Abstract
This article focuses on Airbnb that was one of the most popular sharing models in Economics. This study investigates the Airbnb business performance using customer reviews to calculate the monthly occupancy rate and a yearly income of Airbnb hosts in Amsterdam between 2015 and 2019. This study uses modest and optimistic estimates for the review rate with 0.6 percent and 0.4 percent, respectively, and 3.9 for the average length of stay in Amsterdam. Findings reveal that the visitors increase from May to June, then again in September and October. The monthly occupancy rate of the super host has a higher occupancy rate rather than the regular host at every district. The yearly income of the super hosts in Centrum-West and Centrum-Oost was higher than in other districts, while annual income was most deficient in Gaasperdam - Driemond. In term of average occupancy and number of maximum people per accommodation, accommodations which accommodate more than eleven people have more occupancy rate than others. Customer reviews can be used to calculate the monthly occupancy rate and a yearly income of Airbnb hosts.
Highlights
The development of the sharing economy business that continues to increase is the main competitor for the hotel industry and impacting the tourism industry (Oskam & Boswijk, 2016)
Despite its importance and scale in the tourism and hospitality market, scientists have begun a systematic study of the Airbnb trend, shifting from a model represented in the media to a study goal guided
This study investigates the visualization of the Airbnb business performance model using customer reviews to calculate the monthly occupancy rate and a yearly income of Airbnb hosts
Summary
The development of the sharing economy business that continues to increase is the main competitor for the hotel industry and impacting the tourism industry (Oskam & Boswijk, 2016). Airbnb’s service quality attributes effects on customer satisfaction (Ju et al, 2019), price determinants (Wang & Nicolau, 2017), the construction of home feeling (Zhu et al, 2019), trust evolution (Ert & Fleischer, 2019), super host profile (Setiawan, 2020b), impacts of host quality and quantity attributes (Xie & Mao, 2017), development in Paris (Heo et al, 2019), user review comments (Cheng & Jin, 2019), geospatial analysis (Setiawan, 2020a), and dynamic pricing strategies (Gibbs et al, 2018; Oskam & Boswijk, 2016), price factors (Moreno-Izquierdo et al, 2020), and behaviour (Oskam et al, 2018)
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