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Property Values and Water Quality: A Nationwide Meta-Analysis and the Implications for Benefit-Transfer
Citation:
Guignet, D., Matt Heberling, M. Papenfus, O. Griot, AND B. Holland. Property Values and Water Quality: A Nationwide Meta-Analysis and the Implications for Benefit-Transfer. 2018 Society for Benefit-Cost Analysis Annual Conference, Washington,DC, March 14 - 16, 2018.
Impact/Purpose:
The abstract is to be submitted for consideration to the 2018 Society for Benefit-Cost Analysis Annual Conference. The Society for Benefit-Cost Analysis is an international, multi-disciplinary association working to promote and improve the theory and practice of benefit-cost analysis.
Description:
There is a well-established literature examining how water quality impacts home values, dating back over five decades. Despite this, hedonic property value studies have yet to be used in regulatory analyses of regional and nationwide water quality regulations enacted by the US Environmental Protection Agency. Heterogeneity in local housing markets, the types of waterbodies examined, and the water quality metrics used, are key reasons why the results of these local studies have not been applied to broader policies. Our objective is to synthesize this vast literature, and estimate unit values and value-transfer functions that can be used to assess broader water quality policies. We identify 38 studies in the published and grey literature that examine how home values vary with water quality. Our study is the first meta-analysis to aggregate this literature and systematically calculate comparable within- and cross-study elasticity estimates by accounting for differences in functional forms, assumed price-distance gradients, and baseline conditions. We convert the primary study coefficient estimates to common elasticity measures for both waterfront and near-waterfront homes, and then use monte carlo simulations to estimate the corresponding standard errors. Each study can yield numerous meta-observations due to multiple study areas, water quality metrics, and model specifications. Our resulting meta-dataset contains over n=686 unique elasticity estimates. We find considerable heterogeneity in the meta-dataset in terms of the water quality metrics utilized, the type of waterbody covered, and the region of the US. The majority of estimated elasticities are with respect to water clarity (n=261). Among these studies the mean price elasticity with respect to a one-percent increase in secchi depth is 0.12% (median of 0.04%), with a range from -0.65% to 1.72%. Other common metrics that are examined include dissolved oxygen, fecal coliform, E-coli, pH, chlorophyll, nitrogen, phosphorous, and total suspended solids. We also find that the majority of estimates correspond to freshwater lakes (291), followed by estuaries (280), rivers (86) and small rivers and streams (29). Finally, we find reasonable spatial coverage, with studies covering 20 different states. The one exception, however, is the western US, where there are only four studies (providing just 75 meta-observations). A key contribution of this meta-analysis is in highlighting key gaps in the literature– in terms of the types of waterbodies and regions covered, and the gap between the water quality metrics used by economists versus those currently examined by water quality modelers. We have just completed the meta-dataset, and the next steps are to bin the meta-observations according to common water quality metrics, and estimate meta-analytic Random Effect Size (RES) means, RES meta-regressions, and Random Effects Panel models. We will then statistically test the appropriateness of pooling estimates from different regions and waterbody types, and discuss the implications for benefit-transfer.