National Tenant Survey technical report
November 2024
Applies to England
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Contents
Context and objectives
Background
The Regulator of Social Housing (“RSH”) is a non-departmental public body, sponsored by the Ministry of Housing,
Communities & Local Government (“MHCLG”).
RSH regulate registered providers of social housing in England for a viable, efficient, and well governed social housing sector able to deliver quality homes and services for current and future tenants. Registered providers include local authority landlords and private registered providers (such as not-for-profit housing associations, co-operatives, and for-profit organisations.
In November 2020 the Government published ‘The Charter for Social Housing Residents: Social Housing White Paper’ (the “Social Housing White Paper”). In the Social Housing White Paper, the Government set out a commitment to strengthen the regulator’s consumer regulation role, with the introduction of a proactive consumer regulation regime.
The Social Housing White Paper set an expectation that RSH would bring in a set of tenant satisfaction measures (“TSMs”) on issues that matter to tenants. In September 2022, following a sector-wide consultation, the full set of TSMs were published by the regulator alongside detailed technical requirements.
As part of the TSM requirements, all registered providers are required to undertake tenant perception surveys. These surveys must include the 12 set tenant perception questions, with set response scales, as defined in the TSM requirements. Landlords were first required to undertake the TSM surveys in 2023-24 and were required to report their results to the regulator in June 2024.
Research objectives
The National Tenant Survey (“NTS”) was commissioned to support RSH’s understanding of the TSM results collated by providers. The key objectives of the NTS were to:
- Create a robust independent benchmark for tenant satisfaction data across the perception TSMs.
- Give RSH an early idea of tenant satisfaction and an indication of trends or issues that RSH could consider as TSM results are received.
- Improve RSH understanding of contextual and methodological drivers of TSMs.
In particular, the NTS sought to understand some key research questions, including:
- What is the correlation between results from the TSM questions, which are most likely to drive overall satisfaction, and are there any overlaps between questions?
- What can we understand of the reasons behind tenant responses?What impact (holding other factors constant) do key stock and tenant characteristics have on the average levels of satisfaction?
- How does satisfaction vary for Low Cost Rental Accommodation (LCRA) and Low Cost Home Ownership (LCHO)?
NTS included detailed qualitative and statistical analysis to answer key research questions. Following a competitive tendering exercise, BMG Research were commissioned to undertake the NTS on behalf of the regulator. The analysis presented in this report is that of BMG Research.
Overview of approach
Conducted by BMG, the NTS captures LCRA (Low Cost Rental Accommodation) and LCHO (Low Cost Home Ownership) tenant feedback on the TSM perception questions.
Framed around the research objectives, the questionnaire used in the NTS captured feedback on all 12 TSM perception questions, stock and tenant socio- demographic questions to be used for analysis purposes, and additional key areas of further exploration designed by BMG and RSH allowing us to understand:
- Why tenants provide the rating they do for ‘TP01: Overall satisfaction’,
- Why tenants are not satisfied with the repairs service,
- Why tenants provide the rating they do for ‘TP05: Satisfaction that the home is safe’,
- The nature of a complaint, whether tenants have received a written response to a complaint and how, if at all, complaints handling could have been improved,
- Whether tenants have reported anti-social behaviour to their landlord in the last 12 months
- Which aspects of service delivery tenants would most like their landlord to improve.
Following a pilot, conducted in February 2024, the main fieldwork took place in May and June 2024 capturing responses using an online, telephone and face-to-face method of data collection from 3,287 LCRA tenants and 394 LCHO tenants. Targets and weights have been used to ensure the final sample is representative of the 2021 Census profile of LCRA and LCHO tenants in England.
Following the main fieldwork, 29 further in-depth interviews were conducted by BMG amongst 24 LCRA tenants and 5 LCHO tenants to ascertain what tenants are thinking about when they are answering each TSM perception question.
Analysis focused on undertaking verbatim coding, correlation analysis, factor analysis and regression analysis to allow us to answer the key research questions detailed below.
Regulator of Social Housing National Tenant Survey Analytical Framework
Key research questions
| What impact (holding other factors constant) do key stock and tenant characteristics have on the average levels of satisfaction? | What is the correlation between results from the TSM questions, which are most likely to drive overall satisfaction, and are there any overlaps between questions? Can you understand more about tenant responses to the TSMs by analysing the TSM questions or asking additional questions? | What can we understand of the reasons behind tenant responses? |
BMG analytical approach used
| Bi-Variate analysis Logistic regression - stepwise (stock and tenant characteristics only) |
Logistic regression - stepwise (Hierarchical) Correlation analysis Factor analysis |
Root cause analysis on verbatims Cognitive testing of the TSMs |
Questionnaire
The questionnaire used in the NTS was designed cooperatively by BMG and RSH. All 12 TSM perception questions listed within the TSM tenant survey requirements were used in the questionnaire and followed the guidance set out within the TSM tenant survey requirements e.g., respondents could choose not to provide a response to a question.
To ensure we only spoke to LCRA and LCHO tenants a screener question was used to capture tenure. Any respondent who was not an LCRA nor LCHO tenant was screened out of the survey. This was amended during the pilot stage. Further details on this amendment is listed in the pilot section.
In addition to the 12 TSM perception questions, the following background stock and tenant characteristics were asked in the questionnaire, to understand either the profile of respondent or to be used for analytical purposes:
- Postcode,
- First line of address,
- Region,
- Property type,
- Landlord name,
- Age (or age band),
- Physical or mental health condition or illnesses lasting or expected to last 12 months, and whether the condition, or illness, reduces ability to carry out day-to-day activities,
- Ethnicity,
- Sex and gender identity,
- Number of bedrooms available for use in the household,
- Number of adults and children in the household, and
- Employment status.
The questionnaire also contained some additional questions to further explore key areas of insight including:
- Why tenants provide the rating they did for ‘TP01: Overall satisfaction’ – this was asked as an open-ended question.
- Why tenants are not satisfied with the repairs service – this was asked as a multi-response closed question with a pre-determined list of options. There was an option for a respondent to write in an ‘other’ response if they required. The options provided for this question were amended following the pilot stage. Further information is provided about this below.
- Why tenants provide the rating they do for ‘TP05: Satisfaction that the home is safe’ – this was asked as an open-ended question.
- The nature of a complaint – this was asked as an open-ended question.
- Whether tenants have received a written response to a complaint – this was asked as a closed question.
- How, if at all, complaints handling could have been improved – this was asked as an open-ended question.
- Whether tenants have reported anti-social behaviour to their landlord in the last 12 months – this was asked as a closed question.
- Which aspects of service delivery tenants would most like their landlord to improve – this was asked as a closed question with respondents asked to select their top three improvements from a pre-determined list of options (aligned to the 12 TSM perception questions). There was an option for a respondent to write in an ‘other’ response if they required.
The full questionnaire is detailed in Appendix 3.
Fieldwork
Pilot
Approach
A pilot survey of the NTS was undertaken in February 2024 to test the survey length, to test the sampling process and to ensure that the questionnaire was working as intended.
The pilot consisted of 398 LCRA tenants and 20 LCHO tenants using a mixture of telephone and online methods of data collection.
Table 1 Number of responses to the NTS pilot survey conducted in February 2024 by tenure and method of data collection
| Tenure | Online surveys | Telephone surveys | Total |
|---|---|---|---|
| LCRA | 202 | 196 | 398 |
| LCHO | 18 | 2 | 20 |
Changes following the pilot
Screener question
Following the pilot an amendment was made to the initial tenure screener question. For the pilot the first screener question asked:
Question: ‘Do you currently rent your home from a social housing provider i.e. Local Authority/Council or Housing Association/Trust? This does not include part renting / part buying from a social housing provider as part of a shared ownership.’
Response options:
- Yes – Local Authority / Council
- Yes – Housing Association / Trust
- No
- Don’t know
After analysing the results, this was amended to the following to ensure that private rented sector tenants were being appropriately captured in the questionnaire (and screened out).
Question: ‘Do you currently rent your home? This does not include part renting / part buying from a social housing provider as part of a shared ownership.’
Response options:
- Yes – Local Authority / Council
- Yes – Housing Association / Trust
- Yes – Private landlord
- No
- Don’t know
Repairs follow up question
After reviewing the ‘other’ responses to the repairs question ‘What are the main reasons why you are not satisfied with the repairs service?’, an option for respondents to select ‘Not considering disabilities or vulnerabilities’ was added to the response list.
Number of children in the household
In the pilot, respondents were asked: ‘How many adults and children, including yourself, currently live in your household?’ with the ability to write in either how many adults (18+) or children (under 18), live within the household. To better capture the presence of children in the household, this was amended following the pilot to specifically ask respondents whether they have any children that currently live in the household. Those that said yes were then asked how many children currently live within the household.
Sampling approach
Overview
The sample was designed to capture responses using an online, telephone and face-to-face method of data collection from LCRA and LCHO tenants across England. Screening questions were used to ensure participants were LCRA or LCHO tenants, live in England and were aged 16 or over.
The main fieldwork for the NTS was conducted in May and June 2024. In total 3,287 LCRA tenants and 394 LCHO tenants participated in the survey. A breakdown by method of data collection is shown below.
Table 2 Total number of responses to the NTS by tenure and method of data collection (including pilot interviews)
| Tenure | Online surveys | Telephone surveys | Face-to-face surveys | Total |
|---|---|---|---|---|
| LCRA | 1,885 | 1,017 | 385 | 3,287 |
| LCHO | 184 | 10 | 200 | 394 |
Telephone
For the telephone sample, a telephone database was purchased in areas (Output Areas) of known high stock density of LCRA or LCHO tenants using 2021 Census information stratified by geographic regions.
Due to the challenges of low incidence rates of LCHO tenants, only 10 telephone surveys were conducted with this cohort. Therefore, it was decided to use the face-to-face method of data collection to help target these respondents.
For LCRA tenants, quotas were set by region and age, with monitoring targets set by disability, ethnicity, property type and landlord type to ensure views were captured from a cross-section of respondents. During fieldwork, age targets were amended due to the challenges of achieving telephone surveys amongst younger tenants (under 50 years old), compared to the challenges of achieving online surveys amongst older tenants (over 65 years old). The unweighted profile of the 1,017 LCRA telephone responses is shown later in this section.
Online
For the online sample, respondents were invited to participate in the online survey using three online panel providers.
For LCRA tenants, quotas were set by region and age, with monitoring targets set by disability, ethnicity, property type and landlord type to ensure views were captured from a cross-section of respondents. During fieldwork, as highlighted above, age targets were amended due to the challenges found in capturing responses from older age groups. The unweighted profile of the 1,885 LCRA online responses is shown later in this section.
For LCHO tenants, monitoring targets were set during fieldwork by region, age, disability, ethnicity and property type. The unweighted profile of the 184 LCHO online responses is shown later in this section.
Face-to-face
The face-to-face sample was used to help target LCHO tenants and to help target hard-to-reach LCRA cohorts in the online and telephone samples.
For both LCRA and LCHO tenants, targets were first set by region. Output Areas with the largest proportions of LCHO or LCRA tenants were then selected within each region, selecting no more than two Output Areas per Local Authority. A target was set of 10 interviews per Output Area with monitoring targets set at a total level for LCRA and LCHO tenants by age, disability, ethnicity, property type and landlord type.
The unweighted profile of the 385 LCRA and 200 LCHO tenant face-to-face surveys are shown below.
LCRA unweighted profile by method of data collection
Below outlines the unweighted profile of LCRA tenants by method of data collection. The population statistics have been generated using 2021 Census information except for landlord size and type which were calculated based on RSH’s 2023 SDR/LADR data.
Table 3 Unweighted profile of LCRA telephone responses by method of data collection
| Population | Online | Telephone | Face-to-face | ||
|---|---|---|---|---|---|
| Unweighted base size | 1,885 | 1,017 | 385 | ||
| Geographical region | East Midlands | 8% | 9% | 8% | 8% |
| East of England | 10% | 11% | 10% | 10% | |
| London | 20% | 18% | 19% | 21% | |
| North East | 6% | 7% | 5% | 6% | |
| North West | 14% | 13% | 14% | 16% | |
| South East | 13% | 13% | 14% | 15% | |
| South West | 8% | 9% | 9% | 6% | |
| West Midlands | 11% | 12% | 12% | 10% | |
| Yorkshire and the Humber | 10% | 9% | 9% | 9% | |
| Age | Aged 16 to 34 years | 17% | 15% | 10% | 28% |
| Aged 35 to 49 years | 27% | 31% | 17% | 31% | |
| Aged 50 to 64 years | 30% | 35% | 33% | 29% | |
| Aged 65 years and over | 27% | 18% | 40% | 12% | |
| Unknown | 0% | 1% | 0% | ||
| Disability | Yes | 40% | 51% | 52% | 39% |
| No | 60% | 47% | 45% | 59% | |
| Unknown | 1% | 3% | 2% | ||
| Ethnicity | White | 80% | 88% | 89% | 68% |
| All other ethnic groups | 20% | 12% | 10% | 31% | |
| Unknown | 0% | 1% | 1% | ||
| Landlord type | Private registered provider | 63% | 64% | 53% | 72% |
| Local authority | 37% | 36% | 47% | 28% | |
| Property type | House | 55% | 63% | 53% | 38% |
| Flat | 45% | 35% | 43% | 62% | |
| Unknown | 2% | 4% | 0% | ||
| Landlord size | 0-999 units | 3% | 11% | 5% | 4% |
| 1,000-2,499 units | 2% | 2% | 1% | 0% | |
| 2,500-4,999 units | 7% | 8% | 8% | 5% | |
| 5,000-9,999 units | 17% | 16% | 19% | 8% | |
| 10,000-24,999 units | 30% | 27% | 33% | 22% | |
| 25,000-39,999 units | 17% | 18% | 15% | 27% | |
| >39,999 units | 25% | 19% | 21% | 34% |
LCHO unweighted profile by method of data collection
Below outlines the unweighted profile of LCHO tenants by method of data collection. The population statistics have been generated using 2021 Census information.
