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On the Use of Spline Smoothing in Estimating Hedonic Housing Price Models: Empirical Evidence Using Hong Kong Data

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  • Helen X.H. Bao
  • Alan T.K. Wan

Abstract

Traditionally in estimating hedonic housing price functions, investigators use parametric models involving specific functional forms and a finite number of unknown parameters. Some investigators have questioned whether the underlying theory is capable of conveying sufficient information to enable a correct and successful specification of parametric models and have instead proposed the less restrictive semiparametric approach to the problem. In this paper, we illustrate how the technique of smoothing splines can be used to estimate hedonic housing price models. Smoothing splines are a powerful approach to the analysis of housing data as they are exceptionally flexible in their functional forms and provide a computationally tractable method even with a large number of explanatory variables. Our illustration takes the form of a rather limited, but very promising, application with Hong Kong data. In the forecasting comparison, the spline smoothing procedure outperforms the traditional parametric Box-Cox model in mean square error terms for out-of-sample predictions. Our results also suggest that the distortion caused by underfitting the model is smaller for spline smoothing than for the kernel and k-nearest-neighbor semiparametric procedures. Copyright 2004 by the American Real Estate and Urban Economics Association

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Bibliographic Info

Article provided by American Real Estate and Urban Economics Association in its journal Real Estate Economics.

Volume (Year): 32 (2004)
Issue (Month): 3 (09)
Pages: 487-507

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Handle: RePEc:bla:reesec:v:32:y:2004:i:3:p:487-507

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Cited by:
  1. Robert J. Hill & Daniel Melser, 2007. "Comparing House Prices Across Regions and Time: An Hedonic Approach," Discussion Papers 2007-33, School of Economics, The University of New South Wales.
  2. Melser, Daniel & Syed, Iqbal, 2007. "Life Cycle Pricing and the Measurement of Inflation," MPRA Paper 16722, University Library of Munich, Germany, revised 07 Jul 2008.
  3. Arnstein Gjestland & David McArthur & Liv Osland & Inge Thorsen, 2011. "Relationships between housing prices and commuting flows," ERSA conference papers ersa10p906, European Regional Science Association.
  4. Bing Zhu & Roland Füss & Nico Rottke, 2011. "The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 42(4), pages 542-565, May.
  5. Ekaterina Chernobai & Michael Reibel & Michael Carney, 2011. "Nonlinear Spatial and Temporal Effects of Highway Construction on House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 348-370, April.
  6. Iqbal Syed & Daniel Melser, 2008. "Prices over the Product Life Cycle: An Empirical Analysis," Discussion Papers 2008-25, School of Economics, The University of New South Wales.
  7. Sam K. Hui & Alvin Cheung & Jimmy Pang, 2010. "A Hierarchical Bayesian Approach for Residential Property Valuation:Application to Hong Kong Housing Market," International Real Estate Review, Asian Real Estate Society, vol. 13(1), pages 1-29.
  8. 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.
  9. Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.

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