### Do Homebuyers Differentiate between Tec hnical and Non-Technical Measures of Water Quality? Evidence from a He donic Analysis in South Florida Jeffrey Czajkowski (Corresponding author) Austin College 900 North Grand Avenue, Suite 61579 Sherman, TX 75090 jczajkowski@austincollege.edu 903-813-2815 Okmyung Bin East Carolina University Brewster Building A-435 Greenville, NC 27858-4353 bino@ecu.edu (252) 328-6820

August 2010

Abstract: While most water quality based hedonic models only employ a scientifica lly-based measure of water quality in their analysis, th ere is a recurring concern in th e literature that homebuyers may find these objective measures to be overly technical, no t representative of public perception, or not valid in driving actual home buyer behavior and consequently of little relevance in the home purchase decision and any associated welfare an alysis. We address th is issue by using both traditional objective measures of water quality as well as a less technical and hence more easily understood measure of water quality, “location gr ade,” available to homebuyers in an urban coastal housing market of South Florida. Our re sults indicate that water quality does in fact matter to the waterfront homebuyers, and that th is holds for both technical and non-technical measures of water quality. As the non-technical measure oste nsibly provides homebuyers a simple way to evaluate water quality, our results indicate that they likely use it simply as a warning sign when the location grade is withina failing range. In contrast, the technical measures of water quality appear to convey soph isticated information to homebuyers in assisting their house purchase decision. We further impute implicit prices for both the technical and nontechnical measures of water quality where mean willingness-to-pay (WTP) values for significant estimates range from $3,972 to $23,874.

JEL Classifications: Q25, Q51, R21, D60 Keywords: Hedonic Analysis, Water Quality, South Fl orida Real Estate, Indian River Lagoon South, Technical and Non-techni cal measures of water quality1 I. Introduction Hedonic property value models are used to determine whether a relationship exists between an environmental amenityand housing prices, and if so, to often impute implicit prices for the environmental amenity that can then pot entially be used for an associated welfare analysis. Although hedonic property value studies that utilize air or noise quality as the environmental amenity in the empirical analysis are more prevalent, a number of fairly recent studies that employ water quality as the primary environmental amenity of interest have been applied including Leggett and Bockst ael (2000), Michael et al. (2000) , Poor et al. (2001), Gibbs et al. (2002), Krysel et al. (2003) , Poor et al. (2007), Phaneuf et al. (2008), and Walsh et al., (2010). 1 While most of these studies only employ a scientifically-based measure of water quality, there is a recurring concern in the li terature that homebuyers may find these objective measures to be overly technical, not representati ve of public perception, or not valid in driving actual homebuyer behavior and cons equently of little re levance in the home purchase decision and any associated welfare analysis (Epp and Al -Ani, 1979; Michael et al. 2000; Poor et al., 2001; Lewis et al. 2008; Egan et al ., 2009). The purpose of this study is to address this issue by using in the hedonic analysis both traditional objectiv e measures of water quality as well as a less technical and hence more easily understood measur e of water quality, “location grade”, that is notably a weighted average of the objective m easures available to homebuyers in an urban coastal housing market of South Florida. Water clarity as an objective measure of water quality is most often used in assessing the amenity value of good water quality for waterfront and non-waterfront ho mes. Steinnes (1992), Boyle et al. (1999), Michael et al . (2000), Poor et al. (2001), Gibbs et al. (2002), Krysel et al.

1 A number of other studies utilize a measure of water quality in their hedonic analysis although it is not the primary focus of the research, e.g., Papenfus and Provencher (2005), Hodgkinson and Valadkhani (2009), Horsch and Lewis (2009).2 (2003) all investigate the effect of water clarity on lakefront hom e prices. Walsh et al. (2010) expand the analysis to understand the effect of water clarity on both lakefront and non-lakefront homes in the urban housing market of Orange Count y, Florida. Water clarity is frequently used as an objective measure in the hedonic analysis since it is likely to be perceived by homebuyers, as well as more readily understood, compared to ot her objective measures of water quality. The drawback to using this measure is that “the benefits of improvements in water clarity have ambiguous ecological merit” (Leggett and Bockst ael, pg. 122, 2000). A numb er of other studies have used other objective measures of water quality in the hedonic an alysis including pH (Epp and Al-Ani, 1979), fecal coliform (Leggett and Bockstael, 2000), total suspended solids and dissolved inorganic nitrogen (Poor et al., 2007), and total susp ended solids, total phosphorous, and dissolved oxygen (Phaneuf et al., 2008). While improvements in these other objective measures may have more ecological merit and may be more useful to waterbody managers,

