A loon on every lake: A hedonic analysis of lake water quality in the Adirondacks

Resource and Energy Economics 39 (2015) 1–15

Contents lists available at ScienceDirect

Resource and Energy Economics journal homepage: www.elsevier.com/locate/ree

A loon on every lake: A hedonic analysis of lake water quality in the Adirondacks夽 Carrie M. Tuttle, Martin D. Heintzelman ∗ Institute for a Sustainable Environment and School of Business, Clarkson University, United States

a r t i c l e

i n f o

Article history: Received 18 July 2013 Received in revised form 17 October 2014 Accepted 2 November 2014 Available online 10 November 2014 Keywords: Hedonic analysis Ecosystem services Water quality

a b s t r a c t The Adirondack Park contains over 6 million acres and over 3000 lakes. Approximately 43% of the Park is publically owned and protected to remain “forever wild”. Despite regulatory measures aimed at protecting the natural resources of the Adirondacks, surface water quality is threatened by acid and mercury deposition. This paper uses data on 12,001 property transactions over 9 years in the 12 counties that comprise the Adirondack Park to explore how property owners value lake water quality using fixed effects hedonic analysis. We find that multiple measures of water quality have significant effects on property values including lake acidity, the presence of water milfoil, an invasive species, and the presence of loons, an indicator species. This research provides valuable insight into how water quality and associated ecosystem health are capitalized into property values. Moreover, this research helps partially quantify air pollution impacts on Adirondack property values and could be used to justify additional regulations to further restrict sulfur and mercury emissions which are negatively impacting the Park. © 2014 Elsevier B.V. All rights reserved.

夽 Carrie M. Tuttle is a Research Assistant Professor in the Institute for a Sustainable Environment at Clarkson University. Martin D. Heintzelman is Associate Professor and Fredric C. Menz Scholar of Environmental Economics at Clarkson University School of Business. We would like to thank Kevin Boyle, Bill Desvouges, Patrick Walsh, and Nina Schoch for comments on earlier drafts and presentations of this research, as well as three anonymous referees for comments. All errors are our own. ∗ Corresponding author. Tel.: +1 315 268 6427.

E-mail addresses: [email protected] (C.M. Tuttle), [email protected] (M.D. Heintzelman). http://dx.doi.org/10.1016/j.reseneeco.2014.11.001 0928-7655/© 2014 Elsevier B.V. All rights reserved.

2

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

1. Introduction The Adirondack Park was established in 1892 by the State of New York to protect valuable natural resources. Containing 6.1 million acres, 30,000 miles of rivers and streams, and over 3000 lakes, the Adirondack Park is the largest publically protected area in the United States and is larger than Yellowstone, Everglades, Glacier, and Grand Canyon National Park combined. Approximately 43% of the Park is publically owned and constitutionally protected to remain a “forever wild” forest preserve. The remaining acreage is made up of private land holdings whose development and use are regulated by the Adirondack Park Agency. Despite the regulatory measures aimed at protecting natural resources in the Adirondacks, surface water quality is threatened by acid and mercury deposition created from burning fossil fuels, primarily coal fired power plants in areas outside the Park. Failing septic systems and increased lakefront development have also contributed to reduced lake water quality in the Adirondacks. The effects of acid rain and mercury contamination in the Adirondacks are significant. The Adirondacks are especially susceptible to acid and methylmercury deposition because of their high precipitation, soil composition, and location downwind of coal-fired power plants in the Midwest (Jenkins et al., 2007). The Adirondack Lakes Survey Corporation has determined that 25% of the lakes in their sample are acidified and 80% have low acidification neutralizing capacity (ANC) making them sensitive to further acidification. What makes these numbers especially striking is that many of the lakes in their sample are located in relatively isolated state-owned areas of the Park that are protected from development. Acid deposition damages plants by depleting calcium from their membrane structures, making them more susceptible to cold weather, disease, and other stresses. Soils are also impacted by atmospheric acids by mobilizing aluminum which is toxic to plants and animals. In an aquatic environment, acidification decreases biodiversity and kills fish and invertebrates. Methylmercury levels are especially concerning in aquatic ecosystems as they pose risks to fish, loons and humans. Risk levels vary based on a variety of factors including hydrology/chemistry, proximity to pollutant sources, topography, and land cover (Driscoll et al., 1994). There is also evidence that acidity and mercury may be correlated in Adirondack lakes (Brown et al., 2010). Mercury is an extremely toxic substance causing significant environmental impacts, even at very low concentrations. One gram of mercury, the amount found in a clinical thermometer, will produce enough methylmercury to contaminate a 25 acre lake. That amount of methylmercury will make 2000 fish unsafe for human consumption or decrease the reproductive success of 30 pairs of loons (Jenkins and Keal, 2004). The Clean Air Act (CAA) established National Ambient Air Quality Standards (NAAQS) at both primary and secondary levels. The estimated impacts of the 1990 CAA amendments on sulfur dioxide and nitrogen oxides through 2010 are an additional 16.3 hundred ton per day reduction in SO2 and 18.3 hundred ton per day reduction on NOx. The economic value of the reductions for all the NAAQS parameters is estimated to be $110 billion, primarily the result of avoiding illness and premature death from health effects caused by air pollution (U.S. Environmental Protection Agency, 1999). In 2011, the U.S. EPA issued the Mercury and Air Toxics Standards (MATS), the first standards for power plant emissions of mercury. These regulations will drastically reduce emissions of mercury, and this paper provides some evidence on the potential benefits. This paper uses data on residential transactions in the Adirondack Park between 2001 and 2009 in conjunction with water quality and other ecological data and property characteristics to perform a hedonic analysis that explores the relationship between lake water quality, ecological indicators, and property values. This study is distinctive in the literature for the size of the study area, the large sample of property transactions over 9 years, and the number of lakes included. These attributes allow for the implementation of census block fixed effects analysis to control for omitted variables and other biases common to hedonic analysis. Our data also provides important variation in lake water quality and ecological indicators over both space and time that we use to identify the capitalization effects of water acidity, the common loon, and invasive milfoil. The Adirondack Park is also an important region for study as it is the largest protected area in the mainland United States, and unique in its mix of public and private land, all of which is protected to some degree. The park also includes a large swath of ecologically important wilderness areas unique to the Eastern United States.

