[
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