Wert-Berater, Inc. is an independent car wash feasibility study consultant preparing lender- and agency-ready studies for new construction, acquisitions, expansions, conversions, and multi-site car wash projects — express exterior tunnel, flex-serve, full-service, self-service, and in-bay automatic. Our analysis evaluates traffic and vehicle demand, site access and visibility, competitive saturation, wash-volume capture, retail and unlimited-membership economics, pricing, chemical and utility costs, equipment reserves, stabilization, debt-service coverage, and downside sensitivity for SBA, USDA, conventional, and institutional financing.
Prepared to SBA SOP 50 10 8, USDA 7 CFR Part 5001, and conventional underwriting standards. Fiduciary duty runs to the lender and the agency, never the borrower. Fixed fee quoted within one business day; standard delivery in ten to fifteen business days from a complete data room. 4,000+ engagements since 1998 covering $41.2 billion in evaluated project value. So far in 2026: 41 engagements and $1.54 billion evaluated — 17 SBA, 11 USDA.
Express car wash feasibility is traffic conversion under membership economics. The study converts AADT and retail co-tenancy into wash volume through capture modeling, tests the membership-penetration assumptions that now carry the industry's revenue model, and maps every competing tunnel — operating and announced — because site saturation has become the category's defining risk. Labor-light operating economics are modeled honestly, including the chemical, utility, and equipment-reserve loads that compress the apparent margin.
Methodology combines DOT traffic counts, competitive census with pricing and membership offers, ICA industry benchmarks, and equipment-supplier capital costs independently tested. The model presents retail and membership revenue separately, with membership churn sensitivity, against the program's coverage requirement.
Every Wert-Berater financial model is fully linked with no hardcoded values, so any reviewer can stress any input. Deliverables comprise a complete narrative report and the linked Excel model, with ten-year pro forma, sensitivity analysis at ±5, 10, and 15 percent, interest-rate stress from +0.5 to +3.0 percent, and ratio analysis benchmarked against RMA and IBISWorld data.
SBA engagements are prepared to SOP 50 10 8, including its debt-service-coverage minimums of 1.15x operating and 1.00x global. USDA engagements follow RD Staff Instruction 5001 across the Business & Industry, Community Facilities, REAP, and Value-Added Producer Grant programs. Conventional engagements are built to the lender's stated coverage standard, typically 1.20x. Car washes are financed under SBA 504 and 7(a) — with SOP special-purpose treatment — and conventional structures for multi-site operators; an acquisition may also call for a special-purpose appraisal that addresses the operating business as well as the real estate.
The firm evaluates car wash projects within its fuel-retail and roadside-commercial practice, applying the same capture-rate discipline developed across its gas station and travel center record. Independence is non-negotiable: determinations follow the evidence and are not revised under pressure, and studies are built to pass lender, agency, and third-party review without exception items.
Published engagement record. In Alhambra, Los Angeles County, California, the firm completed an Alhambra express car wash feasibility study covering a ground-up express tunnel on a primary arterial retail corridor, with a total development budget of approximately $7,480,000 evaluated for SBA 504 financing under SOP 50 10 8. Drawings and conditional use permit approvals formed part of the evaluated record, and the coverage analysis was tested against membership-penetration assumptions held below the levels achieved by mature comparable sites. In Chino, San Bernardino County, California, the firm completed a car wash and ground-lease pad highest and best use study. That engagement was a highest-and-best-use analysis rather than a car wash feasibility study: it screened competing uses and concluded that a branded express tunnel with a national drive-through pad produced the highest land residual. It is cited here as evidence of corridor and saturation analysis, not as a completed feasibility determination.
Responsibility for car wash engagements rests with Donald Safranek, MSc, President of Wert-Berater, Inc. The firm does not hold itself out as a car wash operator, equipment supplier, or engineering practice, and does not provide civil engineering, entitlement, or construction services.
A car wash feasibility study consultant answers one question for the lender: will this specific wash, at this specific corner, generate enough volume at a defensible price to cover its operating cost and its debt service? Answering it takes five connected bodies of analysis, and a study that is strong in one and thin in the others will not survive credit review.
Site. The parcel is assessed before any revenue number is written. The analysis covers annual average daily traffic on each frontage, directional splits, turning movements at the controlling intersection, signalization, median treatment, curb cuts, ingress and egress geometry, sight lines and visibility from the approach lane, stacking depth ahead of the pay stations, queuing behaviour at peak, parcel dimensions and usable area, on-site circulation, and co-tenancy with adjacent retail anchors that generate their own trips.
Demand. Traffic alone is not a market. The study measures registered vehicles in the trade area, household counts and vehicles per household, population and income characteristics, commuting patterns and the direction of the peak flow, the geographic extent of the realistic customer trade area, expected washes per customer per year, and the share of the addressable base the site can capture given its access and format.
Competition. The competitive census records every operating wash in the trade area with its tunnel type, lane count, throughput capacity, retail pricing tier, unlimited plan pricing, hours, site quality, brand and age — then adds the pipeline: sites under construction, permitted locations, approved projects, and credible announced projects, each weighted by its drive-time relationship to the subject.
Operations. Wash volume is split between retail transactions and member washes, each with its own ticket and its own variable cost. The operating build covers membership count and churn, chemical consumption per car, water and sewer charges, electricity, labour at the actual hours proposed, maintenance, and a funded equipment replacement reserve.
