Airbnb has become a frequent option when people start to plan their trips. The trend of Airbnb usage has shown that people are switching from regular hotel booking platforms like Expedia, Bookings, etc., to Airbnb while traveling to a new place. Airbnb is not only a platform that offers new options for hotel booking but also a lifestyle-sharing platform that allows travelers to share their living experiences from different destinations. What’s more interesting is that even during the Covid-19 pandemic, when people cannot travel or go outside, they can still interact with homeowners (hosts) on their chosen topic via streaming video. Therefore, those contents successfully attract more users who become passionate about the Airbnb experience and are willing to share their comments or reviews under each topic or location. This paper focuses on the renting industry. We select Airbnb and try to find the relationships behind different variables. We want to look at our database and use different languages to understand Airbnb in New York, such as multiple regression analysis. We have a database before the Covid-19 outbreak, which can fairly reflect the situation during the normal time. In order to find the relatively accurate correlation results, we want to confirm the correlations which confidently proclaimed that ‘calculated_host_listings_count’ has positively correlated with ‘Reviews_per_month’. This paper aims to identify the key elements that most impact customers submitting their monthly reviews and how those reviews motivate other customers to book their upcoming trips through Airbnb continuously.