I often get asked whether a property makes more sense as a short-term rental (Airbnb) or a long-term lease. The intuitive answer—“it depends”—isn't very helpful. What I find useful, and what I’ll walk you through here, is a reproducible stress-test approach that combines occupancy shocks and market comps to give a clearer, data-driven recommendation for your city and your property.
Why you need a stress test, not a fantasy budget
Owners frequently rely on optimistic averages: peak-season nightly rates, best-case occupancy, and zero downtime. That’s dangerous. Short-term rentals (STRs) are volatile—seasonality, platform changes, regulation, and macro shocks (think pandemics or travel downturns) hit occupancy first. Long-term leases are steadier but often yield lower nominal income. A stress test forces you to compare outcomes under adverse but plausible scenarios so you can decide which model fits your risk tolerance and cashflow needs.
Three pillars of my stress-test framework
I use three inputs to model STR performance and compare it to long-term leasing:
Step 1 — Gather reliable comps
Start local and be specific. For STRs I use AirDNA, AllTheRooms, or Mashvisor to extract:
For long-term comps I check Zillow, Rentometer, or local listings to obtain current market rent for a comparable unit. Also note recent lease concessions or landlord-paid utilities that change effective rent.
Step 2 — Build a realistic expense model
STRs have higher operational costs. In my models I include:
For long-term leases: landlord-paid utilities (if any), lower maintenance turnover, periodic repainting and minor repairs. Management fees for long-term are typically lower (8–12% if using a property manager).
Step 3 — Define occupancy shock scenarios
I model at least three scenarios for occupancy:
Apply these shocks to monthly occupancies rather than a single annual number to capture seasonality. For example, a 30% drop in high-season occupancy yields a different revenue loss than the same drop in low-season months.
Step 4 — Run the revenue and cashflow comparison
Create a simple monthly P&L for both strategies over a 12- to 24-month horizon. Key rows should include:
Below is a compact example table you can copy into a spreadsheet and populate with local numbers. Replace the sample numbers with your comps.
| STR (Baseline) | STR (Severe shock -50%) | Long-term Lease | |
|---|---|---|---|
| Avg nightly rate | $150 | $150 | - |
| Avg occupancy (nights/month) | 18 | 9 | - |
| Gross monthly revenue | $2,700 | $1,350 | $1,800 |
| Platform & booking fees | $270 (10%) | $135 | $0 |
| Cleaning & turnover | $300 | $150 | $50 |
| Utilities | $200 | $200 | $100 |
| Mgmt fees | $540 (20%) | $270 | $180 (10%) |
| Maintenance reserve | $150 | $150 | $75 |
| Net monthly income | $1,240 | $445 | $1,395 |
In this sample, the long-term lease is more resilient under a severe occupancy shock. But baseline STR income is higher—hence the importance of deciding whether you can tolerate downside volatility.
Step 5 — Evaluate downside protection and financing
Lenders and your personal cashflow needs matter. STR lenders often underwrite using lower occupancies or cap rates, increasing debt service. If you have a mortgage, run a debt-service coverage test under each scenario. Ask: in the severe shock scenario, can I cover mortgage, insurance, and taxes for X months without dipping into reserves?
Step 6 — Consider non-revenue impacts
Short-term rentals come with extra time and liability costs that aren't always obvious:
These qualitative factors should reduce the effective upside you assume in STR models.
When STR wins vs when long-term wins
STR is typically preferable when:
Long-term is typically preferable when:
Practical tips and tools I use
For fast, defensible analysis:
How I operationalize this for clients
When I advise investors, I run the STR vs long-term model with at least 12 months of monthly granularity and three shock scenarios. I present a quick payoff matrix—expected net income vs worst-case net income—and overlay a risk-adjusted preference. If a property delivers high upside but the downside breaches our 3–6 month liquidity threshold, we usually recommend long-term or a hybrid approach (block long-term during low season and STR in high season, if regulations allow).
If you want, I can produce a simple spreadsheet template pre-filled with formulas and the sample table above so you can plug in your city’s comps and run the scenarios yourself.
