The year 2020 is just months away, and in the latest edition of EXPOSURE — the RMS magazine for risk management professionals, we consider some of the changes that the (re)insurance industry will have undergone in ten years’ time. Mohsen Rahnama, Cihan Biyikoglu and Moe Khosravy from RMS tackle the issues, examining the evolution of risk management, the drivers of technological change, and how all roads lead to a common, collaborative industry platform.Continue reading
The revised earthquake coverages and caps proposed by the New Zealand Earthquake Commission (EQC) came into law as planned on July 1, 2019. As noted in an RMS blog back in February, these well signaled changes – to increase the building coverage from NZ$100,000 to NZ$150,000 and remove the NZ$20,000 contents cover, only had a small effect on the gross average annual loss for both EQC and the private market. Swapping the first layer of contents exposure for a larger, higher layer of building exposure produced a result that was close to neutral.
Examining the Exceedance Probability (EP) curve (see figure 1 below), the changes are small across all return periods. There are small increases in loss for the private market at short return periods (which produce the small increase in average annual loss reported earlier) but very little change at long return periods.
Critically, the modeled gross 1000-year loss to the private market is essentially unchanged, meaning there are no implications with regards to the Reserve Bank of New Zealand (RBNZ) solvency requirements. Further, these EQC coverage changes are not expected to affect the peril balance driving trans-Tasman solvency considerations where both the RBNZ and Australian Prudential Regulation Authority (APRA) standards must be met.
The recent events that shook a relatively remote part of the Mojave Desert region of Eastern California provide an acute reminder of the major risk posed by earthquakes in the state. It has been a while now since California experienced a large earthquake, and the main event in this sequence – with a magnitude of Mw7.1, was the most powerful earthquake to occur in the state in twenty years.
Since then, the field of seismology as well as earth scientific measuring capabilities have undergone quite substantial improvements and innovations. Immediately after the start of the sequence, several coordinated efforts from academic, government and engineering organizations resulted in focused field surveys and the installation of additional, more densely spaced instrumentation to monitor seismicity and surface deformation, in and around the epicentral area.
So far, extraordinary amounts of high-quality data have been collected that will undoubtedly provide new insights and understanding of earthquakes in general and earthquake hazard and risk in (Southern) California, in particular. Work on these new data sets has only just started, but what have we learned so far? Here is a summary of observations and interpretations based on various (preliminary) field surveys, reports and briefings.
It was off to another prestigious London venue last week for the RMS team, to attend the Insurance Post British Insurance Awards at the Royal Albert Hall. In addition to fulfilling lifelong dreams to see Rick Astley perform live, the RMS team was also competing for the Risk and Resilience Award, alongside four other very worthy contenders. And, first presentation of the night, I was delighted to represent RMS to collect this important award.
This award recognized our longstanding charity partner Build Change, who we have worked together with for six years. Both organizations share a mission: to reduce lives lost from disasters by strengthening the built environment in economically deprived areas.
By combining RMS’ risk modeling expertise and institutional support with Build Change’s technical knowledge and grassroots approach, we’ve been able to demonstrate that retrofitting buildings, from homes to schools, in vulnerable neighborhoods across the globe can significantly reduce economic loss and save lives. And one of our many collaborations was an initiative to greatly improve the safety of seismically-vulnerable communities in Colombia.
Without the ability to measure, how do we know if we are making progress?
In December 2012, in preparation for the renewal of the UN Millennium Development Goals, I wrote a report for the U.K. Government Department for International Development (DFID) advocating that catastrophe models should be used to measure progress in disaster risk reduction. I suggested goals could be set to target a 50 percent reduction in expected casualties and a 20 percent reduction in normalized economic losses, over the period of a decade, based on the output of a catastrophe model.
Two years later, the seven targets agreed at the UN meeting on Disaster Risk Reduction, held on March 14–18, 2015, in Sendai, Japan – were a disappointment. The first two targets for “Disaster Mortality” and “Affected People” would simply compare data from 2020-2030 with 2005-2015. The third target was to “reduce direct disaster economic loss in relation to global GDP by 2030”. Yet we know, especially for casualties – even at a global level, a decade is not enough to define a stable mean. For cities and countries, comparing two decades of data will generate spurious conclusions.
And so, it was a relief to see that only two weeks later, the Japanese and Tokyo city governments announced they had set themselves the challenge of halving earthquake casualties over a decade, measured by modeling a hypothetical event based on the M7 1855 Edo earthquake under Tokyo. I referenced this announcement and quoted it widely in presentations, to highlight that risk modeling had been embraced by the country with the most advanced policies for disaster risk reduction.
