When a bad geocode changes your flood risk classification

Julien Crétin — Sun, 19 Apr 2026 08:00:00 GMT

French home insurers don't price flood risk by intuition. They price it by zone, specifically by the PPRI (Plan de Prévention des Risques d'Inondation), a government-issued flood risk map that classifies every address in France into one of three statuses: Risque Existant (known flood zone), Risque non Connu (municipality at risk, precise address not mapped), or no risk.

The classification feeds directly into the premium. A property in a Risque Existant zone typically carries a 10 to 30% surcharge on the natural disaster component of the home insurance premium, and in high-risk zones, coverage can be refused altogether.

What happens when the geocode is wrong? We tested 300 addresses where two geocoders disagreed; 1 in 7 ended up in a different PPRI classification depending on which one was used.

The mechanics

One common approach: an insurer's system geocodes the address, converting a street address into latitude/longitude coordinates, then queries the PPRI database with those coordinates. The system trusts the coordinates it receives; it has no way to verify whether the geocode is accurate.

If the geocode places the address 1.2 km from its actual location, the PPRI query runs against the wrong point on the map. Whether that matters depends entirely on where the wrong point lands: in a flood zone, out of one, or across a municipal boundary where the PPRI itself changes.

What we measured

We ran 300 French addresses through two geocoders, BAN (Base Adresse Nationale, the French open-source reference) and Google Maps, and queried the Géorisques PPRI API for both coordinate pairs. We selected addresses where the two providers disagreed on location by at least 50 metres.

1 in 7 addresses ended up in a different PPRI classification depending on which geocoder was used.

That's not 1 in 7 addresses overall. It's 1 in 7 among addresses where the geocoders already disagreed.

The right question for a portfolio audit isn't "how many addresses have a bad geocode?"; it's "how many addresses have a geocode divergence large enough to cross a PPRI boundary?"

Three cases

On all maps: orange pin = BAN coordinate, green pin = Google coordinate. Red/blue zones = flood risk areas (PPRI). Source: Géorisques / georisques.gouv.fr · © OpenStreetMap contributors.

Case 1 — Loire-Atlantique (dept. 44): 1.3 km, flood zone missed

An address in the Loire-Atlantique estuary area. BAN placed it at 47.2807°N, 2.4322°W, in a known flood zone (Risque Existant). Google placed it at 47.2691°N, 2.4331°W, 1,294 metres away, outside the mapped zone (Risque non Connu). BAN confidence score: 0.681.

An insurer using Google's coordinates would classify this property as outside the flood zone. No surcharge. Potentially a significant underpricing of risk.

Case 2 — Vendée (dept. 85): 1.1 km, flood zone added

An address on the Vendée coast. BAN placed it at 46.4963°N, 1.8072°W, outside the mapped flood zone (Risque non Connu). Google placed it at 46.4974°N, 1.7925°W, 1,135 metres away, inside a flood zone (Risque Existant). BAN confidence score: 0.702.

Here the error runs in the other direction. An insurer using BAN would miss the flood zone entirely, no surcharge applied to a property that should carry one. A loss exposure that doesn't show up in the underwriting model.

Case 3 — Moselle (dept. 57): three addresses, same divergence pattern

Three addresses on the same street in the Moselle valley showed a consistent 1,850 to 1,900 metre gap between BAN and Google. All three: BAN in a flood zone (Risque Existant), Google outside (Risque non Connu). BAN score: 0.551.

This is the pattern that matters at portfolio scale. A single address with a bad geocode is a one-off. Three addresses with the same systematic divergence, likely sharing a street, a building, or a postal code, suggest a structured data quality problem that will recur across every address in that zone.

What this means for a portfolio audit

The 14.5% reclassification rate we observed applies to addresses with a known geocoding divergence. The prior question, how many addresses in a typical insurance portfolio have a divergence large enough to matter, is the one worth answering first.

Our benchmark on 10,000 French addresses suggests that roughly 3% produce a gap of 500 metres or more between BAN and a premium provider. Applied to a portfolio of 1,000,000 home insurance policies, that's approximately 30,000 addresses worth auditing.

At a 14.5% reclassification rate: roughly 4,350 policies may be incorrectly classified for flood risk. For the subset under-classified as standard risk, the annual premium shortfall runs from €33,000 to €196,000. Beyond the premium impact, these policies carry an unrecognised claims exposure: at a conservative 1% annual flood event probability, roughly 20 under-classified policies will generate a claim in any given year, each potentially running to €20,000 to €80,000 in damages on a contract priced for standard risk.

A geocoding audit doesn't require re-underwriting the entire portfolio. It requires identifying the addresses where the geocode is uncertain enough to cross a risk boundary, and those addresses are identifiable before any claim is filed.

The signal is already in your data: the BAN confidence score. Addresses with a score below 0.7 are the ones most likely to produce a divergence large enough to matter. In our sample, every fine-category reclassification came from an address with a BAN score below 0.71.

Running an audit on your portfolio

One simplified approach for a portfolio audit, noting that in practice, any geocoder can be wrong, and more robust workflows would cross-reference three or more providers:

Run your address file through BAN and flag addresses with a confidence score below 0.7
For flagged addresses, run a secondary geocoder and compute the coordinate gap
For addresses with a gap above ~500 metres, query the PPRI API for both coordinate pairs
Surface the cases where the two coordinates produce different PPRI classifications

Steps 1 to 3 can be automated with a cascading geocoding pipeline. Step 4 is a one-time enrichment.

Coordable builds multi-provider geocoding pipelines that automate exactly this kind of cascade, surfacing address uncertainty before it becomes a classification error. If you're thinking about a portfolio audit, we'd be happy to talk through the approach.

Methodology note

Dataset: 300 French addresses drawn from the ADEME DPE database (10,000-address stratified sample across urban and rural zones), geocoded with BAN and Google Maps. Only addresses where both providers returned valid French metropolitan coordinates were included.

Selection: Addresses split into two groups, fine (50m to 2km gap, 200 addresses) and gross (2km to 50km gap, 100 addresses). Gaps above 50km excluded as geocoding errors.

PPRI API: Géorisques /api/v1/resultats_rapport_risque, queried for both coordinate pairs. 72 of 300 address pairs returned network errors (24%), reducing the effective sample. 3 communes returned HTTP 404 (no PPRI data available).

Reclassification rate: Computed on address pairs where both coordinates returned a valid, non-null PPRI status. "Risque Inconnu" normalised to "Risque non Connu" (API variant).

This post is part of a series on geocoding quality in French operations. For the cost impact on last-mile logistics, see our failed delivery cost models and routing impact benchmark.

Coordable (Articles sur insurance)