Airbnb vs Long-Term Rentals: Understanding NYC Real Estate

Posted on Jul 25, 2023

Airbnb, the road to Easy Street?

What’s the best approach for a property owner who wants to generate revenue? Airbnb offers the allure of high nightly prices and the potential for revenue that exceeds the long-term rental market. On the other hand, a rental under lease provides sure income over a certain period of time, not subject to the seasonality of tourism or the changing supply of accommodation for visitors.

This Shiny app aims to explore the relationship between the long-term rental market and Airbnb in NYC neighborhoods. Given the varying popularity and prices of Airbnb across different regions of the city, the dashboard allows the user to view snapshots of the market across each of the boroughs and neighborhoods. By navigating the tabs and filters, a potential real estate investor could determine where and how to invest for the greatest potential revenue.

The data visualized in the app are from two sources depicting the real estate market in March 2023. Inside Airbnb provides data on current Airbnb listings to enable publicly available analysis and foster transparency around Airbnb's impact on residential communities. Each listing includes its neighborhood, number of bedrooms, daily price, guest ratings, and other details. Street Easy, a popular site to search for NYC rentals, provides monthly aggregate data on neighborhood rental market statistics, including median rent and total rental inventory for each neighborhood.

Market Overview

This tab provides an overview of the Airbnb landscape in each borough and neighborhood. The majority of listings are in Brooklyn, and prices are highest in Manhattan. A user can filter further by borough to see the distribution of listings across neighborhoods and by neighborhood to see price distribution for Airbnbs of different sizes.

Supply and Demand

Of course, a listing will only bring in revenue if it is in high demand and continues to generate new customers. While the data doesn’t include any measure of listing popularity through number of guests or number of requests for bookings, we attempt to understand demand for listings through the number of monthly reviews. While an imperfect proxy for demand, reviews do provide some insight into a listing’s total usage. Not every guest stay results in a review, but a listing with more reviews likely has more guests overall. With the exception of Manhattan, each of the boroughs shows a declining number of reviews for listings above $200.

Airbnb and the Rental Market

Airbnb is often blamed for a negative impact on the residential communities where the properties are located. In addition to the adverse effects of having tourists in a residential area, the presence of short-term rentals through Airbnb are said to drive up long-term rental prices. While we can’t evaluate causality, this page illustrates the correlation between long-term rental prices and inventory and different Airbnb metrics. 

We see a positive correlation between Airbnb listing price and neighborhood median rent, and we see that there tend to be fewer monthly Airbnb reviews for listings in neighborhoods with higher median rent. We also see a positive correlation between Airbnb listing inventory and long-term rental inventory. However, without seeing change in the market over time, the true relationship is ambiguous. This correlation may suggest that the presence of Airbnbs is driving up rental prices and causing more turnover, resulting in a greater inventory, but one could also conclude that Airbnb is not having a detrimental effect on the supply of long-term rentals.

Projecting Revenue

As with demand, the data doesn’t offer much insight into listing occupancy rate. Especially with the NYC law requiring a 30-day minimum stay, the number of monthly reviews makes it difficult to approximate a listing’s occupancy rate. Instead, the user can use this dashboard to see the impact of a changing occupancy rate on revenue for Airbnb and long-term rentals. 

With long-term rental monthly revenue on the X-axis and Airbnb monthly revenue on the Y-axis, each point represents the expected values for a single neighborhood. As the occupancy rate shifts, points above the line generate more revenue as an Airbnb, while points below the line generate more revenue as a long-term rental. Given a 100% occupancy rate, almost all neighborhoods would be most profitable for Airbnbs. Given a 25% Airbnb occupancy rate, most neighborhoods would be more profitable as long-term rentals. However, at 65% occupancy, there is a more even split - some neighborhoods would generate more revenue with Airbnbs, while others would generate more with long-term rentals. The user can adjust the slide to a projected occupancy rate to view which neighborhoods fall above or below the line.

Conclusions and Recommendations

What’s missing from this data is a clear view into the popularity and usage of listings, whether by price point, size, or neighborhood. Without an accurate prediction for Airbnb occupancy, the revenue gap between the rental market and the Airbnb market is ambiguous. While Airbnb offers the potential for more revenue from  higher nightly costs, long-term rentals are more stable and predictable. 

Based on some estimates, NYC has an overall Airbnb occupancy rate of 75-85% in 2023. However, more research could be done and data gathered on property costs, travel preferences, and number of travelers per year to get a clearer estimate of expected revenue for an investor. Without more data, we would recommend buying property in a neighborhood where projected revenue is similar for a long-term rental and an Airbnb with an occupancy rate at or below 75% . This would allow for flexibility to shift between an Airbnb or a long-term rental. 

Overall, the dashboard offers a potential investor a means to gather insights about the current market across different regions of the city as they plan to buy property.

About Author

Emmeline Danforth

Data scientist with a background producing analyses, visualizations, and insights to drive decision-making within the education and policy sectors.
View all posts by Emmeline Danforth >

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