Worked Example: SBA 7(a) Flex-Serve Car Wash Feasibility Study in Texas
A worked example of the feasibility study a lender would read for a car wash project in Texas, financed with an SBA 7(a) loan.
What SBA's own loan files show about lending in the Brownsville-Harlingen, TX Metro Area, every asset class together, and what a feasibility study for a project there has to test.
Brownsville-Harlingen is Cameron County, TX alone, with 286 loans and $108.0 million from FY2017 to FY2026, ranking 250 of 928. Two floors match: cancelled approvals total 10.6% through FY2024, and charge-offs total 10.6% across the 113 loans funded out of earlier approval years. A charge-off floor that high on a small base is a signal for any lender. SBA's release leaves out a cancellation ceiling and a five-year loss rate for Brownsville-Harlingen, so none is printed, and a gap must not be read as nil.
Median borrowing sits at $125,000, a mere 0.60x the national level, while the middle loans range from $25,000 to $350,000. Franchise brands account for 9.4%. The programme split is 275 under 7(a) and 11 under 504, and no 504 holder reaches a count large enough to list. Few tracked classes register: coffee shop and snack bar ties hotel and motel at 9, full-service restaurant has 8, medical office 7, and limited-service restaurant ties gas station and truck stop at 6. A total of 220 loans fall in no tracked class. Cameron County holds all 286 loans, so the evidence a study can cite is entirely local.
SBA's holder entries below say where each loan sits; practice is outside their scope. Wells Fargo Bank National Association holds 52 7(a) loans worth only $3.1 million, PNC Bank, National Association 43 worth $5.7 million, PlainsCapital Bank 25 worth $13.6 million, Newtek Bank, National Association 18 worth $4.5 million and PeopleFund 11 worth $1.1 million. PlainsCapital Bank's average loan is several times that of the first two names.
Annual approvals fell to 9 loans worth $830,000 in FY2020 and recovered to 37 loans in FY2024 and $19.6 million in FY2025. A lender reading this will see that the area's record has been rebuilt recently and that older seasoning is limited.
A study should keep cost in line with the small tickets that dominate and should test demand in Cameron County for the specific trade area. Debt service coverage must hold at a charge-off floor near one in ten, and the appraisal has to support value. Since the floors above rest on modest numbers, we advise reading them as warnings, not forecasts. The 2023 delineation sets the county. Nothing on this page implies an approval.
| Loans approved, FY2017 to FY2026 | 286 |
|---|---|
| Gross dollars approved | $108.0 million |
| Median loan | $125,000 |
| Middle half of loans, from | $25,000 |
| Middle half of loans, to | $350,000 |
| SBA 7(a) loans | 275 |
| SBA 504 loans | 11 |
| Franchise share of loans | 9.4% |
| Rank among metro and micro areas by loans | 250 of 928 |
| Median loan against the national median | 0.60x |
| Cancelled after approval, FY2017 to FY2024 approvals (a floor) | 10.6% |
|---|---|
| The same, counting loans still undisbursed as cancelled (a ceiling) | withheld |
| Charged off to date, FY2017 to FY2021 disbursed loans (a floor) | 10.6% |
| Charged off within five years of approval | withheld |
| Loans in that cohort | 113 |
Both rates are floors. SBA withholds the status of open loans, so a loan counts as charged off only once SBA reports it; a rate needs at least 30 loans in its cohort and is otherwise not reported.
SBA loans approved in the Brownsville-Harlingen, TX Metro Area, by fiscal year
| Fiscal year | Loans | Dollars |
|---|---|---|
| FY2017 | 33 | $10.2 million |
| FY2018 | 38 | $11.1 million |
| FY2019 | 33 | $6.8 million |
| FY2020 | 9 | $830,000 |
| FY2021 | 15 | $8.9 million |
| FY2022 | 18 | $3.0 million |
| FY2023 | 34 | $15.0 million |
| FY2024 | 37 | $16.9 million |
| FY2025 | 34 | $19.6 million |
| FY2026 | 35 | $15.7 million |
| Asset class | Loans |
|---|---|
| Not in any of the 39 asset classes | 220 |
| Hotel and motel | 9 |
| Coffee shop and snack bar | 9 |
| Full-service restaurant | 8 |
| Medical office (physicians) | 7 |
| Gas station and truck stop | 6 |
| Limited-service restaurant | 6 |
| Beauty salon | 5 |
Classes with 5 or more loans; smaller ones are withheld, and more where a small one could be derived.
| Lender | Loans | Dollars |
|---|---|---|
| Wells Fargo Bank National Association | 52 | $3.1 million |
| PNC Bank, National Association | 43 | $5.7 million |
| PlainsCapital Bank | 25 | $13.6 million |
| Newtek Bank, National Association | 18 | $4.5 million |
| PeopleFund | 11 | $1.1 million |
Lenders with 5 or more loans here; a lender with fewer is not listed, and loans SBA or a receiver holds are counted but never listed as a lender.
| County | Loans |
|---|---|
| Cameron County, TX | 286 |
Counties with 5 or more loans; smaller ones are not listed.
Metro is the CBSA (metropolitan or micropolitan statistical area) of the loan's project county, July 2023 delineation (OMB Bulletin 23-01). The county is matched by name (manifests/ld2/county-cbsa-map.csv); a loan whose county cannot be placed in a 2023 CBSA is left out of every metro.
All assets: every 7(a) and 504 approval in the metro, including those outside the 39 asset classes.
A value marked withheld rests on fewer than 5 loans, or is withheld so that such a figure cannot be derived from other published figures.
Source: U.S. Small Business Administration, 7(a) and 504 loan-level FOIA files (data.sba.gov), as of 2026-09-30; areas from the U.S. Census Bureau CBSA delineation, July 2023 (OMB Bulletin 23-01); Loan Analytics metro cell MT-15180.
A worked example of the feasibility study a lender would read for a car wash project in Texas, financed with an SBA 7(a) loan.
Loan Analytics prepares the feasibility study a lender reads before it commits: for a commercial project in the Brownsville-Harlingen, TX Metro Area, financed under SBA 7(a), SBA 504, USDA Business and Industry or a conventional bank loan. Tell us the project and the lender's deadline, and we reply with scope, timeline and fee.
Start with the data.
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