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Un análisis del mercado de la vivienda a través de redes neuronales artificiales

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  • CARIDAD Y OCERÍN, J.M.

    (Dpto. de Estadística, I.O. Econometría y Org. Empresas. Universidad de Córdoba.)

  • CEULAR VILLAMANDOS, N.

    (Dpto. de Estadística, I.O. Econometría y Org. Empresas. Universidad de Córdoba.)

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    Abstract

    El análisis del mercado de la vivienda, como parte fundamental en el desarrollo de la Economía Urbana de una región y por agregación de un determinado país, se ha constituido en una de las principales líneas de investigación de la última década. Debido al continuo incremento de los precios en dicho mercado, se están manteniendo interesantes debates acerca de las causas de este pernicioso proceso y de las políticas a aplicar para mejorar el estado de bienestar social. En los últimos años se están replanteando los trabajos de Rosen (1974), emergiendo un nuevo concepto de vivienda como conjunto de atributos que le confieren identidad propia, haciendo que unidades de igual precio puedan ser contempladas por oferentes y demandantes como bienes sustancialmente diferentes. Con este propósito, se introduce la metodología hedónica para analizar el precio de un bien en función de sus características, a través de la estimación de los precios implícitos de sus componentes. El desarrollo de la Inteligencia Artificial permite la utilización de sistemas de redes neuronales como alternativa a los métodos econométricos de modelización tradicional. En el presente trabajo se pretende desarrollar una estructura del tipo perceptrón multicapa como herramienta de predicción del precio de la vivienda. La comparación de los resultados obtenidos con ambos modelos, muestra una considerable mejora en la precisión de las valoraciones con la utilización de sistemas neuronales artificiales. The real state market is a fundamental part of Regional Urban Economics, and it has been intensive research has been developed in the last decade. The increases in the house prices have originated a broad debate over its causes, and the policy necessary to increase the general well being. The classical S. Rosen (1974) paper consider a dwelling as a set of attributes that can be valued different by the consumers, through their hedonic prices. An alternative, based on A.I. developments is proposed: a multilayer perceptron neural network is used to estimate house prices. The results obtained in a case study show statistical advantages over the classical hedonic methodology, with an increased accuracy.

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    Bibliographic Info

    Article provided by Estudios de Economía Aplicada in its journal Estudios de Economía Aplicada.

    Volume (Year): 18 (2001)
    Issue (Month): (Agosto)
    Pages: 67-81

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    Handle: RePEc:lrk:eeaart:18_2_10

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    Related research

    Keywords: Urban economics; housing prices; neural networks; hedonic models.;

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    References

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    1. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74, pages 132.
    2. Phil Graves & James C. Murdoch & Mark A. Thayer & Don Waldman, 1988. "The Robustness of Hedonic Price Estimation: Urban Air Quality," Land Economics, University of Wisconsin Press, vol. 64(3), pages 220-233.
    3. 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..
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