Theoretical and Methodological Foundations of Uncertainty Modeling in Real Estate Markets

Nonso Izuchukwu  Ewurum; Njideka Maryclara  Aguome; Cezary Kowalczyk; Idu Robert Egbenta; Vivita Pukite

doi:10.31648/aspal.10112

Published: 2024-10-301

Vol. 23 No. 3 (2024)

Theoretical and Methodological Foundations of Uncertainty Modeling in Real Estate Markets

Nonso Izuchukwu Ewurum

, Njideka Maryclara Aguome

, Cezary Kowalczyk

, Idu Robert Egbenta

, Vivita Pukite

Acta Scientiarum Polonorum Administratio Locorum

Section: Articles

https://doi.org/10.31648/aspal.10112

Abstract

Motivation: The need to improve the accuracy and reliability of market valuation and risk assessment in real estate markets, especially under conditions of uncertainty.
Aim: To integrate theoretical foundations and methodological approaches for modeling aleatoric and epistemic uncertainties in real estate markets using credal networks and confidence boxes (c-boxes).
Approach: This paper presents a comprehensive theoretical and methodological framework for uncertainty modeling in real estate markets, focusing on the application of credal networks and confidence boxes. It does not include empirical validation or practical case studies, instead providing a detailed conceptual and methodological discussion.
Results: The proposed method demonstrates significant improvements in uncertainty quantification and market analysis accuracy in theoretical terms, offering valuable insights for investors, urban planners, and policymakers. However, empirical validation is suggested for future research to confirm practical applicability.

Keywords:

aleatoric uncertainty, confidence boxes (c-boxes), credal networks, epistemic uncertainty, real estate market valuation, uncertainty modeling

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Ewurum, N. I. ., Aguome, N. M. ., Kowalczyk, C., Egbenta, I. R., & Pukite, V. (2024). Theoretical and Methodological Foundations of Uncertainty Modeling in Real Estate Markets. Acta Scientiarum Polonorum Administratio Locorum, 23(3), 407–423. https://doi.org/10.31648/aspal.10112

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References

Alday, S. S., & Hanea, A. M. (2022). Bayesian networks as a novel tool to enhance interpretability and predictive power of ecological models. Ecological Informatics, 67, 101539. https://doi.org/10.1016/j.ecoinf.2021.101539
Crossref
Google Scholar

Ancy, S. G., & Praveenchandar, J. (2024). An effective machine learning algorithm for forecasting the market value of a house. Proceedings of the 2024 IEEE International Conference on Artificial Intelligence and Computer Systems (ICAAICS). https://doi.org/10.1109/ icaaic60222.2024.10575745
Crossref
Google Scholar

Basili, M., & Pratelli, L. (2024). Uncertainty, imprecise probabilities, and interval capacity measures on a product space. arXiv. https://doi.org/10.48550/ arxiv.2404.15484
Google Scholar

Bovy, E. M., Suilen, M., Junges, S., & Jansen, N. (2024). Imprecise probabilities meet partial observability: Game semantics for robust POMDPs. International Joint Conference on Artificial Intelligence. https://doi. org/10.24963/ijcai.2024/740
Crossref
Google Scholar

Çatak, F. Ö., & Kuzlu, M. (2024). Uncertainty quantification in large language models through convex hull analysis. arXiv. https://doi.org/10.48550/ arxiv.2406.19712
Google Scholar

Chan, K. K. W., Rodionov, D., & Konnikov, E. A. (2024). Dynamic analysis of the residential real estate market. Modern Economy Success, 4(7), 7–15. https://doi. org/10.58224/2500-3747-2024-4-7-15
Crossref
Google Scholar

Chen, Y., Xu, J., Wang, S., & Xu, S. (2024). Economic environment uncertainty and financialization of real estate firms. International Review of Economics & Finance, 93B, 1104–1114. https://doi.org/10.1016/j. iref.2024.05.011
Crossref
Google Scholar

Cortes-Ciriano, I., & Bender, A. (2018). Deep confidence: A computationally efficient framework for calculating reliable errors for deep neural networks. arXiv: Learning. https://doi.org/10.1021/ACS.JCIM.8B00542
Crossref
Google Scholar

Dai, L., & Sheng, X. (2021). The impact of uncertainty on state-level housing markets of the United States: The role of social cohesion. Sustainability, 13(6), 3065. https://doi.org/10.3390/SU13063065
Crossref
Google Scholar

DelSole, T. (2000). A fundamental limitation of Markov models. Journal of the Atmospheric Sciences, 57(13), 2158–2168. https://doi.org/10.1175/1520- 0469(2000)057<2158>2.0.CO;2
Crossref
Google Scholar

