Season ticketing as a risk management tool in professional team sports: A pricing analysis of German soccer and basketball

Huth, Christopher; Kurscheidt, Markus Article Season ticketing as a risk management tool in professional team sports: A pricing analysis of German soccer and basketball Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Huth, Christopher; Kurscheidt, Markus (2022) : Season ticketing as a risk management tool in professional team sports: A pricing analysis of German soccer and basketball, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 15, Iss. 9, pp. 1-18, https://doi.org/10.3390/jrfm15090392 This Version is available at: https://hdl.handle.net/10419/274913 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Huth, Christopher, and Markus Kurscheidt. 2022. Season Ticketing as a Risk Management Tool in Professional Team Sports: A Pricing Analysis of German Soccer and Basketball. Journal of Risk and Financial Management 15: 392. https://doi.org/10.3390/ jrfm15090392 Academic Editors: Hannes Winner, Michael Barth, Martin Schnitzer and Thanasis Stengos Received: 30 April 2022 Accepted: 24 August 2022 Published: 3 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Risk and Financial Management Article Season Ticketing as a Risk Management Tool in Professional Team Sports: A Pricing Analysis of German Soccer and Basketball Christopher Huth 1and Markus Kurscheidt 2,* 1Institute of Sport Science, Universität der Bundeswehr München, 85577 Neubiberg, Germany 2BaySpo—Bayreuth Center of Sport Science, University of Bayreuth, 95440 Bayreuth, Germany *Correspondence: [email protected]; Tel.: +49-921-55-3470 Abstract: Ticket sales remain a significant source of revenue in professional team sports. However, season ticket revenue, as an effective risk-reducing instrument, is rarely analyzed in the literature. This study aims to determine, from a price and product perspective, the extent to which different factors affect season ticket prices. Using three different professional German sports leagues, a ticketpricing model was developed as the empirical model. Consistent with other pricing studies, an ordinary least-squares (OLS) model and a Tobit model were fit. The results indicate that different season ticket rights, type of season ticket, club league membership, fan club membership, club stadium utilization rate, club sporting performance, and club market size have significant negative or positive impacts on season ticket price. Whereas, for example, a reserved seat in the stadium has a positive impact, the population of the club’s city has a negative impact. Based on the results, club managers should consider all traditional season ticket rights and season ticket discounts when calculating season ticket pricing. These and further implications are discussed with respect to the risk management issues of season ticket pricing in light of the COVID-19 pandemic and differences in local market constellations of professional team sports clubs. Keywords: season tickets; pricing; product design; professional team sports; football; basketball; sports finance; Tobit model; OLS model; COVID-19; Europe 1. Introduction In European professional sports, sports clubs’ primary source of revenue has traditionally been gate receipts (Andreff 2009;Fried et al. 2008). In the leading European football leagues, media and commercial revenues are currently higher than ticket revenues. However, especially for clubs in minor football leagues or other sports leagues, tickets remain a significant source of revenue (Huth 2014). While a match day ticket allows the holder to attend a certain game, a season ticket is valid for several games—e.g., all home matches of the regular season—played by a sports club (Huth 2012). Previous research on sports tickets has focused, inter alia, on the pricing of match day tickets (e.g., Alexander 2001;Boyd and Boyd 1998;Coates and Humphreys 2007), the impact of assets on ticket sales (e.g., Brown et al. 2006;Lawson et al. 2008), customer satisfaction with (season) tickets (e.g., Beccarini and Ferrand 2006;McDonald 2010;McDonald et al. 2013;O’Reilly et al. 2008), and ticket sales strategies (e.g., Bruggink and Eaton 1996;Drayer and Martin 2010; Drayer et al. 2012;Iho and Heikkilä 2010). The topic of season tickets, however, is rarely analyzed in the sports economics and sports finance literature. This lack of research is surprising, considering the major role that season tickets play (Wakefield 2006) in generating substantial direct and relatively low-risk revenues for sports clubs (McDonald 2010). The example of Germany’s 1st Football Bundesliga shows that, on average, clubs sell more season tickets (60 percent) than match day tickets (40 percent) (Deutsche Fußball Liga 2013). Indeed, in North American sports J. Risk Financial Manag. 