Bernardi Environmental Risk Index
Our Methodology and Positioning for Natural Disaster Resiliency within Municipal Bond Portfolios
White Paper, May 2026
Created By:
Matthew Bernardi
Thomas Bernardi, CFA, CIPM
Zachary Cronin
Jeremy Williams
Overview
Our firm has developed a proprietary Environmental Risk Index (“ERI” or the “Index”) to both measure and mitigate relative environmental risk within municipal bond portfolios. The Index assesses a county’s exposure to natural disasters and aspects of its financial resiliency – factoring in insurance non-renewal risk. We believe that a municipality’s overall resilience to natural disasters is a function of both this Index and its underlying fiscal health. ERI scores do not independently determine municipal credit risk and are complementary to our internal, single security credit analysis process.
We believe incorporating the Index into our credit analysis process is of growing importance as the cost of natural disasters continues to increase; in 2024, the US incurred approximately $182.7 billion in damages from 27 separate billion-dollar weather related disasters.[1] While the cost of disasters declined in 2025, damages still totaled approximately $115 billion across 23 billion-dollar events.[2] Based on these figures, the average annual cost of natural disasters has increased by approximately 3.60%-6.10% per year[3] – outpacing the 2.40% rate of inflation since 2005
Stress in the insurance market in particular regions poses a growing risk to municipal credit quality. Constrained coverage availability, rising premiums, and higher deductibles can pressure household and business balance sheets, weakening the local tax base and slowing post-disaster economic recovery. Non-renewal rates are especially alarming and deteriorating insurance conditions often signal increasing levels of underinsurance. This can materially impede rebuilding efforts and prolong fiscal strain on state and local governments following a disaster.
Lastly, the importance of measuring natural disaster risk and resiliency is further intensified by the potential for waning federal support for disaster recovery and a ballooning federal debt load. These factors could place significant fiscal pressure on state and local governments during future rebuilding efforts. A lack of federal support may also temper the pace of post-disaster economic recovery, straining tax revenues. It is important to note that the Index is a backward looking tool based on historical data, not a predictor of future events. Our primary data sources are FEMA’s National Risk Index [4] and a Senate Budget Committee report[5] on insurance non-renewal rates. The ERI score ranges from 0 (lowest risk) to 10 (highest risk).
Bernardi ERI | United States Heat Map
Average State ERI
0.67
4.17
Figure 1: In the chart above, we have provided a heat map of each state’s average county-based ERI score. The higher the score, the more risk. The scale runs from red-orange-yellow-green with the former indicating higher risk vs. the latter.
Bernardi Environmental Risk Index Findings
Below is the average ERI score, by state, calculated by averaging the scores of all counties within the given state. According to the Index, Florida leads the nation in environmental risk followed by Oklahoma, Arizona, California, and Hawaii. These five states represent the highest risk levels according to our blended Index of environmental risk and insurance non-renewal rates. Notably, Midwestern states – led by Michigan, Wisconsin, Ohio, Indiana – generally have lower Index scores (less risk), averaging a score of 1.82.[6]
State Environmental Risk Index Rankings
Average county-level ERI score by state — all 50 states.
Higher scores indicate greater relative natural disaster risk and/or less insurance market resiliency.
Higher scores indicate greater relative natural disaster risk and/or less insurance market resiliency.
2.48
National average
#
State
Risk profile
ERI
1
FL
4.17
2
OK
4.11
3
AZ
3.93
4
CA
3.82
5
HI
3.77
6
DE
3.68
7
NV
3.42
8
NJ
3.33
9
SC
3.19
10
MA
3.16
11
CT
3.13
12
RI
3.13
13
TX
3.04
14
KS
3.04
15
MD
3.02
16
ID
2.98
17
NM
2.97
18
ME
2.96
19
LA
2.95
20
NC
2.89
21
UT
2.81
22
CO
2.74
23
AL
2.57
24
WY
2.52
25
AR
2.41
26
MS
2.39
27
NE
2.33
28
GA
2.33
29
MO
2.29
30
IA
2.15
31
SD
2.15
32
VA
2.15
33
OR
2.12
34
NH
2.07
35
IL
1.89
36
MT
1.78
37
MN
1.78
38
KY
1.78
39
NY
1.73
40
WA
1.70
41
AK
1.70
42
PA
1.68
43
TN
1.67
44
IN
1.62
45
ND
1.59
46
OH
1.38
47
WV
1.28
48
VT
1.15
49
WI
1.05
50
MI
0.67
Source: Bernardi Securities — Bernardi Environmental Risk Index, May 2026. Based on FEMA National Risk Index annualized hazard frequency data and Senate Budget Committee property insurance non-renewal rates. ERI scores are ordinal and min-max normalized; they do not represent probabilities of loss or default.
