Editor’s note: This is the second article of a four-part comparison analysis conducted by STR on Airbnb data in Manhattan. The first article summarized the findings of the analysis. This second part examines competition during compression nights. The third article will analyze New York City boroughs. The fourth article will examine special events. STR is the parent company of Hotel News Now.
HENDERSONVILLE, Tennessee—One of the most frequently cited concerns surrounding Airbnb is that the alternative-accommodations provider is reducing the number of compression nights in the hotel industry.
These high-occupancy nights are important due to the rate premiums that hoteliers are able to charge. Some industry insiders also have argued that Airbnb is reducing pricing power on compression nights.
STR, the parent company of Hotel News Now, took a deep dive into the Manhattan data to check the validity of these claims. This analysis shows little evidence that Airbnb causes fewer compression nights or reduces pricing power on those nights.
- Read Part One of STR’s analysis of Airbnb’s minor effect on Manhattan demand.
Compression nights are defined as occupancy at or above 95%. This analysis considered only Airbnb's statistics for private rooms and entire homes, as they are assumed to be most comparative to the typical hotel room.
Compression nights
In 2015, there were 52 compression nights in Manhattan, which is 26 fewer than in 2014. However, 2014 was an outlier that had significantly more compression nights than the long-run average of 47 nights. Over the last 10 years, compression nights have accounted for 18% of annual revenue, even though they’ve made up 13% of the nights on average. In 2015, compression revenue accounted for 19% of the total revenue for the year.
In terms of compression nights by class, luxury, upscale and midscale/economy saw more compression nights in 2015 than the long-run average. However, upper-upscale and upper-midscale hotels experienced two fewer compression nights than average. While this could be partially related to Airbnb, new hotel supply also must be factored in, particularly in the upper-midscale segment, which saw more than 6% supply growth in 2015.
Although 2015 saw fewer compression nights than 2014, pricing power on those nights was stronger in 2015. As the chart above shows, the average-daily-rate premium on compression nights compared to non-compression nights was almost 28%, compared to 24% in 2014. The 2015 rate premium was in line with the ADR premiums of 2012 and 2013, which was before Airbnb had a substantial presence in the city.
Meanwhile, the Airbnb rate premium on hotel compression nights was 4% in the 12 months ending November 2015. Airbnb occupancy on nights where hotels ran 95%-plus occupancy was 57.2%, which was slightly less than Airbnb occupancy on compression nights last year.
All hotel classes achieved rate premiums 20% higher than non-compression nights. Lower-end hotels and Airbnb units realized greater rate premiums than the upper-end. Notably, upscale, upper-midscale and midscale/economy hotels all achieved their highest rate premiums in at least 10 years.
For all non-compression nights, hotel occupancy growth was slightly positive at 0.3%, which brought absolute occupancy to 85.3%. Airbnb occupancy increased 12.6% during non-compression nights to reach 45% in 2015. While this is remarkable growth, the base is lower than hotels, which makes it easier to experience this kind of growth. Rates in both hotels and Airbnb units have dropped slightly this year during non-compression nights at -0.3% and -0.2%, respectively.
Class performance on non-compression nights shows that ADR growth was flat or down across the board, with the exception of luxury hotels and Airbnb units. While hotel occupancy was flat, Airbnb units saw growth across all classes, led by upper-upscale class units. It could be argued that Airbnb’s upper-upscale growth on non-compression nights is part of the reason why hotels in that class experienced fewer compression nights in 2015 than average. However, it also must be recognized that Airbnb comprises 3.1% of supply and less than 1.5% of total upper-upscale demand.
Based on this data, there is little evidence that Airbnb is leading to fewer compression nights or reducing pricing power on those nights. The addition of more than 3,000 new hotel rooms in Manhattan in 2015 arguably had more of an effect on compression nights than Airbnb.
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.