An Aggregate Generalized Nested Logit Model of Consumer Choices : An Application to the Lodging Industry

In this paper, using aggregate data, we demonstrate the ability of the generalized nested logit (Wen and Koppelman, 2000; GNL henceforth) to better capture consumer choice under conditions where consumer tradeoffs among choice items is not ex-ante obvious to the researcher, and data on attributes of consumer choice are incomplete. We extend existing GNL models by using more readily available aggregate data (rather than individual data) while accounting for consumer heterogeneity and endogeneity of firm-choice variables. The empirical application is to the lodging (hotel) industry. The industry classifies properties on the basis of price tiers; it also recognizes that consumers appear to have two idea points (of downtown and airport) for location. However, it appears possible that a consumer might see a property in the same location but a different price tier as a closer substitute than a property of the same price tier at a different location. Hence for this industry, an ex-ante nesting structure based on price tiers or location alone might not capture the complexities of consumer choices. We find that GNL provides a better fit to these data than aggregate logit or aggregate probit. Our results provide managerially useful insights into who might comprise competition for any hotel, i.e. is it a nearby property of the same/different quality tier or is it a distant property of the same/different quality tier. We also briefly discuss the implications of consumer choices for firm profitability by estimating a supply-side model