Unveiling Patterns in European Airbnb Prices: A Comprehensive Analytical Study Using Machine Learning Techniques

TL;DR

This study analyzes European Airbnb prices using machine learning models to identify key factors influencing pricing, providing insights for hosts and travelers and contributing to shared economy pricing research.

Contribution

It introduces a comprehensive analysis of Airbnb pricing in Europe employing advanced regression techniques and diverse determinants, offering new insights into pricing dynamics.

Findings

Location and property type significantly impact prices

Host-related factors influence pricing variability

Machine learning models effectively predict Airbnb prices

Abstract

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.