Tag Archives: catastrophe modeling

The Age of a Roof and The Price You Pay: New Analysis of Hurricane Risk in the U.S.

RMS has completed research on hurricane risk to single-family dwellings using an improved understanding of roof age, which can lead to more accurate loss projections using our models

Residential gable end roof failure in the Bahamas, observed following Hurricane Matthew

Residential gable end roof failure in the Bahamas, observed following Hurricane Matthew

Weak roofs mean losses during hurricanes. During reconnaissance trips to the southeast U.S. and the Bahamas following Hurricane Matthew last fall, RMS experts saw ample evidence of this simple fact.  Their on-the-ground survey highlighted everything from shingle and tile damage to complete roof failures.

Roof weakness significantly influences RMS’ view of structural vulnerability in our North Atlantic Hurricane models, which can factor in a roof’s age, covering, and shape into calculations of potential loss. However, this valuable property data is not captured by many insurers, and this could represent a missed business opportunity to improve underwriting – whether it be pricing or risk selection.

Extending the Data, Refining the Insights

RMS already has a dataset of hurricane claims from over one million single-family dwelling (SFD) homes in Florida and the northeast U.S., representing $240 billion in total insured value. However, this dataset lacks roof characteristics for a majority of the homes, so we augmented it with roof age information obtained from BuildFax, which holds detailed building characteristics for over 90 million properties in over 10,000 U.S. cities and counties. From this enhanced dataset we found:

  • About 70 percent of Florida homes (SFDs) had roofs aged 10 years or older at the time of the 2004-05 hurricanes
  • Roughly half of the Northeast homes (SFDs) had roofs aged 20 years or older at the time of Superstorm Sandy (2012)
  • Only 20% of all homes (SFDs) still had their original roofs, although this proportion was lower for coastal properties than for inland properties

So what was the relationship between roof age and losses? In the second stage of our research, our vulnerability modelers paired the exposure data with 182,000 hurricane claims, totaling $2.25 billion in paid losses, to look for patterns related to roof age.

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Normalized severity of Florida claims from the 2004-05 hurricanes, by roof age and selected wind speed bands, for all risk classes

Normalized severity of Northeast claims from Sandy, by roof age and selected wind speed bands, for all risk classes.

Normalized severity of Northeast claims from Sandy, by roof age and selected wind speed bands, for all risk classes

 

 

 

 

 

 

 

 

 

As expected, we found that homes with older roofs generally corresponded with more claims, and claims of greater severity. This was most evident at the low wind speeds experienced in the Northeast U.S. during Superstorm Sandy, as well as at higher wind speeds experienced in the Florida hurricanes. These graphs show that buildings in Florida with a roof older than 20 years are associated with claims that are between 50-100% more severe, compared with buildings having a roof less than five years old. A similar trend appears in the Northeast, but is muted because of the smaller dataset.

That’s the picture from historical data. But what about modeling potential future events? To answer that question we analyzed the enlarged Florida dataset, focusing on how roof age at a particular location compares to the industry average for that region.

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Change in modeled AAL by Florida county when including roof age information from BuildFax

The change in modeled average annual loss (AAL) by county shows a patchwork of increased and decreased risk that corresponds to the average roof age of properties in each county.

So we can see that using roof age data leads to significant differences in modeled loss within regions.

That’s a valuable insight in itself. But we decided to drill down a little deeper.

 

 

 

From counties to ZIP codes to individual locations

Although the maximum change in AAL was less than 10% at the county level, changes of up to 20% were observed at the level of ZIP codes. These results show that improved understanding of predominant roof age could influence a company to change its regional underwriting strategy or refine its rating territories.

Going more granular still, within each county and ZIP code there is variation in the roof age of individual homes and this is critical to consider when writing new business. The scatter plot below shows the change in AAL at individual locations. Those homes with older roofs produce higher than average AAL and vice versa.

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Change in modeled AAL by location when including roof age information. “Location AAL” (x-axis) represents AAL without roof age

So when we go down to the level of individual locations the impact of roof age data leads to loss changes of up to 50%, demonstrating higher significance than at the regional level. For high hurricane risk locations in Florida with large baseline AALs, this change translates into substantial dollar amounts. That’s crucial to know, revealing key opportunities to improve underwriting practices. For instance, companies might choose to quote more competitively on price for properties with newer roofs.

Unsurprisingly, over time strengthened building codes and practices have led to stronger roofs that are more resilient to hurricane damage. But this research tells us much more – the sheer magnitude of modeled loss changes observed was significant, with clear implications for profitability, as explained by BuildFax CEO Holly Tachovsky:

“These results reveal key opportunities to improve underwriting practices, including pricing and risk selection. A focus on roof age can be the difference-maker for loss ratios in certain geographies. As a result, we see a growing level of sophistication among carriers that want to rate and select with a higher degree of accuracy.”

