Abstract
Several recent incidences of severe waterborne lead exposure have public authorities and communities across the US rethinking their strategies to address aging water infrastructure. One common question: who should pay for updates? This paper provides evidence of positive property value capitalization effects following remediation of private lead service lines in Madison, WI. Using a 16-year panel of property transactions data and a universal and prescriptive policy change, I identify an average post-replacement price effect on the order of 3–4% of a property’s value. This implies a more than 75% average return on public and private remediation costs, suggesting homeowners strongly value the benefits of lead reduction in publicly supplied drinking water.
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Notes
Due to state law prohibiting rate-payer funds being used for private infrastructure upgrades, the utility raised money for this program by renting space on their water towers to cellular providers.
A large proportion of these violations were for monitoring/reporting violations rather an exceedance of the LCR standard. See Olson and Fedinick (2016).
At the time of writing, Wisconsin mandates the disclosure of lead water piping during a real estate transaction when the owner is aware of it. This law (Act 338) was only passed in the 2017 legislation session, however, so disclosure was not mandatory during the period studied in this paper.
Interested readers can refer to MWU (2010) for a detailed description of the decade-long process that led to this conclusion. The short version: the sources and chemistry of Madison’s groundwater made several potential adjustments not chemically or economically viable. Uniform corrosion of lead pipes was not the main cause of the city’s LCR violation. As such, the use of phosphates—a group of chemicals which has elsewhere been shown to reduce lead pipe corrosion—was precluded due to ineffectiveness and widespread concern about damaging discharge into Madison’s much-beloved lakes.
See Madison city ordinances: Sec. 13.18 Cr. by Ord. 12,544.
To further reiterate the point—households were only fined following a request from the utility to remove the private side service line. As I mention below, economies of scale were gained by replacing both public and private pipes of neighboring properties—therefore, the utility often withheld requests for private service line removal until it was practical to concurrently replace the public side. Note that this also explains why there was not a rush to replace all private service lines at the program’s commencement.
Household-level data on the utility’s costs for public-side service lines that were concurrently replaced during the program is not available. However, based on the capital budget allocated to utility-side replacements over the course of the program, and the number of service lines replaced, the utility estimates its average cost of a public-side LSL replacement was $1997.
To better illustrate the program’s scope, according to 2015 ACS data, there are 66,722 single or 2–4 unit housing structures in the entire city of Madison. From the same data source, there are 15,085 housing units in Madison that were built before 1940.
To the best of the MWU’s knowledge, all lead services lines in the city of Madison have been removed. Previously unknown lead services are still occasionally found, but this is rare. For instance, the data show that only 15 lead services were replaced over the 2 years period, 2015–2016.
Appendix Table 6 contains estimation results for a handful of alternative specification assumptions. I estimate a Box–Cox model, whose left- and right-hand-side transformation parameter estimates suggest a log-linear specification is not inappropriate. I also estimate models with time-varying \(\beta _t\) parameters to allow for shifting hedonic equilibria and an alternative temporal fixed effects specification. Results from these alternatives are consistent with those from my preferred specification.
To be precise, the propensity score equation is estimated as a linear function of lot size, square footage, bedrooms, baths, age of home, latitude, longitude, and indicators for porch, patio, deck/balcony, waterfront, wooded, airport noise, traffic noise, garage type, and importantly, year of property sale.
Available on request, omitted for brevity. Across specifications: more space, rooms, and better housing/land characteristics increase property value. Noise decreases property value. Sale prices in summer months are stronger, as expected; a tumble in sales prices in the post housing crisis period are also evident.
All reported estimates of price capitalization effects as a percentage of home value in the text make use of the Halvorsen and Palmquist (1980) coefficient correction: \(e^\gamma - 1\). As effect estimates are small (\(<5\)%), this correction is minute.
For additional robustness, I also estimate my baseline model using only transactions on treated properties that fall within a 3-year or 5-year window of the LSL replacement. As shown in Appendix Table 7, the size of the post-treatment parameter (\(\gamma\)) is largely unaffected, though the magnitude of the negative parameter on treatment group (\(\alpha\)) does increase when considering only samples close to the replacement date.
In theory, this would be possible to directly test if landscaping improvements require building permits. Unfortunately, this in not the case in Madison.
Again, in Appendix Table 7, I estimate these models using only sales that occur in 3- or 5-year windows around treatment. Though I lose statistical power due to a small sample, the estimated magnitude of the effect only diminishes slightly.
