Skip to main content
SpringerLink
Log in

Comparison of Ensemble Learning Models with Expert Algorithms Designed for a Property Valuation System

  • Conference paper
  • First Online:
Computational Collective Intelligence (ICCCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10448))

Included in the following conference series:

Abstract

Three expert algorithms based on the sales comparison approach worked out for an automated system to aid in real estate appraisal are presented in the paper. Ensemble machine learning models and expert algorithms for real estate appraisal were compared empirically in terms of their accuracy. The evaluation experiments were conducted using real-world data acquired from a cadastral system maintained in a big city in Poland. The characteristics of applied techniques for real estate appraisal are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Zurada, J., Levitan, A.S., Guan, J.: A comparison of regression and artificial intelligence methods in a mass appraisal context. J. Real Estate Res. 33(3), 349–388 (2011)

    Google Scholar 

  2. Peterson, S., Flangan, A.B.: Neural network hedonic pricing models in mass real estate appraisal. J. Real Estate Res. 31(2), 147–164 (2009)

    Google Scholar 

  3. Antipov, E.A., Pokryshevskaya, E.B.: Mass appraisal of residential apartments: an application of random forest for valuation and a CART-based approach for model diagnostics. Expert Syst. Appl. 39, 1772–1778 (2012)

    Article  Google Scholar 

  4. Kusan, H., Aytekin, O., Özdemir, I.: The use of fuzzy logic in predicting house selling price. Expert Syst. Appl. 37(3), 1808–1813 (2010)

    Article  Google Scholar 

  5. Kontrimas, V., Verikas, A.: The mass appraisal of the real estate by computational intelligence. Appl. Soft Comput. 11(1), 443–448 (2011)

    Article  Google Scholar 

  6. Musa, A.G., Daramola, O., Owoloko, A., Olugbara, O.: A neural-CBR system for real property valuation. J. Emerg. Trends Comput. Inf. Sci. 4(8), 611–622 (2013)

    Google Scholar 

  7. Jahanshiri, E., Buyong, T., Shariff, A.R.M.: A review of property mass valuation models. Pertanika J. Sci. Technol. 19(S), 23–30 (2011)

    Google Scholar 

  8. McCluskey, W.J., McCord, M., Davis, P.T., Haran, M., McIlhatton, D.: Prediction accuracy in mass appraisal: a comparison of modern approaches. J. Prop. Res. 30(4), 239–265 (2013)

    Article  Google Scholar 

  9. d’Amato, M., Kauko, T. (eds.): Advances in Automated Valuation Modeling AVM: After the Non-agency Mortgage Crisis. Studies in Systems, Decision and Control, vol. 86. Springer, Cham (2017)

    Google Scholar 

  10. Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fus. 16, 3–17 (2014)

    Article  Google Scholar 

  11. Krawczyk, B., Woźniak, M., Cyganek, B.: Clustering-based ensembles for one-class classification. Inf. Sci. 264, 182–195 (2014)

    Article  MathSciNet  Google Scholar 

  12. Burduk, R., Walkowiak, K.: Static classifier selection with interval weights of base classifiers. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015, Part I. LNCS, vol. 9011, pp. 494–502. Springer, Cham (2015)

    Google Scholar 

  13. Fernández, A., López, V., José del Jesus, M., Herrera, F.: Revisiting evolutionary fuzzy systems: Taxonomy, applications, new trends and challenges. Knowl. Based Syst. 80, 109–121 (2015)

    Article  Google Scholar 

  14. Lughofer, E.: Evolving Fuzzy Systems—Methodologies, Advanced Concepts and Applications. STUDFUZZ, vol. 266. Springer, Heidelberg (2011)

    Book  Google Scholar 

  15. Hong, T.-P., Liou, Y.-L., Wang, S.-L., Vo, B.: Feature selection and replacement by clustering attributes. Vietnam J. Comput. Sci. 1, 47–55 (2014)

    Article  Google Scholar 

  16. Pham, T.-N., Nguyen, V.-Q., Tran, V.-H., Nguyen, T.-T., Ha, Q.-T.: A semi-supervised multi-label classification framework with feature reduction and enrichment. J. Inf. Telecommun. 1(2), 141–154 (2017)