RMS remains committed to partnerships with industry experts like BuildFax to communicate the business benefits of emerging trends in the (re)insurance space.

Hurricane Risk on the U.S. East Coast: The Latest RMS Medium-Term Rate Forecast is More Than Just a Number

For the meteorologist in me, hurricane and climate research is fascinating in its dynamism. The last two years have seen continuous scientific debate about the state of Atlantic basin hurricane activity, which we’ve reflected on thoroughly in the RMS blog.

But for the insurance industry, it’s more than just a fascinating debate: business decisions depend on clear insight. It’s more than just a number.

In April with the release of the RMS Version 17 North Atlantic Hurricane Model, we will include the latest biennial update to the industry’s long-term rates, in addition to the RMS medium-term rate forecast.

For the first time since its introduction, the RMS medium-term rate forecast has dipped slightly below the long-term rate.

For the U.S. as a whole, the new 2017-2021 medium-term rate forecast MTRof hurricane landfall frequency is now one percent below the long-term rate for Category 1–5 storms, and six percent for major hurricanes (Category 3–5 storms).

Mind the Tail

The impact of the rate changes on the view of risk will vary from portfolio to portfolio. Measuring the new medium-term rate against the RMS Industry Exposure Database, we see a 16 percent decrease in the U.S. average annual loss (AAL) relative to the previous medium-term rate forecast – mainly driven by lower risk in Florida and the Gulf.

However, to focus solely on the headline AAL-based changes, or the national impacts, ignores the risk implications of the unique atmospheric conditions and key features of the new forecast.

At the 250-year return period, the decrease is more muted – at eight percent – which positions the medium-term rate slightly higher (one percent) than the long-term rate. Unlike with previous below-average periods, persisting warm sea surface temperatures in the Atlantic continue to indicate that the medium-term rate risk remains above the long-term rate in certain key U.S. regions, such as the Northeast.

The Science and Process Underpinning the Medium-Term Rates

Grounded in objective science, we follow a systematic process to develop the biennial medium-term rate each time we update it. We analyze 13 different statistical climate models, which all provide a five-year forecast of activity for the Atlantic basin.

The climate models reflect three main theories of hurricane variability in the Atlantic over recent decades:

  • Shift models identify historical, multi-decadal periods of high or low hurricane activity, which are viewed as natural, inevitable oscillations
  • Sea surface temperature (SST) models identify relationships between SSTs and hurricane landfalls in the past and use these to predict similar patterns in the future
  • Active baseline models suggest that the low activity phase of the 1970s and 1980s was caused not by natural variability, but by high levels of atmospheric aerosols which are not expected to recur in the future

To provide a more reliable forecast, we take a weighted average across all 13 models – based on tests made of each model’s predictive “skill.” These tests compare how well the models predict hurricane activity in sample periods from the past, against what occurred. This rigorous testing process is revisited with each release of the medium-term rate.

The Latest Data – How Do the Climate Models Interpret It?

The new medium-term rate forecast uses updated information from the HURDAT2 hurricane dataset and the latest sea surface temperature data, including the 2014 – 2016 seasons.

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North Atlantic Basin major hurricane counts, 1970-present

The updated hurricane data reveals a four-year stretch of below-average Atlantic major hurricane activity between 2012 and 2015, leading to a five-year average trend that is decreasing.

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North Atlantic Basin main development region sea surface temperatures, 1970-present

On the other hand, sea surface temperatures over this same period have been rising. Energy derived from warm temperatures serves as an important driver for hurricanes – so you would expect to see an increasing rate of hurricanes, not fewer.

When the data is fed into the climate models they do not point in the same direction for future hurricane activity.

The shift models used in our medium-term rate forecast focus on the decrease in major hurricanes and identify the seasons since 2011 as statistically distinct from acknowledged active periods observed since 1950. This could be significant because it may indicate a transition to a quieter phase of hurricane activity, as discussed in Nature Geosciences.

But while the shift models indicate this transition, both the sea surface temperature and active baseline models do not identify a similar transition to a less active hurricane phase, in part based on the warmer Atlantic sea surface temperatures.

It’s Not Just a Factor

As I discussed earlier, the medium-term rate considers multiple drivers of hurricane activity, including sea surface temperatures. Peer-reviewed research highlights the influence of sea surface temperatures (SSTs) on hurricane tracks; thus, analysis of projected SSTs provides different forecasts not only of where along the coastline hurricanes are likely to occur, but at what strength hurricanes will make landfall. This is a process that RMS terms regionalization.

