Coverage is the number every reviewer turns to first. What it means, how it is built, and why a ratio reverse-engineered to clear a covenant is the fastest way to lose a credit committee.
Debt service coverage ratio is net operating income divided by total debt service for the same period. A 1.25x coverage means the project generates a quarter more cash than it needs to make its payments. Below 1.00x it does not cover them at all.
The arithmetic is trivial. Everything that matters is in the inputs: what counts as net operating income, whose debt service is included, which year is being tested, and whether the revenue behind the NOI is evidenced or assumed. Two studies on the same project can report coverage a quarter-turn apart entirely through defensible-looking choices in those four places — which is why a reviewer reads the build, not the ratio.
Minimums vary. A 1.15x to 1.25x range is common across SBA, USDA and conventional lending, but the operative number is whatever your lender's credit policy says, adjusted for asset class and the risk profile of the sponsor. The study does not set the threshold; it reports where the project lands against the one that applies and how durable that position is.
The single most common defect in a rejected study is a projection built backwards from the required coverage. It is easy to spot: occupancy or capture that sits a few points above every comparable in the market, an expense ratio below the peer set with no operating reason given, a ramp that reaches stabilisation faster than any comparable delivery, and — the giveaway — a stabilised coverage of exactly 1.25x.
Reviewers see hundreds of these. Once the coverage line looks engineered, every other number in the report is read with suspicion, including the ones that were right. The remedy is direction of travel: build the market case first, let the revenue fall out of it, and report the coverage that results. If that number does not clear the covenant, the useful output is the analysis of what would have to change — a smaller building, a longer amortisation, more equity, a different rate assumption — not a quietly adjusted occupancy curve.
Every model we deliver is fully linked with no hard-coded outputs, so the underwriter can change an assumption and watch coverage move. Each study reports coverage by year through the ramp and at stabilisation, under a base case built from the market findings, and under stress cases that flex the variables the specific asset is actually sensitive to — occupancy and rate for lodging, throughput and price for processing, census and payer mix for healthcare, enrolment for schools.
We state the break-even point explicitly: the revenue level at which coverage reaches 1.00x, and the level at which it breaches the covenant. A sponsor and a credit committee should both know that number before signing, and it is the first thing an experienced reviewer looks for.
A lender-grade feasibility study for a proposed enterprise is not a business plan dressed up with financial schedules. It is an independent determination of whether projected cash flow, built from verifiable market evidence, is sufficient to service the proposed debt at the lender's required coverage threshold. Every deliverable is designed so a credit officer or agency reviewer can trace each number back to its source without leaving the workbook.
The coverage ratio is only as credible as the revenue line that feeds it, and the revenue line is only as credible as the demand analysis behind it. For a proposed operating business, demand is not asserted — it is counted, sourced and cross-checked against competitive supply before a single dollar of projected revenue is entered into the model.
Primary demand evidence is assembled from sources that are specific to the project type. Retail and food-service proposals draw on consumer expenditure surveys, traffic-count data from state departments of transportation, and trade-area demographic reports. Lodging proposals reference hotel performance data published by recognized hospitality research providers and local convention and visitors bureau statistics. Industrial and manufacturing proposals use utility interconnection records, freight and logistics indices, and purchasing manager surveys. Agricultural and food-processing proposals reference USDA Agricultural Marketing Service price series, cooperative membership records and commodity forward curves. Healthcare and professional-service proposals consult state licensing registries, certificate-of-need filings where applicable, and insured-population data from state insurance commissioners.
Competitive supply is inventoried independently — not taken from the borrower's list. Existing operators are identified through business license databases, Secretary of State filings, health department permits, liquor license registries and direct field observation. Each competitor's estimated capacity and market position is documented so the absorption assumption in the pro forma can be tested against actual available demand rather than an optimistic market-share claim.
Most coverage ratios are decided by a small number of inputs. Identifying those inputs, sourcing them independently and stress-testing them is the analytical core of the engagement. When a projection is engineered backward from a target ratio, it is almost always one of the following assumptions that has been stretched to make the math work — and a trained reviewer will find it.
Coverage minimums are not uniform across lending programs, and the analytical standard applied to reach them differs by agency. Understanding what each reviewing body actually scrutinizes allows the study to be built to the right specification from the first draft rather than revised after a deficiency letter.
Under SBA SOP 50 10 8, the operating coverage minimum is 1.15x and the global coverage minimum is 1.00x. SBA lenders and their reviewing counsel look specifically at whether global cash flow includes all personal obligations of guarantors, whether owner compensation is stated at a defensible market rate, and whether the projection period is long enough to demonstrate stabilized operations rather than a single favorable year. A study that shows 1.15x in year two but deteriorating coverage in years three through five will draw scrutiny regardless of the headline ratio.
USDA Rural Development programs — Business & Industry, Community Facilities, REAP and Value-Added Producer Grant — are governed by RD Staff Instruction 5001 and place additional weight on community economic impact, borrower equity contribution and the feasibility analyst's independence from the transaction. The agency's review process is document-intensive, and the study must address program-specific eligibility criteria alongside financial feasibility.
Conventional lenders typically require 1.20x coverage and focus heavily on the sensitivity table: they want to see the coverage ratio at each stress level, not just the base case. Lenders with portfolio concentration in a particular industry will also compare the study's margin assumptions directly against their own RMA data, so the benchmarking section of the report must be current and correctly classified by NAICS code.
The engagement begins with a fixed fee quoted within one business day of receiving the project description. The fee does not change based on the outcome of the analysis, and no portion of it is contingent on a finding of feasibility. That structure is not incidental — it is what makes the determination credible to a lender or agency reviewer who understands that a contingent fee creates an incentive to reach a favorable conclusion.
Once the fee is accepted, a data room checklist is issued. The checklist specifies exactly what financial, operational and market documentation is required before the clock starts. Standard delivery is 10 to 15 business days from receipt of a complete data room. Rush delivery is available and is quoted at the time of engagement. Incomplete data rooms are the most common source of delay; the checklist is designed to prevent that by making the requirements explicit at the outset.
Every engagement is published to a secure client portal. The financial model — a fully linked Excel workbook with no hardcoded values — remains live in the portal after delivery. When a lender's underwriter or an agency reviewer wants to test a different assumption, they can request a specific input change and see the coverage ratio recalculate in real time rather than waiting for a revised report. Wert-Berater, Inc. has completed 3,969 feasibility studies representing over $41.2 billion in evaluated project value since 1998, and the analytical process described here applies uniformly across that body of work.