Coastal and Gulf states consistently show elevated ERI values, primarily driven by hurricanes, coastal flooding, and riverine flooding. These regions not only face frequent hazards but are susceptible to compounding risks from sea level rise and increasing storm severity. In the interior western states and Pacific region, wildfire, drought conditions, and seismic activity drive higher ERI scores, where destructive storm potential is most prevalent. Counties with the highest ERI scores are concentrated in Southern California, the interior western states, and Oklahoma, with additional hot spots along the coastlines of North Carolina, Louisiana, and Florida.
The western United States exhibits a distinct risk profile, with elevated ERI scores largely driven by wildfires, earthquakes, and drought conditions. Counties in California, Oregon, and Washington face the highest exposure to earthquake and wildfire hazards, while mountain and arid regions are more susceptible to drought and fire frequency. Northern states deal primarily with winter storms and cold waves, though these events typically carry lower destructive potential than high-consequence hazards such as wildfires, tornadoes, and hurricanes.
States with Highest ERI Score
Greatest relative natural disaster exposure among all 50 states
#
State
ERI
Key considerations
1
FL
4.17
‣ High hurricane frequencies in coastal counties
‣ Highest insurance non-renewal rate
2
OK
4.11
‣ Multiple hazards: tornado, hail, drought, and flooding
‣ Ranked 5th in insurance non-renewals
3
AZ
3.93
‣ Multiple hazards with elevated frequency including wildfire, drought, heat wave, riverine flooding
4
CA
3.82
‣ Ranked 3rd in insurance non-renewals
‣ Prevalence of hazards including wildfire, drought, heat wave, earthquake, flooding
5
HI
3.77
‣ Multiple hazards with elevated frequency including volcanic and tsunami activity, as well as earthquake and flooding
‣ Ranked 14th in insurance non-renewals
Source: Bernardi Securities — Bernardi Environmental Risk Index, May 2026. ERI scores are ordinal and min-max normalized; they do not represent probabilities of loss or default.
States with Lowest ERI Score
Lowest relative natural disaster exposure among all 50 states
#
State
ERI
Key considerations
46
OH
1.38
‣ Ranked 23rd in insurance non-renewals
‣ Less frequent severe hazards
47
WV
1.28
‣ Ranked 47th in insurance non-renewals
‣ Less frequent severe hazards
48
VT
1.15
‣ Ranked 36th in insurance non-renewals
‣ Less frequent severe hazards
49
WI
1.05
‣ Ranked 40th in insurance non-renewals
‣ Less frequent severe hazards
50
MI
0.67
‣ Ranked 41st in insurance non-renewals
‣ Less frequent severe hazards
Source: Bernardi Securities — Bernardi Environmental Risk Index, May 2026. ERI scores are ordinal and min-max normalized; they do not represent probabilities of loss or default.
Our analysis attributed an ERI score to 3,147 counties in the United States, with a national county-level average score of 2.34. The charts below show the twenty-five highest and lowest rankings by county. The higher the number, the more risk the county has within the ERI Index.
