Chattaroy, WV
Chattaroy wildfire risk explained
USFS's Wildfire Risk to Communities model puts Chattaroy at the 96th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 397 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Chattaroy at the 97th percentile, close to its 96th-percentile risk score.
Chattaroy's building exposure, zone by zone
85.9% of Chattaroy's 397 buildings sit in USFS's Direct exposure zone, roughly 341 structures close enough to burnable vegetation for flame contact, not just embers — 14.1% fall in the Indirect, ember-only zone and 0% are Minimal.
Where Chattaroy ranks
There's little gap between Chattaroy's 96th national percentile and its 98th percentile inside West Virginia, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Chattaroy ranks 1,403 for wildfire risk (1 is highest) and 19,288 by building count (1 is largest). Within West Virginia alone, it ranks 10 of 429 places by risk. See the full county-by-county picture for West Virginia on its state page.
Shopping for coverage in Chattaroy
At the 96th percentile nationally, Chattaroy carries the very high rating that has pushed some carriers to limit new business in similarly-scored places elsewhere in the country. Worth comparing quotes rather than assuming last year's renewal terms still apply.
Hardening a home in Chattaroy
Because Direct exposure dominates in Chattaroy (85.9%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Chattaroy's figures come from
Chattaroy's 96th-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 Chattaroy's dominant direct exposure actually means, with real examples from across the dataset.