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Proximity, Property Values and Professional Sports Stadiums:
Evidence from Target Field in
Minneapolis Minnesota
Xuanhao He1 and Brady P. Horn1,2,*
Abstract
In the past 25 years, over 30 billion dollars in public funding
has been spent subsidizing
professional stadiums. While there are anecdotal claims of
economic benefits associated with
stadiums justifying these substantial expenditures, there is
little empirical evidence of regional
economic development. One documented positive impact of
professional sports stadiums is an
increase of property values in proximity to newly constructed or
renovated stadiums. However,
recent research evaluating referenda voting outcomes has found
the opposite; voters in close
vicinity of stadiums voted against the stadiums, suggesting that
stadiums may not provide benefits
to households in close proximity to stadiums. In this paper, we
reevaluate the impact of
professional sports stadiums on proximate property values,
evaluating the impact of Target Field,
a professional sports stadium in Minneapolis, Minnesota. Using a
spatial difference-in-difference
identification strategy, we find that there was a sharp decrease
in residential property values
proximate to the stadium. Numerous robustness checks confirm
these results. Overall, these results
contribute to a growing literature suggesting that professional
sports stadiums should not be
subsidized at the levels currently observed in the United
States.
Keywords: Sports Facilities, Property Values, Hedonic,
Proximity, NIMBY
JEL classification: H71, L83, R30, R53
1 (Department of Economics), University of New Mexico, USA
2 Center on Alcoholism, Substance Abuse, and Addictions (CASAA),
University of New Mexico,
USA,
* Corresponding author.
Acknowledgements: We thank Andrew Friedson, Robert Berrens, Alok
Bohara, Xiaoxue Li, and
Bern Dealy for valuable comments and suggestions, thank Garrett
Bing, Matt Sandell of the City
of Minneapolis – Assessor’s Office for the help with housing
data, and thank Maria Dahlen of the
City of Minneapolis - Development Services, Community Planning
and Economic Development
for the generous help with building permits data. All errors are
our own.
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1. INTRODUCTION
Professional sports are a substantial industry in the United
States. It is estimated that over $6 billion
is spent annually attending professional games in the US.1 Also,
the annual TV ad revenue from
professional sports is estimated to be at least $8.47 billion,
or 37% of all television ad revenue.2
Beyond revenue, there are varying, but generally large and
positive estimated non-market benefits
associated with having a professional sports team (Johnson and
Whitehead 2000; Johnson,
Groothuis, and Whitehead 2001; Johnson, Mondello, and Whitehead
2006, 2007; Carlino and
Coulson 2004; Fenn and Crooker 2009).
It is well-known that in order to extract additional rent from
tax payers, professional sports
leagues leave a viable location (or locations) without a sports
team. This creates a credible threat
for sports teams to leave and hence produces pressure on
regional policy makers both to recruit
new teams to a city and to retain existing professional sports
teams. The standard mechanism used
to extract rents from local governments is the subsidization of
professional sports stadiums. Over
the last 25 years, more than 30 billion public funding has been
spent on subsidizing professional
stadiums.3 As Walter Neale pointed out in 1964, the professional
sports industry is a peculiar one,
where organization both creates competitive balance and greater
enjoyment of the sport, but also
places professional sports promoters and owners in a special
position with respect to antitrust laws
(Neale 1964).
1 This number was calculated roughly using data of average
ticket prices and annual attendance for NFL, NHL, NBA,
and MLB in the of 2014 to 2015 season. See
https://www.statista.com/. 2 This number represents advertising
revenue for ABC, CBS, NBC, and Fox in the 2014 to 2015 season.
See
http://adage.com/article/media/sports-account-37-percent-all-tv-ad-dollars/300310/.
3 This calculation represents both building and refurbishing costs
and is based on (Siegfried and Zimbalist 2000), and
https://psmag.com/the-impossible-fight-against-america-s-stadiums-26041189ef3e#.p3w8p4h83.
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Regarding justification for the public expenditures, numerous
benefits have been
associated with professional sports stadiums in terms of quality
of life (Rappaport and Wilkerson
2001), civic pride (Carlino and Coulson 2004), and community
spirit (Johnson et al. 2001). Also,
anecdotal evidence of regional economic development has been
touted, including attracting
businesses, creating jobs, increasing tourism, and increasing
regional property values. However,
the true economic impact of professional sports stadiums is not
well known. Numerous economic
studies have found little to no evidence that stadiums cause
economic benefits (Siegfried and
Zimbalist 2000; Shropshire 1995). Also, there is an uncertain
and possibly negative relationship
between professional sports, sports stadiums and income (Baade
and Dye 1990; Coates and
Humphreys 1999; Noll and Zimbalist 2011; Rosentraub 1999). Also,
recent research suggests that
the opening of a new stadium does not create new business
formation (Harger, Humphreys, and
Ross 2016).
Another economic outcome thought to be impacted by professional
sports stadiums is
property values. While there are mixed findings in terms of the
overall impact of stadiums on
property values, typically property values have been found to
increase with new stadiums (Tu 2005;
Dehring, Depken, and Ward 2007; Ahlfeldt and Maennig 2010; Feng
and Humphreys 2012;
Ahlfeldt and Kavetsos 2014).4 Also, there is evidence that
professional sports stadium have a
positive impact on properties in close proximity to a stadium.
For instance, Tu (2005) found an
increase in the values of property values associated with FedEx
Field, the home of the Washington
Redskins. Feng & Humphreys (2012) evaluated the impact of
every US professional sports
stadium on property values, and found that overall, stadiums had
a significant and positive effects
4 This phenomenon is not exclusive to professional sports.
Friedson and Bogin (2013) found that high quality high
school sports also positively influence property values.
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on property values. Also, Dehring, Depken, & Ward (2007)
found mixed results for the
construction of a new stadium for the Dallas Cowboys in Texas.
