Official Statistics

Background information and quality report: property rental income statistics

Updated 31 October 2023

1. Contact

• Organisation unit: Knowledge, Analysis and Intelligence (KAI)
• Name: K. Secker
• Function: Statistician, KAI Direct Business Taxes
• Mail address: Floor 3, 100 Parliament Street, London, SW1A 2NH
• Email: propertyrentalstatistics@hmrc.gov.uk

2. Statistical presentation

2.1 Data description

This publication provides a breakdown of Self Assessment (SA) property income and expenses declared by unincorporated landlords in the United Kingdom (UK). It is based on SA tax return data, and only includes figures for the latest 5 years of available tax return data.

2.2 Classification system

Breakdowns of the number of individual tax entities, income and allowable expenses are determined based on data submitted by individuals on their SA tax return.

A unique taxpayer reference, assigned to each registered tax entity, is used to aggregate the data.

2.3 Sector coverage

This analysis contains income tax data from SA returns and excludes incorporated landlords that pay Corporation Tax, and landlords reporting their income through Pay As You Earn (PAYE). Only individuals with a registered address in the UK are included.

2.4 Statistical concepts and definitions

Self Assessment

Self Assessment is a system HM Revenue and Customs (HMRC) uses to collect Income Tax.

People and businesses with other income (including property income) must report it in a SA tax return.

Tax Year

The statistics are aggregated into tax years. A tax year stretches from 1 April until 31 March the following calendar year.

Property Income

Property income in this publication refers to the total rents and other income from property.

Allowable Property Expenses

Allowable expenses in this publication are property business costs that are not taxable. They can be deducted from a tax entity’s property income.

2.5 Statistical unit

The unit in the statistics is individuals and other entities required to report Income Tax an SA tax return.

2.6 Statistical population

All tax entities that declare income from renting property in the UK via their SA tax return.

2.7 Reference area

The geographic region covered by the data is the UK.

2.8 Time coverage

The statistics cover the time period from financial year 2016 to 2017 until the latest financial year for which a complete series of SA returns is available.

3. Statistical processing

3.1 Source data

The data for SA income and expenses comes from the SA tax return pages. Property income and allowable property expenses are declared via the SA105, SA200 and SA801. Information on SA return forms can be found on the SA tax return forms homepage.

Geographical region is determined by matching the SA tax returns data with the publicly available National Statistics Postcode Lookup (NSPL) dataset produced by the Office for National Statistics (ONS).

3.2 Frequency of data collection

SA tax returns are filed by taxpayers annually. The data for this publication is extracted after the filing deadline for the most recent tax year.

Each year some returns are submitted after the filing deadline. The returns submitted after the SA tax returns data is extracted will not be included in this publication but will be included in the following years publication. This means that small revisions each year are expected, where a greater number of returns are included following the inclusion of late filers.

The NSPL is issued by the ONS quarterly.

3.3 Data collection

Data on SA Tax receipts and liabilities is sourced from the HMRC tax administrative system. Property income and expense data used in this publication are acquired when a tax entity files the property related boxes in the SA105, SA200 and SA801 returns forms.

3.4 Data validation

Checks carried out on the data include:

• automated checks take place when loading data into the analysis database. Inconsistencies are automatically repaired if possible; otherwise, the record is flagged as invalid
• analysts check that the number of records loaded into the analysis database is as expected

3.5 Data compilation

Aggregating data

Data are aggregated using a unique taxpayer reference number assigned to each tax entity. This unique number does not change across financial years.

Treatment of empty returns

Taxpayers that file a UK property return but do not record non-zero values in any property finance boxes are excluded from the dataset.

4. Quality Management

4.1 Quality assurance

All official statistics produced by KAI must meet the standards in the Code of Practice for Statistics produced by the UK Statistics Authority and all analysts adhere to best practice as set out in the ‘Quality’ pillar.

Analytical Quality Assurance describes the arrangements and procedures put in place to ensure analytical outputs are error free and fit-for-purpose. It is an essential part of KAI’s way of working as the complexity of our work and the speed at which we are asked to provide advice means there is a high risk of error which can have serious consequences on KAI’s and HMRC’s reputation, decisions, and in turn on peoples’ lives.

Every piece of analysis is unique, and as a result there is no single quality assurance (QA) checklist that contains all the QA tasks needed for every project. Nonetheless, analysts in KAI use a checklist that summarises the key QA tasks, and is used as a starting point for teams when they are considering what QA actions to undertake.

