The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.

In 2007, a cash-strapped Brian Chesky came up with a shrewd way to pay his $1,200 San Francisco apartment rent. He would offer “Air bed and breakfast”, which consisted of three airbeds,...

This is the complete breakdown of how much revenue Airbnb makes in commission from listings in each region.

this graph was created in PowerBi, Loocker Studio and R :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fed85af6a3acbfbccab05d78c3bead72b%2Ffoto1.jpg?generation=1734296099094689&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F150a5a47aa99cde0d2bd7b923cbff931%2Ffoto2.jpg?generation=1734296106670510&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F3f839c307652bf17d5ad09563a73225e%2Ffoto3.gif?generation=1734296114482556&alt=media" alt="">

This dataset provides a snapshot of Airbnb listings across major Italian cities offering valuable insights into the short-term rental market in Italy Whether you're interested in pricing trends impact of seasonality superhost classification this dataset has something for you Data refer to a period between September 2023 and September 2024 Key Features City-level data Explore listings in popular cities like Florence Milan Naples Rome and Venice Comprehensive metrics Data includes pricing review scores host details and more Seasonal analysis Data spans different periods allowing for comparisons across seasons Data Dictionary Listings id Unique identifier for each listing Last year reviews Number of reviews received in the twelve months before the scraping data Date of scraping Host since Date the host joined Airbnb Host is superhost Whether the host is a simple host or a superhost Host number of listings Total number of listings the host has Neighbourhood Neighborhood where the listing is located Beds number Bedrooms number Property type Type of room (e.g., entire home private room) Maximum allowed guests Number of guests the listing can accommodate Price Price per night (in Euro) Total reviews Total number of reviews Rating score Overall rating of the listing Accuracy score Accuracy rating Cleanliness score Cleanliness rating Checkin score Check-in rating Communication score Communication rating Location score Location rating Value for money score Value rating Reviews per month Number of reviews per month City Season Time period when the data was scraped (e.g., Early Winter) Bathrooms number Number of bathrooms Bathrooms type Type of bathrooms (shared vs private) Coordinates latitude longitude For visualization reason it is also provide a csv with all city neighbourhoods and the relative geojson Disclaimer This dataset is intended for informational and research purposes only It is not affiliated with Airbnb or any other organization.

See the average Airbnb revenue & other vacation rental data in Bali in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

See the average Airbnb revenue & other vacation rental data in Barcelona in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

Global Airbnb KPI Business Data.

Data Collection Methodology:

The data source is web scraping. We have built validated statistics models to accurately identify the bookings

Data update frequency: Monthly

Data update lag: 15 days

Historical data: Since 2016

Granularity: Per continent, country, or city

Inside Airbnb is an independent, non-commercial set of tools and data that allows you to explore how Airbnb is really being used in cities around the world.By analyzing publicly available information about a city's Airbnb's listings, Inside Airbnb provides filters and key metrics so you can see how Airbnb is being used to compete with the residential housing market.With Inside Airbnb, you can ask fundamental questions about Airbnb in any neighbourhood, or across the city as a whole. Questions such as: "How many listings are in my neighbourhood and where are they?""How many houses and apartments are being rented out frequently to tourists and not to long-term residents?""How much are hosts making from renting to tourists (compare that to long-term rentals)?""Which hosts are running a business with multiple listings and where they?"The tools are presented simply, and can also be used to answer more complicated questions, such as: "Show me all the highly available listings in Bedford-Stuyvesant in Brooklyn, New York City, which are for the 'entire home or apartment' that have a review in the last 6 months AND booked frequently AND where the host has other listings."These questions (and the answers) get to the core of the debate for many cities around the world, with Airbnb claiming that their hosts only occasionally rent the homes in which they live.In addition, many city or state legislation or ordinances that address residential housing, short term or vacation rentals, and zoning usually make reference to allowed use, including: how many nights a dwelling is rented per yearminimum nights staywhether the host is presenthow many rooms are being rented in a buildingthe number of occupants allowed in a rentalwhether the listing is licensedThe Inside Airbnb tool or data can be used to answer some of these questions.The data behind the Inside Airbnb site is sourced from publicly available information from the Airbnb site.The data has been analyzed, cleansed and aggregated where appropriate to faciliate public discussion. Read more disclaimers here.If you would like to do further analysis or produce alternate visualisations of the data, it is available below under a Creative Commons CC0 1.0 Universal (CC0 1.0) "Public Domain Dedication" license.

