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№ 2/2022

№ 2/2022

Fìnansi Ukr. 2022 (2): 72–87
https://doi.org/10.33763/finukr2022.02.072

ACCOUNTING AND AUDIT

BONDAR Mykola 1, KULYK Andriy 2

1SHEE “Kyiv National Economic University named after Vadym Hetman”
OrcID ID : https://orcid.org/0000-0002-1904-1211
2SHEE “Kyiv National Economic University named after Vadym Hetman”
OrcID ID : https://orcid.org/0000-0001-7791-3551


Methods and models of real estate mass appraisal


This article examines the approaches, methods and models of mass appraisal. The article also considers features of application of valuation approaches and methods when conducting mass appraisal taking into account the type of real estate, the impact of price-forming factors of demand and supply and etc. Based on the analysis of scientific works of foreign and domestic scientists, the classification of mass appraisal models is given, which includes parametric multiple regression models, spatial and nonparametric models, as well as more modern methods, in particular, artificial neural networks, genetic algorithms, rough set theory and etc. Hedonistic models as the most common type of parametric multiple regression have been studied. In particular, the mathematical interpretation of this model is given, the key pricing factors that are used as explanatory variables in the construction of the model are analyzed, as well as different types of hedonistic models, their advantages and disadvantages are considered. As complementation of the traditional hedonistic model, spatial models are investigated , while nonparametric models are studied as an alternative. In particular, foreign scientists’ works are analyzed, which consider the comparative analysis of the effectiveness of use of the above-mentioned models. The article also considers modern methods, in particular artificial neural networks, genetic algorithms, rough set theory and expert models, the use of which in the context of mass appraisal is the subject of discussion among scientists. Based on the results of the analysis, criteria are formulated which impact the choice of application of specific mass appraisal methods and models. Further research will be directed towards a more detailed study of the hedonistic model in order to develop the latter as a basic model of mass appraisal on the example of the residential real estate market of one of the districts in Kyiv.

Keywords:mass appraisal, valuation approaches, mass appraisal methods and models

JEL: R30


BONDAR M. . Methods and models of real estate mass appraisal / M. . BONDAR, A. Kulyk // Фінанси України. - 2022. - № 2. - C. 72-87.

