This study investigates the factors that significantly affect the allocation of Airbnbs in Paris, one of the world’s top tourist destinations. Paris is divided into 20 arrondissements (districts), which are further divided into 80 neighborhoods 16 variables are used to explain the distributions of Airbnb offers in Paris. Data were collected using web scraping, INSEE , and QGIS/QuickOSM software. Predictive regression modeling was used for the data analysis. _x000D_
The results showed that bakeries, theaters, ratio of Airbnb and hotel beds (ratio B.H), restaurants, fast-food restaurants, shopping malls, and clothing shops are highly significant factors that affect the distribution of Airbnbs in Paris, but not access to public transport, universities, hotels, tourist attractions, museums beauty and bag shops. The results contribute to our understanding of the sectors of tourism and Airbnb, In addition to determine the factors that influence the distribution of Airbnb in Paris which will give clear idea to the policymaker about the characteristics of each neighborhoodsThe results contribute to our understanding of the future geographical selection of shops, theaters, and restaurants regarding businesses that depend on tourism, and provide insights into real estate investment opportunities for tourist accommodation. The results also contribute to our understanding of when and where hotels should modify their offers to tourists, depending on the spatial and geographical allocation of their properties.

Yoann FADEL
University of Angers

 
ID Abstract: 15