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The Earthquake Insurance Protection Gap: A Tale of Two Countries
Abstract
In this paper, we examine reasons why take-up rates for earthquake insurance are significantly higher in the Lower Mainland of British Columbia than in western Washington state even though earthquake risk is largely the same. Achieving and maintaining high insurance take-up rates for catastrophic events matters because this can play an important role in improving the resiliency of communities. After exploring several factors known to influence the supply and demand of insurance for high-severity but low-frequency events, we find only two key differences: 1) disaster assistance is more readily available in the U.S.; and 2) Canadians are more willing to purchase earthquake insurance when they are told they should. We conjecture that many policy options to increase insurance take-up rates, such as product redesign or cross subsidization, are not likely to be effective in Washington. Making insurance mandatory—either via legislation, making earthquake coverage a prerequisite for a mortgage or embedding it into property taxes—might be the only viable way to increase take-up rates, although these options may be politically difficult to enact.
... Except for the events in New Zealand, all other have low insurance Fig. 1 Ten most expensive earthquakes to the insurance industry worldwide between 1980 and 2020 (Statista 2022) penetration. Some of the main factors of low insurance penetration includes affordability, low risk perception, design and structure of the cat bonds (Bevere and Grollimund 2012; Kelly et al. 2020). These factors are influenced by uncertainty and low confidence in the loss estimates, which tend to increase the price of insurance policies, making them less attractive to potential customers. ...
Risk-based catastrophe bonds require the estimation of losses from the convolution
of hazard, exposure and vulnerability models. These models are affected by different uncertainties that arise from the definition of their input parameters. In this paper, we propose a stochastic approach to treat the uncertainty in the asset location and attributes of the exposure model. The proposed method uses the Monte Carlo sampling approach to generate a stochastic exposure database, where each asset location is generated randomly within the geometric bound of the administration, while the asset attributes (i.e. construction type and material, number of storey, building activity type and number of dwelling) are sampled from distributions built from census data. Finally, a sensitivity analysis is performed to investigate the influence of the spatial resolution of the exposure model on the average annual losses (AAL) and catastrophe bond prices. To this end, we
implement four exposure models, with spatial resolution at the asset, municipality and province levels, on a study region comprising ten provinces in southern Italy. Compared to the proposed model, the exposure model where assets are relocated and aggregated at the geometric centroid of the municipality underestimates AAL by 8% , while a higher difference (up to 20% ) is observed for the exposure model where assets are relocated and aggregated at the geometric centroid of the province. We also consider an exposure model whose asset locations are extracted from publicly available building footprints. Yet, this latter model was incomplete for some provinces resulting in underestimation of AAL up to 90% . Differences in catastrophe bond prices obtained from the four exposure models are less evident, with the exposure model built based on the building footprints showing a difference up to 9.5%.
Recent efforts by the Federal Government of Canada have devoted resources to mitigating disaster risk and identifying sustainable solutions for post-disaster housing reconstruction financing. In 2023, Canada committed funding to set up its National Flood Insurance Program. In parallel, the Department of Finance and Public Safety Canada plans industry engagement on solutions to earthquake insurance. In anticipation of the need to rethink post-earthquake housing reconstruction financing for single-family homes, the present study evaluates the feasibility of implementing three novel financing mechanisms in Canada, drawing inspiration from existing US and New Zealand programs. The proposed mechanisms include a grants program targeting low-to-moderate-income households, a low-interest loan program, and an affordable insurance program. Simulations of the impacts of M7 earthquake in the Strait of Georgia, British Columbia, are used to compare the post-earthquake uninsured losses in the status quo and if each new mechanism was in place before the event. Benefits are assessed through the reduction in uninsured losses, while opportunity losses measure the costs of each program. Results indicate that a loan program with an interest rate above 3.5% could offer benefits surpassing its opportunity cost, albeit with substantial initial expenses. In addition, introducing an affordable insurance program and a disaster fund shows promise but requires robust capitalization in its initial years. A combination of affordable insurance and low-interest loans could alleviate long-term debt for homeowners, particularly for earthquakes causing moderate losses.
Catastrophe bonds (cat bond in short) are an alternative risk‐transfer instrument used to transfer peril‐specific financial risk from governments, financial institutions, or (re)insurers, to the capital market. Current approaches for cat bond pricing are calibrated on seismic mainshocks, and thus do not account for potential effects induced by earthquake sequences. This simplifying assumption implies that damage arises from mainshocks only, while aftershocks yield no damage. Postearthquake field surveys reveal that this assumption is inaccurate. For example, in the 2011 Christchurch Earthquake sequence and 2016–2017 Central Italy Earthquake sequence, aftershocks were responsible for higher economic losses when compared to those caused by mainshocks. This article proposes a time‐dependent aggregate loss model that takes into account seismicity clustering and damage accumulation effects in the computation of damage. The model is calibrated on the seismic events recorded during the recent 2016–2017 Central Italy Earthquake sequence. Furthermore, the effects of earthquake sequence on cat bond pricing is explored by implementing the proposed model on five Italian municipalities. The investigation showed that neglecting time‐dependency may lead to higher difference (up to 45%) in the cat bond price when compared to standard approaches.
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