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

3

Our results indicate that the presence of the common loon, an important indicator species, in addition to being a commonly recognized symbol of the Adirondack region, has a significant positive impact on property values. In addition, we find some evidence that both acidity and the presence of an invasive plant, Eurasian water milfoil reduce property values. Together these results indicate that the ecological properties of lakes in the Adirondack region are important to homeowners and provide a significant benefit. 2. Background and data One natural amenity of New York that draws visitors to the State, and the Adirondacks in particular, is the number of lakes and ponds. Although the State contains over 7600 ponded waters (lakes, ponds, reservoirs, etc.) the actual number is unknown primarily because water bodies under a certain size do not appear on USGS maps. In the Adirondack Park, the number of ponded waters is estimated over 3000. There are many factors which impact the “quality” of these water bodies, and many measures of both these individual factors and overall water quality. A challenge in measuring the benefits of improved water quality is in determining the proper metric to include in the hedonic analysis (Poor et al., 2001). Scientific measures of water chemistry, while important ecologically, may not be observed or understood by market participants. This is an issue not unique to aquatic ecosystems, nor to ecosystems generally, but instead is endemic to hedonic analyses (Chattopadhyay et al., 2004; Baranzini et al., 2010). In the literature on valuing ecosystem services, there is often a mismatch between available measures for analysis and what can be used in next step models of ecosystem services (Boyd and Krupnick, 2013). In this study we use both scientific measures (pH) and ecological endpoints (the presence or absence of loons and the aquatic invasive, milfoil) and find that both are valued by property owners. The latter measures are easily observed while the former may be observed with some effort by homebuyers. There is a substantial literature which attempts to place a value on various measures of water quality through hedonic analysis. In a very early application of hedonic analysis, Epp and Al-Ani (1979) found that reductions in acidity in Pennsylvania streams increased property values. Similarly, Halstead et al. (2003), Horsch and Lewis (2009), and Zhang and Boyle (2010) study the impacts of milfoil on property values in New Hampshire, Wisconsin, and Vermont, respectively, and find significant negative impacts. Several papers look at the effects of clarity and/or trophic status, and find, in general, that reduced water clarity does negatively impact property values, although reduced clarity does not always indicate lower water quality (Boyle et al., 1999; Gibbs et al., 2002; Poor et al., 2007; Walsh et al., 2011). An additional important contribution to this literature uses hedonic analysis to look at the impacts of fecal Coliform bacteria in the Chesapeake Bay (Leggett and Bockstael, 2000). Finally, Banzhaf et al. (2006) use a stated preference approach to value reduced acidification in the Adirondacks and determine that New York residents would be willing to pay between $48 and $107 per household annually for ecological improvements resulting from further reductions in air pollution. Given the large number of lakes and the difficulty in accessing many that are surrounded by forest lands, a comprehensive monitoring program of all Adirondack lakes would be cost prohibitive. However, several separately managed monitoring programs are in place to collect various data at select lakes and data from these programs was used for this study. Some of these programs rely on volunteers to collect data. Methylmercury is expensive to measure and was not included in the ongoing water quality programs utilized for this study. However, Biodiversity Research Institute (BRI) selected the common loon as a suitable indicator species for methylmercury contamination based on ecological, logistical and other criteria including public valuations of natural resources.1 In 1998 BRI began a 3 year study to sample blood mercury levels in 96 loons on 43 lakes in the Adirondacks. That study found that 17%

1 BRI is a 501(c)3 non-profit institute headquartered in Maine with a mission to assess emerging threats to wildlife and ecosystems through collaborative research, and to use scientific findings to advance environmental awareness to inform decision makers. Their research has been published in many peer-reviewed journals, and includes studies across the U.S., Canada, Belize, Costa Rica, and Mexico.

4

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

of the loons sampled were at risk for harmful effects from mercury contamination and of particular interest all of these loons were found on lakes with low pH levels (Schoch and Evers, 2002). In 2001, the Adirondack Cooperative Loon Program, a partnership between BRI, the Wildlife Conservation Society, New York State Department of Environmental Conservation (NYSDEC), and other organizations, began an annual loon census for approximately 200 lakes in the Adirondacks. Perhaps no other species is more of an iconic representation of the Adirondacks, and other “north woods” regions, than the loon. Loons are readily observable and audible. Besides being an indicator species it is possible that buyers value loons simply because they are a beautiful, unique species that is entwined in the history of the Adirondacks and symbolic of a simpler time. For this study, we use loon census data to create a variable indicating whether loons were known to be present at the nearest lake to a property, and the actual number of loons known to be present at the year of sale or closest census year of available data to the year of sale. In addition to the common loon, we also include data on the presence of the invasive aquatic plant, Eurasian water milfoil. The presence of milfoil impacts recreation such as boating and swimming. Data on the presence of milfoil comes from the Adirondack Park Invasive Plant Program and covers the years 1986–2008. These data are not available on an annual basis for each lake, but since it is nearly impossible to eradicate once present, we assume that if detected at any point up to and including the year of sale, then it is recorded as present.2 Both loons and milfoil can be easily observed by buyers when visiting a parcel, although such observations are likely to be done with error. As noted above, acidity affects the entire ecosystem of a lake, including the type and quantity of flora and fauna present. In particular, lakes with lower pH will suffer from decreased biodiversity through the increased bioavailability of aluminum. This has been well documented in the case of the Adirondack Park (Jenkins et al., 2007). New York State categorizes water bodies according to whether the pH is between 6.5 and 8.5, less than 6.5, or greater than 8.5. We follow this convention in our analysis, although we have no observations in our study area with a pH greater than 8.35. In the Adirondacks, long term surface water monitoring programs were initiated by the U.S. Environmental Protection Agency (EPA) in the 1980s and continue to be administered through several different programs today. The Adirondack Lakes Survey Corporation (ALSC) administers one of the largest programs containing 52 lakes and 3 streams that are sampled weekly. For our analysis, we use annual averages of pH for each of the 52 lakes included in the ALSC long term monitoring program. The Citizens’ Statewide Lake Assessment Program (CSLAP) is also administered by the NYSDEC but differs from the ALSC program in its use of volunteers to collect data. The CSLAP has sampled 76 lakes in the Park since 1986 but the number of lakes sampled each year and the parameters analyzed are not as consistent as the ALSC program. Lakes in the interior of the Adirondack Park are underrepresented in this program while mid-sized public access lakes on the edge of the region are overrepresented. Since most property sales occur around this second set of lakes, the CSLAP is especially relevant to our study. Where there is overlap between the lakes monitored by the ALSC and the CSLAP, we use ALSC data since it is more comprehensive than CSLAP data. Finally, there are two other surface water quality monitoring programs that we use to supplement the ALSC and CSLAP data, the National Lake Assessment (NLA) Program and the Adirondack Effects Assessment (AEA) Program. The NLA is administered by the EPA to conduct a national survey of lakes; there are equivalent programs for rivers, estuaries, and wetlands. Out of 909 lakes nationwide included in the program, 12 are in New York State and 5 in the Park. NLA program sampling was performed by the NYSDEC in 2007. The AEA Program is also funded by the EPA and involves collaboration with federal, state and University partners. Program monitoring consists of basic water chemistry and biota (phytoplankton, zooplankton, macrophytes, and fish) for a suite of 35 lakes within the Adirondack Park. Many of the lakes included in the AEA Program overlap those monitored by the ALSC and CSLAP. In our analysis, we use water quality measurements taken during the year of sale, where available, or the measurement taken closest to the time of sale. Since pH levels change very slowly over time, interpolation was not warranted. To form the final pH dataset, we utilized the four sources mentioned above (ALSC, CSLAP, NLA and AEA) to obtain data for 52 lakes around which we observe property

