It Takes a Village: Effects of Community Attributes on Insured Loss

by Marty Ellingsworth, President, ISO Innovative Analytics and Bill Raichle, Vice President, ISO Risk Decision Services

There are indisputable relationships between insured property losses and the attributes of the area surrounding them.

For example, consider this scenario: High concentrations of flammable materials with low water availability, no way to notify officials of an emerging fire, and poor firefighting capabilities are a recipe for fire-hazard mishaps. It can be even more dangerous if the area is hard to access via roadways, surrounded by derelict structures, subject to high winds, subject to hot, dry conditions with atmospheric lightning strikes, and is far from a responding fire station. On their own, any of those factors would likely prompt a risk manager to increase the estimate of potential for a fire loss to occur and for the fire to burn out of control in ways that would raise the severity of the loss. When the factors are compounded, the odds of calamity increase.

Many insurers might either decline to underwrite such a risk or might quote a very high rate, as well as exacting terms and conditions surrounding the contract. Insurers would likely also demand additional mitigation measures before coverage would be initiated. Admittedly, this scenario is an extreme example along a continuum of risk-quality possibilities. In reality, there are many individual attributes with varying degrees of positive or negative effects that combine together into an overall risk equation — what is called the “village effect.”

The “village effect” manifests itself in everyday commonsensical ingredients, including geographic, physical, social, and political attributes. Establishing common sense as an element of risk-management strategy is an area in which the industry continues to make progress. Improved risk-management strategy begins with data being collected in an intelligent and precise manner by qualified and certified professionals. The data-collection baseline is then enhanced via an analytic approach to deploy the data effectively. The analytic paradigm combines specific subject-matter expertise, additional data aggregation, interpretation, verification, validation, and modeling steps. It can also involve the distillation of many companies’ loss experience over a long period of observation. This type of quantification and analysis is fast becoming the standard of modern insurance operations. Subsequent steps to validate the risk equation of the “village effect” often build and extend from this starting point.

Communities vary significantly regarding factors such as geography, weather, roadways and traffic patterns, physical infrastructure, and investments in firefighting, police, flood mitigation, and building-code enforcement — attributes that strongly affect insured loss frequency and severity. To properly administer a program in support of loss cost estimation, insurance providers can develop and maintain insured-loss data by working closely at the grassroots level with local officials across the nation in the assessment of community loss-mitigation capabilities. Such continual collaboration would keep the data fresh while providing educational avenues to show which community attributes can be changed to help lower expected losses.

As the relevant data becomes more readily available and as analytic sophistication grows, insurers and their vendor-partners can develop refined statistical models to help both communities and insurance operations staff better understand complex risk-quality relationships, permitting more accurate underwriting and rating of individual risks.

Understanding how individual community attributes affect loss is difficult, but analytics experts are now using time-tested, objective metrics to evaluate communities and to determine how one community measures up against another. A suitable measurement program can include the following:

close working relationship with local fire and building officials
clear metrics that can be used to rank/score departments nationally
identification of attributes that contribute to measurement of losses and mitigation
qualified staff with the necessary certifications and training to conduct objective surveys

Municipal fire and building-code authorities and other community officials would use such measurement processes to help administer important programs that evaluate components of community infrastructure, such as fire-hazard risk, compliance with up-to-date building codes and construction guidelines, and the geospatial threats of flood damage from surrounding bodies of water.

Many communities strive to show they are safe places to live and conduct business. When communities actually are safer, citizens, policyholders, local businesses, and public authorities benefit from stronger, well-protected communities with reduced risks and losses. And insurers benefit from more accurate risk assessment.

New Insights from Predictive Modeling
Experts in the field of risk assessment make better judgments with better information — on both the subject at risk and the surrounding “risk ecosystem” (i.e., adjacent community risks, local mitigation capabilities, topography of land/water, prevailing weather patterns, and even catastrophe disposition to events caused by society or nature). Such risk-assessment data can deliver the highest value when it surpasses the level of isolated, silo-based information and reaches the level of actionable insight and knowledge for decisive action.

The knowledge of future likelihoods can permit more refined risk assessment for individual and aggregated pools of insurance contracts. This predictive knowledge can allow even more accurate risk-based pricing and targeted marketing approaches for insurers. Meanwhile, communities can leverage the same information and insight to improve the quality of life and the safety of their citizenry.

One key driver of decision-making action for insurers and their policyholders is predictive modeling, the process of incorporating multiple building blocks of contextual information into a framework of statistical analysis. With predictive analytics, compound elements of risk data can be better observed in relation to loss frequency and loss severity. And more intelligent insights about what may occur can be generated. Increased accuracy in forecasting aggregate losses on portfolios of risks is what makes predictive analytics so useful today. The improvements under way in obtaining better data and increasing the quantity of data — in conjunction with more sophisticated modeling technologies — are the factors that will keep predictive analytics relevant tomorrow, especially in light of increasingly interdependent risk equations of the “village effect.”

As communities work to reduce their risks and as insurers refine their rating models to integrate more and more attributes affecting loss, ISO’s predictive analytics and information on geography, public infrastructure, and investments in firefighting, flood mitigation, and building-code enforcement will continue to play a key role in loss mitigation and accurate risk assessment for the purposes of offering insurance coverage.

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