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Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna

Author

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  • Wolfgang Brunauer

    ()

  • Stefan Lang

    ()

  • Peter Wechselberger

    ()

  • Sven Bienert

    ()

Abstract

We 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.

Suggested Citation

  • Wolfgang Brunauer & Stefan Lang & Peter Wechselberger & Sven Bienert, 2008. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," Working Papers 2008-17, Faculty of Economics and Statistics, University of Innsbruck.
  • Handle: RePEc:inn:wpaper:2008-17
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    References listed on IDEAS

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    Cited by:

    1. Ulrich B. Morawetz & Dieter Mayr & Doris Damyanovic, 2016. "Ökonomische Effekte grüner Infrastruktur als Teil eines Grünflächenfaktors. Ein Leitfaden," Working Papers 662016, Institute for Sustainable Economic Development, Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, Vienna.

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    Keywords

    Hedonic regression; submarkets; multiplicative spatial scaling factors; semiparametric models; P-splines;

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