Your projections can accurately reflect how the business will perform — and the lender may still reduce them, replace them, or decline to rely on them. The reason is not pessimism. It is underwriting.
Preparing an SBA application? See our SBA feasibility study consultants service — independent studies built to SOP 50 10 underwriting standards.
A well-built pro forma can accurately reflect how a business is likely to perform and still be rejected, reduced, or replaced during loan underwriting.
That apparent contradiction frustrates many borrowers. They may have strong operating experience, detailed pricing assumptions, signed customer interest, defensible staffing plans, and a clear understanding of the local market. Yet the lender adjusts projected revenue downward, increases operating expenses, delays the assumed ramp-up period, or declines to give credit for certain income entirely.
The issue is not always that the borrower’s projections are wrong. The issue is that lenders must determine whether those projections are sufficiently independent, supportable, repeatable, and conservative to be used in a regulated credit decision.
SBA and conventional lenders evaluate a pro forma based on whether its revenue, operating expenses, growth assumptions, and repayment capacity can be independently supported. Even an accurate forecast may be adjusted if it relies on unsupported market share, aggressive ramp-up assumptions, unverified pricing, or expenses that fall outside recognized industry benchmarks.
For SBA and conventional lenders, a pro forma is not simply a prediction of what could happen. It is evidence supporting the borrower’s ability to repay debt. SBA guidance places responsibility on participating lenders to underwrite loans in accordance with SBA loan-origination policies, while conventional banks must also follow their own credit policies and applicable safety-and-soundness expectations. Cash flow from normal business operations remains central to determining repayment capacity.
A business plan and a credit underwriting model serve different purposes. A business plan asks:
What can this company achieve if management executes its strategy?
A lender’s underwriting model asks:
What level of performance can the bank reasonably rely on when determining whether scheduled debt payments will be made?
Those are related questions, but they are not identical.
Management typically develops projections around the most likely operating case. A lender must evaluate the downside as well as the expected case. Underwriters are expected to make an independent and realistic assessment of projected cash flow rather than relying exclusively on the borrower’s forecast. Banking guidance also emphasizes evaluating whether cash flow remains adequate after debt service, capital expenditures, working-capital needs, and other operating requirements.
This is why an underwriter may “haircut” a forecast even when the borrower’s assumptions appear reasonable. The lender is not necessarily saying the forecast cannot be achieved. The lender is deciding how much of that forecast can prudently be counted as a dependable source of repayment.
Commercial lenders operate within several overlapping layers of responsibility:
SBA Standard Operating Procedure 50 10 establishes loan-origination policies for the 7(a) and 504 programs. The current SOP framework has also undergone updates, including revisions effective in 2025, making it important for lenders to use the applicable version and related notices when documenting a transaction. Our guide to SBA feasibility study requirements under SOP 50 10 8 covers the study-specific provisions in detail.
Conventional lenders do not follow SBA rules on non-SBA loans, but they remain accountable for prudent underwriting. OCC examiner guidance instructs examiners to assess whether lending personnel follow established underwriting standards, documentation requirements, risk tolerances, and bank policies. Policy or underwriting exceptions generally require identification, approval, tracking, and justification.
This creates an important practical reality:
A credible feasibility study should connect the market opportunity to the financial forecast through a transparent chain of evidence. The study should answer five basic questions.
The analysis should identify the relevant customer base, trade area, buying behavior, unmet need, demand drivers, and evidence that customers are willing and able to purchase the proposed product or service. Population alone is rarely enough. A feasibility study should translate demographic or economic information into estimated customer activity. For example:
Saying that a business has “no direct competition” is usually not persuasive. Nearly every business competes with substitutes, existing habits, alternative locations, online providers, internal customer solutions, or the choice to make no purchase. A useful analysis identifies direct competitors, indirect competitors, substitute products and services, competitor pricing, capacity and estimated utilization, customer reviews and positioning, barriers to customer switching, and projects or competitors entering the market.
Market demand does not automatically become company revenue. The study should connect demand to operational capacity through factors such as the number of seats, rooms, units, appointments, machines, or service bays; business hours; staffing levels; average transaction time; production throughput; inventory availability; seasonality; customer-acquisition capacity; and ramp-up time.
A restaurant cannot exceed its seating, table-turn, kitchen, and labor capacity. A medical practice cannot exceed its provider schedules. A hotel cannot sell more occupied room nights than it has available room nights. A manufacturer cannot exceed equipment, labor, shift, and supply-chain capacity without additional investment.
A lender wants to see where each material assumption came from. Stronger assumptions are supported by historical operating results, existing contracts, executed leases, purchase orders, franchise performance information where appropriately used, local competitor pricing, third-party market research, industry financial benchmarks, government data, vendor quotations, payroll estimates tied to an actual staffing plan, and utility, insurance, rent, tax, and maintenance estimates from identifiable sources.
