The Property-Price Effects of Abating 

Nutrient Pollutants in Urban Housing Markets 

Patrick Walsh 

National Center for Environmental Economics 

US EPA 

Washington, DC 20460 

J. Walter Milon 

Department of Economics 

University of Central Florida 

Orlando, FL 32816-1400 

David Scrogin 

Department of Economics 

University of Central Florida 

Orlando, FL 32816-1400 

We are grateful to Jesse Abelson for assistance in constructing the water quality data set and 

Mark Schneider for the GIS programming and to Shelby Gerking, Mark Dickie, and seminar 

participants at the CU Environmental and Resource Economics workshop and the Northeastern 

Agricultural and Resource Economics Association Annual Conference. The views expressed in 

this paper and any errors should be attributed solely to the authors. 
Abstract 

Nutrient pollution from urban stormwater runoff is a widespread problem throughout the U.S. 

Because the sources of stormwater pollution are diverse and non-point in nature, nutrient 

pollution from stormwater runoff is a common property problem. In Florida, several programs 

have been introduced to mitigate the negative environmental impacts of stormwater runoff. 

Among these are programs that give property owners incentives to abate runoff from their 

residences and allow neighborhoods to vote on municipal service taxes to fund water quality 

management at nearby lakes. In this chapter, hedonic property price models are estimated with 

historic sales data from Orange County (Orlando), Florida in order to evaluate the effects of 

abating nutrient pollution on residential property prices. Results indicate that the property price 

effects of nutrient abatement i) differ significantly between lakefront and non-lakefront 

properties, ii) diminish with distance from the waterfront, and are iii) an increasing function of 

the size of the waterbody. 
I. Introduction

 The state of Florida has experienced rapid population growth over the past fifty years. 

The most significant increase has occurred since 2000, with the population rising by more than 

sixteen percent. This growth has been accompanied by increased residential and commercial 

development and investment in public infrastructure. As the landscape has transformed and the 

levels of impervious surface increased, so have the levels of nutrient pollutants in state waters 

due to stormwater runoff. Florida has a long history of stormwater regulation, but nutrient 

loading has emerged as the primary threat to inland waters, and most notably so in the central 

Florida corridor (OCDEP, 2007). Several programs have recently been introduced at the state 

and local levels to mitigate the negative environmental impacts of stormwater runoff. Among 

these are programs that give property owners incentives to abate runoff from their residences and 

allow neighborhoods to vote on municipal service taxes to fund water quality management at 

nearby lakes. 

 In addition to state and local initiatives, a forthcoming federal rule has also changed the 

regulatory environment in Florida, with the EPA proposing to replace the long-standing narrative 

nutrient criteria with numeric nutrient criteria. All waterbodies are to be assigned strict limits for 

the content of several nutrients linked to stormwater runoff, including nitrogen, phosphorous, 

and chlorophyll a. This regulation has the potential to improve water quality throughout the state 

and is expected to require substantial control of nonpoint source pollution (FDEP, 2008). As the 

state is expected to incur significant costs in achieving the proposed standards, it is important to 

also recognize the variety of benefits that can result from stormwater abatement and reductions 

in nutrient pollutants. This chapter investigates one dimension of these benefits: the appreciation 

of property prices in urban housing markets. 
 Hedonic property price models are estimated with a large sample of property sales 

occurring in Orange County, Florida over the period 1996-2004. Of interest is measuring the 

relationship between property prices and several continuous indicators of water quality in local 

lakes, including the trophic state index (TSI), a composite of three nutrient pollutants resulting 

from stormwater runoff. Given that properties located throughout the housing market contribute 

to runoff deposited in local lakes and that the costs of achieving the standards will ultimately be 

shared by tax payers, our analysis includes both waterfront and non-waterfront property sales. 