variations in these meas ures tend to be less obs ervable and hence of le ss value to homebuyers (Leggett and Bockstael, 2000; Micheal et al., 2000). As such, subjective measures of water qual ity which are either more easily perceived by homebuyers, or more easily understood, have been us ed as an alternative to objective measures of water quality in the hedonic analysis. Davi d (1968) used subjective government ratings of poor, moderate, and good from the Department of Conservation and from the Committee on water pollution in her Wisconsin study. In add ition to the objective pH measure Epp and Al-Ani (1979) used a subjective measure of perceived water quality problem s distinct from the pH value obtained from telephone surveys. Poor et al. (2001) examined convergent validity between comparable objective and subjective measures of water clarity. They asked homebuyers to rate the water clarity at the time they purchased the home via mail surveys. Finally, Krysel (2003) in3 addition to using the water clarity objective m easure of water quality included a shoreland quality index developed through non-homebuyer site visits to the lakes in the analysis. Although subjective measures are meant to account for the obj ective measure issues, they potentially have their own drawbacks. If they are not connected to the objective measures in any meaningful way, although percep tible to homebuyers, they are not actionable by the relevant waterbody manager (Michael et al., 2000). In addition, surveys conducted to understand homebuyers subjective ratings are expensive and time-consuming to conduct, as well as fraught with their own inherent survey issues. In this study we use the measure of location grade – the percentage score of 96–100%, 86-95%, 76-85%, 70- 75%, and < 70% (with an associated letter grade of A, B, C, D, or F respectively) – which is subjective in nature being easily understandable by homebuyers; but also significantly addresses the aforementioned subjective measure drawbacks given that it is constructed directly from the associated objective measures of water quality and is easily accessible via the local newspapers and internet. In fact the water quality grading system was designe d precisely for these reasons – it “... gives the average reader a simple way to evaluate the rive r water quality” (Voisinet, 2006) We conduct our hedonic analysis on the urban coastal housing market of Martin County, FL which is predicated upon the waterbodies of the St. Lucie River, St. Lucie Estuary, and Indian River Lagoon. Similar to Poor et al. (2 007) we assume that water quality matters to homebuyers in this area given that the health of th ese waterbodies are an inte gral part of the local history as well as the environmental and eco nomic well-being of the community (SFWMDa, 2010; FLDEP, 2010; SFWMDb, 2010). Citing the well-being of the St. Lucie River and Estuary to the Florida economy and quality of life, Gove rnor Crist recently pledged continued funding (FLDEP, 2010) of the $1 billion Indian River La goon South (IRLS) Everglades restoration effort4 that is currently being conducted to improve wate r quality in this area. In fact the IRLS restoration effort is only one of three major efforts currently underway to improve the water quality of the river, estuar y, and lagoon (SFWMDb, 2010). More over, Martin County residents are “outraged over the systematic destruction of the St. Lucie River and Estuary” (CFPBMC, 2010) and a 2006 poll conducted for the Community Foundations for Palm Beach and Martin Counties (Princeton Survey Research Associates International, 2006) found that eight out of ten Martin County residents indicate protecting the natural environment as the number one priority of Martin County. Using weekly water quality da ta collected by the Florid a Oceanographic Society, we compare hedonic property value model results usi ng the measures of wate r visibility (water clarity), temperature, pH, salinity, and dissolved oxygen to the results us ing the water quality measure of location grade in order to determine if: 1) water quality matters to coastal waterfront homebuyers in Martin County, FL as we have assumed; and 2) whether these homebuyers

differentiate between a techni cal and non-technical measure of water quality. Our results indicate that water quality does in fact matter to Martin County waterfront homebuyers, and that

this holds for both technical and non-technical measures of wate r quality. However, there is

some distinction in how the t echnical and non-technical measures are used in the home purchase decision. As the non-technical measure ostensibly provides homebuyers a simple way to evaluate water quality in their home purchase decisi on, our results indicate that they likely use it simply as a warning sign in their house purchas e decision when the location grade is within a failing range. In contrast, the technical measur es of water quality seem to provide relevant information to homebuyers in assisting their house purchase decision. Higher values of all of the technical measures, excluding diss olved oxygen, increase property values significantly. These5 results indicate that waterfront homebuyers in this market appear to be rela tively sophisticated in terms of their understanding and use of the vary ing technical water quality measures. We hypothesize that the constructi on of the non-technical grade wa ter quality score may also potentially assist in forming this level of s ophistication – i.e., knowledge is gained of the individual parameters in trying to effectivel y understand the overall score. We further impute implicit prices for both the tec hnical and non-technical measures of water quality where mean willingness-to-pay (WTP) values for significan t estimates range from $3,972 to $23,874. These WTP results are potentially notable given th e $1 billion Indian River Lagoon South (IRLS) Everglades restoration effort be ing conducted to improve water quali ty in this area, and the lack of any formal economic benefit analysis conducted to-date (USACE/SFWMD, 2002). This paper is organized as follows: Section II provides an overview of the study area and associated data used; Section III presents the em pirical model; Section IV gives the results; and Section V has the concluding comments.

II. Study Area and Data Martin County is located on the Southeastern Atlantic coast of Florida. The Northeastern portion of the county and its accompanying waterfro nt housing market located on the St. Lucie River, St. Lucie Estuary, and Indian River Lagoon, are analyzed for this study. Analogous to Leggett and Bockstael (2000), this area is well-suited for a hedonic an alysis of water quality due to the substantial and sufficiently varied numbe r of waterfront properties, the lively housing market in the area, as well as the variation in water quality across the St. Lucie River, St. Lucie Estuary, and the Indian River Lagoon.6 The specific home sale data used for the anal ysis are sales of wa terfront properties

located in the Northeastern portion of Martin County from January 2000 to August 2004 as supplied by the Martin County pr operty appraiser’s office. The sales price is adjusted to 2 nd quarter 2004 values 2 using the Office of Federal Housi ng Enterprise Oversight (OFHEO, 2004) housing index for the Fort Pierce-Port St . Lucie Metropolitan Statistical Area. 3 Table 1 provides the summary statistics for housing sales price in 2004 Q2 values as well as for each of the

variables included in th e empirical analysis. A total of 991waterfront property sales were

collected over this timeframe. After deleting the obs ervations with incomplete information, a total of 510 single family residential home sales ar e included in our data. All the relevant data are stored in a geographical information sy stems (GIS) for our spatial analysis. Water quality data for the Martin County wa terfront area is tracked on a weekly basis by the Florida Oceanographic Society (FOS, 2004) for nine separate locations as depicted in the upper-right hand corner of Figure 1. Beside the public availability of data on the FOS website, this data is also published week ly in the local newspapers and therefore it is reasonable to consider it a known, important, and readily available source of wa ter quality data. The specific water quality locations tracked and used for this study include 2. North Fork, 3. South Fork, 5. Wide Middle River, 6. Narrow Middle River, 7. Manatee Pocket, and 8. Inlet Area. 4 Figure 1 additionally presents an example of the weekly WQ data available by location. A total of five distinct objective water quality variables are collected and published weekly – temperature, pH, water visibilit y, salinity, and dissolved oxygen (DO); as well as one objectively

