WildfireRiskFinder

Converse, SC

Converse wildfire risk explained

Elevated
60thpercentile nationally

Converse's 349 buildings earn a 60th-percentile wildfire-risk score nationally under USFS's model — modestly above the national average for wildfire risk. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Converse at the 63rd percentile, close to its 60th-percentile risk score.

Converse's building exposure, zone by zone

349Total buildings
67.1%Direct exposure
33%Indirect exposure
0%Minimal exposure

349 buildings are counted in Converse, and 67.1% of them sit in USFS's Direct exposure zone — flame contact, not just ember cast, is the live possibility for most of them, against 0% rated Minimal.

How Converse compares

Converse's 60th national percentile looks worse in isolation than its 19th ranking inside South Carolina does — this place is on the milder end for its own state, by 41 points. Among the 31,521 US communities USFS scores, Converse ranks 12,650 for wildfire risk (1 is highest) and 20,352 by building count (1 is largest). Within South Carolina alone, it ranks 384 of 474 places by risk. See the full county-by-county picture for South Carolina on its state page.

Converse and the insurance market

At the 60th national percentile, Converse rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.

Lowering exposure, not just insuring around it

Because Direct exposure dominates in Converse (67.1%), the highest-leverage fix is defensible space, not roofing or vents alone — covered in the home-hardening guide.

Where Converse's figures come from

Converse's 60th-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 Converse's dominant direct exposure actually means, with real examples from across the dataset.