The history of competitive sets stems from the late 1980s when STR wanted to create a way for hotels to safely and legally share data in a manner that would allow hotels to effectively benchmark their performance against similar hotels. STR Analytics, a consulting branch of STR, is currently studying more than 36,000 global primary comp sets to help answer hoteliers’ questions about their comp sets and all the financial implications tied to the imperfect science of selecting them.
Competitive sets are chosen based on ADR, similar physical characteristics, location, group vs. transient mix, and a host of other factors. The number of hotels in a competitive set depends on the property and the supply within its competitive market; although the national average is 5.57 properties per comp set.
Since their conception, hoteliers have acquired comp sets, changed comp sets, added comp sets, and used comp sets to understand the economic climate around them, base internal analysis, indexes, and often performance bonuses.
With the support of extensive worldwide STR comp-set data, STR Analytics is looking at comp-set trends at the market, class, and national levels. STR Analytics has utilized this extensive database to build a model, essentially assigning report card-like letter “grades” to every single primary set, using several weighted metrics. The grade depicts how the comp set fits relative to other analogous properties’ comp sets. Using the comp-set grading model, averages have been established on class and market levels.
Following are some interesting facts STR Analytics has discovered regarding comp sets during its analysis:
In a perfectly competitive environment, the name-back percentage (the percentage of hotels you name as a primary competitor who name you as a primary competitor) should be 100 percent; meaning that competitive hotels find each other equally competitive. However, only 45 percent of U.S. hotels and 37 percent of European hotels named as primary competitors are named back as a primary competitor by the same properties.
This does not mean that 55 percent of all comp sets are irrelevant; this merely demonstrates that not all hotels are aligned with the “norm” for similar hotels. Some reasons for this may include:
– a lack of supply within the market
– failing to acknowledge new hotels
– grandfathering a comp set during conversion
– STR sufficiency guidelines
– substantial property uniqueness
– simply not choosing the optimal set from the beginning
– never reconsidering the set in the future
For those hotels not able to achieve an A+ primary comp set, it is imperative to understand the set’s challenges and use that knowledge to strategize for the future. Knowing the weak areas within a comp set can be just as effective as having a highly competitive comp set in helping to interpret indexes and change what could have been seen as a negative, as a positive (or vice versa). Understanding these challenges will also aid in tweaking the comp set to make it more tightly competitive.
In addition to the relative competitiveness of the overall comp set, it is equally as important to discuss the subject property’s “fit” within its comp set. Ideally, the subject property should fit within the comp set and not be a significant outlier in any substantial metric. In addition, the comp set as a whole should ideally form a narrow band of performance in most metrics, within which the subject property competes.
It is wise to revisit the selection of your competitive set every few years to consider new hotels and the economic cycle in order to fully understand your fit within your comp set. Understanding the other properties within your competitive set and your fit within that set can lead to smarter strategic decisions.
Caitlyn Hillyard is business intelligence manager for STR Analytics.
