The Hedonic Price Method in Real Estate and Housing Market Research: A Review of the Literature
AbstractThe Hedonic Price Method (HPM), also known as hedonic regression, is used for estimating the value of a commodity or the demand for a commodity. The HPM has been extensively used in real estate and housing market research in the recent past. In this paper, we discuss theoretical and methodological developments related to hedonic regression and undertake an examination of use of this methodology in the recent real estate and housing literature. We first define the HPM, and explain the fundamentals behind the methodology. The idea behind the HPM is that the commodities are characterized by their constitute properties, hence the value of a commodity can be calculated by adding up the estimated values of its separate properties. In the second part of the paper, we emphasise that the heterogeneous nature of real estate property justifies the use of HPM for estimating their value or demand. We also take a stock of most cited empirical studies on real estate and housing using the HPM, and classify those into several categories. The classification indicates that neighbourhood characteristics of real estate are relatively over-researched as a determinant of price or rent. It also shows that implicit value of structural characteristics is under-researched. In general, implicit value of environmental amenities in the neighbourhood and air pollution are relatively overresearched. The effect of social factors, i.e. racial segregation and crimes on real estate value is under-researched.
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Bibliographic InfoPaper provided by Institute for the Environment and Regional Development, Department of Socioeconomics, Vienna University of Economics and Business in its series SRE-Disc with number sre-disc-2010_03.
Length: 20 pages
Date of creation: 2010
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- Wolfgang Brunauer & Stefan Lang & Wolfgang Feilmayr, 2013. "Hybrid multilevel STAR models for hedonic house prices," Jahrbuch für Regionalwissenschaft, Springer, vol. 33(2), pages 151-172, October.
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