Capital and financial structure. The capital budget covers land and site development, the tunnel and equipment package, the building, paving and stacking lanes, vacuum plazas, water-reclaim equipment, signage, pre-opening cost, and working capital. Those figures drive the stabilization curve, operating cash flow, debt service, debt-service coverage, break-even wash volume, and sensitivity testing on membership penetration, volume, cost, and interest rate. The determination follows from that structure rather than from a headline occupancy or a supplier's pro forma.
A car wash feasibility study prepared by Wert-Berater is not a template dropped over a site address. Every engagement is scoped to the specific format — express exterior, flex-serve, or full-serve tunnel — because the revenue model, cost structure, and underwriting risk differ materially across formats. The study opens with a site-suitability assessment that evaluates ingress and egress geometry, stacking capacity, and parcel constraints before any revenue projection is built. From that foundation, the analysis moves through demand, competitive supply, operating economics, and capital structure to a single, auditable conclusion about feasibility.
Format is not a styling choice; it determines the revenue model, the labour load, the capital budget, and the way the lender should read coverage. The firm scopes each engagement to the format actually proposed.
The dominant new-development format and the one most sensitive to saturation. Revenue concentrates in unlimited membership plans, so throughput capacity, stacking depth ahead of the pay stations, and the vacuum plaza are as important as the tunnel itself. Feasibility turns on how quickly the membership base ramps, how durable it is once a competitor opens inside the drive-time ring, and whether peak-hour demand can physically clear the site.
An express exterior tunnel with optional interior service sold as an add-on. The format carries a higher average ticket and a materially higher labour load than express exterior, and throughput is lower because interior work occupies staff and finishing space. The model must separate the express transaction from the interior upsell, because the two do not scale together and the labour cost attaches only to one.
Labour-intensive, with interior cleaning and detail work included rather than optional. Ticket is the highest of the formats, dwell time is longest, and throughput is lowest. Feasibility depends on local wage rates and staffing availability to a degree the express formats do not, and the membership model is generally a smaller share of revenue.
The firm evaluates both where they are the proposed use, most often as a component of a fuel and convenience site or in smaller or rural trade areas where express tunnel volume cannot be supported. The economics differ fundamentally: capital cost per bay is far lower, labour is minimal, and revenue is transaction-driven rather than membership-driven, which removes the ramp risk of the express model but also removes its recurring revenue base. Bay count, cycle time, and utilisation at peak replace tunnel throughput as the capacity constraint. Where the wash sits alongside fuel, it is modelled inside the gas station and convenience store feasibility study as its own revenue and cost line.
Annual average daily traffic is not a projection of washes. It is the outer boundary of the vehicle population that passes the site, and the great majority of those vehicles will never enter it. The study moves from traffic to revenue through a documented chain, and each link removes volume:
Directional traffic matters more than the headline count. A site on the evening commute side of an arterial, visible on the approach and reachable without crossing a median, will outperform a site with identical AADT on the opposite frontage. Turning movements at the controlling intersection are read from counts rather than assumed, because a left-turn prohibition or an unsignalised entrance placed too close to the intersection removes a large share of nominally available traffic. Morning-oriented and evening-oriented corridors behave differently, and co-tenancy with a grocery anchor, a fuel site, or a drive-through generates trips that a pure pass-by model does not see. Entrance friction — a tight curb cut, a shared drive, a queue that spills toward the road — is treated as a demand constraint, not a design footnote.
The firm does not publish a universal capture rate, and it does not adopt benchmark ranges from competitor reports or equipment-supplier marketing without independent support. Capture is derived per site from the conditions above and is disclosed with its basis, so a reviewing officer can test it rather than accept it.
Demand for a car wash is a function of traffic volume, site accessibility, and the share of passing vehicles a given location can realistically convert. The analysis begins with state and county DOT continuous-count and short-count traffic data, cross-referenced against turning-movement studies where intersection geometry affects capture. Raw AADT is not a revenue number; it is a ceiling that the study discounts through a documented capture-rate methodology calibrated to format and site conditions.
Competitive supply is assembled from multiple independent sources rather than a single database. State and local business-license registries identify operating locations. Municipal permitting portals and planning-commission agendas surface announced projects that have not yet opened. Trade-association directories and ICA membership data provide format and pricing context. Field observation confirms operating hours, lane configuration, and current membership price points for every direct competitor within the trade area.
Demographic and psychographic overlays — vehicle ownership rates, household income distribution, and commuter-pattern data from Census and American Community Survey files — inform the membership-penetration assumption rather than justify it after the fact. Where a proposed site sits inside a market that already shows tunnel density, the study models the existing competitive set at stabilized performance before layering in the subject, so the reviewing officer can see what share remains available rather than what the market produced before saturation began.
Traffic counts measure vehicles passing a point. They do not measure the population of vehicles that could reasonably use the site, and for a membership-driven express wash the resident base often matters more than the pass-by count. A member who lives two miles away and washes weekly is worth more than a commuter who passes daily and never stops.
Where the data supports it the study builds a resident demand base from county and state vehicle registration records, vehicles per household from Census and American Community Survey files, household counts and projected household growth, population and income distribution, owner and renter characteristics where they bear on wash behaviour, employment and commuting patterns, and nearby residential development that is permitted or under construction. Those inputs frame how large the recurring membership pool could be and how fast it might grow.
Resident demand and pass-by traffic overlap. A household inside the trade area may also be a vehicle in the AADT count on the frontage, and adding a resident-based projection to a traffic-based projection double counts the same customer. The study reconciles the two into a single addressable base rather than summing them, and states which component drives the projection. Where the two methods disagree materially, the disagreement is reported rather than averaged away.