The authors found that property
values in the city of Dallas, the location of initially proposed
stadium site, increased following an
announcement to potentially relocate there; however, a
subsequent small decrease in property
values was found in Arlington, where the stadium was eventually
built.5 Finally, outside of the US,
Ahlfeldt and Kavetsos (2014) found an increase in property
values related to New Wembley and
Emirates Stadiums in London, and Ahlfeldt and Maennig (2010)
found increased values associated
with three multifunctional sports arenas in Berlin.
In somewhat of a contrast to the hedonic studies that have found
a positive impact of
professional sports stadiums on proximal property values, there
is growing voting literature finding
that regional amenities may not have a positive welfare effect.
NIMBY, or “Not in My Back Yard,”
is a commonly used term used to characterize people who oppose
projects in their neighborhood.
This NIMBY effect has been found in different kinds of public
projects, including renewable
energy sites (Van der Horst 2007), subsidized housing programs
(Galster, Tatian, and Pettit 2004),
and shopping malls (Dear 1992). In terms of professional sports
stadiums, two studies, evaluating
the impact of professional sports stadiums on referendum voting
outcomes, found that voters near
a proposed sports stadium did not actually support the stadium.
Horn, Cantor, and Fort (2015)
evaluated the voting outcomes for Quest Field in Seattle,
Washington and found a nonlinear effect
of distance, where the lowest support for the stadium was among
people living closet to the
proposed stadium site. Also, Ahlfeldt and Maennig (2012) found
that voters in close proximity to
Allianz Arena, in Munich, Germany, voted against the
stadium.
5 Note, the stadium was originally announced to be built in
Dallas, and then changed to Arlington.
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In this paper, we reevaluate the impact of professional sports
stadiums on proximate
property values. We evaluate the impact of Target Field, a
professional baseball stadium located
in Minneapolis, Minnesota on property values, on single-family
residential houses. One challenge
with using hedonic analysis to evaluate the impact of
professional sports stadiums is omitted
variable bias, either coming from housing structural or
neighborhood attributes. The other is the
natural endogeneity associated with stadium location choices; a
stadium is often chosen to be built
in a neighborhood of lower average housing value than the rest
of the area. To mitigate both, we
use a spatial difference-in-difference (DD) model, which
compares the pre-post difference of
housing value near the stadium with that further away. In this
model, numerous different time cut-
points are evaluated (proposal proposed, bill passing, breaking
ground, and stadium opening).
Overall, we find that there was a sharp decrease in
single-family residential property values
surrounding the stadium in response to the stadium construction
and opening. Standard DD model
finds that single-family residential house price decreased by
9.24% after the stadium broke ground
and by 14.27% after the stadium opened. These results are
verified using various robustness checks.
Overall, these results provide evidence that the impact of
professional sports stadiums on
proximate single-family residential property values is highly
negative in the affected area. This is
additional evidence supporting that professional sports in the
US should not be subsidized at their
current levels.
2. BACKGROUND
Before Target Field, the home field for the Minnesota Twins was
the Hubert H. Humphrey
Metrodome, which opened in 1982. While it was initially
considered novel, after two decades
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Metrodome was outdated and considered one of the worst venues to
watch professional baseball.6
However, it took quite a while for Minnesota to build a new
stadium, and the issue was contentious
and actively debated. Starting in 1997, 11 bills were introduced
in the Minnesota Legislature about
a new professional baseball stadium, and a special session was
called to debate this issue. None of
these bills was passed. Consistent with the standard relocation
threat, Carl Pohlad, the owner of
Minnesota Twins, then attempted to sell the team to a
businessman Don Beaver, who was
speculated to want to move the team to numerous different places
including his hometown,
Charlotte, North Carolina. However, these moves never happened,
as voters rejected potential
public funded stadium proposals.7 In 1998 again stadium bills
were introduced in Minnesota and
failed. In 2002, the legislature passed a bill providing state
financing for a $330 million stadium
in St. Paul, but the plan was turned down by the Twins.
After series of meetings from December 9, 2003 to January 29,
2004, the Stadium
Screening Committee finally selected two viable cites for
construction of a new professional
baseball stadium in the report to Governor Tie Pawlenty on
February 2, 2004 (see Table 1),
including the Minnesota Urban Ballpark, which is now Target
Field, located in downtown
Minneapolis next to the Target Center (a professional basketball
arena). On April 26, 2005, a deal
between the Twins and Hennepin County was reached in which the
Twins would pay roughly one-
third of the stadium’s cost, with the rest being paid for by
increasing sales tax in Hennepin County.
Interestingly, in Minneapolis a referendum was required for any
professional sports facility project
with expense on city resources over $10 million;8 however, the
Minnesota Legislature directly
passed a bill allowing the plan of building a baseball stadium.
The final bill was approved by the
6 See
http://www.espn.com/page2/s/list/worstballparks/010503.html. 7 See
http://www.savetheminnesotatwins.com/articles.html. 8 See ARTICLE
IV and Section 9.4 of the City of Minneapolis Charter (Version Jan
31, 2017).
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Minnesota Legislature on May 20, 2006, and signed into law by
Governor Tim Pawlenty on May
26, 2006, which allowed the county to impose a sale and use tax
at the rate of 0.15 percent.9 After
that, the baseball park broke ground on August 30, 2007, and it
was opened on January 4, 2010.
Overall, the price tag for Target Field was $555 million,
including the cost of site acquisition and
infrastructure. Among that, $350 million (63%) of the project
was subsidized through public
funding in Hennepin County, while only $195 million (35%) was
provided by the baseball team,
Minnesota Twins.
