Disaster Loss Data
NOAA billion-dollar disaster database — disaster economics intelligence.
No listings currently in the marketplace for Disaster Loss Data.
Find Me This Data →Overview
What Is Disaster Loss Data?
Disaster Loss Data encompasses economic intelligence from major natural catastrophe events, tracking the financial impact of climate-related disasters globally. This data type includes comprehensive records of billion-dollar disasters—wildfires, flooding, severe thunderstorms, and other weather events—along with insured and uninsured loss figures. Organizations use this intelligence to understand disaster economics, assess regional risk exposure, and model future loss scenarios. The NOAA billion-dollar disaster database and similar sources provide historical context showing that global natural disaster damage reached approximately $224 billion in 2025, with insured losses around $108 billion, representing the costliest year for non-peak catastrophe perils. This data is critical for insurers, reinsurers, risk managers, and climate-focused investors seeking to quantify climate exposure and forecast financial impact.
Market Data
$224 billion
Global Natural Disaster Damage (2025)
Source: ASIS Online / Global Reinsurance Data
$108 billion
Insured Losses (2025)
Source: Munich Re
78% ($100 billion)
US Share of Global Insured Losses (2025)
Source: Gallagher Re
$60 billion globally; $51 billion in US
Severe Convective Storm Losses (2025)
Source: Gallagher Re
$41 billion
LA Wildfires Insured Loss (January 2025)
Source: Gallagher Re
Who Uses This Data
What AI models do with it.do with it.
Insurance & Reinsurance Industry
Insurers and reinsurers use disaster loss data to model catastrophe exposure, price premiums, and set policy terms. Data from billion-dollar disaster databases informs underwriting decisions and help carriers understand which regions and perils carry the highest financial risk.
Risk Management & Corporate Planning
Enterprise risk teams and business continuity planners rely on disaster loss data to assess supply chain vulnerability and operational disruption exposure. Understanding historical loss patterns helps organizations prioritize disaster recovery investments and insurance procurement.
Climate & Environmental Investment
Climate-focused investors, asset managers, and ESG analysts use disaster loss intelligence to quantify climate-related financial risks and identify sectors or regions facing elevated exposure. This data supports climate scenario analysis and transition planning.
Government & Policy
Government agencies and policymakers use disaster loss data to inform climate adaptation strategy, infrastructure investment decisions, and emergency preparedness budgeting. This intelligence guides national resilience initiatives and risk reduction priorities.
What Can You Earn?
What it's worth.worth.
Disaster Loss Dataset (Basic)
Varies
Historical billion-dollar disaster records with event summaries, loss figures, and geographic tags. Pricing depends on dataset scope, update frequency, and exclusivity.
Insured vs. Uninsured Loss Intelligence (Premium)
Varies
Granular breakdown of insured and uninsured losses by event, region, and peril type. Higher-tier datasets include reinsurer estimates and claims history.
Real-time Disaster Impact Feed (Enterprise)
Varies
Live-updated disaster loss estimates, preliminary damage assessments, and economic impact projections. Enterprise licensing typically includes API access and custom reporting.
Climate Scenario & Stress Test Models (Institutional)
Varies
Advanced modeling incorporating historical disaster loss data with climate projections. Used by insurers and institutional investors for long-term financial risk assessment.
What Buyers Expect
What makes it valuable.valuable.
Accuracy & Verification
Buyers require data sourced from authoritative bodies (NOAA, Munich Re, Gallagher Re, government disaster databases). All loss figures must be cross-verified against insurer and reinsurer estimates; unverified estimates significantly reduce market value.
Granularity & Attribution
Data must include clear event identification (date, location, peril type), loss categorization (insured/uninsured, direct/indirect), and impact metrics by sector or region. Buyers expect structured data that can be filtered, aggregated, and compared across time periods.
Timeliness & Update Frequency
Historical datasets are valued, but buyers increasingly demand near-real-time or daily updated loss estimates during active disaster periods. Data lag of more than a few days reduces competitive value in risk management and trading contexts.
Provenance & Chain of Title
Buyers require clear documentation of data source, methodology, and any transformations applied. Datasets incorporating official NOAA or Munich Re data command premium pricing; third-party aggregations require transparent sourcing.
Complementary Intelligence
High-value datasets combine loss figures with contextual data: affected populations, infrastructure damage, business interruption estimates, climate attribution, and recovery timelines. Buyers reward datasets that provide actionable insight beyond raw loss numbers.
Companies Active Here
Who's buying.buying.
Global reinsurer and catastrophe risk modeler; actively publishes annual natural disaster loss analyses and uses disaster loss data to inform capital allocation and pricing strategy.
Reinsurance broker and risk advisor; publishes detailed Natural Catastrophe and Climate Reports leveraging disaster loss intelligence for client risk advisory and market analysis.
Property & casualty insurers rely on billion-dollar disaster datasets to model catastrophe exposure, update premium rates, and manage portfolio concentration risk by geography and peril.
Large corporations use disaster loss data to assess supply chain resilience, quantify business interruption exposure, and prioritize disaster recovery and business continuity investments.
ESG and climate risk investors use disaster loss intelligence to quantify climate-related financial risk, model long-tail exposure, and inform transition planning and sector allocation.
FAQ
Common questions.questions.
What exactly is included in a 'Disaster Loss Dataset'?
A disaster loss dataset typically includes records of major natural catastrophe events (wildfires, flooding, severe storms, hurricanes, etc.) with associated economic damage figures, insured loss estimates, geographic location, event date, affected infrastructure, and business sectors impacted. High-quality datasets distinguish between total economic loss and insured loss, and may include data from NOAA's billion-dollar disaster database or equivalent authoritative sources.
How much of natural disaster damage is typically insured?
In 2025, approximately $224 billion in global natural disaster damage occurred, but only $108 billion was covered by insurers—representing an insurance gap of roughly 52%. This protection gap varies significantly by region; the United States accounted for 78% of global insured losses ($100 billion), while developing regions typically have much lower coverage rates.
Who are the primary buyers of disaster loss data?
Primary buyers include reinsurers and insurance carriers (who use data for catastrophe modeling and pricing), corporate risk managers and business continuity teams (for supply chain and operational resilience planning), climate-focused investment firms (for ESG and climate risk assessment), and government agencies (for emergency preparedness and adaptation planning).
How frequently is disaster loss data updated, and does real-time matter?
Authoritative sources like Munich Re and Gallagher Re typically publish comprehensive annual reports in January/February for the prior year. However, buyers increasingly value near-real-time or daily updated loss estimates during active disaster periods—particularly for traders, insurers managing active claims, and risk managers responding to emerging events. Data lag of more than a few days significantly reduces competitive value.
Sell yourdisaster lossdata.
If your company generates disaster loss data, AI companies are actively looking for it. We handle pricing, compliance, and buyer matching.
Request Valuation