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An Approach to Property Valuation Based on Market Segmentation with Crisp and Fuzzy Clustering

  • Adrian Malinowski
  • Mateusz Piwowarczyk
  • Zbigniew Telec
  • Bogdan TrawińskiEmail author
  • Olgierd Kempa
  • Tadeusz Lasota
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11055)

Abstract

Property valuation is a complex and time-consuming process which is carried out by qualified real estate appraisers. Number of properties and number of purchase-sale transactions grows year by year. Mass real estate appraisal appears as another big problem. These issues are connected with deficiency of human and time resources. Therefore, numerous studies are carried out on computer systems which can support the real estate appraisers. Automated property valuation systems are also developed. A method utilizing clustering algorithms to automate property valuation according to sales comparison approach was proposed in this paper. A crisp and fuzzy clustering algorithms were employed to divide the properties located in a given city into a number of clusters. These clusters established the basis for property valuation process. The effectiveness of the proposed method was examined and compared with the real estate appraisal based on the spatial partition of an area of the city into cadastral regions and expert zones.

Keywords

Property valuation Mass appraisal Sales comparison approach Expert algorithms K-means C-means Submarket segmentation 

References

  1. 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. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Kusan, H., Aytekin, O., Özdemir, I.: The use of fuzzy logic in predicting house selling price. Expert Syst. Appl. 37(3), 1808–1813 (2010)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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).  https://doi.org/10.1007/978-3-319-49746-4CrossRefGoogle Scholar
  9. 9.
    Goodman, A.C., Thibodeau, T.G.: Housing market segmentation and hedonic prediction accuracy. J. Hous. Econ. 12(3), 181–201 (2003)CrossRefGoogle Scholar
  10. 10.
    Bourassa, S.C., Hoesli, M., Peng, V.S.: Do housing submarkets really matter? J. Hous. Econ. 12, 12–28 (2003)CrossRefGoogle Scholar
  11. 11.
    Chen, Z., Cho, S.-H., Poudyal, N., Roberts, R.K.: Forecasting housing prices under different submarket assumptions. Urban Stud. 46(1), 67–87 (2009)Google Scholar
  12. 12.
    Kauko, T., Hooimeijer, P., Hakfoort, J.: Capturing housing market segmentation: an alternative approach based on neural network modelling. Hous. Stud. 17(6), 875–894 (2002)CrossRefGoogle Scholar
  13. 13.
    Kontrimas, V., Verikas, A.: The mass appraisal of the real estate by computational intelligence. Appl. Soft Comput. 11(1), 443–448 (2011)CrossRefGoogle Scholar
  14. 14.
    Shi, D., Guan, J., Zurada, J., Levitan, A.S.: An innovative clustering approach to market segmentation for improved price prediction. J. Int. Technol. Inf. Manag. 24(1), 15–32 (2015)Google Scholar
  15. 15.
    Hayles, K.: The use of GIS and cluster analysis to enhance property valuation modelling in Rural Victoria. J. Spat. Sci. 51(2), 19–31 (2010)CrossRefGoogle Scholar
  16. 16.
    Wu, C., Sharma, R.: Housing submarket classification: the role of spatial contiguity. Appl. Geogr. 32, 746–756 (2012)CrossRefGoogle Scholar
  17. 17.
    Bourassa, S.C., Cantoni, E., Hoesli, M.: Predicting house prices with spatial dependence: a comparison of alternative methods. J. Real Estate Res. 32(2), 139–159 (2010)Google Scholar
  18. 18.
    Woźniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014)CrossRefGoogle Scholar
  19. 19.
    Krawczyk, B., Woźniak, M., Cyganek, B.: Clustering-based ensembles for one-class classification. Inf. Sci. 264, 182–195 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Burduk, R., Walkowiak, K.: Static classifier selection with interval weights of base classifiers. In: Nguyen, N.T., Trawiński, B., Kosala, R. (eds.) ACIIDS 2015. LNCS (LNAI), vol. 9011, pp. 494–502. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-15702-3_48CrossRefGoogle Scholar
