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Lead Pipes, Prescriptive Policy and Property Values

Environmental and Resource Economics volume 74, pages 1355–1382 (2019)Cite this article

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

  1. 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.

  2. A large proportion of these violations were for monitoring/reporting violations rather an exceedance of the LCR standard. See Olson and Fedinick (2016).

  3. 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.

  4. 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.

  5. See Madison city ordinances: Sec. 13.18 Cr. by Ord. 12,544.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. See Muehlenbachs et al. (2015) or Abbott and Klaiber (2013) for recent applications related to housing markets.

  13. 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.

  14. 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.

  15. 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.

  16. In theory, this would be possible to directly test if landscaping improvements require building permits. Unfortunately, this in not the case in Madison.

  17. 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.

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Authors and Affiliations

  1. Department of Agricultural and Applied Economics, University of Wisconsin-Madison, 427 Lorch Ct, Madison, WI, 53706, USA

    Adam Theising

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Appendix

Appendix

See Tables 6 and 7.

Table 6 This table shows results from alternative specifications for the hedonic model using the data sample that captures the full extent of the market
Full size table
Table 7 This table measures the robustness of baseline and repeat sales results to limiting the temporal window around a lead pipe replacement when constructing the study sample
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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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Keywords

  • Lead pollution
  • Water quality
  • Hedonic valuation
  • Diff-in-diff
  • Property value