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properties built before 1995 were coded as “1” while those built in 1995 or later were coded as “0.” When year is included in the model, the B coefficient can be interpreted as the difference in average water use between properties built before 1995 compared to those built in 1995 or later. The factor “property is a rental” was defined so that rental properties were coded as “1” while all non-rental properties were coded as “0.” Again, the B coefficient represents the difference in average water use between rental properties and non-rental properties. There are a couple ordinal (continuous) variables included in one of more of the models presented below. These include: average number of bedrooms per unit, average rent, and average price charged for water by the local utility. For these variables, the B coefficient represents the average difference in the amount of water used per unit for every unit increase (e.g., bedroom size, dollar of rent, or dollar charged per kgal) in these predictor variables. The three models selected for presentation in this report represent the range developed for this study from fairly simple models involving five independent variables to the complex stepwise regression model that includes numerous independent variables. Because of the inherent range of water use and human behavior associated with the use of water across different properties, cities, and regions, none of the models did a particularly good job of explaining the variability of water use observed in multi-family properties. These models do consistently show a statistically significant reduction in water use attributable to submetering programs at the 95 percent confidence level. The models do not show any statistically significant water savings from RUBS. The coefficient of determination (R 2 value), a measure of the goodness of fit of the model, for these multivariate models were only on the order of 0.15 – 0.3, indicating that these models explain between 15 and 30 percent of the variability of the data. While not a particularly strong result in scientific and engineering research, these values are typical to what is found in studies of human behavior and attitudes in the social sciences. While these models are weakly predictive, they are useful in identifying the most important factors that influence water use in these multi-family properties. Submetering was the only billing methodology consistently found to effect a statistically significant reduction in water use. RUBS achieved statistical significance in only a few of the models developed and in some of those cases the sign of the coefficient indicated an increase in water use associated with the billing practice. Significant efforts were 154
made to determine if there are any verifiable water savings associated with RUBS. None of the models showed a consistent, statistically significant decrease in water use due to RUBS. Water Use Model #1 – Six Independent Variables The first multivariate regression model presented uses a limited set of six independent variables that included average number of bedrooms per unit, year the property was built (1994 and earlier or 1995 and later), whether the property was a rental property or a non-rental property, average price charged for water by the local utility, submetering, and RUBS. Fundamental information and statistics are presented in Table 5.14. The adjusted coefficient of determination (R 2 ) for the model is 0.224. This value indicates that this model explains only 22 percent of the variability in the data. The P-value for the model is 0.00 indicating that whatever fit does exist is statistically significant at the 95% confidence level. Table 5.14 Model #1 summary statistics, coefficient of determination, and significance Adjusted R Std. Error of Degrees of R R Squared Square the Estimate Freedom F P-value 0. 478 0.229 0.224 21.693 955 46.942 .000 Predictors: (Constant), Submetering, Rental property (compared to individually owned or other), RUBS, Property built before 1995 (compared to properties built 1995 or later), Utility’s average commodity charge for water and wastewater, Average number of bedrooms per unit Dependent Variable: Indoor water use per unit (average 2001, 2002) The B coefficients presented in Table 5.15 present the magnitude of the “effect” of the different independent variables in the model. Of particular interest are the coefficients for RUBS and submetering. In Model #1, five factors were statistically significant – average number of bedrooms per unit, property is a rental (vs. non-rental), year property was built (1994 and earlier or 1995 and later), average price charged by the local utility for water and wastewater, and submetering. The only factor that wasn’t statistically significant was RUBS. The effect of submetering and RUBS are shown graphically in Figure 5.9 and Figure 5.10. 155
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NATIONAL MULTIPLE FAMILY SUBMETERIN
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CONTENTS LIST OF TABLES............
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Matched-Pair Analysis .............
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LIST OF TABLES Table ES.1.1 Breakdo
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Table 6.6 Elasticity values, analys
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Figure 4.30 RUBS water bill #6 ....
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investigated local water savings as
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We are grateful to the members of a
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quantitative aspects of separate bi
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3. Owner/postcard survey: To identi
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"averages" of impact property water
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in-rent properties, others used the
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of these models included the billin
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70 Average Annual Water Use Per Uni
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COSTS AND BENEFITS Beyond quantifyi
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service charge, then the resident w
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Billing Method Efficient Fixtures?
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70 Avg. Water Use per MF Unit (kgal
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REGULATORY FRAMEWORK REVIEW The fra
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Table ES.1.12 NSUAA Summary of Stat
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above, the researchers offer the fo
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service is preferable, and would he
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practices (BMPs) should be implemen
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infrastructure of the building and
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multi-family housing. The transfer
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CHAPTER 1 INTRODU CTION More and mo
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In 1998, 15.0%, or 15.4 million, of
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TERMINOLOGY AND DEFINITIONS OF BILL
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submetering. With utility-submeteri
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all the two bedroom units for $20 p
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HOW TO USE THIS REPORT This report
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CHAPTER 2 LITERAT URE REVIEW As the
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A review of the literature unearthe
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The price elasticity of water deman
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expected. Additionally, McClelland
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submetered and five control (in-ren
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Another study might want to target
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CHAPTER 3 RESEARC H METHODS, APPROA
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1. Selection of participating study
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STUDY SITE SELECTION The Study aime
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Figure 3.2 National Multiple Family
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Matched pair protocol/survey - conf
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information given the available bud
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survey. Service companies that prov
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in “business to business” calli
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MATCHED PAIR SAMPLE The purpose of
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The second part of the visit requir
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epresentative of a known population
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51 Table 3.7 Postcard survey statis
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Table 3.8 Comparison of annual wate
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postcard survey. Nevertheless, exam
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confidence interval ranged from 49.
