Product Management · Marketplace Design · Trust and Safety

I wanted to make laundry less of a chore.

I developed Laundromate to make laundry accessible to the 35 million renters who need it most.

Check out Laundromate →
B
Beulin Ashika Nithyanandam
February 2025
The Problem

35 million US renter households have no in-unit laundry.

Most rely on commercial laundromats, paying an average of $4.50 per load2 and travelling roughly half a mile each way.3 The time and cost stack up. Many renters end up waiting longer between washes than they would if a machine were closer.

Most mid-size apartment buildings have shared laundry rooms installed for tenants. Those rooms run at a fraction of their capacity. Landlords earn nothing from the idle hours. Nearby renters have no idea the machines exist.

Supply was never the problem. Access was.
Why I built this

As an international student, I lived in several apartments without in-unit laundry. The nearest laundromat was a 15 to 20 minute walk each way, and the cost and effort of it meant I put off washing longer than I should have. When I eventually moved somewhere with a laundry room in the basement, I thought about all the buildings I had walked past that probably had the same thing sitting idle below street level. That is where this started.

The Concept

What if the machine next door was bookable?

Building laundry rooms have predictable idle time. Renters nearby need access. A platform that connects the two, handles scheduling, and builds trust on both sides could close that gap without requiring any new infrastructure.

Product hypothesis

Renters do not choose laundromats because they prefer them. They choose them because no alternative is visible. If a verified, bookable machine exists within walking distance, the laundromat stops being the default.

Building It

Decisions, decisions, and more decisions.

Revenue model

First idea
Charge guests a small booking fee, under $1 per slot, to fund the platform.
Why it changed
Renters without in-unit laundry already pay more per load than those who have one. Adding a platform fee makes the product part of the problem, not the solution.
Final
Hosts pay a flat monthly or annual subscription. Guests book free, always. Revenue scales with host supply rather than booking volume — the right structure for a marketplace that needs host density before guest retention matters.
V2 ideas
Referral program where hosts who bring in other hosts get a free month. Enterprise tiers for property management companies with multiple buildings.

Slot length and availability

First idea
Hosts set a weekly schedule. Guests book any increment within it.
Why it changed
Shared laundry rooms exist first for building tenants. Unlimited guest availability competes with that. The platform should fill idle time, not displace tenant use.
Final
Hosts set limited windows when the room is typically free. Guests book fixed 2-hour slots within those windows. Each slot is atomic and cannot be split or extended. Tenant access stays protected.
V2 ideas
Separate weekday and weekend windows. Multi-slot booking for large loads. Calendar sync so hosts can block dates.

Guest payment

First idea
Process guest payments through the platform with payouts to hosts.
Why it changed
Building laundry machines already have payment systems: coin-operated, card reader, or app-based (ShinePay, Laundryview). The price per cycle is set by the machine. Adding a second payment layer creates unnecessary friction.
Final
Guests pay the machine directly on arrival. Laundromate handles slot reservations and a one-time $5 refundable deposit for first-time guests, returned after check-in. Hosts list what the machine charges and how it accepts payment.
V2 ideas
Partner with connected payment providers to surface real-time pricing in the listing.

Discovery before sign-up

First idea
Show full listing details, including address and specs, to everyone.
Why it changed
Hosts are sharing the address of a building they manage. Exposing that to unverified visitors before any trust relationship exists is a real concern, particularly for smaller landlords.
Final
Unsigned-in visitors see listing count, machine types, and availability status. Addresses and booking details are only visible after creating a free account.
V2 ideas
A map view showing approximate zones rather than precise pins. Host-controlled visibility tiers.

Host verification

First idea
Self-declaration: hosts confirm they have authority and agree to terms.
Why it changed
A checkbox does nothing to prevent a tenant from listing a machine they do not control, or someone listing a private machine in a shared living space. Both create safety risks for guests.
Final
Every host submits proof of property authority: a deed, management contract, or authorization letter. They upload a photo of the laundry room in a clearly common area and sign a declaration confirming it is not a private residence.
V2 ideas
Automated document verification. Annual re-verification. Additional identity check tier with a verified badge.

Host quality floor

First idea
Let the review system surface quality issues over time.
Why it changed
A passive approach means guests keep booking poor listings until the host churns on their own. That is a bad guest experience and a platform trust problem.
Final
Hosts with ratings below 1.8 stars after a minimum review threshold are flagged, warned, and eventually removed. The subscription is not refunded. This policy is stated on the guest side of the platform.
V2 ideas
Response rate metric alongside rating. Mediation flow for disputes before escalating to delisting.

