WildfireRiskFinder

Oblong, IL

Oblong wildfire risk explained

Low
3rdpercentile nationally

Out of every US place USFS scores, Oblong lands at the 3rd percentile for wildfire risk to structures — among the lower wildfire-risk places nationally — a figure built from its 1,074 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Oblong's burn probability — fire likelihood with no building count factored in — sits at the 3rd percentile nationally.

Where Oblong's buildings actually sit

1,074Total buildings
11.6%Direct exposure
0%Indirect exposure
88.5%Minimal exposure

Most of Oblong's buildings (88.5% of 1,074) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.

Where Oblong ranks

Oblong's 20th-percentile standing inside Illinois outpaces its 3rd 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, Oblong ranks 30,537 for wildfire risk (1 is highest) and 11,437 by building count (1 is largest). Within Illinois alone, it ranks 1,157 of 1,445 places by risk. See the full county-by-county picture for Illinois on its state page.

Oblong and the insurance market

Oblong's low rating (3rd 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

Oblong's 88.5% 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 Oblong's figures come from

Every one of the two percentiles behind Oblong's 30,537-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Oblong's dominant minimal exposure actually means, with real examples from across the dataset.