Why It Matters
You're looking at an STR property and someone throws out a number — "$48,000 a year." Before you commit to that figure, you need to build the projection yourself. That means pulling comparable listings from AirDNA or a similar data tool, confirming what nearby properties actually earn at different occupancy levels, applying seasonal multipliers for peak and shoulder seasons, and deducting platform fees, cleaning costs, and vacancy. The number that survives all that math is what you can realistically underwrite. A projection built on real market data from STR market analysis is the difference between a deal that cash-flows and one that quietly bleeds cash for twelve months before you figure out what went wrong.
At a Glance
- What it is: A financial model projecting gross and net STR income using nightly rates, occupancy rates, seasonality, and platform fees
- Primary inputs: Average daily rate (ADR), projected occupancy percentage, seasonal demand curve, and platform commission
- Key data sources: AirDNA, Rabbu, Mashvisor, and comparable active listings on Airbnb and Vrbo
- Dynamic pricing tools: PriceLabs and similar revenue management software that adjusts rates based on demand signals
- Output: Gross annual revenue, net operating revenue after fees and cleaning costs, and monthly cash flow model
How It Works
Building the revenue baseline. The foundation of any STR revenue projection is the average daily rate (ADR) and projected occupancy for your specific market and property type. You don't estimate these — you pull them from data. AirDNA provides market-level ADR and occupancy data broken down by bedroom count, property type, and neighborhood. Cross-reference that against active comparable listings on Airbnb and Vrbo to see what similar properties charge and how often their calendars are booked. A realistic ADR for a 3-bedroom cabin in a ski market might be $287/night; for a studio condo in a mid-tier beach market, $164/night. Those aren't guesses — they're medians pulled from actual transaction data.
Applying the seasonal demand curve. STR revenue isn't linear. Most markets have 2-4 high-demand windows and multiple shoulder and off-peak stretches where occupancy drops and rates compress. A mountain vacation property might fill at 95% occupancy and $340/night during winter ski season, then drop to 58% occupancy and $195/night in mud season. A beach property flips that curve entirely. To build an accurate annual projection, break the year into monthly or quarterly buckets, assign an occupancy percentage and nightly rate to each, and multiply through. This seasonal modeling is where many investors underestimate — they take a peak-season figure and assume it holds year-round, then wonder why the property underperforms against their spreadsheet. STR market analysis reveals which months are soft before you buy.
Accounting for platform fees and cleaning revenue. Airbnb and Vrbo both take a host service fee — typically 3% on Airbnb's split-fee model, though it can reach 5% in some configurations. Vrbo charges either 8% per booking or a flat annual subscription. These come off your gross revenue before you see a dollar. Cleaning fees are worth modeling carefully: they appear as separate line items on guest receipts, which means they don't reduce your perceived nightly rate, but they do add revenue that partially offsets actual cleaning costs. If your cleaner charges $120 per turnover and you charge guests $130, you're netting $10 per stay — worth tracking in the projection separately. PriceLabs and similar dynamic pricing tools factor platform fees into their rate recommendations so you're always looking at net revenue, not gross.
Projecting net operating revenue. Once you have gross revenue — ADR times occupied nights across all 12 months — you layer in deductions to reach net operating revenue. Typical deductions beyond platform fees: property management (20-30% of gross if using a co-host or manager), channel management software subscriptions ($30-60/month), consumables and restocking (toiletries, paper goods, coffee: $20-40 per turnover), minor repairs and maintenance reserve (1-2% of property value annually), and utility costs above long-term rental baseline. What remains after these deductions but before debt service is your net operating revenue — the figure you use to calculate cap rate and coverage ratios. Guest satisfaction and seamless operations, maintained through strong guest communication and automated messaging systems, support the occupancy rates your projection assumes.
Real-World Example
DeShawn is evaluating a 2-bedroom condo in a Gulf Coast beach market. The seller claims it earned $61,000 last year. Before making an offer, DeShawn builds his own projection using AirDNA data for the submarket.