During higher sea surface temperature periods, the body of warm water over which hurricanes develop expands eastward towards Africa. This expansion increases the likelihood that hurricanes re-curve away from the eastern U.S. coast, towards the northeast and maritime Canada, following paths similar to hurricanes Irene and Sandy.

In the medium-term rate forecast it is regionalization that causes forecasted activity in the U.S. northeast and mid-Atlantic to be above the long-term average, despite a below-average forecast for the U.S. as a whole. This creates a pattern that differs from normal climatological expectations, which would typically be focused on the risk to Texas and Florida – although, obviously, in those southern states the risk does remain higher in absolute terms.

The forecast’s regionalization also produces slightly above long-term risk beyond the 100-year return period, on the industry U.S. exceedance probability curve. At the 250-year return period, for example, while the new medium-term rate has decreased risk by eight percent, the new forecast remains one percent above the long-term rate. Despite decreases in the forecasted frequency of large loss-causing, tail events in Florida and the northeast U.S., warm Atlantic sea surface temperatures continue to support the possibility of these events occurring at a rate above the long-term average.

Delivering the Model

Pre-release data sets for the new medium-term rate are now available in advance of the April release of the updated RiskLink® Version 17 North Atlantic Hurricane Models. These are accompanied by technical documentation describing the process’ methodology and its impact on risk. It will also be concurrently available within Risk Modeler on the RMS(one)® platform.

For further insights from RMS experts on the new forecast, as well as on model updates and the latest on the RMS(one) solutions platform, join us at Exceedance in New Orleans, March 20-23.

Exceedance 2017 – Coming in Just a Few Weeks!

It’s hard to believe but Exceedance 2017 will be here in just a few weeks, and the excitement is building!

Exceedance_6Feb2Many companies are sending their cross-functional teams to fast track their ability to put new capabilities to work. And with good reason. With the releases of Risk Modeler on the RMS(one)® platform and Version 17 in April, attendees will experience more tracks (22) and more sessions (105) than in previous years.

There will also be many opportunities for interaction with model experts, up close team training, networking opportunities, and so much more. Enabling your success is the driving force behind Exceedance!

Here are some highlights of the topics we are preparing for you and your team:

  • Risk Modeler powered by RMS(one): You will obtain a deep understanding of the modeling and analytics that provide the core of the Risk Modeler workflow, including setting up analyses, creating structures and positions, and accessing models from multiple RiskLink® versions for key use cases such as change management, modeling reinsurance programs, and analyzing insurance portfolios.
  • Version 17 North America Earthquake: The changes to the North America Earthquake Models represent the latest view of risk across the U.S., Canada, and Mexico. We will provide the full scope of the update by delving into the model components, including our unique implementation of the latest source models from the USGS, directional loss changes by region and line of business, and detailed loss change exhibits.
  • Event Response: How did your business respond to Hurricane Matthew? Learn what we are doing to enhance RMS Event Response, including future offerings, making it work for your business, addressing the main challenges faced during a real-time event like Hurricane Matthew, and more.
  • U.S. Flood Model: Flood risk management is becoming an increasingly important peril to manage for the insurance industry in the U.S. We’ll provide the latest details on all model components, including the simulation-based model methodology, the innovative vulnerability components of the upcoming RMS U.S. Flood HD Model, and how best to capture opportunities in the evolving U.S. flood market.

The Lab at Exceedance: Solutions, Model Releases, and In-Depth Training with RMS ExpertsExceedance_6Feb

The Lab will be packed with our latest modeling and software releases, in addition to special areas dedicated to research from Horizons (RMS scientific publication) and resilience initiatives across the globe. Over 50 RMS scientists and modelers will be in The Lab to offer technical insights, training, and support – and will be available for personalized discussions.

There’s a Lot to Be Excited About

This is an important year for all of us in the industry, and RMS is ready to meet our commitments to you as we remain on track for a full schedule of delivery throughout 2017. If you’re attending, be sure to let your colleagues know about all Exceedance has to offer.

To see the full agenda with information about the tracks and sessions, The Lab, speakers, networking events, and more, visit the conference website at: exceedance.rms.com. You can also register for Exceedance here. Look for our next blog with more exciting Exceedance updates in the coming days!

Billions in Liabilities: Man-Made Earthquakes at Europe’s Biggest Gas Field

The Groningen gas field, discovered in 1959, is the largest in Europe and produces up to 15 per cent of the natural gas consumed across the continent. With original reserves of more than 100 trillion cubic feet, over the decades the field has been an extraordinary cash cow for the Dutch government and the two global energy giants, Shell and ExxonMobil, which partner in managing the field. In 2014 alone, state proceeds from Groningen were approximately €9.4 billion ($9.8 billion).

