WildfireRiskFinder

Arvin, CA

Arvin wildfire risk explained

High
70thpercentile nationally

Arvin's 4,819 buildings earn a 70th-percentile wildfire-risk score nationally under USFS's model — well above the national norm for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Arvin at the 71st national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.

What "at risk" means for the buildings here

4,819Total buildings
11.6%Direct exposure
12.4%Indirect exposure
76%Minimal exposure

Arvin rates 76% Minimal exposure against just 11.6% Direct and 12.4% Indirect — of 4,819 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.

How Arvin compares

Arvin's 70th national percentile looks worse in isolation than its 27th ranking inside California does — this place is on the milder end for its own state, by 43 points. Among the 31,521 US communities USFS scores, Arvin ranks 9,507 for wildfire risk (1 is highest) and 3,544 by building count (1 is largest). Within California alone, it ranks 1,143 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 Arvin 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.

Lowering exposure, not just insuring around it

Arvin's 76% 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 Arvin's figures come from

The methodology guide shows exactly how USFS turned 4,819 counted buildings into the percentiles shown above for Arvin. The exposure-zones guide covers what Arvin's dominant minimal exposure actually means, with real examples from across the dataset.