Editor’s Note: This is a rebuttal to “Industry uses wrong metrics to project RevPAR.” In it, columnist Joel Ross argues the perceived relationship between revenue per available room and GDP is problematic.
Yes, the prognosticators for the industry missed the worst decline in RevPAR in history, but that was not because of some perceived relationship of RevPAR to GDP. We at STR have been tracking the lodging industry’s performance for more than 25 years and we are not sure that anyone has ever even looked at the relationship between RevPAR and GDP. RevPAR is not the same as room nights sold or lodging demand. That is the relationship that is studied so intently. RevPAR projections are actually just a mathematical outgrowth of room supply, room demand and room rate projections.
The econometric variables that go into each of those projections is varied and quite detailed. Room-supply projections have their own attributes as does room demand and room rates (though everyone is still struggling to isolate those variables that do a good job of projecting room rates). We do not think a forecast for this industry has ever been compiled just using expected changes in GDP or the relationship between current performance and that of previous downturns. While those are key items to look at, they are hardly the sole criteria for a forecast.
No one forecast the September 2008 collapse of Lehman Brothers, the collapse of the financial sector of our economy or the disparaging comments made by President Obama that decimated demand at U.S. luxury hotels and resorts. We also didn’t see any forecasts that predicted the industry would respond to this downturn by slashing room rates by levels never seen by anyone. In addition, we failed to see any media coverage that forecasted the supply projections kept supply growth at historically high levels in spite of collapsing demand.
In hindsight, most projections were off on RevPAR because of the sharp decrease in room rates that no model accurately projected. The supply and demand projections were not as far off, though even minor shifts in these two variables resulted in occupancy performance falling well below expected levels. All of this said, there clearly are problems using GDP numbers, which Ross said “are distorted by various major factors of historic proportions.” The problem stems from the fact that virtually all of the variables used in any model have been distorted by various factors of historic proportions including the industry’s own performance. In this environment, the number of variables one has to look at to develop a solid forecast explodes. It does not narrow down to only one variable like GDP.
Ross suggests “some metrics that really do matter,” including the unemployment rate, the foreclosure rate, international travel, local taxes and issues with leverage and underwriting. STR has been using those metrics along with at least 10 other variables, including capital spending, fuel and airline pricing, consumer price index, industrial production, disposable personal income and currency shifts. There is an evident relationship between changes in GDP and changes in lodging demand, and we sometimes like to show this relationship during our presentations. But to imply that is the sole criteria for developing a RevPAR forecast for the lodging industry is incorrect.
