Published 2024-08-04
Keywords
- Mass property appraisal,
- Machine learning,
- Property valuations,
- Appraisal for property tax,
- Systematic mapping study
Copyright (c) 2024 Carlos Augusto Zilli, Lia Caetano Bastos, Liane Ramos da Silva
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
The use of machine learning models in mass appraisal of properties for tax purposes has been extensively investigated, generating a growing volume of primary research. This study aims to provide an overview of the machine learning techniques used in this context and analyze their accuracy. We conducted a systematic mapping study to collect studies published in the last seven years that address machine learning methods in the mass appraisal of properties. The search protocols returned 332 studies, of which 22 were selected, highlighting the frequent use of Random Forest and Gradient Boosting models in the last three years. These models, especially Random Forest, have shown predictive superiority over traditional appraisal methods. The measurement of model performance varied among the studies, making it difficult to compare results. However, it was observed that the use of machine learning techniques improves accuracy in mass property appraisals. This article advances the field by summarizing the state of the art in the use of machine learning models for mass appraisal of properties for tax purposes, describing the main models applied, providing a map that classifies, compares, and evaluates the research, and suggesting a research agenda that identifies gaps and directs future studies.
Metrics
References
- Almaslukh, B. (2021). A gradient boosting method for effective prediction of housing prices in complex real estate systems. In International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 217–222. https://doi.org/10.1109/taai51410.2020.00047
- Aydinoglu, A. C., Bovkir, R., & Colkesen, I. (2020). Implementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI. Survey Review, 53, 349–365. https://doi.org/10.1080/00396265.2020.1771967
- Benito, B. M. (2021). SpatialRF: Easy Spatial Regression with Random Forest. R package version 1.1.3. https://doi.org/10.5281/zenodo.5525027
- Bicak, D. (2021). Geographical random forest model evaluation in agricultural drought assessment. Master thesis. Charles University, Faculty of Science, Department of Applied Geoinformatics and Cartography. Prague, 2021.
- Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
- Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80, 571–583. https://doi.org/10.1016/j.jss.2006.07.009
- Budgen, D., Turner, M., Brereton, P., & Kitchenham, B. A. (2008). Using mapping studies in software engineering. In Proceedings of Psychology of Programming Interest Group (PPIG), 8, 195–204. https://ppig.org/files/2008-PPIG-20th-budgen.pdf
- Carranza, J. P., Piumetto, M. A., Lucca, C. M., & da Silva, E. (2022). Mass appraisal as affordable public policy: open data and machine learning for mapping urban land values. Land Use Policy, 119, 106211. https://doi.org/10.1016/j.landusepol.2022.106211
- Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE). Geoscientific Model Development Discussions, 7, 1525–1534. https://doi.org/10.5194/gmd-7-1247-2014
- Chen, L., Babar, M. A., & Zhang, H. (2010). Towards an Evidence-Based Understanding of Electronic Data Sources. In Proceedings of the 14th International Conference on Evaluation and Assessment in Software Engineering (EASE), British Computer Society, Swinton, pp 135–138. https://doi.org/10.14236/ewic/ease2010.17
- Dimopoulos, T., & Bakas, N. P. (2019). Sensitivity Analysis of Machine Learning Models for the Mass Appraisal of Real Estate. Case Study of Residential Units in Nicosia, Cyprus. Remote Sensing, 11(24), 3047. https://doi.org/10.3390/rs11243047
- Doumpos, M., Papastamos, D., Andritsos, D. A., & Zopounidis, C. (2021). Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches. Annals of Operations Research, 306, 415–433. https://doi.org/10.1007/s10479-020-03556-1
- Fontoura Júnior, C. F., Uberti, M. S., & Tachibana, V. M. (2020). Mass appraisal of apartment through geographically weighted regression. Boletim de Ciências Geodésicas, 26, e2020005. https://doi.org/10.1590/s1982-21702020000200005
- Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29, 1189–1232. https://doi.org/10.1214/aos/1013203451
- Garcia, D. C. F, Gattaz, C. C., & Gattaz, N. C. (2019). A relevância do título, do resumo e de palavras-chave para a escrita de artigos científicos. Revista de Administração Contemporânea, 23(3), 1–9. https://doi.org/10.1590/1982-7849rac2019190178.
