Service /05 · Real Estate & Public Record Extraction
Real Estate & Public Record Extraction
Turn scattered records into a usable property dataset.
Python data extraction for real estate research teams and property data products. Collect agreed foreclosure, REO, and public record fields, preserve source references, and deliver normalized records.
Sound familiar?
“Our team checks property and foreclosure records one site at a time.”
Your outcomeProperty records your team can filter, reconcile, and use.
- Foreclosure and REO records from agreed public sources
- Property datasets assembled across differing county formats
- A recurring data feed for a real estate research dashboard
Normalized property research data
Agreed property record sources → Normalized property research data
data/property-records.csvreference/source-coverage.csvvalidation/duplicate-review.csvThe handover
What you receive.
- A source coverage inventory with the agreed fields
- A Python extraction and normalization pipeline
- Record identifiers, source URLs, and collection timestamps
- Duplicate rules, exception reports, and reconciliation checks
- CSV or database delivery with documented field definitions
A clear scope from the start.
Service boundaryCoverage is scoped source by source. The output is a research dataset; title verification, legal status, owner contact accuracy, and investment advice are outside the extraction scope.
The system, access requirements, volume, and deadline determine the approach and quote. A representative pilot comes first.
Python collection pipeline, record normalization, and delivery into the platform.
Foreclosure Data HubA useful first brief
What to bring to the walkthrough.
These details help define a representative pilot. An anonymized example is enough to start; access can be arranged after we agree on the scope.
- The counties or source sites you need covered
- Required property fields and sample records your team considers complete
- The update schedule and how the dataset will be used
How we get there
A pilot before the full build.
Define a useful result.
Show me the repetitive task, the tools involved, and what your team needs to receive. Agree on what success looks like.
Verify it on real examples.
Test a small, representative batch. Check access, output quality, and the awkward edge cases.
Put the workflow to work.
Implement the agreed scope with validation, run logs, retries, and clear exceptions.
Keep the result in your control.
Receive the code, setup instructions, and a walkthrough. Agree on any ongoing maintenance.
Questions about this service.
Can you collect records from every county?
Coverage is evaluated for the particular counties and sources you need. Access methods, available fields, and source formats differ, so a pilot establishes feasible coverage before a broader quote.
Can the data feed our existing dashboard?
Yes, when the destination supports a suitable connection. We agree on the schema, stable identifiers, and update behavior, then test a sample import or database delivery.
How are conflicting property records handled?
The workflow retains source references and applies agreed matching rules. Ambiguous addresses or conflicting values are flagged for review instead of silently merging them.
Read the practical guide: Planning a real estate public record extraction pipeline
Start with one workflow.
Tell me the repetitive task and the result you need. We’ll define a small pilot, check it against real examples, and agree on the full scope.