NFIP Flood Loss Risk Screener — Property Loss History Score

Turn a list of property coordinates into a flood loss history score built from FEMA National Flood Insurance Program (NFIP) paid claims. For each property (lat/lon) the actor summarizes past NFIP flood-insurance losses within a radius: claim count, total dollars paid, average and largest payout, a repetitive-loss signal, the most recent loss year, the dominant cause of damage, and a composite 0–100 flood-loss score.

Most tools republish the raw FEMA NFIP claims dump by state and year. This actor does the transform buyers actually want: asset-level loss history — "how much flood-insurance money has been paid out near THIS address, how often, how badly, and how recently."

Who it's for

• P&C insurers & reinsurers — pre-bind flood exposure and repetitive-loss screening.
• Mortgage lenders & CRE due diligence — flood-loss history alongside Phase I / property condition.
• Real estate & appraisal — quantify neighborhood flood-loss burden.
• Risk / cat modeling teams — a quick, keyless historical-loss density signal by point.

Example input

{
  "assets": [
    { "lat": 30.0, "lon": -90.0, "label": "New Orleans metro, LA", "state": "LA" },
    { "lat": 29.7, "lon": -95.4, "label": "Houston Ship Channel, TX", "state": "TX" }
  ],
  "radiusMiles": 5,
  "sinceYear": 2000,
  "maxClaimsPerAsset": 5000,
  "maxResults": 500
}

Output fields (one row per asset)

Field	Meaning
asset_label, asset_lat, asset_lon, state	Echo of the input property
radius_miles	Radius used for aggregation
claim_count	NFIP paid claims within the radius (on/after sinceYear)
total_paid_usd	Sum of building + contents + ICC payments
avg_payout_usd / max_single_payout_usd	Average and largest single payout
most_recent_loss_year	Year of the most recent nearby loss
dominant_cause	Most frequent NFIP cause of damage (human-readable)
repetitive_loss_flag	True when ≥2 claims fall within the radius
flood_loss_score	Editorial 0–100 composite (count + total paid + recency)
nearest_claim_distance_miles	Distance to the closest nearby claim
error, disclaimer, source_url, checked_at	Diagnostics + provenance

Important: how FEMA locates claims (read this)

FEMA censors each loss to its census-tract centroid and rounds latitude/ longitude to about 0.1° (~7 miles). Claims therefore snap onto a coarse grid. Consequences:

• A small radius (e.g. 1 mile) only matches when a property sits on a populated grid point. For arbitrary real properties, use radiusMiles ≥ ~5.
• Metrics describe a tract-level neighborhood, not one parcel.
• flood_loss_score is an editorial 0–100 composite (log-scaled claim count up to 40, log-scaled total paid up to 40, recency up to 20) — not a FEMA rating, flood-zone determination, or actuarial premium.

Use as an MCP tool

This actor is a clean, chainable tool for AI agents via mcp.apify.com: pass one or more {lat, lon, label} assets and get back a structured, field-described flood-loss history row per property — ideal for underwriting copilots and property-risk agents.

FAQ

Where does the data come from? FEMA OpenFEMA FimaNfipClaims (National Flood Insurance Program redacted paid-claims dataset). Keyless, public, historical.

Is this a FEMA flood zone / FIRM lookup? No. It measures historical paid losses near a point, not the regulatory flood zone. Pair it with a FIRM/flood-zone check.

Why is claim_count capped? Dense flood metros can hold tens of thousands of claims at one grid point; maxClaimsPerAsset bounds the scan (most-recent-first) so runs stay fast and cheap. Raise it for a fuller aggregate.

How is it billed? Pay-per-result: one billable Result per asset row. Empty or errored assets still emit a row so you always see what happened.

Nothing came back for my address. Increase radiusMiles (the coordinates are tract-centroid rounded), widen sinceYear, or confirm the point is in a flood-claim region.