MuniScore™
Thousands of financial line items, distilled into a single score. Get an immediate read on any municipality's underlying financial health, compare credits side by side, and decide in seconds whether to move on or dig deeper.
A Different Kind of Rating. Built for a Broader Purpose.
MuniScore was built by former rating agency analysts who have deep respect for the bond rating process. Bond ratings remain the gold standard for assessing debt security, and MuniScore is designed to complement them, not replace them.
Where a bond rating focuses on the security of a specific debt obligation, MuniScore measures the underlying financial operating health of the municipality itself. That distinction matters. A municipality can carry an investment-grade bond rating while quietly running structural deficits, drawing down reserves, or accumulating pension obligations that have not yet surfaced in its debt profile. MuniScore surfaces those dynamics directly, making it useful far beyond the bond market: for analysts assessing fiscal risk, researchers studying municipal finance, government officials benchmarking their own position, and portfolio managers monitoring credits between rating agency reviews.
More Precision Where Bond Ratings Go Quiet.
Rating agency categories are intentionally broad. Two municipalities rated Aa2 may look similar from a bond security standpoint but tell a very different story in their financials. MuniScore's 1-99 scale gives you the granularity to see those differences. When two credits carry the same rating and their bonds are pricing similarly, a MuniScore comparison can surface meaningful gaps in reserve levels, debt trajectories, or budgetary performance that a rating bucket cannot show. Use it to rank credits within a rating category, flag outliers before a rating action occurs, or identify relative value that is not visible from ratings alone.
Score composition
What Goes Into a MuniScore
MuniScore is built on the same dimensions a credit analyst would assess manually: the strength of the local economy, whether revenues are keeping pace with expenditures, the depth of reserve buffers, and the weight of long-term debt and pension obligations.
The four factors are weighted to reflect their reliability as indicators. Reserves receive the highest weight because fund balance is the most consistently reported and stable measure of fiscal health. A single-year operating margin carries less weight because it can swing on one-time items like asset sales or grant timing.
The framework
4 Factors, 9 Metrics
Each factor measures a distinct dimension of financial health. The weight distribution reflects that fund balance reserves are the most reliable indicator, while a single-year operating margin can swing on one-time items.
Economic health of the municipality. A strong tax base and employed population support revenue and reduce social service demand.
- Median Household IncomeHigher ▲
- Unemployment RateLower ▼
- Poverty RateLower ▼
- Bachelor's Degree+Higher ▲
Evaluates whether revenues keep pace with expenditures. Weighted lowest of the four factors, since annual results are often influenced by one-time events like asset sales or grant timing.
- Operating MarginHigher ▲
Quantifies the financial buffer a municipality holds against unexpected shortfalls. Receives the highest weight because fund balance is the most consistently reported and reliable measure of fiscal resilience.
- Unreserved FB / ExpenditureHigher ▲
- Total FB / ExpenditureHigher ▲
Measures how heavily long-term commitments weigh on the municipality's revenue capacity. Uses Total Governmental Fund revenue as the denominator to capture all governmental revenue streams.
- Debt BurdenLower ▼
- Pension & OPEB BurdenLower ▼
Scoring engine
How the Score Is Calculated
Here's how the math works.
Score Each Metric
We compare each raw number to five benchmarks tuned to the municipality's sector: Very Low, Low, Moderate, Strong, and Very Strong. Hit Very Strong, the metric scores 90. Hit Very Low, it scores 10. Land between two benchmarks, the score scales smoothly to where you sit.
Average Into Factors
Each factor's score is the average of its metric scores. If a metric can't be computed, we leave it out rather than counting it as zero — so missing data lowers confidence, not the score itself.
Weight Into MuniScore
The overall MuniScore is a weighted average of the four factors: Economy 25%, Budgetary 10%, Reserves 40%, Debt 25%. If a factor can't be computed, its weight is redistributed across the remaining ones — Economy stays anchored at 25% either way.
From a metric to a score
Suppose the “Strong” benchmark for a city's reserves is X% and the “Very Strong” benchmark is Y%. A city sitting halfway between those two benchmarks earns a score of 82 \u2014 exactly halfway between 75 and 90.
For metrics where lower is better (like debt ratios), the same scaling applies in reverse: heavier debt earns a lower score.
When data is missing
Say a school district's debt schedule isn't available, so we can't compute the Debt factor. Its 25% slot doesn't go to zero \u2014 it shifts to the other financial factors in proportion. Economy stays anchored at 25%:
A MuniScore needs Economy plus at least two financial factors to be reliable. With anything less, we mark the score as Insufficient rather than publishing a number we can't stand behind.
