Municipal Credit Report

Municipal credit analysis for any US city, county, or state — synthesized from 6 federal data sources into a composite credit rating (AAA through CCC) with outlook. This actor gives bond analysts, municipal finance teams, and infrastructure investors an independent, data-driven credit assessment in under 60 seconds, without subscription fees or analyst calls.

The actor queries USASpending federal award records, Federal Reserve (FRED) economic indicators, Bureau of Labor Statistics employment data, Data.gov fiscal datasets, Congressional bill feeds, and FEMA disaster history in parallel. Four scoring models — fiscal stress, funding cliff detection, economic resilience, and climate exposure — combine into one composite score and credit opinion.

What data can you extract?

Why use Municipal Credit Report?

Pulling together federal award data, FRED economic time series, BLS employment figures, FEMA disaster records, and congressional bill feeds manually takes days of analyst time. Even then, you still need to build a scoring framework to synthesize the findings. Credit rating agencies charge subscription fees running into tens of thousands of dollars annually — and their reports are infrequent, backward-looking, and opaque about methodology.

This actor automates the entire municipal credit intelligence workflow. You provide a city or county name. The actor queries 6 federal APIs in parallel, applies 4 scoring models, and delivers a structured credit brief with specific risk signals, funding concentration ratios, employment sector diversity metrics, and climate exposure projections — all in one run.

• Scheduling — run quarterly or monthly to track fiscal health trends over time
• API access — trigger from Python, JavaScript, or any HTTP client with full programmatic control
• Proxy rotation — built-in Apify proxy infrastructure handles rate limits and API throttling
• Monitoring — receive Slack or email alerts when a municipality's credit rating changes
• Integrations — pipe results directly into Excel, Google Sheets, Airtable, or your own bond analysis platform

Features

• 4 independent scoring models that produce sub-scores before combining into a composite: Fiscal Stress (30%), Funding Cliff (20%), Economic Resilience (30%), and Climate Exposure (20%)
• Federal dependency scoring measures total USASpending obligations and funding fragmentation across up to 1,000 award records — flags municipalities where a single funding source exceeds 50% of total federal revenue
• Funding cliff detection identifies awards with expiration dates within 12 months, calculates top-source concentration ratios, and scans Congressional bills for repeal, defund, rescind, or reduce-funding language
• Economic resilience via Herfindahl-Hirschman Index — calculates employment sector diversity across BLS data points; a lower HHI (higher diversity index) indicates a more shock-resistant economic base
• Climate fiscal exposure projection from FEMA disaster records: frequency score, major disaster severity, multi-hazard type diversity, and acceleration trend comparing the last 5 years against prior history
• Annualized disaster cost computed from total FEMA obligations divided by years of available data — gives a dollar figure for climate fiscal drag
• S&P-style credit rating scale: AAA (90+), AA (80–89), A (70–79), BBB (55–69), BB (40–54), B (25–39), CCC (below 25)
• Four outlook designations: WATCH (severe stress or critical funding cliff), NEGATIVE, STABLE, or POSITIVE — driven algorithmically from score thresholds
• Parallel data collection — all 5–6 sub-actors run simultaneously via Promise.allSettled, so a single slow API does not block the report
• Geocoding via Nominatim — converts the municipality name to lat/lon for location-dependent data matching in FEMA and geographic economic queries
• Graceful degradation — if any data source returns zero records, the scoring model uses available data rather than failing the run
• Climate toggle — set includeClimate: false to skip FEMA queries and speed up the report when climate risk is out of scope

Use cases for municipal credit analysis

Municipal bond analysis

Bond analysts and credit committees need independent validation before recommending general obligation or revenue bonds to clients. This actor produces a structured credit brief with specific signal narratives — "22 federal awards totaling $485M — high federal dependency" — that complement or challenge rating agency opinions with real-time federal data.

City and county fiscal monitoring

Finance directors and budget officers can schedule this actor to run quarterly and track fiscal stress trends over time. When the funding cliff score rises or a new cluster of expiring federal awards appears, the signal fires before the problem reaches crisis level — giving departments time to seek replacement revenue or restructure obligations.