3. DATA
Housing price data on single-family residential houses of
Minneapolis for the years 2002 to 2016
was obtained from Minneapolis Open Data Portal.10 All
transactions during this period were
recorded, each observation represents a real property
transaction record, and prices were inflation
adjusted to represent 2016 dollars. To avoid using records of
unfair or mispriced property market
values, only arm’s length transactions considered by Minnesota
Department of Revenue (MNDOR)
were chosen. We further limit the sample to properties built
after 1900, which dropped 184
observations. The building and land characteristics include the
year of built, lot acreage, building
area above ground, number of bedrooms and bathrooms, and if a
property had a fireplace.
One drawback of the housing data is that both the land and
building characteristics (lot size,
number of bedrooms, etc.) of the house come from the assessor’s
data and reflect the status of the
property as of 2016. The data does not capture the housing
characteristics before the change if a
property was remodeled, causing measurement error for the
remodeled properties sold before the
9 See Chapter 257 of Laws of Minnesota 2006. 10 The data can be
found on http://opendata.minneapolismn.gov/.
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remodeling. We addressed this concern in two ways. First, we
acquired all the building and
remodeling permits issued by the city during the sample period
and matched them with the housing
data using GIS. We excluded 1,103 transaction records during
this process. Second, following
previous literature including (Lang, Opaluch, and Sfinarolakis
2014), we defined “flipped” houses
as those sold more than twice in any 6-month time window and
excluded all the earlier transactions
except the last one for these properties.11 This process removed
711 sales records. After cleaning,
the housing data contains 38,816 sales records of 14,838
single-family residential houses.
Table 2 presents the summary statistics for the variables used
in our analysis. The average
property was sold for $401,621, with an average neighboring
property price of $391,167. An
average property has a 0.13 acres’ lot, contains 1,297 square
feet building area above ground, 2.95
bedrooms, 1.74 bathrooms, and an age of 78 years. Also, 38% of
them have at least one fireplace.
Figure 1 displays the full locations of these residential sales
in the city of Minneapolis and Target
Field. Target Field is located at the East end of Interstate
394, near the center of the city. Note that
the proportion of single-family houses sold in close proximity
to the stadium during the sample
period was very low and most transactions happened in the south
of the city. To accurately measure
their proximity to Target Field, we calculated the distance from
each property to the stadium using
the Major Sport Venues dataset obtained from GEOhio Spatial Data
Discovery Portal.
4. EMPIRICAL APPROACH
4.1. Event Study Method
11 Home owners may not apply for permits to build or remodel
their properties, even though they can be fined if
officials find this during the housing assessing process. In
this case, official permits won’t capture the change in
housing characteristics.
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Similar to previous studies (Tu 2005; Ahlfeldt and Kavetsos
2014), 3.5-mile ring centered at
Target Field was chosen as the impacted area. Based on Ellen et
al. (2001), we first applied event
study method to single-family residential houses, located within
3.5-mile of Target Field using the
following empirical specification
Ln(𝑃𝑖𝑗𝑡) = ∑ 𝜃𝑡𝐷𝑖𝑗3.5 ∗ 𝑡59𝑡=1 + 𝛽𝑋𝑖 + 𝛼𝑗 + 𝛿𝑡 + 𝜀𝑖𝑗𝑡 (1)
In this model Ln(𝑃𝑖𝑗𝑡) indicates the natural log of the housing
price for the 𝑖th property, in the 𝑗th
census tract, at the 𝑡th quarter-year. Specifically, fifty-nine
consecutive year-by-quarter dummies
are estimated starting from the first quarter of 2002 and ending
in the third quarter of 201612. To
represent the distance ring from the stadium, 𝐷𝑖𝑗3.5 is a vector
of dummy variable, equal to one if a
property is located within three and a half-mile radius of
Target Field. The variables of interest are
𝜃𝑡’s, which capture the impact of Target Field on properties
within 3.5-mile across quarters.
In terms of other covariates, 𝑋𝑖 indicates a vector of housing
and land characteristics,
including: the age of a property (Age); the square of property
age (Agesq); the lot acreage
(Landsize); the square of lot size (Landsizesq); the square
footage of building area above ground
(Aboveground); the square of above ground building area
(Abovegroundsq); the number of
bedrooms (Bedroom); the number of baths (Bathroom); and a binary
variable indicating if the
property had fireplaces (Fireplace). In addition, census tract
of 2000 fixed effects (𝛼𝑗) were
included to allow for pre-existing neighborhood heterogeneity
and quarter-year fixed effects (𝛿𝑡)
were added to account for the temporal variation in the general
housing market.
4.2. Spatial DD Identification Strategy
12 There’re only two property transactions in Minneapolis in the
last quarter of 2016. So, these transactions were not
included in the event study analysis.
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To formally estimate the impact of each stadium event on
property values, a spatial DD empirical
method is used. Spatial DD models are an increasingly used
method to mitigate the natural
endogeneity associated with location amenities and dis-amenities
(Dealy, Horn, and Berrens 2017;
Linden and Rockoff 2008). In evaluating the impact of
professional sports stadiums, the benefit of
spatial DD models compared with standard pre-post models is that
they mitigate potential
endogeneity between property values and location choices of
stadiums, e.g., self-selection of
stadiums into lower property value neighborhood (Galster,
Tatian, and Smith 1999). It also
alleviates part of the concern over omitted variable bias by
differencing out the unobservable time-
invariant characteristics at both house and neighborhood levels,
assuming that the properties in
close proximity to the stadium and those further away are
similar enough, and assuming the pre-
trend assumption holds. This technique has been used by numerous
authors to evaluate the impact
of professional sports stadiums on residential property values
(Tu 2005; Dehring, Depken, and
Ward 2007).
The standard set up for a spatial DD model, used to evaluate the
impact of a professional
sports stadium, estimates the difference between pre-post
changes in property values in close
proximity to the stadium and pre-post changes in property values
further away. Specifically, to
evaluate the impact of Target Field on proximate property
values, the following empirical
specification is used.