Results indicate that the property price effects of stormwater pollution abatement i) differ 

significantly between lakefront and non-lakefront properties, ii) diminish with distance from the 

waterfront, and are iii) an increasing function of the size of the waterbody. The hedonic valuation 

of water quality and stormwater management in Florida are reviewed in next section. In Section 

III the hedonic property price models are specified, and the construction of the dataset and 

summary statistics are discussed in Section IV. Estimation results and the marginal implicit 

prices of reduced nutrient levels are discussed in Section V, and Section VI concludes. 

II. Literature Review and Background

Hedonic price analyses have been conducted in a variety of settings, ranging from the 

pricing of automobiles (Atkinson and Halvorsen, 1984) to the wages paid in labor markets 

(Garen, 1988). In the non-market valuation literature, hedonic models have been used to measure 

the property price effects of environmental amenities and disamenities, including proximity to 

hazardous waste sites (Kolhase, 1991), improvements in air quality (Smith and Huang, 1995; 

Boyle and Kiel, 2001), and access to open space (Cho et al., 2009). The methodology was 

pioneered by Rosen (1974), who describes an equilibrium between profit maximizing producers 

and utility maximizing consumers. A useful feature of the model for analysis of public programs 
is that under certain conditions the mean implicit price of (or marginal willingness to pay for), 

individual attributes of the good may be estimated. 

If water quality affects the sales prices of surrounding properties, then hedonic models 

may be used to estimate the impact of pollution abatement efforts on property prices. While 

numerous air quality studies have been published, the water quality valuation literature remains 

relatively sparse. This may be because the health risks of exposure to waterborne pollutants may 

be small relative to those of airborne pollutants and because sufficient variation in water quality 

over time and/or waterbodies is needed in order to estimate values with defensible statistical 

significance. 

Early water quality studies used surveys of homeowners, water biologists, or government 

officials to construct proxies of water quality, including David (1968), Epp and Al-Ani (1979), 

and Young (1984). As water sampling became more prevalent and the measurement techniques 

more consistent, studies of housing markets throughout the country began to employ both 

physical indicators, including concentrations of dissolved oxygen and nitrogen (Brashares, 

1985), fecal coliform (Leggett and Bockstael, 2000), and suspended solids (Poor et al., 2007) and 

measures of water clarity or transperancy, most notably secchi depth (Michael et al., 1996; Boyle 

et al., 1999; Michael et al., 2000; Poor et al., 2001; Gibbs et al., 2002; Walsh et al., 2010). 

Several studies have directly examined stormwater’s impact on property prices. Loomis and 

Streiner (1995) and Braden and Johnston (2004) investigated the benefits of flood reduction 

resulting from stormwater management. Bin et al. (2009) focused on the impact of riparian 

buffers adjacent to streams on property prices. And Sander et al. (2010) found that reduced 

stormwater flows resulting from increased residential tree-cover can significantly affect property 

prices. 
 In the case of Florida, nutrient pollutants from stormwater runoff from residential and 

commercial development have grown to be the greatest threat to the quality of public waters 

(FDEP, 1996, 2006, 2008). The state has a long-history of managing and regulating stormwater 

flows and was first in the nation to implement a stormwater permitting program for all new 

development. The Florida Stormwater Rule took effect in 1982 and required all new 

development with the exception of single family residential projects to obtain stormwater permits 

and abate discharges. Figure 1 illustrates the impact of the Rule on phosphorous levels and 

reveals a recent upswing since 2000. This period has experienced considerable population 

growth accompanied by increased residential development throughout the state. New programs 

have since been initiated to mitigate the negative environmental impacts, particularly since single 

family residential development was exempt from the Rule. 

