STR Financial Modeling

The core math of STR income is simple: ADR (average daily rate) multiplied by occupancy percentage, multiplied by 365 days. Yet subtle errors in baseline assumptions—overestimating occupancy, ignoring seasonality, underestimating cleaning costs—transform a supposedly profitable property into a cash-draining liability. This article walks through the mechanics of defensible STR financial models.

Key takeaways

• The fundamental equation is ADR × occupancy % × 365 = annual gross revenue. Errors in any of these three inputs propagate directly to your net income forecast.
• Occupancy rates vary wildly by market, seasonality, and property type: urban downtown properties sustain 75%+ year-round; seasonal destinations (ski resorts, beach towns) swing from 85% in-season to 20% off-season.
• Conservative modeling uses a trailing 12-month average occupancy from comps rather than "best-case" rates. A property that peaks at 80% occupancy three months per year but slumps to 40% the other nine months averages 55%—not 80%.
• Seasonality compounds risk: a Miami beach condo's January occupancy at $280 ADR differs dramatically from its September rate at $110 ADR. Annual models must account for this granularity or deliver misleading projections.
• Expense-to-revenue ratios vary by market and model: cleaning and turnover alone consume 20–40% of gross revenue in urban STRs; management, maintenance, utilities, and insurance push total opex to 50–65% of gross revenue.

The fundamental equation with real numbers

The simplest STR projection uses a single annual ADR and occupancy figure:

Simple model (single market, steady year-round):

ADR:                $150
Occupancy:          65%
Days per year:      365
Gross revenue:      $150 × 0.65 × 365 = $35,587

This model works for markets with flat seasonality (Denver, Austin, Nashville—business travel and leisure distributed evenly). It fails for seasonal markets (Miami, Aspen, coastal areas) where January and July operate under entirely different economics.

Refined model (seasonal adjustment):

Divide the year into 2–4 seasons, each with distinct ADR and occupancy:

High season (Jan–Mar):  ADR $280, occupancy 80%, 90 days = $20,160
Shoulder (Apr–Jun):     ADR $180, occupancy 70%, 92 days = $11,571
Low season (Jul–Sep):   ADR $110, occupancy 45%, 92 days = $4,554
Recovery (Oct–Dec):     ADR $160, occupancy 75%, 91 days = $10,920
Total annual gross:     $47,205

This Miami beachfront example shows how naive annual averaging ($160 avg ADR × 0.65 occupancy × 365 = $37,960) underestimates actual revenue by $9,000. The high-season dominance more than compensates for the summer slump. If you had modeled it with a flat 65% occupancy at $160, you'd underestimate profitability and possibly reject a good deal.

Occupancy: from comparable properties to your property

ADR is observable—you can see nightly rates on live Airbnb listings. Occupancy is harder. It requires either historical data (if you're buying an existing STR) or comps (similar properties in the same market).

Data sources for occupancy benchmarks:

• AirDNA.co: Provides historical occupancy, ADR, and revenue data for anonymized properties by neighborhood. Subscriptions cost $99–499/month depending on access level. Free trial available; consider testing before buying.
• Mashvisor: Offers similar analytics; occupancy heatmaps by zip code. Free version shows market summaries; detailed comps require paid membership.
• Rabbu: Focuses on shorter data windows but includes Airbnb, VRBO, and Booking.com in a single dashboard.
• Manual comps: List 10–20 comparable properties in your target neighborhood on Airbnb. Record their availability (blocked vs. open nights) for a month. Math out occupancy rates from the snapshot. Free but labor-intensive.

A critical distinction: booking data (what platforms report to property owners) often differs from occupancy (nights actually occupied). Airbnb might report "25 bookings per year" but those bookings are 3–5 nights each; occupancy is derived as (total booked nights) / 365.

Operating expense ratios

Gross revenue minus opex equals net profit. The challenge: expenses are highly variable and market-dependent.

Typical expense buckets (as % of gross revenue):

Expense	% of Revenue	Notes
Cleaning & turnover	20–40%	Higher for frequent turnovers (short stays); lower for long-term guests
Utilities	2–6%	Climate, season, guest count matter; furnished properties consume more than vacant ones
Platform fees (Airbnb, VRBO)	14–20%	Bundled fees; varies by platform mix
Co-host / property manager	15–25%	If you delegate operations; self-managed = $0 but personal labor cost is implicit
Maintenance & repairs	3–8%	Older properties, high guest volume, and high-traffic areas (bathrooms, kitchen) increase this
Property tax, insurance	5–15%	Varies by location and liability coverage; STR insurance is costlier than LTR insurance
Supplies & restocking	1–3%	Shampoo, toilet paper, dish soap, linens, detergent
Linens & towel service	2–5%	If outsourced; self-washing reduces this
HOA / community fees	0–10%	If applicable; many condos restrict STRs and charge premium fees
Total opex	50–75%	Conservative operators assume 65% to leave margin for error

