Form S-2: Probable Maximum Loss (PML) 

Provide projections of the insured loss for various probability levels using the hypothetical data 

set provided in the file named “FormA1Input06.xls.” Provide the total average annual loss for 

the PML distribution. If the methodology of your model does not allow you to produce a viable 

answer, please state so and why. 

Part A 

Return 

Time (years) 

Probability of 

Exceedance 

Estimated 

Loss 

($) 

Top Event ________________ 

10,000 0.01% 87,322,608 

5,000 0.02% 82,290,160 

2,000 0.05% 72,977,696 

1,000 0.10% 65,776,844 

500 0.20% 59,484,704 

250 0.40% 50,217,388 

100 1.00% 36,735,988 

50 2.00% 25,406,254 

20 5.00% 14,540,838 

10 10.00% 6,902,197 

5 20.00% 1,895,416 

Part B 

Mean (Total Average Annual Loss) 2,394,788 

Median 7,190 

Standard Deviation 6,962,786 

Interquartile Range 967,097 

Sample Size 

511,500 events 

194



Form S-3: Five Validation Comparisons 

A. Provide five validation comparisons of actual exposures and loss to modeled exposures and 

loss. These comparisons must be provided by line of insurance, construction type, policy 

coverage, county or other level of similar detail in addition to total losses. Include loss as a 

percent of total exposure. Total exposure represents the total amount of insured values (all 

coverages combined) in the area affected by the hurricane. This would include exposures for 

policies that did not have a loss. If this is not available, use exposures for only those policies 

that had a loss. Specify which was used. Also, specify the name of the hurricane event 

compared. 

B. Provide scatter plot(s) of modeled vs. historical losses for each of the five validation 

comparisons. (Plot the historical losses on the x-axis and the modeled losses on the y-axis.) 

Rather than using directly a specific published hurricane wind field, the winds underlying the 

modeled loss cost calculations must be produced by the model being evaluated and should be 

the wind field most emulated by the model. 

Validation comparisons for six companies and seven hurricanes are provided 

below and in Figures 27 through 30. Exposures shown are total values, not just 

those that experienced a loss. Note that some comparisons were performed for 

large insurers, and disclosing the true figures would amount to disclosing the 

insurer and that insurer’s confidential information. To disguise the identity in such 

cases, we have divided all exposures and losses by one constant; that constant 

may vary from comparison table to comparison table, but within one comparison 

the same constant was used on both exposure and loss. 

Note that slight differences between modeled and actual exposures in some 

comparisons result from slightly different mapping from ZIP Code to county, 

between USWIND and the insurer. The insurer provided ZIP Code and county, 

along with detailed address information, for every policy. The modeled county 

exposure entries in the tables shown below are the ones, USWIND automatically 

looked up, based on ZIP Code. By contrast, to provide actual exposure amounts, 

we accumulated exposures according to the county data the insurer provided 

with the data. If the user omitted the county name, but provided the ZIP Code, or 

if the insurer provided a different county name with a record than USWIND 

believes, then a discrepancy would occur. (Many ZIP Codes lie partly in one 

county, partly in another.) However, the discrepancies are small, and neither we 

nor the insurer felt that it indicated a significant error. 

195



(FORM S-3 CONTINUED) 

Totals by Company 

Company Event Year TIV ($M) 

Actual 

($M) 

USWIND 

($M) 

Difference 

A Opal 1995 222,270.00 112.91 116.86 3.5% 

B Andrew 1992 4,578.28 48.20 50.32 4.4% 

C Andrew 1992 1,229.95 19.93 19.12 -4.1% 

D Andrew 1992 793.41 30.75 29.93 -2.7% 

E Andrew 1992 608.67 29.02 30.96 6.7% 

F Charley 2004 495,664.85 1,210.76 1,289.61 6.5% 

F Frances 2004 495,664.85 736.71 778.96 5.7% 

F Ivan 2004 495,664.85 454.95 478.66 5.2% 

F Jeanne 2004 495,664.85 382.10 352.87 -7.6% 

F Wilma 2005 495,664.85 954.74 839.21 -12.1% 

10,000 

1,000 

Modeled Loss ($M) 

100 

10 

10 100 1,000 10,000 

Actual Loss ($M) 

Figure 27. Historical vs. Modeled Losses for Companies A to F 

196




Company C by Line of Business 

Event LOB TIV ($M) Actual ($M) 

USWIND 

($M) 

Difference 

Andrew Mobile Homes 56.16 0.82 0.77 -6.1% 

Fire & Extended 11.80 0.16 0.24 52.9% 

Homeowners 1,017.47 17.28 16.54 -4.3% 

Renters/Tenants 10.99 0.13 0.09 -32.6% 

Landlord 74.29 1.00 1.07 6.9% 

Condominiums 59.25 0.54 0.41 -24.1% 

Total 1,229.95 19.93 19.12 -4.0% 

100 00 

10 00 


1 00 

0.10 

0 01 

0.01 0.10 1.00 10.00 100.00 


Figure 28. Historical vs. Modeled Losses by LOB for Company C 

197




Company D by County 

Event County TIV ($M) Actual ($M) 

USWIND 

($M) 

Difference 

Andrew Broward 234.51 0.50 0.52 4.2% 

Charlotte 25.64 0.00 0.00 0.0% 

Collier 44.65 0.18 0.21 14.2% 

Hendry 2.74 0.00 0.00 0.0% 

Martin 8.22 0.00 0.00 0.0% 

Miami-Dade 203.79 30.01 29.16 -2.8% 

Monroe 0.31 0.00 0.00 0.0% 

Total 793.41 30.75 29.94 -2.5% 

100 00 

10 00 


1 00 

0.10 

0 01 

0.01 0.10 1.00 10.00 100.00 


Figure 29. Historical vs. Modeled Losses by County for Company D 

198




Company E by Line of Business 

Event LOB TIV ($M) Actual ($M) 

USWIND 

($M) 

Difference 

Andrew Homeowner Form 1 0.15 0.02 0.02 -2.5% 

Homeowner Form 3 179.08 7.34 8.98 22.4% 



Homeowner Form 6 52.36 0.63 0.60 -5.3% 

Total 608.67 29.02 30.96 6.7% 

100.00 

10.00 


1.00 

0.10 

0.01 

0.01 0.10 1.00 10.00 100.00 


Figure 30. Historical vs. Modeled Losses by LOB for Company E 

199



Form S-4: Average Annual Zero Deductible Statewide Loss Costs – 

Historical versus Modeled 

A. Provide the average annual zero deductible statewide loss costs produced using the list of 

hurricanes in the Base Hurricane Storm Set based on the 2002 Florida Hurricane 

Catastrophe Fund’s aggregate personal residential exposure data, as of August 1, 2003 

(hlpm2002.exe). 

