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.

According to data from Airbtics.com, the share of international Airbnb guests in Mexico slightly surpassed domestic travelers in May 2020. In April, local tourists accounted for over half of the guests that used the popular online lodging platform, overturning the trend registered in previous months. From ************* to **********, the country registered the highest shares of domestic Airbnb guests, with figures above ** to nearly ** percent. In 2019, domestic spending accounted for ** percent of total spending on travel and tourism in Mexico.

According to data from Airbtics.com, international guests staying at Airbnbs in Greece reached a 13-month low of **** percent of total guests in May 2020, compared to **** percent in May 2019. International guests started to decrease as the COVID-19 outbreak restricted global travel.

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 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.

According to data from Airbtics.com, international Airbnb guests in California reached a 13-month low in May 2020, with only *** percent of guests coming from abroad. Comparatively, in May 2019, **** percent of Airbnb guests in California came from outside of the United States. This decrease in international guests began as travel restrictions due to the coronavirus (COVID-19) pandemic were implemented across the globe.

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...

According to data from Airbtics.com, international guests staying at Airbnbs in the United Kingdom reached a 13-month low of **** percent of total guests as of May 2020, compared to **** percent in May 2019. The ratio of international guests started to decrease in April 2020, as the COVID-19 outbreak restricted travel into the country.

This statistic depicts the total number of Airbnb guest arrivals worldwide from 2016 to 2018. The total number of people residing in an Airbnb accommodation is forcast to amount to approximately ***** million in 2018, up from *** million the previous year.

Barcelona Airbnb Listings

Understanding Rental Prices and Trends in the City

By Kelly Garrett [source]

About this dataset

This dataset contains detailed information about Airbnb listings for the city of Barcelona, Spain, including reviews from guests and hosts, ratings, neighborhoods and more. With over 16000 observations collected from nearly 5000 unique listings, it offers great insight into the demand and popularity of different types of accommodation in Barcelona. It also provides detailed insights into the quality of each listing such as its exact location, number of bedrooms and Cleanliness Rating. Additionally, this dataset gives an opportunity to explore what kind of amenities each listing has to offer (such as parking or internet) and how they affect price range. Ultimately this data allows users to analyze different types of accommodations in Barcelona in order to discover key trends within the rental market - which locations are most popular amongst visitors? Which kinds amenities are associated with higher-priced rentals? How do ratings compare across neighborhoods?

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How to use the dataset

This dataset is a great way to gain insight into what Barcelona has to offer in terms of Airbnb listings. It provides information on over 19,000 listings throughout the city with details such as availability and pricing, as well as listings that include reviews and amenities offered. Through exploring this dataset you will be able to identify trends in Airbnb's presence in Barcelona and make better informed decisions when booking your stay.

To begin using this dataset: - Start by getting an overview of the data by considering the columns present in the dataset such as 'neighbourhood_group',‘room_type’, ‘price’, 'number_of_reviews' etc., and determining how each of these features influence your analysis or search for certain key properties that interest you. - Gain further insight about individual properties through exploring related columns such as 'amenities' or 'host_name'.
- Identify geographic areas that have higher concentrations of Airbnb's using visualizations or clustering techniques to better understand which neighbourhoods have more activity or data points associated with them making for a potentially more enjoyable stay based on customer ratings & reviews etc,.
- Use summary statistics and rankings (such as describing how far you are from main attractions) to examine overall prices across different neighbourhood components within Barcelona during different times of year taking into consideration factors like peak seasonality vs low seasonality before entering any booking agreement via online travel sites etc,.

By following these steps when utilizing this datasets potential it will allow users get a detailed overview of potential options prior to making any final decisions concerning their prized Airbnb stay!

Research Ideas

• Analyzing the correlation between rental prices in different areas and various socioeconomic factors such as median household income, population density, and types of business establishments in those areas.
• Examining differences in amenities offered at different price points to determine how much more a traveler would be willing to pay for certain amenities (ie luxury sheets, spa-like shower setup).
• Analyze the changes in Airbnb listings over time - including number of new/cancelled listings, average nightly price increases or decreases - that can help inform decision making by tourists or local government on investment into the future of tourism in Barcelona

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 No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Kelly Garrett.

Airbnb Prices in European Cities

Determinants of Price by Room Type, Location, Cleanliness Rating, and More

By [source]

About this dataset

This dataset provides a comprehensive look at Airbnb prices in some of the most popular European cities. Each listing is evaluated for various attributes such as room types, cleanliness and satisfaction ratings, bedrooms, distance from the city centre, and more to capture an in-depth understanding of Airbnb prices on both weekdays and weekends. Using spatial econometric methods, we analyse and identify the determinants of Airbnb prices across these cities. Our dataset includes information such as realSum (the total price of the listing), room_type (private/shared/entire home/apt), host_is_superhost (boolean value indicating if host is a superhost or not), multi (indicator whether listing is for multiple rooms or not), biz (business indicator) , guest_satisfaction_overall (overall rating from guests camparing all listings offered by host ), bedrooms, dist (distance from city center) , lng & lat coordinates for location identification etc. We hope that this data set offers insight into how global markets are affected by social dynamics and geographical factors which in turn determine pricing strategies for optimal profitability!

