Lessons from the Texas Homeowners Insurance Crisis Bob Puelz ...

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In other words, a more complete empirical specification, where the relationships of interest are explored, while controlling for other county-specific factors that may be related to risk of loss, better assists in understanding some of the issues surrounding this insurance crisis. A testable hypothesis of interest is whether observed pricing in this market is related to changes in the unexpected claims experience by insurers after accounting for factors “normally” priced in this market and other control variables. Thus, while we want to know with precision whether variations in homeowners premiums have been associated with perils that are the source of expected loss in this market, we are also interested in whether insurers are building into their premium calculus factors, in addition to expected loss elements, that could be less well understood by insurance consumers. Testing of these hypotheses is undertaken while controlling for a variety of county- specific, economic and environmental variables that are expected be related to the economics of the homeowners insurance market and the premium per $1,000 of exposure. County-specific data come from a few sources. First, 1990 and 2000 U.S. Census data are used to proxy information on these variables. Because the time period of the study is from 1996 through 2001, 1990 census data are applied to years 1996 through 1999 and 2000 census data are used for the years 2000 and 2001. Income data, obtained from the Bureau of Economic Analysis, were available for each year of the study. 18 Other control variables include a county’s total housing units; the percentage of rural housing units relative to total housing units; the percentage of units that are vacant relative to total housing units; the percentage of occupied housing that is rented; exist where the association between peril types and premiums is clouded by the absence of variables that are correlated with homeowners loss perils and other independent variables that are also associated with premiums. Panel data methods are used to mitigate this problem. 18 See http://www.bea.gov/bea/regional/reis/. 14
the average household size of an occupied housing unit; the crime rate; and the percentage of the population that is nonwhite. The association between homeowners premiums and perils, while controlling for county- specific environmental and demographic factors, is specified as (1) ln Prem + # + # + # 8 14 20 = # 0 " Deviation " Crime + # " Water + # " Fire + # " Wind + # t$ 1 1 t$ 1 15 + # + # " ln Income + # " ln Size + # 21 9 2 " Theft t$ 1 16 ln Nonwhite + # + # 22 3 10 " Other " Water " ln Hous + # t$ 1 15 + # + # 17 Year + # 23 4 Theft 11 " ln RHous + # " VMM + # 18 t$ 1 5 Other " Fire + # t$ 1 24 + # 12 + # 6 " Liab " VMM " ln Vacant + # 19 " Wind t$ 1 + ! + # t$ 1 7 " Liab 13 " ln Re nt where the dependent variable (Prem) is the natural logarithm of the county premium per $1,000 of exposure in year t. 19 Each of the peril variables — Fire, Wind, Water, Theft, Other, VMM and Liab —are defined as the dollar losses per $1,000 of exposure in a year for a given Texas county. The estimation equation also includes a one-year lag of the peril variables to test whether a given year’s premium also reflects an association with prior year losses and the corresponding lags are included as the last seven variables of the estimated equation (1). Support for the hypothesis that premiums per $1,000 of exposure are related to peril-specific current losses per $1,000 of exposure would be exhibited by statistically significant positive coefficients, β1 through β7. In theory, current pricing in a competitive insurance market is based on expected losses. Thus, if the estimated coefficients on lagged loss perils are positive, then such evidence would support a view that factors other than the current perils themselves are related to the current premium, which increases the dimension by which consumers can misunderstand how their policy is priced. Similarly, an associated question is whether the premium is related to an uncertainty 19 It is important to note that the dependent variable is specific to premiums for homeowners multi-peril contracts; contracts that are limited to residential dwelling owner-occupants or town home unit owner-occupants. See Texas Personal Lines Manual, Insurance Council of Texas, Austin.
Page 1 and 2: Lessons from the Texas Homeowners I
Page 3 and 4: I. Introduction Lessons from the Te
Page 5 and 6: Illinois with company-level data to
Page 7 and 8: [Table 1 about here] might have bee
Page 9 and 10: with the NAIC, and were sourced for
Page 11 and 12: To further explore the Texas settin
Page 13 and 14: amounts are summarized in Table 4.
Page 15: [Figure 4 about here] One glimpse i
Page 19 and 20: units that have been classified as
Page 21 and 22: as equations in levels. The Hansen
Page 23 and 24: while variations in perils help exp
Page 26 and 27: Table 1 Texas Homeowners Premiums W
Page 28 and 29: "Combined" Ratio $1.80 $1.60 $1.40
Page 30 and 31: Table 5 Texas Statewide Homeowners
Page 32 and 33: 35% 30% 25% 20% 15% 10% 5% 0% 60% 5
Page 34 and 35: Cumulative % of Texas Counties 100%
Page 36 and 37: Table 7 GMM Estimates of Empirical
Page 38: Harrington, Scott E. and Greg Nieha

Lessons from the Texas Homeowners Insurance Crisis Bob Puelz ...

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