Abstract
There is no consensus in the hedonic property pricing literature on measures of water quality to use for regulatory policy analysis. This study compares several alternative measures of water quality with a focus on singular and composite nutrient indicators. Our contribution is to compare and contrast these indicators in the context of benefit analysis based on recent regulatory programs for nutrients in the US and EU. Results indicate order of magnitude differences in the benefits derived from the different types of indicators. We find support for a compound indicator that combines three policy-relevant indicators into an overall measure of waterbody health and is significantly related to property values. Given the growing interest in objective criteria for regulating nutrients and other nonpoint source pollutants, these results provide guidance on the selection of indicators in property valuation studies of water quality regulations.
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Notes
For additional information on the application of numeric nutrient criteria, see http://cfpub.epa.gov/wqsits/nnc-development/ (accessed October, 2014).
To meet the requirements of the CWA based on the ‘Impaired Waters Rule’ (IWR) (Florida Administrative Code Ch. 62-303).
If this criteria is exceeded, the waterbody would be included on a ‘planning list’ for further study to determine whether specific nutrient loadings are causing the impairment. If the impairment from nutrient loadings is confirmed, the waterbody would be placed on a ‘verified list’ for TMDL development (FDEP 2004; Norgart 2004).
Florida’s TSI is a modification of Carlson’s (1977) original measure that replaces secchi depth with CHLA due to the limiting influence of colored water and adds equations to adjust the nutrient component for a limiting nutrient (FDEP 1996). In the case of phosphorus-limited lakes (TN/TP \(>\)30),
$$\begin{aligned} \hbox {NUTR}_\mathrm{TSI} = 10 \times [2.36 \times \ln (\hbox {TP} \times 1,000) - 2.38]. \end{aligned}$$For nitrogen-limited lakes(TN/TP \(<\) 10),
$$\begin{aligned} \hbox {NUTR}_\mathrm{TSI} = 10 \times [5.96 + 2.15 \times \ln (\hbox {TN} + .0001)] \end{aligned}$$This distinction is attributable to greater connectivity between limestone aquifers and more alkaline lakes along with higher natural biological productivity in more alkaline lakes (USEPA 2010, pp. 68 – 84).
Semi-log equations were also estimated and had similar qualitative results as the double log form.
Although we do not see it in our data, there may be settings that support a positive correlation between lake area and water quality, which could affect the interpretation and treatment of this variable.
In the empirical analysis we only use clear lakes because there were few colored lakes in the study area.
There are other alternatives for selecting the spatial model that may be less subject to omitted variable bias, such as starting with the spatial Durbin model, as explained in Elhorst (2010). However, since the main differences between the OLS and spatial models were minor differences in magnitude (which did not affect the main focus of the paper), and since the implicit prices are harder to interpret in the spatial Durbin model, we used the general spatial model as the foundation.
One potential weakness of spatial econometric methods is that the SWM needs to be specified in advance, as opposed to being estimated (Elhorst 2010). The researcher therefore must specify the configuration of “neighbors” that influence observations.
Home prices were adjusted to 2002 dollars using the Federal Housing Finance Agency’s home price index.
For additional information on local water quality sampling activities, as well as additional data, see http://www.orange.wateratlas.usf.edu/.
Since local jurisdictions are required to upload data to the State of Florida to fulfill national regulatory requirements, sampling methods are relatively consistent across organizations.
Similar to other recent papers (Mueller and Loomis 2010), we do not find drastic differences in the coefficients of interest between the spatial and non-spatial models. In many cases, the spatial coefficients were slightly smaller than the OLS coefficients.
One exception is that the waterfront dummy is insignificant for the TP regression, although its interaction with TP is highly significant.
For instance, see the EPA website on implementing nutrient policy (http://www2.epa.gov/nutrient-policy-data), which has resources to help states develop nutrient policy, as well as information on the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force.
This was the most recent year with dependable water quality samples for all lakes.
To calculate benefits, the direct effects in the spatial model were used, which omit the lag. This keeps the focus on the direct impact of water quality on home prices, and omits the indirect impact that represents the average impact on the neighborhood. The indirect impacts only make minor differences and do not qualitatively change the results, but are available in an appendix upon request.
Note that some of these hypothetical changes may be considered non-marginal, so the resulting estimates of value may be capitalization impacts as opposed to marginal willingness to pay (see Kuminoff et al. 2010). However, the objective of this analysis is to compare the different water quality indicators, and the issues we highlight apply whether or not a second stage model is used.
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Acknowledgments
We would like to thank David Scrogin and seminar participants at the CU Environmental and Resource Economics workshop, the Northeastern Agricultural and Resource Economics Association Annual Conference, the Southern Economics Association Annual Conference, and the Society for Benefit Cost Analysis Annual Conference for comments and input. The views expressed in this paper and any errors should be attributed solely to the authors, and do not necessarily represent the views of their agency or department.
