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 Re source Economics workshop and the Northeastern Agricultural and Resource Economics Associati on Annual Conference. Th e views expressed in this paper and any errors should be attributed solely to the authors.Abstract Nutrient pollution from urban stormwater runo ff is a widespread problem throughout the U.S. Because the sources of stormwater pollution ar e diverse and non-point in nature, nutrient pollution from stormwater runoff is a common pr operty problem. In Florida, several programs have been introduced to mitigate the negative environmental impacts of stormwater runoff. Among these are programs that give property owne rs incentives to abate runoff from their residences and allow neighborhoods to vote on m unicipal service taxes to fund water quality management at nearby lakes. In this chapter, hedonic property price mode ls are estimated with historic sales data from Orange County (Orlando), Florida in order to ev aluate the effects of abating nutrient pollution on reside ntial property prices. Results i ndicate that the property price effects of nutrient abatement i) differ signi ficantly 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 sin ce 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 infrastruc ture. As the landscape has transformed and the levels of impervious surface increased, so have th e levels of nutrient pollu tants in state waters due to stormwater runoff. Florida has a long hi story of stormwater re gulation, but nutrient loading has emerged as the primary threat to in land waters, and most notab ly so in the central Florida corridor (OCDEP, 2007). Several programs have recently been introduced at the state and local levels to mitigate the negative envi ronmental impacts of stormwater runoff. Among these are programs that give property owners ince ntives to abate runoff from their residences and allow neighborhoods to vote on mu nicipal service taxe s 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 EP A proposing to replace the long-standing narrative

nutrient criteria with numeric nutri ent criteria. All water bodies are to be assigne d strict limits for

the content of several nutrients linked to stor mwater runoff, includi ng nitrogen, phosphorous, and chlorophyll a . This regulation has the potential to improve water quality throughout the state and is expected to require substantial contro l of nonpoint source pollution (FDEP, 2008). As the state is expected to incur signifi cant costs in achieving the proposed standards, it is important to also recognize the variety of bene fits that can result from stormwater abatement and reductions in nutrient pollutants. This chapter investigates one dimension of these be nefits: 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 pr ices and several continuous indi cators of water quality in local lakes, including the trophic stat e index (TSI), a com posite 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 analys is includes both waterfront a nd non-waterfront property sales. Results indicate that the propert y price effects of stormwater pollution abatement i) differ significantly between lakefront and non-lakefront properties, ii) dimi nish with distance from the waterfront, and are iii) an increasing function of the size of the wate rbody. The hedonic valuation of water quality and stormwater management in Fl orida are reviewed in ne xt section. In Section III the hedonic property price models are specif ied, 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 discusse d in Section V, and S ection VI concludes.

II. Literature Review and Background Hedonic price analyses have been conducted in a variety of setti ngs, ranging from the pricing of automobiles (Atkinson and Halvorse n, 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 amen ities and disamenities, including proximity to hazardous waste sites (Kolhase, 1991), improveme nts 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 e quilibrium between profit maximizing producers and utility maximizing consumers. A useful featur e of the model for analysis of public programsis 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 pollu tion 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 us ed surveys of homeowners, wa ter biologists, or government officials to construct proxies of water qual ity, 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 market s 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 stormw ater’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 reside ntial tree-cover can signi ficantly affect property prices.In the case of Florida, nutrient pollutant s from stormwater runo ff from residential and commercial development have grown to be the gr eatest threat to the quality of public waters (FDEP, 1996, 2006, 2008). The state has a long-hist ory of managing and re gulating 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 sin ce 2000. This period has experien ced considerable population growth accompanied by increased residential de velopment 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 C ounty is investigated in this chapter. This particular program dates to the 1970’s and al lows neighborhoods to vote on the imposition of a property tax to fund projects at specific lakes, including stormw ater 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 th e 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 ta x paid by all residential property owners within the neighborhood (about $150 per ho usehold). 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 be rm and swale system, restoring lakeshorevegetation and littoral zo nes, and removing upland invasive tr ee 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 reside ntial property prices. The nutrient indicators include total nitrogen (TN), tota l phosphorous (TP), and chlorophyll a (CH). Elevated levels of

