1. A Comprehensive Review of the Evidence of the Impact of Surface Water Quality on Property Values
2. Early US Studies: 1968–1983
3. Middle Era US Studies: 1984–2003
4. Recent US Studies: 2003–2017
5. International Studies
Conflicts of Interest
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|Author (Year) |
|Study Site/Location||Year(s) of Data||Method, Sample Size, (Adjusted) R2 (as Applicable and Listed)||Dependent|
|Water Quality Variable(s)||Key Findings Regarding Water Quality Impacts on Property Values|
|60 artificial lakes in Wisconsin||1952, 1957, and 1962||Series of linear regressions, 2131 lakefront tracts, 0.20–0.69||Per acre value of land, per acre value of improvements, and number of improvements (all assessed)||Dummy variables to represent poor, moderate, and good quality||Value increased with rising water quality: tracts on poor quality lakes saw no premium; tracts on moderate quality lakes saw a significant premium in three of five models; tracts on good quality lakes saw a significant premium in four of five models.|
|Dornbusch and Barrager (1973)|
Barrager (1974) *
|San Diego Bay, CA; Kanawha River, WV; Willamette River, OR; Ohio River, PA||1960 and 1970||Six sets of multiple regressions (one for San Diego Bay, two for Kanawha River, two for Willamette River, one for Ohio River), 0.10–0.72||Change in value of single family residences during study period (based on sales prices and assessed values)||Distance from water front (two forms: linear and inverse proportion)||Regression results conclusive in four of six cases. Contribution to property value increased with improvement in water quality within study period calculated for houses located at 100 ft, 500 ft, 1000 ft, and 2000 ft from water front.|
|Fisher, Starler, and Fisher (1976) *||Lake Erie and Chautauqua Lake, NY||1950 to 1973||Property value index based on actual and interpolated sales prices with visual comparison of index values for waterfront, lakeside, and upland parcels across the time period and two study sites, 686 parcels along Erie and 751 on Chautauqua||N/A||N/A||Erie: overall index increased from 42.72 in 1950 to 151.10 in 1973 (254%), waterfront 40.31 to 116.10 (188%), close to lake 38.04 to 162.22 (326%), upland 44.41 to 158.08 (256%). Chautauqua: overall index 42.72 to 151.10 (286%), waterfront 40.31 to 116.10 (406%), close to lake 38.04 to 162.22 (255%), upland 44.41 to 158.08 (248%).|
|Epp and Al-Ani (1979) *||Small rivers and streams (12 clean, 13 polluted) in PA||1969 to 1976||Four hedonic models, 212 properties within 700 feet of river or stream (93 near a clean stream, 119 near a polluted stream), 0.65–0.74||Log of sales prices of non-farm rural single family houses||Perceived quality (PQ), pH, interaction of PQ/pH with % population change||Pooled sample: increasing water quality—according to all four quality variables—had a significant positive impact on price. Paired sample: pH significant (positive) along clean streams, insignificant along polluted streams, interaction term significant (positive) along both.|
|Feenberg and Mills (1980)||29 beaches (mix of fresh and salt water) in Boston, MA||1970||Hedonic model, 506 census tracts||Log of median values of owner-occupied houses||Concentration of oil and turbidity at nearest beach|
(both squared over distance)
|Concentration of oil and level of turbidity both had significant negative impact on median values.|
|Rich and Moffit (1982)||Housatonic River in MA||1957–1975||Hedonic model, 42 residential parcels, sales price within 1/8 of a mile of the river||Log of sales price per acre||Pre (n = 31) and post (n = 11) cleanup of pollution||Post-abatement property value increase of $37 per occupied acre.|
|Willis and Foster (1983)||Housatonic River, MA and Winoski River, Montpelier, VT||1962–1980||Multiple hedonic model, 81 or 40 properties within 1500 feet of the river, 0.68–0.78||Sales prices of single family homes||Purchase date relative to improvement in water quality (pre or post)||Hedonic results were not supportive; surveys suggested homeowners had little awareness of water quality.|
|Author (Year) |
|Study Site/Location||Year(s) of Data||Method, Sample Size, (Adjusted) R2 (as Applicable and Listed)||Dependent Variable(s)||Water Quality Variable(s)||Key Findings Regarding Water Quality Impacts on Property Values|
