Consumer Aversion to Algorithm-Driven Price Volatility: Empirical Investigation of Airbnb

Abstract

Dynamic-pricing algorithms have enabled frequent price adjustments to improve 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 the price volatility across travel dates leads to quality concerns, whereas the price volatility across temporal distances leads to fairness concerns. Our findings imply the algorithmic pricing optimality may not be achieved by ignoring consumers’ behavioral reactions.

Keywords: pricing algorithm, dynamic pricing, volatility aversion, human-AI interaction, Airbnb