Estimating the implicit price of energy efficiency improvements in the residential housing market: A hedonic approach

JOURNAL

OF URBAN

ECONOMICS

25, 52-67 (1989)

Estimating the Implicit Price of Energy Efficiency improvements in the Residential Housing Market: A Hedonic Approach* TERRY M. DINAN+ AND JOHN A. MIRANOWSKI* ‘Office of Policy Analysis, U.S. Environmental Protection 20460, and *Director, Resources and Technology Division, U.S. Department of Agriculture

Agency, Washington, D.C. Economic Research Service,

Received July 17.1986; revised August 5,1986

I. INTRODUCTION Many fuel cost saving devices may be incorporated into new homes or existing homes. Insulation, passive solar applications, and storm windows are examples of such investments. This paper addressesthe issueof whether the fuel savings resulting from these efficiency improving investments are capitalized into housing values. This issue is of interest from the homeowner’s point of view, as well as from a policy point of view. A homeowner faced with the decision of. whether to invest in efficiency improvements benefits from information on both the fuel savings which accrue to him/her during the period he/she owns the home and the amount of the initial investment cost that will be recouped when the house is sold. The issue is significant from a policy point of view because failure of the housing market to capitalize fuel savings may result in less investment in efficiency improvements than is socially optimum. Failure of durable resale values to reflect the capitalized value of fuel savings has been cited as a causeof high consumer discount rates for energy durables. Chernoff [2] claims that, while life cycle cost analysts evaluate costs and benefits over the physical life of a durable, individual owners evaluate costs and benefits over the period they expect to own the durable. He argues that owners do not consider the present value of efficiency benefits that will accrue in the period following their ownership period because they do not expect these benefits to be incorporated into the sale price of the used durable [2, p. 831. Since most energy efficiency improving durables are sold with the house (e.g., insulation, high efficiency furnaces, *We are grateful for the helpful comments of Daniel Hamblin (Oak Ridge National Laboratory) and Pat Huelrnau (Iowa State University). Please address correspondence to Terry M. Dinan, Office of Policy Analysis, U.S. Environmental Protection Agency, PM-221, 401 M St., SW., Washington, DC 20460. 52 0094-1190/89 $3.00 Copyright All ri@ts

(c, 1989 by Academic Press, Inc. of reproduction in any form reserved.

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storm windows) the relevant issue is whether the capitalized fuel savings due to these investments are incorporated into the selling price of the house. Policy proposals for home energy ratings rest on the assumption that the housing market does not effectively capitalize efficiency improvements; however, previous research has not adequately addressed this issue. In this paper, a hedonic price model is developed to determine the impacts of energy efficiency on housing prices. The hedonic technique has been widely utilized in economic research, e.g., to analyze the impact of construction quality on automobile prices [4] and air quality on housing prices [6, 111. The hedonic model developed in this paper provides an estimate of the implicit price paid for an increase in energy efficiency in the Des Moines, Iowa housing market. The impact of efficiency improvements on housing prices was previously addressed by Johnson and Kaserman [7]; however, their study utilizes annual fuel bills as an independent variable, as opposed to a measure of energy efficiency. This confounds information on efficiency improvements with information on occupant characteristics, such as preferences for internal temperature settings during the heating season and use of air conditioning during the cooling season. In addition, a linear functional form for the hedonic equation was utilized in their study. A linear functional form liits the amount of information which can be obtained about the impact which efficiency improvements have on housing resale values [3] and may bias the resulting implicit prices [lo]. A further discussion of these points may be found in Sections III and IV. The hedonic model developed in this paper resolves both problems. II. THE THEORETICAL

MODEL

Freeman describes the hedonic technique as “a method for estimating the implicit price of the characteristics which differentiate closely related products in a product class” [3, p. 781. In this study the hedonic technique is applied to the housing market. Houses are differentiated by their size, number of rooms, location, quality of construction, energy efficiency, and numerous other structural and neighborhood characteristics. The hedonic technique is used to determine the effect each of these characteristics has on the selling price of the house. A basic assumption of the model is that each house may be described by a vector of characteristics: H = (h,,

h, )...,

h”).

hi is characteristic i of the house. The price of each house may then be written as a function of the price-determining characteristics:

P = P(h,, hz,..., h,),

(2)

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where P is the selling price of the house. If the function relating the price of the house to its characteristics can be identified, then the implicit price associated with any given characteristic, holding all other factors constant, is

--8P ahi - 'h/

(3)

Phi represents the increase in P that an individual must pay to obtain one more unit of housing characteristic hi. The implicit price associated with an increase in energy efficiency is of particular interest in this study. III.