But now, costs to the Dutch government are mounting as the courts have ordered that compensation is paid to nearby propery owners for damage caused by the earthquakes induced by extracting the gas. Insurers who were covering liabilities at the field now find that the claims have the potential to extend beyond the direct shaking damage to include the reduction in property values caused by this ongoing seismic crisis. And the potential for future earthquakes and their related damages has not disappeared – a situation which again illustrates the importance of modeling the risk costs of liability coverages, a new capability on which RMS is partnering with its sister company Praedicat.

The Groningen gas reservoir covers 700 square miles and, uniquely among giant gas fields worldwide, it is located beneath a well-populated and developed region. The buildings in this region, which half a million people live and work in, are not earthquake resistant: 90% of properties are made from unreinforced masonry (URM).

The ground above the gas field has been subsiding as the gas has vented out from the 10,000-feet deep porous sandstone reservoir and the formation has compacted. This compaction helps squeeze the gas out of reservoir, but has also led to movement on pre-existing faults that are present throughout the sandstone layer, a small number of which are more regional in extent. And these sudden fault movements radiate earthquake vibrations.

How A Shake Became a Seismic Crisis

The first earthquake recorded at the field was in December 1991 with a magnitude of 2.4. The largest to date was in August 2012 with a magnitude of 3.6. In most parts of the world, such an earthquake would not have significant consequences, but on account of the shallow depth of the quake, thick soils and poor quality building construction in the Groningen area, there were more than 30,000 claims for property damage, dwarfing the total number from the previous two decades.

Since the start of 2014 the government has limited gas production in an attempt to manage the earthquakes, with some success. But the ongoing seismicity has had a catastrophic effect on the property market, which has been compounded by a class-action lawsuit in 2015. It was filed on behalf of 900 homeowners and 12 housing co-operatives who had seen the value of their properties plummet. The judge ruled that owners of the real estate should be compensated for loss of their property’s market value, even when the property was not up for sale. The case is still rumbling on through the appeal courts but if the earlier ruling stands, then the estimates of the future liabilities for damage and loss of property value range from €6.5 billion to €30 billion.

Calculating the Risk

While earthquakes associated with gas and oil extraction are known from other fields worldwide, the massive financial risk at Groningen reflects the intersection of a moderate level of seismicity with a huge concentration of exposed value and very weak buildings. And although limiting production since 2014 has reduced the seismicity, there still remains the potential for further highly damaging earthquakes.

Calculating these risk costs requires a fully probabilistic assessment of the expected seismicity, across the full range of potential magnitudes and their annual probabilities. Each event in the simulation can be modeled using locally-calibrated ground motion data as well as expected property vulnerabilities, based on previous experience from the 2012 earthquake.

There is also the question of how far beyond actual physical damage the liabilities have the potential to extend and where future earthquakes can affect house values. The situation at Groningen, where it took almost thirty years of production before the earthquakes began, highlights the need for detailed risk analysis of all energy liability insurance covers for gas and oil extraction.

The Cost of Shaking in Oklahoma: Earthquakes Caused by Wastewater Disposal

It was back in 2009 that the inhabitants of northern Oklahoma first noticed the vibrations. Initially only once or twice a year, but then every month, and even every week. It was disconcerting rather than damaging until November 2011, when a magnitude 5.6 earthquake broke beneath the city of Prague, Okla., causing widespread damage to chimneys and brick veneer walls, but fortunately no casualties.

The U.S. Geological Service had been tracking this extraordinary outburst of seismicity. Before 2008, across the central and eastern U.S., there were an average of 21 earthquakes of magnitude three or higher each year. Between 2009-2013 that annual average increased to 99 earthquakes in Oklahoma alone, rising to 659 in 2014 and more than 800 in 2015.

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During the same period the oil industry in Oklahoma embarked on a dramatic expansion of fracking and conventional oil extraction. Both activities were generating a lot of waste water. The cheapest way of disposing the brine was to inject it deep down boreholes into the 500 million year old Arbuckle Sedimentary Formation. The volume being pumped there increased from 20 million barrels in 1997 to 400 million barrels in 2013. Today there are some 3,500 disposal wells in Oklahoma State, down which more than a million barrels of saline water is pumped every day.

It became clear that the chatter of Oklahoma earthquakes was linked with these injection wells. The way that raising deep fluid pressures can generate earthquakes has been well-understood for decades: the fluid ‘lubricates’ faults that are already poised to fail.

