Shawneeland, VA
Shawneeland, VA's wildfire risk, in USFS's own numbers
Out of every US place USFS scores, Shawneeland lands at the 67th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 912 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Shawneeland at the 64th percentile, close to its 67th-percentile risk score.
Where Shawneeland's buildings actually sit
100% of Shawneeland's 912 buildings sit in USFS's Direct exposure zone, roughly 912 structures close enough to burnable vegetation for flame contact, not just embers — 0% fall in the Indirect, ember-only zone and 0% are Minimal.
Where Shawneeland ranks
Shawneeland scores 67th nationally and 79th within Virginia — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Shawneeland ranks 10,447 for wildfire risk (1 is highest) and 12,625 by building count (1 is largest). Within Virginia alone, it ranks 147 of 681 places by risk. See the full county-by-county picture for Virginia on its state page.
Shawneeland and the insurance market
At the 67th percentile nationally, Shawneeland carries the 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.
What would actually reduce this score
Because Direct exposure dominates in Shawneeland (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Shawneeland's figures come from
Every one of the two percentiles behind Shawneeland's 10,447-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Shawneeland's dominant direct exposure actually means, with real examples from across the dataset.