BOULDER, Colorado—All competitors are not created equal. It's a basic concept to understand, but operators who sift through their daily analytics are constantly challenged by this simple fact.
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Carter Wilson
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Orly Ripmaster
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The STAR report provides the most meaningful benchmarking data in the industry, but in order to protect data confidentiality, reports must include at least four competitors, some of which may be less comparable and/or competitive than others in the set. Once included, all properties in the competitive set are treated as equal; each property is considered 100 percent competitive against one another. However, a new tool allows you to see your competitive strength in a way never seen before.
Consider the following example. You operate a La Quinta Inn in a small suburban market. For the purposes of your STAR report, you list as competitors a Hampton Inn, Holiday Inn Express, Courtyard by Marriott, a Comfort Inn and Best Western. For illustrative purposes, we'll use one week's worth of data from September 2009. The following chart illustrates your hotel's revenue-per-available-room penetration results from that week.
Click image to enlarge.
RevPAR penetration levels that week for the La Quinta ranged from 82 percent on Wednesday to 136 percent on Thursday; the overall average for the month was 98 percent. These data are meaningful to give you an idea about where you sit in your competitive environment on an average basis, but it doesn't consider the fact that, in reality, your property might fiercely compete with one or two of the properties in this set and only compete on a secondary level with the others.
The new tool developed by STR Analytics is the Market-Weighted Penetration Index Analysis, which uses a multiple regression model to objectively determine what competitors are most statistically significant to your hotel and then readjusts your penetration index accordingly.
Multiple regression is a flexible, statistical process of analyzing several variables, namely a dependent variable (i.e. your property's daily RevPAR) to any other independent variables (i.e. competitors' daily RevPAR). In simple terms, the primary indicating factor in the regression analysis is R2.
R2 = Coefficient of determination. R2 represents the proportion of the variance in one variable that is explained by the other variables. In other words, R2 represents how well your property’s RevPAR variations are explained by the variations in your market.
For example: If R2 is 0.85 that indicates that 85 percent of the total variation in your hotel's RevPAR can be explained by the relationship between your hotel and its competitive set. Strong relationships indicate high levels of competition within a competitive market. Assuming your competitive set is the most appropriate to benchmark your performance (which may have changed in the new economy and is worth examining) then the R2 should be significant.
The significance of R2 indicates the confidence level an analyst can have in making an estimate from that statistical model, and provides a benchmark of how well outcomes are likely to be predicated from that model. The higher the statistical significance the less likely the estimated value could occur by chance.
The multiple regression analysis output comes in the form of a linear equation, which is analogous to an improved competitive set performance metric. The equation objectively gives greater weights to those competitive properties that have the strongest competitive relationships and lower weights on properties with less direct interactions. This equation amplifies the traditional competitive set “average" and is objectively based on the competitive interactions of the market and what the market’s performance would expect from your hotel's performance. Rather than benchmarking how well you performed to the traditional market average, The Market-Weighted Penetration Index determines how well your property performed relative to this weighted equation.
Back to the example, after running the hotels in this set through a multiple-regression model (with the La Quinta as the dependent variable), the resultant R2 and beta weights (which establish the relative statistical importance of each independent competitor) are used to produce a new RevPAR penetration index. The results are shown in the following table.
Click image to enlarge.
The Market-Weighted Penetration Index shows a similar trend as the original but tells a different story for the La Quinta. In fact, the weighted index reveals an average of 30 penetration points higher than the traditional index for this sample week, suggesting the La Quinta is actually more competitively forceful relative to those properties with whom the La Quinta is most statistically significant than the traditional market average indicated.
This methodology becomes particularly useful over longer periods—months or years—where discrepancies from ill-selected competitive sets become more pronounced. Such discrepancies allow owners to revisit assumptions about the nature of their competitive sets and perhaps provide justification for revisiting the selection of properties that comprise them.
In a nutshell, the Market-Weighted Penetration Index uses statistics to apply greater weight to the more relevant properties in your competitive set. The objective nature of the mathematical formula enables STR Analytics to preserve the confidentiality of the data in a way that would not be available if a client ordered a STAR or Trends Report in which he or she defined the weights subjectively. This tool is just one of many being launched by STR Analytics to help owners and operators increase their profitability and gain an elevated understanding of their competitive environments. If you are interested in this analysis for your property or portfolio, contact STR Analytics (www.stranalytics.com).
You can reach Carter at:
4940 Pearl East Circle, Suite 103
Boulder, CO 80301
(303) 396-1644 (Direct)
(303) 449-6587 (FAX)
cwilson@STRanalytics.com
