Holbrook, NY (11741) Flooding & Climate Risk Profile

The primary drivers of climate-related financial risk in Holbrook, NY (11741) are Inland Flooding, Hurricane, and Earthquake. Based on recent federal data, homeowners in this market face an estimated average annual insurance premium of $2,215, with a local policy non-renewal rate of 0.4%.

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 11741

FEMA Flood Maps for 11741 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
$3,205,736
Annualized Property Exposure

Primary Risks

Inland Flooding

$3,205,736

Expected Annual Loss for Zip Code 11741

33.3Score

Relatively Moderate compared to US average

Hurricane

$597,389

Expected Annual Loss for Zip Code 11741

73.7Score

Relatively Moderate compared to US average

Earthquake

$304,224

Expected Annual Loss for Zip Code 11741

53.8Score

Relatively Low compared to US average

Insurance Market Stability

Avg. Annual Premium (2022)

$2,215
Latest Market Rate

Year-over-Year Change

+0.6%
20212022

Market Retreat (Non-Renewals)

0.37%

Higher rates indicate insurers are actively reducing exposure to ZIP 11741 due to climate-linked risk.

Underwriting Stress (Loss Ratio)

34.0%

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
$3,205,736
Score: 33.3
MAJOR DRIVER
Hurricane
$597,389
Score: 73.7
MAJOR DRIVER
Earthquake
$304,224
Score: 53.8
Cold Wave
$230,642
Score: 38.3
Heat Wave
$112,377
Score: 21.5
Strong Wind
$103,932
Score: 43.9
Tornado
$62,817
Score: 18.8
Lightning
$52,307
Score: 35.4
Ice Storm
$45,566
Score: 54.4
Winter Weather
$32,743
Score: 61.4
Hail
$2,699
Score: 9.9
Wildfire
$1,867
Score: 45.0
Landslide
$18
Score: 43.7

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: 33.3
🌀High Investment

Hurricane Mitigation

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

Risk Score: 73.7
🏠Low Investment

Earthquake Mitigation

General property maintenance and insurance review recommended.

Risk Score: 53.8

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