Gains from algorithmic pricing: Evidence from Airbnb
Yinan Wang Biography: An economics PhD candidate at NYU Stern, who focuses on the platform economy and online marketplaces, with a particular interest in the effects of algorithmic pricing on welfare.
In the evolving landscape of digital commerce, a significant portion of transactions is shifting to decentralized platforms such as Amazon, Ebay, Airbnb. These platforms empower small sellers by allowing them to directly set their own prices. However, this democratization of selling comes with distinct challenges, particularly for amateur sellers who might not have the same resources as professional vendors. Especially on Airbnb, most property owners are individuals or small-scale operators who possibly managing their listings part-time. Pricing frictions exist as sellers fail to respond to changes in demand across time. They do not have sufficient time or access to crucial market information to effectively manage their prices. Furthermore, the effort and cost of adjusting prices can be prohibitive for these peer sellers. These amateur sellers often lack the sophistication in terms of economic understanding, consumer behavior, and competitive pricing strategies, which can hinder their ability to set prices that maximize profits or drive sales.
In 2015, Airbnb introduced "Smart Pricing" which is designed to assist hosts in optimizing their rental prices. It adjusts nightly rates automatically based on a variety of factors, including changes in demand for similar listings, seasonal trends, and local events. My research delves into the effects of hosts adopting automated pricing tools in decentralized digital marketplaces, specifically focusing on Airbnb. The primary goal is to distinguish between the private gains hosts receive from adopting such technologies and the broader equilibrium effects that these technologies induce across the marketplace. To achieve this, the study employs a structural demand model and a dynamic pricing model tailored to analyze the adoption and implications of pricing technologies. The analysis quantitatively assesses the efficiency gains attributable to the adoption of automated pricing tools, providing a clear picture of how these tools influence individual listings and the overall market dynamics. By computing optimal prices, the research also allows for counterfactual scenarios that help quantify potential gains for hosts if they were to upgrade their pricing strategies through technological means.
