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Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal

Author

Listed:
  • Steven Peterson

    (Virginia Commonwealth University)

  • Albert B. Flanagan

    (Williams Appraisers, Inc.)

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.

Suggested Citation

  • Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.
    • Handle: RePEc:jre:issued:v:31:n:2:2009:p:147-164
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    References listed on IDEAS

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    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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