Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal
AbstractUsing 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.
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Bibliographic InfoArticle provided by American Real Estate Society in its journal journal of Real Estate Research.
Volume (Year): 31 (2009)
Issue (Month): 2 ()
Contact details of provider:
Postal: American Real Estate Society Clemson University School of Business & Behavioral Science Department of Finance 401 Sirrine Hall Clemson, SC 29634-1323
Web page: http://www.aresnet.org/
Postal: Diane Quarles American Real Estate Society Manager of Member Services Clemson University Box 341323 Clemson, SC 29634-1323
Find related papers by JEL classification:
- L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services
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