Saturation is the defining risk in this category, and it is the reason a market that looks attractive at the study date can be oversupplied by the time the subject opens. A competitive census limited to sites already operating will systematically overstate opportunity, because express tunnel development moves faster than the entitlement-to-opening cycle of the subject itself.
The competitive inventory therefore covers five tiers, each treated differently:
For each competitor the census records tunnel type and length, wash lane count, rated and practical throughput, membership programme and monthly plan pricing, retail price tiers, operating hours, access and visibility, site quality, age and condition, brand or franchise affiliation, and observable customer experience. Sources are municipal permitting portals and planning-commission agendas, state and local business-license registries, trade-association directories, and direct field verification of pricing and configuration. Nothing is carried on a single unverified source.
The analysis then models the competitive set at the subject's opening date rather than at the study date, so the reviewing officer sees the supply the project will actually face. Where the pipeline is heavy, the study reports what share of demand remains after credible additions are absorbed, and says plainly when the answer is that the corridor will not support another tunnel. Speculative projects are disclosed with their status and probability; they are not converted into certainty, and they are not quietly omitted because they weaken the conclusion.
Fair share is a discipline, not a formula, and there is no universal percentage that makes a car wash feasible. What the concept provides is a way to test whether a projected wash volume is reasonable in the context of the supply the site competes against.
Where the data supports it, the study establishes fair-share context by relating the subject's practical throughput capacity to the total practical capacity of the competitive set, including the pipeline additions expected by opening. That produces a reference point: the share of market throughput the subject would hold if every site performed alike. Real sites do not perform alike, so the reference point is the beginning of the analysis rather than its conclusion.
The study then compares the subject's projected wash volume against addressable market demand and asks whether the implied capture is defensible given access and turning movements, visibility from the approach, operating format, pricing position relative to the set, the competitive membership offers already in the market, brand strength or its absence, co-tenancy and adjacent trip generators, and the maturity of the competitors the subject must take share from. A projection that requires the subject to outperform every established operator in the corridor from its first year is identified as such.
The firm does not publish an "acceptable" capture percentage, and treats any study that applies one across markets as unsupported. Required capture is reported as a number the project must achieve, tested against the site's actual characteristics, and stressed downward to show what happens to coverage if it is not achieved.
The unlimited wash club is the centre of the modern express model and the single largest source of error in sponsor projections. Recurring monthly revenue is genuinely valuable, but it is not the same thing as profit, and a study that treats membership growth as costless will overstate coverage.
The model separates the two customer populations completely. Retail customers pay per visit at a posted tier price and visit irregularly. Members pay a fixed monthly fee and, having paid it, wash far more often — which is the point of the plan from the customer's perspective and the risk of it from the operator's. Each population is projected with its own count, its own frequency, and its own contribution.
The membership build covers the monthly plan price and the tier structure, member count by month, average washes per member per month, churn, gross membership billings, promotional and introductory pricing and the rate at which promotional members convert to full price, and the incremental chemical, water, and electricity cost created by member wash frequency. That last item is the one most often omitted: a member washing eight times a month at a fixed price consumes eight times the variable cost, and beyond a certain frequency the marginal member reduces margin rather than adding to it. The study models variable cost against actual projected member usage rather than applying an industry-average expense ratio to revenue.
Mature-location membership levels are not assumed at opening. A site that reaches a strong penetration rate in year three did not have it in month two, and the coverage that matters to a lender is the coverage in the early loan years. Membership counts are ramped, tested at low, base, and high penetration, and reported with the coverage ratio each scenario produces. Our companion article on car wash demand, memberships and lender underwriting walks through how that analysis is assembled.
Where the engagement warrants it, membership is modelled month by month from opening to stabilization rather than as an annual average, because an annual average hides the months in which coverage is thinnest and the loan is newest.
The monthly build tracks new memberships sold, cancellations, and net member additions; the opening promotion and the introductory rate offered to fill the base quickly; the conversion of promotional members to mature pricing and the attrition that occurs at the moment the price steps up; seasonality in both sign-ups and cancellations; the effect of a competitor opening inside the drive-time ring during the ramp; and, for multi-site operators, the transferability of memberships between locations, which changes both the value of the plan to the customer and the attribution of revenue between sites.
Churn is the input sponsors most often understate. A plan with strong gross additions and high cancellations can show attractive sales while the net base barely moves, and because the cancelled member consumed variable cost while enrolled, the operator paid for volume that did not persist. The study models gross and net separately and tests the coverage outcome at elevated churn.
An aggressive ramp is the most efficient way to make a weak project look feasible. Pulling the stabilization date forward and raising the terminal membership count can carry a marginal site to an acceptable coverage ratio on paper without changing anything about the corner it sits on. For that reason the ramp assumption is stated explicitly, sourced where comparable evidence exists, and stressed — and the study reports what the coverage becomes if stabilization takes longer than projected.
Retail revenue is not one number multiplied by one price. A tunnel typically sells a basic wash, one or more mid-tier packages, and a premium package, and the mix between them determines the effective ticket far more than the posted top price does.
The study builds retail volume by service tier, projecting transactions at each price point rather than applying a single blended average to total wash count. It then models upgrade behaviour — the share of customers who move up from the entry package at the pay station — along with discounting, coupon and fleet programmes, seasonal promotions, and any bundled package pricing. The resulting retail average ticket is an output of the mix, not an input asserted at the top of the model.
Retail transactions and member washes are kept separate throughout. Combining them and applying one average price is the most common arithmetic failure in car wash projections: member washes carry no incremental revenue at the point of sale but do carry incremental variable cost, so folding them into a retail average simultaneously overstates revenue per wash and understates cost per wash. The two streams are projected independently and consolidated only at the revenue line.