Table 4 Unweighted profile of LCHO telephone responses by method of data collection
| Population | Telephone | Online | Face-to-face | ||
|---|---|---|---|---|---|
| Unweighted base size | 10 | 184 | 200 | ||
| Geographical region | East Midlands | 7% | 0 | 10% | 5% |
| East of England | 11% | 1 | 8% | 11% | |
| London | 22% | 2 | 26% | 23% | |
| North East | 2% | 0 | 2% | 5% | |
| North West | 9% | 1 | 10% | 10% | |
| South East | 23% | 3 | 20% | 22% | |
| South West | 11% | 1 | 9% | 9% | |
| West Midlands | 8% | 2 | 8% | 11% | |
| Yorkshire and the Humber | 5% | 0 | 7% | 5% | |
| Age | Aged 16 to 34 years | 25% | 1 | 37% | 22% |
| Aged 35 to 49 years | 34% | 2 | 35% | 38% | |
| Aged 50 to 64 years | 24% | 5 | 21% | 28% | |
| Aged 65 years and over | 17% | 2 | 7% | 13% | |
| Unknown | 0 | 0% | 0% | ||
| Disability | Yes | 19% | 3 | 23% | 22% |
| No | 81% | 6 | 74% | 78% | |
| Unknown | 1 | 3% | 1% | ||
| Ethnicity | White | 85% | 9 | 74% | 82% |
| All other ethnic groups | 15% | 0 | 24% | 17% | |
| Unknown | 1 | 1% | 2% | ||
| Property type | House | 60% | 6 | 65% | 59% |
| Flat | 40% | 4 | 29% | 41% | |
| Unknown | 0 | 6% | 0% |
Cognitive interviews
Cognitive interviews have been used in the NTS to gain insight into how respondents are answering each TSM perception question.
Conducted following the main fieldwork and recruited using an opt-in from the main fieldwork sample, 29 in-depth interviews were conducted by BMG amongst 24 LCRA tenants and 5 LCHO tenants. Broad targets were set to ensure BMG interviewed a cross-section of tenants amongst those who have recently had a repair completed in their home, made a complaint, have a communal area or have reported anti-social behaviour to their landlord in the last 12 months. All respondents received a £30 gift voucher for participating and giving up their time. This is standard practice as these interviews take 30 to 45 minutes on average to complete.
For the cognitive interviews respondents answered the NTS survey online whilst sharing their screen with a BMG interviewer and were asked to verbalise their thoughts as they attempted to answer the survey questions. For certain TSM perception questions there were specific prompts that BMG asked respondents to answer, to better understand:
- The key issues that drive satisfaction with repairs & maintenance,
- The nature of complaints perceived by tenants, including how far they appear to meet the complaint definition in the Housing Ombudsman’s Complaint Handling Code, and reasons for reporting dissatisfaction with complaint handling.
- What issues are reflected in ‘TP05: Satisfaction that the home is safe’, ‘TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods’, and ‘TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour’.
- How far some questions on engagement (e.g., TP06 – TP08) reflect service interactions or more general tenant communication & engagement.
- What are the issues that drive lower levels of satisfaction amongst LCHO tenants.
The feedback from the interviews has then been analysed and reported back within the main findings document.
Data production
Quality checks & data cleaning
To prepare the dataset for analysis, BMG carried out the following data checks:
- Ensured routing was followed correctly.
- Assessed for any missing data.
- Verified that screen-outs were applied correctly.
- Confirmed adherence to exclusivity on multi-coded responses (e.g., “Don’t know” on TP02C).
- Checked that the correct number of responses were provided for the improvement questions
- Examined the dataset for any duplicate records.
- Verified that derived variables in the script setup functioned correctly (e.g., age, region, landlord type).
- Conducted logic checks on the reported number of adults and children within each household..
- Reviewed for any potential outliers or “speeders” (e.g., rapid completion times).
Following these checks, BMG then:
- Added derived variables to the dataset (discussed below).
- Included back-coded responses.
- Inserted coded verbatim responses.
Postcode validation
During data collection, BMG employed a postcode lookup to verify postcode accuracy and used this to append both 2011 and 2021 Output Area (OA) and Lower-layer Super Output Area (LSOA) information using the Office of National Statistics (ONS) postcode lookup file.
Data Validation
For LCRA tenants, to ensure that the sample comprised only of LCRA tenants, BMG removed any respondents who did not know or refused to provide their landlord’s name.
Data Security
All data collection was collected and stored in line with the General Data Protection Regulation (GDPR) and UK Data Protection Act 2018. Any personal data collected (e.g., postcode or first line of address) has been deleted after use (i.e., once the derived variables have been created).
Coding
Responses from the fully open-ended questions were collated and detailed code frames were created by BMG and RSH, reflecting the themes discussed by respondents. A separate code frame was created for those satisfied and those dissatisfied at ‘TP01A Reason for overall satisfaction’ and ‘TP05B Reason for home is safe’, and then one code frame each for ‘TP09C Nature of complaint’ and ‘TP09E Complaint handling improvements’. A verbatim comment can have more than one detailed code. These detailed codes have then been summarised into key overarching themes. The full results to open ended questions can be found in the Appendix section 1 (LCRA tenants) and section 2 (LCHO tenants).
Variables used for analysis
Below lists the variables BMG used for the analysis including how variables were derived.
Deprivation
The Index of Multiple Deprivation (IMD), produced by the Ministry of Housing, Communities & Local Government, offers a relative measure of deprivation across England at the Lower-layer Super Output Area (LSOA) level. The most recent release is IMD2019. BMG used the LSOA data (derived from postcode) to append the following IMD statistics: raw score, quintile, quartile, and decile. It is important to note that IMD2019 is linked to the 2011 LSOA boundaries. The IMD quartile was found to be the most predictive of overall satisfaction and was therefore used in further analysis.
Region & Rural-Urban Classification
Where respondents provided a postcode, BMG used the ONS postcode lookup file to append Government Office Regions (GOR) to the data. Respondents had the option to decline to provide their postcode; in such instances, they were asked to specify their region of residence.
BMG used the 2011 rural-urban classification (RUC2011), produced by the ONS, to indicate whether a respondent lived in an urban or rural area. RUC2011 was produced at the OA level, categorising each OA into eight categories. An OA was classified as ‘urban’ if it appeared in the 2011 built-up area file with a population of 10,000 people or more. All remaining OAs were classified as ‘rural’. BMG used the OA appended to the data (derived from the respondent’s postcode) to match the dataset. The limitation of this dataset was that it did not consider population changes at the OA level since 2011. Following initial analysis, London, which is classified entirely as a ‘Major Conurbation’, was placed in its own category as it was deemed important to determine whether the differences in the data was true when conducting further analysis (discussed later in this report). As the urban-rural classification cannot be used alongside the region in regression analysis - as the two measures are too closely related - it was decided that the urban-rural classification would be used for further analysis.
Table 5 Rural -urban classification used in NTS
| 2011 rural-urban classification categories | Final Categories | |
|---|---|---|
| Urban | Major conurbation | London |
| Minor conurbation | Urban conurbation excl. London | |
| City and town | Urban city and town | |
| City and town in a sparse setting | Urban city and town | |
| Rural | Town and fringe | Rural |
| Town and fringe in a sparse setting | Rural | |
| Village and dispersed | Rural | |
| Village and dispersed in a sparse setting | Rural |
Property age
BMG used the first line of the addresses and postcode captured within the survey, in conjunction with an online property checker tool, which utilises HM Land Registry data, to append the property build year to the dataset.
Landlord size & type
During data collection, respondents could either select their landlord from a list provided by the RSH or enter their landlord’s name manually. When a name was submitted, BMG back-coded this to align with the RSH dataset. The RSH landlord database was then used to append landlord size bands and landlord type to the data.
Household composition
BMG used the number of adults and children collected within the survey to derive household composition within the dataset.
Number of bedrooms
Respondents were asked to specify the number of bedrooms in their property from a list ranging from one to four or more bedrooms.
Overcrowding
BMG developed a binary variable to measure overcrowding, using the number of bedrooms and the total household size (number of children plus adults). Households with more than two people per bedroom were classified as overcrowded.
Ethnicity
Respondents were asked to select their ethnicity from the ONS Census 2021 ethnic group classification. Respondents had the option to manually enter their ethnicity, and this has been back-coded in the dataset.
Property type
Respondents were asked to select which property type they live in, and this has then been grouped together within the dataset as shown below.
Table 6 Property type groups
| Property Type | Grouped property type |
|---|---|
| Terraced house | House |
| Semi-detached house | |
| Detached house | |
| Bungalow | |
| Converted flat | Flat |
| Purpose built flat, low rise (less than six storeys high) | |
| Purpose build flat, high rise (at least six storeys high) | |
| Other | Unknown |
| Don’t know/Prefer not to say |
Age
Respondents were asked to specify their age in years. If they declined to provide this information, they were given the option to select from a banded list of age ranges.
Disability
Respondents were asked if they have any physical or mental health conditions or illnesses that is lasting, or expected to last, 12 months or more.
Sex
Respondents were asked to select their sex. A question on gender identity was also asked, however, has not been used for analysis purposes due to the limited number of respondents who indicated that the gender they identify with is not the same as their sex registered at birth.
Dependent TSM perception questions used in analysis
Where a TSM question is used in the regression analysis detailed in this report, the original scale was recoded into a binary variable distinguishing those who were satisfied from those who were not. The table below details how the questions were transformed into binary variables:
Table 7 Binary variable details
| TSM Question Likert Scale | Final Binary Variable |
|---|---|
| Very satisfied | Satisfied |
| Fairly satisfied | |
| Neither satisfied nor dissatisfied | Not Satisfied |
| Fairly dissatisfied | |
| Very dissatisfied | |
| Not applicable/Don’t know | Missing (NULL) |
| No response/Missing/Not Answered | Missing (NULL) |
Independent TSM perception questions used in analysis
Where the TSM perception questions are used as independent variables in the regression analysis detailed in this report, they were converted to numeric values ranging from 2 to -2. In this instance, however, the ‘Not applicable/Don’t know’ responses were recoded to zero to minimise the impact of missing respondents in the subsequent analysis. For ‘TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour’, 618 (18%) respondents selected ‘Not applicable/Don’t know,’ which is too many to exclude from any further analysis. This issue would be compounded when using multiple TSM questions as independent variables. The table below details how the TSM questions were recoded into numeric independent variables. BMG opted not to impute these values for two reasons: they are unlikely to be missing at random, and using other survey questions for imputation could amplify any existing correlations and associations.
Table 8 Final numeric variables
| TSM Question Likert Scale | Final Numeric Variable |
|---|---|
| Very satisfied | 2 |
| Fairly satisfied | 1 |
| Neither satisfied nor dissatisfied | 0 |
| Fairly dissatisfied | -1 |
| Very dissatisfied | -2 |
Weighting
Weighting for LCRA
To weight the LCRA dataset, BMG used the final cleaned dataset, to check:
- How representative the cleaned dataset is by region, age, disability, ethnicity, landlord type and property type against the England profile of LCRA tenants using the 2021 Census data.
- Which of the key characteristics has a significant impact on a respondents satisfaction with ‘TP01: Overall satisfaction’.
Given the importance of the NTS, it was decided that BMG shall weight the data to all known stock and tenant characteristics to ensure the final dataset is representative of the England profile of LCRA tenants. Following analysis of the LCRA dataset, it was also decided to weight the data by landlord size, as this was found to have a significant impact on LCRA tenant satisfaction with ‘TP01: Overall satisfaction’. BMG used a Random Iterative Method (RIM) procedure to generate the weights.
Table 9 LCRA Weighting profile
| Population | Unweighted profile | Weighted profile | ||
|---|---|---|---|---|
| Geographical region | East Midlands | 8% | 8% | 8% |
| East of England | 10% | 11% | 10% | |
| London | 20% | 19% | 20% | |
| North East | 6% | 6% | 6% | |
| North West | 14% | 14% | 14% | |
| South East | 13% | 14% | 13% | |
| South West | 8% | 8% | 8% | |
| West Midlands | 11% | 11% | 11% | |
| Yorkshire and the Humber | 10% | 9% | 10% | |
| Age | Aged 16 to 34 years | 17% | 15% | 17% |
| Aged 35 to 49 years | 27% | 27% | 27% | |
| Aged 50 to 64 years | 30% | 34% | 30% | |
| Aged 65 years and over | 27% | 24% | 27% | |
| Unknown | 0% | 0% | ||
| Disability | Yes | 40% | 50% | 39% |
| No | 60% | 48% | 59% | |
| Unknown | 2% | 2% | ||
| Ethnicity | White | 80% | 86% | 79% |
| All other ethnic groups | 20% | 14% | 20% | |
| Unknown | 1% | 1% | ||
| Landlord type | Private registered provider | 63% | 62% | 63% |
| Local authority | 37% | 38% | 37% | |
| Property type | House | 55% | 57% | 54% |
| Flat | 45% | 41% | 44% | |
| Unknown | 3% | 3% | ||
| Landlord size | 0-999 units | 3% | 8% | 3% |
| 1,000-2,499 units | 2% | 1% | 2% | |
| 2,500-4,999 units | 7% | 7% | 7% | |
| 5,000-9,999 units | 17% | 16% | 17% | |
| 10,000-24,999 units | 30% | 28% | 30% | |
| 25,000-39,999 units | 17% | 17% | 17% | |
| >39,999 units | 25% | 21% | 25% |
Weighting efficiency
Following weighting, the total effective sample size for LCRA tenants is 2,813 respondents which provides a confidence interval of ±1.85% for an observed statistic of 50% at 95% confidence level. Details of the weighting for LCRA tenants is listed below.