But induced seismicity is an issue for energy companies worldwide, not just in the South Central states of the U.S.. And it presents a challenge for insurers, as earthquakes don’t neatly label themselves ‘induced’ and ‘natural.’ So their losses will also be picked up by property insurers writing earthquake extensions to standard coverages, as well as potentially by the insurers covering the liabilities of the deep disposal operators.

Investigating the Risk

Working with Praedicat, which specializes in understanding liability risks, RMS set out to develop a solution by focusing first on Oklahoma, framing two important questions regarding the potential consequences for the operators of the deep disposal wells:

  • What is the annual risk cost of all the earthquakes with the potential to be induced by a specific injection well?
  • In the aftermath of a destructive earthquake how could the damage costs be allocated back to the nearby well operators most equitably?

In Oklahoma detailed records have been kept on all fluid injection activities: well locations, depths, rates of injection. There is also data on the timing and location of every earthquake in the state. By linking these two datasets the RMS team was able to explore what connects fluid disposal with seismicity. We found, for example, that both the depth of a well and the volume of fluid disposed increased the tendency to generate seismic activity.

Earthquakes in the central U.S. are not only shallow and/or human-induced. The notorious New Madrid, Mo. earthquakes of 1811-1812 demonstrated the enormous capacity for ‘natural’ seismicity in the central U.S., which can, albeit infrequently, cause earthquakes with magnitudes in excess of M7. However, there remains the question of the maximum magnitude of an induced earthquake in Oklahoma. Based on worldwide experience the upper limit is generally assumed to be magnitude M6 to 6.5.

Who Pays – and How Much?

From our studies of the induced seismicity in the region, RMS can now calculate the expected total economic loss from potential earthquakes using the RMS North America Earthquake Model. To do so we run a series of shocks, at quarter magnitude intervals, located at the site of each injection well. Having assessed the impact at a range of different locations, we’ve found dramatic differences in the risk costs for a disposal well in a rural area in contrast to a well near the principal cities of central Oklahoma. Reversing this procedure we have also identified a rational and equitable process which could help allocate the costs of a damaging earthquake back to all the nearby well operators. In this, distance will be a critical factor.

Modeling Advances for Manmade Earthquakes

For carriers writing US earthquake impacts for homeowners and businesses there is also a concern about the potential liabilities from this phenomenon. Hence, the updated RMS North America Earthquake Model, to be released in spring 2017, will now include a tool for calculating property risk from induced seismicity in affected states: not just Oklahoma but also Kansas, Ohio, Arkansas, Texas, Colorado, New Mexico, and Alabama. The scientific understanding of induced seismicity and its consequences are rapidly evolving, and RMS scientists are closely following these developments.

As for Oklahoma, the situation is becoming critical as the seismic activity shows no signs of stopping: a swarm of induced earthquakes has erupted beneath the largest U.S. inland oil storage depot at Cushing and in September 2016 there was a moment magnitude 5.8 earthquake located eight miles from the town of Pawnee – which caused serious damage to buildings. Were a magnitude 6+ earthquake to hit near Edmond (outside Oklahoma City) our modeling shows it could cause billions of dollars of damage.

The risk of seismicity triggered by the energy industry is a global challenge, with implications far beyond Oklahoma. For example Europe’s largest gas field, in the Netherlands, is currently the site of damaging seismicity. And in my next blog, I’ll be looking at the consequences.

[For a wider discussion of the issues surrounding induced seismicity please see these Reactions articles, for which Robert Muir-Wood was interviewed.]

Indonesia’s Protection Gap – How the Sumatra Earthquake Shows that Coverage Must Spread

On December 7, 2016, a shallow magnitude 6.5 earthquake struck northern Sumatra in Indonesia, severely damaging or destroying more than ten thousand homes and many businesses, as well as causing over a hundred deaths. The disaster struck a poorer area away from the major cities, where the standards of building design, construction methods, and material quality are not sufficient to withstand such an earthquake.

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USGS Shake map for Mw 6.5 Earthquake

We have up-to-date research on local building design and construction practices in Indonesia, which we have incorporated into the latest version of the RMS® Indonesia Earthquake Model. This research was done last year when members of the RMS vulnerability team, including me, visited southeast Asia as part of the process to update the model. We held workshops with local earthquake engineering experts who practice there, and attended an earthquake engineering conference, as well as visiting commercial and industrial buildings, including those under construction, to see first-hand how they were designed and built.

