Geocoding methodology decides climate exposure more than the API you pick
Geocoding looks like a plumbing problem: address in, coordinates out. At portfolio scale, the method you pick is a risk input.
**Three methods, three error profiles.**
**Rooftop.** Parcel centroids or building footprints. Accuracy 1-5 meters. Works for residential and commercial addresses in the US, UK, parts of EU. Still fails for PO boxes, suite numbers, rural routes, most emerging-market addresses.
**Interpolation.** Places a point along a street segment based on number range. Accuracy 10-50 meters. Off by half a block in dense urban areas, worse in suburbs. A 30-meter shift can move a record from a 100-year floodplain into a 500-year zone. FEMA defines those as different risk categories.
**Centroid fallback.** Postal code or admin area polygon center. Accuracy 500m to 10km. Cat modeling literature quantifies the cost: AAL estimates shift 20-40% when you fall back from rooftop to postal centroid, before any hazard input has changed.
**Where this compounds.**
- JRC flood polygons resolve at building scale. 30m shifts matter.
- Wildfire WUI classifications change at 1-hectare resolution.
- Protected area overlay (WDPA) for EUDR is binary: inside or outside the polygon.
For anyone scoring climate risk across 500 to 5,000 sites, the gate before hazard scoring is quality validation, not just geocoding: did the API return rooftop or interpolated? What's the confidence score? Did it silently fall back to centroid? Most batch workflows return a single coordinate column with no grade attached.
The methodology guide walks through the decision tree for portfolio workflows: five quality checks, rooftop vs interpolated thresholds, batch considerations.
Open question for teams running portfolio-scale geocoding for compliance or risk screening: do you gate on confidence score, or re-route low-quality records through a second provider? Single-provider vs waterfall is the main methodology fork we see in practice.
Posted Apr 20, 2026, 6:29 AM UTC