WildfireRiskFinder

Inland, NE

How exposed is Inland to wildfire?

Moderate
25thpercentile nationally

Inland's 78 buildings earn a 25th-percentile wildfire-risk score nationally under USFS's model — close to the middle of USFS's national wildfire-risk range. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Inland's burn probability — fire likelihood with no building count factored in — sits at the 26th percentile nationally.

What "at risk" means for the buildings here

78Total buildings
16.7%Direct exposure
28.2%Indirect exposure
55.1%Minimal exposure

USFS classifies 55.1% of Inland's buildings as Minimal exposure, the largest of the three zones here by a wide margin over 16.7% Direct and 28.2% Indirect — a landscape-level risk rather than a building-by-building one.

How Inland compares

Inland's risk sits at a similar level relative to Nebraska (13th 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, Inland ranks 23,516 for wildfire risk (1 is highest) and 29,975 by building count (1 is largest). Within Nebraska alone, it ranks 506 of 583 places by risk. See the full county-by-county picture for Nebraska on its state page.

Inland and the insurance market

Inland's moderate rating (25th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Lowering exposure, not just insuring around it

With 55.1% of buildings rated Minimal exposure, Inland gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.

Where Inland's figures come from

Every one of the two percentiles behind Inland's 23,516-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Inland's dominant minimal exposure actually means, with real examples from across the dataset.