2 Despite having July and August 2004 sales data, a 2 nd -quarter index is chosen (the last complete quarter of home sales) such that April through August 2004 home sale prices were not adjusted. 3 It should be noted that this index did experience a significant overall increase of 56% between 3 rd quarter 2001 and 2 nd quarter 2004 as compared to a 15% increase from 4 th quarter 1998 to 3 rd quarter 2001, a prior but similar length of time. These results do raise some concerns in regard to the equilibrium assumptions of the hedonic model. 4 Location 1. Winding North Fork is a part of St. Lucie County for which home sales were not collected. No home sales from the original data were provided for location 4. Winding South Fork, and location 9. IRL had significant water quality issues consistently throughout the study time period7 based, yet less technica l, compiled measure of water quality – location grade. All of the distinct objective water quality measures, excluding temperature, are brie fly explained in the weekly published data with water visibility and disso lved oxygen given corres ponding labels of poor, fair, and good over specified range s of values; and pH and salinity given corresponding labels of poor (above or below range) or good over specified ranges of values. These labels of poor/above or below range, fair, and good for each of the four measures excluding temp erature are then used to calculate an equally weighted location grade percentage value. For example, from Figure 2 WQ area 2. North Fork had pH va lue of 8.0 = “good”, a water visibi lity value of 36.4% = “fair”, a salinity value of 7.4 ppt = “good”, and a DO value of 7.9 mg/L = “good”. Good, fair, and poor/above or below range values are equal to 100%, 52%, and 0% respectively. Therefore the location grade percentage value for North Fork during this week is de termined by the equally weighted average of (100%+ 52%+100%+100%)/4 = 88%, or a B corresponding letter grade (Voisinet, 2006). In this wa y, location grade capture s the variations in the otherwise less observable yet more ecologically meaningful objective measures of WQ such as DO, salinity, and packages them in less technical, more famili ar, and hence more understandable format for residents and homebuyers to use. Conseque ntly, we collect and use the location grade percentage value, in addition to the traditio nal and more technical objective measures of temperature, pH, water visibi lity, salinity, and dissolved oxygen in our hedonic analysis. 5 Each home sale is assigned to a water qual ity location by viewing the actual location of the home through the use of both the constructe d GIS database and Martin County property

5 As can be seen in Figure 2, not all locations report WQ data every week. Significant gaps occurred with 6. Narrow Middle River, 7. Manatee Pocket, and 8. Inlet Area in 200 4, as well as 8. Inlet Area for 2000, 2001, and 2003. Consequently, corresponding location waterfront home sales during these time periods were excluded from the analysis.8 appraiser website (MCPA, 2004) overlaid upon the given water quality location map. 6 The associated WQ data for each location is then assigned to each home by the date of the sale. Following the literature, we use th e median annual value during the year of the sale for our WQ measures in the empirical analysis. There are 16 homes located directly in the middle of the North and South Forks that are assigned to a cr eated location of 2.5 with associated WQ data taken to be the average of the two North and S outh Forks locations. Figure 2 presents the home sales as per their assigned WQ locations from our GIS database. As shown in Table 1, there exists sufficient variation in the WQ data across locations. We also include land square f ootage, home square footage, and the number of bathrooms in the estimated hedonic price functions. These thr ee structural variables ar e all squared to allow for non-linearity in them. Being that these are wa terfront homes, a series of dummy variables are

also included to indicate whether the house has a pool, boat lift, dock, etc 7 . Other dummies

indicated whether the house ha d more than one structure 8 and notably the type of exterior wall (1=concrete block) due to the fr equency of hurricanes in Florid a. Three nei ghborhood variables are selected in order to capture ethnicity (percen t white), age (percent older than 65), and socioeconomic demographics (percent of household owners). This neighborhood information is obtained from the 2000 census data (FGDL, 2004), a nd using the constructed GIS database, each home is spatially joined to its corresponding census tract leve l. Lastly, we also include the mortgage interest rate 9 which is commonly thought to be one of the main drivers of housing sales

6 Our visual approach minimizes potential errors from using the Euclidean distance. For example, some houses located on St. Lucie Estuary could be closer to a monitoring site on Indian River Lagoon. It represents a unique feature of the landscape in the study area. 7 These data were listed under improvements on the property appraiser website and no date for the improvement was given. Therefore, it was assumed that if an impr ovement was listed it was there as the time of sale. 8 If the home did have more than one structure, the square footage and number of baths were summed and the other dummy variables were taken from the largest structure listed. 9 Monthly national interest rate for a 30 year fixed rate mortgage as provided by HSH Associates (HSH, 2004)9 and prices. Despite the significance of interest rates in purchasi ng a home, this is typically not

included in the environmental hedonic mode ls reviewed in the literature.

III. Model Estimation Hedonic price models are based on the intuit ive notion that the co mponent values of various attributes of heteroge neous goods are reflected in pric e differentials (Rosen 1974). Hedonic property price models use observations on property values, t ypically residential properties, to infer values for non-traded attribut es such as environmental quality. We assume that the housing supply is fixed and the prices of existing houses are demand determined. 10 The equilibrium hedonic price f unction is represented by: ) , , ( w n s P P  , [1]

where P is the property price, which is a f unction of structural characteristics, s , neighborhood characteristics, n , and water quality, w . The hedonic price function emerges from competitive bidding among housing buyers when housing supply is taken as given. The housing market is in equilibrium when buyers have maximized their utility, ) , , , , ( α y w n s U u  , subject to a budget constraint, y w n s P m   ) , , ( , where U is a strictly concave util ity function with the usual properties, y is the non-housing numeraire good, m is consumer income, and α is the vector of variables representing demographic factors, knowledge of water quality, and water quality management expectations. Assuming that P (  ) is continuously differentiable, the first derivative of [1] with respect to any con tinuous attributes produces an estimate of the representative households’ marginal willingness to pay for an additional unit of that attribute:

