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Identifying the Current Status of Real Estate Appraisal Methods


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Alsawan, N. M., & Alshurideh, M. T. (2023). The application of artificial intelligence in real estate valuation: A systematic review. International Conference on Advanced Intelligent Systems and Informatics, Search in Google Scholar

Alzain, E., Alshebami, A. S., Aldhyani, T. H., & Alsubari, S. N. (2022). Application of artificial intelligence for predicting real estate prices: The case of Saudi Arabia. Electronics (Basel), 11(21), 3448. Search in Google Scholar

Bidanset, P., McCord, M., Davis, P., & Sunderman, M. (2019). An exploratory approach for enhancing vertical and horizontal equity tests for ad valorem property tax valuations using geographically weighted regression. Journal of Financial Management of Property and Construction. Search in Google Scholar

Binoy, B., Naseer, M., & Anil Kumar, P. (2022). Spatial variation of the determinants affecting urban land value in Thiruvananthapuram, India. International Journal of Housing Markets and Analysis. Search in Google Scholar

Binoy, B., Naseer, M., Anil Kumar, P., & Lazar, N. (2022). A bibliometric analysis of property valuation research. International Journal of Housing Markets and Analysis, 15(1), 35–54. https://doi.org/10.1108/IJHMA-09-2020-0115 Search in Google Scholar

Bogin, A. N., & Shui, J. (2020). Appraisal accuracy and automated valuation models in rural areas. The Journal of Real Estate Finance and Economics, 60(1-2), 40–52. https://doi.org/10.1007/s11146-019-09712-0 https://doi.org/10.1007/s11146-019-09727-7 Search in Google Scholar

Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Routledge. https://doi.org/10.1201/9781315139470 Search in Google Scholar

Chongwilaikasaem, S., & Chalermyanont, T. (2022). Flood hazards and housing prices: A spatial regression analysis for Hat Yai, Songkhla, Thailand. International Journal of Housing Markets and Analysis(ahead-of-print). Search in Google Scholar

Deaconu, A., Buiga, A., & Tothăzan, H. (2022). Real estate valuation models performance in price prediction. International Journal of Strategic Property Management, 26(2), 86–105. https://doi.org/10.3846/ijspm.2022.15962 Search in Google Scholar

Dimopoulos, T., & Bakas, N. (2019). Sensitivity analysis of machine learning models for the mass appraisal of real estate. Case study of residential units in Nicosia, Cyprus. Remote Sensing (Basel), 11(24), 3047. https://doi.org/10.3390/rs11243047 Search in Google Scholar

Foryś, I. (2022). Machine learning in house price analysis: Regression models versus neural networks. Procedia Computer Science, 207, 435–445. https://doi.org/10.1016/j.procs.2022.09.078 Search in Google Scholar

Fu, Q. (2022). Real estate tax base assessment by deep learning neural network in the context of the digital economy. Computational Intelligence and Neuroscience, 2022, 5904707. https://doi.org/10.1155/2022/5904707 PMID:35983153 Search in Google Scholar

Gabrielli, L., & French, N. (2021). Pricing to market: Property valuation methods–a practical review. Journal of Property Investment & Finance, 39(5), 464–480. https://doi.org/10.1108/JPIF-09-2020-0101 Search in Google Scholar

Gao, Q., Shi, V., Pettit, C., & Han, H. (2022). Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia. Land Use Policy, 123, 106409. https://doi.org/10.1016/j.landusepol.2022.106409 Search in Google Scholar

García-Magariño, I., Medrano, C., & Delgado, J. (2020). Estimation of missing prices in real-estate market agent-based simulations with machine learning and dimensionality reduction methods. Neural Computing & Applications, 32(7), 2665–2682. https://doi.org/10.1007/s00521-018-3938-7 Search in Google Scholar

Gnat, S. (2021). Property mass valuation on small markets. Land (Basel), 10(4), 388. https://doi.org/10.3390/land10040388 Search in Google Scholar

Gnat, S., & Doszyn, M. (2020). Parametric and non-parametric methods in mass appraisal on poorly developed real estate markets. https://doi.org/10.35808/ersj/1740 Search in Google Scholar

Guliker, E., Folmer, E., & van Sinderen, M. (2022). Spatial determinants of real estate appraisals in the Netherlands: A machine learning approach. ISPRS International Journal of Geo-Information, 11(2), 125. https://doi.org/10.3390/ijgi11020125 Search in Google Scholar

Hernández, C., & Rosales, I. (2021). Building models to predict real estate list prices using ensemble machine learning algorithms. Proceedings of the International Conference on Industrial Engineering and Operations Management, Rome, Italy, August 2-5, 2021. Search in Google Scholar

