Parklawn, CA
Parklawn wildfire risk explained
Out of every US place USFS scores, Parklawn lands at the 59th percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 451 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
Separately from the risk score above, Parklawn's burn probability — fire likelihood with no building count factored in — sits at the 62nd percentile nationally.
What "at risk" means for the buildings here
Parklawn rates 98.5% Minimal exposure against just 1.6% Direct and 0% Indirect — of 451 buildings counted, few sit close enough to burnable vegetation for USFS to flag them individually.
Parklawn against the rest of the country
Parklawn ranks lower within California (17th percentile statewide) than its 59th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Parklawn ranks 13,040 for wildfire risk (1 is highest) and 18,249 by building count (1 is largest). Within California alone, it ranks 1,310 of 1,570 places by risk. See the full county-by-county picture for California on its state page.
What this risk score means for insurance
Because Parklawn is in California, a wildfire-hazard disclosure is legally required before a sale closes here — unusual nationally, since only these two states mandate it. The state's FAIR Plan alone carried 668,609 policies by the end of 2025. Full detail in the disclosure-law guide.
What would actually reduce this score
With 98.5% of buildings rated Minimal exposure, Parklawn gets less benefit from structure hardening than a Direct- or Indirect-dominant place would — the home-hardening guide explains why the zone matters.
Where Parklawn's figures come from
The methodology guide shows exactly how USFS turned 451 counted buildings into the percentiles shown above for Parklawn. The exposure-zones guide covers what Parklawn's dominant minimal exposure actually means, with real examples from across the dataset.