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A workshop with local experts

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International Conference – Jogja Earthquake in Reflection (May, 2016)

This on-the-ground research provided insights into Indonesia’s rules and practices around construction, seismic design, code enforcement, as well as information on the relative quantities of different types of buildings in the country. We discovered significant differences between mainstream construction and those buildings covered by earthquake insurance, namely:

  • Past earthquakes have demonstrated that single family dwellings and/or low rise buildings are the most vulnerable building types compared to those built for commercial and industrial use, because of a lack of engineering design, poor construction, and lower material quality.
  • Buildings outside of major cities are mostly low rises and they may not be designed for earthquake risk.
  • Major cities such as Jakarta, Bandung, and Surabaya enforce a strict structural design review process for the construction of mid- and high-rise buildings.
  • Insurance penetration rates are higher for commercial and industrial buildings in and near major cities, with much lower penetration for residential properties in rural areas.

It’s perhaps not surprising that if poorer communities have less insurance protection, that they also cannot afford to invest in the higher quality construction that is designed to better withstand earthquakes. This is one of the primary reasons for the ‘protection gap’. As these countries become more developed, there’s the potential for that gap to start closing. In fact, Indonesia is one of the fastest growing economies in southeast Asia, with the property insurance and (re)insurance market expanding rapidly.

But as the earthquake disaster demonstrated, there are still many poorer regions with low insurance penetration which are also prone to repeated natural disasters. Sadly, there is still a long way to go before people in those places benefit from the resilience in their built environment which other, richer parts of the world may take for granted.

Exceedance 2017 Is Coming to New Orleans!

Welcome to the first in a series of blogs leading up to Exceedance 2017, March 20-23.

We’re looking forward to the event, which will be held at the Hyatt Regency New Orleans. Situated less than a mile from the historic French Quarter, and about a mile-and-a-half from Jackson Square, it’s a great location in the heart of the ‘Big Easy.’

This year’s theme, ‘Create Resilience,’ reflects the strength and spirit of New Orleans, including the tremendous progress made in the ten years since the devastation caused by Hurricane Katrina. Exceedance 2017 will emphasize how innovation, analytics, and ingenuity can create more resilience in our global catastrophe risk management practices.

Hands-On Training for Risk Modeler on the RMS(one) Platform and Version 17

With the release of Risk Modeler on the RMS(one)® platform and Version 17 upcoming in April, this year’s Exceedance schedule is focused on training and enablement. It’s the only place to get key insights into these new RMS releases – and be trained to assess risk more effectively.

Exceedance2017Exceedance will feature over 22 speakers and provide many opportunities to dive deep into more than 20 new models, including North America Earthquake, North Atlantic Hurricane, and major advances in science, software, and HD-simulation models.

The agenda is designed to provide attendees with all the information they need for our new solutions developed for a rapidly changing market. Solutions that will increase operational effectiveness, agility, resilience, and business growth.

Take Some Time to Have Some Fun

Along with experiencing all there is to see and learn at Exceedance, there are plenty of opportunities to relax and have some fun with the following pre-conference activities:

Golf at TPC Louisiana: Enjoy a round at TPC Louisiana, rated one of Golfweek’s “Best Courses You Can Play.” It’s a great place for you and your colleagues to experience a one-of-a-kind day on a championship golf course.

Tour the Lower 9th Ward: Join the Make It Right Foundation for a walking tour of the Lower 9th Ward. You’ll experience first-hand how innovative partnerships and community-led design sessions are transforming the neighborhood that was most devastated by Hurricane Katrina.

Horse-Drawn Carriage Ride and Cooking Class: Journey through the French Quarter by carriage, where you’ll pass through the city’s eighteenth- and nineteenth-century French and Spanish architecture. Then, satisfy your appetite with chef extraordinaire Amy Sins who will guide you through an interactive culinary experience that ends with a delectable meal.

Spirits and Spirits – an Evening Tour: Take a guided evening stroll through the spooky side of the old French Quarter. You’ll hear tales from the city’s storied history, and perhaps even encounter a ghost or two. Then enjoy local cocktail favorites at one of New Orleans’s oldest restaurants, a former Spanish armory.

To learn more about these events, visit the Exceedance website. If you’re ready to register, fill out your form.

Exceedance will be here soon, so look for our next blog in two weeks. It will include the latest information on the session tracks and content, as well as details of the keynote speakers.

Understanding Risk Accumulations in Taiwan’s Science Parks

“The 6.4 magnitude Tainan earthquake in February 2016 resulted in a sizeable insured loss from the high-tech industrial risks and reminded the insurance industry of the potential threat from the risk accumulated in science parks.” (A.M. Best Special Report, Sept 2016)

Reading the sentence above you might be forgiven for wondering why science parks would give insurers and reinsurers any particular cause for concern. But consider this statistic: although Taiwan’s three major science and industrial parks occupy only 0.1% of the island’s total land mass, they represent 16% of Taiwan’s overall manufacturing – they are hugely significant, both economically and with regards to the insured exposure in Taiwan.