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    Lughofer, E.: Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications. STUDFUZZ. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-18087-3CrossRefzbMATHGoogle Scholar
  23. 23.
    Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Exploration of bagging ensembles comprising genetic fuzzy models to assist with real estate appraisals. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 554–561. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04394-9_67CrossRefGoogle Scholar
  24. 24.
    Krzystanek, M., Lasota, T., Telec, Z., Trawiński, B.: Analysis of bagging ensembles of fuzzy models for premises valuation. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds.) ACIIDS 2010. LNCS (LNAI), vol. 5991, pp. 330–339. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12101-2_34CrossRefGoogle Scholar
  25. 25.
    Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: Investigation of the eTS evolving fuzzy systems applied to real estate appraisal. J. Multiple-Valued Log. Soft Comput. 17(2–3), 229–253 (2011)Google Scholar
  26. 26.
    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)CrossRefGoogle Scholar
  27. 27.
    Trawiński, B.: Evolutionary fuzzy system ensemble approach to model real estate market based on data stream exploration. J. Univ. Comput. Sci. 19(4), 539–562 (2013)MathSciNetGoogle Scholar
  28. 28.
    Telec, Z., Trawiński, B., Lasota, T., Trawiński, G.: Evaluation of neural network ensemble approach to predict from a data stream. In: Hwang, D., Jung, Jason J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS (LNAI), vol. 8733, pp. 472–482. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11289-3_48CrossRefGoogle Scholar
  29. 29.
    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 (LNAI), vol. 9011, pp. 472–483. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-15702-3_46CrossRefGoogle Scholar
  30. 30.
    Lasota, T., et al.: 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 (LNAI), vol. 9330, pp. 566–577. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24306-1_55CrossRefGoogle Scholar
  31. 31.
    Trawiński, B., et al.: Comparison of expert algorithms with machine learning models for a real estate appraisal system. In: The 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA 2017). IEEE (2017)Google Scholar
  32. 32.
    Trawiński, B., Lasota, T., Kempa, O., Telec, Z., Kutrzyński, M.: Comparison of ensemble learning models with expert algorithms designed for a property valuation system. In: Nguyen, N.T., Papadopoulos, George A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 317–327. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67074-4_31CrossRefGoogle Scholar
  33. 33.
    Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)zbMATHGoogle Scholar
  34. 34.
    Ankrest, M., Breunig, M., Kriegel, H., Sander, J.: OPTICS: Ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD 1999, pp. 49–60, Philadelphia PA (1999)Google Scholar
  35. 35.
    Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)CrossRefGoogle Scholar
  36. 36.
    Cox, E.: Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration. Elsevier, Boston (2005)zbMATHGoogle Scholar
  37. 37.
    Vendramin, L., Campello, R.J.G.B., Hruschka, E.R.: Relative clustering validity criteria: a comparative overview. Stat. Anal. Data Min. 3(4), 209–235 (2010)MathSciNetGoogle Scholar
  38. 38.
    Tibshirani, R., Walther, G., Hastie., T.: Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B 63(2), 411–423 (2001)Google Scholar
  39. 39.
    Wu, K.-L., Yang, M.-S.: A cluster validity index for fuzzy clustering. Pattern Recogn. Lett. 26, 1275–1291 (2005)CrossRefGoogle Scholar
  40. 40.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Intell. Inf. Syst. J. 17(2–3), 107–145 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Adrian Malinowski
    • 1
  • Mateusz Piwowarczyk
    • 1
  • Zbigniew Telec
    • 1
  • Bogdan Trawiński
    • 1
    Email author
  • Olgierd Kempa
    • 2
  • Tadeusz Lasota
    • 2
  1. 1.Faculty of Computer Science and ManagementWrocław University of Science and TechnologyWrocławPoland
  2. 2.Department of Spatial ManagementWrocław University of Environmental and Life SciencesWrocławPoland

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