Common Mistakes
By David Jones, MAI, CCIM · Published 2026 · 9 min read
We review feasibility studies produced by other firms as part of our supplemental review and replacement study services. We also hear from lenders who have rejected studies and need a credible replacement. Over years of this work, we have identified a consistent set of errors that cause studies to fail. These are not obscure technical mistakes. They are fundamental analytical failures that experienced credit officers spot immediately.
If you are a borrower commissioning a study, this list tells you what to watch for in the deliverable you receive. If you are a lender reviewing a study, this list gives you a diagnostic framework. If you are a consultant, consider this a quality checklist.
What goes wrong: the study inventories every existing competitor but does not identify facilities under construction, in permitting, or publicly announced. A self-storage market that appears undersupplied at 6.5 SF per capita today may have three facilities in the pipeline that will push the ratio above 10.0 SF per capita within 18 months.
Why it sinks the study: the SBA processing center analyst or the lender's credit officer knows to ask "what's being built?" If the study cannot answer that question, the demand projection is built on incomplete data. The entire financial model is compromised because the revenue projection assumes a competitive landscape that will not exist by the time the subject property opens.
How to get it right: check local planning department records for pending permits and conditional use applications, search commercial real estate databases for announced projects, conduct a physical site visit to observe construction activity, and call the planning department directly to ask about pending applications in the relevant property category.
What goes wrong: the study's revenue projection is within 2% to 3% of the borrower's business plan. The analyst either copied the borrower's numbers directly or reverse-engineered assumptions to produce a matching result. The projections are not independent; they are the borrower's projections with a consulting firm's letterhead.
Why it sinks the study: the entire purpose of a third-party feasibility study is independent validation. SOP 50 10 8 requires CDCs to perform independent analysis of repayment ability. A study that mirrors the borrower's plan does not provide independence. Experienced credit officers test this by comparing the study's projections to the business plan. If they match too closely, the study loses credibility.
How to get it right: build projections bottom-up from market data (traffic counts, comp set performance, demographic demand models) without reference to the borrower's plan. Compare the independent projection to the borrower's numbers only after the model is complete. Divergence of 5% to 20% is normal and healthy. If the projections happen to align, document the independent methodology that produced the same result.
What goes wrong: the study either omits sensitivity analysis entirely or includes a "pessimistic" scenario where DSCR still shows a comfortable 1.25x. The pessimistic case was calibrated to avoid an uncomfortable conclusion rather than to identify the real downside risk.
Why it sinks the study: credit committees need to understand the downside. If the pessimistic scenario never threatens the DSCR threshold, the stress test was not a stress test. An SBA processing center analyst who sees a pessimistic DSCR of 1.25x against a base case of 1.40x will question whether the analyst understands the purpose of sensitivity analysis.
How to get it right: the pessimistic scenario should produce a DSCR between 0.85x and 1.05x for most projects. It should identify the specific conditions that would cause the project to fail (15% to 25% revenue reduction, 6 to 12 month absorption delay, simultaneous cost increases). Include a breakeven analysis that identifies the exact occupancy, revenue, or volume level where DSCR hits 1.0x. That breakeven number is the most useful metric in the entire study for credit decision-making.
What goes wrong: the study uses demographic data from the 2020 Census without updating to current estimates, cites STR data that is 12+ months old, or references competitor pricing that was collected 6+ months before the report date. Markets move. A study using 2023 data to support a 2026 loan decision is analyzing a market that may no longer exist.
Why it sinks the study: SBA processing center analysts check data dates. A study submitted in 2026 with a competitive inventory dated January 2025 will be questioned. If a new competitor opened between January 2025 and the submission date, the study is materially incomplete.
How to get it right: core market data should be no more than 6 months old at the time of the study. Demographics should use the most recent ACS estimates (not decennial Census alone). Competitive pricing should be verified within 30 days of the report date. STR data should cover the trailing 12 months ending within 3 months of the report date.
What goes wrong: the study includes only the 3 or 4 competitors that are most favorable to the subject property (older, lower quality, higher priced) while omitting newer, stronger competitors that present a more challenging comparison. Or the study describes competitors generically ("several establishments in the area") without naming, locating, or profiling each one.
Why it sinks the study: a lender or SBA analyst who knows the market will immediately spot missing competitors. If the study omits the brand-new Hampton Inn that opened last year 1.5 miles from the subject hotel, the analyst's confidence in the entire document collapses. Selective competitor inclusion is a form of bias that undermines the study's independence.
How to get it right: inventory every competing facility within the defined trade area, including those that are stronger than the subject property. Document each by name, address, capacity, pricing, condition, and competitive positioning. If a competitor is superior to the subject property on multiple dimensions, acknowledge it and explain how the subject property will compete.
What goes wrong: the study validates market demand and financial viability but never confirms that the proposed use is legally permitted on the site. The borrower discovers after the study is complete (and the loan is in process) that the property requires a rezoning, a conditional use permit with uncertain approval, or a Certificate of Need that could take 12+ months.