Table 10 LCRA Weighting efficiency
| LCRA weighting efficiency | |
|---|---|
| Sample size | 3287 |
| Effective Sample Size | 2813 |
| Weighting efficiency | 86% |
| Min weight | 0.22 |
| Max weight | 2.44 |
| Weighting ratio | 11.02 |
Weighting for LCHO
To weight the LCHO dataset, BMG used the final cleaned dataset, to check:
- How representative the cleaned dataset is by region, age, disability, ethnicity, and property type against the England profile of LCHO tenants using the 2021 Census data.
- Which of the key characteristics has a significant impact to a respondents satisfaction with ‘TP01: Overall satisfaction’.
Given the smaller sample size amongst LCHO tenants, a more pragmatic approach to weighting must be used. Following a review of the dataset, it was decided to weight the data by region and property type.
Table 11 LCHO Weighting profile
| Population | Unweighted profile | Weighted profile | ||
|---|---|---|---|---|
| Geographical region | East Midlands | 7% | 7% | 7% |
| East of England | 11% | 10% | 11% | |
| London | 22% | 24% | 22% | |
| North East | 2% | 4% | 2% | |
| North West | 9% | 10% | 9% | |
| South East | 23% | 21% | 23% | |
| South West | 11% | 9% | 11% | |
| West Midlands | 8% | 10% | 8% | |
| Yorkshire and the Humber | 5% | 6% | 5% | |
| Age | Aged 16 to 34 years | 25% | 28% | 28% |
| Aged 35 to 49 years | 34% | 36% | 36% | |
| Aged 50 to 64 years | 24% | 25% | 26% | |
| Aged 65 years and over | 17% | 10% | 10% | |
| Disability | Yes | 19% | 23% | 24% |
| No | 81% | 76% | 75% | |
| Unknown | 2% | 2% | ||
| Ethnicity | White | 85% | 79% | 80% |
| All other ethnic groups | 15% | 20% | 19% | |
| Unknown | 2% | 1% | ||
| Property type | House | 60% | 62% | 58% |
| Flat | 40% | 36% | 39% | |
| Unknown | 3% | 3% |
Weighting efficiency
Following weighting, the total effective sample size for LCHO tenants is 380 respondents which provides a confidence interval of ±5.03% for an observed statistic of 50% at 95% confidence level.
Table 12 LCHO Weighting efficiency
| LCHO weighting efficiency | |
|---|---|
| Sample size | 394 |
| Effective Sample Size | 380 |
| Weighting efficiency | 97% |
| Min weight | 0.57 |
| Max weight | 1.49 |
| Weighting ratio | 2.63 |
Significance testing
Statistical significance is a measure used to determine the likelihood that the results observed in a survey are due to specific factors rather than chance. It helps to assess whether patterns and differences found in the data are genuine and can reliably inform conclusions about the broader population.
As this survey used quotas rather than random probability sampling, statistical significance is indicative rather than definitive. Nevertheless, it remains a useful gauge of where differences may be meaningful. When significant differences between sub-groups and the total sample are identified, the ‘total sample’ refers to the overall sample minus the specific sub-group in question. Significant differences are reported at a 95% confidence level and are indicated with up or down arrows on charts throughout the report.
The report examines significance in two ways: as a bivariate relationship and as part of regression analysis, which controls for other factors. For instance, it’s possible that variable A shows a significant association with variable B, while variable B is also significantly associated with variables C and D, and C and D are likewise associated with A. Regression analysis aims to isolate unique associations between variables. Thus, if variable B appears as significant in the regression analysis, this indicates a significant association with A while accounting for the influence of variables C and D.
Correlation analysis
Correlation analysis explains the extent in which there is a linear relationship between two questions or characteristics. Correlation analysis has been used on the NTS to evaluate the associations between individual TSM questions, and tenant and stock characteristics, to identify any strong correlations that could pose issues in further analysis. It has also been used to allow us to understand how closely aligned the views expressed by tenants for each TSM perception question are with each other.
Stock and tenant characteristics
To evaluate associations between the independent variables (stock and tenant characteristics), cross-tabulations and chi-squared tests were conducted. Chi-squared tests are well-suited for assessing relationships between two nominal (unordered) categorical variables. Although certain variables were ordinal, dummy coding was used in the regression analysis, effectively treating these variables as nominal and disregarding their inherent order. Therefore, the chi-squared tests remained appropriate for identifying significant associations between the independent variables.
The table below displays significant and non-significant associations between tenant and stock characteristics, with a tick indicating significant associations and a cross indicating non-significant associations. Most tenant and stock characteristics show significant associations with each other. However, this does not preclude their use in future regression analyses.
Table 13 Stock and tenant characteristics correlation matrix
| Landlord Size | Urban / Rural/ London | IMD - Quartile | Year Built | Landlord Type | Property Type | Age | Disability | Ethnicity | Sex | Property size | Household composition | Overcrowded | Employment Status | Region | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Landlord Size | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| Urban / Rural / London | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| IMD - Quartile | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖ | ✔ | ✖ | ✖ | ✔ | ✖ | ✔ | ✔ | |
| Year Built | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ | ✖ | ✔ | ✔ | |
| Landlord Type | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖ | ✖ | ✔ | ✖ | ✖ | ✖ | ✔ | ✔ | |
| Property Type | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| Age | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| Disability | ✔ | ✔ | ✖ | ✔ | ✖ | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| Ethnicity | ✔ | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ | ✔ | ✖ | ✖ | ✔ | ✔ | ✔ | ✔ | |
| Sex | ✔ | ✖ | ✖ | ✖ | ✖ | ✔ | ✔ | ✖ | ✖ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| Property size | ✔ | ✔ | ✖ | ✔ | ✖ | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ | ✔ | ✔ | ✖ | |
| Household composition | ✔ | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| Overcrowded | ✔ | ✔ | ✖ | ✖ | ✖ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| Employment Status | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | |
| Region | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ | ✔ |
Generalized Variance Inflation Factor statistics
To assess concerns related to multicollinearity in the context of regression analysis, BMG examined the Adjusted Generalized Variance Inflation Factor (GVIF) statistics footnote 1, which are displayed in the table below. Multicollinearity refers to the situation where two or more independent variables in a regression model are highly correlated, meaning they share a significant amount of variance. This can lead to unstable coefficient estimates, making it difficult to determine the individual effect of each variable on the dependent variable. Typically, Adjusted GVIF values above 2 may indicate moderate multicollinearity that could require attention, while values exceeding 5 suggest high multicollinearity, which can lead to instability in regression coefficients.
Most variables, such as ‘Landlord Size’ (1.0), ‘IMD - Quartile’ (1.0), ‘Landlord Type’ (1.1), ‘Disability’ (1.1), and ‘Overcrowded’ (1.1), exhibit adjusted GVIF values close to 1. This indicates minimal multicollinearity, suggesting that these variables share little variance with others in the model.
In contrast, ‘Property Type’ (1.2), ‘Property Size’ (1.2), and ‘Household Composition’ (1.2) have slightly higher adjusted GVIF values; however, these remain well below concerning thresholds.
The age of the respondent exhibits an adjusted GVIF of 1.3, while the adjusted GVIF for employment status is 1.4. Although these values indicate some degree of multicollinearity, they remain below the critical threshold of 2.
An initial analysis indicated that the predictive power of ‘Employment Status’ mainly stems from the distinction between retirees and non-retirees. Notably, employed individuals of working age do not demonstrate significantly different levels of satisfaction with the services provided by their landlord compared to those who are not working. Among retirees, 92% are aged over 65, and 86% of individuals in this age group are retired.
When recalculating the Adjusted GVIF using a binary variable for ‘Employment Status’ (retired vs. not retired), the adjusted GVIF increases to 1.9, approaching the threshold of 2. This result, combined with the observation that respondents’ employment status is highly predictive of overall satisfaction when age is not considered, but becomes statistically insignificant when age is considered, led to the decision with RSH to prioritise respondents’ age over their employment status in any further regression analyses.
Table 14 GVIF for tenant and stock characteristics
| Stock and tenant characteristics | Adjusted GVIF |
|---|---|
| Landlord Size | 1.05 |
| Urban/Rural/London | 1.08 |
| IMD - Quartile | 1.05 |
| Year Built | 1.04 |
| Landlord Type | 1.06 |
| Property Type | 1.17 |
| Age | 1.29 |
| Disability | 1.12 |
| Ethnicity | 1.12 |
| Sex | 1.04 |
| Property Size | 1.21 |
| Household Composition | 1.16 |
| Overcrowded | 1.10 |
| Employment Status | 1.44 |
TSM perception questions
As mentioned previously, all TSM perception questions listed in the table below were recoded into numeric values for their use as independent variables. The following table presents the Pearson correlation matrix for the independent TSM variables. A value of 1 indicates perfect positive correlation, -1 perfect negative correlation. Values above 0.5 indicates strong correlation, and above 0.7 indicates a very strong relationship. The correlation matrix reveals that all independent variables demonstrate high correlations. However, it is essential to note that none of the correlation coefficients exceed the critical threshold of 0.8, which is commonly used to identify simple multicollinearity.
Table 15 TSM perception question Pearson correlation matrix
| TP01: Overall satisfaction | TP02: Satisfaction with repairs | TP03: Satisfaction with the time taken to complete most recent repair | TP04: Satisfaction that the home is well maintained | TP05: Satisfaction that the home is safe | TP06: Satisfaction that the landlord listens to tenant views and acts upon them | TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | TP08: Agreement that the landlord treats tenants fairly and with respect | TP09: Satisfaction with the landlord’s approach to handling complaints | TP10: Satisfaction that the landlord keeps communal areas clean and well maintained | TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TP01: Overall satisfaction | 0.70 | 0.64 | 0.77 | 0.68 | 0.74 | 0.68 | 0.71 | 0.67 | 0.56 | 0.61 | 0.51 | |
| TP02: Satisfaction with repairs | 0.70 | 0.74 | 0.73 | 0.63 | 0.67 | 0.62 | 0.65 | 0.57 | 0.52 | 0.55 | 0.47 | |
| TP03: Satisfaction with the time taken to complete most recent repair | 0.64 | 0.74 | 0.68 | 0.56 | 0.65 | 0.61 | 0.59 | 0.55 | 0.46 | 0.54 | 0.44 | |
| TP04: Satisfaction that the home is well maintained | 0.77 | 0.73 | 0.68 | 0.75 | 0.74 | 0.70 | 0.71 | 0.62 | 0.57 | 0.63 | 0.51 | |
| TP05: Satisfaction that the home is safe | 0.68 | 0.63 | 0.56 | 0.75 | 0.67 | 0.64 | 0.66 | 0.56 | 0.54 | 0.56 | 0.48 | |
| TP06: Satisfaction that the landlord listens to tenant views and acts upon them | 0.74 | 0.67 | 0.65 | 0.74 | 0.67 | 0.77 | 0.75 | 0.74 | 0.58 | 0.69 | 0.58 | |
| TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | 0.68 | 0.62 | 0.61 | 0.70 | 0.64 | 0.77 | 0.73 | 0.65 | 0.56 | 0.67 | 0.57 | |
| TP08: Agreement that the landlord treats tenants fairly and with respect | 0.71 | 0.65 | 0.59 | 0.71 | 0.66 | 0.75 | 0.73 | 0.66 | 0.57 | 0.66 | 0.55 | |
| TP09: Satisfaction with the landlord’s approach to handling complaints | 0.67 | 0.57 | 0.55 | 0.62 | 0.56 | 0.74 | 0.65 | 0.66 | 0.54 | 0.64 | 0.58 | |
| TP10: Satisfaction that the landlord keeps communal areas clean and well maintained | 0.56 | 0.52 | 0.46 | 0.57 | 0.54 | 0.58 | 0.56 | 0.57 | 0.54 | 0.67 | 0.56 | |
| TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | 0.61 | 0.55 | 0.54 | 0.63 | 0.56 | 0.69 | 0.67 | 0.66 | 0.64 | 0.67 | 0.64 | |
| TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | 0.51 | 0.47 | 0.44 | 0.51 | 0.48 | 0.58 | 0.57 | 0.55 | 0.58 | 0.56 | 0.64 |
Variance Inflation Factor (VIF) statistics
To further evaluate multicollinearity, we can also examine the Variance Inflation Factor (VIF) statistics. VIF measures the extent to which the variance of an estimated regression coefficient increases due to multicollinearity. Generally, VIF values exceeding 5 or 10 indicate significant multicollinearity that could distort the regression results. Thus, while the correlation matrix provides initial insights into relationships between variables, VIF analysis offers a more detailed understanding of the multicollinearity issues among the independent variables. BMG has analysed this by all respondents, by those who had a repair in the last 12 months, by those who reported they had made a complaint in the last 12 months and by those who indicated living in a property with a communal area. This allows for those corresponding TSM perception questions to be used in the analysis. As shown in the table overleaf this analysis finds:
- All Respondents: The highest VIF is 3.6 for ‘TP06: Satisfaction that the landlord listens to tenant views and acts upon them’, suggesting moderate multicollinearity but within acceptable limits. Other variables, like ‘TP04: Satisfaction that the home is well maintained’ (3.2) and ‘TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them’ (3.1), also show moderate correlations with other predictors in this group.
