Hurricane Barry estimates are coming in fact now, with the latest from Corelogic suggesting that the insured wind and flood loss will be from $300 million to as much as $600 million, excluding any claims for the NFIP.
That’s a higher estimate than RMS’ that we explained earlier called for insurance and any reinsurance industry losses from recent hurricane Barry not to exceed $500 million, 50% of which is expected to fall to the National Flood Insurance Program (NFIP).
Previously, risk modeller Karen Clark & Company had forecast an up to $300 million private re/insurance market loss, so excluding the NFIP.
Meaning Corelogic’s figure is quite a bit higher, as even the lowest end of its range at $300 million is higher than the upper-end of RMS’ estimate ($250m if you take off the 50% of expected NFIP impacts).
Yet again this highlights the divergence in results from catastrophe risk models, as well as the difficulty that can present the insurance and reinsurance industry in coming to a determination for the impacts of catastrophe loss event so soon after it occurs.
Corelogic breaks down its estimate into flood and wind, with the private insurance market flood loss not expected to exceed $100 million, but the insured wind loss from hurricane Barry pegged in a range from $300 million to $500 million.
That wind loss estimate alone is higher than RMS’ total estimate of private insurance and reinsurance losses from the storm, while KCC’s estimate sits right at the lowest end of it.
Corelogic says that insured residential and commercial flood loss covered by NFIP policies is estimated to be between $100 million and $200 million from hurricane Barry, with uninsured flood loss estimated to be approximately $100 million.
Adding it all together, the $300 million to $600 million private market losses from wind and flood, plus the NFIP’s $100 million to $200 million, plus the around $100 million of uninsured flood, gives a total “insurable” loss estimate of $600 million to $900 million for hurricane Barry.
Unfortunately these estimates diverge so significantly as to render themselves less useful. It underscores the fact risk models often prove directional at best for specific loss impact figures, while remaining integral to the industry’s ability to analyse, measure and approximate exposure.
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