Reducing Information Asymmetry: Economic Value of Airbnb Signals in a Small College Town

Trust is the backbone of platform operations. Airbnb platform designers implement various methods including trust-building mechanisms, user interface (UI) artifacts, and reputation management systems. Previous research has explored trust in Airbnb, yet little is known about the economic value of trust indicators in smaller cities and rural areas. Thus, this thesis investigates the economic significance of User Interface (UI) artifacts, including the “Superhost” badge, star ratings, and listing photographs, within the peer-to-peer accommodation sector in the Columbia Metropolitan Area. Drawing on signaling theory, we conducted quantitative analysis, to examine the relationship between trust indicators and listing prices, controlling factors like amenities and cleaning fees. Findings of this study indicate that Superhost status significantly increases revenue, but its effect is smaller compared to the number of reviews and number of photos (hereinafter referred to as visual cues and organic trust signals). Thus, understanding the economic impact of these trust signals provides valuable insights into how reputation systems reinforce price premiums, particularly in understudied markets outside major cities. Signaling theory posits that informational clues help to reduce uncertainty where information asymmetry exists. Both the buyer and seller operate in a market equilibrium that is called Pareto optimization (where two parties have conflicting goals). Under these conditions, any change from the equilibrium position worsens the outcome for one of the parties. In signaling theory, this equilibrium is a moving target and continues to adjust over time. From the buyer’s perspective, the amount of time required to sort through the sellers’ options is the cost of collecting more information. This cost is weighed with the benefit of risk reduction or lowering the risk of making a bad booking decision. The seller needs to present the best signals to catch the buyer’s attention. This thesis examines how these signals mitigate information asymmetry throughout the traveler decision-making process.