Teams amend and adapt it as they see fit, to take account of the level of risk associated with their analysis, and the different QA tasks that are relevant to the work.

At the start of a project, during the planning stage, analysts and managers make a risk-based decision on what level of QA is required.

Analysts and managers construct a plan for all the QA tasks that will need to be completed, along with documentation on how each of those tasks are to be carried out and turn this list into a QA checklist specific to the project.

Analysts carry out the QA tasks, update the checklist, and pass onto the Senior Responsible Officer for review and eventual sign off.

4.2 Quality assessment

The QA for this project adhered to the framework described in section 4.1, Quality assurance, and the specific procedures undertaken were as follows:

Stage 1: Specifying the question

Up to date documentation was agreed with stakeholders setting out outputs needed and by when; how the outputs will be used; and all the parameters required for the analysis.

Stage 2: Developing the methodology

Methodology was agreed and developed in collaboration with stakeholders and others with relevant expertise, ensuring it was fit for purpose and would deliver the required outputs.

Stage 3: Building and populating a model/piece of code

• analysis was produced using the most appropriate software and in line with good practice guidance
• data inputs were checked to ensure they were fit-for-purpose by reviewing available documentation and, where possible, through direct contact with data suppliers
• QA of the input data was carried out
• the analysis was audited by someone other than the lead analyst checking code and methodology

Stage 4: Running and testing the model/code

• results were determined to be explainable and in line with expectations.

Stage 5: Drafting the final output

• checks were completed to ensure internal consistency (for example, totals equal the sum of the components)
• the final outputs were independently proofread and checked

5. Relevance

This section covers the degree to which statistical information meets user needs. See the GSS Quality Statistics in Government guidance for additional information on the relevance dimension of quality.

5.1 User needs

This analysis is likely to be of interest to users under the following broad headings:

• national government, policy makers and MPs
• regional and local governments
• academia and research bodies
• media
• business community
• general public

5.2 User satisfaction

Formal investigations into user satisfaction have not been undertaken, however KAI are always open to ideas for new analysis to meet changing user requirements.

5.3 Completeness

It is a legal requirement that all qualifying tax entities submit a SA Tax return, at the required time. Penalties exist for non-compliance. The statistics contained in this report can therefore be considered as complete.

6. Accuracy and reliability

6.1 Overall accuracy

This analysis is based on administrative data, and accuracy is addressed by eliminating non-sampling errors as much as possible through adherence to the quality assurance framework.

The potential sources of error include:

• tax entities entering incorrect information onto the SA Tax Return form
• human or software error when entering the returns data into the SA tax system
• tax entities not completing their Tax Return form by the required date
• mistakes in the programming code used to analyse the data and produce the statistics

6.2 Sampling error

Samples are not used to compile the analysis, instead it is based on administrative data from HMRCs SA tax system. Sampling error is therefore not relevant.

6.3 Non-sampling error

Coverage error

All qualifying tax entities must register with HMRC for SA Income Tax when they start trading and inform HMRC when they cease trading. Coverage error is therefore not relevant.

Measurement error

The main sources of measurement error could be categorised as respondent errors and include the following:

• tax entities may make errors entering their information onto the SA Tax Return form, whether this is done on paper or electronically
• SA Return data is subsequently entered onto the system either manually or by electronic transmission, which is another point at which data may be altered due to human or software error

There is a risk that errors involving very large profits or tax amounts may distort the overall statistics. To mitigate against this, checks are conducted on the analysis database before the statistics are produced, and any incorrect large values detected are altered.

Nonresponse error

When analysing tax liabilities for the latest available year, figures are not necessarily available for all tax entities, as some may not have completed their SA Return by the required date.

Statistics that are more accurate will be available in the following year’s publication, by which time almost all tax entities will have completed returns and assessments, and this error will have been mitigated.

Processing error

It is possible that errors exist in the programming code used to analyse the data and produce the statistics. This risk is reduced through developing a good understanding of the complexities of Income Tax, and thoroughly reviewing and testing the programs that are used.

6.4 Data revision

Data revision - policy

The United Kingdom Statistics Authority (UKSA) Code of Practice for Official Statistics requires all producers of Official Statistics to publish transparent guidance on the policy for revisions.