References
Abadie A, Imbens GW (2011) Bias-corrected matching estimators for average treatment effects. J Bus Econ Stat 29:1–11
Abbott JK, Klaiber HA (2013) The value of water as an urban club good: a matching approach to community-provided lakes. J Environ Econ Manag 65(2):208–224
Aizer A, Currie J, Simon P, Vivier P (2017) Do low levels of blood lead reduce children’s future test scores? Am Econ J Appl Econ 10:307–341
Andersen P, Gill R (1982) Cox's Regression Model for Counting Processes: A Large Sample Study. Ann Stat 10(4):1100–1120
Bae H (2012) Reducing environmental risks by information disclosure: evidence in residential lead paint disclosure rule. J Policy Anal Manag 31(404):431
Billings S, Schnepel K (2017) The value of a healthy home: lead paint remediation and housing values. J Public Econ 153:69–81
Christensen P, Keiser D, Lade G (2019) Economic effects of environmental crises: evidence from Flint, Michigan. Working Paper, Department of Economics, Iowa State University
Cornwall D, Brown R, Via S (2016) National survey of lead service line occurrence. J AWWA 118(4):E182–191
Currie J, Davis L, Greenstone M, Walker WR (2015) Environmental health risks and housing values: evidence from 1,600 toxic plant openings and closings. Am Econ Rev 105(2):678–709
Environmental Defense Fund (EDF) (2017) Grading the nation: state disclosure policies for lead pipes. https://www.edf.org/sites/default/files/content/edf_lsl_state_disclosure_report_final-031317.pdf. Accessed 9 Jan 2017
Edwards M, Lambrinidou Y, Scott R, Schwartz P (2009) Gaps in the EPA lead and copper rule that can allow for gaming of compliance: DC WASA 2003–2009. https://oversight.house.gov/wp-content/uploads/2016/03/Marc-Edwards-Final-3-15-2016.pdf. Accessed 9 Jan 2017
Ferrie J, Rolf K, Troesken W (2012) Cognitive disparities, lead plumbing, and water chemistry: prior exposure to water-borne lead and intelligence test scores among World War Two U.S. Army enlistees. Econ Hum Biol 10(1):98–111
Gazze L (2019) The price and allocation effects of targeted mandates: evidence from lead hazards. Working Paper, Energy and Environment Lab, University of Chicago
Halvorsen R, Palmquist R (1980) The interpretation of dummy variables in semi-logarithmic equations. Am Econ Rev 70(3):474–475
Ho D, Imai K, King G, Stuart E (2007) Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal 15:199–236
Kuminoff N, Parmeter C, Pope J (2010) Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities? J Environ Econ Manag 60(3):145–160
Muehlenbachs L, Spiller E, Timmins C (2015) The housing market impacts of shale gas development. Am Econ Rev 105(12):3633–3659
Madison Water Utility (MWU) (2010) Lead and copper rule compliance sampling: summary. https://www.cityofmadison.com/sites/default/files/city-of-madison/water-utility/documents/2010LCRSamplingACfinal.pdf. Accessed 9 Jan 2017
Nevin R (2007) Understanding international crime trends: the legacy of preschool lead exposure. Environ Res 104:315–336
Ngai LR, Tenreyro S (2014) Hot and cold seasons in the housing market. Am Econ Rev 104(12):3991–4026
Nriagu JO (1983) Saturnine gout among roman aristocrats: did lead poisoning contribute to the fall of the Empire? N Engl J Med 308:660–663
Olson E, Fedinick KP (2016) What’s in your water? Flint and beyond. NRDC report 16-06. Natural Resources Defense Council
Pope J (2008) Buyer information and the hedonic: the impact of a seller disclosure on the implicit price for airport noise. J Urban Econ 63:498–516
Rau T, Reyes L, Urzua S (2015) Early exposure to hazardous waste and academic achievement: evidence from a case of environmental negligence. J Assoc Environ Resour Econ 2:527–563
Renner R (2010) Exposure on tap: drinking water as an overlooked source of lead. Environ Health Perspect 118:69–74
Reyes JW (2007) Environmental policy as social policy? The impact of childhood lead exposure on crime. BE J Econ Anal Policy 7(1):1–43
Rosen S (1974) Hedonic prices and implicit markets: product differentiation in pure competition. J Political Econ 82(1):34–55
Tanellari E, Bosch D, Boyle K, Mykerezi E (2015) On consumers’ attitudes and willingness to pay for improved drinking water quality and infrastructure. Water Resour Res 51:47–57
Taylor L, Phaneuf D, Liu X (2016) Disentangling property value impacts of environmental contamination from locally undesirable land uses: implications for measuring post-cleanup stigma. J Urban Econ 93:85–98
Troesken W (2006) The great lead water pipe disaster. MIT Press, Cambridge
Troesken W (2008) Lead water pipes and infant mortality in turn-of-the-century Massachusetts. J Hum Resour 43(3):553–75
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Theising, A. Lead Pipes, Prescriptive Policy and Property Values. Environ Resource Econ 74, 1355–1382 (2019). https://doi.org/10.1007/s10640-019-00372-5
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DOI: https://doi.org/10.1007/s10640-019-00372-5