Butler Beach, FL (32080) Flooding & Climate Risk Profile

The primary drivers of climate-related financial risk in Butler Beach, FL (32080) are Inland Flooding, Hurricane, and Coastal Flooding. Based on recent federal data, homeowners in this market face an estimated average annual insurance premium of $4,810.

Understanding the Dollars

Expected Annual Loss (EAL) is a statistical average of property damage for this entire zip code over a standard year across all properties.

  • / It represents the "average cost" rather than a guaranteed yearly bill.
  • / It can be used to compare the relative risk from different hazards and across different neighborhoods.

Zip Code Risk Map

Flood Plain Analysis

Localized Flood Dynamics in 32080

FEMA Flood Maps for 32080 identify the "100-year" and "500-year" floodplains (1% and 0.2% annual chance), but modern climate risk analysis suggests that nearly 25% of flood insurance claims originate from properties outside of these designated high-risk zones.

Use the map above to better understand risk by looking at both the FEMA flood plain maps and FEMA Risk Inventory maps by census tract. Standard FEMA maps may not account for 'flash flooding' from intense rain events.
FEMA Designation vs. Reality
Relatively Moderate
Relative Vulnerability
$2,287,815
Annualized Property Exposure

Primary Risks

Inland Flooding

$2,287,815

Expected Annual Loss for Zip Code 32080

37.7Score

Relatively Moderate compared to US average

Hurricane

$1,925,781

Expected Annual Loss for Zip Code 32080

87.8Score

Relatively High compared to US average

Coastal Flooding

$1,435,388

Expected Annual Loss for Zip Code 32080

92.6Score

Relatively High compared to US average

Insurance Market Stability

Avg. Annual Premium (2022)

$4,810
Latest Market Rate

Year-over-Year Change

-3.4%
20212022

Market Retreat (Non-Renewals)

0.00%

Higher rates indicate insurers are actively reducing exposure to ZIP 32080 due to climate-linked risk.

Underwriting Stress (Loss Ratio)

22.0%

A ratio over 70% suggests insurers are paying out nearly all premiums as claims, forcing future price hikes.

Historical Market Trends

Toggle series below to compare costs vs. market stress indicators

Historical Trends & Forecasting

Compare premium costs against underlying risk factors.

Financial Risk Inventory

MAJOR DRIVER
Inland Flooding
$2,287,815
Score: 37.7
MAJOR DRIVER
Hurricane
$1,925,781
Score: 87.8
MAJOR DRIVER
Coastal Flooding
$1,435,388
Score: 92.6
Tornado
$723,319
Score: 66.5
Heat Wave
$182,155
Score: 46.7
Lightning
$133,388
Score: 73.8
Wildfire
$76,777
Score: 67.8
Strong Wind
$49,161
Score: 36.1
Earthquake
$38,778
Score: 22.7
Cold Wave
$21,521
Score: 20.2
Hail
$2,777
Score: 14.5
Landslide
$16
Score: 36.9

Recommended Mitigation Strategies

Recommended investments to protect your property value and reduce insurance liability based on your local risk profile.

💧Medium Investment

Inland Flooding Mitigation

Install a smart sump pump with battery backup and extend downspouts 10ft from foundation.

Risk Score: 37.7
🌀High Investment

Hurricane Mitigation

Install permanent hurricane shutters or upgrade to impact-resistant windows.

Risk Score: 87.8
🏠Low Investment

Coastal Flooding Mitigation

General property maintenance and insurance review recommended.

Risk Score: 92.6

Methodology and Sources

Spatial Climate Risk Modeling

The Expected Annual Loss (EAL) and hazard risk scores are derived from the FEMA NRI zip code dataset using a population-weighted spatial join. Because Zip Codes and Census Tracts do not share perfectly aligned boundaries, we utilize US Census Block Group population centroids to identify where residents actually live.

Financial & Insurance Metrics

The pysical resilence score is calculated by synthesizing Expected Annual Loss (EAL) against the total building replacement value within a jurisdiction. This creates a "Loss Ratio" that measures physical resilience. We supplement this with ZIP-code level data from the U.S. Treasury's Federal Insurance Office (FIO), monitoring trends in premium growth, loss ratios, and policy non-renewals to identify emerging "Insurance Deserts."

Primary Data Sources

Nearby Locations