When ZIP Codes Break Google Distance Matrix: Why Geocoding Fails and a Simple Address Fix

Learn why Google Distance Matrix API returns wrong results for ZIP codes and how to fix it by using full addresses like 01033, MA, USA to stabilize geocoding.

Distance Matrix occasionally returns wildly wrong results when you feed it ZIP codes. A typical case: sending “01033” as an origin sometimes resolves to Kyiv, Ukraine instead of Granby, MA—despite region hints like region='us' and despite Google Maps showing the right place when you search manually. Here’s what’s going on and how to stabilize your results without rewriting your entire integration.

Example of the issue

The setup looks straightforward: a list of Massachusetts ZIPs as origins and a destination address.

orig_zips = ["01033", "02108"]
dst_point = "Boston, MA, USA"
maps_client.distance_matrix(
    orig_zips,
    dst_point,
    mode="driving",
    region="us",
    traffic_model="best_guess",
)

Root cause

Distance Matrix doesn’t compute distance from a raw string. It first calls Geocoding under the hood to turn what you send into a place_id and only then computes distance. The documentation states:

“If you pass an address, the service geocodes the string and converts it to a latitude/longitude coordinate to calculate distance.”

And the Geocoding FAQs clarify the same flow:

“When the Directions API (Legacy) or Distance Matrix API (Legacy) is queried with an address string rather than a place ID or latlng, they use the same backend as the Geocoding API to convert that address into a place ID prior to calculating directions.”

Postal codes are inherently ambiguous inputs. Sometimes they geocode to the intended locality; sometimes they don’t. Because Distance Matrix relies on that first geocoding step, ZIP-only origins can resolve incorrectly. Adding region='us' doesn’t guarantee the disambiguation you expect.

How to fix it

Avoid bare postal codes as origins and destinations. Use properly formatted addresses that constrain the search. One practical pattern is to expand the ZIP with state and country, following the address style suggested in the Distance Matrix docs. For example, instead of "01033" use "01033, MA, USA".

orig_inputs = ["01033, MA, USA", "02108, MA, USA"]
dst_point = "Boston, MA, USA"
maps_client.distance_matrix(
    orig_inputs,
    dst_point,
    mode="driving",
    region="us",
    traffic_model="best_guess",
)

This doesn’t change your program’s flow; it simply provides more precise address strings for the geocoding step, reducing misresolutions like the Kyiv case.

Why this matters

Distance and routing features are only as good as the locations they’re computed from. If geocoding guesses wrong, every downstream metric—distance, time, even pricing or SLA estimates—can be off by orders of magnitude. Knowing that Distance Matrix geocodes first, and that postal codes are weak identifiers, helps you harden inputs and avoid silent failures.

Takeaways

If you must keep using the Distance Matrix API (Legacy), don’t pass ZIP codes by themselves. Prefer fully qualified address strings such as “01033, MA, USA.” Geocoding is most effective with properly formatted addresses, and this small change removes most ambiguity without refactoring your integration.