Demand Uncertainty in the Hotel Industry

Leisten, Matthew

doi:https://doi.org/10.21985/n2-z7gm-mj76

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Demand Uncertainty in the Hotel Industry

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When demand is volatile and uncertain, prices often cannot adequately respond to demand shifts because these shifts are not known when prices are set. In this dissertation, I use the hotel industry--- an industry with a high degree of demand uncertainty and capacity constraints, which amplify the cost of setting the wrong price--- to study three aspects of demand uncertainty and pricing: how economists can measure uncertainty, how firms mitigate demand uncertainty, and how firms may otherwise adapt to demand uncertainty if it cannot be fully mitigated. In the first chapter of this dissertation, I develop a new identification strategy that leverages differences in how firms' prices respond to common knowledge demand shifts relative to non-common knowledge demand shifts. Firms' price policy functions--- the mapping from their information sets to prices--- and the information structure itself are semiparametrically identified under fairly general conditions. I then apply this technique to investigate why some hotels have better information about demand than others. I use a year of hotel-level price and occupancy data from eighteen different U.S. college towns. I leverage college events, such as home football games and graduation weekend, as key common-knowledge demand shifters to identify the quality of information hotels have about demand and rivals' prices. Hotels affiliated with large, upscale chains, which provide their franchisees with the most sophisticated revenue management tools, have better information than rivals, all else equal, but this advantage is overridden by weaker incentives for franchisees affiliated with these chains to gather information. These large-chain franchisees pay higher royalties on the revenues they gain from better demand forecasting, but bear the full private cost of whatever information they gather beyond what is provided by the chain. In the second chapter, I extend this model to examine how hotels gather information about demand in equilibrium. I establish that, in a relatively broad class of games, information is \textit{complementary}: if rivals obtain better information about the state of the world (e.g., a market-wide demand shifter), each firm has a greater incentive to gather information as well. Counterfactuals using the model and estimates from the previous chapter demonstrate that complementarities can induce hotels to begin investing rapidly in better demand forecasting tools in response to each other. Relatively small improvements in demand forecasting may thus be amplified in equilibrium. This mechanism may explain why some markets have widespread IT adoption, while others do not. If a firm cannot fully mitigate its uncertainty over demand, it can make other choices to better adapt to the uncertainty. In my third chapter, I show that firms facing demand uncertainty may adapt by changing their capacity investment. I distinguish between demand \textit{volatility} as demand fluctuations to which capacity cannot respond but prices can, and demand \textit{uncertainty} as demand fluctuations to which neither capacity nor prices can respond. In a simple model capacity choice, converting uncertainty to volatility and vice versa has a theoretically ambiguous effect on capacity investment. Empirically, more demand uncertainty induces hotels to be larger, but leads to fewer hotels in a given market, whereas the effects of volatility are insignificant. These results suggest that hotels use capacity as insurance against mispricing, thereby substituting long-run capacity adjustments for short-run price adjustments.

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