 One of the programs administered in Orange County is investigated in this chapter. This 

particular program dates to the 1970’s and allows neighborhoods to vote on the imposition of a 

property tax to fund projects at specific lakes, including stormwater management and aquatic 

plant control. Support by 66% of homeowners leads to adoption of either a municipal service tax 

unit (MSTU) or a municipal service benefit unit (MSBU) within the neighborhood. The MSTU 

takes the form of a millage on residential property taxes so that assessed property values dictate 

the tax payment, whereas the MSBU is a flat tax paid by all residential property owners within 

the neighborhood (about $150 per household). There are currently 21 lakes managed through the 

MSTU program and 10 lakes managed under the MSBU program. In addition to the 

MSTU/MSBU program, Orange County instituted the Financial Incentives Program in 2004 as 

part of Florida’s Clean Lakes Initiative. This program provides homeowners with a rebate of up 

to $1,000 dollars for installing a stormwater berm and swale system, restoring lakeshore 
vegetation and littoral zones, and removing upland invasive tree species. Public participation 

exhausted the program’s $150,000 budget in 2008. 

In the next section, we examine the benefits of public efforts to reduce nutrient levels in 

Orange County lakes as reflected through residential property prices. The nutrient indicators 

include total nitrogen (TN), total phosphorous (TP), and chlorophyll a (CH). Elevated levels of 

these indicators can lead to reduced water clarity and foul odor, reduced levels of oxygen and 

increased levels of bacteria, and ultimately undermine the health of the aquatic and surrounding 

terrestrial ecosystems (US EPA, 2003). These pollutants also appear in the Florida numeric 

nutrient criteria that were proposed by EPA in 2009.1

 The criteria assign numeric thresholds to 

lakes, streams, springs, and canals for nutrient content, effectively replacing the state’s longstanding narrative nutrient criteria. TN, TP, and CH are also the inputs of the fourth water 

quality variable used in this paper: the trophic state index (TSI). The TSI was originally 

developed by Carlson (1977) to be a comprehensive indicator of lake health.2 Similar indicators 

have appeared in other water quality valuation studies (Pendleton et al., 2001; Massey et al., 

2006) and have a long history in the conservation biology literature (Ribaudo et al., 2001), 

including studies of tropical forestation (Sheil et al., 2004), marine ecosystem quality (Muxika et 

al., 2005; Blocksom and Johnson, 2009), and biodiversity (Feest et al., 2010). 

III. Model Specification 

 The hedonic property price model specifies the selling price of a property as a stochastic 

function of structural characteristics of the property, environmental amenities, and other 

 

1 http://www.epa.gov/waterscience/standards/rules/florida/

2

 The TSI used here is calibrated to local Florida conditions and accounts for the nutrientlimiting concept. A TSI value between 0-59, 60-69, or 70-100 indicates, respectively, the 

biological health of the water body is good, fair, or poor. 
locational attributes within the housing market. In specifying the model and obtaining the 

implicit value water quality, households are assumed to be freely mobile within the market to 

choose housing bundles that maximize utility over the attributes (Kuminoff, 2009). To allow the 

implicit price of water quality to vary between properties and between water bodies, water 

quality enters the model through multiple interaction variables. The linear-in-parameters, linearin-variables specification of the model is: 

01 2 3 4 5

67 8

* *

* ' ' ''

P Waterfront WQ Waterfront WQ Distance Distance WQ

Area Area WQ MSTU

β β ββ β β

β β β γ φ ψδε

=+ + + + +

+ + + + + +++ SN lt (1) 

where P references the sales price; Lakefront is a dummy variable distinguishing lakefront from 

non-lakefront properties; WQ is a continuous measure of water quality in the nearest lake at the 

time of sale; Distance is a measure of the property proximity to the waterfront; Area is the 

surface water coverage of the nearest lake; MSTU indicates whether the home pays an 

MSTU/MSBU fee; S is a vector of structural attributes; N is a vector of location and 

neighborhood attributes; t is a vector of time dummy variables; l is a vector of lake dummy 

variables; and ε is random error. 