ERI National Average of all Counties: 2.34
It is important these values are interpreted correctly. ERI scores are min-max normalized, and nonlinear. This means the scale is ordinal rather than ratio-based. For example, a score of 6.0 does not represent twice the risk as a score of 3.0. Instead, a higher score indicates higher rates of insurance non-renewals, more potential hazards, more frequency, or both. Furthermore, these scores are not probabilistic and should not be used to calculate the likelihood of a specific event in a given year.
| # | State | County | ERI Score |
|---|---|---|---|
| 1 | CA | San Bernardino | 10.00 |
| 2 | CA | San Diego | 10.00 |
| 3 | CA | Riverside | 10.00 |
| 4 | ID | Twin Falls | 8.37 |
| 5 | ID | Elmore | 7.82 |
| 6 | CO | Weld | 7.82 |
| 7 | OK | Pittsburg | 7.12 |
| 8 | NC | Carteret | 6.90 |
| 9 | AK | Aleutians West | 6.81 |
| 10 | FL | Collier | 6.53 |
| 11 | UT | Washington | 6.51 |
| 12 | CA | Los Angeles | 6.49 |
| 13 | CO | Washington | 6.47 |
| 14 | NV | Elko | 6.47 |
| 15 | LA | St. Bernard | 6.43 |
| 16 | OK | Haskell | 6.38 |
| 17 | FL | Palm Beach | 6.37 |
| 18 | OK | Latimer | 6.37 |
| 19 | OK | Osage | 6.31 |
| 20 | SC | Charleston | 6.28 |
| 21 | OK | LeFlore | 6.27 |
| 22 | FL | Hendry | 6.25 |
| 23 | AZ | Mohave | 6.20 |
| 24 | CA | Ventura | 6.14 |
| 25 | NV | Humboldt | 6.14 |
| # | State | County | ERI Score |
|---|---|---|---|
| 1 | MI | Schoolcraft | 0.12 |
| 2 | MI | Presque Isle | 0.14 |
| 3 | AK | North Slope | 0.16 |
| 4 | MI | Alpena | 0.19 |
| 5 | MI | Luce | 0.19 |
| 6 | MI | Mackinac | 0.23 |
| 7 | MI | Emmet | 0.24 |
| 8 | MI | Montmorency | 0.25 |
| 9 | MI | Cheboygan | 0.26 |
| 10 | MI | Alcona | 0.26 |
| 11 | MI | Benzie | 0.27 |
| 12 | WI | Florence | 0.28 |
| 13 | AK | Dillingham | 0.30 |
| 14 | MI | Menominee | 0.31 |
| 15 | WI | Menominee | 0.32 |
| 16 | MI | Dickinson | 0.33 |
| 17 | MI | Chippewa | 0.34 |
| 18 | MI | Manistee | 0.36 |
| 19 | MI | Lake | 0.36 |
| 20 | MI | Iosco | 0.37 |
| 21 | MI | Delta | 0.37 |
| 22 | MI | Otsego | 0.37 |
| 23 | MI | Missaukee | 0.37 |
| 24 | MI | Ogemaw | 0.37 |
| 25 | MI | Oscoda | 0.37 |
Source: Bernardi ERI scores derived from FEMA National Risk Index and county-level property insurance non-renewal data (U.S. Senate Budget Committee, ‘Next to Fall,’ 2024). Data as of time of analysis.
Property Insurance Policy Non-Renewal Rates Across the Country
Not surprisingly, insurance market stress is emerging in regions with the highest environmental risk. In 2023, county-level non-renewal rates spiked in parts of Florida and North Carolina, led by Glades County, FL (16.2%), aptly named Dare County, NC (12.9%), and Washington County, NC (12.2%). As shown in the tables below, several additional coastal counties in North Carolina and Florida recorded non-renewal rates in the 9–11% range. Smaller, yet significant, clusters also appeared in pockets in Massachusetts and California. The correlation between high ERI scores and elevated non-renewal rates underscores the compounding challenge: the dual pressure of rising hazard severity and declining insurance availability (or surging premiums making insurance cost prohibitive) in the most exposed counties.
Average National Insurance Non-Renewal Rate: 1.17%[7]
Our ERI takes this into account if the insurance non-renewal for a specific county is higher than the national average. As a result, the ERI score is assigned a 10% penalty.