Ln(𝑃𝑖𝑗𝑡) = 𝜃1𝜏𝑖𝑡𝑃𝐿 + 𝜃2𝜏𝑖𝑡
𝐵𝐼𝐿𝐿 + 𝜃3𝜏𝑖𝑡𝐵𝑅 + 𝜃4𝜏𝑖𝑡
𝑂𝑃𝐸𝑁 + 𝜃5𝐷𝑖𝑗3.5 + (𝜃6𝜏𝑖𝑡
𝑃𝐿 + 𝜃7𝜏𝑖𝑡𝐵𝐼𝐿𝐿 +
𝜃8𝜏𝑖𝑡𝐵𝑅 + 𝜃9𝜏𝑖𝑡
𝑂𝑃𝐸𝑁)𝐷𝑖𝑗3.5 + 𝛽𝑋𝑖 + 𝛼𝑗 + 𝛿𝑡 + 𝜀𝑖𝑗𝑡
(2)
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Similar to the prior model, 𝑃𝑖𝑗𝑡 indicates the housing price for
the 𝑖th property, in the 𝑗th census
tract, at the 𝑡th time period, and a semi-log model (natural log
of price) is used.13 In this DD
specification, four distinct time events are included. First,
𝜏𝑖𝑡𝑃𝐿 is a dummy variable, equal to one
if a property sale happened after the Target Field proposal was
proposed (Feb 2, 2004), and before
the final bill of Target Field was approved (May 21, 2006).
Second, 𝜏𝑖𝑡𝐵𝐼𝐿𝐿 is a dummy variable,
equal to one if a property sale happened after the Target Field
final bill was passed, and before the
groundbreaking of Target Field (Aug 30, 2007). Third, 𝜏𝑖𝑡𝐵𝑅 is a
dummy variable, equal to one if a
transaction happened after the groundbreaking, and before the
opening of Target Field (January 4,
2010). Fourth, 𝜏𝑖𝑡𝑂𝑃𝐸𝑁 is a dummy variable, equal to one if a
transaction happened after the stadium
was opened.
The DD is created by including both the time and distance dummy
variables individually
and their interactions. In this specification 𝜃1 , 𝜃2 , 𝜃3 , 𝜃4
will capture pre-existing temporal
differences and 𝜃5 will capture pre-existing locational
differences; thus allowing 𝜃6, 𝜃7, 𝜃8, and
𝜃9 to capture Target Field time cut-points on proximal property
values.14 Specifically, 𝜃6 will
capture the change in property values associated with the
stadium proposal being proposed, 𝜃7
will capture the change in property values associated with the
final bill being passed, 𝜃8 will
capture the change in property values associated with the
stadium breaking ground, and 𝜃9 will
capture changes in values associated with Target Field opening.
In terms of other relevant aspects
of our model, 𝑋𝑖 is the same vector of housing characteristics
as the previous model. Also, like the
13 Results are similar to alternate functional form
specifications. 14 Note that quarter-year fixed effects are also
included into the model (with the first quarter of 2002 as the
base
category). Thus, 𝜃1- 𝜃5 should be interpreted as pre-existing
time and location differences after the time effect has been
removed.
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previous model, census-tract locational fixed effects (𝛼𝑗) and
quarter-year time fixed effects (𝛿𝑡)15
are included.
4.3. Spatial DD With Multiple Distance Rings
Further, the exact impacted distance ring of Target Field is not
known as a priori. As a robustness
check, we defined eight separated distance rings surrounding the
stadium, starting from 1.5-mile
radius around the stadium and increasing at a step of 0.5-mile:
0-1.5 miles, 1.5-2 miles, 2-2.5 miles,
2.5-3 miles, 3-3.5 miles, 3.5-4 miles, 4-4.5 miles, more than
4.5 miles. We ran the spatial DD
model on all the rings together except the last one, more than
4.5 miles. The formal specification
of the model is as follows:
Ln(𝑃𝑖𝑗𝑡) = 𝜃1𝜏𝑖𝑡𝑃𝐿 + 𝜃2𝜏𝑖𝑡
𝐵𝐼𝐿𝐿 + 𝜃3𝜏𝑖𝑡𝐵𝑅 + 𝜃4𝜏𝑖𝑡
𝑂𝑃𝐸𝑁 + ∑ 𝜃5𝑢𝐷𝑖𝑗
𝑣
7
𝑢=1
+ ∑(𝜃6𝑢𝜏𝑖𝑡
𝑃𝐿 + 𝜃7𝑢𝜏𝑖𝑡
𝐵𝐼𝐿𝐿 + 𝜃8𝑢𝜏𝑖𝑡
𝐵𝑅 + 𝜃9𝑢𝜏𝑖𝑡
𝑂𝑃𝐸𝑁)𝐷𝑖𝑗𝑣
7
𝑢=1
+ 𝛽𝑋𝑖 + 𝛼𝑗
+ 𝛿𝑡 + 𝜀𝑖𝑗𝑡 , 𝑣 = 0 − 1.5, 1.5 − 2, … , 3.5 − 4, 4 − 4.5
(3)
Similar to model (2), 𝐷𝑖𝑗𝑡𝑣 is a set of distance dummies
representing the seven closest distance rings
excluding the furthest ring from the stadium. The coefficients
of interest are still 𝜃6𝑢, 𝜃7
𝑢, 𝜃8𝑢, and
𝜃9𝑢, which reflects the impact of each Target Field event on the
properties in each distance ring.
Houses sold more than 4.5 miles away from the stadium are
treated as the comparison group for
all treated houses in the rings with radiuses less than 4.5
miles. A consistent impact across various
ring specifications will strengthen our results of the stadium
impact on nearby property value.
