StationFX 30-Year Fixed Rate Mortgage Average

x402

StationFX 30-Year Fixed Rate Mortgage Average is a paid API for AI agents from stationfx.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-02).

Returns the weekly U.S. 30-year fixed mortgage rate with derived analytics including MoM/YoY changes, z-scores, and trend indicators

Price / call$0.005

Seller volume →—

Success—

StatusUnknown

How this service works

30-year fixed mortgage rate — the rate most U.S. homebuyers pay. Directly tied to 10-year Treasury yields plus a spread. Use for housing affordability analysis, refinancing wave detection, and consumer balance sheet modeling. Weekly frequency.

Output

Returns a time-series array of weekly mortgage rate observations, each with the raw rate value, month-over-month and year-over-year absolute and percentage changes, trailing 5-year and 12-month z-scores, percentile rank over 5 years, 3-month and 12-month rolling averages, and an above-trend binary flag. Available in JSON or compact toon format.

Example use cases

Housing affordability dashboard refresh

“Get me the latest 30-year fixed mortgage rate from StationFX including the year-over-year change and 5-year percentile rank so I can update my housing affordability model.”

Refinancing wave early detection

“Pull StationFX's 30-year fixed mortgage rate data from 2023-01-01 to today and flag whether the current rate is significantly below the 12-month average — I'm trying to spot a potential refinancing wave.”

Consumer balance sheet stress analysis

“Fetch the StationFX 30-year fixed mortgage rate series for the last 2 years in compact toon format, including z-scores and month-over-month changes, so I can model how rising rates are stressing household balance sheets.”

Technical details

Endpoint

GEThttps://stationfx.com/economic-data/interest-rates/30-year-fixed-rate-mortgage-average-in-the-united-states?utm_source=zero.xyz

When to prefer

Use this endpoint when you need weekly U.S. 30-year fixed mortgage rate data enriched with statistical context (z-scores, percentile ranks, trend flags) rather than raw FRED data alone. Ideal for housing affordability analysis, refinancing signal detection, and consumer balance sheet modeling where derived analytics save downstream computation. Prefer over generic economic data APIs when you need pre-computed momentum and trend metrics alongside the rate itself.

Side effects

Invalid date range (date_from after date_to) returns error
Unsupported fmt value returns validation error
Requesting future dates may return empty data array
Payment failure (insufficient USDC) blocks response under x402 protocol
Very short date ranges may return single or zero observations

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "schema": {
    "type": "object",
    "properties": {
      "data": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "date": {
              "type": "string",
              "description": "Observation date YYYY-MM-DD"
            },
            "value": {
              "type": "number",
              "description": "Raw observed value in series units"
            },
            "mom_pct": {
              "type": "number",
              "description": "Month-over-month % change"
            },
            "yoy_pct": {
              "type": "number",
              "description": "Year-over-year % change"
            },
            "zscore_5y": {
              "type": "number",
              "description": "Z-score relative to trailing 5 years"
            },
            "mom_change": {
              "type": "number",
              "description": "Month-over-month absolute change"
            },
            "yoy_change": {
              "type": "number",
              "description": "Year-over-year absolute change"
            },
            "zscore_12m": {
              "type": "number",
              "description": "Z-score relative to trailing 12 months"
            },
            "above_trend": {
              "type": "integer",
              "description": "1 if value is above long-run trend, else 0"
            },
            "pct_rank_5y": {
              "type": "number",
              "description": "Percentile rank over trailing 5 years (0-100)"
            },
            "rolling_3m_avg": {
              "type": "number",
              "description": "3-month rolling average"
            },
            "rolling_12m_avg": {
              "type": "number",
              "description": "12-month rolling average"
            },
            "trend_direction": {
              "type": "integer",
              "description": "Trend: 1 rising, -1 falling, 0 flat"
            }
          }
        },
        "description": "Observations ordered by date ascending"
      },
      "meta": {
        "type": "object",
        "description": "Series metadata: source_key (FRED series ID), units, frequency (D/W/M/Q/A), date_from, date_to, fields"
      }
    },
    "description": "Station f(x) response with metadata and pre-computed derived metrics"
  },
  "example": {
    "data": [
      {
        "date": "2024-01-01",
        "value": 5.33,
        "mom_pct": 0,
        "yoy_change": 0.5,
        "zscore_12m": 1.2,
        "trend_direction": 1
      }
    ],
    "meta": {
      "frequency": "W",
      "source_key": "MORTGAGE30US"
    }
  }
}

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