Spend less time standardizing addresses, more time analyzing

When working with large amounts of address data, simply standardizing the addresses to make them workable can take longer than doing the actual analysis.

If you're pulling together data from multiple sources or user-inputted data, this can be even more time-consuming. Geocodio takes away this frustration by parsing addresses into their components, allowing you to quickly standardize a list of addresses.

Spend less time standardizing addresses, more time analyzing
Avertra

We bought a list of addresses from the USPS that was incomplete. Using Geocodio, were able to get the missing information for thousands of addresses in a few minutes. It was simple and easy.

Sebastien Benoit, SAP Director, Avertra Corporation

Address Parsing

Split addresses up into their components: street, city, state, ZIP, and county.

Address Completion

Add missing components, including as city/state (if ZIP given), ZIP (if city/state given), and county.

Address Normalization

Get all of the addresses in a consistent format.

Example Input: "42370 ## Bob Hoppe RANCH MraGE"

Output

  • number: 42370
  • street: Bob Hope
  • suffix: Dr
  • formatted_street: Bob Hope Dr
  • city: Rancho Mirage
  • county: Riverside County
  • state: CA
  • zip: 92270
  • lat: 33.739482744493 lng: -116.40828650441

First 2,500 lookups per day are free, no credit card required

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API Documentation

We included parsed and standardized results with all API lookups for both single address and batch processing.
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Standardize Addresses in a Spreadsheet

We'll also parse and standardize all spreadsheet uploads. You can download a sample result here.
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Address Parsing Library

If you just need address parsing and don't need geocoding, we recommend using an open-source library like Libpostal. (Not affiliated with Geocodio.)
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