10 The fixed housing supply is a common assumption in the sh ort-run or for situations where the stocks of existing houses dominate the market (Palmquist 2004).10 w n s z z w n s P

z y w n s U

, , , ) , , ( ) , , , , (

  

    α [2]

where  is the marginal utility of income. Considerable attention has been given to examining spatial dependence in estimated hedonic price functions (Kim, Phipps, and Anse lin 2003; Bin et al. 2008; Carruthers and Clark 2010). Spatial dependence arises because houses in a neighborhood share similar location amenities or because they have similar structur al characteristics due to similar timing of construction (Anselin and Bera 1998). The existe nce of spatial dependence implies that a sample contains less information than an uncorrelated one, and that the loss of information should be acknowledged in estimation to prop erly carry out statistical infe rence. Regression diagnostics based on Ordinary Least Squares (OLS) estimati on and the Lagrange Multiplier (LM) test statistics suggest the first-order spatial erro r hedonic model which is given in equation [3]. 11

, u w n s P ln

k k k j j j i i i

        

##   

        [3]

where ln P is the log of sales price,  ,  ,  , and  are the unknown parameters to be estimated,  is an independent random error term,  is the spatial autoregressive coefficient, Π is the spatial weighting matrix, and u is a vector of independent and identically distributed random error terms. This model assumes that one or more omitted variables in the hedonic equation vary spatially, and thus the error terms are spatially autocorrelated. In this specification, the OLS

11 Alternative models to incorporate the spatial dependence include a spatially lagged dependent variable model, which assumes that the spatially weighted sum of neighborhood housing prices enters as an explanatory variable in the hedonic price function. Failing to account for spatial lag dependence leads to biased and inconsistent parameter estimates, whereas failing to account for spatial error depe ndence leads to inefficien cy. The robust Lagrange Multiplier (LM) tests for Table 2 showed no spatial lag dependence (  2 = 0.002; p-value = 0.967) but indicated spatial error dependence (  2 = 3.190; p-value = 0.074). For all columns in Table 3, the robust LM test also suggested the spatial error models. Ne ither the spatial lag nor the general m odel with both spatial lag and error terms were significant in any models considered.11 estimator remains unbiased but is no longer efficient due to the nonspherical error covariance. Efficient estimators are obtained by utilizing th e particular structure of the error covariance implied by the spatial process. The spatial autoregressive error models are estimated via maximum likelihood (ML). The estimation is im plemented within the GeoDa v.0.9.5-i (2004) environment in conjunction with ArcView GIS 3.3 extensions. The elements of the weighting matrix are bi nary indicators that identify observations within a neighborhood:  ij = 1 when observations i and j are neighbors and  ij = 0 otherwise. By convention, the diagonal elements of the weighti ng matrix are set to zero and row elements are standardized such that they sum to one, facilitating the interpretation of results derived from the weighting matrix as an average of nei ghborhood values. The neighborhood is usually determined by distance contiguity—t he binary indicator is set to ‘1’ if two corresponding houses are within some specified distan ce of one another. Using met hods suggested Anselin and Bera (1998), we experimented with di fferent weights matrices, and in this analysis use a spatial weighting matrix that identifies properties within a 0.1 mile as nonzero elements. This spatial weighting matrix was based on a comparison of the fits for several alternative specifications using a range of distances. The distance cut-off of 0.1 mile resulted in overall the best fit and the regression results reported hereafter are based on this weighting matrix.

IV. Results Recall that in estimating the first-order spatia l error hedonic model of [3], the log of sales price is used as the dependent variable acros s all of our models. Table 2 provides the ML estimation results of the spatial autoregressi ve hedonic model for the nontechnical WQ measure using location grade percentage value. We expe rimented with several alternative specifications12 and found that the primary results were robust to alternative functional forms. Estimation results exhibit a significant spatial au toregressive coefficient ( λ = 0.139), suggesting that a spatial dependence in our primary sample of housing prices indeed exists. Intuition would suggest that homebuyers interpret any “poor” water quality de signation – or location gr ade percentage value at or below 75% corresponding to a D or F le tter grade – as a warn ing sign in their house purchase decision, but could be indifferent between higher percen tage grades. This intuition seems to be especially plausible given the varied amount of weighted technical information contained in the score, as well as the relativ ely tight grading scale employed where only 25 percentage points separate an A from an F. 12 Consequently, we use a quadratic specification for location grade in addition to the other continuous va riables such as lot size , total structure square footage, and number of bathrooms in order to capture any non-linear effects. Regression coefficients for the location grade variables have expected signs and are st atistically significant. These results indicate that water quality does in fact matter to coastal waterfront homebuyers in Martin County, FL, and that the non-technical version of location grade ostensibly provides homebuyers a simple way to evaluate water quality in their home purchase decision. Moreover, the quadratic specification seems to capture the diminishing marginal effects of the location grade variable where our results indicate that hi gher location grade percen tage values increase property values significantly , but at a decreasing rate. 13 It is interesting to examine which part of the water quality matters to homebuyers the most. To address this question we look at tech nical WQ measures separa tely. Table 3 provides the ML estimation results of the spatial autoregr essive hedonic models for five technical WQ