For example, the Hsinchu Science Park (HSP), known for semiconductor production, employs more than 150,000 people and contributes over $32 billion in revenues – approximately 6% of national GDP. By one estimate HSP represents over $319 billion in total insured values. In addition, some of the latest high tech areas within HSP, such as advanced “clean rooms,” present additional challenges due to their vulnerability to ground shaking or power interruption. The importance of this risk was observed in February’s Tainan earthquake where some significant losses to high-tech industrial risks were caused by damage to the equipment and the related business interruption due to power outage.

Improving data quality for advanced and detailed modeling is an important way to manage these risks, concludes the A.M. Best report quoted above. This is so as to accurately assess the potential loss impact on insurers’ books. RMS has already been analysing earthquake risk in Taiwan for 12 years – long before this year’s Mw 6.4 event – and in that time our view of seismic risk in Taiwan has not changed, since our model benefits from spectral response-based hazard and damage functions, that even include local liquefaction and landslide susceptibilities.

The 1999 Chi-Chi Earthquake (known in Taiwan as the 921 Earthquake) was the key event in building the RMS® Taiwan Earthquake Model in terms of the quake’s seismicity, ground motion, soil secondary effects and building response. Since then there have been no significant events to justify a re-calibration of the components of the model. In fact, the damages observed in this year’s event were broadly in line with RMS’ expectations and validated the robustness of the current model.

But although A.M. Best views the Taiwan insurance industry as prudently managed with relatively high catastrophe management capability, there are still lessons to be learnt from the 2016 event, and RMS has solutions which offer additional insight into understanding the risk posed by these business parks in Taiwan.

Concentration of Exposure into Science Parks

The RMS® Asia Industrial Clusters Catalogs were released in 2014 to identify hotspots of exposure, and profile their risk. The locations and geographic extent of the science parks within Taiwan are detailed to help understand risk accumulations for industrial lines and develop more robust risk management strategies.

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Example of industrial cluster captured in the RMS Taiwan Industrial Clusters Catalog. The red outline illustrates the digitized boundaries of the Formosa Petrochemical Co. Plant in Yunlin Hsien.

High Fragility of the Semiconductor Industry

For coding of Industrial Plants, the RMS® Industrial Facilities Model (IFM) captures the unique nature of different industrial risks, as a high percentage of property value is often associated with machinery and equipment (M&E) and stock. This advanced vulnerability model supports the earthquake model to define the damageability of a comprehensive set of industrial facilities more accurately, and calculate the financial risk to these specific types of facilities, including building, contents, and business interruption (BI) loss estimates. The IFM differentiates the risks for different types of business within the science parks, and highlights the higher fragility of semiconductor plants compared to other industrial units, as shown below.

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Lessons Learnt?

The huge damage from the 1999 Chi Chi earthquake has not halted the rapid development of Taiwan’s science parks in this seismically active area – indeed the island’s third biggest science park has since been built there. But this year’s comparatively small Mw 6.4 event further highlighted the substantial exposures concentrated within this sector, reminding the industry of the potential for significant losses without sound accumulation management practices, informed by the best modeling insights.

See How Quickly and Easily You Can Access the Exposure Metrics That Matter

Exposure Manager is a risk management solution that provides executives, underwriters, risk analysts, and other decision-makers with the exposure analytics needed to offer a comprehensive view of risk and understand loss potential.

As the first solution released on the RMS(one) platform, Exposure Manager was developed based on the understanding that organizations not only need quick and reliable assessments of exposure concentrations, but also the right tools to ensure they can access key metrics and insights.

The videos below illustrate two of the important capabilities that enhance users’ ability to build portfolio intuition faster and quickly access the metrics that are most important.

Build Portfolio Intuition Faster provides insights into how Exposure Manager enables customers to quickly and efficiently derive deeper portfolio insights using an intuitive and user-friendly interface.

With a customizable interface that conveys the information that’s most important to the user, Exposure Manager’s analytics, enabled by an intuitive best-in-class user experience, can be configured without knowledge of SQL or support from IT.

This enhances the ability for customers to create quick insights into their portfolio or perform a deep dive into their book to make quick assessments.

Access Metrics That Matter shows how Exposure Manager leverages the RMS financial model to provide an exposed limit metric. This offers a consistent view of loss potential to enable precise identification of loss drivers.

The flexible interface provides users with precise control to quickly make informed decisions about their book and help identify threats and opportunities in the portfolio.

All of these benefits allow customers to become more incisive about their portfolio.

Earthquake Hazard: What Has New Zealand’s Kaikoura Earthquake Taught Us So Far?