2022,15, 392. https://doi.org/10.3390/jrfm15090392 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2022,15, 392 2 of 18 leagues, sellouts and waiting lists for season tickets are the rule rather than the exception (Boyd and Boyd 1998;DeSerpa 1994). Surprisingly, season tickets are normally sold at a discount. This discount amounts to, on average, 27.52 percent in Germany’s 1st Football Bundesliga (Huth 2014), even though this league had an average stadium utilization rate of more than 90 percent (Statista 2015) in the pre-COVID-19 age. These two examples show that many sports clubs could increase their profits by raising their season ticket prices (Boyd and Boyd 1998). This study aims to determine, from a price and product perspective, the extent to which different factors affect season ticket prices. In this context, the role of season ticket discounts and different monetary and nonmonetary season ticket rights for season ticket holders were particularly considered. For the present study, three different leagues were selected to facilitate comparisons among different market situations. The considered leagues were chosen because they had different stadium utilization rates, from very high (1st Football Bundesliga, 93 percent) through intermediate (Basketball Bundesliga, 84 percent) to low (2nd Football Bundesliga, 59 percent), and, therefore, different supply and demand markets. This is particularly important in the context of the COVID-19 pandemic representing a fundamental business risk, as sports leagues are currently faced with larger free capacities in their stadiums due to lags in returning attendance demand after spectator lockdowns (Huth and Kraus 2021). For this reason, this question should be considered in any study conducted at this time. This paper broadens the literature considerably. To the best of our knowledge, no previously published study has focused empirically on season ticket pricing and rights for season ticket holders. To date, studies on sports season tickets have primarily concentrated on predicting season ticket renewal, including churn rates (Katz et al. 2019;Lee et al. 2020; McDonald 2010;McDonald et al. 2014;McDonald and Stavros 2007) and satisfaction among season ticket holders (Beccarini and Ferrand 2006;McDonald et al. 2013;Won and Lee 2022). This first empirical exploratory study of season ticket pricing thus provides several useful insights into relevant factors for season ticket pricing. In addition, the findings may help sports clubs develop more customized season ticket arrangements and, therefore, achieve better supply and demand matching. The findings also illuminate what season ticket rights should be offered to create an attractive product for both sports clubs and season ticket holders. This could become particularly important in the post-COVID period, as initial data indicate that the demand for tickets has decreased since the pandemic compared with the period before it (Huth and Kraus 2021). Thus, it is even more important for clubs to design a product that is tailored to fit the current situation in the form of season tickets. This paper is structured as follows: The following section provides a short description of the economics of season tickets. Subsequently, key aspects of sports ticket pricing are presented. Next, the method used in this study is presented in terms of the empirical model, data collection, and data description. Then, key findings are discussed and interpreted in the Results Section. A concluding section then discusses the key implications of the paper. 