Galante, M., Giove, S., & Rosato, P. (2024). Neural networks and linear models in real estate appraisal: The impact of sets selection procedures. Valori e Valutazioni [Values and Evaluations], 35, 45–68. https://doi.org/10.48264/vvsiev-20243505
Crossref
Google Scholar

Gofman, M. O., Pekna, H., & Solonets, V. (2024). Parameters of the real estate market as a business environment. Včeni zapiski unìversitetu “KROK” [Academic records of the university “KROK”], 74, 31–41. https://doi.org/10.31732/2663-2209-2024-74-31-41
Crossref
Google Scholar

Golomoziy, V., Mishura, Y., & Kladívko, K. (2024). A discrete-time model that weakly converges to a continuous-time geometric Brownian motion with Markov switching drift rate. Frontiers in Applied Mathematics and Statistics, 10, 1450581. https://doi. org/10.3389/fams.2024.1450581
Crossref
Google Scholar

Hanea, A. M., Christophersen, A., & Alday, S. S. (2022). Bayesian networks for risk analysis and decision support. Risk Analysis, 42(6), 1240–1261. https://doi. org/10.1111/risa.13938
Crossref
Google Scholar

Hong, F., Wei, P., Song, J., Faes, M., Valdebenito, M. A., & Beer, M. (2023). Combining data and physical models for probabilistic analysis: A Bayesian augmented space learning perspective. Probabilistic Engineering Mechanics, 73, 103474. https://doi. org/10.1016/j.probengmech.2023.103474
Crossref
Google Scholar

Jiang, C., Li, J., Wang, W., & Ku, W. S. (2021). Modeling real estate dynamics using temporal encoding. Proceedings of the 29th International Conference on Advances in Geographic Information Systems. https:// doi.org/10.1145/3474717.3484254
Crossref
Google Scholar

Juliani, F., & Maciel, C. D. (2024). Bayesian networks supporting management practices: A multifaceted perspective based on the literature. Journal of Indus¬trial Engineering and Management Innovation, 4(1), 100231. https://doi.org/10.1016/j.jjimei.2024.100231
Crossref
Google Scholar

Kapon, S., Del Carpio, L., & Chassang, S. (2024). Using divide-and-conquer to improve tax collection. Quarterly Journal of Economics, qjae018. https://doi. org/10.1093/qje/qjae018
Crossref
Google Scholar

Kim, J.-S. (2024). A study on the forecasting of real estate market using algorithms. Daehan Bu’dongsan Haghoeji [Korean Real Estate Association], 42(2), 159–168. https://doi.org/10.37407/kres.2024.42.2.159
Crossref
Google Scholar

Lafi, M., & Chammem, H. (2023). The uncertainty of eco¬nomic policies in the United States and its impact on the stock markets of some African countries: An empirical study with VAR modeling. Technium Social Sciences Journal, 56(1). https://doi.org/10.47577/tssj. v56i1.10725
Crossref
Google Scholar

Lin, Y., Rong, H., Yang, W., Lin, J., & Zheng, C. (2024). A novel integrated urban flood risk assessment approach based on one-two dimensional coupled hydrodynamic model and improved projection pursuit method. Journal of Environmental Management, 312, 121910. https://doi.org/10.1016/j.jenvman.2024.121910
Crossref
Google Scholar

Marques, S. H. (2023). Simulation and optimisation techniques for construction of a probability box structure. 15th Chaotic Modeling and Simulation International Conference, 177–187. https://doi. org/10.1007/978-3-031-27082-6_15
Crossref
Google Scholar

Mustafa, N., Hassan, G. F., Elrefaie, M. A. M., & Afifi, S. (2024). Different factors affecting the real estate market in the Egyptian context. International Journal of Housing Markets and Analysis. https://doi. org/10.1108/ijhma-03-2024-0044
Crossref
Google Scholar

Naumova, O., Naumova, M., & Karpenko, S. (2024). Pricing real estate market: The uncertainty aspect. Journal of Real Estate Research, 73, 75–82. https://doi. org/10.31732/2663-2209-2024-73-75-82
Crossref
Google Scholar

Nechval, N. A., Bērziņš, G., & Nechval, K. N. (2024). Novel constructing adequate simultaneous predictive limits or confidence intervals for future outcomes via pivotal quantities and ancillary statistics in the case of parametric uncertainty of applied real-life models. Aeronautics and Aerospace Open Access Journal, 8(1), 110–113. https://doi.org/10.15406/aaoaj.2024.08.00197
Crossref
Google Scholar