2

The milfoil data does not allow us to be more specific than year of sale when measuring the presence or absence of the data.

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

5

Table 1 Data sources. Description

Time frame

Source

Adirondack human impact model Adirondack loon census Census blocks Cultural amenities Distance to environment pollutant sources GIS shapefile of lakes Invasive species

2008 2001–2009 2010 Unknown 2008

SUNY-Environmental State & Forestry Biodiversity Research Institute New York State GIS Clearinghouse Adirondack Park Agency New York State Department of Environmental Conservation

Unknown Between 1986 and 2008 2001 2009 2009 2009 2004 2001–2009 Unknown 2001–2009 2001–2009 2002–2006 2007

New York State Department of Environmental Conservation Adirondack Park Invasive Plant Program

Land cover Marina, point file of locations Parcel layer Parcel level details Populated places Property sales Roads Water quality data Water quality data Water quality data Water quality data

United States Geologic Survey Generated from NYSORP property classification codes Provided by individual counties New York State Office of Real Property North American Atlas GIS Group New York State Office of Real Property Adirondack Park Agency Adirondack Lake Survey Corporation Citizens Lake Survey Program Adirondack Effects Assessment Program National Lake Assessment Program

transactions. Each of these programs is administered by reputable federal or state agencies and pH samples are collected by trained personnel according to written standard sampling procedures. Our database of real estate transactions includes detailed parcel-level information on land cover, property and building characteristics, access to public utilities including road distance to libraries, schools, hospitals, and airports. We also have data on road distance to recreation opportunities such as hiking trails, parks and campsites. Table 1 summarizes the data sources we utilize in this analysis and their sources. To form the final dataset, we utilize ESRI ArcView Geographic Information System (GIS) software and STATA data analysis and statistical software. Table 2 presents selected summary statistics for our dataset. Only arms-length transactions and those observations with complete data for key variables are kept in the final dataset. Nine parcels are also dropped that have transacted more than 5 times between 2001 and 2009. These transactions occur over a 1–2 year span and appear to be subdivisions which do not have updated parcel acreages in the New York State Office of Real Property Taxation Services (NYSORPTS) database.3 Other transactions are dropped due to unpopulated variables (i.e., building size, lot size, number of bathrooms, building age, etc.) or missing data that result when NYSORPTS data is joined with other spatial files in GIS (i.e., missing land cover, census data, proximity measures, etc.). Fig. 1 depicts the study area, the centroids of transacted parcels, and shows the Adirondack Park perimeter, also referred to as the “blue line”, in relation to the 12 counties that have a portion of their towns in the Park. Concentrated transactions occur in some of the most desirable places for second homes in the region (Lake George, Old Forge, and the High Peaks region). 3. Methodology The hedonic pricing method is based on the premise that the characteristics of a good determine its price on the open market. Hedonic pricing models infer the individual price impact of various characteristics on the good’s overall price by taking advantage of variation in home prices and characteristics across space and time. More specifically, the price of the good is regressed on the good’s characteristics using an econometric model (Rosen, 1974). The foundation for hedonic modeling is well established

3 NYSORPTS maintains a database of real property transactions and property characteristics. Data are input by local assessors and primarily utilized for taxation purposes.

6

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

Table 2 Select summary statistics. Variable

All parcels

Std. dev. all parcels

Within 0.05 Mi. water

Std. dev. 0.05 Mi. water

Mean sale price ($) Personal property ($10,000)

$179,190 0.041

$271,083 0.746

$362,557 0.127

$473,515 1.493

Water quality characteristics Loon present yr. sale (d) No. of loons present yr. sale Invasive species present (d) pH ≥6.5 and ≤8.5 (d) pH <6.5 (d) pH unknown (d)

0.260 1.010 0.492 0.337 0.012 0.646

0.439 2.465 0.500 0.473 0.108 0.478

0.372 1.663 0.635 0.418 0.026 0.556

0.483 3.476 0.482 0.493 0.159 0.497

Proximity Characteristics Distance to nearest lake (miles) Distance to lake2ˆ Lakefront Size of lake (acres) Distance to nearest forest (miles) Distance to blue line (miles) Distance to nearest road (feet) Distance to nearest rec. area (miles) Distance to population center1 Distance to population center2 Distance to population center3 Distance to population center4 Distance to population center5

1.075 4.705 0.166 6783 2.877 12.75 1859 5.40 46 31 103 121 220

1.884 26.476 0.372 12,197 4.586 10.403 3462 4.05 24 13 16 31 30

0.011 0.0003 0.761 9222 2.079 13.45 2972 4.68 48 34 99 124 220

0.014 0.0006 0.427 13,579 3.573 10.944 4999 3.55 21 13 18 30 30

Structure/parcel characteristics Building age (years) Lot size (acres) Living area (sqft) Bedrooms Fireplaces Full baths Half baths Estate Seasonal residence Mobile home Multiple unit year round Other property class Land cover open water Land cover low intensity Land cover medium intensity Land cover high intensity Land cover forest Land cover field Land cover wetlands Condition fair Condition average Condition good Condition excellent Buyer outside Adirondacks Sale post 2006

40 6.927 1508 2.820 0.400 1.483 0.251 0.001 0.154 0.002 0.027 0.016 0.034 0.113 0.025 0.002 0.409 0.069 0.110 0.124 0.746 0.106 0.007 0.456 0.168

