Real estate loans: shaping credit decision with macroeconomic Monte Carlo approach

Purpose Traditional real estate valuation techniques, such as discounted cash flows (DCF), often fail to explicitly account for the risks of real estate investments. This paper proposes an innovative strategy integrating Monte Carlo simulations to enhance decision-making in real estate finance. Design/methodology/approach The framework incorporates Monte Carlo simulations of macroeconomic and financial scenarios and risk variables specific to the real estate industry. It introduces a consistent macroeconomic approach, considering the correlation between macrofinancial variables. Findings The paper shows how the methodology allows for risk-return optimization by identifying the best possible loan terms and interest rates. It also offers insights into monitoring the simulation KPIs, providing tools for continuous investment evaluation. Practical implications By constructing a “Credit Decision Map”, the study provides financial institutions with a valuable tool to guide their decisions and assess the associated risks in real estate lending. Originality/value This work introduces a consistent macroeconomic Monte Carlo framework, thereby enhancing the robustness of risk assessment methodologies in real estate finance. Additionally, it provides a “Credit Decision Map” to optimize structuring of real estate loan.