- Respondents who had a repair: VIF values are similar to the “All Respondents” group, with TP06 again showing the highest VIF at 3.9. Variables directly related to repair satisfaction—TP02 (3.0) and TP03 (2.6)—also display acceptable multicollinearity levels, indicating that repair satisfaction measures correlate moderately with other variables but do not exceed concerning thresholds.
- Respondents who made a complaint: In this subset, TP06 (3.8), TP07 (3.0), TP08 (3.0) and TP04 (2.9) exhibit the highest VIFs, suggesting moderate multicollinearity. This pattern indicates that satisfaction related to tenant communication and home maintenance correlates with other satisfaction measures in respondents who have made complaints.
- Respondents living a property with communal areas: The TP06 variable, which focuses on tenant communication, has a VIF of 3.5 in this subset, similar to the other groups. Unique to this group, ‘TP10: Satisfaction that the landlord keeps communal areas clean and well maintained’ has a VIF of 2.0, indicating limited multicollinearity specific to communal area satisfaction measures.
Across all subsets, VIF values are below the common threshold of 5, suggesting that multicollinearity is unlikely to introduce significant distortion in the regression models. The TP06 variable consistently shows the highest VIF across subsets, suggesting it may share variance with other satisfaction metrics more than other variables do. However, the VIF remains below critical levels, indicating this variable’s multicollinearity does not pose an immediate concern.
Table 16 VIF statistics on TSM perception questions
| All Respondents | Respondents Who Had a Repair | Respondents Who Made a Complaint | Respondents with Communal Areas | |
|---|---|---|---|---|
| TP02: Satisfaction with repairs | 3.0 | |||
| TP03: Satisfaction with the time taken to complete most recent repair | 2.6 | |||
| TP04: Satisfaction that the home is well maintained | 3.2 | 3.8 | 2.9 | 3.2 |
| TP05: Satisfaction that the home is safe | 2.5 | 2.6 | 2.4 | 2.5 |
| TP06: Satisfaction that the landlord listens to tenant views and acts upon them | 3.6 | 3.9 | 3.8 | 3.5 |
| TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | 3.1 | 3.2 | 3.0 | 3.2 |
| TP08: Agreement that the landlord treats tenants fairly and with respect | 2.9 | 3.1 | 3.0 | 3.1 |
| TP09: Satisfaction with the landlord’s approach to handling complaints | 2.5 | |||
| TP10: Satisfaction that the landlord keeps communal areas clean and well maintained | 2.0 | |||
| TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | 2.5 | 2.5 | 2.6 | 3.0 |
| TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | 1.8 | 1.8 | 2.0 | 2.1 |
Factor analysis
Overview of our approach
The correlations among the TSM questions reveal a strong association, indicating that these questions may be capturing common underlying themes. To investigate the nature and structure of this shared variability, BMG conducted Principal Component Analysis (PCA), a method closely related to factor analysis. Both PCA and factor analysis aim to reduce a larger set of variables into a smaller, more interpretable set of factors or components allowing us to identify themes within the data. Principal Component Analysis (PCA) was selected over factor analysis due to the small determinant of the correlation matrix; readers should note that, in this context, PCA and factor analysis yield very similar outcomes.
Factory analysis findings
All Respondents
All independent TSM perception questions asked to all respondents, except ‘TP01: Overall Satisfaction’, were included in a PCA with varimax rotation. The table below, presents the Kaiser-Meyer-Olkin (“KMO”) Measure of Sampling Adequacy and Bartlett’s Test of Sphericity. The KMO measure, which assesses whether the variables share enough common variance to justify PCA , was close to 1.0, indicating excellent sampling adequacy for PCA. Bartlett’s Test of Sphericity, testing whether the correlation matrix differs from an identity matrix (where only diagonal elements are 1, and all others are 0), was significant, confirming the data’s suitability for PCA. The determinant of the correlations matrix was more than 0.0001, a requirement forPCA. Varimax rotation, assuming uncorrelated components, was applied to simplify interpretation by maximising variance on each component, so that each question loads strongly onto only one component.
Table 17 KMO and Bartlett’s Test - All respondents
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | 0.926 | |
|---|---|---|
| Bartlett’s Test of Sphericity | Approx. Chi-Square | 16159.919 |
| df | 21 | |
| Sig. | <.001 |
The table below presents the communalities for each question. Communalities represent the amount of variance in each TSM perception question (or variable) that is explained by the final PCA solution. Higher communalities indicate that the extracted components effectively account for a significant portion of the variance in that variable. The communalities, ranging from 0 to 1, reveal how much of this variance is captured by the final PCA solution. Values closer to 1 indicate that the PCA model explains a substantial portion of the variance in these variables.
Generally, a communality value above 0.7 is considered good, indicating that the extracted components account for a substantial portion of the variance in each variable. The high communalities in this table suggest that the PCA model effectively captures the underlying patterns in respondents’ satisfaction levels.
Table 18 Communalities - All Respondents
| Question | Communalities |
|---|---|
| TP04: Satisfaction that the home is well maintained | 0.843 |
| TP05: Satisfaction that the home is safe | 0.925 |
| TP06: Satisfaction that the landlord listens to tenant views and acts upon them | 0.83 |
| TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | 0.862 |
| TP08: Agreement that the landlord treats tenants fairly and with respect | 0.8 |
| TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | 0.993 |
| TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | 0.997 |
The table below outlines the variance explained by the PCA. The table presents both the initial eigenvalues and the rotation sums of squared loadings for each component.
The first component has an eigenvalue of 4.883, explaining 69.76% of the total variance. This indicates that this component alone accounts for most of the variance in the data. The high value suggests that it captures a primary underlying construct, likely related to overall satisfaction with the services provided by the landlord.
In assessing the internal consistency of the variables, a Cronbach’s alpha value above 0.9 was observed, indicating excellent reliability. This high internal consistency implies that the questions are indeed measuring the same construct, reinforcing the interpretation that they reflect a cohesive concept of overall satisfaction.
Although one could extract a single factor to represent overall satisfaction, which would simplify the analysis, this approach would not provide sufficient insight. Instead, BMG and RSH opted to extract four components based on their interpretability and the fact that together, these components explain 89% of the total variance. This decision allows for a more nuanced understanding of the different dimensions of satisfaction rather than oversimplifying it into one overarching measure.
Table 19 Total Variance Explained - All Respondents
| Component | Initial Eigenvalues | Rotation Sums of Squared Loadings | ||||
|---|---|---|---|---|---|---|
| Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
| 1 | 4.883 | 69.757 | 69.757 | 2.258 | 32.26 | 32.26 |
| 2 | 0.635 | 9.067 | 78.825 | 1.782 | 25.455 | 57.715 |
| 3 | 0.405 | 5.786 | 84.61 | 1.142 | 16.317 | 74.032 |
| 4 | 0.327 | 4.677 | 89.287 | 1.068 | 15.255 | 89.287 |
| 5 | 0.284 | 4.055 | 93.342 | |||
| 6 | 0.245 | 3.499 | 96.841 | |||
| 7 | 0.221 | 3.159 | 100 |
The table below, displays the loadings of each question on the four extracted components from the PCA following varimax rotation. Each loading indicates the strength and direction of the relationship between the questions and the respective components. A factor loading lower than 0.4 is generally considered to mean that the question does not contribute to that theme (therefore have been removed from the table), a figure greater than 0.6 is generally considered to mean that the question has a significant contribution to that theme.
The first component (Communication & respect) accounts for 32% of the variance, while the second component (Safety & maintenance) explains 26%. The third component (Handling anti-social behaviour) accounts for 16%, and the final component (Neighbourhood management) explains 15%. Together, these components account for 89% of the total variance. However, it is important to note that some components are not entirely distinct. For instance, ‘TP04: Satisfaction that the home is well maintained’ loads onto both the ‘Communication & Respect’ and ‘Safety & Maintenance’ components, indicating that this perception influences both areas.
Table 20 Rotated Component Matrix = All respondents
| TSM perception question | Communication & respect | Safety & maintenance | Handling anti-social behaviour | Neighbourhood management |
|---|---|---|---|---|
| TP04: Satisfaction that the home is well maintained | 0.475 | 0.718 | - | - |
| TP05: Satisfaction that the home is safe | - | 0.87 | - | - |
| TP06: Satisfaction that the landlord listens to tenant views and acts upon them | 0.72 | - | - | - |
| TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | 0.813 | - | - | - |
| TP08: Agree that the landlord treats tenants fairly and with respect | 0.735 | - | - | - |
| TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | - | - | - | 0.833 |
| TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | - | - | 0.904 | - |
| % of Variance | 32.3 | 25.5 | 16.3 | 15.3 |
Respondents who had a repair
All TSM perception questions asked respondents who had a repair completed in the past 12 months were analysed using PCA, resulting in five components that explained 90% of the variance. The KMO measure, Bartlett’s test, and communalities confirmed the suitability of PCA. The identified components were Communication & Respect (24% variance), Repairs Service (23%), Safety & Maintenance (19%), Handling Anti-social Behaviour (13%), and Neighbourhood Management (12%).
Respondents Who Had a Repair and made a complaint
PCA was conducted on TSM perception questions asked of respondents who had both a repair completed and submitted a complaint within the last 12 months. The analysis identified five components, explaining 87% of the variance: Repairs Service (20%), Safety & Maintenance (19%), Communication & Respect (17%), Neighbourhood Management (17%), and Complaint Handling (15%).
Respondents with Communal Areas
PCA was conducted on respondents who had a repair completed in the past 12 months were analysed using PCA, yielding five components that explained 91% of the variance. The identified components were Communication & Respect (23% variance), Safety & Maintenance (21%), Communal Area Maintenance (18%), Handling anti-social behaviour (17%) and Neighbourhood contribution (14%).
Regression analysis
Attitudinal regression models
Overview of approach
In the correlations section, we discussed how the TSM perception questions are interrelated, highlighting that simple bivariate correlations might be misleading as they do not reveal the unique contribution each TSM perception makes to overall satisfaction. To uncover each TSM perception’s unique effect on satisfaction, regression analysis was conducted.
Models were applied to multiple subsets of respondents to understand each perception’s unique effect, as certain perceptions were only relevant to specific groups; for instance, questions related to property repairs were only asked to respondents who had received a repair in the past 12 months.
BMG performed both a broad model to identify significant drivers of satisfaction, while accounting for inter-relationships among TSM questions, and a streamlined model to assess the relative importance of significant perceptions alone. This streamlined model excludes non-significant perceptions to avoid distortion in the relative importance of each perception.
Logistic regression was performed on ‘TP01: Overall Satisfaction’, which was recoded into a binary variable (e.g., ‘Very Satisfied/Satisfied’ vs. ‘NOT Satisfied’). Logistic regression predicts the odds of a binary event occurring—in this case, the odds of being satisfied with the services provided by the landlord.
In the logistic regression analysis, beta values (coefficients) indicate the strength and direction of the relationship between each predictor variable and the outcome variable. A positive beta value suggests that as the predictor increases, the odds of the outcome occurring (e.g., being satisfied) also increase, while a negative beta value implies a decrease in the odds. The estimated coefficient reflects the expected change in the log-odds of the outcome for a one-unit change in the predictor variable.
To evaluate the model’s explanatory power, Nagelkerke R Square was employed. Nagelkerke R Square ranges from 0 to 1, with higher values indicating a better model fit. It is analogous to R Square in linear regression, which quantifies the proportion of variance in a continuous dependent variable that can be explained by the independent variables. However, while R Square can be interpreted directly in linear regression, Nagelkerke R Square serves as a pseudo–R Square in logistic regression.
Variable selection
Logistic regression was conducted on six subsets of respondents, incorporating TSM questions asked of all individuals within each subset. The six subsets included: all respondents who completed a survey; those who had a repair carried out in the last 12 months; those who had a repair and made a complaint in the last 12 months; respondents who have communal areas; those who reported anti-social behaviour (ASB); and finally, those who did not report any anti-social behaviour.
Broad model
The broad model includes all available TSM perception questions that were asked universally across respondents within each subset. The table below summarises the results of this model, which analysed responses from 3,130 participants, with only 4.7% of responses missing.
This model achieved a Nagelkerke R Square of 0.666, indicating a strong improvement over the null (intercept-only) model in predicting satisfaction. The Hosmer-Lemeshow Test result is significant, indicating that observed responses deviate from the expected outcomes, which could suggest a lack of perfect fit. However, with such a large sample size, even small deviations are likely to yield significant results.
Another way to examine model fit is through the calibration curve, which compares expected outcomes with actual results. Here, the observed data were not substantially different from expectations, mitigating concerns about the significant Hosmer-Lemeshow result. This suggests that, despite the test outcome, the model’s fit remains robust.
BMG also analysed the standardized residuals, which aligned with our expectations, as less than 5% of the cases fell outside of ±1.96. Notably, only three cases exceeded a value of 3 (0.01%).
BMG investigated cases that might have undue influence on the model. A case was deemed influential if its leverage values were three times the average, Cook’s distance (greater than 1) or if DFBETA values exceeded 2 divided by the square root of the sample size. A total of 179 cases were identified as potentially influential, which is relatively high but not unexpected given the categorical nature of the data collection. These influential cases were removed from the regression to verify whether the model generally held, and it did. No cases were excluded from the reported models solely based on being classified as influential, as this is not the best practice.