32 1.642102 0.467750 1.059 0.613 0.760 0.470 0.034 0.361 0.047 0.162 0.127 0.133 0.263 0.133 0.036 0.399 0.202 0.235 0.329 0.435 0.308 0.083 0.498 0.374

37 9.149 1537 2.784 0.593 1.541 0.251 0.005 0.367 0.001 0.008 0.023 0.134 0.042 0.006 0.001 0.453 0.038 0.143 0.084 0.783 0.114 0.011 0.699 0.183

27 214 970 1.193 0.708 0.905 0.471 0.070 0.482 0.034 0.091 0.150 0.241 0.167 0.064 0.029 0.384 0.150 0.259 0.277 0.412 0.318 0.106 0.459 0.387

Observations

12,001

12,001

2624

2624

and has been used to value a variety of characteristics that could affect home values including proximity to neighborhood parks, land cover, viewscapes, proximity to wind turbines, Adirondack Park land zoning classifications, and proximity to other environmental amenities and open space (Netusil, 2005; Heintzelman and Tuttle, 2012; Tuttle and Heintzelman, 2013; Sanders and Polasky, 2009). By

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

7

Fig. 1. Location of transactions in the Adirondack Park.

providing evidence about peoples’ willingness to pay for environmental amenities, like water quality, policy makers can use less subjective means to make decisions that affect public goods. In practice, the values derived from hedonic models must overcome several potential econometric challenges to form the basis for sound policy decisions. Common empirical issues include arbitrary functional form, omitted variables bias, endogeneity, spatial dependence and autocorrelation, heteroskedasticity, and multicollinearity (Gujarati and Porter, 2010). More errors may be induced based on the nature of the characteristics being assessed or the selective (non-random) nature of the dataset itself. Despite these obstacles, the hedonic literature over the last 40 years has established techniques to correct for these empirical issues. Although our data is more comprehensive than many studies, we certainly do not have information on all the characteristics that determine the price of a property, and many of these characteristics may in fact be unobservable. When there is correlation between unobserved characteristics and those included in the model, the model will give biased coefficient estimates. We address this issue of omitted variables, as well as possible spatial dependence, using fixed effects analysis at the level of the census block (Greenstone and Gayer, 2009; Kuminoff et al., 2010).4 In addition, we control for spatial autocorrelation by allowing clustering of error terms for parcels within census blocks but enforcing independence of error terms across census blocks. In this way, our model also controls for heteroskedasticity (Wooldridge, 2002).5 4 Spatial fixed effects allow us to control for omitted variables that are varying over space, but not any that might vary over time. 5 The calculated Moran’s I for our model is highly significant with a z-score of 82.74, indicating that we do have spatial autocorrelation in our data. We control for this with our local area fixed effects and error clustering. A more complete spatial

8

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

Fixed effects can, theoretically, be applied in our dataset at the census block, census block group, county, individual lake, or individual parcel level. The smaller the geographic area of the fixed effects the better the control for omitted variables bias, but this comes at the price of statistical power. The estimated coefficients for the explanatory variables rely on variation in those variables within the scale of the fixed effects and this variation is decreasing with smaller scales of fixed effects. In the case of individual lake or parcel fixed effects, we would require variation in the explanatory variables over time, which is often not available. It is for this reason that we use census block fixed effects. At this scale we can rely on both temporal and small-scale spatial variation in our explanatory variables, while significantly reducing the potential impact of the common biases of the hedonic approach. Our general specification is adapted from Bertrand et al. (2004) and Parmeter and Pope (2011). We employ a standard log-linear specification of the hedonic price function. Following Kuminoff et al. (2010), we test a more general Box–Cox specification and estimate a theta of 0.0876 which supports the use of a log-linear specification for our model. Our specification can be summarized as below: ln pijt = t + ˛j + zit ˇ + ij ıj + jt + it

(1)

where pijt represents the price of the property i in the fixed effects group j at time t; t represents a set of time series dummy variables for the month and year of sale; ˛j represents the census block fixed effects; zit represents the water quality variables we are concerned with (i.e., presence of loons, water milfoil and pH at the nearest lake) as described in Section 2; ij represents property characteristics; jt and  it represent the fixed effects group error and individual error terms, respectively. Amongst the property characteristics that we include are the building age, lot size, square footage, number of bedrooms, number of fireplaces, number of full and half bathrooms, whether or not the home is classified as an “Estate,” seasonal residence, mobile home, or multi-family residence, or other property type (all relative to a single family year-round home), the share of the parcel of various land cover types (water, low, medium, or high intensity, forest, field, or wetlands), and the assessed condition of the property. We include a dummy variable to indicate the home address of the buyer as inside or outside of the Adirondack Park, and include year and month dummies, as well as an indicator for whether the sale was pre- or post-2006 as a simplistic way to account for any effects of the financial crisis. We also include distance to the nearest lake as a linear measurement and in quadratic form, a dummy variable indicating whether the property is lakefront, and the size of the nearest lake in all regressions. We also include proximity variables for the nearest state forest, nearest road, and nearest recreational area as well as the distance to the Adirondack Park border (the Blue Line) to account for effects of being closer to the center or fringes of the park. Finally, we include variables representing the distance to the nearest population centers of various sizes from <10,000 residents to more than 3 million residents.6 These variables are also represented in Eq. (1) by ij . All variables are listed in Table 2 with summary statistics. 4. Results The results presented in this paper utilize census block level fixed effects for our full dataset (Table 3) and separately for only those properties located within ∼0.05 miles of a lake (Table 4).7 Tables 3 and 4 present the regression results for the water quality variables only, although a full set of controls was included in each of these regressions. Table 5 presents the results for all of these controls for Model 1, and full results for all models are available upon request. All regressions include the same

econometric approach is computationally challenging given the size of our dataset and our approach is, in effect, a spatial econometric analysis where we assume a simple spatial weights matrix with ones in those places where the two parcels are within the same census block and zeroes elsewhere. 6 Population center categories are as follows 1: 1–9999 people; 2: 10,000–99,999; 3: 100,000–999,999; 4: 1,000,000–2,999,999; 5: >3,000,000 from the North American Atlas GIS Group. 7 The authors have designated waterfront parcels as those within 0.05 miles (264 ) of a lake. While this designation is somewhat arbitrary, the designation takes into consideration the minimum lot size requirements typically required by the Adirondack Park Agency for waterfront parcels and adjusts for some margin of error that occurs when lake layers and parcels layers are combined in GIS.

