Abstract copyright UK Data Service and data collection copyright owner.

The Acorn geodemographic classification is a long-running classification developed by CACI Limited. Acorn operates by merging geography with demographics and details about consumer characteristics and behaviours. Supported by advanced AI methods, comprehensive input data, and detailed product literature, Acorn provides precise information and enables an in-depth understanding of the different types of consumers in every part of the country.

The current classification groups the entire United Kingdom population into 7 categories, 22 groups and 65 types. The data is available at unit postcode level. Further information may be found on the CACI ACORN microsite.

Use of the data requires approval from the data owner or their nominee and is restricted to those based at a Higher Education or Further Education institution. Please see the Data Access section for further information.

For the second edition (October 2024) data and documentation files for 2024 have been added to the study.

Main Topics:

Variables include: unit postcode; large user flag; deleted flag; ACORN category; ACORN group; ACORN type.

NOTE: Please choose the download option to access the profiles A collection of ward socio-economic profiles created using the resident/customer classification tool Acorn. These profiles are created using Cheshire East addresses from the National Address Gazetteer, then categorised at postcode. The postcode is based on the predominant classification of the households in a particular postcode. For more information, including information about the sources of data, please visit the data providers website.

Percentage of pupils achieving level 5 or above in Key Stage 3 tests by ACORN category of pupil residency

Source: Department for Education and Skills (DfES)

Publisher: Department for Children Schools and Families (DCSF)

Geographies: Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), National

Geographic coverage: England

Time coverage: 2006

Type of data: Administrative data

Notes: Acorn category based on pupil postcode

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

This dataset contains Wellbeing Acorn geodemographic segmentation codes (group and type) for each household in every wave of Understanding Society, together with a household identification number (hidp) allowing it to be linked to the main Understanding Society data files. The dataset is produced by matching the Wellbeing Acorn segmentation against every Understanding Society household at the postcode level.

The Wellbeing Acorn segmentation system itself is developed and maintained by CACI Ltd and is designed by analysing demographic data, social factors, health and wellbeing characteristics in order to provide an understanding of the population’s wellbeing across the country. Group is the higher layer containing 5 segments providing a snapshot of the population from the least healthy to the healthiest. The more granular level is Type, containing 25 segments, to provide more detailed insights about the population to better understand their demographic, lifestyle and health characteristics. For details on the Acorn segmentation structure and how is it is produced please refer to the documentation and the Caci website.

These data have more restrictive access conditions than those available under the standard End User Licence (see 'Access data' tab for more information).

NOTE: Please choose the download option to access the profiles. A collection of ward wellbeing profiles created using the resident/customer classification tool Acorn. These profiles are created using Cheshire East addresses from the National Address Gazetteer. The postcode is based on the predominant classification of the households in a particular postcode. For more information, including information about the sources of data, please visit the data providers website.

Abstract copyright UK Data Service and data collection copyright owner.

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

This dataset contains Wellbeing Acorn geodemographic segmentation codes (group and type) for each household in every wave of Understanding Society, together with a household identification number (hidp) allowing it to be linked to the main Understanding Society data files. The dataset is produced by matching the Wellbeing Acorn segmentation against every Understanding Society household at the postcode level.

The Wellbeing Acorn segmentation system itself is developed and maintained by CACI Ltd and is designed by analysing demographic data, social factors, health and wellbeing characteristics in order to provide an understanding of the population’s wellbeing across the country. Group is the higher layer containing 5 segments providing a snapshot of the population from the least healthy to the healthiest. The more granular level is Type, containing 25 segments, to provide more detailed insights about the population to better understand their demographic, lifestyle and health characteristics. For details on the Acorn segmentation structure and how is it is produced please refer to the documentation and the Caci website.

These data have more restrictive access conditions than those available under the standard End User Licence (see 'Access data' tab for more information).

NOTE: Please choose the download option to access the profiles A collection of ward socio-economic profiles created using the resident/customer classification tool Acorn. These profiles are created using Cheshire East addresses from the National Address Gazetteer, then categorised at postcode. The postcode is based on the predominant classification of the households in a particular postcode. For more information, including information about the sources of data, please visit the data providers website.

Average (mean) rating for 'Happy Yesterday' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Percentage of responses in the range 0-6 for 'Worthwhile' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.