From the regression we observe that ‘TP04: Satisfaction that the home is well maintained’ is highly significant. In the column labelled ‘EXP(B),’ the variable has a value of 2.6. This indicates that for each unit increase in TP04, we can expect the odds of being satisfied to increase by a factor of 2.6.
Table 21 Logistic regression – All respondents
| B | S.E. | Wald | df | Sig. | Exp(B) | |
|---|---|---|---|---|---|---|
| Constant | -0.47 | 0.088 | 28.43 | 1 | <.001 | 0.6 |
| TP04: Satisfaction that the home is well maintained | 0.942 | 0.073 | 165.6 | 1 | <.001 | 2.6 |
| TP05: Satisfaction that the home is safe | 0.236 | 0.071 | 11.11 | 1 | <.001 | 1.3 |
| TP06: Satisfaction that the landlord listens to tenant views and acts upon them | 0.407 | 0.077 | 28.18 | 1 | <.001 | 1.5 |
| TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | 0.177 | 0.075 | 5.551 | 1 | 0.018 | 1.2 |
| TP08: Agreement that the landlord treats tenants fairly and with respect | 0.545 | 0.087 | 39.47 | 1 | <.001 | 1.7 |
| TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | 0.112 | 0.075 | 2.242 | 1 | 0.134 | 1.1 |
| TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | 0.126 | 0.065 | 3.745 | 1 | 0.053 | 1.1 |
Relative importance of broad models
To understand the relative importance of the variables (TSM perception questions), BMG calculated Shapley importance scores. These scores provide a robust method for assessing the contribution of each variable to the model’s predictive power. Shapley scores represent the average marginal contribution of a variable across all possible combinations of variables. This approach offers several advantages: it evaluates each variable’s contribution based on its unique influence, ensuring that the importance of each variable is assessed independently. Additionally, Shapley scores maintain their reliability even in the presence of correlated independent variables, which is crucial in regression models where multicollinearity can distort the influence of individual variables. Furthermore, the scores are intuitive, allowing stakeholders to easily grasp which variables have a meaningful impact on the outcome.
For each of the broad models undertaken, the table below displays the relative importance, the number of observations in each model, and the models’ predictive power, as indicated by the Nagelkerke R Square value. Those variables which are not found to be significant for those relevant models have been underlined.
Table 22 Relative importance scores of broad logistic regression models
| TSM perception question | All | Had repair | Had repairs and made a complaint | Has communal areas | Reported ASB | Not reported ASB |
|---|---|---|---|---|---|---|
| TP02: Satisfaction with repairs | - | 12 | 9 | - | - | - |
| TP03: Satisfaction with the time taken to complete most recent repair | - | 8 | 7 | - | - | - |
| TP04: Satisfaction that the home is well maintained | 27 | 20 | 18 | 26 | 23 | 27 |
| TP05: Satisfaction that the home is safe | 13 | 11 | 13 | 10 | 13 | 13 |
| TP06: Satisfaction that the landlord listens to tenant views and acts upon them | 17 | 12 | 10 | 16 | 17 | 16 |
| TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | 12 | 10 | 9 | 12 | 11 | 13 |
| TP08: Agreement that the landlord treats tenants fairly and with respect | 16 | 13 | 12 | 15 | 15 | 17 |
| TP09: Satisfaction with the landlord’s approach to handling complaints | - | - | 12 | - | - | - |
| TP10: | ||||||
| Satisfaction that the landlord keeps communal areas clean and well maintained | - | - | - | 6 | ||
| TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | 9 | 8 | 7 | 8 | 9 | 9 |
| TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | 6 | 5 | 3 | 6 | 12 | 5 |
| Nagelkerke R Square | 67% | 69% | 69% | 67% | 65% | 67% |
| N | 3,130 | 1,983 | 642 | 1528 | 508 | 2,622 |
Streamlined model
The streamlined model uses the same TSM perception questions or variables as the broad model but processes them through a stepwise procedure, using the log-likelihood ratio as a criterion for selection. The log-likelihood ratio is a statistical measure that compares how well different models explain the data; it helps determine if adding or removing a variable improves the model. This approach employs both forward and backward selection methods, systematically adding or removing variables based on their statistical significance in predicting the outcome. This ensures that only the most relevant variables are retained in the final model, enhancing its interpretability and predictive power.
The model has a Nagelkerke R Square value of 0.66, which is quite similar to that of the broader model. This indicates that the removal of the variable ‘TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods’ has had little effect on the model’s predictive power. While the streamlined model is largely comparable to the broader model, it is essential to recognise that the stepwise procedure can result in previously non-significant variables being included or significant variables being excluded. This outcome is a natural consequence of the variable selection process inherent in a stepwise approach. For example, ‘TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour’ becomes significant once TP11 is removed. Further exploration finds that TP12 is only significant amongst those who have reported anti-social behaviour to their landlord in the last 12 months, which explains why we have created two separate models for these cohorts.
Standard checks were performed on the streamlined model, including assessments for influential cases and an analysis of the standardized residuals, along with Hosmer-Lemeshow tests and calibration charts. Once again, the removal of influential cases did not alter the model’s overall robustness.
Table 23 Logistic regression (streamlined) – All respondents
| B | S.E. | Wald | df | Sig. | Exp(B) | |
|---|---|---|---|---|---|---|
| Constant | -0.46 | 0.087 | 27.75 | 1 | <.001 | 0.6 |
| TP04: Satisfaction that the home is well maintained | 0.953 | 0.073 | 172.1 | 1 | <.001 | 2.6 |
| TP05: Satisfaction that the home is safe | 0.229 | 0.071 | 10.56 | 1 | 0.001 | 1.3 |
| TP06: Satisfaction that the landlord listens to tenant views and acts upon them | 0.436 | 0.075 | 33.68 | 1 | <.001 | 1.5 |
| TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | 0.189 | 0.074 | 6.454 | 1 | 0.011 | 1.2 |
| TP08: Satisfaction that the landlord treats me fairly and with respect | 0.556 | 0.085 | 42.38 | 1 | <.001 | 1.7 |
| TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | 0.155 | 0.061 | 6.36 | 1 | 0.012 | 1.2 |
Relative importance of streamlined models
Shapley importance scores were calculated in the same manner as for the broader model, and the analysis was repeated on the same subsets as before. Below is a table that displays the relative importance for each subset model, along with the Nagelkerke R Square value and the number of cases for each model.
Table 24 Relative importance scores – Streamlined model
| All | Had repair | Had repairs and made a complaint | Has communal areas | Reported ASB | Not reported ASB | |
|---|---|---|---|---|---|---|
| TP02: Satisfaction with repairs | 15 | 16 | ||||
| TP04: Satisfaction that the home is well maintained | 28 | 22 | 26 | 33 | ||
| TP05: Satisfaction that the home is safe | 14 | 12 | 18 | 32 | 28 | |
| TP06: Satisfaction that the landlord listens to tenant views and acts upon them | 19 | 15 | 22 | 14 | ||
| TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | 14 | 12 | 16 | 28 | 17 | |
| TP08: Agreement that the landlord treats tenants fairly and with respect | 18 | 15 | 20 | 20 | 13 | |
| TP09: Satisfaction with the landlord’s approach to handling complaints | 21 | 22 | 17 | |||
| TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | 9 | 10 | ||||
| TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour | 7 | 9 | 18 | |||
| Nagelkerke R Square | 66% | 68% | 69% | 66% | 65% | 67% |
| N | 3138 | 2040 | 677 | 1550 | 526 | 2657 |
Stock and tenant characteristic regression models
Overview of approach
As discussed in the correlations section, tenant and stock characteristics are interrelated and show significant associations. However, examining bivariate associations between overall satisfaction and each characteristic individually may be misleading, as this approach does not account for the interrelationships within the data. To reveal each tenant and stock characteristic’s unique contribution to overall satisfaction, logistic regression was again employed.
BMG conducted a broad model including a comprehensive selection of tenant and stock characteristics. Due to issues with multicollinearity and similarity, not all possible measures could be included; for example, employment status and age could not be used within the same model. The broad model enabled BMG to identify the characteristics significantly associated with overall satisfaction. Additionally, a streamlined model was produced using the stepwise procedure described previously for the perceptions regressions. This streamlined model helped uncover the relative importance of each significant characteristic.
Variable selection
The variables listed in the Data Production section were used as independent variables, following consultation with RSH. Employment status was excluded due to its association with age, as discussed in the Correlations section. Additionally, a derived variable was created to measure household composition instead of using the separate counts of adults and children within a household. This derived variable was found to be more predictive and easier to interpret, providing more meaningful insights.
Broad model
The table below presents the results of the broad model, with ‘TP01: Overall Satisfaction’ as the dependent variable. The model has a Nagelkerke R Square value of 0.09, which is low but expected, as it only includes stock and tenant characteristics without any attitudinal variables. It’s important to note that while the model may have a low R-squared value, it is still significantly better than the null model (mean prediction). The model includes 2,909 respondents, with 11.5% missing cases.
BMG also investigated cases that might have undue influence on the model. A case was considered influential if its leverage values were three times the average, Cook’s distance was greater than 1, or if DFBETA values exceeded 2 divided by the square root of the sample size. A total of 2 cases were identified as potentially influential. Influential cases were removed from the regression to verify whether the model generally held, and it did. No cases were excluded from the reported model solely for being classified as influential, as this is not considered best practice. Also, Hosmer and Lemeshow Test was not significant removing the need for a calibration curve.
The details of the regression model can be summarised as follows:
- Landlord size: Landlord size significantly affects tenant satisfaction. For landlords with between 5,000 and 39,999 units, the coefficient is -0.43 (Exp(B) = 0.7), indicating that tenants in this category have 30% lower odds of being satisfied compared to those with fewer than 5,000 units. Similarly, for landlords with more than 39,999 units, the coefficient is -0.89 (Exp(B) = 0.4), suggesting that tenants of these landlords have 60% lower odds of satisfaction compared to those with fewer than 5,000 units.
- Urban/Rural/London: Location has a significant impact on tenant satisfaction. For tenants in urban conurbations, the coefficient is 0.27 (Exp(B) = 1.3), indicating a 30% increase in the odds of satisfaction compared to those in London. Tenants living in urban cities and towns have a coefficient of 0.41 (Exp(B) = 1.5), reflecting a 50% increase in their odds of satisfaction. Similarly, those in rural areas show a coefficient of 0.42 (Exp(B) = 1.5), indicating a 50% increase in odds of satisfaction compared to tenants in London.
- IMD - Quartile: The overall model indicates a non-significant effect for the Index of Multiple Deprivation (IMD) quartiles (p = 0.745), meaning that these quartiles do not significantly predict tenant satisfaction.
- Year Built: The year built also does not significantly predict satisfaction, with p-values for all categories exceeding 0.05 (p=0.602).
- Landlord Type: There are no significant differences in satisfaction based on provider type, with all p-values indicating non-significance (P=0.35).
- Property Type: No significant differences in satisfaction are observed based on property type, as indicated by p-values exceeding (0.081).
- Age: Age has a significant influence on tenant satisfaction. For individuals aged 35 to 54, the coefficient is -0.29 (Exp(B) = 0.7), meaning their odds of being satisfied are 30% lower compared to those under 35. The coefficient for the 55 to 64 age group is -0.08 (Exp(B) = 0.9), which is not statistically significant. In contrast, for those aged 65 and older, the coefficient is 0.68 (Exp(B) = 2.0), suggesting that older adults have odds of being satisfied that are twice those of individuals under 35.
- Disability: The coefficient is -0.31 (Exp(B) = 0.7), indicating that individuals with disabilities have 30% lower odds of being satisfied compared to those without disabilities.
- Ethnicity: The ethnicity variable shows no significant effect on satisfaction (p = 0.675).
- Sex: Males have a coefficient of 0.43 (Exp(B) = 1.5), suggesting their odds of being satisfied are 50% higher than females.
- Property Size: No significant differences were found across different property sizes (P=0.386).
- Household Composition: There are no significant differences in satisfaction across different household compositions, (P=0.087).
- Overcrowding: The overcrowding variable shows no significant impact on satisfaction (p = 0.931).