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

9

Table 3 Water quality regression results for all parcels. Variables

Model 1

Model 2

Model 3

Model 4

Model 5

Loons present yr of sale (d) No. of loons present yr of sale pH poor pH unknown Invasive species present

0.103*** – – – –

– 0.015*** – – –

– – −0.202** −0.226*** –

– – – – 0.004

– 0.010*** −0.226*** −0.233*** −0.064***

Month and year dummies Clustered errors Constant Observations Adjusted R2

Y Y 9.310*** 12,001 0.438

Y Y 9.188*** 12,001 0.438

Y Y 11.222*** 12,001 0.445

Y Y 8.981*** 12,001 0.437

Y Y 11.559*** 12,001 0.446

Note: Full results with all controls are available upon request. * p < 0.1. ** p < 0.05. *** p < 0.01. Table 4 Water quality regression results for parcels within 0.05 miles of waterfront. Variables

Model 1

Model 2

Model 3

Model 4

Model 5

Loons present yr of sale (d) No. of loons present yr of sale pH poor pH unknown Invasive species present

0.107** – – – –

– 0.012** – – –

– – −0.261* −0.352*** –

– – – – 0.011

– 0.009* −0.271* −0.347*** −0.017

Month and year dummies Clustered errors Constant Observations Adjusted R2

Y Y 6.785*** 2624 0.533

Y Y 6.517*** 2624 0.532

Y Y 10.250*** 2624 0.547

Y Y 6.643*** 2624 0.531

Y Y 10.087*** 2624 0.547

Note: Full results with all controls are available upon request. * ** ***

p < 0.1. p < 0.05. p < 0.01.

property, parcel and proximity characteristics, as described above. Separate models are run for each of the loon presence dummy, loon count, pH dummies, and the milfoil dummy (illustrated as M1–M4 in Tables 3 and 4). We then run a combined regression model that includes pH dummies, number of loons, and presence of milfoil at the nearest lake (M5). Table 6 illustrates the percentage impact of the water quality measures on property values from the combined regression and the loon dummy variable regression for both the full and waterfront datasets following Halvorsen and Palmquist (1980).8 The presence of loons on the nearest lake is included in separate regressions as both a dummy variable (M1) and the total number of loons present at the time of sale (M2). In the combined model, the number of loons is included with presence of milfoil, and pH dummy variables (M5).9 The loon dummy variable yielded positive and significant coefficients of 0.107 in the model with only waterfront

8 In our dataset there are 12 counties, 85 municipalities, 313 lakes, and 732 census blocks. We attempted several other fixed effects models including county, municipality, census block group, lake, and repeat sales. County, municipality, and census block level fixed effects generally produced similar results in terms of significance and sign; however variations in magnitude were evident. Repeat sales fixed effects models were attempted but did not produce reportable results due to the limited panel data for water quality variables and the significant reduction in transactions. The lake fixed effects models also have limited variation of panel data across individual lakes and produces only slightly better results than the repeat sales models. 9 We compared the waterfront regressions to those using the full sample of homes for Model 5 (the combined regression including loons, acidity, and milfoil) and, using a z-test, found only a few coefficients that are significantly different at the 95% confidence level. Most importantly, none of the variables of interest are significantly different (although not all of these coefficients are significantly different from zero). The results of this test are available from the authors.

10

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

Table 5 Sample full regression results for all parcels, Model 1. Variables

Model 1

Loons present yr of sale (d) No. of loons present yr of sale pH poor pH unknown Invasive species present

0.103*** – – – –

Distance to nearest lake (miles) Distance to lake2ˆ Waterfront Size of lake (acres) Distance to nearest forest Distance to blue line Distance to nearest road Distance to nearest rec. area Distance to population center1 Distance to population center2 Distance to population center3 Distance to population center4 Distance to population center5 Personal property ($10,000) ln Sqft. of structure ln Property size (acres) Building age Building age2ˆ No. of bedrooms No. of full baths No. of half baths No. of fireplaces Seasonal Estate Multiple unit year round Mobile home Other property class Land cover open water Land cover low intensity Land cover medium intensity Land cover high intensity Land cover forest Land cover field Land cover wetlands Condition fair Condition average Condition good Condition excellent Buyer outside Adirondacks Sale post 2006

−0.023* 0.001 0.844*** 5.6E−06*** −0.002 0.009*** −0.000 −0.022*** −0.010*** −0.011*** 0.003* 0.002 −0.007* 0.047*** 0.338*** 0.036*** 0.014*** −0.000*** 0.015 0.103*** 0.096*** 0.191*** −0.123*** 0.675*** −0.231*** −0.230 0.333*** 0.192*** −0.068** −0.191*** −0.473*** 0.014 0.127*** 0.076* 0.164*** 0.434*** 0.363*** 0.565*** 0.178*** 0.705***

Month and year dummies Clustered errors Constant Observations Adjusted R2

Y Y 9.310*** 12,001 0.438

* ** ***

p < 0.1. p < 0.05. p < 0.01.

parcels and 0.103 in our model with the complete dataset. This implies an 11% premium for properties that are located near a lake that has loons. In addition, in models where we include the number of loons, rather than just a dummy variable, we are able to estimate the marginal value of an additional loon. In these models, this marginal value is between 0.9 and 1.5 percent of the sales price, which

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

11

Table 6 Interpretation of coefficients for all parcels and waterfront parcels Model 1 (M1) – “loons present year of sale” and Model 5 (M5) – “combined” results. All parcels

Loons present yr of sale (d) – M1 No. of loons present yr of sale – M5 Invasive species present – M5 pH <6.5 (d) – M5

Waterfront

%Change

Mean property value impact ($)

Median property value impact ($)

11%*** 1%*** −6%*** −18%***

$21,803 $1819 ($10,459) ($30,144)

$13,386 $1117 ($6421) ($18,507)

%Change

11%** 1%* – −24%*

Mean property value impact ($)

Median property value impact ($)

$46,158 $3308 – ($69,734)

$28,009 $2007 – ($42,205)

Notes: 1. Percentages are calculated using Halvorsen and Palmquist’s (1980) equation, 100g = 100(ec − 1), where c is the coefficient derived from the regression and g is the relative percentage effect on the price. 2. Property value impact calculations use mean value of full dataset $179,190 and waterfront dataset $362,557. The median property values for the full dataset and waterfront datasets are $110,000 and $220,000, respectively. * ** ***

p < 0.1. p < 0.05. p < 0.01.

