Comparative analysis of neural network models for premises valuation using SAS Enterprise Miner

Abstract

The experiments aimed to compare machine learning algorithms to create models for the valuation of residential premises were conducted using the SAS Enterprise Miner 5.3. Eight different algorithms were used including artificial neural networks, statistical regression and decision trees. All models were applied to actual data sets derived from the cadastral system and the registry of real estate transactions. A dozen of predictive accuracy measures were employed. The results proved the usefulness of majority of algorithms to build the real estate valuation models. © 2009 Springer-Verlag Berlin Heidelberg.