An examination of underlying consumer demand and sport pricing using secondary market data

Highlights

► The purpose of the study is to understand demand, consumer surplus, and pricing (in)efficiency. ► Secondary market prices, instead of number of transactions, explain changes in consumer demand. ► Data indicate that NFL teams may be able to sell 20,000 additional seats for each game. ► There is approximately $260,000 in consumer surplus per game that is captured by resellers.

Abstract

The growth of the secondary ticket market has given sport managers a new way to understand consumer demand for tickets. In the secondary market, transaction prices and the number of transactions are highly variable and respond directly to consumer preferences, making it ripe for exploration. Using secondary market data for the NFL provided by a secondary market firm, the purpose of the current study is to understand a variety of traditional sport economics issues such as demand, consumer surplus, and pricing (in)efficiency. Results show that secondary market prices, instead of number of transactions, respond to the factors commonly associated with consumer demand. Further, the data indicate that teams may be able to sell 20,000 additional seats for each game. However, given that teams cannot easily add this number of seats (and may not want to given the NFL's blackout rule), there is approximately $260,000 in consumer surplus per game that is captured by resellers.

Keywords:

• Consumer demand
• Sport pricing
• Consumer surplus
• Price inefficiency
• Secondary market
• Tickets

Notes

1 Tel.: [phone].

2 Tel.: [phone].

3 Profit maximising may be more appropriately labeled as “loss minimising” for some sport organisations. We acknowledge further that some organisations, such as European football clubs in open league structures have been shown in the literature to not act in a profit maximising manner, as per the work of authors including Andreff and Szymanski (2007), Késenne (1996), and Szymanski (2003). We contend, nonetheless, that these organisations operate on the business side as “revenue maximisers”, thus not affecting the discussion of pricing and the secondary market here.

4 If an NFL game is not sold out 72 h in advance of the game's start time, it will not be shown on television within the market of the home team. This is commonly known as the “blackout” rule.

5 For a more comprehensive list of sport demand studies, see Borland and MacDonald (2003).

6 Beginning with the 2010 season, the San Francisco Giants instituted a dynamic pricing policy for all of their single game tickets that adjusts prices up or down up to the morning of a game based on common demand factors.

7 As described by Spindler (2003) and Spindler and de Vanssay (2000), this curve can be approached, in theory, by using an entry price and then a per use price in other contexts. The notion of personal seat licenses by franchises upon construction of a new (or refurbished) facility stems from attempting to extract all of the consumer surplus or engage in perfect price discrimination.

8 Welfare is defined as the sum of consumer surplus (total amount that customers are willing to pay minus what they actually pay) plus producer surplus (or profit when not accounting for fixed costs). With a vertical supply curve, there is no deadweight loss in measuring welfare (see Krugman, Wells, & Olney, 2006).

9 For the sake of simplicity, this assumes that there are no costs for the primary seller. Without loss of generality, this assumption can be relaxed. The existence of a secondary market does not change the supply curve of the primary seller (i.e., the stadium is the same size with the same total costs of operation).

10 Leslie and Sorensen (2009), p. 3.

11 In order to protect their financial data, the firm chose to remain anonymous.

12 A Sargan test (with a p-value of 0.08) shows that the quantity of tickets is endogenous and that the chosen instruments help improve the estimation of the equation.

13 The sign on week of the season is negative and significant. It is highly correlated with week × current home team winning percentage. When the latter is removed and the analysis re-run, week of the season becomes insignificant (and R² drops to 0.49).

14 Given that lagged home team winning percentage is used, the total number of observations is 239.

15 Additionally, teams are beginning to use VTP and dynamic pricing to capture some of these surpluses. Although, there will always be secondary markets because some sellers purchased a ticket (whether it was dynamically priced or subject to VTP) and may have to resell due to unforeseen circumstances. Also, the threat of television blackouts will cause owners to be more risk-averse with respect to ticket pricing, and some resellers are simply lower cost providers of the search to find buyers (or have different revenue functions, as described by Swofford).