Financial Assumptions for Automotive Repair Shops

Why modeling auto repair is harder than it looks

An automotive repair shop is a deceptively simple business on the surface: cars come in, technicians diagnose and fix them, customers pay, and the shop repeats. The reality is that financial performance is determined less by “how many cars” and more by a tight set of operational levers that cascade into unit economics: labor mix (how many billable hours are actually sold—and at what effective rate), parts gross profit discipline (markup strategy, sourcing behavior, returns), and physical capacity (bay layout, parking, staging, and workflow bottlenecks). The reason these assumptions matter for lenders and feasibility studies is that small errors compound quickly: a few percentage points on parts margin, or a small shortfall in car count per bay, can swing free cash flow meaningfully.

Demand-side conditions also matter because they shape sustainable throughput and pricing. The U.S. vehicle fleet is aging—average vehicle age reaching 12.8 years in 2025 in analyses—typically increasing the frequency and complexity of repair work over time. At the same time, the industry’s constraint is often labor, not consumer need: technician recruiting and retention pressures feed directly into wage levels and the achievable billed-hours-per-tech.

A clean way to structure assumptions is to model the shop as a production system with two “factories” operating in tandem:

• The labor factory converts technician time into billable hours and charges an effective labor rate.

• The parts factory converts procurement and inventory handling into delivered parts on a repair order, priced via markup strategy and constrained by customer price sensitivity and competition.

The physical plant (square footage, bay geometry, and parking/staging) determines how efficiently those factories run. Capacity limits are real: you can’t bill hours you can’t physically stage, lift, test, and deliver.

Labor mix assumptions that actually drive revenue

What “labor mix” means in a finance model

In shop modeling, “labor mix” is often used loosely, so it’s worth being explicit. In feasibility work, it typically bundles three distinct mixes:

1. Revenue mix: labor dollars vs parts dollars (sometimes expressed as a parts-to-labor ratio).

2. Work mix: maintenance, diagnostics, repair categories (affecting gross margin, bay time, and comeback risk).

3. Capacity mix: technicians, advisors, and bays (who is the constraint at each growth stage).

All three show up in unit economics.

The parts-to-labor ratio as a first-order benchmark

A widely cited rule-of-thumb is the parts-to-labor ratio, commonly considered “normal” in the range of 0.8 to 1.0 (i.e., $0.80–$1.00 of parts sales for each $1.00 of labor sales). That single ratio implies a practical revenue mix range:

• If parts-to-labor = 0.8, then parts share of (parts+labor) revenue is 0.8 / (1.8) ≈ 44%.

• If parts-to-labor = 1.0, then parts share is 1.0 / (2.0) = 50%.

So a common base case for a general repair shop is ~45–50% parts revenue and ~50–55% labor revenue, before considering shop supplies, disposal fees, tires, or sublet work.

This is not a law of physics. The ratio shifts materially by shop type: a tire-heavy business tends to be more parts/material intensive, while diagnostic-heavy or European-specialist shops may push more labor per RO. What matters is that the ratio is a fast way to sanity-check whether the shop is implicitly underpricing labor or over-discounting parts.

Labor rate: posted vs effective, and why lenders should care

Financial models often plug in a single “labor rate” (e.g., $140/hr). In reality, two rates matter:

• Posted labor rate: the published door rate.

• Effective labor rate: what the shop actually realizes after discounting, menu pricing, mix of billed hours, warranty adjustments (if relevant), and write-downs.

The gap between the two—often created unintentionally by discounting habits or inconsistent estimating—can decide whether the shop hits its target gross margin.

In the 2025 industry survey, shops that track KPIs show notably stronger “quality of earnings” indicators than those that do not, including a much higher share reporting posted labor rates above $130. The same survey also provides distributions for sales conversion (closing ratio) and ARO (average repair order), which are leading indicators of how effectively labor capacity is monetized.

A useful piece of hard, non-national-but-credible labor-rate evidence comes from the labor rate survey: it reported a statewide general shop labor rate around $151 in Oregon in early 2024 and noted diagnostic rates often run higher than general labor. This is not a universal rate, but it shows that $150+ posted rates are not unusual in at least some U.S. markets—important when underwriting “reasonable” revenue per billed hour.

Technician wages and the “loaded cost” trap

To model labor gross margin credibly, you need the “true” technician cost per sold hour. The median annual wage for automotive service technicians and mechanics was $49,670 (May 2024) per . Real shops can be above or below that, especially depending on specialty, flat-rate structure, and market tightness, but the BLS median is a defensible anchor for a base case.

