Underwaterwriting: from theory to empiricism in regional mortgage markets in the U.S.

Abstract

This article provides the theoretical foundation for the concept of “Underwaterwriting,” which can be understood as various informational and institutional limitations related to environmental exposure and climate change impacts—specifically flooding and sea level rise inundation—shaping firm participation in mortgage markets. Underwaterwriting suggests that the unevenness of scientific knowledge and local soft information, as well as the institutional barriers for the utilization of that information, could result in determinations of risk that may not accurately reflect long-term asset performance or credit loss. These informational asymmetries may result in assignments of risk that reflect a degree of arbitrariness or inaccuracy that may operate to strand assets and shed or increase market share in ways that are inefficient and may otherwise lead to negative public externalities. Consistent with this theory, this article provides evidence that concentrated local lenders are transferring risk in high-risk coastal geographies in the Southeast Atlantic and Gulf Coasts (U.S.) through increased securitization of mortgages. These findings provide an impetus for supporting more robust analysis of climate-risk in light of forthcoming accounting rules that require an upfront accounting of forward-looking credit losses.

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Notes

  1. 1.

    Loans not for the purchase of 1–4 family homes are excluded from the sample analyzed herein as these are viewed as materially different from 4 family homes. In particular, multifamily condominiums and manufactured housing are each subject to substantially different flood risk profiles and the market footprint of each of these residence categories is correlated with a number of factors. To maximize homogeneity of the underlying collateral stock, conditional on observables, analysis is limited to 1–4 family homes. Furthermore, the sample is limited to loans for the purchase of 1–4 family homes, excluding refinanced loans.

  2. 2.

    This is of course not equivalent to the standard definition of the Herfindahl-Hirschman index (HHI) used in the antitrust literature to measure the concentration of a given market. However, given the similarity of our lender diversification measure to the standard HHI, we follow the shorthand of Loutskina and Strahan 2011 and refer to our measure as HHI throughout.

  3. 3.

    While restricting analysis to loans in census tracts within two miles of the coast significantly reduces the sample size, this does not limit the validity of the analysis described herein. In particular, this paper focuses on the effect of SLR exposure in coastal mortgage markets and uses a convention similar to that found elsewhere in the literature (see Bernstein et al. 2019) to define the geographic extent of coastal markets. Moreover, sufficient variation in SLR exposure still remains when limiting analysis to tracts within two miles of the coast: in the final sample used herein, over half of loans are not exposed to a SLR scenario of 0.30 m.

  4. 4.

    Note that the SLR exposure field captures exposure to different SLR scenarios based on the share of a census tract which would inundated in each scenario. Importantly, this exposure field is based on inundation modeling and does not take a stance on a particular SLR projection. In other words, this SLR exposure field captures the share of a census tract which would be inundated were there to be, for example, 0.30 m. of global mean SLR. It is therefore a static object based on inundation scenarios. This SLR exposure field does not capture, for example, the share of a census tract which will be inundated by 2100 due to SLR nor does it capture the amount of SLR to which a census tract is exposed over the time period examined in the analysis. With that said, estimates suggest that there is a 90% probability of global mean SLR of between 0.2 and 2.0 m. by 2100 (Parris et al. 2012), which suggests that the results using the 0.30 m. SLR inundation scenario may be interpreted as the estimated effect of exposure to some lower bound of end-of-century SLR.

  5. 5.

    While the lender-tract-year panel is unbalanced, the number of tracts remains relatively constant and the number of lenders increases slightly over the time period included in the final panel. Thus, attrition is not of major concern. Though balancing the panel is considered, this approach is sub-optimal given that a large number of unbalanced units (bank-tracts with missing observations for certain years in the final panel) are highly concentrated lenders. Exclusion of these observations would bias the results given the important role of lender concentration.

  6. 6.

    Note that counties are supersets of census tracts, so aggregating lender-tract-year marginal effects to county marginal effects is feasible.

  7. 7.

    Annual interaction term coefficients are obtained by interacting a set of binary variables indicating the year in which a lender-tract-year observation occurs with the main interaction term of interest.

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Correspondence to Jesse M. Keenan.

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Appendix

Appendix

Table 2 Description of variables in final panel
Table 3 Summary statistics for final panel
Table 4 Model Selection
Table 5 Robustness to different SLR scenarios
Table 6 Regional analysis of loan retention
Table 7 Robustness to past flooding exposure
Table 8 Linear probability model of acceptance and retention behavior
Fig. 5
figure5

Loan acceptance and retention behavior over time. Year-by-year coefficient estimates and 95% confidence intervals on Exposurec × HHIb, t from regressions of a acceptance rate and b retention rate on the interaction term of interest and a set of controls. Annual interaction term coefficients are obtained by interacting a set of binary variables indicating the year in which a lender-tract-year observation occurs with the main interaction term of interest. Exposurec is a continuous variable measuring the share of a census tract c’s total area that is exposed to inundation under SLR of 0.30 m. Heteroskedasticity-robust standard errors clustered at the census tract-level are used to construct reported confidence intervals. Observations are at the lender-tract-year-level and include all coastal lender-tract-year observations in the Gulf Coast and Southeast states (AL, FL, GA, LA, MS, NC, SC, TX) for the years 2009–2017. Specifications include the same lender, borrower, and census tract controls as those included in the specifications in Table 1 as well as lender-, census tract-, and county-by-year fixed effects

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Keenan, J.M., Bradt, J.T. Underwaterwriting: from theory to empiricism in regional mortgage markets in the U.S.. Climatic Change (2020). https://doi.org/10.1007/s10584-020-02734-1

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Keywords

  • Climate adaptation
  • Sea level rise
  • Climate-risk
  • Mortgage market
  • Banking
  • Housing