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Property valuation with artificial neural network: the case of Athens

Author

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  • Angelos Mimis
  • Antonis Rovolis
  • Marianthi Stamou
Abstract
The purpose of this article is to examine the application of an artificial neural network (ANN) approach in property valuation. The approach has been enhanced by the use of a geographic information system (GIS) to enrich the explanatory variables and model the spatial dimension of the problem. The sample data used contain information of 3150 properties in the broader area of Athens. Various internal physical (structure quality and quantity) and external environmental characteristics (neighbourhood characteristics and transportation access) of the properties are available. In order to incorporate these environmental variables, the GIS was used to employ location-based characteristics. In our approach, the multilayer perception network has been employed and the results have been compared with the traditional approach of the spatial lag model. The comparison demonstrates that ANN gives more consistent predictions in the area of Athens. Our results reveal the non-linear relationships of the value of a property with respect to floor space and age. Finally, spatial variation of the values of the properties in broader area of Athens is illustrated.

Suggested Citation

  • Angelos Mimis & Antonis Rovolis & Marianthi Stamou, 2013. "Property valuation with artificial neural network: the case of Athens," Journal of Property Research, Taylor & Francis Journals, vol. 30(2), pages 128-143, June.
    • Handle: RePEc:taf:jpropr:v:30:y:2013:i:2:p:128-143
    DOI: 10.1080/09599916.2012.755558
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    1. Karolien De Bruyne & Jan Van Hove, 2013. "Explaining the spatial variation in housing prices: an economic geography approach," Applied Economics, Taylor & Francis Journals, vol. 45(13), pages 1673-1689, May.
    2. Stanley McGreal & Paloma Taltavull de La Paz, 2011. "An analysis of factors influencing accuracy in the valuation of residential properties in Spain," Journal of Property Research, Taylor & Francis Journals, vol. 29(1), pages 1-24, May.
    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..
    4. Allan Din & Martin Hoesli & Andre Bender, 2001. "Environmental Variables and Real Estate Prices," Urban Studies, Urban Studies Journal Limited, vol. 38(11), pages 1989-2000, October.
    5. Peter Malpass, 2004. "Fifty Years of British Housing Policy: Leaving or Leading the Welfare State?," European Journal of Housing Policy, Taylor and Francis Journals, vol. 4(2), pages 209-227.
    6. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    7. Ramesh Sharda, 1994. "Neural Networks for the MS/OR Analyst: An Application Bibliography," Interfaces, INFORMS, vol. 24(2), pages 116-130, April.
    8. Patric H. Hendershott & John C. Weicher, 2002. "Forecasting Housing Markets: Lessons Learned," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 30(1), pages 1-11.
    9. Liao, Wen-Chi & Wang, Xizhu, 2012. "Hedonic house prices and spatial quantile regression," Journal of Housing Economics, Elsevier, vol. 21(1), pages 16-27.
    10. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    11. 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.
    12. K.C. Lam & C.Y. Yu & C.K. Lam, 2009. "Support vector machine and entropy based decision support system for property valuation," Journal of Property Research, Taylor & Francis Journals, vol. 26(3), pages 213-233, August.
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    Cited by:

    1. Damian Przekop, 2022. "Artificial Neural Networks vs Spatial Regression Approach in Property Valuation," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 14(2), pages 199-223, June.
    2. Horvath, Sabine & Soot, Matthias & Zaddach, Sebastian & Neuner, Hans & Weitkamp, Alexandra, 2021. "Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis," Land Use Policy, Elsevier, vol. 107(C).
    3. Polixeni Iliopoulou & Elissavet Feloni, 2022. "Spatial Modelling and Geovisualization of House Prices in the Greater Athens Region, Greece," Geographies, MDPI, vol. 2(1), pages 1-21, February.
    4. Ti-Ching Peng, 2021. "The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 1-32, February.
    5. GLUMAC Brano & DES ROSIERS François, 2018. "Real estate and land property automated valuation systems: A taxonomy and conceptual model," LISER Working Paper Series 2018-09, Luxembourg Institute of Socio-Economic Research (LISER).
    6. Michalis Doumpos & Dimitrios Papastamos & Dimitrios Andritsos & Constantin Zopounidis, 2021. "Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches," Annals of Operations Research, Springer, vol. 306(1), pages 415-433, November.
    7. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    8. Áron Horváth & Blanka Imre & Zoltán Sápi, 2016. "The International Practice of Statistical Property Valuation Methods and the Possibilities of Introducing Automated Valuation Models in Hungary," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 15(4), pages 45-64.

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