Realistic three-scenario model:

Assume a $150 ADR property with 65% occupancy in an urban market:

Gross revenue:       $150 × 0.65 × 365 = $35,587

Conservative scenario (65% opex):
Opex:                $35,587 × 0.65 = $23,131
Net:                 $12,456

Moderate scenario (60% opex):
Opex:                $35,587 × 0.60 = $21,352
Net:                 $14,235

Optimistic scenario (55% opex):
Opex:                $35,587 × 0.55 = $19,573
Net:                 $16,014

Your actual outcome depends on execution (hiring reliable cleaners, choosing reputable co-hosts, handling maintenance proactively). New operators typically land in the "conservative" scenario; experienced operators with systems in place hit "moderate." The "optimistic" scenario is rare without significant leverage (self-managing, large portfolio spreading fixed costs).

Sensitivity analysis: testing assumptions

Small changes to ADR or occupancy cascade into large profit swings. Model your assumptions' sensitivity:

Base case: $150 ADR, 65% occupancy, 60% opex ratio = $14,235 net.

If ADR drops to $140 (competition increases, market softens):

Gross revenue:       $140 × 0.65 × 365 = $33,189
Net (60% opex):      $33,189 × 0.40 = $13,276 (−$959, or −7%)

If occupancy drops to 55% (new market, seasonal downturn):

Gross revenue:       $150 × 0.55 × 365 = $30,037
Net (60% opex):      $30,037 × 0.40 = $12,015 (−$2,220, or −16%)

If opex rises to 70% (unexpected maintenance, higher cleaning costs):

Gross revenue:       $150 × 0.65 × 365 = $35,587
Net:                 $35,587 × 0.30 = $10,676 (−$3,559, or −25%)

Occupancy is the most leveraged variable. A 10-percentage-point occupancy drop (65% to 55%) reduces net income by 16%. If you're buying a property betting on 70% occupancy to meet debt service, a realistic market correction to 55–60% could put you underwater. Conservative operators assume occupancy 5–10 points below comps to buffer against overoptimism.

Purchase decision threshold

Use net income projections to validate whether a property justifies its purchase price.

Example: A $400,000 condo purchase, financed at 6.5% 30-year mortgage = $2,530/month debt service, or $30,360 annual.

Projected net STR income: $14,235 (from moderate scenario above).

Net STR income:      $14,235
Debt service:        −$30,360
Cash flow gap:       −$16,125 (negative cash flow)

This property does not support itself through STR revenue alone. You're betting on:

1. Property appreciation (long-term real estate inflation, 2–3% annually)
2. Loan paydown (each mortgage payment builds equity)
3. Tax deductions (depreciation, interest, expenses reduce taxable income)

These are valid long-term strategies, but they're speculative. If you can't cover the mortgage from STR income within 2–3 years or expect appreciation to accelerate, the property is overleveraged for STR use. Investors in hot markets (2021–2022 in Austin, Denver, Nashville) often bought into negative-cashflow STRs expecting appreciation; many faced losses when rates rose in 2023–2024 and prices stalled.

Common modeling errors

Error 1: Using peak-season ADR for annual projections. You list in January at $280/night and assume that's your annual rate. June rate is $110. Annual average is $180, not $280.

Error 2: Ignoring turnover costs. "Revenue is ADR × occupancy." You forget that 65% occupancy at a 4-night average stay means 59 turnovers per year, each costing $120 in cleaning. That's $7,080 in opex before utilities, supplies, or management.

Error 3: Underestimating maintenance. "It's a new property; maintenance will be minimal." New properties still have small failures—a water heater needs replacing, guest damages a light fixture, the HVAC develops a rattle. Budget 3–8% of gross revenue, not 1%.

Error 4: Confusing occupancy terminology. Some platforms report "booking frequency" (number of bookings), others report "occupancy" (nights booked / nights available). A property with 25 bookings at 4 nights each = 100 nights booked = 27% occupancy, not 25%.

Error 5: Betting on growth. "Year 1 will be conservative at 60% occupancy, but by year 3 it'll hit 75%." This narrative sounds good but rarely materializes. Market capacity, seasonality, and competitive saturation are structural, not temporary. Model conservatively and be pleasantly surprised, not the reverse.

Financial modeling flowchart

Related concepts

• ./01-str-vs-ltr-the-honest-comparison.md
• ./04-adr-and-occupancy-by-market.md
• ./06-str-operating-expenses.md

Next

Once you've modeled your property's financial potential, you need to validate those assumptions against real-world ADR and occupancy data for your specific market. Different cities exhibit vastly different dynamics—Miami beach commands $280 nightly while suburban Denver averages $120. Tools like AirDNA and Mashvisor provide market benchmarks, but you also need to know how to interpret them and where they fall short. The next article covers market analysis in detail.