AI Income Risk · Real Estate

Myrtle Beach-Conway-North Myrtle Beach, SC

ProofIndex score 39/100 (Low Risk) · medium confidence

ProofIndex score: 39/100 — Low Risk risk.

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

Myrtle Beach-Conway-North Myrtle Beach, SC (United States) is relatively insulated in ProofIndex’s scoring, with a ProofIndex score of 39/100 (Low Risk). The biggest contributor is housing leverage — how leveraged local mortgage borrowers are — loan-to-value, debt-to-income, and price-to-income.

Key risk drivers

Housing Leverage — 48/100
How leveraged local mortgage borrowers are — loan-to-value, debt-to-income, and price-to-income.
Resilience Factors — 33/100
Local economic cushioning — savings, benefits, and job diversity that absorb an income shock.
Employment Vulnerability — 30/100
How exposed local jobs are to AI automation, based on the area’s occupation mix.
Income Concentration — 27/100
How concentrated local income is in higher-paid, AI-exposed work.

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 MSA occupation proxy · Proxy · MSA · vintage 2020-2024 ACS 5-year. MSA occupation exposure is inferred from local education and parent-state sector mix.
Income Concentration — US Census Bureau — ACS 5-year
ACS MSA indicators · Official statistics · MSA · vintage 2020-2024 ACS 5-year. Official survey data at metro level; occupation exposure still uses derived sector/education mapping.
Housing Leverage — Zillow Research
Zillow metro home value index · Market data · MSA · vintage May 2026 file release. Market data series for metro home values; some credit and rent inputs are modelled.
Rental Market Exposure — Zillow Research
Zillow metro home value index · Market data · MSA · vintage May 2026 file release. Market data series for metro home values; some credit and rent inputs are modelled.
Resilience Factors — US Department of Labor ETA + ProofIndex model
US metro resilience estimate · Derived · MSA · vintage 2026. Derived from state-level safety-net parameters and metro labor-market characteristics.

Frequently asked questions

Is Myrtle Beach-Conway-North Myrtle Beach, SC at risk from AI job losses?

Less than most — Myrtle Beach-Conway-North Myrtle Beach, SC is relatively insulated among the United States markets ProofIndex tracks. It scores 39/100 (Low Risk) overall, and 30/100 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 Myrtle Beach-Conway-North Myrtle Beach, SC housing market to AI automation?

Myrtle Beach-Conway-North Myrtle Beach, SC scores 39/100 (Low 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 housing leverage at 48/100 — how leveraged local mortgage borrowers are — loan-to-value, debt-to-income, and price-to-income. Housing leverage — loan-to-value, debt-to-income, and price-to-income among local borrowers — scores 48/100. Local resilience — savings, benefits, and job diversity that absorb an income shock — scores 33/100 and pulls the composite down. Scores are normalized 0–100 within United States, so this ranks Myrtle Beach-Conway-North Myrtle Beach, SC against other United States markets rather than against markets abroad.

Why is Myrtle Beach-Conway-North Myrtle Beach, SC rated Low Risk?

Low Risk is the risk tier ProofIndex assigns to a composite score of 39/100. The composite weights employment vulnerability at 35%, income concentration at 25%, housing leverage at 25%, and subtracts local resilience at 15%. For Myrtle Beach-Conway-North Myrtle Beach, SC the risk pillars rank housing leverage 48/100, employment vulnerability 30/100, income concentration 27/100. Confidence in the underlying data for this area is medium.

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