B. Provide a comparison with the statewide loss costs produced by the model on an average 

industry basis. 

Average Annual Zero Deductible Statewide Loss Costs 

Time Period Historical Hurricanes Produced by Model 

Current Year $2.14 Billion $2.52 Billion 

Previous Year $1.87 Billion $2.38 Billion 

Second Prior $1.68 Billion $2.12 Billion 

Percentage Change Current 

Year/Previous Year 

Percentage Change Current 

Year/Second Prior 

14.5% 5.9% 

26.8% 18.7% 

C. Provide the 95% confidence interval on the differences between the mean of the historical 

and modeled loss. 

Based on the historical storm set for the 107 year experience period (1900 

through 2006) provided by the Commission and using the Florida Hurricane 

Catastrophe Fund’s 2002 aggregated exposure data resulted in a statewide 

historical annual average zero deductible loss of $2.14 billion and a modeled 

annual average zero deductible loss of $2.52 billion. 

The difference can be shown to be statistically insignificant as follows: 

Let X i (i=1…81) represent the losses from the 81 historical events, which 

occurred over 107 years. Then the historical annual loss cost A is given by: 

A = ∑ X i / 107 (where i = 1…81) = $2.14 Billion 

200



The standard error of A is given by: 

S.E (A) = SQRT(A 2 /81 + 81* Var ({X i }) /107 2 ) = $0.47 Billion 

where Var ({X i }) is the variance of the historical losses (from the 81 storms). This 

assumes that the X i have identical independent distributions and the frequency 

has a Poisson distribution. 

Using the t-test the two-tailed 90% confidence for the true annual loss cost 

interval (narrower than the 95% confidence interval) is given by the range: 

A1 = A - 1.671 * S.E (A) = $1.35 Billion 

A2 = A + 1.671 * S.E (A) = $2.92 Billion 

The modeled annual loss cost ($2.52 Billion) is within the above range, so the 

difference between the historical and the modeled results is not statistically 

significant. 

201




C-1 Documentation 

A. The modeler shall maintain a primary document binder, containing a 

complete set of documents specifying the model structure, detailed 

software description, and functionality. Development of each section 

shall be indicative of accepted software engineering practices. 

EQECAT maintains all such documentation, and will have it available to the 

professional team during the on-site visit. 

B. All computer software (i.e., user interface, scientific, engineering, 

actuarial, data preparation, and validation) relevant to the modeler’s 

submission shall be consistently documented and dated. 



C. Documentation shall be created separately from the source code. 



202



C-2 Requirements 

Disclosure 

The modeler shall maintain a complete set of requirements for each 

software component as well as for each database or data file accessed by a 

component. 

EQECAT maintains such requirements and documentation, and will have it 

available to the professional team during the on-site visit. 

1. Provide a description of the documentation for interface, human factors, functionality, 

documentation, data, human and material resources, security, and quality assurance. 

EQECAT maintains a set of documents describing the specifications and 

product requirements for user interfaces, database schema, client 

customizations, security considerations, user manuals, and references. 

The above documentation will be available to the professional team during 

the on-site visit. 

203



C-3 Model Architecture and Component Design 

The modeler shall maintain and document (1) detailed control and data flow 

diagrams and interface specifications for each software component, and (2) 

schema definitions for each database and data file. Documentation shall 

be to the level of components that make significant contributions to the 

model output. 

The design levels of the software have been documented, including software 

components and interfaces, data files, and database elements. This 

documentation will be shown to the professional team during the on-site visit. 

204



C-4 Implementation 

A. The modeler shall maintain a complete procedure of coding guidelines 

consistent with accepted software engineering practices. 

EQECAT maintains such a procedure. 

B. The modeler shall maintain a complete procedure used in creating, 

deriving, or procuring and verifying databases or data files accessed by 

components. 

EQECAT maintains such a procedure. 

C. All components shall be traceable, through explicit component 

identification in the flow diagrams, down to the code level. 

All components are traceable in this manner. 

D. The modeler shall maintain a table of all software components affecting 

loss costs, with the following table columns: (1) Component name, (2) 

Number of lines of code, minus blank and comment lines; and (3) 

Number of explanatory comment lines. 

This table will be available for review by the professional team. 

E. Each component shall be sufficiently and consistently commented so 

that a software engineer unfamiliar with the code shall be able to 

comprehend the component logic at a reasonable level of abstraction. 

Yes, the source code is commented in this manner. Also, EQECAT maintains live 

intranet source code documentation for the analysis engines. The model is based 

upon published research modified as appropriate by EQECAT’s meteorological, 

engineering, and statistical personnel. System data is organized and maintained 

in tables, binary files, or flat files, depending upon the type of analysis. The 

underlying model including algorithm implementation and technical assumptions 

along with the procedures used for updating the system data will be available for 

review by the professional team during the on-site visit. The overall system 

design has been implemented using standard software engineering techniques. 

System documentation is maintained to define critical system functionality in 

terms of Data Flow Diagrams, Structure Charts, and the corresponding narratives 

which describe how each module functions. This information is available for onsite 

review. 

205



Disclosure 

1. Specify the hardware, operating system, other software, and all computer languages 

required to use the model. 

Details regarding the required hardware, operating system, and other 

software are given in Standard G-1, Disclosure 2. The calculational 

components of the model have been developed in C++; other components 

have been developed in C++ and Java. 

206



C-5 Verification 

A. General 

For each component, the modeler shall maintain procedures for 

verification, such as code inspections, reviews, calculation 

crosschecks, and walkthroughs, sufficient to demonstrate code 

correctness. 