State aid prioritization

State treasurers and intergovernmental affairs offices can batch-analyze dozens of municipalities to triage which local governments face the highest fiscal stress and climate exposure. This informs state aid distribution, emergency assistance decisions, and targeted fiscal intervention programs.

Infrastructure investment screening

Private capital, municipal bond funds, and infrastructure investors evaluating water, transportation, or energy bond issuances can use this actor to screen issuer creditworthiness before deploying due diligence resources. A BBB or below rating with a WATCH outlook triggers deeper review.

Policy research and academic analysis

Researchers studying the relationship between federal funding patterns, economic structure, and municipal fiscal stability can extract comparable credit profiles for dozens of municipalities and build cross-sectional datasets. All scoring formulas are documented and reproducible.

ESG and climate risk assessment

ESG-focused investors and rating agencies looking to quantify physical climate risk at the municipal level can use the climate exposure module independently — FEMA disaster frequency, major disaster count, disaster type diversity, and acceleration trend produce a defensible, data-sourced climate risk score for any US municipality.

How to generate a municipal credit report

1. Enter the municipality name — Type a US city, county, or state in the Municipality field (e.g., "Detroit", "Cook County", or "Puerto Rico"). Larger municipalities with more federal award history produce richer analyses.
2. Add the state abbreviation — Provide the two-letter state code (e.g., "MI") in the State field to improve geocoding accuracy and narrow data queries to the correct jurisdiction.
3. Choose whether to include climate analysis — Leave the default (true) for a full 4-model report. Set to false to skip FEMA data and receive a faster 3-model analysis.
4. Click Start and download results — The run completes in under 60 seconds. Download the full report as JSON, CSV, or Excel from the Dataset tab.

Input parameters

Input examples

Standard full report — most common use case:

{
  "municipality": "Detroit",
  "state": "MI",
  "includeClimate": true
}

County-level analysis with climate:

{
  "municipality": "Cook County",
  "state": "IL",
  "includeClimate": true
}

Fast report without climate scoring:

{
  "municipality": "Stockton",
  "state": "CA",
  "includeClimate": false
}

Input tips

• Include the state — providing "MI" alongside "Detroit" prevents geocoding the wrong jurisdiction and improves USASpending search relevance
• Use exact government names — "Cook County" outperforms "Chicago suburbs"; "Puerto Rico" works as a territory name
• Disable climate for speed — setting includeClimate: false skips the FEMA actor call and typically cuts run time by 15–20 seconds
• Larger municipalities produce richer reports — cities with more federal awards, BLS sector coverage, and FEMA disaster history generate more populated signal lists and more reliable scores

