# Laundromat Model See how customer demand, machine use, pricing, and utilities affect a laundromat's cash flow and value. - Canonical: https://finamodel.com/templates/laundromat - Excel download: https://finamodel.com/templates/laundromat.xlsx - Category: Consumer - Model type: Operating model - Difficulty: Intermediate - Audiences: Investors & analysts, Founders & operators, Private equity associates, Laundromat operators, Search-fund investors, Lenders, Laundromat and multi-site operators, Search-fund and PE buyers, Equipment and acquisition lenders, Investors and analysts - Tags: laundromat, self-service-laundry, coin-laundry, operating-model, dcf, self-service laundry, roll-up, utilisation ## Overview This model helps you plan a single laundromat or a growing group of stores. It connects machines, customer usage, pricing, wash-and-fold services, and other income to the real costs of utilities, rent, staff, and equipment. Use it to assess a new site, acquisition, or expansion plan. The summary shows how changes in store count, pricing, and utilisation affect profitability, cash flow, and business value. ## What's included - Capacity inputs: Year-1 stores, new stores per year, washers and dryers per store, operating days - Utilisation: Year-1 utilisation with an annual ramp and a practical ceiling - Throughput: washer and dryer turns per day driving annual cycles - Pricing: washer and dryer vend per cycle, wash-dry-fold rate per pound, ancillary per store, price escalation - Cost structure: attendants per store, wage, benefits, wage growth, utilities, rent, supplies, marketing, SG&A, depreciation, tax - Capital and working capital: maintenance capex, build-out cost per store, NWC, base-year revenue - Valuation: WACC, terminal growth, net debt, shares outstanding - Operations sheet: store roll-forward, washer and dryer fleet, utilisation ramp, annual cycles, wash-dry-fold pounds, attendants - Revenue sheet: washer and dryer vend, self-service subtotal, wash-dry-fold, ancillary, total revenue - P&L sheet: revenue to net income with headcount-driven labour and depreciation, margins, identity check - FCF sheet: NOPAT, depreciation add-back, capex, change in NWC, unlevered FCF, discount factor, PV - Valuation sheet: sum of PV, terminal value, enterprise value, equity value, value per share, implied EV/EBITDA - Dashboard with stores, annual cycles, utilisation, revenue per store, EBITDA margin, EV, per share, and revenue mix - Cost structure: attendants per store, wage, benefits, wage growth; utilities, rent, supplies, marketing, SG&A, depreciation (% of revenue); tax - Capital and working capital: maintenance capex %, build-out cost per store, NWC % of revenue growth, base-year revenue - Store roll-forward (opening + new = closing) and per-store washer and dryer fleet - Cycle throughput build: machine turns per day x operating days x a utilisation ramp, plus a wash-dry-fold pound count - Revenue build: washer and dryer vend per cycle, a wash-dry-fold service per pound, and ancillary income per store - P&L through attendant labour and the utility-, rent- and supplies-heavy cost stack to EBITDA - Unlevered free-cash-flow bridge (NOPAT + depreciation - capex - change in working capital) discounted at WACC - DCF to enterprise value, equity value and value per share, plus a one-page dashboard ## Laundromat Financial Model: How the Template Works This laundromat financial model helps evaluate a single store or a multi-site operator. It connects store growth, machine fleet, utilisation, vend pricing and service revenue to utility-heavy costs, labour and capital spending. The result is a seven-year forecast with cash flow and a discounted valuation, useful for assessing a new site, acquisition or expansion plan. ### Operating drivers: stores, machines and throughput The model starts with a store roll-forward: opening stores plus new greenfield additions give closing stores. Closing stores times washers and dryers per store determine the machine fleet. - Each machine turns a set number of cycles per day, multiplied by operating days and a utilisation factor. Utilisation begins at a Year 1 input and ramps annually to a practical ceiling, reflecting that new stores fill gradually and peak-hour bunching prevents flat-out operation. - Total annual cycles, the sum of washer and dryer cycles, is the key volume driver, while wash-dry-fold pounds scale with store count. ### Revenue build: cycles, pounds and ancillary income Self-service revenue comes from washer and dryer cycles multiplied by the per-cycle vend price, escalated from Year 1. That separates washer and dryer economics. - A wash-dry-fold service adds pounds processed per store at a price per pound, also escalated. Ancillary income, such as vending, change machines and detergent sales, scales per store. - Together they produce total revenue and a revenue-per-store headline. Because vend pricing is a single blended rate for coin and card, the model still captures processing costs on the cost side. ### Cost stack and margin mechanics Most costs are percentages of revenue, but utilities and rent are the signature burden: water, sewer, gas, electricity and a retail lease together absorb roughly a third of revenue before labour. Attendant labour is headcount-driven