Case study
Fllat: how all-inclusive pricing became a revenue stream
1,200+ tenants. 8+ cities. The all-inclusive pricing model was killing margins. We built the system that turned a cost problem into a revenue stream.
At a glance
1,200+
Tenants
8+
US cities
$210,000+
Annualised financial impact
260+
Staff hours saved per year
About Fllat
United States
Fllat provides fully furnished coliving and student housing across major US cities: Miami, New York, Austin, New Orleans, Boston and more. Their residents are students, interns and young professionals who want a simple deal. One price. Everything included. Move in with a suitcase.
The all-inclusive model is Fllat’s competitive advantage. It removes friction from the booking process. It simplifies pricing. It eliminates the anxiety of unexpected bills. Residents love it. It converts exceptionally well.
It was also quietly destroying Fllat’s margins.
All-inclusive pricing at scale
Here is the economics problem that nobody tells you about all-inclusive coliving pricing.
At 50 rooms, you can absorb utility overruns. You price in a buffer, maybe 10-15% above estimated utility costs, and most months you come out even. Some residents use less. Some use more. It averages out.
At 300 rooms, it stops averaging out. The heavy users start to dominate. Air conditioning units running 24/7 in Miami summer. Heaters cranked in New York winter. Three-hour showers. Washing machines running daily for one person. The “average” resident does not exist anymore. The variance kills you.
At 1,200 rooms across 8 cities, it becomes a P&L crisis.
The utility problem was the most expensive symptom, but the underlying issue was that Fllat’s operational infrastructure had not scaled with its geographic footprint.
Fllat’s numbers: all-inclusive pricing covered electricity, Wi-Fi and weekly housekeeping. On paper, the buffer should have been sufficient. In practice, utility overruns were eroding 3-4% of total unit economics. On a business running at 19-20% margins, that 3-4% represents nearly a fifth of the entire profit. The difference between a business that can reinvest in growth and one that is barely breaking even.
No centralised platform
Each city operated semi-independently. Miami had its own spreadsheets. New York had its own. Listings, inquiries, tenant records and financial data lived in different systems in different cities. There was no single source of truth for anything.
Manual rent collection consuming 30+ hours per month
With 1,200 tenants paying across 8 cities through bank transfers, cards and payment apps, reconciliation was a multi-day ordeal every month. The finance team spent more time tracking who had paid than managing the business.
Heavy OTA dependency
Roughly 85% of bookings came through third-party platforms charging 15-20% commissions. Fllat’s own website could showcase rooms but could not close bookings. Every lead that came through the website had to be converted via email or phone, a process that lost 40-50% of prospects.
Maintenance in the dark
Eight cities. Hundreds of properties. Maintenance requests tracked in city-specific WhatsApp groups. No centralised view. No SLA tracking. No escalation logic. A broken AC in Miami during August could go unfixed for days if the local team was overwhelmed.
What JumboTiger built
The multi-city PMS
We built Fllat’s PMS from a blank product requirements document to a live platform. The system was designed from day one for multi-city, mixed-duration coliving.
Multi-city inventory management: every room across every city visible in one dashboard. Real-time availability that updates the moment a booking is confirmed, a lease is signed, or a move-out is processed. No more emailing the Miami team to ask if Room 304 is available.
Direct booking engine, integrated into fllat.com. A prospective tenant can browse available rooms, see photos and pricing, select their dates, upload documents, sign their lease and pay their deposit in one session. No emails. No phone calls. No waiting. Direct bookings went from 15% to 38% of total bookings within 6 months.
Automated rent collection: tenants set up recurring payments on move-in. The system matches payments automatically, flags late payments at day 1, sends automated reminders at day 3, and escalates to the city manager at day 7. Monthly reconciliation time dropped from 30+ hours to under 8.
Maintenance ticketing: a centralised system with city-level routing. A maintenance request in Austin goes to the Austin team. A request in Boston goes to the Boston team. Every ticket has an SLA timer. Every escalation is automatic. Every resolution is confirmed by the resident.
Multi-city dashboard: occupancy, revenue, CPOR, ADR and NOI by city and by property, updated in real time. The leadership team can see the entire business in one screen instead of compiling 8 separate city reports.
