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Energy labels and economic search
Energy labels and economic search An example from the residential real estate market Robert W. Gilmer
Home energy rating systems are popular and widely used programmes to encourage energy ejiciency in the residential housing market. With the actual rating perhaps conveyed to prospective buyers as a posted label, these rating systems ofleer a variety of benejts. This paper focuses on the ability of these systems to help buyers identtfy more quickly the properly priced house. An economic search model is applied to a unique sample of energy-eficient homes in Minnesota. The search bene$ts are modest but positive in our case study, but could be larger in a more diverse housing market. Keywords:
Energy
efficiency;
Rating
systems;
Home energy rating systems are popular and widely used programmes to encourage energy efficiency in the residential housing market. Each house - new or existing - is rated as it enters the real estate market. The rating is an authoritative assessment of the energy condition of the home, and it might be conveyed to prospective buyers in the form of a posted label. Such a label would be analogous to those seen on such appliances as refrigerators, freezers, dishwashers, room air conditioners, clothes washers, and furnaces throughout the USA and Europe. For example, the fuel economy label posted on new cars in the USA tells buyers the automobile’s rating in miles/gallon, provides an annual fuel cost estimate, and gives the range of fuel economy available in similar models. Home energy rating and labelling systems are widely used in the USA, and they have been adopted through many administrative and institutional channels (Hendrickson[6]). Several state governments
The author is on the Chief Economist Staff, Tennessee Valley Authority, 400 W Summit Hill Drive, ESD84, Knoxville, Tennessee 37902, USA. This report was written while the author was a visiting researcher at the Center for Energy Research, Education, and Service, Ball State University, Muncie, Indiana. The views expressed are those of the author and not necessarily those of CERES or the Tennessee Valley Authority. Final manuscript
received
0140-9883/89/030213-06
12 November
1988.
Housing
sponsor these programmes, including Alaska, Arkansas, Massachusetts, Minnesota and Vermont. A number of investor-owned utilities operate rating systems under the auspices of the Edison Electric Institute; several large utilities (eg Pacific Gas and Electric, Duke Power and the Tennessee Valley Authority) have their own programmes. Municipalities and regional governments also operate these home energy rating systems at a local level. The details of specific programmes vary widely in the extent of the housing market included in the programme, the coverage of the energy-related aspects of the house, the participation of homeowners (mandatory v voluntary), and in the means in which information is conveyed to buyers (eg as a prescriptive list of potential energy efficiency improvements or as an energy consumption estimate). Home energy-rating systems offer a variety of benefits to participants in the real estate market. Among the most important of these benefits are: (i) that these labels assure market-wide recognition of conservation investments and energy-efficient design; (ii) they make explicit the tradeoff between higher mortgage payments that accompany highly efficient housing and the lower monthly energy bills they offer; (iii) they assure homeowners that investments in energy efficiency made today will be recognized at the time of sale; and (iv) they help buyers to zero in more quickly on the properly priced house. All of these benefits are discussed below, but in this paper we will focus on item number four. These informational benefits that shorten
$03.00 0 1989 Butterworth & Co (Publishers) Ltd
213
Energy labels and economic search: R. W. Gilmer
the economic search for the potential homeowner are important to the design and implementation of these programmes, but prove difficult to quantify and are neglected in most discussions of these programmes. Estimates of these benefits are offered below using an economic search model and a specific example of energy-efficient housing in Minnesota. The search benefits prove to be positive but modest in our example, but could be much larger in a more diversified housing market. The methodology is equally appropriate for labels applied to other durable goods such as autos or home appliances.
Energy labels and the mortgage market The mortgage market provided the strongest impetus for the implementation of home energy-rating systems. The cost of building an energy-efficient home is typically higher than standard construction, but these homes offer lower utility and fuel bills. The inability of the mortgage market to recognize this tradeoff offered much of the original stimulus for these programmes (Millhone and Schuck[14]). Energy-rating systems serve as documentation of the lower energy bills available from efficient design, and transmit this data to buyers, appraisers, and lenders in primary and secondary mortgage markets. The mortgage market’s failure to acknowledge the advantages of energy-efficient housing has been a barrier to solar, superinsulated, and other innovative housing designs. The high interest rates of recent years excluded large numbers of prospective buyers, because lenders require a minimum income-to-debt ratio of 25-28%. A decision to incur high initial costs in favour of energy efficiency could sharply reduce the number of prospective buyers, and leads builders to avoid energy-efficient but capital-intensive designs. In 1979 the two largest purchasers of secondary mortgages in the US, the Federal Home Loan Mortgage Association and the Federal National Mortgage Association, changed their underwriting guidelines to decrease the income-to-debt ratio for energy-efficient construction (Millhone and Schuck [14], p 11). Despite this change, primary lenders in many markets remained reluctant to make such loans, apparently undecided on the proper definition of energy-efficient homes. In 1986 both major secondary lenders altered their underwriting guidelines again to allow the home rating sheet for particular labelling systems to serve as documentation for home mortgages (Home Energy Rating News [7]). A number of rating systems now qualify, and where available they fill an important gap in the market for innovative housing.
