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Foreclosure Filing Acceleration Intelligence

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Foreclosure Filing Acceleration Intelligence

Foreclosure Filing Acceleration Intelligence

Turn foreclosure and court-record snapshots into property, lender and geography-level filing acceleration and distress intelligence.

Pricing

from $10.50 / 1,000 results

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Developer

Rafael Barreto Haddad

Rafael Barreto Haddad

Maintained by Community

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2

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1

Monthly active users

12 hours ago

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Turn foreclosure and court-record snapshots into property, lender and geography-level filing acceleration and distress intelligence.

Why use this Actor

Distressed-asset teams need recurring foreclosure acceleration signals across properties and geographies instead of one-off filings. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

  • Foreclosure acceleration rather than static records.

  • Property and geography-level snapshot comparison.

  • Debt amount and repeated-filing concentration.

  • Distress actions for acquisition and risk workflows.

  • Reusable Dataset-based monitoring.

  • Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

  • Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

  • Distressed real estate.
  • Lenders.
  • Servicers.
  • Investment firms.
  • AI agents.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.015 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

  • Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
  • Scores are decision-support signals, not predictions or guarantees.
  • Keep stable identifiers across snapshots for best change detection.
  • Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Input

Provide currentItems as JSON records or point currentDatasetId to an Apify Dataset. For recurring comparisons, provide previousItems or previousDatasetId. Keep stable entity identifiers across snapshots whenever possible. maxItems limits Dataset reads. Optional Gen2 fields can compare the current decision metric with a prior analysis and estimate economic impact only when the user explicitly supplies an impact value.

Output

The default Dataset receives one structured intelligence report with record counts, new and removed records, ranked entity signals, decision confidence, regression status, executive decision, recommended action, and transparent economic-impact fields when enabled. The same report is stored in INTELLIGENCE_REPORT for downstream automations and agent workflows.

Compatible upstream datasets

This Actor analyzes normalized records rather than scraping restricted sources itself. Useful upstream families include:

  • county/court foreclosure filings.
  • foreclosure and judicial-auction datasets.
  • property datasets used to enrich distressed-asset monitoring.

The input aliases were expanded for these workflows. Verify field semantics when an upstream dataset uses source-specific names; snapshot identifiers should remain stable between periods.