Public infrastructure and housing prices: An application of geographically weighted regression within the context of hedonic prices

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Juan Carlos Duque
Hermilson Velásquez Ceballos
Jorge Agudelo


Real state, GWR, Geographically Weighted Regression, Hedonic prices, Metro station.


The analysis of externalities in real state has been matter of study during the past few years. In this paper we use both conventional and spatial econometric model, as well as geographically weighted regression models, to measure the effect of the San Javier Metro Station (in Medellín, Colombia) on the housing prices of the surrounding area.

The main finding of this study is that the metro station has a positive impact on the prices of houses located within a radius of 600 meter from the station. However, the railroad track accessing the station has a negative impact on housing prices located nearby.


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Agudelo, G. (2010). Dependencia Espacial: Detección, Validación y Modelación. TesisMaestría en Matemáticas Aplicadas. EAFIT.

Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrech: Kluwer.

Anselin, L. (2003). Spatial externalities, spatial multipliers, and spatial econometrics. International Regional Science, 26, 153–166.

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

Beaty, J. (1952). Rental real estate often a good investment. Med Econ, 5(6), 93–94.

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.

Brunsdon, C., Fotheringham, S., Charlton, M. (1998). Geographically weighted regression – modeling spatial non-stationarity. The Statistician,47(3), 431-443.

Can, A. (1992). Specification and estimation of hedonic house Price models. Regional Sciences and Urban Economics, 22, 453-474.

Carroll, T., Clauretie, T., Jensen, J. (1996). Living next to godliness: Residential property values and churches. Journal of real estate finance and economics. 12(3): 319 – 330.

Cho, S., Bowker, J., Park, W. (2006). Measuring the Contribution of Water and Green Space Amenities to Housing Values: An Application and Comparison of Spatially Weighted Hedonic Models.Journal of Agricultural and Resource Economics, 31(03), 485 – 507.

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

Dubin, R., Goodman, A. (1982). Valuation of education and crime neighborhood characteristics through hedonic housing prices.Population and environment, 5(3), 166–181.

Fotheringham, A., Brunsdon, C., and Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationship. John Wiley and Sons, Ltd., West Sussex, UK.

Frech, H., Lafferty, R. (1984). The effect of the California coastal commission on housing prices.Journal of Urban Economics, 16(1), 105–123.

Grudnitski, G., Do, Q. (1997). Adjusting the value of houses located on a golf course. The appraisal Journal,65(3), 261-266.

Lancaster, K. (1966). A new approach to consumer theory.Journal of Political Economy, 74(1), 132–157.

LeSage, JP. (2004). A family of geographically weighted regression models.Advances in spatial econometrics. Methodology, tools and applications, 241-264.

LeSage J., Peace, K. (2004). Spatial statistics and real estate.Journal of Real Estate Finance Economics, 29, 147–148.

LeSage, J., Peace, K (2009). Introduction to Spatial Econometrics.CRC press.

Medina, Carlos; Morales, Leonardo y JairoNuñez.(2010). Quality of life in urban neighbourhoods of Bogotá and Medellín. The Quality Life in Latin American Cities: Markets and Perceptio. Editado por Eduardo Lora, Andrew Powell, Bernard M.S. van Praag y Pablo Sanguinetti, BID y BM.

Mei, C., He, S., Fang, T. (2004). A note on the mixed geographically weighted regression model. Journal of Regional Science, 44(1), 143-157.

Mennis, J. (2006). Mapping the results of geographically weighted regression. Cartographic Journal, 43(2), 171-179.

Pace, R., Kelley, R., Sirmans, C. (1998). Spatial Statistics and Real Estate. Journal of Real Estate Finance and Economics, 17, 5–13.

Pavlyuk, D. (2009). Statistical analysis of the relationship between public transport accesibility and flat prices in Riga. Transport and telecomunication,10(02), 26–32.

Portney, P. (1981). Housing prices, health efects, and valuing reductions in risk of death. Journal of environmental economics and management, 8(1), 72–8.

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

Yu, D. (2004). Modeling housing market dynamics in the city of Milwaukee: a geographically weighted regression aproach. Recuperado de: http://www.ucgis. org/ucgisfall2004/studentpapers/files/danlinyu.pdf