The Risks of Hazard

Next week I will be heading to Asia to try and find out how insurers, brokers, and reinsurers are handling their risk assessment, particularly for flood risk. Asia is an exciting market for property insurers, with economies growing quickly and property values becoming valuable enough to stimulate thriving markets for insurance — especially for natural catastrophe coverages. It is also a breeding ground for innovation right now, as everyone is working hard to ensure they have the necessary tools to understand risk from all natural catastrophes (and Asia gets them all!). This article by James Nash (of Guy Carpenter) summarizes the situation in Asia and their response to it very well.

Intermap has partnered with Anchor Point, a wildfire mitigation specialist based in Boulder, Colorado, to offer its No-HARM wildfire model to InsitePro subscribers. In today's blog post, I asked Anchor Point COO Chris White to discuss the advantages of No-HARM over publicly available wildfire models.

When a property insurer underwrites a specific natural peril, one of the first things they need to do is identify a model to use for determining the risk at the locations they will quote. That hunt almost always starts with Google searches of government organizations that might have a model available for use. For those underwriting flood outside the USA, there are flood models published by local and regional governments. Meanwhile, those underwriting flood within the USA of course have the flood insurance rate maps (FIRMs) published by FEMA. For those underwriting wildfire in the USA, the search might start with HAZUS from FEMA (although they don’t have wildfire module) and end with the U.S. Forest Service (USFS).

The use of risk models and maps to predict the likelihood and intensity of flooding has an inherent, although not immediately apparent, flaw: dependability. Flood maps and models are relatively static, updated every few years (maybe), while weather and climate are extremely dynamic. So how can static models predict dynamic events dependably? A recent study suggests they really can’t.

One of the things that make natural catastrophes inherently unpredictable is the ever-changing nature of the natural world. This is a trivial observation, but it is fundamental to understanding how peril models work and what their limitations are. This post will discuss flood specifically, but the case is the same for all natural catastrophic phenomena.

Here in Colorado, where Intermap is based, one of the most dramatic fires in recent memory was the Waldo Canyon fire, which engulfed some of the western suburbs of Colorado Springs in 2012. It was an intense wildfire that stormed down the front range incredibly fast and consumed almost 350 homes. However, most of those houses weren’t behind the fire-line — they were ignited by embers blown by the same winds that blew the fire down the mountains.

One of the regular topics of this blog has been the importance of having the best data for the job when trying to understand risk, as well as understanding what can and can’t be done with the data at hand. Earlier this month (March 2015), there was a fantastic case study showcasing this principle in action in the U.K. flood insurance market.