214
Conservation and the energy label Another frequently cited benefit of the energy label is its contribution to energy efficiency and conservation. The standards that must be met to achieve economic efficiency in conservation are stringent (Johnson and Kaserman [9]). First, each homeowner must use the social discount rate to compute the value of energy saved by conservation investments made today. Second, since the typical homeowner will live in the house for fewer years than the life of the conservation investment, he or she must be able to recover the value of the remaining life at the time of sale. Further, the capitalization of the energy savings in the sale price must occur at the social rate of discount. There is mounting evidence that these standards are not being met. The capitalization process does seem to work, but apparently not at appropriate rates of discount. According to a number of different studies, energy savings do seem to be capitalized in housing prices. Laquatra [lo] examined the sale price of a sample of highly energy-efficient homes in Minnesota, and controlled for the effect of housing and neighbourhood characteristics using an hedonic equation. He found that potential energy savings, known to buyers through an energy labelling system, did affect the final sales price significantly. Other studies have drawn similar conclusions. Johnson and Kaserman [9] examined housing sales in Tennessee, and found that past energy bills for the home were a good predictor of sales price if housing and neighbourhood characteristics were properly taken into account. In Lubbock, Texas an infrared photograph from a fly-over of the city allowed Corgel, Goebel and Wade [4] to classify the houses as energy efficient or inefficient. This classification scheme proved a good predictor of the sales value of the house. The study by Johnson and Kaserman found little divergence between the social discount rate and the rate at which energy efficiency is capitalized in the sale price of the house. This study is an exception, however, and the weight of evidence rests on the side of a strong divergence between these rates and potentially serious underinvestment in energy conservation. A recent survey by Train [ 181 of 28 studies and reports summarized the discount rates implicit in consumer decisions on thermal integrity, appliances, autos and other decisions. The results are shown in Table 1. The rates shown are real rates that abstract from inflation. Although there is some overlap with the rates of 3-6% often employed in economic and engineering studies to reflect social discount rates, most of these studies indicate real rates that are much higher. Train identifies three studies that specifically concern themse!ves with thermal integrity. Arthur D.
ENERGY ECONOMICS
July 1989
Energy labels and economic search: R. W. Gilmer Table 1. Average discount rates (%). Range of rates observed
Choice made Thermal
lC-32
integrity
Space heating
4.4-36
system
3.2-29
Air conditioners
39-100
Refrigerators Other
18-67
appliances
241
Automobiles Other,
3.1-22.5
unspecified
Source: Train
[IS].
Little [2] estimated discount rates for the thermal shell of 32% and for windows and doors the rate was 10%. Corum and O’Neil [S] found discount rates for thermal shell measures that ranged from lO-21% by fuel type and 634% by city. Cole and Fuller [3] found average discount rates of 26% for the thermal shell in a national survey and 12% for the Pacific Northwest. Thus the bulk of the evidence indicates that there is an educational and informational role for energy rating systems and labels. Even if issued only as a list of needed improvements or as certification that houses meet a minimum standard of efficiency, the efficiency effects of the label will be salutary. If the label contains an energy consumption estimate or an approximation of the monthly bill, it can directly address the capitalization issue. With such an estimate in hand, most consumers will compare houses by adding the monthly energy bill to the mortgage payment. Implicit in this addition is the assessment of energy savings at the mortgage rate of interest. Mortgage rates are much closer to the social discount rate as defined by most economic and engineering studies than the private rates cited in Train’s survey. In this format the energy label allows the buyer and seller to make simple and reasonable calculations that move the economic outcome sharply toward the social optimum.
falls in two parts: (i) a standard model of economic search: and (ii) a measure of the influence of the energy label on the length of search. The methodology applied here to housing is equally applicable to other durable purchases. The consumer is assumed to assess the price of alternative housing choices using an hedonic equation of the following form.
P=P(X, Z)+e
(1)
~=b,+b,X,+b,X,+...+b,X,+aZ
(2)
e - N(0,
(3)
The price of the house is assumed to be dependent on a vector of characteristics of the house and its neighbourhood, X = (X,, X,, . . . , A’,}; on a single summary measure of energy efficiency (I); and a random error term (e) that is normally distributed with constant variance. Models of random search behaviour originated in the literature on employment search (McCall [ 121 and Lippman and McCall [ll]) provide an extensive survey. In our case the potential homebuyer ventures out to find a home once each period; the house is offered at price P. The cost of the search (c) includes all out-of-pocket expenses plus the value of lost work or leisure. The length of search will depend on the cost of search and on the distribution of housing prices, g(e). With each search the prospective buyer will compute the implicit price from the hedonic equation (p), compare it to the asking price(P), and hope to find the implicit price ‘sufficiently below’ the asking price. P--P>e*>O
Large durable purchases such as appliances, autos and houses offer a wide range of different features and prices. Comparing products and selecting the best buy can be an expensive and time-consuming process. The energy label is an aid to consumers in narrowing the available selection, and it shortens the time spent in pursuit of the right purchase. In this section we will quantify these search benefits in a specific example of energy-efficient homes. The methodological approach
ENERGY ECONOMICS
July 1989
(4)
Once this critical value of e* is exceeded the search is over. Its value is determined by equating the marginal cost of search to the expected marginal return from additional search.
c=
The search for a new house
a’)
sY e*
e - e*)g(e) de
(5)
The higher (lower) the marginal cost of search, the lower (higher) the reservation differential e* will be set. To assign a value to the energy label we will solve this search problem twice. We will solve it first with exact information available on energy efficiency. That is, with an energy label providing the searcher with the exact information on the value of Z for each house. We will solve it a second time with the exact value of Z unknown, but approximated from a few general
215
Energy labels and economic search: R. W. Gilmer
characteristics
of the energy system in the house.
