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Boosting the accuracy of hedonic pricing models

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  • van Wezel, M.C.
  • Kagie, M.
  • Potharst, R.

Abstract

Hedonic pricing models attempt to model a relationship between object attributes and the object's price. Traditional hedonic pricing models are often parametric models that suffer from misspecification. In this paper we create these models by means of boosted CART models. The method is explained in detail and applied to various datasets. Empirically, we find substantial reduction of errors on out-of-sample data for two out of three datasets compared with a stepwise linear regression model. We interpret the boosted models by partial dependence plots and relative importance plots. This reveals some interesting nonlinearities and differences in attribute importance across the model types.

Suggested Citation

  • van Wezel, M.C. & Kagie, M. & Potharst, R., 2005. "Boosting the accuracy of hedonic pricing models," Econometric Institute Research Papers EI 2005-50, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:7145
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    References listed on IDEAS

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    Cited by:

    1. Martijn Kagie & Michiel Van Wezel, 2007. "Hedonic price models and indices based on boosting applied to the Dutch housing market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(3‐4), pages 85-106, July.
    2. Felix Lorenz & Jonas Willwersch & Marcelo Cajias & Franz Fuerst, 2023. "Interpretable machine learning for real estate market analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(5), pages 1178-1208, September.

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