The northeastern end of the South Island is a tectonically complex region with the plate motion primarily accommodated through a series of crustal faults. On November 14, as the Kaikoura earthquake shaking began, multiple faults ruptured at the same time culminating in a Mw 7.8 event (as reported by GNS Science).

The last two weeks have been busy for earthquake modelers. The paradox of our trade is that while we exist to help avoid the damage this natural phenomenon causes, the only way we can fully understand this hazard is to see it in action so that we can refine our understanding and check that our science provides the best view of risk. Since November 14 we have been looking at what Kaikoura tells us about our latest, high-definition New Zealand Earthquake model, which was designed to handle such complex events.

Multiple-Segment Ruptures

With the Kaikoura earthquake’s epicenter at the southern end of the faults identified, the rupture process moved from south to north along this series of interlinked faults (see graphic below). Multi-fault rupture is not unique to this event as the same process occurred during the 2010 Mw 7.2 Darfield Earthquake. Such ruptures are important to consider in risk modeling as they produce events of larger magnitude, and therefore affect a larger area, than individual faults would on their own.

Map showing the faults identified by GNS Sciences as experiencing surface fault rupture in the Kaikoura Earthquake.
Source: http://info.geonet.org.nz/display/quake/2016/11/16/Ruptured +land%3A+observations+from+the+air

In keeping with the latest scientific thinking, the New Zealand Earthquake HD Model provides an expanded suite of events that represent complex ruptures along multiple faults. For now, these are included only for areas of high slip fault segments in regions with exposure concentrations, but their addition increases the robustness of the tail of the Exceedance Probability curve, meaning clients get a better view of the risk of the most damaging, but lower probability events.

Landsliding and Liquefaction

While most property damage has been caused directly by shaking, infrastructure has been heavily impacted by landsliding and, to a lesser extent, liquefaction. Landslides and slumps have occurred across the region, most notably over Highway 1, an arterial route. The infrastructure impacts of the Kaikoura earthquake are a likely dress rehearsal for the expected event on the Alpine Fault. This major fault runs 600 km along the western coast of the South Island and is expected to produce an Mw 8+ event with a probability of 30% in the next 50 years, according to GNS Science.

As many as 80 – 100,000 landslides have been reported in the upper South Island, with some creating temporary dams over rivers and in some cases temporary lakes (see below). These dams can fail catastrophically, sending a sudden increase of water flow down the river.

 

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Examples of rivers blocked by landslides photographed by GNS Science researchers.

Source: http://info.geonet.org.nz/display/quake/2016/11/18/ Landslides+and+Landslide+dams+caused +by+the+Kaikoura+Earthquake

 

 

 

 

 

 

 

 

Liquefaction occurred in discrete areas across the region impacted by the Kaikoura earthquake. The Port of Wellington experienced both lateral and vertical deformation likely due to liquefaction processes in reclaimed land. There have been reports of liquefaction near the upper South Island towns (Blenheim, Seddon, Ward), but liquefaction will not be a driver of loss in the Kaikoura event to the extent it was in the Christchurch earthquake sequence.

RMS’ New Zealand Earthquake HD Model includes a new liquefaction component that was derived using the immense amount of new borehole data collected after the Canterbury Earthquake Sequence in 2010-2011. This new methodology considers additional parameters, such as depth to the groundwater table and soil-strength characteristics, that lead to better estimates of lateral and vertical displacement. The HD model is the first probabilistic model with a landslide susceptibility component for New Zealand.

Tsunami

The Kaikoura Earthquake generated tsunami waves that were observed in Kaikoura at 2.5m, Christchurch at 1m, and Wellington at 0.5m. The tsunami waves arrived in Kaikoura significantly earlier than in Christchurch and Wellington indicating that the tsunami was generated near Kaikoura. The waves were likely generated by offshore faulting, but also may be associated with submarine landsliding. Fortunately, the scale of the tsunami waves did not produce significant damage. RMS’ latest New Zealand Earthquake HD Model captures tsunami risk due to local ocean bottom deformation caused by fault rupture, and is the first model in the New Zealand market to do this, that is built from a fully hydrodynamic model.

Next Generation Earthquake Modeling at RMS

Thankfully the Kaikoura earthquake seems to have produced damage that is lower than we might have seen had it hit a more heavily populated area of New Zealand with greater exposures – for detail on damage please see my other blog on this event.

But what Kaikoura has told us is that our latest HD model offers an advanced view of risk. Released only in September 2016, it was designed to handle such a complex event as the Kaikoura earthquake, featuring multiple-segment ruptures, a new liquefaction model at very high resolution, and the first landslide susceptibility model for New Zealand.