The models have been extensively tested to verify that calculated results are 

consistent with the intended simulation approach. A variety of methods have 

been employed. These include algorithm verification through comparison to 

independently developed software packages, hand calculations, and sensitivity 

analyses. 

Extensive validation testing of the software generated wind fields has been 

performed to confirm that generated wind speeds are consistent with 

observations. Numerous analyses have been conducted using actual insurance 

portfolio data to confirm the reasonableness of resulting answers. 

B. Component Testing 

1. The modeler shall use testing software to assist in documenting and 

analyzing all components. 

Testing software is used to assist in documenting and analyzing all components. 

2. Unit tests shall be performed and documented for each component. 

Unit tests have been performed and documented for each component relevant to 

residential hurricane loss costs in Florida. 

3. Regression tests shall be performed and documented on incremental 

builds. 

A suite of automated regression tests is regularly run on the software to ensure 

integrity of the various components as well as the results produced by the 

integrated system. 

Quality assurance documentation includes a description for each test case from 

the regression testing suite. 

4. Aggregation tests shall be performed and documented to ensure the 

correctness of all model components. Sufficient testing shall be 

performed to ensure that all components have been executed at least 

once. 


integrity of the various components as well as the results produced by the 

integrated system. 

207



C. Data Testing 

1. The modeler shall use testing software to assist in documenting and 

analyzing all databases and data files accessed by components. 

Testing software is used to assist in documenting and analyzing all databases 

and data files accessed by components. 

2. The modeler shall perform and document integrity, consistency, and 

correctness checks on all databases and data files accessed by the 

components. 

Client data is extensively tested during the import process into the EQECAT 

system to confirm its accuracy. Field level validation is performed to confirm that 

every data element within each record falls within known ranges. Data not falling 

within known ranges is marked as an error or a warning in a log depending upon 

the severity of the problem. Child/parent and other key relationships are also 

checked. A summary log is displayed at the end of import process denoting the 

number records which have warnings or errors. 

Disclosures 

1. State whether the model produces the same loss costs if it runs the same information 

more than once without changing the seed of the random number generator. 

Yes, it will produce the same loss costs. 

2. Provide an overview of the component testing procedures. 


integrity of the various components as well as the correctness and consistency of 

results produced by the integrated system. 

208



C-6 Model Maintenance and Revision 

Disclosure 

A. The modeler shall maintain a clearly written policy for model revision, 

including verification and validation of revised components, databases, 

and data files. 

EQECAT has a clearly written policy for model revision with respect to 

methodologies and data, including verification and validation of revised 

components, databases, and data files. 

B. A revision to any portion of the model that results in a change in any 

Florida residential hurricane loss cost shall result in a new model 

version number. 

A revision to any portion of the model that results in a change in any Florida 

residential hurricane loss cost results in a new model version number. 

C. The modeler shall use tracking software to identify all errors, as well as 

modifications to code, data, and documentation. 

EQECAT uses tracking software to identify all errors, as well as modifications to 

code, data, and documentation. 

EQECAT’s policies and procedures for model revision will be made available to 

the professional team during the on-site visit. 

1. Identify procedures used to maintain code, data, and documentation. 

EQECAT has a series of ISO procedures regarding the maintenance of code, 

data, and documentation, and these will be made available to the professional 

team. 

209



C-7 Security 

Disclosure 

The modeler shall have implemented and fully documented security 

procedures for: (1) secure access to individual computers where the 

software components or data can be created or modified, (2) secure 

operation of the model by clients, if relevant, to ensure that the correct 

software operation cannot be compromised, (3) anti-virus software 

installation for all machines where all components and data are being 

accessed, and (4) secure access to documentation, software, and data in 

the event of a catastrophe. 

In accordance with standard industry practices, EQECAT has in place security 

procedures for access to code, data, and documentation, including disaster 

contingency, and for maintenance of anti-virus software on all machines where 

code and data are accessed. Procedures are also in place to ensure that 

licensees of the model cannot compromise the correct operation of the software. 

These procedures will be made available to the professional team during the onsite 

visit. 

1. Describe methods used to ensure the security and integrity of the code, data, and 

documentation. 

The model can only be used by authorized users. Authorized user accounts 

are created by a trusted administrator. The program files of the model are in 

machine code and can not be reverse engineered or tampered with. The data 

files (vulnerability curves, hazard etc.) are in binary format and can not be 

tampered with. The output from the model is always labeled with the analysis 

parameters and other information needed to repeat a particular analysis - 

thus, reports of the program can not be misused or altered to present 

incorrect information. 

210


Appendices 

Appendix 1 - Credentials of Selected Personnel 

211


Appendices 

CREDENTIALS 

Dr. James R. (Bob) Bailey has over 15 years experience as a technical consultant, researcher, 

and project manager. His doctoral work in Civil Engineering included an emphasis on wind 

engineering, specifically wind effects on buildings and components. He is experienced in 

subjects related to construction materials, solid mechanics, dynamics, numerical analysis, 

structural analysis and design. He served as a consultant to NASA by performing an on-site 

inspection at the Marshall Space Flight Center to assess the structural integrity of buildings 

subject to tornado winds. He also has performed on-site inspections of commercial high-rise 

buildings in Dallas to evaluate the performance of structurally-glazed window glass systems 

subject to extreme wind events. He is a member of the API subcommittee that is developing a 

new wind loading specification for drilling masts and derricks. Dr. Bailey holds a Ph.D., M.S., 

and B.S. in Civil Engineering from Texas Tech University. 

Dr. James J. Johnson, Consultant to EQECAT, has more than 30 years of project management 

and civil/nuclear engineering experience, serving the insurance/reinsurance, Fortune 500, and 

nuclear (domestic and international) industries. From its creation in 1994 until 2000 he headed 

the EQECAT division, a group that provides catastrophic risk management services to the global 

insurance and reinsurance industries, including catastrophe modeling software, portfolio and 

single site analysis, risk management consulting, training, and information. In addition, Dr. 

Johnson has participated in the development, implementation, and teaching of seismic risk and 

seismic margin assessment methodologies. He has participated in seismic PRAs of over 20 

nuclear power plants. His participation encompasses many aspects including hazard definition, 

seismic response and uncertainty determination, detailed walkdowns, and fragility assessment. 