|Young and Teti (1984)||St. Albans Bay, Lake Champlain, VT||1976 to 1981||Two hedonic models, 93 seasonal homes adjacent to lake, 0.67–0.76||Sales prices of residential property||Objective measure: dummy to represent location on bay; subjective measure: rating by panel of local experts||Objective measure: Average property on bay sold for $4500 (20%) less than similar property outside bay, total loss of value for 430 bayside dwellings was $2 million (1981 dollars). Subjective measure: Average property on bay sold for $4200 (19%) less than similar property outside bay.|
|Brashares (1985)||78 lakes in southeast Michigan||1977||Multiple hedonic models, up to 2370 properties (178 on lake), 0.66–0.73.||Log of sales prices of houses||Of a starting list of 40, only two were employed in final models: squared values of turbidity and fecal coliform level||Turbidity and fecal coliform had significant negative impact on lakefront prices. Only turbidity was significant when canal front houses included. Neither were significant for houses with deeded lake access. Coliform was significant for houses with access via a public site.|
|Kirshner and Moore |
|Tiburon (T) and Foster City (FC), San Francisco Bay, CA||1984 to 1986||Four hedonic models (linear and log-log forms), 117 properties in town on clean part of bay (T) and 159 in town with much lower water quality (FC), 0.70–0.76||Sales prices of single family houses||Dummy variable to represent location immediately proximate to water||Linear models: Implicit marginal price of water proximity was $65,000 in T and $24,000 in FC. Log models: Implicit price of proximity was 20% of property value in T, 9% in FC. All at 99% significance. Differences between two sets of prices significant at 95%.|
|Mendelsohn, Hellerstein, Huguenin, Unsworth, and Brazee |
|Harbor in New Bedford, MA||1969 to 1988||Multiple panel data (repeat sale) regressions (linear and semi-log forms), 1916 sales of 780 properties within two miles of the harbor, 0.27–0.49||Sales prices of single family houses||PCB zone (based on hazard level of harbor water closest to property)||Properties subject to PCB contamination sold for $7000–$10,000 less than non-contaminated properties (relative to the average price of $71,630, all in 1989 dollars). Conservative estimate of total impact on single family property prices: $35.9 million.|
|Steinnes (1992) *||53 lakes in northern Minnesota||Not stated||Three hedonic models, seasonal use leased lots on 53 lakes (only land values considered, unit of analysis = lakes not lots), 0.31–0.74||Total price, average price, and average price per front foot of lots (all appraised)||Water clarity (per Secchi disk readings)||Water clarity had a significant positive impact in all three models; additional foot of clarity added $3384 to the total price of lots, $206 to the average price per lot, or $1.99 to the average price per front foot.|
|Michael, Boyle, and Bouchard (1996)||22 lakes in Maine||1990 to 1994||Four hedonic models, 543 lakefront properties in four markets (90, 84, 214, 155), 0.37–0.65||Sales prices per front foot of single family houses and unimproved land||Water clarity (per Secchi disk), interacted with lake area||Effect of water quality was significant in all four models; implicit price of a 1-m improvement in water clarity (per Secchi disk readings) on average sales price ranged from $11 per foot frontage (on Echo Lake) to $200 per foot frontage (Sabbattus Lake).|
|Boyle, Poor, and Taylor (1999) *||25 lakes in Maine||1990 to 1995||12 hedonic models (linear, semi-log and Cobb-Douglas forms; first and second stage analysis), 249 lakefront properties in four markets (48, 112, 68, 21)||Sales prices of houses||Natural log of minimum water clarity during summer months of purchase year (Secchi disk), interacted with lake area||Water clarity significant (negative) in semi-log and Cobb-Douglas models. Average implicit price of visibility (per meter, at mean visibility for average lake): Bangor $2337, Waterville $2695, Lewiston/Auburn $4235, Camden $12,938.|
|Leggett and Bockstael (2000) *||Anne Arundel County, Chesapeake Bay, MD||1993 to 1997||Eight hedonic models (linear, semi-log, double-log, inverse semi-log forms), 1183 waterfront properties, 0.39–0.76||Sales prices and residual land prices of properties||Median fecal coliform count in year of sale (inverse distance-weighted average of counts at three nearest monitoring stations)||Effect of fecal coliform count negative in all eight cases, significant at 5% in seven and at 10% in one; change of 100 coliform counts per 100 mL produced 1.5% change in price; mean effect per 100 count change ranged from $5114 to $9824 (mean reading was 103 counts/100 mL, with range from 4 to 2300).|
