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The impact of location on housing prices: applying the Artificial Neural Network Model as an analytical tool

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  • Laura Fernández-Durán

    ()

  • Alicia Llorca
  • Nancy Ruiz
  • Soledad Valero
  • Vicente Botti
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    Abstract

    The location of a residential property in a city directly affects its market price. Each location represents different values in variables such as accessibility, neighbourhood, traffic, socio-economic level and proximity to green areas, among others. In addition, that location has an influence on the choice and on the offer price of each residential property. The development of artificial intelligence, allows us to use alternative tools to the traditional methods of econometric modelling. This has led us to conduct a study of the residential property market in the city of Valencia (Spain). In this study, we will attempt to explain the aspects that determine the demand for housing and the behaviour of prices in the urban space. We used an artificial neutral network as a price forecasting tool, since this system shows a considerable improvement in the accuracy of ratings over traditional models. With the help of this system, we attempted to quantify the impact on residential property prices of issues such as accessibility, level of service standards of public utilities, quality of urban planning, environmental surroundings and other locational aspects.

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    File URL: http://www-sre.wu.ac.at/ersa/ersaconfs/ersa11/e110830aFinal01595.pdf
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    Bibliographic Info

    Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa11p1595.

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    Date of creation: Sep 2011
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    Handle: RePEc:wiw:wiwrsa:ersa11p1595

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    1. Darla K Munroe, 2007. "Exploring the determinants of spatial pattern in residential land markets: amenities and disamenities in Charlotte, NC, USA," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 34(2), pages 336-354, March.
    2. Brasington, David M. & Hite, Diane, 2005. "Demand for environmental quality: a spatial hedonic analysis," Regional Science and Urban Economics, Elsevier, vol. 35(1), pages 57-82, January.
    3. Stephen Gibbons & Stephen Machin, 2008. "Valuing school quality, better transport, and lower crime: evidence from house prices," Oxford Review of Economic Policy, Oxford University Press, vol. 24(1), pages 99-119, spring.
    4. 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..
    5. Goodman, Allen C. & Thibodeau, Thomas G., 1998. "Housing Market Segmentation," Journal of Housing Economics, Elsevier, vol. 7(2), pages 121-143, June.
    6. Dewees, D. N., 1976. "The effect of a subway on residential property values in Toronto," Journal of Urban Economics, Elsevier, vol. 3(4), pages 357-369, October.
    7. Quigley, John M., 1985. "Consumer choice of dwelling, neighborhood and public services," Regional Science and Urban Economics, Elsevier, vol. 15(1), pages 41-63, February.
    8. Geoghegan, Jacqueline & Wainger, Lisa A. & Bockstael, Nancy E., 1997. "Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS," Ecological Economics, Elsevier, vol. 23(3), pages 251-264, December.
    9. Cervero, Robert & Landis, John, 1997. "Twenty years of the Bay Area Rapid Transit system: Land use and development impacts," Transportation Research Part A: Policy and Practice, Elsevier, vol. 31(4), pages 309-333, July.
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