Abstract
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.
Highlights
The development of the Internet-based industry has always been in a hot area where emerging industries are constantly flowing, especially those related to the sharing economy, are growing more rapidly[1]
This project collects more than 50 types of information about 1003 rental houses on Airbnb in New York, USA in 2020 through the Internet, excavates the corresponding important characteristics and establishes the corresponding rental status prediction model
Through this research, based on the short-term rental information of merchants on Airbnb in New York in April 2020, we found that the most influential factor on the status of rental housing is usually the corresponding rental status information of the merchants in the past
Summary
The development of the Internet-based industry has always been in a hot area where emerging industries are constantly flowing, especially those related to the sharing economy, are growing more rapidly[1]. Too fast development often brings many unavoidable potential problems. Utilizing the powerful computing power of modern computers and combining statistical methods such as machine learning to predict the future state of the studied variables, we can take measures in advance to prevent or even avoid problems. I obtained more than 50 kinds of information on 1003 rental houses on Airbnb in New York in April 2020. We observed the impact of time and region on the state of housing rental through visual analysis
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