Vina, CA
Vina wildfire risk explained
Vina sits at the 89th percentile nationally for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — per USFS's Wildfire Risk to Communities model, built from its 179 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Vina at the 91st national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
Vina's building exposure, zone by zone
55.9% of Vina's 179 buildings sit in USFS's Direct exposure zone, roughly 100 structures close enough to burnable vegetation for flame contact, not just embers — 44.1% fall in the Indirect, ember-only zone and 0% are Minimal.
Where Vina ranks
Inside California, Vina sits at just the 60th percentile even though it scores 89th nationally — the state's overall wildfire exposure is high enough to make this a relatively quiet corner of it. Among the 31,521 US communities USFS scores, Vina ranks 3,332 for wildfire risk (1 is highest) and 25,659 by building count (1 is largest). Within California alone, it ranks 631 of 1,570 places by risk. See the full county-by-county picture for California on its state page.
Shopping for coverage in Vina
Because Vina 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.
Hardening a home in Vina
Because Direct exposure dominates in Vina (55.9%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Vina's figures come from
Every one of the two percentiles behind Vina's 3,332-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Vina's dominant direct exposure actually means, with real examples from across the dataset.