Nguyen, V. Q. T., & Vergara-Alert, C. (2023). Political uncertainty and housing markets. Journal of Housing Economics, 61, 101952. https://doi.org/10.1016/j. jhe.2023.101952
Crossref
Google Scholar

Pavesi, L., Volpi, E., & Fiori, A. (2024). Flood risk assessment through large-scale modeling under uncertainty. Natural Hazards and Earth System Sciences. https://doi.org/10.5194/nhess-2024-114
Crossref
Google Scholar

Peng, L., Wang, Y., & Pan, Z. (2024). A modeling study of insurance and real estate risk assessment in the context of global climate change. Highlights in Business, Economics and Management, 3(2), 123–134. https://doi.org/10.54097/6sf0hz30
Crossref
Google Scholar

Polotskaya, K., Muñoz-Valencia, C. S., Rabasa, A., Quesada, J. A., Orozco-Beltrán, D., & Barber, X. (2024). Bayesian networks for the diagnosis and prognosis of diseases: A scoping review. Machine Learning and Knowledge Extraction, 6(2), 1243–1262. https://doi.org/10.3390/make6020058
Crossref
Google Scholar

Pulkkinen, S., Ahmed, A., & Cui, Y. (2023). DEUCE v1.0: A neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties. Geoscientific Model Development. https://doi. org/10.5194/egusphere-2023-1100
Google Scholar

Qin, Y., & Li, G. (2024). Consensus reaching with the dynamic hybrid trust network in group decision making based on an extensive bounded confidence. Expert Systems with Applications, 255A, 124448. https://doi.org/10.1016/j.eswa.2024.124448
Crossref
Google Scholar

Qu, Y., Azlina, A., & Kassim, M. (2023). The impact of economic policy uncertainty on investment in real estate corporations based on sustainable development: The mediating role of house prices. Sustainability, 15(21), 15318. https://doi.org/10.3390/su152115318
Crossref
Google Scholar

Sabbatini, F., & Calegari, R. (2024). Untying black boxes with clustering-based symbolic knowledge extraction. Intelligenza Artificiale [Artificial Intelligence], 18(1), 21–34. https://doi.org/10.3233/ia-240026
Crossref
Google Scholar

Saranathan, A. M., Werther, M., Balasubramanian, S. V., Odermatt, D., & Pahlevan, N. (2024). Assessment of advanced neural networks for the dual estimation of water quality indicators and their uncertainties. Frontiers in Remote Sensing, 5, 1383147. https://doi. org/10.3389/frsen.2024.1383147
Crossref
Google Scholar

Siemon, M. S. N., Moeslund, T. B., Norton, B., & Nasrollahi, K. (2024). Bounding boxes and probabilistic graphical models: Video anomaly detection simplified. arXiv. https://doi.org/10.48550/arxiv.2407.06000
Google Scholar

Yang, J. (2023). Data-driven investment strategies in in¬ternational real estate markets: A predictive analytics approach. International Journal of Computer Science and Information Technology, 3(1), 32. https://doi. org/10.62051/ijcsit.v3n1.32
Crossref
Google Scholar

Yang, T., Han, C., Luo, C., Gupta, P., Phillips, J. M., & Ai, Q. (2024). Mitigating exploitation bias in learning to rank with an uncertainty-aware empirical Bayes approach. ACM Digital Library, 1486–1496. https://doi.org/10.1145/3589334.3645487
Crossref
Google Scholar

Yavorska, V. V., & Shynkarenko, S. L. (2024). Modern real estate market of Ukraine: Risks and opportuni ties in city renewal. Visnik Odeskogo Nacionalnogo Unìversitetu [Bulletin of the Odessa National Univer¬sity], 1(44), 305–382. https://doi.org/10.18524/2303- 9914.2024.1(44).305382
Google Scholar

Zhang, K., Chen, N. J., Liu, J., Yin, S., & Beer, M. (2023). An efficient meta-model-based method for uncertainty propagation problems involving non-parameterized probability-boxes. Reliability Engineering & System Safety, 238, 109477. https://doi. org/10.1016/j.ress.2023.109477
Crossref
Google Scholar

Zhang, X., Ma, X., & Xiao-jia, Z. (2024). Empirical study on real estate mass appraisal based on dynamic neural networks. Buildings, 14(7), 2199. https://doi. org/10.3390/buildings14072199
Crossref
Google Scholar

Zhang, L., Li, C., Su, H., Xu, Y., Da Ronch, A., & Gong, C. (2024). An efficient uncertainty propagation method for nonlinear dynamics with distribution-free P-box processes. Chinese Journal of Aeronautics. https://doi. org/10.1016/j.cja.2024.05.028
Crossref
Google Scholar