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Estimation of Hedonic Price Functions via Additive Nonparametric Regression

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We model a hedonic price function for housing as an additive nonparametric regression. Estimation is done via a backfitting procedure in combination with a local polynomial estimator. It avoids the pitfalls of an unrestricted nonparametric estimator, such as slow convergence rates and the curse of dimensionality. Bandwidths are chosen using a novel plug in method that minimizes the asymptotic mean average squared error (AMASE) of the regression. We compare our results to alternative parametric models and find evidence of the superiority of our nonparametric model. From an empirical perspective our study is interesting in that the effects on housing prices of a series of environmental characteristics are modeled in the regression. We find these characteristics to be important in the determination of housing prices. Copyright Springer-Verlag 2005

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Paper provided by East Carolina University, Department of Economics in its series Working Papers with number 0116.

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Handle: RePEc:wop:eacaec:0116

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Cited by:
  1. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Applying a CART-based approach for the diagnostics of mass appraisal models," MPRA Paper 27646, University Library of Munich, Germany.
  2. Kagie, M. & van Wezel, M.C., 2006. "Hedonic price models and indices based on boosting applied to the Dutch housing market," Econometric Institute Research Papers EI 2006-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  3. Lee Chun Chang & Hui-Yu Lin, 2012. "The Impact of Neighborhood Characteristics on Housing Prices-An Application of Hierarchical Linear Modeling," International Journal of Management and Sustainability, Pak Publishing Group, vol. 1(2), pages 31-44, 12-2012.
  4. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
  5. 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.
  6. Robert J. Hill & Daniel Melser, 2007. "Comparing House Prices Across Regions and Time: An Hedonic Approach," Discussion Papers, School of Economics, The University of New South Wales 2007-33, School of Economics, The University of New South Wales.
  7. Bontemps, Christophe & Simioni, Michel & Surry, Yves R., 2005. "Hedonic Housing Prices and Agricultural Pollution: An Empirical Investigation on Semiparametric Models," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark, European Association of Agricultural Economists 24709, European Association of Agricultural Economists.
  8. repec:asg:wpaper:1006 is not listed on IDEAS
  9. Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, Springer, vol. 42(3), pages 987-1009, June.
  10. Wolfgang Brunauer & Stefan Lang & Nikolaus Umlauf, 2010. "Modeling House Prices using Multilevel Structured Additive Regression," Working Papers 2010-19, Faculty of Economics and Statistics, University of Innsbruck.
  11. W. Brunauer & S. Lang & P. Wechselberger & S. Bienert, 2010. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," The Journal of Real Estate Finance and Economics, Springer, Springer, vol. 41(4), pages 390-411, November.
  12. Okmyung Bin, 2005. "A semiparametric hedonic model for valuing wetlands," Applied Economics Letters, Taylor & Francis Journals, Taylor & Francis Journals, vol. 12(10), pages 597-601.
  13. Celia Bilbao & Amelia Bilbao & José Labeaga, 2010. "The welfare loss associated to characteristics of the goods: application to housing policy," Empirical Economics, Springer, Springer, vol. 38(2), pages 305-323, April.

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