The model is estimated separately for each water quality variable due to a high degree of 

correlation between TN, TP, and CH in the sample, highlighting another potential advantage of 

using comprehensive trophic state indices for hedonic water quality valuation. Water quality 

enters (1) as its own variable and through interactions with several other variables. The 

interaction with the lakefront dummy reflects the hypothesis that water quality affects waterfront 

property prices differently than non-waterfront property prices due to their unique location 

within the landscape. The interaction with the distance variable reflects the hypothesis that the 
effect of water quality on property price depends upon the property’s proximity to the nearest 

lake. Lastly, the interaction with the area variable reflects the hypothesis that the effect of water 

quality on property price depends upon the size of the lake. A similar specification of the model 

that includes these three interaction variables was investigated by Walsh et al. (2010). 

The lake dummy variables control for lake-specific attributes that do not vary over time, 

such as private or public access or the existence of a boat launch. The main tradeoff with these 

variables is that the lake area variable cannot be used alone in the regression (although it still 

appears as an interaction), since it does not change over time. However, as long as lake area does 

not change significantly over the study area, its impact should be captured with the lake 

dummies. The one caveat is that there is the risk that the area interaction with water quality may 

overstate the impact of lake area on the value of water quality. 

Two final modeling issues are potential spatial autocorrelation and functional form 

specification. Spatial autocorrelation can be induced by unobserved neighborhood and landscape 

attributes common to property sales located within close proximity to one another. In the 

presence of spatial dependence the assumption of independent errors does not hold and the 

parameter estimates are inconsistent (Anselin, 1999) This concern has been raised in several 

hedonic property studies, with spatial lag and spatial error econometric models estimated to test 

and correct for spatial dependence in the errors.3

 The findings are mixed, with some studies 

reporting improvements in fit (Gelfand et al., 2004; Case et al., 2004) and others reporting results 

that are robust to the error specification (Leggett and Bockstael, 2000; Kim et al., 2003; and 

 

3

 The spatial lag model is written Y = ρWY + Xβ + u, where Y is the dependent variable, W is a 

spatial weights matrix, X is a vector of regressors, β and ρ are parameters to be estimated, and u 

is an i.i.d. normal random error. The spatial error model appears as Y = Xβ + g, where g = λWg + 

u, and β and λ are parameters. Estimates of ρ and λ may be used to test for spatial dependence in 

the errors. See Anselin (1988) and LeSage (1999) for technical details on spatial econometric 

models. 
Muller and Loomis, 2008). For completeness, estimation is performed under the null of zero 

spatial correlation and with the spatial lag specification. Since it has been shown that the 

specification of the spatial weights matrix (SWM) can impact regression results (Bell and 

Bockstael, 2000), two variations of SWM are used in the present paper; one based on inverse 

distance and the other based on the 15 nearest neighbors.4

 Maximum likelihood is used to 

estimate the spatial lag models, as explained in LeSage (1999). 

The functional form of the model must also be specified. While there is no theoretical 

basis for the functional form, Cropper et al. (1988) suggest that semi-log and double-log models 

are superior to more complicated models in the face of mis-specification and proxy variables. 

Both regular Box-Cox tests and spatially-robust Box-Cox tests using double-length regression 

tests (Le and Li (2008) and Le (2009)) favored the double-log specification appearing in 

expression (1). Furthermore, both hedonic (Michael et al., 2000) and limnology (Hoyer et al., 

2002) sources recommend transforming the water quality variables into natural logarithms since 

marginal changes may be more recognizable at low levels. 

IV. Data 

The data include 146 Orange County lakes that vary in size from approximately 1 acre to 

1,800 acres.5

 Table 1 contains a summary of the sizes of the included lakes. All of the water 

quality indicators were obtained from EPA’s STORET database, with missing observations obtained 

 

4

 The inverse distance SWM is created with appraisal practices in mind; homes sold within a 

distance of a half mile and within a time period of six months previous and three months forward 

are considered neighbors. If property j is a neighbor to property i, Wij = 1/dij, where dij is the 

distance between the two homes. The nearest neighbor specification uses Wij = 1 if property j is 

one of the 15 nearest properties to home i. Both types of SWM are row-standardized. 

