Sensitivity Analysis Frameworks

One-variable sensitivity holds all assumptions constant except one, then varies that single input across a defined range to observe the impact on a target output (typically IRR or Cash-on-Cash). For example, varying the exit cap rate from 6.0% to 9.0% in 0.25% increments while holding all other assumptions constant reveals the IRR at each exit cap level. This analysis identifies the break-even point for each variable—the value at which your target return threshold is no longer met. Build one-variable tables for each of the five most impactful assumptions: vacancy rate, rent growth rate, exit cap rate, interest rate, and renovation cost (for value-add deals).

Two-variable sensitivity (also called a data table or matrix) varies two inputs simultaneously, showing the target output for every combination. The classic real estate sensitivity table shows IRR at the intersection of exit cap rate (columns) and NOI growth rate (rows). This reveals how the deal performs under combined adverse conditions—for example, what happens if both rent growth disappoints AND cap rates expand? The two-variable table is the most widely used sensitivity tool in professional real estate underwriting because it captures the interaction between assumptions that a one-variable analysis cannot.

A tornado chart ranks variables by their impact on the target output, displayed as horizontal bars emanating from a central baseline. Each bar shows the range of the target output when that variable moves from its pessimistic to optimistic estimate while all other variables remain at base case values. The variable with the widest bar has the greatest impact. In most multifamily acquisitions, the tornado chart reveals that exit cap rate and rent growth rate have the greatest impact on IRR, followed by vacancy, interest rate, and expense growth. This prioritization tells the investor where to focus due diligence effort—spend the most time validating the assumptions that matter most.