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Sparks RE API – Property Factual Extractor
x402
Sparks RE API – Property Factual Extractor is a paid API for AI agents from sparks-re-api.sparksdigital-re.workers.dev, paid per call via x402, $0.03/call, status unknown (last checked 2026-10-30).
Parses unstructured real estate listing text into structured property fields using regex pattern matching
Price / call$0.03
Success—
StatusUnknown
How this service works
AI-powered real estate tools for property normalization, FHA compliance scanning, and investor metrics calculation. Powered by x402 micropayments.
Output
A JSON object with up to 11 structured property fields: address, city, state, zip, bedrooms (integer), bathrooms (integer), square_feet (integer), lot_size (string), year_built (integer), property_type (string, e.g. 'SFR'), and raw_price (string). Fields not found in the input text may be null or omitted. Values are regex-parsed — agent should validate before downstream transactional use.
Example use cases
MLS copy normalization for CRM import
“I have this raw MLS remark text and I need to load it into our CRM as structured fields — can you parse out the address, city, state, zip, bed count, bath count, square footage, lot size, year built, property type, and price from it: '4BR/3BA Colonial at 112 Birchwood Dr, Columbus OH 43215, built 2004, 2,400 sqft, 0.3-acre lot, asking $425,000.'”
Investor deal screening from email blasts
“I get property deal emails in plain text with no consistent format — can you extract the key specs from this one so I can run my investment calculations: 'Off-market duplex, 2001 vintage, 1,600 sqft each unit, 3 bed 2 bath per side, 0.18 acres, 5522 Elm St Dayton OH 45402, seller wants $310k.'”
Listing data pipeline for property aggregator
“We're ingesting scraped listing descriptions and need them structured — please parse this into property fields: 'Cozy 2-bedroom, 1-bathroom bungalow at 331 Oak Lane, Carmel IN 46032. Built 1965, 980 sq ft, small 0.12-acre lot, priced to sell at $189,900.'”
Technical details
Endpoint
POSThttps://sparks-re-api.sparksdigital-re.workers.dev/property/factual?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have free-form or MLS listing text and need fast, cheap structured extraction of standard property fields without AI inference overhead. At $0.03/call it is cost-effective for high-volume pipelines. Prefer it over the AI-powered sibling endpoint when speed and cost matter more than handling edge cases, and over manual parsing when you need consistent JSON output. If the text is highly ambiguous or uses non-standard formats, consider the Gemini-powered sibling endpoint instead.
Side effects
- Ambiguous or abbreviated text may cause fields to be null or incorrectly parsed — regex cannot handle all formatting variations
- Unconventional price formats (e.g. 'two ninety-five') may not be captured in raw_price
- property_type may default to null if not explicitly stated or not matching known abbreviations
- Heavily abbreviated MLS shorthand may fail to match regex patterns for square footage or lot size
- Non-US address formats may parse city/state/zip incorrectly or not at all
- Payment failure (insufficient USDC balance) returns 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| raw_text | string | Free-form property listing text to parse |
Output
Schema · Response
{
"type": "json",
"example": {
"zip": "46220",
"city": "Indianapolis",
"state": "IN",
"address": "789 Maple Ave",
"bedrooms": 3,
"lot_size": "0.25 acres",
"bathrooms": 2,
"raw_price": "$295,000",
"year_built": 1998,
"square_feet": 1850,
"property_type": "SFR"
}
}Tags
x402
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