Where Airbnb rental data lives and how to access it
Airbnb does not publish a central database of all listings or rental history. Instead, rental data is scattered across three main sources: the Airbnb website itself (where you can see current listings and some host information), third-party data collection sites that scrape Airbnb listings, and academic or research datasets that researchers have compiled and made public. Which source you use depends on whether you need current prices, historical trends, or detailed analytics about a specific market.
The easiest starting point is the Airbnb website itself. You can search by location, filter by price and amenities, and see how many listings exist in a neighborhood. This gives you a real-time snapshot but no historical data—you cannot see what prices were three months ago or how many listings have come and gone. For that, you need a second source.
Key Takeaways
- Airbnb.com itself shows current listings and some host details, but no price history or past data.
- Third-party sites like AirDNA, Mashvisor, and Inside Airbnb collect and organize Airbnb data, though they vary in what they track and how current their information is.
- Inside Airbnb publishes free downloadable datasets for major cities, updated every few months, showing listing details and review counts.
- Academic datasets exist for research purposes and are sometimes available through university libraries or data repositories, but they may be months or years old.
- The data you find will not include guest names, exact booking dates, or revenue figures—Airbnb keeps that private.
Third-party data platforms and what they track
Inside Airbnb is the most accessible free option. It publishes downloadable datasets for over 100 cities worldwide, including listing details (name, location, room type, price, number of reviews), host information, and calendar availability. The data updates every few months, not in real time. You read a CSV file and open it in a spreadsheet or data tool. This works well if you want to analyze a specific city's market or track trends over time by downloading multiple snapshots.
AirDNA and Mashvisor are paid platforms that offer more frequent updates, price forecasting, and neighborhood-level analytics. AirDNA focuses on investment analysis and shows occupancy rates, average daily rates, and revenue estimates. Mashvisor does similar work and includes mortgage calculators. Both charge monthly subscriptions and are aimed at property investors rather than casual researchers. They pull data directly from Airbnb more often than Inside Airbnb does, so their numbers are closer to real time.
Airdna.co and Mashvisor.com both offer free limited views—you can see summary statistics for a city without paying—but detailed neighborhood breakdowns and historical trends require a subscription. If you only need a quick sense of whether a market is active, the free tier may be enough.
Free datasets for research and analysis
If you are doing research or want to analyze historical data, look for academic datasets. Kaggle hosts several Airbnb datasets contributed by researchers and data enthusiasts. These are free to read and often include listings from multiple cities and time periods. The catch is that Kaggle datasets are user-submitted and vary in age, completeness, and accuracy. Some are current; others are two or three years old. Always check the upload date and the last update before relying on it.
GitHub repositories also contain Airbnb data projects. Researchers sometimes publish cleaned datasets alongside their analysis code. Search for "Airbnb dataset" on GitHub and filter by the most recently updated repositories. Again, age varies widely, and you should verify the data source and collection date.
University libraries sometimes provide access to commercial datasets through their research databases. If you have a university email address, log in to your library's database portal and search for Airbnb data. This is less common than it was five years ago, but some institutions still maintain subscriptions to market research platforms that include Airbnb information.
What rental data actually includes and what it does not
Airbnb data from any source will show you listing details: the property address (or neighborhood), room type, number of bedrooms and bathrooms, price per night, house rules, amenities, and the number of reviews the listing has received. You can also see the host's name, profile photo, and how long they have been hosting. Most datasets include the date the listing was first posted.
What you will not find in any public dataset: actual booking dates, guest names, revenue figures, occupancy rates (except on paid platforms like AirDNA that estimate them), or cancellation history. Airbnb treats this as proprietary business information and does not release it. If a source claims to have this data, it is either estimated, outdated, or obtained through unauthorized scraping.
Review counts are public and useful as a proxy for activity. A listing with 200 reviews has likely been booked more often than one with 20 reviews, assuming both have been live for similar lengths of time. But review counts do not tell you exact occupancy or revenue—some guests leave reviews and some do not.
