Skip to main content
SpringerLink
Log in

Optimization of Location Attractiveness Zones for the Purpose of Property Mass Appraisal

  • Conference paper
  • First Online:
Book cover Experimental and Quantitative Methods in Contemporary Economics (CMEE 2018)

Part of the book series: Springer Proceedings in Business and Economics ((SPBE))

  • 678 Accesses

Abstract

Mass land appraisal is a specific type of appraisal. A suitable algorithm used for mass appraisal constitutes one of the main determinants of the quality of achieved results. Many of the algorithms cluster real estate in accordance with specific criteria, such as a real estate type, its characteristics or location. Assuming a “real estate location” as a clustering basis, we use terms such as a taxation zone (which originates from a basic application of mass appraisal, which involves ad valorem real estate taxation), along with an elementary terrain (which originates from the nomenclature used in spatial planning). The main goal of this chapter is to find the solution to the designation of such zones, or real estate clusters for the purpose of mass land appraisal conducted with the use of Szczecin Algorithm of Real estate Mass Appraisal (SAREMA). Each mass appraisal algorithm has its own specificity, and consequently, it may pose various requirements to designated zones or real estate clusters (here referred to as location attractiveness zones—LAZ’s). The chapter presents the methods of LAZ’s designation formed on the basis of the existing cadastral districts, real estate market analysis, and hierarchical clustering. The chapter is additionally focused on the manner of determining LAZ optimal number and consequently the number of properties that will be included in an automated mass appraisal process in a given zone. The uniformity of isolated LAZ’s was subject to verification with the use of an entropy ratio, especially constructed for that purpose. The study was conducted on the land plots located in the city of Szczecin.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arguelles, M., Benavides, C., Fernandez, I.: A new approach to the identification of regional clusters: hierarchical clustering on principal components. Appl. Econ. 46(21), 2511–2519 (2014). https://doi.org/10.1080/00036846.2014.904491

    Article  Google Scholar 

  2. Boongoen, T., Iam-On, N.: Cluster ensembles: a survey of approaches with recent extensions and applications. Comput. Sci. Rev. 28, 1–25 (2018). https://doi.org/10.1016/j.cosrev.2018.01.003

    Article  Google Scholar 

  3. Bradbury, K.L., Mayer, C.J., Case, K.E.: Property tax limits, local fiscal behavior, and property values: evidence from Massachusetts under Proposition 2,5. J. Public Econ. 80, 287–311 (2001)

    Article  Google Scholar 

  4. Davidson, I., Ravi, S.S.: Agglomerative hierarchical clustering with constraints: theoretical and empirical results. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) Knowledge Discovery in Databases: PKDD 2005. Lecture Notes in Computer Science vol. 3721, pp. 59–70. Springer, Berlin, Heidelberg (2005). https://doi.org/10.1007/11564126_1

    Google Scholar 

  5. Dedkova, O., Polyakova, I.: Development of mass valuation in Republic of Belarus. Geomatics Environ. Eng. 12(3), 29–39 (2018). https://doi.org/10.7494/geom.2018.12.3.29

    Article  Google Scholar 

  6. Doszyń, M.: Ekonometryczna wycena nieruchomości. Studia i Prace Wydziału Nauk Ekonomicznych i Zarządzania 26, 41–52 (2012)

    Google Scholar 

  7. Fang, Y.X., Wang, Y.H.: Selection of the number of clusters via the bootstrap method. Comput. Stat. Data Anal. 56, 468–477 (2012). https://doi.org/10.1016/j.csda.2011.09.003

    Article  Google Scholar 

  8. Guo, G.: Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). Int. J. Geogr. Inf. Sci. 22(7), 801–823 (2008). https://doi.org/10.1080/13658810701674970

    Article  Google Scholar 

  9. Hozer, J.: Regresja wieloraka a wycena nieruchomości. Rzeczoznawca Majątkowy 2, 13–14 (2001)

    Google Scholar 

  10. Hozer, J., Kokot, S., Foryś, I., Zwolankowska, M., Kuźmiński, W.: Ekonometryczny algorytm masowej wyceny nieruchomości gruntowych. Uniwersytet Szczeciński, Stowarzyszenie Pomoc i Rozwój, Szczecin (1999)

    Google Scholar 

  11. Hozer, J., Kokot, S., Kuźmiński, W.: Metody analizy statystycznej rynku w wycenie nieruchomości. PFSRM, Warszawa (2002)

