Valuing water quality in the United States using a national dataset on property values.

High-quality water resources provide a wide range of benefits, but the value of water quality is often not fully represented in environmental policy decisions, due in large part to an absence of water quality valuation estimates at large, policy relevant scales. Using data on property values with nationwide coverage across the contiguous United States, we estimate the benefits of lake water quality as measured through capitalization in housing markets. We find compelling evidence that homeowners place a premium on improved water quality. This premium is largest for lakefront property and decays with distance from the waterbody. In aggregate, we estimate that 10% improvement of water quality for the contiguous United States has a value of $6 to 9 billion to property owners. This study provides credible evidence for policymakers to incorporate lake water quality value estimates in environmental decision-making.

Spatial Heterogeneity in Hedonic Price Effects for Lake Water Quality.

This study uses Zillow's ZTRAX property transaction database to investigate variation in hedonic price effects of water clarity on single-family houses throughout the United States. We consider five spatial scales and estimate models using different sample selection criteria and model specifications. Our results indicate considerable spatial heterogeneity both within and across the four U.S. Census regions. However, we also find heterogeneity resulting from different types of investigator decisions, including sample selection and modelling choices. Thus, it is necessary to use practical knowledge to consider the limits of market areas and to investigate the robustness of estimation results to investigator choices. (JEL Q51).

Hedonic property values and water quality: A meta-analysis of commodity, market, and methodological choices.

This study quantitatively reviews the hedonic literature examining surface water quality to assess how attributes of the commodity, housing market, and methodological choices lead to variation in the significance and expected sign of the estimated property value effects (i.e., elasticities). We conduct a meta-analysis of 29 studies with 290 unique estimates, published or released between 1985 and 2017, and find evidence based on probit meta-regression models that some of the definitions and decisions made in primary studies do influence the estimated relationship between water quality and home prices. Our most robust evidence suggests that methodological choices (e.g., accounting for spatial dependence, or if the water quality measure was based on something other than in situ measurement) have a critical role in determining the likelihood of finding a significant and theoretically expected result; and perhaps most importantly, it is not always selections that reflect best practices that lead to this finding. This study can help identify potential concerns with data and modeling choices in the collective hedonic literature focused on water quality.

Using benefit transfer to estimate housing value increases from improved water clarity: A case study of lakes in Kosciusko County, Indiana.

This study provides step-by-step guidance for practitioners and local stakeholders on how to use existing study results to conduct benefit transfer (BT), and ultimately make informed predictions of how improvements in lake water clarity may benefit surrounding communities. The procedures are demonstrated using a publicly available meta-dataset developed by the United States Environmental Protection Agency, and a subsequent meta-analysis that synthesizes the literature on how improvements in water clarity impact home values. The BT procedures are demonstrated using a case study of 14 large lakes in Kosciusko County, Indiana. Lake-specific average increases in home values, as well as the value of the housing stock in aggregate, are calculated for illustrative improvements in lake water clarity. This analysis provides a critical bridge to better connect high-quality, academic research with real-world policy analysis, and ultimately serves to better equip local governments and stakeholders to make more informed policy and land use decisions.

Property values, water quality, and benefit transfer: A nationwide meta-analysis.

We construct a comprehensive, publicly-available meta-dataset based on 36 hedonic studies that examine the effects of water quality on housing values in the United States. The meta-dataset includes 656 unique estimates and entails a cluster structure that accounts for price effects at different distances. Focusing on water clarity, we estimate reduced-form meta-regressions that account for within-market dependence, statistical precision, housing market and waterbody heterogeneity, publication bias, and methodological practices. While we find evidence of systematic heterogeneity, the out-of-sample transfer errors are large. We discuss the implications for benefit transfer and future work to improve transfer performance.

Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs.

Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.

Do Housing Prices Reflect Water Quality Impairments? Evidence from the Puget Sound.

Using data from Washington state and empirical methods to control for confounding factors, we estimate the effects of water quality impairments on residential housing prices. We also examine the role of individual water quality parameters such as fecal coliform and dissolved oxygen that are used to judge the attainment of water quality standards. Results indicate that impairments have a negative effect on home values. Using a hedonic model that includes fine-resolution spatial fixed effects, we find that being located adjacent to an impaired waterbody leads to an annualized depreciation of$1,942. We find that water quality impairments due to fecal coliform have the largest effects. Given the importance of evaluating the economic benefits of water quality, the use of impairment data to evaluate these benefits on a larger scale could lead to improved allocation of resources to protect the myriad beneficial uses that depend on water quality.