is broadly consistent with the dummy variable results given that the average lake with loons in our sample had approximately 3.9 loons. These results fit nicely with anecdotal evidence that potential buyers often query realtors about the presence of loons on a lake when they are considering purchasing a lake home in the Adirondacks, and that realtors have been known to contact the BRI to check the loon census for a particular lake. It is not clear whether loons are desired because of their natural beauty, unique tremolo, or because the loons are a sensitive species that buyers may see as a proxy for overall lake health, although the combined regression (M5) suggests that there is a premium for loons even controlling for acidity and invasive milfoil. Lake acidification is captured in our model through the use of a pH dummy variable. In our full dataset pH levels range from 4.62–8.35 for 4250 observations at 54 lakes with 141 observations at lakes with poor pH and 4109 observations at lakes with good pH. There are 7751 observations (65%) in the full dataset for which pH data is not available. These observations have been assigned a dummy variable of pH unknown. In our waterfront subset 68 observations occur at lakes with poor pH, 1097 at lakes with good pH, and 1459 at lakes with unknown pH levels. Both datasets yielded surprising large and negative results given the very small number of lakes with poor pH. Having a pH level less than 6.5 will reduce property values by approximately 20% in our full dataset and approximately 23% for waterfront parcels relative to lakes with known good pH, depending on the exact specification. Results were also significant for unknown pH values in both datasets, indicating that buyers discount waterfront properties with unknown pH values by 29% and properties in the full dataset by 21%, again relative to lakes with known good pH. While it is strange that we observe larger negative coefficients for the pH unknown variable relative to the poor pH variable, it is important to note that the coefficients on these factors are not statistically different from one another at the 95% confidence level in any of our models. The proper interpretation of these results is probably that, rather than discounts for poor pH or unknown pH, what we are actually seeing is a premium for lakes with known good pH. Given the long history of acid deposition in the Adirondack Park, lakes which are non-acidic during our study period are presumably less likely to become acidic in the future and may be demonstrating a natural resilience. Since pH may not be widely known by potential buyers, it may be surprising to see such clear results, especially given our obvious data limitations. However, acidification impacts individual species of aquatic plants and animals, disturbs the aquatic food web, and affects the entire lake ecosystem (Baker and Christensen, 1991; Driscoll et al., 2003; Jenkins et al., 2007). Lakes with low pH are likely to have less biotic diversity including having fewer fish, amphibians, and other species which are readily observable by potential home buyers. Since we are unable to include comprehensive data on the abundance of fish species, and since non-acidic lakes are likely to support healthier and more numerous fish populations, this may be a large driver. Fishing is a major recreational activity in the

12

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

Adirondack Park. It is also the case that acidity is likely to be correlated with the presence of mercury (Simonin et al., 2008, Brown et al., 2010), which we are unable to include, but which is highly toxic to humans and ecosystems and is highly prevalent in the Adirondacks. The presence of waterborne invasives impacts recreational opportunities by affecting boating, swimming and fishing; and prior literature confirms property values can be negatively affected (Halstead et al., 2003; Horsch and Lewis, 2009; Zhang and Boyle, 2010). In the full dataset, the milfoil variable presented with a negative and highly significant coefficient suggesting a discount of about 6% in our combination regression (M5) which also included the presence of loons and pH. It was positive and not significant when included on its own, in both datasets (M4). In the waterfront dataset milfoil is not significant, although it is still negative in the combined specification.10 Importantly, we are only observing the presence of milfoil at the lake level, so that all parcels on an invaded lake are counted as such, but it may be that only portions of some lakes are invaded, especially for the larger lakes. One reason for this is that the most common vector for the transport of invasive species, including milfoil, is boats (Rothlisberger et al., 2010), and, anecdotally, milfoil invasions are most common in the vicinity of public boat launches (Hilary Smith, Director, Adirondack Invasive Plant Program, personal communication, May 13, 2014). Thus, our estimates of the effect of invasion may be biased toward zero since some parcels may be on an invaded lake while not actually being directly impacted by that invasion. It seems possible that this effect may be especially relevant for waterfront parcels where an invasion out one’s back door is likely to be considerably more important than one down the lake near the public beach, for instance. In terms of more general characteristics, homeowners prefer to be closer to lakes as indicated by the negative coefficient of distance to the nearest lake and seem to favor larger lakes over smaller ones. Lake size is included in acres and found to be positive and highly significant with a relatively small coefficient (10−6 ). Distance to the nearest State forest and road is not significant, most likely because there is little variation in these variables. All the parcels in the Adirondack Park are relatively close to forests and road density is also fairly consistent throughout the populated areas of the Park. We also included a variable representing the distance of the parcels to the park perimeter (i.e., the blue line). This variable presented with positive and highly significant results indicating that buyers prefer to be located toward the interior regions of the park, perhaps because of the protection that provides to development or because of the proximity to the high peaks region, which is a popular destination for visitors and seasonal residents. Structure and parcel characteristics appear predominantly as expected. Larger homes with larger lots, more bedrooms, bathrooms and fireplaces are preferred. Buyers also capitalize the value of personal property into their purchase price. Home values increase as condition codes improve and rankings of fair, good, average and excellent all produce positive results. Building age is positive and significant indicating that buyers prefer older homes. This is somewhat unusual since one would expect buyers to prefer newer homes, which would likely be in need of less maintenance and capital improvements. However, this result may indicate some other unobserved attributes related to the age of the home such as the increased prevalence of modular homes in our study area in new construction. Land cover types of low, medium and high intensity use all have negative and significant impacts on property values in the full dataset, discounting property values from 7% to 32%. Intensity levels reflect the amount of vegetation versus constructed material cover in residential areas. According to the United States Geological Survey (USGS) definitions for land cover, high intensity has less than 20% vegetative cover while low intensity has between 20% and 70%. Fields and open water are both positive and indicate a premium for these land cover types of 15% and 23%, respectively. Land cover results for the waterfront properties are less significant due to the limited variability in the smaller dataset. Only low intensity and high intensity land cover types affect waterfront property values. These results indicate that buyers of waterfront property place a large preference on vegetative cover and will discount low intensity property by 16% and high intensity property by 71%.11 These results are

10 Waterfront observations comprise 22% or the total transactions in our dataset. Of the 2624 waterfront observations, 64% have a nearest lake that is infected with milfoil, compared to 49% in the full dataset. 11 There are 9 properties classified as high intensity land use type in the waterfront dataset.

