How to Answer “What are the main drivers of returns in an LBO (IRR/MOIC), and how would you sensitize them?” in Private Equity Interviews
In private equity interviews, a common technical prompt is: “What are the main drivers of returns in an LBO (IRR/MOIC), and how would you sensitize them?” A strong answer shows you understand the drivers of returns in LBO as a clean bridge from enterprise value to equity value—and that you can translate that bridge into a small number of high-signal sensitivities.
At the private equity associate role level, interviewers want you to separate MOIC (value created) from IRR (value created and timing), then explain how you would run an LBO sensitivity analysis that is interpretable, avoids double-counting, and highlights the true investment returns drivers.
What Private Equity Interview Questions Reveal About Return Drivers
This sits near the centre of many private equity interview questions because it tests investor thinking, not just modelling vocabulary. You’re being assessed on whether you can map inputs (price, leverage, operating plan, cash conversion, exit) to outputs (equity proceeds, MOIC, IRR) in a cause-and-effect chain.
They’re also testing judgement on what to sensitise. Good candidates focus on independent levers (e.g., entry multiple, leverage, EBITDA path, exit multiple, hold period) and let the model produce dependent outcomes (e.g., debt paydown). Weak answers either sensitise everything or choose variables that overlap, making it impossible to attribute what moved returns.
Finally, they’re assessing communication under time pressure: can you give a clear IRR/MOIC explanation, propose 2–3 sensitivity “grids” with realistic ranges, and summarise what actually dominates upside/downside for this deal?
Answer Framework: Drivers of Returns in an LBO
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Step 1: Clarify MOIC vs IRR and the equity value bridge
Start with a repeatable framing: MOIC answers “how much money did we make?”; IRR answers “how fast did we make it?”. That immediately signals you understand why the same MOIC can imply different IRRs.
Then explain the equity value bridge in plain English:
- Entry: equity cheque = purchase price (plus fees) minus debt raised.
- Hold period: operating performance drives EBITDA, but free cash flow is what pays down debt (or funds distributions).
- Exit: exit equity value = exit EV (exit EBITDA × exit multiple) minus net debt at exit.
This bridge sets up your sensitivities: you’re going to vary the inputs that change entry equity, free cash flow/deleveraging, and exit proceeds—rather than listing random model lines.
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Step 2: Lay out the main factors affecting LBO IRR and MOIC
Group the investment returns drivers into three buckets so the answer is easy to follow.
1) Entry terms (starting point):
- Entry multiple / purchase price: lower price increases MOIC and usually IRR.
- Leverage and debt cost: higher leverage can increase returns by shrinking the equity cheque, but higher interest burden and tighter covenants can reduce flexibility.
2) Value creation + cash conversion (engine of deleveraging):
- EBITDA trajectory: revenue growth, pricing, margins.
- Cash conversion: capex intensity, working capital, taxes, one-offs → drives free cash flow.
3) Exit + timing (realisation):
- Exit multiple: often a major swing factor because much of value is realised at exit.
- Hold period and interim distributions: disproportionately impacts IRR because timing changes the discounting effect.
Add the key nuance: debt paydown is typically an output, not an independent “driver” to sensitise separately.
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Step 3: Build an LBO sensitivity analysis that isolates independent levers
Describe a practical sensitivity plan you could implement quickly in a case study.
Core two-way grids (show MOIC and IRR side-by-side):
- Exit multiple × exit EBITDA (or EBITDA growth rate): separates valuation from operating delivery and usually explains most dispersion.
- Entry multiple × starting leverage: shows underwriting discipline versus financing.
IRR-focused timing grid:
- Hold period × cash conversion (FCF margin) with exit multiple held constant: highlights why faster deleveraging/earlier cash back can lift IRR even if MOIC is similar.
Keep it clean (avoid double-counting):
- If you sensitise EBITDA or cash conversion, let interest and debt paydown flow through mechanically.
- Don’t also haircut net debt manually in the same grid.
- Keep debt terms consistent across operating cases unless the point of the sensitivity is credit tightening/widening.
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Step 4: Choose realistic ranges and explain what the outputs mean
Close by showing you can interpret sensitivities like an investor.
Range-setting: anchor multiples to trading/transaction comps and a credible re-rating story; anchor leverage to debt capacity, cyclicality, and lender appetite; anchor operating ranges to history plus a conservative execution band.
Interpretation:
- If MOIC swings materially, it’s typically exit multiple and/or EBITDA path.
- If IRR swings more than MOIC, it’s typically timing (hold period, interim distributions, speed of deleveraging).
