WildfireRiskFinder

Connerton, FL

Connerton wildfire risk explained

Very High
92ndpercentile nationally

Connerton's 1,250 buildings earn a 92nd-percentile wildfire-risk score nationally under USFS's model — in USFS's highest wildfire-risk band nationally. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Connerton at the 94th 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

1,250Total buildings
44%Direct exposure
56%Indirect exposure
0%Minimal exposure

Indirect exposure is dominant in Connerton (56% of 1,250 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 44% sit in the Direct zone.

Where Connerton ranks

Connerton scores 92nd nationally and 86th within Florida — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Connerton ranks 2,415 for wildfire risk (1 is highest) and 10,350 by building count (1 is largest). Within Florida alone, it ranks 136 of 955 places by risk. See the full county-by-county picture for Florida on its state page.

What this risk score means for insurance

Connerton's very high rating (92nd 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.

Hardening a home in Connerton

With ember exposure the dominant pattern in Connerton (56% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.

Where Connerton's figures come from

Every one of the two percentiles behind Connerton's 2,415-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Connerton's dominant indirect exposure actually means, with real examples from across the dataset.