WildfireRiskFinder

Oxford, IA

Oxford, IA's wildfire risk, in USFS's own numbers

High
60thpercentile nationally

USFS's Wildfire Risk to Communities model puts Oxford at the 60th national percentile for risk to structures, well above the national norm for wildfire risk — a score built from 446 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

Fire likelihood alone (USFS's burn-probability figure) ranks Oxford at the 61st national percentile — 1 points above its risk-to-structures score, a gap driven by how much is actually built there.

Where Oxford's buildings actually sit

446Total buildings
10.5%Direct exposure
0%Indirect exposure
89.5%Minimal exposure

Oxford rates 89.5% Minimal exposure against just 10.5% Direct and 0% Indirect — of 446 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.

Oxford against the rest of the country

Compare Oxford's two percentiles: 84th within Iowa, only 60th nationally — a gap of 23 points that marks it as unusually exposed for its own state. Among the 31,521 US communities USFS scores, Oxford ranks 12,480 for wildfire risk (1 is highest) and 18,347 by building count (1 is largest). Within Iowa alone, it ranks 167 of 1,017 places by risk. See the full county-by-county picture for Iowa on its state page.

Shopping for coverage in Oxford

Oxford's high rating (60th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.

Lowering exposure, not just insuring around it

With 89.5% of buildings rated Minimal exposure, Oxford gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.

Where Oxford's figures come from

The methodology guide shows exactly how USFS turned 446 counted buildings into the percentiles shown above for Oxford. The exposure-zones guide covers what Oxford's dominant minimal exposure actually means, with real examples from across the dataset.