THE DATA

The sample used in this study consisted of 234 detached, single-family homes sold in Des Moines, Iowa, during the January 1982-June 1982 period. To construct the hedonic model, information was needed on the selling price, structural characteristics, and neighborhood characteristics of each home. Information on the selling price and structural characteristics of each home was obtained from the Greater Des Moines Board of Realtors. Age and floor area were obtained from the city and county assessor offices of Des Moines. For each home, the median income of the appropriate census tract was obtained from the 1980 census, and the distance of the home from the central business district was measured. Table 1 indicates the variable names, sources, means, and standard deviations. The implicit price of energy efficiency is of particular interest in this study. Since no measure of the structural efficiency of the sample homes was available, a proxy variable was constructed. The proxy variable, F, was constructed by adjusting the actual winter fuel expenditures for differences in billing periods, differences in internal temperature settings, and differences in heated floor space.’ F reflects the per square foot expenditure necessary to maintain the sample house at 65°F in an average Des Moines heating season. F, therefore, reflects variations in ejlkiency leuefs among houses, rather than variations in house size or occupant characteristics. F was calculated by determining the fuel expenditures per degree-day per square-foot for each home and then multiplying by the average number of degree-days in an average Des Moines heating season, -F;.= ( l$/HDDi)

( l/Areai)

(6550))

(4) where Fj = adjusted fuel expenditures per heated square foot of house i. Fj reflects the heating expenditures per square-foot that house j would incur in ‘To calculate F, the December 1982 through February 1983 fuel expenditures of each home were obtained from Iowa Power; the internal temperature setting of each home was obtained from the homeowners through a mail survey; and the number of degree-days in the 1982 heating season was obtained from the Energy Extension Office at Iowa State University.

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TABLE 1 Observed Housing Characteristics for Des Moines Sample Source”

Mean

1 1 1 1 1 2 1 1 1 1 1

67,082 3.0256 1.6207 0.64957 0.63248 1,253.0 11,599 0.92308 0.54701 0.74359 0.68376

44,197 0.79658 0.66939 0.47813 0.48316 530.87 9,868.7 0.26704 0.49885 0.43759 0.46600

1

0.6667

0.47242

1 1 1 1 1 2

0.17521 0.89316 0.35897 0.53419 0.39744 29.543

0.38096 0.32314 0.48073 0.49990 0.49042 22.066

3

0.13988

0.05214

4

0.34819

0.09677

5

21,079

5467.1

6

4.7223

3.3939

Variable name

Description

PRICE BED BATH FAMRM DINRM SQFT LOT BASE DISH RANGE DISP CA

Selling price of house Number of bedrooms Number of bathrooms Family room present* Dining room present* Square-feet of floor area Square-feet of lot area Basement present’ Dishwasher present’ Cooking range present’ Garbage disposal present’ Central air-conditioning present’ Window air conditioner present’ Garage present * Single garage present’ Double garage present’ Fireplace present* Age of house Adjusted fuel bills per square-foot of heated floor area Predicted fuel bills per square-foot of heated floor area (normalized for temperature differences) Medium income of appropriate census tract, used as a proxy for neighborhood status Miles from the central business district

WA GAR GARl GAR2 FP AGE F

NBHD

LOC

Standard deviation

“Sources: 1, Multiple Listing Service, Des Moines, Iowa; 2, City and County Assessor’s Office; 3, based on information from the homeowners, Iowa Power, and the Energy Extension Office at Iowa State University; 4, estimated in this study; 5, 1980 Census of Population and Housing Census Tracts [15], Des Moines, Iowa, Standard Metropolitan Area; and 6, Des Moines City map. %hcates a qualitative variable.

an average heating season if the internal temperature was maintained at 65’F. HDDi = heating degree-days for household j in the December 1982 through February 1983 billing period. Areaj = square feet of heated floor

