Do Homebuyers Differentiate between Technical and Non-Technical Measures of 

Water Quality? Evidence from a Hedonic 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 scientifically-based measure of 

water quality in their analysis, there is a recurring concern in the literature that homebuyers may 

find these objective measures to be overly technical, not representative of public perception, or 

not valid in driving actual homebuyer behavior and consequently of little relevance in the home 

purchase decision and any associated welfare analysis. We address this 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 grade,” available to homebuyers in an urban 

coastal housing market of South Florida. Our results indicate that water quality does in fact 

matter to the waterfront homebuyers, and that this holds for both technical and non-technical 

measures of water quality. As the non-technical measure ostensibly 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 within a failing range. In contrast, the technical 

measures of water quality appear to convey sophisticated 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 Florida Real Estate, Indian River Lagoon 

South, Technical and Non-technical measures of water quality 
 1 

I. Introduction

Hedonic property value models are used to determine whether a relationship exists 

between an environmental amenity and housing prices, and if so, to often impute implicit prices 

for the environmental amenity that can then potentially 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 Bockstael (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 literature that homebuyers may find these objective 

measures to be overly technical, not representative of public perception, or not valid in driving 

actual homebuyer behavior and consequently of little relevance 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 objective measures of water quality as well as a less 

technical and hence more easily understood measure of water quality, “location grade”, that is 

notably a weighted average of the objective measures 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 homes. 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 home 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 County, 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 other 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 Bockstael, pg. 122, 2000). A number of other studies 

have used other objective measures of water quality in the hedonic analysis 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 suspended 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 measures tend to be less observable and hence of less value to homebuyers 

(Leggett and Bockstael, 2000; Micheal et al., 2000). 

 As such, subjective measures of water quality which are either more easily perceived by 

homebuyers, or more easily understood, have been used as an alternative to objective measures 

of water quality in the hedonic analysis. David (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 addition to the objective pH measure Epp and Al-Ani 

(1979) used a subjective measure of perceived water quality problems 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) in 
 3 

addition to using the water clarity objective measure 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 objective measure issues, they 

potentially have their own drawbacks. If they are not connected to the objective measures in any 

meaningful way, although perceptible 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 designed precisely for these reasons – it “... gives the average reader 

a simple way to evaluate the river 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. (2007) we assume that water quality matters to 

homebuyers in this area given that the health of these waterbodies are an integral part of the local 

history as well as the environmental and economic 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, Governor Crist recently pledged continued funding 

(FLDEP, 2010) of the $1 billion Indian River Lagoon South (IRLS) Everglades restoration effort 
 4 

that is currently being conducted to improve water 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, estuary, and lagoon (SFWMDb, 2010). Moreover, 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 data collected by the Florida Oceanographic Society, we 

compare hedonic property value model results using the measures of water visibility (water 

clarity), temperature, pH, salinity, and dissolved oxygen to the results using 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 technical 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 water quality. However, there is 

some distinction in how the technical 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 decision, our results indicate that they likely use it 

simply as a warning sign in their house purchase decision when the location grade is within a 

failing range. In contrast, the technical measures 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 dissolved oxygen, increase property values significantly. These 
 5 

results indicate that waterfront homebuyers in this market appear to be relatively sophisticated in 

terms of their understanding and use of the varying technical water quality measures. We 

hypothesize that the construction of the non-technical grade water quality score may also 

potentially assist in forming this level of sophistication – i.e., knowledge is gained of the 

individual parameters in trying to effectively understand the overall score. We further impute 

implicit prices for both the technical and non-technical measures of water quality where mean 

willingness-to-pay (WTP) values for significant estimates range from $3,972 to $23,874. These 

WTP results are potentially notable given the $1 billion Indian River Lagoon South (IRLS) 

Everglades restoration effort being conducted to improve water quality 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 empirical 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 waterfront 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 analysis of water quality due 

to the substantial and sufficiently varied number 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 analysis are sales of waterfront properties 

located in the Northeastern portion of Martin County from January 2000 to August 2004 as 

supplied by the Martin County property appraiser’s office. The sales price is adjusted to 2nd

quarter 2004 values2

 using the Office of Federal Housing 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 the empirical analysis. A total of 991waterfront property sales were 

collected over this timeframe. After deleting the observations with incomplete information, a 

total of 510 single family residential home sales are included in our data. All the relevant data 

are stored in a geographical information systems (GIS) for our spatial analysis. 

