Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis
利用希腊大量历史房价数据，比较回归、特征价格方程和人工神经网络等模型在非有效市场中的估值与预测能力，并提出组合预测规则以提高精度。
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 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.