Read the working paper
INSEAD Working Paper 2015/62/TOM
We study price competition in markets with large numbers (in magnitude of hundreds or thousands) of potential competitors, using the hotel industry as a test bed. We address two methodological challenges: simultaneity bias and high dimensionality. Simultaneity bias arises from joint determination of prices in competitive markets. We propose an instrumental variable approach to address simultaneity bias in high dimensions. The novelty of the idea is to exploit online search and clickstream data to uncover demand shocks at a granular level, with sufficient variations both over time and across hotels in order to obtain valid instruments at a large scale. We then develop a methodology to identify relevant competitors in high dimensions combining the instrumental variable approach with high dimensional l-1 norm regularization. Our approach is in contrast to many existing applications of high dimensional statistical models that, using either regression or clustering techniques, are primarily concerned with correlation rather than causality. We apply this data-driven approach to identify price competition patterns in the New York City hotel market. We found: 1) engagement in competition-based revenue management is prevalent across branded and non-branded hotels and across all quality tiers. It explains 35.0% of the within-hotel price variation, an additional 12.0% on top of demand based factors; 2) Branded hotels are more capable of preserving geographical and quality boundaries than independent hotels. Also, budget hotels are more likely to cross geographical boundaries but less likely to cross quality boundaries than upper scale hotels. 3) Branded hotels are both more influential and more influenced in setting prices.