15 We also tried month-year fixed effects. The estimates on the
distance dummy and event period interactions are very
similar in magnitudes and significant at 1% level.
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4.4. Repeated Sales Spatial DD Identification Strategy
An important consideration is that the assumption of similarity
between proximal houses and those
located further away from the stadium might fail. The unobserved
housing attributes, such as the
material of the floor or the house, and neighborhood attributes,
such as crime rates and green space
around the house could be substantially different between houses
within the downtown area and
those at the edge of the city. To further address this concern,
we adopted repeated sales approach,
which removes all unobservable attributes at both levels as long
as they’re unchanged over time.
To implement this approach, we restricted the sample to be
houses sold more than once in the
sample period and included parcel level fixed effects to the
spatial DD model. Formally, our model
is as follows:
Ln(𝑃𝑖𝑗𝑡) = 𝜃1𝜏𝑖𝑡𝑃𝐿 + 𝜃2𝜏𝑖𝑡
𝐵𝐼𝐿𝐿 + 𝜃3𝜏𝑖𝑡𝐵𝑅 + 𝜃4𝜏𝑖𝑡
𝑂𝑃𝐸𝑁 + (𝜃6𝜏𝑖𝑡𝑃𝐿 + 𝜃7𝜏𝑖𝑡
𝐵𝐼𝐿𝐿 + 𝜃8𝜏𝑖𝑡𝐵𝑅 +
𝜃9𝜏𝑖𝑡𝑂𝑃𝐸𝑁)𝐷𝑖𝑗
3.5 + 𝛾𝑖 + 𝛿𝑡 + 𝜀𝑖𝑗𝑡 (4)
Different from the model (2), parcel-level fixed effects 𝛾𝑖
control all the temporal constant
characteristics. So, the housing attributes 𝑋𝑖, locational
difference dummy 𝐷𝑖𝑗3.5, census tract fixed
effects 𝛼𝑗 are omitted from the model (4). All the variations
used to estimate 𝜃’s come from houses
sold at least twice, once before the proposal was proposed and
once after a corresponding stadium
event. Again, 𝜃1 , 𝜃2 , 𝜃3 , and 𝜃4 will explain the temporal
change of housing value after each
stadium event on properties outside the 3.5-mile range.
Similarly, 𝜃6, 𝜃7, 𝜃8, and 𝜃9 will capture
the changes in property values within 3.5-mile attributable to
Target Field.
4.5. Spatial Effects
Another concern is over the spatial and temporal correlations
across properties, which has been
examined intensively in the hedonic studies (e.g., Box et al.
2005; Se Can and Megbolugbe 1997).
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To address this, we use an instrumental variable approach (Se
Can and Megbolugbe 1997; Tu
2005).16 This approach uses weights that are created using the
prior transactions of neighboring
houses. Specifically, weights are calculated for each property
using the following equation.
𝐶𝑂𝑀𝑃𝐴𝑅𝐴𝐵𝐿𝐸𝑖𝑡 = ∑ 𝑤𝑖𝑘𝑃𝑅𝐼𝐶𝐸𝑘𝑡𝑛𝑘=1 = ∑ [
1
𝑑𝑖𝑘
∑1
𝑑𝑖𝑘
𝑛𝑘=1
]𝑛𝑘=1 𝑃𝑅𝐼𝐶𝐸𝑘𝑡 (5)
In this equation, again following Se Can and Megbolugbe (1997),
𝑃𝑅𝐼𝐶𝐸𝑘 is the 𝑘𝑡ℎ neighboring
house price located within 1.8-mile distance from subject
property and 6-month prior to the
transaction date of the subject house. The weight applied to
each neighborhood property
transaction (𝑤𝑖𝑘) is based on its inverse distance 1 𝑑𝑖𝑘⁄ to the
property. Weights are normalized to
1 for each subject property.
4.6. Housing Boom and Bust
The final concern is over our sample period, 2002-2016, which
covers the latest housing bubble.
While proposal and possibly final bill pass happened in the
housing boom, the stadium
construction and opening happened during the housing bust. While
spatial DD model addresses
the pre-existing difference between the housing price within
3.5-mile of the stadium and that
outside of it, the similar timing between housing market
disruption and the stadium events could
cause a spurious relationship between the stadium events and the
relative change in the housing
value proximate to the stadium. Based on the suggestions in
(Boyle et al. 2012) and similar to
(Lang, Opaluch, and Sfinarolakis 2014), we added two
interactions between lot size and its square
with year fixed effects into both the spatial DD model and
repeated sales spatial DD model,
16 Maximum likelihood estimation with spatial lag and spatial
error models are often carried out (Anselin 1988). This
approach requires calculation of 𝑛 × 𝑛 spatial weighting matrix,
which is not feasible in the context of large number of varying
properties transacted over time. That’s why instrumental variable
method is preferred.
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15
allowing the land value within Minneapolis to change over years.
It will at least partially alleviate
our concern over the omitted variable bias caused by housing
bubble, if both models stay
unaffected after the addition.
5. RESULTS
5.1. Graphical Evidence
The marginal effects of interaction terms between the distance
dummy and quarters from equation
(1) are plotted in Figures 2. Four cut points are included in
the figure, including the quarter when
the proposal was proposed (Feb 2, 2004), the final bill was
approved and signed into law (May 21,
2006), the quarter when Target Field’s construction broke ground
(Aug 30, 2007), and the quarter
when Target Field opened (January 4, 2010). The figure shows the
average value of residential
properties within 3.5 miles were not significantly different
from that outside the range before the
stadium construction finally broke ground. This supports the
common pre-trend assumption for
the validity of spatial DD method. The value inside 3.5 miles
started to drop sharply after breaking
ground and this trend continued after the opening of the
stadium. Overall, this graph shows a
continuously negative trend for properties sold within the 3.5
miles of the stadium after the
construction began.