these indicators can lead to reduced water cl arity and foul odor, reduced levels of oxygen and increased levels of bacteria, and ultimately unde rmine the health of the aquatic and surrounding terrestrial ecosystems (US EPA, 2003). These po llutants 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 troph ic state index (TSI). The TSI was originally developed by Carlson (1977) to be a co mprehensive indicator of lake health. 2 Similar indicators have appeared in other water quality valuation studies (Pendl eton et al., 2001; Massey et al., 2006) and have a long history in the conservation biology litera ture (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 prope rty 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 calibra ted 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 wa ter body is good, fair, or poor.locational attributes within the housing market. In specifying the model and obtaining the implicit value water quality, househ olds 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 betw een properties and between water bodies, water quality enters the model through multiple interact ion 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 dis tinguishing lakefront from non-lakefront properties; WQ is a continuous measure of water qua lity 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 wate r 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 st ate indices for hedoni c water quality valu ation. Water quality enters (1) as its own variable and through interacti ons with several ot her variables. The

interaction with the lakefront du mmy reflects the hypothesis that wa ter quality affects waterfront property prices differently than non-waterfront property prices due to their unique location within the landscape. The interaction with the distance variab le reflects the hypothesis that theeffect of water quality on property price depe nds upon the property’s proximity to the nearest lake. Lastly, the interaction with the area variable reflects the hypot hesis that the effect of water quality on property price depends upon the size of the lake. A sim ilar specification of the model

that includes these three interaction variab les was investigated by Walsh et al. (2010). The lake dummy variables control for lake-spe cific 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 us ed alone in the regr ession (although it still appears as an interaction), sin ce it does not change over time. However, as long as lake area does not change significantly over the study area, its impact should be cap tured 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 ar e potential spatial autocorrelation and functional form specification. Spatial autocorrelation can be induced by unobserved neighborhood and landscape attributes common to property sa les located within close proxim ity to one another. In the presence of spatial dependence the assumption of independent errors does not hold and the parameter estimates are inconsistent (Anselin, 199 9) This concern has be en raised in several hedonic property studies, with spatial lag and spa tial error econometric mode ls estimated to test and correct for spatial de pendence in the errors. 3 The findings are mixed, with some studies reporting improvements in fit (Gelfand et al., 2004; Case et al., 2004) and ot hers reporting results

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

3 The spatial lag model is written Y = ρ WY + X β

+ 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 = X β

+ g , where g = λ W g

+

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, estim ation is performed under the null of zero spatial correlation and with th e spatial lag specification. Sin ce it has been s hown that the specification of the spatial weights matrix (S WM) can impact regres sion results (Bell and Bockstael, 2000), two variations of SWM are used in the presen t 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-l og and double-log models are superior to more complicated models in th e face of mis-specifica tion and proxy variables. Both regular Box-Cox tests and spatially-robus t Box-Cox tests using double-length regression tests (Le and Li (2008) and Le (2009)) favor ed the double-log specifi cation appearing in expression (1). Furthermore, both hedonic (Mich ael et al., 2000) and limnology (Hoyer et al., 2002) sources recommend transforming the water quali ty variables into natu ral 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 appr aisal practices in mind; homes sold within a distance of a half mile and within a time period of six months pr evious and three months forward are considered neighbors. If property j is a neighbor to property i , W ij = 1/d ij , where d ij is the distance between the two homes. The nearest neighbor specification uses W ij = 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 statistic s on the water quality variables and illustrates significant variation in each vari able over the nine year sample period. The property data were obtained from the Orange County Property Appraiser (OCPA). These data contain all single family propert y 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 additi onal home attribute data, including the number of bedrooms and bathrooms, size of the parc el, size of the home, ho me age, the existence of a pool, and if the home is classified as lake front, canal-front, or golf-front. The OCPA also provided latitude and longitude coordinates of each home, whic h were geo-coded on GIS maps

to calculate the distance to the central busine ss district (CBD: downtow n Orlando) and distance

to the nearest lake. 7 Through the OCPA data it is also pos sible 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 neighborh ood 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 sp atial regression with the nearest neighbor SWM (NN15), and a spatial regression with an i nverse-distance SMW (DT) . The models provide nearly identical fits to th e data as reflected by the R 2 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 lake s was also relevant. A lake count variable was used in preliminary regressions, which expresse d the number of lakes within 1,000 meters from each parcel. This variable was in significant in all specifications. 8 The full set of estimation results is available in an appendix upon request.robust to the error specificati on. A likelihood ratio test (LeSage, 1999) reje cts 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 qua lity interaction term is significant and negative, indicating that the mean marginal value of water qual ity is greater for waterfront