Table 25 Logistic Regression - Stock and Tenan Characteristics -Overall Satisfaction
| B | S.E. | Wald | df | Sig. | Exp(B) | |
|---|---|---|---|---|---|---|
| Constant | 1.11 | 0.309 | 12.90 | 1 | <.001 | 3.0 |
| Landlord Size | 39.57 | 2 | <.001 | |||
| 5,000 - 39,999 units | -0.43 | 0.127 | 11.24 | 1 | <.001 | 0.7 |
| > 39,999 units | -0.89 | 0.144 | 37.99 | 1 | <.001 | 0.4 |
| <5,000 units | ||||||
| Urban/Rural/London | 11.11 | 3 | 0.011 | |||
| Urban conurbation excl. London | 0.27 | 0.131 | 4.25 | 1 | 0.039 | 1.3 |
| Urban city and town | 0.41 | 0.127 | 10.54 | 1 | 0.001 | 1.5 |
| Rural | 0.42 | 0.178 | 5.41 | 1 | 0.02 | 1.5 |
| London | ||||||
| IMD - Quartile | 1.23 | 3 | 0.745 | |||
| 2 | 0.01 | 0.205 | 0.00 | 1 | 0.977 | 1.0 |
| 3 | -0.12 | 0.192 | 0.37 | 1 | 0.542 | 0.9 |
| 4 - Most Deprived | -0.02 | 0.188 | 0.01 | 1 | 0.937 | 1.0 |
| 1 - Least Deprived | ||||||
| Year Built | 2.74 | 4 | 0.602 | |||
| 1950-1966 | -0.04 | 0.15 | 0.09 | 1 | 0.768 | 1.0 |
| 1966-1982 | 0.10 | 0.152 | 0.40 | 1 | 0.526 | 1.1 |
| 1983-2023 | 0.11 | 0.155 | 0.46 | 1 | 0.498 | 1.1 |
| Unknown | 0.14 | 0.139 | 1.04 | 1 | 0.307 | 1.2 |
| Pre-1950 | ||||||
| Landlord Type | 1.411 | 2 | 0.494 | |||
| Local Authority | 0.09 | 0.096 | 0.87 | 1 | 0.35 | 1.1 |
| Housing Association/Trust | ||||||
| Property Type | 2.912 | 2 | 0.233 | |||
| Flats | -0.19 | 0.109 | 3.05 | 1 | 0.081 | 0.8 |
| Houses | ||||||
| Age | 56.72 | 3 | <.001 | |||
| 35 to 54 | -0.29 | 0.129 | 5.20 | 1 | 0.023 | 0.7 |
| 55 to 64 | -0.08 | 0.153 | 0.25 | 1 | 0.618 | 0.9 |
| 65+ | 0.68 | 0.167 | 16.37 | 1 | <.001 | 2.0 |
| Under 35 | ||||||
| Disability | ||||||
| Yes | -0.31 | 0.088 | 12.46 | 1 | <.001 | 0.7 |
| No | ||||||
| Ethnicity | ||||||
| All other ethnic groups | 0.06 | 0.131 | 0.18 | 1 | 0.675 | 1.1 |
| White | ||||||
| Sex | ||||||
| Male | 0.43 | 0.092 | 21.62 | 1 | <.001 | 1.5 |
| Female | ||||||
| Property Size | 1.90 | 2 | 0.386 | |||
| Two | 0.17 | 0.125 | 1.87 | 1 | 0.172 | 1.2 |
| Three or more | 0.17 | 0.153 | 1.20 | 1 | 0.273 | 1.2 |
| One | ||||||
| Household Composition | 6.57 | 3 | 0.087 | |||
| Single adult with children | -0.14 | 0.184 | 0.61 | 1 | 0.436 | 0.9 |
| 2+ adults No children | -0.30 | 0.12 | 6.34 | 1 | 0.012 | 0.7 |
| 2+ adults with children | -0.21 | 0.158 | 1.73 | 1 | 0.188 | 0.8 |
| Single adult no children | ||||||
| Overcrowded | ||||||
| Yes | -0.02 | 0.212 | 0.01 | 1 | 0.931 | 1.0 |
| No |
Broad model
BMG conducted the same regression analysis 12 times, designating each of the 12 TSM questions as the dependent variable. As with the attitudinal regression, Shapley importance scores were calculated. The chart below presents the Shapley importance scores for each of the 12 regressions, along with the Nagelkerke R Square value and the number of observations for each regression model. Characteristics underlined are significant.
Table 26 Broad model relative importance - Stock and Tenant Characteristics – all perception TSMs
| Tenant and stock characteristic | TP01: Overall satisfaction | TP02: Satisfaction with repairs | TP03: Satisfaction with the time taken to complete most recent repair | TP04: Satisfaction that the home is well maintained | TP05: Satisfaction that the home is safe | TP06: Satisfaction that the landlord listens to tenant views and acts upon them | TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | TP08: Agreement that the landlord treats tenants fairly and with respect | TP09: Satisfaction with the landlord’s approach to handling complaints | TP10: Satisfaction that the landlord keeps communal areas clean and well maintained | TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Landlord Size | 23 | 22 | 26 | 16 | 6 | 21 | 16 | 21 | 33 | 14 | 20 | 18 |
| Urban/Rural/London | 7 | 10 | 10 | 4 | 12 | 6 | 8 | 7 | 2 | 2 | 2 | 2 |
| IMD - Quartile | 1 | 3 | 2 | 0 | 1 | 3 | 2 | 1 | 3 | 6 | 1 | 2 |
| Year Built | 1 | 0 | 3 | 3 | 4 | 3 | 7 | 4 | 12 | 2 | 5 | 9 |
| Landlord Type | 2 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 7 | 1 | 1 |
| Property Type | 3 | 5 | 3 | 1 | 1 | 2 | 3 | 3 | 6 | 2 | 2 | 4 |
| Age | 35 | 33 | 24 | 42 | 42 | 33 | 35 | 27 | 18 | 28 | 35 | 30 |
| Disability | 7 | 7 | 1 | 6 | 5 | 8 | 9 | 11 | 4 | 0 | 5 | 10 |
| Ethnicity | 0 | 1 | 4 | 1 | 2 | 0 | 1 | 0 | 7 | 1 | 3 | 6 |
| Sex | 13 | 7 | 7 | 13 | 7 | 13 | 8 | 17 | 5 | 23 | 17 | 10 |
| Property Size | 1 | 4 | 2 | 2 | 2 | 3 | 2 | 4 | 1 | 0 | 3 | 1 |
| Household Composition | 6 | 8 | 15 | 9 | 15 | 7 | 7 | 4 | 5 | 13 | 6 | 6 |
| Overcrowded | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 4 | 3 | 1 | 1 |
| Nagelkerke R Square | 9% | 9% | 7% | 11% | 11% | 10% | 9% | 8% | 14% | 9% | 8% | 7% |
| N | 2909 | 1871 | 1838 | 2889 | 2873 | 2746 | 2769 | 2811 | 821 | 1394 | 2642 | 2312 |
Streamlined model
Streamlined models were created using the same stepwise procedure applied in the attitudinal regression, with ‘TP01: Overall Satisfaction’ as the dependent variable. The final model has a Nagelkerke R Square value of 0.08, which, although small, is significantly better than the null model (constant only). There were three influential cases, but the outliers fell within the expected range, and the Hosmer and Lemeshow Test was not significant. Below is a table displaying the Shapley relative importance scores calculated for each of the 12 regression models, each set with one of the 12 TSM questions as the dependent variable. The table includes the Shapley importance scores, Nagelkerke R Square values, and the number of cases used in each regression.
Table 27 Streamlined model relative importance - Stock and Tenant Characteristics – all perception TSMs
| Tenant and stock characteristic | TP01: Overall satisfaction | TP02: Satisfaction with repairs | TP03: Satisfaction with the time taken to complete most recent repair | TP04: Satisfaction that the home is well maintained | TP05: Satisfaction that the home is safe | TP06: Satisfaction that the landlord listens to tenant views and acts upon them | TP07: Satisfaction that the landlord keeps tenants informed about things that matter to them | TP08: Agreement that the landlord treats tenants fairly and with respect | TP09: Satisfaction with the landlord’s approach to handling complaints | TP10: Satisfaction that the landlord keeps communal areas clean and well maintained | TP11: Satisfaction that the landlord makes a positive contribution to neighbourhoods | TP12: Satisfaction with the landlord’s approach to handling anti-social behaviour |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Landlord Size | 26 | 24 | 34 | 16 | 5 | 23 | 19 | 22 | 54 | 24 | 24 | 18 |
| Urban/Rural/London | 11 | 14 | 16 | 6 | 17 | 8 | 9 | 12 | 2 | |||
| IMD - Quartile | 2 | |||||||||||
| Year Built | 9 | |||||||||||
| Landlord Type | 8 | 1 | ||||||||||
| Property Type | 3 | 4 | ||||||||||
| Age | 44 | 47 | 25 | 47 | 47 | 37 | 47 | 35 | 24 | 33 | 47 | 30 |
| Disability | 6 | 5 | 6 | 5 | 10 | 11 | 12 | 11 | 4 | 10 | ||
| Ethnicity | 12 | 4 | 6 | |||||||||
| Sex | 13 | 10 | 8 | 13 | 6 | 11 | 11 | 16 | 21 | 20 | 10 | |
| Property Size | 0 | 0 | 3 | 3 | 4 | 1 | ||||||
| Household Composition | 17 | 12 | 17 | 8 | 13 | 6 | ||||||
| Overcrowded | 1 | |||||||||||
| Nagelkerke R Square | 8% | 8% | 6% | 10% | 11% | 10% | 8% | 8% | 9% | 7% | 7% | 7% |
| N | 3,082 | 1980 | 1959 | 3022 | 2966 | 2863 | 2586 | 2967 | 918 | 1557 | 2887 | 2312 |
Appendix
1 - Coding (LCRA)
1.1 - Reasons for satisfaction at ‘TP01: Overall satisfaction’
Coded responses to TP01A. Why are you satisfied with the service provided by your landlord?
| Unweighted base size | 2291 | |
|---|---|---|
| Summary: Any customer service | 26% | |
| Issues/queries dealt with quickly/promptly/efficiently | 12% | |
| Good customer service | 11% | |
| Good communication/easy to contact/kept informed | 2% | |
| Deal with issues/sort out queries | 2% | |
| Summary: Any repairs | 18% | |
| Repairs dealt with quickly/promptly/efficiently | 16% | |
| Carry out repairs | 2% | |
| Good quality of work | 1% | |
| Attend appointments on time/within time slot | >1% | |
| Generally happy/satisfied | 14% | |
| No problems/issues | 10% | |
| Summary: Any quality/safety of home | 3% | |
| Good quality/condition of home/property | 3% | |
| Feeling safe in home | >1% | |
| Affordable rent | 2% | |
| Area is kept clean | 2% | |
| Management of ASB | 1% | |
| Other | 1% | |
| Don’t know | 6% |
1.2 - Reasons for dissatisfaction at ‘TP01: Overall satisfaction’
Coded responses to TP01A. Why are you dissatisfied with the service provided by your landlord?
| Unweighted base size | 571 | |
|---|---|---|
| Summary: Any repairs | 64% | |
| Outstanding repairs | 39% | |
| Slow/poor responses to repairs/queries | 28% | |
| Poor quality workmanship | 9% | |
| Summary: Any customer service | 16% | |
| Poor customer service/rude/not listening | 10% | |
| Poor communication | 6% | |
| Outstanding queries/issues | >1% | |
| Summary: Any property maintenance | 11% | |
| Poor quality/condition of home/property | 9% | |
| Property maintenance | 2% | |
| Summary: Any costs | 8% | |
| Paying for services which we don’t receive | 5% | |
| High level of rent | 3% | |
| Summary: Any other maintenance | 7% | |
| Communal maintenance | 6% | |
| Greenery maintenance | 1% | |
| Not dealing with problem neighbours | 4% | |
| Not looking after vulnerable tenants | 4% | |
| Not helping with trying to move | 1% | |
| Do not feel safe in home | >1% | |
| Other | 2% | |
| Don’t know | >1% |
1.3 Reasons for satisfaction at ‘TP05: Home that is safe’
Coded responses to TP05B. What are the main reasons why you are satisfied that your landlord provides a home that is safe?
| Unweighted base size | 2494 | |
|---|---|---|
| Summary: Any building safety compliance | 17% | |
| Regular safety checks incl. gas/fire/electrical | 15% | |
| Good fire safety measures | 2% | |
| Generally feel safe | 14% | |
| Summary: Any security of building | 12% | |
| Building security incl. intercom, CCTV cameras | 7% | |
| Secure doors/windows (unspecified) | 2% | |
| Secure doors/windows (communal) | 2% | |
| Secure doors/windows (in property) | 1% | |
| Summary: Any customer service | 11% | |
| Generally good service | 4% | |
| Speed of response/act promptly on issues/requests | 3% | |
| Deal with issues | 2% | |
| Good customer service incl. helpful, caring, friendly | 1% | |
| Good communication (Easy to contact//get updates) | >1% | |
| Summary: Any repairs/maintenance | 10% | |
| Carry out repairs/maintenance | 7% | |
| Speed of response/act promptly | 3% | |
| Good standard of workmanship | >1% | |
| No issues/problems | 10% | |
| Good property condition | 6% | |
| Good area/neighbourhood | 3% | |
| Other | 1% | |
| Don’t know | 11% |
1.4 Reasons for dissatisfaction at ‘TP05: Home that is safe’
Coded responses to TP05B. What are the main reasons why you are dissatisfied that your landlord provides a home that is safe?
| Unweighted base size | 420 | ||
|---|---|---|---|
| Summary: Any repairs | 50% | ||
| Taking too long to deal with repairs | 26% | ||
| Not tackling damp/mould | 17% | ||
| Not doing repair/fixing issues | 4% | ||
| Poor workmanship/quality of work | 4% | ||
| Summary: Any security of building | 17% | ||
| Broken locks/insecure access points | 12% | ||
| Issues with doors/windows (in property) | 4% | ||
| Issues with doors/windows (communal) | 2% | ||
| Issues with doors/windows (unspecified) | 1% | ||
| Crime and ASB | 14% | ||
| Don’t listen to our concerns | 11% | ||
| Issue with boiler (inc. heating/hot water) | 5% | ||
| Lack of support for vulnerable tenants | 2% | ||
| Other | 4% | ||
| Don’t know | 2% |
1.5 Nature of complaint
Coded responses to TP09C. What was the nature of your complaint?