Average Non-Renewal Rate by State
Hover over a state to see its rate
Rising non-renewal rates indicate insurers’ growing reluctance to underwrite natural disaster risk, reflecting their internal assessments of heightened disaster frequency and severity. This has also led to a surge in insurance premiums which creates a challenging environment where even if coverage is available, it has become increasingly cost prohibitive. Declining insurance coverage increases the potential for financial stress following a disaster and likely indicates constituents are under insured on a broader scale. Declining insurance availability and affordability materially reduce the attractiveness of these locations for both existing residents and prospective taxpayers.
FEMA Risk Index Shortfalls in Measuring Natural Disaster Risk & Resiliency
FEMA provides highly granular, county-level data on natural disaster risk, which serves as the foundational input to our Index. This data plays a critical role in enhancing the public’s understanding of disaster exposure across the United States. We hope FEMA continues to measure and maintain this data over long time horizons, as it will enable deeper analysis and more informed risk assessments of environment and financial risk over time.
From a municipal credit perspective, however, the FEMA framework requires refinement. The FEMA National Risk Index incorporates an economic value component, that systematically assigns higher risk scores to larger and more economically valuable counties. While this construction is intuitive from a damage-estimation standpoint – higher concentrations of economic assets imply greater absolute potential losses – it can be misleading when applied to municipal credit analysis, in our opinion.
In our view, retaining the economic value input misrepresents credit risk and distorts relative risk rankings among municipal obligors.
When evaluated through the lens of municipal credit risk, this approach conflates exposure with credit vulnerability. Higher economic value is frequently associated with larger, more diversified, and economically dynamic jurisdictions. These characteristics are not weaknesses from a credit standpoint; rather, they are key drivers of fiscal resilience. In addition, larger counties tend to have broader geographic footprints, allowing disaster impacts to be absorbed across a wider economic base. These characteristics enhance resilience and support a municipality’s ability to recover from the damage of a natural disaster.
To better align the index with municipal credit fundamentals, we removed the economic value component of FEMA’s National Risk Index and rely exclusively on FEMA’s natural disaster probability variable. In our view, retaining the economic value input misrepresents credit risk and distorts relative risk rankings among municipal obligors.
In the context of municipal credit analysis, higher underlying economic value is more appropriately interpreted as an indicator of resilience, for several reasons:
- Greater aggregate wealth enhances a jurisdiction’s capacity to levy taxes or fees to finance disaster recovery and maintain essential operations
- Wealthier jurisdictions typically exhibit broader economic and revenue diversification, reducing reliance on any single industry or taxpayer, which improves shock absorption.
- Larger, economically significant regions carry heightened national and regional importance, increasing the likelihood of federal or state assistance after a major disaster.
- Higher economic value often reflects entrenched commercial activity, strategic location, or natural advantages that support faster and more durable post-disaster recovery.
Conversely, smaller and less wealthy obligors generally operate with narrower and more concentrated economic footprints. In these cases, disaster damage is more likely to impair a materially larger share of the tax base, resulting in disproportionate fiscal stress, weaker recovery capacity, and elevated credit risk.
This distinction is illustrated by recent California wildfire events. The 2025 Los Angeles–area wildfires were geographically extensive and economically destructive, with estimated losses in the tens to hundreds of billions of dollars. While these events created near-term fiscal pressure on the City of Los Angeles and surrounding jurisdictions, the region’s scale, wealth, and highly diversified revenue base materially enhanced its capacity to absorb losses and fund recovery costs over time.
By contrast, the 2018 Camp Fire in Paradise, California destroyed approximately 95% of the town’s structures, effectively eliminating most of its economic base. Although the absolute dollar losses were significantly lower than those incurred in Los Angeles, the proportional economic and fiscal impact on Paradise was far more severe, resulting in substantially higher credit impairment.
This comparison underscores why absolute economic damage is an insufficient proxy for municipal credit risk, and why isolating disaster probability—rather than asset value— provides a more analytically sound framework for evaluating long-term municipal resilience and creditworthiness. Any environmental score, however, must be paired with a fiscally oriented credit assessment to fully evaluate an obligor’s overall credit quality and financial resilience.
