WildfireRiskFinder

Ina, IL

Ina, IL's wildfire risk, in USFS's own numbers

Low
14thpercentile nationally

USFS's Wildfire Risk to Communities model puts Ina at the 14th national percentile for risk to structures, among the lower wildfire-risk places nationally — a score built from 377 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Ina's burn probability — fire likelihood with no building count factored in — sits at the 14th percentile nationally.

What "at risk" means for the buildings here

377Total buildings
22%Direct exposure
0%Indirect exposure
78%Minimal exposure

USFS classifies 78% of Ina's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 22% Direct and 0% Indirect — a landscape-level risk rather than a building-by-building one.

Ina against the rest of the country

Within Illinois, Ina ranks higher (51st percentile) than it does nationally (14th) — one of the more fire-exposed places in a state that, overall, scores lower than that. Among the 31,521 US communities USFS scores, Ina ranks 27,094 for wildfire risk (1 is highest) and 19,715 by building count (1 is largest). Within Illinois alone, it ranks 706 of 1,445 places by risk. See the full county-by-county picture for Illinois on its state page.

What this risk score means for insurance

Ina's low rating (14th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

What would actually reduce this score

Ina's 78% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.

Where Ina's figures come from

Every one of the two percentiles behind Ina's 27,094-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Ina's dominant minimal exposure actually means, with real examples from across the dataset.