Geocoding to the Postcode Centroid Is Silently Costing You (A Data Problem)

If you build risk or pricing models on property data, here's a quiet bug worth checking: how are your locations geocoded? At a lot of insurers, every risk is snapped to the postcode centroid — one point representing an area that can span kilometres. For admin, fine. For anything spatially sensitive — flood, wildfire, storm surge, accumulation — it's a systematic error that costs real money.

The centroid problem

A centroid places every risk in that postcode at the same point: the geographic middle. But hazards are sharply local. Flood risk flips across a single street depending on which side of a contour you're on. Snap all risks to one point and you've thrown away exactly the spatial precision the peril cares about.

Crucially, this isn't random noise that averages out. It's structured error: risks near a river but on high ground get overrated; risks in the same postcode but in the floodplain get underrated. You overcharge safe customers (who leave) and undercharge dangerous ones (who stay). Adverse selection, manufactured by your own geocoding.

Where it bites

• Cat models. A cat model is only as precise as the exposure you feed it. Centroid coordinates → modelled PML and accumulation built on points that can be off by kilometres.
• Flood. The peril most sensitive to precise location, handed a point accurate to a neighbourhood.
• Accumulation. "How much exposure within 1km of this coastline?" is unanswerable if exposure is snapped to centroids.

Keep the precision metadata (the part people skip)

Don't just geocode more precisely — record how precise each result is. A geocode that resolved to a rooftop and one that fell back to a postcode are not the same fact, and your model should be able to tell them apart.

{
  "risk_id": "P-88213",
  "lat": 51.5079,
  "lon": -0.0877,
  "geocode_precision": "rooftop",   // rooftop | street | postcode_centroid
  "confidence": 0.98
}

What good looks like

1. Geocode to the structure, captured at quote time with a confidence score.
2. Store the precision level so downstream models can weight or exclude low-confidence points.
3. Re-geocode the back book — the in-force portfolio is where the accumulated error lives; a one-off enrichment pass often reveals exposure you didn't know you had.
4. Feed precise coordinates into cat and pricing models — they were always capable of using them; they were starved of them.

This is a data-quality problem, not a modelling one — which is exactly why it's fixable without touching the actuaries' work.

Full write-up:

Geocoding Is Quietly Costing You: Exposure at the Address, Not the Postcode →

From IntelliBooks' series on the data foundation under insurance AI.

How does your pipeline track geocode confidence — or does low-confidence silently get treated the same as rooftop?