Returns Analysis & Sensitivity

The returns analysis is where everything comes together. After building the sources and uses, projecting the operating model, and constructing the debt schedule, the final step is calculating how much money the PE sponsor makes and testing how sensitive that return is to changes in key assumptions. This lesson covers exit equity value calculation, the MOIC and IRR metrics, how to decompose returns into their three drivers, and the sensitivity and scenario analysis frameworks that PE professionals use to evaluate risk.

Returns analysis is not just an output of the model. It is the primary decision-making tool. Investment committees approve or reject deals based on whether the projected returns meet the fund's hurdle rate under a range of plausible scenarios. A deal that looks attractive in the base case but falls apart under modest stress is not a good deal.

Every dollar of equity value created in an LBO can be attributed to one of three drivers. Decomposing the return into these components helps PE professionals understand where the value came from and evaluate whether the thesis is realistic.

1. EBITDA Growth
The contribution from EBITDA increasing during the hold period. Calculated as: (Exit EBITDA - Entry EBITDA) x Exit Multiple. If EBITDA grew from $50M to $70M and the exit multiple is 8x, the EBITDA growth contribution is ($70M - $50M) x 8x = $160M.

2. Multiple Expansion
The contribution from selling at a higher multiple than the purchase multiple. Calculated as: (Exit Multiple - Entry Multiple) x Entry EBITDA. If the entry multiple was 7x and exit was 8x on $50M of entry EBITDA, the multiple expansion contribution is (8x - 7x) x $50M = $50M. Note: a combined term captures the interaction of growth and expansion: (Exit EBITDA - Entry EBITDA) x (Exit Multiple - Entry Multiple).

3. Debt Paydown (Deleveraging)
The contribution from reducing the debt balance during the hold period. Calculated simply as: Entry Debt - Exit Debt. If debt went from $300M to $180M, the deleveraging contribution is $120M.

These three components should sum to the total equity value creation (Exit Equity - Entry Equity). In a well-structured deal, EBITDA growth and debt paydown are the primary drivers. Multiple expansion is often assumed at zero in the base case because it is the least predictable and least controllable driver.

A sensitivity table (also called a data table) shows how returns change when two key assumptions vary simultaneously. It is one of the most important outputs of any LBO model because it reveals the range of potential outcomes and highlights which assumptions matter most.

Common sensitivity table combinations:

Entry Multiple vs. Exit Multiple: Shows how returns vary with purchase price and sale price. This is the most standard table in any LBO presentation. A deal bought at 8x and sold at 8x generates a very different return than one bought at 10x and sold at 8x (multiple compression).

EBITDA Growth Rate vs. Exit Multiple: Isolates the interaction between operational performance and exit valuation. Reveals whether the deal works even with modest growth if the exit multiple holds, or whether it requires aggressive growth to hit the hurdle.

Leverage (Debt/EBITDA) vs. EBITDA Growth: Shows the trade-off between financial risk and operational performance. Higher leverage boosts returns if growth materializes but increases downside if it does not.

A typical sensitivity table is a 5x5 or 7x7 grid. The base case sits in the center, with two to three increments above and below for each variable. Returns below the fund's hurdle rate are often highlighted to show where the 'floor' of acceptable outcomes lies.

A good LBO is not defined by a single number but by a returns profile that meets the fund's criteria across a range of plausible outcomes. Most PE funds target a gross MOIC of 2.5x or higher and a gross IRR of 20% or higher. Deals that achieve these thresholds primarily through EBITDA growth and debt paydown (rather than relying on multiple expansion) are considered higher quality because those drivers are more controllable.

The best deals exhibit several characteristics: predictable, recurring revenue that makes the operating model reliable; high FCF conversion that enables rapid deleveraging; a credible path to EBITDA growth through pricing, operational improvements, or add-on acquisitions; and a purchase price that leaves room for multiple contraction without destroying the return. The returns analysis and sensitivity framework taught in this lesson is the tool that reveals whether a deal has these qualities or whether it is only attractive under optimistic assumptions.

With this lesson, you have completed the full LBO modeling toolkit: from the conceptual overview and sources-and-uses table, through the paper LBO shortcut, to the full operating model, debt schedule, and returns analysis. This is the core analytical framework of private equity investing.