Weaker assumptions are based primarily on management optimism, unverified conversations, national statistics applied to a small local market without adjustment, top-performing competitors, online asking prices with no evidence of actual transactions, unsupported market-share percentages, broad statements such as “demand is growing,” or revenue goals reverse-engineered to produce acceptable debt-service coverage.
A feasibility study should not only present a base case. It should explain what happens when performance is weaker than expected. Typical sensitivity tests include lower sales volume, slower customer ramp-up, reduced pricing, higher wages, higher food or material costs, construction delays, higher interest rates, increased vacancy, lower occupancy, and additional working-capital needs.
Banking guidance encourages assessment of cash-flow sensitivity when key variables change. In real-estate-related transactions, these variables can include rental rates, vacancy, operating expenses, capitalization rates, construction activity, absorption, and local economic conditions.
No single source automatically makes a revenue forecast acceptable. Lenders generally look for multiple forms of corroboration.
Existing-company history is often the strongest starting point. The lender can compare projections with tax returns, interim financial statements, bank activity, customer concentration, historical margins, and prior growth. A projection showing 8% growth after several years of stable operations may be easier to support than a projection showing 60% growth without a documented change in capacity, contracts, location, or strategy.
Executed agreements can provide strong support, but lenders will still assess cancellation rights, contract length, customer credit quality, performance conditions, renewal risk, customer concentration, whether the contract represents gross billings or net revenue, and whether the borrower has the capacity to perform.
A letter of intent is usually less persuasive than an enforceable contract. A customer survey is usually less persuasive than deposits, reservations, executed orders, or demonstrated pre-opening sales activity.
Capacity modeling is especially useful for startups and expansions. For example, projected revenue for a 100-room hotel might be modeled as:
Available rooms × assumed occupancy × average daily rate
Projected revenue for a dental practice might be modeled as:
Provider hours × appointment utilization × average net collection per visit
Projected revenue for a self-storage facility might be modeled as:
Rentable units × physical occupancy × economic occupancy × average effective rent
This approach allows the lender to test each driver independently rather than accepting one large revenue number.
Local evidence is usually more persuasive than a national growth statistic standing alone. Useful support can include local pricing, customer counts, traffic data, competitor capacity, permitting activity, employment growth, household characteristics, tourism activity, housing development, and business formation.
The data must match the proposed trade area and customer profile. County-level statistics may not adequately support a neighborhood business, while a small-radius analysis may not capture the true trade area of a destination operation.
Industry data can help establish whether a forecast is within a reasonable range. It is particularly useful for reviewing gross margins, payroll, occupancy costs, inventory turnover, working-capital needs, profitability, and leverage. However, industry averages do not prove that a particular business will achieve the average. They are a reasonableness test — not a substitute for company-specific analysis.
A statement such as “the company only needs 2% of a $500 million market” sounds conservative but may be meaningless. The $500 million figure may describe a national market, while the business operates within a 15-mile radius. It may include customer segments the company cannot serve. It may measure retail sales while the borrower earns wholesale revenue. It may also ignore competitors, capacity, and customer-acquisition costs.
A defensible market-share calculation must use the correct geographic area, customer population, spending category, and revenue definition.
Many projections assume the business reaches mature operating performance within the first few months. Lenders often use a longer ramp-up period because new businesses need time to hire, train, build awareness, generate reviews, refine operations, and convert prospects. The projection may eventually be correct, but the timing can still make it unsuitable for debt underwriting.
Professional practices, healthcare providers, contractors, subscription businesses, and other companies may bill more than they collect. Lenders may distinguish among gross billings, contractual adjustments, discounts, refunds, bad debt, third-party reimbursement, timing of collections, and net recognized revenue. The relevant cash-flow figure is generally the amount the business can reasonably expect to collect — not merely the amount it plans to invoice.
Pipeline reports, inquiries, social-media interest, waitlists, and verbal commitments may be useful indicators, but they are not the same as contracted revenue. A lender may give partial or no credit to this income unless the conversion assumptions are supported by historical data or other reliable evidence.
Forecasts may be reduced when sales assumptions exceed the facility’s practical capacity or require staffing, equipment, licensing, inventory, or working capital not included in the financing plan.
A borrower may intend to charge premium prices. The lender will ask whether the market has demonstrated a willingness to pay them. A competitor’s posted price does not necessarily establish realized revenue. Discounts, promotions, payer contracts, commissions, vacancy, refunds, and product mix can materially reduce the effective price.
Borrowers tend to focus on revenue reductions, but expense adjustments can be equally important.