- Gnat, S. (2021). Property Mass Valuation on Small Markets. Land, 10(4), 388. https://doi.org/10.3390/land10040388
- Ho, W., Tang, B., & Wong, S. W. (2020). Predicting property prices with machine learning algorithms. Journal of Property Research, 38, 48–70. https://doi.org/10.1080/09599916.2020.1832558
- Hong, J., Choi, H., & Kim, W. (2020). A house price valuation based on the random forest approach: the mass appraisal of residential property in South Korea. International Journal of Strategic Property Management, 24(3), 140–152. https://doi.org/10.3846/ijspm.2020.11544
- IAAO (2013). Standard on Ratio Studies. International Association of Assessing Officers. Kansas City, IAAO.
- IEEE (2022). The Impact of Technology in 2022 and Beyond: IEEE Global Survey. Available at: https://transmitter.ieee.org/impact-of-technology-2022/ (accessed 23 May 2022)
- Islam, M. D., Li, B., Lee, C., & Wang, X. (2022). Incorporating spatial information in machine learning: The Moran eigenvector spatial filter approach. Transactions in GIS, 26, 902–922. https://doi.org/10.1111/tgis.12894
- Ja’afar, N.S., Mohamad, J., & Ismail, S. (2021). Machine learning for property price prediction and price valuation: a systematic literature review. Planning Malaysia, 19(3), 411–422. https://doi.org/10.21837/pm.v19i17.1018
- Kim, J., Lee, Y., Lee, M., & Hong, S. (2022). A comparative study of machine learning and spatial interpolation methods for predicting house prices. Sustainability, 14(15), 9056. https://doi.org/10.3390/su14159056
- Kitchenham, B. A., & Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and Software Technology, 55, 2049–2075. https://doi.org/10.1016/j.infsof.2013.07.010
- Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing systematic literature reviews. In Software Engineering, Technical Report EBSE- 2007-01, 1-57. Departament of Computer Science Keele University, Keele.
- Kitchenham, B. A., Brereton, P., & Budgen, D. (2010). The educational value of mapping studies of software engineering literature. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering, Volume 1, 589–598. https://doi.org/10.1145/1806799.1806887
- Kitchenham, B. A., Mendes, E., & Travassos, G. H. (2007). Cross versus within-company cost estimation studies: a systematic review. IEEE Transactions on Software Engineering, 33, 316-329. https://doi.org/10.1109/tse.2007.1001
- Liporoni, A. S. (2014). Avaliação em massa com ênfase em planta de valores. In IBAPE/SP Engenharia de Avaliações, 2. ed., 2, 664.
- Manasa, J., Gupta, R., & Narahari, N. (2021). Machine learning based predicting house prices using regression techniques. In Proceedings of the 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2020), 624–630. https://doi.org/10.1109/icimia48430.2020.9074952
- Mayer, M., Bourassa, S.C., Hoesli, M. & Scognamiglio, D. (2019). Estimation and updating methods for hedonic valuation. Journal of European Real Estate Research, 12(1), 134–150. https://doi.org/10.1108/JERER-08-2018-0035
- McCluskey, W., Deddis, W. G., Mannis, A., McBurney, D., & Borst, R. A. (1997). Interactive application of computer assisted mass appraisal and geographic information systems. Journal of Property Valuation and Investment, 15, 448–465. https://doi.org/10.1108/14635789710189227
- Mete, M. O., & Yomralioglu, T. (2022). GIS & machine learning based mass appraisal of residential properties in England & Wales. In 30th Annual Geographical Information Science Research UK (GISRUK). https://doi.org/10.5281/zenodo.6410120
- Nakamura, W. T., Oliveira, E., Oliveira, E. H., Redmiles, D. F., & Conte, T. (2022). What factors affect the UX in mobile apps? A systematic mapping study on the analysis of app store reviews. Journal of Systems and Software, 193, 111462. https://doi.org/10.1016/j.jss.2022.111462
- Nejad, M. Z., Lu, J., & Behbood, V. (2017). Applying dynamic Bayesian tree in property sales price estimation. In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 1–6. https://doi.org/10.1109/ISKE.2017.8258810
- Nejad, M. Z., Lu, J., Asgari, P., & Behbood, V. (2016). The effect of google drive distance and duration in residential property in Sydney, Australia. In World Scientific Proceedings Series on Computer Engineering and Information Science. https://doi.org/10.1142/9789813146976_0101
