, Njideka Maryclara Aguome
, Cezary Kowalczyk
, Ikechukwu William-Johnson Ozigbo
, Jacek Kil
https://doi.org/10.31648/aspal.10716
Motivation: Real estate markets, particularly in emerging economies such as Nigeria, are subject to significant uncertainties. These can be broadly categorized into aleatoric uncertainties, which arise from inherent market variability, and epistemic uncertainties, which arise from incomplete knowledge and data gaps. Traditional valuation models often struggle to fully capture these uncertainties, making property investment decisions more challenging.
Aim: This study aims to explore the integration of credal networks and confidence boxes (c-boxes) as an innovative approach to modeling uncertainty in real estate markets. The research focuses on the property market in Enugu, Nigeria, and demonstrates how this methodology improves the accuracy and reliability of market valuations compared to conventional probabilistic models.
Approach: Combining ex post facto analysis with expert surveys, the study applied a hybrid model incorporating credal networks and c-boxes to quantify both aleatoric and epistemic uncertainties. Empirical validation was conducted on property valuation data from the Enugu market, using Monte Carlo simulations and probabilistic sensitivity analysis to assess the model’s robustness.
Results: The findings revealed significant aleatoric uncertainty in property value appreciation and construction costs, while epistemic uncertainty was more pronounced in rental yields and mortgage rates. The credal network approach outperformed traditional models, providing an average improvement of 24.09% in valuation accuracy and reliability, thereby offering a more comprehensive framework for decision-making in property markets.
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