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Mr. Jiaqi Shi
Ph.D. Candidate in Information Systems
Paul Merage School of Business
University of California, Irvine

Dynamic-pricing algorithms facilitate frequent price adjustments to optimize sales. Yet, overly frequent price fluctuations may complicate consumers’ purchase decisions. This paper empirically investigates how algorithm-driven price volatility influences the occupancy rates of more than 105,000 rental properties in New York City listed on Airbnb. Because properties on Airbnb can be booked up to 12 months in advance, we compile two price-volatility measures: a property’s frequency of price changes across travel dates on a given booking date (i.e., volatility over travel dates) and a property’s frequency of price changes across booking dates on a given travel date (i.e., volatility over temporal distances). For both measures, the occupancy rates increase from flat pricing to a certain degree of dynamic pricing. However, the occupancy rates start to decrease when prices become too volatile, controlling for the magnitudes of price-level variation. A series of mechanism checks suggest that price volatility across travel dates leads to quality concerns, whereas price volatility across temporal distances leads to fairness concerns. Our findings suggest optimal algorithmic pricing cannot be achieved without considering consumers’ behavioral responses.