Why it sinks the study: a project that is financially feasible but cannot obtain the required permits is not feasible. The site and regulatory assessment is not optional. Zoning confirmation, permitting pathway identification, and regulatory compliance screening are baseline requirements.
How to get it right: verify the zoning designation and confirm the proposed use is permitted. Identify all required permits (building, health department, liquor, TTB, CON) and estimate the timeline for each. Flag any permits that are discretionary (CUP, variance, rezoning) and assess the probability of approval based on local precedent.
What goes wrong: the study reads like it could apply to any city in America. The market analysis section uses generic language ("the area is experiencing growth") without specific local references. The demand model uses national averages rather than trade-area-specific data. The financial projections use industry-average expense ratios without adjusting for local wage rates, utility costs, or tax rates.
Why it sinks the study: a template study signals that the analyst did not conduct genuine market research. Credit officers can distinguish between an analysis that was built for a specific market and one where the city name was swapped into a boilerplate document. Template studies also miss local factors (a major employer closing, a highway bypass under construction, a competing development in permitting) that national data cannot capture.
How to get it right: 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, and data points sourced from local agencies are markers of genuine market-specific research.
What goes wrong: the DSCR is calculated against only the SBA mortgage while ignoring equipment leases, lines of credit, seller carry-back notes, or other obligations the business must service from cash flow. A $450,000 NOI divided by $300,000 in SBA mortgage payments produces a 1.50x DSCR. But if the business also has $120,000 in annual equipment lease payments, the real DSCR is 1.07x.
Why it sinks the study: the lender underwrites against total debt service, not just the SBA loan. A DSCR that excludes material obligations is materially overstated. This is one of the first things an experienced credit officer checks: does the debt service line include everything?
How to get it right: identify every debt obligation the business must service (SBA mortgage, equipment financing, lines of credit, seller notes, partner distributions if contractually required). Sum them into total annual debt service. Calculate DSCR as NOI divided by total debt service. If the borrower has personal guarantees on other debts that could be satisfied from the business, note them in the risk assessment even if they are not included in the primary DSCR calculation.
What goes wrong: the study was prepared entirely from desktop data. The analyst never visited the subject property, never observed the competitive environment, never drove the trade area, and never photographed the site or competitors. The site assessment section either does not exist or is based on Google Street View images that may be 2+ years old.
Why it sinks the study: desktop-only studies miss critical information. A competitor that Google Maps shows as a functioning hotel may have closed 6 months ago. A site that looks accessible on a map may have a median barrier that blocks left turns. A building that appears adequate in satellite view may have a collapsing roof visible only from the ground. For any project above $500,000, a study without a site visit should be viewed with significant skepticism.
How to get it right: conduct an in-person site visit. Photograph the subject property from multiple angles. Photograph key competitors. Drive the trade area. Observe traffic patterns, access points, and construction activity. Include the photographs and site visit observations in the report. If the project is in a remote location that makes a site visit impractical, disclose this limitation explicitly and explain what alternative verification methods were used.
What goes wrong: the analyst has a financial relationship with the borrower beyond the study fee, was engaged by a mortgage broker with a commission interest in loan approval, or has a marketing model that depends on producing positive conclusions (and therefore never issues negative recommendations). The study reads like advocacy rather than analysis. Every finding is framed positively. Risks are minimized or omitted. The conclusion was predetermined before the research began.
Why it sinks the study: independence is the foundation of credibility. A study that reads like a sales document for the project, rather than an objective evaluation, will not survive scrutiny from experienced credit officers. If the lender discovers that the analyst has never issued a negative recommendation across hundreds of studies, the credibility of every positive conclusion is questionable.
How to get it right: engage an analyst with no financial interest in the loan's approval. The fee should be fixed and payable regardless of the conclusion. The analyst should be able to demonstrate a track record that includes conditional and negative recommendations, not just positive ones. An analyst who has never said "no" is not performing independent analysis. For a detailed framework on evaluating analyst independence, see our quality evaluation checklist.
A deficient feasibility study does not just risk a loan denial. It creates cascading consequences. The borrower loses 4 to 8 weeks waiting for the study to be reviewed and rejected. The replacement study costs another $4,900 to $25,000 and takes another 2 to 3 weeks. The loan closing is delayed by 6 to 12 weeks total. If the borrower has a purchase contract with a closing deadline, the delay can kill the deal entirely. And if a deficient study somehow clears underwriting and the loan later defaults, the lender faces a purchase/repair demand from the SBA, which can result in the SBA denying the guarantee claim.
The lesson: getting the study right the first time is not just about quality. It is about protecting the timeline, the deal, and the guarantee.
Every feasibility study we deliver is built to survive lender review on the first submission. Independent projections, complete competitive inventory with pipeline, honest sensitivity analysis, in-person site visit, and a clear recommendation. Request a scope of work and fixed-fee quote within 24 hours.
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