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

These are the Airbnb statistics on gross revenue by country.

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

In 2017, the majority of Airbnb users in the United States and Europe were between the ages ** to **. People in older age groups generally made up a smaller share of Airbnb users. Only **** percent of Airbnb users were aged 65 or older - indicating that Airbnb is more popular among younger users. Airbnb popularity The accommodation rental and sharing website Airbnb is gaining popularity all over the world. This can most likely be attributed to the company allowing for cheaper accommodation alternatives and a more personal experience of a location. In 2018, there were forecast be around ***** million Airbnb guest arrivals worldwide – and the average number of guests per listing was **. A survey found that ** percent of European and American Airbnb users were ‘very satisfied’ with their experience. On the other hand, *** percent stated that they were ‘somewhat dissatisfied’ or ‘not at all satisfied’ with using the accommodation sharing platform. Why not use Airbnb? Despite the large amount of people being satisfied with their Airbnb experience, there still remain people in Europe and the U.S. that do not want to use their service. A survey found that the most common reason for people not to use Airbnb was privacy concerns – with ** percent of the respondents expressing this concern.

New York City accounted for ****** Airbnb listings in late 2024. Meanwhile, Los Angeles had ****** listings, making it the city with the most Airbnb listings in the ranking.

In 2017, the number of adults using Airbnb in the United States amounted to **** million, up from ** million the previous year. This figure is forecast to reach **** million by 2022. Why do people use Airbnb? The privately owned accommodation, rental and sharing website Airbnb has gained popularity all over the world. This is due to multiple factors including cheaper lodging alternatives, a more authentic experience, uniqueness of accommodation and more. A survey found that ** percent of U.S.-based & European Airbnb users were ‘very satisfied’ with their experience. On the other hand,**** percent stated that they were ‘somewhat dissatisfied’ or ‘not at all satisfied’ with using the accommodation sharing platform. Why don't people use Airbnb? Despite the large number of people who are satisfied with their Airbnb experience, there still remain those in Europe and the U.S. that do not want to use the company's services. The most common reason for people not to use Airbnb is privacy concerns, according to a 2017 survey – with ** percent of respondents expressing this fear.

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.

Leverage our Airbnb dataset to gain comprehensive insights into global short-term rental markets. Track property details, pricing trends, reviews, availability, and amenities to optimize pricing strategies, conduct market research, or enhance travel-related applications. Data points may include listing ID, host ID, property type, price, number of reviews, ratings, availability, and more. The dataset is available as a full dataset or a customized subset tailored to your specific needs.

San Diego Airbnb Listings

Location, Amenities and Reviews

By Ali Sanne [source]

About this dataset

This dataset provides a comprehensive look at all active Airbnb listings in San Diego. It includes detailed information such as host information, location details, amenities offered, and reviews from past guests. With this dataset you can explore the list of Airbnb properties close to you, assess their suitability for staycations or business trips alike and understand the local market trends in a matter of minutes. Get an inside peek into each listing's features such as transportation options nearby, access to digital conveniences like guest profile pictures and phone verification requirement if any; property amenities including bed type, bathrooms and bedrooms; local neighborhood overviews; house rules; review scores rating from previous guests on different aspects like accuracy and communication etc.; security deposits or cleaning fees required by hosts, among others. With the data points provided here you can answer questions about your upcoming stays or become an informed owner/host in this dynamic sharing economy space. The listings dataset includes columns such as: listing_url ,name ,summary ,space ,description ,neighborhood_overview ,notes ,transit ,access interaction house_rules thumbnail_url host_url host_name host_since host_location host_about host_response time host response rate host acceptance rate host is super host host neighbourhood hosting listings count hosting total listings count hosting has profile pic street neighbourhoods cleansed city state zip code market smart location country code latitude longitude is location exact property type room type accommodates bathrooms bedrooms beds bed types amenities square feet nightly price price per stay security deposit cleaning fees guest included extra people minimum nights maximum nights number of reviews number of stays first review last review review scores rating review scores accuracy review scores cleanliness trial scores check-in trailed scores communications trailed score locations trial score values requires license instant bookable is business travel ready cancellation policy require guess profile picture require guess phone verifications

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How to use the dataset

This dataset provides a comprehensive look at all the Airbnb listings in San Diego, California. It contains detailed information about each Airbnb listing, including host name and contact details, location and amenities, and reviews. With this data, users can get an accurate picture of the state of the San Diego Airbnb market and analyze trends in the data to make more informed decisions on how to use their resources.

