StationFX Housing Starts (New Privately-Owned Units Started)

x402

StationFX Housing Starts (New Privately-Owned Units Started) 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 monthly U.S. new residential construction starts data with derived metrics including MoM/YoY changes, z-scores, percentile ranks, and rolling averages.

Price / call$0.005

Seller volume →—

Success—

StatusUnknown

How this service works

Number of new residential construction projects begun. Measures actual housing supply entering the pipeline. Sensitive to mortgage rates, builder confidence, and economic conditions. Monthly frequency with full derived metrics.

Output

Returns a JSON array of monthly observations, each containing the observation date, raw housing starts value (in units), and a full suite of derived metrics: month-over-month and year-over-year absolute and percent changes, 12-month and 5-year z-scores, 5-year percentile rank, 3-month and 12-month rolling averages, and a binary above-trend flag.

Example use cases

Monthly housing supply pipeline check

“Give me the latest U.S. new housing starts figure and tell me how it compares to last month and last year — include the month-over-month and year-over-year percent changes.”

Mortgage rate impact on construction

“Pull housing starts data from January 2022 through December 2023 so I can see how the construction pipeline responded when mortgage rates surged — I want the raw values and YoY percent changes.”

Housing market trend alert system

“Get me the current housing starts z-score relative to the trailing 5 years and its percentile rank, so I can tell whether new construction is running hot or cold compared to recent history.”

Technical details

Endpoint

GEThttps://stationfx.com/economic-data/housing/new-privately-owned-housing-units-started-total-units?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need U.S. residential construction starts data enriched with pre-computed momentum metrics (MoM, YoY, z-scores, rolling averages) rather than raw series data alone. Ideal for economic analysis agents, real estate market monitors, or macro dashboards that need to contextualize housing supply trends without performing their own statistical derivations.

Side effects

Invalid date format returns a 400 error
date_from after date_to returns empty or error response
Requesting future dates returns no data or partial results
Invalid field names in the fields parameter may return nulls or errors
Payment failure (x402) if USDC balance is insufficient

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": "M",
      "source_key": "HOUST"
    }
  }
}

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