Abstract
Using a large sample of 46,467 residential properties spanning1999–2005, we demonstrate using matched pairs that, relative tolinear hedonic pricing models, artificial neural networks (ANN)generate significantly lower dollar pricing errors, have greaterpricing precision out-of-sample, and extrapolate better frommore volatile pricing environments. While a single layer ANNis functionally equivalent to OLS, multiple layered ANNs arecapable of modeling complex nonlinearities. Moreover, becauseparameter estimation in ANN does not depend on the rank ofthe regressor matrix, ANN is better suited to hedonic modelsthat typically utilize large numbers of dummy variables.
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