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The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates

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Abstract

This article compares results from non-spatial and new spatial methods to examine the reliability of welfare estimates (direct and multiplier effects) for locational housing attributes in Seattle, WA. In particular, we assess if OLS with spatial fixed effects is able to account for the spatial structure in a way that represents a viable alternative to spatial econometric methods. We find that while OLS with spatial fixed effects accounts for more of the spatial structure than simple OLS, it does not account for all of the spatial structure. It thus does not present a viable alternative to the spatial methods. Similar to existing comparisons between results from non-spatial and established spatial methods, we also find that OLS generates higher coefficient and direct effect estimates for both structural and locational housing characteristics than spatial methods do. OLS with spatial fixed effects is closer to the spatial estimates than OLS without fixed effects but remains higher. Finally, a comparison of the direct effects with locally weighted regression results highlights spatial threshold effects that are missed in the global models. Differences between spatial estimators are almost negligible in this study.

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Notes

  1. Freeman (1999).

  2. Deaton and Muellbauer (1980), pp. 124.

  3. At this point an additional assumption is being made; Eq. (2) will be continuous if there is sufficient variation in the characteristics that define the houses. The richness of the data that will be used in this study will guarantee that the assumption is satisfied.

  4. Our sample consists of 5,035 observations. The average number of links (i.e. the average number of neighboring observations) is 5.96 and only 11% of the weights are different from zero. Farber et al. (2009a, b) have recently emphasized that these properties of the spatial weights matrix are likely to affect the power of LM and other tests. Results from the simulations reported in the papers show that with sample sizes of 1,000 observations the power of the test is already reasonably high. Therefore, given our sample size we can certainly conclude that our results are not affected by the topology of our sample. Additionally, results using alternative weights matrices were consistent and are available from the authors.

  5. Breusch and Pagan (1979).

  6. All computations were performed using the libraries sphet (Piras, 0010), spdep (Bivand et al. 2010a) and spgwr (Bivand et al. 2010b) of the R statistical software (R Development Core Team 2009).

  7. The results are robust to alternative bandwidth choices, including ten nearest neighbors (i.e. considering a larger number of non-zero covariances).

  8. For an extensive treatment of the topic see, for example, McMillen (1996), Brunsdon et al. (1996, 1998), McMillen and Redfearn (2010) and Fotheringham et al. (2002).

  9. Where P is usually taken to be the average house price.

  10. The term premium has been widely used in the hedonic literature to refer to the coefficient estimates when the corresponding variables is categorical (see e.g. Beron et al. 2004). The name stems from the fact that it refers to the coefficient of a variable that is not continuous, and therefore it indicates the additional price—hence a premium, that individuals are willing to pay for a certain dichotomous characteristic, e.g. view or access to a highway.

  11. For comparison purposes we also estimated the premiums using the transformation described in Eq. (16). The results obtained are qualitatively similar to those reported in Tables 4 and 5. Detailed results are available from the authors.

  12. There is a discussion in the literature on which variables to include in a hedonic model from the long list of possible determinants of prices. For instance, school district information is often included, which is not part of this analysis since Seattle has a unified school district. See Dubin and Sung (1990) on the problem of which location variables to choose.

  13. After a Thiessen polygon conversion of points, a queen weights matrix was used to calculate the weighted averages.

  14. The trend surface results are not included in this article but are available from the authors upon request.

  15. Results for premiums calculated according to the transformation: \(\hat{\beta}_c=\exp(\hat{\beta})-1\) were qualitatively similar for both direct and multiplier effects. Results can be obtained from the authors.

  16. Confidence bands for the S2SLS estimates are calculated using the HAC standard errors with an Epanechnikov kernel. In the non-spatial models and for the direct effect computation, the standard errors are those reported for the regression coefficients. For the spatial multiplier, the standard error of both \(\hat{\beta}\) and \(\hat{\rho}\) must be accounted for jointly, which we implement by means of the delta method (see Greene 2003, for further details).

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Acknowledgments

We would like to thank the three reviewers and the journal editor for very helpful improvement suggestions. An earlier version of this paper benefited from feedback at the 54th North American Meeting of the Regional Science Association International in Savannah, GA (November 2007). Gianfranco Piras gratefully acknowledges financial support from the Iniciativa Milenio, MIDEPLAN (Chile) and the authors further acknowledge support from Arizona State University’s GeoDa Center for Geospatial Analysis and Computation.

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Authors and Affiliations

  1. GeoDa Center, Arizona State University, Tempe, AZ, USA

    Julia Koschinsky

  2. The World Bank, Washington, DC, USA

    Nancy Lozano-Gracia

  3. Regional Research Institute, West Virginia University, 886 Chestnut Ridge Road, P.O. Box 6825, Morgantown, WV, 26506-6825, USA

    Gianfranco Piras

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  1. Julia Koschinsky
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  2. Nancy Lozano-Gracia
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  3. Gianfranco Piras
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Correspondence to Gianfranco Piras.

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Koschinsky, J., Lozano-Gracia, N. & Piras, G. The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates. J Geogr Syst 14, 319–356 (2012). https://doi.org/10.1007/s10109-011-0148-6

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