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area of house j. 6550 = the number of heating degree-days in an average Des Moines heating season (using 65°F as the base temperature). Note that HDDj in (4) is a house specific measure of heating degree-days. This was utilized to control for the differences in fuel bills due to differences in occupant lifestyle patterns and temperature preferences. A heating degree-day is a lo difference between the internal temperature of the home and the external temperature over a 24-hour period. Therefore, HDD, varies by the billing period and the internal temperature of the home, HDD, = BDDi + D,(I;

- 65”),

(5)

where BDDj += heating degree-days in the December 1982 through February 1983 bilhng period for house j (using 65°F as the base temperature), Dj = number of days in the billing period for house j, and T, = average internal temperature of house j during the December-February billing period. Information on the number of hours occupants were home during the day and the temperature level which was maintained when occupants were (1) not at home; (2) at home, but sleeping; and (3) at home and awake was utilized to obtain Tj. Preliminary analysis indicated that the normalization for temperature settings and heated floorspace did not totally eliminate the lifestyle component of F. Although F was adjusted for the internal temperature setting of each home, imperfect information on temperature setting may have caused an incomplete adjustment. Additionally, no adjustment was made for appliance usage. In homes which are heated by natural gas2 a high level of appliance usage will increase the cost of heating the home.3 Since an increase in F may reflect an increase in appliance usage or an increase in internal temperature setting, F is not a fixed exogenous measure of the structural efficiency of the sample homes. Thus, it is not uncorrelated with the error term, and the coefficient of F is biased [8]. A three-step process was used to obtain an exogenous, predicted measui-e of the structural efficiency of the sample homes. First, the adjusted fuel bills per square foot, F, were regressed against a vector of structural and neighborhood characteristics. Next, F was regressed on the subset of these characteristics

‘Over 90% of homes in the Des Moines study area are heated by natural gas (Clark Bruebaker, Iowa Power, Des Moines, personal communication, 1982). ‘Most appliances are operated by electricity. Since the price of electricity in Des Moines is nearly three times as high as the price of natural gas ($ZO/MBTU and 7/MBTU, respectively), a high level of appliance usage in gas-heated homes will increase the relative cost of heating the home; i.e., although appliances generate internal heat gains, they utilize a more expensive fuel than the primary heating fuel.

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that were significant at the 95F confidence level in explaining the variation in F, and a predicted value, F, was obtained. Finally, using the coefficients obtained in the second step, the effects which reflect household lifestyle were eliminated from the predicted value. A linear functional form was used in the regres$on to obtain I”, therefore, the impact of lifestyle characteristics on F could be evaluated independently of the structural characteristics. The residual value, f ‘, is an exogenous predicted value of the structural efficiency of the sample homes.4 P was then used as an independent variable in the hedonic model. The use of fi as an independent variable facilitates examination of the impact of efficiency improvements on housing prices. In a previous study, Johnson and Kaserman [7] utilized a predicted value of annual fuel bills as an independent variable in a hedonic model of the Knoxville, Tennessee housing market. They collected data on the utility bills of each sample home for the 12-month period beginning 2 months after the purchase of the house. This variable, denoted U, was then regressed against $u.tural and proxy household characteristics to obtain a predicted value, U. U was used as an independent variable in the hedonic model. The use of 6 to investigate the impact which efficiency improvements have on housing prices has several limitations. First, the fuel bills collected for each house reflect different billing periods because the fuel bills for each house were not collected for the same 1Zmonth period. Therefore, differences in energy prices and weather conditions encountered during the time periods over which bills were collected create variation in U among households. Second, U reflects differences in lifestyles. Households in which an occupant is at home most of the time tend to have higher values of U than households in which all members are gone during the day. Finally, U reflects differences in internal temperature preferences during the heating season, and differences in preferences for air conditioning use during the cooling season. The predicted value, fi, eliminates the stochastic element of U but does not eliminate the-occupant effect; i.e., it merely makes the occupant effect a predicted, and, therefore, fixed element of fi. 3 is designed, however, to eliminate the impact which occupant characteristics have on fuel bills. The coefficient of 8’ may then be used to determine the impact which efficiency improvements have on housing values. IV. THE MODEL ESTIMATION A fundamental issue in estimating the hedonic price function (2) is choosing the functional form. A frequent criticism of hedonic studies is that the functional form is chosen on the basis of convenience [5]. Rosen demonstrated that the hedonic price function is a reduced form equation, “A similar procedure is used by Witte et al. [16] to obtain an index of dwelling unit quality.

