WildfireRiskFinder

Hartley, IA

Hartley wildfire risk explained

Low
15thpercentile nationally

USFS scores Hartley at the 15th national percentile for wildfire risk to structures (among the lower wildfire-risk places nationally), a figure built from 1,100 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Hartley at the 14th national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.

Hartley's building exposure, zone by zone

1,100Total buildings
18.4%Direct exposure
0%Indirect exposure
81.6%Minimal exposure

Most of Hartley's buildings (81.6% of 1,100) 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.

Hartley against the rest of the country

Hartley scores 15th nationally and 25th within Iowa — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Hartley ranks 26,750 for wildfire risk (1 is highest) and 11,273 by building count (1 is largest). Within Iowa alone, it ranks 762 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.

Hartley and the insurance market

Hartley's low rating (15th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.

Hardening a home in Hartley

Hartley's 81.6% 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 Hartley's figures come from

Every one of the two percentiles behind Hartley's 26,750-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Hartley's dominant minimal exposure actually means, with real examples from across the dataset.