Airbnb Occupancy Rate by City and Zip Code: What the Data Can Tell You

Looking up an Airbnb occupancy rate by city feels like it should be a solved problem: type the city, read the number, decide. It is not, and the reason matters more than the number. City-level figures are built by inferring bookings from public calendars, they average neighbourhoods that behave nothing alike, and by the time one is published the supply that produced it has already changed. This guide explains what a city figure can and cannot tell you, why zip-code data is weaker still, and gives you a method that produces something more useful for the address you actually care about.

What is the Airbnb occupancy rate by city?

Short answer: there is no single authoritative figure for a city, only estimates that disagree. Every one is produced by sampling public calendars and modelling the rest, and every one blends areas within the city that perform completely differently. Use a city figure as background, never as a benchmark for one listing.

• What a city figure is: a modelled average across a sample of listings in an administrative boundary
• What it is not: a measurement, a benchmark for your address, or something two sources will agree on
• What to use instead: a manual sample of the listings a guest is actually choosing between when they choose you

Why two sources give two different answers for the same city

Two sources disagree about the same city because they use different denominators, different boundaries and different periods, and because neither can tell a booked night from a blocked one. Those differences compound rather than cancel out, so both figures can be internally correct and still useless placed side by side.

In plain English: when two sources disagree about a city, neither is necessarily lying. They are answering slightly different questions and presenting both answers as one word: occupancy.

Why occupancy varies inside a single city

Within one city, occupancy varies by distance to the reason people came, by transport, by parking, by bedroom count and by how dense local supply is. A city figure averages all of that into a single percentage that describes no actual listing — and the spread it hides is usually wider than the gap between two cities.

• Distance to the reason people came — ten minutes further out can halve midweek demand while leaving the weekend untouched
• Transport — near a station, parking is irrelevant; twenty minutes out, it decides whether you appear in results at all
• Bedroom count — one-bedrooms and four-bedrooms on the same street serve different travellers with different weekly patterns
• Local supply density — one block with forty listings and the next with four behave like separate markets
• Regulation by district — some cities restrict short-term letting in one zone and not another, which caps supply on one side of a road

The size of that internal spread is the part worth holding on to: occupancy usually varies more between two neighbourhoods of one city than it does between two cities. Which is also the answer to whether you should benchmark against your city at all. You should not — compare instead against ten listings a guest would genuinely consider alongside yours, same bedroom count, same standard, walking distance.

Averaging all of that into one percentage produces a number that describes no actual listing in the city, including yours.

Where to find Airbnb occupancy statistics by city

Short answer: nowhere authoritative, and that is the finding rather than a gap in this guide. Published Airbnb occupancy statistics come from third parties modelling public calendars, so two sources disagree about the same city and neither can be reproduced. What you can produce yourself, in an afternoon, is a comparable fill rate for the ten listings you actually compete with.

• Platform data: exact, but only about your own listing
• Third-party statistics: broad coverage, modelled, unreproducible at address level
• Public registers: in regulated cities, an exact count of legal supply — a real statistic, and more useful than occupancy for judging a market
• Your own sample: rough, current, and about your street

Can I get an Airbnb occupancy rate by zip code?

Yes, tools will show you an occupancy rate by zip code, and it deserves more caution than the city figure rather than less. The method is identical; the sample is far smaller, so one blocked calendar or a single operator running twelve units can move the average on their own. Precision is not accuracy.

• Fewer listings per zip means each blocked calendar has a larger effect on the estimate
• A single professional operator running twelve units can move a small zip's average on their own
• Zip boundaries are postal, not economic — they were drawn for mail delivery, and they cut through markets rather than around them
• The narrower the geography, the more the confidence interval matters — and it is almost never published

The practical version: a zip-level figure is useful for spotting a large difference between two areas, and unreliable for anything finer than that. If you are deciding between two streets, count calendars rather than reading an estimate.

Meet Elena: the city number that was right and useless

Elena was assessing a two-bedroom in a mid-sized city. All figures below are illustrative.

• City-level estimate she started with: 64 percent, which looked healthy
• Her own manual sample, ten comparable two-bedrooms within a ten-minute walk: 48 percent for the same window
• The same sample filtered to buildings with parking: 71 percent
• What she learned: the city figure was an average of two groups, and which group her property joined depended on one amenity
• Cost of the parking space: a fraction of what the occupancy gap was worth over a year

Before: a single citywide number and a decision that looked like a coin flip. After: a sample that revealed the variable actually splitting the market. Why it wins: the useful question was never "what is this city's occupancy" but "what separates the full calendars here from the empty ones".

How to get a real occupancy figure for your city or neighbourhood

To get a real occupancy figure for your neighbourhood, count unavailable nights across ten comparable listings for a window 45 to 75 days out, then divide by the total nights you counted. Forty minutes, no subscription, and the result describes the listings a guest is genuinely choosing between when they choose you.

• Choose the window: 45 to 75 days out, so impulse bookings have not landed but planners have
• Build the comp set: ten listings, same bedroom count, same standard, walkable distance — not the whole city
• Count unavailable nights for the next 30 days in each calendar, and total them
• Divide: unavailable nights over total nights counted, which gives you a comparable fill rate
• Split the sample by one variable at a time — parking, bedrooms, distance — to find what separates the full from the empty
• Repeat monthly: one reading is a snapshot, three are a trend

Remember what you are counting: unavailable, not booked. That is the same limitation every paid tool has, applied honestly. The full method, and where each source stops being reliable, is in how to find Airbnb occupancy rates.