Payroll assumptions may omit employer payroll taxes, workers’ compensation, health benefits, overtime, training, recruiting, turnover, management coverage, paid time off, contract labor, and wage inflation. A staffing plan should show positions, headcount, wage rates, hours, start dates, payroll burden, and the relationship between staffing and capacity.
A business may appear profitable because the owner’s labor is not included at market cost. The lender may normalize owner compensation, particularly when the business must hire a manager or skilled employee if the owner becomes unavailable.
New equipment may have limited repair costs initially, but the business will eventually incur maintenance and replacement expenses. Real estate, vehicles, machinery, furniture, technology, and specialized systems may require recurring reserves even when they are under warranty.
Startup forecasts often assume substantial initial marketing followed by a sharp reduction. Lenders may question this when the company’s revenue depends on continuous customer acquisition, referrals, paid media, promotions, or sales staff.
A lender may replace informal estimates with actual quotes, historical costs, lease terms, tax records, or comparable operating data.
Growth can consume cash. Inventory purchases, accounts receivable, deposits, payroll timing, and seasonal fluctuations may require funding before related revenue is collected. A profitable income statement does not guarantee adequate cash flow.
Lenders need external reference points that can be consistently applied and documented.
RMA’s Annual Statement Studies — now associated with ProSight Financial Association following the merger of RMA and BAI — have historically been built from anonymized financial statements submitted by participating financial institutions. These datasets are used to compare borrowers with companies in similar industries and size categories.
IBISWorld provides industry research, market information, forecasts, financial ratios, and benchmarking information. Its commercial-lending materials describe the use of industry averages and financial ratios to compare a borrower’s performance with industry peers.
Other commonly used sources may include U.S. Census Bureau data, Bureau of Labor Statistics data, Bureau of Economic Analysis data, trade-association reports, CoStar or similar real-estate information, franchise disclosure information, state licensing and regulatory databases, local government planning data, industry-specific operating surveys, appraisals and market studies, and internal lender portfolio data.
These sources help lenders answer several questions:
Industry benchmarking allows the underwriter to identify assumptions that require further explanation. It also creates a record showing that the lender did not rely solely on management’s opinion.
A benchmark can be authoritative for underwriting purposes without being perfectly representative of a specific business. This distinction is essential.
Industry data may combine companies with different business models, geographic markets, customer segments, revenue sizes, ownership structures, product mixes, accounting policies, facility costs, levels of maturity, and distribution channels.
A highly automated operator may outperform the industry’s labor ratio. A premium provider may achieve higher revenue per customer. A vertically integrated business may have different gross margins. A company in a high-cost metropolitan area may spend more on wages and occupancy than a national benchmark suggests.
Therefore, a borrower’s pro forma can be more accurate for the proposed operation than the broad industry average. But the borrower must demonstrate why. It is not enough to say, “Our company will be different.” The feasibility study should quantify the difference and provide evidence for it. For example:
The industry payroll benchmark is 32% of revenue. The proposed operation projects payroll at 25%. The difference is supported by self-service technology, a reduced front-desk staffing model, executed technology pricing, a position-level labor schedule, and operating results from three comparable locations using the same model.
That is substantially stronger than:
Payroll will be lower because management plans to operate efficiently.
A forecast can ultimately prove accurate and still have been inappropriate for the lender to rely upon at the time of approval. Credit decisions are made based on evidence available before the future occurs.
Suppose a borrower projects $2 million in first-year revenue. The business later achieves $2.1 million. That outcome does not necessarily mean the lender should have accepted the original projection if, at underwriting, the forecast depended on unsupported market share, undocumented pricing, an aggressive opening schedule, and no evidence of customer conversion. The forecast was correct in outcome but weak in methodology.
Conversely, a carefully supported forecast can later prove incorrect because of unexpected economic, competitive, regulatory, or operational events. A sound feasibility study does not guarantee the result. It demonstrates that the conclusion was reasonable based on the available evidence.
Lenders therefore care about both the projected number and the process used to derive the number. A borrower’s intuition can be right. A lender still needs a supportable credit file.
A useful way to evaluate projections is to divide assumptions into three categories.
The result could occur, but there is limited evidence supporting its likelihood. Example: the business could capture 10% of the local market.
Evidence suggests the result is more likely than not. Example: existing demand, competitor utilization, pricing research, pre-opening sales, and operational capacity support the projected customer volume.
The assumption is sufficiently documented, appropriately conservative, and capable of surviving independent review, sensitivity testing, and loan-committee scrutiny.
Not every probable outcome is bankable at its full value. A lender may believe the business will perform well while underwriting only 75% or 80% of management’s expected revenue. The difference is a margin of safety, not necessarily a rejection of the business model.
Build revenue from operating drivers. Do not begin with a desired annual sales number. Build from units, customers, transactions, prices, utilization, capacity, and timing.
