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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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    Cited by:

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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.
    7. Elena Bykowa & Maria Skachkova & Ivan Raguzin & Irina Dyachkova & Maxim Boltov, 2022. "Automation of Negative Infrastructural Externalities Assessment Methods to Determine the Cost of Land Resources Based on the Development of a “Thin Client” Model," Sustainability, MDPI, vol. 14(15), pages 1-29, July.
    8. Numan Jamal A. A. & Yusoff Izham Mohamad, 2024. "Identifying the Current Status of Real Estate Appraisal Methods," Real Estate Management and Valuation, Sciendo, vol. 32(4), pages 12-27.
    9. Elena Bykowa & Tatyana Banikevich & Natalia Zalivatskaya & Oksana Pirogova, 2024. "Modeling the Cadastral Value of Land Plots of Gardening and Horticultural Non-Profit Partnerships Taking into Account the Influence of Local Factors of the Territory," Land, MDPI, vol. 13(7), pages 1-17, July.
    10. Hosseini, Hamid & Atazadeh, Behnam & Rajabifard, Abbas, 2025. "Towards intelligent land administration systems: Research challenges, applications and prospects in AI-driven approaches," Land Use Policy, Elsevier, vol. 157(C).

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