Download a list of care homes in England for Excel

Build and refresh a supplier territory list

If you sell equipment, training, software or other services to care homes, start with the registered locations that fit your territory. For example, preview Surrey care homes with at least 40 registered beds, then adjust the area, care type or specialism in the list builder.

1. Check that the list fits your work.Review the sample, available business details and missing-field counts. Registered beds describe capacity; they do not establish a home's budget, staffing needs or purchasing intent.
2. Use the list for today’s task.Download up to 25 matching rows free. A larger filtered CSV costs £1.99, paid once, with no account needed. Open it in your spreadsheet or import it into your CRM.
3. Save the selection with annual access.If you need to return to this list, choose Save this territory after a successful preview. Sign in or create an account, obtain annual access if needed, then name and save your territory. Its first capture becomes your reviewed baseline.
4. Check for changes when you return.Open your saved territory and choose Check for changes. Review locations entering or leaving the selection and changed published details. Only Mark reviewed accepts the displayed comparison as the baseline for next time. See the example below before signing up.

This is organisational data for account research, without named decision-maker emails or vacancy information. Published phones and websites may be missing. Use the same Surrey selection in an AI assistant or API workflow.

Compare care-home territories before choosing your list

For supplier territory planning or recruitment research, compare registered locations, provider counts and recorded beds in two authorities. Choose the care type, specialism or minimum beds that fit your task, inspect the matching providers and preview the actual locations free.

Open the free territory report with Surrey selected. Every report shows its DataScoop import date and missing-field coverage. Registered capacity is not a count of occupied or vacant beds; provider IDs identify registered operators, not verified ultimate owners.

From a care-home list to an Excel spreadsheet

1. Choose the homes you need.Use the area selector above. Keep care homes selected, then add a service type or specialism if your task needs it. The Devon example shows how to narrow the list to nursing homes.
2. Check the sample before paying.Review the matching-record count and import date. Download up to 25 matching rows free from the list builder to check the columns and any missing values. The preview shows the first matching rows.
3. Import the CSV into Excel.Choose Data → From Text/CSV, select the downloaded file, use UTF-8 and a comma delimiter. Keep CQC IDs and postcodes as text to preserve their formatting. In Google Sheets, use File → Import → Upload.
4. Filter and sort the whole table.In Excel, select the records and turn on Data → Filter. Use Service types for the kind of care, or sort the whole table by Provider to review a group’s locations together. Keep blank bed counts and ratings separate from recorded values.
5. Download the full list when the sample fits.If the result has 25 rows or fewer, the free download contains the whole list. Larger filtered lists cost £1.99, paid once. Keep the original CSV and note its filters and import date before editing a copy.

What the care-home CSV columns mean

Each row is a registered location. A provider with ten homes can appear in ten rows. These are the fields most useful when building or checking your spreadsheet; the CQC dataset page has the full column list.

On a small screen, scroll the table sideways. Blank source values stay blank in the CSV. The final line beginning with # credits the source; keep it out of counts and calculations.

Free CQC download or a filtered CSV?

CQC makes its source data available free. Choose the original files if you want to do the filtering yourself, or check a DataScoop sample before buying a larger filtered list.

CQC free download versus DataScoop: the same task measured

We completed a real retrieval on 1 November 2026: care-home locations in the exact Surrey local authority with at least 40 recorded beds. Each route produced a UTF-8 CSV containing Location ID, name, beds, local authority, postcode, overall rating/status and website. IDs were preserved as text and blank values stayed blank.

This saved measurement predates the ratings supplement published later on 1 November 2026. The original files and counts below are retained; check the current preview and its source dates for corrected coverage.

On a small screen, scroll the table sideways to compare both routes. Keyboard users can focus the table and use the arrow keys.

How to complete it with CQC’s free files

1. Open Using CQC data and download Care directory with filters. The ordinary directory CSV does not supply the bed-count field needed for this task.
2. Open the workbook’s HSCA_Active_Locations sheet in a spreadsheet application that supports ODS. Filter Care home? to Y, Location Local Authority to exactly Surrey, and Care homes beds to at least 40.
3. Keep the seven columns listed above, preserve Location IDs as text, and export to CSV. Check the workbook’s README for its source date. This run used the 1 October workbook.

How we completed it with DataScoop

1. Request careHome=true, localAuthorityExact=Surrey and minBeds=40 with strict filter validation.
2. Read all 8 anonymous JSON pages of up to 25 rows, checking the applied filters, total and import date throughout.
3. Keep the same seven columns, write the CSV and read it back to validate row counts and IDs.

The measured DataScoop route used free API pagination, with no account or payment. It did not measure the browser’s paid CSV download or checkout. In the browser, preview this exact list and choose the existing sample or full-download offer.

Equal totals do not mean identical data

187 Location IDs appear in both outputs; 1 appears only in the official selection and 1 only in DataScoop’s selection. Names, bed counts, local authorities, postcodes and websites match across the shared locations. Rating/status labels differ for 58 shared locations, including 57 where the official file has a label and DataScoop’s value is blank.

The official workbook provides more rating/status labels for this measured selection. Use it when that coverage matters. These snapshots have different dates; the differences do not establish errors, openings, closures or verified rating changes. A DataScoop import date is not a publisher release date or the date each record was checked.

What this measurement establishes

It records actual files, filters, transfer sizes, matching IDs and field coverage. Both CSVs were checked using prepared Python code. We did not measure a person’s Excel preparation time or typical customer effort. Machine load affected the technical timings, so we make no time-saving claim.

Download the aggregate evidence (JSON) · Read the measurement method · Download the reproducible Python script. A rerun uses the then-current sources and may return different results. The script selects organisational fields only; it does not output manager names or personal contact fields.

Contains public sector information licensed under the Open Government Licence v3.0. Source: Care Quality Commission (CQC). DataScoop is independent of CQC. Open Government Licence v3.0.

Coverage and source dates

This guide covers CQC-registered locations in England. The wider register also includes homecare agencies, dentists, GPs and other services, so keep the care-home filter on when you need residential or nursing homes. Scotland, Wales and Northern Ireland have separate regulators and are not included.

The CSV contains organisational register fields and published business details. Manager names and personal email lists are excluded. CQC controls when its records are published; the DataScoop import date does not make each record current to that day. Use the CQC profile URL in the CSV to check a home’s latest status or inspection before a time-sensitive decision.