It's tempting to forecast a short-term rental the same way as a long-term one — take a nightly rate, multiply by 365, and call it the annual forecast. That overstates the number badly, because it ignores the mechanic that actually drives short-term rental income: occupancy is never 100%, and it isn't stable across the year.
Different mechanics, not just different yields
A long-term rental (LTR) forecast is built on a fixed weekly or monthly rent that changes only at defined points — lease renewal or an indexed rent review. A short-term rental (STR) forecast has no fixed rent at all; income is the product of a nightly rate that moves constantly and an occupancy rate that moves with it. That makes STR forecasting a fundamentally different exercise, not just a higher-yield version of the same one.
How a long-term rental forecast runs
An LTR forecast runs off lease terms and an indexed rent-review cadence: a known rent for the life of the lease, then a defined increase at renewal (often benchmarked to CPI or the local market rate). That structure makes LTR income low-volatility and highly predictable — the main forecasting risk is a vacancy gap between tenancies, not income variability while tenanted.
Annual income ≈ Weekly rent × 52 − vacancy weeks × weekly rent
How a short-term rental forecast runs
An STR forecast runs off an occupancy curve multiplied by an average daily rate (ADR) trend, with seasonality layered on top and platform fees deducted from the gross figure:
Annual income ≈ Σ (nights available × occupancy rate × ADR) − platform fees
Each of those inputs varies by season — peak periods can run near-full occupancy at a premium ADR, while shoulder and off-peak periods can see materially lower occupancy at a discounted rate. A realistic STR forecast models months or seasons separately rather than applying one blended annual figure, and deducts platform fees (typically a percentage of the booking) and cleaning/turnover costs, which don't exist in an LTR forecast at all.
Occupancy × ADR, by season
Model peak, shoulder and off-peak periods separately — a single blended annual occupancy rate hides the seasonal swing that actually drives income variability.
Platform fee drag
Booking platform commissions and payment processing fees reduce gross booking revenue before it reaches you — model this as its own deduction, not folded into a vague expense line.
Why STR needs a range, not a number
Because occupancy and ADR both move with market conditions, local regulation, and seasonality, an STR forecast is inherently more volatile and harder to pin to a single number than an LTR forecast. Rather than presenting one projected figure, build a low / base / high scenario range — e.g. a conservative occupancy assumption, a realistic base case, and an optimistic peak-season-weighted case — so the forecast communicates its own uncertainty instead of implying false precision.
Use the short-term rental yield calculator to test occupancy and ADR assumptions against each other and see how sensitive the net yield is to each one before committing to a single forecast figure.
Which one applies to your property
A quick way to decide which mechanic applies:
- Fixed lease, tenant occupies for months or years — use the LTR mechanic (lease terms, rent-review cadence).
- Nightly or weekly bookings, variable occupancy — use the STR mechanic (occupancy × ADR by season, minus platform fees).
- Considering switching between the two — see can I manage short-term rentals for the operational and regulatory considerations before the forecast even starts.
Try the calculators