and escalates with wages. - Vend pricing escalates at the same assumed rate as wages. EBITDA margin expands modestly because operating leverage works through the utilisation ramp: labour scales only with store count, while revenue also benefits from higher utilisation, so revenue grows faster than labour. - Card processing fees on the card/app share of self-service vend modestly dampen that expansion. ### Cash flow, valuation and dashboard outputs Unlevered free cash flow is NOPAT plus depreciation, less maintenance capex and growth capex for new stores, less the change in working capital, which is favourable because cash is collected at the machine. - The DCF sums the present value of explicit cash flows and a Gordon-growth terminal value to enterprise value, then subtracts net debt for equity value and value per share. - The dashboard summarises stores, cycles, utilisation, revenue, EBITDA, margin, enterprise value and value per share, with trend charts and a waterfall. - Note the public download is a values-only preview. ## Throughput and the machine fleet drive revenue Revenue is the product of a fixed washer and dryer fleet, how many cycles each machine turns, and the vend price. The model makes store count, per-store machine counts, machine turns per day, and a utilisation ramp explicit, so annual cycles and revenue per store are transparent operating metrics an analyst can flex against the cost stack rather than a top-down growth rate. ## Designed for one-edit responsiveness Every input, the build pipeline, the machine fleet, throughput, the full pricing and cost stack, capex, working capital, and the WACC, is a named-range cell. Edit one and the operations build, revenue, P&L, free-cash-flow bridge, valuation, and dashboard all recompute. No formula rewrites are needed to test a pricing, utility, or expansion scenario. ## An unlevered DCF, not an EBITDA shortcut A laundromat carries real depreciation and capex on machines and store fit-out and favourable working capital because cash is collected at the machine, so the model bridges to unlevered free cash flow and discounts it at a WACC with a Gordon-growth terminal value. Enterprise value bridges through net debt to equity value and a per-share figure, and the implied EV/EBITDA falls out as a sanity check against the mid-single-digit sector range. ## Workbook structure ### Cover Workbook overview, sheet legend, units, and tab-colour key. - Title and scope framing - Sheet-by-sheet purpose summary - Units and tab-colour legend ### Assumptions Every driver in one sheet: stores, machines, throughput, pricing, costs, capital, valuation. - Year-1 stores, new stores per year, washers and dryers per store, operating days - Utilisation with an annual ramp and a practical ceiling - Washer and dryer turns per day - Washer and dryer vend, wash-dry-fold rate, ancillary per store, price escalation - Attendants per store, wage, benefits, wage growth, and the percent-of-revenue cost lines, tax - Maintenance capex, build-out cost per store, NWC, base-year revenue - WACC, terminal growth, net debt, shares ### Operations Stores, machines, cycles, and staff. - Opening plus new stores equals closing stores - Washer and dryer fleet equals closing stores times per-store machines - Utilisation ramps from a Year-1 input, capped at a ceiling - Annual cycles equal machines times turns times operating days times utilisation - Wash-dry-fold pounds processed per store - Attendants per store and cycles per attendant ### Revenue Revenue by stream. - Washer and dryer vend equal cycles times vend price times escalation - Self-service vend subtotal - Wash-dry-fold equals pounds times rate times escalation - Ancillary per store times escalation - Total revenue ### P&L Revenue to net income. - Total revenue from the Revenue sheet - Attendant labour equals attendants times wage times wage growth times a benefits load - Utilities, rent, supplies, marketing, and SG&A as a percent of revenue - EBITDA equals revenue less total operating costs - Depreciation, EBIT, tax on positive EBIT, net income, margins, identity check ### FCF Unlevered free cash flow bridge. - EBIT and unlevered tax from the P&L - NOPAT equals EBIT less unlevered tax - Add back depreciation - Maintenance capex on revenue and growth capex on new stores - Change in net working capital on revenue growth - Unlevered free cash flow - Discount factor and PV of UFCF ### Valuation Discounted cash flow. - Sum of PV of explicit UFCF - Gordon-growth terminal value and its PV - Enterprise value - Less net debt to equity value - Shares outstanding and value per share - Implied EV/EBITDA ### Dashboard Headline metrics and revenue mix. - Stores, annual cycles, utilisation, revenue per store - Revenue and EBITDA - EBITDA margin - Enterprise value and value per share - Revenue mix across self-service, wash-dry-fold, and ancillary ## Features - **Throughput and the machine fleet drive revenue:** Revenue is the product of a fixed washer and dryer fleet, how many cycles each machine turns, and the vend price - the model makes store count, per-store machine counts, machine turns per day, and a utilisation ramp explicit, so annual cycles and revenue per store are transparent operating metrics rather than a top-down growth rate. - **The