2. Characteristics of Season Tickets A season ticket can be assigned to a subscription market in which customers allocate—mostly contractually—the majority of their business to one provider for a certain period of time (Dawes 2014). In this sense, a season ticket is quite similar to other subscription market products, such as newspaper subscriptions, phone services, and insurance (Sharp et al. 2002). In this context, Simmons (2006) explains that season ticket holders usually have particularly close emotional, temporal, and financial links to their preferred sports club. Lee et al. (2020) and McDonald (2010) add that season ticket holders are the most loyal and involved among sports club supporters. McDonald and Stavros (2007) illustrate that, in extreme cases, a season ticket is purchased for altruistic reasons to help ensure a club’s financial survival. Season tickets are normally marketed at the beginning of the new season by sports clubs (McDonald 2010;Simmons 2009). J. Risk Financial Manag. 2022,15, 392 3 of 18 Regarding the economic aspects of season tickets, consumers buying them must bear a valuation risk. Neither weather conditions (Borland and MacDonald 2003;Iho and Heikkilä 2010;Parlasca 1993), nor the team’s playing quality and performance (Simmons 2009) can be evaluated at the time of purchase. Simultaneously, season ticket holders’ risk can be identified as a key advantage from sports clubs’ point of view, as sports clubs generate a major portion of ticket revenues before a new season starts, allowing them to invest in and plan for the professional squad or other assets (McDonald 2010). This financial advantage also has a risk-minimizing benefit for sports clubs. Revenues from season tickets are guaranteed regardless of the sports club’s future sporting success. By selling a large portion of available seats as season tickets, clubs do not experience lost revenues in the case of underperformance. Season ticket revenues are thus somewhat decoupled from a club’s sporting performance, whereby financial risk decreases and planning security increases (Huth 2012). Customer loyalty is an additional advantage because, in addition to club membership, season tickets can be considered a long-term customer loyalty instrument (Simmons 2009), which reduces the risk of the season ticket holder changing team affiliation. Additionally, sports clubs receive extensive customer data that can be used for numerous other research areas, such as specialized offers in cooperation with a club’s sponsors (McDonald 2010). In fact, from the perspective of clubs in Germany’s 1st and 2nd Football Bundesliga, planning security, and, therefore, less financial risk, is the greatest benefit, ranking ahead of customer loyalty, stadium utilization, and club support (Huth 2014). A key disadvantage of season tickets, however, is the accorded discount. Based on a complete utilization of the stadium, season ticket sales reduce ticket revenues. The discount is given because season ticket holders underwrite a certain part of the mentioned risk (Simmons 2006). Therefore, the season ticket discount can be considered a risk premium for season ticket holders’ accepted risk. In addition, Salant (1992) argues that for the holder, a season ticket also constitutes an insurance contract in the form of a renewable option for preferable regular-season seating and access to highly valued playoff games. Therefore, season ticket holders are willing to pay a certain risk premium. In addition to emphasizing the risk argument, Beccarini and Ferrand (2006) illustrate that the season ticket discount positively influences season ticket holders’ satisfaction. Compared with match day tickets, season tickets often contain additional property rights (DeSerpa 1994;McDonald and Stavros 2007). Property rights theory distinguishes four kinds of rights: (1) the right to use the good (usus), (2) the right to formally and materially modify the good (abusus), (3) the right to retain the returns of the good’s use (usus fructus), and (4) the right to sell the good completely or partially (Alchian and Demsetz 1972;Pejovich 1976). The right to use is relevant for season ticket holders because they have the right to attend all the home matches of a given club. Additionally, the right to sell is relevant if a season ticket holder is unable to attend a game and is inclined to sell the ticket for that match to another person. The two other rights are more or less irrelevant for season ticket holders because a season ticket does not generate financial returns, such as dividends, and is not modifiable because the club, not the customer, defines its conditions. These rights are reserved by sports clubs. The season ticket holder does have the opportunity to sell tickets for special games profitably on the secondary market, thus making the third right also potentially relevant. However, many clubs are now punishing such sales of tickets above the official price. Concerning season ticket rights, the right to attend a certain number of matches, e.g., all home matches of a season, is the fundamental right of a season ticket holder. This bundling of matches provides three major advantages for season ticket holders. First, they have the convenience of buying one season ticket instead of several match day tickets. Second, they are charged lower transaction costs. Third, they have the guarantee of attending the top games against the league’s best or most prestigious clubs. Clubs also grant a certain number of additional season ticket rights, such as the right to a reserved place in the stadium, the right of preemption for special matches (e.g., playoff games or (inter)national cup competitions), the option to repurchase for the next season and discounts in the fan J. Risk Financial Manag. 2022,15, 392 4 of 18 shop. Sports clubs commonly grant the right of entry to all home games, the right to a reserved place in the stadium, a purchase option for special matches, and the option to repurchase for the next season (Huth 2014). Season ticket rights can be classified into monetary and nonmonetary rights. Nonmonetary season ticket rights bear the advantage that clubs can offer them without incurring high additional costs. Some of these rights, such as the option to buy additional tickets for special matches or the renewal period for season tickets, can be offered completely cost free. In contrast, monetary season ticket rights cost a club a certain amount of its real return. These rights mostly concern discounts on the season ticket itself, on merchandise in the fan shop, or on stadium magazines. However, these discounts can also be considered to promote sales because they can stimulate demand for other club products and services. In contrast to match day ticket pricing (e.g., Noll 1974;Scully 1989;Forrest et al. 2002), quantitative pricing has not been the subject of any study to date. DeSerpa (1994), however, theoretically discusses the rationality of ostensibly low season ticket prices. Although many games sell out in different sports, the seller prices below the myopic short-term demand price to give fans a reason to purchase season tickets. DeSerpa (1994) also notes that underpriced season tickets are optimal if fans prefer to attend only a portion of the ticketed games and to resell the tickets for the remaining matches. Season tickets must be priced sufficiently low so that holders are able to at least recoup their initial investment after assuming the transaction costs of resale. The additional offered rights also represent a certain value that must be quantified. Season ticket rights such as the right of preemption for special matches can be interpreted as a kind of option right. The season ticket holder has the option—but not the obligation—to buy tickets for special matches. In financial mathematics, these options are evaluated by various option-pricing models, such as the Black–Scholes model (Black and Scholes 1973) and the binominal model of Cox et al. (1979). Therefore, every offered (season ticket) right has a certain value. However, additional season ticket rights can be evaluated differently by their holders, and in extreme cases, no supplemental right may be attractive to its holder. Concerning both season ticket discounts and season ticket rights, previous research indicates that no correlation or a weak correlation exists between the number of rights and the season ticket discount offered by sports clubs (Huth 2014). Therefore, a low discount does not compensate for a large number of rights, or vice versa. However, the findings indicate that less competitive clubs offer more season ticket rights than more competitive clubs. 3. Empirical Model, Methods, and Data 3.1. Empirical Model A ticket pricing model was developed for the empirical model. Different regression models were selected for the analysis. In line with other pricing studies (e.g., Alexander 2001;Paul and Weinbach 2013;Salaga and Winfree 2015), an ordinary least-squares (OLS) model, followed by a Tobit model (Greene 2003), was calculated. However, no zero values for the season ticket price exist in the present dataset. A subsample was created by separating the survey participants according to their responses regarding whether they had ever bought a season ticket. Thus, the subsample contains only participants who have (ever) purchased a season ticket. Additionally, when the dependent variable is season ticket prices, there is no upper limit truncation, as is habitually seen when attendance is used as the response variable; thus, the venue capacity constraint is avoided (Salaga and Winfree 2015). Hence, the Tobit model can be considered an alternative to the OLS model, especially with regard to OLS regression results. OLS and Tobit regressions focusing on the season ticket price (PRICE) and the logarithm price (LNPRICE) as dependent variables were run. The log-linear form of the second dependent variable LNPRICE was used to avoid misspecification problems (Gerrard et al. 2007). In line with the early empirical work of Demmert (1973), Noll (1974), and Schofield (1983), demographic, sportand team-specific, and economic variables were J. Risk Financial Manag. 2022,15, 392 5 of 18 considered. Additionally, the survey participants’ preferences for different season ticket rights were considered. The general form of the model with season ticket prices (the actual price or the log price) as the dependent variable is as follows: DEP = β0+β1DISCOUNT + β2ALLGAMES + β3SEAT + β4REPURCH + β5PREEMP + β6GATE + β7PTRANS + β8PARK + β9PRESENT + β10STORE + β11MAG + β12SPONS + β13TRANSFER + β14INVIT + β15FRIEND + β16YEARS + β17MEMBERC + β18MEMBERFC + β19MEMBERU + β20AGE + β21AGE2 + β22SEX +β23EDU + β24INC + β25STAND + β26DFL1 + β27DFL2 + β28UTIL + β29SUCC + β30POPUL + β31GDP + e 3.2. Data Description and Measurement As mentioned above, two dependent variables were considered. First, PRICE measures the season ticket price most recently paid by participants in the survey. Alternatively, LNPRICE is the log of PRICE. The independent variables in the regression models were as follows: First, fifteen typically monetary and nonmonetary season ticket rights, including season ticket discounts, were considered. For their selection, season ticket flyers of all the considered sports clubs were analyzed, and fifteen possible season ticket rights were identified. To consider the role of the link between fans and clubs, which Simmons (2006) identified as important, four club-link-related variables were selected. Participants indicated whether they were club members (MEMBERC), fan club members (MEMBERFC), or Ultras group members (MEMBERU). YEARS indicates the number of years that season ticket holders have held season tickets. Additionally, five categories of sociodemographic data on participants were considered in the analysis: their age (AGE), age squared (AGE2), sex (SEX), highest educational level (EDU), and net income (INC). Participants’ real per-capita income (INC) was used because previous research has found that income is an important economic determinant of demand and attendance decisions (Bruggink and Eaton 1996;Feehan 2009). Additionally, the type of season ticket (STAND) was selected to control for the monetary difference between standing and seating season tickets. Second, DFL1 and DFL2 controlled for whether a season ticket was valid in the 1st Football Bundesliga (DFL1) or 2nd Football Bundesliga (DFL2); the Basketball Bundesliga was the omitted category. In addition, clubs’ stadium utilization (UTIL) and success in the previous season (SUCC) were considered. Teams with high stadium utilization and success were expected to have higher season ticket prices because these clubs have more power to charge higher prices. Previous studies indeed indicate that good sporting performance boosts subsequent attendance (Feehan 2009;Simmons 1996). Finally, two macroeconomic variables were considered to control for a club’s market size and potential. Wilson and Sim (1995) and Schmidt and Berri (2001) underline the relevance of market size. Market size is usually described using the population of a club’s hometown (POPUL) (Simmons 2009), whereas market potential is described using the local GDP of a club’s city or region (GDP). The data were collected from the German Federal Statistical Office (Statistisches Bundesamt 2015). In addition to a general regression that included all three selected leagues and both types of season tickets (seating and standing), regressions that split the survey data into the three leagues and two types of season tickets were conducted. Table 1gives an overview of the two considered dependent variables and the 31 selected independent variables. J. Risk Financial Manag. 2022,15, 392 6 of 18 Table 1. Overview of variables. Variable Description Scale Dependent variable PRICE Price of season ticket (in EUR) Metric LNPRICE Logarithmic price of season ticket Metric Preference(s) for season ticket rights DISCOUNT Season ticket discount (5-point scale) Ordinal ALLGAMES Guarantee to see all matches live (5-point scale) Ordinal SEAT Reserved seat in the stadium (5-point scale) Ordinal REPURCH Option to repurchase for next season (5-point scale) Ordinal PREEMP Right of preemption for special matches (5-point scale) Ordinal GATE Special entrance for STH (5-point scale) Ordinal PTRANS Ticket for public transport (5-point scale) Ordinal PARK Parking area for STH (5-point scale) Ordinal PRESENT Special present for STH (5-point scale) Ordinal STORE Special discounts in fan shop (5-point scale) Ordinal MAG Special price for stadium magazine (5-point scale) Ordinal SPONS Special discounts with club’s partners (5-point scale) Ordinal TRANSFER Transferability of season ticket (5-point scale) Ordinal INVIT Invitations to specific events (5-point scale) Ordinal FRIEND Free entrance to friendly matches (5-point scale) Ordinal Season ticket holder club-related variables MEMBERC Club member (1 = yes; 0 = no) Nominal MEMBERFC Fan club member (1 = yes; 0 = no) Nominal MEMBERU Ultras group member (1 = yes; 0 = no) Nominal YEARS Period of holding season ticket (in years) Metric Sociodemographic data AGE Age of participant (six categories) Ordinal AGE2 Age2Metric SEX Sex of participant (0 = male; 1 = female) Nominal EDU Highest educational level of participant (seven categories) Ordinal INC Net income of participant (six categories) Ordinal Fixed-effects variables STAND Standing season ticket (1 = standing; 0 = seating) Nominal DFL1 1st Football Bundesliga (1 = 1st DFL; 0 = other) Nominal DFL2 2nd Football Bundesliga (1 = 2nd DFL; 0 = other) Nominal UTIL Stadium utilization (in %) Metric SUCC Club’s sporting success (league position) Metric POPUL Population of club’s city Metric GDP Local 2012 GDP of club’s city (or region) Metric 3.3. Data Collection and Descriptive Results As mentioned above, a comparative approach was chosen to track season ticket pricing under different league market conditions. Three leagues were selected according to the criterion of stadium utilization. This approach was used to find three leagues with different stadium utilization rates and, therefore, different market situations to consider different ticket markets with potentially different pricing models. The selected leagues were the 1st and 2nd Bundesliga in football, Germany’s preferred sports, and the 1st Bundesliga in basketball, a sport with growing popularity. J. Risk Financial Manag. 2022,15, 392 7 of 18 In the present study, data from a standardized online questionnaire were combined with different secondary data sources. Accordingly, some variables were collected from club-related data sources, such as stadium utilization rates and clubs’ individual rankings during the previous season. Macroeconomic data were collected from the official homepages of clubs’ home cities and from official data obtained from the German Federal Statistical Office. As noted above, neither the study, nor the data collected were affected by the COVID-19 pandemic. Thus, a market environment that was as “normal” as possible and that was not influenced by an extreme situation can be assumed. The data were collected in the 2014/15 season. Other variables were collected via a standardized online questionnaire to reduce cost and time factors (Li et al. 2008;Wright 2005). Another advantage of this approach was that season ticket holders from all the considered sports leagues across Germany were able to participate (Bartlett 2005). The questionnaire tool Qualtrics was used for online sampling. The questionnaire had four major parts. First, the participants were filtered by the criterion of being a season ticket holder or nonholder to prevent nonholders from answering the central questions, which focused on season ticket rights. Afterwards, season ticket holders were questioned about the league and the club for which they purchased their season tickets. Season ticket holders’ relationship with the chosen clubs was also analyzed. They declared whether they were club members, fan club members, or Ultras group members. Additionally, the participants were asked how long they had held season tickets. These questions aimed to elucidate the relationship between season ticket holders and clubs. The next part—the focus of the study—asked the participants to evaluate season ticket rights according to their subjective judgements via 5-point Likert scales (from 1 = do not agree to 5 = fully agree) to assess their attitudes or, rather, preferences (Jones 2015; Revilla et al. 2014). Sociodemographic data on the respondents were collected in the survey’s final section. In total, N= 1076 football and basketball fans participated in the online survey, and 762 of these participants had held a season ticket in the past. The link to the survey was distributed in various club forums and via social media (e.g., Facebook). The distribution of respondents over the three analyzed leagues was approximately uniform (1st Football Bundesliga with a share of 28 percent, 2nd Football Bundesliga with 35 percent, and Basketball Bundesliga with 36 percent). Thus, the comparative study approach is also reflected in the sampling. In all, 45 percent of season ticket holders bought a standing season ticket; thus, the present sampling more or less represents the real allocation of standing and seating season tickets. Table 2shows the summary statistics for the variables used in the regression analysis. The mean price paid for a season ticket by the survey participants was EUR 236.84. Notably, the highest mean season ticket price was identified for the 1st Football Bundesliga (EUR 286.02), followed by the Basketball Bundesliga (EUR 250.53), and the 2nd Football Bundesliga (EUR 180.36). Independent of the league, the mean paid standing season ticket cost was EUR 149.65, and the mean seating season ticket cost was EUR 309.21. The highest-rated season ticket rights were the guarantee to see all matches live in the stadium (ALLGAMES) and the right of preemption for special matches (PREEMP). The results also indicate season ticket holders’ preference for the season ticket discount, which was rated the third-most-important season ticket component. Analyzing differences in preferences among the three leagues, a Kruskal–Wallis test (Kruskal and Wallis 1952) indicated that evaluations of season ticket rights significantly differ in most cases. REPURCH, for example, is especially relevant for the 1st Football Bundesliga’s season ticket holders. Considering the high average stadium utilization rate, this result is logical because the season ticket limit is exhausted for most clubs in the 1st Football Bundesliga. J. Risk Financial Manag. 2022,15, 392 8 of 18 Table 2. Summary statistics. Variable Mean SD Min Max Mean DFL1 Mean DFL2 Mean BBL PRICE 236.84 125.68 50 782 289.54 181.86 253.71 LNPRICE 5.337 0.510 3.912 6.662 5.541 5.09 5.43 DISCOUNT 4.356 0.939 1 5 4.30 4.23 4.45 ALLGAMES 4.713 0.694 1 5 4.84 4.60 4.73 SEAT 4.142 1.130 1 5 4.10 4.04 4.34 REPURCH 4.283 0.968 1 5 4.56 4.21 4.26 PREEMP 4.491 0.822 1 5 4.56 4.62 4.38 GATE 3.140 1.290 1 5 2.77 3.27 3.21 PTRANS 3.540 1.342 1 5 3.72 3.69 3.21 PARK 2.733 1.341 1 5 2.41 2.59 3.12 PRESENT 2.734 1.341 1 5 2.36 2.67 2.99 STORE 3.059 1.279 1 5 2.68 2.99 3.31 MAG 2.471 1.168 1 5 2.43 2.53 2.43 SPONS 2.661 1.203 1 5 2.31 2.59 3.01 TRANSFER 4.083 1.058 1 5 3.98 3.92 4.35 INVIT 3.001 1.214 1 5 2.62 2.85 3.38 FRIEND 3.227 1.200 1 5 2.95 3.23 3.37 MEMBERC 0.472 0.499 0 1 0.75 0.62 0.17 MEMBERFC 0.364 0.481 0 1 0.38 0.39 0.31 MEMBERU 0.077 0.267 0 1 0.12 0.11 0.39 YEARS 5.915 3.684 1 15 6.36 6.25 5.14 AGE 2.827 1.278 1 6 2.87 2.94 2.98 AGE2 9.626 8.224 1 36 9.11 10.11 10.79 SEX 0.190 0.393 0 1 0.14 0.11 0.31 EDU 4.434 1.327 1 7 4.61 4.22 4.36 INC 2.988 1.426 1 6 3.13 3.04 3.10 STAND 0.447 0.498 0 1 0.48 0.59 0.29 DFL1 0.281 0.450 0 1 - - - DFL2 0.337 0.473 0 1 - - - UTIL 80.617 20.141 32.4 100 95.1 57.3 90.8 SUCC 10.748 4.138 1 18 10.87 10.72 10.65 POPUL 560,889.2 784,113.4 12,785 3,375,000 494,309 911,908 298,675 GDP 44,212.67 15,915.77 19,108 105,059 44,387 39,240 48,501 4. Results and Discussion 4.1. General Results A variance inflation factor (VIF) test was performed on each regression to test for multicollinearity. The results indicate that none of the VIF values for the regression models exceeds 4.63, except for the variables AGE and AGE2. This value is under the threshold of 10 (Baum 2006;Beckham et al. 2012;Wooldridge 2013), indicating no issues with multicollinearity. Therefore, no variables were excluded from the regression analyses. Additionally, robust standard errors were specified in the OLS models with PRICE as the dependent variable based on significant Breusch–Pagan/Cook–Weisberg and White (1980) test results. The analysis below focuses on the results of the semilog OLS regressions. The results of the semilog Tobit regressions are presented in Appendix Afor comparison. Table 3 presents the estimation results for the different regression models. The * notations denote statistical significance at the 10 (*), 5 (**), and 1 (***) percent levels. J. Risk Financial Manag. 2022,15, 392 15 of 18 Appendix A Table A1. Results of Tobit regression with LNPRICE. Dependent Variable LNPRICE Variable ALL BBL BULI2 BULI1 STANDING SEATING DISCOUNT −0.0211 0.0056 −0.0257 −0.0559 −0.0589 −0.0416 (0.012) (0.023) (0.017) (0.025) (0.014) (0.019) ALLGAMES −0.0021 −0.0076 0.0005 −0.0225 0.0963 *−0.0653 (0.018) (0.031) (0.023) (0.045) (0.022) (0.026) SEAT 0.0705 *** 0.0857 *0.0825 *0.1001 ** 0.0922 *0.1097 *** (0.011) (0.022) (0.016) (0.020) (0.012) (0.020) REPURCH 0.0375 0.0719 0.0196 −0.0033 −0.0663 0.0828 ** (0.014) (0.022) (0.020) (0.032) (0.016) (0.022) PREEMP −0.0335 −0.0147 −0.0446 −0.0223 −0.0417 −0.0189 (0.015) (0.023) (0.024) (0.030) (0.018) (0.021) GATE −0.0574 ** 0.0487 −0.0910 ** −0.1264 ** −0.0647 −0.0700 (0.010) (0.017) (0.014) (0.021) (0.012) (0.014) PTRANS −0.0319 −0.0563 −0.0182 −0.0184 −0.0477 −0.0117 (0.009) (0.015) (0.013) (0.017) (0.011) (0.012) PARK 0.0368 0.0404 0.01193 0.1099 ** 0.0520 0.0404 (0.101) (0.017) (0.016) (0.020) (0.013) (0.014) PRESENT −0.0013 0.0024 0.0785 −0.0378 0.0630 −0.0177 (0.013) (0.020) (0.019) (0.029) (0.017) (0.017) STORE 0.0226 −0.0354 0.0865 −0.0330 −0.0278 0.0416 (0.012) (0.021) (0.018) (0.026) (0.016) (0.018) MAG −0.0180 −0.0259 −0.1107 ** 0.0620 −0.0700 0.0065 (0.012) (0.021) (0.018) (0.025) (0.016) (0.017) SPONS 0.0594 ** 0.0887 ** −0.0819 0.1289 ** −0.0417 0.1128 ** (0.012) (0.019) (0.018) (0.027) (0.016) (0.017) TRANSFER −0.0066 0.0218 −0.0128 0.0213 0.0220 −0.0172 (0.011) (0.022) (0.015) (0.021) (0.013) (0.016) INVIT 0.0102 −0.0284 0.0566 0.0082 0.0153 0.0110 (0.012) (0.020) (0.017) (0.025) (0.014) (0.017) FRIEND −0.0344 −0.0627 0.0194 −0.0956 0.0105 −0.0810 (0.011) (0.019) (0.015) (0.022) (0.013) (0.015) YEARS 0.0225 0.1129 *** 0.0465 −0.0866 −0.0674 0.0814 ** (0.003) (0.056) (0.005) (0.006) (0.004) (0.0043) MEMBERC 0.0379 0.0609 0.0229 0.0192 0.1004 ** 0.0126 (0.025) (0.051) (0.033) (0.048) (0.030) (0.038) MEMBERFC −0.0974 *** −0.0438 −0.1610 *** −0.1262 *** −0.0359 −0.1634 *** (0.023) (0.041) (0.036) (0.043) (0.029) (0.034) MEMBERU 0.0017 −0.0174 −0.0205 0.0075 −0.0060 0.0118 (0.041) (0.096) (0.056) (0.066) (0.040) (0.080) SEX −0.0031 0.0162 0.0087 −0.0163 −0.0456 0.0382 (0.028) (0.041) (0.051) (0.059) (0.038) (0.039) AGE 0.1671 0.4974 ** 0.2927 −0.3466 * 0.0815 0.3900 ** (0.044) (0.070) (0.071) (0.087) (0.060) (0.063) AGE2 −0.0981 −0.3871 ** −0.2385 0.3466 ** 0.0135 −0.3076 * (0.006) (0.010) (0.010) (0.013) (0.010) (0.009) EDU −0.0130 −0.0465 −0.0247 0.0299 −0.0084 −0.0298 (0.009) (0.015) (0.013) (0.018) (0.012) (0.012) INC 0.1471 *** 0.1788 *** 0.1385 *** 0.1341 ** 0.2501 *** 0.1523 *** (0.010) (0.018) (0.015) (0.019) (0.013) (0.015) STAND −0.569 *** −0.4635 *** −0.6518 *** −0.7096 *** (0.026) (0.049) (0.040) (0.046) DFL1 0.2069 *** 0.2382 *** 0.3019 *** (0.033) (0.043) (0.047) DFL2 0.0546 −0.2591 *** 0.2177 *** (0.047) (0.056) (0.070) J. Risk Financial Manag. 2022,15, 392 16 of 18 Table A1. Cont. Dependent Variable LNPRICE Variable ALL BBL BULI2 BULI1 STANDING SEATING UTIL 0.1374 *** 0.2411 *** 0.0597 0.0652 −0.0848 0.3235 *** (0.001) (0.003) (0.001) (0.005) (0.001) (0.002) SUCC 0.0833 *** 0.0262 0.0463 0.0672 0.1432 *** 0.0802 * (0.003) (0.006) (0.007) (0.006) (0.004) (0.005) POPUL −0.2205 *** −0.1268 *** −0.2972 *** −0.0670 −0.1712 *** −0.3423 *** (1.74 ×10−8) (4.03 ×10−8) (2.46 ×10−8) (7.66 ×10−8) (2.25 ×10−8) (2.47 ×10−8) GDP −0.0284 −0.0495 −0.0593 −0.0033 −0.0613 −0.0416 (7.08 ×10−7) (1.81 ×10−6) (9.11 ×10−7) (2.18 ×10−6) (8.28 ×10−7) (1.05 ×10−6) Constant 4.970 *** 3.738 *** 5.257 *** 5.566 *** 4.915 *** 4.463 *** (0.153) (0.301) (0.021) (0.585) (0.191) (0.24) N 762 277 269 216 345 417 McFadden’s R20.773 0.767 0.984 0.814 1.102 0.633 McKelvey and Zavoina’s R20.681 0.640 0.701 0.695 0.366 0.497 Note: * p< 0.10, ** p< 0.05, *** p< 0.01. References Alchian, Armen A., and Harold Demsetz. 1972. 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