Comparison of expert algorithms with machine learning models for real estate appraisal

Abstract

Machine learning models require numerous training examples to provide reliable predictions of real estate prices. Expert algorithms could be applied wherever only several training samples are available. The accuracy of two expert algorithms based on the sales comparison approach was experimentally examined using real-world data derived from a cadastral system and registry of real estate transactions. The performance of the algorithms was compared with three data driven regression models for property valuation. Statistical analysis of the obtained results was conducted.