Model selection for inhomogeneous real estate market data in Germany

Analyzing the real estate market using modern machine learning (ML) methods is increasingly becoming a common approach. The variables (factors) influencing the real estate market (purchase price or value) behave non-linearly often. For this reason, the ML-methods seem to outperform the previously established linear regression models – especially in modelling bigger datasets from large spatial submarkets or long timespans. However, many approaches found in the literature use the same influencing parameters known from the multiple linear regression models for the new non-parametric approaches. It remains unclear whether there are further influencing variables that only prove significant in a non-linear model. The selection of influencing factors is understood here as model selection: In this work, we investigate model selection approaches on inhomogeneous German real estate transaction data from Brandenburg, Saxony and Lower Saxony. The aim of the research is an improved automatization in the context of model selection starting from raw data. As functional submarket, we aggregate multi-family houses with apartments to increase the sample size. The dataset has several data gaps in explaining parameters e.g. living space. Furthermore, the influencing variables differ between apartments and multi-family houses. We are therefore developing a method to model this inhomogeneity in a single approach (e.g. factor analysis). We consider Artificial Neural Networks (ANN), Random Forest (RF) and Gradient Boosting (GB) as ML-models for which the model selection is performed. We compare the found parameters with classical model selection used for a linear approach.