Boosting the accuracy of hedonic pricing models

M.C. van Wezel, Martijn Kagie () and R. Potharst
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R. Potharst: Erasmus Econometric Institute

No EI 2005-50 Revision_Date: 2009-07-29, Econometric Institute Report from Erasmus University Rotterdam, Econometric Institute

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.

Keywords: conjoint analysis; data mining; gradient boosting; ensemble learning; hedonic pricing; marketing; pricing (search for similar items in EconPapers)
Date: 2005-12-02

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Persistent link: http://EconPapers.repec.org/RePEc:dgr:eureir:1765007145

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