Removing the wealth variable in FEMA’s risk framework materially alters the relative risk ranking. For example, this change moves Los Angeles down from the riskiest county in the FEMA National Risk Index to the ERI’s 12th riskiest. Of the top ten riskiest ERI scored counties, the average population[8] is 883,861 with a median of 209,514 vs. FEMA’s average population of 3,213,614 and median of 2,299,920. These differences illustrate how the ERI partly mitigates the size and wealth bias embedded in FEMA’s index, resulting in a more appropriate assessment of natural disaster risk for municipal credit analysis.
Each scoring framework will inherently attribute more risk to larger counties given the increased chance of a natural disaster due to their larger size. Furthermore, there is an increased risk multiple disasters could occur in a larger land mass vs. smaller.
Los Angeles County, CA is nearly 2.5x larger than Butte County, CA (location of the Camp Fire) and geographically more exposed to more risks. It has more coastal flooding, drought, earthquake, heatwave, landslide, riverine flooding, wind, tornado, tsunami, and wildfire risk due to its location and size. Therefore, before any weights are ascribed to disaster risk, Los Angeles County is going to have a higher risk score.
Cook County presents another great example of a county that is unfairly penalized by FEMA for its wealth and size. It is ranked as the 13th riskiest county within the FEMA risk framework. Within the Bernardi ERI, Cook is ranked the 4th riskiest among all State of Illinois counties, and 704th nationwide. Within Illinois, the counties of Alexander, Jackson, and White all rank higher (riskier) within our ERI framework.
The table below presents the twenty-five counties with the highest risk according to the ERI and FEMA Index. For each county, the table also shows its ranking under the other index and the difference between the two rankings. This difference is reported in the fifth column of each section. A higher ranking indicates greater assessed risk under the respective index.
You will see significant risk ranking differences between the two indexes. This is primarily due to the ERI removing the size/wealth factor which FEMA incorporates. Generally, larger counties score higher (riskier) within the FEMA rankings vs. the ERI. Twelve different states are represented in the ERI’s top twenty-five riskiest counties, while FEMA has only eight. The median population of the ERI top twenty-five riskiest counties is 70,400 vs. 1,080,000 for FEMA’s top twenty-five.
Evaluating an obligor’s exposure to natural disaster risk requires analysis beyond the headline ERI score. This is particularly important for large counties, where a single county-level score may mask meaningful variation among the dozens—or even hundreds—of underlying municipal obligors within its boundaries. In addition to assessing an issuer’s distinct financial and management profile, investors should incorporate more granular hazard indicators – such as wildfire risk maps and floodplain data – to form a more complete view of natural disaster risk.
ERI: Insights into Geographic Positioning within Municipal Bond Portfolios
Today, positioning a municipal portfolio based on geography is driven by tax status and benchmark weightings. We argue that natural disaster probability and resilience should also be a major factor. For actively managed portfolios with no state of residence tax incentives, portfolios should be allocated to obligors with relatively lower natural disaster risk and higher resilience.
If the cost of recovering from natural disasters continues to rise and outpace inflation, and if the federal government’s role in post-disaster assistance becomes less certain, measuring relative natural disaster risk will become increasingly critical to municipal credit research.
We use the ERI to guide geographic portfolio construction on a relative risk basis, both across states and within them. The index indicates that the Midwest offers the most favorable natural disaster risk profile within the municipal bond market. Importantly, we do not believe this view is broadly reflected in current market consensus, nor is it embedded in valuations, as many Midwest issuers continue to offer attractive spread.
Several structural factors help explain why Midwest municipal issuers often trade at wider spreads. The largest issuers – those most heavily weighted in major benchmarks and therefore attracting the greatest index-driven demand – are largely located outside the Midwest, frequently in regions with higher natural disaster exposure. These issuers also tend to be based in high–income-tax states where in-state residents benefit from double tax-exempt income. Because a significant amount of investable wealth is concentrated in New York and California, this plays a major role in capital flows and pricing. Strong demand from benchmark-oriented strategies and local investors seeking state tax exemption tends to compress yields on these securities, effectively muting any yield premium that might otherwise arise from elevated natural disaster risk.
Barring tax status, this calls for a strong overweight of portfolios to Midwestern locations
Additionally, the average Midwestern town is smaller than coastal cities.[9] This reduces the likelihood that Midwest-based municipalities are included in benchmarks given their smaller size. Therefore, the large fund complexes and passive strategies that mimic the benchmarks have a relatively lower weighting to these geographies.