See the average Airbnb revenue & other vacation rental data in Pittsburgh in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

gradio/NYC-Airbnb-Open-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is used in the introductory course Explore and Search for data in ODS Academy, Opendatasoft's training portal.Inside Airbnb is an independent, non-commercial set of tools and data that allows you to explore how Airbnb is really being used in cities around the world.By analyzing publicly available information about a city's Airbnb's listings, Inside Airbnb provides filters and key metrics so you can see how Airbnb is being used to compete with the residential housing market.With Inside Airbnb, you can ask fundamental questions about Airbnb in any neighbourhood, or across the city as a whole. Questions such as: "How many listings are in my neighbourhood and where are they?""How many houses and apartments are being rented out frequently to tourists and not to long-term residents?""How much are hosts making from renting to tourists (compare that to long-term rentals)?""Which hosts are running a business with multiple listings and where they?"The tools are presented simply, and can also be used to answer more complicated questions, such as: "Show me all the highly available listings in Bedford-Stuyvesant in Brooklyn, New York City, which are for the 'entire home or apartment' that have a review in the last 6 months AND booked frequently AND where the host has other listings."These questions (and the answers) get to the core of the debate for many cities around the world, with Airbnb claiming that their hosts only occasionally rent the homes in which they live.In addition, many city or state legislation or ordinances that address residential housing, short term or vacation rentals, and zoning usually make reference to allowed use, including: how many nights a dwelling is rented per yearminimum nights staywhether the host is presenthow many rooms are being rented in a buildingthe number of occupants allowed in a rentalwhether the listing is licensedThe Inside Airbnb tool or data can be used to answer some of these questions.The data behind the Inside Airbnb site is sourced from publicly available information from the Airbnb site.The data has been analyzed, cleansed and aggregated where appropriate to faciliate public discussion. Read more disclaimers here.If you would like to do further analysis or produce alternate visualisations of the data, it is available below under a Creative Commons CC0 1.0 Universal (CC0 1.0) "Public Domain Dedication" license.

See the average Airbnb revenue & other vacation rental data in Chiang Mai in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

What makes your data unique? - We have our proprietary AI to clean outliers and to calculate occupancy rate accurately.

How is the data generally sourced? - Web scraped data from Airbnb. Scraped on a weekly basis.

What are the primary use-cases or verticals of this Data Product? - Tourism & DMO: A one-page CSV will give you a clear picture of the private lodging sector in your entire country. - Property Management: Understand your market to expand your business strategically. - Short-term rental investor: Identify profitable areas.

Do you cover country X or city Y?

We have data coverage from the entire world. Therefore, if you can't find the exact dataset you need, feel free to drop us a message. Our clients have bought datasets like 1) Airbnb data by US zipcode 2) Airbnb data by European cities 3) Airbnb data by African countries.

See the average Airbnb revenue & other vacation rental data in Summit County in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

These datasets include information about Airbnb listings in the Boston area processed from data released by insideairbnb.com. Inside Airbnb produces monthly data releases about Airbnb activity for select regions internationally. This data includes listings from Inside Airbnb’s “Boston” and “Cambridge” releases. AIRBNB.Listing is a listing-level file that contains information about the rental properties listed on Airbnb. Listing data has been aggregated across census tracts to generate AIRBNB.CT, which includes ecometrics that describe neighborhoods in terms of listing frequency and pricing .

Metrics that can be unearthed will be ones contained in the email booking invoice such as Hotel name, type of room, dates, check in and check out times, price paid, duration of stay. We can go back to 5 years of history.

We also have cancellation emails.

Any hotel vendor can be requested too. We will conduct a search in our database to see if it justifies a parser build to extract the data.

Please contact michelle@measurable.ai for a demo or more data samples.

