Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Emerson Kohl, Junior Ivan O'Neil
Lead Presenter's Name
Emerson Kohl
Lead Presenter's College
DB College of Business
Faculty Mentor Name
Dr. Youngran Choi
Abstract
While the U.S. domestic airline industry generates hundreds of billions in annual revenue, the mechanics of airfare pricing remain remarkably opaque to travelers and analysts alike. Prices for geographically similar routes often diverge sharply, driven by a complex interplay of structural constraints and market pressures. This study elucidates the primary drivers of ticket pricing by analyzing high-frequency data from the Bureau of Transportation Statistics DB1B database. Utilizing an Ordinary Least Squares framework, the research evaluates how route distance, carrier concentration, passenger throughput, and seasonal fluctuations collectively dictate average fares. By quantifying these relationships in Python, the analysis seeks to isolate the "competition effect" from purely operational costs like fuel and distance. Preliminary results suggest that while distance remains a foundational cost driver, market density and the presence of low-cost carriers exert disproportionate downward pressure on prices. These findings offer critical insights for consumers navigating a volatile market, as well as for policymakers evaluating the impact of industry consolidation on consumer welfare.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Airline Ticket Anaysis
While the U.S. domestic airline industry generates hundreds of billions in annual revenue, the mechanics of airfare pricing remain remarkably opaque to travelers and analysts alike. Prices for geographically similar routes often diverge sharply, driven by a complex interplay of structural constraints and market pressures. This study elucidates the primary drivers of ticket pricing by analyzing high-frequency data from the Bureau of Transportation Statistics DB1B database. Utilizing an Ordinary Least Squares framework, the research evaluates how route distance, carrier concentration, passenger throughput, and seasonal fluctuations collectively dictate average fares. By quantifying these relationships in Python, the analysis seeks to isolate the "competition effect" from purely operational costs like fuel and distance. Preliminary results suggest that while distance remains a foundational cost driver, market density and the presence of low-cost carriers exert disproportionate downward pressure on prices. These findings offer critical insights for consumers navigating a volatile market, as well as for policymakers evaluating the impact of industry consolidation on consumer welfare.