Midwestern states benefit from lower ERI scores due to lower annual frequencies of highly destructive hazards. Primarily, the lack of hurricanes, earthquakes, and wildfire hazards. Our model weights those hazards heavily so the lack of annualized frequency brings the ERI score down relative to areas where those hazards are prevalent. Additionally, the lack of these hazards within the Midwest leads to lower insurance non-renewal rates so the ERI scores are not penalized like western and coastal states.
These variables are correlated, but if one believes the trends will continue, the Midwest will become an even more relatively attractive place to safely defend against natural disaster risk. Barring tax status, this calls for a strong overweight of portfolios to Midwestern locations.
Conclusion
Defaults in the municipal bond market are exceptionally rare. To date, we do not know of any natural disaster–driven defaults among general obligation bonds or essential-service revenue bonds. Where disasters have contributed to payment failures, those cases have been confined to smaller, non-essential or commercially oriented revenue issuers with highly concentrated economic bases.
That said, natural disaster risk is becoming an increasingly material variable in municipal credit analysis. Greater storm severity, wildfire exposure, floodplain expansion, insurance market retrenchment, and demographic shifts are all altering the risk profile of certain issuers. As a result, portfolio construction should account for geographic risk.
Complete avoidance of disaster exposure is not practical in a diversified municipal strategy. However, risk can be meaningfully mitigated through disciplined security selection and the use of indexes such as the ERI to maintain exposure levels below the national average. Practically, this implies an overweight to Midwest issuers and a relative underweight to coastal credits where natural disaster frequency and insurance market stress are higher.
In the near term, municipal yields are predominantly influenced by technical factors—supply/demand imbalances, fund flows, and issuance calendars. Over the medium to long term, relative value is more closely anchored to tax policy, Treasury rate movements, and macroeconomic conditions. An open question is whether geographic risk differentials—particularly exposure to disaster risk—will increasingly command a pricing premium or discount at the state and local level, thereby becoming a more explicit component of spread differentiation across obligors. We believe it should and utilizing indexes such as the ERI are a starting point to provide guidance.
Appendix & Resources
FEMA Risk Index Detail:
The FEMA Score is made up of expected annual loss times a community risk factor. Expected annual loss is made up of annualized frequency, exposure, and historic loss ratio. The community risk factor is a function of social vulnerability and community resilience. Values are calculated at the Census tract level, with county values calculated by summing the values from their tracts.
The Community Risk Factor scales the value such that counties with higher relative Social Vulnerability and/or lower Community Resilience will have higher index values.
Resources:
FEMA Risk Index: https://www.fema.gov/flood-maps/products-tools/national-risk-index
Senate Budget Committee Report on Insurance Non-renewal rates: https://www.budget.senate.gov/imo/media/doc/next_to_fall_the_climate-driven_insurance_crisis_is_here__and_getting_worse.pdf
[1] https://coast.noaa.gov/states/stories/
[2] https://www.climatecentral.org/climate-matters/2025-in-review/
[3] This data is sourced from the National Oceanic and Atmospheric Administration (NOAA), which only covers events with at least $1 billion in damages. The annual growth rate is based on their average data from 2005-2015 vs. the 2024 and 2025 figures. This data can be found here: https://www.ncei.noaa.gov/access/billions/summary-stats/US/2000-2010. These calculations deserve a caveat, in that though the growth of the cost of natural disasters has outpaced inflation, this NOAA-based data could be biased towards a higher relative growth rate. Given the price level has increased drastically due to inflation, it is more likely a billion dollar event will occur today than ten, let alone, twenty years ago. As the value of property increases, so does the probability of billion dollar events used in NOAA’s data.