Dr. Johnson has contributed to over 80 technical reports and journal articles and is a member of 

the Earthquake Engineering Research Institute, American Society of Civil Engineers, and other 

technical organizations. Dr. Johnson holds a Ph.D. and M.S. in civil engineering from the 

University of Illinois, and a B.C.E. in civil engineering from the University of Minnesota. He is 

also a licensed Civil Engineer in California. 

Dr. Mahmoud Khater, Senior Vice President of EQECAT, has more than 20 years of 

engineering experience in natural hazards risk and reliability assessment; in the insurance, 

power, industrial, and commercial sectors; and in the behavior of structures and lifelines under 

seismic and wind loading. His experience includes seismic, fire, and hurricane hazard and risk 

assessments for single buildings, lifeline systems, and portfolios of properties. Since joining 

EQECAT, Dr. Khater has served as EQECAT’s project and technical manager for the 

development of state-of-the-art probabilistic analysis computer programs for application to civil 

engineering problems, seismic risk analysis and hurricane risk assessment. Responsibilities have 

included several earthquake and hurricane structural response analyses and portfolio analyses. 

Dr. Khater holds a Ph.D. in structural engineering from Cornell University, and a M.Sc. and 

M.Bc. in structural engineer from Cairo University in Egypt. He is an active member in the 

Earthquake Engineering Research Institute and the American Society of Engineers. 

Dr. Omar Khemici has over 20 years of extensive professional experience in structural 

engineering and natural hazard risk assessment and mitigation. As a Director for EQECAT, he 

provides technical direction and support to a variety of key projects. He performed the QA 

verification of different USWIND modules through hand calculations, and participated in the 

212


Appendices 

development of the USWIND and USQUAKE vulnerability functions. He recently 

participated in the development of the USWildfire. Dr. Khemici is project manager for jobs 

with the primary insurance, reinsurance companies, and financial institutions. Dr. Khemici 

graduated from Stanford University in 1982 and is a licensed Civil Engineer in California. 

Raymond Kincaid, Senior Vice President of EQECAT, has more than 20 years of experience in 

natural hazards risk management. For the last 10 years he has directed the GUI portion of the 

development of several software products used to assess and manage insurance portfolio risk 

resulting from catastrophic events including hurricanes, earthquakes, high winds, and flood. 

Products developed under his guidance include USWIND, USQUAKE, UKWIND and 

UKFLOOD. He also has extensive experience in the design and analysis of structures to resist 

extreme loadings including earthquakes, hurricane, blast, and nuclear weapons effects. Mr. 

Kincaid has directed major natural phenomena and seismic hazard analysis programs for 

numerous government, manufacturing and commercial clients. Representative clients include the 

Department of Energy, U.S. Postal Service, Allendale Insurance, Pacific Bell, Anheuser-Busch, 

3M, Northrop, Unisys, General Foods, Litton, Parker-Hannifin, and Rockwell International. 

Thomas I. Larsen, Senior Vice President of EQECAT, has more than 15 years of professional 

structural engineering, research, computer programming, and project management experience. 

He recently participated in the development of the USWIND and USQUAKE natural 

catastrophe financial risk assessment software programs. This includes project management for 

analyses for selected clients, review of the software methodology for consistency and 

completeness, and compilation of post-earthquake/hurricane damage and loss experience data. 

Prior work at EQECAT includes natural catastrophe hazard (earthquake and related perils such 

as tsunami and fire following, hurricane and other windstorm, and volcano) and/or risk analysis 

for many different regions including Australia, Chile, Iceland, Italy, New Zealand, Puerto Rico, 

the Sakhalin Islands, and the Caspian Sea area. Mr. Larsen holds a M.Eng. in structural 

engineering from the University of California in Berkeley and B.S. in structural engineering 

from Stanford University. He is presently a licensed civil engineer in California. 

David F. Smith has more than 10 years of professional experience in hurricane model design, 

natural hazard research, software development, and project management. He recently 

participated in the development of the USWIND and USQUAKE natural catastrophe financial 

risk assessment software programs. This includes development of the hazard portions of both 

programs, risk analyses for selected clients, and review of the software methodology for 

consistency and completeness. Mr. Smith also managed the development of the hazard portion of 

the EQECAT hurricane/typhoon models for Japan and the Caribbean. Prior work at EQECAT 

includes natural catastrophe hazard and/or risk analysis for many different regions including 

Puerto Rico, Jamaica, Costa Rica, the Philippines, and Japan. Mr. Smith holds a M.S. in 

geophysics from Yale University and a B.S. in mathematics from the University of Chicago. 

213


Appendices 

Appendix 2 - Independent Review 

214


Appendices 

The Engineering, Statistical, and Scientific Validity of EQECAT 

USWIND Modeling Software 

IMPLICATIONS FOR CATASTROPHE MODELING WITHIN THE 

COMMERCIAL HIGHLY PROTECTED RISK PROPERTY INSURANCE INDUSTRY 

Peter J. Kelly 

Lixin Zeng 

Arkwright Mutual Insurance Company 

Abstract 

The validity of EQECAT USWIND 1 modeling software is reviewed from several perspectives. 

Using several external sources for hurricane data, it is found that the storm data set represents the 

historical and expected long term storm patterns well and generally without bias. By reviewing 

storm damage estimates against a theoretical understanding of the wind effects on structures as 

well as actual experience, it was found that the model’s damage estimates reasonably reflect the 

physical properties of force and damage and that the system has no systematic bias in its damage 

estimation logic. One minor shortcoming in damage estimation was uncovered in the manner that 

USWIND uses geocoding during initial data import, especially for areas with very large zip 

codes. The vendor has corrected this problem in subsequent versions of the software. 

All in all, the EQECAT modeling package represents a very well conceived and thoroughly 

researched natural disaster modeling environment for hurricanes. External data and expert 

opinion have been incorporated into the software. Our independent experiments as well as the 

advice of meteorological and structural experts lead us to conclude that the systems is an 

excellent tool for managing the risk of natural disasters in the commercial property insurance 

industry. 

Presented November 7, 1996 at the ACI Conference for Catastrophe Reinsurance, New York, 

NY. 