Output example

{
  "municipality": "Detroit, MI",
  "geocoded": {
    "lat": 42.3314,
    "lon": -83.0458,
    "display": "Detroit, Wayne County, Michigan, United States"
  },
  "generatedAt": "2026-03-20T14:22:10.000Z",
  "includeClimate": true,
  "compositeScore": 42,
  "creditRating": "BB",
  "outlook": "NEGATIVE",
  "strengths": [],
  "risks": [
    "ELEVATED fiscal stress — federal dependency 22 awards totaling $485M",
    "HIGH funding cliff — 5 programs expiring, concentration ratio 38%",
    "VULNERABLE economic base — limited sector diversity"
  ],
  "allSignals": [
    "$485M in federal obligations — high federal dependency",
    "22 federal awards — fragmented funding base",
    "Unemployment rate 6.8% — above healthy threshold",
    "Funding concentration ratio 38% — losing top source would be severe",
    "5 federal awards expiring within 12 months — funding cliff approaching",
    "3 sectors showing job losses — weakening employment base",
    "14 FEMA disasters on record — high natural hazard zone",
    "5 major disaster declarations — recurring severe events"
  ],
  "scoringModels": {
    "fiscalStress": {
      "score": 65,
      "federalGrants": 22,
      "totalFederalAmount": 485000000,
      "economicIndicatorCount": 11,
      "unemploymentSignals": 4,
      "stressLevel": "ELEVATED",
      "signals": [
        "$485M in federal obligations — high federal dependency",
        "22 federal awards — fragmented funding base",
        "Unemployment rate 6.8% — above healthy threshold"
      ]
    },
    "fundingCliff": {
      "score": 58,
      "expiringPrograms": 5,
      "concentrationRatio": 0.38,
      "legislativeThreats": 2,
      "cliffRisk": "HIGH",
      "signals": [
        "Funding concentration ratio 38% — losing top source would be severe",
        "5 federal awards expiring within 12 months — funding cliff approaching"
      ]
    },
    "economicResilience": {
      "score": 35,
      "employmentSectors": 6,
      "diversityIndex": 0.72,
      "growthSectors": 2,
      "resilienceLevel": "VULNERABLE",
      "signals": [
        "3 sectors showing job losses — weakening employment base"
      ]
    },
    "climateExposure": {
      "score": 48,
      "totalDisasters": 14,
      "majorDisasters": 5,
      "disasterTypes": ["Severe Storm", "Flood", "Winter Storm", "Tornado"],
      "exposureLevel": "MODERATE",
      "annualizedCost": 12500000,
      "signals": [
        "14 FEMA disasters on record — high natural hazard zone",
        "5 major disaster declarations — recurring severe events"
      ]
    }
  },
  "dataSummary": {
    "federalAwards": 22,
    "fredIndicators": 11,
    "blsDataPoints": 18,
    "datagovDatasets": 7,
    "congressBills": 9,
    "femaDisasters": 14
  },
  "rawData": {
    "topFederalAwards": [{ "award_id": "DETP-MI-2024-001", "total_obligation": 185000000, "recipient": "Detroit Water & Sewerage", "period_of_performance_current_end_date": "2026-09-30" }],
    "economicIndicators": [{ "series_id": "DETR312URN", "title": "Detroit Unemployment Rate", "value": 6.8 }],
    "employmentData": [{ "series_id": "SMU26198200000000001", "value": 82400, "change": -1200 }],
    "fiscalDatasets": [{ "title": "Detroit Pension Obligation Analysis", "description": "Pension underfunding assessment 2023" }],
    "relevantBills": [{ "bill_number": "HR 4521", "title": "Reduce Urban Infrastructure Funding Act", "status": "Referred to Committee" }],
    "femaDisasters": [{ "disasterNumber": "4559", "incidentType": "Severe Storm", "declarationDate": "2021-07-15", "totalObligatedAmount": 28000000 }]
  }
}

Output fields

How much does it cost to generate a municipal credit report?

Municipal Credit Report uses pay-per-run pricing — approximately $0.15 per report, which includes all 6 parallel sub-actor calls and compute costs.

You can set a maximum spending limit per run to control costs. The actor stops when your budget is reached.

Compare this to Bloomberg Municipal Analytics at $2,000–$5,000/month, or a single Moody's credit opinion at $5,000–$25,000 per issuance. With this actor, a team analyzing 50 municipalities monthly spends approximately $7.50 with no subscription commitment. Apify's free tier includes $5 of monthly credits — enough to run 30 free assessments.

Municipal credit analysis using the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/municipal-credit-report").call(run_input={
    "municipality": "Detroit",
    "state": "MI",
    "includeClimate": True
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['municipality']}: {item['creditRating']} ({item['compositeScore']}/100) — {item['outlook']}")
    for risk in item.get("risks", []):
        print(f"  RISK: {risk}")
    for signal in item.get("allSignals", []):
        print(f"  SIGNAL: {signal}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("ryanclinton/municipal-credit-report").call({
    municipality: "Detroit",
    state: "MI",
    includeClimate: true
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
for (const item of items) {
    console.log(`${item.municipality}: ${item.creditRating} (${item.compositeScore}/100) — ${item.outlook}`);
    console.log("Risks:", item.risks);
    console.log("Fiscal stress:", item.scoringModels.fiscalStress.stressLevel);
    console.log("Funding cliff:", item.scoringModels.fundingCliff.cliffRisk);
}

cURL

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/ryanclinton~municipal-credit-report/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"municipality": "Detroit", "state": "MI", "includeClimate": true}'

# Fetch results (replace DATASET_ID from the run response above)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

How Municipal Credit Report works

Phase 1 — Geocoding and parallel data collection

The actor first submits the municipality name (and optional state) to the Nominatim geocoder, which returns a lat/lon coordinate and standardized display name. This coordinate is used to disambiguate location-dependent data queries.

