AI Income Risk · Real Estate

North Carolina

ProofIndex score 56/100 (Moderate Risk) · medium confidence

ProofIndex score: 56/100 — Moderate Risk risk.

Scoring period 2026-Q2 · medium confidence in the underlying data

North Carolina (United States) shows moderate exposure in ProofIndex’s scoring, with a ProofIndex score of 56/100 (Moderate Risk). The biggest contributor is employment vulnerability — how exposed local jobs are to AI automation, based on the area’s occupation mix.

Key risk drivers

Employment Vulnerability — 63/100
How exposed local jobs are to AI automation, based on the area’s occupation mix.
Income Concentration — 55/100
How concentrated local income is in higher-paid, AI-exposed work.
Housing Leverage — 36/100
How leveraged local mortgage borrowers are — loan-to-value, debt-to-income, and price-to-income.
Resilience Factors — 24/100
Local economic cushioning — savings, benefits, and job diversity that absorb an income shock.

Where this score comes from

Every ProofIndex pillar is computed from published statistics. These are the sources behind this area's score, with the vintage of each and the limitation we know about it.

Employment Vulnerability — US Census Bureau + ProofIndex model
Derived ACS occupation proxy · Proxy · State · vintage 2020-2024 ACS 5-year. Occupation exposure is inferred from sector and education signals, not direct OEWS occupation employment.
Income Concentration — US Census Bureau — ACS 5-year
ACS state indicators · Official statistics · State · vintage 2020-2024 ACS 5-year. Official survey data; state-level values can hide MSA-level concentration.
Housing Leverage — Zillow Research
Zillow home value index · Market data · State · vintage May 2026 file release. Market data series; rental and mortgage stress inputs may be derived from home-value and income relationships.
Rental Market Exposure — Zillow Research
Zillow home value index · Market data · State · vintage May 2026 file release. Market data series; rental and mortgage stress inputs may be derived from home-value and income relationships.
Resilience Factors — US Department of Labor ETA
US safety-net parameters · Official statistics · State · vintage 2026. Statutory and public program parameters; savings and absorption are still proxy estimates.

Frequently asked questions

Is North Carolina at risk from AI job losses?

Moderately — North Carolina sits mid-pack among the United States markets ProofIndex tracks. It scores 56/100 (Moderate Risk) overall, and 63/100 on employment vulnerability — how much of the local occupation mix overlaps with work today’s AI can already do. ProofIndex measures how exposed local incomes are, not how many jobs will go: a higher score means more of the local wage base sits in work that AI is changing.

How exposed is the North Carolina housing market to AI automation?

North Carolina scores 56/100 (Moderate Risk) on ProofIndex, which blends how exposed local incomes are to AI with how leveraged the local housing market is. The largest single contributor is employment vulnerability at 63/100 — how exposed local jobs are to AI automation, based on the area’s occupation mix. Housing leverage — loan-to-value, debt-to-income, and price-to-income among local borrowers — scores 36/100. Local resilience — savings, benefits, and job diversity that absorb an income shock — scores 24/100 and pulls the composite down. Scores are normalized 0–100 within United States, so this ranks North Carolina against other United States markets rather than against markets abroad.

Why is North Carolina rated Moderate Risk?

Moderate Risk is the risk tier ProofIndex assigns to a composite score of 56/100. The composite weights employment vulnerability at 35%, income concentration at 25%, housing leverage at 25%, and subtracts local resilience at 15%. For North Carolina the risk pillars rank employment vulnerability 63/100, income concentration 55/100, housing leverage 36/100. Confidence in the underlying data for this area is medium.

See the full North Carolina report

Full pillar breakdowns, peer benchmarking, and data sources with confidence ratings.

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