Airbnb Analytics

This Airbnb analytics report explores key trends across listings, focusing on property types, room types, booking preferences, and regional performance. The analysis highlights the most popular property and room categories, identifies how many listings allow instant booking, and ranks cities based on guest activity using review counts as a proxy for visits. Overall, the insights provide a clear snapshot of listing behavior, customer preferences, and market opportunities within the Airbnb platform. 

The world of Airbnb is bustling with over 279,000 listings curated by more than 182,000 hosts, there's a lot happening behind every booking. At the heart of it all, hosts and guests are shaping the short-term rental landscape.

As we explored the data, a few names rose to the top. Listings like "ZWEI ZIMMER WOHNUNG" stood out for their popularity, while cities such as Paris emerged as guest favorites—showing strong activity and demand. It’s clear: the urban charm still captures hearts.

Diving deeper, we discovered that the platform is largely dominated by entire apartments, making up 65% of all listings. Private rooms follow closely, especially in larger apartments, offering a more personal, budget-friendly option for travelers. Interestingly, 58% of these listings support instant booking, suggesting a growing preference for speed and convenience among guests.

But it’s not just about where you stay, it’s also about who hosts you. Our analysis found that while 72% of hosts are verified (a reassuring signal for guests), many listings still lack data on response times, leaving potential gaps in guest communication and trust.

From high-performing hosts to room type trends and booking preferences, this analysis shines a light on how Airbnb operates beneath the surface. For stakeholders, these insights offer a clear direction: strengthen host responsiveness, enhance guest trust, and tailor offerings based on real user behavior.

I independently managed the entire project, from data cleaning and transformation to building the analytics dashboard in Power BI. Working solo, I handled every stage — creating calculated columns, designing visuals, and structuring insights for easy interpretation. The final outcome was a clear, interactive dashboard that revealed key loyalty trends, supporting data-driven decision-making for customer retention and marketing efforts.