Hedonic analysis with locally weighted regression: An application to the shadow cost of housing regulation in Southern California
AbstractThis paper investigates the role of hedonic model misspecification through inappropriate geographic aggregation in the debate over the effects of housing regulation. We use locally weighted regression (LWR) techniques and geo-referenced data to allow the housing hedonic parameters to vary over space. This modeling strategy better represents micro-market realities and the importance of location as a prime determinant of housing prices. Our results, based on a unique dataset of almost 14,000 single-family home sales between 1993 and 2001 in Southern California, suggest regulation had strong direct impacts on the housing market as suggested by Glaeser and Gyourko (2003) and Cheung et al. (2009a) and not indirectly through increased land scarcity as suggested by Davis and Palumbo (2007).
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Bibliographic InfoArticle provided by Elsevier in its journal Regional Science and Urban Economics.
Volume (Year): 40 (2010)
Issue (Month): 6 (November)
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Web page: http://www.elsevier.com/locate/regec
Hedonic analysis Housing regulation Land price Locally weighted regression Geographically weighted regression Geo-referenced data Semi-parametric econometrics;
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