|Michael, Boyle, and Bouchard (2000) *||22 lakes in Maine||1990 to 1994||27 hedonic models, 531 lakefront properties in three markets (89, 295, 147)||Sales prices of single family houses and unimproved land (tract max 20 acres)||Semi-log of nine variations on current, historical, and seasonal change water clarity measures (per Secchi disk)||Most of the nine clarity variables tested were significant across two or three markets; implicit prices of clarity varied across markets; within each market, differences between prices were not significantly different, but substantial enough to produce different policy outcomes if any one was used as a single point estimate in a cost-benefit analysis.|
|Boyle and Taylor (2001) *||34 freshwater lakes and ponds in Maine||1990 to 1995||Eight hedonic models, 318 lakefront properties in four markets (55, 158, 74, 31), 0.38–0.81||Sales prices of properties||Natural log of water clarity (per Secchi disk) interacted with lake size||Coefficients on water clarity variable significant in all eight markets. No significant differences were found between coefficients on clarity variables within each market. Implicit prices for clarity were $2000–$8000 per meter.|
|Poor, Boyle, Taylor, and Bouchard (2001) *||Freshwater lakes and ponds in Maine||1990 to 1995||Eight hedonic models, 348 lakefront properties in four markets (56, 174, 52, 66), 0.49–0.75||Sales prices of properties||Natural log of water clarity (measured both objectively and subjectively) interacted with lake size||Coefficients on water clarity variables (objective and subjective) were significant in two of four markets. Augusta: Implicit price of subjective water clarity was $2756, objective was $2600, 6% differential was not significant at the 10% level. Lewiston: subjective $8985, objective $6279, 43% differential was significant at the 1% level. Equations with objective measures of clarity were better predictors of price.|
|Gibbs, Halstead, Boyle, and Huang (2002) *||69 public access lakes in 59 towns in New Hampshire||1990 to 1995||Four hedonic models, 447 lakefront properties in four markets (115, 178, 80, 74), 0.43–0.67||Sales prices of properties||Natural log of water clarity (per Secchi disk) interacted with lake area||Water clarity was significant and positive in all four models; implicit price/value of a 1-m change in clarity from average (%) was $1135/$1268 (0.91%) in Conway/ Milton market, $5541/$6122 (3.50%) in Winnipesaukee, $3923/$4411 (3.39%) in Derry/Amherst, $9756/$11,094 (6.64%) in Spofford/Greenfield.|
|Krysel, Boyer, Parson, and Welle (2003)||37 lakes in the headwaters of the Mississippi River, MN||1996 to 2001||12 hedonic models, 1205 lakeshore properties in six markets, 0.29–0.53||Sales prices of single family properties and assessed values of land||Natural log of water clarity in year property sold (per Secchi disk) interacted with lake area||Water clarity was significant and positive in all 12 models; implicit increase (decrease) in property price per front foot for a 1-m increase (decrease) in clarity ranged from $1.08 to $423.58 ($1.43–$594.16); total increase (decrease) in lakeshore property price for a 1-m increase (decrease) in clarity ranged from $30,467 to $93,425,651 ($36,264–$150,560,122).|
|Location of Property||One Month Model||One Year Model|
|1/8 mile from waterfront||1.93||4.21||11.42||8.03|
|1/4 mile from waterfront||1.50||3.28||8.89||6.26|
|1/2 mile from waterfront||0.91||1.99||5.39||3.80|
|1 mile from waterfront||0.34||0.73||1.98||1.40|
|2 miles from waterfront||0.05||0.10||0.27||0.19|
|4 miles from waterfront||0.00||0.00||0.00||0.00|
|Author (Year) |
|Study Site/Location||Year(s) of Data||Method, Sample Size, (Adjusted) R2 (as Applicable and Listed)||Dependent|
|Water Quality Variable(s)||Key Findings Regarding Water Quality Impacts on Property Values|
|Kashian, Eiswerth, and Skidmore (2006) *||Three lakes in Walworth County, WI||1987, 1995, and 2003||Hedonic model, 314 homes assessed at three time points = 942 observations, 0.60||Assessed values of residential properties||Water clarity (per Secchi disk)||Effect of water clarity the was significant (positive). A one-foot increase in clarity was associated with a $5207 increase in price of an average property.|
