Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis

In this paper we develop an automatic valuation model for property valuation using a large database of historical prices

from Greece. The Greek property market is an inefficient, non-homogeneous 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 covers a big range of properties across time and includes the Greek financial crisis period

which led to tremendous changes in the dynamics of the real estate market. We formulate and compare linear and nonlinear

models based on regression, hedonic equations, spatial analysis 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 to identify 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 performance. Our results

indicate that the proposed methodology constitutes an accurate tool for property valuation in non- homogeneous,

newly developed markets.