AirDNA shows comparable 2-bedroom units averaged $187/night ADR with 71% annual occupancy — implying 259 occupied nights and $48,433 in gross revenue, not $61,000. That $12,567 gap is suspicious. DeShawn maps the seasonal curve: December through February runs 52% occupancy at $141/night; March through May jumps to 84% at $203/night; June through August peaks at 91% at $229/night; September through November drops to 58% at $163/night.
Running each quarter: Q1 = 52% × 90 nights × $141 = $6,595. Q2 = 84% × 91 nights × $203 = $15,511. Q3 = 91% × 92 nights × $229 = $19,188. Q4 = 58% × 92 nights × $163 = $8,707. Gross total: $49,901.
He then deducts: Airbnb 3% fee = $1,497. Cleaning costs net of guest fees = $1,840. Property management at 25% = $12,475. Software and consumables = $960. Maintenance reserve at 1.5% of $385,000 value = $5,775. Net operating revenue: $27,354. At a $385,000 purchase price, that's a 7.1% cap rate — lower than the seller's claim implied, but acceptable for the market. DeShawn makes a lower offer based on his own numbers, not the seller's.
Pros & Cons
- Forces a property-by-property income analysis before closing, preventing overreliance on optimistic seller claims or peak-season anecdotes
- Seasonal modeling reveals when cash flow turns thin — giving you a reserve strategy before you own the property, not after
- Net revenue calculation exposes the full cost stack (platform fees, management, consumables, maintenance) that simple ADR-times-occupancy estimates ignore
- Comparable data from AirDNA and active listings creates an independently verifiable baseline rather than relying on owner-supplied financials
- Dynamic pricing assumptions built from tools like PriceLabs reflect actual demand elasticity in the market, producing more reliable rate inputs than static averages
- Historical AirDNA data reflects past market conditions — a market with new hotel supply, policy changes, or shifting tourism patterns may perform materially differently going forward
- Seasonal curves require submarket-level granularity; city-wide averages can mask wide variation between neighborhoods or property types in the same city
- Operator quality is not captured in any data model — a well-managed listing earns 15-25% more than a mediocre one at comparable rates, which means projections represent averages, not ceilings
- Cleaning costs, restocking, and minor repairs are variable and difficult to forecast precisely for a property you haven't operated
- Revenue projections cannot account for platform algorithm changes, new local STR regulations, or shifts in guest communication expectations that affect review scores and search ranking
Watch Out
Never use a seller's trailing-twelve-months revenue as your projection. Sellers frequently provide their best year, their best season, or cherry-picked months. Some use gross revenue before platform fees, which inflates the headline number by 3-8%. Always pull your own data from AirDNA or comparable active listings, build your own seasonal model, and treat seller financials as a verification input, not a starting point.
Occupancy rates in data tools reflect the market average — not what a new listing earns on day one. New properties take 60-120 days to accumulate reviews and earn algorithmic ranking. Build in a ramp-up period in your first-year projection: assume 40-50% lower occupancy for months one through three, scaling up to market-rate occupancy by month four or five. Ignoring the ramp-up period is one of the most common reasons first-year STR cash flow disappoints.
Dynamic pricing tools like PriceLabs optimize within the demand environment — they don't create demand. If your market has 300 comparable listings competing for 10,000 annual guest nights, no pricing algorithm closes that gap. Verify market supply-to-demand balance in your STR market analysis before assuming rate optimization will close a revenue shortfall.
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The Takeaway
An STR revenue projection is the research work that separates a real deal from a hope. You're combining nightly rate data from AirDNA, a seasonal demand curve built from comparable listings, platform fee deductions, and a full operating cost stack to arrive at a net revenue figure you can actually underwrite. The projection only holds if your occupancy assumptions are grounded in market data, your seasonal model reflects how the specific submarket actually behaves, and your cost estimates include cleaning, management, software, consumables, and maintenance — not just the mortgage. Strong operational systems — including automated messaging and consistent guest communication — are what make the occupancy rates in your model achievable once you own the property. Get the projection wrong and you're not discovering the error until twelve months of operations tell you.