Economics of Flood Damage Claims - Large Events Dominate Overall Losses - Should Effective Mitigation Strategies Focus On "The Big Ones"

What type of meteorologic events dominate flood damage claims? Is it from many frequent small storms or a few infrequent black-swan events? Understanding what size of events cause the most losses can help us focus on the most effective flood loss mitigation measures - this is essential for achieving high returns on investment in flood mitigation strategies. By reducing flood losses in an economically efficient manner, high benefit-cost ratios can be achieved.

This post summarizes the distribution of FEMA's flood damage claims and explores what type of storm events - big or small - govern extreme weather losses.

FEMA summarizes the number of payout claims and the total value of payouts for significant flood events, i.e., those with 1500 or more payouts. The total value of payouts, adjusted for inflation to 2018 dollars, is plotted below against the total number of claims in the events between 1978 and 2017:

FEMA Average Claim Amount for Various Event Sizes (Number of Claims) - Small Significant Flood Events with a Minimum of 1500 Claims Per Event

The median number of payouts is 4115 with a median payout amount of $29,700, adjusted to 2018 dollars. This is over a total of 116 flood events from 1978 to 2017.

Larger storm result in more extensive flood damages and numbers of payout claims as shown on the following chart that labels some of the largest tropical storm / hurricane events:
FEMA Average Claim Amount for Various Event Sizes (Number of Claims) - Small and Large Significant Flood Events with a Minimum of 1500 Claims Per Event
How do the larger events affect the flood damage and payout values? The average flood claim payout of $36,200 is above the median value reflecting the skew in catastrophic event distribution - the right tail of rare black-swan events in the probability distribution of events pulls the average above the median.

Five of the 116 event have claim counts that are over ten to forty times the median number of claims. That is, Hurricane Ike and Irene had over 40,000 claims compared to the median count of just over 4000 claims. And Hurricane Harvey had over 160,000 claims. The losses are greater for these larger events with the best-fit line showing average claim values of over $50,000 to over $120,000 for these largest significant events. What effect do these claim counts have on weighted claim amount - they increase the claim-count-weighted average loss to $60,600 - more than double the median claim amount per event that is not weighted by the number of claims in each event.

So when looking at the economic losses associated with a significant flood event, we need to consider the size of the event. And when we develop strategies and best practices for flood resilient communities and flood risk mitigation, striving for significant damage reduction and return on investment in averted flood damage losses, we must also consider what events cause the most damages. Canada's Disaster Mitigation and Adaptation Fund (DMAF), for example, requires return on investment (ROI) evaluations for eligible risk reduction projects. It would appear that to achieve meaningful flood damage reduction ROI we must target solutions toward events leading to the most damages

Looking at FEMA's significant flood events, data show that 3 of 116 events account for 54% of the total inflation-adjusted damages. Those 3 events are Hurricane Harvey, Superstorm Sandy and Hurricane Katrina. And the top 20 events, each with total event claims of over $500M, account for 81% of the total claims. So it is clear that to reduce the bulk of flood damages we have to consider how to increase resiliency in existing communities during the largest storm events. If we target flood risk reduction for the small catastrophic events, the smaller 97 events, we will be addressing only 20% of the total claim value. So the 80/20 rule, the Pareto principle, does apply to flood damage reduction.

FEMA Inflation Adjusted Significant Flood Event Payout Distribution - Pareto Distribution and the 80/20 Rule

The impact on a few recent large events on damages helps show sample-bias in catastrophic event losses as explored in a previous post. That is, up to 2004 prior to Hurricane Katrina, the distribution of losses based on the 1978-2004 sample of events did not consider the true 'population' distribution of flood events that includes very extreme, right-tail events. As Fleming demonstrated in "Yep, We're Skewed", short samples with high skew underestimate losses of the true population of events.


For statistics geeks:

Could it be that the common chorus of explaining recent floods losses as being due to climate change may in fact be explained simply by statistics and larger sample sizes overcoming short sample biases (underestimation)?

Could it be that growth in high risk areas is driving flood damages higher? AON Benfield's review of Hurricane Harvey suggests that growth in at-risk areas explains some flood impacts:

 "Given the volume of water, local infrastructure across southeast Texas was simply unable to handle such an enormous amount of rainfall in a short amount of time. This led to major water run-off that quickly accumulated across a very large area. With so much residential and commercial growth throughout this part of the state – combined with abundant concrete and poor absorbing clay soil –this only worsened the flood impact."

Solutions to flood risk mitigation therefore cannot only be local infrastructure solutions to convey enormous amounts of water but rather land use planning policies to direct development and redevelopment away from high flood risk areas. As AON Benfield notes " Hurricane Harvey’s rainfall reached the 1,000-year rainfall return period based on many time intervals during the course of a number of hours and days.", and it is not cost effective, or technically feasible, to have local infrastructure convey the runoff from events of this magnitude.


Canada Connection (for those appreciate tree-sauce, skatey-punchy, and noble antler cows):

CatIQ claim datasets have been used to evaluate flood damages across Canada according to the size of the flood event (i.e., related to the number of claims). A similar pattern of increasing damages with increasing event size and distribution is apparent in the CatIQ datasets. In contrast to the FEMA claims noted above for many hurricane events, the CatIQ data reflect basement flooding claims primarily, as overland flooding has not been insured in the past and is not widely held. What is the magnitude of these Canadian claims? Aviva Canada provided this summary of claim trends and magnitude:

"In 2014, water damage claims accounted for 44% of dollars paid out on all Aviva Canada property damage claims, compared with 39% in 2004. The average cost per residential water damage claim has increased significantly – going from $11,709 in 2004 to $16,070 in 2014, a 37% increase."

So basement flooding damages are significantly less than FEMA's large scale catastrophe claims. CatIQ data shows that for larger events (those with higher claim counts) the average claim amount does increase above the Aviva Canada values noted above. Comprehensive benefit-cost analysis used to develop ROI rankings for flood mitigation projects would apply the lower range of typical damages to frequent to moderate events and the higher damages/claim amounts to the frequent events, factored by their probabilities. The most frequent storms, typically 5 to 10 year return period events as in a recent study by Atkins for the US EPA do not generate damages.

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