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Housing Market Fluctuations and the Implicit Price of Water Quality: Empirical Evidence from a South Florida Housing Market

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Abstract

In this study we utilize a hedonic property price analysis to examine changes in the implicit price of water quality given housing market fluctuations over time. We analyze Martin County, Florida waterfront home sales from 2001 to 2010 accounting for the associated significant real estate fluctuations in this area through flexible econometric controls in space and time. We apply a segmented regression methodology to identify housing market price instability over time, interact water quality with these identified market segmentations, and embed these interactions within a spatial fixed effect model to further account for any spatial heterogeneity in the waterfront market. Results indicate that water quality improvement is associated with higher property values. We find no evidence that the economic downturn crowded out concern for the water quality in this area. We further impute an implicit prices of $2614, evaluated at the sample mean, for 1 % point increase in the water quality grade.

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Notes

  1. Recent hedonic literature suggests that shocks to preferences, income, or amenities can change the shape of the equilibrium hedonic price function and ignoring this adjustment can bias estimates for marginal willingness to pay (Kuminoff et al. 2010). A quasi-experimental design or a difference-in-differences approach has been offered to mitigate the bias. As our data do not allow such approach, we attempt to estimate the time-varying implicit prices with the interactions between time period dummies and the water quality measures within the spatial fixed effects framework.

  2. During the waterfront identification, we tried the distance of 200, 150, 125, 100 and 50 feet from the waterbody. Less-than-125 (i.e. 100 feet and 50 feet) criteria provided a very limited number of observations. More-than-125 criteria (i.e. 200 and 150 feet) included many second and third row waterfront properties. After visual inspection, we concluded the 125-feet criteria was the best definition of “waterfront” properties in our study area that there are no properties between the parcel and the water.

  3. Some of the 2243 waterfront single family homes are located in southeast and northwest corner are far away from the water quality testing stations and thus excluded from the analysis.

  4. Waterfront homes also likely represent a different market than non-waterfront homes in Martin County given that this particular area of Martin County, FL is not a beach and recreational swimming area, but rather primarily a recreational boating area where direct access to the water matters. On average in any given year during this timeframe, waterfront homes sold for more than four times as much as non-waterfront homes.

  5. South—Size Class B/C means population size between 50,000 and 1,500,000 which is appropriate for Martin County (Martin County population is 126,731 in 2000 and 146,318 in 2010).

  6. The peak of the FHFA HPI for Port St. Lucie MSA also occurred during this time frame.

  7. As of November 11, 2010 a tenth location was added, 10. Intracoastal Waterway South, which is excluded in this analysis given it only being present for less than 2 months of our ten years timeframe.

  8. Location 1. Winding North Fork is a part of St. Lucie County for which home sales were not collected.

  9. Water visibility and DO are given corresponding labels of poor, fair, or good over specified ranges of values; and pH and salinity are 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 Fig. 3 Location 2. North Fork had a 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 that week is determined by the equally weighted average of (\(100\,\%+52\,\%+100\,\%+100\,\%)/4 = 88\,\%\), or a B corresponding letter grade (Voisinet 2006).

  10. We understand attributing the water quality based on the shortest straight line distance from a property to the nearest monitoring site may lead to some errors (e.g., the properties that face to the St Lucie Estuary may be attributed the water quality in the Indian River Lagoon). Thus, we visually checked all the waterfront properties and used editing tools to manually reassign the homes to the appropriate water testing site if necessary.

  11. Over the ten year time period, not all locations report WQ data every week. Gaps occurred in 2004 and 2005 for all locations stemming from the hurricanes that impacted this geographic area, and water quality location 8. Inlet Area was particularly problematic from 2000 to 2005. Where water quality data was missing during these time periods, corresponding location waterfront home sales were excluded from the analysis.

  12. Based upon a census block analysis we conducted we do not believe this endogeneity issue to be present in the study area. From this analysis, in general census block vacant rates for single-family homes are higher where water quality is higher.

  13. We plotted the series and visually determined where the initial guesses would be located. We checked whether or not slightly different initial guesses would lead to very large differences. In this case, there were not any major changes with slightly different initial conditions. Moreover, the confidence intervals for the breakpoints did not overlap.

  14. Lesage and Pace (2009) note that a spatial weights matrix can be constructed in a number of different ways including a first-order contiguity weight matrix or a nearest-neighbor weight matrix based on m nearest-neighboring regions. The spatial weights should be truly exogenous to the model and the range of dependence allowed by the structure of the weights matrix should be constrained to avoid identification problems. In spite of their lesser theoretical appeal, the alternative spatial weights based on social network and distance decay have been considered in the literature.

  15. Robust Lagrange Multiplier (LM) tests showed no spatial lag dependence (\(\chi ^{2}= 72.605\); p value \(< 0.001\)) but indicated spatial error dependence (\(\chi ^{2}= 0.351\); p value \(< 0.553\)) for our baseline model.

  16. Spatial dependence can be also incorporated using 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. When both types of spatial dependence occur, the general model that includes both the spatial error and spatial lag terms can be considered. 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.

  17. Akaike info criterion rewards goodness of fit, but it also includes a penalty that is an increasing function of the number of estimated parameters. Given a set of candidate models for the data, the preferred model is the one with the minimum AIC value.

  18. More precisely, the estimated coefficients are the elasticity estimates, and the marginal effects would depend on the data points.

  19. An idea similar to investors seeking out higher returns from value stocks during a recession. Motley Fool (2011) found that returns to value stocks outperformed growth stocks during periods of recessions since 1969.

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Authors and Affiliations

  1. Department of Economics, East Carolina University, Brewster Building A-435, Greenville, NC, 27858, USA

    Okmyung Bin

  2. Wharton Risk Management and Decision Processes Center, University of Pennsylvania, Room 526.7 Huntsman Hall, Philadelphia, PA, 19104, USA

    Jeffrey Czajkowski

  3. I.M. Systems Group, 3206 Tower Oaks Boulevard, Suite 300, Rockville, MD, 20852, USA

    Jingyuan Li

  4. 306 C. Maxwell Stanley Hydraulics Laboratory, Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA, 52242, USA

    Gabriele Villarini

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  1. Okmyung Bin
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  2. Jeffrey Czajkowski
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  3. Jingyuan Li
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  4. Gabriele Villarini
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Correspondence to Okmyung Bin.

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The authors are respectively, Professor of Economics, East Carolina University; Willis Re Research Fellow, Wharton Risk Management and Decision Processes Center, University of Pennsylvania; Coastal Resource Analyst, I.M. Systems Group; and Assistant Professor of Civil and Environmental Engineering, University of Iowa.

Appendix

Appendix

See Tables 5 and 6.

Table 5 Estimation results for the hedonic models without temporal breakpoints: visibility as the water quality measurement
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Table 6 Estimation results for the hedonic models with temporal breakpoints: visibility as the water quality measurement
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Bin, O., Czajkowski, J., Li, J. et al. Housing Market Fluctuations and the Implicit Price of Water Quality: Empirical Evidence from a South Florida Housing Market. Environ Resource Econ 68, 319–341 (2017). https://doi.org/10.1007/s10640-016-0020-8

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