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Appendix
Appendix
Additional coefficients
TN | TP | CH | TSI | |||||
|---|---|---|---|---|---|---|---|---|
Coeff. | SE | Coeff. | SE | Coeff. | SE | Coeff. | SE | |
Constant | 7.094\(^{***}\) | 0.009 | 6.888\(^{***}\) | 0.008 | 7.115\(^{***}\) | 0.006 | 7.786\(^{***}\) | 0.015 |
Waterfront | 0.225\(^{***}\) | 0.010 | \(-\)0.021 | 0.045 | 0.284\(^{***}\) | 0.010 | 0.478\(^{***}\) | 0.058 |
ln(dist lakes) | \(-0.129^{***}\) | 0.004 | \(-0.093^{***}\) | 0.013 | \(-0.144^{***}\) | 0.004 | \(-0.250^{***}\) | 0.018 |
Canalfront | 0.003 | 0.019 | 0.008 | 0.019 | 0.006 | 0.019 | 0.008 | 0.019 |
Golffront | 0.119\(^{***}\) | 0.032 | 0.112\(^{***}\) | 0.032 | 0.110\(^{***}\) | 0.032 | 0.113\(^{***}\) | 0.032 |
ln(bath) | 0.087\(^{***}\) | 0.005 | 0.087\(^{***}\) | 0.005 | 0.087\(^{***}\) | 0.005 | 0.087\(^{***}\) | 0.005 |
Pool | 0.006\(^{**}\) | 0.003 | 0.006\(^{**}\) | 0.003 | 0.006\(^{**}\) | 0.003 | 0.006\(^{**}\) | 0.003 |
ln(age) | \(-0.090^{***}\) | 0.001 | \(-0.090^{***}\) | 0.001 | \(-0.090^{***}\) | 0.001 | \(-0.090^{***}\) | 0.001 |
ln(area heated) | 0.515\(^{***}\) | 0.006 | 0.516\(^{***}\) | 0.006 | 0.516\(^{***}\) | 0.006 | 0.517\(^{***}\) | 0.006 |
ln(area prcl) | 0.179\(^{***}\) | 0.004 | 0.180\(^{***}\) | 0.004 | 0.179\(^{***}\) | 0.004 | 0.179\(^{***}\) | 0.004 |
ln(dist cbd) | \(-\)0.009 | 0.014 | \(-0.023^{*}\) | 0.014 | \(-\)0.008 | 0.014 | \(-0.029^{**}\) | 0.014 |
Near airport | \(-\)0.028 | 0.019 | \(-0.031^{*}\) | 0.018 | \(-0.031^{*}\) | 0.019 | \(-0.033^{**}\) | 0.018 |
ln(income) | 0.106\(^{***}\) | 0.012 | 0.113\(^{***}\) | 0.012 | 0.106\(^{***}\) | 0.012 | 0.115\(^{*}\) | 0.012 |
% Black | \(-0.369^{***}\) | 0.041 | \(-0.393^{***}\) | 0.040 | \(-0.379^{***}\) | 0.041 | \(-0.397^{***}\) | 0.040 |
% Over 65 | 0.036 | 0.059 | 0.050 | 0.058 | 0.039 | 0.058 | 0.055 | 0.058 |
ClearLow | 0.044\(^{**}\) | 0.018 | \(-\)0.019 | 0.048 | 0.018\(^{***}\) | 0.016 | 0.056 | 0.040 |
Year 1996 | \(-0.842^{***}\) | 0.005 | \(-0.839^{***}\) | 0.004 | \(-0.840^{***}\) | 0.004 | \(-0.839^{***}\) | 0.005 |
Year 1997 | \(-0.791^{***}\) | 0.005 | \(-0.786^{***}\) | 0.004 | \(-0.788^{***}\) | 0.004 | \(-0.787^{***}\) | 0.005 |
Year 1998 | \(-0.723^{***}\) | 0.005 | \(-0.719^{***}\) | 0.004 | \(-0.721^{***}\) | 0.004 | \(-0.719^{***}\) | 0.004 |
Year 1999 | \(-0.632^{***}\) | 0.005 | \(-0.630^{***}\) | 0.004 | \(-0.633^{***}\) | 0.004 | \(-0.631^{***}\) | 0.004 |
Year 2000 | \(-0.524^{***}\) | 0.005 | \(-0.523^{***}\) | 0.004 | \(-0.526^{***}\) | 0.005 | \(-0.524^{***}\) | 0.005 |
Year 2001 | \(-0.405^{***}\) | 0.005 | \(-0.406^{***}\) | 0.005 | \(-0.407^{***}\) | 0.005 | \(-0.406^{***}\) | 0.005 |
Year 2002 | \(-0.301^{***}\) | 0.005 | \(-0.299^{***}\) | 0.005 | \(-0.299^{***}\) | 0.004 | \(-0.299^{***}\) | 0.005 |
Year 2003 | \(-0.169^{***}\) | 0.004 | \(-0.167^{***}\) | 0.004 | \(-0.167^{***}\) | 0.004 | \(-0.167^{***}\) | 0.004 |
Rho | 0.012\(^{***}\) | 0.002 | 0.018\(^{***}\) | 0.001 | 0.017\(^{***}\) | 0.001 | 0.018\(^{***}\) | 0.001 |
Lambda | 0.904\(^{***}\) | 0.000 | 0.893\(^{***}\) | 0.000 | 0.901\(^{***}\) | 0.000 | 0.891\(^{***}\) | 0.000 |
\(\hbox {R}^{2}\) | 0.9281 | 0.9278 | 0.9280 | 0.9278 | ||||
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Walsh, P.J., Milon, J.W. Nutrient Standards, Water Quality Indicators, and Economic Benefits from Water Quality Regulations. Environ Resource Econ 64, 643–661 (2016). https://doi.org/10.1007/s10640-015-9892-2
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DOI: https://doi.org/10.1007/s10640-015-9892-2