In recent years big financial institutions are interested in creating and maintaining property valuation models. The main objective is to use reliable historical data in order to be able to forecast the price of a new property in a comprehensive manner and provide some indication for the uncertainty around this forecast. In this paper we develop an automatic valuation model (AVM) for property valuation using a large database of historical prices from Greece.
The Greek property market is an inefficient, nonhomogeneous market, still at its infancy and governed by lack of information. As a result modelling the Greek real estate market is a very interesting and challenging problem.
The available data cover a wide range of properties across time and include the financial crisis period in Greece which led to tremendous changes in the dynamics of the real estate market. We formulate and compare linear and non-linear models based on regression, hedonic equations and artificial neural networks. The forecasting ability of each method is evaluated out-of-sample. Special care is given on measuring the success of the forecasts but also on identifying the property characteristics that lead to large forecasting errors. Finally, by examining the strengths and the performance of each method we apply a combined forecasting rule to improve forecasting accuracy. Our results indicate that the proposed methodology constitutes an accurate tool for property valuation in a non-homogeneous, newly developed market.
We perform an extensive out-of-sample analysis in four non-overlapping data sets. In contrast to previous studies, our results indicate that NNs constantly outperform traditional valuation methods. In this study the proposed NN was fine tuned and extra care was taken to avoid overfitting.
Finally, we try to identify the property characteristics that lead to large forecasting errors. Our results indicate that the forecasting error increases when the residence area is above 120m2 or the property is a house or large land area is included. Similarly, very old properties (built before 1950) lead to larger forecasting errors. However, it is worth to mention that our analysis revealed that NNs are less sensitive to the changes of these characteristics
The results of this study can potentially have significant policy and fiscal implications. It can help both the government and the public sectors like commercial banks. To start with it can help the central and local governments in planning and implementing their fiscal policies, both at micro and macro level and can promote economic and development sustainability.
Second, it can have significant impact in operational efficiency of commercial banks. The proposed AVM can be adapted in applications such as mortgage quality control or appraisal review, loss mitigation analysis, portfolio valuation and appraisal process redesign.
The study has been published in The Journal of Operational Research Society and it is a collaborative work of Dr. Antonis K. Alexandridis, Dimitrios Karlis, Dimitrios Papastamos and Dimitrios Andritsos.