Methodology

Each metro receives a composite risk score from 0 to 100, derived from multiple structural factors scored as cross-sectional percentile ranks. Higher scores indicate greater cycle risk relative to other metros.

The core model uses public data exclusively. No proprietary inputs, no black-box signals, no forecasts. Scores measure where a metro sits relative to its peers — not whether prices will go up or down.

The composite score is built from six structural factors spanning price dynamics, building activity, affordability conditions, labor markets, and population flows. Each factor is scored as a percentile rank across the full metro universe, then combined with equal weighting.

Factors with positive economic signals (strong employment, net in-migration) are inverted so that lower percentiles always correspond to lower risk across all six dimensions.

Each factor is ranked across all metros with valid data. The composite score is the equally-weighted average of all factor percentiles, rounded to the nearest integer. This cross-sectional approach measures relative positioning — where a metro stands compared to its peers in the current month.

All input data is sourced from public statistical agencies and updated on their respective publication schedules. Some factors lag by weeks, others by months. The system carries forward the most recent available value when newer data has not yet been published.

Bands are descriptive labels, not predictive probabilities. “High Risk” means a metro is in the highest quintile of cycle exposure relative to peers — it does not predict a price decline.

The platform presents multiple complementary layers of analysis. Each layer adds context without modifying the layers below it.

Start with the composite score and risk band for relative positioning. Check market signals for near-term context. National regime layers provide the macro backdrop. Cycle phase and county divergence add structural depth.

A national composite is calculated from the metro-level factor data. When sustained directional shifts are detected, a regime indicator activates:

• Tightening: National conditions worsening across multiple structural factors
• Easing: National conditions improving across multiple structural factors

The overlay provides macro context. It does not change metro rankings or scores.

A quarterly national indicator that classifies credit market conditions using mortgage rate dynamics, credit spreads, and lending standards. The system draws on over 35 years of historical data to contextualize current conditions.

The credit regime does not affect metro scores. It provides a macro lens on whether the financing environment is amplifying or dampening housing market activity nationally.

A quarterly national indicator that classifies the construction supply pipeline using housing starts, completions, and units under construction. The system draws on over 55 years of historical data to identify supply imbalances.

The supply regime does not affect metro scores. It provides a macro lens on whether the national construction pipeline is balanced, building, or oversaturated.

Two supplementary signal layers provide near-term market context using a mix of public and industry-standard datasets. These signals do not affect the composite score or metro rankings.

Liquidity

Measures how quickly homes are selling relative to recent history, based on inventory conditions and time-on-market patterns.

Trend reflects the recent directional movement of market conditions: improving, stable, or worsening.

Valuation

Tracks whether rents are keeping pace with home prices. When prices significantly outpace rents, it can indicate stretched valuations — though not necessarily an imminent correction.

Each metro is classified by its current building permit activity relative to its own historical norms. This captures whether a metro is building more or less than its typical baseline — independent of the composite score.

Permit status does not affect the composite score. It provides supply-side context for understanding whether local building conditions are expanding or contracting.

Each metro is classified into one of four housing cycle phases based on a combination of its risk positioning, market signal conditions, and score momentum. The classification provides a macro framing for where a metro sits in the housing cycle.

Cycle phase does not affect the composite score or metro ranking.

Metros may also carry a phase risk annotation when additional signal combinations suggest caution:

• Late Cycle — Expansion phase with sustained valuation compression. Prices running significantly ahead of rents.
• Valuation Lag — Recovery phase where liquidity is normalizing but rent-price ratios remain compressed.

Each county within a metro receives a structural composite score based on a subset of the metro-level factors adapted for county-level data availability. Counties are scored relative to their siblings within the same metro, capturing internal variation rather than national positioning.

The divergence score measures how much structural variation exists within a metro. Low divergence means the metro composite is broadly representative. High divergence means conditions vary significantly across counties — the metro-level score may mask important local differences.

For each metro, the system identifies a risk driver (highest-risk county) and stabilizer (lowest-risk county) to highlight the structural extremes within the metro.

The suburb/ZIP lookup is a discovery layer, not a scoring layer. No suburb-level scores are computed, no ZIP-level rankings exist, and no neighborhood-level modeling is performed.

Searching for a suburb or ZIP code resolves to its parent county and metro, then displays the corresponding county and metro risk data.

The scoring universe includes all US metropolitan statistical areas that meet minimum data coverage requirements across all structural factors and have sufficient population. The universe is periodically updated to reflect current OMB delineations.

The core composite model uses public data exclusively — sourced from federal statistical agencies. Supplementary signal layers incorporate industry-standard datasets for near-term market context.

All data is publicly available. The system's value lies in the integration, scoring methodology, and layered analytical framework — not in proprietary data access.

Public data inherently lags. Some inputs update monthly, others quarterly or annually. The system is designed around these rhythms, using carry-forward logic to maintain continuity between publication cycles.

• Public data lags — some factors update monthly, others annually with multi-month delays. Carry-forward smooths gaps but cannot capture sudden shifts.
• Cross-sectional percentile model — during uniform national shocks, metro rankings compress toward the center. The national regime layers mitigate but do not eliminate this.
• Signal thresholds are calibrated to current conditions and may be refined as the system accumulates more historical observation.
• County-level scoring uses a reduced factor set due to data availability constraints. Counties in smaller metros may have compressed score ranges.
• Suburb/ZIP lookup resolves to county and metro data only. No sub-county modeling is performed.
• Equal factor weighting is a simplifying assumption. Different weighting schemes may be more appropriate for specific analytical contexts.

This product ranks relative metro cycle conditions. It does not predict price declines, estimate probabilities, or provide buy/sell advice. Past cycle patterns may not repeat. All scores reflect lagging public data and should be considered alongside current local market conditions.