Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna
AbstractWe apply additive mixed regression models (AMM) to estimate hedonic price equations. Non-linear effects of continuous covariates as well as a smooth time trend are modeled non-parametrically through P-splines. Unobserved district-specific heterogeneity is modeled in two ways: First, by location specific intercepts with the postal code serving as a location variable. Second, in order to permit spatial variation in the nonlinear price gradients, we introduce multiplicative scaling factors for nonlinear covariates. This allows highly nonlinear implicit price functions to vary within a regularized framework, accounting for district-specific spatial heterogeneity. Using this model extension, we find substantial spatial variation in house price gradients, leading to a considerable improvement of model quality and predictive power.
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Bibliographic InfoPaper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2008-17.
Date of creation: Aug 2008
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Hedonic regression; submarkets; multiplicative spatial scaling factors; semiparametric models; P-splines;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-09-13 (All new papers)
- NEP-ECM-2008-09-13 (Econometrics)
- NEP-GEO-2008-09-13 (Economic Geography)
- NEP-MKT-2008-09-13 (Marketing)
- NEP-URE-2008-09-13 (Urban & Real Estate Economics)
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