Sour John, OK
Sour John wildfire risk explained
USFS scores Sour John at the 92nd national percentile for wildfire risk to structures (in USFS's highest wildfire-risk band nationally), a figure built from 63 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Sour John at the 94th national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
100% of Sour John's 63 buildings sit in USFS's Direct exposure zone, roughly 63 structures close enough to burnable vegetation for flame contact, not just embers — 0% fall in the Indirect, ember-only zone and 0% are Minimal.
How Sour John compares
Sour John ranks lower within Oklahoma (57th percentile statewide) than its 92nd national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Sour John ranks 2,561 for wildfire risk (1 is highest) and 30,555 by building count (1 is largest). Within Oklahoma alone, it ranks 361 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Shopping for coverage in Sour John
Sour John's 92nd-percentile, very 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.
Hardening a home in Sour John
Because Direct exposure dominates in Sour John (100%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.
Where Sour John's figures come from
The methodology guide shows exactly how USFS turned 63 counted buildings into the percentiles shown above for Sour John. The exposure-zones guide covers what Sour John's dominant direct exposure actually means, with real examples from across the dataset.