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reflecting both supply and demand influences. Therefore, the functional form may not be determined from information about either the underlying supply or demand equations [ 131. Since the economic relationship between the implicit price of a given characteristic and the level of that characteristic or other characteristics may not be determined a priori, it is important to choose a functional form which does not predetermine the form of these relationships. Freeman demonstrated the economic implications associated with alternative functional forms. He specified eight alternative forms of the hedonic price function and demonstrated that of these eight forms, only the Box-Cox transformation allows both (1) the implicit price of a given characteristic to depend on the level of other characteristics and (2) the implicit price of a given characteristic to either increase or decrease as the level of the characteristic itself increases [3]. Johnson and Kaserman [7] utilized a linear functional form to investigate the relationship between energy efficiency and housing prices. A linear functional form imposes the restriction that the implicit price of efficiency improvements is constant across all efficiency levels; i.e., an efficiency improvement in a very efficient house is valued the same as an efficiency improvement in an extremely inefficient house. In addition, the use of a linear functional form requires the implicit price of energy efficiency to be independent of the level of other house characteristics; e.g., the implicit price of an increment of efficiency must be the same in a very old house as in a new house, Finally, if the true functional form of the hedonic equation is not linear, the restriction of linearity may result in bias in the resulting coefficients [lo]. To avoid placing undue restrictions on the functional form of the hedonic price equation and to enable the investigation of the relationship between the implicit price of efficiency and the level of all independent variables (including the level of efficiency itself) a Box-Cox model was used. A full Box-Cox model may be specified as

where pm = -PB - 1

8

= lnP, jp’

h?i = L-,

’

820 8=0

-

1 I

= In hi,

xi

# 0

A, = 0.

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Due to computational limitations, using a full Box-Cox model, in which the dependent variable and each of the independent variables may take on a different power transformation factor, was not feasible. In solving for the appropriate 8 and Ai values, it was necessary to constrain all of the Xi values to be equal. Therefore, all the continuous independent variables have the same power transformation factor; i.e., all Xi = X. If the values of 8 and X are constrained to equal 1, the model reduces to a linear form. If 8 and h are constrained to equal 0, the model reduces to a log-linear form. If the value of 19is set equal to 0 and the value of X is set equal to 1, then a semilog model results. Therefore, all of the functional forms that are commonly used in hedonic models, the linear, semilog, and log-linear forms, are subcategories of the Box-Cox model used in this study. A hedonic model containing all the independent variables listed in Table 1 was estimated. The value of the log-likelihood function for this specification of independent variables is at a maximum when the Box-Cox transformations of 6 = -0.10 and A = 0.22 are used. The implicit prices of housing characteristics obtained from this model specification (denoted SF) are reported in Table 2. In estimating hedonic price models, there is a possibility of coefficient bias due to excluded explanatory variables. To explore how sensitive the implicit prices obtained are to the specification of independent variables, a restricted hedonic price function was estimated. RANGE and DISP, which were not significant in the full model, were excluded from the restricted regression, while DISH, which was significant in the full model, was included. Since no other appliances or kitchen characteristics are included in the restricted equation, DISH serves as a proxy variable, denoting a modern kitchen. In the full model neither DINRM nor FAMRM were significant. It was thought that multicollinearity may have prevented either of these variables from being significant. DINRM, therefore, was excluded from the restricted model to see if FAMRM became significant. Finally, the window air-conditioning variable, WA, was excluded from the restricted model, and GARl and GAR2 were condensed into one variable, GAR. Neither of the neighborhood variables (LOC and NBHD) were significant in the full model. However, these were not excluded from the restricted model since no other neighborhood variables were available. The value of the log-likelihood function for the restricted specification of independent variables is at a maximum with the Box-Cox transformations of 8 = -0.10 and X = 0.29. The implicit prices of housing characteristics obtained from this model specification (denoted as @,) are reported in Table 2. As revealed in Table 2, the implicit prices of the housing characteristics contained in both the full and restricted models are not substantially different. For 8 of the 13 variables common to both models, the implicit

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TABLE 2 Implicit Prices Obtained Using Alternative Specifications of Independent Variables’

Variable neme +d

BED BATH FAMRM LOT’ DISH CA WA GAR GARl GAR2 FP AGE NBHD’ LOC SQFT BASE DINRM RANGE DISP

Full modelb

Restricted model’

