Algorithm-Driven Hedonic Real Estate Pricing – An Explainable AI Approach

Algaba, E., Fragnelli, V., & Sánchez-Soriano, J. (Eds.). (2019). Handbook of the Shapley value. CRC Press. https://doi.org/10.1201/9781351241410 Search in Google Scholar

Alonso, W. (1964). Location and land use: Toward a general theory of land rent. Harvard University Press., https://doi.org/10.4159/harvard.9780674730854 Search in Google Scholar

Angrist, J. D., & Pischke, J. S. (2010). The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. The Journal of Economic Perspectives, 24(2), 3–30. https://doi.org/10.1257/jep.24.2.3 Search in Google Scholar

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 Search in Google Scholar

Arribas, I., García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2016). Mass appraisal of residential real estate using multilevel modelling. International Journal of Strategic Property Management, 20(1), 77–87. https://doi.org/10.3846/1648715X.2015.1134702 Search in Google Scholar

Basu, S., & Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. The Journal of Real Estate Finance and Economics, 17, 61–85. https://doi.org/10.1023/A:1007703229507 Search in Google Scholar

Bogin, A. N., & Shui, J. (2020). Appraisal accuracy and automated valuation models in rural areas. The Journal of Real Estate Finance and Economics, 60(1-2), 40–52. https://doi.org/10.1007/s11146-019-09712-0 https://doi.org/10.1007/s11146-019-09727-7. Search in Google Scholar

Borgoni, R., Michelangeli, A., & Pontarollo, N. (2018). The value of culture to urban housing markets. Regional Studies, 52(12), 1672–1683. https://doi.org/10.1080/00343404.2018.1444271 Search in Google Scholar

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

Cellmer, R. (2013). Use of spatial autocorrelation to build regression models of transaction prices. Real Estate Management and Valuation, 21(4), 65–74. https://doi.org/10.2478/remav-2013-0038 Search in Google Scholar

Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). https://doi.org/10.1145/2939672.2939785 https://doi.org/10.5194/acp-20-8063-2020. Search in Google Scholar

Deppner, J., & Cajias, M. (2022). Accounting for spatial autocorrelation in algorithm-driven hedonic models: A spatial cross-validation approach. The Journal of Real Estate Finance and Economics, 68, 235–273. https://doi.org/10.1007/s11146-022-09915-y Search in Google Scholar

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. https://doi.org/10.1007/s11146-023-09944-1 PMID:38625136 Search in Google Scholar

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451 Search in Google Scholar

Gilliland, M. (ed.). (2010). The Business forecasting deal: exposing myths, eliminating bad practices, providing practical solutions. John Wiley & Sons., https://doi.org/10.1002/9781119199885 Search in Google Scholar

Holzinger, A., Saranti, A., Molnar, C., Biecek, P., & Samek, W. (2022). Explainable AI methods – A brief overview. In A. Holzinger, R. Goebel, R. Fond, T. Moon, K. R. Müller, & W. Samek (Eds.), xxAI – Beyond explainable AI (pp. 13–38). Springer., https://doi.org/10.1007/978-3-031-04083-2_2 Search in Google Scholar

Hong, J., Choi, H., & Kim, W. S. (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

Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82, 657–673. https://doi.org/10.1016/j.landusepol.2018.12.030 Search in Google Scholar

Kauko, T. (2006). What makes a location attractive for the housing consumer? Preliminary findings from metropolitan Helsinki and Randstad Holland using the analytical hierarchy process. Journal of Housing and the Built Environment, 21, 159–176. https://doi.org/10.1007/s10901-006-9040-y Search in Google Scholar

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 Search in Google Scholar

Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political Economy, 74(2), 132–157. https://doi.org/10.1086/259131 Search in Google Scholar

Lewis, C. D. (1982). Industrial and business forecasting methods. Butterworths. Search in Google Scholar

Li, Z. (2022). Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845. https://doi.org/10.1016/j.compenvurbsys.2022.101845 Search in Google Scholar

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 Search in Google Scholar

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 30. ISBN: 978-1-5108-6096-4. Search in Google Scholar

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 Search in Google Scholar

Molnar, C. (2020). Interpretable Machine learning. A guide for making black box models explainable. Lean Publishing. Search in Google Scholar

Montero, J. M., & Fernández-Avilés, G. (2014). Hedonic Price Model. In A. C. Michalos (Ed.), Encyclopedia of quality of life and wellbeing research (pp. 2834–2837). Springer., https://doi.org/10.1007/978-94-007-0753-5_1279 Search in Google Scholar