Equipment throughput and site throughput are different numbers, and the gap between them is where projections fail. A tunnel rated by its supplier at a given cars-per-hour figure will not achieve that rate if vehicles cannot reach the pay stations, queue on site, and clear the exit without conflict.
The analysis evaluates stacking depth ahead of the pay stations and whether the queue can hold peak-hour arrivals without spilling onto the public road; pay-station count and transaction time, which frequently constrain the site before the tunnel does; rated tunnel throughput and the conveyor speed actually proposed; vacuum stall count and dwell time, since a full vacuum plaza backs up the exit and stalls the tunnel behind it; on-site circulation and the separation of entering, washing, and vacuuming traffic; entrance and exit conflicts with adjacent uses; overflow behaviour when the stack is full; road access, turn permissions, and curb-cut placement; adjacent retail circulation and shared drives; queue interference with neighbouring tenants; total lot size and usable area; and tunnel orientation relative to the frontage and the approach direction.
Peak-hour capacity, not average daily capacity, is the binding constraint. A site that can process its projected annual volume in theory may still be unable to serve a Saturday afternoon, and the demand lost at peak is not recovered on a Tuesday morning. The study models the peak and reports achievable site throughput as distinct from rated equipment throughput.
This is a feasibility and capacity assessment. Wert-Berater does not provide civil engineering, traffic engineering, or site design services, and the study does not substitute for a civil or traffic engineer's work where the jurisdiction requires one.
Variable cost per car is a project-specific number built from local tariffs and supplier schedules. There is no universal cost per wash, and applying one is a reliable way to misstate margin in either direction.
The cost build addresses fresh water consumption per vehicle at the equipment configuration proposed; the local water tariff including tiered and seasonal rates; sewer and wastewater discharge charges, which in many jurisdictions are billed on water consumption and can exceed the water charge itself; any surcharge or pre-treatment requirement applicable to vehicle-wash discharge; water-reclaim equipment and the share of throughput it genuinely offsets, net of its own maintenance and energy cost; electricity for motors, dryers, lighting, and vacuums, at the applicable commercial tariff and demand-charge structure; natural gas where heated water or heated dryers are proposed; and chemical consumption per vehicle by service tier, since a premium wash consumes materially more than a basic one.
Those inputs produce a cost per wash that differs by tier and by market. The study uses local tariffs and project-specific equipment data wherever they are available, states the source of each, and tests the result for volume sensitivity — because several of these costs scale with wash count while the fixed charges beneath them do not, and member wash frequency drives the variable line hardest.
A car wash is an industrial facility that runs a heavy duty cycle in a wet, chemically aggressive environment. Its equipment wears out on a schedule, and a projection that ignores that schedule reports cash flow the operator will not keep.
The capital and reserve analysis covers the tunnel package and conveyor, dryers and blowers, pumps and motors, pay stations and access control, vacuum systems, the water-reclaim system, point-of-sale and membership management technology, signage, and the wear components — brushes, cloth, belts, nozzles, seals — that are consumed continuously rather than replaced once.
Routine maintenance is expensed. Major component replacement is modelled as a funded reserve, sized to the expected life of each component at the projected throughput, not deferred to a residual assumption at the end of the projection period. This matters to the coverage conclusion: a tunnel can report strong EBITDA in years one through five precisely because it has not yet replaced anything, and a lender reading that figure as durable free cash flow is reading a number that will decline as the replacement cycle arrives. Higher throughput accelerates the cycle, so the reserve scales with volume rather than sitting as a fixed percentage. The study presents coverage both before and after the funded reserve so the reviewer can see the difference.
Weather affects car wash revenue everywhere, but not in the same direction and not by the same magnitude, and a seasonality curve borrowed from another market is worse than none.
Rainfall suppresses same-day volume in every market, but its annual effect depends on whether precipitation is concentrated in a short season or spread across the year. Snow and road salt in northern markets produce the opposite effect: winter becomes a peak season driven by corrosion concern, and the undercarriage wash becomes a primary purchase rather than an upgrade. Freezing conditions can close a site outright or restrict operating hours, which caps volume regardless of demand. Drought and municipal water restrictions are a distinct and increasingly material risk in parts of the western United States, where restrictions may exempt reclaim-equipped commercial washes or may not, depending on the jurisdiction. Extreme heat and dust drive volume upward in arid markets. Local seasonality — tourism, agricultural cycles, school calendars, and commuting patterns — shapes the monthly curve independently of weather.
Where historical climate data for the specific market is relevant to the projection, it is used to shape the monthly revenue curve and to test the months in which coverage is thinnest. The seasonality assumption is stated for the market being studied and is not carried over from another region.
There is no generic answer, and any figure quoted without reference to a specific cost structure and capital stack should be treated as marketing. Two washes with identical daily car counts can have entirely different feasibility conclusions.
Break-even is an output of the financial model, calculated from the retail and member mix, the retail average ticket produced by the tier mix, the monthly membership price and average member wash frequency, chemical cost per car, water, sewer and electricity cost per car, labour at the hours actually proposed, maintenance and the funded equipment reserve, fixed operating expenses including rent or ground lease, insurance, property tax, marketing and management, and the debt service produced by the actual loan structure.