Next, all 5 or 6 sub-actors execute simultaneously via Promise.allSettled. The actor calls USASpending search (up to 1,000 federal award records), FRED economic data (regional and national time series), BLS employment data (sector-level employment and change figures), Data.gov dataset search (queried with "fiscal budget debt" terms), and Congress bill search (queried for municipal funding legislation). If includeClimate is enabled, FEMA disaster search runs in the same parallel batch. Individual sub-actor timeouts are set to 120 seconds each; the outer run waits for all to settle before proceeding.

Phase 2 — Four scoring model execution

Fiscal Stress scoring processes USASpending records to calculate total federal obligation amount and award count. High reliance on federal grants scores up to 20 points; funding fragmentation (20+ awards) adds up to 10. FRED series are scanned for UNRATE (unemployment above 5% = 8 points, above 8% = additional 7), GDP contraction (10 points), and poverty rate above 15% (5 points). BLS employment change fields identify job-loss sectors, contributing up to 25 points. Data.gov dataset titles and descriptions are scanned for debt, pension, deficit, and bankruptcy keywords, adding up to 20 points. Maximum raw fiscal stress score: 100.

Funding Cliff scoring sorts USASpending award obligations and computes the top-award concentration ratio. A ratio above 50% scores 35 points; above 30% scores proportionally. Award end dates are compared against a 12-month forward window — each expiring award adds 5 points up to 25. Congressional bill titles are scanned for repeal, defund, eliminate, reduce funding, and rescind keywords; bills that have passed committee score double. FRED federal funds rate above 4% adds a fiscal tightening signal. Maximum raw funding cliff score: 100.

Economic Resilience scoring builds a sector employment map from BLS series titles, then applies a Herfindahl-Hirschman Index calculation. A fully diversified economy (HHI near 0) scores the maximum 30 diversity points; single-sector concentration (HHI above 0.25) triggers a signal. FRED indicators are scanned for positive GDP growth (8 points), population growth (5 points), median income above $50,000 (5 points), and unemployment below 4% (7 points). BLS growth-to-decline ratios across all sectors contribute up to 25 points. Data.gov dataset titles provide infrastructure proxies — education, transit, tech, and health keywords add to resilience. This model is inverted: higher score means better credit quality.

Climate Exposure scoring processes FEMA records for disaster frequency (2 points per event up to 20), major disaster count (5 points each up to 20), and disaster type diversity (4 points per type up to 20). An acceleration test compares disasters in the last 5 years against prior history — if recent frequency exceeds historical frequency, an acceleration score of up to 20 points is added. Annualized disaster cost is computed from total FEMA obligations divided by years of data (capped at 25 years).

Phase 3 — Composite scoring and credit opinion

The composite credit quality score is a weighted average: (100 − fiscal stress) × 0.30 + (100 − funding cliff) × 0.20 + resilience × 0.30 + (100 − climate exposure) × 0.20. This produces a score on a 0–100 scale where 100 represents maximum credit quality.

Credit rating thresholds: AAA ≥ 90, AA ≥ 80, A ≥ 70, BBB ≥ 55, BB ≥ 40, B ≥ 25, CCC below 25. Outlook logic: WATCH when fiscal stress level is SEVERE or funding cliff is CRITICAL; NEGATIVE when fiscal score exceeds 60 or climate score exceeds 70; POSITIVE when resilience exceeds 70 and fiscal stress is below 30; STABLE otherwise.

Tips for best results

1. Always include the state abbreviation. "Detroit" alone could geocode to Detroit, Oregon. Adding "MI" ensures USASpending, FEMA, and BLS queries resolve to Wayne County, Michigan.

2. Use official government entity names. Queries like "Los Angeles County" or "New York City" perform better than "LA County" or "NYC" because federal award databases store recipient names in their formal legal form.

3. Run comparisons by batching via the API. Submit 10–20 municipalities programmatically, collect the compositeScore and creditRating fields, and sort to identify the highest-risk jurisdictions in a state or region.

4. Disable climate for pure fiscal analysis. When screening for bond credit quality without environmental ESG criteria, set includeClimate: false to run 5 sub-actors instead of 6, cutting typical run time by 15–20 seconds.