utility burden is modelled, not buried:** The laundromat signature is the weight of utilities and rent: water, sewer, gas and electricity to run the machines plus a retail lease absorb roughly a third of revenue before labour. Each is a transparent percent of revenue, and attendant labour is headcount-driven and escalates at the wage-growth rate, so the EBITDA margin responds the way a real operator would expect. - **An unlevered DCF, not an EBITDA shortcut:** A laundromat is equipment- and fit-out-intensive and carries favourable working capital because cash is collected at the machine, so the model bridges EBITDA to cash through NOPAT, depreciation, maintenance and growth capex, and the change in working capital, then discounts the unlevered free-cash-flow stream at a WACC with a Gordon-growth terminal value to a defensible enterprise and equity value. - **Throughput meets the utility burden:** Revenue is driven by how many cycles the fleet turns at a given vend price, while the single largest variable cost - water, gas and electricity - scales with that same throughput, so the two move together. - **A fixed fleet with a utilisation ramp:** Stores x per-store washers and dryers set the fleet; machine turns per day, operating days and a utilisation ramp (with a practical ceiling) set how hard it works. - **Collected-at-the-machine cash flow:** Working capital is favourable because cash is collected at the machine, so the unlevered FCF bridge reflects a genuinely cash-generative format. ## Use cases - **Intrinsic valuation:** Set the build pipeline, machine throughput, vend pricing, the cost stack, and a WACC, and read the enterprise value, equity value, value per share, and implied EV/EBITDA. Sense-check the multiple against the mid-single-digit range that coin-laundry operators change hands at. - **Roll-up and pipeline planning:** Flex new stores per year and the build-out cost per store to see how the development pipeline consumes cash and lifts cycle volume, and watch revenue per store and the EBITDA margin respond as the estate scales. - **Throughput and utility stress test:** Cut the utilisation ceiling or push the utilities and rent lines higher to model a soft-demand year or a spike in energy and water costs, and read the EBITDA-margin and valuation impact as the cost burden moves against pricing. - **Roll-up underwriting:** Flex the build pipeline, throughput, vend pricing and the cost stack and watch enterprise value, the EBITDA margin and revenue per store move together. - **Equipment and acquisition finance:** Lenders use the per-store machine fleet, build-out cost and cash flow to size equipment and acquisition facilities. - **Pricing and utilisation analysis:** Test a vend-price increase or a utilisation uplift and see the EBITDA-margin impact net of the utility cost it drives. ## Frequently asked questions ### What is a laundromat model? A laundromat model captures the seven-year operating economics and intrinsic value of a multi-store self-service laundromat (coin / card laundry) operator. It rolls a store count forward, derives a washer and dryer fleet, converts machine turns and a utilisation ramp into annual cycles, prices self-service vend plus a wash-dry-fold service and ancillary income, runs the cost stack to EBITDA, and discounts an unlevered free-cash-flow stream to enterprise value, equity value, and value per share. It is how a private-equity associate, search-fund operator, or lender values a laundromat platform. ### How is laundromat revenue built? Revenue is driven by the machine fleet and its throughput: washer and dryer cycles equal the number of machines times turns per day times operating days times a utilisation factor, and self-service revenue is those cycles times a per-cycle vend price. A wash-dry-fold service is layered on as pounds processed per store at a price per pound, and ancillary income from vending, change machines, and detergent sales scales per store. ### Why are utilities and rent so important? Washing and drying are energy- and water-intensive, and a laundromat sits in leased retail space, so utilities and rent together absorb roughly a third of revenue before labour, far more than most retail formats. The model carries each as a transparent percent of revenue so an analyst can stress energy costs or lease terms and watch the EBITDA margin move. ### Why an unlevered DCF instead of an EBITDA multiple? A laundromat runs healthy EBITDA margins but carries real depreciation and capex on machines and store fit-out, so EBITDA overstates cash. The model bridges to unlevered free cash flow, NOPAT plus depreciation, less capex, less the change in working capital, which is favourable because cash is collected at the machine, and discounts it at a WACC, then adds a Gordon-growth terminal value. The implied EV/EBITDA falls out as a sanity check rather than as the valuation input. ### Can I make it a levered or single-store model? The template is a single-entity unlevered DCF. For an equity-IRR view, add an equipment-financing schedule and bridge to levered free cash flow; for a single store, set the estate to one store and size the machine counts, throughput, and wash-dry-fold volume to that location. 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