Hjort, A., Pensar, J., Scheel, I., & Sommervoll, D. E. (2022). House price prediction with gradient boosted trees under different loss functions. Journal of Property Research, 39(4), 338–364. https://doi.org/10.1080/09599916.2022.2070525 Search in Google Scholar

Hjort, A., Scheel, I., Sommervoll, D. E., & Pensar, J. (2023). Locally interpretable tree boosting: An application to house price prediction. Decision Support Systems, •••, 114106. Search in Google Scholar

Ho, W. K., Tang, B.-S., & Wong, S. W. (2021). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48–70. https://doi.org/10.1080/09599916.2020.1832558 Search in Google Scholar

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 Search in Google Scholar

Horvath, S., Soot, M., Zaddach, S., Neuner, H., & Weitkamp, A. (2021). Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis. Land Use Policy, 107, 105475. https://doi.org/10.1016/j.landusepol.2021.105475 Search in Google Scholar

Huang, Y. (2019). Predicting home value in California, United States via machine learning modeling. Statistics, Optimization & Information Computing, 7(1), 66–74. https://doi.org/10.19139/soic.v7i1.435 Search in Google Scholar

Hurley, A. K., & Sweeney, J. (2022). Irish property price estimation using a flexible geo-spatial smoothing approach: What is the impact of an address? The Journal of Real Estate Finance and Economics, 68, 355–393. PMID:38482270 Search in Google Scholar

Iban, M. C. (2022). An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants. Habitat International, 128, 102660. https://doi.org/10.1016/j.habitatint.2022.102660 Search in Google Scholar

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. Search in Google Scholar

Jayantha, W. M., & Oladinrin, O. T. (2020). Bibliometric analysis of hedonic price model using CiteSpace. International Journal of Housing Markets and Analysis, 13(2), 357–371. https://doi.org/10.1108/IJHMA-04-2019-0044 Search in Google Scholar

Kalliola, J., Kapočiūtė-Dzikienė, J., & Damaševičius, R. (2021). Neural network hyperparameter optimization for prediction of real estate prices in Helsinki. PeerJ. Computer Science, 7, e444. https://doi.org/10.7717/peerj-cs.444 PMID:33977129 Search in Google Scholar

Kang, J., Lee, H. J., Jeong, S. H., Lee, H. S., & Oh, K. J. (2020). Developing a forecasting model for real estate auction prices using artificial intelligence. Sustainability (Basel), 12(7), 2899. https://doi.org/10.3390/su12072899 Search in Google Scholar

Koktashev, V., Makeev, V., Shchepin, E., Peresunko, P., & Tynchenko, V. (2019). Pricing modeling in the housing market with urban infrastructure effect. Journal of Physics: Conference Series. Search in Google Scholar

Krämer, B., Stang, M., Doskoč, V., Schäfers, W., & Friedrich, T. (2023). Automated valuation models: improving model performance by choosing the optimal spatial training level. Journal of Property Research, •••, 1–26. Search in Google Scholar

Kucklick, J.-P., & Müller, O. (2023). Tackling the accuracy-interpretability trade-off: Interpretable deep learning models for satellite image-based real estate appraisal. ACM Transactions on Management Information Systems, 14(1), 1–24. https://doi.org/10.1145/3567430 Search in Google Scholar

Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political Economy, 74(2), 132–157. https://doi.org/10.1086/259131 Search in Google Scholar

Lazar, N., & Chithra, K. (2021). Comprehensive bibliometric mapping of publication trends in the development of Building Sustainability Assessment Systems. Environment, Development and Sustainability, 23, 4899–4923. https://doi.org/10.1007/s10668-020-00796-w Search in Google Scholar

Lee, C. (2022a). Designing an optimal neural network architecture: an application to property valuation. Property Management (ahead-of-print). Search in Google Scholar

Lee, C. (2022b). Training and Interpreting Machine Learning Models: Application in Property Tax Assessment. Real Estate Management and Valuation, 30(1), 13–22. https://doi.org/10.2478/remav-2022-0002 Search in Google Scholar

Lee, H., Han, H., Pettit, C., Gao, Q., & Shi, V. (2023). Machine learning approach to residential valuation: A convolutional neural network model for geographic variation. The Annals of Regional Science, •••, 1–21. Search in Google Scholar

Lin, W., Shi, Z., Wang, Y., & Yan, T. H. (2021). Unfolding Beijing in a hedonic way. Computational Economics, •••, 1–24. Search in Google Scholar

Lisi, G. (2019). Sales comparison approach, multiple regression analysis and the implicit prices of housing. Journal of Property Research, 36(3), 272–290. https://doi.org/10.1080/09599916.2019.1651755 Search in Google Scholar