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particular property (e.g., the mont
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State PUC officials - water commiss
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10. The PROPID number appearing on
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These costs represent the commodity
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The postcard survey provided inform
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Table 4.4 Average number of units f
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Two-thirds of the properties that r
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Percent of Surveyed Properties with
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$700 Average Rent Per Bedroom ($) $
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Percent of Surveyed Properties with
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Impact properties were also more li
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Table 4.8 Issues considered before
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Percent of Surveyed Impact Properti
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60% Percent of Surveyed Properties
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From the visits, it was found that
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100% Percent of Surveyed Residents
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Roughly similar proportions of resi
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inserts for toilets (21%) or low-fl
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proportion were unsure, 37% from su
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Table 4.18 Ownership and rental rat
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Figure 4.17 Submeter water bill #1
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Figure 4.18 Submeter water bill #2
Page 156 and 157: Figure 4.20 Submeter water bill #4
Page 158 and 159: wastewater rates are not shown so t
Page 160 and 161: Figure 4.23 Hot water hybrid water
Page 162 and 163: RUBS Sample Bills Seven sample RUBS
Page 164 and 165: wastewater rates are not shown so t
Page 166 and 167: Figure 4.28 RUBS water bill #4 from
Page 168 and 169: Figure 4.30 RUBS water bill #6 RUBS
Page 170 and 171: directly to and then paid by the th
Page 172 and 173: never subtracting for common area w
Page 174 and 175: Late Fees At the start of water bil
Page 176 and 177: 124
Page 178 and 179: intuitively clear, and matches comm
Page 180 and 181: Indoor water use was normalized on
Page 182 and 183: Table 5.3 Summarized water use anal
Page 184 and 185: easonable assumption since there is
Page 186 and 187: Figure 5.1 shows the frequency dist
Page 188 and 189: 40 Difference in Annual Water Use p
Page 190 and 191: after 1995. By including only those
Page 192 and 193: 70 Average Annual Water Use Per Uni
Page 194 and 195: Table 5.11 Statistical tests compar
Page 196 and 197: Factors that Influence Water Use Th
Page 198 and 199: Table 5.12 Association of categoric
Page 200 and 201: Indoor Water Use per Unit in 2001 I
Page 202 and 203: Table 5.13 Continuous factors from
Page 204 and 205: Multivariate Models to Determine Im
Page 208 and 209: 70 Average Annual Water Use Per Uni
Page 210 and 211: A = Beta coefficient for “Propert
Page 212 and 213: substantially reduces the sample si
Page 214 and 215: Table 5.17 Model #2 coefficients an
Page 216 and 217: 0.00 indicating that whatever fit d
Page 218 and 219: commodity charge for water and wast
Page 220 and 221: properties having generally lower w
Page 222 and 223: 110 Average Annual Water Use Per Un
Page 224 and 225: analysis, intended to control best
Page 226 and 227: Table 5.27 Paired samples test, all
Page 228 and 229: Table 5.30 Estimated water use befo
Page 230 and 231: Table 5.33 Comparison of water savi
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Page 234 and 235: sector only reduced usage by 18% (A
Page 236 and 237: Table 6.1 Avoided costs from submet
Page 238 and 239: submetered. Complete responses of c
Page 240 and 241: included in this cost/benefit analy
Page 242 and 243: Table 6.3 Cost and benefit for owne
Page 244 and 245: Perceived Concerns In the resident
Page 246 and 247: With a service fee, residents could
Page 248 and 249: Table 6.4 Economic return for a res
Page 250 and 251: ANALYSIS OF PRICE ELASTICITIES Econ
Page 252 and 253: concluded that if a single elastici
Page 254 and 255: esults from this analysis point to
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over which they have control than d
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According to the basic consumer cho
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The consumer choice problem can be
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210
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water systems from compliance with
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and methods of practice. NIST publi
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Table 7.1 NSUAA Summary of State Re
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New Surveys of State Agencies and W
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the AWWA WaterStats 1999 database.
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Measures, which dispatches a public
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Mississippi, Missouri, Pennsylvania
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226 Table 7.6 Summary of utility re
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DISCUSSION - KEY EXAMPLES OF STATE
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ill. Condominium associations are g
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under construction after January 1,
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Weights and Measures Regulation In
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from the submetering. Meter testing
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clean and decent housing or the lac
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240
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NATIONAL ORGANIZATIONAL STRUCTURE F
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complaints in all 50 states and app
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- Meters must demonstrate in testin
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- Plumbing systems are space constr
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(2) Twisted or kinked flex supply l
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252
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Estimated Water Use By Different Bi
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there are clear benefits to these s
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study site. These values were then
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immediately strengthens the owner's
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among the nation's key strategies f
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all repair and replacement decision
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These findings underscore the obser
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varies with volume of use) for mult
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Recommendation 6 - Efficient plumbi
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financially responsible for their o
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individual unit, the volume allocat
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RECOMMENDATIONS FOR FURTHER RESEARC
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Fixed rates - Part of a master mete
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Total-capture submetering - a type
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282
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Corral, L.R. 1997. Price and Non-Pr
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McClelland, L. 1980. Encouraging En
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Young, C.E., K.R. Kinsley and W.E.
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