Laurette Coetzee, Senior Account Manager, ESRI South Africa.
Let's imagine you have two houses in the same suburb, with similar replacement values, comparable sum insured and broadly similar policyholder profiles. On paper, this pairing may appear similar. And they are only four kilometers apart. Traditional underwriting models would likely classify them in the same rating zone and generate roughly similar premiums.
But one of these houses is located on a steep hill where flash floods are common after heavy rains. The second, despite being on higher ground, remains in the open field, posing a security risk. Each building tells a very different story. One has solar panels, battery backup and smart leak detector. The other has old plumbing and an old roof.
An insurer relying on suburban-level averages still sees two nearly identical homes, and both customers receiving the same premium. But it's clear that these are two very different homes with unique risk profiles. Yet both properties receive the same insurance premium.
This logic is the same for two commercial properties in the city center, where the distance between buildings may be a block or two. One is in a complex where municipal infrastructure is old, making it more prone to burst water pipes and sewer backups. While other benefits are from recently improved utilities and more reliable service delivery. Once again, two buildings located at completely different distances from each other can have very different risk profiles.
Business Case for Hyperlocal Underwriting
According to Laurette Coetzee, senior account manager at ESRI South Africa, hyperlocal underwriting assesses insurability based on specific geographic data, tied to a property's location rather than an average. “What's important here is context. Using location data, insurers can access an individual asset,” she says.
By using detailed data, insurers can price policies more accurately. This also makes it possible to provide quick quotes and personalized terms. When an insurer can accurately map the exact location and characteristics of insured assets, actual claims settlement is faster, improving customer satisfaction. Coetzee explains that this makes it easier to identify anomalies that may indicate fraud.
Additionally, location data and hyperlocal underwriting enable insurers to identify risk concentrations. She shares the example of an insurer with multiple policies in a flood-prone area. Each represents an acceptable individual risk, but if a major storm occurs, the business will be at extreme risk and could suffer significant financial losses. Armed with hyperlocal insights, insurers can rebalance portfolios before risk concentration becomes a costly problem. “In the past, it would not have been possible to analyze so much data. But by incorporating artificial intelligence (AI) into the process, insurers can untangle complex relationships and reveal rich insights,” she adds.
Hyperlocal underwriting assesses insurability based on specific geographic data, tied to a property's location rather than an average. (Image: ESRI)
Returning to the two houses in the initial example, an insurer relying on traditional underwriting would average the two different risks, with the lower risk customer subsidizing the higher risk customer. But with more specific location intelligence and related data, these two assets that once looked alike no longer look the same. In a world where data is driving better decisions in every industry, this type of information helps modern insurers do the same.
Hyperlocal underwriting is not just another underwriting enhancement. This represents a fundamental shift towards more accurate pricing, stronger portfolio management, better capital flexibility and better risk selection. In an increasingly competitive market, precision is becoming one of the industry's most important sources of sustainable competitive advantage.
feel free to access to If you want to learn more about how hyperlocal underwriting can be implemented within your organization or want to discuss practical use cases that are already helping insurers make more confident, risk-informed decisions.