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Relationship between value of open space and distance from housing locations within a community

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Abstract

This research uses a sequence of hedonic spatial regressions for a metropolitan housing market in the Southeastern United States to explore a new procedure that establishes the relationship between the value attributable to open space and distance from housing locations (a “distance-decay function”) within a given community. A distance-decay function allows identification of the range of distance over which open space affects housing values and the estimation of a proxy for the value added to nearby houses resulting from hypothetical open space preservation. Ex post analyses of the open-space regression coefficients suggest marginal implicit price functions for three types of open space that decay as open space area increases with respect to house location. After controlling for other factors in the spatial hedonic model, simple distance-decay functional relationships were established between the implicit prices of developed open space, forest-land open space, and agriculture-wetland open space and the buffer radius of the open-space areas surrounding a given housing location. The proposed method may be useful for identifying the range over which preferences for different types of open space are exhibited.

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Notes

  1. Adding the open space acreage for each succeeding ring as explanatory variables in one hedonic equation may be alternative approach. This would be more parsimonious than the approach that was used in this paper. However, considering the high degree of correlation between the ring buffers (0.29–0.99 for developed open space, 0.45–0.99 for agriculture-wetland open space, and 0.40–0.99 for forest-land open space), serious multicollinearity is anticipated. In fact, the mean variance inflation factor based on the OLS estimates using the ring buffers (instead of the cumulative buffers) in single hedonic model is estimated to be 27,121, which confirms the serious multicollinearity.

  2. Because the study area has multiple centers of cultural and business opportunities, three CBDs (i.e., downtown and two other concentrations of retail and commercial buildings) are used.

  3. The elements of the contiguity matrix were interacted with an n by n matrix containing a continuous decay function in each position. The resulting matrix therefore discounts the influence of sales transactions between more distant neighbors. The elements of the combined matrix were W = (w ij  > 0)/d ij , where d ij  = Euclidean distance between locations i and j, and w ij  = 1 if i and j were neighbors. The final matrix was row standardized. The average number of neighbors was 6.0, with the minimum and maximum eigenvalues (e) of the combined weighting matrix were −1.0 and 1.0, respectively. The values set the bounds for the AR lag and error parameters as [e −1min , e −1max ] = [−1, 1] (Anselin 1988).

  4. The detail descriptions about the both approaches are available on page 302 in Larch and Walde (2008).

  5. Previous research has sequentially measured open space using land cover data (e.g., Acharya and Bennett 2001; Nelson et al. 2004; Poudyal et al. 2009). Land cover data, including the national land cover database (NLCD), has some distinct advantages. The NLCD database is standardized with respect to definitions used to classify satellite images into specific land cover categories. The information is also publicly available to researchers. There may be drawbacks, however, to using NLCD data (Kline et al. 2009). Concern has been expressed that the NLCD does not accurately reflect low density residential development patterns, particularly in rural areas (Irwin and Bockstael 2007). However, the overall accuracy rate of the NLCD data in urban areas is reported to be sound. For example, the overall accuracy in all urban areas of upstate New York and Pennsylvania is 88.7%, indicating relatively good coverage (Walton 2005). The focus of this research is not on rural areas per se, but on the valuation of open space in an established urban/suburban housing market. In this case, because aerial remote images of urban areas including developed open space and forest-land open space, are generally accurately interpreted with imperviousness accuracies ranging from 83 to 91% and tree canopy accuracies ranging from 78 to 93%, a static picture of the urban physical characteristics is likely sufficient (Irwin and Bockstael 2007; Walton 2005).

  6. In general, there is no consensus which weights are most appropriate for any econometric study (Anselin 1988), and the selection of appropriate weight matrices remains a challenge to practitioners (Le Gallo and Etur 2003). Florax and Rey (1995) discuss some problems that may arise if spatial weights matrices are poorly selected. In an ex post analysis, the residuals of the error model were tested for spatial autocorrelation using the hybrid W matrix. The advantage of the hybrid W matrix is that it accounts for interactions within a given neighborhood of adjacent transactions but discounts the influence of more distant observations. The null hypothesis of no spatial error dependence could not be rejected at any conventional level of significance. As another sensitivity analysis, a simple contiguity and an inverse distance matrix (both row standardized) were use in each of the 50 regressions. The same conclusions were obtained: spatial error was significant across the regressions, while spatial lag was not. While it is difficult to conclude that the W matrix used in this study is the best of all possible neighborhood specifications, the ex post LM error tests and the Wald tests of the second and third sets of regressions using the contiguity and inverse distance matrices are encouraging in this respect.

  7. In a study examining the aggregate effect of surrounding agricultural and forested lands on the value of residential exurban property, differing open-space effects are also found (Geoghegan et al. 1997). In their study, within a tenth of a kilometer radius, the portion of open space positively impacts land values, but negatively influences land prices within a one-kilometer buffer.

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Authors and Affiliations

  1. Department of Agricultural Economics, University of Tennessee, 2621 Morgan Circle, 314-D Morgan Hall, Knoxville, TN, 37996-4518, USA

    Seong-Hoon Cho

  2. Department of Agricultural Economics, University of Tennessee, 2621 Morgan Circle, 321A Morgan Hall, Knoxville, TN, 37996-4518, USA

    Dayton M. Lambert

  3. Department of Agricultural Economics, University of Tennessee, 2621 Morgan Circle, 325 Morgan Hall, Knoxville, TN, 37996-4518, USA

    Seung Gyu Kim

  4. Department of Agricultural Economics, University of Tennessee, 2621 Morgan Circle, 308B Morgan Hall, Knoxville, TN, 37996-4518, USA

    Roland K. Roberts

  5. Department of Agricultural Economics, University of Tennessee, 2621 Morgan Circle, 321 Morgan Hall, Knoxville, TN, 37996-4518, USA

    William M. Park

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  1. Seong-Hoon Cho
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Correspondence to Seong-Hoon Cho.

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Cho, SH., Lambert, D.M., Kim, S.G. et al. Relationship between value of open space and distance from housing locations within a community. J Geogr Syst 13, 393–414 (2011). https://doi.org/10.1007/s10109-010-0126-4

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