Abstract
The experiments aimed to compare machine learning algorithms to create models for the valuation of residential premises, implemented in popular data mining systems KEEL, RapidMiner and WEKA, were carried out. Six common methods comprising two neural network algorithms, two decision trees for regression, and linear regression and support vector machine were applied to actual data sets derived from the cadastral system and the registry of real estate transactions. A dozen of commonly used performance measures was applied to evaluate models built by respective algorithms. Some differences between models were observed.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fernández, J.C., Herrera., F.: KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems. Soft Computing 13(3), 307–318 (2009)
González, M.A.S., Formoso, C.T.: Mass appraisal with genetic fuzzy rule-based systems. Property Management 24(1), 20–30 (2006)
Hagquist, C., Stenbeck, M.: Goodness of Fit in Regression Analysis – R2 and G2 Reconsidered. Quality & Quantity 32, 229–245 (1998)
Król, D., Lasota, T., Trawiński, B., Trawiński, K.: Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal. International Journal of Hybrid Intelligent Systems 5(3), 111–128 (2008)
Lasota, T., Pronobis, E., Trawiński, B., Trawiński, K.: Exploration of Soft Computing Models for the Valuation of Residential Premises using the KEEL Tool. In: Nguyen, N.T., et al. (eds.) 1st Asian Conference on Intelligent Information and Database Systems (ACIIDS 2009), pp. 253–258. IEEE, Los Alamitos (2009)
McCluskey, W.J., Anand, S.: The application of intelligent hybrid techniques for the mass appraisal of residential properties. Journal of Property Investment and Finance 17(3), 218–239 (1999)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006), pp. 935–940 (2006)
Nguyen, N., Cripps, A.: Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. J. of Real Estate Res. 22(3), 3131–3336 (2001)
Soibelman, W.L., González, M.A.S.: A Knowledge Discovery in Databases Framework for Property Valuation. J. of Property Tax Assessment and Admin. 7(2), 77–106 (2002)
Taffese, W.Z.: Case-based reasoning and neural networks for real state valuation. In: Proceedings of the 25th IASTED International Multi-Conference: Artificial Intelligence and Applications, Innsbruck, Austria (2007)
Waller, B.D., Greer, T.H., Riley, N.F.: An Appraisal Tool for the 21st Century: Automated Valuation Models. Australian Property Journal 36(7), 636–641 (2001)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Worzala, E., Lenk, M., Silva, A.: An Exploration of Neural Networks and Its Application to Real Estate Valuation. J. of Real Estate Res. 10(2), 185–201 (1995)
Wyatt, P.: The development of a GIS-based property information system for real estate valuation. Int. J. Geographical Information Science 111(5), 435–450 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Graczyk, M., Lasota, T., Trawiński, B. (2009). Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-04441-0_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04440-3
Online ISBN: 978-3-642-04441-0
eBook Packages: Computer ScienceComputer Science (R0)