Goal: Turn raw MLS sales into a clean, actionable prospect list by enriching records with Assessor data.

Why an API?
MLS tells you what sold. The Assessor API adds who owns it, where they receive mail, and when/how title changed (deed date & type). That lets us:

  • Spot true ownership changes (deed date ≠ MLS COE)

  • Exclude out-of-state investors or recent refis

  • Deduplicate and focus only on the highest-value targets

Setup & Prep

  • Dedicated machine (no sleep/screensavers), updates installed

  • Clean source data (drop junk rows)

  • Get API key + docs from MCTA

  • Plan the pipeline before you code

API Best Practices

  • Throttling: add short delays; avoid hammering or getting rate-limited/blocked

  • Error handling: log failures, skip bad APNs, keep the run going

  • Batching: process 1k–5k APNs at a time with checkpoints so you can pause/resume

AAEL Prompt Kit (How we built it with AI)

  1. Ask AI to summarize endpoints (propertyinfo, valuations, residential-details, owner-details) + required headers/auth.

  2. Co-design the workflow (read CSV → dedupe by newest COE → fetch ≥2 endpoints/APN → 1s delay → save in batches).

  3. Write the fetch function first, then expand to merge endpoints.

  4. Add retries + exponential backoff + logging.

  5. Batch processing: save targets_api_partNNN.csv, track progress so restarts are painless.

  6. Final assembly, then test with 10 APNs before scaling.

Takeaway: APIs transform a flat MLS export into a precision prospect list you can actually market to—systematically, scalably, and politely.

#PropTech #RealEstateData #Mortgage #LeadGen #API #Python #DataEngineering #AAEL #MaricopaCounty #MLS

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