https://orcid.org/0000-0002-0794-6064
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Abstract
Salt marshes provide a myriad of critical ecosystem services yet have been threatened by urban expansion, sea level rise, and the armoring of coastal property. This study uses a hedonic property price method to evaluate the effects of salt marshes on residential property values in the Eastern Shore of Virginia. Contrary to findings from the wetland literature, results show an insignificant relationship between proximity to salt marsh and residential property values. In contrast, results show a statistically significant increase in property values with increasing proportions of salt marsh within 250 to 500 meters of the parcel. Furthermore, the increase in value is found to be larger for proportions of preserved relative to non-preserved salt marsh in this proximity of the home. The findings of this study can contribute to understanding the spatial patterns for housing premiums stemming from salt marshes. The information can be used to guide coastal management agencies on how to conserve salt marshes in a way that promotes residential property values.
Acknowledgements
I would like to thank professors Robert J. Johnston, Jaqueline Geoghegan, and Wayne Gray for their feedback and support on this project. I would also like to thank the two anonymous reviewers for their comments and suggestions that led to significant improvements in the analysis and final manuscript.
Notes
1 For example, according to the Virginia Coastal Management Zone Program Coastal Needs Assessment and Strategy 2016 report, the state lost 0.154 percent of its salt marshes and 1.44 percent of its total wetlands between 1996 and 2011. Of the total wetlands lost, 3.2 square miles had been inundated and 13.7 square miles had been developed.
2 This program is a network of Virginia state agencies and local governments which administers coordinated coastal policies for the protection of its resources and the strengthening of its economy.
3 In general, there is large empirical evidence that shows the enhancement of property values near open space due to benefits such as these (McConnell and Walls Citation2005). Wetlands (including salt marsh) serve as key habitats for hundreds of species, and provide recreational opportunities for birdwatching, fishing, and hunting. The value of these recreational services has been well studied (Woodward and Wui Citation2001; Brander, Florax, and Vermaat Citation2006).
4 For example, in Virginia, any development that impacts wetlands requires a Virginia Water Protection Permit from the Virginia Marine Resources Commission (VMRC) and Department of Environmental Quality (DEQ).
5 These effects cannot be completely separated since they are spatially intertwined. For example, the threat and realization of stifled development cannot be separated from salt marsh (dis)amenities. In other words, proportions of salt marsh within proximities of the parcel will capture the net effect on home value.
6 Geoghegan (Citation2002) and Irwin (Citation2002) find price premiums associated with preserved relative to non-preserved agricultural and forest land. In contrast, Geoghegan, Lynch, and Bucholtz (Citation2003) find that preserved agricultural and forest land have positive effects on housing values in two of three Maryland counties while non-preserved agricultural and forest land either have negative or no effect. In a more recent study, Yoo and Ready (Citation2016) find that preserved agricultural land has a positive effect on housing values in one Pennsylvania county and a negative effect in another.
7 A box-cox test was also used to test the appropriate functional form, however, it rejected both linear and log forms, and suggested an alternative nonlinear form (theta = 0.409, lambda = 0.515) that would be difficult to interpret.
8 Other econometric approaches used to address bias stemming from temporally invariant unobservables such as fixed effects, random effects, first-differences, and difference-in-differences cannot be applied since the data does not contain repeat sales (further description of the data is provided below).
9 A spatial weighting model was also examined as a potential solution (using an inverse distance matrix with a 500-meter cutoff). However, since this required correctly specifying the parametric form of the error-correlation structure (an incorrect specification can result in biased standard errors), it was not preferred over the chosen approach (Bishop et al. Citation2020).
10 For a list of statewide land trusts, see: https://www.dcr.virginia.gov/land-conservation/whereto4.
11 Most of these preserves are publicly accessible (with the exceptions of Pickett’s Harbor and Cape Charles) and are frequently visited for sighting their rare plants, animals, and natural communities (e.g., birdwatching opportunities such as waterfowl and migratory songbirds) (https://www.dcr.virginia.gov/natural-heritage/natural-area-preserves/).
12 Prices were deflated using the Bureau of Labor Statistic’s Historical Price Index for All Urban Consumers U.S. City Average. https://www.bls.gov/regions/midwest/data/consumerpriceindexhistorical_us_table.pdf.
13 This included the removal of duplexes, double-family homes, vacant land (determined using improvement values and visual inspection in Google Maps), sales of multiple homes, and sales larger than 20 acres (the maximum for suburban residential zoning). Erroneous information was determined by cross-referencing the raw data with Accomack and Northampton Counties’ online portals using the parcel ID (https://parcelviewer.geodecisions.com/Accomack/Account/Logon; https://parcelviewer.geodecisions.com/northampton/Account/Logon). Arms-length sales were determined using buyer and seller surnames. 29 sales under $10,000 were not considered arms-length and excluded.
14 Environmental variables (representing amenities or disamenities) are included in the model to mitigate omitted variable bias. For example (natural or manmade) features may impact key model results pertaining to salt marsh’s effect on home price if they are correlated with both. Beaches are included because they may provide value to nearby homes and be positively correlated with salt marsh proximities to homes. Failure to include this variable would place an upward bias on the effect of salt marsh on home price. Similarly, coastal distance and elevation are included because shorter coastal distances and higher elevations are hypothesized to be positively correlated with home price and (positively and negatively) correlated with salt marsh locations. Other included controls are based on findings from past salt marsh valuation studies (Atreya, Kriesel, and Mullen Citation2016) and the open space valuation literature, as well as features specific to the Eastern Shore.
15 Waterviews were indicated on property cards and determined by visual inspection (checking for obstructions) in Google Maps. Elevation and slope measurements were calculated using data from the U.S. Geographical Survey.
16 Salt marsh land cover includes all marine and estuarine intertidal wetlands as defined in the Cowardin et al. Citation1979 classification system.
17 Measurements that use proportions are superior to those that use areas since they are not confounded by ring size. Results are consistent when using alternative definitions of salt marsh size, including proportions within concentric buffers that are (and are not) mutually exclusive with salt marsh contained in other buffers.
18 All percentage effects on home sales price for non-logged variables are calculated using , where
represents the coefficient for the explanatory variable of interest.
19 The Wald Test found that including SM500M provided a statistically significant improvement in model fit over the regression in column (1) at the 0.04 percent level (this was found to be at the 0.03 percent level when applying a Likelihood-Ratio Test).
20 In calculating the implicit price of greater proportions of salt marsh within 250- to 500-meters of the parcel, the distance to salt marsh is set to the average for homes that have positive amounts of salt marsh in that ring.