Exploration of Soft Computing Models for the Valuation of Residential Premises Using the KEEL Tool

Abstract

The experiments aimed to compare data driven soft computing models for the valuation of residential premises were conducted using the KEEL tool (Knowledge Extraction based on Evolutionary Learning). Twelve regression algorithms were applied to actual data sets derived from the cadastral system and the registry of real estate transactions. The 5x2-fold cross validation and nonparametric statistical tests were applied. The results proved the usefulness of the tool to carry out laborious investigation in a relatively short time. Further research is needed to determine to what extent data coming from such sources allow to build the real estate valuation models.