5. Interpret signals, not just ratings. The allSignals array contains the specific findings that drove each sub-score. A BBB rating with a signal reading "5 federal awards expiring within 12 months" tells a different story than a BBB driven solely by moderate climate exposure.

6. Schedule quarterly monitoring. Use Apify's scheduler to run the same municipality every quarter. Compare compositeScore across runs to detect deteriorating fiscal trends before they reach a WATCH outlook.

7. Combine with WHOIS and company research for private placement. For municipal bond private placements, combine this actor with Company Deep Research to cross-reference the issuing entity's organizational profile alongside the jurisdiction's credit profile.

Combine with other Apify actors

Limitations

• US jurisdictions only — all data sources (USASpending, FRED, BLS, FEMA, Congress) are US federal databases. Non-US municipalities will return sparse or empty data, making scores unreliable.
• Not a substitute for official credit ratings — the composite rating is derived from public data using documented formulas. It does not incorporate non-public financial statements, audit reports, or issuer disclosure documents that rating agencies receive directly.
• Data latency — FRED and BLS data may lag 30–90 days behind current conditions. USASpending award records are typically updated weekly. FEMA disaster records are generally current within 30 days.
• Small municipalities return fewer signals — cities with populations under 50,000 may have few USASpending records, limited BLS sector coverage, and no FEMA disaster history, producing scores that rest on a narrow evidence base. The dataSummary field shows record counts so you can assess data density.
• Congressional bill scanning is keyword-based — the actor identifies legislation containing repeal, defund, or rescind language in bill titles. Bills with indirect fiscal impact (e.g., Medicaid cuts affecting county health budgets) are not captured by this method.
• No real-time bond market pricing — the actor does not query yield spreads, CUSIP pricing, or secondary market data. Credit quality scores reflect structural fiscal conditions, not current market sentiment.
• Climate model covers declared disasters only — the FEMA model uses official disaster declarations and obligation amounts. Localized flooding or severe weather events that did not reach federal declaration threshold are not captured.
• Composite weights are fixed — the 30/20/30/20 weighting of fiscal stress, funding cliff, resilience, and climate is not configurable. Analysts who weight climate risk differently must apply their own weighting to the four raw sub-scores.

Integrations

• Zapier — trigger municipal credit reports on a schedule and push ratings into Airtable or Notion for team review
• Make — build multi-step automations that run a report, check if the rating is BB or below, and notify a Slack channel automatically
• Google Sheets — export composite scores, ratings, and risk signals into a spreadsheet for portfolio tracking or board presentations
• Apify API — batch-submit dozens of municipalities programmatically and retrieve structured JSON for integration with bond analysis platforms
• Webhooks — trigger downstream processes (email, CRM update, alert) when a run completes with a rating below a configured threshold
• LangChain / LlamaIndex — feed the structured credit brief JSON into an LLM pipeline to generate narrative credit memos or investor summaries

Troubleshooting

• Score is 0 or very low despite a major city input — This usually means the USASpending or FRED sub-actor returned empty results. Check the dataSummary field: if federalAwards and fredIndicators are both 0, the municipality name may not have matched any records. Try adding the state abbreviation or using the full official jurisdiction name (e.g., "City of Detroit" vs "Detroit").

• Run times exceed 90 seconds — The actor waits for all parallel sub-actors to complete. If one source is slow (FRED or USASpending occasionally has elevated API latency), the run will hold until the 120-second per-actor timeout. This is normal behavior and not a bug. Enable run sharing (see below) if you want us to investigate a specific slow run.

• Climate score is missing from the output — If includeClimate was set to false, the climateExposure field is intentionally excluded from scoringModels and the composite weighting adjusts to fiscal stress (43%), funding cliff (29%), and resilience (43%). Verify your input JSON includes "includeClimate": true if you expected climate data.

• geocoded field is null — Nominatim could not resolve the input to a known geographic entity. This happens with informal names ("the Rust Belt"), very small localities, or misspellings. Use the official county or city name as recognized by the US Census Bureau.

Responsible use

• This actor queries only publicly available federal government databases (USASpending, FRED, BLS, FEMA, Congress, Data.gov) and open geographic APIs (Nominatim/OpenStreetMap).
• The credit ratings produced are derived from public data and documented algorithms. They are not official credit opinions and must not be represented as such to investors or regulators.
• Comply with applicable financial regulations when using credit analysis outputs in investment decisions, bond offering documents, or regulated financial communications.
• For guidance on web scraping and data use legality, see Apify's guide.

