1. Datasets
  2. H&M
H&M

H&M Locations dataset (August 2026)

3,616 locations across 83 countries and 2,574 cities, with 11 fields per row. Available in CSV and ZIP formats.

Rows
3,616
Fields
11
Countries
83
Collected
Aug 2026
Browse H&M locations →

License

Licensed under CC BY 4.0. Requires attribution. License & attribution guide →

Fields

# Column Field
1 store_id Store ID
2 store_name Store Name
3 country_code Country Code
4 country Country
5 city City
6 region Region
7 zip_code ZIP Code
8 address Premium Address
9 coordinates Coordinates
10 phone_number Premium Phone Number
11 opening_hours Premium Opening Hours

Sample rows

The first 5 rows, straight out of the file.

store_id store_name country_code country city region zip_code address Premium coordinates phone_number Premium opening_hours Premium
US0282 Alderwood Mall US United States Lynnwood Washington 98037 3000 184th Street SW, 98037, Lynwood [47.82990,-122.27308] +1 855 466 7467 Mo-Sa 11:00-20:00; Su 11:00-19:00
US0146 South Coast Plaza US United States Costa Mesa California 92626 3333 Bear Street, 92626, Costa Mesa [33.69267,-117.89318] +1 855 466 7467 Mo-Sa 10:00-20:00; Su 11:00-19:00
US0148 Menlo Park US United States Edison New Jersey 08837 510 Menlo Park, 08837, Edison [40.54659,-74.33541] +1 855 466 7467 Mo-Th 10:00-20:00; Fr-Sa 10:00-21:00; Su 12:00-18:00
US0244 Westfield Wheaton US United States Wheaton-Glenmont Maryland 20902 11160 Veirs Mill Road, 20902, Wheaton [39.03661,-77.05568] +1 855 466 7467 Mo-Th 10:00-20:00; Fr-Sa 10:00-21:00; Su 11:00-19:00
US0323 Valley River Center US United States Eugene Oregon 97401 293 Valley River Center, 97401, Eugene [44.06759,-123.10499] +1 855 466 7467 Mo-We 10:00-21:00; Th 10:00-20:00; Fr-Sa 10:00-21:00; Su 11:00-19:00

Get the full dataset (Free)

In the free version, coordinates are rounded to two decimal places (1 km precision) and premium fields are not available.

To get over these limits, check out our paid plans.

Use it directly

Only meant for experimentation. Using it directly in production pipeline is strictly prohibited.

Python
import pandas as pd

df = pd.read_csv("https://bizlocationdb.com/locations/hm?download=csv")
print(df.head())
DuckDB
SELECT country, count(*) AS n
FROM read_csv('https://bizlocationdb.com/locations/hm?download=csv')
GROUP BY 1 ORDER BY n DESC;