Underwriting commonly fails in one place: models treat wages as the only direct cost of labor. In practice, employers carry payroll taxes, insurance, paid leave, and other benefits. In BLS Employer Costs for Employee Compensation data, wages and salaries are about 70% of total employer compensation costs for private industry workers, with benefits around 30%. That implies a rough “load factor” on wages (wage → fully loaded employer cost) that can be closer to ~1.3x to 1.4x depending on benefit richness and establishment size.

If a feasibility model assumes, implicitly or explicitly, that technician cost is $25/hr (unloaded) but labor is sold at $140/hr, it may appear the shop prints money. But if loaded cost is closer to $33–$36/hr (plus flags like productivity losses), the real labor gross profit per sold hour is lower, and the break-even point rises.

Productivity and efficiency assumptions should be conservative

Most financial models assume a stable conversion of paid technician hours to billed hours. Reality is lumpy: diagnostic bottlenecks, parts delays, rechecks, and workflow variation will erode theoretical capacity.

The Ratchet+Wrench survey highlights that only a subset of KPI-tracking shops report high productivity and efficiency thresholds (e.g., productivity above 90% and efficiency above 100%), and KPI tracking is correlated with stronger performance outcomes. This matters because many models quietly embed “high-performance shop” assumptions (near-peak productivity) when underwriting an average operation.

A disciplined approach is to model three levels:

• Base case: realistic, not aspirational (moderate productivity, some downtime, normal parts delays).

• Upside case: KPI-driven operation (better inspection process and scheduling discipline).

• Downside case: hiring gaps or weaker service advising (lower car count and lower sold hours per day).

That scenario structure is aligned with how survey data actually distributes.

Parts markup and parts gross profit discipline

Start by separating markup from margin

Many operators (and some spreadsheets) mix up markup and margin. Markup is the percentage added to cost; margin is gross profit as a share of selling price. Confusing these two creates modeling errors, especially when applying a parts matrix. Trade guidance repeatedly emphasizes the distinction because it changes what “50%” means in dollars.

What parts gross profit looks like in current shop surveys

Benchmarking parts gross profit is messy because accounting practices vary (some shops net out returns differently; some treat shop supplies as parts; some do not). Still, survey evidence gives a usable anchor.

In ’s 2025 shop survey reporting, the most common respondent bracket for gross profit on parts was in the 51–60% range, and cited “industry experts” recommending 55–60% or more as a benchmark target. The sample size shown in the report excerpt is substantial (hundreds of shops), which makes this directionally meaningful even though it is still survey-based rather than audited financials.

Meanwhile, Ratchet+Wrench’s 2025 survey suggests that KPI-tracking shops are more likely to report parts gross profit above 50% than non-tracking shops. Even if you don’t accept 55–60% as “average,” it is consistently framed as a target for well-run general repair shops.

Why a parts matrix exists (and how to model it)

A parts matrix is the most common way to operationalize parts pricing discipline: lower-cost parts carry higher markup, higher-cost parts carry lower markup, with the goal of a stable target margin across the portfolio. PartsTech’s guidance describes this structure explicitly—higher markups on lower-cost parts and lower markups on higher-cost parts—to reach the target overall margin.

A real-world example of this tiering appears in PartsTech content: an illustrative matrix might apply ~60% markup to a $100 part and ~25% markup to a $2,000 part. The point is not that those exact numbers fit every shop; it’s that the shape of the curve matters. Without a matrix, shops often underprice small parts (where handling costs are high relative to ticket size) and overprice large parts (where the customer “price shock” is greatest), pulling margins toward the middle in the worst way.

Reconciling OEM MSRP behavior with independent markup targets

It can look contradictory that dealer parts pricing often references lower markup levels than independent shop targets. An explanation: “MSRP” is itself a manufacturer matrix, and one reported average in dealer-focused commentary is that MSRP pricing often pays around 50–60% markup over cost (with some OEMs lower). That is a statement about manufacturer MSRP design and dealer perceptions, not about what independent shops should charge. But it does help anchor a key modeling reality: “retail” prices in the ecosystem are matrix-driven across the board; they are not simply cost-plus-10%.

For independent feasibility models, the right modeling move is not to import dealer MSRP markups directly, but to underwrite a target parts gross profit margin (e.g., 50–58% depending on positioning) and then back into implied effective markup by parts price tiers.