homes. The distance interaction te rm 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 eff ects 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 nutri ent criteria proposed by EPA are significantly related to property prices. Finally, in all specifications the MSTU/MSBU coefficient is positive and significant, suggesti ng that neighborhoods that participate in the program realize price effects from water qu ality 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 th e 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 pr ohibitively large for that procedure. Using the same data, Walsh (2009) uses out-of-sample pr ediction to compare the two models, gauged by six categories of prediction error from Case et al. (2004). The spatial la g model outperforms the spatial error model in five of th e six categories. Furthermore, th ere 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 ch anges 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) smeari ng 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 propertie s obtained from the spatial and non-spatial specifications of the model. To facilitate comparison across indicato rs and with past SDM li terature (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 numeri c nutrient criteria imply di fferent changes for each affected lake, the current approach should provi de 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 specif ication. TSI has the high est waterfront implicit price at approximately $9,600, equiva lent to 5% of the average home price (2% of the average waterfront price of $452,646). Althou gh only slightly larger than the implicit price of TN, this is roughly three times larger than the CH wate rfront 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 imp licit prices closer for homes

10 For comparison to other hedonic papers, a one foot change in SD M 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 im pact of distance on the implicit prices, Figure 2 contains a graph of th e implicit price gradients of each indicator. At 100 meters from the lake, TSI has an implicit pri ce of $1,800, with TN slightly less. TN has the farthest extent, with pos itive implicit prices beyond 600 meters from the lake. TP has the shortest extent, going to zero at approximately 350 meters away. Nonetheless, a ll of the water quality variables yield positive non-wate rfront prices for homes severa l 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 f ee highlighted in black. Homes with in 600 meters of the lake are colored grey to illustrate the potential implicit pri ce 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 en rolled in the MSTU. Homes that pay the MSTU see a property price in crease 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 propert y 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 sour ces of pollution have been regulated, with the latter increasing in importance since the 1982 Stormwater Rule. Ho wever, 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 (FDE P, 2008). Furthermore, 24% ofassessed waters are currently degrading over time. Urban stormwater is a persistent threat to Florida waters, contributing nutri ents to waterbodies through a di verse 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 an alysis. The present pape r analyzed potential benefits to improvements in th e targets of the new criteria through a hedoni c property analysis. The results presented here indicate that prope rty prices are signifi cantly affected by the pollutants targeted by the numeric nutrient criteria. Four indicat ors 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. Furthe rmore, the marginal value of water quality

increased with lake area in thr ee of the four quality variable s. Results are supportive of the compound TSI variable, since it co uld incorporate changes in TN , TP, and CH, while accounting for the nutrient limiting concept. The TSI repres ents a useful indicato r for future benefits analysis in Florida and other ar eas, since it can be calibrated to account for local conditions. For instance, Ohio uses a TSI that has been cal ibrated using SDM inst ead of chlorophyll a. The estimated implicit prices illustrate in centives for homeowners to contribute to stormwater improvement activities, made possible by Orange County policies like the MSTU/MSBU program. Results indicate that home s participating in the program receive two benefits from membership. Firs t, 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 wate r quality improvements can benefit homes outside of the designated MSTU/MSBU boundary, yielding a positive externality from the program. Asthe returns to traditional stormwater regulation decrease over time, policies like those instituted in Orange County may represent comple mentary methods of combating pollution.References Anselin, L. (1988). Spatial Econometrics: Methods and Models . Norwell, MA, Kluwer

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Resources Bulletin 20(2): 163-166.Figure 1: Average Phosphorous in Florida Waterbodies

Source: FDEP, 2008Figure 2: Distance, Water Qu ality, and Property PricesFigure 3: Lake Holden MSTUTable 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 146 284.74Table 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 67.058Table 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 12.2Table 4: Selected Hedonic Estimation Results WQ WQ*WF Dist*WQ Area*WQ Pay MSTU ρ R 2 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.