Comparison of Machine Learning Algorithms for Mass Appraisal of Real Estate Data

Atasoy, T., & Tanrıvermiş, H. (2024). Gayrimenkul Türevleri, Gayrimenkul Türev Fiyatlandırma Modelleri Ve Türkiye’de Bir Uygulama (Real Estate Derivatives, Real Estate Derivative Pricing Models and an Application in Turkey). The Journal of Academic Social Science Studies, 16 (98), 461-494. Search in Google Scholar

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 Search in Google Scholar

Borst, R. A. (1991). Artificial neural networks: The next modelling/calibration technology for the assessment community. Property Tax Journal, 10(1), 69–94. Search in Google Scholar

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655 Search in Google Scholar

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 Search in Google Scholar

Breiman, L., & Cutler, A. (2005). Random Forests. Berkeley. In. Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the performance of random forest versus multiple regression for predicting prices of the apartments. ISPRS International Journal of Geo-Information, 7(5), 168. https://doi.org/10.3390/ijgi7050168 Search in Google Scholar

Chen, H., Zhang, Z., Yin, W., Zhao, C., Wang, F., & Li, Y. (2022). A study on depth classification of defects by machine learning based on hyper-parameter search. Measurement, 189, 110660. https://doi.org/10.1016/j.measurement.2021.110660 Search in Google Scholar

Dambon, J. A., Fahrländer, S. S., Karlen, S., Lehner, M., Schlesinger, J., Sigrist, F., & Zimmermann, A. (2022). Examining the vintage effect in hedonic pricing using spatially varying coefficients models: A case study of single-family houses in the Canton of Zurich. Swiss Journal of Economics and Statistics, 158(1), 2. https://doi.org/10.1186/s41937-021-00080-2 Search in Google Scholar

Dellstad, M. (2018). Comparing three machine learning algorithms in the task of appraising commercial real estate. Degree project in computer science and engineering. Stockholm, Sweden. 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

Gültekin, A., Dikmen, Ç., Erciyes, A., & Örgü, D. (2017). An examination on evolution of sustainability in the context of United Nations Sustainable Development Goals: Turkey case. 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

Hong, J., & Kim, W. (2022). Combination of machine learning-based automatic valuation models for residential properties in South Korea. International Journal of Strategic Property Management, 26(5), 362–384. 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

Kontrimas, V., & Verikas, A. (2007). Neural networks based screening of real estate transactions. Neural Network World, 17(1), 17. Search in Google Scholar

Kontrimas, V., & Verikas, A. (2011). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11(1), 443–448. https://doi.org/10.1016/j.asoc.2009.12.003 Search in Google Scholar

Lam, K. C., Yu, C., & Lam, K. (2008). An artificial neural network and entropy model for residential property price forecasting in Hong Kong. Journal of Property Research, 25(4), 321–342. https://doi.org/10.1080/09599910902837051 Search in Google Scholar

Lenk, M. M., Worzala, E. M., & Silva, A. (1997). High-tech valuation: Should artificial neural networks bypass the human valuer? Journal of Property Valuation and Investment, 15, 8-26. Search in Google Scholar

Lerman, P. (1980). Fitting segmented regression models by grid search. Journal of the Royal Statistical Society. Series C, Applied Statistics, 29(1), 77–84. Search in Google Scholar

McCluskey, W. (1996). Predictive accuracy of machine learning models for the mass appraisal of residential property. New Zealand Valuers Journal, 16(1), 41-47. Search in Google Scholar

Morano, P., & Tajani, F. (2013). Bare ownership evaluation. Hedonic price model vs. artificial neural network. International Journal of Business Intelligence and Data Mining, 8(4), 340–362. https://doi.org/10.1504/IJBIDM.2013.059263 Search in Google Scholar

Musa, A. G., Daramola, O., Owoloko, A., & Olugbara, O. (2013). A neural-CBR system for real property valuation. Journal of Emerging Trends in Computing and Information Sciences, 4(8), 611–622. Search in Google Scholar

Özkan, G., Yalpır, Ş., & Uygunol, O. (2007). An investigation on the price estimation of residable real-estates by using artificial neural network and regression methods. XIIth Applied Stochastic Models and Data Analysis International conference (ASMDA), Chania, Crete, Greece, Search in Google Scholar