[4] https://www.fema.gov/sites/default/files/documents/fema_national-risk-index_technical-documentation.pdf
[5] https://www.budget.senate.gov/imo/media/doc/next_to_fall_the_climate-driven_insurance_crisis_is_here__and_getting_worse.pdf
[6] Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin
[7] In lieu of counties, Alaska uses “Boroughs” and Louisiana uses “Parishes”
[8] 2020 Census figures
[9] According to the U.S. Census Bureau’s 2024 Annual Estimates of the Resident Population for Incorporated Places (SUB-IP-EST2024-POP), the median incorporated place in the Midwest has a population of approximately 700 — the lowest of the four official Census regions — compared to approximately 1,400 nationally, 2,400 in the Northeast, 1,400 in the South, and 2,700 in the West, across 19,479 total incorporated places nationwide. Source: U.S. Census Bureau, Population Division, May 2025.
IMPORTANT DISCLOSURES
General — Not Investment Advice
This white paper is published by Bernardi Securities, Inc. (“BSI”), a registered broker-dealer and member of FINRA and MSRB. This white paper is provided for informational and educational purposes only and does not constitute investment advice, a solicitation, or an offer to buy or sell any security or financial instrument. Nothing in this white paper should be construed as a recommendation to purchase or sell any specific security or to adopt any particular investment strategy. Past performance and historical data referenced herein are not indicative of future results. All investments involve risk, including the possible loss of principal.
Proprietary Model — Bernardi Environmental Risk Index (ERI)
The Bernardi Environmental Risk Index (“ERI” or “Index”) is a proprietary analytical tool developed by BSI. The ERI is based on BSI’s internal methodology and reflects BSI’s assumptions, judgments, weightings, and data inputs as of the date indicated herein. The ERI is one of several factors considered in BSI’s investment and credit analysis process and does not represent the sole basis for any investment decision. The ERI is subject to periodic revision, and scores may change as underlying data is updated. Prior ERI scores may not be directly comparable to current scores following any methodology revision.
Data Sources
ERI scores are derived primarily from two data sources: (1) the FEMA National Risk Index (NRI), a county-level dataset published by the Federal Emergency Management Agency measuring annualized frequency of eighteen natural hazards, available at fema.gov/flood-maps/products-tools/national-risk-index; and (2) county-level property insurance non-renewal rate data sourced from the U.S. Senate Budget Committee report on insurance non-renewal rates (“Next to Fall,” 2024), available at budget.senate.gov. Data is utilized as of the most recent available vintage at the time of index construction. BSI does not independently verify third-party data and makes no representation as to its accuracy, completeness, or fitness for any particular purpose. FEMA has noted that its National Risk Index data is “intended for use as a tool for broad, nationwide comparisons” and that “modeling natural hazard risk for the entire country has inherent uncertainty and inaccuracy.” BSI adopts this same caveat with respect to ERI scores derived therefrom.
Model Risk and Limitations
The ERI is a quantitative scoring model based on historical hazard frequency data and BSI’s analytical framework. Like all models, the ERI is subject to inherent limitations, including: reliance on historical patterns that may not predict the frequency, severity, or geographic distribution of future natural disaster events; data gaps or inconsistencies at the county or regional level in the underlying FEMA and insurance datasets; potential misclassification of risk due to intra-county geographic variation not captured at the county level; and the possibility that factors not captured in the model — including climate change trajectory, land use changes, infrastructure investment, and policy changes — may materially affect natural disaster risk or municipal bond credit quality. The ERI should not be relied upon as a complete or definitive assessment of natural disaster risk, municipal credit quality, or investment suitability.
Forward-Looking Statements and Historical Resilience
Certain statements in this white paper regarding expected trends, future conditions, and the potential impact of natural disasters on municipal bond markets are forward-looking in nature and subject to significant uncertainty. Actual outcomes may differ materially from those anticipated. Historical resilience of the municipal bond market to natural disaster events does not guarantee that such resilience will continue in future events. The increasing frequency and severity of natural disasters, rising insurance non-renewal rates, and climate-related financial risks may affect municipal bond issuers in ways not fully captured by historical data. BSI undertakes no obligation to update this white paper to reflect subsequent developments.
Publication Date
This white paper reflects BSI’s research, analysis, and views as of May 2026 and is subject to change without notice. The ERI scores and insurance non-renewal data presented herein reflect data available as of the time of analysis. BSI undertakes no obligation to update this white paper following its initial publication.