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Hedonic price models and indices based on boosting applied to the Dutch housing market

Author

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  • Kagie, M.
  • van Wezel, M.C.

Abstract

We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables. We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance with up to 40% compared with Linear Regression. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price. Finally, we construct hedonic price indices and compare these to the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam.

Suggested Citation

  • 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.
  • Handle: RePEc:ems:eureir:7665
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    File URL: https://repub.eur.nl/pub/7665/EI%202006-17.pdf
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    References listed on IDEAS

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    1. Carlos Martins-Filho & Okmyung Bin, 2005. "Estimation of hedonic price functions via additive nonparametric regression," Empirical Economics, Springer, vol. 30(1), pages 93-114, January.
    2. Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
    3. Ariel Pakes, 2003. "A Reconsideration of Hedonic Price Indexes with an Application to PC's," American Economic Review, American Economic Association, vol. 93(5), pages 1578-1596, December.
    4. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74, pages 132-132.
    5. Helen X.H. Bao & Alan T.K. Wan, 2004. "On the Use of Spline Smoothing in Estimating Hedonic Housing Price Models: Empirical Evidence Using Hong Kong Data," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(3), pages 487-507, September.
    6. R. Kelley Pace, 1998. "Appraisal Using Generalized Additive Models," Journal of Real Estate Research, American Real Estate Society, vol. 15(1), pages 77-100.
    7. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    8. Anglin, Paul M & Gencay, Ramazan, 1996. "Semiparametric Estimation of a Hedonic Price Function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 633-648, Nov.-Dec..
    9. John M. Clapp, 2004. "A Semiparametric Method for Estimating Local House Price Indices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(1), pages 127-160, March.
    10. Gencay, Ramazan & Xian, Yang, 1996. "A forecast comparison of residential housing prices by parametric versus semiparametric conditional mean estimators," Economics Letters, Elsevier, vol. 52(2), pages 129-135, August.
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    Cited by:

    1. Olivier Schöni, 2014. "Asymptotic Properties of Imputed Hedonic Price Indices," SERC Discussion Papers 0166, Spatial Economics Research Centre, LSE.
    2. Schöni, Olivier, 2014. "Asymptotic properties of imputed hedonic price indices," LSE Research Online Documents on Economics 64500, London School of Economics and Political Science, LSE Library.

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