Artificial Neural Networks as Automated Valuation Modelling for Real Estate Valuation

• Applicants: Dr.-Ing. Matthias Soot; Professorin Dr.-Ing. Alexandra Weitkamp
• Subject Area: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 530964147

Volatile markets require timely real estate market information. Automated valuation methods can contribute to this. Many studies focus on multiple linear regression. This method reaches its limits when it comes to assessing large spatial submarkets that exhibit nonlinear dependencies and want to include submarkets with few transactions. Artificial neural networks (ANN), on the other hand, have the advantage of being able to take this into account. The overall aim is to introduce ANN for automated real estate valuation. For this purpose, sample size, complexity, parameter estimation, acceptance and applicability for real estate valuation including low purchase price locations with ANN will be researched and further developed. KNN require large amounts of data. The data scarcity of real estate valuation is countered by data augmentation methods, e.g., aggregation or inclusion of asking prices. Quantifying the bounds of minimal prediction risk requires evaluation of complexity. This motivates the study of the Vapnik-Chervonenkis dimension as a complexity measure or the transformation of ANNs into a Kalman filter estimation framework. The obtained results are validated mathematically and by experts, and the acceptance of ANNs is analyzed. A special focus is on the applicability for markets with few transactions in Austria and Germany. This project differs from previous research in its holistic approach, which includes fundamental investigations and developments regarding the sample size and data types, the complexity of the KNN-based models used, parameter estimation and uncertainty considerations, as well as validation and acceptance.

• DFG Programme: Research Grants
• International Connection: Austria
• Partner Organisation: Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
• Cooperation Partner: Professor Dr.-Ing. Hans-Berndt Neuner