090°Open data
Open datasets, fully documented — searchable here, and readable by any LLM.
5 datasets
Mortgage-Rate Signals (derived)
Weekly US mortgage-rate signals from the Freddie Mac Primary Mortgage Market Survey (redistributed by FRED, 1971 ->): 30-year and 15-year fixed rates with 13-week momentum, 30-week change volatility, 3-sigma anomaly flags vs a trailing 52-week baseline, 4-week drift forecasts, cross-series ranks, the mortgage-minus-10y-Treasury spread, a 52-week rate-shock gauge, high/low rate flags, and a 10-year affordability z-score. All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED); underlying data: Freddie Mac, U.S. Department of the Treasury.
U.S. Department of Housing and Urban Development
Annual HUD Fair Market Rents for ~3,160 U.S. counties and county-equivalents, FY2018-FY2027: monthly 0-4 bedroom FMRs with year-over-year change, 5-year CAGR, acceleration, within-state ranks, national percentiles, z-scores, rent-surge/drop flags, and population-weighted state and national context averages. Keyless HUD USER county FMR files (revised editions).
Freddie Mac
Weekly US mortgage-rate pressure intelligence from Freddie Mac's Primary Mortgage Market Survey full-history archive (keyless XLSX, 1971-04-02 -> present; 30y/15y fixed rates with fees/points and the 5/1 ARM through the November 2022 survey revamp). Per week: 30y-15y spread, 1/4/13-week changes in basis points, 52-week change, trailing-52-week z-score, 13-week change volatility, a documented 5-state rate_regime classifier, high/low-rate flags, and a documented 0-100 pressure_score composite (high = expensive and rising: a demand headwind for rate-sensitive businesses). Keyless replacement path for the FRED-key-gated fred_mortgage_signals dataset, rebuilt from the primary source. Who joins this: a subscription business joins weekly churn or expansion revenue to week_ending to model household-budget-pressure-driven cancellations; an online shop selling big-ticket or durable goods joins weekly orders to week_ending to capture rate-driven demand swings; a sales team times territory outreach on rate_regime shifts. Caveat: Freddie Mac occasionally restates the latest weekly observation the following Thursday, so momentum flags on the newest week carry revision risk. Source: Freddie Mac.
Shelter Inflation Signals (derived)
Monthly signals derived from BLS shelter CPI series (redistributed by FRED): 30-month annualized change volatility, 3-month momentum, year-over-year change, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-month cross-series volatility rank, the shelter premium (shelter YoY minus headline CPI YoY — the affordability wedge) and the OER-minus-rent divergence gauge. Covers rent of primary residence from 1915 and owners' equivalent rent from 1983. All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED); underlying data: U.S. Bureau of Labor Statistics.
US Home-Price & Rate-Lock Pulse (derived)
Monthly US home-price & rate-lock pulse: S&P/Case-Shiller national home price index (FRED CSUSHPISA; MoM %/YoY %, trailing-12m z-score of the monthly change, 12-month acceleration in pp, trailing-10y percentile rank) and the 30-year fixed mortgage rate (Freddie Mac PMMS via FRED MORTGAGE30US, weekly observations averaged to calendar months; MoM/YoY point changes, trailing-12m z-score, trailing-10y percentile rank), plus a deterministic affordability_pressure_score = 0.5*price_hist_pct_rank + 0.5*rate_hist_pct_rank (0-100; high = expensive homes at expensive rates) and a rule-based market_regime (frozen / rate_shocked / thawing / overheating / cooling / balanced). Panel 1987-01 -> latest published month (Case-Shiller lags ~2 months). Upstream: FRED keyless fredgraph.csv (no API key). One row per year_month, country_code=USA. Who joins this: a subscription business joins monthly home-price momentum to customer cohorts on year_month to model housing-wealth effects on retention and upgrades; an online shop joins it to home-category demand forecasts on year_month.
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