FF

ia

- 11.48* 6,798* 8,524* 3,762 52.00* 7,026; 3,181 60.81

- 11.6* 6,363* 8,733* 3,592 53.94’ 8,088* 3,741 11,243* 1,324 - 157; 28.32 191 17.29* 11,182* -

11,541’ 10,413: 1,340 - 177’ 28.31 69.31 14.28; 11,008* 4,589 2,477 2,408

Percentage change i$ - Pa ” 'F

0.0131 0.0640 0.0245 0.0452 0.0373 0.1511 0.1482 0.0119 0.1130 0.0004 1.7557 0.2108 0.0158

“The implicit prices given here are obtained when all variables are at their mean level. ‘Implict price obtained using Box-Cox model with B = -0.10, h = 0.22. ‘Implicit price obtained using Box-Cox model with: B = -0.10, X = 0.29. dImplicit price listed is JP/( a$1 in RFE) = aP/aP + square feet of heated floor area, where RFE = required fuel expenditures necessary to maintain the house at 65°F in an average heating season. eMeasured in hundreds of square feet. ‘Measured in hundreds of dollars. *Indicates implicit price is significant at a 95% confidence level.

prices differ by less than 10%. The implicit price of $‘, the key variable of interest in this study, changed by less than 2%. These results are consistent with those of Butler [l]. Butler estimated both a full and a restricted hedonic price function of the St. Louis housing market and did not find substantial differences in the implicit prices which were obtained in the two models. Further investigation was undertaken to determine if the implicit prices of housing characteristics were sensitive to changes in the functional form of the hedonic model. For comparison purposes, the linear, semilog, and

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TABLE 3 Implict Prices Obtained under Alternative Functional Forms Variable name FfJ

BED BATH FAMRM LOT’ DISH CA GAR FP AGE NBHDd SQFT BASE LOC

Expected sign C-1

(+I (+) (+I (+I c+> (+) (+) (+I t-1 (+) (+) (+) C-1

Linear model (9

semilog model (8

- 11.39 3,524 17,572* 2.146 147.50. 4,517 - 1,262 4,425 -915 -125 31.69 26.96: 4,051 -331

- 21.03: 5,222* 8,765* 3,676 45.20. 8,276; 3,574 11,059’ 1,729 - 160; 40.24 16.38* 8,976 -240

Log-linear model cv - 7.19* 5,813* 9,981* 3,951 26.82; 6,917* 4,618* 10,811* 1,095 -118s 21.14 20.24* 10,637’ - 116.44

Box-Cox” model (9 - 11.63* 6,363* 8,733* 3,592 53.94’ 8,088* 3,641 11,243* 1.324 - 157% 28.31 17.29* 11,182’ 191

” Box-Cox model was estimated with B = - 0.10, h-0.29. ‘Implicit price listed is aP/( a$1 in RFE) = aP/aP + square feet of heated floor area, where RFE = required fuel expenditures necessary to maintain the house at 65°F in an average heating season. ‘Measured in hundreds of square feet. dMeasured in hundreds of dollar. *Indicates implicit price is significant at a 95% confidence level.

log-linear models,5 which are frequently used in hedonic analyses, were estimated. The restricted specification of independent variables was used in estimating these models. Table 3 indicates the implicit prices of housing characteristics which are obtained under the four model specifications. The implicit price obtained for a given housing characteristic is sensitive to changes in the form of the hedonic price function. For example, the implicit price of an additional 100 square feet of lot area (LOT) is over six times greater using the linear model relative to log-linear model. Not only do the magnitudes of the implicit prices change under different model specifications, but in three cases the signs of the implicit prices change as well. The implicit price of central air-conditioning (CA), a fireplace (FP), and the distance of the home from the central business district (LOC) have different signs under different model specifications. A likelihood ratio test revealed that we cannot reject that the linear, semilog, and log-linear models are significantly different from the Box-Cox model at a 95% confidence level. ‘These are each restricted forms of the Box-Cox model.

