Part 2/3 of disaster estimation - Understanding the expected monetary loss

In part 1 and part 1b of this series, we reviewed some of the ways in which disaster estimation modelers go about estimating the probability of occurence of a catastrophic event. The next phase is the estimation of expected dollar losses when the catastrophe does take place. What would be some of the impacts on the economic activity within a region and how widespread would be the impacts?

This is where the 'sexiness' of model building techniques meets the harsh realities of extensive ground work and data gathering. When a disaster does occur, the biggest disruptions are usually to life and property. Then there are additional longer term impacts to the economic activity of the region and this is driven both directly by the damage to life and property and also indirectly by the impacts to business continuity and ultimately by the confidence that consumers and tradespeople alike continue to have about doing business in the region. Lets examine this one piece at a time.

The disruptions to life and property can be examined by the number of dwellings or business properties that are built specifically to resist the type of disaster event we are talking about. In the case of fires, it is the number of properties that are built with the right building codes that are built under the right safety codes. This type of information requires some gathering but is publicly available information from the property divisions of several counties. In the case of hurricanes, it can be the number of houses that are constructed after a certain year when stricter building codes started to be enforced. This type of data gathering is extremely effort intensive but is often the difference between a good approximate model versus a really accurate model that can be used for insurance pricing decisions. With a competitive market like insurance where there are many companies operating essentially on price, the ability to build accurate models is a powerful edge.

The damage to life often has a very direct correlation to the amount of property damage. Also with the early warning systems in place ahead of disasters (except earthquakes, I suppose), it has become quite common to have really large disasters like hurricanes not resulting in any major loss of life. One significant example was Hurricane Katrina where more than a thousand people lost their lives in the Gulf Coast area and particularly in New Orleans.

In the next article in the series, I will provide an overview of the ReInsurance market. Which is where a lot of this probabilistic modeling ultimately gets applied.