Table 2. Coefficients of physical and locational attributes hedonic equation for housing prices.
Z=l+v
(6)
~=d,+d,Z,+...+d,i,
(7)
0 - N(0, 6;)
(8)
The consumer judges the energy system by the vector of characteristics {I,, . . . , I,,,}, and uses 1 rather than the exact value of the energy label. If the random error v is uncorrelated with the Ei or with U, the Zi are as proxy variables (Maddala [ 131). The use of proxies yields estimates of E(P (X, 7) that are biased in small samples but consistent (Aigner Cl]). The error variance of the hedonic equation, with 1 used to approximate Z, is 0: = uz + a’~:. Thus the change in the distribution of housing prices between the case of the energy label known and the energy label approximated is a2a2 The value 1;; the energy label is estimated by solving Equation (5) with a fixed value of c and with g(e) alternatively N(0, a2) and N(0, a:).’ The expected number of searches to be made is .Z= l/p where p is the probability that e > e*. The expected number of searches is computed with the label known (.Zi) and with the label approximated (.Z2).The value of the label as a means of reducing search costs is c(.Zz- Zi) where J2 > J,.
A case study To apply this methodology we used a unique sample of energy-efficient homes in Minneapolis and St Paul, Minnesota (Laquatra [lo]). Its uniqueness stems from having an explicit measure of thermal integrity - an energy label - available to prospective buyers. Other studies of capitalized energy efficiency approximated the thermal integrity of the house by using past billing data or by other means. The houses in this sample were all highly energy efficient with a thermal integrity factor that averaged 3 Btu/ft2/day, or about half the standard for other new houses built in Minnesota in this 1981-82 time period (Hutchinson, Fagerson and Nelson [8]). The hedonic equation estimated by Laquatra for these houses is shown in Table 2. Variables describing housing quality and location are listed on the left; the bi coefficients and the associated r-statistics are given on the right side of the table for both ordinary least squares (OLS) and for weighted least squares (WLS). ’ Because the stochastic elements of our model are normally distributed, the solution is a straightforward application of the truncated normal distribution. See Mood and Graybill [15], p 138.
216
Variable
OLS
in the
WLS
Size of lot (ft*)
0.52 (0.55)
0.05 (0.53)
Finished
floor area (ft’)
3.45 (1.09)
2.87 (0.93)
Dummy
for duplex
3104 (1.97)
2901 (1.86)
Dummy
for attached
- 8446 (- 5.12)
(- 5.31)
in
0.81 (10.85)
0.83 (11.31)
($)
12.92 (8.36)
12.97 (8.50)
- 934 (- 4.24)
(- 4.33)
1633 (6.04)
1671 (6.16)
0.6295 (- 2.54)
0.6722 (- 2.58)
unit unit
Median value of housing census tract ($) School
district
spending
Mean journey-to-work
(minutes)
Distance from interstate (miles) Thermal
integrity
ramp
RZ
0.630
0 Note: The r-statistics
3323 are given in parentheses
- 8820
- 968
0.672 3288
below the coefficient.
Laquatra used WLS in his study to correct for heteroskedasticity, and we follow his example here.2 Both equations show a significant relationship between thermal integrity and price. How well could these prices have been approximated with only indirect and partial information available about thermal integrity? A list of proxy variables for thermal integrity, all energy-specific characteristics of the houses in the sample, is given on the left side of Table 3. These proxies are regressed on the measured thermal integrity factor, and the resulting coefficients and t-statistics are on the right side of the table. A positive test for heteroskedasticity suggested the use of WLS. Only the dummy variable for night insulation, the dummy variable for solar gain with sunspace, and the dummy variable for superinsulated houses are strongly significant. Both the need for night insulation (eg insulated coverings for windows) and the solar sunspace are indicators of less efficient designs, while the superinsulated houses are more efficient than average. The data in Tables 2 and 3 allows us to compute 2 The results shown here use Theil’s [17] weighted least squares, and they are very close to Laquatra’s results. An alternative correction for heteroskedasticity suggested by White [19] improved on Laquatra’s equation, particularly by showing a significant coefficient on the square footage of the house. For our own application the difference in these alternative models was negligible, and the measured error variance nearly the same in all of them.
ENERGY ECONOMICS
July 1989
Energy labels and economic search: R. W. Gilmer Table 3. Coefficients of tbe equation efficiency to the proxy variables.
relating
Proxy variable
measured
energy
OLS
WIS
R-value of ceiling insulation
- 0.00 (-0.15)
- 0.00 (- 0.32)
R-value
-0.01 (- 0.84)
- 0.02 (- 0.93)
- 0.32 (- 2.08)
(- 1.84)
- 0.50 (- 1.44)
- 0.46 (- 1.31)
Dummy variable for solar gain with sunspace
- 1.75 (5.57)
(- 5.45)
Dummy variable for solar gain with greenhouse
0.08 (0.30)
(E,
Dummy house
0.40 (2.09)
0.45 (2.38)
R2
0.526
0.524
0
0.4481
0.4474
of wall insulation
Dummy variable insulation
for night
Dummy variable glazed glass
for triple-
variable
for superinsulated
Note: The t-statistics
are given in parentheses
- 0.29
- 1.73
below the coefficient.
the variance of the hedonic equation if we use proxy variables:
a2 = 0’ + a202 P ”
a2P = (-
2599)’
(0.4474)’
as the energy label shortens
Marginal cost of search ($1
Number of searches
Value of energy label 0)
5 10 15 20 25 30 40 50 60 70 80 90
74 41 30 24 20 18 14 12 11 10 9 8
21 25 27 29 30 32 35 37 39 41 42 44
the energy system of the homes they examined, when this willingness to pay is measured solely in terms of the reduced search costs. This sample of homes fell in a very narrow range of energy efficiency, with all of them designed to meet very high standards. A more typical search would be across a spectrum of new and existing construction with much higher variance in energy efficiency. The value of the energy label seems likely to increase sharply as it narrows choices among a more diverse group of homes.
