Real Estate Valuation in Areas with Few Transactions Using a Robust Bayesian Hedonic Model

• Applicants: Dr.-Ing. Hamza Alkhatib; Professorin Dr.-Ing. Alexandra Weitkamp
• Subject Area: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
• Term: from 2014 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 260668532

The classical procedures of real estate evaluation works especially well if sufficient informations from different partial markets are available. Right there, statistic procedures (hedonic procedures e.g. regression analysis) are usually used within the framework of the sale comparison approach in order to predict an accurate market value. In areas with few transactions classical statistic evaluation approaches provide only unreliable results or even fail, since these approaches require suitable sample sizes; normally 15 purchases per independent variable in the regression analysis are needed. Therefore, the situation in areas with few transactions represents a special challenge for the methodology and the approach to predict the market value adequately. The goal of the research project is it to develop an innovative model, which enables reliable evaluation in situations with few transactions. For this propose a robust Bayesian approach should be developed. In this approach, it is possible to integrate expert knowledge into data supported models, like the multiple regression analysis, which can deals with small sample size. Special challenges, with which the research project deals, concern on both the data characteristic and on the prior knowledge. The random samples (data and transactions) exhibit a very small data extent; they are contaminated with outliers and show heterogeneity in the variances. The prior knowledge will be generated or collected from several qualitatively different sources, so that these informations should be weighted among themselves and with the data additionally. In order to solve the different tasks in this submitted project a robust Bayesian model will be developed which works with few data and can combine qualitatively different prior knowledge. Other than the valuation practice, the data are not assumed to obey the normal distribution; a method will be, therefore, developed which determines the correct distribution function. The weighting will be carried out by means of variance component estimation. Monte Carlo methods will be developed to enable the numerical solution of the robust Bayesian hedonic model. In the first instance, data from submarkets with numerous transactions are used. By means of closed loop simulation, areas with few transactions will be simulated, in which the data are systematically reduced, and afterwards different outlier types will be simulated. In order to validate the results, the application of the developed approach will be carried out in real submarkets with few transactions. As a result, the developed model is able to work efficiently in data with small sample range (even if the selected sample contains some outliers) in combination of prior knowledge (collected by expert interviews, approval certificate and offering data), so that a reliable and i.e. more accurate market value with quality statements can be predicted.

• DFG Programme: Research Grants