Your Voter Rolls Are a Mess: Address Data Is Why

A single household submits three voter registration forms over a decade. “123 MLK Jr Boulevard” on the first. “123 Martin Luther King Jr Blvd” on the second. “123 MLK Blvd” on the third. Three registrations, one household, and a voter roll that now shows three separate entries.

Multiply that across millions of registrations and you get voter rolls that are wildly inaccurate. Studies have found that US voter registration lists contain millions of records that are invalid or inaccurate — duplicate registrations, outdated addresses, and entries that can’t be matched to a real residence.

The same problem plagues census enumeration. Self-reported addresses on census forms vary wildly in format, and small inconsistencies lead to residents being assigned to the wrong precinct, block, or enumeration area.

The Real Cost of Inconsistent Addresses

When address data is dirty, everything downstream breaks:

• Duplicate voter registrations erode public trust in election integrity and create administrative overhead for election officials
• Wrong precinct assignments mean voters receive incorrect ballots or are turned away at the wrong polling location
• Inaccurate population counts lead to misallocated federal funding — Census Bureau estimates show that each person missed costs their community roughly $2,400 per year in lost federal funding
• Redistricting errors compound when the underlying address data can’t reliably assign households to geographic boundaries

Manual address verification is prohibitively expensive at scale. Election offices and census bureaus process millions of records. Having humans review each one for formatting consistency isn’t feasible.

The Fix: Standardize Every Submitted Address

Standardizing each submitted address into structured components and canonical expansions standardizes input before it’s matched against official address and boundary datasets. GoodVat’s Address Standardization API does this in a single call.

curl -X POST "https://api.goodvat.com/v1/address/normalize" \
  -H "Authorization: Bearer $GOODVAT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "query": "77 Elm Street, Cook County, Chicago IL 60601" }'

{
  "houseNumber": "77",
  "road": "elm street",
  "postcode": "60601",
  "suburb": "cook county",
  "city": "chicago",
  "state": "il",
  "country": "USA",
  "expansions": [
    "77 elm street cook county chicago il 60601 usa",
    "77 elm street cook county chicago illinois 60601 usa"
  ]
}

County, borough, or ward context shows up in suburb or stateDistrict — isolated as its own field instead of buried in a free-text line. The expansions array standardizes street suffixes ("St" → "Street"), while city and postcode narrow the match to the correct voting district.

10ms p99 response time. Batch-process millions of registration records in minutes. 200+ countries for international government applications. $5/mo unlimited requests.

Accurate Data, Accurate Democracy

Every duplicate registration, every wrong precinct assignment, every miscounted household traces back to address data that wasn’t standardized. The fix is simple, fast, and costs less than a single manual review.

Read the docs and start cleaning your address records at scale.