Creola, LA
How exposed is Creola to wildfire?
Out of every US place USFS scores, Creola lands at the 65th percentile for wildfire risk to structures — well above the national norm for wildfire risk — a figure built from its 81 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Creola at the 66th national percentile — 1 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
USFS classifies 100% of Creola's buildings as Direct exposure, higher than its 0% Indirect share and far above its 0% Minimal share — a profile where 81 structures sit close enough to vegetation that lot clearing matters most.
How Creola compares
Creola's 81st-percentile standing inside Louisiana outpaces its 65th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Creola ranks 11,183 for wildfire risk (1 is highest) and 29,846 by building count (1 is largest). Within Louisiana alone, it ranks 95 of 488 places by risk. See the full county-by-county picture for Louisiana on its state page.
Creola and the insurance market
Creola's high rating (65th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
What would actually reduce this score
With 100% of Creola in Direct exposure, defensible-space clearing and ember-resistant construction (vents, Class-A roofing) address the exposure this page's own numbers describe, not a generic checklist. See the home-hardening guide.
Where Creola's figures come from
Every one of the two percentiles behind Creola's 11,183-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Creola's dominant direct exposure actually means, with real examples from across the dataset.