Have a personal or library account? Click to login

Testing the Effectiveness of Outlier Detecting Methods in Property Classification

Real Estate Management and Valuation
Volume 28 (2020): Issue 4 (December 2020)
By:
Open Access
|Dec 2020

References

  1. Aggarwal, C. C. (2017). Outlier Analysis (2nd ed.). Springer Publishing Company. https://doi.org/10.1007/978-3-319-47578-310.1007/978-3-319-47578-3
    Search in Google ScholarBack to article
  2. Angiulli, F., & Pizzuti, C. (2002). Fast Outlier Detection in High Dimensional Spaces, In: Elomaa T., Mannila H., Toivonen H. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2002. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol. 2431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45681-3_210.1007/3-540-45681-3_2
    Search in Google ScholarBack to article
  3. Baldominos, A., Blanco, I., Moreno, A. J., Iturrarte, R., Bernárdez, Ó., & Afonso, C. (2018). Identifying Real Estate Opportunities Using Machine Learning. Applied Sciences (Basel, Switzerland), 8(11), 2321. https://doi.org/10.3390/app811232110.3390/app8112321
    Search in Google ScholarBack to article
  4. Batóg, B., & Foryś, I. (2011). Logit models in the analysis of transactions on the Warsaw residential market in Polish: Modele logitowe w analizie transakcji na warszawskim rynku mieszkaniowym. Studia i Materiały Towarzystwa Naukowego Nieruchomości, 19(3), 33–48.
    Search in Google ScholarBack to article
  5. Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. SIGMOD Record, 29(2), 93–104. https://doi.org/10.1145/335191.33538810.1145/335191.335388
    Search in Google ScholarBack to article
  6. Cook, R. D. (1977). Detection of Influential Observation in Linear Regression. Technometrics, 19(1), 15–18.
    Search in Google ScholarBack to article
  7. Domingues, R., Filippone, M., Michiardi, P., & Zouaoui, J. (2018). A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, 406–421. https://doi.org/10.1016/j.patcog.2017.09.03710.1016/j.patcog.2017.09.037
    Search in Google ScholarBack to article
  8. Doszyń, M. (Ed.). (2020). Attribute influence matrix calibration system in Szczecin’s algorithm of mass property valuation. University of Szczecin.
    Search in Google ScholarBack to article
  9. Doszyń, M., Gnat, S., & Bas, M. (2017). The Econometric Procedures of Specific Transactions Identification. Folia Oeconomica Stetinensia, 17(1), 20–30. https://doi.org/10.1515/foli-2017-000210.1515/foli-2017-0002
    Search in Google ScholarBack to article
  10. Etel, L., & Dowgier, R. (2013). Local taxes and charges – time for a change in Polish: Podatki i opłaty lokalne – czas na zmiany, Białystok. Temida : Casopis o Viktimizaciji, Ljudskim Pravima i Rodu, 2.
    Search in Google ScholarBack to article
  11. Głuszak, M., & Marona, B. (2015). Cadastral tax. Economic conditions of the property taxation reform in Polish Podatek katastralny. Ekonomiczne uwarunkowania reformy opodatkowania nieruchomości. Poltext.
    Search in Google ScholarBack to article
  12. Gnat, S. (2009). Analysis of the effects of replacing current property tax with ad valorem property tax in a sample municipality. Folia Oeconomica Stetinensia, 8(16), 82-98.10.2478/v10031-009-0022-6
    Search in Google ScholarBack to article
  13. Gnat, S. (2010). Use of operational research methods in modelling the impact of cadastral tax on the financial situation of the municipality in Polish: Wykorzystanie metod badań operacyjnych w modelowaniu wpływu podatku katastralnego na sytuację finansową gminy. Doctoral Dissertation, Univesrity of Szczecin, Szczecin.
    Search in Google ScholarBack to article
  14. Gnat, S. (2018). Analysis of Communes’ Potential Fall in Revenue Following Introduction of ad Valorem Property Tax. Real Estate Management and Valuation, 26(1), 63–72. https://doi.org/10.2478/remav-2018-000610.2478/remav-2018-0006
    Search in Google ScholarBack to article
  15. Grubbs, F. E. (1969). Procedures for Detecting Outlying Observations in Samples. Technometrics, 11(1), 1–21. https://doi.org/10.1080/00401706.1969.1049065710.1080/00401706.1969.10490657
    Search in Google ScholarBack to article
  16. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer. https://doi.org/10.1007/978-0-387-84858-710.1007/978-0-387-84858-7
    Search in Google ScholarBack to article
  17. Hawkins, D. (1980). Identification of Outliers. Chapman and Hall. https://doi.org/10.1007/978-94-015-3994-410.1007/978-94-015-3994-4
    Search in Google ScholarBack to article
  18. He, Z., Xu, X., & Deng, S. (2003). Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9-10), 1641–1650. https://doi.org/10.1016/S0167-8655(03)00003-510.1016/S0167-8655(03)00003-5
    Search in Google ScholarBack to article
  19. Johnson, R. (1992). Applied Multivariate Statistical Analysis. Prentice Hall.
    Search in Google ScholarBack to article
  20. Jolliffe, I. (2002). Principal Component Analysis (2nd ed.). Springer.
    Search in Google ScholarBack to article