FAQ

How does this municipal credit rating compare to Moody's, S&P, or Fitch? The rating scale (AAA through CCC) follows standard bond rating conventions, but this actor produces an independent, data-driven assessment — not an official rating agency opinion. It uses only publicly available federal data with documented scoring formulas, making the methodology transparent and reproducible. Use it as a supplementary analytical layer, not a replacement for official ratings required in regulated bond offerings.

What data sources does the municipal credit report pull from? The actor queries 6 sources in parallel: USASpending (federal award obligations), FRED (Federal Reserve economic time series), BLS (Bureau of Labor Statistics employment data), Data.gov (open fiscal and budget datasets), Congress.gov (bill search for legislative funding threats), and FEMA (disaster declaration and obligation history). All sources are free US government APIs.

How many municipalities can I analyze in one run? Each run analyzes one municipality. To analyze multiple jurisdictions, submit separate runs via the API — it takes roughly 10 lines of Python to loop through a list of municipalities and collect results. A batch of 50 municipalities typically costs $7.50 and completes in under an hour when run sequentially.

How accurate is the funding cliff detection? The funding cliff model identifies awards with expiration dates within 12 months and bills with defunding language in their titles. It does not have visibility into ongoing grant negotiations, renewal likelihood, or informal federal agency communications — factors that a government relations team would incorporate. Treat the cliff score as a structured screening signal, not a definitive cliff prediction.

Does the municipal credit report work for territories, tribal governments, or special districts? Territories like Puerto Rico work well — they have substantial USASpending and FEMA records. Tribal nations and special districts (water districts, school districts) often have limited federal award records and sparse BLS sector data, which reduces score reliability. Check dataSummary.federalAwards to assess data coverage before interpreting the score.

How long does a typical municipal credit report run take? Most runs complete in 45–60 seconds. The bottleneck is the slowest of the 6 parallel sub-actor calls. Setting includeClimate: false typically reduces run time by 15–20 seconds by eliminating the FEMA data call.

Can I schedule this actor to monitor a municipality's fiscal health over time? Yes. Use Apify's built-in scheduler to run the same municipality monthly or quarterly. The generatedAt timestamp and compositeScore field allow you to track rating trends over time. You can also set a webhook to notify your team when the rating drops below a threshold you define.

What does a WATCH outlook mean, and how serious is it? WATCH is the most severe outlook designation. It is triggered when the fiscal stress level is SEVERE or the funding cliff risk is CRITICAL. WATCH means the current data pattern is consistent with conditions that precede municipal fiscal distress. It should prompt immediate deeper investigation — not necessarily action — using primary source documents like CAFRs and bond indentures.

Is it legal to use federal government data for commercial credit analysis? Yes. All data sources queried by this actor are publicly available US government databases published under open data policies. USASpending, FRED, BLS, FEMA, Data.gov, and Congress.gov data are explicitly made available for public use, including commercial applications. You should consult a financial regulatory attorney before using the outputs in regulated investment advisory communications.

How is the economic resilience HHI calculated? The actor builds an employment sector map from BLS series titles, sums employment values per sector, and computes the Herfindahl-Hirschman Index as the sum of squared market shares across sectors. The HHI is then inverted (1 − HHI) to produce a diversity index where 1.0 represents perfect diversification and 0 represents complete single-sector concentration. The diversity index scales to a maximum of 30 points in the resilience sub-score.

What happens if one of the 6 data sources returns no results? The actor uses Promise.allSettled for parallel execution, which means a failed or empty sub-actor call does not crash the run. The corresponding scoring model simply operates on an empty dataset and contributes 0 to its component score. The dataSummary field shows the record count for each source so you can identify which inputs had coverage gaps.

How is the climate exposure annualized cost calculated? The actor sums all FEMA obligation amounts across disaster records, then divides by the number of years between January 1, 2000 and today (capped at 25 years). This gives an average annual fiscal cost from federally declared disasters. It does not include uninsured losses, infrastructure rebuild costs not captured in FEMA obligations, or productivity losses from disaster events.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.