The path of least resistance explaining tourist mobility patterns in destination areas using Airbnb data

Airbnb and COVID-19: SPACE-TIME vulnerability effects in six world-cities

“…The volume and extent of the Airbnb data allows testing several theories. For instance, Türk et al (2021) show the validity of “ the path of least resistance ” principle for explaining the spatial behaviour of tourists in 25 major tourist destination cities by Airbnb data. Research topics also include the analysis of the relationship between the traditional hotel industry and Airbnb, and also the implications of Airbnb for the regular housing market.…”

Analysis of the Tourist’s Behavior in Lisbon Using Data from a Mobile Operator

“…In the article entitled "The path of least resistance explaining tourist mobility patterns in destination areas using Airbnb data", Umut Turk et all [15], 2021, resorted to data provided by the Airbnb local accommodation platform to identify which are the 25 most attractive tourist destinations the world, having as motivation to do so the fact that a lack of knowledge on the topic was identified. Initially, an assessment was made of the quality of Airbnb's offer and prices in each of the locations and subsequently the prices and quality of the transport network were evaluated in each of the locations studied.…”

The effects of location before and during COVID-19

“…Location is a widely used characteristic in exploring real estate ( Dubin, 1992 ) and is one of the most desired by Airbnb guests ( Visser et al, 2017 ). Not surprisingly, in the existing research on listings' performance, locational patterns have been used more and more ( Benítez-Aurioles, 2018 ; Boto-García, 2022a ; Boto-García et al, 2021 ; Oskam et al, 2018 ; Türk et al, 2021 ; Xie and Mao, 2017 ; Yang and Mao, 2020 ). The location's effect can be measured using different groups of characteristics: environmental, social/economic, and accessibility related.…”

“…Recently, a study analyzed 25 cities and explored the effect that the distance to transportation generated. It concluded that “1 % increase in distance to the nearest bus stops generates 2 % decline in prices, and 1 % increase in distance to railway stations decreases prices by 8 %” ( Türk et al, 2021 , p. 7). In addition, public transport is a sustainable urban tourism mode ( Le-Klähn et al, 2015 ) that has been scarcely studied in the Airbnb literature.…”

“…Public transport is used most frequently by visitors to small areas, and the educational level of tourists and the price of public transport significantly influence the transport mode choice ( Le-Klähn et al, 2015 ). Being located close to public transportation has a positive impact on listing rates ( Türk et al, 2021 ).…”