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.

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. In 2023, Airbnb reported that ** percent of their hosts identified as women.

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.

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...

The statistic shows the share of Airbnb hosts in Italy in 2016, broken down by gender. As of the survey period, about ** percent of the hosts were females.

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.

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.

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.

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Data Set Information

This data set describes the listing activity of Airbnb homestays in New Orleans, Louisiana, as part of the Inside Airbnb initiative. The data set was compiled on November 7, 2021. See the New Orleans Airbnb data visually here.

Some personally identifying information has been removed from the data uploaded here.

Contents

The following Airbnb activity is included in this New Orleans data set:

Listings, including full descriptions and average review score (new_orleans_airbnb_listings.csv) Reviews, including unique id for each reviewer and detailed comments (reviews.csv)

Acknowledgements

Data credit goes to Murray Cox and Inside Airbnb. The original source for this particular New Orleans data can be found here--where you can also find information on the different listing ids and their price and availability for different calendar dates (if you're interested in looking at how Airbnb rental listing price fluctuates over time).

Context

The data set can be used to answer some interesting questions, such as: - Can you predict how much a short-term rental in New Orleans should charge per night based on it's location and amenities? - Can you describe the vibe of each neighborhood in using listing descriptions? - What are the most common amenities to have among short-term rental listings in New Orleans? - What elements contribute to a popular or highly-rated listing? - Is there any noticeable difference in favorability among different NOLA neighborhood/areas and what could be the reason for it?

Furthermore, it's also important to note that Inside Airbnb (provider of dataset) is a mission driven activist project with the objective to provide data that quantifies the impact of short-term rentals on housing and residential communities; and also provides a platform to support advocacy for policies to protect cities from the impacts of short-term rentals.

According to travel guides, New Orleans is one of the top ten most-visited cities in the United States. It was severely affected by Hurricane Katrina in August 2005, which flooded more than 80% of the city, killed more than 1,800 people, and displaced thousands of residents, causing a population decline of over 50%. Since Katrina, major redevelopment efforts have led to a rebound in the city's population. Concerns about gentrification, new residents buying property in formerly closely knit communities, and displacement of longtime residents have all been a major discussion topic.

Bearing the given context in mind, this data set shared by Inside Airbnb also allows you to ask fundamental questions about Airbnb in any neighbourhood, or across the city as a whole, 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 questions (and their 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 year - minimum nights stay - whether the host is present - how many rooms are being rented in a building - the number of occupants allowed in a rental - whether the listing is licensed

(Visit their site for more details.)

This statistic shows the revenue of the leading commercial Airbnb hosts in New York City between 2010 and 2014. The leading commercial host using Airbnb in New York City generated approximately **** million U.S. dollars in revenue from private short-term rentals between 2010 and 2014.

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.

The Airbnb Accommodation Booking Data Warehouse (2020-2024) is a dataset for business intelligence, and it has a dimensional model comprising four dimension tables and one fact table.

The Dim_Date table provides detailed date information from 2020 to 2024, including day, month, quarter, and weekday details for time-based analysis. The Dim_Host table captures information about property hosts, such as superhost status, total listings, and response times. Dim_Property contains details of accommodations, including location, property type, room type, number of rooms, and pricing. Dim_Customer includes customer demographics such as age group, gender, nationality, and customer segment.

The central Fact_Bookings table records booking transactions, including revenue, nights booked, guests, and fees. Each booking links to specific hosts, customers, properties, and dates through foreign keys.

The dataset supports multi-year analysis of booking trends, revenue performance, customer behaviour, and host activity. It enables insights into seasonal patterns, location performance, and customer segmentation, allowing for strategic decisions in pricing, marketing, and operational planning.

The statistic shows the share of Airbnb hosts renting their home in Italy in 2015. As of the survey period, about ** percent of the hosts were renting their secondary home.

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

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.

This statistic presents the number of Airbnb hosts and guests in London (UK) from July 2016 to July 2018. In total there were around 75,700 active listings for Airbnb properties in London in the year to July 2018, receiving a total of 2.2 million inbound guests.

Comprehensive Airbnb dataset for Tallinn, Estonia providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

Comprehensive Airbnb dataset repository offering detailed vacation rental analytics worldwide including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.