Simulation‐based excess return model for real estate development

Abstract

Purpose An acute need exists for a practical quantitative risk management‐based real estate investment underwriting methodology that clearly helps guide decision making and addresses the shortcomings of discounted cash flow (DCF) modeling by evaluating the full range of probable outcomes. This paper seeks to address this issue. Design/methodology/approach The simulation‐based excess return model (SERM) is an original methodology developed based on an application of Monte Carlo simulation to project risk assessment combined with the widely practiced DCF modeling. A case study is provided where results of the modeling are compared with traditional DCF risk models and with prior projects with known outcomes. Findings This paper lays out a practical method for stochastic quantitative risk management modeling for real estate development projects and illustrates that for identical projects risk‐adjusted returns derived with the use of SERM may differ significantly from returns provided by traditional discounted cash flow analysis. SERM corrects serious shortcomings in the DCF methodology by incorporating stochastic tools for the measurement of the universe of outcomes. It further serves to condense the results of Monte Carlo simulations into a simplified metric that can guide practitioners and which is easily communicational to decision makers for making project funding decisions. Practical implications SERM offers a simple, practical decision‐making method for underwriting projects that addresses the limitations of the prevailing methodologies via: stochastic assessment of the range of outcomes; interdependence of input variables; and objective risk premium metrics. Originality/value This paper presents an original methodology for making project‐funding decisions for real estate development projects that is based on Monte Carlo simulation combined with DCF analysis. The methodology presented here will have value for real estate developers, investors, project underwriters, and lenders looking for a practical and objective method for project valuation and risk management than is offered by traditional DCF analysis. A review of literature did not reveal analogous methodologies for risk management.

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