Boosting the accuracy of hedonic pricing models

Abstract

Hedonic pricing models attempt to model a relationship between object attributes andthe object's price. Traditional hedonic pricing models are often parametric models that sufferfrom misspecification. In this paper we create these models by means of boosted CARTmodels. 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 datasetscompared with a stepwise linear regression model. We interpret the boosted models by partialdependence plots and relative importance plots. This reveals some interesting nonlinearitiesand differences in attribute importance across the model types.pricing;marketing;data mining;conjoint analysis;ensemble learning;gradient boosting;hedonic pricing