Shop supplies, environmental, and disposal fees: small line items that can still matter

Fee practices vary heavily by jurisdiction and by shop philosophy. As a consumer education example, notes that repair bills often include hazardous waste disposal/environmental recycling and shop supplies fees, and that these can vary by state and locality; it also notes shops may charge flat fees or itemize.

For modeling, you need two separate decisions:

1. Whether you assume a fee line exists at all (some markets push back; some shops embed these costs in pricing).

2. How the fee is calculated (flat, capped percentage of labor, or percentage of labor+parts).

Industry trade commentary has historically referenced shop supplies charges often being a percentage of labor with a cap (e.g., 5–10% of labor with a maximum per RO).

A critical compliance caveat: in some jurisdictions, fee practices are regulated. The has stated that automotive repair dealers may not charge general fees just to cover overhead; any fees must be directly related to repair work or payment method and must be properly disclosed. This is not an academic point—if you’re underwriting a California shop, the “misc fee % of invoice” line can be legally problematic depending on how it’s framed and disclosed.

Space requirements and the physical constraint behind “car count per bay”

Space is not just CapEx and rent—it is a throughput variable. If the shop cannot stage cars, move vehicles safely, and avoid internal gridlock, labor efficiency collapses and the model’s revenue per tech becomes unachievable.

Bay geometry: what a “standard bay” really implies

For planning around lifts, provides a clear rule-of-thumb: a standard two-post lift commonly needs about 12 feet of ceiling height and approximately a 12 ft × 24 ft operational area (with variation by model). This 288 sq ft “box” is a useful minimum, but it’s not the whole story: you also need tool access, doors opening, and technician circulation.

Installation guidance from equipment documentation reinforces the clearance issue, explicitly recommending at least 3 feet clearance around the lift and a minimum 12-foot ceiling for overhead clearance in a bay layout context, while also emphasizing compliance with local building and fire codes.

The implication for models: using only the 12×24 bay rectangle tends to understate required service area and leads to unrealistic occupancy/throughput forecasts.

Typical shop size by bays from survey data

PartsTech’s 2025 survey excerpt shows an “average shop size” of 6 bays, and indicates that the majority of surveyed shops have 8 bays or fewer. That provides a practical base case for feasibility models: if you’re underwriting a single-location independent shop, 4–8 bays is the center of the distribution, not 15.

Converting “bays” into required square footage

A practical (and conservative) rule in underwriting is that gross building square footage per working bay exceeds the simple bay rectangle because you need:

• internal drive aisles and circulation

• toolboxes and shared equipment zones (tire machines, brake lathes if used, battery/charging areas)

• parts receiving and short-term staging

• service advisor and customer-facing space (even minimal)

• restrooms, mechanical/electrical rooms, and storage

A useful reference point for how non-bay functions consume area comes from a vehicle operations/maintenance facility design guide hosted on the Whole Building Design Guide platform (a U.S. government–affiliated building guidance hub) for facilities. While it is not written specifically for private independent shops, it is valuable as a planning analog because it explicitly breaks out support functions and highlights that circulation/walls can be modeled as a percentage factor in space planning (the guide’s example for administrative functions uses a 20% walls and circulation factor).

In practical shop underwriting, this usually leads to a two-layer space assumption:

• Service area per bay (with working clearance and aisle share): often modeled in the range of ~300–450 sq ft per bay depending on layout discipline and vehicle mix, anchored by 12×24 (288 sq ft) plus clearance logic.

• Support space as a share of service area: often 25–60% depending on whether the shop has customer lounge, training room, tire storage, and how much work is sublet.

Because every building is different, the defensible approach in a feasibility study is to show the calculation explicitly (bay rectangle → add clearance → add circulation/support factor) rather than dropping a single “sq ft per bay” with false precision.

Parking and external staging: often dictated by code, not preference

Even if the building “fits,” many municipalities impose minimum parking based on service bays. An article by by includes an example of parking requirements for automobile dealers that explicitly includes three parking spaces per service bay for repair garage areas. This is not “the rule everywhere,” but it demonstrates how common it is for zoning to treat service bays as parking generators.

From a unit economics standpoint, inadequate parking/staging space shows up as:

• reduced daily car count per bay (internal congestion)

• higher technician downtime (waiting for vehicles to be moved or for space to open)

• longer cycle times (vehicles occupying bays while waiting on parts)

So parking is not a soft “nice-to-have” assumption; it is connected to the throughput variable you will later multiply into revenue.