The study reports two distinct thresholds. Operating break-even is the daily wash volume at which revenue covers all operating costs including the reserve, before debt service. Debt-service break-even is the volume at which the project also covers principal and interest and reaches the coverage ratio the programme requires — 1.15x operating and 1.00x global for SBA under SOP 50 10 8, or the conventional lender's stated standard, commonly 1.20x. The gap between the two is the project's margin of safety, and it is reported as a number rather than described as adequate.
The reason identical car counts produce different conclusions is that the mix beneath them differs. A site at 300 cars per day with a high member share, a low average ticket, and expensive water may fail where a site at 250 cars per day with a stronger tier mix, cheaper utilities, and less debt succeeds comfortably. Break-even is therefore calculated per project and stress-tested, never quoted as a rule of thumb.
A small number of inputs account for most of the variance in a car wash pro forma. A study that does not isolate and stress each one independently is not a feasibility study — it is a presentation. Wert-Berater treats each assumption as a variable with a tested range, not a point estimate defended by optimism.
Those levers are set out in full in the sections above — membership penetration and churn, the retail wash count and ticket mix, chemical and utility cost per car, the funded equipment replacement reserve, the stabilization timeline, and the site-specific labour load. What matters for the determination is not that each is discussed but that each is treated as a variable with a tested range rather than a point estimate defended by optimism. Every one of them is run at a low, base, and high case, and the coverage ratio produced at each boundary is reported rather than summarised.
Each assumption is documented with its source, its tested range, and the coverage ratio it produces at the boundary of that range.
A start-up and an acquisition are different analytical problems, and analysing an acquisition as though it were a start-up discards the most valuable evidence in the file.
New construction has no operating history, so every revenue figure is a projection. The analysis carries unproven wash volume, an unbuilt membership base and its ramp, site development and entitlement risk, the equipment package and construction budget with contingency, the construction period and its carrying cost, working capital through the ramp, and the competitive pipeline that may open before or shortly after the subject does. Coverage in years one and two is the critical test, because that is when the loan is newest and the membership base is smallest.
Acquisition replaces projection with evidence wherever the evidence is reliable. The analysis begins with historic wash volume by month, the actual member count and its trend, realised churn, actual realised pricing rather than posted pricing, and reported operating costs tested against utility bills and supplier invoices. It then examines what will change under new ownership: the physical condition and remaining life of the existing equipment, deferred maintenance and the capital required to correct it, any rebranding or conversion planned and the membership attrition that typically accompanies a price or brand change, renovation downtime, and the transferability and retention of the existing membership base after the sale.
Historic performance is tested rather than accepted. Volume may have been supported by discounting that the buyer does not intend to continue, or by the absence of a competitor that has since opened. Conversely, a well-run site with documented history carries materially less risk than any start-up projection, and the study says so where the record supports it.
Neither model is universally superior, and the study does not treat brand affiliation as a proxy for feasibility. What it does is model the two cost and revenue structures accurately and test which one the specific project and operator can support.
Franchise brings brand recognition that can shorten the membership ramp in a market where the brand is already known, a developed operating system, marketing support, and vendor and equipment standards that reduce specification risk. Against that it carries an initial franchise fee, ongoing royalties on gross revenue, contributions to a marketing fund, mandated vendors and equipment that may cost more than alternatives, and territorial and operational restrictions. Royalties are modelled on gross revenue, which means they are paid on membership billings whether or not those members were profitable.
Independent operation avoids the royalty and marketing fees entirely and allows local pricing, local branding, and free vendor selection. The trade is that the operator carries the entire marketing burden of building recognition from nothing, has no established system to fall back on, and depends far more heavily on their own execution — which makes sponsor experience a more significant underwriting factor than it is under a franchise.
The decision interacts with financing. Franchise fees and royalties reduce the cash flow available for debt service, while brand strength may accelerate the ramp that determines early coverage. The study models the actual fee structure proposed rather than a generic percentage, and tests coverage both ways where the sponsor is genuinely choosing between them. Our article on franchise versus independent underwriting sets out how lenders read the distinction across asset types.
Car washes present a specific underwriting profile that differs from general retail. SBA reviewing officers apply SOP 50 10 8 and flag express tunnels as special-purpose properties under the collateral analysis, which affects loan-to-value treatment and requires the feasibility study to address liquidation risk alongside operating feasibility. The study is prepared to meet those documentation expectations without requiring the lender to request supplemental analysis after submission.
For SBA 7(a) and 504 engagements, the study confirms that projected debt-service coverage meets the 1.15x operating and 1.00x global minimums and presents the global calculation with all affiliated obligations included. Saturation risk is addressed explicitly, because reviewing officers in high-density markets have begun requiring evidence that the trade area supports an additional location rather than accepting sponsor-supplied comparables.
USDA Business & Industry engagements under 7 CFR Part 5001 require the same coverage discipline and add a community-impact and market-need narrative. The study addresses both without conflating them.
Where the wash is a component of a fuel site rather than a standalone facility, it is modelled inside the gas station and c-store feasibility study as its own revenue, cost and margin line before consolidation.
Conventional lenders financing multi-site operators typically require 1.20x coverage and place additional weight on the sponsor's operating track record and the transferability of the membership base. The study addresses membership portability and the degree to which revenue is site-specific versus brand-dependent, because that distinction affects both coverage sustainability and collateral value in a workout scenario.
Sponsors frequently ask for a "site study" when what the lender requires is a feasibility study, and the two are not interchangeable. The distinction is worth stating before an engagement is scoped, because ordering the narrower product and submitting it to credit wastes weeks.
A site study answers whether the corner works. It covers traffic counts and directional flow, visibility and the approach, access, turning movements and curb cuts, the competitive set within the trade area, parcel dimensions and usable area, and stacking and circulation capacity. It is a useful screening tool during site selection, particularly for an operator comparing several parcels before committing to one.
