Quality Indicators
By David Jones, MAI, CCIM · Published 2026 · 8 min read
Experienced SBA credit officers can identify a deficient feasibility study within 5 minutes of opening the document. They are not reading every word in the first pass. They are checking specific elements: the executive summary's clarity, the competitive inventory's completeness, the sensitivity analysis's rigor, and the reliance letter's addressees. If any of these raise concerns, the study gets flagged for deeper review or rejection.
This guide identifies the red flags lenders look for, written from both perspectives. For lenders, it is a quality checklist. For borrowers, it is a preview of what your study will be evaluated against, so you can assess quality before submitting.
Feasibility study red flags fall into six categories. Any single red flag may not kill the study; multiple red flags almost always do.
| Category | What It Signals | Severity |
|---|---|---|
| Overly optimistic projections | Revenue assumptions not supported by market evidence | High — often causes rejection |
| Insufficient market data | Study was rushed or analyst lacks market access | High — fundamental credibility issue |
| Template-quality analysis | Generic content with light customization | High — fails SBA SOP independence requirement |
| Missing or weak sensitivity analysis | Downside scenarios not tested or designed to avoid uncomfortable conclusions | High — credit committee red flag |
| Unvalidated operator assumptions | Borrower's claims accepted without independent verification | Moderate to high — depends on materiality |
| No site visit evidence | Desktop-only analysis missing local conditions | Moderate — more severe for larger projects |
The most common red flag. Revenue projections that exceed market evidence, expense ratios below industry benchmarks, occupancy or utilization rates above comp set performance. The study's numbers look great on paper but do not match what the market actually supports.
| Warning Sign | What Credit Officer Does | Defensible Alternative |
|---|---|---|
| Projected occupancy above comp set stabilized occupancy | Questions why subject will outperform established competitors | Project at comp set average or below; justify if above |
| ADR or rate 10%+ above comp set | Questions pricing power assumption | Rate at comp set average; explicit positioning argument if above |
| Labor cost ratio below industry benchmark | Questions whether labor model is realistic | Apply industry benchmark from BLS or trade association; local adjustment |
| Revenue growth above 4% annually beyond stabilization | Flags as hockey-stick; requires market justification | Project 2-4% growth; justify higher with specific data |
| Expense ratios below 45% when industry norm is 55% | Questions expense completeness | Apply industry norms; identify specific efficiencies if lower |
| Year 1 ramp reaching stabilization | Questions absorption trajectory | Realistic ramp curve; stabilization in Year 2 or Year 3 |
The test credit officers apply: can the projection be explained with specific market evidence? "Our projected 75% occupancy reflects the subject property's superior location, newer facility, and the market's average 72% occupancy per STR data" is defensible. "The subject hotel is projected to reach 75% occupancy" with no supporting evidence is not.
Feasibility studies rely on market data to validate demand. A study without adequate data is operating on assumption, which does not satisfy SBA's independent analysis requirement.
Warning signs. Demographic analysis limited to a single source (typically Census) without commercial demographic data (ESRI, Placer.ai). Competitive inventory that names fewer than 3 competitors in a competitive market. Absence of specialized industry data (no STR for hotels, no CoStar for multifamily, no NACS benchmarks for c-stores). Data cited with dates over 12 months old. No primary research for rural or emerging markets where database coverage is limited.
What it signals. The analyst either lacks access to the appropriate data sources, rushed the engagement, or does not recognize which data is needed. Any of these indicate that the analysis underlying the conclusions may be unreliable.
What credit officers check. Is the data recent (within 6-12 months)? Are the sources appropriate for the property type? Is the depth of data proportional to the project size? A $500,000 project might justifiably use lighter data than a $5 million project, but basic commercial data should still be present.
Template-based studies read like they could apply to any city in America because they could. The analyst swapped the subject property name into a boilerplate document, added a few local data points to disguise the template origin, and delivered the result as original analysis.
| Template Indicator | Why It Signals Template |
|---|---|
| Generic market language ('the area is experiencing growth') | Could describe any metro area in the US; no specific local evidence |
| Industry trends that could apply anywhere | National trends without local application |
| Competitive discussion without named competitors | No specific analysis; competitors should be named by address |
| Financial projections with round numbers and predictable ratios | No trade-area-specific adjustment |
| Recommendations section that reads as boilerplate | Conclusion not tied to the specific analysis in the preceding sections |
| Identical paragraph structure across multiple studies by same firm | Template with variable substitution; not original analysis |
| Generic photos (stock imagery, not site visit photos) | Indicates desktop-only work |
| Data citations that don't match specific market | National data applied without market-specific validation |
The defense against template-quality analysis is simple: every market analysis should contain findings that could only result from analyzing the specific trade area. Named local employers, specific development projects, local economic events, data points sourced from local agencies. If the study lacks these specifics, it is working from a template.