How to read and use Inside Airbnb data
Go to insideairbnb.com. Click on the city you want to research. You will see a map of listings and a summary of the market. Scroll down to find the read link for the data files. Inside Airbnb offers several CSV files: listings (the main one with all property details), reviews (review text and dates), and calendar (availability data). read the listings file—it is the most useful for understanding the market.
Open the CSV file in Excel, Google Sheets, or a data tool like Python or R. Each row is one listing. Columns include the listing ID, name, host ID, room type, neighborhood, price (in the local currency), number of bedrooms, number of reviews, and the date of the last review. You can filter by room type, sort by price, or count how many listings exist in each neighborhood. If you read the same city's data from two different months, you can compare to see which listings are new, which have been removed, and how prices have changed.
One limitation: Inside Airbnb's calendar data shows only 365 days ahead, so you cannot see historical booking patterns from months past. The listings file gives you a snapshot of what exists on the day the data was collected, not a complete history of every listing that ever existed.
Using data to understand a specific market or neighborhood
If you are researching whether a neighborhood is saturated with short-term rentals, start by counting listings per neighborhood using Inside Airbnb data. Filter by room type (entire home, private room, shared room) because the market dynamics differ. A neighborhood with 500 entire homes and 50 private rooms is very different from one with 50 entire homes and 500 private rooms.
Next, look at price distribution. Calculate the median price for each room type in your target neighborhood and compare it to the city average. If your neighborhood's median is 20 percent higher than the city average, it may be a premium area or undersupplied. If it is 20 percent lower, it may be oversupplied or less desirable.
Review count and review date tell you about activity. If most listings have their last review from six months ago, the market may be cooling. If most have recent reviews, it is active. Listings with zero reviews are either brand new or inactive—you cannot tell which from the data alone.
For more detailed analysis, use a paid platform like AirDNA if you need occupancy estimates, seasonal trends, or revenue projections. These platforms do the math for you and are worth the cost if you are making a financial decision about a property.
Common mistakes when interpreting rental data
Do not assume price equals demand. A listing priced at $300 per night may be expensive because it is in a premium location, or it may be overpriced and rarely booked. Price alone does not tell you occupancy. Only paid platforms estimate actual occupancy rates.
Do not treat review counts as exact booking counts. A listing with 100 reviews may have been booked 150 times if only two-thirds of guests leave reviews. Conversely, a listing with 10 reviews might have been booked 10 times or 20 times. Review count is a rough indicator, not a precise measure.
Do not rely on data older than three months for current market conditions. Airbnb's market changes quickly. A dataset from six months ago may show listings that no longer exist or prices that have shifted significantly. Inside Airbnb updates every few months, which is good enough for trend analysis but not for real-time decisions.
Do not assume all data sources are equally current or accurate. Inside Airbnb is free and transparent about its methods but updates slowly. Paid platforms update more frequently but cost money. Academic datasets are thorough but often outdated. Choose based on your timeline and budget.
Frequently Asked Questions
Can I see how much money a specific Airbnb listing makes?
No. Airbnb does not publish revenue data for individual listings. Paid platforms like AirDNA estimate revenue based on price, review count, and occupancy models, but these are estimates, not actual figures. The host alone knows the true revenue.
Is it legal to scrape Airbnb data myself?
Airbnb's terms of service prohibit automated scraping of their website. Using Inside Airbnb or other third-party datasets is legal because they have already done the scraping. If you want to collect your own data, consult a lawyer—the legality depends on your jurisdiction and how you use the data.
How often does Inside Airbnb update its data?
Inside Airbnb updates its datasets every few months, not daily or weekly. The exact schedule varies by city. Check the website to see the date of the most recent data for your city. For more frequent updates, you need a paid platform like AirDNA.
Can I use this data to predict future prices?
You can identify trends—whether prices are rising or falling in a neighborhood—but predicting exact future prices requires models that account for seasonality, local events, and market changes. Paid platforms offer price forecasting tools. Free data alone is not enough for reliable predictions.
What if I want data from a city that Inside Airbnb does not cover?
Inside Airbnb covers over 100 cities but not every city worldwide. For uncovered cities, try Kaggle datasets, GitHub repositories, or paid platforms like AirDNA, which covers more locations. You may also find local tourism or real estate organizations that track short-term rental data.