A full feasibility study contains all of that and then answers whether the business supports its debt. It adds market demand quantification, the retail and membership revenue model, the capital budget, the operating expense build, the debt structure and its amortisation, debt-service coverage against the programme requirement, break-even volume, sensitivity and interest-rate stress testing, and a documented feasibility determination. Only the second satisfies an SBA, USDA, or conventional lender's feasibility condition.
A related distinction applies where the use itself is still open. If the question is which use a parcel should carry, that is a highest and best use analysis rather than a feasibility study — our article on highest and best use versus feasibility explains the difference between selecting the use and testing a use already selected. A feasibility study begins after the car wash has been chosen.
Three documents are routinely requested in the same financing and are routinely confused. They answer different questions, they are prepared by different parties, and one does not substitute for another.
A business plan is the sponsor's document. It sets out the operator's strategy, management team, marketing approach, and financial goals, and it is advocacy for the project by the party that wants it financed. That is a legitimate purpose, but it is not independent, and a lender cannot rely on it as third-party verification. Our comparison of a feasibility study and a business plan sets out the difference in detail.
A feasibility study is prepared by an independent third party whose duty runs to the lender and the reviewing agency. It tests whether the specific proposed project can achieve the volume, pricing, and margin required to cover its operating cost and debt service, and it reaches a determination that may be unfavourable. Wert-Berater's fee is fixed at engagement and is not contingent on the finding.
An appraisal answers a different question again: what is the real property or business interest worth? It produces a value conclusion, typically for collateral purposes, under the appraisal standards applicable to the assignment. Feasibility asks whether the project works; appraisal asks what it is worth. Our note on feasibility study versus appraisal covers where each is required. Most SBA and USDA car wash financings require both, and neither one satisfies the requirement for the other.
The engagement begins with a fixed-fee quote delivered within one business day of inquiry. The fee is stated before any work begins and does not change based on the finding. No fee is contingent on a favorable determination, and no determination is revised because a sponsor or lender prefers a different outcome. Fiduciary duty runs to the lender and the reviewing agency.
Standard delivery is ten to fifteen business days from receipt of a complete data room. The data room for a car wash engagement typically includes the site plan and survey, equipment specifications and supplier quotes, any existing traffic studies, proposed membership pricing, and the sponsor's operating history if the project involves an experienced operator. Incomplete submissions extend the timeline; the engagement clock starts when the data room is complete, not when the engagement is signed.
Rush delivery is available and is quoted at the time of engagement. Every completed study is published to a secure client portal where the linked Excel model remains live. Because no values are hardcoded, a reviewer or loan officer can change any input — membership penetration, chemical cost, interest rate, stabilization period — and watch every downstream ratio recalculate in real time. That transparency is not a feature added for convenience; it is a structural requirement of independent analysis. A model that cannot be interrogated is not an independent model.
A car wash feasibility study consultant is an independent third party engaged to test whether a specific proposed wash can generate the volume, pricing, and margin needed to cover its operating costs and its debt service. The work spans site and traffic analysis, trade-area demand, the competitive census including the development pipeline, retail and membership revenue modelling, the capital budget, the operating expense build, and coverage and sensitivity testing against the lending programme’s requirement.
The defining feature is independence. Wert-Berater’s fiduciary duty runs to the lender and the reviewing agency rather than the borrower, the fee is fixed at engagement and is not contingent on the finding, and a determination is not revised because a sponsor or lender prefers a different one.
A complete engagement delivers a narrative report and a fully linked Excel financial model. The narrative covers site suitability and access, traffic and capture analysis, trade-area demand including registered vehicles and households, the competitive census with operating and pipeline supply, the proposed operating format, retail and membership revenue projections, the capital budget, the operating expense build including utilities, chemicals, labour and equipment reserves, the stabilization curve, debt-service coverage, break-even volume, sensitivity and interest-rate stress testing, and a documented feasibility determination.
The model contains no hardcoded values, so any reviewer can change any input — membership penetration, chemical cost, interest rate, stabilization period — and watch every downstream ratio recalculate. For SBA and USDA engagements the report is organised to the documentation expectations of SOP 50 10 8 and 7 CFR Part 5001 respectively.
Demand is built from two bases that are then reconciled rather than added. The pass-by base starts from state and county DOT traffic counts on each frontage, adjusted for direction, time of day, turning movements, and access constraints. The resident base is built from vehicle registration records, households and vehicles per household, population and income characteristics, and commuting patterns within the realistic trade area.
Both bases are then combined with an expected wash frequency per customer and the share of that population the site can realistically capture. Because a resident of the trade area may also be a vehicle in the traffic count on the frontage, the two are reconciled into a single addressable base — summing them would double count the same customer.
Capture is derived per site, not taken from a published benchmark. The study moves from total AADT to addressable vehicles, then to realistically accessible traffic after median restrictions, turn prohibitions, signalisation and curb-cut placement are applied, then to site capture, and finally to wash transactions split between retail and member visits.
The capture assumption at the end of that chain reflects visibility from the approach lane, access quality, operating format, pricing position, brand, competition, and co-tenancy. Wert-Berater does not publish a universal capture rate and does not adopt competitor or equipment-supplier benchmark ranges without independent support. The rate used is disclosed with its basis so a reviewing officer can test it.