Article original in Ukrainian (pp. 72 - 87) DownloadDownloads :126
1. Gloudemans, R. J., & Almy, R. R. (2011). Fundamentals of Mass Appraisal. Kansas City: IAAO.
2. Kauko, T., & D’Amato, M. (2008). Mass Appraisal Methods: An international perspective for property valuers. RICS Research.
3. Borst, R. A., & McCluskey, W. J. (2008). Using geographically weighted regression to detect housing submarkets: modeling large-scale spatial variations in value. Journal of Property Tax Assessment & Administration, 5 (1), 21–54. Retrieved from researchex- change.iaao.org/jptaa/vol5/iss1/2.
4. McCluskey, W., Davis, P., Haran, M., McCord, M., & McIlhatton, D. (2012). The potential of artificial neural networks in mass appraisal: the case revisited. Journal of Financial Managementof Propertyand Construction, 17 (3), 274–292. doi.org/10.1108/13664381211274371
5. Kirichek, Yu. O., Land, Ie. O., & Haidenko, Ie. Yu. (2012). Valuation of real estate, including land for tax purposes. Bulletin of Prydniprovs’ka State Academyof Civil Engineering and Architecture, 12, 7–12 [in Ukrainian].
6. Drapikovskyi, O. I., & Ivanova, I. B. (2013). Models of mass assessment of urban lands. Bulletin of Prydniprovs’ka State Academy of Civil Engineering and Architecture, 7, 19–28 [in Ukrainian].
7. IVSC. (2019). International Valuation Standards. London, UK.
8. IAAO (2012). Standard on Mass Appraisal of Real Property. Kansas City, Missouri, USA. Retrieved from www.iaao.org/media/standards/StandardOnMassAppraisal.pdf.
9. Wang, D., & Jing Li, V. (2019). Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review. Sustainability, 11 (24).doi.org/10.3390/su11247006
10. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers. doi.org/10.1007/978-94-015-7799-1
11. Lockwood, T., & Rossini, P. (2011). Efficacy in Modelling Location within the Mass Appraisal Process. Pacific Rim Property Research Journal, 17 (3), 418–442. doi.org/10.1080/14445921.2011.11104335
12. Dimopoulos, T., & Moulas A. (2016). A Proposal of a Mass Appraisal System in Greece with CAMA System: Evaluating GWR and MRA techniques in Thessaloniki Muni- cipality. Open Geosciences, 8 (1), 675–693. doi.org/10.1515/geo-2016-0064
13. Wu, C., Ye, X., Ren, F., & Du, Q. (2018). Modified Data-Driven Framework for Housing Market Segmentation. Journal of Urban Planning and Development, 144 (4). doi.org/10.1061/(ASCE)UP.1943-5444.0000473
14. Belyaeva, A. V. (2012). Spatial models in mass appraisal of real estate. Computer Research and Modeling, 4 (3), 639–650. doi.org/10.20537/2076-7633-2012-4-3-639-650
15. D’Amato, M. (2010). A Location Value Response Surface Model for Mass Appraising: An Iterative Location Adjustment Factor in Bari, Italy. International Journal of Strategic Property Management, 14 (3), 231–244. doi.org/10.3846/ijspm.2010.17
16. Verkooijen, W. J. H. (1996). Neutral networks in Economic Modelling (Doctoral dissertation). Tilburg University, Center for Economic Research.
17. Messe, R., & Wallace, N. (1991) Nonparametric Estimation of Dynamic Hedonic Price Models and Construction of Residential Housing Price Indices. Real Estate Economics, 19 (3), 308–332. doi.org/10.1111/1540-6229.00555
18. Pace, R. K. (1995). Parametric, Semiparametric, and Nonparametric Estimation of Cha- racteristic Values within Mass Assessment and Hedonic Pricing Models. The Journal of Real Estate Finance and Economics, 11 (3), 195–217. doi.org/10.1007/BF01099108
19. Mc Cluskey, W. J., & Anand, S. (1999). The application of intelligent hybrid techniques for the mass appraisal of residential properties. Journal of Property Investment & Finance, 17 (3), 218–238. doi.org/10.1108/14635789910270495
20. McCluskey, W. J., McCord, M., Davis, P. T., Haran, M., & McIlhatton, D. (2013). Prediction accuracy in mass appraisal: A comparison of modern approaches. Journal of Property Research, 30 (4), 239–265. doi.org/10.1080/09599916.2013.781204
21. Worzala, E., Lenk, M., & Silva, A. (1995). An Exploration of Neural Networks and Its Application to Real Estate Valuation. Journal of Real Estate Research, 10 (2), 185–201. doi.org/10.1080/10835547.1995.12090782
22. Nguyen, N., & Cripps, A. (2001). Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks. Journal of Real Estate Research, 22 (3), 313–336. doi.org/10.1080/10835547.2001.12091068
23. Cooley, R. E., Pack, A. D., Hobbs, M., & Clewer, A. D. E. (1994). A Genetic Algorithm for Modelling Locational Effects on Residential Property Prices. In The Cutting Edge 1994 Conference Proceedings, pp. 179–193.
24. Ahn, J. J., Byun, H. W., Oh, K. J., & Kim, T. Y. (2012). Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting. Expert Systems with Appli- cations, 39 (9), 8369–8379. doi.org/10.1016/j.eswa.2012.01.183
25. Pawlak, Z. (1982). Rough Sets. International Journal of Computer and Information Science, 11, 341–356. doi.org/10.1007/BF01001956
26. Pawlak, Z. (1991). Rough Sets. Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Publisher. doi.org/10.1007/978-94-011-3534-4
27. D’Amato, M. (2002). Appraising Properties with Rough Set Theory. Journal of Property Investment and Finance, 20 (4), 406–418. doi.org/10.1108/14635780210435074
28. D’Amato, M. (2004). A comparison between MRA and Rough Set Theory for Mass Appraisal. A case in Bari. International Journal of Strategic Property Management, 8 (4), 205–217. doi.org/10.3846/1648715X.2004.9637518
29. D’Amato, M. (2004). Un’applicazione della RST per mass appraisal: il caso di Amsterdam. Rivista del Consulente Tecnico, 2, 260–282. Retrieved from iris.poliba.it/ handle/11589/7739#.YXWXm5rP1PZ.
30. Del Giudice, V., De Paola, P., & Cantisani, G. B. (2017). Rough Set Theory for Real Estate Appraisals: An Application to Directional District of Naples. Buildings, 7 (1). doi.org/10.3390/buildings7010012
31. Kilpatrick, J. (2018). Expert problem solving practice in commercial property valuation: an exploratory study. Journal of Property Investment & Finance, 36 (4). doi.org/10.1108/JPIF-05-2017-0037
32. Ferreira, F. A. F.; Spahr, R. W., & Sunderman, M. A. (2016). Using multiple criteria decision analysis (MCDA) to assist in estimating residential housing values. International Journal of Strategic Property Management, 20 (4), 354–370. doi.org/10.3846/1648715X.2015.1122668
33. Naderi, I., Sharbatoghlie, A., & Vafaeimehr, A. (2012). Housing valuation model: An investigation of residential properties in Tehran. International Journal of Housing Markets and Analysis, 5 (1), 20–40. doi.org/10.1108/17538271211206644