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

13

not surprising since buyers in the Adirondacks prefer to be located in less developed areas (Tuttle and Heintzelman, 2013). Non-local buyers are willing to pay a premium for Adirondack real estate of 19% for properties in the full sample and 10% for parcels in the waterfront sample. Properties that are classified as seasonal and multifamily units are discounted by 12% and 26%, respectively, while estates sell for a 100% premium.12 Estates are defined by NYSORPTS as a residential property of 5 acres or more with a luxurious residence and auxiliary buildings.13 Discounting of seasonal property is obvious since these homes are only usable a portion of the year when weather permits.14 Mobile homes and multifamily residences are also valued less than single family homes, controlling for other parcel and property characteristics.15 Table 6 utilizes the mean property values for the full dataset and the waterfront parcels to illustrate the property impacts from loons, pH and invasive species in dollar terms. The average waterfront property located on an acidified lake will sell for $69,554 less than one located on a lake with good pH, all else equal. If the nearest lake has loons, then property values for the average home will be $46,158 higher than waterfront property on a lake without loons.16 The marginal value of a loon is $3308 for waterfront parcels and $1819 for the full dataset. Finally, full dataset results show that the presence of invasive species on the nearest lake decreases property values by $10,459. These results suggest the importance of water quality as measured through these metrics to property values in the Adirondacks and other similar regions. 5. Discussion and conclusion Our results indicate that various measures of water quality have significant effects on property values. The presence of loons, which can be affected by lake acidity and methyl mercury concentrations, has a positive impact on property values, even when controlling for acidity. Buyers appear to have a sense of the ecosystem health in their nearest lake and are capitalizing their preferences into property prices. These results can be utilized by policy makers and stakeholders to illustrate the economic impact of lake water pollution so that the costs of conservation can be more accurately compared to costly treatment measures to repair affected lakes after they are polluted and to the costs of regulation to prevent further deterioration. The results also provide a framework for communicating water quality information to the public in a manner that relates personal observation and experiences to scientific data. In other words, while the general public may not understand the complexities of acid deposition or be aware of which lakes are acidified, they are likely to have observed differences in biodiversity of lakes where they have recreated. The success of environmental programs relies on public support which to a great extent is dependent on the ability of policy makers to draw on the personal experiences of the public to develop an effectively communication strategy regarding environmental policy. In this manner, this research provides tangible measures that can be utilized to help further public communication on water quality. Our results clearly demonstrate that homeowners in the Adirondacks are being financially impacted by air pollution that is generated outside of the park, perhaps lending support to the argument that the Mercury and Air Toxics Standards and other regulations are needed to protect more sensitive and

12 An alternate model was run by dropping the 14 observations that represent Estates. 12 of the 14 observations for Estates are waterfront properties. Dropping Estates did not alter the significance of any coefficients and had negligible impacts on the magnitude of coefficients. Dropping Estates did decrease the mean property value for the full dataset from $179,190 to $177,498 and the maximum property value decreased from $6.25 M to $5.8 M. In the waterfront dataset the mean property value dropped from $362,557 to $355,849. Since the altered model (without Estates) did not produce significantly different and do not change the results of the paper as presented, the authors have elected to retain the existing models presented in Tables 3–5. 13 Results presented for the full dataset census block fixed effects model present in Table 4 (M5). 14 Seasonal properties are those that are not equipped with heating and water for year-round use. 15 A combined regression, similar to Model 5 in Tables 3 and 4, was also run using the non-waterfront parcels. This model produced significant results for the presence of loons (0.009), poor pH (−0.19), and presence of milfoil (−0.079) and was generally consistent with the results for the full dataset and waterfront dataset. These results are not presented in the paper but are available by request from the authors. 16 In our study area the number of loons present on the lakes in our study area ranges from 0 to 28. Our dataset contains 144 lakes with loons and 3119 parcels transacted near these lakes.

