| Issue |
E3S Web Conf.
Volume 251, 2021
2021 International Conference on Tourism, Economy and Environmental Sustainability (TEES 2021)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 5 | |
| Section | Analysis of Energy Industry Economy and Consumption Structure Model | |
| DOI | https://doi.org/10.1051/e3sconf/202125101017 | |
| Published online | 15 April 2021 | |
Airbnb Short-term Housing Rental Status Prediction Model Under the Impact of the COVID-19 Pandemic
School of Mathematics and Science, Leshan Normal University, 614000 Leshan, China
* Corresponding author: luzhixiang1998@gmail.com
With the vigorous development of the sharing economy, the short-term rental industry has also spawned many emerging industries that belong to the sharing economy. However, due to the impact of the COVID-19 pandemic in 2020, many sharing economy industries, including the short-term housing leasing industry, have been affected. This study takes the rental information of 1,004 short-term rental houses in New York in April 2020 as an example, through machine learning and quantitative analysis, we conducted statistical and visual analysis on the impact of different factors on the housing rental status. This project is based on the machine learning model to predict the changes in the rental status of the house on the time series. The results show that the prediction accuracy of the random forest model has reached more than 94%, and the prediction accuracy of the logistic model has reached more than 74%. At the same time, we have further explored the impact of time span differences and regional differences on the housing rental status.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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