Disentangling the Impacts of Environmental Contamination from Locally Undesirable Land-uses (LULUs) on Residential Pro

1. Disentangling the Impacts of Environmental Contamination from Locally Undesirable Land-uses (LULUs) on Residential Property Values Xiangping Liu, Laura Taylor, and Daniel Phaneuf June 25, 2010

2. Literature on Environmentally Contaminated Sites • Use distance to a site listed on National Priority List (NPL) to proxy its impact • Study a single or several NPL sites in one area • Summarized in Kiel and Williams (2007)

3. Literature on Environmentally Contaminated Sites, contd. • Heterogeneity of the impact by site • Kiel and Williams (2007; all NPL sites) • Stigma • Messer et al. (2006) • McCluskey and Rausser (2003) • Dale et al. (1999)

4. Our Contribution • Particular focus is to separate two impacts: • Contamination/cleanup (listing/delisting) • LULU (commercial or industrial properties) • Control for sites that are on both federal and state priority lists

5. Our Contribution, contd. • Use accurate site boundary rather than the centroid • Estimate heterogeneous impacts of site types • Commercial vs. industrial sites • Landfill, solvent, and military sites vs. other sites • Use parcel-level transaction data, with careful attention to local unobservable community characteristics

6. Identification strategy--geographic matching • Key issue: identify treated group & construct unobserved counterfactual for the treated observations. • Distance matters, but distance can be correlated with local unobservable characteristics. • Local community characteristics, if not controlled for, could bias the estimation.

7. Identification strategy, contd. • Treated observations: • Residential properties within 0.3-mile buffer to the boundary of a listed site • Identify land-use of each listed site • Control observations: • Residential properties within 0.3-mile buffer to the boundary of a clean commercial/industrial (COM/IND) site • Identify land-use of each “clean” site • Match clean COM or IND sites to contaminated sites based on their spatial locations

8. Identification strategy--geographic matching • Match treated & control observations based on their spatial locations: • Treated residential properties are within 0.3 mile buffer of listed site

9. Illustration of geographic matching

10. Identification strategy--geographic matching • Match treated & control observations based on their spatial locations: • Treated residential properties are within 0.3 mile buffer of listed site • Create buffer ring of 0.7-1.0 mile from boundary of listed site

11. Illustration of geographic matching

12. Identification strategy--geographic matching • Match treated & control observations based on their spatial locations: • Treated residential properties are within 0.3 mile buffer of listed site • Create buffer ring of 0.7-1.0 mile from boundary of listed site • Any “clean” commercial/industrial site lying within buffer ring serves as a control com/ind site

13. Illustration of geographic matching

14. Identification strategy--geographic matching • Match treated & control observations based on their spatial locations: • Treated residential properties are within 0.3-mile buffer of listed site • Create buffer ring of 0.7-1.0 mile from centroid of listed site • Any “clean” commercial/industrial site lying within buffer ring serves as a control com/ind site • Control residential properties are within 0.3-mile buffer of control com/ind site

15. Illustration of geographic matching

16. Identification strategy--geographic matching • Match treated & control observations based on their spatial locations: • Treated residential properties are within 0.3-mile buffer of listed site • Create buffer ring of 0.7-1.0 mile from centroid of listed site • Any “clean” commercial/industrial site lying within buffer ring serves as a control com/ind site • Control residential properties are within 0.3-mile buffer of control com/ind site

17. Illustration of geographic matching

18. Heterogeneous effects and local community level unobservable • Conduct analysis for COM and IND sites separately • Examine the impact by site types, eg. landfill, solvent, military sites, and other sites separately • Control for site fixed effect • Check the robustness of the result by controlling both site fixed effect and local community characteristics

19. Data • Study area: Minneapolis/St. Paul metropolitan statistical area, Minnesota • Contaminated sites: All sites listed on state or federal registers of contaminated sites. • 108 sites (51 listed after 1990 / 59 delisted after 1990) • Site boundaries (manual identification) • Land-use • Contaminate type • Residential property transactions 1990-2007 • housing attributes: acres, # rooms, bedrooms, baths, age, school district.

20. Data • Community characteristics: • Census demographics at block group level are compiled from Geolytics • Local land-use characteristics (within 0.5 miles or 1 mile) • % land in: commercial use; industrial use; residential use; apartments, open space, water, highway

21. Legend Control residential Listed sites Treated residential Clean com/ind Example of treated & control residential properties

22. Empirical specification • Mathematical representation of estimating equations --Listing Ln(sales price)=α*treat+β*tl+γ*treat_tl +constant+α*X+ site fixed effect+ time effect… --De-listing Ln(sales price)=α*treat+β*tdl+γ*treat_tdl +constant+α*X+ site fixed effect+ time effect…

23. Preliminary sample results: listing • All listed sites, no impact heterogeneity in land-use of site

24. Preliminary sample results: listing, continued. • Heterogeneity by land-use: Commercial versus Industrial (all covariates & most recent transaction)

25. Preliminary sample results: de-listing All de-listed sites, no impact heterogeneity in land-use of site

26. Preliminary sample results: de-listing, continued • Heterogeneity by land-use: Commercial versus Industrial (all covariates & most recent transaction)

27. Preliminary sample results: landfill, solvent, military sites separately • Heterogeneity by site types: Landfills, solvent sites and military sites (all covariates & most recent transaction)

28. Conclusion • The effect of contamination--listing a site reduces nearby residential property value 3-7 % • The listing has larger impact on residential property surrounding a industrial site than a commercial site • about 7-12% for a COM site • 47% an IND site • Delisting some NPL/PLP sites improves nearby residential property by 5%. • However, there exist stigma effect of cleanup on the nearby residential property for the landfill sites.

29. Work-to-do • Matching NPL/PLP sites based on local community characteristics rather than geographical location (a propensity score matching or matching on local community characteristics directly). This method allows us to further check how strong local community characteristics or unobservables affect estimation results. • Separate regressions for detached residential properties, townhouse, and condo

30. The EndThank you!

31. Preliminary sample results: de-listing, continued • Heterogeneity by land-use: Commercial versus Industrial (all covariates & most recent transaction)

32. Preliminary sample results: de-listing, continued De-listed sites, exluding landfill, solvent & military sites

33. Preliminary sample results: de-listing, continued De-listed sites, only landfill, solvent & military sites