+ (3288)’
Conclusions
12.16 (106) (TV= 3487 > CT= 3288
Thus the standard error of the hedonic equation increases 5.7% when the proxies are used in place of explicit information about thermal integrity. The economic search model can be solved repeatedly as the marginal cost of search is varied. For specific costs, Table 4 shows the number of searches that would be conducted and savings available to the homeowner because of the energy label. The number of searches is unrealistically large at low marginal costs, but the availability of such low-cost searches is probably limited. The potential homeowner will face a rising marginal cost curve as the search expands over wider areas (Salop [ 163). The average house in this sample cost about $75 000. Given the minimum family income required to qualify for a mortgage on this house, and assuming one or two hours travel time, the marginal cost of search is $25-40. This implies 14-20 searches and savings from the energy label of $3&35. The curve relating marginal search cost to the value of the label is quite flat, and all costs over a range of $25-60 yield savings of $30-40. In this example potential homebuyers would have been willing to pay $30-40 for explicit information on
ENERGY ECONOMICS
Tabid 9. !3avings to the homebuyer economic search.
July 1989
This paper focused on the benefits of energy labels during the course of economic search. We found these benefits were positive but modest in our case study of highly energy-efficient homes, but that they had the potential to be considerably larger in more diverse housing markets. The fact that positive benefits accrue to both buyer and seller in this market (as the seller is given a showcase for diligent efforts to save energy) suggests that both should contribute to the financing of labelling programmes. Inspection fees and labelling fees for sellers and a real estate transfer tax on buyers are examples of appropriate financing mechanisms. The third major beneficiary of these programmes is the general public as the label encourages the efficient use of energy. Some general subsidy is called for on these grounds. The home energy label fills an important gap in the mortgage market by helping lenders recognize the innovative home. These programmes move individual conservation efforts toward the social optimum in several ways: they bring information on the scope and content of beneficial energy improvements to the attention of potential buyers at the time of sale; they encourage existing homeowners to make improvements to their existing home; they may narrow the gap between public and private rates of discount.
217
Energy labels and economic search: R. W. Gilmer
References D. J. Aigner, ‘MSE dominance of least squares with errors of observation’, Journal of Econometrics, Vol 2, 1974, pp 365-372. Arthur D. Little, Measuring the Impact of Residential Conservation Programs, Electric Power Research Institute, Palo Alto, CA, 1984. H. Cole and R. Fuller, Residential Energy Decision Making, Hittman Associates, Columbia, MD, 1980. J. B. Corgel, P. R. Goebel and C. E. Wade, ‘Measuring energy efficiency for selection and adjustment of comparable sales’, The Appraisal Journal, Vol 50, 1982, pp 71-78. K. Corum and D. O’Neil, ‘Investment in energy-efficient houses’, Energy, Vol 7, 1982, pp 389400. P.L. Hendrickson, ‘Implementation of voluntary residential energy efficiency rating/labeling systems’, in J. Harris and C. Blumstein, eds, In What Works?: Documenting Energy Conservation in Buildings, American Council for an Energy- Efficient Economy, Washington, DC, 1984. Home Energy Rating News, 1987, Vol 1, No 1. M. Hutchinson, M. Fagerson and G. Nelson, ‘Measured thermal performance and the cost of conservation for a group of energy-efficient Minnesota houses’, in Harris and Blumstein, op tit, Ref 6. R. C. Johnson and D. L. Kaserman, ‘Housing market
218
10
11
12
13
18
19
capitalization of energy-efficient durable goods’, Economic Inquiry, Vol 21, 1983, pp 374-386. J. Laquatra, ‘Housing market capitalization of thermal integrity, Energy Economics, Vol 8, No 2, 1986, pp 134138. S. A. Lippman and J. J. McCall, ‘The economics ofjob search: a survey’, Economic Inquiry, Vol 13, 1976, pp 155-189, 347-368. J. J. McCall, ‘Economics of information and job search’, Quarterly Journal of Economics, Vol 84, 1970, pp 113-126. G. S. Maddala, Econometrics, McGraw-Hill, New York, 1977. J. Millhone and L. Schuck, ‘Defining residential energy efficiency in residential lending practices’, in Harris and Blumstein, op tit, Ref 6. A. M. Mood and F. A. Graybill, Introduction to the Theory of Statistics, McGraw-Hill, New York, 1963. S. C. Salop ‘Systematic job search and employment’, Review of Economic Studies, Vol 40, 1973, pp 191-201. H. Theil, Econometrics, John Wiley and Sons, New York, 1971. K. Train, ‘Discount rates in consumer’s energy-related decisions: a review of the literature’, Energy, Voi 10, 1985, pp 1243-1253. H. White, ‘A heteroskedasticity-consistent covariance estimator and a direct test for hetroskedasticity’, Econometrica, Vol 48, 1980, pp 817-838.
