Comparison of Bagging, Boosting and Stacking Ensembles Applied to Real Estate Appraisal

The experiments aimed to compare three methods to create ensemble models implemented in a popular data mining system WEKA, were carried out. Six common algorithms comprising two neural network algorithms, two decision trees for regression, and linear regression and support vector machine were used to generate individual committees. All algorithms were employed to actual data sets derived from the cadastral system and the registry of real estate transactions. Nonparametric Wilcoxon signed-rank tests to evaluate the differences between ensembles and original models were conducted. The results obtained show there is no single algorithm which produces the best ensembles and it is worth to seek an optimal hybrid multi-model solution.