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Artificial Neural Networks for Predicting Real Estate Prices || Redes neuronales artificiales para la predicción de precios inmobiliarios

Author

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  • Núñez Tabales, Julia M.

    () (Faculty of Economics, University of Cordoba (Spain))

  • Caridad y Ocerin, José María

    () (Faculty of Economics, University of Cordoba (Spain))

  • Rey Carmona, Francisco J.

    () (Faculty of Economics, University of Cordoba (Spain))

Abstract

Econometric models, in the estimation of real estate prices, are a useful and realistic approach for buyers and for local and fiscal authorities. From the classical hedonic models to more data driven procedures, based on Artificial Neural Networks (ANN), many papers have appeared in economic literature trying to compare the results attained with both approaches. We insist on the use of ANN, when there is enough statistical information, and will detail some comparisons to hedonic modeling, in a medium size city in the South of Spain, with an extensive set of data spanning over several years, collected before the actual downturn of the market. Exogenous variables include each dwelling's external and internal data (both numerical and qualitative), and data from the building in which it is located and its surroundings. Alternative models are estimated for several time intervals, and enabling the comparison of the effects of the rising prices during the bull market over the last decade. || Los modelos econométricos en la valoración de precios inmobiliarios constituyen una herramienta útil tanto para los compradores como para las autoridades locales y fiscales. Desde los modelos hedónicos clásicos hasta los planteamientos actuales a través de redes neuronales artificiales (RNA), han tenido lugar numerosas aportaciones en la literatura económica que tratan de comparar los resultados de ambos métodos. Insistimos en el empleo de RNA en el caso de disponer de suficiente información estadística. En este trabajo se aplica dicha metodología en una ciudad de tamaño medio situada en el sur de España, utilizando una extensa muestra de datos que comprende varios años precedentes a la crisis actual. Las variables utilizadas -tanto cuantitativas como cualitativas- incluyen datos externos e internos de la vivienda, del edificio en el que está localizada, así como de su entorno. Se construyen varios modelos alternativos para distintos intervalos de tiempo, siendo capaces de estimar los efectos de los precios crecientes del mercado alcista durante la década pasada.

Suggested Citation

  • Núñez Tabales, Julia M. & Caridad y Ocerin, José María & Rey Carmona, Francisco J., 2013. "Artificial Neural Networks for Predicting Real Estate Prices || Redes neuronales artificiales para la predicción de precios inmobiliarios," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 29-44, June.
  • Handle: RePEc:pab:rmcpee:v:15:y:2013:i:1:p:29-44
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    References listed on IDEAS

    as
    1. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    2. Elaine M. Worzala & Margarita Lenk & Ana Silva, 1995. "An Exploration of Neural Networks and Its Application to Real Estate Valuation," Journal of Real Estate Research, American Real Estate Society, vol. 10(2), pages 185-202.
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    More about this item

    Keywords

    house prices; artificial neural networks (ANN); valuation; econometric modeling; precios de la vivienda; redes neuronales artificiales (RNA); valoración; modelos econométricos;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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