Louati, A., Lahyani, R., Aldaej, A., Aldumaykhi, A., & Otai, S. (2022). Price forecasting for real estate using machine learning: A case study on Riyadh city. Concurrency and Computation, 34(6), e6748. https://doi.org/10.1002/cpe.6748 Search in Google Scholar

Luo, H., Zhao, S., & Yao, R. (2021). Determinants of housing prices in Dalian city, China: Empirical study based on hedonic price model. Journal of Urban Planning and Development, 147(2), 05021017. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000698 Search in Google Scholar

Mankad, M. D. (2022). Comparing OLS based hedonic model and ANN in house price estimation using relative location. Spatial Information Research, 30(1), 107–116. https://doi.org/10.1007/s41324-021-00416-3 Search in Google Scholar

Marzagão, T., Ferreira, R., & Sales, L. (2021). A note on real estate appraisal in Brazil. Revista Brasileira de Economia, 75, 29–36. Search in Google Scholar

Mayer, M., Bourassa, S. C., Hoesli, M., & Scognamiglio, D. (2022). Machine learning applications to land and structure valuation. Journal of Risk and Financial Management, 15(5), 193. https://doi.org/10.3390/jrfm15050193 Search in Google Scholar

Museleku, E. K. (2022). Modelling apartments values in the Nairobi metropolitan area, Kenya. Property Management (ahead-ofprint). Search in Google Scholar

Niu, J., & Niu, P. (2019). An intelligent automatic valuation system for real estate based on machine learning. Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., & Brennan, S. E. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88, 105906. Search in Google Scholar

Pai, P.-F., & Wang, W.-C. (2020). Using machine learning models and actual transaction data for predicting real estate prices. Applied Sciences (Basel, Switzerland), 10(17), 5832. https://doi.org/10.3390/app10175832 Search in Google Scholar

Parmar, T., Mori, H., Poriya, D., Vanani, V., Chauhan, S., & Bhagat, Y. (2018). Identification of methodology used in real estate property valuation. International Research Journal of Engineering Technology, 5(2), 170–173. Search in Google Scholar

Pollestad, A. J., Brandrud Næss, A., & Oust, A. (2024). Towards a better uncertainty quantification in automated valuation models. Available at SSRN 4706470. https://doi.org/10.2139/ssrn.4706470 Search in Google Scholar

Przekop, D. (2022). Artificial neural networks vs spatial regression approach in property valuation. Central European Journal of Economic Modelling and Econometrics, 199-223-199-223. Search in Google Scholar

Rahman, S. N. A., Maimun, N. H. A., Razali, M. N. M., & Ismail, S. (2019). The artificial neural network model (ANN) for Malaysian housing market analysis. Planning Malaysia, 17. Search in Google Scholar

Rampini, L., & Re Cecconi, F. (2022). Artificial intelligence algorithms to predict Italian real estate market prices. Journal of Property Investment & Finance, 40(6), 588–611. https://doi.org/10.1108/JPIF-08-2021-0073 Search in Google Scholar

Renigier-Biłozor, M., Źróbek, S., Walacik, M., Borst, R., Grover, R., & d’Amato, M. (2022). International acceptance of automated modern tools use must-have for sustainable real estate market development. Land Use Policy, 113, 105876. https://doi.org/10.1016/j.landusepol.2021.105876 Search in Google Scholar

Rico-Juan, J. R., & Taltavull de La Paz, P. (2021). Machine learning with explainability or spatial hedonics tools? An analysis of the asking prices in the housing market in Alicante, Spain. Expert Systems with Applications, 171, 114590. https://doi.org/10.1016/j.eswa.2021.114590 Search in Google Scholar

Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55. https://doi.org/10.1086/260169 Search in Google Scholar

Rowley, J., & Slack, F. (2004). Conducting a literature review. Management Research News, 27(6), 31–39. https://doi.org/10.1108/01409170410784185 Search in Google Scholar

Sa’at, N. F., Maimun, N. H. A., & Idris, N. H. (2021). Enhancing the accuracy of Malaysian house price forecasting: A comparative analysis on the forecasting performance between the hedonic price model and artificial neural network model. Planning Malaysia, 19. Search in Google Scholar

Shetty, D. V., Rao, B. P., Prakash, C., & Vaibhava, S. (2020). Multiple regression analysis to predict the value of a residential building and to compare with the conventional method values. Journal of Physics: Conference Series. Search in Google Scholar

Shi, D., Zhang, H., Guan, J., Zurada, J., Chen, Z., & Li, X. (2023). Deep learning in predicting real estate property prices: A comparative study. https://doi.org/10.1088/1742-6596/1706/1/012118 Search in Google Scholar

