Boosting the accuracy of hedonic pricing models
AbstractHedonic 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.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2005-50.
Date of creation: 02 Dec 2005
Date of revision:
conjoint analysis; data mining; ensemble learning; gradient boosting; hedonic pricing; marketing; pricing;
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