 Water quality data for the Martin County waterfront 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 weekly in the local newspapers and therefore it is reasonable to 

consider it a known, important, and readily available source of water 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 visibility, salinity, and dissolved oxygen (DO); as well as one objectively 

 

2

 Despite having July and August 2004 sales data, a 2nd-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 3rd quarter 2001 and 

2nd quarter 2004 as compared to a 15% increase from 4th quarter 1998 to 3rd 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 period 
 7 

based, yet less technical, compiled measure of water quality – location grade. All of the distinct 

objective water quality measures, excluding temperature, are briefly explained in the weekly 

published data with water visibility and dissolved oxygen given corresponding labels of poor, 

fair, and good over specified ranges 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 temperature are then used 

to calculate an equally weighted location grade percentage value. For example, from Figure 2 

WQ area 2. North Fork had pH value of 8.0 = “good”, a water visibility 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 determined by the equally 

weighted average of (100%+52%+100%+100%)/4 = 88%, or a B corresponding letter grade 

(Voisinet, 2006). In this way, location grade captures 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 familiar, and hence more understandable format for 

residents and homebuyers to use. Consequently, we collect and use the location grade 

percentage value, in addition to the traditional and more technical objective measures of 

temperature, pH, water visibility, salinity, and dissolved oxygen in our hedonic analysis.5

 

Each home sale is assigned to a water quality location by viewing the actual location of 

the home through the use of both the constructed 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 2004, 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 the 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 created location of 2.5 with associated WQ data 

taken to be the average of the two North and South 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 footage, home square footage, and the number of bathrooms 

in the estimated hedonic price functions. These three structural variables are all squared to allow 

for non-linearity in them. Being that these are waterfront homes, a series of dummy variables are 

also included to indicate whether the house has a pool, boat lift, dock, etc7

. Other dummies 

indicated whether the house had more than one structure8

 and notably the type of exterior wall 

(1=concrete block) due to the frequency of hurricanes in Florida. Three neighborhood variables 

are selected in order to capture ethnicity (percent 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), and using the constructed GIS database, each 

home is spatially joined to its corresponding census tract level. Lastly, we also include the 

mortgage interest rate9 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 improvement 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 purchasing a home, this is typically not 

included in the environmental hedonic models reviewed in the literature. 

III. Model Estimation 

Hedonic price models are based on the intuitive notion that the component values of 

various attributes of heterogeneous goods are reflected in price differentials (Rosen 1974). 

Hedonic property price models use observations on property values, typically residential 

properties, to infer values for non-traded attributes 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 function is represented by: 

P  P(s, n,w) , [1] 

where P is the property price, which is a function 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, ) u  U(s, n,w, y,α , subject to a budget 

constraint, m  P( , where s, n,w)  y U is a strictly concave utility 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 continuous 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 short-run or for situations where the stocks of existing 

houses dominate the market (Palmquist 2004). 
 10 

z s n w

z

P s n w

z

U s n w y , , , ( , , , , ) ( , , )    



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 Anselin 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 structural characteristics due to similar timing of 

construction (Anselin and Bera 1998). The existence 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 properly carry out statistical inference. Regression diagnostics 

based on Ordinary Least Squares (OLS) estimation and the Lagrange Multiplier (LM) test 

statistics suggest the first-order spatial error hedonic model which is given in equation [3].11 

u,

ln P s n w

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 dependence leads to inefficiency. 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. Neither the spatial lag nor the general model 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 the 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 implemented 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 binary 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 weighting 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 neighborhood values. The neighborhood is usually 

determined by distance contiguity—the binary indicator is set to ‘1’ if two corresponding houses 

are within some specified distance of one another. Using methods suggested Anselin and Bera 

(1998), we experimented with different 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 spatial error hedonic model of [3], the log of sales 

price is used as the dependent variable across all of our models. Table 2 provides the ML 

estimation results of the spatial autoregressive hedonic model for the nontechnical WQ measure 

using location grade percentage value. We experimented with several alternative specifications 
 12 

and found that the primary results were robust to alternative functional forms. Estimation results 

exhibit a significant spatial autoregressive 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 designation – or location grade percentage value 

at or below 75% corresponding to a D or F letter grade – as a warning sign in their house 

purchase decision, but could be indifferent between higher percentage grades. This intuition 

seems to be especially plausible given the varied amount of weighted technical information 

contained in the score, as well as the relatively 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 variables 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 statistically 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 higher location grade percentage 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 technical WQ measures separately. Table 3 provides 

the ML estimation results of the spatial autoregressive 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 several alternative specifications and found 

that the primary results were robust to alternative functional forms.15 The estimation results 

exhibit significant spatial autoregressive coefficients (λ = 0.144, 0.148, 0.135, 0.148, and 0.143 

respectively), suggesting that a spatial dependence in our primary sample of housing prices 

indeed exists. Regression coefficients for the WQ variables have expected signs and are 

statistically significant for all coefficients except dissolved oxygen, indicating once more that 

water quality does in fact matter to coastal waterfront homebuyers in Martin County, FL. 

Moreover, these results indicate that waterfront homebuyers in this market appear to be relatively 

sophisticated in term of their understanding and use of the varying technical 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 quality score may also potentially assist in forming 

this level of sophistication. 

As seen in the literature, water clarity/visibility is one of the most used technical 

measures of water quality in a hedonic analysis 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 and salinity results. Both 

higher water visibility and salinity have a positive association with the house sales price and their 

 

14 We also estimated the model with all of the water quality 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 indicate that the higher pH is associated with higher water front property 

values, indicating that the abundant aquatic life may be valued by home buyers. Dissolved 

oxygen and water temperature are negatively correlated 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 insignificant at any conventional level. Adding 

credence to this insignificant dissolved oxygen result, Voisinet (2006) found that dissolved 

oxygen rarely falls into the range of values deemed poor, and if this did occur it was a relatively 

short-lived phenomenon. 