|Carey and Leftwich (2007)||Lake Greenwood, Greenwood County, SC||1980 to 2006||Two hedonic models, 548 and 295 properties within 1000 feet of western shore of lake, 0.76–0.79||Sales prices of properties (including both houses and lots)||Sale during 1999 algal bloom; chlorophyll-a level at time of sale; location within ½ mile of a National Pollutant Discharge Elimination Service (NPDES) site||None of the water quality variables reached statistical significance in either model.|
|Poor, Pessagno, and Paul (2007) *||St. Mary’s River watershed, MD||1999 to 2003||Two hedonic models, 1231 and 1377 properties, 0.34–0.35||Log of sales prices of single family houses||Yearly averages of dissolved inorganic nitrogen (DIN) and total suspended solids (TSS) at 26 monitoring stations (two separate regressions)||Both quality variables were significant and negative. Marginal implicit price for 1 milligram per liter increase in TSS and DIN (based on mean sales price) was $1086 and −$17,642, respectively.|
|Morgan, Hamilton, and Chung (2010)||Two rivers (Middle, not polluted, and South, polluted) in Augusta County, VA||Not stated||Four spatial-lag hedonic models, 2069 and 1252 properties on Middle and South Rivers, respectively||Log of assessed total value (house + land) and of assessed land value||Natural log of distance to Middle or South River||Coefficients on distance to river were negative and significant in all four models. Marginal willingness to pay to locate 1 foot closer to Middle River: $5.41 (total value) and $2.67 (land value). Marginal willingness to pay to locate 1 foot closer to South River: $3.77 (total value) and $1.41 (land value). Value of improving South River quality to that of Middle River between $7.3 and $12 million.|
Walsh, Milon, and Scrogin (2010) (2011) *
|146 lakes in Orange County, FL||1996 to 2004||Multiple hedonic models (including spatial lag), 54,712 properties within 1000 m of a lake (1496 lakefront), 0.893–0.894||Log of sales prices of single family properties||Log of mean annual water quality (Secchi depth) in nearest lake at time of sale, interacted with (i) dummy variable to represent lakefront properties, (ii) distance to waterfront, and (iii) lake area||Water clarity variable was significant (positive) in four of six models reported. Variables interacting clarity with proximity, distance, and area were significant in all models reported. Mean marginal value of water clarity was significantly higher on the waterfront. Implicit price of water clarity declined rapidly with distance from waterfront and increased with lake size. Benefits realized by broader market exceeded those accruing to owners on waterfront.|
|Cho, Roberts, and Kim (2011) *||10 of 18 sub-watersheds of the Pigeon River watershed, North Carolina and Tennessee||2001 to 2004||Six spatial hedonic models (four for NC, two for TN), 595 properties in NC and 497 in TN||Sales prices of detached single family houses||Impairment status of subwatershed (two dummy variables, one each for rivers and streams); water view (four dummy variables, view of (un)impaired river or stream); water proximity (four variables, natural log of euclidean distance to nearest (un)impaired portion of river or stream)||River impairment dummy: Significant (negative) in five of six models (three of four in NC, one of two in TN). Stream impairment dummy: Insignificant in all four NC models, significant (negative) in both TN models. Water view variables: All insignificant across all six models. Water proximity: Insignificant across all six models for impaired rivers, for impaired streams, and for unimpaired streams; significant (negative) for unimpaired rivers across all six models.|
|Bin and Czajkowski (2013) *||St. Lucie River and Estuary and Indian River Lagoon, Martin County, southeastern Atlantic coast of Florida||2000 to 2004||Eight spatial hedonic models, 510 waterfront properties||Log of sales prices of single family residences||Non-technical: water quality location letter grade (weighted average of pH, visibility, salinity, and dissolved oxygen (DO) (entered as grade, grade squared, and dummy)). Technical: pH and DO (linear and squared), visibility and salinity (level, level squared, and dummy for fair or good).||Non-technical measures significant in two of 10 cases; technical measures significant in 16 of 30 cases. Marginal willingness to pay (90% lower bound—mean—90% upper bound, at mean sales price): location grade (%) $474−$43,158−$84,400; visibility (%) $13,552−$36,070−$58,749; salinity (parts per thousand) $1647−$31,938−$61,486; pH (1/10 unit) $3536−$7531−$11,479; dissolved oxygen (mg/L)—$30,584−$14,052−$1628.|
