WildfireRiskFinder

Scio, OR

Scio wildfire risk explained

Moderate
32ndpercentile nationally

USFS's Wildfire Risk to Communities model puts Scio at the 32nd national percentile for risk to structures, close to the middle of USFS's national wildfire-risk range — a score built from 482 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Scio's burn probability — fire likelihood with no building count factored in — sits at the 35th percentile nationally.

Where Scio's buildings actually sit

482Total buildings
15.8%Direct exposure
84.2%Indirect exposure
0%Minimal exposure

USFS puts 84.2% of Scio's 482 buildings in the Indirect exposure zone, versus 15.8% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.

Where Scio ranks

There's little gap between Scio's 32nd national percentile and its 30th percentile inside Oregon, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Scio ranks 21,438 for wildfire risk (1 is highest) and 17,674 by building count (1 is largest). Within Oregon alone, it ranks 295 of 420 places by risk. See the full county-by-county picture for Oregon on its state page.

Shopping for coverage in Scio

Scio's 32nd-percentile score lands in Oregon, the only two states requiring a wildfire disclosure at sale. Oregon's own hazard map feeds directly into that disclosure requirement. The disclosure-law guide covers what it requires.

Lowering exposure, not just insuring around it

With ember exposure the dominant pattern in Scio (84.2% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.

Where Scio's figures come from

Scio's 32nd-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 Scio's dominant indirect exposure actually means, with real examples from across the dataset.