Author information: peter_kelly@arkwright.com and lixin_zeng@arkwright.com 

1 USWIND is a trademark of EQECAT, Incorporated, headquartered in San Francisco. 

215


Appendices 

Introduction 

Use of natural disaster modeling software for windstorm exposures has increased in recent years 

with the advent of improved software and the increase in natural disaster-caused damage during 

the most recent 10 years. For insurers in the Highly Protected Risk (HPR) commercial property 

marketplace, these software programs represent an important advance and a significant 

challenge. While some insurers take the time to calibrate these systems by examining the damage 

estimates that come from software, few take the time to review the probabilistic and scientific 

content of these systems in light of recent research and advances in the scientific community. 

This is unfortunate because based on our experience, the damage-based error to portfolio 

calculations will generally be well less than one order of magnitude, but errors due to the 

probabilistic and scientific components of the system can be several orders of magnitude. 

In this paper, we first survey the current research on the long term probabilistic characteristics of 

tropical cyclones conducted at governmental agencies and scientific community. The scientific 

basis of the USWIND tropical cyclone modeling component of the software are then assessed. 

Next, the design of the wind damage calculation is reviewed. Based on this assessment, the 

validity of the software is tested and evaluated through a series of experiments; first to validate 

the damage calculations and then to validate the probabilistic storm database, which because of 

its proprietary nature, requires a special simulation process. 

The software that was used in all analysis presented in this paper is USWIND 3.07.05. 

I. An assessment of current scientific research 

Tropical cyclone (TC) is a generic term for hurricane, typhoon and other tropical vortices. A 

severe TC is the most devastating natural disaster in terms of property damage and loss of life. 

Studying TC activity is therefore one of the most important objectives of meteorological 

agencies and scientific communities around the world. For insurers' underwriting and/or 

reinsurance decision making, accurate long term probabilistic characteristics of TC activity is 

needed. Both observational and theoretical studies have been undertaken to address these issues: 

Direct historical observations: the National Hurricane Center (NHC) has archived reliable 

observations of the North Atlantic basin (including the North Atlantic Ocean, Caribbean Sea, and 

Gulf of Mexico) TC activity since 1886 and eastern Pacific observations since 1949. The data 

captured includes position of storm center, central pressure, and maximum sustained wind speed 

every six hours. Based on the data, scientists at NOAA/National Weather Service calculated the 

probability distributions of TC (in particular, hurricane) frequency, intensity and track 

parameters along the 3000 miles coast line of the eastern and southeastern United States [Ho et 

al., 1987; Neumann, 1987]. The results of these studies are widely used by storm-surge 

modelers, climate researchers, and insurers. 

216


Appendices 

General circulation model (GCM) simulations: GCM is the numerical simulation of atmospheric 

and oceanic circulation based on our knowledge of dynamics and physics. High speed computers 

and advanced observing technology (e.g. environmental satellites) have enabled researchers to 

study TC activity with unprecedented detail. The latest work in this area has been undertaken by 

scientists at Max-Plank Institute for Meteorology in Germany [Bengtsson et al. 1995]. They run 

their GCM on time scales from five years to multi-decades, and have successfully simulated 

realistic TC activity globally. 2 

Observational study and GCM simulation have their respective advantages and shortcomings. 

The former is directly based on historical data but representativeness of the available data is 

uncertain and needs to be further assessed. The latter is based on physics and thus is more robust. 

However, GCM’s high computational demand limits its ability to fully resolve TC activity and 

keeps it from being widely adopted. 

Bengtsson et al. [1995] compared a GCM simulation to the observations of TC experience 

during a period of twenty years. It was found that the GCM and observations reveal similar 

frequency and geographical and seasonal distributions of TC activity. This comparison serves as 

an independent verification to the validity of the observational data. However, detailed 

examination of the data set showed that the early observations are biased toward higher hurricane 

activity because the wind measurements were biased high prior to the 1960s. An empirical 

correction was designed by Landsea [1993], and is used in our investigation. 

2 A GCM designed for regional climatology study is usually teamed with a nested LAM (limited 

area model) in order to obtain a resolution fine enough to describe the region of interest. Studies 

have demonstrated encouraging results of the GCM/LAM simulation of regional climate in 

Europe [Giorgi et al., 1990] and North America [Hewitson and Crane, 1992]. The regional 

distribution of important climatic variables are shown to be realistically reproduced. In 

particular, Giorgi et al. [1990] illustrated the ability of the GCM/LAM to provide detailed 

features of European winter storms. 

Admittedly, no attempt has yet to be made to simulate tropical cyclones with a GCM/LAM. As Bengtsson [1995] 

showed that a GCM itself can simulate the TC activities with reasonable accuracy, it is our belief that future 

GCM/LAM work will substantially improve such simulations. We plan to work with experts in this field to initiate 

studies along this path. 

217


Appendices 

II. The scientific basis of the EQECAT software USWIND 

USWIND is a wind storm hazard modeling software package developed by EQECAT, Inc. for 

the eastern and southeastern United States, Hawaii and Puerto Rico. Given detailed location 

structural information and insurance policy financial information (e.g. building location, 

construction and content type, total insured value, deductible, reinsurance, etc.), USWIND 

calculates the annual expected damage in dollar amount and percentage. Additionally, it 

estimates non-exceedance damage at any given probability level (e.g. the 95% non-exceedance 

damage). This program consists of three main steps: (1) construction of an applicable probability 

storm data set, and (2) damage calculations based on this data set, and (3) financial analysis 

(which is not part of this study.) 

The scientific basis for the first step stems from the study by Ho et al. [1987, see section I], 

which is documented in NOAA Technical Report - NWS 38. The probability distributions of 

landfalling hurricanes along the eastern and southeastern United States are calculated based on 

historical observations. Although the history of TC records is not long (about 100 years), the 

observational data have been proven to be reasonably representative. An example of such prove 

is the agreement between the observations and GCM simulation [Bengtsson et al., 1995]. 

The probability distributions of TC activity are then sampled by a computer simulation scheme 

(Latin-Hypercubic simulation with variance reduction) to create a data set including 465,000 

storms. The characteristics (such as location, intensity, etc.) of these storms is stochastically 

assigned. These storms are then imposed on an insurance portfolio. 

