Severance, CO
Severance wildfire risk explained
Out of every US place USFS scores, Severance lands at the 75th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 3,817 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Severance's burn probability — fire likelihood with no building count factored in — sits at the 74th percentile nationally.
Severance's building exposure, zone by zone
Severance rates 71.3% Minimal exposure against just 28.7% Direct and 0.1% Indirect — of 3,817 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Where Severance ranks
Inside Colorado, Severance sits at just the 44th percentile even though it scores 75th 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, Severance ranks 8,033 for wildfire risk (1 is highest) and 4,410 by building count (1 is largest). Within Colorado alone, it ranks 267 of 472 places by risk. See the full county-by-county picture for Colorado on its state page.
What this risk score means for insurance
At the 75th percentile nationally, Severance 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.
Lowering exposure, not just insuring around it
Even with 71.3% of Severance outside USFS's Direct and Indirect zones, basic hardening — gutter maintenance, ember-resistant vents — is inexpensive relative to the regional burn-probability risk noted above. Details in the home-hardening guide.
Where Severance's figures come from
The methodology guide shows exactly how USFS turned 3,817 counted buildings into the percentiles shown above for Severance. The exposure-zones guide covers what Severance's dominant minimal exposure actually means, with real examples from across the dataset.