Why this topic, and why now
Climate hazards are increasingly material for credit markets. When extreme events disrupt income and damage collateral, the effects can persist through delinquency, default, recovery values, and the timing of cash flows that back securitized products. Yet many credit frameworks were built around assumptions that are better suited to cyclical macro shocks than to physical hazards that can be clustered, location specific, and repeatedly disruptive.
This chapter was motivated by a practical question. If physical climate risk is rising, how should credit securitization and mortgage pricing mechanisms evolve so that risk is measured more accurately and managed more transparently, without ignoring distributional and affordability consequences.
What the chapter studies
The chapter examines the relationship between climate related threats and credit securitization markets, with a particular focus on blue lining patterns that may emerge in hazard prone regions when pricing and guarantee fees respond to local risk. In simple terms, blue lining refers to the potential for households and communities in high hazard areas to face worse credit access or higher costs because climate exposure is priced in, sometimes unevenly.
The chapter also argues that current security charges and fee structures may not fully incorporate climate risk exposure in a forward looking way. That gap matters because the fee and pricing layer is one of the main places where risk signals are transmitted to lenders, borrowers, and investors.
A second focus is the role of government sponsored enterprises and the broader ecosystem of credit risk transfer and securitization. If a large share of mortgage credit risk is intermediated through standardized channels, then the design of pricing adjustments and guarantee related charges becomes central to how climate risk is allocated across private markets and public balance sheets.
Where AI fits, and what it can add
A key challenge in climate credit work is translation. Physical risk is described in terms of hazards, exposure, and vulnerability, while credit risk is measured through default likelihood, loss severity, prepayment behavior, and cash flow timing. These worlds connect, but not always linearly.
In the chapter, AI driven modeling is positioned as a useful complement to standard econometric approaches, especially when relationships are nonlinear, state dependent, and driven by interactions between borrower characteristics, geography, and macro conditions. Rather than treating climate exposure as a background control, the objective is to integrate climate signals into predictive and scenario based analysis in a way that can inform stress testing and securitization design.
One practical takeaway is that model choice matters less than the modeling discipline. Clear feature design, careful validation, and transparent limits are critical when climate inputs are uncertain and can change over time as hazards evolve and adaptation investments occur.
The policy lever: loan level price adjustments
A core policy element discussed is loan level price adjustments, often shortened to LLPAs. In many mortgage systems, LLPAs are add on fees or pricing adjustments that reflect borrower and loan characteristics such as credit score, loan to value, occupancy, and product type. The chapter’s broader point is that, when climate exposure meaningfully affects expected losses, pricing mechanisms should be able to incorporate that signal in a consistent and explainable way.
However, pricing reform has tradeoffs. If climate risk is priced more aggressively, affordability and access can be affected, and impacts may fall disproportionately on lower income households and communities with limited ability to relocate or self insure. A climate aware approach therefore needs to consider both risk management and distributional outcomes.
This is why I framed the discussion around strategic fee adjustments rather than a single mechanical rule. The goal is to improve alignment between risk measurement and pricing, while keeping the design transparent enough to be scrutinized and debated.
What surprised me while writing
Two themes stood out while developing the chapter.
First, the financial stability dimension is easy to understate. If climate exposures are correlated across borrowers and regions, then losses and downgrades can become system wide rather than idiosyncratic. That raises the value of scenario analysis and system level stress testing frameworks, not only loan level prediction.
Second, the incentives are complex. Pricing can encourage resilience investment, but it can also accelerate exclusion if households face higher charges without feasible adaptation paths. This tension is one reason the chapter highlights complementary instruments such as catastrophe bonds and insurance linked securities as part of a broader resilience toolkit, rather than relying only on mortgage pricing to carry the full burden.
Implications and next steps
For researchers, the next steps are empirical and interdisciplinary. Better integration of post 2020 disaster experience can strengthen external validity, especially across different hazard types and institutional settings. The chapter highlights future work that extends the analytical framework using post 2020 disaster data, including Hurricanes Harvey, Ida, and Ian, to validate machine learning extensions and improve generalizability.
For practitioners, the immediate implication is that climate risk needs to be treated as a factor that can change both expected loss and tail risk. That affects securitization performance, credit enhancement design, investor disclosures, and how pricing adjustments should be interpreted.
For policymakers, the key takeaway is design. Pricing adjustments and guarantee fee structures are not only technical parameters. They shape incentives, access, and risk allocation, so climate integration should be approached with transparency, measured implementation, and clear evaluation criteria.
References and supporting links
-
Merchant, M. (2026). Adapting Credit and Asset-Backed Financing to Climate Threats: AI-Driven Modeling and LLPA Policy Reform. Springer, Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-3-032-10895-1_7
-
Network for Greening the Financial System (NGFS). Climate scenarios for central banks and supervisors. https://www.ngfs.net/en/publications-and-statistics/publications/ngfs-climate-scenarios-central-banks-and-supervisors
-
Bolton, P. et al. (2020). The Green Swan: Central Banking and Financial Stability in the Age of Climate Change. Bank for International Settlements. https://www.bis.org/publ/othp31.pdf
-
Giglio, S., Kelly, B., Stroebel, J. (2021). Climate Finance. Annual Review of Financial Economics. https://www.annualreviews.org/content/journals/10.1146/annurev-financial-102620-103311
-
Bernstein, A., Gustafson, M., Lewis, R. (2019). Disaster on the Horizon: The Price Effect of Sea Level Rise. Journal of Financial Economics. https://www.sciencedirect.com/science/article/abs/pii/S0304405X19300807
-
Thomson, H. et al. (2023). Systemic financial risk arising from residential flood losses. Nature Climate Change. https://pmc.ncbi.nlm.nih.gov/articles/PMC10162782/
-
Ouazad, A., Kahn, M. (2019). Mortgage finance and climate change: Securitization dynamics in the aftermath of natural disasters. NBER Working Paper. https://www.nber.org/system/files/working_papers/w26322/w26322.pdf
-
Fannie Mae. Loan Level Price Adjustments (LLPA) overview and matrix resources. https://singlefamily.fanniemae.com/media/9391/display