12 Starting in 2006, a new grading methodology was employed including a wider percentage grading scale (Voisinet, 2006) 13 In runs of our model using only a linear specification of location grade, this non-technical measure of water quality was not statistically significant.13 measures entered separately into each equation - water visibility, temperature, pH, salinity, and dissolved oxygen. 14 Again, we experimented with seve ral alternative speci fications and found that the primary results were robust to alternative functional forms. 15 The estimation results exhibit significant sp atial autoregressive coefficients ( λ = 0.144, 0.148, 0.135, 0.148, and 0.143 respectively), suggesting that a spatial dependence in our primar y sample of housing prices indeed exists. Regression coefficients for th e WQ variables have e xpected signs and are statistically significant for all coefficients except dissolved oxygen, indicating once more that water quality does in fact matter to coastal wa terfront homebuyers in Martin County, FL. Moreover, these results indicate th at waterfront homebuyers in this market appear to be relatively sophisticated in term of their understanding a nd use of the varying t echnical water quality

measures. Outside of the fact that the waterbodies are an integral part of the local history as well as the environmental and economic well-being of the community, we hypothesize that the construction of the non-technical grade water qualit y score may also potentia lly assist in forming this level of sophistication. As seen in the literature, water clarity/vis ibility is one of the most used technical measures of water quality in a hedonic analys is due to its likely perception and ease of understanding by homebuyers. Further, Voisinet (2006) suggests that water visibility and salinity are the two key water quality measures in indicating the health of the waterbodies in the study area. Thus, we are particularly interested in the water visibility an d salinity results. Both higher water visibility and salinit y have a positive association with the house sales price and their

14 We also estimated the model with all of the water qua lity measures together, except for location grade, as buyers may care about multiple measures of water quality. Due to the high correlations, the individual measures were largely insignificant although they were collectively significant. Combined with the results from the collective water quality measure (i.e., location grade), the individual parameter estimates should reflect component values of the water quality to potential homebuyers in the urban coastal housing market. 15 Runs of our models including a quadratic specification for the technical water quality measures analogous to the location grade estimation provided results that the linear specification was preferred due to insignificance of the quadratic terms.14 coefficients are significant at the 1% level. The pH scale may go from 1 to 14, but the pH of natural water ranges between 6.5 and 8.5, where all our observations locate. The pH value in a productive water body will range between 7.5 and 8. 5 and in less productive waters between 6.5

and 7.5. Our results in dicate that the higher pH is associated with higher water front property values, indicating that the abundant aquatic li fe may be valued by home buyers. Dissolved oxygen and water temperature are negatively co rrelated and normally exhibit high seasonal variations. However, they are relatively steady in our study area and within a tolerance range for most fish and aquatic life. Water temperature coefficient is positive and significant at 5% level while dissolved oxygen concentration is insigni ficant at any conventi onal level. Adding credence to this insignifican t dissolved oxygen result, Voisinet (2006) found th at dissolved oxygen rarely falls into the range of values deemed poor, and if this did occur it wa s a relatively short-lived phenomenon. We impute implicit prices for both the tec hnical and non-technical measures of water quality, with Table 4 reporting the estimated ma rginal willingness to pay (WTP) for the various WQ measures. A bootstrapping procedure is us ed to generate confidence intervals for the marginal willingness to pay (Krinsky and R obb 1986). The procedure generates 5,000 random

variables from the distribution of the estimated parameters and computes 5,000 marginal WTP estimates. The marginal WTP estimates are sort ed in ascending order, and the 95% confidence bounds are found by dropping the top and bottom 2.5% of the estimates. We find that mean WTP to increase location grade by one point is $3, 972, evaluated at the mean value of sales price and the location grade of 70%. Lower and uppe r 95% confidence interval estimates are $3,375 and $4,554, respectively. Increasing water visibil ity by one percent resu lts in $5,263 increase in average property value, ceteris paribus . The 95% lower bound and upper bounds for WTP for15 visibility are $1,843 and $8,719, re spectively. The mean WTP for water temperature, evaluated at the mean sales price, is $23,752 with a relativ ely wide 95% confidence interval. Given a small range of pH values, the marginal change in defined as a one-tenth increase. The mean WTP to increase pH level by a one-tenth point is $11,09 0. Also, the mean WTP for salinity is $23,875 with a lower bound of $16,870 and upper bound of $30,946. While we are primarily interested in the result s of our water quality variables, we briefly

discuss results from the structur al and neighborhood variables used. Across all specifications of the different WQ measures, lot si ze, total structure square foot age, the dummy variables for a pool/patio enclosure and an in-ground pool, and inte rest rate are consistently significant at the 1% level with the expected signs. Given the pr ominence of hurricane thr eats in this area, the non-significance of our concrete bl ock exterior wall dummy variable is surprising. The fact that most our neighborhood variables ar e insignificant is also unexpect ed, although the percentage of the population older than 65 is sign ificant at the 5% or 10% level in some of the regressions. Lastly, the insignificance of the water features of these waterfront homes such as a boat lift or dock is notable. As the semi-log functional form is used for estim ation, the estimated coefficient is interpreted as the percentage change in th e mean sales price for an additional unit of the variable. For example, the existence of in -ground pool increases a home’s value in our study area by approximately 10-15% per structure while a pool/patio enclosure lowe rs property values.

V. Conclusions This study utilizes a unique WQ dataset for an urban coastal housing market in Martin County, Florida to find that various WQ measures affect waterfront housing prices in this setting. Using weekly water quality data collected by the Florida Oceanographic Society, we compare16 hedonic property value model results using the obj ective and more technical measures of water visibility (water cl arity), temperature, pH, salinity, and dissolved oxygen with the results using a less technical and hence more easily understood m easure of water quality, location grade. Our results indicate that water quality does in f act matter to the waterfront homebuyers in South Florida, and that this holds for both technical and non-technical measures of water quality. We impute implicit prices for both the technical an d non-technical measures of water quality where mean willingness-to-pay (WTP) values for signifi cant water quality measures range from $3,972 to $23,874. These WTP results are potentially notable given the $1 billion Indian River Lagoon South (IRLS) Everglades restorati on effort being conducted to improve water quality in this area, and the lack of any formal economic benef it analysis conducted to -date (USACE/SFWMD, 2002).