Unit economics built from bays, car count, ARO, and margins

The most useful unit: revenue per bay per day

A practical lender-grade unit economics model starts with a simple identity:

Annual revenue ≈ (Bays) × (Cars per bay per day) × (Operating days) × (Average repair order).

You can then break ARO into labor and parts and apply gross profit assumptions.

Two survey anchors are especially helpful here:

• PartsTech reports an average daily car count of 2.2 vehicles per bay in its 2025 findings excerpt.

• Ratchet+Wrench’s 2025 survey indicates that for many shops, average repair order is above $600, with distribution across bands from $400–$599 up through $1,000+.

These are not perfect, but they are concrete enough to underwrite a base case and stress tests.

A base-case example: a typical 6-bay general repair shop

Below is a worked example designed to be transparent—every number is either anchored in survey data or clearly labeled as an underwriting choice.

Capacity and demand assumptions

• Bays: 6 (survey “average shop size”).

• Cars per bay per day: 2.2 (survey average).

• Operating days: 250 (modeling convention: ~5 days/week × 50 weeks; adjust by local practice).

• ARO: $700 (a conservative midpoint consistent with Ratchet+Wrench showing a large share of shops in $600–$999 bands and >$1,000 for a meaningful subset).

Implied annual car count and revenue

• Annual cars ≈ 6 × 2.2 × 250 = 3,300 vehicles/year.

• Annual revenue ≈ 3,300 × $700 = $2.31M/year.

Labor vs parts split

Assume parts-to-labor ratio of 0.9 (within the 0.8–1.0 “normal” range).

• Labor revenue share = 1 / (1 + 0.9) = 52.6%

• Parts revenue share = 0.9 / (1 + 0.9) = 47.4%

So revenue split:

• Labor revenue ≈ $2.31M × 52.6% = $1.22M

• Parts revenue ≈ $2.31M × 47.4% = $1.09M

Gross profit assumptions (and why they’re defensible)

• Target labor gross profit: base-case 65% (PartsTech coaching content recommends 60–75% and suggests starting at 65%).

• Target parts gross profit: base-case 55% (PartsTech survey excerpt and cited benchmark range).

These targets produce a blended gross margin around:

• Blended gross profit ≈ (1.22M×65%) + (1.09M×55%)

  
  

  ≈ $0.79M + $0.60M = $1.39M, or ~60% gross margin.

Is ~60% blended gross margin plausible? Ratchet+Wrench’s 2025 distribution shows a meaningful share of shops reporting overall gross profit margins of 60% or more, while the largest single bin is 50–59%. So 60% is best treated as an upper base case for a well-run shop; a more conservative “typical” blended GM might be 52–57% depending on how technician wages are classified (COGS vs operating expense) and whether the shop sells tires (which often compress margins).

A lender-grade model should therefore show at least two margin cases:

• Conservative blended GM: 52–55% (aligned to the most common Ratchet+Wrench bin).

• Optimized blended GM: 58–60% (aligned to high-performing targets and the “60%+” subset).

Operating cost structure: build it from headcount, not magic percentages

Percent-of-sales expense ratios are convenient but can hide bad logic. A better approach is to underwrite headcount and pay levels, then check whether the implied payroll-to-sales ratio is reasonable.

To translate wages into employer cost, use:

• Technician wage anchor: BLS median $49,670/year.

• Benefit load anchor: BLS ECEC indicates benefits ~29.8% of compensation cost for private industry workers.

A simplified conversion (illustrative):

• Unloaded hourly wage ≈ 49,670 / 2,080 ≈ $23.9/hr.

• Loaded hourly cost (rough) ≈ $23.9 / 0.702 ≈ $34/hr using the ECEC wage share for private industry as an approximation.

Now embed an operating structure (example, not a universal template):

• 6 technicians (one per bay in a staffing-constrained system)

• 2 service advisors (front counter)

• 1 manager/owner-operator (could be an expense or a distribution depending on underwriting)

This gives you a payroll envelope that, in many plausible pay configurations, lands in the ~30–35% of sales range for a $2.3M store—consistent with common advisory benchmarks that total payroll should be kept roughly at or below ~35% of sales for many independent shops.

