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Determining the Influence of Real Estate Features on Prices with Partial Dependence Plots: A Case Study in Szczecin, Poland

Open Access
|Oct 2024

References

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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.
  14. 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
  15. 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
  16. 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
  17. 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
  18. Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43. https://doi.org/10.1145/3233231
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. Molnar, C. (2022). Interpretable Machine Learning (2nd ed.).
  25. Molnar, C., & Freiesleben, T. (2024). Supervised machine learning for science (1st ed.).
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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.
  31. 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
  32. 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.
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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.
  41. 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
  42. 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
Language: English
Page range: 105 - 116
Published on: Oct 16, 2024
Published by: Real Estate Management and Valuation
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Sebastian Gnat, published by Real Estate Management and Valuation
This work is licensed under the Creative Commons Attribution 4.0 License.