WildfireRiskFinder

Argo, IA

Argo wildfire risk explained

Low
9thpercentile nationally

Argo sits at the 9th percentile nationally for wildfire risk to structures — among the lower wildfire-risk places nationally — per USFS's Wildfire Risk to Communities model, built from its 59 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Argo's burn probability — fire likelihood with no building count factored in — sits at the 9th percentile nationally.

What "at risk" means for the buildings here

59Total buildings
37.3%Direct exposure
0%Indirect exposure
62.7%Minimal exposure

Most of Argo's buildings (62.7% of 59) 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.

How Argo compares

There's little gap between Argo's 9th national percentile and its 18th percentile inside Iowa, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Argo ranks 28,641 for wildfire risk (1 is highest) and 30,675 by building count (1 is largest). Within Iowa alone, it ranks 829 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.

Argo and the insurance market

Argo's low rating (9th 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 62.7% of buildings rated Minimal exposure, Argo gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.

Where Argo's figures come from

Every one of the two percentiles behind Argo's 28,641-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Argo's dominant minimal exposure actually means, with real examples from across the dataset.