218


Appendices 

III. The damage estimation component of the EQECAT software USWIND 

Storm damage is determined by accumulating site specific calculations. These calculations 

incorporate wind field information from the probability storm data set and individual location 

engineering considerations (e.g. construction, roofing, and cladding.) The damage calculation 

itself is a function of the projected maximum wind speed, peak gusts, and storm surge. The 

factors are the independent variables in a model for which the dependent variable is the percent 

of damage that results from a storm. The functions which relate wind speed to damage are called 

“vulnerability” curves within the industry. EQECAT provides a set of vulnerability functions 

with its software. Each curve represents a vulnerability function for different structure types. 

Customized vulnerability functions can also be created for unique locations by creating new 

curves, or by combining the existing EQECAT’s curves. 

The resulting site damage is adjusted for the financial structure of the insurance policy including 

local or policy deductible, limits, and site specific (“facultative”) reinsurance recoveries. These 

net amounts are then accumulated and adjusted for portfolio level (“treaty”) reinsurance 

recoveries. 

IV. Assessing the Simulated EQECAT USWIND Damage Calculation 

As a 150 year old commercial property engineering company with an insurance capacity, 

Arkwright has developed a process for estimating individual site wind damage which is very 

complex. Initially, general storm parameters are taken into consideration. These storm 

parameters include wind speed, storm diameter and shape, forward speed and direction, central 

and external barometric pressure, and rainfall. Before translating this data in localized forces, 

local terrain data are analyzed. This terrain information consists of elevation, distance from 

coast, roughness, drainage, nearby structures (as well as storage and vegetation), and local tide 

patterns. The final component of input to the process is the local facility structural engineering 

information. This structural information includes roof construction (design, geometry, flashing, 

and anchorage), overall building envelope design and openings, wall construction (material, 

design, cladding, and glass), canopies and overhangs, and contents information (amount, 

susceptibility to water and wind damage, and desirability to looters.) 

From these inputs an Arkwright engineer can estimate the resulting forces that can be expected 

to be exerted upon a building during a storm. These forces include the overall wind field, number 

and speed of projectiles, storm and tidal surge, wind gusts (speed, pattern, and duration), and salt 

deposit (for corrosion damage estimation.) 

For a given profile of forces exerted upon a well defined structure, an experienced property 

engineer can then estimate (through theory and experience) resulting damage. This part of the 

process begins with identification of the likely initial failure mode (roof uplift, balcony collapse, 

etc.) The likely failure mode is a function of the most exposed structural component. In addition 

to assessing what structural component will fail, the extent of failure must be estimated -- based 

on the forces exerted upon the structure. Next, any subsequent or resulting failure must be 

219


Appendices 

estimated in order to complete the chain of damage producing events. Lastly, based on many 

losses and financial guidelines 3 , the financial extent of the event is determined. 

In an ideal modeling environment, this entire process would be represented in a detailed 

calculation that would take place for every storm and for every structure. Based on computing 

power, intended use, and inherent uncertainties in other parts of the model (the probabilistic 

storm data set), such a detailed approach is simply not practical within an application such as 

USWIND. 

The approach that EQECAT has chosen is to incorporate vulnerability curves for standard 

structure types. These vulnerability curves were developed by EQECAT using research done by 

Dr. Kishor Mehta (Director of the Wind Engineering Research Center) and Dr. James McDonald 

(Director of the Institute for Disaster Research) at Texas Tech University, where damage 

analysis for storms for the last 25 years has been conducted. In addition to this work, EQECAT 

used claims data from all the major storms of the last 30 years that is contained in the National 

Hurricane Research Project at Travelers. This data was analyzed by Dr. Don G. Friedman and 

John Mangano. Additionally, EQECAT used internal investigations of hurricanes Andrew, Iniki, 

Marilyn, Bob, Opal, and typhoon Angela as well as claims data from hurricanes Hugo, Andrew, 

Iniki, and Opal from companies that participated in the development of USWIND. 

While this research record is impressive, Arkwright also has extensive and well documented loss 

experience. With this data in hand and since the vulnerability curves can be customized, the issue 

of validity testing for the vulnerability curves is not as important as it is for the mathematical and 

meteorological content of the system. After an extensive calibration exercise, Arkwright 

developed a library of customized vulnerability curves. In addition to the customized curves 

however, Arkwright does use some curves that come directly form the EQECAT set, so the 

provided vulnerability functions do warrant review. 

The first test used to validate the vulnerability curves was to compare the changes in damage to 

the changes in the kinetic energy at different wind speeds. With all other things being equal, the 

damage should be proportional to the square of the velocity (wind speed) because it is closely 

related to the pressure that the wind exerts on a building. The well know formula for kinetic 

energy bears this out. The formula for kinetic energy is 

K.E. = 1/2 m v 2 

where m = mass of air, and 

v = velocity (wind speed) 

Since properties (especially commercial properties) withstand considerable force before any 

damage results, the proportionality that we wish to test is: 

i. D = 0 ; for v d < v x 

ii. D ~ 1/2 m (v x - v d ) 2 ; for v x > v d 

where D = damage to building, 

v x = velocity (wind speed), and 

3 These guidelines include the standard regional costs of materials and labor as well as increased, or inflated, cost of 

construction after a natural disaster due to a high demand for construction materials and personnel 

220


Appendices 

v d = velocity (wind speed) where damage ceases to be zero. 

Focusing on equation ii and removing the constants (1/2, and m) which only affect the scale of 

the proportionality , we get: 

D ~ (v x - v d ) 2 

To test this comparison, we ran a simple test using individual facilities of varying structure types 

using the scenario storm capability of USWIND and compiled damage statistics for various wind 

speeds. One example of these analyses is presented in figure 1 where the damage estimates for 

an industrial high rise building with average cladding is reviewed. 

USWIND Damage Estimate Calculation 

For a single facility at various wind speeds 

70 

75 

80 

90 

100 

110 

120 

130 

140 

150 

160 

170 

180 

190 

Sq. of Wind Speed (Axis 1) / 

Facility Damage (Axis 2) 

Wind Speed (MPH) 

Square of Wind Speed 

USWIND Damage 

Figure 1. A comparison of the USWIND estimated wind damage at a commercial 

facility versus the square of the difference between the wind speed and the 

point at which damage ceases to be zero (v d ); for this example, the point v d 

was 70 mph. A constant or proportionality of .0018 was included for 

scaling. 

