Models lose accuracy for super-cat storms, warns JLT Re

Loss estimates produced by modelling firms for north Atlantic hurricanes are unreliable and often wide of the mark warns JLT Re, as it calls for greater transparency to help solve the issue. In a new report analysing loss estimates provided by AIR and RMS for major Atlantic hurricanes since 2004, JLT Re says models proved to be more accurate for storms that produced moderate losses but, as seen with the 2017 trio of hurricanes, are wide of the mark for superstorms. David Flandro, global head of analytics at JLT Re, said the risk transfer market has been sceptical about hurricane loss estimates during the past 15 years, adding that variations shown in the report explain why. “At first glance, there is an overriding trend towards significant loss underestimation (the 2017 hurricanes apart), and it is not even immediately apparent that the range of loss estimates narrows during the lifespan of storms, or that they always become more accurate,” he said. Losses from hurricanes with super-cat characteristics, including Katrina and Sandy, were significantly underestimated by both modellers. JLT Re says the models do not work well for hurricanes where losses extend beyond wind and into areas that are either not modelled or not understood. Last year’s Harvey, Irma and Maria storms tested the loss models. AIR’s loss forecast was significantly under the final bill for Harvey, while it overestimated for Irma and fell within the lower range of forecasts for Maria. At the same time, RMS’s loss estimate was in range for Harvey, significantly over the final bill for Irma and under for Maria. Josh Darr, lead meteorologist at JLT Re, said: “This highlights the inherent difficulties modelling companies face in predicting losses for complex hurricane events that strike highly-populated urban areas. These types of events often bring unforeseen consequences that cause losses to spiral.” Mr Darr said that estimates for Hurricanes Katrina, Ike and Sandy show that catastrophe models have struggled to generate accurate loss ranges. In each case, unmodelled loss components accounted for a significant proportion, if not the majority, of the total cost, he said. Mr Darr noted that risk modelling firms have recalibrated models to include previously unmodelled factors. The report notes that the accuracy of models for Harvey, Irma and Maria were mixed, but given the amount of data collected from the storms and technical developments, they should produce greater accuracy in future. Keith Leung, head of catastrophe modelling – international at JLT Re, said greater transparency around underlying assumptions used in the modelling process would help the market and improve accuracy. “2017 reinforced the importance of understanding differences between estimates. Working with a range of modelled, post-event views can be valuable as long as the important drivers are clearly highlighted, particularly in situations where significant divergences occur,” said Mr Leung. “Catastrophe modelling firms can assist the market further here by better communicating the key drivers of loss within each estimate and providing more transparency around the various assumptions being applied. It is likewise incumbent on market participants to review rigorously, or even challenge, some of the more extreme loss estimates released by modelling firms,” he added.

Models lose accuracy for super-cat storms, warns JLT Re

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