- Niu, J., & Niu, P. (2019). An intelligent automatic valuation system for real estate based on machine learning. In Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC19), 1–6. https://doi.org/10.1145/3371425.3371454
- Pai, P., & Wang, W. (2020). Using machine learning models and actual transaction data for predicting real estate prices. Applied Sciences, 10(17), 5832. https://doi.org/10.3390/app10175832
- Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008). Systematic Mapping Studies in Software Engineering. In 12th International Conference on Evaluation and Assessment in Software Engineering (EASE). BCS Learning & Development. https://doi.org/10.14236/EWIC/EASE2008.8
- Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: an update. Information and Software Technology, 64, 1–18. https://doi.org/10.1016/j.infsof.2015.03.007
- Rodríguez, P., Haghighatkhah, A., Lwakatare, L.E., Teppola, S., Suomalainen, T., Eskeli, J., Karvonen, T., Kuvaja, P., Verner, J. M., & Oivo, M. (2017). Continuous deployment of software intensive products and services: a systematic mapping study. Journal of Systems and Software, 123, 263–291. https://doi.org/10.1016/j.jss.2015.12.015
- Sevgen, S. C., & Aliefendioglu, Y. (2020). Mass apprasial with a machine learning algorithm: random forest regression. Journal of Information Technologies, 13(3), 301–311 . https://doi.org/10.17671/gazibtd.555784
- Steurer, M., Hill, R., & Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research, 38, 99–129. https://doi.org/10.1080/09599916.2020.1858937
- Su, H., Shen, W., Wang, J., Ali, A., & Li, M. (2020). Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests. Forest Ecosystems, 7, 1–20. https://doi.org/10.1186/s40663-020-00276-7
- Uberti, M. S., Antunes, M. A., Debiasi, P., & Tassinari, W. D. (2018). Mass appraisal of farmland using classical econometrics and spatial modeling. Land Use Policy, 72, 161–170. https://doi.org/10.1016/j.landusepol.2017.12.044
- Uludag, O., Philipp, P., Putta, A., Paasivaara, M., Lassenius, C., & Matthes, F. (2022). Revealing the state of the art of large-scale agile development research: a systematic mapping study. Journal of Systems and Software, 194, 111473. https://doi.org/10.1016/j.jss.2022.111473
- Valier, A., & Micelli, E. (2020). Automated models for value prediction: a critical review of the debate. Valori e Valutazioni, 24, 151–162. https://www.researchgate.net/publication/348962087
- Velumani, P., Priyadharshini, B., Mukilan, K., & Shanmugapriya (2022). A mass appraisal assessment study of land values using spatial analysis and multiple regression analysis model (MRA). Materials Today: Proceedings, 66, 2614–2625. https://doi.org/10.1016/j.matpr.2022.07.224
- Wang, D., & Li, V. J. (2019). Mass appraisal models of real estate in the 21st century: a systematic literature review. Sustainability, 11(24), 7006. https://doi.org/10.3390/su11247006
- Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., & Regnell, B. (2012). Experimentation in Software Engineering. Heidelberg, Springer Science & Business Media. https://doi.org/10.1007/978-3-642-29044-2
- Xu, L., & Li, Z. (2021). A new appraisal model of second-hand housing prices in China’s first-tier cities based on machine learning algorithms. Computational Economics, 57, 617–637. https://doi.org/10.1007/s10614-020-09973-5
- Yan, Z., & Zong, L. (2020). Spatial prediction of housing prices in beijing using machine learning algorithms. In Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence, 64–71. https://doi.org/10.1145/3409501.3409543
- Yee, L.W., Bakar, N. A., Hassan, N. H., Zainuddin, N. M., Yusoff, R. C., & Rahim, N. Z. (2021). using machine learning to forecast residential property prices in overcoming the property overhang issue. In 2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 1–6. https://doi.org/10.1109/iicaiet51634.2021.9573830
- Yilmazer, S., & Kocaman, S. (2020). A mass appraisal assessment study using machine learning based on multiple regression and random forest. Land Use Policy, 99, 104889. https://doi.org/10.1016/j.landusepol.2020.104889
- Zhang, R., Du, Q., Geng, J., Liu, B., & Huang, Y. (2015). An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study. Habitat International, 46, 196–205. https://doi.org/10.1016/j.habitatint.2014.12.001
- Zhao, Y., Chetty, G., & Tran, D. T. (2019). Deep Learning with XGBoost for Real Estate Appraisal. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 1396–1401. https://doi.org/10.1109/SSCI44817.2019.9002790