ENERGY
ECONOMICS
July
1989
Home energy rating systems are popular and widely used programmes to encourage energy ejiciency in the residential housing market. With the actual rating perhaps conveyed to prospective buyers as a posted label, these rating systems ofleer a variety of benejts. This paper focuses on the ability of these systems to help buyers identtfy more quickly the properly priced house. An economic search model is applied to a unique sample of energy-eficient homes in Minnesota. The search bene$ts are modest but positive in our case study, but could be larger in a more diverse housing market. Keywords:
Energy
efficiency;
Rating
systems;
Home energy rating systems are popular and widely used programmes to encourage energy efficiency in the residential housing market. Each house - new or existing - is rated as it enters the real estate market. The rating is an authoritative assessment of the energy condition of the home, and it might be conveyed to prospective buyers in the form of a posted label. Such a label would be analogous to those seen on such appliances as refrigerators, freezers, dishwashers, room air conditioners, clothes washers, and furnaces throughout the USA and Europe. For example, the fuel economy label posted on new cars in the USA tells buyers the automobile’s rating in miles/gallon, provides an annual fuel cost estimate, and gives the range of fuel economy available in similar models. Home energy rating and labelling systems are widely used in the USA, and they have been adopted through many administrative and institutional channels (Hendrickson[6]). Several state governments
The author is on the Chief Economist Staff, Tennessee Valley Authority, 400 W Summit Hill Drive, ESD84, Knoxville, Tennessee 37902, USA. This report was written while the author was a visiting researcher at the Center for Energy Research, Education, and Service, Ball State University, Muncie, Indiana. The views expressed are those of the author and not necessarily those of CERES or the Tennessee Valley Authority. Final manuscript
received
0140-9883/89/030213-06
12 November
1988.
Housing
sponsor these programmes, including Alaska, Arkansas, Massachusetts, Minnesota and Vermont. A number of investor-owned utilities operate rating systems under the auspices of the Edison Electric Institute; several large utilities (eg Pacific Gas and Electric, Duke Power and the Tennessee Valley Authority) have their own programmes. Municipalities and regional governments also operate these home energy rating systems at a local level. The details of specific programmes vary widely in the extent of the housing market included in the programme, the coverage of the energy-related aspects of the house, the participation of homeowners (mandatory v voluntary), and in the means in which information is conveyed to buyers (eg as a prescriptive list of potential energy efficiency improvements or as an energy consumption estimate). Home energy-rating systems offer a variety of benefits to participants in the real estate market. Among the most important of these benefits are: (i) that these labels assure market-wide recognition of conservation investments and energy-efficient design; (ii) they make explicit the tradeoff between higher mortgage payments that accompany highly efficient housing and the lower monthly energy bills they offer; (iii) they assure homeowners that investments in energy efficiency made today will be recognized at the time of sale; and (iv) they help buyers to zero in more quickly on the properly priced house. All of these benefits are discussed below, but in this paper we will focus on item number four. These informational benefits that shorten
$03.00 0 1989 Butterworth & Co (Publishers) Ltd
213
Energy labels and economic search: R. W. Gilmer
the economic search for the potential homeowner are important to the design and implementation of these programmes, but prove difficult to quantify and are neglected in most discussions of these programmes. Estimates of these benefits are offered below using an economic search model and a specific example of energy-efficient housing in Minnesota. The search benefits prove to be positive but modest in our example, but could be much larger in a more diversified housing market. The methodology is equally appropriate for labels applied to other durable goods such as autos or home appliances.
Energy labels and the mortgage market The mortgage market provided the strongest impetus for the implementation of home energy-rating systems. The cost of building an energy-efficient home is typically higher than standard construction, but these homes offer lower utility and fuel bills. The inability of the mortgage market to recognize this tradeoff offered much of the original stimulus for these programmes (Millhone and Schuck[14]). Energy-rating systems serve as documentation of the lower energy bills available from efficient design, and transmit this data to buyers, appraisers, and lenders in primary and secondary mortgage markets. The mortgage market’s failure to acknowledge the advantages of energy-efficient housing has been a barrier to solar, superinsulated, and other innovative housing designs. The high interest rates of recent years excluded large numbers of prospective buyers, because lenders require a minimum income-to-debt ratio of 25-28%. A decision to incur high initial costs in favour of energy efficiency could sharply reduce the number of prospective buyers, and leads builders to avoid energy-efficient but capital-intensive designs. In 1979 the two largest purchasers of secondary mortgages in the US, the Federal Home Loan Mortgage Association and the Federal National Mortgage Association, changed their underwriting guidelines to decrease the income-to-debt ratio for energy-efficient construction (Millhone and Schuck [14], p 11). Despite this change, primary lenders in many markets remained reluctant to make such loans, apparently undecided on the proper definition of energy-efficient homes. In 1986 both major secondary lenders altered their underwriting guidelines again to allow the home rating sheet for particular labelling systems to serve as documentation for home mortgages (Home Energy Rating News [7]). A number of rating systems now qualify, and where available they fill an important gap in the market for innovative housing.
