References
- Alenany, E., Lekham, L. A., & Lu, S. (2021). Integrated clustering regression for real estate valuation. Real Estate Finance, Available at SSRN: https://ssrn.com/abstract=3835967
- 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 Preprint at https://doi.org/10.20944/preprints201810.0297.v1
- Banerjee, D., & Dutta, S. (2017). Predicting the housing price direction using machine learning techniques. 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2998–3000. https://doi.org/10.1109/ICPCSI.2017.8392275
- Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Classification and Regression Trees, 1–358. https://doi.org/10.1201/9781315139470
- Cellmer, R. (2014). The possibilities and limitations of geostatistical methods in real estate market analyses. Real Estate Management and Valuation, 22(3), 54–62. https://doi.org/10.2478/remav-2014-0027
- Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016, 785–794. https://doi.org/10.1145/2939672.2939785
- Chowhaan, M. J., Nitish, D., Akash, G., Sreevidya, N., & Shaik, S. (2023). Machine learning approach for house price prediction. Asian Journal of Research in Computer Science, 16(2), 54–61. https://doi.org/10.9734/ajrcos/2023/v16i2339
- Choy, L. H. T., & Ho, W. K. O. (2023). The Use of Machine Learning in Real Estate Research. Land (Basel), 12(4), 740. Advance online publication. https://doi.org/10.3390/land12040740
- Çılgın, C., & Gökçen, H. (2023). Machine learning methods for prediction real estate sales prices in Turkey. Revista de la Construcción, 22(1), 163–177. https://doi.org/10.7764/RDLC.22.1.163
- Derdouri, A., & Murayama, Y. (2020). A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan. Journal of Geographical Sciences, 30(5), 794–822. https://doi.org/10.1007/s11442-020-1756-1
- Durganjali, P., & Pujitha, M. V. (2019). House resale price prediction using classification algorithms. 6th IEEE International Conference on Smart Structures and Systems, ICSSS 2019. https://doi.org/10.1109/ICSSS.2019.8882842
- Georgiadis, A. (2018). Real estate valuation using regression models and artificial neural networks: An applied study in Thessaloniki. RELAND: International Journal of Real Estate & Land Planning, 1(0), 292–303. https://doi.org/10.26262/RELAND.V1I0.6485
- Gu, J., Zhu, M., & Jiang, L. (2011). Housing price forecasting based on genetic algorithm and support vector machine. Expert Systems with Applications, 38(4), 3383–3386. https://doi.org/10.1016/j.eswa.2010.08.123
- Hamizah Zulkifley, N., Abdul Rahman, S., Ubaidullah, N. H., & Ibrahim, I. . (2020). House price prediction using a machine learning model: A survey of literature. International Journal of Modern Education and Computer Science, 12(6), 46–54. https://doi.org/10.5815/ijmecs.2020.06.04
- Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems & their Applications, 13(4), 18–28. https://doi.org/10.1109/5254.708428
- Ho, W. K. O., 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
- Jha, S. B., Pandey, V., Jha, R., & Babiceanu, R. (2020). Machine learning approaches to real estate market prediction problem: A case study. ArXiv:2008.09922. https://doi.org/10.48550/arXiv.2008.09922
- Kang, Y., Zhang, F., Peng, W., Gao, S., Rao, J., Duarte, F., & Ratti, C. (2021). Understanding house price appreciation using multi-source big geo-data and machine learning. Land Use Policy, 111, 104919. Advance online publication. https://doi.org/10.1016/j.landusepol.2020.104919
- Kim, J., Lee, Y., Lee, M.-H., & Hong, S.-Y. (2022). A comparative study of machine learning and spatial interpolation methods for predicting house prices. Sustainability 2022, Vol. 14, Page 9056, 14(15), 9056. https://doi.org/10.3390/su14159056
- Loh, W. Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 1(1), 14–23. https://doi.org/10.1002/widm.8
- 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
- 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, 11(11), 2100. https://doi.org/10.3390/land11112100
- Morillo Balsera, M. C., Martínez-Cuevas, S., Molina Sánchez, I., García-Aranda, C., & Martinez Izquierdo, M. E. (2018). Artificial neural networks and geostatistical models for housing valuations in urban residential areas. Geografisk Tidskrift, 118(2), 184–193. https://doi.org/10.1080/00167223.2018.1498364
- Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology 2006 24:12, 24(12), 1565–1567. https://doi.org/10.1038/nbt1206-1565
- Oladunni, T., & Sharma, S. (2016). Hedonic housing theory – A machine learning investigation. 522–527. https://doi.org/10.1109/ICMLA.2016.0092
- Park, B., & Bae, K. J. (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
- Plakandaras, V., Gupta, R., Gogas, P., & Papadimitriou, T. (2015). Forecasting the U.S. real house price index. Economic Modelling, 45, 259–267. https://doi.org/10.1016/j.econmod.2014.10.050
- Ren, X., Mi, Z., & Georgopoulos, P. G. (2023). Socioexposomics of COVID-19 across New Jersey: A comparison of geostatistical and machine learning approaches. Journal of Exposure Science & Environmental Epidemiology, 34, 197–207. https://doi.org/10.1038/s41370-023-00518-0 PMID:36725924
- 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
- Santhanam, R., Uzir, N., Raman, S., Banerjee, S., & Nishant Uzir Sunil, R. R. S. (2016). Experimenting XGBoost Algorithm for Prediction and Classification of Different Datasets Experimenting XGBoost Algorithm for Prediction and Classification of Different Datasets. International Journal of Control Theory and Applications, 9. https://www.researchgate.net/publication/318132203
- Sutton, C. D. (2005). Classification and regression trees, bagging, and boosting. Handbook of Statistics, 24, 303–329. https://doi.org/10.1016/S0169-7161(04)24011-1
- Tchuente, D., & Nyawa, S. (2022). Real estate price estimation in French cities using geocoding and machine learning. Annals of Operations Research, 308(1–2), 571–608. https://doi.org/10.1007/s10479-021-03932-5
- Thamarai, M., & Malarvizhi, S. P. (2020). House price prediction modeling using machine learning. International Journal of Information Engineering and Electronic Business, 12(2), 15–20. https://doi.org/10.5815/ijieeb.2020.02.03
- Truong, Q., Nguyen, M., Dang, H., & Mei, B. (2020). Housing price prediction via improved machine learning techniques. Procedia Computer Science, 174, 433–442. https://doi.org/10.1016/j.procs.2020.06.111
- Üzümcü, A. C., & Eliguzel, N. (2023). Predictive Analysis Using Web Scraping for the Real Estate Market in Gaziantep. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 12(1), 17–24. https://doi.org/10.17798/bitlisfen.1155725
- Wong, T. T., & Yeh, P. Y. (2020). Reliable accuracy estimates from K-fold cross validation. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1586–1594. https://doi.org/10.1109/TKDE.2019.2912815
- Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295–316. https://doi.org/10.1016/j.neucom.2020.07.061