Why Gas Stations Require Specialized Feasibility Analysis
Gas stations and convenience stores operate within one of the most data-intensive segments of commercial real estate. Unlike single-use commercial properties, a gas station is a multi-revenue-center business where fuel economics, convenience retail performance, and ancillary revenue streams must each be independently modeled and stress-tested against lender underwriting standards.
 

SBA lenders classify gas stations as special-purpose properties under SOP 50 10 8. This classification triggers mandatory third-party feasibility analysis for virtually all SBA 7(a) and 504 loan applications involving gas station acquisition, construction, or expansion. The SBA 504 program requires a 15% equity injection for special-purpose properties (20% if the borrower is also a startup), making the feasibility study a critical component of the capital stack evaluation.
 

USDA Business and Industry guaranteed loans are also available for gas stations in eligible rural areas (populations under 50,000), with loans up to $25 million and 80% federal guarantee. The feasibility study requirements under 7 CFR Part 5001 apply to all USDA-guaranteed gas station projects. Compare SBA vs. USDA financing options.

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Beyond regulatory compliance, the feasibility study serves a practical underwriting function: it provides lenders with independently constructed projections that validate whether the project can generate sufficient cash flow to service its debt obligations in a market where fuel margins are razor-thin and profitability depends heavily on convenience store performance.

What Our Gas Station Feasibility Studies Include

Every MMCG gas station feasibility study is engineered to address the full spectrum of analytical requirements that credit committees, SBA loan reviewers, and USDA Rural Development offices evaluate.
 

• Fuel volume projections and margin analysis. We construct independent gallon-per-day (GPD) projections using AADT traffic counts, capture rate modeling, competitive supply analysis, and logistical setting evaluation. Our fuel revenue models distinguish between branded and unbranded supply economics, account for diesel and DEF demand where applicable, and project net fuel margins under multiple price scenarios. The average U.S. gas station moves approximately 3,000 to 4,000 gallons per day; our models evaluate where the subject property falls within that range and why.
   

• AADT traffic analysis and capture rate modeling. Annual Average Daily Traffic data forms the foundation of fuel volume projections. We source AADT counts from state DOT databases, FHWA records, and mobile location analytics, then apply capture rate methodology calibrated to the station's logistical position: corner vs. mid-block, signalized intersection access, ingress/egress configuration, visibility from roadway, and directional traffic splits. Traffic quality matters more than traffic quantity. A station on a 4,300 AADT rural highway can outperform one on a 60,000 AADT urban corridor if logistical factors favor it.
   

• Convenience store revenue modeling. Fuel sales typically represent approximately 70% of total revenue but only 30% of total profit. The convenience store generates the inverse: roughly 30% of revenue but 70% of profit. Our c-store projections model inside sales per square foot, product category margins (tobacco, beverages, prepared food, packaged snacks, lottery), and the impact of foodservice programs. Prepared food margins reach 55% to 65%, and foodservice now represents nearly 29% of total c-store in-store sales nationally, a share that has more than doubled over two decades.
   

• Competitive landscape assessment. We profile every competing fuel retailer within the defined trade area, typically a one- to five-mile radius depending on market density. Each competitor is evaluated on fuel pricing, brand affiliation, c-store size and quality, traffic access, and operational hours. The analysis quantifies competitive saturation and identifies defensible positioning opportunities for the subject station.

 

• Financial projections and DSCR analysis. Independent ten-year revenue and expense projections constructed from fuel volume models, c-store performance benchmarks, and comparable operating data. Every projection includes debt service coverage ratio analysis against SBA, USDA, and lender-specific thresholds, sensitivity testing under adverse fuel price and volume scenarios, and breakeven analysis identifying the minimum GPD required to sustain operations.

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