Skip to main content

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

  • Conference paper
  • First Online:
Computational Collective Intelligence (ICCCI 2018)

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.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

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. 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 

  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)

    Article  Google Scholar 

  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. 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. 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. 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 

  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/10.1007/978-3-319-49746-4

    Book  Google Scholar 

  9. Goodman, A.C., Thibodeau, T.G.: Housing market segmentation and hedonic prediction accuracy. J. Hous. Econ. 12(3), 181–201 (2003)

    Article  Google Scholar 

  10. Bourassa, S.C., Hoesli, M., Peng, V.S.: Do housing submarkets really matter? J. Hous. Econ. 12, 12–28 (2003)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  13. 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 

  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. 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)

    Article  Google Scholar 

  16. Wu, C., Sharma, R.: Housing submarket classification: the role of spatial contiguity. Appl. Geogr. 32, 746–756 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  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_48

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  22. Lughofer, E.: Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications. STUDFUZZ. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-18087-3

    Book  MATH  Google Scholar 

  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_67

    Chapter  Google Scholar 

  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_34

    Chapter  Google Scholar 

  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. 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 

  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)

    MathSciNet  Google Scholar 

  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_48

    Chapter  Google Scholar 

  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_46

    Chapter  Google Scholar 

  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_55

    Chapter  Google Scholar 

  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. 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_31

    Chapter  Google Scholar 

  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)

    MATH  Google Scholar 

  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. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)

    Article  Google Scholar 

  36. Cox, E.: Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration. Elsevier, Boston (2005)

    MATH  Google Scholar 

  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)

    MathSciNet  Google Scholar 

  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. Wu, K.-L., Yang, M.-S.: A cluster validity index for fuzzy clustering. Pattern Recogn. Lett. 26, 1275–1291 (2005)

    Article  Google Scholar 

  40. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Intell. Inf. Syst. J. 17(2–3), 107–145 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bogdan Trawiński .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Malinowski, A., Piwowarczyk, M., Telec, Z., Trawiński, B., Kempa, O., Lasota, T. (2018). An Approach to Property Valuation Based on Market Segmentation with Crisp and Fuzzy Clustering. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98443-8_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98442-1

  • Online ISBN: 978-3-319-98443-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics