Facility Age and Ownership in Major American Team
Sports Leagues: The Effect on Team Franchise Values
Phillip A. Miller
Minnesota State University, Mankato*
Oct 2008
*
Department of Economics
Morris Hall 150
Minnesota State University, Mankato
Mankato, MN 56001
phillip.miller@mnsu.edu
1
Facility Age and Ownership in Major American Team Sports Leagues: The Effect
on Team Franchise Values
This paper examines the franchise values of American professional sports teams in the NBA, the NFL, and
the NHL. It is theoretically argued that team franchise values depend on the ownership status of the facility
in which the team plays. If a team owns its playing facility, it capitalizes the value of the facility in the
team franchise value, driving the latter higher. If a team plays in a facility owned by another entity, the
franchise value should be lower. The empirical evidence suggests that the franchise values of NFL and
NHL teams are higher for teams that own their playing facilities. No such effect is found for NBA teams.
2
1. Introduction2
The 1991 to 2004 period was active for designers and builders of sports facilities.
According to Table 1, three of the twenty-either National Football League (NFL) teams
that existed in 19913 played in stadiums ten years old or younger. Nine of twenty-seven
National Basketball Association (NBA) teams and two of twenty-one National Hockey
League (NHL) played in arenas aged ten years or less. Contrast those numbers with 2004
where seventeen of twenty-nine NBA teams, twenty-two of the thirty NHL teams, and
sixteen of thirty-two 32 NFL teams played in such facilities. The average age of an
NBA arena was 18.5 years in 1991, but only 11.4 years old in 2004. The average age of
an NHL arena was 30.3 years in 1991 and 11.9 years in 2004. The average age of an
NFL stadium was 26.1years in 1991 and 19.3 years in 2004.
Many sports facilities have been built with public assistance ranging from
subsidies for construction, land acquisition, and infrastructure. A source of controversy,
public subsidies continue to be sought by teams and given at various levels of
government.
Sports teams often claim they need new facilities to help them remain
competitive. The data on this, however, is spotty (for example, see Quinn et al (2003)).
What is more certain is a new facility’s effect on attendance. New facilities tend to
generate fan interest per-se, a so-called “honeymoon effect” (for example, see Clapp and
Hakes (2005)). Some fans attend games not so much because of the competition but to
experience the new facility.
1
There is evidence that the honeymoon effect transfers to franchise values.
Alexander and Kern (2004) show that the franchise values of NBA, NHL, and Major
League Baseball (MLB) teams increase when they move into new facilities. Miller
(2007) shows that MLB teams, on average, realize an increase in franchise value when
they move into a new facility. Humphreys and Mondello (forthcoming), on however,
find no significant evidence that facility ages affect franchise values in the four major
American sports leagues.
Miller (2007) argues that when a facility is built with public funds, it is generally
owned by a public authority. But when a team builds its facility without public
construction subsidies, it owns facility and its value is capitalized into the franchise
value. Miller (2007) finds empirical evidence for this in MLB teams. Humphreys and
Mondello (forthcoming) find evidence that teams in the four major sports in the US that
own their facilities have higher franchise values.
Why, then, do so many teams seek public funding for facilities and effectively
transfer ownership to public bodies if private ownership drives franchise values higher?
Miller (2007) argues that if the cost of constructing a new facility in exceeds the
incremental value that private ownership provides, then teams will seek public assistance
for construction.
In this paper I empirically examine Miller’s findings for the other 3 major sports
leagues in the US: the NHL, the NBA, and the NFL. The paper adds to the literature on
sports franchise values and it also adds to the literature on the honeymoon effect as well
as the public finance literature on the relationship between the government and sports.
The rest of the paper is organized as follows: section 2 presents the empirical model and
2
the data used in the analysis, section 3 presents the empirical results and discusses the
findings, section 4 concludes.
2. The Empirical Model and the Data
If franchise owners and all potential buyers are profit-maximizers and assuming
perfect foresight and common discount rates, the value of a sports franchise will therefore
be equal to the present value of future profits. Consequently, franchise values will be
functions of the determinants of team profitability and the ownership status of the facility.
This suggests an empirical model of the form
Fti  X ti    ti .
(1)
Fti is the logarithm of the real franchise value of team i in year t, X it is a matrix of
independent variables that impact the team’s value, including variables that control for
the age and ownership status of the facility.  is a vector of parameters to be estimated.
 it is a vector of random error terms.
The X it matrix includes variables that control for team profitability, including the
logarithms of SMSA real per-capita income and population in each team’s home
metropolitan area, both of which control for both revenue potential and team payroll
costs.
I include team winning percentage in the current year and in the previous year in
the regressions for the NBA and the NFL. I include team points standings in the current
season and the previous season for NHL teams. Because fans prefer winning, I expect
the coefficient on these team quality measures to be positive.
3
I also include facility age in the models. Facility age is defined as the difference
between the season in which it first opened and the time period of the observation. For
example, a team playing in 2005 in a facility opened in 1972 will have a twenty-three
year old facility. If a facility’s age is defined as zero, then the team is playing its first
season in a new stadium.
As noted above in the discussion of honeymoon effects, a new facility presents a
novelty that draws people simply to experience it. But this novelty should diminish over
time. Older facilities, on the other hand, may present a historical value to fans, and fans
may attend games to experience historical stadiums. Therefore, I include facility age in
linear and quadratic form.
I also control for fan loyalty by using the number of seasons the team has been in
the current city. The interested reader is directed to Depken (2000) who examines fan
loyalty in MLB and (2001) in the NFL. This variable is included linearly and
quadratically.
I include an ownership dummy equal to one for teams playing in stadiums owned
by that team plus an interaction term between private ownership and the age of the
facility.
The data for the analysis are drawn from American NBA and NFL teams and are
taken from the period 1991 to 2005 and for American NHL teams drawn from the period
1991-20044. SMSA population and per-capita income data was obtained from the
Bureau of Economic Analysis’ Regional Economic Information System (REIS).
Franchise value data was generated by Financial World and Forbes during this period
and was obtained from Rod Fort’s website (www.rodneyfort.com). No franchise values
4
were available for NHL or NBA teams in 1998, so those years were dropped from the
analysis. In addition, no franchise values were reported for expansion teams.
Team win percent data for the NBA and the NFL and points data for the NHL was
also obtained from Rod Fort’s website. Each team’s ownership status for its facility was
obtained from Wikipedia.com entries, Ballparks.com entries, several online press articles,
and team corporate web sites. In cases where team ownership and facility ownership fall
under different corporations that are subsidiaries of the same parent company, the facility
was considered to be owned by the team. For example, Atlanta Spirit, LLC owns Philips
Arena in Atlanta, the Atlanta Hawks of the NBA, and the Atlanta Thrashers of the NHL.
Because Atlanta Spirit owns both teams and the arena, both the Hawks and the Thrashers
are assumed to play in their own arena.
Lastly, since the data used in the analysis is panel, there are two sources of
randomness in the error term  it : one between teams and one over time. Consequently I
use estimation techniques to control for the two sources of error. Hausman tests rejected
the equivalence of fixed and random effects, so fixed effects estimation was used since it
yields consistent parameter estimates. In addition, Wooldridge tests showed the presence
of autocorrelation. So each model was assumed to have an AR(1) error process.
3. Empirical Results
Table 2 gives the means of the variables used in the regressions. Table 3 reports
the results of the regressions. Standard errors are listed below each estimated coefficient.
Models 1 and 2 are for the NBA, models 3 and 4 are for the NFL, and models 5 and 6 are
5
for the NHL. Models 1, 3, and 5 control for team ownership of the stadium via a dummy
variable equal to one if the team owns its stadium. Models 2, 4 and 6 utilize an
interaction term between the age of the stadium and the private ownership dummy
quadratically.
Within leagues, the coefficient on the logarithm of per-capita income changes
little with model specification. All coefficients are positive and significant, suggesting
that teams in higher per-capita income cities have higher franchise values, all else equal.
In the NBA, a 1% increase in per-capita income increases franchise values by
approximately 0.4%, suggesting relatively insensitive changes in franchise vales driven
by changes in per-capita incomes. NFL franchise values, conversely, are more sensitive
to changes in real per-capita incomes: a 1% increase in local area per-capita income
increases franchise values higher by approximately 1%. NHL franchise values exhibit
weak sensitivity to changes in per-capita income, similar to the NBA in magnitude.
Franchise values are relatively unresponsive to changes in the logarithm of
population in every model. Because the three leagues studied in this analysis maintain
franchises in the largest cities, save for the Green Bay Packers of the NFL, there is little
variation between the populations of cities. In addition, the largest cities tend to sport
more franchises in each league, yielding a smaller range of population-per-sports team
values. This further mitigates the effects on franchise values.
Current season win percent does not significantly affect the franchise values of
NBA or NFL teams. However, previous season win percent drives the franchise value of
NBA teams higher. According to both models 1 and 2, if the average team wins 10%
more of its games, its franchise values increases by approximately 1.7% in the following
6
season. NFL teams’ franchise values are statistically unaffected by changes in lagged
win percent. This may be due to the way revenue is generated and shared in the NFL.
During the study period, the bulk of team revenue was generated via the NFL’s national
television contract, revenue that was shared equally among the teams. Local gate
revenue is split between the home and visiting teams, with the home team keeping 60%
of the gate receipts. This equitable revenue sharing system smoothes out variations in
revenues across teams and may lessen the impact of team quality on franchise values.
Current season point standings do not affect the franchise values of NHL teams.
Lagged points, however, has a positive and significant impact on NHL franchise values.
According to models 5 and 6, a one-point increase drives next year’s franchise value
higher by nearly 0.21%.
The linear term on city tenure is insignificant in every regression, but its quadratic
term has a positive and significant effect on the franchise values of NBA teams and a
negative and significant effect on NHL teams, but no significant effect on NFL teams.
The results suggest that the longer an NBA team resides in a city, the more valuable it
becomes, suggesting a dominance of fan loyalty over honeymoon effects. However, the
value of NHL teams does not increase with tenure, suggesting a dominance of
honeymoon effects over fan loyalty. The lack of relationship between city tenure and
NFL values may reflect the equitable revenue sharing system in the NFL. Any
honeymoon or loyalty effects that would normally arise in team revenues are spread
throughout the league via revenue sharing.
Recall that the age of the facility appears in regressions 1, 3, and 5 linearly and
quadratically, but with no interactions. It appears with a linear and a quadratic interaction
7
with the private ownership dummy in models 2, 4, and 6. There is no significant
evidence that either the age of the facility or its ownership status effects NBA franchise
values.
But this is not the case with the NFL regressions. The linear terms on facility age
are negative and significant in both regressions and the quadratic terms are positive and
significant in both regressions. Moreover, the estimated coefficients are robust to the
adding of the interaction terms. The estimated parameters of the linear and quadratic
interaction terms are both positive and significant.
The estimates suggest that the age of NHL facilities does not significantly affect
NHL franchise values. However, if a US NHL team owns its facility its franchise value
is 20.5% higher on average. So while facility age does not appear to be a determinant of
NHL franchise values, facility ownership status does matter.
The effect of a one-unit change in facility age on NFL franchise values is
complex. To interpret this effect, rewrite equation 3 in terms of the variables that contain
facility age as follows:
Fti  X 'ti  '1 AGEti   2 AGEti2   3 AGEti Dti   4  AGEti Dti    it .
2
(2)
AGEti is the age of team i’s facility in year t, Dti is a dummy variable equal to one if
team i played in its own facility in year t, X 'ti is a matrix of all other variables contained
in the X ti matrix in equation (1), 1   4 are parameters to be estimated,  ' is a vector
of the other parameters to be estimated, and  it is the error term defined in equation (1).
8
Models 1, 3, and 5 are estimated by omitting the interaction terms. Therefore, the
proportional effect of a one-unit change in facility age on franchise values in those
models is given by 1  2 2 AGEit .
Models 2, 4, and 6 are estimated by including the interaction terms. Therefore,
the proportional effect of a one-unit change in facility age is given by 1  2 2 AGEit if a
team does not own its own stadium ( Dti  0 ) and by 1   3   2 2   4 AGEit if it
does own its own stadium ( Dti  1 ).
Figure 1 presents the proportional effect of a 1-year change in an NFL team’s
facility, all else equal, using model (4). The figure suggests that if the stadium is brand
new (0 years old), a one-year change in the age of the stadium causes the team’s
franchise value to fall by 0.53% if it does not own its own stadium and to increase by
over 12.7% if it does own its stadium. However, the rate of growth is much higher for
teams playing in their own facilities vs. teams playing in facilities they do not own. But
as the facility ages, the growth rate of franchise values increases regardless of ownership
status. If the facility is 20 years old, a one-year change in age drives the franchise value
down by 0.18% if the team does not own the facility. But if the team owns its facility,
the franchise value increases by over 20%.
5. Conclusion
This paper examines the effect of facility ages and ownership status on the
franchise values of American NBA, NFL, and NHL teams and, therefore, extends the
work of Miller (2007). It is argued that teams that play in their own facilities will realize
9
higher franchise values because they can capitalize the value of the facility in that of the
team. The empirical results find evidence of this for NHL and NFL teams but not for
NBA teams.
2
Comments made by the folks at the Wisconsin Economic Association:
a. If teams realize higher franchise value, why not build with private funds. This is an issue I take up in
the Oct 2007 JSE paper… I need to take it up here.
b. I could dummy out renovations….. or I could put years since renovation in as a regressor
c. I should mention the public/private blend of ownership, but I also need to mention that sweetheart deals
for public facilities can mimic private ownership very well. So my private ownership dummy doesn’t
control for these kinds of deals.
d. Should at least mention, if not control for, a measure of the consumption value that ownership gets from
owning a team.
e. I need to mention something about the problems associated with using the Forbes data. For instance, if
the Forbes data actually tends to underestimate the value of franchises, then my percent change values
overestimate the true percent change values. Also, sorta, kinda like Humphreys and Modello did, I can
control for the difference between sale and estimated by, for instance, finding the ratio of sale to estimated
franchise value for teams that have sold and adjust all franchise values by that amount.
f. Is the “error” associated with Forbes data, as compared to sales data, consistent over time. If not, I have
a problem.
3
The NFL regular season and its playoffs overlap calendar years. In this analysis, I refer to an NFL season
as the calendar year in which the regular season began. The NBA and the NHL regular seasons overlap
calendar years. Throughout the analysis, I refer to the calendar year in which the regular season ended as
the “year” or “season” for these two leagues. For example, the 1994-95 regular season is referred to as the
1995 season.
4
The 2004-05 NHL season was cancelled as the result of a lockout.
10
References
Alexander, Donald L. and William Kern (2004), The Economic Determinants of
Professional Sports Franchise Values, Journal of Sports Economics 5, 51-66
Ballparks.com http://www.ballparks.com
Christopher M. Clapp and Jahn K. Hakes (2005), “How Long a Honeymoon? The Effect
of New Stadiums on Attendance in Major League Baseball”, Journal of Sports
Economics 6, 237-263
Depken, Craig A. II (2000), Fan Loyalty in Professional Sports: An Extension to the
National Football League, Journal of Sports Economics 2, 275-284
Depken, Craig A. II (2001), Fan Loyalty and Stadium Funding in Professional Baseball,
Journal of Sports Economics 1, 125-138
Humphreys, Brad R. and Michael Mondello (forthcoming), “Determinants of Franchise
Values in North American Professional Sports Leagues: Evidence from a Hedonic
Price Model”, International Journal of Sports Finance
Miller, Phillip A. (2007), “Private Financing and Sports Franchise Values: The Case of
Major League Baseball”, Journal of Sports Economics 5, 449-467
Quinn, Kevin G., Paul B. Bursik, Christopher P. Burick, and Lisa Raethz (2003), “Do
New Digs Mean More Wins? The Relationship between a New Venue and a
Professional Sports Team's Competitive Success”, Journal of Sports Economics
4.3, 167-182
Rodney Fort’s Website http://www.rodneyfort.com
Wikipedia http://www.wikipedia.org
11
Table 1
Stadium Opening Information
NBA
1991
Franchise
Atlanta Hawks
Boston Celtics
Chicago Bulls
Cleveland Cavaliers
Dallas Mavericks
Denver Nuggets
Detroit Pistons
Golden State Warriors
Houston Rockets
Indiapolis Pacers
Los Angeles Clippers
Los Angeles Lakers
Miami Heat
Milwaukee Bucks
Minnesota Timberwolves
New Jersey Nets
Charlotte Hornets
New York Knicks
Orlando Magic
Philadelphia 76ers
Phoenix Suns
Portland Trail Blazers
Sacramento Kings
San Antonio Spurs
Seattle SuperSonics
Utah Jazz
Washington Bullets
Average
Average Excluding
Expansion Teams
2004
Year Stadium Age of
Opened
Stadium
1973
1928
1930
1975
1981
1976
1989
1967
1976
1975
1960
1968
1989
1989
1991
1982
1989
1969
1990
1968
1966
1961
1989
1969
1963
1970
1974
18
63
61
16
10
15
2
24
15
16
31
23
2
2
0
9
2
22
1
23
25
30
2
22
28
21
17
Franchise
Year
Stadium
Opened
Atlanta Hawks
Boston Celtics
Chicago Bulls
Cleveland Cavaliers
Dallas Mavericks
Denver Nuggets
Detroit Pistons
Golden State Warriors
Houston Rockets
Indiapolis Pacers
Los Angeles Clippers
Los Angeles Lakers
Memphis Grizzlies*
Miami Heat
Milwaukee Bucks
Minnesota Timberwolves
New Jersey Nets
New Orleans Hornets
New York Knicks
Orlando Magic
Philadelphia 76ers
Phoenix Suns
Portland Trail Blazers
Sacramento Kings
San Antonio Spurs
Seattle SuperSonics
Toronto Raptors*
Utah Jazz
Washington Wizards
2000
1996
1995
1995
2002
2000
1989
1967
2004
2000
2000
2000
1992
2000
1989
1991
1982
1999
1969
1990
1997
1993
1996
1989
2003
1963
2000
1992
1998
Age of
Stadium
4
8
9
9
2
4
15
37
0
4
4
4
12
4
15
13
22
5
35
14
7
11
8
15
1
41
4
12
6
11.2
18.5
11.4
*New Team Since 1991
12
Table 1 continued
NFL
1991
Franchise
Phoenix Cardinals
Atlanta Falcons
Buffalo Bills
Chicago Bears
Cincinnati Bengals
Cleveland Browns
Dallas Cowboys
Denver Broncos
Detroit Lions
Green Bay Packers
Indianapolis