The base case DSCR is one number. Sensitivity analysis tests what happens when the base case doesn't happen. A feasibility study without meaningful sensitivity analysis is presenting a single scenario without context.
| Sensitivity Red Flag | What It Indicates |
|---|---|
| No sensitivity analysis at all | Study is incomplete; must be returned for revision |
| 'Pessimistic' scenario shows DSCR still above 1.20x | Stress test calibrated to avoid uncomfortable conclusions, not to identify real risk |
| Only one variable tested (revenue only) | Missing expense, rate, and absorption sensitivity |
| No breakeven analysis | Credit committee cannot identify the margin of safety |
| Sensitivity conclusions not tied to base case findings | Generic analysis disconnected from the specific project |
| Pessimistic scenario DSCR calculated but not interpreted | Raw numbers without analytical commentary |
A credible pessimistic scenario produces DSCR between 0.85x and 1.05x for most projects. If the pessimistic scenario produces a higher DSCR than that range, either the base case is extremely conservative (unusual) or the stress test was too gentle (common). The pessimistic case should force the lender to confront whether the project can survive adversity, not provide reassurance that adversity cannot occur.
Feasibility studies should validate the borrower's key operational assumptions, not simply accept them. A borrower claiming to be able to achieve 25% food cost on a restaurant concept requires validation that the menu pricing, sourcing model, and volume assumptions can actually produce that ratio.
Warning signs. Expense ratios that match the borrower's business plan exactly without independent calculation. Labor structure that assumes productivity levels above industry norms without documentation. Equipment costs that use borrower estimates rather than vendor quotes. Marketing budgets that seem unrealistic for a startup or new location.
What credit officers check. Are the operational assumptions benchmarked against industry data? Did the analyst independently verify key cost estimates or accept borrower figures? Is the operating model actually achievable given the proposed scale and staffing?
The borrower's claims are not the enemy. They may well be accurate. The issue is whether the feasibility analyst independently validated them. If the borrower claims 28% labor cost and BLS data shows the relevant wages producing 34% labor cost at the projected revenue level, the analyst should address the discrepancy. Accepting the borrower's number without addressing the inconsistency is the red flag.
For any project above $500,000, a site visit should be standard. A feasibility study without site visit evidence is working from desktop sources that can miss critical local conditions.
| Missing Site Visit Evidence | What Might Have Been Missed |
|---|---|
| No photos of subject property | Current condition, visible deferred maintenance, signage adequacy |
| No photos of competitors | Comparable quality assessment, condition comparison |
| No observations of traffic patterns | Actual traffic flow versus database counts (which can be dated) |
| No documentation of surrounding conditions | Adjacent land uses, pending construction, neighborhood conditions |
| Site description reads as Google Street View summary | No ground-level observations that only a site visit provides |
| No notes on access, parking adequacy, visibility | Real-world site characteristics that affect business performance |
Site visit evidence should include photographs (subject property from multiple angles, competitors, neighborhood context), dated observations (the date of the site visit should be documented), and ground-level detail (access points, signage, conditions that only in-person observation provides).
When a credit officer opens a feasibility study, they complete an initial quality assessment in under 5 minutes by checking specific elements. The assessment is not comprehensive; it is a screening decision about whether to trust the document for deeper review.
| Check | What They Look For | Time |
|---|---|---|
| Reliance letter | Addressed to lender and SBA; signed by named analyst | 15 seconds |
| Executive summary recommendation | Clear feasible / conditional / not feasible statement with DSCR | 30 seconds |
| Sensitivity analysis table | Pessimistic scenario DSCR; breakeven identification | 45 seconds |
| Competitive inventory | Competitors named by address; count matches expected market density | 60 seconds |
| Site visit evidence | Photographs of subject and competitors; dated observations | 30 seconds |
| Analyst qualifications | Appraisal Institute affiliation, professional credentials, SBA experience | 30 seconds |
| Overall formatting and professionalism | Professional layout, consistent formatting, defensible writing | 30 seconds |
If the 5-minute review raises multiple concerns, the study is flagged for deeper review or rejection. If the 5-minute review passes, the study gets read substantively. Borrowers can protect themselves by applying the same 5-minute review to draft studies before submission: if the draft fails the screening checks, request revisions before delivery to the lender.
For a complete quality evaluation framework, see our 15-point feasibility study evaluation checklist. For the full list of common errors, see our blog post on top 10 mistakes that sink feasibility studies.
Our feasibility studies are built to withstand the screening checks experienced credit officers apply: proper reliance letter, clear recommendation, rigorous sensitivity analysis, complete competitive inventory, documented site visits, and credentialed analyst. Request a scope of work and fixed-fee quote within 24 hours.
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