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V. ANALYSIS

AND

MIRANOWSKI

OF MODEL

RESULTS

Nine of the 14 independent variables in the restricted Box-Cox model are significant at a 95% confidence level. Only one of the variables does not have the hypothesized sign6 The implicit prices obtained for the age of the home, the presence of central air-conditioning, the presence of a fireplace, the number of bathrooms, and the lot size are within the range of values found in other studies which used individual homes as units of observation (rather than census tract data). These studies are: Linneman [lo], Kain and Quigley [9], and Johnson and Kaserman [7]. The implicit prices obtained for a basement and a garage were both higher than those found in the comparison studies. The difference between the implicit prices of a basement and garage obtained in this study and those found in the comparison studies could be attributed to differences in functional forms, differences in included explanatory variables, or the more severe weather in Des Moines, Iowa. The frequency of tornadoes and harsh winters may cause the demand for basements and garages to be higher in Des Moines than in some other areas. None of the other studies estimated the implicit price of a dishwasher. The implicit price of a dishwasher obtained in this study is $8088. Intuitively, this seems to be an unreasonably high value. It is possible that this implicit price reflects not only the value of the dishwasher, but also the value of several excluded variables. Homes having dishwashers may tend to have larger kitchens and include more modem kitchen appliances and furnishings. A positive correlation between the dishwasher variable and these excluded variables would cause the coefficient on DISH to be biased upward, reflecting the entire value of a modem kitchen, rather than just the value of a dishwasher. The variable of key interest in this study is 9. The implicit price of p (reported in Table 3) is - $11.63. This indicates that, when all independent variables are at their mean values, the expected selling price of a house is increased by $11.63 due to a $1 decrease in the level of fuel expenditures necessary to maintain the house at 65°F in an average heating season. For convenience, this level of fuel expenditure will be referred to as “required fuel expenditures.” Since @’ is designed to reflect the structural efficiency component of household fuel bills, the implicit price of fi is interpreted as the implicit price of an efficiency improvement which results in $1 decrease in the level of required fuel expenditures. ?he variable LOC (miles from central business district) does not have the hypothesized sign. This result may be due to the limited number of neighborhood characteristics which were available for inclusion in the model. A positive covariance between LOC and other neighborhood characteristics, such as school quality and air quality, could be responsible for the positive coefficient obtained in our analysis [2].

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Some investments that increase the winter heating efficiency of a home increase the summer cooling efficiency of the home as well (e.g., ceiling insulation). A relevant question is whether the implicit price of a $1 decrease in required annual winter fuel expenditures is equal to the present value of the dollar’s worth of winter fuel savings alone, or whether the implicit price includes the present value of summer cooling savings as well. To explore this issue, the hedonic model was estimated with an interaction term between $’ and CA (a variable indicating the presence of central air-conditioning) included as an independent variable. If home purchasers include summer cooling effects in the premium that they arc .Lting to pay for an increase in winter heating efficiency, then it is expected that the coefficient on the interaction term would be negative; i.e., in homes that have air-conditioning it is expected that a decrease in required fuel expenditures (an increase in efficiency) will increase the expected selling price of the home more than in homes with no air-conditicr: .Iig. The coefficient on the interaction term was negative; however, it MS not significant at a 95% confidence level.7 Based on these results, it could not be concluded that the implicit price of $’ includes the present value of summer cooling benefits as well as the present value of one dollar’s worth of winter fuel savings. In interpreting the implicit price of #‘, it must be recalled that the hedonic price function is nonlinear. The implicit price was cbtained by assuming that P and all other characteristic levels NY dt their mean values. An advantage of the Box-Cox model is that it allows the implicit price of a housing characteristic to rely on the level of the characteristic itself, as well as the levels of other housing characteristics. The first derivative of the price function with respect to fi indicates a negative relationship between required fuel bills per square foot and housing prices; i.e., cYP/afi < 0. The second derivative of the hedonic function with respect to @’ is positive; i.e., d’P/JE’I’ > 0. A negative first derivative and a positive second derivative indicate that the relationship between the housing prices and required fuel bills per square foot is decreasing at a decreasing rate. This implies that increases in efficiency are valued more in efficient homes than in inefficient homes. One possible explanation for this result is that efficiency improvements that are made to homes that are initially relatively efficient may tend to be improvements that are readily visable (and, therefore, easily documented by a seller) and may have significant secondary benefits in addition to their fuel saving benefits; e.g., they may enhance the attractiveness of the home. Finally, the signs of the derivatives obtained in this analysis indicate that as the energy efficiency of a house approaches zero, the value of the house will not be reduced to zero,