5

 Only named lakes are used in the analysis. 
from three municipalities. Table 2 contains summary statistics on the water quality variables and 

illustrates significant variation in each variable over the nine year sample period. 

The property data were obtained from the Orange County Property Appraiser (OCPA). 

These data contain all single family property sales during the time period 1996-2004. All singlefamily home sales within 1,000 meters of the 146 lakes are used in the analysis, yielding 54,716 

total property sales.6 The property data also contain additional home attribute data, including the 

number of bedrooms and bathrooms, size of the parcel, size of the home, home age, the existence 

of a pool, and if the home is classified as lakefront, canal-front, or golf-front. The OCPA also 

provided latitude and longitude coordinates of each home, which were geo-coded on GIS maps 

to calculate the distance to the central business district (CBD: downtown Orlando) and distance 

to the nearest lake.7

 Through the OCPA data it is also possible to identify which homes pay an 

MSTU/MSBU fee. Thirteen percent of the properties paid a tax at the time of sale. Parcels were 

also mapped to census tracts to obtain neighborhood variables such as income, race, and age 

demographics. Summary statistics appear in Table 3. 

V. Estimation Results

 The estimated water quality coefficients appear in Table 4.8 For each water quality 

variable, three models are estimated: OLS, a spatial regression with the nearest neighbor SWM 

(NN15), and a spatial regression with an inverse-distance SMW (DT). The models provide 

nearly identical fits to the data as reflected by the R2

 statistics, indicating that the models are 

 

6

 Properties that sold for less than $30,000 were excluded from the dataset. 

7

 There was concern that the distance to other lakes was also relevant. A lake count variable was 

used in preliminary regressions, which expressed the number of lakes within 1,000 meters from 

each parcel. This variable was insignificant in all specifications. 

8

 The full set of estimation results is available in an appendix upon request. 
robust to the error specification. A likelihood ratio test (LeSage, 1999) rejects the hypothesis of 

no spatial dependence at the 99% level.9

 The spatial lag coefficient (ρ) is small but significant in 

each model. 

In all models, the waterfront and water quality interaction term is significant and 

negative, indicating that the mean marginal value of water quality is greater for waterfront 

homes. The distance interaction term is positive and significant in all specifications, indicating 

that the marginal value of water quality decreases as a property’s distance to the nearest lake 

increases. The hypothesis about the interactive effects of water quality and lake size is supported 

for TN, CH, and TSI but is rejected for TP. It is of the correct sign, but insignificant, in the TP 

models. Overall, the pollutants targeted by the numeric nutrient criteria proposed by EPA are 

significantly related to property prices. Finally, in all specifications the MSTU/MSBU 

coefficient is positive and significant, suggesting that neighborhoods that participate in the 

program realize price effects from water quality improvements above and beyond those of nonparticipants. 

Since the water quality variables are all expressed in different units, the percentage 

changes implied by the various coefficients in Table 4 are not easily compared. The marginal 

effect, or implicit price, of a change in each of the water quality indicators is more useful for this 

purpose. From the spatial lag specification of the model (1), the implicit value of water quality 

may be written: 

 

9

 While robust Lagrange multiplier tests are normally used to select between the spatial error and 

lag models, the size of the present dataset is prohibitively large for that procedure. Using the 

same data, Walsh (2009) uses out-of-sample prediction to compare the two models, gauged by 

six categories of prediction error from Case et al. (2004). The spatial lag model outperforms the 

spatial error model in five of the six categories. Furthermore, there were only minor differences 

in the magnitudes of the coefficients between the spatial error and spatial lag models. 
( ) 23 5 7

1 * *ln( ) *ln( ) 1

P P

WF Distance Area

WQ WQ ββ β β

ρ

∂

= ++ +

∂ −

         (2) 

