STR: Airbnb's impact on NYC's boroughs
 
STR: Airbnb's impact on NYC's boroughs
24 FEBRUARY 2016 8:20 AM

Data shows that Airbnb units in the boroughs of New York City do not significantly affect key performance indicators of hotels.

By Claudia Alvarado, Steve Hennis, Jessica Haywood
STR


Editor’s note: This is the third part of an analysis series conducted by STR on Airbnb data in New York City. Airbnb provided STR with data on its operations in the New York City market—the largest data set Airbnb has provided to a third-party for independent analysis. Airbnb's data included only aggregate daily metrics; no host-level or other individually identifiable information was shared. STR was not remunerated in any way for its analysis, and its participation in this analysis was not contingent upon developing or reporting predetermined results. STR is the parent company of Hotel News Now.

 
HENDERSONVILLE, Tennessee—The first two parts of the STR analysis on Airbnb focused on Manhattan, but we didn’t want to ignore the outer boroughs, which also are affected by Airbnb’s presence. 
 
This article examines the outlying New York City boroughs of the Bronx, Brooklyn, Queens and Staten Island (Note: Hotel data in the Bronx cannot be reported to protect hotel confidentiality). This analysis considered only Airbnb's statistics for private rooms and entire homes, as they are assumed to be most comparable to the typical hotel room.




Manhattan contains 84.5% of all hotel rooms and 55.1% of all Airbnb units in the New York City market. The second largest borough for hotels and Airbnb differs. Queens has the second most hotel rooms with 10.4% of New York City hotel supply. By contrast, Brooklyn is Airbnb’s second largest borough, containing 36.7% of all Airbnb units in the city. The number of Airbnb units in Brooklyn outnumbers hotel rooms. 



Airbnb makes up nearly 59% of all roomnights available (Airbnb plus hotels) in Brooklyn, which is up from 53% in 2014. Airbnb’s share of roomnights sold and room revenue in Brooklyn is not as significant as supply but both are growing. In Queens and Staten Island, Airbnb’s share has increased the most of any New York borough. In both boroughs, Airbnb’s share of demand and revenue has doubled year over year.

    
In general, Airbnb units are about half as full as hotel rooms. However, occupancy in Airbnb units is increasing, particularly in the boroughs where hotel supply is more limited. Airbnb saw the Bronx’s occupancy increase 22% and Staten Island’s grow 11%. The remaining three boroughs experienced Airbnb occupancy growth of about 6% year over year. Conversely, hotel occupancy growth ranged from -4% (Staten Island) to +2.7% (Queens). However, as indicated in the chart above, hotels are generally full, so expecting growth at the same rate experienced by Airbnb is unrealistic. 



The occupancy chart above shows how Airbnb occupancy follows a similar seasonal pattern to that of hotels but with a one-month lag. Also, hotels experience less volatility in monthly occupancy. The chart illustrates that in spite of the substantial growth in Airbnb supply, hotels have been able to sustain high levels of occupancy. The correlation between hotel and Airbnb occupancy is 0.64, when one might expect those numbers to be in perfect synchronization.



In terms of rate, hotels in each of the boroughs achieved rates that were between 47% and 62% higher than Airbnb units. While Airbnb experienced more occupancy growth than hotels last year, rate growth for both Airbnb and hotels was flat. Queens, which was the exception, saw rate growth of 3% in hotels and 4% for Airbnb units.



The table above details the correlations between Airbnb metrics and hotel performance. The strongest correlation is between hotel average daily rate and Airbnb’s demand and revenue (0.81 and 0.80, respectively). This could indicate that as hotels increase their room rates, Airbnb benefits by capturing more price-sensitive demand. However, there are obvious confounding factors here. High-demand nights in New York City likely drive higher rates in hotels as they fill up, as well as Airbnb properties achieving higher roomnights sold.
 
Compression in the boroughs



 
The three outer boroughs all saw compression nights increase significantly in 2015. Queens experienced the largest increase in compression nights with 21 more than the previous year. The number of compression nights in Brooklyn increased by 18 year over year. Finally, Staten Island compression nights increased four nights. During these 2015 compression nights, hotels in the outer boroughs were able to raise rates 11.2% to 17.5% higher than on non-compression nights. In both Brooklyn and Queens, the ADR premium on compression nights increased significantly over the previous year.



 
During hotel compression nights, Airbnb units were a little over half-full, on average. However, this is approximately 40% higher than on non-compression nights. While hotels achieved significant rate premiums on compression nights, Airbnb hosts charged slightly more on compression nights, again indicating a lack of widespread revenue-management knowledge among Airbnb hosts. 

The data suggests that Airbnb might be filling a void in the New York City market by providing a different lodging option at a much lower price point. While it is difficult to deny that some demand might be moving from hotels to Airbnb, it’s also difficult to deny that Airbnb is not generating incremental new demand. 
 
Hotel performance in the broader New York City market continues to be quite strong. That said, Airbnb’s presence is expanding in each of the boroughs. The hotel industry must continue to monitor not just Airbnb but all alternative paid accommodations moving forward.
 
About the study
Airbnb provided STR with data on its operations in the Manhattan market—the largest data set Airbnb has provided to a third-party for independent analysis. Airbnb's data included only aggregate daily metrics; no host-level or other individually identifiable information was shared. Metrics requested by STR and utilized for the purpose of the analysis included supply, demand, revenue by borough, class and trip length. STR was not remunerated in any way for its analysis, and its participation in this analysis was not contingent upon developing or reporting predetermined results.
 

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