We impute implicit prices for both the technical and non-technical measures of water 

quality, with Table 4 reporting the estimated marginal willingness to pay (WTP) for the various 

WQ measures. A bootstrapping procedure is used to generate confidence intervals for the 

marginal willingness to pay (Krinsky and Robb 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 sorted 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 upper 95% confidence interval estimates are $3,375 

and $4,554, respectively. Increasing water visibility by one percent results in $5,263 increase in 

average property value, ceteris paribus. The 95% lower bound and upper bounds for WTP for 
 15 

visibility are $1,843 and $8,719, respectively. The mean WTP for water temperature, evaluated 

at the mean sales price, is $23,752 with a relatively 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 $11,090. 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 results of our water quality variables, we briefly 

discuss results from the structural and neighborhood variables used. Across all specifications of 

the different WQ measures, lot size, total structure square footage, the dummy variables for a 

pool/patio enclosure and an in-ground pool, and interest rate are consistently significant at the 

1% level with the expected signs. Given the prominence of hurricane threats in this area, the 

non-significance of our concrete block exterior wall dummy variable is surprising. The fact that 

most our neighborhood variables are insignificant is also unexpected, although the percentage of 

the population older than 65 is significant 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 estimation, the estimated coefficient 

is interpreted as the percentage change in the 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 lowers 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 compare 
 16 

hedonic property value model results using the objective and more technical measures of water 

visibility (water clarity), temperature, pH, salinity, and dissolved oxygen with the results using a 

less technical and hence more easily understood measure of water quality, location grade. Our 

results indicate that water quality does in fact 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 and non-technical measures of water quality where 

mean willingness-to-pay (WTP) values for significant 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 restoration effort being conducted to improve water quality in this area, 

and the lack of any formal economic benefit 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 that 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 based 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 homebuyers in this market appear to be relatively 

sophisticated in term of their understanding and use of the varying technical water quality 

measures. Higher values of all of the technical measures of water quality, excluding dissolved 

oxygen, increase property values significantly. Our results continue 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 

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SFWMDb (South Florida Water Management District), Retrieved 2010, “Focus on the St. Lucie 

River”, 

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Memo 

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#10-02 
 20 

Table 1. Summary Statistics 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 10.108a 2.442 0.0000 

Sold in 2001 (=1) -0.168c 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 0.005a

 0.001 0.0000 

Lot square footage squared -5.61e-06a 1.43e-06 0.0001 

Total housing square footage 0.486a 0.048 0.0000 

Total housing square footage 

squared -0.027a

 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) -0.176a

 0.053 0.0009 

In-ground pool (=1) 0.122a

 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 -0.231a

 0.067 0.0006 

Location grade 0.103c

 0.062 0.0951 

Location grade squared -0.001c 4.01e-04 0.0777 

LAMBDA 0.139b

 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 denote 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 13.344a 0.646 11.715a 1.094 3.961b 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 0.005a 0.001 0.005a 0.001 0.004a 0.001

Lot square footage 

squared -5.19e-06a 1.43e-06 -5.10e-06a 1.45e-06 -4.10e-06a 1.42e-06

Total housing square 

footage 0.467a 0.048 0.483a 0.048 0.439a 0.047

Total housing square 

footage squared -0.026a 0.005 -0.027a 0.005 -0.024a 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) -0.146a 0.053 -0.154a 0.053 -0.143a 0.051

In-ground pool (=1) 0.138a 0.046 0.136a 0.046 0.138a 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 -0.007b 0.003 -0.005c 0.003 -0.007b 0.003

Percent of owner 

occupied 0.005 0.003 0.003 0.003 0.004 0.003

National 30 year fixed 

interest rate -0.232a 0.067 -0.231a 0.067 -0.224a 0.066

Water quality 0.006a 0.002 0.025b 0.011 1.183a 0.212

LAMBDA 0.144a 0.054 0.148a 0.054 0.135b 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 denote 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 13.040a 0.619 14.431a

 0.748

Sold in 2001 (=1) -0.176b 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 0.003a 0.001 0.005a

 0.001

Lot square footage squared -3.91e-06a 1.40e-06 -5.69e-06a

 1.43e-06

Total housing square 

footage 0.430a 0.047 0.488a

 0.048

Total housing square 

footage squared -0.023a 0.005 -0.027a

 0.005

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) -0.149a 0.051 -0.173a

 0.052

In-ground pool (=1) 0.144a 0.044 0.126a

 0.046

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 -0.007a 0.003 -0.003 0.003

Percent of owner occupied 0.004 0.003 0.001 0.003

National 30 year fixed 

interest rate -0.233a 0.065 -0.239a

 0.068

Water quality 0.025a 0.004 -0.080 0.049

LAMBDA 0.148a 0.054 0.143a

 0.054

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 denote 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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