# Invest In Wash: full reference

Invest In Wash is a laundromat research and modeling platform operated by ERLV Inc. Public pages include a New York map preview, buyer workflows, equipment guidance, data methodology, and an illustrative deal simulator. The national workspace requires an invitation and sign-in. Recorded evidence, missing facts, and model assumptions are labeled separately. Invest In Wash is a research aid; it is not a broker, dealer, or investment advisor.

Access: workspaces are provisioned by invitation, with no self-service sign-up or checkout. The map, comparison, and calculator require sign-in. The overview page at https://investinwash.com/ includes a public New York map preview of up to 200 locations.

## Capabilities

### Map
- Mapped laundromat locations with business status, reviews, and recorded competitors counted within 1, 3, and 5 miles (straight-line).
- Filters: equipment on file, listing on file, minimum review count, and open, temporarily closed, or permanently closed status.

### Store analysis
Each store opens to four views: Diligence, Market, Operations, and Records. Depending on what is on file, these show:
- Status, hours, phone, and website.
- Competitors within 1, 3, and 5 miles.
- ZIP-level renter and owner households from Census ACS 5-year estimates, shown only where the store-to-ZIP link is certified.
- ZIP-level average adjusted gross income per tax return from IRS SOI (the ZIP's tax filers, not the store's revenue).
- County unemployment from the BLS Local Area Unemployment Statistics series.
- Reported crime from FBI and New York DCJS agency data, with reporting coverage shown beside the figures.
- Posted machine prices, wash-and-fold pricing, and listing asking price, as recorded observations.
- Equipment on file, with purchase year where recorded.
- Building permits, county assessor parcels, and NYC PLUTO and ACRIS records.
- Business registry links: Texas sales tax permits, Pennsylvania sales tax licenses, and city business records for Chicago, Los Angeles, Miami-Dade County, New Orleans, New York City, Seattle, and San Francisco.

### Comparison
Two to four stores side by side: business status, population, renter household share, median household income, reviews, competitors within 1 and 3 miles, wash-and-fold pricing evidence, and straight-line distance between stores. Values that are not on file are labeled Not on file.

### Deal simulator (public)
An illustrative 28-day operating model with three presets. Inputs: washers per store, turns per washer per day, wash + dry revenue per turn, utilities and labor as a share of gross revenue, and the financed share of capital. Outputs: gross revenue, operating EBITDA, cash after scheduled debt, cash across 12 modeled periods, cash-on-cash return, debt service coverage, and break-even turns. Presets are assumptions, not seller records.

### Investment calculator (signed in)
A month-by-month forecast built from user inputs:
- Washer groups (count, vend price, turns per day, availability, cycle time); dryer revenue by ratio, cycles, or paid minutes; services; utilities; and other costs.
- Utilities are entered as monthly amounts or as quantities priced at the user's own tariff. The model does not supply utility rates.
- Loans with interest-only periods and balloon payments; sales tax and income tax.
- Outputs: EBITDA, cash after debt service, funding gap and reserve, year-two cash-on-cash return and debt service coverage, payback month, XIRR, and break-even turns per day checked against capacity.
- Scenario sensitivity, an evidence-coverage percentage, and a verdict against the owner's targets, where available.

## Investment strategies
- Buy & Operate: screen existing stores to acquire and run. Locations below the review minimum stay on the map, unranked.
- Buy & Retool: inspect the recorded fleet and choose a retool scope (Cosmetic, Light, Mid, or Heavy). Missing equipment evidence stays unknown.
- Lease & Build, Neighborhood: screen areas using certified renter-household evidence (renters hold at least 45% of occupied homes) and recorded competition. Existing stores provide market context, not targets.
- Lease & Build, Daily Needs: a daily-errand location thesis. Grocery anchors and traffic counts are not yet in the data, so every Daily Needs area stays unconfirmed for now.

## Data conventions
- Evidence records name their source and, where one exists, the date it was observed or recorded.
- A missing value is shown as Not on file and a missing date as Date not recorded; neither is estimated or treated as zero.
- Model outputs are labeled as modeled; preset and sample inputs are labeled as illustrative.
- The legacy screening score from April 2026 is labeled as such because its inputs were not recorded.
- Ownership is never inferred from a contact, taxpayer, officer, manager, registered agent, or name match.