Natural Occupancy Shifts in Hotel Markets

Abstract

The natural occupancy rate underpins room price adjustment models for hotel market evaluations; however, models that assume that this rate is time invariant and everywhere constant may introduce error when forecasting future real room rates and benchmarking market strength. We use monthly STR room rate and occupancy data for five large U.S. metropolitan hotel markets to estimate natural occupancy differences in time and across markets. The notion of a time-varying natural occupancy aligns with changing market equilibrium. When aggregating over the entire sample, long-run average occupancy is reasonably good approximation of estimated constant natural occupancy. We use a Markov switching model to determine the likely equilibrium occupancy for unique periods in time, and these results inform Tse and Fischer's model to estimate time-and market-specific natural occupancy. Using the estimated natural occupancy rates for prevailing market regimes produces better fitting predictions than either constant natural occupancy or long-run average occupancy. Accordingly, they are most appropriate for practical applications.

The Influence of Occupancy Change on Hotel Market Equilibrium

“…deRoos estimated the natural occupancy level on a nationwide basis to be 62.9%, with individual markets showing natural occupancy levels ranging from 56.4% to 75.9%. Gallagher and Corgel (2018) subsequently identified that natural occupancy rates are time varying and regime varying and that the natural occupancy rates are higher for higher priced hotels. Their research, which included monthly occupancy and ADR observations from 1988 through 2017, suggested that natural occupancy rates range from 60% to 77% depending on the city, lodging subgroup, and whether the market is in a normal or recessionary environment.…”

“…The fixed and perishable nature of room inventory coupled with widely fluctuating demand patterns compel hotels to continuously adjust their prices and their sales and marketing tactics (Kimes, 1989), making the process of managing revenues more complex (Erdem & Jiang, 2016;Kimes, 2015). Understanding market dynamics is critical not only for investors making decisions about where to build new hotels (deRoos, 1999) but also for managers and revenue managers tasked with optimizing revenues and managing inventory for their properties (Gallagher & Corgel, 2018).…”

“…The studies by deRoos (1999), Corgel (2005), and Gallagher and Corgel (2018) have framed the hotel 953849C QXXXX10.1177/1938965520953849Cornell Hospitality QuarterlyDenton and Sandstrom research-article2020 1 Washington State University, Pullman, USA 2 Washington State University, Vancouver, USA equilibrium theory, which posits that hotel markets have natural occupancy rates at which real average rates remain stable, increasing at the rate of expense inflation. Occupancy levels exceeding this equilibrium rate contribute to increased rate growth, and occupancies below equilibrium contribute to below-inflationary rate growth.…”

“…The seminal study by deRoos reported that R 2 values rarely exceed .15, with .10 considered to be a good fit (deRoos, 1999). Gallagher and Corgel's recent study found R 2 coefficients of .18 to .20 for the top 60 markets, with individual markets achieving coefficients ranging from .18 to .53 (Gallagher & Corgel, 2018). These low levels and wide ranges of explanatory power indicate that other factors should be explored to try and better explain the forces that drive average rate growth in hotel markets.…”

“…The purpose of this study is to expand on the established theory of hotel market equilibrium using data from a single metropolitan market. The first objective of the study is to establish whether prior year occupancy levels (Gallagher & Corgel, 2018) or current year occupancy levels (deRoos, 1999) are more appropriate predictors of average rate growth and whether using daily occupancy observations provides additional reliability compared with monthly observations. Second, this study explores whether the simple linear regression methodology used in past studies provides the best tool for predicting average rate growth or whether additional factors or nonlinearity may be observed by focusing on a more robust data set involving a single metropolitan market.…”