  In the burgeoning market of short-term rentals, understanding pricing
dynamics is crucial for a range of stake-holders. This study delves into the
factors influencing Airbnb pricing in major European cities, employing a
comprehensive dataset sourced from Kaggle. We utilize advanced regression
techniques, including linear, polynomial, and random forest models, to analyze
a diverse array of determinants, such as location characteristics, property
types, and host-related factors. Our findings reveal nuanced insights into the
variables most significantly impacting pricing, highlighting the varying roles
of geographical, structural, and host-specific attributes. This research not
only sheds light on the complex pricing landscape of Airbnb accommodations in
Europe but also offers valuable implications for hosts seeking to optimize
pricing strategies and for travelers aiming to understand pricing trends.
Furthermore, the study contributes to the broader discourse on pricing
mechanisms in the shared economy, suggesting avenues for future research in
this rapidly evolving sector.
