Chittenango, NY (13037) Flooding & Climate Risk Profile

The primary drivers of climate-related financial risk in Chittenango, NY (13037) are Inland Flooding, Tornado, and Lightning. Based on recent federal data, homeowners in this market face an estimated average annual insurance premium of $912, 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

Significant Flood Exposure in 13037

FEMA Flood Maps for 13037 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 High
Relative Vulnerability
$2,695,857
Annualized Property Exposure

Primary Risks

Inland Flooding

$2,695,857

Expected Annual Loss for Zip Code 13037

75.5Score

Relatively High compared to US average

Tornado

$136,567

Expected Annual Loss for Zip Code 13037

47.0Score

Relatively Moderate compared to US average

Lightning

$84,109

Expected Annual Loss for Zip Code 13037

81.1Score

Relatively High compared to US average

Insurance Market Stability

Avg. Annual Premium (2022)

$912
Latest Market Rate

Year-over-Year Change

+2.7%
20212022

Market Retreat (Non-Renewals)

0.40%

Higher rates indicate insurers are actively reducing exposure to ZIP 13037 due to climate-linked risk.

Underwriting Stress (Loss Ratio)

77.0%

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
$2,695,857
Score: 75.5
MAJOR DRIVER
Tornado
$136,567
Score: 47.0
MAJOR DRIVER
Lightning
$84,109
Score: 81.1
Cold Wave
$83,243
Score: 40.8
Strong Wind
$66,109
Score: 61.9
Ice Storm
$33,388
Score: 74.4
Heat Wave
$28,465
Score: 16.5
Hurricane
$24,497
Score: 51.7
Winter Weather
$23,516
Score: 79.2
Earthquake
$20,773
Score: 24.0
Hail
$10,254
Score: 38.2
Wildfire
$676
Score: 48.7
Landslide
$74
Score: 56.1

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: 75.5
🌪️High Investment

Tornado Mitigation

Reinforce garage doors and consider a FEMA-approved safe room or storm cellar.

Risk Score: 47.0
🏠Low Investment

Lightning Mitigation

General property maintenance and insurance review recommended.

Risk Score: 81.1

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