Kansas City Chiefs
Miami Dolphins
Minnesota Vikings
New England Patriots
New Orleans Saints
New York Giants
New York Jets
Los Angeles Raiders
Philadelphia Eagles
Pittsburgh Steelers
San Diego Chargers
Seattle Seahawks
San Francisco 49ers
Los Angeles Rams
Tampa Bay Buccaneers
Houston Oilers
Washington Redskins
Average
Average Excluding
Expansion Teams
2004
Year Stadium Age of
Opened
Stadium
1958
1966
1973
1924
1970
1931
1971
1948
1975
1957
1984
1972
1987
1982
1971
1975
1976
1976
1923
1971
1970
1967
1976
1960
1960
1967
1965
1961
33
25
18
67
21
60
20
43
16
34
7
19
4
9
20
16
15
15
68
20
21
24
15
31
31
24
26
30
26.1
Franchise
Arizona Cardinals
Atlanta Falcons
Baltimore Ravens*
Buffalo Bills
Carolina Panthers*
Chicago Bears
Cincinnati Bengals
Cleveland Browns
Dallas Cowboys
Denver Broncos
Detroit Lions
Green Bay Packers
Houston Texans*
Indianapolis
Jacksonville Jaguars*
Kansas City Chiefs
Miami Dolphins
Minnesota Vikings
New England Patriots
New Orleans Saints
New York Giants
New York Jets
Oakland Raiders
Philadelphia Eagles
Pittsburgh Steelers
San Diego Chargers
Seattle Seahawks
San Francisco 49ers
St Louis Rams
Tampa Bay Buccaneers
Tennessee Titans
Washington Redskins
Year
Stadium
Opened
1958
1992
1998
1973
1996
1924
2000
1999
1971
2001
2002
1957
2002
1984
1995
1972
1987
1982
2002
1975
1976
1976
1966
2003
2001
1967
2002
1960
1995
1998
1999
1997
Age of
Stadium
46
12
6
31
8
80
4
5
33
3
2
47
2
20
9
32
17
22
2
29
28
28
38
1
3
37
2
44
9
6
5
7
19.3
21.2
*New Team Since 1991
13
Table 1 continued
NHL
1991
Franchise
Boston Bruins
Buffalo Sabres
Calgary Flames
Hartford Whalers
Chicago Blackhawks
Quebec Nordiques
Minnesota North Stars
Detroit Red Wings
Edmonton Oilers
Los Angeles Kings
Montreal Canadiens
New Jersey Devils
New York Islanders
New York Rangers
Philadelphia Flyers
Winnipeg Jets
Pittsburgh Penguins
St Louis Blues
Toronto Maple Leafs
Vancouver Canucks
Washington Capitals
Average
Average Excluding
Expansion Teams
2004
Year Stadium Age of
Opened
Stadium
1928
1941
1983
1976
1930
1951
1967
1980
1975
1966
1925
1982
1973
1969
1968
1956
1962
1968
1932
1969
1974
63
50
8
15
61
40
24
11
16
25
66
9
18
22
23
35
29
23
59
22
17
Franchise
Year
Stadium
Opened
Mighty Ducks of Anaheim*
Atlanta Thrashers*
Boston Bruins
Buffalo Sabres
Calgary Flames
Carolina Hurricanes
Columbus Blue Jackets*
Chicago Blackhawks
Colorado Avalanche
Dallas Stars
Detroit Red Wings
Edmonton Oilers
Florida Panthers*
Los Angeles Kings
Minnesota Wild*
Montreal Canadiens
Nashville Predators*
New Jersey Devils
New York Islanders
New York Rangers
Ottawa Senators*
Philadelphia Flyers
Phoenix Coyotes
Pittsburgh Penguins
San Jose Sharks*
St Louis Blues
Tampa Bay Lightning*
Toronto Maple Leafs
Vancouver Canucks
Washington Capitals
1994
2000
1996
1997
1983
2000
1983
1995
2000
2002
1980
1975
1999
2000
2001
1997
1997
1982
1973
1969
1996
1997
2004
1962
1994
1995
1997
2000
1996
1998
30.3
Age of
Stadium
10
4
8
7
21
4
21
9
4
2
24
29
5
4
3
7
7
22
31
35
8
7
0
42
10
9
7
4
8
6
11.9
13.5
*New Team Since 1991
14
Table 2
Summary Statistics
NBA
NFL
Variable
Facility Age
Years in City
Team Win Percent
Lagged Team Win
Percent
Points
Lagged Points
SMSA Population
Real Franchise Value
($Mil)
SMSA Real Percapita Income
Number of
Observations
NHL
Mean
Std. Dev.
Mean
Std. Dev.
Mean
Std. Dev.
