Applying Reference Class Forecasting to Multifamily Investments: Identifying and Capturing Operational Alpha through the Outside View

Abstract

This thesis applies Reference Class Forecasting (RCF) to multifamily real estate underwriting as a means of countering optimism bias, strategic misrepresentation, and other distortions embedded in the traditional “inside view.” Adapted from its proven application in infrastructure and corporate capital budgeting, RCF anchors projections in the actual performance distributions of comparable assets rather than in deal-specific narratives. The research centers on the development of the “Comp Warehouse,” a structured repository of property-level financials organized by market, asset class, vintage, and unit scale. By benchmarking assumptions against statistically valid reference classes, the approach enforces empirical discipline and highlights opportunities for “operational alpha”—the marginal increase in net operating income (NOI) achieved when underperforming assets converge on median peer performance. A South Florida case study demonstrates the method’s utility in an acquisition context. Analysis of 48 assets across Melbourne, Miami, Fort Lauderdale, and West Palm Beach shows that while rent levels cluster tightly around market medians, operating expenses vary widely, producing large dispersion in realized NOI. Applying the framework to a 191-unit Class A property in Fort Lauderdale illustrates how RCF can ground underwriting assumptions by distinguishing between defensible revenue-driven growth strategies and less plausible expense-reduction projections proposed in a bidding scenario. Recognizing constraints of both scale and frequency, this thesis also explores artificial intelligence as a tool for automating the ingestion and standardization of operating statements and rent rolls. Properly deployed in a human-in-the-loop framework, AI can reduce data friction, expand sample sizes, and sharpen forecasting precision. The contribution of this thesis is twofold: it demonstrates the feasibility of applying RCF to the multifamily sector—an asset class whose relative standardization, liquidity, and data availability make it especially conducive to outside-view benchmarking—and it situates the methodology within a technology-native architecture designed to scale empirical discipline, enhance underwriting rigor, and systematically capture operational alpha.