A Comparison of the Performance of Brand-Affiliated and Unaffiliated Hotel Properties

Research has shown that performance differences exist between brand-affiliated hotels and unaffiliated properties. However, the extant empirical results are mixed. Some research has shown that brands outperform unaffiliated hotels on various metrics, whereas other research has shown the opposite. This article analyzes this issue using a matched-pair approach where we compare the performance differences of brand-affiliated and unaffiliated properties between 1998 and 2010. The matched-pair approach ensures that local competitive conditions as well as hotel characteristics are the same across the comparison pair. In addition, all potential omitted-variable bias and model misspecifications are avoided. Thus, to address our research question, we compare branded hotels with unaffiliated properties that are identical in age, market segment, location, and duration of operation, as well as having a similar number of rooms. Our analysis shows that performance differentials are present, albeit not systematic. We found no consistent advantages in all segments for either the affiliated hotels or the comparable unaffiliated properties, taking into account our comparison factors. That said, the methodology of our approach yields results that are more informative to the affiliation choice of owners and to the growth strategies of hotel brand–owner companies than those of previous empirical studies.

Group Forecasting Accuracy in Hotels

Yield management helps hotels more profitably manage the capacity of their rooms. Hotels tend to have two types of business: transient and group. Yield management research and systems have been designed for transient business in which the group forecast is taken as a given. In this research, forecast data from approximately 90 hotels of a large North American hotel chain were used to determine the accuracy of group forecasts and to identify factors associated with accurate forecasts. Forecasts showed a positive bias and had a mean absolute percentage error (MAPE) of 40% at two months before arrival; 30% at one month before arrival; and 10-15% on the day of arrival. Larger hotels, hotels with a higher dependence on group business, and hotels that updated their forecasts frequently during the month before arrival had more accurate forecasts.