Singers Glen, VA
How exposed is Singers Glen to wildfire?
Singers Glen's 166 buildings earn a 69th-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 Singers Glen at the 69th national percentile — 0 points below its risk-to-structures score, a gap driven by how much is actually built there.
Where Singers Glen's buildings actually sit
166 buildings are counted in Singers Glen, and 100% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
Where Singers Glen ranks
Singers Glen scores 69th nationally and 82nd 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, Singers Glen ranks 9,660 for wildfire risk (1 is highest) and 26,195 by building count (1 is largest). Within Virginia alone, it ranks 125 of 681 places by risk. See the full county-by-county picture for Virginia on its state page.
What this risk score means for insurance
Singers Glen's 69th-percentile, high rating is the kind of score behind a broader national pattern: insurers pulling back from the highest wildfire-risk markets, non-renewing or declining new policies outright, most visibly in California. Shopping around, not assuming automatic renewal, tends to matter here.
Lowering exposure, not just insuring around it
Singers Glen's 100% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.
Where Singers Glen's figures come from
Singers Glen's 69th-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 Singers Glen's dominant direct exposure actually means, with real examples from across the dataset.