|Gorelick (2014)||99 lakes in Rhode Island||1988 to 2012||Hedonic model, up to 97,352 properties within 5 miles of a lake (3315 lakefront), 0.69||Log of sales prices of single family houses||Good water quality (lake with chlorophyll concentration ≤ 7.2 ppm) interacted with dummy variable for lakefront, log of distance to nearest lake, and lake size||Water quality had significant positive impact on sales price; lakefront property sales price increase is possible with the improvement of all state lakes from (extremely) poor to good ($1,465,230) $9,560,224 (total > $11 m); the value of quality was held constant over the short and long term.|
|Netusil, Kincaid, and Chang (2014) *||Two urbanized watersheds in Portland, Oregon (Johnson Creek) and Vancouver, Washington (Burnt Bridge Creek)||Not stated||Multiple OLS and spatial autoregressive (SAR) hedonic models, 5093 (WA) and 10,479 (OR) properties, 0.57–0.72||Log of sales prices of single family houses||Fecal coliform (FC), pH, dissolved oxygen (DO), stream temperature (temp), total suspended solids (TSS); annual averages as well as wet (November–April) and dry (May–October) seasons; properties within ¼, ½, 1 mile, or more than 1 mile from creek||Not all results are presented. For the dry season in Johnson Creek: DO was significant (positive) at all four distances for the OLS and two SAR models; FC was significant (negative) in 11 of 12 cases; pH was only significant (positive) at distances >1 mile; temp was significant (negative) for two of three models within a 1-mile buffer and all three models at distances >1 mile; TSS was significant (positive) for the OLS model within ¼ mile and all models within ½ and 1 mile.|
|Bin, Czajkow-ski, Li, and Villarini (2015) (2016) *||St. Lucie River and Estuary and Indian River Lagoon, northeast Martin County, southeastern Atlantic coast of Florida||2001 to 2010||Multiple spatial hedonic models, with and without spatial fixed effects and temporal breakpoints, 1526 waterfront properties||Log of sales prices of single family residences||Water quality location grade (annual mean percentage score at nearest monitoring station in year of sale, grade incorporates temperature, pH, visibility, salinity, and dissolved oxygen) (linear in 2015, log in 2016)||Water quality perceived to be valuable by waterfront homebuyers throughout the real estate expansion and contraction periods, i.e., concern for the environment not crowded out. Marginal WTP for a 1% increase in water quality was $1754 (2015 study) or $2614 (2016 study, with log measure of quality).|
|Florida Realtors® (2015)||St. Lucie River, Martin County, and Caloosahatchee River, Lee County, FL||2010 to 2013||12 hedonic models, 7975 (Martin) and 48,572 (Lee) properties, 0.86–0.88||Log of sales prices of single family properties||Both counties: water clarity (per Secchi disk) and levels of dissolved oxygen. Lee County only: levels of chlorophyll-a and turbidity.||Lee County: Three of four water quality measures were significant. Martin County: One of two water quality measures was significant. Clarity was the most influential of the water quality variables. Impact of quality declined with distance from the waterfront. A one-foot improvement in water clarity would result in an aggregate increase in property values of $541 million (Lee) and $428 million (Martin).|
|Ramach-Andran (2015)||Three Bays, Barnstable, Cape Cod, MA||2005 to 2013||Four hedonic models, n not stated, 0.68–0.72||Sales prices of single family homes||Concentration of nitrogen||Coefficient on nitrogen variable was negative and significant, indicating an average price reduction of 0.61% for each 1% increase in nitrogen concentration.|
|Tuttle and Heintzelman (2015) *||52 lakes in Adirondack Park, New York||2001 to 2009||10 fixed effects hedonic models, five for all 12,001 parcels and five for 2624 parcels within 0.05 miles of water, 0.44–0.55||Log of sales prices of residential parcels||Presence/absence of loons present (dummy), number of loons present, presence/absence of Eurasian water milfoil (dummy), annual average pH (<6.5 (poor), 6.5–8.5, or unknown) (measured at (or at closest time to) time of sale)||Presence of loons, number of loons, and poor or unknown pH were significant when entered individually for all parcels and just waterfront parcels; presence of milfoil was insignificant in both cases. Number of loons, poor or unknown pH, and presence of milfoil were significant when entered simultaneously for all parcels; number of loons and poor or unknown pH were significant for just waterfront parcels. Presence and number of loons had a positive effect, while the influence of poor or unknown pH and presence of milfoil (when significant) was negative.|