The five benchmarks, and the scores they earn
Fair comparisons
Benchmarks Tailored by Sector
Every metric is scored against benchmarks calibrated for the municipality's sector. Cities, counties, school districts, and states are each evaluated against thresholds derived from their own structural realities — revenue flexibility, expenditure rigidity, fund-balance norms, and pension burdens differ materially across entity classes.
The chart on the right shows what this looks like in practice: the actual score density curves differ in shape, center, and dispersion across sectors. A school district with 30% reserves is strong for its sector but average for a city — sector-specific benchmarks correct for that systematic bias.
Score density by sector
% of sectorDistributions reshape by entity class: cities and counties skew similarly, states cluster tightly around the median, school districts sit a little lower and flatter. Each curve is normalized to the size of its own sector.
Sector-specific thresholds
Five sectors get their own benchmark grids — city, county, school district, state, and an “all” fallback for special districts. The grids reflect Moody's tier boundaries on shared metrics, then diverge where structural differences matter (e.g., school districts have higher NPL tolerance to absorb teacher pension burdens).
Fixed across fiscal years
The same thresholds apply to every fiscal year, so a score of 75 in FY2016 carries the same meaning as 75 in FY2024. Because we don't re-rank against shifting peer percentiles, real macro effects — pandemic stimulus, post-2022 normalization — are visible in the time series rather than washed out by a rolling baseline.
Statistical validity
The Numbers Behind the Scores
MuniScore is built on 90,026 scored (entity, fiscal year) pairs across 18,261 unique municipalities and 10 fiscal years. The distribution is calibrated to a midpoint near 50 — the “A” credit boundary on every published rating-agency grid we consulted. Mean = 69.7, median = 72.1.
MuniScore distribution
n = 90,026Every (EIN, fiscal year) pair we've scored, bucketed into 5-point bins. Calibrated to a midpoint near 50 — the “A” credit boundary.
Tier breakdown
90,026 scoredFive tiers, calibrated to credit-quality boundaries. The bulk of the population sits in the Moderate band — by design, since that band corresponds to “A” credit quality.
Mean MuniScore over time
FY2016–FY2025Because benchmarks are fixed rather than peer-percentile, scores are directly comparable across years — and macro shifts (federal stimulus, recovery, normalization) are visible. The shaded band spans the 10th–90th percentile each year.
Factor correlation matrix
Pearson rHow tightly do the four factor scores move together? Strong correlations between two factors would suggest we're measuring the same thing twice. Weak correlations confirm the factors capture distinct dimensions of fiscal health.
| Economy | Budgetary | Reserves | Debt | |
|---|---|---|---|---|
| Economy | — | 0.02 | -0.05 | 0.02 |
| Budgetary | 0.02 | — | 0.33 | 0.03 |
| Reserves | -0.05 | 0.33 | — | 0.05 |
| Debt | 0.02 | 0.03 | 0.05 | — |
Coverage by sector
Scored (EIN, fiscal year) pairs by sector, with the share that earned a HIGH confidence badge and the average number of metrics computed (out of nine).
Charts above are computed live from the production municipality_scores table, refreshed hourly. Per-row scoring data is never exposed to the client — only pre-aggregated bins, percentiles, and correlation summaries.
Data completeness
Confidence Badges & Missing Data
Not every municipality has complete data for all 9 metrics. MuniScore handles this through dynamic weight redistribution: when a factor can't be scored, its weight is proportionally redistributed among the remaining financial factors.
A MuniScore requires Economy plus at least 2 financial factors. About 76% of scored municipalities receive High confidence, meaning 7 or more of the 9 metrics were available.
Annual Comprehensive Financial Reports
Audited financial statements extracted directly from municipal ACFRs. Digitized and standardized for FY2016–2024.
Census American Community Survey
Economic data from the Census Bureau's 5-year ACS at place, MSA, and state levels. Automatic 3-tier fallback. For the most recent fiscal years where ACS data is not yet available, we use our own forecasted values.
Transparency
Everything Traces Back to Audited Public Data.
Every MuniScore is built entirely on audited public data. Financial data is extracted directly from ACFRs filed with municipalities and standardized against a consistent accounting framework. Economic data comes from the U.S. Census Bureau's American Community Survey.
No proprietary estimates, no black boxes. Every input can be traced back to a source document.
See It in Action
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