‘The 1 value of the interaction coefficient was equal to - 1.17

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i.e., totally inefficient homes still have a positive value due to the shelter that they provide. By examining a 2P/iS'$ aAge the relationship between the implicit price of energy efficiency and house age may be obtained. 8 2P/i39 aAge -C 0 indicating that the increase in P due to a decrease in 8’ (i.e., an increase in efficiency) is less in older homes than in newer homes. There is no a priori reason to assume that energy efficiency is valued more highly in new than in old homes. It is possible, however, that the average age of fuel saving investments in old homes is greater than the average age of fuel saving investments in new homes. For example, in a house that is 2 years old, no fuel saving investment could be more than 2 years old. In a house that is 30 years old, the fuel saving investments could be up to 30 years old and, therefore, would have a short remaining life. This implies that capitalized fuel savings from the efficiency investments in a 30-year-old home may be less than those from the efficiency investments in a 2-year-old home. Only one of the three comparison studies included fuel expenditures in the hedonic price model. Johnson and Kaserman [7] included a predicted fuel bill variable in their hedonic model of the Knoxville, Tennessee housing market. They found that the implicit price of increased fuel expenditures is - $20.73, or a $1 increase in annual fuel expenditures will decrease the selling price of the home by $20.73, cetera paribus. As discussed in Section III, the predicted fuel expenditure variable used by Johnson and Kaserman reflects differences in occupant preferences as well as variations in structural efficiency. Their study results are not directly comparable to ours, since our independent variable, @‘, is designed to reflect variations in structural efficiency only. Aside from the fact that the two variables are not directly comparable, differences in functional form of the hedonic price equations used in the two studies would be expected to lead to differences in the implicit prices obtained. We utilized a flexible functional form as opposed to the linear model utilized by John and Kaserman. As demonstrated in Section V, differences in functional form specification may cause substantial variation in the resulting implicit prices. Finally, there may be differences in the underlying demand and supply functions for energy-efficient homes in Knoxville and Des Moines. Our study results indicate that the Des Moines housing market capitalizes efficiency improvements into housing values; however, it does not indicate whether efficiency improvements are priced “correctly.” In an efficient market, it is expected that the implicit price of a $1 decrease in required fuel expenditures would be equal to the expected present discounted value of a stream of $1 annual fuel savings. However, imperfections in the housing market, such as lack of information on the efficiency levels of alternative homes, may prevent efficiency improvements from being fully capitalized in the housing market. To calculate Des Moines

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home buyers’ expectations of the present value of $1 worth of annual fuel savings, it is necessary to have information on their fuel price expectations, discount rates, and the average expected remaining life of fuel saving devices in the sample homes. Without this information we were unable to conclude whether or not the Des Moines housing market prices fuel saving investments efficiently. If assumptions are made about the average expected remaining life of fuel saving investments in the sample and the fuel price expectations of Des Moines home buyers, an estimate of the consumer discount rate implied by the hedonic price obtained in our analysis may be derived. If, for example, it is assumed that consumers expect fuel prices to rise at a real rate of 4% annually,8 and the average remaining life of fuel saving investments in the sample is 20 years, the implicit price of $11.63 would imply a real discount rate of 10%. This is at the low end of the range of discount rates which have been estimated for new purchases of thermal integrity improvements and space heating equipment. In a review of studies which estimate discount rates, Train [14] reports a range of discount rates between 10 and 30% for thermal integrity improvements and between 5 and 35% for new space heating equipment. Regardless of whether the housing market prices fuel savings efficiently, knowledge of the implicit price of energy efficiency provides valuable information for homeowners who do not plan to own their homes over the entire life of the fuel saving investments. The implicit price may be used to obtain an estimate of the average resale value of fuel saving investments.9 This estimate is equal to the reduction in required annual fuel expenditures caused by each fuel saving device multiplied by the implicit price of a $1 reduction in required fuel expenditures. For example, in a 40 x 20-foot single story home located in Des Moines, Iowa, an increase in above-grade wall insulation from an R-25 to an R-35 level is expected to result in an $8.16 decrease in the level of required annual fuel expenditures.1° The estimated resale value of the increased wall insulation, therefore, is $94.87 (i.e., the $8.16 annual savings multiplied by $11.63, the implicit price of a $1 reduction in required fuel expenditures). By this method, the average resale value of any fuel saving device may be estimated. The implicit price used in the example provided is the marginal valuation obtained when all independent variables are at their mean level. Estimated resale values of a sAccording to the Office of Policy, Planning, and Analysis [12] forecasts, the real price of natural gas in the Des Moines area was expected to rise by approximately 4% annually at the time this study was undertaken. ‘The resale value of a fuel saving investment is defined as the expected increase in the market value of the home due to the inclusion of the efficiency improvement. “In calculating the savings it was assumed that a 75% efficient gas furnace was used as the auxiliary heat source.

