Comprehensive Airbnb dataset for Lisbon, Portugal 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.

The top 13 Airbnb markets in 2025 are: 1. Lisbon - Strict regulations, 13,342 listings, 82% occupancy rate, €115 daily rate. See other 12 places.

The weekly year-over-year bookings for Airbnb properties in Lisbon fell dramatically due to the outbreak of the (COVID-19) coronavirus in early 2020; In the week ending April 12, Airbnb only achieved *** percent of last years bookings in the Portuguese capital.

Dataset is from http://tomslee.net/airbnb-data-collection-get-the-data

room_id: A unique number identifying an Airbnb listing. The listing has a URL on the Airbnb web site of http://airbnb.com/rooms/room_id

host_id: A unique number identifying an Airbnb host. The host’s page has a URL on the Airbnb web site of http://airbnb.com/users/show/host_id

room_type: One of “Entire home/apt”, “Private room”, or “Shared room”

borough: A subregion of the city or search area for which the survey is carried out. The borough is taken from a shapefile of the

city that is obtained independently of the Airbnb web site. For some cities, there is no borough information; for others the borough may be a number. If you have better shapefiles for a city of interest, please send them to me.

neighborhood: As with borough: a subregion of the city or search area for which the survey is carried out. For cities that have both, a neighbourhood is smaller than a borough. For some cities there is no neighbourhood information.

reviews: The number of reviews that a listing has received. Airbnb has said that 70% of visits end up with a review, so the number of reviews can be used to estimate the number of visits. Note that such an estimate will not be reliable for an individual listing (especially as reviews occasionally vanish from the site), but over a city as a whole it should be a useful metric of traffic.

overall_satisfaction: The average rating (out of five) that the listing has received from those visitors who left a review.

accommodates: The number of guests a listing can accommodate.

bedrooms: The number of bedrooms a listing offers.

price: The price (in $US) for a night stay. In early surveys, there may be some values that were recorded by month.

minstay: The minimum stay for a visit, as posted by the host.

latitude and longitude: The latitude and longitude of the listing as posted on the Airbnb site: this may be off by a few hundred metres. I do not have a way to track individual listing locations with

last_modified: the date and time that the values were read from the Airbnb web site. The first line of the CSV file holds the column headings.

Here are the cities, the survey dates, and a link to download each zip file.

Aarhus Survey dates: 2016-10-28 (2258 listings), 2016-11-26 (1900 listings), 2017-01-21 (2167 listings), 2017-02-21 (2295 listings), 2017-03-30 (2323 listings), 2017-04-18 (2398 listings), 2017-04-28 (2360 listings), 2017-05-15 (2437 listings), 2017-06-19 (2802 listings), 2017-07-28 (3142 listings)

In 2025, Lisbon reported the highest number of Airbnb listings per 1,000 inhabitants among the selected European destinations. Overall, the Portuguese capital had roughly 45 Airbnb listings per 1,000 inhabitants that year. Paris and Florence followed on the list, with around 40 and 36 Airbnb listings per 1,000 inhabitants in 2025, respectively.

Check out TravelingToLisbon reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Check out Sarah E Pedro Apartments reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Dados apresentados nos arquivos

listings.csv - contém um total de 19638 registros de imóveis a serem alugados na cidade de Lisboa, com dados distribuidos em 77 colunas. Os dados apresentam informações básicos do imóvel disponibilizado para aluguel tendo como chave "id". Calendar.csv - registra as informações diárias de agendamento dos imóveis, possuindo 7171900 linhas com as informações distribuidas em 7 colunas. Cada item de "listing_id" possui 365 registros com sua disponibilidade e preço da reserva para todos os dias do ano. reviews.csv - apresenta os comentários feitos para os imóveis disponibilizados. Nem todos possuem informações registradas e nâo verifiquei necessidade de uso desse dataset.

Check out Bridge2Lisbon Team Cavalcanti Freudenfeld reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

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...

This is a merged dataset of 9 famous cities in Europe.

Amsterdam, Athens, Barcelona, Berlin, Budapest, Lisbon, Paris, Rome and Vienna.

The original Dataset was really messy and lacked describing appropriate information.

Perform analysis and tell a story you'd like to tell with this dataset.

Column names are self-explanatory.

Have fun exploring.

Check out Portas Do Céu reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Check out Trip2Portugal reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Check out Qualquer Destino reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Check out BeGuest reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Check out Time Cooler reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Check out Interhome Group reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Check out 7 Hills Apartments reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.

Check out Feels Like Home Portugal reviews by guests and hosts. Compare its performance to the Lisbon Airbnb market average. Decide if it’s the right choice for you.