214
Conservation and the energy label Another frequently cited benefit of the energy label is its contribution to energy efficiency and conservation. The standards that must be met to achieve economic efficiency in conservation are stringent (Johnson and Kaserman [9]). First, each homeowner must use the social discount rate to compute the value of energy saved by conservation investments made today. Second, since the typical homeowner will live in the house for fewer years than the life of the conservation investment, he or she must be able to recover the value of the remaining life at the time of sale. Further, the capitalization of the energy savings in the sale price must occur at the social rate of discount. There is mounting evidence that these standards are not being met. The capitalization process does seem to work, but apparently not at appropriate rates of discount. According to a number of different studies, energy savings do seem to be capitalized in housing prices. Laquatra [lo] examined the sale price of a sample of highly energy-efficient homes in Minnesota, and controlled for the effect of housing and neighbourhood characteristics using an hedonic equation. He found that potential energy savings, known to buyers through an energy labelling system, did affect the final sales price significantly. Other studies have drawn similar conclusions. Johnson and Kaserman [9] examined housing sales in Tennessee, and found that past energy bills for the home were a good predictor of sales price if housing and neighbourhood characteristics were properly taken into account. In Lubbock, Texas an infrared photograph from a fly-over of the city allowed Corgel, Goebel and Wade [4] to classify the houses as energy efficient or inefficient. This classification scheme proved a good predictor of the sales value of the house. The study by Johnson and Kaserman found little divergence between the social discount rate and the rate at which energy efficiency is capitalized in the sale price of the house. This study is an exception, however, and the weight of evidence rests on the side of a strong divergence between these rates and potentially serious underinvestment in energy conservation. A recent survey by Train [ 181 of 28 studies and reports summarized the discount rates implicit in consumer decisions on thermal integrity, appliances, autos and other decisions. The results are shown in Table 1. The rates shown are real rates that abstract from inflation. Although there is some overlap with the rates of 3-6% often employed in economic and engineering studies to reflect social discount rates, most of these studies indicate real rates that are much higher. Train identifies three studies that specifically concern themse!ves with thermal integrity. Arthur D.
ENERGY ECONOMICS
July 1989
Energy labels and economic search: R. W. Gilmer Table 1. Average discount rates (%). Range of rates observed
Choice made Thermal
lC-32
integrity
Space heating
4.4-36
system
3.2-29
Air conditioners
39-100
Refrigerators Other
18-67
appliances
241
Automobiles Other,
3.1-22.5
unspecified
Source: Train
[IS].
Little [2] estimated discount rates for the thermal shell of 32% and for windows and doors the rate was 10%. Corum and O’Neil [S] found discount rates for thermal shell measures that ranged from lO-21% by fuel type and 634% by city. Cole and Fuller [3] found average discount rates of 26% for the thermal shell in a national survey and 12% for the Pacific Northwest. Thus the bulk of the evidence indicates that there is an educational and informational role for energy rating systems and labels. Even if issued only as a list of needed improvements or as certification that houses meet a minimum standard of efficiency, the efficiency effects of the label will be salutary. If the label contains an energy consumption estimate or an approximation of the monthly bill, it can directly address the capitalization issue. With such an estimate in hand, most consumers will compare houses by adding the monthly energy bill to the mortgage payment. Implicit in this addition is the assessment of energy savings at the mortgage rate of interest. Mortgage rates are much closer to the social discount rate as defined by most economic and engineering studies than the private rates cited in Train’s survey. In this format the energy label allows the buyer and seller to make simple and reasonable calculations that move the economic outcome sharply toward the social optimum.
falls in two parts: (i) a standard model of economic search: and (ii) a measure of the influence of the energy label on the length of search. The methodology applied here to housing is equally applicable to other durable purchases. The consumer is assumed to assess the price of alternative housing choices using an hedonic equation of the following form.
P=P(X, Z)+e
(1)
~=b,+b,X,+b,X,+...+b,X,+aZ
(2)
e - N(0,
(3)
The price of the house is assumed to be dependent on a vector of characteristics of the house and its neighbourhood, X = (X,, X,, . . . , A’,}; on a single summary measure of energy efficiency (I); and a random error term (e) that is normally distributed with constant variance. Models of random search behaviour originated in the literature on employment search (McCall [ 121 and Lippman and McCall [ll]) provide an extensive survey. In our case the potential homebuyer ventures out to find a home once each period; the house is offered at price P. The cost of the search (c) includes all out-of-pocket expenses plus the value of lost work or leisure. The length of search will depend on the cost of search and on the distribution of housing prices, g(e). With each search the prospective buyer will compute the implicit price from the hedonic equation (p), compare it to the asking price(P), and hope to find the implicit price ‘sufficiently below’ the asking price. P--P>e*>O
Large durable purchases such as appliances, autos and houses offer a wide range of different features and prices. Comparing products and selecting the best buy can be an expensive and time-consuming process. The energy label is an aid to consumers in narrowing the available selection, and it shortens the time spent in pursuit of the right purchase. In this section we will quantify these search benefits in a specific example of energy-efficient homes. The methodological approach
ENERGY ECONOMICS
July 1989
(4)
Once this critical value of e* is exceeded the search is over. Its value is determined by equating the marginal cost of search to the expected marginal return from additional search.
c=
The search for a new house
a’)
sY e*
e - e*)g(e) de
(5)
The higher (lower) the marginal cost of search, the lower (higher) the reservation differential e* will be set. To assign a value to the energy label we will solve this search problem twice. We will solve it first with exact information available on energy efficiency. That is, with an energy label providing the searcher with the exact information on the value of Z for each house. We will solve it a second time with the exact value of Z unknown, but approximated from a few general
215
Energy labels and economic search: R. W. Gilmer
characteristics
of the energy system in the house.
