When you apply for a home loan, the lender is mostly underwriting you — your income, your credit, your job. Commercial real estate works differently. The borrower matters, but the asset is the star of the show. An underwriter is really answering one question: can this property service its own debt, on its own cash flow, with room to spare? Everything else flows from that.
Understanding the sequence of that analysis is the single best thing a property owner can do to get a faster, cleaner, more confident term sheet. Here is how the work actually unfolds at the desk.
It starts with net operating income
Net operating income — NOI — is the property's annual income after operating expenses but before debt service. The underwriter does not simply accept your number. They rebuild it: they take the rent roll, strip out income that isn't durable, normalize expenses to a realistic run rate, and often apply a vacancy factor even if you are fully leased today. The result is an underwritten NOI that is usually more conservative than the one on your marketing flyer. That figure becomes the foundation for everything that follows.
Then comes the coverage test
Once NOI is set, the lender tests debt service coverage — the ratio of NOI to the property's annual debt service (principal plus interest). The debt service coverage ratio, or DSCR, is the heartbeat of commercial underwriting. A commonly required minimum is around 1.25x, meaning the property generates roughly 25% more income than it needs to cover the loan payments. That cushion is not arbitrary; it is the lender's margin of safety against a soft year, a lost tenant, or a rate reset.
If a deal pencils at exactly 1.0x coverage, the property earns precisely enough to pay the mortgage and not a dollar more — and no disciplined lender will fund that. The coverage requirement is often what caps your loan amount well before the value of the building does.
Leverage is the second constraint
After coverage, the underwriter checks loan-to-value (LTV) — the loan amount against the appraised value of the asset. Different property types carry different leverage ceilings. As a general rule, conventional commercial mortgages run up to about 75% LTV, multifamily can reach roughly 80%, and owner-occupied properties (where the business uses the space) can go as high as about 90%. Your loan is sized to the lower of what coverage allows and what leverage allows — the binding constraint wins.
Why both tests exist
Coverage and leverage protect against two different failure modes. DSCR protects against the income side: can the property keep paying through a rough patch? LTV protects against the value side: if the lender ever has to sell the collateral, is there enough equity beneath the loan to get repaid? A deal can clear one test and fail the other. A trophy building with thin cash flow can be low-leverage and still fail on coverage; a high-yield asset in a weak market can cover comfortably and still get trimmed on value.
What this means for your file
The practical takeaway is that the cleaner your income story, the smoother your underwriting. Hand the lender a rent roll that ties to your leases, operating statements that reconcile to your tax returns, and an honest read on vacancy and capital needs. Every gap an underwriter has to fill, they fill conservatively — and conservative assumptions shrink your loan. Owners who present a tight, reconciled income picture routinely get sized closer to the leverage ceiling, because the underwriter has less reason to discount.
Commercial underwriting is not a black box. It is a structured stress test of an income stream. Learn to read your own deal the way the desk will — NOI first, then coverage, then leverage — and you stop being surprised by the term sheet and start shaping it.
Joseph Snado runs the Keystone desk in the Selective Capital business-funding network and reviews every file. Questions go straight to him at (561) 915-1002.
Educational only — not financial, legal, investment, or tax advice. Figures cited are from the sources above and reflect 2025–26 industry data.
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