Estimates of the implicit value of marginal changes in TN, TP, CH, and TSI may be obtained by 

substituting the coefficient estimates reported in Table 4 and the sample means of the respective 

independent variables into (2). Given that the dependent variable is in natural logarithms, an 

estimate of E(P|X) obtained with the Duan (1983) smearing estimator is used in place of the 

sample mean of property price. Table 5 reports the mean implicit prices of water quality for 

lakefront and non-lakefront properties obtained from the spatial and non-spatial specifications of 

the model. To facilitate comparison across indicators and with past SDM literature (Michael et 

al., 2000; Poor et al., 2001; Gibbs et al., 2002; Krysel et al., 2003), implicit prices are expressed 

as a 17% change.10 Since the Florida numeric nutrient criteria imply different changes for each 

affected lake, the current approach should provide a tool that can be adapted to individual 

conditions. 

 Table 5 indicates that the spatial models do not cause large differences in the magnitudes 

of the implicit prices, although the standard errors of the spatial waterfront implicit prices are 

notably lower than those of the non-spatial specification. TSI has the highest waterfront implicit 

price at approximately $9,600, equivalent to 5% of the average home price (2% of the average 

waterfront price of $452,646). Although only slightly larger than the implicit price of TN, this is 

roughly three times larger than the CH waterfront implicit price ($3,200). The effect of 

waterfront on the implicit prices is clear from the Table, where TN has the largest non-waterfront 

prices; at the average non-waterfront lake distance of 456 meters, the implicit prices of the other 

indicators are essentially zero. However, the non-waterfront implicit prices closer for homes 

 

10 For comparison to other hedonic papers, a one foot change in SDM corresponds to a 17% 

change in the SDM level of the average lake in the sample. 
closer to the lake are considerably larger. To better illustrate the impact of distance on the 

implicit prices, Figure 2 contains a graph of the implicit price gradients of each indicator. At 100 

meters from the lake, TSI has an implicit price of $1,800, with TN slightly less. TN has the 

farthest extent, with positive implicit prices beyond 600 meters from the lake. TP has the shortest 

extent, going to zero at approximately 350 meters away. Nonetheless, all of the water quality 

variables yield positive non-waterfront prices for homes several hundred meters around a lake. 

 An interesting application is a comparison of MSTU boundaries to the extent of benefits 

implied by the implicit prices. Figure 3 contains a map of the parcels surrounding Lake Holden, 

with those that pay the MSTU fee highlighted in black. Homes within 600 meters of the lake are 

colored grey to illustrate the potential implicit price gradient, and other parcels are white. In this 

map, there appears to be a positive externality from the MSTU; water quality increases from 

MSTU projects can impact a substantial number of additional homes not enrolled in the MSTU. 

Homes that pay the MSTU see a property price increase from the water quality improvement, as 

well as the 3% increase from simply being in the MSTU. This type of policy appears to be quite 

efficient, since the annual fee paid by homes is a fraction of the property price appreciation that 

results. 

VI. Discussion and Conclusions

 The State of Florida has had an expansive program of water quality management over the 

last half century. Both point and nonpoint sources of pollution have been regulated, with the 

latter increasing in importance since the 1982 Stormwater Rule. However, of those waterbodies 

evaluated by the FDEP, 28% of river and stream miles, 25% of lake acres, and 59% of estuary 

square miles are classified as having poor water quality (FDEP, 2008). Furthermore, 24% of 
assessed waters are currently degrading over time. Urban stormwater is a persistent threat to 

Florida waters, contributing nutrients to waterbodies through a diverse array of sources. EPA’s 

proposed numeric nutrient criteria are designed to halt the progress of nutrient pollution by 

imposing standards on all water bodies, replacing the previous narrative criteria that required 

time and resource-intensive waterbody-specific analysis. The present paper analyzed potential 

benefits to improvements in the targets of the new criteria through a hedonic property analysis.