14

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

ecologically diverse areas such as the Adirondacks. Through the adoption of more stringent controls on sulfur, nitrogen and mercury emissions, pollution in the Adirondacks should decrease, resulting in fewer acidic lakes, impaired water bodies, and more lakes with loons, fish and aquatic diversity. Similarly, our results provided some limited support for efforts to reduce the spread of milfoil to otherwise unaffected lakes as we see some evidence that such invasions do harm property values. Our results do not speak to the financial impacts on local businesses that rely on tourism or other broader economic impacts. It is realistic to assume, however, that if homeowners prefer unimpaired lakes with generally good water quality and populations of fish and loons then tourists visiting the Adirondacks also share the same preferences, not least because of the rental market. That is, sales prices of vacation homes are likely to reflect the rental values of those properties. So, it is not far-fetched to suggest that lower (higher) property values may in part be caused by decreased (increased) rental demand for properties with certain ecological characteristics. Tourism in the Adirondacks is a $900 million dollar industry that provides 17% of the total employment in the region (Tourism Economics, 2010). Additional research in this area would be beneficial to quantify the economic impacts of water pollution on local businesses in the Adirondacks, thereby providing a more comprehensive assessment of the impacts of declining water quality on the region. In the current economic climate, funding for conservation measures is heavily scrutinized and some policy makers argue that environmental protection is too costly to sustain. Through a deeper understanding of the value of conservation in the Adirondack Park, policy makers may be better equipped with the tools needed to communicate the importance of these programs and to more accurately and fully evaluate their costs and benefits, including often neglected externalities. If property owners know how water quality affects their personal investment, they may be willing to self-fund initiatives to prevent the spread of localized detriments like aquatic invasive species. Moreover, the residents of the Adirondacks may be willing to implement more protective measures to maintain loon breeding habitat, protect biodiversity, and minimize anthropogenic impacts which make the Adirondacks a truly unique public/private park. References Baker, J.P., Christensen, S., 1991. Effects of acidification on biological communities in aquatic ecosystems. Acidic Depos. Aquat. Ecosyst., 83–106. Banzhaf, S., Burtraw, D., Evans, D., Krupnick, A., 2006. Valuation of natural resource improvements in the Adirondacks. Land Econ. 82 (3), 445–464. Baranzini, A., Schaerer, C., Thalmann, P., 2010. Using measured instead of perceived noise in hedonic models. Transp. Res. D: Transp. Environ. 15 (8), 473–482. Bertrand, M., Duflo, E., Mullainathan, S., 2004. How much should we trust differences–indifferences estimates? Q. J. Econ. 119 (1), 249–275. Boyd, J., Krupnick, A., 2013. Using ecological production theory to define and select environmental commodities for nonmarket valuation. Agric. Resour. Econ. Rev. 42 (1), 1–32. Boyle, K.J., Poor, P.J., Taylor, L.O., 1999. Estimating the demand for protecting freshwater lakes from eutrophication. Am. J. Agric. Econ. 81 (5), 1118–1122. Brown, D., Goncharov, A., Paul, E., Simonin, H., Carpenter, D.O., 2010. The relationship between Adirondack lake pH and levels of mercury in yellow perch. J. Aquat. Anim. Health 22 (4), 280–290. Chattopadhyay, S., Braden, J.B., Patunru, A., 2004. Perceived environmental value in revealed data: evidence from the housing market. In: Presented at the Allied Social Sciences Association (AERE) Meetings, held in San Diego CA, January 3–5, 2004. Driscoll, C.T., Driscoll, K.M., Mitchell, M.J., Raynal, D.J., 2003. Effects of acidic deposition on forest and aquatic ecosystems in New York State. Environ. Pollut. 123, 327–336. Driscoll, C.T., Yan, C., Schofield, C.L., Munson, R., Holsapple, J., 1994. The mercury cycle and fish in the Adirondack lakes. Environ. Sci. Technol. 28, 136A–143A. Epp, D.J., Al-Ani, K.S., 1979. The effect of water quality on rural nonfarm residential property values. Am. J. Agric. Econ. 61 (3), 529–534. Gibbs, J.P., Halstead, J.M., Boyle, K.J., Huang, J., 2002. A hedonic analysis of the effects of lake water clarity on New Hampshire lakefront properties. Agric. Resour. Econ. Rev. 31 (1), 39–46. Greenstone, M., Gayer, T., 2009. Quasi-experiments and experimental approaches to environmental economics. J. Environ. Econ. Manage. 57 (1), 21–44. Gujarati, D.N., Porter, D.C., 2010. Essentials of Econometrics, fourth ed. McGraw-Hill Corporation Inc., New York, NY. Halstead, J.M., Michaud, J., Hallas-Burt, S., 2003. Hedonic analysis of effects of a nonnative invader (Myriophyllum heterophyllum) on New Hampshire (USA) lakefront properties. Environ. Manage. 30 (3), 391–398. Halvorsen, R., Palmquist, R., 1980. The interpretation of dummy variables in semilogarithmic equations. Am. Econ. Rev. 70 (3), 474–475. Heintzelman, M.D., Tuttle, C.M., 2012. Values in the wind: a hedonic analysis of wind power facilities. Land Econ. 88 (3), 571–588.

C.M. Tuttle, M.D. Heintzelman / Resource and Energy Economics 39 (2015) 1–15

15

Horsch, E.J., Lewis, D.J., 2009. The effects of aquatic invasive species on property values: evidence from a quasi-experiment. Land Econ. 85 (3), 391–409. Jenkins, J., Keal, A., 2004. The Adirondack Atlas. Syracuse University Press, Syracuse, NY. Jenkins, J., Roy, K.M., Driscoll, C., Buerkett, C., 2007. Acid Rain in the Adirondacks: An Environmental History. Cornell University Press, Ithaca/London. Kuminoff, N.V., Parmeter, C.F., Pope, J.C., 2010. Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities? J. Environ. Econ. Manage. 60 (3), 145–160. Leggett, C.G., Bockstael, N.E., 2000. Evidence of the effects of water quality on residential land prices. J. Environ. Econ. Manage. 39 (2), 121–144. Netusil, N.R., 2005. The effect of environmental zoning and amenities on property values: Portland Oregon. Land Econ. 81 (2), 227–246. Parmeter, C., Pope, J.C., 2011. Quasi-experiments and hedonic property value methods. In: John, A.L., Michael, K.P. (Eds.), Handbook on Experimental Economics and the Environmental. Edward Elgar Publishing. Poor, P.J., Boyle, K.J., Taylor, L.O., Bouchard, R., 2001. Objective versus subjective measures of water clarity in hedonic property value models. Land Econ. 77 (4), 482–493. Poor, P.J., Pessagno, K.L., Paul, R.W., 2007. Exploring the hedonic value of ambient water quality: a local watershed-based study. Ecol. Econ. 60 (4), 797–806. Rosen, S., 1974. Hedonic prices and implicit markets: product differentiation in pure competition. J. Polit. Econ. 82 (1), 34–55. Rothlisberger, J.D., Chadderton, W.L., McNulty, J., Lodge, D.M., 2010. Aquatic invasive species transport via trailered boats: what is being moved, who is moving it, and what can be done. Fisheries 35 (3), 121–132. Sanders, H.A., Polasky, S., 2009. The value of views and open space: estimates from a hedonic pricing model for Ramsey County, Minnesota USA. Land Use Policy 26 (3), 837–845. Schoch, N., Evers, D.C., 2002. Monitoring mercury in common loons: New York Field Report, 1998–2000. In: Report BRI 2001-01 Submitted to U.S. Fish & Wildlife Service and New York State Department of Environmental Conservation. Biodiversity Research Institute, Falmouth, ME. Simonin, H., Loukmas, J., Skinner, L., Roy, K., 2008, April. Strategic monitoring of mercury in New York State Fish. In: New York State Energy Research and Development Authority Report 08-11. Tourism Economics – An Oxford Economics Company, 2010. The Economic Impact of Tourism in New York State, Philadelphia, PA. Tuttle, C.M., Heintzelman, M.D., 2013. The value of forever wild: an economic analysis of land use in the Adirondacks. Agric. Resour. Econ. Rev. 42 (1), 119–138. US EPA, 1999. The Benefits and Costs of the Clean Air Act 1990–2010. Office of Policy Analysis, Washington, D.C. Walsh, P., Walter, M.J., Scrogin, D., 2011. The spatial extent of water quality benefits in urban housing markets. Land Econ. 87 (4), 628–644. Wooldridge, J.M., 2002. Econometric Analysis of Cross Section and Panel Data. The MIT Press, Boston, MA. Zhang, C., Boyle, K.J., 2010. The effect of aquatic invasive species (Eurasian watermilfoil) on lakefront property values. Ecol. Econ. 70 (2), 394–404.