Let’s imagine you have two homes, in the same suburb, with similar replacement values, comparable sums insured and broadly similar policyholder profiles. On paper, the pair might appear identical. And they’re just four kilometres apart. Traditional underwriting models would likely classify them within the same rating territory and produce almost identical premiums.
But one of these homes is located on a steep hillside where flash flooding is common after heavy rains. The other, despite being on higher ground, backs onto an open field, which creates security risks. Each building tells a very different story. One has solar panels, battery backup and smart leak detectors. The other has outdated plumbing and an ageing roof.
The insurer that relies on suburb-level averages still sees two nearly identical homes, and both customers receive 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 premiums.
The argument is similar for two commercial properties in the city centre, where the difference between the buildings could be a block or two. One is in a precinct with ageing municipal infrastructure, making it more susceptible to burst water pipes and sewer backups. While the other benefits from recently upgraded utilities and more reliable service delivery. Once again, two buildings located just streets apart can have very different risk profiles.
The 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, linked to a property’s location rather than averages. “What’s important here is context. Using location data, insurers can drill down to an individual property,” she says.
By using granular data, insurers can price policies more accurately. This also makes it possible to provide quicker quotes and personalised terms. When an insurer can accurately map the precise location and characteristics of insured assets, settling genuine claims happens faster, which improves 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 an example of an insurer with several policies in a flood-prone area. Each represents an acceptable individual risk, but if there were a major storm, the business would be overexposed and could incur significant financial losses. Armed with hyperlocal insights, insurance firms can rebalance portfolios before risk concentration becomes a costly problem. “In the past, analysing so much data wouldn’t have been possible. But by incorporating artificial intelligence (AI) into the process, insurers can untangle complex relationships and reveal richer insights,” she adds.
Returning to the two homes in the opening example, the insurer relying on traditional underwriting will average two very different risks, with the lower-risk customer subsidising the higher-risk one. But with more specific location intelligence and related data, these two properties that once looked so similar now look nothing alike. In a world where data is driving smarter decisions across every industry, this type of information helps modern insurers do the same.
Hyperlocal underwriting is not simply another underwriting enhancement. It represents a fundamental shift towards more precise pricing, stronger portfolio management, improved capital resilience and better risk selection. In an increasingly competitive market, that precision is becoming one of the industry’s most important sources of sustainable competitive advantage.
Feel free to reach out if you’d like to explore how hyperlocal underwriting can be applied within your organisation or to discuss practical use cases that are already helping insurers make more confident, risk-informed decisions.
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