In 2025, Victoria’s capital, Melbourne, came out on top as Australia’s Airbnb hub, home to the highest number of Airbnb listings across the Australian cities and regions represented, with over ****** listings. Sydney and the Gold Coast also emerged as Airbnb powerhouses in the country, with the next highest listing volumes that year. Which Airbnb locations are proving most profitable? Airbnb is one of the most popular online travel accommodation booking brands among Australians, according to a 2025 survey. While Melbourne leads in sheer Airbnb listing numbers, other regions are proving more lucrative for Airbnb hosts regarding income. Noosa Heads in Queensland generated the highest annual Airbnb revenue countrywide, exceeding ******* Australian dollars. Surfers Paradise and the Sunshine Coast followed closely, indicating that Queensland’s coastal areas show promising returns for Airbnb hosts as they are popular among holidaymakers looking for short-term rentals. Occupancy rates and average daily charges Yet, it was Perth that had the highest occupancy rate across Australia’s key Airbnb markets, with an ** percent average, with Airbnb rentals in areas like Surfers Paradise, Brisbane, and the Gold Coast also enjoying high occupancy. This high occupancy rate in Perth may be attributed to a balanced supply and demand in the market. Mornington Peninsula had the highest average Airbnb daily rates across Australia’s cities and regions, with Noosa Heads and Shoalhaven also fetching premium prices. These regional disparities in revenue versus occupancy and daily rates highlight the importance of location in the Airbnb market. Some areas experience elevated demand and can afford to charge higher rates due to their unique appeal to tourists, proximity to certain attractions and the seafront, or limited accommodation availability.

Airbnb Global Accommodation and Reviews

Exploring Location-Based Listing Data

By Debayan Kar [source]

About this dataset

The Airbnb Global Dataset contains a wealth of information about the locations, availability, reviews and other details related to short-term rentals available around the world. Use this dataset to explore how guests rate their experiences, discover new places in various neighbourhood groups and geographical locations, compare prices of different room types, consider minimum nights required for bookings and more! With this data set you can evaluate factors associated with: host name; neighbourhood group; latitude & longitude; room type; price; minimum nights required for bookings; number of reviews - both in total and over the last 12 months (number_of_reviews_ltm); license (if applicable); last review received; average number of reviews per month (reviews per month) as well as calculated host listing counts which reflect seasonal demand variations. With this information at your fingertips you could travel anywhere your heart desires - so let's turn those dreams into reality!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The following guide will help you get started in your journey to gain insights from this data set.

First, specify the fields that you want to focus on. In order to do this, make sure you take into consideration the columns available within this dataset. By doing so, not only are you able to hone in on specific aspects of Airbnb accommodation and reviews (i.e neighborhood groups, room types or even pricing), but also identify themes or common trends among listings which could prove useful when formulating hypotheses.

Once you have identified which fields will be useful for analysis, it is important that they are converted into appropriate data types if they need any sort of conversion at all (i.e converting strings to integers). Moreover, make sure there are no inconsistencies across your features when exploring the entries in those columns; take care of them before any substantial analysis is done.

You are now ready for some exploratory analysis! Start by creating visualizations such as bar graphs or box plots in order to get an overview of particular aspects related to listings (i.e distribution of prices around a neighbourhood group) - these can be very useful indicators! Then try out correlations between different exponential variable datasets such as availability_365 versus minimum_nightsand explore how they fluctuate with changes in pricing over time - examining how these relationships relate over different locations can yield interesting results like unexpected concentration points which demand research! Another field worth exploring would be reviews associated with each listing by digging down into their components like ratings breakdowns under different criteria such as security/price value ratio etc.. All these evaluations should give an excellent outline on what potential customers might look out for while browsing through options online so as entrepreneurs we can hover upon those trends specially mentioning needs fulfilled during our advertisement campains.... Lastly examine publicly available information about each host such as number_of_reviews or calculated_listings count variation over time , with ability provided here we have ample opportunities predicting customer opinion about newly created businesses offering same services...so many things one could dive deep !

Overall , after gaining ample amount insights taking about current market scenario it’s best suggested procuring feedback from active host & using it devise plans bringing mutual mutually beneficial solutions making both hosts & guests happy . This is where creativity play huge role designing perks forming long lasting trust inducing relationship between service providers &

Research Ideas

• Predicting price points for Airbnb listings based on factors such as room type, neighborhood group, and reviews.
• Identifying areas with a high demand for Airbnb rentals, by looking at the ratio of availability to number of reviews for listings in different neighborhoods.
• Analyzing guest satisfaction levels based on factors such as room type and location, by correlating the reviews_per_month with the number_of_reviews indicator and other variables in the dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description ...

This data provides a detailed window into how travelers across Europe are making choices between Airbnb, boutique hotels, and chain hotels, and how those choices are influenced by perceived value, authenticity, and price sensitivity. It spans major tourism markets such as Paris, Barcelona, Rome, Berlin, Amsterdam, Vienna, Prague, Lisbon, Athens, and Dubrovnik, while layering in demographic details including age, income, and household type. By capturing these sentiment drivers alongside actual accommodation choice percentages, the data goes beyond occupancy statistics or market reports and instead reveals the deeper psychology of why travelers choose where to stay.