There is no generic figure, and any number quoted without reference to a specific cost structure and capital stack should be treated as marketing. Break-even depends on the retail and member mix, the average ticket produced by the tier mix, monthly membership pricing and member wash frequency, chemical and utility cost per car, labour, maintenance and the funded equipment reserve, fixed operating expenses, and the debt service produced by the actual loan structure.
Two washes with identical daily car counts can reach opposite conclusions. A site at 300 cars per day with a high member share, a low average ticket, and expensive water can fail where a site at 250 cars per day with a stronger tier mix and cheaper utilities succeeds. The study calculates the threshold for the specific project and stress-tests it.
Saturation is measured against the supply the subject will face at its opening date, not the supply operating at the study date. The competitive census records every operating wash in the trade area with its tunnel type, lane count, practical throughput, retail and membership pricing, hours, access, age, brand and site quality, verified by field observation rather than a database listing alone.
The analysis then relates total competitive practical capacity, including credible pipeline additions, to the addressable demand in the trade area, and reports what share remains available to the subject. Where the corridor will not support another tunnel, the study says so.
Pipeline supply is tiered by certainty rather than treated as a single category. Sites under construction are treated as operating by the subject’s opening date. Permitted locations carry a high probability of completion. Approved projects holding entitlements or a conditional use permit but not yet permitted carry a meaningful probability. Credible announced projects — publicly announced or under land contract — are disclosed and weighted.
Sources include municipal permitting portals, planning-commission agendas, business-license registries and field verification. Speculative projects are never converted into certainty, and they are never quietly omitted because they weaken the conclusion. Both treatments are disclosed so the reviewer can see what was assumed.
Members and retail customers are projected as two separate populations. The membership build covers the monthly plan price and tier structure, member count by month, average washes per member per month, churn, gross billings, promotional and introductory pricing with the conversion rate to full price, and the incremental chemical, water and electricity cost created by member wash frequency.
That last item is the one most often omitted from sponsor projections. A member washing eight times a month at a fixed price consumes eight times the variable cost, so beyond a certain frequency the marginal member compresses margin rather than adding to it. Variable cost is modelled against projected member usage rather than as an industry-average expense ratio applied to revenue. Mature-location penetration is never assumed at opening.
Churn is modelled monthly, with gross new memberships and cancellations tracked separately so that net additions are visible. A plan can show strong gross sales while the net base barely moves, and because a cancelled member consumed variable cost while enrolled, the operator paid for volume that did not persist.
The analysis addresses seasonality in both sign-ups and cancellations, the attrition that occurs when introductory pricing steps up to mature pricing, the effect of a competitor opening inside the drive-time ring during the ramp, and membership transferability between locations for multi-site operators. Coverage is then re-tested at elevated churn to show the downside.
They are projected independently and consolidated only at the revenue line. Retail volume is built by service tier — basic, mid-tier and premium — with upgrade behaviour, discounting and package mix modelled, so the retail average ticket is an output of the mix rather than an asserted input. Member washes are driven by member count and average wash frequency and generate no incremental revenue at the point of sale.
Combining the two and applying one blended average price is the most common arithmetic failure in car wash projections: it simultaneously overstates revenue per wash and understates variable cost per wash.
The model reports two distinct thresholds. Operating break-even is the daily wash volume at which revenue covers all operating costs including the funded equipment reserve, before debt service. Debt-service break-even is the volume at which the project also covers principal and interest and reaches the coverage ratio the programme requires.
The gap between the two is the project’s margin of safety, and it is reported as a number rather than described as adequate. Both thresholds are recalculated under the sensitivity and interest-rate stress scenarios.
From local tariffs and supplier schedules for the specific project, never from a universal cost per wash. The build covers fresh water consumption per vehicle at the equipment configuration proposed, the local water tariff including tiered and seasonal rates, sewer and wastewater discharge charges, any pre-treatment or surcharge requirement applicable to vehicle-wash discharge, water-reclaim equipment and the share of throughput it genuinely offsets net of its own energy and maintenance cost, electricity including demand charges, natural gas where heated water or dryers are proposed, and chemical consumption per vehicle by service tier.
Cost per wash therefore differs by tier and by market. Each source is stated, and the result is tested for volume sensitivity because these costs scale with wash count while the fixed charges beneath them do not.
Routine maintenance is expensed; major component replacement is modelled as a funded reserve, sized to the expected life of each component at the projected throughput rather than deferred to a residual assumption at the end of the projection period.
This materially affects the coverage conclusion. A tunnel can report strong EBITDA in its early years precisely because it has not yet replaced anything, and a lender reading that figure as durable free cash flow is reading a number that will decline as the replacement cycle arrives. Because higher throughput accelerates wear, the reserve scales with volume rather than sitting as a fixed percentage of revenue. Coverage is presented both before and after the reserve.
There is no single qualifying AADT figure, and a threshold quoted without reference to access, format and competition is not meaningful. A site with high traffic on the wrong side of a divided arterial, with no left-turn access and poor visibility from the approach, can convert less volume than a lower-count site with a signalised entrance and strong co-tenancy.
What the study evaluates is the traffic that can realistically reach the site: directional splits, turning movements at the controlling intersection, median treatment, curb-cut placement, signalisation, and the time-of-day orientation of the flow. Traffic volume is a necessary input, not a qualifying test on its own.
Access is assessed from the driver’s approach rather than from a site plan in isolation. The analysis covers ingress and egress geometry, curb-cut placement relative to the controlling intersection, turn permissions and median restrictions, signalisation, sight lines and visibility on the approach, shared drives and adjacent retail circulation, and the friction a driver experiences entering and leaving at peak.