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given investment will vary according to the age and initial efficiency level of the home. VI. SUMMARY In this study a hedonic price model has been used to examine the impact which efficiency improvements have on housing values. Our study represents an improvement over a previous attempt to examine this issue in that the independent variable used in our analysis is designed to reflect variation in structural efficiency levels among sample homes, rather than variation in occupant lifestyles and preferences. In addition, a flexible functional form is used to prevent bias due to m&specification of the hedonic price equation. The study results reveal that efficiency improvements are capitalized into housing prices in Des Moines, Iowa. At the average efficiency level of homes in the sample, an efficiency improvement which results in a $1 decrease in the level of expenditures necessary to maintain the house at 65’F (in the average heating season) will increase the expected selling price of the house by $11.63. The premium obtained for an equivalent efficiency improvement will be greater than $11.63 in a relatively efficient home, and less than $11.63 in a relatively inefficient home. Without further information concerning the price expectations and discount rates utilized by Des Moines home buyers, and the average remaining life of fuels saving investments in the sample, it cannot be determined whether the housing market is pricing fuel savings efficiently. Regardless of whether or not the implicit price of fuel savings is the outcome of an efficient market, information on this implicit price facilitates the estimation of an average resale value of fuel saving investments. In addition, the results of this study refute the hypothesis that efficiency improving investments are not capitalized into housing prices. REFERENCES 1. V. R. Butler, The specification of hedonic indexes for urban housing, Land Ecunom., 58, 96-108 (1982). 2. H. Chernoff, Individual purchase criteria for energy-related durables: The misuse of life cycle cost, Energy J., 4 (4), 81-86 (1983). 3. A. M. Freeman, “The Benefits of Environmental Improvement: Theory and Practice,” Johns Hopkins Univ. Press, Baltimore (1979). 4. Z. Griliches, Hedonic price indexes for automobiles: An econometric analysis of quality change, in “Price Indexes and Quality Change” (Z. Griliches, Pd.), pp. 55-87, Harvard Univ. Press, Cambridge, MA (1971). 5. R. Halvorsen and H. 0. Pollalcowski, Choice of functional form for hedonic price equations, J. Urban Econom., 10,37-49. 6. D. Harrison, Jr., and D. L. Rubenfeld, Hedonic housing prices and the demand for clean air,” J. Environ. Econom. Management, 5, 81-102 (1978).

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7. R. Johnson and D. L. Kasserman, Housing market capitalization of energy saving durable good investment, Econom. Inquiry, 21,374-386 (1983). 8. G. G. Judge, R. Carter I-Ii& W. E. Gritliths, H. Liitkepohl, and T.-C. Lee, “Introduction to the Theory and Practice of Econometrics,” Wiley, New York (1982). 9. J. F. Kain and J. M. Quigley, Measuring the value of housing quality, J. Amer. Starist. Assoc., 65, 532-548. 10. P. Linneman, Some empirical results on the nature of the hedonic price function for the urban housing market, J. Urban Econom., 8,47-68 (1980). 11. J. P. Nelson, Residential choice, hedonic prices, and the demand for urban air quality, J. Urban Econom., 5,357-369 (1978). 12. Office of Policy, Planning, and Analysis, “National Energy Policy Plan,” U.S. Department of Energy, Washington, DC (1983). 13. S. Rosen, Hedonic prices and implicit markets: Product differentiation in pure competition, J. PO/it. Econom., 82, 34-55 (1974). 14. K. Train, Discount rates and consumer energy related decisions: A review of the literature, Energy, 10 (12), 1243-1253 (1985). 15. U.S. Department of Commerce, “1980 Census of Population and Housing Census Tracts, Des Moines, Iowa Standard Metropolitan Area,” PHC80-2-139 (1980). 16. A. D. Witte, J. Sumka, and H. Erekson, An estimate of a structural hedonic price model of the housing market: An application of Rosen’s theory of implicit markets, Econometha, 47, 1151-1173 (1979).