At its heart, the data measures the trade-offs travelers make. Some value price above all else, seeking the cheapest option and showing high sensitivity to even small changes in nightly rates. Others prioritize authenticity, looking for cultural immersion, unique architecture, or a connection to the community, a sentiment often tied to boutique hotels or Airbnb stays. Still others rate perceived value, balancing comfort, service, and cost in ways that may lean toward chain hotels where consistency and loyalty programs come into play. By quantifying these three sentiment drivers alongside accommodation choice, the data enables a holistic view of the European hospitality landscape that is not just descriptive but predictive.

For hotel operators, this data provides granular competitive intelligence. A chain hotel executive in Berlin can see not only how many travelers are opting for chain hotels versus Airbnb or boutiques, but also the sentiment scores that drive those choices. If authenticity consistently scores low for chain hotels, it suggests a strategic opening to localize offerings, integrate cultural experiences, or adjust marketing. Boutique hotel managers in Lisbon can benchmark how their authenticity score compares to Airbnb in the same city, providing evidence for whether they should double down on differentiation or compete more aggressively on price. Airbnb hosts and platform managers can assess whether travelers in cities like Athens or Dubrovnik are primarily choosing Airbnb for price sensitivity or for perceived authenticity, and then adjust host guidelines and search rankings to align with those motivations.

Tourism boards and city governments can use this data to shape destination strategies. In cities where authenticity is highly valued, they may promote cultural experiences and boutique stays that highlight heritage and local life. In cities where price sensitivity dominates, they may anticipate pressure on affordability and design policies to balance visitor demand with resident quality of life. Tracking sentiment alongside accommodation choice allows policymakers to see whether interventions such as limiting Airbnb licenses or incentivizing boutique hotels are having the intended effect.

For travel agencies and online booking platforms, this data provides immediate commercial value by informing recommendation algorithms. If Millennials traveling to Barcelona are shown to favor Airbnb due to high authenticity scores, platforms can tailor recommendations to match those preferences and increase conversion rates. If Boomers traveling to Vienna demonstrate high perceived value scores for chain hotels, agencies can design targeted campaigns that emphasize comfort, service, and reliability. By embedding demographic segmentation, the data enables personalization that goes beyond generic marketing and aligns with actual consumer psychology.

Investors and financial analysts also gain critical foresight from this data. The growth of Airbnb has often been framed in broad, disruptive terms, but this data dissects the nuance of where Airbnb’s advantage comes from and how strong it is in different markets. In Amsterdam, for example, Airbnb may dominate with authenticity but show weaker perceived value compared to boutique hotels. In Prague, chain hotels may hold firm due to loyalty programs and price competitiveness. Understanding these dynamics city by city allows investors to make sharper decisions about where to allocate capital, which hotel groups are most resilient, and where regulatory risks may matter most.

Marketing agencies and brand strategists can mine the sentiment scores for creative direction. A boutique hotel in Lisbon may craft campaigns around the theme of authenticity if the data shows that is the strongest differentiator for their target demographic. A chain hotel group in Rome might emphasize value and consistency if those resonate more strongly with middle-income families. Airbnb itself can use the data to position its brand differently across cities, leaning into affordability in one market and cultural immersion in another. The combination of quantitative percentages and sentiment scores creates a bridge between analytics and storytelling, enabling brands to market with evidence rather than assumption.

The demo...

The total revenue of Airbnb reached **** billion U.S. dollars in 2024. This was an increase over the previous year's total of **** billion. The decrease in revenue in 2020 can be attributed to the coronavirus (COVID-19) pandemic, which caused travel disruption across the globe. When breaking down Airbnb revenue by region, ***************************************, brought in the most revenue in 2024. Where are Airbnb’s biggest markets? Airbnb is a home sharing economy platform that operates in many countries around the world. The company’s biggest market is in ************* where Airbnb’s gross booking value amounted to **** billion U.S. dollars. Meanwhile, Latin American travelers stayed more nights with Airbnb on average than those in the Asia Pacific region. How did COVID-19 impact Airbnb? The COVID-19 pandemic impacted the travel and tourism industry worldwide, with many countries initiating stay at home orders or travel bans to prevent the spread of the virus. In addition to a decrease in revenue in 2020, the company also experienced a reduction in the number of nights and experiences booked with Airbnb. Bookings fell to under *** million in 2020 due to these travel restrictions. In 2024, Airbnb reported over *** million booked nights and experiences, a significant increase over the previous year.