AI Income Risk · Real Estate

Středočeský kraj

ProofIndex score 73/100 (High Risk) · medium confidence

ProofIndex score: 73/100 — High Risk risk.

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

Středočeský kraj (Czech Republic) ranks among the higher-risk markets ProofIndex tracks, with a ProofIndex score of 73/100 (High Risk). The biggest contributor is income concentration — how concentrated local income is in higher-paid, AI-exposed work.

Key risk drivers

Income Concentration — 85/100
How concentrated local income is in higher-paid, AI-exposed work.
Housing Leverage — 80/100
How leveraged local mortgage borrowers are — loan-to-value, debt-to-income, and price-to-income.
Employment Vulnerability — 78/100
How exposed local jobs are to AI automation, based on the area’s occupation mix.
Resilience Factors — 66/100
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 — ČSÚ — VŠPS
Labour Force Survey occupation mix · Official statistics · Regional · vintage 2024. Occupation mix is direct at regional level; smaller districts may inherit and adjust regional structure.
Income Concentration — ČSÚ — Czech Statistical Office
Czech statistical indicators · Official statistics · Regional / district · vintage 2024-2025. Official regional data; some district-level housing inputs are modelled from available market and labor indicators.
Housing Leverage — ČSÚ — Czech Statistical Office
Czech statistical indicators · Official statistics · Regional / district · vintage 2024-2025. Official regional data; some district-level housing inputs are modelled from available market and labor indicators.
Rental Market Exposure — ČSÚ — Czech Statistical Office
Czech statistical indicators · Official statistics · Regional / district · vintage 2024-2025. Official regional data; some district-level housing inputs are modelled from available market and labor indicators.
Resilience Factors — ČSÚ — Czech Statistical Office
Czech statistical indicators · Official statistics · Regional / district · vintage 2024-2025. Official regional data; some district-level housing inputs are modelled from available market and labor indicators.

Frequently asked questions

Is Středočeský kraj at risk from AI job losses?

Relative to other Czech Republic markets, yes — Středočeský kraj ranks among the higher-risk markets ProofIndex tracks. It scores 73/100 (High Risk) overall, and 78/100 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 Středočeský kraj housing market to AI automation?

Středočeský kraj scores 73/100 (High 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 income concentration at 85/100 — how concentrated local income is in higher-paid, AI-exposed work. Housing leverage — loan-to-value, debt-to-income, and price-to-income among local borrowers — scores 80/100. Local resilience — savings, benefits, and job diversity that absorb an income shock — scores 66/100 and pulls the composite down. Scores are normalized 0–100 within Czech Republic, so this ranks Středočeský kraj against other Czech Republic markets rather than against markets abroad.

Why is Středočeský kraj rated High Risk?

High Risk is the risk tier ProofIndex assigns to a composite score of 73/100. The composite weights employment vulnerability at 35%, income concentration at 25%, housing leverage at 25%, and subtracts local resilience at 15%. For Středočeský kraj the risk pillars rank income concentration 85/100, housing leverage 80/100, employment vulnerability 78/100. Confidence in the underlying data for this area is medium.

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