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How to use the dataset

This dataset can be used by individuals and companies to gain insight on the cost of Airbnb listings in some of the most popular European cities. It contains information on a variety of attributes such as room type, cleanliness rating, guest satisfaction, distance from the city centre, and more. In addition to exploring general trends in prices across Europe, this dataset can be used for deeper spatial econometric analysis.

To begin using this dataset for your own research or analysis project: - Download the files which contain both weekday and weekend listings data for European cities. - Familiarize yourself with the columns included in each file; these provide descriptions of various attributes associated with each listing.
- Calculate any desired summary statistics - average price per night per city or room type etc. - using statistical software (e.g Excel).
- Perform spatial econometric analysis if desired; use specialized packages such as spdep or spatialreg in R to identify determinants of Airbnb price levels across Europe (e.g., metro distance). - Visualize your results with GIS software if necessary to more easily understand patterns between variables like proximity/location and price level (e.g., QGIS).

By leveraging both descriptive and inferential methods while taking advantage of geographic information systems (GIS), users can apply this dataset to many interesting questions related to rental prices on Airbnb in Europe!

Research Ideas

• Analyzing spatial trends in Airbnb prices across Europe and finding the most favorable cities for hosting.
• Comparing differences between weekday vs weekend booking patterns to project rental rates for vacationers and business travelers in European cities.
• Using spatial econometrics methods to find important determinants of Airbnb prices in order to provide insights into areas of opportunity for improvement, or assess the effectiveness of existing policy changes concerning vacation rentals

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 No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: vienna_weekdays.csv | Column name | Description | |:-------------------------------|:---------------------------------------------------------------------------| | realSum | The total price of the Airbnb listing. (Numeric) | | room_type | The type of room being offered (e.g. private, shared, etc.). (Categorical) | | room_shared | Whether the room is shared or not. (Boolean) | | room_private | Whether the room is private or not. (Boolean) | | **per...

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

The vacation rental website market is experiencing robust growth, driven by increasing demand for unique travel experiences and the flexibility offered by vacation rentals compared to traditional hotels. The rise of remote work and the increasing popularity of multi-generational travel are further fueling this expansion. While the exact market size for 2025 is unavailable, considering a plausible CAGR of 15% (a conservative estimate given industry trends) and a hypothetical 2019 market size of $50 billion, the 2025 market size could be estimated at approximately $90 billion. This substantial valuation reflects the market's maturity and the significant investment from major players like Airbnb, Booking Holdings, and Expedia Group, who are continuously innovating to enhance user experiences and broaden their offerings. The competitive landscape is highly fragmented, with both established giants and smaller niche players vying for market share. This competition drives innovation in areas such as dynamic pricing, property management software, and enhanced guest communication tools. Technological advancements, like improved search functionalities, virtual tours, and AI-powered recommendations, are key drivers of growth. However, challenges such as regulatory hurdles in various jurisdictions, concerns around property safety and guest security, and the impact of economic downturns pose potential restraints on future growth. Segmentation within the market includes various property types (apartments, villas, houses), target demographics (families, couples, groups), and booking platforms (direct booking websites, online travel agencies). Future growth will likely depend on effective addressal of these restraints, ongoing technological development, and the continued expansion into emerging markets. The forecast period (2025-2033) promises sustained expansion, with the CAGR likely to remain in the double digits, reflecting continued digitalization and a preference for personalized travel options. Specific regional growth will vary depending on factors such as tourism infrastructure, economic conditions, and regulatory environments. Key players will need to focus on strategic acquisitions, technological innovation, and effective marketing to maintain competitiveness and capture market share in this dynamic and rapidly growing sector. Success will hinge on leveraging data analytics to improve operational efficiency, personalization of services, and the proactive management of risk associated with security and regulatory compliance.

According to data from Airbtics.com, international guests staying at Airbnbs in France reached a 13-month low of ** percent of total travel as of May 2020, compared to **** percent in May 2019. The rate of international guests started to decrease in April 2020, as the COVID-19 outbreak restricted travel into the country.

The number of guest nights, nights, and stays for short-term lets offered through online collaborative economy platforms (Airbnb, Booking.com and Expedia Group).

In 2019, guests who stayed in Airbnb listings in Taiwan spent around *** million U.S. dollars, up from *** million U.S. dollars in the previous year. In 2019, there were approximately ****** Airbnb listings in Taiwan.

According to data from Airbtics.com, international guests staying at Airbnb's in Ireland reached a 13-month low of **** percent of total guests in May 2020, compared to **** percent in May 2019. The ratio of international guests started to decrease in April 2020, as the COVID-19 outbreak restricted travel.