  21. Kontrimas, V., & Verikas, A. (2006). Tracking of doubtful real estate transaction by outlier detection methods: A comparative study. Information Technology and Control, 35(2), 94–105.
    Search in Google ScholarBack to article
  22. Liu, F. T., Ting, K. M., & Zhou, Z. 2008, Isolation Forest, Eighth IEEE International Conference on Data Mining, Pisa, 2008, 413-422.
    Search in Google ScholarBack to article
  23. Liu, H., Shah, S., & Jiang, W. (2004). On-line outlier detection and data cleaning. Computers & Chemical Engineering, 28(9), 1635–1647. https://doi.org/10.1016/j.compchemeng.2004.01.00910.1016/j.compchemeng.2004.01.009
    Search in Google ScholarBack to article
  24. Maimon, O. (ed.), & Rokach, L. (ed.). (2005). Data Mining and Knowledge Discovery Handbook. Springer-Verlag. https://doi.org/10.1007/b10740810.1007/b107408
    Search in Google ScholarBack to article
  25. Morano, P., De Mare, G., & Tajani, F. (2013). LMS for Outliers Detection in the Analysis of a Real Estate Segment of Bari. In B. Murgante, . . . (Eds.), Lecture Notes in Computer Science: Vol. 7974. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Springer. https://doi.org/10.1007/978-3-642-39649-6_3310.1007/978-3-642-39649-6_33
    Search in Google ScholarBack to article
  26. Ng, K. H., & Khor, K. (2016). Rapid identification of outstanding real estate investment trusts with outlier detection algorithms. Journal of Theoretical and Applied Information Technology, 88(2), 321–330.
    Search in Google ScholarBack to article
  27. Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249. https://doi.org/10.1016/j.sigpro.2013.12.02610.1016/j.sigpro.2013.12.026
    Search in Google ScholarBack to article
  28. Prastawa, M., Bullitt, E., Ho, S., & Gerig, G. (2004). A brain tumor segmentation framework based on outlier detection. Medical Image Analysis, 8(3), 275–283. https://doi.org/10.1016/j.media.2004.06.007 PMID:1545022210.1016/j.media.2004.06.007
    Search in Google ScholarBack to article
  29. Raschka, S. (2018). MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack. Journal of Open Source Software, 3(24), 638. https://doi.org/10.21105/joss.0063810.21105/joss.00638
    Search in Google ScholarBack to article
  30. Sammut, C. (ed.), & Webb, G. I. (ed.). (2017). Encyclopedia of Machine Learning and Data Mining (2nd ed.). Springer Publishing Company. https://doi.org/10.1007/978-1-4899-7687-110.1007/978-1-4899-7687-1
    Search in Google ScholarBack to article
  31. Stefansky, W. (1972). Rejecting Outliers in Factorial Designs. Technometrics, 14(2), 469–479. https://doi.org/10.1080/00401706.1972.1048893010.1080/00401706.1972.10488930
    Search in Google ScholarBack to article
  32. Śpiewak, B. (2018). Application of Chosen Methods of Robust Estimation: Baarda’s and Huber’s in Search for Outliers in the Real Estate Market Modeling. Folia Oeconomica Stetinensia, 18(1), 27–38. https://doi.org/10.2478/foli-2018-000310.2478/foli-2018-0003
    Search in Google ScholarBack to article
  33. Trojanek, M., & Kisiała, W. (2016). The Diversification of Communes’ Revenue from Real Estate Across Provinces. Real Estate Management and Valuation, 24(2), 36–49. https://doi.org/10.1515/remav-2016-001210.1515/remav-2016-0012
    Search in Google ScholarBack to article
  34. Wójtowicz, K. (2006). Analysis of potential effects of real estate tax system reform in Poland in Polish: Analiza potencjalnych skutków reformy systemu opodatkowania nieruchomości w Polsce. Finanse Publiczne. UMCS.
    Search in Google ScholarBack to article
  35. Worden, K., Manson, G., & Fieller, N. R. J. (2000). Damage detection using outlier analysis. Journal of Sound and Vibration, 229(3), 647–667. https://doi.org/10.1006/jsvi.1999.251410.1006/jsvi.1999.2514
    Search in Google ScholarBack to article
  36. Zhao, Y., Nasrullah, Z., & Li, Z. (2019). PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of Machine Learning Research, 20(96), 1–7.
    Search in Google ScholarBack to article
  37. Zhu, C., Kitagawa, H., Papadimitriou, S., & Faloutsos, C. (2011). Outlier detection by example. Journal of Intelligent Information Systems, 36, 217–247. https://doi.org/10.1007/s10844-010-0128-110.1007/s10844-010-0128-1
    Search in Google ScholarBack to article
  38. Zyga, J. (2016). Connection Between Similarity and Estimation Results of Property Values Obtained by Statistical Methods. Real Estate Management and Valuation, 24(3), 5–15. https://doi.org/10.1515/remav-2016-001710.1515/remav-2016-0017
    Search in Google ScholarBack to article
DOI: https://doi.org/10.1515/remav-2020-0033 | Journal eISSN: 2300-5289
Journal RSS Feed
Language: English
Page range: 81 - 92
Published on: Dec 12, 2020
Published by: Real Estate Management and Valuation
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
outlier detection,
logistic regression,
real estate market analysis,
property taxation
Related subjects:
Business and economics,
Political economics,
Political economics, other

© 2020 Sebastian Gnat, published by Real Estate Management and Valuation
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 28 (2020): Issue 4 (December 2020)Next article