Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal

Abstract: Using a large sample of 46,467 residential properties spanning 1999-2005, we demonstrate using matched pairs that, relative to linear hedonic pricing models, artificial neural networks (ANN) generate significantly lower dollar pricing errors, have greater pricing precision out-of-sample, and extrapolate better from more volatile pricing environments. While a single layer ANN is functionally equivalent to OLS, multiple layered ANNs are capable of modeling complex nonlinearities. Moreover, because parameter estimation in ANN does not depend on the rank of the regressor matrix, ANN is better suited to hedonic models that typically utilize large numbers of dummy variables