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.

Monthly occupancy rate, ADR and seasonality trends for Airbnb listings in Lisboa, Portugal. Based on aggregated real-time market data.

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.

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 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.

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 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.

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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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.

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