Table 2. Coefficients of physical and locational attributes hedonic equation for housing prices.
Z=l+v
(6)
~=d,+d,Z,+...+d,i,
(7)
0 - N(0, 6;)
(8)
The consumer judges the energy system by the vector of characteristics {I,, . . . , I,,,}, and uses 1 rather than the exact value of the energy label. If the random error v is uncorrelated with the Ei or with U, the Zi are as proxy variables (Maddala [ 131). The use of proxies yields estimates of E(P (X, 7) that are biased in small samples but consistent (Aigner Cl]). The error variance of the hedonic equation, with 1 used to approximate Z, is 0: = uz + a’~:. Thus the change in the distribution of housing prices between the case of the energy label known and the energy label approximated is a2a2 The value 1;; the energy label is estimated by solving Equation (5) with a fixed value of c and with g(e) alternatively N(0, a2) and N(0, a:).’ The expected number of searches to be made is .Z= l/p where p is the probability that e > e*. The expected number of searches is computed with the label known (.Zi) and with the label approximated (.Z2).The value of the label as a means of reducing search costs is c(.Zz- Zi) where J2 > J,.
A case study To apply this methodology we used a unique sample of energy-efficient homes in Minneapolis and St Paul, Minnesota (Laquatra [lo]). Its uniqueness stems from having an explicit measure of thermal integrity - an energy label - available to prospective buyers. Other studies of capitalized energy efficiency approximated the thermal integrity of the house by using past billing data or by other means. The houses in this sample were all highly energy efficient with a thermal integrity factor that averaged 3 Btu/ft2/day, or about half the standard for other new houses built in Minnesota in this 1981-82 time period (Hutchinson, Fagerson and Nelson [8]). The hedonic equation estimated by Laquatra for these houses is shown in Table 2. Variables describing housing quality and location are listed on the left; the bi coefficients and the associated r-statistics are given on the right side of the table for both ordinary least squares (OLS) and for weighted least squares (WLS). ’ Because the stochastic elements of our model are normally distributed, the solution is a straightforward application of the truncated normal distribution. See Mood and Graybill [15], p 138.
216
Variable
OLS
in the
WLS
Size of lot (ft*)
0.52 (0.55)
0.05 (0.53)
Finished
floor area (ft’)
3.45 (1.09)
2.87 (0.93)
Dummy
for duplex
3104 (1.97)
2901 (1.86)
Dummy
for attached
- 8446 (- 5.12)
(- 5.31)
in
0.81 (10.85)
0.83 (11.31)
($)
12.92 (8.36)
12.97 (8.50)
- 934 (- 4.24)
(- 4.33)
1633 (6.04)
1671 (6.16)
0.6295 (- 2.54)
0.6722 (- 2.58)
unit unit
Median value of housing census tract ($) School
district
spending
Mean journey-to-work
(minutes)
Distance from interstate (miles) Thermal
integrity
ramp
RZ
0.630
0 Note: The r-statistics
3323 are given in parentheses
- 8820
- 968
0.672 3288
below the coefficient.
Laquatra used WLS in his study to correct for heteroskedasticity, and we follow his example here.2 Both equations show a significant relationship between thermal integrity and price. How well could these prices have been approximated with only indirect and partial information available about thermal integrity? A list of proxy variables for thermal integrity, all energy-specific characteristics of the houses in the sample, is given on the left side of Table 3. These proxies are regressed on the measured thermal integrity factor, and the resulting coefficients and t-statistics are on the right side of the table. A positive test for heteroskedasticity suggested the use of WLS. Only the dummy variable for night insulation, the dummy variable for solar gain with sunspace, and the dummy variable for superinsulated houses are strongly significant. Both the need for night insulation (eg insulated coverings for windows) and the solar sunspace are indicators of less efficient designs, while the superinsulated houses are more efficient than average. The data in Tables 2 and 3 allows us to compute 2 The results shown here use Theil’s [17] weighted least squares, and they are very close to Laquatra’s results. An alternative correction for heteroskedasticity suggested by White [19] improved on Laquatra’s equation, particularly by showing a significant coefficient on the square footage of the house. For our own application the difference in these alternative models was negligible, and the measured error variance nearly the same in all of them.
ENERGY ECONOMICS
July 1989
Energy labels and economic search: R. W. Gilmer Table 3. Coefficients of tbe equation efficiency to the proxy variables.
relating
Proxy variable
measured
energy
OLS
WIS
R-value of ceiling insulation
- 0.00 (-0.15)
- 0.00 (- 0.32)
R-value
-0.01 (- 0.84)
- 0.02 (- 0.93)
- 0.32 (- 2.08)
(- 1.84)
- 0.50 (- 1.44)
- 0.46 (- 1.31)
Dummy variable for solar gain with sunspace
- 1.75 (5.57)
(- 5.45)
Dummy variable for solar gain with greenhouse
0.08 (0.30)
(E,
Dummy house
0.40 (2.09)
0.45 (2.38)
R2
0.526
0.524
0
0.4481
0.4474
of wall insulation
Dummy variable insulation
for night
Dummy variable glazed glass
for triple-
variable
for superinsulated
Note: The t-statistics
are given in parentheses
- 0.29
- 1.73
below the coefficient.
the variance of the hedonic equation if we use proxy variables:
a2 = 0’ + a202 P ”
a2P = (-
2599)’
(0.4474)’
as the energy label shortens
Marginal cost of search ($1
Number of searches
Value of energy label 0)
5 10 15 20 25 30 40 50 60 70 80 90
74 41 30 24 20 18 14 12 11 10 9 8
21 25 27 29 30 32 35 37 39 41 42 44
the energy system of the homes they examined, when this willingness to pay is measured solely in terms of the reduced search costs. This sample of homes fell in a very narrow range of energy efficiency, with all of them designed to meet very high standards. A more typical search would be across a spectrum of new and existing construction with much higher variance in energy efficiency. The value of the energy label seems likely to increase sharply as it narrows choices among a more diverse group of homes.