    Google Scholar 

  17. Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Exploration of bagging ensembles comprising genetic fuzzy models to assist with real estate appraisals. In: Yin, H., Corchado, E. (eds.) IDEAL 2009, LNCS, vol. 5788, pp. 554–561. Springer, Heidelberg (2009)

    Google Scholar 

  18. Krzystanek, M., Lasota, T., Telec, Z., Trawiński, B.: Analysis of bagging ensembles of fuzzy models for premises valuation. In: Nguyen, N.T., et al. (eds.) ACIIDS 2010, LNAI, vol. 5991, pp. 330–339. Springer, Heidelberg (2010)

    Google Scholar 

  19. Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Investigation of the eTS evolving fuzzy systems applied to real estate appraisal. J. Mult. Valued Logic Soft Comput. 17(2–3), 229–253 (2011)

    Google Scholar 

  20. Lughofer, E., Trawiński, B., Trawiński, K., Kempa, O., Lasota, T.: On employing fuzzy modeling algorithms for the valuation of residential premises. Inf. Sci. 181, 5123–5142 (2011)

    Article  Google Scholar 

  21. Trawiński, B.: Evolutionary fuzzy system ensemble approach to model real estate market based on data stream exploration. J. Univers. Comput. Sci. 19(4), 539–562 (2013)

    MathSciNet  Google Scholar 

  22. Telec, Z., Trawiński, B., Lasota, T., Trawiński, G.: Evaluation of neural network ensemble approach to predict from a data stream. In: Hwang, D., et al. (eds.) ICCCI 2014, LNAI, vol. 8733. Springer, Heidelberg (2014)

    Google Scholar 

  23. Lasota, T., Sawiłow, E., Trawiński, B., Roman, M., Marczuk, P., Popowicz, P.: A Method for merging similar zones to improve intelligent models for real estate appraisal. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS, vol. 9011, pp. 472–483. Springer, Cham (2015). doi:10.1007/978-3-319-15702-3_46

    Chapter  Google Scholar 

  24. Lasota, T., Sawiłow, E., Telec, Z., Trawiński, B., Roman, M., Matczuk, P., Popowicz, P.: Enhancing intelligent property valuation models by merging similar cadastral regions of a municipality. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS, vol. 9330, pp. 566–577. Springer, Cham (2015). doi:10.1007/978-3-319-24306-1_55

    Chapter  Google Scholar 

  25. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann, Burlington (2016)

    Google Scholar 

  26. Krasnoborski, J.: Management of a cadastral map on a mobile platform (in Polish). Engineer’s thesis. Wroclaw University of Science and Technology, Wrocław (2015)

    Google Scholar 

  27. Piwowarczyk, M.: Web application to aid in real estate appraisal (in Polish). Engineer’s thesis. Wroclaw University of Science and Technology, Wrocław (2015)

    Google Scholar 

  28. Talaga, M.: Mobile application to aid in real estate appraisal (in Polish). Engineer’s thesis. Wroclaw University of Science and Technology, Wrocław (2015)

    Google Scholar 

  29. Trawiński, B., Telec, Z., Krasnoborski, J., Piwowarczyk, M., Talaga, M., Lasota, T., Sawiłow, E.: Comparison of expert algorithms with machine learning models for a real estate appraisal system. In: 2017 IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA 2017). IEEE (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński, Zbigniew Telec & Marcin Kutrzyński

  2. Department of Spatial Management, Wrocław University of Environmental and Life Sciences, Wrocław, Poland

    Tadeusz Lasota & Olgierd Kempa

Authors
  1. Bogdan Trawiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tadeusz Lasota
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olgierd Kempa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zbigniew Telec
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marcin Kutrzyński
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bogdan Trawiński .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management , Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Trawiński, B., Lasota, T., Kempa, O., Telec, Z., Kutrzyński, M. (2017). Comparison of Ensemble Learning Models with Expert Algorithms Designed for a Property Valuation System. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67074-4_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67073-7

  • Online ISBN: 978-3-319-67074-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation