Mr. Gallagher

Revenue Management

Net Operating Income Forecasts

Looking Under the Top Line

By Bram Gallagher, Economist, CBRE Hotels' Americas Research

The most widely reported hotel performance metrics include occupancy, average daily rate (ADR), and revenue per available room (RevPAR), with RevPAR frequently used as the measure of market financial strength. While RevPAR itself is a good proxy for profitability when the market is in a typical state, it potentially obscures the performance of hotels when the market veers away from the norm. Pass-through performance (i.e., the percent change in bottom-line income given the percent change in top-line revenue), or measures that are net of a market-wide average of expenses, are increasingly of interest as record-breaking occupancies make RevPAR movements an incomplete measure of hotel performance.

To analyze the change in market conditions, CBRE Hotels' Americas Research is developing a technique to produce forecasts of net operating income (NOI). This measure takes the sum of a hotel's revenue from all sources and subtracts from it all departmental expenses, property taxes, management fees, insurance expenses and capital reserves. Although NOI closely tracks RevPAR, it also allows expenses and non-room revenue to vary based on occupancy. This potentially gives a fuller impression of a hotel's performance during the recent period of persistently elevated occupancy.

To find out what the relationships between occupancy, expenses, and revenues are, we turn to the Trends® in the Hotel Industry data: approximately 65,000 hotel statements from 1994 to 2016. These statements are unweighted and so are, by themselves, not representative of all hotels in aggregate; however, the statements are from a broad geographic distribution and all levels of performance. We examine these properties' expenses and revenues to understand the effect of varying occupancy levels across markets. Real average daily rate (RADR) is included as a control variable with the assumption that ADR is correlated with unobserved factors like the quality of service or the cost-of-living immediately proximate to the hotel. Subsampling could be used to obtain market-specific effects or even to estimate chain-scale specific effects. Here we use all useable records from the entire sample, or 50,507 complete records, to obtain national results.

An important consideration when trying to determine the cause-and-effect relationship of occupancy on expenses and revenues is that while higher occupancy produces higher expenses as more rooms need to be cleaned and linens washed, higher expenses also may increase occupancy when they are used for marketing or enhancing the guest experience. We attempt to correct for this situation, called endogeneity, by using the 3-stage least squares technique on a system of equations. This widely-used technique in economic analyses produces consistent results in the presence of endogeneity, and in this case will provide a better estimate of the effect of occupancy on NOI holding other variables constant. The model produced highly statistically significant results, and three-quarters of the variation in rooms expenses were explained by the variables in our model. Other expenses achieved a fit ranging from 60%-96% of the variation explained, although no other expense category is as generally variable as rooms expense. Considering the parsimony of our model's specification, this is good evidence that our model is doing its job of relating occupancy to expenses.

Trends® has an impressive, large sample of individual hotels. To get broadly representative NOI estimates, national-level STR data for supply, demand, and room revenue are added to the analysis and fitted to the relationships estimated from Trends®. This method then is applied to forecast RevPAR data from Hotel Horizons® to obtain forecast NOI. The assumption underlying this formulation is that while hotels that submitted statements to the Trends® sample are not representative of all the hotels in a market, the effect of occupancy and ADR on revenues is consistent within a sample. The result is a single time series of nationally representative annual data of observed RevPAR and estimated NOI from 1991 to 2016 and forecast RevPAR and NOI through 2020.

In 2015, national hotel occupancy reached the highest annual occupancy for all years recorded by STR with an average annual occupancy of 68.3%. Previously, from 1988 to 2014, the long-run annual average occupancy had been 64.4%-- a nearly 4 percentage point differential. Real RevPAR grew 5% year-over-year in 2015, but we do not expect that expenses changed in the same way as they did in 2004, when occupancy was 63.7% and real RevPAR grew 5%.

Expense growth is also related to the growth rate of occupancy, which is in turn related to level occupancy. Because of the historically cyclical nature of the lodging market, occupancy growth is, on average, lower when occupancy is high and higher when occupancy is low. We observe this pattern in the above mentioned years also. Occupancy growth in 2004, when level occupancy was low, was 4% year-over-year. 2015, on the other hand, had an occupancy growth rate of 1.1% year-over-year. Adapting expectations and models to these new occupancy levels will continue to be important, since we do not forecast national average annual occupancy dipping much below 68%, or positive occupancy growth, for the foreseeable future.