5.2. Baseline Spatial DD
Table 3 presents the results of baseline spatial DD regression
on single-family residential houses
in Minneapolis. Spatial IV, or the spatial and temporal variable
Comparable, is included to in both
regression. Column 2 adds two interactions between lot size and
lot size square with year fixed
effects. To begin with, recall that both time and location fixed
effects are included in the model as
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16
well as the Target Field time cut-points and the distance
parameter. Thus, parameter estimates for
the Target Field time and distance cut points should be
interpreted as the difference from the fixed
effects. Second, recall that events are specified as
non-overlapping dummies (i.e. each time dummy
ends when a new time period starts), so each can be interpreted
as the average impact of Target
Field observed in that time window. Third, recall that the
coefficients on interactions between the
distance dummy and time-cut points are interpreted as the
average effect of each Target Field event
on property values within 3.5-mile for the corresponding time
period.
Turning to the results, all time dummy parameters are
insignificant, suggesting the quarter-
year fixed effects well control temporal heterogeneity of the
housing market in Minneapolis. The
small and insignificant estimate on distance dummy 𝐷𝑖𝑗𝑡3.5
suggests there’s not pre-event spatial
difference in housing values across treatment statuses. Among
the variables of interest, the
groundbreaking and opening interactions are negative and
significant at 1% confidence level. To
explain in detail, residential property values within 3.5-mile
of the stadium are found to drop by
9.24% after the stadium broke ground and by 14.27% after the
stadium opened. In addition, the
interaction between proposal and distance dummy is positive and
significant at 10% level,
suggesting a small positive effect might be capitalized into the
housing market after the stadium
proposal was made. Further, these results stay consistent with
or without adding the interaction
terms between lot size and year fixed effects.
5.3. Spatial DD With Multiple Distance Rings
To verify the choice of 3.5-mile distance ring as the range for
impacted area, we created eight
distance rings around the stadium and ran the spatial DD model
on them to test the effect of stadium
across rings over time. Each cell combination (Table 4) presents
the amount of housing
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17
transactions volume and the corresponding column percentage for
each ring and stadium event
period. Based on the distance to Target Field, this table shows
more than a third of the total
transactions happened outside the 4.5-mile radius of the
stadium, while only less than 2%
happened within 1.5-mile distance ring. In terms of the stadium
time cut-points, the transaction
volume percentage dropped by 1-2 percentage points after the
stadium final bill was passed for the
rings with radiuses between 1.5 and 2.5 miles and by around 1
percentage point after the
construction broke ground for all the rings within 3.5 miles
distance range. This reduction in
transaction volume suggests that the housing boom and bust
affected the city housing market
during the sample period. After the opening of the stadium,
however, the percentages started to
increase for the majority of the spatial rings, implying the
recovery of the housing market. Overall,
the proportion of transactions within each cell is relatively
constant across periods.
Recall that the distance ring beyond 4.5-mile and its
interactions with each stadium event
are omitted from the Model (3) and so treated as the comparison.
The model estimates the average
effects of each stadium event on property values within each
distance ring compared to those
outside the 4.5-mile range. Table 5 presents the selected
estimates on the distance ring and event
dummy interactions from Model (3) regression results. Each cell
combination presents the estimate
on a corresponding ring and event dummy interaction and its
standard error in parenthesis. The
table shows consistent and strong negative effects of Target
Field breaking ground and opening on
the property value within 3.5 miles of the stadium.
Specifically, the stadium breaking ground
reduced the property value within 1.5-mile ring by 17.22%, that
between 1.5- and 2-mile by 15.3%,
that between 2.5- and 3-mile by 12.19%, and that between 3- and
3.5-mile by 10.42%. Similarly,
the stadium opening decreased the property value within 1.5-mile
ring by 14.53%, that between
1.5- and 2-mile by 14.10%, that between 2- and 2.5-mile by
19.27%, that between 2.5- and 3-mile
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18
by 16.56%, and that between 3- and 3.5-mile by 14.70%. Overall,
these estimated effects decrease
as ring radius increases and drop sharply beyond 3.5-mile radius
in terms of both magnitudes and
significances. Also, the stadium proposal was associated with a
positive impact on the property
value within 2.5- and 3-mile (5.45% increase), though this
positive effect is not consistent across
rings.
5.4. Repeated Sales Spatial DD Identification Strategy
As another robustness check, we further addressed potential
omitted variable bias using repeated
sales approach. After including parcel fixed effects, this
approach removes all the pre-existing
unobserved characteristics both within and outside of the
parcel. The variations within the same
parcel (house) over event periods are used to estimate the
coefficients in Model (4).
Table 6 presents the results of Model (4). Both regressions use
parcel fixed effects and
year-by-quarter fixed effects. Recall that the time invariant
factors, including 3.5-mile distance
dummy, housing attributes, and census tract fixed effects, are
not omitted. Similar to spatial DD
model, the estimates on the interactions reflect the effects of
stadium events on the property value
within 3.5-mile distance ring during the events’ periods.
Consistent with the spatial DD results
(Table 3), we found strong and negative effects of both
construction and opening of Target Field,
being significant at 1% level. In particular, construction and
opening were associated with 11.49%
and 15.46% reduction each in proximate single-family housing
price based on Column 1.
Interestingly, repeated sales model shows positive and
consistent effect of ball park proposal on
the same proximate properties, with a 6.56% increase based on
Column 1.
6. DISCUSSION AND CONCLUSION
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19
Substantial societal resources are spent in the United States
subsidizing professional sports. While
there are obviously welfare gains provided by sports leagues in
terms of enjoyment of watching
sports, there is limited evidence of economic gains associated
with professional sports leagues.