The results presented here indicate that property prices are significantly affected by the 

pollutants targeted by the numeric nutrient criteria. Four indicators of nutrient pollution were 

employed, with a new compound variable, TSI, introduced to the hedonic literature. The 

marginal values of these pollutants were found to be considerably larger for waterfront homes 

and diminish with distance to the lake. Furthermore, the marginal value of water quality 

increased with lake area in three of the four quality variables. Results are supportive of the 

compound TSI variable, since it could incorporate changes in TN, TP, and CH, while accounting 

for the nutrient limiting concept. The TSI represents a useful indicator for future benefits 

analysis in Florida and other areas, since it can be calibrated to account for local conditions. For 

instance, Ohio uses a TSI that has been calibrated using SDM instead of chlorophyll a. 

The estimated implicit prices illustrate incentives for homeowners to contribute to 

stormwater improvement activities, made possible by Orange County policies like the 

MSTU/MSBU program. Results indicate that homes participating in the program receive two 

benefits from membership. First, homes get an average 3-5% property price increase from 

membership alone. Second, if the programs are successful at improving water quality, property 

prices increase further. Additionally, the water quality improvements can benefit homes outside 

of the designated MSTU/MSBU boundary, yielding a positive externality from the program. As 
the returns to traditional stormwater regulation decrease over time, policies like those instituted 

in Orange County may represent complementary methods of combating pollution. 
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Figure 1: Average Phosphorous in Florida Waterbodies 

Source: FDEP, 2008 
Figure 2: Distance, Water Quality, and Property Prices 
Figure 3: Lake Holden MSTU 
Table 1: Lake Size Description 

Size (Acres) Number of Lakes Average Size 

0-50 82 16.72 

50-100 23 74.16 

100-300 26 162.35 

>300 15 818.10 

Total 146 284.74 
Table 2: Water Quality Summary Statistics 

TSI TN TP CH

1996-1998 

 Mean 44.756 0.788 0.035 11.484 

 St. Dev 12.622 0.287 0.034 11.044 

 Min 16.470 0.220 0.003 0.681 

 Max 77.175 2.281 0.299 72.431 

1999-2001 

 Mean 42.488 0.845 0.034 9.892 

 St. Dev 14.529 0.334 0.034 11.684 

 Min 11.190 0.154 0.005 0.350 

 Max 87.495 2.565 0.350 69.331 

2002-2004 

 Mean 46.351 0.839 0.038 11.411 

 St. Dev 11.978 0.360 0.031 10.583 

 Min 11.580 0.176 0.004 0.500 

 Max 78.070 2.450 0.350 67.058 
Table 3: Descriptive Statistics (N = 54,712) 

Variable Units Mean Std. Dev. Min Max 

Sales Price 2002 Dollars 206,891.00 211,640.90 30,048 3,986,184 

Heated Area Square Feet 1,984.096 969.632 506 23,728 

Area of Parcel Square Meters 1,122.693 1,205.603 53.510 98,582.010

% Pay MSTU/MSBU 13.08 

% Waterfront 2.7 

Number of Bedrooms 3.304 0.844 1 16 

Home Age Years 18.759 14.992 0.5 99 

% With Pool 20.7 

Distance to Nearest Lake Meters 455.525 273.013 2 1,130.420 

Area of Nearest Lake Acres 284.736 453.150 0.997 1,853.161 

Distance to CBD Meters 9,253.748 5,149.971 506.290 28,589.640

% In airport noise zone 15.7 

Latitude Coordinate Degrees 653,265.700 7,930.728 635,754.200683,568.200

Longitude Coordinate Degrees 505,663.300 6,285.382 487,273.600517,649.500

% Canalfront 0.3 

% Golffront 0.1 

% of Population White 78.2 

% of Population Black 12.3 

% of Population > 65 11.2 

Median Household Income 2002 Dollars 56,578.670 23,993.800 9,641 174,169 

% of sales in 1996 9.2 

% of sales in 1997 10.9 

% of sales in 1998 12.2 

% of sales in 1999 13.4 

% of sales in 2000 11.4 

% of sales in 2001 9.9 

% of sales in 2002 9.9 

% of sales in 2003 11.0 

% of sales in 2004 12.2 
Table 4: Selected Hedonic Estimation Results 

 WQ WQ*WF Dist*WQ Area*WQ Pay MSTU ρ R2

TN 

OLS -0.083**

(0.042) 