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. https://doi.org/10.3390/land11112100 Search in Google Scholar

MSCI. (2022). Private real estate: Valuation and sale price comparison 2021. Search in Google Scholar

NBP. (2022). Raport o sytuacji na rynku nieruchomości mieszkaniowych i komercyjnych w Polsce w 2021 r. [Report on the situation in the residential and commercial real estate market in Poland in 2021]. Search in Google Scholar

Niu, F., & Liu, W. (2017). Modeling urban housing price: The perspective of household activity demand. Journal of Geographical Sciences, 27, 619–630. https://doi.org/10.1007/s11442-017-1396-2 Search in Google Scholar

Osland, L. (2010). An application of spatial econometrics in relation to hedonic house price modeling. Journal of Real Estate Research, 32(3), 289–320. https://doi.org/10.1080/10835547.2010.12091282 Search in Google Scholar

Pace, R. K., & Hayunga, D. (2020). Examining the information content of residuals from hedonic and spatial models using trees and forests. The Journal of Real Estate Finance and Economics, 60, 170–180. https://doi.org/10.1007/s11146-019-09724-w Search in Google Scholar

Przekop, D. (2022). Artificial neural networks vs spatial regression approach in property valuation. Central European Journal of Economic Modelling and Econometrics, 14, 199–223. https://doi.org/10.24425/cejeme.2022.142630 Search in Google Scholar

Rico-Juan, J. R., & Taltavull de La Paz, P. T. (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 Search in Google Scholar

Rosen, S. (1974). Hedonic Prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55. https://doi.org/10.1086/260169 Search in Google Scholar

Saha, A., Basu, S., & Datta, A. (2023). Random forests for spatially dependent data. Journal of the American Statistical Association, 118(541), 665–683. https://doi.org/10.1080/01621459.2021.1950003 Search in Google Scholar

Sevgen, S. C., & Tanrivermiş, Y. (2024). Comparison of machine learning algorithms for mass appraisal of real estate data. Real Estate Management and Valuation, 32(2), 100–111. https://doi.org/10.2478/remav-2024-0019 Search in Google Scholar

Shapley, L. (1953). 17. A Value for n-Person Games. In H. Kuhn & A. Tucker (Eds.), Contributions to the theory of games (Vol. AM-28, pp. 307–318). Princeton University Press., https://doi.org/10.1515/9781400881970-018 Search in Google Scholar

Steurer, M., Hill, R. J., & Pfeifer, N. (2021). Metrics for evaluating the performance of machine learning based automated valuation models. Journal of Property Research, 38(2), 99–129. https://doi.org/10.1080/09599916.2020.1858937 Search in Google Scholar

Straszhem, M. (1987). The theory of urban residential location. In Handbook of Regional and Urban Economics (Vol. 2, pp. 717–757). Elsevier., https://doi.org/10.1016/S1574-0080(87)80004-4 Search in Google Scholar

Talebi, H., Peeters, L. J., Otto, A., & Tolosana-Delgado, R. (2022). A truly spatial random forests algorithm for geoscience data analysis and modelling. Mathematical Geosciences, 54, 1–22. https://doi.org/10.1007/s11004-021-09946-w Search in Google Scholar

Statistical Office in Warsaw. (2022). Panorama dzielnic Warszawy w 2021 r. [Panorama of Warsaw districts in 2021]. 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

Wheaton, W. C. (1977). Income and urban residence: An analysis of consumer demand for location. The American Economic Review, 67(4), 620–631. https://www.jstor.org/stable/1813394 Search in Google Scholar

Wu, Y., Wei, Y. D., & Li, H. (2020). Analyzing spatial heterogeneity of housing prices using large datasets. Applied Spatial Analysis and Policy, 13, 223–256. https://doi.org/10.1007/s12061-019-09301-x Search in Google Scholar

Yoshida, T., Murakami, D., & Seya, H. (2024). Spatial prediction of apartment rent using regression-based and machine learningbased approaches with a large dataset. The Journal of Real Estate Finance and Economics, 69, 1–28. https://doi.org/10.1007/s11146-022-09929-6 Search in Google Scholar

Public Transport Authority in Warsaw. (2022). Informator statystyczny 2021 [Statistical guide 2021]. Search in Google Scholar

Public Transport Authority in Warsaw. (2022). Informator statystyczny nr XII (333) [Statistical guide no. XII (333)]. Search in Google Scholar

Zyga, J. (2019). Data selection as the basis for better value modelling. Real Estate Management and Valuation, 27(1), 25–34. https://doi.org/10.2478/remav-2019-0003 Search in Google Scholar