CASE STUDY DEEP DIVES

How We Normalized Property Records Across 932 Counties

What breaks when you combine assessor data from hundreds of county systems, and the schema decisions that kept 4.2M records usable.

9 min readMixed2026-04-11

County systems are not just different, they are incompatible

The project looked simple on paper: pull county property records and merge into one table. In reality, each county stack encodes parcel IDs, owner names, and assessed values differently. We ended up classifying county portals into 14 template groups before extraction logic became stable.

Normalization required a fixed schema and aggressive mapping

Raw fields exceeded 200 naming variants. We reduced everything to 42 canonical fields and pushed all source-specific quirks into mapping rules. This is what made downstream filtering and analytics reliable instead of constantly patching edge cases.

Freshness came from monthly change detection, not re-scraping everything blind

Instead of full rewrites every cycle, we compare new pulls with previous snapshots and emit deltas for ownership transfer, tax shifts, and lien updates. That reduced analyst review workload and made alerts actionable.

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