There is some distinction in how the technical and non-technical measures are used in the home purchase decision. Our results suggest th at homebuyers use the non-technical measure as a warning sign when the location grade is within a failing range. It is an advantage of using a more easily understood yet scientifically ba sed objective measure of water quality over a traditional pure objective measure of water quality that may not be as easily understood by homebuyers. In contrast, waterfront homebuyer s in this market appear to be relatively sophisticated in term of their understanding a nd use of the varying t echnical water quality measures. Higher values of all of the technical measures of water quality, excluding dissolved oxygen, increase property values significantly. Our results con tinue to advance the current notion in the WQ hedonic literature that more efforts need to be aimed at understanding what particular WQ variable to be included in the analysis.17 REFERENCES Anselin, L., and A. Bera. 1998. “Spatial Dependence in Linear Regression Models with an Introduction to Spatia l Econometrics.” In Handbook of Applied Economic Statistics , eds A. Ullah and D. Giles. Bin, O., T. Crawford, J.B. Kruse, and C.E. Landry. 2008. “Flood Prone with a View: Coastal Housing Market Response to Risk and Amenity.” Land Economics , 84: 434–48. Boyle, K., Poor, J., Taylor, L., 1999. “Estimatin g the Demand for Protec ting Freshwater Lakes from Eutrophication”, American Journal of Agricultural Economics , 81:5:1118-1122. Carruthers, J., and D. Clark. Valuing E nvironmental Quality: A Space-Based Strategy, Journal of Regional Science, in press

. CFPBMC (Community Foundation for Palm Beach and Martin Counties), Retrieved 2010, “Problems in Paradise: Getting Along with Mother Nature”, http://www.cfpbmc.org/newsarticle.cfm ?articleID=114314&page id=27493&SiteID=183 7&PTSIDEBAROPTID=0 David, E., 1968. “Lakeshore Property Values: A Gu ide to Public Investment in Recreation”, Water Resources Research , 4:4:697-707. Egan, K., Herriges, J., Kling, C., Downing, J ., 2009. “Valuing Water Quality as a function of Water Quality Measures”, American Journal of Ag ricultural Economics , 91:1:106-123. Epp, D., Al-Ani, K.S., 1979. “The Effect of Water Quality on Rural Nonfarm Residential Property Values”, American Journal of Agricultural Economics , 61:529-534. FLDEP (Florida Department of Environmental Protection), Retrieved 2 010. “Governor Crist Reaffirms Commitment to Everglades Restor ation during Tour of St. Lucie River and Estuary”, http://www.dep.state.fl .us/secretary/news/2010/03/0318_01.htm FOS (Florida Oceanographic Society) St. Lucie River Estuary Water Quality Data, Retrieved October 2004 to January 2005 from http: //www.floridaoceanogra phic.org/water.htm Gibbs, J., Halstead, J., Boyle, K., Huang, J., 2002. “An Hedonic Analysis of the Effects of Lake Water Clarity on New Hampshire Lakefront Properties”, Agricultural and Resource Economics Review , 31:1:39-46. Hodgkinson, A., Valadkhani, A., 2009. “Community Valuations of Environmental Quality in Coastal Lakes: Lake Illawarra Case St udy”, University of Wollongong Working Paper 09-03. Horsch, E., Lewis, D., 2008. “The Effects of Aq uatic Invasive Species on Property Values: Evidence from a Quasi-Random Experiment”, Land Economics , 85:3:391-409.18 HSH Associates, Retrieved N ovember 2004 from www.hsh.com Kim, C., T. Phipps, and L. Anselin. 2003. “Measuring the Benefits of Air Quality Improvement: A Spatial Hedonic Approach.” Journal of Environmenta l Economics and Management , 45: 24-39. Krysel, C., Boyer, E., Parson, C., Welle, P., 2003. “Lakeshore Property Values and Water Quality: Evidence from Property Sales in the Mississippi Headwaters Region”, Submitted to the Legislative Commission on Minnesota Resources by the Miss issippi Headwaters Board and Bemidji State Universtiy. Leggett, C., Bockstael, N., 2000. “Evidence of the Effects of Water Quality on Residential Land Prices.” Journal of Environmental Economics and Management , 39:121-144. Lewis, L., Bohlen, C., Wilson, S., 2008. “Dam s, Dam Removal, and River Restoration: A Hedonic Property Value Analysis” Contemporary Economic Policy , 26:2:175-186. MCPA (Martin County Property Appraiser), Re trieved October to November 2004 from http://paoweb.martin.fl.us/ Michael, H., Boyle, K., Bouchard, R., 2000. “Doe s the Measurement of E nvironmental Quality Affect Implicit Prices Estim ated from Hedonic Models?”, Land Economics , 76:2:283- 298. OFHEO (Office of Federal Housing Enterprise Oversight), Retrieved November 2004 from http://www.ofheo.gov/index.asp Palmquist, R. "Property Value Models," in K-G Maler and J. Vincent, eds., Handbook of Environmental Economics , volume 2, North Holland, 2004. Papenfus, M., Provencher, B., 2005. “A Hedonic Analysis of Environmental Zoning: Lake Classification in Vilas Count y, Wisconsin”, University of Wisconsin-Madison Working Paper. Phaneuf, D., Smith, V.K., Palmquist, R., Pope, J., 2007. “Integrating Prope rty Value and Local Recreation Models to Value Ecosystem Services in Urban Watersheds”, Land Economics, 84:3:361-381. Poor, P., Boyle, K., Taylor, L., Bouchard, R., 2001. “Objective versus Su bjective Measures of Water Clarity in Hedonic Property Value Models”, Land Economics , 77:4:482-493. Poor, P.J., Pessagno, K., Paul, R., (2007). “Expl oring the Hedonic Value of Ambient Water Quality: A Local Watershed-based Study”, Ecological Economics, 60:797-806. Princeton Survey Research Associates Internatio nal, 2006. “Problems in Paradise: The People of19 Palm Beach and Martin Counties Speak Up”, Report Prepared for the Community Foundation for Palm Beach and Martin Counties. Rosen, S. 1974. Hedonic Prices and Implicit Ma rkets: Product Differe ntiation in Pure Competition. Journal of Political Economy 82(1): 34-55. SFWMDa (South Florida Water Management Dist rict), Retrieved 2010. “St. Lucie Estuary Overview”, http://www.sfwmd.gov/portal/pag e/portal/levelthree/Coastal%20Watersheds SFWMDb (South Florida Water Management Distri ct), Retrieved 2010, “Foc us on the St. Lucie River”, http://www.sfwmd.gov/portal/page/portal/xrepo sitory/sfwmd_reposit ory_pdf/stlucie.pdf Steinnes, D., 1992. “Measuring the Economic Value of Water Quality”, Annals of Regional Science, 26:171-176. Stevenson, S., 2004. “New Empirical Evidence on Heteroscedasticity in Hedonic Housing Models”, Journal of Housing Economics , 13:136-153. Taylor, L., 2003. The Hedonic Method. In P. Champ, K. Boyle, and T. Brown (Eds.), A Primer on Nonmarket Valuation (pgs. 331-393). Kluwer A cademic Publishers. USACE/SFWMD (United States Army Corps of E ngineers, South Florida Water Management District), 2002. Central and Southern Florida Proj ect Indian River Lagoon – South (IRLS) Feasibility Study, Final Integr ated Feasibility Re port and Supplemental Environmental Impact Statement

