Estimation of Hedonic Price Functions via Additive Nonparametric Regression

We model a hedonic price function for housing as an additive nonparametric regression. Estimation is done via a back ̄tting procedure in combination with a local polynomial estimator. It avoids the pitfalls of an unrestricted nonparametric estimator, such as slow convergence rates and the curse of dimensionality. Bandwidths are chosen via a novel plug in method that minimizes the asymptotic mean average squared error (AMASE) of the regression. We compare our results to an alternative parametric model and ̄nd evidence of the superiority of our nonparametric model. From an empirical perspective our study is interesting in that the e®ects on housing prices of a series of environmental characteristics are modeled in the regression. We ̄nd these characteristics to be important in the determination of housing prices.