NOI Change From Trough to Peak

The resulting estimated and predicted changes to national NOI and RevPAR are illustrated in Exhibit 1.

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In the period following the Great Recession, NOI grew at a faster rate than RevPAR, peaking at 12.0% year-over-year growth in 2011. By 2016, however, NOI growth approaches zero and remains below RevPAR until 2018. As illustrated in Exhibit 1, and for the hotelier, moderated RevPAR growth belied a much steeper drop in NOI growth in 2015.

Turning to Exhibit 2, we can see the explanation for the difference: revenue growth is more sensitive to changes in occupancy than expense growth. During times of increasing occupancy, revenues grow more quickly than expenses because fixed costs are spread out to more rooms, while the opposite occurs in times of decreasing occupancy. While RevPAR and NOI growth rates are highly correlated, RevPAR change will almost always understate the gain realized or the pain experienced by the hotelier, the exception being when occupancy holds steady.

Occupancy grew on a year-over-year basis from 2010-2015. Whenever occupancy growth is above zero, revenue growth is observed to be higher than expense growth. Starting in 2016, occupancy levels off, and is forecast to hold steady or slightly decline. Correspondingly, revenues are forecast to grow at about the same or a slower speed than expenses for the foreseeable future, and NOI growth will be slightly lower than RevPAR growth during 2017.

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Evaluating Market Performance With NOI

The revenue-manager, investor, or developer can use the NOI forecast to give a clearer picture of the future strength of a hotel market. High levels of occupancy suggest that the market is reaching a peak, and that occupancy will hold steady or decline. Caution should be taken when using RevPAR as a substitute for NOI performance. Although RevPAR growth works well when occupancy is stable, if the occupancy rate starts to drop, expenses will not fall as quickly as revenue does, and the hotel executive should prepare for NOI decreases that are larger than RevPAR decreases. Conversely, if for a particular market we expect the occupancy to grow, revenues should grow more quickly than expenses, and NOI will increase faster than RevPAR.

Example: A revenue manager can justify maintaining current rates rather than reducing them if NOI increases more than 3% during 2017. Assuming that NOI grows at the same rate as RevPAR, that is 3.1% in 2017, this manager concludes that current rates are justified. However, as shown in this article, NOI does not grow at the same rate as RevPAR. NOI grows according to our forecast of 2.2%, and the threshold NOI needed to justify current rates will not be met. In retrospect, maintaining rates will not have been justified.

Of course, individual market forecasts will differ depending on local conditions, and individual hotels can be exceptions. For example, in some markets and property types, non-room revenue is becoming increasingly important. In these areas, RevPAR may understate the marginal impact of another room sold since ADR is the only revenue metric captured in RevPAR. A strategy to lower prices to attract guests that might spend additional money at the hotel restaurant, on room service, or for rental facilities may not make sense from a strict RevPAR perspective. On the other hand, it's possible that a hotel with meaningful non-room revenue might find its NOI growing even as its RevPAR shrinks or remains flat.

Another limitation of these estimates is that the effects of occupancy are assumed to be constant. It may be the case that per-room expenses increase with occupancy after some threshold compression occupancy is reached. The national average likely has not reached this threshold, but it could be that individual hotels have, and even submarkets like New York near Times Square, which is fast approaching annual average occupancy of 90%.

These scenarios reinforce the intuitive understanding that RevPAR may not tell the whole story of a hotel market's financial performance. Hotel NOI can tell more because it incorporates expenses' and non-room revenues' relationship to occupancy. In the current environment, with occupancy records being broken in many markets, NOI is especially important now as the conventional rules of market performance and RevPAR may not work in unconventional times.

Bram Gallagher is an Economist for CBRE Hotels’ Americas Research. He maintains performance forecasting models for 60 major U.S. hotel markets, performs consulting work for a destination marketing organization, creates tools for hotel developers, and examines the effects natural disasters have on hotel occupancy. His background in statistical modeling and understanding of how markets operate have allowed him to reveal powerful, new insight into the future of the lodging industry. Mr. Gallagher has worked as a professor at Middle Tennessee State University and Berry College. He left Academia for a research position in the lodging industry in 2015 to better learn how economics is practiced. Mr. Gallagher can be contacted at 404-812-5189 or bram.gallagher@cbre.com Please visit http://www.cbre.com for more information. Extended Bio...

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