. Voisinet, B., 2006. “New Grading for the St. Lu cie Estuary”, Florida Oceanographic Society Memo Walsh, P., Milon, J.W., Scrogin, D., 2010. “The Spatial Extent of Wate r Quality Benefits in Urban Housing Markets”, Nati onal Center for Environmental Economics Working Paper #10-0220 Table 1. Summary Statis tics for the Hedonic Data Variable Mean Std Dev Minimum Maximum House sales price in 2004 Q2 values 937,294.72 851,731.89 89,907.39 7,224,467.27 Sold in 2001 (=1) 0.21 0.40 0.00 1.00 Sold in 2002 (=1) 0.22 0.41 0.00 1.00 Sold in 2003 (=1) 0.21 0.41 0.00 1.00 Sold in 2004 (=1) 0.13 0.33 0.00 1.00 Lot square footage (in thousands) 26.22 45.22 2.60 883.69 Total housing square footage (in thousands) 2.69 1.50 0.56 13.39 Number of bathrooms 2.75 1.18 1.00 10.00 Concrete block exterior walls (=1) 0.53 0.50 0.00 1.00 Fireplace (=1) 0.37 0.48 0.00 1.00 Pool/patio enclosure (=1) 0.24 0.42 0.00 1.00 In-ground pool (=1) 0.54 0.50 0.00 1.00 Boat lift (=1) 0.33 0.47 0.00 1.00 Waterfront dock (=1) 0.77 0.42 0.00 1.00 Home special feature (=1) 0.29 0.45 0.00 1.00 Percent of population that is white  94.98 7.29 41.72 99.08 Percent of population that is age 65 or over  32.76 7.99 14.81 51.30 Percent of households that are owner occupied  80.67 8.91 36.59 92.57 National 30 year fixed interest rate 6.93 0.88 5.43 8.71 Location grade (%) 80.82 7.81 63.00 88.00 Water visibility (%) 49.10 13.84 31.20 77.80 Water temperature (  F) 75.79 2.82 71.60 80.60 pH 8.01 0.12 7.80 8.20 Salinity (ppt) 15.75 7.41 1.00 30.40 Dissolved oxygen (mg/L) 6.42 0.49 5.70 7.70 Notes: The number of observations is 510. Asterisk (  ) denotes the 2000 census track level data. The national 30 year fixed interest rate is measured at the month and year of sale.21 Table 2. ML Estimation Results for the Spatial Hedonic Model – Nontechnical WQ Measure

Variable Coefficient Std. Error p-value Constant 10.108 a 2.442 0.0000 Sold in 2001 (=1) -0.168 c 0.098 0.0854 Sold in 2002 (=1) 0.011 0.121 0.9291 Sold in 2003 (=1) -0.207 0.166 0.2140 Sold in 2004 (=1) -0.117 0.160 0.4657 Lot square footage 0.005 a 0.001 0.0000 Lot square footage squared -5.61e-06 a 1.43e-06 0.0001 Total housing square footage 0.486 a 0.048 0.0000 Total housing square footage squared -0.027 a 0.005 0.0000 Number of bathrooms 0.099 0.071 0.1617 Number of bathrooms squared -0.008 0.009 0.3674 Concrete block exterior walls (=1) -0.001 0.040 0.9715 Fireplace (=1) -0.005 0.045 0.9042 Pool/patio enclosure (=1) -0.176 a 0.053 0.0009 In-ground pool (=1) 0.122 a 0.046 0.0079 Boat lift (=1) 0.039 0.042 0.3547 Waterfront dock (=1) 0.055 0.048 0.2494 Home special feature (=1) 0.024 0.044 0.5947 Percent of population that is white -0.001 0.004 0.7631 Percent of population that is age 65 or over -0.003 0.003 0.3295 Percent of households that are owner occupied 0.002 0.003 0.5822 National 30 year fixed interest rate -0.231 a 0.067 0.0006 Location grade 0.103 c 0.062 0.0951 Location grade squared -0.001 c 4.01e-04 0.0777 LAMBDA 0.139 b 0.054 0.0106 Log likelihood -288.792 Akaike info criterion 625.584 Schwarz criterion 727.209 Notes: Dependent variable is natural log of sales price. Superscripts a, b, and c deno te significance at the 0.01, 0.05, and 0.10 levels, respectively.22 Table 3. ML Estimation Results for the Spatial Hedonic Model – Technical WQ Measures

