Oil City, PA (16301) Flooding & Climate Risk Profile

The primary drivers of climate-related financial risk in Oil City, PA (16301) are Inland Flooding, Strong Wind, and Lightning. Based on recent federal data, homeowners in this market face an estimated average annual insurance premium of $994, with a local policy non-renewal rate of 0.6%.

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 16301

FEMA Flood Maps for 16301 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
Very High
Relative Vulnerability
$3,666,612
Annualized Property Exposure

Primary Risks

Inland Flooding

$3,666,612

Expected Annual Loss for Zip Code 16301

52.7Score

Very High compared to US average

Strong Wind

$246,552

Expected Annual Loss for Zip Code 16301

77.8Score

Relatively High compared to US average

Lightning

$200,099

Expected Annual Loss for Zip Code 16301

86.2Score

Very High compared to US average

Insurance Market Stability

Avg. Annual Premium (2022)

$994
Latest Market Rate

Year-over-Year Change

+1.4%
20212022

Market Retreat (Non-Renewals)

0.59%

Higher rates indicate insurers are actively reducing exposure to ZIP 16301 due to climate-linked risk.

Underwriting Stress (Loss Ratio)

29.0%

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
$3,666,612
Score: 52.7
MAJOR DRIVER
Strong Wind
$246,552
Score: 77.8
MAJOR DRIVER
Lightning
$200,099
Score: 86.2
Tornado
$154,476
Score: 38.6
Hurricane
$30,280
Score: 48.5
Cold Wave
$17,918
Score: 19.8
Earthquake
$15,121
Score: 14.1
Winter Weather
$13,586
Score: 52.2
Hail
$10,342
Score: 31.2
Heat Wave
$8,905
Score: 3.2
Ice Storm
$6,179
Score: 18.5
Landslide
$1,089
Score: 71.2
Wildfire
$636
Score: 42.0

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: 52.7
💨Low Investment

Strong Wind Mitigation

Trim large trees back from the roofline and reinforce roof-to-wall connectors (hurricane straps).

Risk Score: 77.8
🏠Low Investment

Lightning Mitigation

General property maintenance and insurance review recommended.

Risk Score: 86.2

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