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