Decision-useful takeaway: state which variables dominate this deal and why (e.g., “returns are most sensitive to exit multiple because most equity value is realised at exit, and the business delevers steadily but not enough to offset multiple compression”). This is what distinguishes strong LBO interview prep from reciting drivers.
Model Answer with IRR MOIC Explanation
The main drivers of returns in an LBO are entry price, leverage and cost of debt, operating performance and cash conversion during the hold, and the exit multiple and timing. MOIC measures how much equity value you create, while IRR is MOIC plus when cash comes back.
I’d frame it as an equity bridge. At entry, the equity cheque is purchase price (plus fees) minus the debt you can raise. During the hold, EBITDA growth and margin expansion matter, but the real mechanism for equity value accretion is free cash flow—capex and working capital drive cash conversion, and that cash either pays down debt or can be distributed. At exit, equity proceeds are exit EV—exit EBITDA times an exit multiple—minus net debt.
So the key variables I’d call out are: entry multiple, starting leverage and interest rate, EBITDA trajectory (growth and margins), FCF conversion (capex/working capital/taxes), hold period, and exit multiple.
For sensitivities, I’d keep it tight and attributable. First, an exit multiple × exit EBITDA (or EBITDA CAGR) grid, because terminal value usually explains a lot of the MOIC/IRR dispersion and it cleanly separates valuation from operating delivery. Second, an entry multiple × leverage grid to show how underwriting price and financing interact. Then for IRR specifically, I’d run hold period × FCF conversion with the exit multiple held constant to show how faster deleveraging or earlier monetisation changes IRR even when MOIC is similar.
I’d avoid double-counting by treating debt paydown as an output of free cash flow and I’d sanity-check that the leverage and multiple ranges are realistic for the sector and credit environment.
- Opens with a standalone snippet that cleanly distinguishes MOIC vs IRR (useful for interviewer notes).
- Uses an equity bridge to connect drivers to equity proceeds, not just a list of assumptions.
- Sensitivity grids isolate independent levers (valuation × operations; entry × leverage; timing × cash conversion).
- Explicitly avoids the common double-counting trap (sensitising deleveraging directly).
Common Mistakes in LBO Sensitivity Analysis
- Treating MOIC and IRR as interchangeable and forgetting hold period and interim distributions can move IRR dramatically.
- Sensitising dependent outputs (e.g., debt paydown) instead of the underlying cash flow assumptions that cause paydown.
- Changing multiple levers at once in a “downside case” grid, which prevents attribution of what drove the return change.
- Using implausible sensitivity ranges (extreme multiple expansion or leverage) that wouldn’t clear basic comps or credit constraints.
- Talking only about EBITDA and ignoring cash conversion (capex and working capital), even though cash flow drives deleveraging.
- Showing grids without an investor takeaway (what dominates, what breaks, and what you’d underwrite hardest).
Follow-Ups: Sensitivity Analysis for LBO Returns
If you could run only one sensitivity analysis for LBO returns, what would it be and why?
Exit multiple × exit EBITDA (or EBITDA growth), because it captures the biggest valuation and operating drivers in one view and usually explains most MOIC/IRR dispersion.
Why can higher leverage increase MOIC, and when can it hurt outcomes?
Higher leverage reduces the entry equity cheque (lifting MOIC), but higher interest burden and tighter covenants can slow deleveraging and increase downside risk, which can reduce IRR and resilience.
How do you explain a situation where MOIC is similar across cases but IRR changes a lot?
That’s usually timing—different hold periods, faster debt paydown, or earlier distributions—so the multiple of money is similar but the cash returns arrive sooner.
What’s typically the bigger driver: EBITDA growth or exit multiple?
Often the exit multiple and exit EBITDA together dominate because a large share of value is realised at exit, but the balance depends on leverage, hold period, and cash generation.
How would you pick realistic sensitivity ranges in a case study?
Anchor multiples to comps and a plausible re-rating narrative, leverage to debt capacity and cyclicality, and operating ranges to history plus a conservative execution band.
LBO Interview Prep for the Private Equity Associate Role
- Practise a 90-second version: MOIC vs IRR → equity bridge → three driver buckets → 2–3 sensitivity grids.
- Memorise two default grids for LBO interview prep: exit multiple × EBITDA and entry multiple × leverage, and be ready to say what each isolates.
- Add one IRR-specific lens (e.g., hold period × FCF conversion) to show you understand timing, not just terminal value.
- After each grid, force a one-line IC-style conclusion: “Returns are most sensitive to X because Y.”
- Use AceTheRound to run this question live and get feedback on whether your sensitivities avoid double-counting and whether your ranges sound credible for a private equity associate interview.
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