Hedonic Price Equation Research Articles

The Demand for Housing: Reply

It is gratifying to find that one's work has not gone unread but has motivated others to work on the same problem and offer improvements. As it happens, however, the study by Erekson and Witte [1] differs from my own [2] in one critical procedure, and it is this, I believe, which introduces bias to their seemingly unrelated regression (SUR) results. Since I did something else, I doubt that their finding of bias in their own results is applicable to my own. Both procedures have merit, so it is useful to point out the differences. E-W have estimated hedonic price equations for each housing market in their sample. Prices of housing characteristics to be used in the demand study are obtained from these by calculating

The Air Pollution and Property Value Debate: Some Empirical Evidence

In recent issues of this REVIEW, Myrick Freeman and Kenneth Small have helped to clarify some confusion which grew out of a study of air pollution and property values by Ridker and Henning (1967). Ridker and Henning (hereafter referred to as R-H) performed a multiple regression of property values on air pollution, housing characteristics, accessibility, and neighborhood characteristics using cross-section data for the St. Louis metropolitan area. As Freeman (1974a) and Small (1975) point out, the early debate on the R-H study was largely sidetracked with the issue of whether one can predict the aggregate change in property values resulting from an improvement in urban air quality. A more appropriate focus for policy purposes is the question of whether property value data can be used to estimate households' willingness to pay for cleaner air. The R-H study calculated willingness to pay by first multiplying the air pollution coefficient obtained in their linear regression by the change in air pollution estimated for each household, and then summing over all St. Louis households. The air pollution coefficient was thus inteipreted as the average willingness to pay for air quality improvements for all St. Louis households. Freeman (1971) had correctly pointed out in an earlier note that the R-H procedure of calculating willingness to pay is inappropriate because a hedonic housing regression cannot in itself isolate demand and supply elements; and Freeman was not very sanguine about the possibilities of doing so. Without taking a position on whether the true willingness to pay for clean air can be determined empirically, both Freeman and Small put the earlier empirical work into perspective by showing that the pollution coefficient in the housing value regression does provide a correct measure of the 'mnarginal valuation placed by individual consumers on uniform changes in pollution levels (Small, 1975, p. 105). As a theoretical point, this result is reassuring, since it suggests that benefit estimates obtained from studies like that of Ridker and Henning may be reasonable if the air quality improvement is small. Unfortunately, however, the air quality improvements generated by the Clean Air Act Amendments of 1970 and 1977 are distinctly nonmarginal (pollution reductions are often predicted to be 50% or more). No empirical evidence has been available thus far to assess the magnitude of the bias associated with using marginal valuations to estimate the benefits from these nonmarginal pollution reductions.' This note attempts to fill this gap. Several authors have argued as a matter of principle that the willingness to pay for nonmarginal air quality improvements can be obtained from property value data if the proper conceptual procedure is used.2 Using the approach suggested by these papers and an empirical focus suggested by Rosen (1974), we have elsewhere provided estimates of households' willingness to pay for clean air as well as details concerning the nature of the data and possible specification biases (Harrison and Rubinfeld, 1978). In this paper we confine our discussion to the magnitude of the bias that results from using the air pollution coefficient in the hedonic housing price equation directly to estimate the willingness to pay of urban households for a nonmnarginal air quality improvement. Section II summarizes the model we use to estimate the demand for nonmarginal changes in air quality. In section III we distinguish three potential sources of bias in the R-H procedure, and assess the quantitative importance of each with data for the Boston Metropolitan area.

Residential choice, hedonic prices, and the demand for urban air quality

This paper uses a two-step estimation procedure suggested by Sherwin Rosen to estimate structural demand and supply equations for urban air quality. In the first step, a hedonic price equation is estimated for residential property values for the Washington, D. C., SMSA for 1970. In the second step, a set of marginal hedonic prices is generated. These prices and the quantity of clean air (reciprocal of air pollution) are used as endogenous variables in a simultaneous equation model. Empirical results indicate a price elasticity of demand between −1.2 and −1.4 and a unitary income elasticity.

Hedonic housing prices and the demand for clean air

This paper investigates the methodological problems associated with the use of housing market data to measure the willingness to pay for clean air. With the use of a hedonic housing price model and data for the Boston metropolitan area, quantitative estimates of the willingness to pay for air quality improvements are generated. Marginal air pollution damages (as revealed in the housing market) are found to increase with the level of air pollution and with household income. The results are relatively sensitive to the specification of the hedonic housing price equation, but insensitive to the specification of the air quality demand equation.

Automobiles and Hedonic Quality Measurement

Are price indexes biased upward because of quality change? One of the few pieces of firm evidence on this question is the study by Zvi Griliches (1961, 1964), who concluded that almost all of the recorded rise in the new automobile component of the Consumer Price Index (CPI) between 1954 and 1960 could be attributed to substantial improvement in the quality of automobiles. In view of the ubiquity of the quality problem in index number construction and the paucity of knowledge on the nature and extent of quality change, it is not surprising that Griliches' findings have been cited extensively in support of the charge that there is serious upward bias in our price indexes because of failure to make sufficient allowance for quality improvement. The present paper is devoted to further consideration of the problem. For the 1960-65 period, it is found that the Hedonic measures employed by Griliches indicate negligible quality improvement in automobiles and provide no substantiation for the belief in an upward quality bias in the CPI. However, certain biases in the Hedonic indexes themselves limit their validity as measures of quality change.