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House Price Prediction: Hedonic Price Model vs. Artificial Neural Network

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  • Limsombunchai, Visit

Abstract

The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.

Suggested Citation

  • Limsombunchai, Visit, 2004. "House Price Prediction: Hedonic Price Model vs. Artificial Neural Network," 2004 Conference, June 25-26, 2004, Blenheim, New Zealand 97781, New Zealand Agricultural and Resource Economics Society.
    • Handle: RePEc:ags:nzar04:97781
    DOI: 10.22004/ag.econ.97781
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    References listed on IDEAS

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    1. Messonnier, Mark L. & Luzar, E. Jane, 1990. "A Hedonic Analysis Of Private Hunting Land Attributes Using An Alternative Functional Form," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 22(2), pages 1-7, December.
    2. Messonier, Mark L. & Luzar, E. Jane, 1990. "A Hedonic Analysis of Private Hunting Land Attributes Using an Alternative Functional Form," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 22(2), pages 129-135, December.
    3. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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