| Unweighted base size | 943 | |
|---|---|---|
| Summary: Any repairs/maintenance | 59% | |
| Taking too long to deal with repairs | 14% | |
| Damp/mould in property | 11% | |
| Not tackling repairs/maintenance in general | 9% | |
| Problems with plumbing/leaks | 9% | |
| Problem with heating | 6% | |
| Leaking roof/guttering | 3% | |
| Issues with quality of bathroom | 2% | |
| Structural repairs | 1% | |
| Poor quality of workmanship | 1% | |
| Summary: Any ASB | 17% | |
| Anti-social behaviour | 15% | |
| Lack of cleanliness incl. fly-tipping | 2% | |
| Summary: Any customer service/ complaint handling | 5% | |
| Poor customer service | 2% | |
| Lack of communication | 2% | |
| Taking too long to deal with issues | 2% | |
| Summary: Any estate management | 4% | |
| Pest control | 2% | |
| Issues with Grounds maintenance | 2% | |
| Summary: Any charges (rent, service charge) | 2% | |
| Issues with service charge | 1% | |
| Issues with rent | 1% | |
| Problems with parking | 1% | |
| Other | 3% | |
| Don’t know | 4% |
1.6 Improving complaint handling
Coded responses to TP09E. How, if at all, could your landlord have improved the way your complaint was handled?
| Unweighted base size | 943 | |
|---|---|---|
| Could not have been improved | 17% | |
| Summary: Any customer service | 60% | |
| Taken action/resolved issues/be more responsive | 28% | |
| Dealt with more quickly/met expected deadlines | 14% | |
| Better level of communication | 13% | |
| Listened/taken it more seriously | 4% | |
| Better customer service incl. show empathy | 3% | |
| Keep promises/do what they said they would | 2% | |
| Be more honest/open/transparent | 1% | |
| Be more organised/efficient | 1% | |
| Follow up/investigate the issue | 3% | |
| Better workmanship/quality of work | 3% | |
| Provided compensation/reimbursement | 1% | |
| Had to escalate (including take to court) | 1% | |
| Other | 1% | |
| Don’t know | 9% |
2 - Coding (LCHO)
2.1 Reasons for satisfaction at ‘TP01: Overall satisfaction’
Coded responses to TP01A. Why are you satisfied with the service provided by your landlord?
| Unweighted base size | 197 | |
|---|---|---|
| Summary: Any customer service | 21% | |
| Good customer service | 12% | |
| Issues/queries dealt with quickly /efficiently | 5% | |
| Good communication/easy to contact/kept informed | 4% | |
| Deal with issues/sort out queries | 1% | |
| No problems/issues | 18% | |
| Generally happy/satisfied | 13% | |
| Affordable rent | 4% | |
| Summary: Any repairs or maintenance | 3% | |
| Repairs dealt with quickly/promptly/efficiently | 3% | |
| Good quality of work | 1% | |
| Summary: Any quality/safety of home | 3% | |
| Good quality/condition of home/property | 2% | |
| Feeling safe in home | 1% | |
| Area is kept clean | 1% | |
| Other | 3% | |
| Don’t know | 14% |
2.2 Reasons for dissatisfaction at ‘TP01: Overall satisfaction’
Coded responses to TP01A. Why are you dissatisfied with the service provided by your landlord?
| Unweighted base size | 108 | |
|---|---|---|
| Rent and service charges | 38% | |
| Summary: Any repairs or maintenance | 33% | |
| Outstanding repairs | 20% | |
| Slow/poor responses to repairs | 13% | |
| Poor quality workmanship | 1% | |
| Summary: Any customer service | 26% | |
| Poor customer service/rude/not listening to resident | 14% | |
| Poor communication | 13% | |
| Summary: Any other maintenance | 11% | |
| Communal maintenance | 8% | |
| Greenery maintenance | 6% | |
| Lack of support for vulnerable tenants | 6% | |
| Summary: Any property maintenance | 4% | |
| Property maintenance | 2% | |
| Poor quality/condition of home | 2% | |
| Management of ASB incl. neighbour issues | 3% | |
| Outstanding complaints | 1% | |
| Other | 3% | |
| Don’t know | 2% |
2.3 Reasons for satisfaction at ‘TP05: Home that is safe’
Coded responses to TP05B. What are the main reasons why you are satisfied that your landlord provides a home that is safe?
| Unweighted base size | 279 | |
|---|---|---|
| Generally feel safe | 16% | |
| Summary: Any security of building | 13% | |
| Building security incl. intercom, CCTV cameras, key fobs | 9% | |
| Secure doors/windows (communal) | 2% | |
| Secure doors/windows (unspecified) | 1% | |
| No issues/problems | 12% | |
| Summary: Any customer service | 11% | |
| Generally good service | 6% | |
| Good customer service incl. helpful, caring, friendly | 2% | |
| Speed of response/act promptly on issues/requests | 1% | |
| Good communication (Easy to contact//get updates) | 1% | |
| Deal with issues | 1% | |
| Summary: Any building safety compliance | 4% | |
| Regular safety checks incl. gas/fire/electrical | 3% | |
| Good fire safety measures | 1% | |
| Summary: Any repairs/maintenance | 4% | |
| Carry out repairs/maintenance | 3% | |
| Speed of response/act promptly on repairs/maintenance | 1% | |
| New build property | 4% | |
| Good area/neighbourhood | 2% | |
| Other | 1% | |
| Don’t know | 17% |
2.4 Reasons for dissatisfaction at ‘TP05: Home that is safe’
Coded responses to TP05B. What are the main reasons why you are dissatisfied that your landlord provides a home that is safe?
| Unweighted base size | 43 | ||
|---|---|---|---|
| Summary: Any repairs | 44% | ||
| Taking too long to deal with repairs | 41% | ||
| Not tackling damp/mould | 2% | ||
| Summary: Any security of the building | 28% | ||
| Broken locks/insecure access points (inc people getting in) | 22% | ||
| Issues with doors/windows (communal) | 3% | ||
| Issues with doors/windows (in property) | 2% | ||
| Crime and ASB | 15% | ||
| Don’t listen to our concerns | 12% | ||
| Issues with boiler (including heating/hot water) | 4% | ||
| Other | 0% | ||
| Don’t know | 0% |
2.5 Nature of complaint
Coded responses to TP09C. What was the nature of your complaint?
| Unweighted base size | 100 | |
|---|---|---|
| Summary: Any repairs/maintenance | 39% | |
| Not tackling repairs/maintenance in general | 9% | |
| Damp/mould in property | 8% | |
| Issues with front door (broken/unsecure) | 8% | |
| Taking too long to deal with repairs | 6% | |
| Problems with plumbing/leaks (internal) | 4% | |
| Structural repairs | 4% | |
| Leaking roof/guttering (external) | 2% | |
| Problem with heating incl. boiler issues | 1% | |
| Issues with quality of bathroom (inc. fixtures/fittings) | 1% | |
| Summary: Any customer service/ complaint handling | 14% | |
| Lack of communication (not being kept updated/informed) | 9% | |
| Poor customer service | 3% | |
| Taking too long to deal with issues | 2% | |
| Summary: Any ASB | 10% | |
| Anti-social behaviour | 8% | |
| Lack of cleanliness incl. fly-tipping | 3% | |
| Summary: Any charges (rent, service charge) | 8% | |
| Issues with service charge | 7% | |
| Issues with rent | 1% | |
| Poor level of security incl. CCTV, lighting, intercom | 7% | |
| Problems with parking | 5% | |
| Grounds maintenance | 4% | |
| Other | 2% | |
| Don’t know | 7% |
2.6 Improving complaint handling
Coded responses to TP09E. How, if at all, could your landlord have improved the way your complaint was handled?
| Unweighted base size | 100 | ||
|---|---|---|---|
| Could not have been improved | 11% | ||
| Summary: Any customer service | 62% | ||
| Taken action/resolved issues/be more responsive | 34% | ||
| Dealt with more quickly/met expected deadlines | 13% | ||
| Better level of communication incl. providing written/verbal responses | 10% | ||
| Better customer service incl. show empathy, be apologetic | 5% | ||
| Listened/taken it more seriously | 3% | ||
| Be more honest/open/transparent | 2% | ||
| Keep promises/do what they said they would | 1% | ||
| Better workmanship/quality of work | 3% | ||
| Following up/investigating the issue | 2% | ||
| Had to escalate (including take to court) | 2% | ||
| Provided compensation/reimbursement | 2% | ||
| Other | 0% | ||
| Don’t know | 9% |
3.1 - Questionnaire
Screening demographics
Base: All respondents
SINGLE RESPONSE
S1A. Do you currently rent your home?
This might be either from a social housing landlord (i.e. local authority/council or housing association/trust) or private landlord. This does not include part renting / part buying from a social housing provider as part of a shared ownership.
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes – From a local authority/council | ||
| 2 | Yes – From a housing association/trust | ||
| 4 | Yes – From a private landlord | SCREENOUT | |
| 3 | No | S1B | |
| 97 | Don’t know | S1B |
Base: Those who said No or Don’t know at S1A
SINGLE RESPONSE
S1B. Do you currently have shared ownership of your home ie part renting/part buying from a social housing provider?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No | SCREENOUT | |
| 97 | Don’t know | SCREENOUT |
Base: All respondents
ASK ALL, OPEN RESPONSE
S2A. What is your full postcode? Please note that this information will only be used by BMG Research to explore geographical variation in responses. No respondent will be identifiable from this data. Your postcode will not be passed back to the Regulator of Social Housing.
Please include a space where applicable, e.g., M1 5EU
[_________________]
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 98 | Prefer not to say | FIX, EXCLUSIVE | GO TO S2c |
Base: All who provided postcode at S2A
ASK ALL, OPEN RESPONSE
S2b. And what is the first line of your address? Please note that this information will only be used by BMG Research to ascertain property age and explore variation in responses by this. This data will be kept completely confidential and not passed back to the Regulator of Social Housing.
[_________________]
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 98 | Prefer not to say | FIX, EXCLUSIVE | GO TO S3 |
Base: If online or CATI and refused postcode at S2A
SINGLE RESPONSE
S2C. Which region do you live in?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | East Midlands | ||
| 2 | East of England | ||
| 3 | London | ||
| 4 | North East | ||
| 5 | North West | ||
| 6 | South East | ||
| 7 | South West | ||
| 8 | West Midlands | ||
| 9 | Yorkshire & the Humber | ||
| 10 | Outside of England | SCREENOUT |
Base: All respondents
SINGLE RESPONSE
S3. What type of property do you live in?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Terraced house | ||
| 2 | Semi-detached house | ||
| 3 | Detached house | ||
| 4 | Bungalow | ||
| 5 | Converted flat | ||
| 6 | Purpose built flat, low rise (less than six storeys high) | ||
| 7 | Purpose build flat, high rise (at least six storeys high) | ||
| 95 | Other (please specify) | OPEN RESPONSE | |
| 97 | Don’t know | ||
| 98 | Prefer not to say |
IF SHARED OWNER (S1B=YES): For reference when we refer to ‘landlord’ we mean the social housing provider you bought/are buying your property from.
Base: All respondents
SINGLE RESPONSE
S4. What is the name of your landlord?
BRING UP A LIST OF PROVIDERS AS PEOPLE TYPE (see separate list of landlords)
| Fixed codes | Answer list | Scripting notes | Routing |
| — | — | — | — |
| | WRITE IN | | |
| 95 | Other (please specify) | | |
| 97 | Don’t know | | |
| 98 | Prefer not to say | | |
INTRO TEXT
Now some questions about you to make sure we have captured views from a cross section of [tenants/shared owners based on S1A/B]. We recognise that you might consider some of these questions to be personal or sensitive, in which case you are free not to answer them. If you provide information and then wish to withdraw your consent you can do this at any time by contacting 0800 358 0337 or [email protected].
The information you provide will be used for the sole purpose of helping to understand how views vary among [tenants/shared owners based on S1A/B] across England.
Base: All respondents
OPEN RESPONSE, FORCE NUMERIC, SCREEN OUT IF < 16, CAP AT 110
S5A. Please can you tell me your age at your last birthday?
Please type your response in the box below
[_________________]
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 98 | Prefer not to say | FIX, EXCLUSIVE | GO TO S5B |
Base: Where do not want to provide exact age (S5A = 98)
SINGLE RESPONSE
S5B. Can you tell us which band your age falls within?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Under 16 | close | |
| 2 | 16 to 24 | ||
| 3 | 25 to 34 | ||
| 4 | 35 to 44 | ||
| 5 | 45 to 49 | ||
| 6 | 50 to 54 | ||
| 7 | 55 to 64 | ||
| 8 | 65 to 74 | ||
| 9 | 75+ | ||
| 98 | Prefer not to say |
Base: All respondents
SINGLE RESPONSE
S6A. Do you have any physical or mental health conditions or illnesses lasting or expected to last 12 months or more?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | S6B | |
| 2 | No | S7 | |
| 98 | Prefer not to say | S7 |
Base: Those who said Yes at S6A
SINGLE RESPONSE
S6B. Do any of your conditions or illness reduce your ability to carry out day-to-day activities?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes, a lot | ||
| 3 | Yes, a little | ||
| 3 | Not at all | ||
| 98 | Prefer not to say |
Base: All respondents
SINGLE CODE
S7. How would you describe your ethnicity?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| White | heading not Code | ||
| 1 | English, Welsh, Scottish, Northern Irish or British | ||
| 2 | Irish | ||
| 3 | Gypsy or Irish Traveller | ||
| 4 | Roma | ||
| 5 | Any other white background (please specify) | OPEN RESPONSE | |
| Asian or Asian British | heading not Code | ||
| 6 | Indian | ||
| 7 | Pakistani | ||
| 8 | Bangladeshi | ||
| 9 | Chinese | ||
| 11 | Any other Asian background (please specify) | OPEN RESPONSE | |
| Black, Black British, Caribbean or African | heading not Code | ||
| 12 | African | ||
| 13 | Caribbean | ||
| 14 | Any other Black/ African/ Caribbean background (please specify) | OPEN RESPONSE | |
| Mixed or multiple ethnic groups | heading not Code | ||
| 15 | White and Black Caribbean | ||
| 16 | White and Black African | ||
| 17 | White and Asian | ||
| 18 | Any other Mixed/ Multiple ethnic background (please specify) | OPEN RESPONSE | |
| Other ethnic group | heading not Code | ||
| 19 | Arab | ||
| 95 | Any other ethnic group (please specify) | OPEN RESPONSE | |
| 97 | Don’t know | ||
| 98 | Prefer not to say |
Section A: TSM questions
INTRO TEXT
To begin we’d like to ask some questions about the service your landlord provides. [IF RENT FROM LA/COUNCIL AT S1A : Please note this in relation to the housing service provided by the local authority/council and does not include other council services such as refuse collection or recycling.] [IF SHARED OWNER AT S1B: Just to remind you when we refer to ‘landlord’ we mean the social housing provider you bought/are buying your property from.]