Data revision - practice

The statistics in this analysis include tax administration data for the latest five years of available data. Slight revisions in the outputs are expected for reasons that include:

• late submission of SA tax returns. This may result in a greater number of taxpayers, and higher income and expense figures when the information is extracted the following year
• amendments to correct errors in the original tax administration data

The largest revisions each year are expected to be for the latest year in the previous publication, where the impact of late submissions is likely to be greatest.

7. Timeliness and punctuality

7.1 Timeliness

The SA tax return filing deadline is after the end of the taxable period. For example, the deadline for online tax returns for the 2020 to 2021 tax year was 31 January 2022.

This analysis is published in the autumn, providing statistics for tax years that end in the previous year. The reason for the delay is due in part to the time required to complete the complex analysis and quality assurance. It is also to limit the impact of SA Tax Returns being submitted beyond the filing deadline. If publication was brought forward to earlier in the year, there would be many missing returns. This would lead to less reliable statistics being released.

7.2 Punctuality

In accordance with the Code of Practice for official statistics, the exact date of publication will be given not less than one calendar month before publication on both the Schedule of updates for HMRC’s statistics and the Research and statistics calendar of GOV.UK.

Any delays to the publication date will be announced on the HMRC National Statistics website.

The full publication calendar can be found on both the Schedule of updates for HMRC’s statistics and the Research and statistics calendar of GOV.UK.

8. Coherence and comparability

8.1 Geographical comparability

This analysis contains a breakdown of income and expenses for regions within the United Kingdom. These are comparable with one another.

8.2 Comparability over time

There are no changes leading to comparability issues over the published time series.

8.3 Coherence – cross domain

There are no instances where different sources are used to provide data for the same variables.

Tax entities do not report the region their registered address is in, so they have been assigned a region based on the ONS’s National Statistics Postcode Lookup (NSPL). There are no coherence issues associated with this, as the data sources are matched on postcode, which is standardised across the data sources.

Coherence - sub-annual and annual statistics

All statistics are presented as annual outputs. No coherence issues exist.

8.4 Coherence - internal

Rounding of numbers may cause some minor internal coherence issues as the figures within a table may not sum to the total displayed. Effort has been made to ensure totals between tables remain constant where appropriate.

9. Accessibility and clarity

There haven’t been any press releases linked to this data over the past year.

9.1 News release

There haven’t been any press releases linked to this data over the past year.

9.2 Publication

The tables and associated commentary are published on the Property rental income statistics webpage of GOV.UK. They are published in an accessible HTML and ODS format.

The documents comply with the accessibility regulations set out in the Public Sector Bodies (Websites and Mobile Applications) (No. 2) Accessibility Regulations 2018.

Further information can be found in HMRC’s accessible documents policy.

9.3 Online data bases

This analysis is not used in any online databases.

9.4 Micro-data access

Access to this data is not possible in micro-data form, due to HMRC’s responsibilities around maintaining confidentiality of taxpayer information.

9.5 Other

There are no other dissemination formats available for this analysis.

9.6 Documentation on methodology

All methodology can be found in the Statistical processing section of this report.

9.7 Quality documentation

All official statistics produced by KAI, must meet the standards in the Code of Practice for Statistics produced by the UK Statistics Authority and all analysts adhere to best practice as set out in the Quality pillar.

Information about quality procedures for this analysis can be found in section 4 of this document.

10. Cost and burden

Because all necessary data for this publication is obtained from an administrative data source, i.e., SA tax administration data, there is no additional burden on tax entities or HMRC tax inspectors to provide information.

It is estimated to take about 20 days FTE to produce the annual analysis and publication. This is minimised using- reproducible analytical pipelines.

More information on reporting cost and burden (PDF, 4.54MB)

11. Confidentiality

This section covers confidentiality policy and practice.

11.1 Confidentiality – policy

HMRC has a legal duty to maintain the confidentiality of taxpayer information.

Section 18(1) of the Commissioners for Revenue and Customs Act 2005 (CRCA) sets out our duty of confidentiality.

This analysis complies with this requirement.

11.2 Confidentiality – data treatment

The statistics in these tables are presented at an aggregate level so identification of individual tax entities is not possible.

Disclosure in this analysis is avoided by applying rules that prevent categories of data containing:

• small numbers of contributors
• small numbers of contributors that are very dominant

If a cell within a table is determined to be disclosive, its contents would be suppressed either by removing the data or combining categories.

Further information on anonymisation and data confidentiality best practice can be found on the Government Statistical Service’s website.