Cudahy, CA
Cudahy, CA's wildfire risk, in USFS's own numbers
Cudahy sits at the 45th percentile nationally for wildfire risk to structures — modestly above the national average for wildfire risk — per USFS's Wildfire Risk to Communities model, built from its 4,248 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Cudahy's burn probability — fire likelihood with no building count factored in — sits at the 50th percentile nationally.
What "at risk" means for the buildings here
USFS classifies 99.8% of Cudahy's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 0.2% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.
Where Cudahy ranks
Cudahy's 45th national percentile looks worse in isolation than its 8th ranking inside California does — this place is on the milder end for its own state, by 37 points. Among the 31,521 US communities USFS scores, Cudahy ranks 17,417 for wildfire risk (1 is highest) and 4,017 by building count (1 is largest). Within California alone, it ranks 1,452 of 1,570 places by risk. See the full county-by-county picture for California on its state page.
What this risk score means for insurance
Because Cudahy is in California, a wildfire-hazard disclosure is legally required before a sale closes here — unusual nationally, since only these two states mandate it. The state's FAIR Plan alone carried 668,609 policies by the end of 2025. Full detail in the disclosure-law guide.
What would actually reduce this score
Cudahy's 99.8% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.
Where Cudahy's figures come from
Cudahy's 45th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Cudahy's dominant minimal exposure actually means, with real examples from across the dataset.