Multi-Dimensional AI-Based Modeling of Real Estate Investment Risk: A Regulatory and Explainable Framework for Investment Decisions

Avraham Lalum (), Lorena Caridad López del Río and Nuria Ceular Villamandos
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Avraham Lalum: Department of Statistics, Business and Applied Economics, University of Córdoba, 14002 Córdoba, Spain
Lorena Caridad López del Río: Department of Financial Economics and Operations Management, University of Seville, 41018 Seville, Spain
Nuria Ceular Villamandos: Department of Statistics, Business and Applied Economics, University of Córdoba, 14002 Córdoba, Spain

Mathematics, 2025, vol. 13, issue 21, 1-66

Abstract: The real estate industry, known for its complexity and exposure to systemic and idiosyncratic risks, requires increasingly sophisticated investment risk assessment tools. In this study, we present the Real Estate Construction Investment Risk (RECIR) model, a machine learning-based framework designed to quantify and manage multi-dimensional investment risks in construction projects. The model integrates diverse data sources, including macroeconomic indicators, property characteristics, market dynamics, and regulatory variables, to generate a composite risk metric called the total risk score. Unlike previous artificial intelligence (AI)-based approaches that primarily focus on forecasting prices, we incorporate regulatory compliance, forensic risk assessment, and explainable AI to provide a transparent and accountable decision support system. We train and validate the RECIR model using structured datasets such as the American Housing Survey and World Development Indicators, along with survey data from domain experts. The empirical results show the relatively high predictive accuracy of the RECIR model, particularly in highly volatile environments. Location score, legal context, and economic indicators are the dominant contributors to investment risk, which affirms the interpretability and strategic relevance of the model. By integrating AI with ethical oversight, we provide a scalable, governance-aware methodology for analyzing risks in the real estate sector.

Keywords: real estate; risk management; artificial intelligence; investment decisions; machine learning; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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