Separate facts from assumptions. Clearly label documented amounts, third-party estimates, industry benchmarks, management assumptions, contingent revenue, and sensitivity variables.
Cite every material assumption. A reviewer should be able to trace the source of rent, wages, pricing, occupancy, customer counts, supply costs, utilities, insurance, and market growth.
Reconcile the feasibility study with the financial model. The written study and the spreadsheet should use the same number of units, opening date, pricing, occupancy, staffing, capacity, market share, growth rate, and expense assumptions. Internal inconsistencies weaken confidence in the entire analysis.
Explain every material deviation from industry norms. Being better than average is possible. Unsupported superiority is not persuasive. Where the projection differs from RMA, IBISWorld, trade data, or lender benchmarks, explain the operational reason and provide evidence.
Include a realistic ramp-up. Show monthly projections during the startup or expansion period. Annual figures can conceal early cash deficits and working-capital pressure.
Provide base, downside, and break-even cases. The lender should be able to see expected performance, performance under stress, the minimum sales needed to cover operating costs and debt service, and the additional liquidity required if ramp-up is delayed.
Avoid false precision. A five-year forecast showing revenue of exactly $4,873,216 may appear less credible than a model built from clearly explained drivers. Precision is not the same as accuracy.
Address the lender’s likely adjustments before submission. Review the model as an underwriter would: What revenue would be excluded? What expenses are understated? Which assumptions lack third-party support? What happens if opening is delayed? Is there enough working capital? Does debt service remain covered in a downside case? Are margins materially better than industry benchmarks — and why should the lender accept those differences?
A feasibility study should not merely conclude that a project is feasible. It should state the conditions under which the business is feasible, the principal assumptions driving that conclusion, the evidence supporting those assumptions, the risks that could cause performance to fall below projections, the amount of capital and liquidity needed, the break-even point, the expected ramp-up period, the results of sensitivity testing, any material differences from accepted industry benchmarks, and the mitigation available if those differences do not materialize.
A credible conclusion might read:
The proposed business appears financially feasible provided that it opens by the projected date, achieves at least 70% of the base-case customer volume by month 12, maintains an average realized price of no less than a stated minimum, and retains a stated amount of post-closing liquidity. The downside analysis indicates that debt-service coverage becomes inadequate if revenue falls materially below the base case without corresponding expense reductions or additional working capital.
That is more useful to a lender than an unqualified statement that “the market is strong and the project will be successful.”
The borrower, feasibility consultant, and lender are not necessarily working from competing versions of reality. They are applying different standards to the same uncertain future. The borrower asks what the business can reasonably achieve. The consultant asks whether the market and operating model support the proposed investment. The lender asks how much projected cash flow can be prudently relied upon to repay debt — and whether that conclusion can be supported through accepted sources, documented methods, internal policy, and regulatory review.
That is why your pro forma may be on the money while the lender’s underwritten case is lower. The solution is not simply to make projections more conservative. It is to make them more traceable, testable, locally relevant, capacity-based, independently supported, and clearly reconciled with recognized industry information. A compelling pro forma tells the story of the business. A bankable pro forma also proves how the story was calculated.
An SBA lender generally looks for credible evidence of market demand, competition, pricing, operational capacity, startup costs, working-capital needs, projected cash flow, and the business’s ability to repay the proposed loan.
Yes. A lender can adjust or reject projected revenue and expenses when assumptions are unsupported, inconsistent with industry data, overly aggressive, or insufficiently documented.
Lenders use industry research and financial benchmarks to compare a borrower’s projected margins, payroll, occupancy costs, working-capital requirements, and profitability with similar businesses.
A bankable pro forma is based on traceable assumptions, realistic capacity, local market evidence, credible pricing, documented expenses, reasonable ramp-up periods, and sensitivity testing.
The required projection period depends on the lender, loan program, and transaction. Startup and expansion applications commonly include detailed monthly projections for the initial period and annual projections for later years. See our discussion of the ten-year pro forma.
Yes, but material differences should be supported by specific evidence such as technology, contracts, operating history, lower facility costs, proprietary processes, or results from comparable locations.
A business plan explains the company’s strategy and operations. A feasibility study independently evaluates whether the market, operating model, financial assumptions, and risks support the proposed project.
Disclaimer: This article provides general educational information and does not constitute legal, accounting, lending, or SBA-compliance advice. Requirements vary by lender, transaction, loan program, collateral type, and the SBA procedures in effect when an application is processed.
President, Wert-Berater, Inc. — independent feasibility study consultants since 1998. More than 4,000 feasibility studies completed across all 50 states and internationally, evaluating $40.2 billion in project value for SBA, USDA, EB-5, conventional, and institutional financing decisions. Fiduciary duty runs to the lender and agency in every engagement.
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