Variable Water Visibility Water Temperature pH Coeff. Std. Error Coeff. Std. Error Coeff. Std. Error Constant 13.344 a 0.646 11.715 a 1.094 3.961 b 1.865 Sold in 2001 (=1) -0.056 0.092 -0.091 0.091 -0.042 0.089 Sold in 2002 (=1) -0.020 0.118 -0.141 0.125 -0.137 0.117 Sold in 2003 (=1) -0.057 0.161 -0.207 0.164 -0.093 0.156 Sold in 2004 (=1) -0.087 0.160 -0.116 0.159 -0.175 0.155 Lot square footage 0.005 a 0.001 0.005 a 0.001 0.004 a 0.001 Lot square footage squared -5.19e-06 a 1.43e-06 -5.10e-06 a 1.45e-06 -4.10e-06 a 1.42e-06 Total housing square footage 0.467 a 0.048 0.483 a 0.048 0.439 a 0.047 Total housing square footage squared -0.026 a 0.005 -0.027 a 0.005 -0.024 a 0.005 Number of bathrooms 0.107 0.070 0.104 0.070 0.109 0.069 Number of bathrooms squared -0.009 0.009 -0.008 0.009 -0.010 0.009 Concrete block exterior walls (=1) 0.012 0.040 0.015 0.040 0.028 0.039 Fireplace (=1) -0.006 0.044 -0.007 0.044 -0.008 0.043 Pool/patio enclosure (=1) -0.146 a 0.053 -0.154 a 0.053 -0.143 a 0.051 In-ground pool (=1) 0.138 a 0.046 0.136 a 0.046 0.138 a 0.045 Boat lift (=1) 0.045 0.042 0.045 0.042 0.046 0.041 Waterfront dock (=1) 0.068 0.047 0.066 0.047 0.057 0.046 Home special feature (=1) 0.007 0.044 0.009 0.044 -0.004 0.043 Percent of population that is white -0.001 0.004 -1.69e-04 0.004 0.003 0.004 Percent of population that is age 65 or over -0.007 b 0.003 -0.005 c 0.003 -0.007 b 0.003 Percent of owner occupied 0.005 0.003 0.003 0.003 0.004 0.003 National 30 year fixed interest rate -0.232 a 0.067 -0.231 a 0.067 -0.224 a 0.066 Water quality 0.006 a 0.002 0.025 b 0.011 1.183 a 0.212 LAMBDA 0.144 a 0.054 0.148 a 0.054 0.135 b 0.054 Log likelihood -286.969 -288.638 -276.181 Akaike info criterion 619.939 623.277 598.363 Schwarz criterion 717.331 720.668 695.754 Notes: Dependent variable is natural log of sales price. Superscripts a, b, and c deno te significance at the 0.01, 0.05, and 0.10 levels, respectively.23 Table 3. ML Estimation Results for the Spatial Hedonic Model – Technical WQ Measures (cont.)

Variable Salinity Dissolved Oxygen Coeff. Std. Error Coeff. Std. Error Constant 13.040 a 0.619 14.431 a0.748 Sold in 2001 (=1) -0.176 b 0.089 -0.089 0.091 Sold in 2002 (=1) -0.130 0.115 -0.075 0.120 Sold in 2003 (=1) 0.049 0.156 -0.145 0.161 Sold in 2004 (=1) -0.016 0.155 -0.104 0.161 Lot square footage 0.003 a 0.001 0.005 a0.001 Lot square footage squared -3.91e-06 a 1.40e-06 -5.69e-06 a 1.43e-06 Total housing square footage 0.430 a 0.047 0.488 a0.048 Total housing square footage squared -0.023 a 0.005 -0.027 a0.005 Number of bathrooms 0.105 0.068 0.102 0.071 Number of bathrooms squared -0.009 0.009 -0.008 0.009 Concrete block exterior walls (=1) 0.009 0.038 0.003 0.040 Fireplace (=1) -0.006 0.043 0.001 0.045 Pool/patio enclosure (=1) -0.149 a 0.051 -0.173 a0.052 In-ground pool (=1) 0.144 a 0.044 0.126 a0.046 Boat lift (=1) 0.042 0.040 0.041 0.042 Waterfront dock (=1) 0.042 0.046 0.068 0.047 Home special feature (=1) 0.005 0.042 0.021 0.044 Percent of population that is white 0.003 0.004 -0.002 0.004 Percent of population that is age 65 or over -0.007 a 0.003 -0.003 0.003 Percent of owner occupied 0.004 0.003 0.001 0.003 National 30 year fixed interest rate -0.233 a 0.065 -0.239 a0.068 Water quality 0.025 a 0.004 -0.080 0.049 LAMBDA 0.148 a 0.054 0.143 a0.054 Log likelihood -270.619 -289.979 Akaike info criterion 587.237 625.958 Schwarz criterion 684.629 723.349 Notes: Dependent variable is natural log of sales price. Superscripts a, b, and c deno te significance at the 0.01, 0.05, and 0.10 levels, respectively.24 Table 4. Marginal Willingness to Pay for Water Quality Measures Variable Marginal Willingness to Pay ($) 95% Lower Bound Mean 95% Upper Bound Location grade (%) 3,375.22 3,972.04 4,554.23 Water visibility (%) 1,843.47 5,262.57 8,719.41 Water temperature (  F) 3,323.51 23,751.53 43,765.30 pH 7,269.66 11,090.04 14,912.39 Salinity (ppt) 16,870.15 23,874.32 30,945.79 Dissolved oxygen (mg/L) -163,120.71 -74,692.00 18,727.80 Notes: The marginal willingness to pay is evaluated at the observed mean value of sales price. A marginal change of the location grade is defined as one percentage point increase from 70%. A marginal change in pH is defined as a one-tenth increase. A bootstrapping procedure is used to generate 95% confidence intervals for the marginal willingness to pay (Krinsky and Robb 1986). The reported confidence intervals are based on 5,000 sets of random parameter vectors from the distribution of the estimated parameters.25 Figure 1. Example of Martin County Weekly Water Quality Data & Collection Locations

(Source: FOS, 2004)26 Figure 2. Waterfront Home Sales by WQ Monitoring Location



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