Antipov, E. A., & Pokryshevskaya, E. B. (2012). Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications, 39(2), 1772–1778. https://doi.org/10.1016/j.eswa.2011.08.077
Baldominos, A., Blanco, I., Moreno, A. J., Iturrarte, R., Bernárdez, Ó., & Afonso, C. (2018). Identifying real estate opportunities using machine learning. Applied Sciences (Basel, Switzerland), 8(11), 2321. Advance online publication. https://doi.org/10.3390/app8112321
Bilgilioğlu, S. S., & Yılmaz, H. M. (2023). Comparison of different machine learning models for mass appraisal of real estate. Survey Review, 55(388), 32–43. https://doi.org/10.1080/00396265.2021.1996799
Clark, S. D., & Lomax, N. (2018). A mass-market appraisal of the English housing rental market using a diverse range of modelling techniques. Journal of Big Data, 5(1), 43. Advance online publication. https://doi.org/10.1186/s40537-018-0154-3 PMID:30931238
Del Giudice, V., De Paola, P., Forte, F., & Manganelli, B. (2017). Real estate appraisals with bayesian approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples. Sustainability (Basel), 9(11), 2138. Advance online publication. https://doi.org/10.3390/su9112138
Demetriou, D. (2017). A spatially based artificial neural network mass valuation model for land consolidation. Environment and Planning. B, Urban Analytics and City Science, 44(5), 864–883. https://doi.org/10.1177/0265813516652115
Deppner, J., von Ahlefeldt-Dehn, B., Beracha, E., & Schaefers, W. (2023). Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach. The Journal of Real Estate Finance and Economics, 1–38. Advance online publication. https://doi.org/10.1007/s11146-023-09944-1 PMID:38625136
Dimopoulos, T., Tyralis, H., Bakas, N. P., & Hadjimitsis, D. (2018). Accuracy measurement of Random Forests and Linear Regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus. Advances in Geosciences, 45, 377–382. https://doi.org/10.5194/adgeo-45-377-2018
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd Editiom). Springer. https://doi.org/doi.org/10.1007/978-1-0716-1418-1
Khamis, A. Bin, & Kamarudin, N. K. (2014). Comparative study on estimate house price using statistical and neural network model. International Journal of Scientific & Technology Research, 3(12), 126–131.
Kok, N., Koponen, E.-L., & Martínez-Barbosa, C. A. (2017). Big data in real estate? From manual appraisal to automated valuation. Journal of Portfolio Management, 43(6), 202–211. https://doi.org/10.3905/jpm.2017.43.6.202
Krämer, B., Nagl, C., Stang, M., & Schäfers, W. (2023). Explainable AI in a real estate context – Exploring the determinants of residential real estate values. Journal of Housing Research, 32(2), 204–245. https://doi.org/10.1080/10527001.2023.2170769
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. Advance online publication. https://doi.org/10.1145/3567430
Lee, C. (2022). 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
Lorenz, F., Willwersch, J., Cajias, M., & Fuerst, F. (2023). Interpretable machine learning for real estate market analysis. Real Estate Economics, 51(5), 1178–1208. https://doi.org/10.1111/1540-6229.12397
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. J., Zulkarnain Daud, D., & Kamarudin, N. (2014). Boosted regression trees: An application for the mass appraisal of residential property in Malaysia. Journal of Financial Management of Property and Construction, 19(2), 152–167. https://doi.org/10.1108/JFMPC-06-2013-0022
Metzner, S., & Kindt, A. (2018). Determination of the parameters of automated valuation models for the hedonic property valuation of residential properties: A literature-based approach. International Journal of Housing Markets and Analysis, 11(1), 73–100. https://doi.org/10.1108/IJHMA-02-2017-0018
Mora-Garcia, R. T., Cespedes-Lopez, M. F., & Perez-Sanchez, V. R. (2022). Housing price prediction using machine learning algorithms in COVID-19 times. Land (Basel), 11(11), 2100. Advance online publication. https://doi.org/10.3390/land11112100
Morano, P., Tajani, F., & Locurcio, M. (2018). Multicriteria analysis and genetic algorithms for mass appraisals in the Italian property market. International Journal of Housing Markets and Analysis, 11(2), 229–262. https://doi.org/10.1108/IJHMA-04-2017-0034
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. Advance online publication. https://doi.org/10.3390/app10175832
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Pérez-Rave, J. I., Correa-Morales, J. C., & González-Echavarría, F. (2019). A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes. Journal of Property Research, 36(1), 59–96. https://doi.org/10.1080/09599916.2019.1587489
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Proceedings of the 32nd International Conference on Neural Information Processing Systems, 6639–6649.
Seagraves, P. (2024). Real Estate Insights: Is the AI revolution a real estate boon or bane? Journal of Property Investment & Finance, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JPIF-05-2023-0045
Trawiński, B., Telec, Z., Krasnoborski, J., Piwowarczyk, M., Talaga, M., Lasota, T., & Sawiłow, E. (2017). Comparison of expert algorithms with machine learning models for real estate appraisal. 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 51–54. https://doi.org/10.1109/INISTA.2017.8001131
Wang, X., Wen, J., Zhang, Y., & Wang, Y. (2014). Real estate price forecasting based on SVM optimized by PSO. Optik (Stuttgart), 125(3), 1439–1443. https://doi.org/10.1016/j.ijleo.2013.09.017
Yoo, S., Im, J., & Wagner, J. E. (2012). Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY. Landscape and Urban Planning, 107(3), 293–306. https://doi.org/10.1016/j.landurbplan.2012.06.009
Zaddach, S., & Alkhatib, H. (2014). Least squares collocation as an enhancement to multiple regression analysis in mass appraisal applications. Journal of Property Tax Assessment & Administration, 11(1), 47–66.
Zhou, G., Ji, Y., Chen, X., & Zhang, F. (2018). Artificial neural networks and the mass appraisal of real estate. International Journal of Online Engineering, 14(3), 180–187. https://doi.org/10.3991/ijoe.v14i03.8420
Zurada, J., Levitan, A., & Guan, J. (2011). A comparison of regression and artificial intelligence methods in a mass appraisal context. Journal of Real Estate Research, 33(3), 349–388. https://doi.org/10.1080/10835547.2011.12091311