Access is treated as a demand constraint rather than a design detail, because it directly limits the share of passing traffic the site can capture. This is a feasibility assessment: Wert-Berater does not provide civil or traffic engineering services and the study does not substitute for an engineer’s work where the jurisdiction requires one.
Rated equipment throughput and achievable site throughput are treated as different numbers. The analysis evaluates stacking depth ahead of the pay stations and whether the queue can hold peak-hour arrivals without spilling onto the public road, pay-station count and transaction time, conveyor speed as actually proposed, vacuum stall count and dwell time, on-site circulation and the separation of entering, washing and vacuuming traffic, entrance and exit conflicts, overflow behaviour when the stack is full, and tunnel orientation relative to the frontage.
Peak-hour capacity is the binding constraint rather than average daily capacity. A site that can process its projected annual volume in theory may still be unable to serve a Saturday afternoon, and demand lost at peak is not recovered on a Tuesday morning.
Stabilization is a project-specific output driven by the membership ramp, not a standard period. The model builds member count month by month from opening — gross additions, cancellations, net additions, promotional conversion and seasonality — until the base reaches a steady state, and reports the coverage ratio in each of the intervening periods.
Those early periods matter most to a lender because that is when the loan is newest and the membership base is smallest. An aggressive ramp assumption is the most efficient way to make a marginal project appear feasible, so the assumption is stated explicitly, sourced where comparable evidence exists, and stressed to show what coverage becomes if stabilization takes longer than projected.
Wert-Berater quotes a fixed fee within one business day of inquiry. The fee does not vary with the finding and is not contingent on loan approval. Because scope differs by format, site complexity, and lending program, the firm does not publish a single price; contact the firm directly for a same-day quote specific to your project.
Standard delivery is ten to fifteen business days from a complete data room. Rush delivery is available and is quoted at engagement. The timeline begins when all required site, equipment, competitive, and financial documents are received — not when the engagement letter is signed. Incomplete submissions are the most common cause of delay.
A typical data room includes the site plan and survey, equipment specifications and supplier capital quotes, proposed membership and retail pricing, any existing traffic or environmental studies, and — for experienced operators — operating history from existing locations. The firm provides a data-room checklist at engagement. Studies cannot be completed accurately from a site address and a business plan alone.
Yes. SBA engagements are prepared to SOP 50 10 8, addressing the debt-service-coverage minimums of 1.15x operating and 1.00x global, with the global calculation presented inclusive of all affiliated obligations. Express tunnels are generally treated as special-purpose properties in the collateral analysis, which affects loan-to-value treatment, so the study addresses liquidation risk alongside operating feasibility.
A feasibility study is an independent analytical determination and not a guarantee of any lending outcome. Approval rests with the lender, the certified development company and the reviewing agency.
Yes. The same SOP 50 10 8 standards, coverage tests and special-purpose collateral treatment apply, and the study is organised so the lender does not need to request supplemental analysis after submission. Where the wash is being acquired rather than built, the analysis is built on the historic operating record and tested for what changes under new ownership.
As with 504, the study meets agency documentation expectations but does not guarantee approval.
Conventional lenders typically set their own coverage standard, commonly 1.20x, and place greater weight than the agencies on the sponsor’s operating track record and on the durability of the revenue base. For multi-site operators they generally require a global coverage view alongside site-level analysis.
Particular attention falls on membership portability and on the degree to which revenue is site-specific rather than brand-dependent, because that distinction affects both coverage sustainability and collateral value in a workout. The study is built to the lender’s stated standard and addresses those questions directly.
Yes, and an acquisition is not analysed in the same way as a start-up. The analysis begins with evidence rather than projection: historic wash volume by month, actual member count and trend, realised churn, realised rather than posted pricing, and reported operating costs tested against utility bills and supplier invoices.
It then models what changes under new ownership — equipment condition and remaining life, deferred maintenance and the capital needed to correct it, any rebranding or price repositioning and the membership attrition that typically accompanies it, renovation downtime, and retention of the transferred membership base. Historic performance is tested rather than accepted, since volume may have depended on discounting the buyer will not continue or on the absence of a competitor that has since opened.
Three factors distinguish car wash underwriting: the express tunnel's revenue model depends heavily on membership penetration, which is a behavioral assumption rather than a historical fact; the property is special-purpose, which limits collateral recovery; and announced competition can materially change the competitive set between study date and opening. A credible study isolates and stress-tests all three rather than presenting a single-point projection.
No. A feasibility study is an independent analytical determination, not a guarantee of any lending outcome. Wert-Berater prepares studies to SBA SOP 50 10 8 and USDA 7 CFR Part 5001 standards so they meet agency documentation requirements, but approval decisions rest with the lender and the reviewing agency, not with the feasibility firm.
Yes. Multi-site engagements address each location's individual capture and coverage position and then consolidate to a global coverage calculation that includes all affiliated debt. Conventional lenders financing portfolio operators typically require the global view alongside site-level analysis. The engagement scope and fee are quoted per site with a consolidated component; contact the firm to discuss the specific structure.
Qualify a project. Tell us about the project and the program. We will tell you the truth about it — scope, timeline, and fee confirmed before work begins.
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Wert-Berater, Inc. is an independent provider of feasibility studies and other related services. The firm does not provide financing or equity investment advice, and does not arrange, broker, or place debt or equity capital of any kind.
All appraisal assignments are performed by Bruce E. Jones, MAI, ASA-GC, BCA, CMEA, a member of the Appraisal Institute since 2006, a staff member of Wert-Berater, Inc. and owner of Special Purpose Realty Valuation.