Parametric regression models of hedonic price functions suffer from two main specification issues: the identification of appropriate dependent and independent variables, and the choice of functional form. Although the first issue remains relevant with the use of nonparametric regression models, the second issue becomes irrelevant since these models do not presume functional forms a priori. We estimate a linear parametric model via OLS, which fails a common specification test, before showing that recently developed nonparametric regression methods outperform it significantly. In addition to estimating the models, we compare the out-of-sample prediction performance of the OLS and nonparametric models. Our data reveals that the nonparametric models provide more accurate predictions than the parametric model.