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Estimating School Proximity Effects on Housing Prices: the Importance of Robust Spatial Controls in Hedonic Estimations

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Abstract

The authors use a data set of over 20,000 residential housing sales in 2010 and 2011 in San Diego County to measure the school proximity effects on nearby residential housing. Prior research supports the existence of a “school proximity premium” for housing located near K-12 schools. However, since these investigations have lacked important spatial controls, the authors investigate whether their inclusion affects outcomes. In addition, the authors separate out the effects for proximity to public and private elementary schools, measure the distance from schools using a more precise distance measurement and utilize a very large data set on the West Coast of the US. Counter to prior investigations, the authors find strong evidence for a “school proximity penalty,” for public elementary schools suggesting that proximity is perceived to be a net negative for homebuyers in San Diego County. When spatially dividing the sample area into a coastal and inland region, the authors find the results for the inland region for public schools more closely resemble the results from prior investigations (with a “delayed” proximity premium) while the coastal results demonstrate a very strong “proximity penalty” effect. Results presented here indicate that failure to include robust spatial controls (including zip code and census tract fixed effects) could have important implications for hedonic housing price estimates of this kind.

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Notes

  1. US census bureau estimates for 2012, http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

  2. US census bureau estimates for 2012, the population of San Diego county is 3,177, 063

  3. US census bureau estimates for 2012, http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

  4. Following Halvorsen and Palmquist (1980) and Thornton and Innes (1989), it can be shown mathematically that for logarithmic transformations, the coefficient is interpreted as 100*(Exp(β)−1)%, where β is the estimated coefficient of the independent variable

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Authors and Affiliations

  1. Burnham-Moores Center for Real Estate, School of Business Administration, University of San Diego, San Diego, CA, 92110, USA

    Vivek Sah

  2. School of Business Administration, University of San Diego, San Diego, CA, 92110, USA

    Stephen J. Conroy & Andrew Narwold

Authors
  1. Vivek Sah
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  2. Stephen J. Conroy
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  3. Andrew Narwold
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Correspondence to Stephen J. Conroy.

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Sah, V., Conroy, S.J. & Narwold, A. Estimating School Proximity Effects on Housing Prices: the Importance of Robust Spatial Controls in Hedonic Estimations. J Real Estate Finan Econ 53, 50–76 (2016). https://doi.org/10.1007/s11146-015-9520-5

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  • DOI: https://doi.org/10.1007/s11146-015-9520-5

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