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Property Mass Valuation on Small Markets

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  • Sebastian Gnat

    (Department of Econometrics and Statistics, Institute of Economics and Finance, University of Szczecin, Mickiewicza 64, 71-101 Szczecin, Poland)

Abstract

The main bases for land taxation are its area or value. In many countries, especially in Eastern Europe, reforms of property taxation, including land taxation, are being carried out or planned, introducing property value as a tax base. Practice and research in this area indicate that such a change in the tax system leads to large changes in land use and reallocation. The taxation of land value requires construction of mass valuation system. Different methodological solutions can serve this purpose. However, mass land valuation requires a large amount of information on property transactions. Such data are not available in every case. The main objective of the paper is to evaluate the possibility of applying selected algorithms of machine learning and a multiple regression model in property mass valuation on small, underdeveloped markets, where a scarce number of transactions takes place or those transactions demonstrate little volatility in terms of real property attributes. A hypothesis is verified according to which machine learning methods result in more accurate appraisals than multiple regression models do, considering the size of training datasets. Three types of models were employed in the study: a multiple regression model, k nearest neighbor regression algorithm and XGBoost regression algorithm. Training sets were drawn from a larger dataset 1000 times in order to draw conclusions for averaged results. Thanks to the application of KNN and XGBoost algorithms, it was possible to obtain models much more resistant to a low number of observations, a substantial number of explanatory variables in relation to the number of observations, a low property attributes variability in the training datasets as well as collinearity of explanatory variables. This study showed that algorithms designed for large datasets can provide accurate results in the presence of a limited amount of data. This is a significant observation given that small or underdeveloped real estate markets are not uncommon.

Suggested Citation

  • Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:4:p:388-:d:532550
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    References listed on IDEAS

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    1. Jasmina Ćetković & Slobodan Lakić & Marijana Lazarevska & Miloš Žarković & Saša Vujošević & Jelena Cvijović & Mladen Gogić, 2018. "Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    2. Alexander N. Bogin & Jessica Shui, 2020. "Appraisal Accuracy and Automated Valuation Models in Rural Areas," The Journal of Real Estate Finance and Economics, Springer, vol. 60(1), pages 40-52, February.
    3. Alexander N. Bogin & Jessica Shui, 2020. "Correction to: Appraisal Accuracy and Automated Valuation Models in Rural Areas," The Journal of Real Estate Finance and Economics, Springer, vol. 61(4), pages 730-731, November.
    4. Jozef Zurada & Alan S. Levitan & Jian Guan, 2011. "A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context," Journal of Real Estate Research, American Real Estate Society, vol. 33(3), pages 349-388.
    5. Konstantinos Tzioumis, 2017. "Appraisers and Valuation Bias: An Empirical Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 45(3), pages 679-712, July.
    6. Usman Hamza & Lizam Mohd & Adekunle Muhammad Usman, 2020. "Property Price Modelling, Market Segmentation and Submarket Classifications: A Review," Real Estate Management and Valuation, Sciendo, vol. 28(3), pages 24-35, September.
    7. Marco Locurcio & Pierluigi Morano & Francesco Tajani & Felicia Di Liddo, 2020. "An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context," Sustainability, MDPI, vol. 12(14), pages 1-29, July.
    8. Pace, R Kelley, 1996. "Relative Performance of the Grid, Nearest Neighbor, and OLS Estimators," The Journal of Real Estate Finance and Economics, Springer, vol. 13(3), pages 203-218, November.
    9. W.J. McCluskey & M. McCord & P.T. Davis & M. Haran & D. McIlhatton, 2013. "Prediction accuracy in mass appraisal: a comparison of modern approaches," Journal of Property Research, Taylor & Francis Journals, vol. 30(4), pages 239-265, December.
    10. Earl D. Benson & Arthur L. Schwartz, Jr., 2000. "An Examination of Vertical Equity Over Two Reassessment Cycles," Journal of Real Estate Research, American Real Estate Society, vol. 19(3), pages 255-273.
    11. Mark A. Sunderman & John W. Birch & Roger E. Cannaday & Thomas W. Hamilton, 1990. "Testing for Vertical Inequity in Property Tax Systems," Journal of Real Estate Research, American Real Estate Society, vol. 5(3), pages 319-334.
    12. Yunjong Kim & Seungwoo Choi & Mun Yong Yi, 2020. "Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
    13. Steffen Metzner & Andreas Kindt, 2017. "Determination of the parameters of automated valuation models for the hedonic property valuation of residential properties," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 11(1), pages 73-100, December.
    14. Jorge Iván Pérez-Rave & Juan Carlos Correa-Morales & Favián González-Echavarría, 2019. "A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes," Journal of Property Research, Taylor & Francis Journals, vol. 36(1), pages 59-96, January.
    15. Bradbury, Katharine L. & Mayer, Christopher J. & Case, Karl E., 2001. "Property tax limits, local fiscal behavior, and property values: evidence from Massachusetts under Proposition," Journal of Public Economics, Elsevier, vol. 80(2), pages 287-311, May.
    16. Arthur Korteweg & Morten Sorensen, 2016. "Estimating Loan-to-Value Distributions," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 44(1), pages 41-86, February.
    17. Vincenzo Del Giudice & Pierfrancesco De Paola & Fabiana Forte & Benedetto Manganelli, 2017. "Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples," Sustainability, MDPI, vol. 9(11), pages 1-17, November.
    18. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    19. Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
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    5. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    6. Sevgen Sibel Canaz & Tanrivermiş Yeşim, 2024. "Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 100-111.

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