Property valuation with artificial neural network: the case of Athens

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.