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Debt Capacity Analysis Walkthrough

How to size a credit facility from scratch — the way direct lenders actually do it, using cash flow constraints and downside recovery, not just EBITDA multiples.

The Example Company

This walkthrough uses a vertical SaaS company as the subject. Software businesses are instructive for debt capacity analysis because they exhibit characteristics that justify above-average leverage — high recurring revenue, low physical capital requirements, and strong unit economics — yet they also present unique risks like customer concentration and technological disruption that a lender must underwrite carefully.

This company is a compelling credit candidate. 85% recurring revenue means the vast majority of the business renews automatically with contractual obligations. 112% NRR means the existing customer base is growing — even without a single new customer, revenue grows. The lender's job is to translate these quality attributes into a defensible debt sizing.

Part 1 — Cash Flow-Based Debt Sizing

The correct way to size debt is not to look up a leverage multiple in a comp table. It is to work backward from the minimum coverage ratio the lender requires and solve for the maximum debt that meets that constraint. Direct lenders typically require a minimum FCCR of 1.5x on entry.

Step 1: Calculate EBITDA - Capex (Numerator of FCCR)

Step 2: Determine the Maximum Debt Service at 1.5x FCCR

If FCCR must be at least 1.5x, the maximum allowable fixed charges (interest + mandatory amortization) are:

Step 3: Solve for Maximum Debt Given Rate and Amortization Assumptions

Assume a first lien term loan at SOFR + 350bps (approximately 8.4% all-in with SOFR at 4.9%) and 1% annual amortization — standard for a TLB structure. Let D = total debt.

The cash flow constraint allows up to approximately $511M of debt. This is the ceiling — sizing beyond this point would push FCCR below 1.5x even in the base case.

Part 2 — Leverage Multiple Check

After the cash flow analysis sets a ceiling, the lender cross-checks against market leverage norms for the sector. For software businesses, direct lenders have become increasingly comfortable with higher leverage multiples due to the predictability of ARR-based cash flows and strong recovery dynamics (customer contracts have real value as collateral in distress).

Software Sector Leverage Context

For our vertical SaaS company with 85% recurring revenue and 112% NRR, the market norm supports 5x–7x leverage. The cash flow model derived a ceiling of 6.4x at a 1.5x FCCR covenant. Both approaches converge on a similar number — this is a good sign that the credit is being sized appropriately rather than being stretched.

Enterprise Value Sanity Check

At 4.75x first lien leverage, the debt is $380M on $80M EBITDA. The sponsor likely paid an 11x EBITDA acquisition multiple — $880M enterprise value. That implies 43% equity / 57% debt at closing.

An LTV of 43% at entry is conservative for a software deal. Even if the company is sold at a distressed 6x multiple ($480M EV), the first lien lender recovers 100% — there is $100M of buffer between distressed EV and the senior debt. This validates the sizing from a recovery perspective.

Part 3 — Recovery and Collateral Sizing

For software companies, traditional asset-based collateral analysis is largely irrelevant — there are no factories, inventory, or receivables in the traditional sense. Instead, lenders focus on enterprise value as the collateral, with the implicit assumption that the customer contracts and IP have significant value even in distress.

Distressed Enterprise Value Analysis

The severe stress scenario — a 35% EBITDA decline and a 6x distressed multiple — results in an $82 recovery on the dollar for the first lien. This is an acceptable outcome for a direct lender. Only in a catastrophic scenario (software business experiencing both major customer losses and forced sale) does the lender take a meaningful principal loss.

Part 4 — Final Debt Structure

After working through cash flow constraints, leverage benchmarks, and recovery analysis, the lender proposes a final structure. For this vertical SaaS company, a conservative first lien unitranche makes sense — simple structure, lower cost than a split first/second lien, and appropriate given the business quality.

This structure is intentionally conservative. At 3.75x leverage, the lender has material headroom before any covenant concern. The $50M revolver provides liquidity for seasonal needs or bolt-on M&A without adding permanent leverage. The 1% amortization is modest — the lender is primarily relying on cash flow coverage and enterprise value recovery rather than rapid de-leveraging.

Cross-Sector Debt Capacity Comparison

The framework above applies across sectors, but the inputs — max leverage, required coverage, distressed multiple — differ materially. The table below shows how a lender approaches debt sizing differently for a SaaS company versus an industrial manufacturer versus a specialty retailer with comparable EBITDA.

The industrial manufacturer gets less leverage despite having hard assets — its EBITDA is more cyclical and the recovery multiple in a forced sale is lower. The retailer gets the least leverage despite potentially having the most tangible assets because retail inventory is notoriously difficult to liquidate at book value and the EBITDA can swing violently with consumer spending.

Interview Questions & Model Answers

Q: How do you size a debt facility for a software company?

I'd work through three parallel analyses and take the most conservative answer. First, cash flow sizing: what is the maximum debt that allows FCCR to remain above 1.5x assuming a 1% amort TLB at current market rates? For a company with $80M EBITDA and $8M capex, that gets you to roughly $380M–$510M depending on the exact rate and covenant level. Second, a leverage multiple check against sector comparables — vertical SaaS in the current direct lending market supports 5x–6.5x, so $400M–$520M. Third, a recovery check: at a 6x distressed multiple, what's the first lien recovery? If it's below 90% recovery, the sizing is probably too aggressive. The final number should satisfy all three tests.

Q: A company has high recurring revenue but negative EBITDA. Can it support debt?

In rare cases, yes — ARR-based lending has emerged for high-growth SaaS companies with negative EBITDA but strong ARR and high gross margins. Lenders size debt as a multiple of ARR (typically 0.3x–0.5x ARR) rather than EBITDA, because the ARR is the contracted cash flow stream that will eventually convert to EBITDA as the company scales. The critical underwriting criteria are gross margin (must be >70% to demonstrate unit economics work), NRR (must be >100% to show the base is growing), and runway (the company must not burn through the revolver before EBITDA is positive). This is a niche structure and most direct lenders still require positive EBITDA — but it exists for the right credit.