From there, you underwrite the non-payroll fixed cost stack:

• facility rent/lease and utilities (highly local)

• shop insurance, software stack, waste disposal services

• tool and equipment leases (if any), training, and marketing

This is where I cannot honestly give a single “industry correct” number without knowing geography, property type, and whether the shop owns real estate. Those inputs can move occupancy cost by multiples and can change DSCR materially. The right feasibility practice is to model rent per square foot from local comparables and to show the sensitivity (rent and payroll are almost always the top two fixed-cost risk drivers).

Break-even logic: make it explicit

Once you have a blended contribution margin (after variable costs), break-even is straightforward:

Break-even revenue = Fixed costs / Contribution margin %

This approach is standard in break-even analysis guidance for repair shops and is consistent with mainstream contribution-margin logic.

A few observations matter more than the algebra:

• If daily car count per bay is 2.2 and ARO is $700, the shop can look “healthy.” If car count slips to 1.8 because of staffing gaps or poor scheduling, revenue falls ~18% with fixed costs largely unchanged.

• A parts gross profit shortfall (e.g., 55% target vs 45% actual) is a silent killer because it reduces contribution margin without visibly changing car count. PartsTech’s own survey material implies many shops underperform recommended parts margin targets, supporting the need to underwrite conservatively unless strong evidence exists.

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Using dealer financial disclosures as a “sanity check,” not a proxy

Public dealer groups show how outsized service and parts can be as a profit engine—even when it is a smaller share of sales. For example, disclosed in SEC filings that service and parts represented 11.7% of dealership revenue but 41.7% of dealership gross profit in 2024. This is not the same business as an independent shop, but it reinforces the underwriting principle: parts and service profitability is often what stabilizes the overall enterprise.

A feasibility-ready set of assumption ranges

The table below consolidates the most finance-relevant drivers into ranges that can be defended with available survey and technical guidance. Where survey data provides distributions rather than means, the ranges are framed to support base/upside/downside cases.

Driver	Conservative underwriting range	Optimized / upside range	Evidence anchor
Bays (single-location independent)	4–8	8–12	PartsTech excerpt shows average 6 bays and most ≤8.
Cars per bay per day	1.7–2.2	2.2–2.7	PartsTech reports average 2.2 vehicles per bay/day.
Average repair order	$450–$650	$650–$900+	Ratchet+Wrench shows many shops with ARO in $400–$999 bands and a meaningful share ≥$1,000.
Parts-to-labor ratio	0.8–1.0	0.7–0.9 (labor-heavy diagnostics)	“Normal” range cited as ~0.8–1.0 in industry commentary.
Target parts gross profit margin	45–52%	55–60%	PartsTech survey excerpt and benchmark guidance; KPI shops more likely >50%.
Target labor gross profit margin	55–65%	65–75%	PartsTech coaching guidance recommends ~60–75% and starting at 65%.
Overall gross profit margin (common-size)	45–55%	55–60%+	Ratchet+Wrench distribution shows most common 50–59% and a material share 60%+.
Bay operational footprint (2-post lift)	~12×24 ft minimum	larger for trucks / heavy work	Rotary rule-of-thumb and equipment clearance guidance.
Minimum lift clearance and ceiling	12 ft ceiling; ≥3 ft clearance	14–16 ft for taller vehicles	Snap-on equipment guidance.
Parking requirement (often zoning-driven)	2–4+ stalls per bay + office rules	higher for quick-turn	Planning literature gives examples including 3 stalls per service bay (context: dealer repair areas).

This set is intentionally practical: it’s built to populate a lender model without pretending that every market behaves identically.

The failure modes lenders see over and over

A feasibility model for an automotive repair shop typically breaks in a few predictable places:

First, car count per bay is treated as stable when it’s actually the output of staffing, scheduling, parts availability, and bay layout. PartsTech’s 2.2 cars/bay/day metric is an average; underwriting it as a guarantee instead of a performance KPI can inflate revenue projections.

Second, models often assume high parts margin without a mechanism. If the shop does not have a pricing matrix, disciplined estimating, and internal controls against ad hoc discounting, the model should not assume benchmark-level parts gross profit. Survey evidence suggests many shops underperform the recommended margin targets, which supports conservative underwriting unless operational evidence is strong.

Third, underwriters fail to reconcile labor gross margin targets with labor market reality. Technician median wages provide a base anchor, but the true employer cost includes benefits and taxes, and labor shortages can push wages above “median.”

Finally, space planning is reduced to rent per square foot, ignoring throughput. If the shop physically cannot stage vehicles (including parking required by code), then the model’s implied revenue per bay will not materialize.