PlumBot: Bot: Creating new article from JSON

2026-03-12T00:00:04Z

Bot: Creating new article from JSON

New page

📉 '''Mean damage ratio''' is an [[Definition:Actuarial science | actuarial]] metric that expresses the average [[Definition:Loss | loss]] as a proportion of the total [[Definition:Insured value | insured value]] across a defined set of [[Definition:Risk | risks]] or events, commonly used in [[Definition:Property insurance | property insurance]] and [[Definition:Catastrophe model | catastrophe modeling]]. If a hurricane causes $50 million in losses to a portfolio with $500 million in total insured value, the mean damage ratio is 10%. The metric distills the relationship between exposure and damage into a single, comparable figure, making it indispensable for benchmarking peril severity and calibrating [[Definition:Pricing | pricing]] models.

⚙️ Catastrophe modelers and [[Definition:Reinsurance | reinsurance]] [[Definition:Underwriter | underwriters]] rely on mean damage ratios at multiple levels of granularity — by individual property, by geographic zone, by [[Definition:Construction class | construction type]], or across an entire [[Definition:Portfolio | portfolio]]. [[Definition:Catastrophe model | Catastrophe models]] from vendors like [[Definition:AIR Worldwide | AIR]], [[Definition:RMS | RMS]], and [[Definition:CoreLogic | CoreLogic]] generate event-level damage ratios by simulating how physical hazards (wind speed, flood depth, ground shaking) interact with building characteristics through [[Definition:Vulnerability function | vulnerability functions]]. Aggregating these event-level ratios across thousands of simulated scenarios produces the mean damage ratio, which feeds into [[Definition:Expected loss | expected loss]] calculations, [[Definition:Probable maximum loss (PML) | PML]] estimates, and [[Definition:Excess of loss reinsurance | excess-of-loss]] layer pricing.

💡 A seemingly small shift in the mean damage ratio can translate into enormous financial consequences for insurers and [[Definition:Reinsurer | reinsurers]] managing large property books. After major loss events, the industry often reassesses historical damage ratios to determine whether existing models underestimated vulnerability — as happened following the 2017 Atlantic hurricane season and the 2011 Tōhoku earthquake. Updating mean damage ratios in light of new evidence drives adjustments to [[Definition:Technical price | technical pricing]], [[Definition:Reserve | reserves]], and [[Definition:Capital | capital]] requirements. For [[Definition:Insurtech | insurtech]] firms building next-generation exposure analytics, improving the accuracy and granularity of damage ratio estimation — using satellite imagery, IoT sensor data, or machine learning — represents a significant competitive opportunity.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe model]]
* [[Definition:Vulnerability function]]
* [[Definition:Probable maximum loss (PML)]]
* [[Definition:Expected loss]]
* [[Definition:Loss ratio (L/R)]]
* [[Definition:Insured value]]
{{Div col end}}

Definition:Mean damage ratio - Revision history

PlumBot: Bot: Creating new article from JSON