What the number means once you have it

A comparable fill rate is only useful next to a rate. The market that fills fastest is frequently the one that has competed its nightly price down, which is set out with the arithmetic in what counts as a good Airbnb occupancy rate. The structural signals that separate a durable high-occupancy area from a temporarily busy one are in which markets have the highest Airbnb occupancy rates.

If your own listing is below its comp set, the gap is almost never explained by the city. Work through how to increase your Airbnb occupancy rate, which ranks nine levers by how fast each one acts, and check the settings covered in Airbnb listing tips before touching your price.

Which cities publish real short-term rental data

There is one category of genuinely reliable city-level information, and it is not occupancy: the registration data that regulated cities publish themselves. Where short-term letting requires a licence, the register is a public list of legal supply.

• What it gives you: how many legal listings exist, and often whether the number is capped
• Why that beats an occupancy estimate: it is a count rather than a model, and supply is the variable that decides next year
• Where to look: the city or municipal government site, usually under short-term rental, tourist accommodation or lodging licensing
• What it will not tell you: anything about how full those listings are

Pairing a public register with your own manual sample is the strongest free method available: one gives you legal supply exactly, the other gives you fill rate approximately, and together they answer the question a single occupancy percentage pretends to.

Reading a city figure without being misled by it

City estimates are not useless. They are useful for exactly one thing: comparing places at a coarse level, when both figures come from the same source and the same period.

• Same source, same period, two cities: a fair comparison, because the method errors apply equally to both
• Two sources, one city: not a comparison at all
• A city figure and your own dashboard: never comparable, because one is modelled and one is exact
• A city figure as a target: the mistake that produces unnecessary discounts

Two practical notes on buying data. Every published figure describes a period that has already closed, and supply moves faster than demand, so check listing counts alongside occupancy or you are reading history. And a subscription earns its price when you are choosing between markets or running enough units that a one-point error costs real money; for one to five listings your own dashboard plus a manual comp sample answers the same question for nothing.

If you only remember one line from this article, make it that one: use city data to rank places against each other, never to judge a listing. The judgement set is the ten listings a guest sees beside yours, and building it takes an afternoon. What to do about the gap once you find it is covered in filling gap nights automatically and in the analytics tools guide, which explains what the platform reports to you for free.

Best cities to own an Airbnb, and best cities for Airbnb investment

Short answer: the best cities to own an Airbnb are not the ones with the highest occupancy, and the best cities for Airbnb investment are not always the same as either. Occupancy answers how full a calendar gets; ownership is judged on what the property demands of you for years, and investment on price, yield and regulatory durability.

• Highest occupancy — frequently the markets where rates were competed down until a full calendar became necessary
• Best to own — steady year-round demand, stable regulation, available cleaners, and close enough to reach
• Best to invest — a favourable price-to-rate ratio today, which is a moment rather than a property of the city

The criteria and the arithmetic that separate them are in best places to buy an Airbnb. Using an occupancy ranking as an investment shortlist is the most common way people buy into a market on the way down.

Myths about city-level occupancy data

Myth: a city occupancy figure is a benchmark for my listing.

Reality: it is an average across neighbourhoods, property types and operators that behave nothing alike. Your benchmark is ten comparable listings you could walk to.

Myth: zip-code data is more accurate because it is more specific.

Reality: narrower geography means a smaller sample, so each blocked calendar and each professional operator distorts it more. Precision and accuracy are not the same thing.

Myth: if two sources disagree, one of them is wrong.

Reality: they are usually measuring different denominators over different boundaries and periods. Both can be internally correct and still useless side by side.

If you are comparing cities because you are choosing where to buy rather than where you already are, occupancy is the input and not the decision. Best places to buy an Airbnb sets out the arithmetic that turns a market read into a yes or a no on a specific property.

Mistakes hosts make with city occupancy data

• Benchmarking against the city. The most common single error, and it produces unnecessary discounts more than anything else on this list.
• Comparing a city estimate to their own dashboard figure. One is modelled from outside, one is exact. The gap between them is partly just measurement.
• Reading a single month as the rate. Short-term rental demand is seasonal and lumpy; one reading catches whatever was true that afternoon.
• Ignoring supply growth. Today's city occupancy with listing counts rising fast is a number with an expiry date on it.
• Buying on a zip-level estimate. The sample behind a small zip is often a handful of listings, one of which may be a twelve-unit operator.

What to do with the difference between you and your comp set

Once you have a comparable fill rate, the gap between it and your own figure is the only number worth acting on. Three of the levers that close it have to run continuously, which is what makes them hard to sustain by hand.

BnBGenius handles those three — replies at any hour, gap-night and extension offers through the Upsell Engine, and reviews posted inside the window after checkout — for $10 per month flat across any number of listings, with the first 500 messages free. It does not sell market data and it does not set your rate; both of those stay where they belong, with the tools and the host. If you are weighing the whole operation against paying a person a share of revenue, see AI property management for 1-5 Airbnb listings, and if you are not listed yet, how Airbnb works for owners covers what the platform does and what it leaves to you. Rules that vary by location live in the Airbnb Help Center.