|Walsh, Griffiths, Guignet, and Klemick (2015)||14 counties on Chesapeake Bay, Maryland||1996 to 2008||Multiple general spatial hedonic models using four different spatial weight matrices (each county modeled separately), 229,513 properties, “approximately 0.7 to 0.9”||Log of sales prices of single family houses and townhouses||Linear and log of KD, the water-column light attenuation coefficient (average one-year and three-year spring-summer values at/prior to time of sale), interacted with distance from bay (dummy variables to represent bayfront, or 0–500, 500–1000, 1000–1500, or 1500–2000 m from bay)||Not all results are presented. For the one-year model using KD: Eight of 14 coefficients were significant (all negative) for waterfront properties, seven (three positive, four negative) for 0–500 m, six (one positive, five negative) for 500–1000 m. For the one-year model using ln(KD): Seven of 14 coefficients were significant (all negative) for waterfront, three (all negative) for 0–500 m, six (two positive, four negative) for 500–1000 m. For the three-year model using KD: Eight of 14 coefficients were significant (one positive, seven negative) for waterfront, 10 (five positive, five negative) for 0–500 m, nine (four positive, five negative) for 500–1000 m. For the three-year model using ln(KD): Seven of 14 coefficients were significant (one positive, six negative) for waterfront, 11 (six positive, five negative) for 0–500 m, nine (four positive, five negative) for 500–1000 m.|
|Walsh and Milon |
|76 lakes in Orange County, Florida||1996 to 2004||Multiple general spatial hedonic models, 33,670 properties up to 1000 m from a lake, 0.93 for all six models reported||Log of sales prices of residential single family properties||Log of: water quality (WQ), WQ interacted with waterfront location, distance to and area of nearest lake, dummy for clear, low alkalinity lake. Six measures of water quality: total nitrogen (TN), total phosphorus (TP), chlorophyll-a (CHLA), TN + TP + CHLA, trophic state index, one-out all-out indicator based on TN, TP, and CHLA.||Significance of coefficients is summarized in Table 5. Inconsistent results in model with TN, TP, and CHLA entered simultaneously indicated a correlation between indicators that reduced the significance of each individual indicator. The sign on coefficients was as expected in six of eight cases, insignificant in one case, and of an unexpected sign in the one-out all-out indicator model. Benefits of improving nutrient levels calculated for five representative lakes; order of magnitude differences in $ benefits were found.|
|Liu, Opaluch, and Uchide (2017) *||Narragansett Bay, Rhode Island||1992 to 2013||Multiple semilog linear OLS and spatial hedonic models, 40,433 transactions of 27,040 properties, 0.78–0.88 for OLS||Log of sales prices of single-family residential properties||Concentration of chlorophyll (in micrograms per liter) (i) for all years up to and including transaction year (“informed model” and (ii) in the five most recent summer months prior to purchase (“myopic model”)||Water quality variable showed expected significant negative coefficient in 19/24 cases for the “informed model” (negative but insignificant in the other five cases) and in 0/12 cases for the “myopic model” (insignificant in all cases). Under the informed model, poor water quality in bay reduced price of homes within one mile, with the greatest impact on houses closest to the shoreline. Total aggregated present value of benefits (discounted to the year of 2017) with a 25% decrease in chlorophyll concentration was $45.52 million.|
|Kung, Guignet, and Walsh (2017)||Long Island Sound, New York||2003 to 2015||Six spatial (SAC) hedonic models, up to 16,926 properties within five kilometers of the sound, 0.79||Natural log of transaction prices of single family and town homes||Natural log of enterococcus level (in colony-forming units per 100 mL at waters closest to each home (controlling for beach closures in five of six cases)||Negative price effects of enterococcus counts extend up to one kilometer from Long Island Sound based on nearest water, up to 2.5 km for nearest accessible beach, and up to 3 km for beach closures. Effect of beach closure most likely to be statistically significant.|