Visually, the test for proportionality is well met. A generally well held structural engineering 

principle may explain the difference observed between the damage curve and the kinetic energy 

curve in the center of the chart. This principle relates to loss control -- mitigating factors that 

help to minimize the damage when a loss occurs. Loss control is especially effective in 

commercial properties where measures such as bracing, flashing, and protection for storage are 

likely to be employed. Loss control measures tend to be effective at moderate to severe wind 

speeds (below 150 mph, for instance) and the damage falls below that expected from a 

theoretical kinetic energy standpoint as the loss control measures mitigate the damage. At 

catastrophic wind speeds however (above 150 mph), the loss control measures cease to be as 

effective as the wind forces overcome the capability of the measures to withstand the energy, 

giving way, and allowing failure to such an extent that the damage appears far more consistent 

with the theoretical kinetic energy curve. 

221


Appendices 

The next test was an analysis of actual losses to see if the damage calculations had any 

systematic bias. To do this, 58 Florida locations were randomly chosen and damage from 

hurricane Andrew from 1992 was adjusted for inflation and compared to the results from a 

“scenario storm” from the historical storm dataset of USWIND. When this was done, the totals 

were very close. The actual losses after adjusting for inflation for these locations totaled 

approximately $100 million and the estimate from USWIND was high by $14 million. 

Furthermore, of the 58 locations, 28 estimates were below the location damage and 30 were 

above the location damage. This data is presented graphically in figure 2. Here a 45-degree line 

is drawn and the distribution pattern of USWIND/actual loss points can be observed. In a system 

with perfect prediction, all points would lie on the line. In a system with random error, the points 

will not lie on the line, but there will be equal numbers and an even pattern of points above the 

line and below. 

Hurricane Andrew Damage Comparison 

USWIND estimates versus actual losses 

USWIND Estimates 

100,000,000 

10,000,000 

1,000,000 

100,000 

10,000 

1,000 

100 

10 

1 

1 100 10,000 1,000,000 100,000,000 

Actual Losses 

Figure 2. A comparison of actual versus USWIND modeled damage for 58 actual 

locations within Florida using hurricane Andrew. 

Our conclusions from a damage perspective then is that USWIND properly models the physical 

properties of forces versus damage and that the system has no systematic bias in its damage 

estimation logic. 

222


Appendices 

V. EQECAT USWIND - Validity of the probabilistic storm data set 

The main challenge to the evaluation of the probabilistic storm data set is that the data are not 

available to the user due to their proprietary nature. To alleviate this problem, a special 

simulation experiment is designed. 

We created a uniform portfolio consisting of commercial buildings located 10 miles apart along 

the coast of the eastern and southeastern United States. They have the same construction type, 

content and insurance policy. This portfolio is used as input to USWIND, whose probabilistic 

calculation gives the annual expected and non-exceedance damage at these locations (Figure 3). 

Because the portfolio is uniform, all of the geographical variability in damage is due to the 

probabilistic distribution of USWIND’s storm data set, and is independent of the damage 

calculation. Therefore, a comparison of the damage variability with the geographical distribution 

of hurricane activity will independently verify whether or not the probabilistic storm data set is 

consistent with observations. 

3.5 

3 

USWIND Damage Distribution 

For an evenly distributed uniform portfolio 

Damage Percentage 

2.5 

2 

1.5 

1 

0.5 

0 

100 

350 

600 

850 

1100 

1350 

1550 

1800 

2050 

2260 

2510 

2760 

Milepost Location (every 10 miles) from Texas to Maine 

mean 

90th percentile 

Figure 3. USWIND estimated annual Damage (%) to the uniform portfolio. There 

is a building every 10 nautical miles. Solid line: expected; dashed line: 

90% non-exceedance. 

To compare this simulated damage calculation against historical data, actual storm experience 

will be reviewed. The two aspects of hurricane activity most relevant to wind damage are 

hurricane frequency and intensity, shown in Figures 4a and 4b, respectively. Data for these 

graphs comes from NOAA Technical Report - NWS 38. 

223


Appendices 

US Landfalling Hurricanes 

Actual experience 1886-1995 

20 

18 

16 

Number of Hurricanes 

14 

12 

10 

8 

6 

4 

2 

0 

130 

310 

490 

670 

850 

1030 

1210 

1390 

1570 

1750 

1930 

2110 

2290 

2470 


2650 

3010 

Figure 4a. Number of landfalling hurricanes along the eastern US coast (1886-1995). Data from North 

Atlantic Tropical Cyclone Best Track Data from National Hurricane Center. 

100 

90 

US Maximum Sustained 1-minute Wind Speeds 

Actual experience 1886-1995 

Wind Speed (MPH) 

80 

70 

60 

50 

40 

30 

130 

310 

490 

670 

850 

1030 

1210 

1390 

1570 

1750 

1930 

2110 

2290 

2470 


2650 

3010 

Figure 4b. One-minute sustained maximum wind speed of landfalling along the eastern 

and southeastern US coast (during the period of 1886-1995.) Data from North 

Atlantic Tropical Cyclone Best Track Data from National Hurricane Center. 

These two parameters (hurricane frequency and intensity) are combined to form an estimate of 

wind damage based on the fact that wind damage is proportional to the square of the wind speed 

224


Appendices 

and the associated kinetic energy (see section IV.) This estimate is then compared with 

USWIND calculation (Figure 5a). 

2.5 

Mean Damage Comparison; USWIND vs Historical Estimate 


Damage Percentage 

2 

1.5 

1 

0.5 

0 

100 

350 

600 

850 

1100 

1350 

1550 

1800 

2050 

2260 

2510 

2760 


USWIND 

Historical 

Figure 5a. Annual expected wind damage (solid line: USWIND; dashed line: 

independent estimate based on historical data). 

The geographical distribution of their difference is shown in Figure 5b. The two estimates 

generally agree well along about 70% of the coast line. USWIND estimates, however, 

demonstrate some noticeable difference from historical data: (1) much larger than expected local 

variations of damage near miles post 1400 (southern Florida); (2) consistent underestimate at the 

eastern part of Gulf coast (mile post 100 - 600); (3) overestimate at west Florida and New 

England coasts. 