-0.125*

(0.025) 

0.032*

(0.004) 

-0.009*

(0.003) 

0.047*

(0.005) --- 0.8938 

NN15 -0.094**

(0.041) 

-0.124*

(0.019) 

0.032*

(0.004) 

-0.008*

(0.003) 

0.046*

(0.005) 

-0.003*

(0.000) 0.8943 

DT -0.081**

(0.041) 

-0.127*

(0.019) 

0.032*

(0.004) 

-0.009*

(0.003) 

0.047*

(0.005) 

0.001*

(0.000) 0.8937 

TP 

OLS -0.088*

(0.032) 

-0.124*

(0.012) 

0.019*

(0.002) 

-0.002 

(0.002) 

0.034*

(0.005) --- 0.8943 

NN15 -0.102*

(0.029) 

-0.123*

(0.009) 

0.018*

(0.002) 

-0.001 

(0.002) 

0.034*

(0.005) 

0.003*

(0.000) 0.8948 

DT -0.090*

(0.029) 

-0.124*

(0.009) 

0.019*

(0.002) 

-0.002 

(0.002) 

0.034*

(0.005) 

0.001*

(0.000) 0.8943 

CH 

OLS -0.014 

(0.015) 

-0.064*

(0.008) 

0.010*

(0.001) 

-0.004*

(0.001) 

0.040*

(0.005) --- 0.894 

NN15 -0.015 

(0.014) 

-0.064*

(0.005) 

0.010*

(0.001) 

0.004*

(0.001) 

0.039*

(0.005) 

-0.003*

(0.000) 0.8946 

DT -0.015 

(0.014) 

-0.015*

(0.014) 

0.010*

(0.001) 

-0.004*

(0.001) 

0.040*

(0.005) 

0.001*

(0.000) 0.8941 

TSI 

OLS -0.164*

(0.051) 

-0.168*

(0.025) 

0.045*

(0.005) 

-0.008*

(0.003) 

0.032*

(0.005) --- 0.8943 

NN15 -0.176*

(0.048) 

-0.167*

(0.016) 

0.046*

(0.004) 

-0.007**

(0.003) 

0.032* 

(0.005) 

-0.003*

(0.000) 0.8948 

DT -0.168*

(0.048) 

-0.169*

(0.016) 

0.046*

(0.004) 

-0.008*

(0.003) 

0.032*

(0.005) 

0.001*

(0.000) 0.8943 

N = 54,712. *, **, and *** denote significance at the 1%, 5%, and 10% levels, respectively. 

Standard errors appear in parentheses. 
Table 5: Implicit Price of a 17% Change in Indicator 

 Mean Waterfront 

Implicit Price 

Mean Non-Waterfront 

Implicit Price 

TN 

OLS 8,076.10

(857.24)

378.64

(167.31)

NN15 7,870.13

(612.84)

363.96

(157.68)

DT 7,650.60

(616.44)

242.59

(156.80)

TP 

OLS 5,812.44

(434.55) ---

NN15 5,736.77

(318.74) ---

DT 5,883.09

(320.57) ---

CH 

OLS 3,217.50

(258.68)

28.12

(59.19)

NN15 3,239.11

(177.21)

23.51

(51.75)

DT 3,261.50

(178.25)

9.74

(52.06)

TSI 

OLS 9,673.93

(810.33) ---

NN15 9,573.05

(515.81) ---

DT 9,729.90

(518.86) ---

Standard errors appear in parentheses.