|Wolf and Klaiber (2017) *||Six counties surrounding four inland lakes in Ohio||2009 to 2015||Two hedonic models (semi-log and spatially heterogeneous), 15,866 properties, 0.74||Log of sales prices of single family homes||Microcystin concentration levels (two to six months prior to sale)||Overall, a negative and significant capitalization effect of algae contamination of 11.53% was found. When algae impacts were varied by lake proximity, crossing the 1 μg/L microcystin threshold significantly reduced the value of lakefront (−22%) and near lake (between 20 and 300 m, −11%) properties, though no impact was found beyond 300 m.|
|Klemick, Griffiths, Gaigaet, and Walsh (forthcoming) *||Meta-analysis: 14 counties on Chesapeake Bay, Maryland; benefit transfer: DC, Delaware, Virginia, four additional Maryland counties||1996 to 2008||Meta-analysis of 70 estimates of water clarity, used to estimate property value impacts of pollution reduction policies using benefit transfer techniques||Log of sales prices of homes||Log of water-column light attenuation coefficient (KD)||Importance of water clarity increased with proximity to the bay. Ten percent improvement in one-year light attenuation led to a statistically significant property value increase of 0.6% for waterfront properties, and 0.1% for non-bayfront homes extending out to 500 m. Aggregate near-waterfront property values were projected to increase by $400–$700 million in response to water clarity improvements.|
|Author (Year) |
|Study Site/Location||Year(s) of Data||Method, Sample Size, (Adjusted) R2 (as Applicable and Listed)||Dependent|
|Water Quality Variable(s)||Key Findings Regarding Water Quality Impacts on Property Values|
|No author (no date)||Rivers, canals, and lakes, the Dommel, Netherlands||2005||12 hedonic models (six OLS, six spatial error), 5358 properties, 0.76–0.84||Log of sales prices of properties||Water turbidity (per Secchi disk), nitrogen (N) concentration (three forms: continuous linear, continuous quadratic, and set of categories)||Secchi depth was significant (positive) in 11 of 12 models; N level was significant for all continuous measures (positive for linear, negative for quadratic) and one half of categorical measures. In the best model, a 1-decimeter increase in visibility was associated with a 3.6% increase in price, for a N max price premium of 4.3% at a concentration of 4.2 mg/L.|
|Large number of lakes and rivers, and Baltic coastline, in Finland||2004||OLS and SAR hedonic models, 1844 (2010) or 1806 (2014) waterfront lots, 0.31–0.39||Log of sales prices of unbuilt summer house lots (2014 version excluded upper and lower 1% of sales)||Five-class water usability index (poor-passable-satisfactory-|
good-excellent) based on 15 ecological and chemical criteria that influence recreation use
|Implicit price estimates for water quality relative to satisfactory class (2014 dataset, in €): poor −19,931 to −32,216 (−65 to −105%), passable −4190 to −4521 (−14 to −15%), good 2729–4169 (9–14%)), excellent 5877–9272 (19–30%). Weak evidence that WTP is non-linear.|
|Clapper and Caudill (2014) *||74 lakes in North Ontario, Canada||2010||Six OLS hedonic models (linear, log-linear and log-log forms), 253 lakefront cottages, 0.14–0.57||Linear and log of sales prices and sales prices per square foot (psq)||Water clarity (per Secchi disk) or log of water clarity||Clarity was significant (positive) in all six models. A one-foot increase in water clarity led to price premiums of $13,390 (linear model) or 2% (log-linear) and price per square foot premiums of $9.50 (linear) and 2% (log-linear). Log-log models: 1% increase in clarity increased price by 0.3% (both price measures).|
|Chen (2017) *||Pearl River and its tributaries, Guangzhou, southern China||2013||Three OLShedonic models, 968 apartments, 0.61–0.62||Log of sales prices of apartments||Model I (dummy only): Dummy for water quality was significant (positive). Model II (dummy and interaction with low floor): Dummy for water quality was significant (positive), interaction with low floor was significant (positive). Model III (dummy and interaction with restoration): Dummy for water quality was significant (positive), interaction with restoration was significant (positive).|
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