What is a Good Occupancy Rate for Airbnb? Benchmarks by Market
Average Airbnb occupancy rates by market type, seasonal patterns, and how to estimate occupancy for underwriting a short-term rental investment.
Occupancy rate is the single most important variable in short-term rental underwriting. A 10-percentage-point swing in occupancy can flip a deal from profitable to cash-flow negative. Yet most new STR investors either use wildly optimistic assumptions or have no idea what realistic occupancy looks like in their target market.
This post covers actual occupancy benchmarks by market type, how seasonality affects your numbers, and how to build a defensible occupancy estimate for deal analysis.
How Airbnb Occupancy Rate is Calculated
The formula is straightforward:
Occupancy Rate = (Booked Nights / Available Nights) x 100
A property available 365 days per year that books 255 nights has a 69.9% occupancy rate.
Two important nuances:
- Available nights matter. If you block 60 days for personal use, your denominator is 305, not 365. Some data providers report based on available nights; others use calendar nights. Know which you are looking at.
- Booked does not mean paid. Cancellations, especially with flexible policies, can create a gap between booked and realized occupancy. Assume 2-5% cancellation drag on top of your occupancy estimate.
Average Occupancy Rates by Market Type
These ranges are based on aggregated data from AirDNA, AllTheRooms, and Mashvisor across U.S. markets. They represent annual averages for entire-home listings.
| Market Type | Low End | Median | Top Quartile | Examples |
|---|---|---|---|---|
| Major urban (gateway cities) | 55% | 68% | 78% | NYC, LA, Chicago, Miami |
| Beach/resort (year-round) | 50% | 65% | 76% | Maui, Key West, San Diego |
| Beach/resort (seasonal) | 35% | 52% | 65% | Outer Banks, Cape Cod, Gulf Shores |
| Mountain/ski resort | 30% | 48% | 62% | Big Bear, Gatlinburg, Park City |
| Suburban (near major city) | 40% | 55% | 67% | Suburbs of Austin, Nashville, Denver |
| College town | 35% | 50% | 63% | Ann Arbor, Gainesville, Clemson |
| Small city / secondary market | 35% | 50% | 62% | Chattanooga, Boise, Asheville |
| Rural / remote cabin | 25% | 42% | 55% | Poconos, Smoky Mountain cabins |
The median across all U.S. short-term rental markets typically falls between 50-60%. If you are underwriting a deal at 75%+ occupancy, you are assuming top-quartile performance, which should require strong evidence.
Seasonality Patterns
Annual averages obscure the reality that most STR markets have dramatic seasonal swings. Understanding your market’s seasonality is critical for cash flow planning.
Beach/Resort Markets
| Quarter | Typical Occupancy | Revenue Share |
|---|---|---|
| Q1 (Jan-Mar) | 35-50% | 15-20% |
| Q2 (Apr-Jun) | 60-80% | 25-30% |
| Q3 (Jul-Sep) | 75-95% | 35-40% |
| Q4 (Oct-Dec) | 30-50% | 15-20% |
Beach markets generate 60-70% of annual revenue in a 4-5 month window. You need to survive the off-season on reserves or by pivoting to medium-term rentals during slow months.
Urban Markets
| Quarter | Typical Occupancy | Revenue Share |
|---|---|---|
| Q1 (Jan-Mar) | 50-60% | 20-22% |
| Q2 (Apr-Jun) | 65-75% | 26-28% |
| Q3 (Jul-Sep) | 70-80% | 28-30% |
| Q4 (Oct-Dec) | 55-70% | 22-26% |
Urban markets are the most stable, with less pronounced seasonality. Business travel, events, and steady tourism provide a more consistent baseline. The dip in Q1 is real but manageable.
Mountain/Cabin Markets
| Quarter | Typical Occupancy | Revenue Share |
|---|---|---|
| Q1 (Jan-Mar) | 55-75% | 28-32% |
| Q2 (Apr-Jun) | 30-45% | 15-20% |
| Q3 (Jul-Sep) | 50-70% | 25-30% |
| Q4 (Oct-Dec) | 45-65% | 22-28% |
Mountain markets often have two peaks: winter (ski season) and summer (hiking/escape-the-heat season). Spring and fall are the shoulder seasons where occupancy craters.
How to Estimate Occupancy for Deal Underwriting
Do not guess. Use data. Here is a systematic approach:
Step 1: Pull Comparable Data
Use AirDNA, Mashvisor, or AllTheRooms to pull occupancy data for comparable listings within 2-3 miles of your target property. Filter for:
- Same property type (entire home, not shared rooms)
- Similar bedroom count (within +/- 1 bedroom)
- Similar amenities (pool, hot tub, etc.)
- Active listings with at least 12 months of booking history
Pull data for 10-20 comparable listings.
Step 2: Analyze the Distribution
Look at the median, not the average. A few superhost listings with 90%+ occupancy will skew the average upward. The median gives you a more realistic baseline for a new listing.
Also note the spread. If the 25th percentile is at 40% and the 75th percentile is at 70%, that is a wide range, which means execution quality (listing photos, pricing strategy, reviews) has a huge impact.
Step 3: Apply a Ramp-Up Discount
New listings without reviews typically perform 15-25% below market averages for the first 3-6 months. Factor this into your Year 1 projections:
- Months 1-3: Market median minus 20%
- Months 4-6: Market median minus 10%
- Months 7-12: Market median
- Year 2+: Market median to 75th percentile (with good reviews)
Step 4: Stress Test
Run your deal at three occupancy levels:
- Conservative: 25th percentile of comps (downside case)
- Base case: Median of comps
- Optimistic: 75th percentile of comps
If the deal does not work at the conservative level, you need to understand how much risk you are taking. If it does not work at the base case, walk away.