Research Ideas

• Creating targeted marketing campaigns based on the demographics of Airbnb hosts and renters in San Diego. This would involve analyzing the various data points related to host information and location, as well as guest preferences such as amenities and reviews, to identify potential target segments for businesses interested in advertising in San Diego.
• Developing an accurate pricing algorithm for Airbnb listings by taking into account factors like property type, room type, square footage, amenities offered and other relevant characteristics like cleanliness and responsiveness ratings from the reviews of previous guests that can affect pricing decisions.
• Using artificial intelligence (AI) algorithms to help predict whether a listing will be successful or not based on past trends of certain characteristics such listing location, ratings from previous guests etc., thus helping hosts make informed decisions about list pricing and marketing activities needed to build more successful listings over time.

Acknowledgements

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

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication...

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As of December, 2024, there were over ** thousand listings for room and apartment rentals in London on the Airbnb website, the highest of any other major European city. Airbnb listings were also high in Paris, Rome and Madrid. Paris accounted for around ** thousand listings, while Rome and Madrid had over ** and ** thousand, respectively. Controversy of Airbnb in Europe Airbnb has become an increasingly popular option for tourists looking for local accommodation. Visitors are attracted to using Airbnb properties instead of hotels and other traditional travel accommodation mainly due to cheaper prices, but also for the location, and to gain an authentic experience. However, the site is facing ongoing legal problems, with some destinations moving to ban or restrict rentals from the site because they worsen housing problems and undermining hotel regulations. Many European cities, including Amsterdam and Paris, have placed limits on the length of rentals, and others such as Barcelona have introduced strict regulations for hosts. The rise of Airbnb Airbnb is one of the most successful companies in the global sharing economy. The company was founded in San Francisco, California in 2008, after being conceived by two entrepreneurs looking for a way to offset their high rental costs. Airbnb was developed as an online platform for hosts to rent out their properties on a short-term basis. It now competes with other online travel booking websites, including Booking.com and Expedia.

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Airbnb & Short term rental statistics.

Available columns - average occupancy rate - average daily rate - revenue - active listing

Timeframe - 2024 TTM (2023 June - 2024 May) - 2023 TTM, 2022 TTM

This data covers following cities:

Brasilia Buenos Aires Mexico City El Calafate Tamarindo São Paulo Panama City Santa Teresa Medellin Cancun Santiago Playa del Carmen Manaus Cartagena Bariloche Bogota Antigua and Barbuda Rio De Janeiro Florianopolis Lima Havana Punta Arenas Salvador de Bahia Cusco Cali Tijuana Oxahaca Punta del Este Jericoacoara Quito Foz do Iguacu Bonito

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

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 data provided by Inside Airbnb for the city of Lisbon contains detailed information about the accommodation listings available on the Airbnb platform in that region. Below is an explanation of the main columns included in the data:

• id: The unique identifier of the Airbnb listing.
• host_id: The unique identifier of the host responsible for the listing.
• host_name: The name of the host.
• host_since: The date the host registered on the Airbnb platform.
• host_location: The location of the host.
• host_total_listings_count: The total number of listings the host has.
• neighborhood: The neighborhood where the property is located.
• neighborhood_group: The neighborhood group to which the neighborhood belongs.
• latitude: The latitude of the property's location.
• longitude: The longitude of the property's location.
• room_type: The type of room offered in the listing (e.g., entire house/apartment, private room, shared room).
• bathrooms_text: The description of the bathrooms available on the property.
• bedrooms: The number of bedrooms in the property.
• price: The price per night for the listing.
• minimum_nights: The minimum number of nights required to book the property.
• maximum_nights: The maximum number of nights allowed to book the property.
• has_availability: Indicates whether the property is available.
• availability_365: The number of days the property can book over a year.
• number_of_reviews_ltm: The number of reviews received in the last 12 months.
• review_scores_rating: The average score of reviews received by the property.
• license: The license or registration of the property, if applicable.
• calculated_host_listings_count: The number of listings the host has in total.
• calculated_host_listings_count_entire_homes: The host owns the number of entire listings (entire house/apartment).
• reviews_per_month: The average number of reviews the property receives per month.

These columns provide a comprehensive view of the characteristics of available accommodation listings in Lisbon during the period in which the data was collected. This is a valuable data source for exploratory analysis, predictive modeling, and research related to the shared hosting industry in Lisbon. It provides a detailed look at listing features, including information about hosts, property locations, room type, pricing, availability, and reviews received.