WildfireRiskFinder

Grant, IA

Grant wildfire risk explained

Elevated
56thpercentile nationally

Out of every US place USFS scores, Grant lands at the 56th percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 115 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 Grant at the 57th percentile, close to its 56th-percentile risk score.

Where Grant's buildings actually sit

115Total buildings
26.1%Direct exposure
0%Indirect exposure
73.9%Minimal exposure

Most of Grant's buildings (73.9% of 115) 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.

Grant against the rest of the country

Compare Grant's two percentiles: 77th within Iowa, only 56th nationally — a gap of 21 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Grant ranks 13,802 for wildfire risk (1 is highest) and 28,411 by building count (1 is largest). Within Iowa alone, it ranks 232 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.

What this risk score means for insurance

Grant's elevated rating (56th 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

Grant's 73.9% 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 Grant's figures come from

Grant's 56th-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Grant's dominant minimal exposure actually means, with real examples from across the dataset.