Universal Credit: Habitual Residence Test
The question
To ask the Secretary of State for Work and Pensions, how many Universal Credit claims failed the Habitual Residence Test in each month since 1 July 2024.
Answered by Andrew Western
The table below gives the number of Universal Credit (UC) Habitual Resident test (HRT) assessments that resulted in a ‘fail’ decision from 1 July 2024 to 31 December 2025.
Month Decision Entered on Admin System | Number of UC HRT ‘Fail’ Decisions |
July 2024 | 8,000 |
August 2024 | 8,000 |
September 2024 | 7,000 |
October 2024 | 9,000 |
November 2024 | 10,000 |
December 2024 | 7,000 |
January 2025 | 8,000 |
February 2025 | 9,000 |
March 2025 | 10,000 |
April 2025 | 9,000 |
May 2025 | 9,000 |
June 2025 | 9,000 |
July 2025 | 9,000 |
August 2025 | 8,000 |
September 2025 | 8,000 |
October 2025 | 9,000 |
November 2025 | 8,000 |
December 2025 | 8,000 |
For context, the total and average monthly number of Universal Credit (UC) Habitual Resident test (HRT) assessments that resulted in a ‘fail’ decision in each financial year from 2022/23 – 2024/25 is as follows:
Date Decision Entered on Admin System | Number of UC HRT ‘Fail’ Decisions | Average monthly number of UC HRT 'Fail' Decisions |
April 2022 to March 2023 | 92,000 | 8,000 |
April 2023 to March 2024 | 99,000 | 8,000 |
April 2024 to March 2025 | 100,000 | 8,000 |
Notes:
- Not all HRT passes lead to a UC award as claimants need to meet all eligibility criteria.
- The Habitual Residence Test (HRT) is nationality blind. It is applied to British citizens returning from abroad to check for factual habitual residency in the UK, as well as to foreign nationals to check they have an immigration status permitting access to public funds, have a qualifying right to reside, and that they are factually habitually resident in the UK.
- All figures are rounded to the nearest thousand decisions.
- An individual may have multiple HRT assessments and multiple passes.
- These figures are not Official Statistics. These figures stem from administrative data and represent the best estimates using current methodologies and assumptions about the data. Future improvements in methodology may lead to different subsequent estimates.
- Figures are for the UK.
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