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Assessing the Economic Impact of Sports Facilities on Property Values: A Spatial Hedonic Approach By: Xia Feng and Brad R. Humphreys
I. Research Question
Brad Humphreys, Dennis Coates and many other urban economists have conducted research in the field of sports arenas and urban development. However, most research has focused on identifying and analyzing tangible, economic benefits of sports arenas on cities. Differentiating itself from prior research on the intangible benefits of sports arenas on cities, Xia Feng and Brad Humphreys’ paper proposes a spatial hedonic model that estimates the intangible benefits of two sports facilities in Columbus, Ohio on residential property values.
This discussion of the benefits of sports stadiums stems from the willingness of cities and towns to subsidize construction of expensive sports stadiums. As the rise in the size of these subsidies has coincided with the boom in the construction of new stadiums, urban economists conducted research on the costs and benefits of construction of new stadiums and arenas. Proponents of these subsidies posit income increases, job creation and multiplier effects (due to new spending) as tangible, positive impacts of building new sports stadiums. However, contrary to the aforementioned claims, made mostly by consulting firms (usually hired by the respective sports franchises), the findings from years of economic research have shown no positive impact of building new stadiums on cities. In fact, econometric evidence has shown that professional sports facilities can have little effect to net negative effects on the local economy.
Regardless of these well-respected and well-supported research projects, cities continue to subsidize the construction of sports stadiums. The continuation of this policy decision, which research finds in general to be neither cost-effective for cities nor beneficial to cities, forces consideration of intangible benefits. Few papers have empirically estimated the intangible benefits, such as the increased civic pride, increased city attractiveness or increased cultural benefits, of building sports stadiums. A couple papers have examined the impact of sports facilities on property values with varying results, and this study adds to the literature by providing new evidence based on data from different locations and different sports. Most importantly, this study does not ignore spatial effects. Spatial autocorrelation is the correlation among values of a single variable due to their close locational positions on a two-dimensional (2-D) surface. Spatial autocorrelation could have caused biased estimates and model misspecification in the few earlier models on the subject of stadium presence’s impact on housing prices
II. Theoretical Background
Because of the difficulty of measuring “intangible benefits or costs”, Feng and Humphreys assume that the presence of a stadium would be viewed as an intangible characteristic and the presence of a sports stadium would be capitalized in housing prices. Housing prices tend to be spatially correlated due to common neighborhood characteristics.
Feng and Humphreys use an adaptation of the spatial lag hedonic model:
(I − ρW y) −1 = I + ρW + ρ 2W2 + . . .
This model links each observation of the dependent variable to all observations of the explanatory variables through a spatial multiplier.
Using transactions data, containing observations on 9,504 single-family housing units, for the year 2000, Feng and Humphreys analyze the values of residential housing around Nationwide Arena and Crew Stadium in Columbus. The data set includes housing and neighborhood characteristics such as lot size, school quality, environmental quality and number of fireplaces.
To account for aspects of the model that were not incorporated into the adapted spatial lag hedonic model, certain modifications were made to the model. To account for the presence of Ohio Stadium, dummy variables were created. To control for the effects of businesses on housing values, Feng and Humphreys controlled for the number of commercial establishments in each zip code, which allowed the business-related variables to capture some of the effects of business location on residential property values.
III. Empirical Model
Known as a spatial weighting matrix, this symmetric matrix is used to define the locations for which the values of the random variables are correlated, and the rows in the weights matrix are standardized. The features of both housing markets and individual housing data make the definition of the spatial weights matrix W especially important. The aforementioned matrices specify “neighborhood sets”, and these neighborhood sets capture spatial interaction. Feng and Humphreys use GeoDa to specify the neighborhoods and to define the spatial weights matrix, and begin by using four different spatial weights to create the matrices. Next, Feng and Humphreys use the log-log form of the hedonic housing price with the appropriate spatial lags to best estimate the parameters.
IV. Results and Discussion
The results of the research of Feng and Humphreys suggest that the presence of sports facilities in Columbus have a significant positive distance-decaying effect on surrounding house values. For Nationwide Arena, at the average, all else equal, for each 1% decrease in the distance to the arena is associated with a 0.175% increase in the price of the average house. In dollar terms, a 1% decrease in distance from each house to the arena, on average, increases the price of an average house by $222. The primary variable used to evaluate the effects of sports facilities on surrounding housing values is the distance between each house and the sports facility, and analysis of this parameter shows that the presence of sports facilities has positive effects (though they diminish with distance) on housing values. Importantly, Feng and Humphreys also show that prior OLS models, which did not account for spatial autocorrelation, overestimated the distance parameters, and did not correct for heteroskedasticity when present.
This paper elevated the credibility of the larger economic argument by finding the general importance of factoring spatial autocorrelation into property value modeling. With regard to policy decisions, professional sports facilities generate intangible benefits in the local economy, and cities do have a rational economic argument to lodge in support of provision of subsidies to sports stadiums. While the costs of public support rarely exceed the cost of public funding for the stadiums directly, the subsequent rise in property values can set the foundation for more substantial growth in adjacent areas, and give the city’s business community the confidence necessary to invest. Feng and Humphreys offered a more precise method of analyzing costs and benefits, and show that there are positive effects (contrary to most research) of building sports facilities at least in this one example. This paper offers answers, and poses new questions. What other benefits can be discovered? How close can economists make it to quantifying the efficient subsidy level for stadiums and arenas?
 Humphreys, Brad & Feng, Xia. “Assessing the Economic Impact of Sports Facilities on Property Values: A Spatial Hedonic Approach.” LASE/NAASE Working Paper Series 8.12 (2008): 1-20. Web. 25 March 2015.