Base: All respondents
SINGLE RESPONSE
TP01. Taking everything into account, how satisfied or dissatisfied are you with the service provided by your landlord?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 99 | No response |
Base: All respondents
OPEN RESPONSE
TP01A. Why are you [Feed in response from previous question] with the service provided by your landlord?
Please answer in the box below
[_________________]
| 97 | Don’t know | ||
|---|---|---|---|
| 98 | Prefer not to say |
Base: All tenants (S1A=YES)
SINGLE RESPONSE
TP02. Has your landlord carried out a repair to your home in the last 12 months?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No | ||
| 99 | DO NOT READ OUT – No response |
Base: All who have had a property repair (TP02 = 1)
SINGLE RESPONSE
TP02B. How satisfied or dissatisfied are you with the overall repairs service from your landlord over the last 12 months?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 99 | No response |
Base: All who are not satisfied with their property repair (TP02B = 3-5)
MULTI RESPONSE, randomise rows
TP02C. What are the main reasons why you are not satisfied with the repairs service?
Please select all that apply
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Time and effort to report repairs | ||
| 2 | The time taken before work starts | ||
| 3 | The speed of completion | ||
| 4 | The attitude of staff or workers | ||
| 5 | The overall quality of work | ||
| 6 | Not being kept informed throughout the process | ||
| 7 | Not feeling listened to | ||
| 8 | Work is incomplete | ||
| 9 | Work has not started | ||
| 10 | Not considering disabilities or vulnerabilities | ||
| 95 | Other (please specify) | OPEN RESPONSE BOX | |
| 97 | Don’t know | Fix, EXCLUSIVE | |
| 98 | Prefer not to say | FIX, EXCLUSIVE |
Base: All who have had a property repair (TP02 = 1)
SINGLE RESPONSE
TP03. How satisfied or dissatisfied are you with the time taken to complete your most recent repair after you reported it?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 99 | No response |
Base: All tenants (S1A=YES)
SINGLE RESPONSE
TP04. How satisfied or dissatisfied are you that your landlord provides a home that is well maintained?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 99 | No response |
Base: All respondents
SINGLE RESPONSE
TP05. Thinking about the condition of the property or building you live in, how satisfied or dissatisfied are you that your landlord provides a home that is safe?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 97 | Not applicable/ don’t know |
Base: All respondents except those who said not applicable/don’t know at TP05
OPEN RESPONSE
TP05b. What are the main reasons why you are [RESPONSE FROM TP05] that your landlord provides a home that is safe?
Please answer in the box below
[_________________]
| 97 | Don’t know | ||
|---|---|---|---|
| 98 | Prefer not to say |
Base: All respondents
SINGLE RESPONSE
TP06. How satisfied or dissatisfied are you that your landlord listens to your views and acts upon them?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 97 | Not applicable/ don’t know |
Base: All respondents
SINGLE RESPONSE
TP07. How satisfied or dissatisfied are you that your landlord keeps you informed about things that matter to you?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 97 | Not applicable/ don’t know |
Base: All respondents
SINGLE RESPONSE
TP08. To what extent do you agree or disagree with the following “my landlord treats me fairly and with respect”?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Strongly agree | ||
| 2 | Agree | ||
| 3 | Neither agree nor disagree | ||
| 4 | Disagree | ||
| 5 | Strongly disagree | ||
| 97 | Not applicable/ don’t know |
Base: All respondents
SINGLE RESPONSE
TP09. Have you made a complaint to your landlord in the last 12 months?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No | ||
| 99 | No response |
Base: All who have made a complaint (TP09 = 1)
SINGLE RESPONSE
TP09B. How satisfied or dissatisfied are you with your landlord’s approach to complaints handling?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 99 | No response |
Base: All who have made a complaint (TP09 = 1)
INTRO
If you have made more than one complaint in the last twelve months, please think about your most recent complaint when answering the following questions.
Base: All who have made a complaint (TP09 = 1)
OPEN RESPONSE
TP09c. What was the nature of your complaint?
Please answer in the box below
[_________________]
| 97 | Don’t know / Can’t remember | ||
|---|---|---|---|
| 98 | Prefer not to say |
Base: All who have made a complaint (TP09 = 1)
SINGLE RESPONSE
TP09D. Did your landlord provide a written response to your complaint?
INCLUDE AS INFO IF HOVER/CLICK ON WRITTEN RESPONSE: This would be a letter or email explaining your landlord’s decision having considered your complaint.
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No | ||
| 3 | My complaint is ongoing | ||
| 97 | Don’t know |
Base: All who have made a complaint (TP09 = 1)
OPEN RESPONSE
TP09E. How, if at all, could your landlord have improved the way your complaint was handled?
Please answer in the box below
[_________________]
| 96 | Could not have been improved | ||
|---|---|---|---|
| 97 | Don’t know | ||
| 98 | Prefer not to say |
Base: All respondents
SINGLE RESPONSE
TP10. Do you live in a building with communal areas, either inside or outside, that your landlord is responsible for maintaining?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No | ||
| 97 | Don’t know |
Base: All who have a communal area (TP10 = 1)
SINGLE RESPONSE
TP10B. How satisfied or dissatisfied are you that your landlord keeps these communal areas clean and well maintained?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 99 | No response |
Base: All respondents
SINGLE RESPONSE
TP11. How satisfied or dissatisfied are you that your landlord makes a positive contribution to your neighbourhood?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 97 | Not applicable/ don’t know |
Base: All respondents
SINGLE RESPONSE
TP12. How satisfied or dissatisfied are you with your landlord’s approach to handling anti-social behaviour?
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Very satisfied | ||
| 2 | Fairly satisfied | ||
| 3 | Neither satisfied nor dissatisfied | ||
| 4 | Fairly dissatisfied | ||
| 5 | Very dissatisfied | ||
| 97 | Not applicable/ don’t know |
Base: All respondents
SINGLE RESPONSE
TP12B. Have you reported anti-social behaviour to your landlord in the last 12 months?
Please select all that apply
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No | ||
| 99 | No response |
Base: All tenants (S1A=YES)
MULTIPLE RESPONSE – MAXIMUM 3, RANDOMISE ROWS
TP13A. In which areas would you most like your landlord to improve the service you receive?
Please select up to 3
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Repairs service | ||
| 2 | Keeping your home well maintained | ||
| 3 | Providing a home that is safe | ||
| 4 | Listening to your views and acting upon them | ||
| 5 | Keeping you informed about things that matter to you | ||
| 6 | Treating you fairly and with respect | ||
| 7 | Approach to complaints handling | ||
| 8 | Keeping communal areas clean and well maintained | ||
| 9 | Making a positive contribution to your neighbourhood | ||
| 10 | Approach to handling anti-social behaviour | ||
| 11 | No improvements needed | FIX, EXCLUSIVE | |
| 95 | Other (please specify) | OPEN RESPONSE BOX | |
| 97 | Don’t know | FIX, EXCLUSIVE |
Base: All with shared ownership (S1B=YES)
MULTIPLE RESPONSE – MAXIMUM 3, RANDOMISE ROWS
TP13B. In which areas would you most like your landlord to improve the service you receive?
Please select up to 3
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Repairs to your building | ||
| 2 | Providing a home that is safe | ||
| 3 | Listening to your views and acting upon them | ||
| 4 | Keeping you informed about things that matter to you | ||
| 5 | Treating you fairly and with respect | ||
| 6 | Approach to complaints handling | ||
| 7 | Keeping communal areas clean and well maintained | ||
| 8 | Making a positive contribution to your neighbourhood | ||
| 9 | Approach to handling anti-social behaviour | ||
| 10 | Process of purchase or staircasing | ||
| 11 | No improvements needed | FIX, EXCLUSIVE | |
| 95 | Other (please specify) | OPEN RESPONSE BOX | |
| 97 | Don’t know | FIX, EXCLUSIVE |
Section B: Demographics
INTRO TEXT
Thank you for your responses. Now some final questions about you to make sure we have captured views from a cross section of [tenants/shared owners based on S1/S1A/B]. We recognise that you might consider some of these questions to be personal or sensitive, in which case you are free not to answer them. If you provide information and then wish to withdraw your consent you can do this at any time by contacting 0800 358 0337 or [email protected].
The information you provide will be used for the sole purpose of helping to understand how views vary among [tenants/shared owners based on S1/S1A/B] across England.
Base: All respondents
SINGLE RESPONSE
B1A. What is your sex? A question about gender identity will follow.
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Female | ||
| 2 | Male | ||
| 98 | Prefer not to say |
Base: All respondents
SINGLE RESPONSE
B1B. Is the gender you identify with the same as your sex registered at birth?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No (write in gender identity) | OPEN RESPONSE | |
| 98 | Prefer not to say |
Base: All respondents
SINGLE RESPONSE
B2. How many bedrooms are available for use only by this household? Please include all rooms built or converted for use as bedrooms.
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | One | ||
| 2 | Two | ||
| 3 | Three | ||
| 4 | Four or more | ||
| 98 | Prefer not to say |
Base: All respondents
OPEN RESPONSE, WRITE IN UP TO 20
B3. How many adults, including yourself, currently live in your household?
_______ adults (18+) [answer option must be 1 or higher]
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 98 | Prefer not to say |
B3B. Are there any children that currently live in the household?
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ASK B3C. | |
| 2 | No | ||
| 98 | Prefer not to say |
B3c. How many children currently live in your household?
_______ children (Under 18) [answer option must be 1 or higher]
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 98 | Prefer not to say |
Base: All respondents
SINGLE response
B4. Which of the following best describes your current employment status?
If you have more than one source of employment, please think about the one which you spend the most time doing.
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Employed (full-time, part-time or self-employed) | ||
| 2 | Studying or on a training programme | ||
| 3 | Unemployed or long-term sick | ||
| 4 | Looking after home / Homemaker | ||
| 5 | Retired | ||
| 6 | Unpaid work for a business, community or voluntary organisation | ||
| 95 | Other (please specify) | ||
| 98 | Prefer not to say |
Base: All respondents
SINGLE response
B5. Finally, BMG Research and the Regulator of Social Housing are looking to conduct some further research and explore some of the responses provided in more detail. Would you be happy for BMG Research to contact you again in the next 12 months as part of this follow up research?
Please select one only
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No |
Base: All respondents
SINGLE response
B6. Would you also be happy for the Regulator of Social Housing, or a research organisation working on their behalf, to contact you again in the next 12 months as part of any follow up research they may be doing?
Please note this would involve passing your survey responses along with your name and contact details back to them. This would be transferred and stored securely in line with the Regulator’s privacy notice which is available on their website (Regulator of Social Housing privacy notice - GOV.UK www.gov.uk).
Your name and contact details will be held by them for no longer than 12 months and will be disposed of securely. If you wish to withdraw your consent at any time please contact [email protected]
Please select all that apply
| Fixed codes | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No |
IF B5 OR B6 = 1
B7. Thank you. Please can I take your name, email address and/or contact telephone number.
Name: [OPEN]
Email address: [OPEN]
Contact number: [OPEN]
CLOSING TEXT
You have reached the end of the survey. Thank you for taking the time to answer our questions. Your input is really appreciated.
[ONLINE: Please click next to submit your responses.]
[CATI: Just to remind you may name is ^GetCatiInterviewerName()^ calling from BMG Research.]
If you’re unhappy with the service from your landlord you should first follow your landlord’s complaints process whether it’s a housing association or local authority.
If you wish to know more about how to make a complaint about your landlord you can find more details on this on the Housing Ombudsman’s website or you can contact them on 0300 111 3000. The Housing Ombudsman Service is a free, independent and impartial service which helps to resolve individual tenant complaints.]
SURVEY EXPERIENCE PILOT
[ONLINE/CATI:
p1. With regards to the survey, how much do you agree or disagree with the following statements?
STATEMENTS
The survey was easy to understand
The length of the survey was appropriate
The survey flowed well
The survey was repetitive
Please select one only
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Strongly agree | ||
| 2 | Agree | ||
| 3 | Neither agree nor disagree | ||
| 4 | Disagree | ||
| 5 | Strongly disagree | ||
| 97 | Not applicable/ don’t know |
p2A. Were there any questions you found difficult to answer?
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No |
p2B. Which question was this and why?
Please answer in the box below
[_________________]
p3A. Do you have any suggestions to improve the survey?
| Code | Answer list | Scripting notes | Routing |
|---|---|---|---|
| 1 | Yes | ||
| 2 | No |
p3B. Please specify?
Please answer in the box below
[_________________]
1: BMG used an adjusted GVIF values (GVIF^(1/(2*Df)) to consider the different number of categories in each variable ↩