    Google Scholar 

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

    Google Scholar 

  13. Kantardzic, M.: Data Mining. Concepts, Models, Methods, and Algorithms. Wiley-IEEE Press (2003)

    Google Scholar 

  14. Kokot, S.: Model wielu regresji pojedynczych w wycenie nieruchomości. Studia i Materiały Towarzystwa Naukowego Nieruchomości 12(1), 106–122 (2004)

    Google Scholar 

  15. Kolesnikov, A., Trichina, E., Kauranne, T.: Estimating the number of clusters in a numerical data set via quantization error modeling. Pattern Recogn. 48(3), 941–952 (2015). https://doi.org/10.1016/j.patcog.2014.09.017

    Article  Google Scholar 

  16. Korteweg, A., Sorensen, M.: Estimating loan-to-value distributions. Real Estate Econ. 44(1), 41–86 (2016). https://doi.org/10.1111/1540-6229.12086

    Article  Google Scholar 

  17. Kotkowski, P.: Propozycja nowej klasyfikacji terenów miejskich. Acta Universitatis Lodziensis, Folia Geographica Socio-Oeconomica 2, 115–124 (1999)

    Google Scholar 

  18. Lis, C.: Sieci neuronowe a masowa wycena nieruchomości, vol. 318, pp. 331–338. Zeszyty Naukowe Uniwersytetu Szczecińskiego, Prace Katedry Ekonometrii i Statystyki, “Mikroekonometria w Teorii i Praktyce” (2001)

    Google Scholar 

  19. Müller, A.C., Guido, S.: Introduction to Machine Learning with Python. O’Reilly, Sebastopol (2016)

    Google Scholar 

  20. Park, B., Bae, J.K.: Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia housing data. Expert Syst. Appl. 42, 2928–2934 (2015)

    Article  Google Scholar 

  21. Prystupa, M.: O potrzebie dalszych prac nad zastosowaniem regresji wielorakiej w wycenie nieruchomości. Rzeczoznawca Majątkowy 4, 16–17 (2000)

    Google Scholar 

  22. Raschka, S., Mirjalili, V.: Python Machine Learning. Packt Publishing, Birmingham, Mumbai (2017)

    Google Scholar 

  23. Sawiłow, E.: Analiza wybranych metod modelowania wartości katastralnych nieruchomości. Acta Scientiarum Polonorum Geodesia et Descriptio Terrarum 8(2), 27–38 (2009)

    Google Scholar 

  24. Suchecka, J. (ed.): Statystyka przestrzenna. C.H. Beck, Warsaw (2014)

    Google Scholar 

  25. Surowiec, G., Malczewska, A.: Automatyzacja procesu wyodrębniania stref izowartościowych nieruchomości w oparciu o zdjęcia lotnicze. Archiwum Fotogrametrii, Kartografii i Teledetekcji 11, 21–35 (2001)

    Google Scholar 

  26. Tzioumis, K.: Appraisers and valuation bias: an empirical analysis. Real Estate Econ. 45(3), 679–712 (2017)

    Article  Google Scholar 

  27. Unpingco, J.: Python for Probability, Statistics, and Machine Learning. Springer International Publishing (2016)

    Google Scholar 

  28. Wiśniewski, R.: Zastosowanie sztucznych sieci neuronowych do wyceny masowej. Wycena 1, 15–20 (1998)

    Google Scholar 

  29. Wu, X., Ma, T., Cao, J., Tian, Y., Alabdulkarim, A.: A comparative study of clustering ensemble algorithms. Comput. Electr. Eng. 68, 603–615 (2018). https://doi.org/10.1016/j.compeleceng.2018.05.005

    Article  Google Scholar 

  30. 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), 349–387 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Economics and Management, University of Szczecin, ul. Mickiewicza 64, 71-101, Szczecin, Poland

    Sebastian Kokot & Sebastian Gnat

Authors
  1. Sebastian Kokot
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastian Gnat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastian Gnat .

Editor information

Editors and Affiliations

  1. Faculty of Economics and Management, Institute of IT in Management, University of Szczecin, Szczecin, Poland

    Kesra Nermend

  2. Faculty of Economics and Management, Institute of IT in Management, University of Szczecin, Szczecin, Poland

    Małgorzata Łatuszyńska

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kokot, S., Gnat, S. (2020). Optimization of Location Attractiveness Zones for the Purpose of Property Mass Appraisal. In: Nermend, K., Łatuszyńska, M. (eds) Experimental and Quantitative Methods in Contemporary Economics. CMEE 2018. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-30251-1_20

Download citation

Publish with us

Policies and ethics

Search

Navigation