One economic benefit of professional sports stadiums, that is
generally found, is an increase in
property values in proximity to a stadium. In this paper, we
used a spatial DD method to evaluate
the impact of three discrete Target Field events (proposal,
final bill pass, groundbreaking, and
opening) on residential property values. We find that Target
Field resulted in a substantial drop in
single-family residential houses in the vicinity of the stadium
both after the groundbreaking and
the opening of the stadium. Several reasons might explain this.
The proximate properties could be
negatively affected by ground level air pollutions, noise
pollution, light pollution, and increased
traffic congestion around the new stadium. Also, the negative
impact on residential properties
could be driven by increased (violent) crime rate surrounding
the new stadium, especially when
the stadium is being used (Kurland, Johnson, and Tilley 2014;
Rees and Schnepel 2009).
Overall, we find strong NIMBY effect of Target Field events on
proximate single-family
residential housing market. In specific, groundbreaking, and
opening of Target Field each reduced
the property values within 3.5-mile radius of the stadium by
9.24% and 14.27%. NIMBY effect
rises from the perception that negative externalities from a
potential project outweighs its benefits
for local community. Potential NIMBY effects with regards to
professional sports stadiums are
clear. First, new stadiums cause negative effects to local
community around them, including air
pollution, noise pollution, and traffic congestion. Second, the
benefit of new sports stadiums in
terms of enjoyment, e.g., quality of life or civic pride, is
capitalized beyond the local community
to all the fans. Third, taxpayers dislike extra taxes being
raised to fund stadiums. In a survey
conducted between 01/31/12 and 02/02/12 for a new professional
football stadium in Minnesota,
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20
68% of Minnesota voters thought a new stadium should be built
with private funding entirely.17
This aversion to pay for sports stadiums might be strongest in
communities proximate to new
sports stadiums, based on recent literature of referendum voting
outcomes on professional sports
stadiums. Fourth, this attitude could be strengthened by a
unwarranted political process, to which
local community residents might respond in a NIMBY attitude
(Kuhn and Ballard 1998; Kemp
1992). In the same survey, 77% of Minnesota voters thought a
public vote should be put before
any tax dollars being used for a new stadium. The state law to
publicly fund Target Field, which
circumvented ballot referendum, might cause NIMBY fashion. In
sum, the results of this paper
provide implications for the public debate over funding
professional sports stadiums using tax
payers’ money. It may not be worthwhile for tax payers to fund
sports stadiums, especially for
those living in close proximity to them.
17 See
http://www.surveyusa.com/client/PollReport.aspx?g=5a67e54f-5eb1-4515-9662-b080012b50f8
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21
Figure 1. Single Family Residential Houses Sales in
Minneapolis
Notes: Housing sales data and administrative boundaries were
obtained from Minneapolis Open Data Portal, 2002 –
2016. Interstate data was acquired from Minnesota Geospatial
Commons. Target Field data was obtained from
GEOhio Spatial Data Discovery Portal.
-
22
Figure 2. Estimated Impact of Within 3.5-mile for Single-family
Houses
Notes: Impact of being located within 3.5-mile of Target Field
is estimated and predicted from the marginal effects
of interaction terms between the distance dummy and quarters in
equation (1). The regression includes housing
characteristics, spatial IV, year-by-quarter fixed effects.
Robust standard errors clustered by census tract. Source:
Housing sales data was obtained from Minneapolis Open Data
Portal, 2002 – 2016. Distances were calculated in
ArcMap using data from Minnesota Geospatial Commons. Target
Field data was obtained from GEOhio Spatial Data
Discovery Portal.
-
23
Table 1 Target Field Events Timeline
Event Name Detail Date
Proposal The baseball park proposal was proposed in the
final report by the Stadium Screening Committee
to Governor Tim Pawlenty
Monday, February 2, 2004
Finalbill The final bill of the park was approved and
signed into law
Sunday, May 21, 2006
Breakground The baseball park construction began Thursday,
August 30, 2007
Open The baseball park opened Monday, January 4, 2010
-
24
Table 2 Summary Statistics (N=38,816)
Variable Description Mean SD Min Max
Price Sale price (2016 $) 401,621 342316 13,000 7,767,659
Comparable Avg. neighbor house price within 1.8-mile prior
6-month (2016 $) 391,167 151691 93,295 2,346,528
Landsize Lot size (acres) 0.13 0.04 0.02 0.91
Aboveground Square footage of building above ground (sqft) 1,297
478.99 120 6972
Bedroom # of bedrooms 2.95 0.88 1 8
Bathroom # of bathrooms 1.74 0.81 1 8
Age Property age (years) 77.98 20.69 0 116
Fireplace Dummy variable 1, if the house has fireplace(s), 0
otherwise 0.38 0.48 0 1
Notes: Housing sales data was obtained from Minneapolis Open
Data Portal, 2002-2016.
