Unit Economics

The valuation of a car wash platform is ultimately the sum of its unit economics, scaled and discounted. Getting the unit model right is therefore non-negotiable. This page focuses on the express exterior format, since that is what most institutional investing is built around, with notes on how other formats differ.

Revenue

Revenue at a modern express site comes from three main sources:

Retail wash revenue. Single-wash transactions at posted prices, typically $10–25 depending on tier and market. The retail mix matters more in early-stage sites and in markets where membership penetration hasn’t matured.

Membership revenue. Unlimited monthly plans priced at $20–35 across most operators, with premium tiers running higher. Once a site is mature, membership is usually the largest single revenue line and the most predictable.

Ancillary revenue. Vacuums (sometimes paid, often free as a member benefit), detail services, fragrances, ceramic add-ons. Ancillary is rarely the headline, but on a well-run site it can contribute several points of margin.

Two derived metrics matter most for benchmarking and for membership accounting:

• Revenue per car (RPC): total revenue divided by total cars washed. Members typically wash 2–4 times per month, so a member paying $25/month at three washes is implicitly paying about $8.30 per car. That dilutes RPC relative to retail-only pricing. A mature membership-heavy site can show an RPC of $9–14, lower than its posted retail prices suggest, and that is normal.
• Capture rate: the share of customers who upgrade from base wash to a higher tier or from single wash to membership. Capture is one of the clearest indicators of operating quality and is a major target of post-close value creation.

Cost structure

Costs at an express site are remarkably consistent across well-run operators:

Labor. The biggest variable controllable cost, typically 12–20% of revenue depending on market wage rates and management discipline. Express sites are designed to run lean, but undisciplined scheduling can erode margin fast.

Chemicals and supplies. Soaps, waxes, sealants, tire dressing, fragrance, towels. Typically 4–8% of revenue. Operators with strong supplier programs and good water reclaim can pull this number down.

Utilities — water and sewer. Highly market-dependent. Western U.S. and drought-exposed markets can see water and sewer costs at 4–7% of revenue; lower-cost markets run 2–4%. Water reclamation systems materially affect this line and have meaningful payback.

Utilities — electricity. Tunnel motors, dryers, and lighting. Typically 2–4% of revenue.

Equipment maintenance and capex. Day-to-day maintenance runs about 2–4% of revenue. Major equipment refresh — conveyor, dryers, arches — typically comes on a 5–7 year cycle and should be budgeted separately rather than buried in maintenance.

Marketing. Variable but typically 1–3% of revenue at established sites, higher during new-site ramp and during membership push campaigns.

Property costs. Real estate taxes, insurance, and (if leased) rent. On owned real estate, this is taxes and insurance only, typically 2–4% of revenue. On leased properties, rent can run materially higher and needs to be modeled explicitly.

Corporate overhead. Allocated G&A from a platform — typically 4–8% of revenue at the site level once a platform has back-office scale.

EBITDA margins

Put together, a well-operated mature express site lands in the 35–50% site-level EBITDA margin range. The top of that range requires high membership penetration, disciplined pricing tiers, strong capture, owned real estate, and lean labor. The bottom of the range is where most acquired independent sites sit on day one.

At the platform level, after corporate overhead, EBITDA margins typically settle in the high 20s to mid 30s percent range. The difference between site-level and platform-level margins is one of the more common sources of underwriting error — sponsors who buy into pitches showing site margins without normalizing for corporate cost.

The ramp curve

New-build express sites do not produce mature unit economics on day one. The typical ramp pattern looks something like:

• Year 1: revenue at perhaps 40–60% of stabilized, with significant marketing spend driving membership acquisition.
• Year 2: revenue climbs to 65–85% of stabilized as membership compounds and word-of-mouth builds.
• Year 3–4: stabilization, with membership penetration approaching mature levels and operating costs normalizing.
• Year 5+: mature steady state, with growth tied to pricing, membership penetration improvements, and market dynamics.

Acquired sites that already have an operating history skip the early ramp but may need an integration ramp of their own — particularly if branding, membership programs, or pricing tiers are being changed.

The levers that actually move the numbers

Operating value creation in car washes is not exotic. It is a small set of levers applied with discipline:

• Membership penetration. Each point of membership penetration is worth real money in both EBITDA and exit multiple. Most acquired independents have penetration well below mature platform levels.
• Tier mix. Moving customers up from base to top-tier washes adds revenue at near-zero incremental cost.
• Capture. The percentage of customers who say yes to the upsell at the menu. Training, signage, and menu design move this meaningfully.
• Throughput at peak. Most sites are capacity-constrained on weekend afternoons. Reducing tunnel cycle time and stacking efficiency unlocks revenue without adding sites.
• Labor productivity. Right-sizing shifts to actual demand patterns rather than to historical staffing habits.

These are not new ideas in the industry. They are also not uniformly executed, which is exactly what creates the dispersion between top-quartile and median operators that drives platform-level returns.

What this means for underwriting

When evaluating a site or a platform, the model should pressure-test the assumptions behind each of these lines rather than accept blended pro-forma margins. The most common dressing-up tactics in seller-prepared models are: overstating membership counts (we’ll cover ghost members in the membership page), understating maintenance capex, and treating one-time marketing spend as recurring revenue contribution. Build your own unit model from observable inputs — wash counts, RPC, member roster, labor schedule — rather than relying on the seller’s EBITDA bridge.