+ (3288)’
Conclusions
12.16 (106) (TV= 3487 > CT= 3288
Thus the standard error of the hedonic equation increases 5.7% when the proxies are used in place of explicit information about thermal integrity. The economic search model can be solved repeatedly as the marginal cost of search is varied. For specific costs, Table 4 shows the number of searches that would be conducted and savings available to the homeowner because of the energy label. The number of searches is unrealistically large at low marginal costs, but the availability of such low-cost searches is probably limited. The potential homeowner will face a rising marginal cost curve as the search expands over wider areas (Salop [ 163). The average house in this sample cost about $75 000. Given the minimum family income required to qualify for a mortgage on this house, and assuming one or two hours travel time, the marginal cost of search is $25-40. This implies 14-20 searches and savings from the energy label of $3&35. The curve relating marginal search cost to the value of the label is quite flat, and all costs over a range of $25-60 yield savings of $30-40. In this example potential homebuyers would have been willing to pay $30-40 for explicit information on
ENERGY ECONOMICS
Tabid 9. !3avings to the homebuyer economic search.
July 1989
This paper focused on the benefits of energy labels during the course of economic search. We found these benefits were positive but modest in our case study of highly energy-efficient homes, but that they had the potential to be considerably larger in more diverse housing markets. The fact that positive benefits accrue to both buyer and seller in this market (as the seller is given a showcase for diligent efforts to save energy) suggests that both should contribute to the financing of labelling programmes. Inspection fees and labelling fees for sellers and a real estate transfer tax on buyers are examples of appropriate financing mechanisms. The third major beneficiary of these programmes is the general public as the label encourages the efficient use of energy. Some general subsidy is called for on these grounds. The home energy label fills an important gap in the mortgage market by helping lenders recognize the innovative home. These programmes move individual conservation efforts toward the social optimum in several ways: they bring information on the scope and content of beneficial energy improvements to the attention of potential buyers at the time of sale; they encourage existing homeowners to make improvements to their existing home; they may narrow the gap between public and private rates of discount.
217
Energy labels and economic search: R. W. Gilmer
References D. J. Aigner, ‘MSE dominance of least squares with errors of observation’, Journal of Econometrics, Vol 2, 1974, pp 365-372. Arthur D. Little, Measuring the Impact of Residential Conservation Programs, Electric Power Research Institute, Palo Alto, CA, 1984. H. Cole and R. Fuller, Residential Energy Decision Making, Hittman Associates, Columbia, MD, 1980. J. B. Corgel, P. R. Goebel and C. E. Wade, ‘Measuring energy efficiency for selection and adjustment of comparable sales’, The Appraisal Journal, Vol 50, 1982, pp 71-78. K. Corum and D. O’Neil, ‘Investment in energy-efficient houses’, Energy, Vol 7, 1982, pp 389400. P.L. Hendrickson, ‘Implementation of voluntary residential energy efficiency rating/labeling systems’, in J. Harris and C. Blumstein, eds, In What Works?: Documenting Energy Conservation in Buildings, American Council for an Energy- Efficient Economy, Washington, DC, 1984. Home Energy Rating News, 1987, Vol 1, No 1. M. Hutchinson, M. Fagerson and G. Nelson, ‘Measured thermal performance and the cost of conservation for a group of energy-efficient Minnesota houses’, in Harris and Blumstein, op tit, Ref 6. R. C. Johnson and D. L. Kaserman, ‘Housing market
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10
11
12
13
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19
capitalization of energy-efficient durable goods’, Economic Inquiry, Vol 21, 1983, pp 374-386. J. Laquatra, ‘Housing market capitalization of thermal integrity, Energy Economics, Vol 8, No 2, 1986, pp 134138. S. A. Lippman and J. J. McCall, ‘The economics ofjob search: a survey’, Economic Inquiry, Vol 13, 1976, pp 155-189, 347-368. J. J. McCall, ‘Economics of information and job search’, Quarterly Journal of Economics, Vol 84, 1970, pp 113-126. G. S. Maddala, Econometrics, McGraw-Hill, New York, 1977. J. Millhone and L. Schuck, ‘Defining residential energy efficiency in residential lending practices’, in Harris and Blumstein, op tit, Ref 6. A. M. Mood and F. A. Graybill, Introduction to the Theory of Statistics, McGraw-Hill, New York, 1963. S. C. Salop ‘Systematic job search and employment’, Review of Economic Studies, Vol 40, 1973, pp 191-201. H. Theil, Econometrics, John Wiley and Sons, New York, 1971. K. Train, ‘Discount rates in consumer’s energy-related decisions: a review of the literature’, Energy, Voi 10, 1985, pp 1243-1253. H. White, ‘A heteroskedasticity-consistent covariance estimator and a direct test for hetroskedasticity’, Econometrica, Vol 48, 1980, pp 817-838.
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ECONOMICS
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1989