Detailed analysis and investigation with EQECAT revealed that the cause for the difference is 

USWIND’s inconsistent handling of user-supplied lat/lon coordinates during portfolio data 

import. Specifically, USWIND sometimes incorrectly assigns zip code centroid locations to 

properties rather than using the user-supplied lat/lon coordinates. The problem generally occurs 

when street address is missing. Because of this problem, USWIND’s probabilistic storm 

calculation will effectively treat buildings as if they are at the center of the zip code zone in 

which they are located, unless users manually enter the distance to the coastline. As a result, a 

building in a larger zip code zone is treated as if it were farther from the coast than one in a 

smaller zip code zone, and consequently is expected to sustain less wind damage. For example, 

most of the Gulf coast states have larger zip code zones than New England states do, USWIND 

estimate tend to be lower in former than in the latter area. The sharp local minimums around mile 

post 1400 (Figure 5b) are also found to be located in large zip code zones of the Everglades. 

225


Appendices 

Mean Damage Difference; USWIND vs Historical Estimate 


200% 

Estimate Diff. (USW-Hist)/Hist 

150% 

100% 

50% 

0% 

-50% 

-100% 

100 

350 

600 

850 

1100 

1350 

1550 

1800 

2050 

2260 

2510 

2760 


Figure 5b. Comparison of annual expected damage estimates by USWIND and 

historical data (USWIND estimates versus historical estimates; historical 

estimates used as the basis.) 

EQECAT worked closely with Arkwright after Arkwright identified this problem to determine 

just how and why the problem was occurring. Based on this work, EQECAT has indicated that 

they have corrected the problem in version 4.0 of the software. An analysis of this correction is 

not included in this report, but a preliminary test of the correction performed at EQECAT’s 

headquarters and reviewed jointly by Arkwright and EQECAT indicates that the correction does 

indeed fix the problem. 

VI. USWIND Summary and Implications for the Insurance Industry 

Our simulation experiment confirms that the historical hurricane observations are an appropriate 

basis for tropical cyclone disaster modeling. These observations are indeed reflected in the 

USWIND probabilistic data base. Also, the damage calculations are reliable and generally 

without bias. 

As was mentioned in section V, EQECAT has listened to, help document, and correct the one 

problem encountered in this study. Based on early analysis, version 4.0 will correct the problem 

and for the time being (until upgrade to 4.0 is done at Arkwright), Arkwright will use an 

empirical correction for the lat/lon zip code centroid problem. 

The implications of this work for the commercial highly protected risk property insurance 

industry lie as much in the process of completing the work than in the conclusions. Certainly, the 

discovery of any systematic bias would have been worthy of discovery. Since the natural hazard 

modeling software is used to make multi-million dollar reinsurance decisions as well as capacity 

226


Appendices 

allocation decisions, any error or bias in the software could prove extremely costly. However, 

based on our work, the software is free of bias and as a result, Arkwright can confidently 

incorporate USWIND into decisions about capacity or reinsurance. 

The day-to-day operational implications for Arkwright that stem from the lessons learned in 

completing this study are significant. They include the following: 

(1) The software combines the expertise of structural engineering, meteorological, mathematical, 

statistical, and economic scientists with the expertise of finance, accounting, and insurance 

professionals. Apparently because of the diversity of expertise in these disciplines among their 

potential customers, vendors of natural disaster modeling packages invest far too little in 

documentation and as a result, validity assessment and damage calibration are activities that were 

very difficult and time consuming. Whatever the reasons behind the documentation issue, the 

insurer must take responsibility for creating a controlled production environment where tests can 

be completed and analysis can be done. An insurer should also expect to invest significant time 

in building expertise in using a natural disaster modeling package. 

(2) Validating the software is a very worthwhile exercise because it provides a benchmark for 

new releases of the program. It also has the benefit of fostering a stronger relationship between 

the designers of the software and the scientists within the insurer’s organization. Doing this 

requires a significant investment in time, money, and people, but the alternative is to write 

insurance with less than complete understanding of the risks involved. The insight that is gained 

by doing such an analysis is enormous. As a result of this work, new understandings and indeed 

new questions arose concerning the portfolio and the reinsurance program. For Arkwright, 

performing this study raised as many questions about the unique characteristics of the insurance 

portfolio (to be addressed through subsequent research) as it settled about the software. 

(3) The potential customer set for these packages is relatively small but the functionality is 

relatively rich. Because of this, it is very likely that a customer will encounter (because of the 

combinations of the structure, the storm, the policy, and the reinsurance) a situation that has 

never been seen in software development. In light of this, the insurer must form and maintain a 

strong relationship with the support organization of the vendor. A validation exercise, by its 

design is likely to manifest this situation. While the experience can be trying and even frustrating 

for both parties, the long term result is well worth the effort as the insurer gains a greater 

understanding of the peril and the software and the vendor gains a better understanding of the 

client. 

227


Appendices 

References 

Bengtsson-L., Botzet-M. and Esch-M., Hurricane-type vortices in a general circulation model, 

Tellus, vol. 47A, no. 2, pp.175-196, Feb. 1995. 

Giorgi-F., Rosaria-M. and Visconti-G., Use of a limited-area model nested in a general 

circulation model for regional climate simulation over Europe, Journal of Geophysical Research, 

vol. 95, no. D11, pp. 18413-31, 20 Oct. 1990. 

Hewitson-B. and Crane-R-G., Regional climates in the GISS global circulation model: synopticscale 

circulation, Journal of Climate, vol. 5, no. 9, pp. 1002-11, Sept. 1992. 

Ho-F., Su-J., Hanevich-K., Smith-R. and Richards-F., Hurricane climatology for the Atlantic and 

Gulf Coast of The United States, NOAA Technical Report NWS 38, April, 1987. 

Landsea-C-W., A climatology of intense (or major) Atlantic hurricanes, Monthly Weather 

Review, vol. 121, no. 6, pp. 1703-13, June 1993. 

Nuemann, C., The National Hurricane Center risk analysis program, NOAA Technical Memo 

NWS NHC 38, November, 1987. 

228

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