Dobbins, CA
Dobbins, CA's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Dobbins at the 100th national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 407 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Dobbins's burn probability — fire likelihood with no building count factored in — sits at the 99th percentile nationally.
What "at risk" means for the buildings here
USFS classifies 100% of Dobbins's buildings as Direct exposure, higher than its 0% Indirect share and far above its 0% Minimal share — a profile where 407 structures sit close enough to vegetation that lot clearing matters most.
Where Dobbins ranks
Dobbins scores 100th nationally and 94th within California — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Dobbins ranks 120 for wildfire risk (1 is highest) and 19,075 by building count (1 is largest). Within California alone, it ranks 77 of 1,570 places by risk. See the full county-by-county picture for California on its state page.
What this risk score means for insurance
Dobbins sits in California, one of two states that legally require a wildfire-risk disclosure at sale. California's FAIR Plan, the state's insurer of last resort, ended 2025 with 668,609 residential policies after adding 21,859 in Q4 alone — the kind of market shift a 100th-percentile score like Dobbins's can end up mattering for. More on the disclosure law.
What would actually reduce this score
Because Direct exposure dominates in Dobbins (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Dobbins's figures come from
The methodology guide shows exactly how USFS turned 407 counted buildings into the percentiles shown above for Dobbins. The exposure-zones guide covers what Dobbins's dominant direct exposure actually means, with real examples from across the dataset.