A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context

Abstract

This paper describes a comparative study where severalregression and artificial intelligence (AI)-based methods are usedto assess properties in Louisville, Kentucky. Four regressionbasedmethods [traditional multiple regression analysis (MRA),and three non-traditional regression-based methods, SupportVector Machines using sequential minimal optimizationregression (SVM-SMO), additive regression, and M5P trees],and three AI-based methods [neural networks (NNs), radial basisfunction neural network (RBFNN), and memory-based reasoning(MBR)] are applied and compared under various simulationscenarios. The results indicate that non-traditional regressionbasedmethods perform better in all simulation scenarios,especially with homogeneous data sets. AI-based methodsperform well with less homogeneous data sets under somesimulation scenarios.