HailScore
Methodology
How HailScore risk scores are computed: data sources, modeling approach, and limitations.
HailScore publishes hail risk scores at neighborhood resolution across the Hail Belt and High Plains. Every score on this site is computed by PerilScore using the same data layer used by insurance and risk management professionals.
Data sources
We start from public scientific records. Every input is auditable, and we don’t use proprietary or paywalled data.
- NOAA Storm Events Database: decades of recorded hail events including stone diameter, location, and time.
- NEXRAD radar-derived hail signatures: Maximum Estimated Size of Hail (MESH) and related radar products for high-resolution coverage.
- Population-adjusted reporting corrections: an adjustment for the well-known reporting bias where populated areas record more hail events than rural areas.
Modeling approach
We fit a frequency-magnitude distribution per location using the historical record, then derive return-period stone sizes (1-in-10, 1-in-50, 1-in-100 year). The result is a single 0 to 10 probability score at each neighborhood-scale sample point (about 5 km²).
Output metrics include long-run hail frequency, expected maximum stone size by return period, and a roof-fragility damage proxy for both standard and impact-rated (Class 4) roof types.
Resolution
Scores are computed at neighborhood resolution: approximately 5 km² sample points across the contiguous US. This is much finer than the county-level averages most public hail data provides.
Update cadence
The model is refreshed annually following the end of severe-weather season, incorporating the prior year’s hail events and radar-derived signatures. Major model updates are versioned and disclosed.
Validation
Models are evaluated against held-out historical events and benchmarked against published reference datasets where available. The exact validation protocol is documented in the PerilScore technical papers.
Limitations
- Forecast boundary. HailScore reflects long-run probability from the historical record. For active storm timing, use National Weather Service guidance.
- Property-specific detail. Scores reflect a neighborhood-scale sample point. For a property-specific score that incorporates roof type, age, and impact-resistance class, use the free PerilScore app.
- Reporting bias is corrected but not eliminated. Sparsely populated areas still have higher uncertainty.
Attribution
Risk scores powered by PerilScore. Visit perilscore.com for the full platform, API access, and commercial-use licensing.
Methodology
Public data. Real science. No black boxes.
Every score is computed from decades of public weather records using physics-based probability modeling. It's the same approach used by insurance and risk management professionals.
- Decades of public weather data
Hurricane tracks, storm intensities, fire perimeters, hail reports, all drawn from public scientific archives. We don't use proprietary data. You can audit every input.
- Physics-based probability modeling
Scores reflect how the actual peril behaves: wind fields, fire spread, ground shaking, and storm tracks. The model keeps the physics visible instead of flattening every place into a broad average.
- Used by professionals
The same PerilScore data layer is used by insurance and risk management professionals. We publish it here so anyone can find authoritative risk numbers for their location.
Frequently asked questions
Where does the hail risk score come from?
Why is hail risk so concentrated geographically?
Does this account for stone size?
Want the full picture for a specific property?
The scores on this site show the representative hail layer for a local area. Enter a street address to add building age, construction type, roof details, occupancy, surroundings, and property-level context.
Free results for any US street address.