Direct REST API syndication for Single-Family Rental (SFR) aggregators, private credit funds, and secondary mortgage buyers. Continuous municipal citation dockets, 30-year continuous mortgage note amortization, sub-meter aerial satellite roof scoring, and unmasked ownership contacts.
Traditional wholesaler lists have a 65% fallout rate due to hidden debt and drive-by cancellations. FirstStrike solves this at the database level.
We ingest raw building code violations, IPMC § 108 emergency condemnation orders, and IPMC § 505 water shutoff curb-valve cuts directly from city databases—45 to 90 days before properties appear on public lis pendens or foreclosure dockets.
Our financial engine runs continuous monthly compounding amortization against recorded notes, Freddie Mac historical PMMS benchmarks, and recorded municipal tax liens. We calculate exact clearance headroom: Clearance Spread = Buy Ceiling - Senior Debt.
Every parcel is audited via 15cm resolution aerial orthophoto tiles. Our computer vision engine detects blue emergency tarps, granular shingle loss, structural ridge board sagging, and yard blight, generating an algorithmic roof rot score (1-100) that eliminates physical drive-by visits.
import os
import requests
import pandas as pd
# Authenticated Institutional Gateway Consumer
API_KEY = os.getenv("FIRSTSTRIKE_API_KEY", "fs_syndicate_live_master88a")
URL = "https://ghostdeal.app/api/v1/syndicate/feed"
headers = {
"X-FirstStrike-Key": API_KEY,
"Content-Type": "application/json"
}
# Quantitative Fund Query: High-Equity Deals with Positive Clearance Headroom
payload = {
"states": ["TX", "FL", "PA"],
"asset_class": "DISTRESSED_HOUSES",
"clearance_status": "DEAL_CLEARS",
"min_equity_pct": 0.40,
"max_debt_ltv": 0.65,
"limit": 100
}
response = requests.post(URL, json=payload, headers=headers)
response.raise_for_status()
data = response.json()
print(f"Connected Fund: {response.headers.get('X-Enterprise-Fund')}")
print(f"Total Matching in Vault: {data['meta']['total_matching_in_vault']}")
# Ingest into Pandas DataFrame for quantitative algorithmic bidding
assets = data.get("assets", [])
df = pd.json_normalize(assets)
print(df[["property.address", "underwriting.estimated_market_value_arv", "underwriting.clearance_spread_usd"]].head())Every record streamed by the syndication feed adheres to strict ground-truth schema guidelines.
DEAL_CLEARS (≥$15k spread), TIGHT_EQUITY_RISK ($0–$15k), or SHORT_SALE_REQUIRED (<$0).
Net cash headroom above total senior debt: Investor Buy Ceiling - Total Senior Debt.
Strict 70% rule acquisition ceiling: (ARV * 0.70) - Estimated Rehab.
Recommended strike price providing $15k+ assignment margin while fully clearing senior liens.
Annual prepaid institutional contracts with dedicated API throughput, enterprise SLAs, and sub-35ms query latency.
Ideal for regional fix-and-flip funds, private lenders, and local debt buyers.
Tailored for institutional SFR aggregators (1,000–5,000 doors) and multi-state private credit funds.
Unrestricted access for Wall Street secondary debt buyers, REITs, and national aggregators.