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Trust & CompanyCostco USA Weekly Warehouse Location & Opening Date Dataset
A weekly file of every Costco warehouse with 12 columns — warehouse id, name, full address, city, state, postal code, latitude and longitude, phone, time zone and opening date. One row per location, geocoded and ready to map.
The column that makes this more than an address list is opening_date. It goes back to 1978 in the supplied sample, so the file is a footprint history as well as a snapshot — you can reconstruct the estate at any point in the past four decades.
The file already exists, because this pipeline runs every week whether you buy it or not. Most vendors start collecting after you order — which is why they quote a lead time. Here the most recent file is in your account within minutes of payment, with an API key issued at the same time.
What you get, in plain terms
Four things. This is a location dataset, not a product or pricing one — if you need Costco pricing, say so and we will scope it separately.
Geocoded, not just addressed
Latitude and longitude on every row, so the file maps without a geocoding step and without the errors one would introduce.
Opening dates back to 1978
Reconstruct the estate as it stood in any past year, or watch new openings arrive week over week.
Warehouse formats distinguished
Standard warehouses sit alongside Business Centres and Home Showrooms, named as Costco names them rather than flattened into one type.
However you want it
Direct download, REST API, Amazon S3, Google Cloud, Snowflake or SFTP. GeoJSON and Shapefile available on request.
Fields included in this dataset
All 12 columns, in the exact order they appear in the file — this list is taken straight from the sample, not from a brochure. The free sample ships with a data dictionary giving an example value for each one.
Sample rows from the real file
Real rows from the sample file, not an illustration. The free sample is 50 warehouses with all 12 columns.
Coverage
Every Costco warehouse in the United States, refreshed weekly so new openings and closures appear within days.
| Region | States | Warehouses | Earliest opening in sample |
|---|---|---|---|
| West | CA, WA, OR, NV, AZ | 312 | 1983 |
| South | TX, FL, GA, AL, AR | 186 | 1984 |
| Midwest | IL, OH, MI, MN, MO | 141 | 1986 |
| Northeast | NY, NJ, MA, PA, CT | 128 | 1985 |
| Mountain | CO, UT, ID, MT, WY | 74 | 1987 |
| Alaska & Hawaii | AK, HI | 19 | 1984 |
| Puerto Rico | PR | 4 | 1998 |
Need international Costco locations, or the same file for another chain? We run location datasets for most major US and international retailers — tell us which and we will confirm what exists before you buy anything.
Historical data
Because opening_date is in the file itself, historical footprint is available without buying a history product — you can reconstruct the estate at any past date from a single current file. Weekly snapshots from January 2025 onwards are available if you also need closure dates, which the current file cannot show you.
Need more data points?
We can extend this dataset beyond the standard 12 columns — catchment polygons and drive-time bands are the most requested additions, along with competitor overlays, square-footage estimates and opening-hours capture. Tell us what you would query and we will say whether it is collectable before anything else happens.
Request custom fields“We were paying for a POI feed that gave us addresses and made us geocode them ourselves. Having coordinates and an opening date in the same row removed two steps from a weekly job.”
What people use this dataset for
Site selection teams
Map the competitor estate with real coordinates and measure distance to your candidate sites without geocoding first.
Catchment analysts
Build drive-time and trade-area models on accurate points rather than centroid-matched postcodes.
Retail strategy
Track openings week over week and reconstruct how the footprint has grown since 1978.
Data teams
Use it as a clean POI reference layer to join against your own transaction or footfall data.
About Costco warehouse location data
Costco operates a comparatively small estate of very large warehouses, which makes its footprint unusually informative for retail analysis. Each location serves a wide catchment, so the presence or absence of a warehouse in a market is a meaningful signal rather than noise.
Why geocoded points beat addresses
Geocoding addresses yourself introduces error, and for large-format retail that error matters — a point placed at a postcode centroid instead of the actual site can sit a mile from the entrance and distort every drive-time calculation built on it. Latitude and longitude are captured directly here, so the point is the site.
What the opening date gives you
It turns a snapshot into a history. With opening_date on every row you can filter the current file to any past date and see the estate as it stood — useful for backtesting expansion models or explaining a market that changed five years ago. The one thing it cannot show you is closures, since a closed warehouse is no longer in the file. If you need that, weekly snapshots are available from January 2025.
Reading the name field carefully
Costco runs several formats under the same brand. Business Centres serve commercial customers with a different assortment, and Home Showrooms are furniture-only. They appear in the file named as Costco names them. Counting rows without reading names will overstate the number of full-line warehouses in a market, and in the supplied sample three of the fifty rows were non-standard formats.
Is collecting this data legal?
Collecting publicly visible business location information is generally lawful in most jurisdictions. Actowiz collects only public pages, respects robots.txt and site terms, holds no personal data, and aligns with GDPR and CCPA. We are ISO 9001 and ISO 27001 certified, and this dataset carries documented provenance so your legal team can review the source before you buy.
Frequently asked questions
timeZone is there, which is what most catchment work actually needs, but trading hours can be added on a custom feed.
Related datasets, same field names
These join to the file above without any reconciliation work, which is why most buyers take more than one.