Soltani, A., Heydari, M., Aghaei, F., & Pettit, C. J. (2022). Housing price prediction incorporating spatio-temporal dependency into machine learning algorithms. Cities (London, England), 131, 103941. https://doi.org/10.1016/j.cities.2022.103941 Search in Google Scholar

Stanišić, N., Radojević, T., & Stanić, N. (2021). Appraisal of apartments in Belgrade using hedonic regression: Model specification, predictive performance, suitability for mass appraisal, and comparison with machine learning methods. In Artificial Intelligence: Theory and Applications (pp. 293-312). Springer. Search in Google Scholar

Steurer, M., Hill, R. J., & Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research, 38(2), 99–129. https://doi.org/10.1080/09599916.2020.1858937 Search in Google Scholar

Štubňová, M., Urbaníková, M., Hudáková, J., & Papcunová, V. (2020). Estimation of residential property market price: Comparison of artificial neural networks and hedonic pricing model. Emerging Science Journal, 4(6), 530–538. https://doi.org/10.28991/esj-2020-01250 Search in Google Scholar

Teoh, E. Z., Yau, W.-C., Ong, T. S., & Connie, T. (2023). Explainable housing price prediction with determinant analysis. International Journal of Housing Markets and Analysis, 16(5), 1021–1045. https://doi.org/10.1108/IJHMA-02-2022-0025 Search in Google Scholar

Terregrossa, S. J., & Ibadi, M. H. (2021). Combining housing price forecasts generated separately by hedonic and artificial neural network models. Asian Journal of Economics. Business and Accounting, 21(1), 130–148. Search in Google Scholar

Tomić, H., Ivić, S. M., Roić, M., & Šiško, J. J. L. U. P. (2021). Developing an efficient property valuation system using the LADM valuation information model: A Croatian case study. 104, 105368. Search in Google Scholar

Wang, D., & Li, V. J. (2019). Mass appraisal models of real estate in the 21st century: A systematic literature review. Sustainability (Basel), 11(24), 7006. https://doi.org/10.3390/su11247006 Search in Google Scholar

Wang, Z., Wang, Y., Wu, S., & Du, Z. (2022). House price valuation model based on geographically neural network weighted regression: The case study of Shenzhen, China. ISPRS International Journal of Geo-Information, 11(8), 450. https://doi.org/10.3390/ijgi11080450 Search in Google Scholar

Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971 Search in Google Scholar

Xu, X., & Zhang, Y. (2021). House price forecasting with neural networks. Intelligent Systems with Applications, 12, 200052. https://doi.org/10.1016/j.iswa.2021.200052 Search in Google Scholar

Yacim, J. A., & Boshoff, D. G. B. (2020). Neural networks support vector machine for mass appraisal of properties. Property Management, 38(2), 241–272. https://doi.org/10.1108/PM-09-2019-0053 Search in Google Scholar

Yasnitsky, L. N., Yasnitsky, V. L., & Alekseev, A. O. (2021). The complex neural network model for mass appraisal and scenario forecasting of the urban real estate market value that adapts itself to space and time. Complexity, 2021, 1–17. https://doi.org/10.1155/2021/5392170 Search in Google Scholar

Yildirim, H. (2019). Property value assessment using artificial neural networks, hedonic regression and nearest neighbors regression methods. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 7(2), 387–404. https://doi.org/10.15317/Scitech.2019.207 Search in Google Scholar

Zaki, J., Nayyar, A., Dalal, S., & Ali, Z. H. (2022). House price prediction using hedonic pricing model and machine learning techniques. Concurrency and Computation, 34(27), e7342. https://doi.org/10.1002/cpe.7342 Search in Google Scholar

Zhan, C., Liu, Y., Wu, Z., Zhao, M., & Chow, T. W. (2023). A hybrid machine learning framework for forecasting house price. Expert Systems with Applications, 233, 120981. https://doi.org/10.1016/j.eswa.2023.120981 Search in Google Scholar

Zhang, H., Li, Y., & Branco, P. (2023). Describe the house and I will tell you the price: House price prediction with textual description data. Natural Language Engineering, •••, 1–35. https://doi.org/10.1017/S1351324923000360 Search in Google Scholar

Zhao, X., Zuo, J., Wu, G., & Huang, C. (2019). A bibliometric review of green building research 2000–2016. Architectural Science Review, 62(1), 74–88. https://doi.org/10.1080/00038628.2018.1485548 Search in Google Scholar

Zhao, Y., Chetty, G., & Tran, D. (2019). Deep learning with XGBoost for real estate appraisal. 2019 IEEE symposium series on computational intelligence. SSCI. Search in Google Scholar

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