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Property valuation with artificial neural network: the case of Athens

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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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    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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