2023-02-20-美联储-家庭_银行和保险公司对迈阿密飓风的风险敞口_风险分析流程（英）_56页_366kb

Summary of "Household, Bank, and Insurer Exposure to Miami Hurricanes: a flow-of-risk analysis"

• Authors: Benjamin N. Dennis
• Institution: Federal Reserve Board
• Date: February 7, 2023
• Research Focus: This paper analyzes the flow-of-risk across entities in the event of hurricane damage to residential real estate in Miami, focusing on climate change impacts.

Key Methodology

1. Flow-of-Risk Framework:

   • Models hurricane damage through a structured approach tracing losses across:
     • Insurers: First-loss absorbers (net of deductibles).
     • Borrowers: Absorb losses if insurance is insufficient.
     • Creditors: Bear losses if borrowers default.

2. Scenarios:

   • Business-as-Usual (BAU): Extrapolates current market trends (price growth, insurance coverage) without accounting for climate change.
   • Hurricane Ian Spillover Effects: Simulates a reaction to Hurricane Ian, including reduced insurance coverage, halted construction in flood zones, and price depreciation.
   • Cautious Markets: Assumes widespread insurance adoption, proactive adaptation measures, and lower default propensity driven by climate awareness.

3. Data Sources:

   • HAZUS-FEMA Tool: Maps hurricane damage by property type.
   • Home Mortgage Disclosure Act (HMDA): Tracks mortgage flows and balances across institutions.
   • National Flood Insurance Program (NFIP): Assesses flood insurance penetration.

Key Findings

• In a Cat 5 hurricane in 2050, unmitigated losses under BAU scenario approach $226 billion, while spillover and cautious scenarios reduce losses to $63 billion and $98.9 billion, respectively.
• Bank losses vary significantly by scenario:
  • BAU: 54.8% of a bank’s Miami portfolio could be lost.
  • Hurricane Ian: 29.5%.
  • Cautious Markets: 19.3%.
• Insurance coverage is a primary buffer but insufficient, leading to cascading losses through borrowers and creditors.

Policy Implications

The study underscores the need for robust climate scenario analyses in financial stability monitoring. Policymakers and regulatory bodies should improve data integration across climate, insurance, and housing sectors. Strengthening climate resilience through adaptation measures (e.g., elevating properties, flood insurance uptake) reduces systemic risks.

Limitations

• Limited insurance and price data.
• Simplified default models.
• Does not account for market, counterparty, or operational risks.

Appendix Notes

• Detailed loss calculations presented in tables for each scenario.
• Dynamic model for mortgage cohorts and cohort equity quantifies strategic default risk.
• Requires complementary models for holistic climate risk assessment.