RE/TERM Intelligence March 2026 Free · Updated Annually

AI is the most important macro variable
in real estate right now.

As AI restructures white-collar employment, cities built on tech-worker demand face a new headwind. RE/TERM is the only free tool that quantifies this risk — by metro, with defensible methodology drawn from observed AI usage data and federal employment statistics.

Live Labor Signal

National labor market data pulled live from FRED · BLS · ADP. These are the leading indicators that precede housing demand shifts.

Overall Unemployment

—

U.S. headline rate · BLS monthly · Baseline context for all other signals

White Collar Unemployment

—

Professional & Business Services · BLS · When this exceeds headline, white collar stress is structural not cyclical

Entry-Level Signal

—

College grads age 20–24 · BLS · This cohort = first-time buyers. Rising rate = shrinking buyer pool in 3–5 years

ADP Private Payrolls

—

Total nonfarm private · ADP monthly · Overall hiring velocity across 26M+ employees

ADP White Collar Payrolls

—

Professional & Business Services · ADP · Negative = white collar employers actively shedding headcount

White Collar vs. Headline Gap

—

Spread: Prof/Business Services rate minus UNRATE · Positive = white collar unemployment running above national average

The thesis

74%

Programmer AI exposure

Anthropic's observed exposure score for computer programmers — derived from real Claude usage mapped to O*NET task definitions. Three-quarters of daily programming tasks are actively being handled by AI today.

−14%

Entry-level hiring slowdown

Post-ChatGPT, workers aged 22–25 entering high-exposure occupations dropped ~14%. This cohort is the first-time buyer. The demand signal weakens before it shows in ZHVI — days-to-pending and inventory move first.

4.2×

Seattle tech concentration

Seattle has 14.4% of its workforce in computer and math occupations — 4.2× the national average. That concentration is what drove $700K+ median home prices. It's the same concentration now contracting at the margins.

AI Disruption Index — 16 Metros

Score = Σ(occupation share × observed exposure) for each metro, minus the U.S. national baseline (15.2%). Positive = above-average AI employment exposure in local workforce mix. Tier cutoffs: >+2pp High · +0.5–2pp Medium · <+0.5pp Low.

Metro	Score vs. U.S.	Tier	Tech %	Finance %	Housing Impact

Sources: Anthropic Economic Index observed exposure scores · BLS OES May 2024 metro employment shares · RE/TERM computation

What This Means for Housing

AI disruption translates to housing demand through one channel: the marginal buyer.

High Exposure

Buyer pool contraction at the top

Tech workers are the marginal buyer for $600K+ homes in Seattle, Austin, and Boston. AI-driven restructuring contracts that pool before layoffs appear in headlines. Watch entry-level job postings and days-to-pending as leading indicators — ZHVI is a lagging signal.

Seattle · Boston · Austin · New York · Denver · Atlanta · Chicago · Dallas · LA · Phoenix · Charlotte · Tampa · Nashville

Medium Exposure

Diversification provides partial insulation

Finance and professional services face AI pressure but disruption is uneven — advisory and relationship roles lag behind back-office automation. Mid-market price points limit downside vs. coastal peers. Corporate relocation demand provides a partial offset.

Houston · Miami

Low Exposure — Net Beneficiary

Coastal displacement creates an in-migration tailwind

Las Vegas sits below the national AI exposure baseline — the only RE/TERM metro in that position. Its hospitality-driven economy means AI automation is late-cycle here. As high-exposure metros restructure, workers seeking affordability may accelerate in-migration to low-exposure markets. The same dynamic that drove Sun Belt growth 2020–2023 could repeat, driven by AI displacement rather than remote work.

Las Vegas

Methodology

Fully citable. Annually refreshed. Built on two public datasets.

Step 1 · Exposure data

Anthropic Economic Index

Observed exposure scores for 22 SOC major occupation groups. Derived from real Claude usage data mapped to O*NET task definitions. "Observed" = what AI is actually doing today, not theoretical capability. Key scores: Computer & Math 35.8% · Business & Finance 28.4% · Office & Admin 34.3% · Legal 32.8%.

Anthropic labor market paper →

Step 2 · Employment shares

BLS OES May 2024

Occupational Employment and Wage Statistics — employment share by SOC major group for each metro MSA. Published annually each May by the Bureau of Labor Statistics. 16 RE/TERM metros matched to their corresponding MSA definitions. Next release: May 2026.

BLS OES metro data →

Step 3 · Composite score

Weighted Average Formula

For each metro, compute the employment-weighted average of observed exposure scores across all occupation groups. Subtract the U.S. national baseline to get the score in percentage points above or below average.

Metro Score =
Σ(occ_share × obs_exposure)
− U.S. baseline (15.2%)

See scores in Analytics dashboard →

Limitations

What This Index Doesn't Capture

Exposure ≠ unemployment. No broad joblessness increase from AI has been observed yet. The signal appears as hiring slowdowns and entry-level contraction — not layoffs. This index measures demand fragility, not immediate price impact. Treat it as a risk flag layered on top of ZHVI, inventory, and migration data — not a standalone forecast.

See the full picture per city

AI disruption signal lives inside Analytics — alongside live Zillow, FRED, and Census data.

Open Analytics → Deal Analyzer →

Anthropic Economic Index — Labor Market Impacts (March 2026) · Anthropic Economic Index — Economic Primitives (January 2026) · BLS OES May 2024 Metro Area Estimates · Not investment advice. RE/TERM is a free tool by Milairo.

AI is the most important macro variablein real estate right now.

AI is the most important macro variable
in real estate right now.