Over the last two years, I started searching for some update on this initiative. What kind of progress in risk reduction was being achieved, whether the targets for Tokyo would be met? And I found my original links had all stopped connecting. Perhaps in my enthusiasm I had dreamt it?
With the start of the U.S. wildfire season on the horizon, in the latest edition of EXPOSURE – the RMS magazine for risk management professionals, wildfire is our lead story, as we examine whether it now needs to be considered a peak peril. The 2017 and 2018 California wildfires have forced one of the biggest re-evaluations of a natural peril since Hurricane Andrew in 1992, as the industry begins to comprehend the potential loss severities.
The article argues that there are similarities with U.S. wildfire as there was with North Atlantic hurricane in 1992 – catastrophe models were relatively new and had not gained market-wide adoption, and many organizations were not systematically monitoring and limiting large accumulation exposure in high-risk areas. Find out why a rethink is required about how the risk management industry currently analyzes the exposure and the tools it uses.
With the release of version 18.1 on April 22 from RMS, there is plenty to explore, validate and put into production.
Updated Insights on North Atlantic Hurricane Risk
Starting with the RMS North Atlantic Hurricane (NAHU) Models, version 18.1 (v18.1) includes updates to the long-term and medium-term event rates throughout the Atlantic Basin, historical event reconstructions from recent seasons, and hazard and line-of-business specific vulnerability enhancements informed by new data and RMS building research.
Why the Saffir-Simpson Hurricane Intensity Scale had five levels we don’t know. The digits on a hand? Better than three, but lower resolution than the dozen rungs for wind speeds or earthquake intensity? Whatever the reason it seems to work.
In the late 1960s, Herbert Saffir, a Florida building engineer, was sent by the United Nations to study the hurricane vulnerability of low-cost housing in the Caribbean. He realized something was needed to rank hurricane destructiveness. Saffir had some “Richter envy” from observing the ease with which seismologists now communicated with the public. In 1971, he contacted Robert Simpson, head of the National Hurricane Center to help link damage levels with wind speeds.
Seeing the opportunity to communicate evacuation warnings, Simpson also added details around the height of advancing storm surges. Better information was clearly needed, after the loss of life in Hurricane Camille on the Mississippi coast in 1969.
Professor Ilan Noy holds a unique ”Chair in the Economics of Disasters” at the Victoria University of Wellington, New Zealand. He has proposed in a couple of research papers that instead of counting disaster deaths and economic costs, we should report the “expected life-years” lost, not only for human casualties but also for the life-years of work that will be required to repair all the damage to buildings and infrastructure.
The idea is based on the World Health Organization’s Disability Adjusted Life Years (DALYs) lost through disease and injury (WHO 2013). The motivation is to escape from the distortion introduced by measuring the impact of global disasters in dollars, as loss from the richest countries will always dominate this metric. Noy’s proposal converts injuries into life-years lost, based on how long it takes for the injured to return to complete health, while also factoring the degree of permanent disability multiplied by its duration. This is topped up by a “welfare reduction weight” for all those exposed to a disaster. The final component of the index attempts to capture how many years of human endeavor is lost to recovering the buildings and assets destroyed in the disaster.
There is plenty to argue over in terms of how deaths, injury and damage should be combined. In particular, the assumption that additional work to rebuild a city, is the same as a shortened life, seems somewhat reductive.
Across the global risk management community, we are bombarded by new information every day. As risk professionals we have to prioritize how we give our attention to new information. From an RMS perspective, when we release new model insights, we know there is a need to be concise and boil down huge research projects into just the important details. But there is a concern that the top-level results get taken as a uniform value that can be applied across the board, losing vital nuance.
When RMS released its New Zealand Earthquake High-Definition (HD) model in mid-2016, an important message was that the annual average loss (AAL) had increased by 30 percent. The ground-up, all-lines, countrywide AAL increased 30 percent relative to the previous version of the model released in 2007. An increase in loss came as no surprise after the Canterbury Earthquake Sequence of 2010/11 – see our New Zealand earthquake blogs.
The HD model was launched at two industry seminars in Wellington and Auckland and came with online documentation: some 44 pages of Understanding Changes in Results and 114 pages of model methodology, supplementary materials on our RMS OWL client portal and a team of modelers happy to talk about their work.
Faced with this information, one approach is to note that the New Zealand market is very consolidated so industry figures should be useful guides for actual portfolios. Let’s just use the old model and scale it by 30 percent. “She’ll be right”, as they like to say in New Zealand. But with two models being so different, this scaling-up would not make sense. Why are they so different?