WildfireRiskFinder

Detmold, MD

Detmold wildfire risk explained

Elevated
58thpercentile nationally

Detmold's 52 buildings earn a 58th-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.)

Fire likelihood alone (USFS's burn-probability figure) ranks Detmold at the 56th national percentile — 3 points below its risk-to-structures score, a gap driven by how much is actually built there.

What "at risk" means for the buildings here

52Total buildings
86.5%Direct exposure
13.5%Indirect exposure
0%Minimal exposure

52 buildings are counted in Detmold, and 86.5% 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.

Where Detmold ranks

Detmold's 83rd-percentile standing inside Maryland outpaces its 58th national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Detmold ranks 13,109 for wildfire risk (1 is highest) and 30,875 by building count (1 is largest). Within Maryland alone, it ranks 87 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.

What this risk score means for insurance

At the 58th national percentile, Detmold rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.

What would actually reduce this score

Detmold's 86.5% Direct-exposure share means lot clearing around the structure typically outweighs any single material upgrade here. The home-hardening guide ranks the options for a place shaped like this.

Where Detmold's figures come from

The methodology guide shows exactly how USFS turned 52 counted buildings into the percentiles shown above for Detmold. The exposure-zones guide covers what Detmold's dominant direct exposure actually means, with real examples from across the dataset.