14.29
26.64
0.51
13.32
12.84
0.15
23.21
37.36
0.50
17.39
22.59
0.19
16.39
27.59
-
15.52
24.70
-
0.51
0.15
0.50
0.19
-
-
4,989,406
4,765,214
4,320,850
4,242,717
82.76
81.76
6,577,973
18.32
18.58
5,734,012
$207.26
$95.40
$448.10
$256.04
$143.58
$66.76
$38,599.34
$5,997.81
$38,913.59
$6,024.44
$40,080.62
$5,733.45
367
447
251
15
Model
Table 3
Fixed Effect AR1 Coefficient Estimates
NBA
1
2
3
Log of Real Per Capita Income
NFL
NHL
4
5
0.4039349*** 0.4093754*** 1.004675*** 0.9896605*** 0.4160979*
6
0.5069236**
0.1373164
0.1375269
0.1210126
0.1154062
0.2421364
0.2452752
Log of Population
-0.0172152
-0.0210551
-0.0188325
-0.0280056
0.238963
0.2016794
0.096015
0.0961892
0.0645938
0.0624048
0.1766445
0.1807334
Win Percent
-0.0128052
-0.0178638
-0.0256069
-0.0191989
0.0576261
0.0580118
0.0002249
0.0002515
0.0004632
0.0004711
Lagged Win Percent
0.1680251*** 0.1699232***
0.0620357
0.0623775
0.0373111
0.0363273
0.051806
0.0534263
0.0381723
0.0369706
Points
Lagged Points
0.0020639*** 0.0020796***
0.0004561
0.0004669
-0.0005335
-0.0012569
Facility Age
-0.0001656
0.0023984
0.0027449
Facility Age Squared
-0.0000247
-0.0000216
0.0000507
0.0000533
0.0000373
0.0000346
0.0000609
0.0000645
Tenure in City
-0.0082163
-0.0086565
-0.0043648
-0.0021479
0.0111257
0.0129205
0.0102077
0.0102472
0.0078994
0.0082337
Tenure in City Squared
0.0011214*** 0.0011173***
0.0001757
Team Owns Facility Dummy
0.000661
0.0001767
-0.0682907
Facility Age - Team Ownership Interaction Squared
Intercept
rho_ar
sigma_u
sigma_e
rho_fov
n
RSq: Within
Between
Overall
Breusch-Pagan Test for Random Effects
Hausman Test
Wooldridge Test for Autocorrelation
0.0024864
0.0022253
0.0000921** 0.000089***
0.0054506
0.0052787
0.000061
0.0000292
0.0000725
0.0000703
-0.0914321
0.056413
Facility Age - Team Ownership Interaction
-0.0054554** -0.0053209**
0.0030854
0.00358
0.0000543
0.000021
-0.0004216* -0.0004786**
0.0002208
0.0002382
0.205488**
0.1165414
0.1007081
0.0033681
0.1325833***
0.0109449
0.0446713
-0.0058797
0.0187319
-0.0002173
0.0017801*
0.000291
0.0003825
0.0010061
0.000704
0.547443*** 0.5314587*** -3.482021*** -3.814593*** -2.594377*** -2.806093***
0.0615866
0.1186935
0.1150764
0.0724791
0.0736083
0.75031126
0.61407013
0.11798347
0.96439892
0.7506042
0.61067549
0.11823312
0.96386938
0.89645247
0.2492291
0.14012078
0.75982778
0.89312146
1.5650721
0.13542865
0.99256788
0.89771374
0.61268589
0.1166086
0.96504316
0.90391276
0.70637959
0.11853316
0.97261306
340
340
415
415
227
227
0.6671
0.2401
0.2561
0.6663
0.2482
0.2599
0.2166
0.0242
0.2524
0.2803
0.0013
0.0076
0.5033
0.2187
0.0675
0.4759
0.3151
0.1099
83.03***
231.16***
128.41***
84.77***
265.18***
134.644***
220.24***
117.98***
336.827***
220.72***
55.41***
344.064***
42.08***
154.13***
71.374***
50.98***
67***
73.076***
***Coefficient is significant at the 1% level or less
**Coefficient is significant at most at the 5% level but more than the 1% level
*Coefficient is significant at most at the 10% level but more than the 5% level
16
Figure 1
Proportional Effects of a One-year Change in Facility Age - NFL
30.00%
25.00%
15.00%
10.00%
5.00%
40
38
36
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0.00%
0
Proportional Change
20.00%
-5.00%
Facility Age
Not Owned by Team
Owned by Team
17