New city onboarding in under 10 days. Previously, expanding to a new city took 4-6 weeks of setup. With the platform, adding a new city is a configuration task, not a build task. Properties, rooms, pricing, team access and workflows are templated.
The utility billing system: the revenue engine
This is the system that changed Fllat’s P&L. The problem was clear: all-inclusive pricing was eroding 3-4% of margins. But simply removing the all-inclusive model would destroy Fllat’s competitive advantage. Residents choose Fllat precisely because of the simplicity. Taking that away would hurt conversion rates significantly. The solution had to preserve the all-inclusive experience while protecting margins, and the methodology we developed rests on three data points.
Data point 1, the reverse-engineered margin target: Fllat’s operating margins were 19-20%. The target was 21.5%. That 1.5% gap told us exactly how much utility cost we needed to cap. This is the financial constraint.
Data point 2, the reasonable usage benchmark: we researched and defined what a fair, reasonable amount of electricity, Wi-Fi and housekeeping should cost per person per month. Not the cheapest possible. Not unlimited. The amount that a responsible, normal resident would use. This is the fairness constraint.
Data point 3, the eight-month historical average: we pulled actual utility bills across Fllat’s entire portfolio for the previous 8 months and averaged them. This gave us the real-world baseline. Not estimates. Not projections. Actual data. This is the reality constraint.
We averaged all three data points and set caps at $120-$150 per tenant per month, varying by city and property type. The system automatically tracks usage against caps, calculates monthly overages, and generates chargeback invoices.
How we framed it
The framing mattered as much as the calculation. The tenant-facing wording was: “The offer of rent all-inclusive with utilities includes fair-use caps to ensure responsible consumption and our contribution to sustainable development goals. The following limits are included in your rent: [electricity cap], [Wi-Fi cap], [housekeeping frequency]. Usage exceeding these caps will be billed separately on a monthly basis.”
We did NOT change existing tenants’ agreements. All caps applied to new tenants only, phased in naturally over 3-4 months.
We explained caps during the move-in conversation. Transparency, not surprise.
We framed it around sustainability, not cost-cutting. Residents respond to “responsible consumption” differently than “we are capping your usage.”
Result: a 99% acceptance rate. Virtually zero pushback. Not a single lease was lost because of utility caps.
The financial impact of the utility system
Monthly utility chargebacks recovered: $10,400
Annualised recovery: $124,800
Margin improvement from utilities alone: 19-20% to 21.2%
At Fllat’s projected scale of 3,000-5,000 tenants: $300,000-$500,000 per year in recovered margin
SEO infrastructure and website rebuild
To reduce OTA dependency, we built the organic acquisition layer.
City-level landing pages for all 8 markets, for example “coliving in Miami” and “shared housing Austin”
25+ university proximity pages, for example “student housing near NYU” and “rooms near UT Austin”
Neighbourhood guides with local pricing, transit and lifestyle content
Blog infrastructure for long-tail organic capture
Direct booking CTA optimisation throughout the site
Result: organic traffic increased 340% in 6 months. Direct bookings rose from 15% to 38%, saving an estimated $86,000 per year in OTA commissions.
The results
| Metric | Before | After |
|---|---|---|
Monthly utility chargebacks recovered | $0 | $10,400 |
Annualised revenue from utility system | $0 | $124,800 |
Utility impact on unit economics | 3-4% erosion | Under 1.5% |
Tenant acceptance of utility caps | N/A | 99% |
Margin (from utilities alone) | 19-20% | 21.2% |
Rent reconciliation time (monthly) | 30+ hours | Under 8 hours |
Direct booking rate | ~15% | 38% |
OTA commission savings (annualised) | N/A | $86,000+ |
Time to onboard a new city | 4-6 weeks | Under 10 days |
Organic traffic (6-month change) | Baseline | +340% |
Total annualised financial impact: $210,000+ in recovered revenue, from utility chargebacks plus OTA commission savings. 260+ staff hours saved per year. Complete operational visibility across 8 cities.
The takeaway
Fllat’s case proves something that most operators have not internalised: operational technology is not a cost centre. It is a revenue engine. The utility billing system alone recovers $124,800 per year. The direct booking engine saves $86,000+ in OTA commissions. Combined, that is $210,000+ in annual financial impact from technology that cost a fraction of that to build. The system does not just manage operations. It generates money.
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