What Makes the Market Tix: A Machine Learning Analysis of the Resale Concert Ticket Market

Abstract

The resale concert ticket market is a largely unexplored domain in terms of rigorous statistical analysis. It is a market in which tickets are sold at multiples of their face value minutes after they are released to the public on the primary market. In this paper, I provide a number of predictive models on data collected from resale ticket websites in order to explain how prices behave in the secondary ticket market. I then introduce parametric models to explain the factors that influence price changes in this market. I am able to use these models to explain how some tickets tend to behave over the course of their life on the secondary market, as well as some factors that influence this behavior. I find that Long Short Term Memory Neural Networks perform extremely well in a predictive capacity for both average and individual ticket prices. The inputs to the parametric models are affected the most by the number of days remaining until a concert and the population of the city where the concert takes place. These models find that the shape of the trend of ticket prices tends to be similar between concerts, while the starting price of a ticket varies significantly between concerts. The findings of this paper shed some light on the confusing secondary market and provide insight for buyers and sellers in both the primary and secondary concert ticket markets.