Ravikumar, A. S. (2017). Real estate price prediction using machine learning. Dublin, National College of Ireland. Search in Google Scholar

Sampathkumar, V., Santhi, M. H., & Vanjinathan, J. (2015). Forecasting the land price using statistical and neural network software. Procedia Computer Science, 57, 112–121. https://doi.org/10.1016/j.procs.2015.07.377 Search in Google Scholar

Saraç, E. (2012). Yapay sinir ağları metodu ile gayrimenkul değerleme (Real estate valuation with artificial neural networks method) İstanbul Kültür Üniversitesi/Fen Bilimleri Enstitüsü/İnşaat Mühendisliği (Istanbul Kültür University/Institute of Natural and Applied Sciences/Civil Engineering)]. Search in Google Scholar

Sawant, R., Jangid, Y., Tiwari, T., Jain, S., & Gupta, A. (2018). Comprehensive analysis of housing price prediction in pune using multi-featured random forest approach. 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Search in Google Scholar

Selim, H. (2009). Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2), 2843–2852. Search in Google Scholar

Sisman, S., Akar, A. U., & Yalpir, S. (2023). The novelty hybrid model development proposal for mass appraisal of real estates in sustainable land management. Survey Review, 55(388), 1–20. https://doi.org/10.1080/00396265.2021.1996797 Search in Google Scholar

Tabales, J. M. N., Caridad, J. M., & Carmona, F. J. R. (2013). Artificial neural networks for predicting real estate price. Revista de Métodos Cuantitativos para la Economía y la Empresa, 15, 29–44. Search in Google Scholar

Tay, D.P.H. & Ho, D.K.H. (1992). Artificial intelligence and the mass appraisal of residential apartments. Journal of Property Valuation and Investment, 10(2), 525-540. https://doi.org/10.1108/14635789210031181 Search in Google Scholar

Torres-Pruñonosa, J., García-Estévez, P., & Prado-Román, C. (2021). Artificial neural network, quantile and semi-log regression modelling of mass appraisal in housing. Mathematics, 9(7), 783. https://doi.org/10.3390/math9070783 Search in Google Scholar

Tursun, A. (2023). Gayrimenkul Pazar Analizinde Sistem Dinamiği Yaklaşımı ve Uygulaması (System Dynamics Approach and Application in Real Estate Market Analysis). Nobel. Search in Google Scholar

Unel, F. B., & Yalpir, S. (2023). Sustainable tax system design for use of mass real estate appraisal in land management. Land Use Policy, 131, 106734. https://doi.org/10.1016/j.landusepol.2023.106734 Search in Google Scholar

Valier, A. (2020). Who performs better? AVMs vs hedonic models. Journal of Property Investment & Finance, 38(3), 213–225. https://doi.org/10.1108/JPIF-12-2019-0157 Search in Google Scholar

Vapnik, V. (1998). Statistical learning theory New York. NY, Wiley. Search in Google Scholar

Varma, A., Sarma, A., Doshi, S., & Nair, R. (2018). House price prediction using machine learning and neural networks. 2018 second international conference on inventive communication and computational technologies (ICICCT). Search in Google Scholar

Wilson, I. D., Paris, S. D., Ware, J. A., & Jenkins, D. H. (2002). Residential property price time series forecasting with neural networks. In Applications and Innovations in Intelligent Systems IX (pp. 17-28). Springer. Search in Google Scholar

Worzala, E., Lenk, M., & Silva, A. (1995). An exploration of neural networks and its application to real estate valuation. Journal of Real Estate Research, 10(2), 185–201. https://doi.org/10.1080/10835547.1995.12090782 Search in Google Scholar

Xin, J. G., & Runeson, G. (2004). Modeling property prices using neural network model for Hong Kong. International Real Estate Review, 7(1), 121–138. Search in Google Scholar

Yılmaz, M., & Bostancı, B. (2023). Investigation of Real Estate Tax Leakage Loss Rates with ANNs. Buildings, 13(10), 2464. https://doi.org/10.3390/buildings13102464 Search in Google Scholar

Yu, H., & Wu, J. (2016). Real estate price prediction with regression and classification. CS229 (Machine Learning). Final Project Reports. Search in Google Scholar