Parsons, WV
Parsons wildfire risk explained
Out of every US place USFS scores, Parsons lands at the 64th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 852 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Parsons's burn probability — fire likelihood with no building count factored in — sits at the 62nd percentile nationally.
What "at risk" means for the buildings here
Most of Parsons's buildings (76.2%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
How Parsons compares
There's little gap between Parsons's 64th national percentile and its 55th 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, Parsons ranks 11,426 for wildfire risk (1 is highest) and 13,152 by building count (1 is largest). Within West Virginia alone, it ranks 193 of 429 places by risk. See the full county-by-county picture for West Virginia on its state page.
What this risk score means for insurance
Parsons's high rating (64th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
What would actually reduce this score
Parsons's 76.2% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Parsons's figures come from
The methodology guide shows exactly how USFS turned 852 counted buildings into the percentiles shown above for Parsons. The exposure-zones guide covers what Parsons's dominant indirect exposure actually means, with real examples from across the dataset.