Guest check-in and no-shows

First idea
No formal check-in. Hosts trust that guests who book will show up.
Why it changed
No-shows waste the host's window and erode trust in the platform. Without a consequence, the behaviour goes unchecked and hosts stop listing.
Final

Three options were considered before landing on photo check-in.

REJECTED
Rotating PIN
Reusable after one visit.
REJECTED
QR code
Can be shared remotely.
REJECTED
Geofencing
GPS unreliable in basements.
CHOSEN: PHOTO CHECK-IN
Guests photograph the machine on arrival within 15 minutes of slot start. Timestamped and geotagged. No-shows get a strike. Three strikes result in temporary suspension. Hosts see a guest's strike count before confirming.
V2 ideas
Machine-level transaction data from connected payment systems can confirm whether a cycle actually ran. As these become standard in multifamily buildings, photo check-in becomes a fallback.
The Present Version

How laundromate works today

Guest
1.Searches by street address or shares location
2.Sees nearby listing count; signs up to unlock details
3.Books a 2-hour slot, free
4.Gets a text reminder and access code 30 min before
5.Checks in with a photo of the machine on arrival
6.Pays the machine directly and washes
Host
1.Submits verification and laundry room photo
2.Lists machine specs, pricing, and availability window
3.Gets notified when a booking is made
4.Gets notified when a guest checks in
5.Machine collects payment directly
6.Pays monthly or annual subscription
Booking flow
01
Search
Street address or location, sorted by proximity.
02
Browse
Machine specs, cycle pricing, payment type, host rating.
03
Book
Pick a 2-hour slot. First-timers hold a refundable $5 deposit.
04
Remind
SMS 30 min before. Address, access code, machine details.
05
Check in
Photo of machine on arrival. 15-minute window.
06
Wash
Pay the machine. Deposit released. Rate the host.
Trade-offs

The decisions that were tough

The tension Decision What it cost
Showing addresses before sign-up drives discovery but exposes host locations to unverified visitors. Teasers show listing count and availability. Full details require an account. Slower conversion from organic discovery. Host trust is worth more at this stage.
Coin-op machines leave no digital trail. Usage cannot be confirmed. Photo check-in confirms presence. Machine usage is inferred from arrival. Cannot confirm cycle count in V1. Accepted as a limitation until connected payment infrastructure is more prevalent.
A guest booking fee would scale revenue directly with booking volume. Guests book free. Revenue scales with host supply only. Slower top-line growth. The positioning trade-off is correct.
Hosts want maximum open hours to maximise earnings. Hosts set limited windows to protect resident access to an amenity they pay for. Hosts may feel earning potential is capped. Tenant priority is non-negotiable.
What Is Still Open

Open questions and next priorities

V2 Priority
Verifying machine usage, not just arrival
Photo check-in proves a guest arrived. It does not confirm they ran a cycle. The fix is machine-level data from connected payment systems. As ShinePay, Laundryview, and similar services become standard in multifamily buildings, cycle confirmation becomes possible without new product work.
Watch
Coin-operated machines as a lower-trust tier
Coin-op listings cannot be verified at the usage level. They are labelled clearly. The long-term path is nudging hosts toward connected payment methods. Coin-op remains in V1 to maximise supply at launch.
Infrastructure
Geographic expansion
The product is built for dense urban rental markets. The prototype covers Boston, Chicago, Austin, Seattle, and Denver. New York and San Francisco are the logical next cities. The unit economics and host acquisition playbook shift in larger, more anonymous buildings. That is a product design problem before it is a sales one.
V2 Priority
Host network effects
Property managers often manage multiple buildings. A single acquisition can become three or five listings. A referral mechanic where hosts who bring in other hosts receive a free month makes that explicit and shortens the sales cycle in a segment where word of mouth already drives decisions.
Interactive prototype
See how it works
Full guest and host flows, booking, check-in, messaging, host dashboard.
This is a working prototype built to document the concept and flows. It is not a production product. Open prototype →
Sources
  1. Harvard Joint Center for Housing Studies. America's Rental Housing 2022. jchs.harvard.edu
  2. Coin Laundry Association. State of the Industry Report 2023. coinlaundry.org
  3. US Census Bureau. American Housing Survey, 2021. census.gov