Interacting coastal based ecosystem services: recreation and water quality in Puget Sound, WA.

Coastal recreation and water quality are major contributors to human well-being in coastal regions. They can also interact, creating opportunities for ecosystem based management, ecological restoration, and water quality improvement that can positively affect people and the environment. Yet the effect of environmental quality on human behavior is often poorly quantified, but commonly assumed in coastal ecosystem service studies. To clarify this effect we investigate a water quality dataset for evidence that environmental condition partially explains variation in recreational visitation, our indicator of human behavior. In Puget Sound, WA, we investigate variation in visitation in both visitation rate and fixed effects (FE) models. The visitation rate model relates the differences in annual recreational visitation among parks to environmental conditions, park characteristics, travel cost, and recreational demand. In our FE model we control for all time-invariant unobserved variables and compare monthly variation at the park level to determine how water quality affects visitation during the summer season. The results of our first model illustrate how visitation relates to various amenities and costs. In the FE analysis, monthly visitation was negatively related to water quality while controlling for monthly visitation trends. This indicates people are responding to changes in water quality, and an improvement would yield an increase in the value of recreation. Together, these results could help in prioritizing water quality improvements, could assist the creation of new parks or the modification of existing recreational infrastructure, and provide quantitative estimates for the expected benefits from potential changes in recreational visitation and water quality improvements. Our results also provide an example of how recreational visitation can be quantified and used in ecosystem service assessments.

Catching the right wave: evaluating wave energy resources and potential compatibility with existing marine and coastal uses.

Many hope that ocean waves will be a source for clean, safe, reliable and affordable energy, yet wave energy conversion facilities may affect marine ecosystems through a variety of mechanisms, including competition with other human uses. We developed a decision-support tool to assist siting wave energy facilities, which allows the user to balance the need for profitability of the facilities with the need to minimize conflicts with other ocean uses. Our wave energy model quantifies harvestable wave energy and evaluates the net present value (NPV) of a wave energy facility based on a capital investment analysis. The model has a flexible framework and can be easily applied to wave energy projects at local, regional, and global scales. We applied the model and compatibility analysis on the west coast of Vancouver Island, British Columbia, Canada to provide information for ongoing marine spatial planning, including potential wave energy projects. In particular, we conducted a spatial overlap analysis with a variety of existing uses and ecological characteristics, and a quantitative compatibility analysis with commercial fisheries data. We found that wave power and harvestable wave energy gradually increase offshore as wave conditions intensify. However, areas with high economic potential for wave energy facilities were closer to cable landing points because of the cost of bringing energy ashore and thus in nearshore areas that support a number of different human uses. We show that the maximum combined economic benefit from wave energy and other uses is likely to be realized if wave energy facilities are sited in areas that maximize wave energy NPV and minimize conflict with existing ocean uses. Our tools will help decision-makers explore alternative locations for wave energy facilities by mapping expected wave energy NPV and helping to identify sites that provide maximal returns yet avoid spatial competition with existing ocean uses.

Examination of physical and regulatory variables leading to small dam removal in Wisconsin.

The decision to remove or repair a dam depends on multiple variables, many of which encompass both physical and social factors. In Wisconsin, the Department of Natural Resources is mandated to inspect small dams every ten years. A safety inspection often acts as a trigger event to a dam removal or repair decision. Although the issues surrounding a dam removal decision are often couched as ecological, these decisions are influenced by their social and regulatory context. In this work, we examine descriptive variables of Wisconsin dams that were inspected and consequently removed or maintained between 1985 and 1990. We hypothesize that geographic location, height of dam, size of impoundment, age of dam, and type of ownership determine the likelihood of a safety inspection, and the subsequent likelihood of removal. Using a logistic model, we find that publicly owned dams had the greatest probability of inspection after 1985. Of these dams, older dams and those with smaller impoundments were most likely to be removed. We were unable to build a strong predictive model for dam removal with our suite of variables, suggesting that a community's decision to remove or maintain a dam is complex and heterogeneous.