The Effect of a Sports Stadium on Housing Rents: An Application of Geographically Weighted Regression

Main Article Content

Jorge Enrique Agudelo Torres
Gabriel Alberto Agudelo Torres
Luis Ceferino Franco Arbeláez
Luis Eduardo Franco Ceballos


Housing Rents, Geographically Weighted Regression, Colombia


Researchers have determined that real estate prices vary in continuous ways as a function of spatial characteristics.  In this study we examine whether geographically weighted regression (GWR) provides different estimates of price effects around a sports stadium than more traditional regression techniques.  We find that an application of GWR with hedonic prices finds that the stadium has a negative external effect on housing rents that extends outward 560 meters, in contrast to the positive external effect on housing rents found using a conventional estimation technique.


Download data is not yet available.
Abstract 1767 | PDF Downloads 701 HTML Downloads 955


Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers.

Anselin, L. (1999). Spatial Data Analysis with SpaceStatTM and ArcView. Workbook (3.a edición). Department of Agricultural and Consumer Economics, University of Illinois, Urbana, IL 61801.

Arce, R. de, Mahía, R. (2008). Conceptos básicos sobre la heterocedasticidad en el modelo básico de regresión lineal y tratamiento con Eviews. Madrid: Universidad Autónoma de Madrid.

Basu, S., Thibodeau, TG. (1998). Analysis of spatial autocorrelation in house prices. The Journal of Real Estate Finance and Economics, 17, 61-85.

Beaty, J. (1952). Rental real estate often a good investment. Med Econ., 5(6): 93-4.

Bitter, C., Mulligan, G., & Dall’erba, S. (2007). Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9(1), 7-27.

Can, A. (1992). Specification and estimation of hedonic house Price models. Regional Sciences and Urban Economics, 22, 453-74.

Chasco, C. (2003). Econometría espacial aplicada a la predicción - extrapolación de datos microterritoriales. [Tesis doctoral]. Madrid. Universidad Autónoma de Madrid. Consejería de Economía e Innovación Tecnológica.

Chávez, Y., Medina, P. (2012). Diferencia de gastos según tamaño y composición familiar: una aplicación para Ecuador usando escalas de equivalencia. Analítika, Revista de Análisis Estadístico, 4(1), 3-20.

Dewey, L., Turo, P. de (1950). Should I invest in real estate? Med Econ., 28, 3, 85-93.

Lancaster, K. (1966). A new approach to consumer theory. Journal of Political Economy, 74(1), 132-57.

Hernández, S. (2013). La violencia del fútbol. Obtenido de:útbol/

Lu, B., Charlton, M., & Fotheringham, A. S. (2011). Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data. Procedia Environmental Sciences, 7, 92-7.

Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation and pure competition. Journal of Political Economy, 82, 34-55.

Selim, H. (2009). Determinants of house prices in Turkey: Hedonic regression. Expert Systems with Applications, 36(2), 2843-5.

Sheppard, S. (1999). Hedonic analysis of housing markets. En P. C. Cheshire & E. S. Mills (Eds.), Handbook of regional and Urban Economics, vol. 3 (pp. 1595-1635). North Holland, Amsterdam.

Yu, D. (2004). Modeling housing market dynamics in the city of Milwaukee: a geographically weighted regression approach. Obtenido de:

Most read articles by the same author(s)