Use the Airbnb calculator to model all three scenarios quickly.
What Drives Higher Occupancy
Not all listings are created equal. These factors consistently correlate with above-median occupancy:
Listing Quality
- Professional photography (20-30% more bookings per multiple studies)
- Detailed, accurate descriptions
- Fast response time (under 1 hour)
- Superhost status (earned after maintaining 4.8+ rating and 90%+ response rate)
Amenities That Move the Needle
The amenities with the highest correlation to occupancy and nightly rate premiums:
| Amenity | Occupancy Impact | Rate Premium |
|---|---|---|
| Hot tub | +8-15% | +15-25% |
| Pool | +5-12% | +10-20% |
| EV charger | +3-5% | +5-8% |
| Game room | +3-8% | +5-15% |
| Pet-friendly | +5-10% | +5-10% |
| Dedicated workspace | +3-7% | +3-5% |
These premiums vary by market. A hot tub in a mountain cabin market adds far more than a hot tub in downtown Miami.
Dynamic Pricing
Hosts using dynamic pricing tools (PriceLabs, Beyond, Wheelhouse) consistently outperform those with static pricing. These tools adjust nightly rates based on demand, seasonality, local events, and competitor pricing. The occupancy lift from proper pricing is typically 5-15 percentage points.
Minimum Stay Strategy
- 1-night minimums maximize occupancy but increase turnover costs and cleaning wear
- 2-night minimums are the sweet spot for most markets — reduces gaps while keeping occupancy high
- 3-7 night minimums reduce management burden but can drop occupancy 10-20%
Many operators use variable minimums: 2-night minimum on weekdays, 3-night on weekends, 4-7 night during peak season.
Occupancy vs. Revenue: The Rate-Occupancy Trade-Off
High occupancy is not always the goal. Consider two scenarios for the same property:
| Metric | Strategy A | Strategy B |
|---|---|---|
| Nightly rate | $150 | $200 |
| Occupancy | 80% | 62% |
| Booked nights | 292 | 226 |
| Gross revenue | $43,800 | $45,200 |
| Cleaning turns (~) | 95 | 70 |
| Cleaning cost ($65/turn) | $6,175 | $4,550 |
| Net after cleaning | $37,625 | $40,650 |
Strategy B generates more net revenue with 25 fewer cleaning turns and lower wear on the property. This is why revenue-per-available-night (RevPAN) is a better metric than occupancy alone.
RevPAN = Nightly Rate x Occupancy Rate
Strategy A RevPAN: $150 x 0.80 = $120 Strategy B RevPAN: $200 x 0.62 = $124
Optimize for RevPAN, not occupancy.
Worked Example: Underwriting a Cabin in Gatlinburg, TN
You are evaluating a 2-bed cabin listed at $275,000. AirDNA shows the following for comparable 2-bed cabins in the area:
- 25th percentile occupancy: 42%
- Median occupancy: 56%
- 75th percentile occupancy: 68%
- Average nightly rate: $165
Scenario analysis (annual gross revenue only):
| Scenario | Occupancy | Booked Nights | Gross Revenue |
|---|---|---|---|
| Conservative | 42% | 153 | $25,245 |
| Base case | 56% | 204 | $33,660 |
| Optimistic | 68% | 248 | $40,920 |
With estimated annual operating expenses of $22,000 (before mortgage) and annual mortgage payments of $15,600 (assuming 25% down at 7.25%):
| Scenario | NOI | Cash Flow | CoC Return |
|---|---|---|---|
| Conservative | $3,245 | -$12,355 | -18.0% |
| Base case | $11,660 | -$3,940 | -5.7% |
| Optimistic | $18,920 | +$3,320 | 4.8% |
This deal only works in the optimistic scenario. At median occupancy, you are losing nearly $4,000 per year. Unless you have strong conviction that you can operate in the top quartile, this deal does not pencil at $275,000. You would need to negotiate the price down or confirm that nightly rates can be pushed higher.
Compare both short-term and long-term strategies for any property using the Airbnb vs long-term rental analysis.
Frequently Asked Questions
What is a good occupancy rate for Airbnb?
A good occupancy rate depends on your market, but generally 60-70% annual occupancy is solid performance for a well-managed entire-home listing. Above 75% is excellent and typically indicates either strong market demand or slightly underpriced nightly rates. Below 50% sustained occupancy usually signals a problem with listing quality, pricing, or market fit.
How do I find occupancy rate data for my market?
AirDNA is the most comprehensive paid source (starts at $20/month for a single market). Free alternatives include checking Airbnb search results for date availability (tedious but free), Mashvisor’s limited free data, and asking local STR property managers for their portfolio averages. Local STR Facebook groups are also surprisingly good sources of real data from operators.
Does Airbnb occupancy rate include blocked dates?
It depends on who is reporting. AirDNA and most analytics platforms calculate occupancy based on available nights (excluding owner-blocked dates). Airbnb’s own host dashboard shows occupancy based on available nights. When comparing data across sources, confirm whether blocked dates are included in the denominator.
How long does it take for a new Airbnb listing to reach full occupancy?
Most new listings take 3-6 months to reach their market’s median occupancy rate. The first 1-2 months are typically the slowest as you have no reviews. Airbnb gives new listings a temporary visibility boost, but converting views to bookings without social proof is harder. Budget for 60-80% of normal revenue in the first 6 months.
Should I lower my price to increase occupancy?
Only if your RevPAN (nightly rate x occupancy rate) increases. Dropping your rate by 20% to gain 10% more occupancy is a losing trade. Use dynamic pricing tools to optimize the rate-occupancy balance automatically. The goal is to maximize net revenue after cleaning and turnover costs, not to maximize booked nights.