Tickfaw, LA
Tickfaw wildfire risk explained
USFS's Wildfire Risk to Communities model puts Tickfaw at the 78th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 438 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Tickfaw at the 79th national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.
Tickfaw's building exposure, zone by zone
438 buildings are counted in Tickfaw, and 82.9% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.
Tickfaw against the rest of the country
Tickfaw scores 78th nationally and 91st within Louisiana — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Tickfaw ranks 6,936 for wildfire risk (1 is highest) and 18,502 by building count (1 is largest). Within Louisiana alone, it ranks 44 of 488 places by risk. See the full county-by-county picture for Louisiana on its state page.
What this risk score means for insurance
At the 78th percentile nationally, Tickfaw 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.
What would actually reduce this score
With 82.9% of Tickfaw 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 Tickfaw's figures come from
Tickfaw's 78th-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 Tickfaw's dominant direct exposure actually means, with real examples from across the dataset.