Tucson, AZ (85730) Flooding & Climate Risk Profile

The primary drivers of climate-related financial risk in Tucson, AZ (85730) are Inland Flooding, Heat Wave, and Wildfire. Based on recent federal data, homeowners in this market face an estimated average annual insurance premium of $949, with a local policy non-renewal rate of 1.2%.

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 85730

FEMA Flood Maps for 85730 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
$9,942,442
Annualized Property Exposure

Primary Risks

Inland Flooding

$9,942,442

Expected Annual Loss for Zip Code 85730

71.4Score

Very High compared to US average

Heat Wave

$4,807,299

Expected Annual Loss for Zip Code 85730

96.8Score

Very High compared to US average

Wildfire

$270,264

Expected Annual Loss for Zip Code 85730

58.1Score

Relatively High compared to US average

Insurance Market Stability

Avg. Annual Premium (2022)

$949
Latest Market Rate

Year-over-Year Change

+4.5%
20212022

Market Retreat (Non-Renewals)

1.17%

Higher rates indicate insurers are actively reducing exposure to ZIP 85730 due to climate-linked risk.

Underwriting Stress (Loss Ratio)

60.0%

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
$9,942,442
Score: 71.4
MAJOR DRIVER
Heat Wave
$4,807,299
Score: 96.8
MAJOR DRIVER
Wildfire
$270,264
Score: 58.1
Earthquake
$178,480
Score: 39.0
Lightning
$92,178
Score: 45.4
Hail
$42,591
Score: 42.0
Strong Wind
$34,824
Score: 19.5
Tornado
$17,289
Score: 4.7
Winter Weather
$6,600
Score: 27.9
Hurricane
$142
Score: 13.3
Landslide
$0
Score: 10.7

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: 71.4
☀️Low Investment

Heat Wave Mitigation

Ensure attic insulation is R-49+ and consider a dual-fuel backup generator for AC.

Risk Score: 96.8
🔥Low Investment

Wildfire Mitigation

Create a 5ft 'non-combustible' zone around your home using gravel or pavers instead of mulch.

Risk Score: 58.1

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