References
- 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
- Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. ArXiv, abs/1810.11363. https://api.semanticscholar.org/CorpusID:26037613
- Doszyń, M. (2020). Econometric support of a mass valuation process. Folia Oeconomica Stetinensia, 20(1), 81–94. https://doi.org/10.2478/foli-2020-0005
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7
- 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
- Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43. https://doi.org/10.1145/3233231
- 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
- Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007
- Molnar, C. (2022). Interpretable Machine Learning (2nd ed.).
- Molnar, C., & Freiesleben, T. (2024). Supervised machine learning for science (1st ed.).
- 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
- Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications, 42(6), 2928–2934. https://doi.org/10.1016/j.eswa.2014.11.040 https://doi.org/10.1016/j.eswa.2015.03.005
- 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.
- Samek, W. (2023). Explainable deep learning: concepts, methods, and new developments. Explainable Deep Learning AI: Methods and Challenges, 7–33. https://doi.org/10.1016/B978-0-32-396098-4.00008-9
- 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
- Sirmans, G. S., Macpherson, D. A., & Zietz, E. N. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 1–43. http://www.jstor.org/stable/44103506 https://doi.org/10.1080/10835547.2005.12090154
- 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
- Yavuz Ozalp, A., & Akinci, H. (2017). The use of hedonic pricing method to determine the parameters affecting residential real estate prices. Arabian Journal of Geosciences, 10(24), 535. https://doi.org/10.1007/s12517-017-3331-3 https://doi.org/10.1007/s12517-017-3351-z
- 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