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- W2024397072 abstract "The hedonic price technique is frequently used for valuing attributes of housing. In such an analysis a hedonic price function is specified: p = p(x). p is the price of a house and x is a vector of attributes of the house. The attributes include structural features such as size, age, garage, and fireplace and locational features such as neighborhood quality, crime, access to downtown, and air quality. The function is interpreted as an equilibrium price schedule for houses (see Rosen [5]). Its first derivative with respect to an element of x is an implicit price for that attribute. The price function is estimated typically using cross-sectional data from a housing market. I argue that the measure of each locational attribute in a hedonic price function should be weighted by lot size and that the measure of each structural attribute should not be weighted. The usual regression weights neither locational nor structural attributes. By my argument, if two houses are identical in every respect except that the measure of lot size of the first is twice that of the second, then the measure for air quality, access to parks, and so on for the first should be twice that of the second. I also argue that failure to weight locational attributes in a hedonic regression that should be weighted gives biased implicit price estimates for structural and locational attributes. The direction and degree of bias depend on the correlations between the attributes and lot size in the housing market, on the variance of lot size in the market, and on the lot size of the observation for which implicit price is being estimated." @default.
- W2024397072 created "2016-06-24" @default.
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- W2024397072 date "1990-05-01" @default.
- W2024397072 modified "2023-10-16" @default.
- W2024397072 title "Hedonic prices and public goods: An argument for weighting locational attributes in hedonic regressions by lot size" @default.
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- W2024397072 doi "https://doi.org/10.1016/0094-1190(90)90004-7" @default.
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