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

Autores/as

  • Julia M. Núñez Tabales Faculty of Economics University of Cordoba
  • José María Caridad y Ocerin Faculty of Economics University of Cordoba
  • Francisco J. Rey Carmona Faculty of Economics University of Cordoba

Palabras clave:

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

Resumen

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.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Allen, W.C. and Zumwalt, J.K. (1994): Neural Networks: a word of caution. Working Paper. Colorado State University.

Bonissone, P.P. and Cheetham, W. (1997): “Financial applications of fuzzy case-based reasoning to residential property valuation”. Fuzz- IEEE, 1, pp. 37–44.

Borst, R. (1991): “Artificial Neural Networks: The Next Modelling / Calibration Technology for the Assessment Community?”. Property Tax Journal, IAAO, 10(1), pp. 69–94.

Caridad, J.M. and Ceular, N. (2001): “Un análisis del mercado de la vivienda a través de Sistemas de Redes Neuronales”. Revista de Estudios de Economía Aplicada, 18, pp. 67–81.

Cechin, A., Souto, A., and Aurelio, M. (2000): “Real estate value at Porto Alegre city using Artificial Neural Networks”. Sixth Brasilian Symposium on Neural Networks Proceedings, 22-25 November, pp. 237–242.

Collins, A. and Evans, A. (1994): “Artificial Neural Networks: an application to residential valuation in the U.K”. Journal of Property Valuation and Investment, 11(2), pp. 195–204.

Do, A. and Grudnitski, G. (1992): “A Neural Network Approach to Residential Property Appraisal”. The Real Estate Appraiser, 58(3), pp. 38–45.

Freeman, J. and Skapura, D.M. (1993): Redes neuronales algoritmos, aplicaciones y técnicas de programación. Ed. Wilmington Addison-Wesley.

Gallego, J. (2004): “La inteligencia artificial aplicada a la valoración de inmuebles. Un ejemplo para valorar Madrid”. Revista CT/Catastro, 50, pp. 51–67.

García Rubio, N. (2004): Desarrollo y aplicación de redes neuronales artificiales al mercado inmobiliario: aplicación a la ciudad de Albacete. Tesis Doctoral. Universidad de Castilla-La Mancha (España).

Hamzaoi, Y.E. and Hernández, J.A. (2011): “Application of Artificial Neural Networks to predict the selling Price in the real estate valuation”. 10th Mexican International Conference on Artificial Intelligence, November 26-December 04, pp. 175–181.

Haykin, S. (1999): Neural networks: A comprehensive foundation. Ed. Prentice-Hall.

Haynes, J.D. and Tan, C.N.W. (1993): An Artificial Neuronal Network real estate price predictor. IEEE Computer Society Press: USA.

Jaén, M. and Molina, A. (1995): Modelos econométricos de tenencia y demanda de vivienda. Servicio de Publicaciones de la Universidad de Almería (España).

Karakozova, O.A. (2000): Comparison between neural network and multiple regression approaches: An application to residential valuation in Finland. Swedish School of Economics and Business Administration.

Kauko, T., Hooimaijer, P., and Hakfoort, J. (2002): “Capturing housing market segmentation: An alternative approach based on neural network modeling”. Housing Studies, 17(6), pp. 875–894.

Lara, J. (2005): “Aplicación de las redes neuronales artificiales al campo de la valoración inmobiliaria”. Mapping, 104, pp. 64–71.

Lashley, K. (1929): Brain mechanisms and intelligence. University of Chicago Press.

Limsombunchai, V., Gan, C., and Lee, M. (2004): “House price prediction: Hedonic Price Model vs. Artificial Neural Network”. American Journal of Applied Sciencies, 1(3), pp. 193–201.

Liu, J., Zhang, X., and Wu, W. (2006): “Application of fuzzy neural network for real estate prediction”. LNCS, 3973, pp. 1187–1191.

McCluskey, W., Dyson, K., McFall, D., and Anand, S. (1996): “Mass appraisal for property taxation: an artificial intelligence approach”. Land Economics Review; 2(1), pp. 25–32.

McCulloch, W.S. and Pitts, W. (1943): “A logical calculus of the ideas immanent in nervous activity”. Bulletin of Mathematical Biophysics, 5, pp. 115–133.

National Statistics Institute (Instituto Nacional de Estadística –INE-): http://www.ine.es

Nguyen, N. and Cripps A. (2001): “Predicting housing value: a comparison of multiple regression analysis and artificial neural networks”. Journal of Real Estate Research, 22(3), pp. 314–336.

Richardson, H.W. (1973): Economía Regional. Teoría de la localización, estructuras urbanas y crecimiento regional. Ed. Vicens Vives. Barcelona.

Rosen, S. (1974): “Hedonic Prices and Implicit Markets: Product Differentiation in Pure competition”. Journal of Political Economy, 82, pp. 34–55.

Rossini, P. (1998): “Improving the results of artificial neural network models for residential valuation”. Four Annual Pacific-Rim Real Estate Society Conference. Perth, Western Australia, 19-21 January.

Rumellhart, D. and McClelland, J. (1986): Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge: MIT.

Saura, P. (1995) Demanda de características de la vivienda en Murcia. Secretariado de Publicaciones de la Universidad de Murcia.

Selim, H. (2009): “Determinants of house prices in Turkey: Hedonic regression versus artificial neural network”. Expert Systems with Applications, 36, pp. 2843–2852.

Tay, D.P. and Ho, D.K. (1992): “Artificial intelligence and the mass appraisal of residential apartment”. Journal of Property Valuation & Investment, 10, pp. 525–540.

Worzala, E., Lenk, M., and Silva, A. (1995): “An exploration of neural networks and its application to real estate valuation”. Journal of Real Estate Research, 10(2), pp. 185–201.

Publicado

2016-11-04

Cómo citar

Núñez Tabales, J. M., Caridad y Ocerin, J. M., & Rey Carmona, F. J. (2016). 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, 15, Páginas 29 a 44. Recuperado a partir de https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2218

Número

Sección

Artículos