WildfireRiskFinder

Obion, TN

Obion wildfire risk explained

Moderate
25thpercentile nationally

Out of every US place USFS scores, Obion lands at the 25th percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 656 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Obion at the 27th percentile, close to its 25th-percentile risk score.

Where Obion's buildings actually sit

656Total buildings
41.6%Direct exposure
57.9%Indirect exposure
0.5%Minimal exposure

Indirect exposure is dominant in Obion (57.9% of 656 buildings): far enough from burnable vegetation to avoid flame contact, close enough for wind-blown embers. Only 41.6% sit in the Direct zone.

How Obion compares

Obion's risk sits at a similar level relative to Tennessee (10th percentile statewide) as it does nationally (25th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Obion ranks 23,559 for wildfire risk (1 is highest) and 15,120 by building count (1 is largest). Within Tennessee alone, it ranks 450 of 502 places by risk. See the full county-by-county picture for Tennessee on its state page.

Obion and the insurance market

At the 25th national percentile, Obion rates moderate for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.

Lowering exposure, not just insuring around it

With ember exposure the dominant pattern in Obion (57.9% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.

Where Obion's figures come from

The methodology guide shows exactly how USFS turned 656 counted buildings into the percentiles shown above for Obion. The exposure-zones guide covers what Obion's dominant indirect exposure actually means, with real examples from across the dataset.