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.