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25
Table 3 Spatial DD
(1) (2)
VARIABLES
𝜏𝑖𝑗𝑡𝑃𝐿 0.0140 0.0145
(0.0355) (0.0357)
𝜏𝑖𝑗𝑡𝐵𝐼𝐿𝐿 0.0539 0.0541
(0.0399) (0.0399)
𝜏𝑖𝑗𝑡𝐵𝑅 0.0524 0.0516
(0.0503) (0.0503)
𝜏𝑖𝑗𝑡𝑂𝑃𝐸𝑁 0.109 0.219
(0.334) (0.337)
𝐷𝑖𝑗𝑡3.5 -0.0124 -0.0128
(0.0263) (0.0265)
𝜏𝑖𝑗𝑡𝑃𝐿 ∗ 𝐷𝑖𝑗𝑡
3.5 0.0236* 0.0237*
(0.0121) (0.0123)
𝜏𝑖𝑗𝑡𝐵𝐼𝐿𝐿 ∗ 𝐷𝑖𝑗𝑡
3.5 -0.0142 -0.0145
(0.0166) (0.0164)
𝜏𝑖𝑗𝑡𝐵𝑅 ∗ 𝐷𝑖𝑗𝑡
3.5 -0.0969*** -0.0966***
(0.0286) (0.0286)
𝜏𝑖𝑗𝑡𝑂𝑃𝐸𝑁 ∗ 𝐷𝑖𝑗𝑡
3.5 -0.154*** -0.152***
(0.0382) (0.0381)
N 38,816 38,816
R-squared 0.485 0.485
Land-Year Interactions NO YES
Year-Quarter FE YES YES
Census-Tract FE YES YES
Spatial IV YES YES Notes: Regressions use semi-log prices as
dependent variables. All the regressions
include same housing characteristics (refer to Table 1 for
details), census tract fixed
effects, year-by-quarter fixed effects, and robust standard
errors clustered by census
tract. *** p
-
26
Table 4 Housing Transaction Distribution by Distance and Event
Period Event Periods
Distance Ring Pre-proposal Proposal Finalbill Breakground Open
Total
< 1.5 miles 194 182 75 75 159 685 2.12% 1.78% 1.73% 1.59%
1.52% 1.76%
1.5 - 2 miles 566 652 239 213 491 2161 6.19% 6.39% 5.50% 4.53%
4.71% 5.57%
2 - 2.5 miles 792 951 290 253 613 2899 8.67% 9.32% 6.68% 5.38%
5.88% 7.47%
2.5 - 3 miles 995 1177 420 390 877 3859 10.89% 11.54% 9.67%
8.29% 8.41% 9.94%
3 - 3.5 miles 1109 1222 555 525 1181 4592 12.14% 11.98% 12.78%
11.16% 11.32% 11.83%
3.5 - 4 miles 1287 1368 610 691 1521 5477 14.08% 13.41% 14.05%
14.69% 14.58% 14.11%
4 - 4.5 miles 1152 1337 577 678 1393 5137 12.61% 13.11% 13.29%
14.42% 13.35% 13.23%
> 4.5 miles 3043 3312 1577 1878 4196 14,006 33.30% 32.47%
36.31% 39.93% 40.23% 36.08%
Total 9138 10,201 4343 4703 10,431 38,816
100% 100% 100% 100% 100% 100%
Notes: Each cell combination represents the number of housing
transactions and its column percentage in a
corresponding event period and a distance ring.
-
27
Table 5 Selected Estimates from Spatial DD with Multiple
Distance Rings
(N = 38,816, R square = .4857) Event Periods
Distance Ring Proposal Finalbill Breakground Open
< 1.5 miles 0.0552 0.0553 -0.189*** -0.157** (0.0398)
(0.0422) (0.0599) (0.0646)
1.5 - 2 miles 0.0429* -0.0425 -0.166*** -0.152** (0.0246)
(0.0342) (0.0397) (0.0617)
2 - 2.5 miles 0.0417* 0.0163 -0.0413 -0.214*** (0.0219) (0.0337)
(0.0460) (0.0565)
2.5 - 3 miles 0.0531*** -0.0267 -0.130*** -0.181*** (0.0178)
(0.0311) (0.0369) (0.0610)
3 - 3.5 miles -0.0131 -0.0219 -0.110** -0.159*** (0.0191)
(0.0291) (0.0483) (0.0576)
3.5 - 4 miles 0.00412 -0.00632 -0.0687* -0.0576 (0.0181)
(0.0240) (0.0352) (0.0387)
4 - 4.5 miles 0.0213 0.00790 -0.00852 -0.0269 (0.0140) (0.0230)
(0.0324) (0.0423)
Notes: Each cell combination represents the estimate and its
standard error on a corresponding event period and a
distance ring. The regression uses semi-log prices as dependent
variables, includes the housing characteristics (refer
to Table 2 for details), census tract fixed effects,
year-by-quarter fixed effects. Robust standard errors are
clustered
by census tract in parentheses. *** p
-
28
Table 6 Repeated Sales Spatial DD
(1) (2)
VARIABLES
𝜏𝑖𝑗𝑡𝑃𝐿 0.0141 0.0132
(0.0476) (0.0478)
𝜏𝑖𝑗𝑡𝐵𝐼𝐿𝐿 0.0650 0.0632
(0.0553) (0.0558)
𝜏𝑖𝑗𝑡𝐵𝑅 0.110 0.107
(0.0811) (0.0816)
𝜏𝑖𝑗𝑡𝑂𝑃𝐸𝑁 -0.0600 -0.433***
(0.0682) (0.159)
𝜏𝑖𝑗𝑡𝑃𝐿 ∗ 𝐷𝑖𝑗𝑡
3.5 0.0635*** 0.0656***
(0.0187) (0.0187)
𝜏𝑖𝑗𝑡𝐵𝐼𝐿𝐿 ∗ 𝐷𝑖𝑗𝑡
3.5 0.0253 0.0255
(0.0266) (0.0265)
𝜏𝑖𝑗𝑡𝐵𝑅 ∗ 𝐷𝑖𝑗𝑡
3.5 -0.122*** -0.121***
(0.0367) (0.0369)
𝜏𝑖𝑗𝑡𝑂𝑃𝐸𝑁 ∗ 𝐷𝑖𝑗𝑡
3.5 -0.168*** -0.166***
(0.0435) (0.0434)
N 23,978 23,978
Within Parcel R-squared 0.152 0.153
Number of parcel 10,579 10,579
Land-Year Interactions NO YES
Year-Quarter FE YES YES
Spatial IV YES YES Notes: Regressions use semi-log prices as
dependent variables. All the regressions
include parcel fixed effects, year-by-quarter fixed effects, and
robust standard errors
clustered by census tract. *** p
-
29
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