Summary regression report 2024
Delivering better value for money
Applies to England
Documents
Details
Contents
- Executive summary
- Introduction
- Key factors influencing VFM performance
- Key findings from the analysis
- How should the data in this report be used?
Executive summary
Value for Money (VFM) is critical to the sector in achieving its objectives. Making efficient use of resources and running effective organisations allows Private Registered Providers (providers) to achieve efficient service delivery for their tenants as well as other important objectives such as improving the quality of their existing property and delivering new homes for those most in need.
The regulator of social housing has a statutory objective to ensure that providers perform their functions efficiently, effectively and economically and that value for money is obtained from the public investment in social housing.
The VFM Standardfootnote 1 places an expectation on boards to be accountable for their decisions. Boards must ensure appropriate strategies are in place to maximise value for money in meeting their objectives.
Providers are also required to report against a range of VFM measuresfootnote 2 which we publish annually. This encourages transparency and consistency and enables meaningful comparison with similar organisations.
Since collecting VFM performance data, our analysis has consistently shown that there is a wide range of VFM performance across the sector. In 2018, we conducted regression analysis on the range of VFM metrics to understand the factors influencing VFM performance based on data that was available to us at that time. This analysis concluded that some of the variation in performance could be explained by measurable factors such as supported housing, region of operation, deprivation and Large-Scale Voluntary Transfer (LSVT) status. Moreover, it concluded that only half of the variation relating to providers’ headline costsfootnote 3 could be explained by some of these factors.
Since we last published comparable analysis in 2018, the sector has changed significantly. There has been continued sector consolidation, with a series of mergers between the largest organisations. The sector’s financial position is now weaker than it was in 2018, and expenditure both at an aggregate level, and per property, has risen sharply. Between 2018 and 2022footnote 4, the sector has juggled with a number of competing pressures and challenges. During the early half of this period, the sector was subject to a different rent settlement, meaning providers were required to reduce rent on most properties by 1% per annum between 2016 and 2020. Rent increases were permitted to rise by CPI+1% pa again from 2020 onwards, however this was offset by higher reinvestment costs relating to fire remediation and building safety costs, energy efficiency, damp and mould, and a range of other stock quality pressures.
Over the same period, we continued to expand our data collection requirements to provide us with valuable insights about providers’ stock characteristics.
We have long suspected that this additional data could improve our understanding and help to explain the most significant factors that drive the wide range of reported VFM performance across the sector. A key aim of our analysis has been to assess whether newly available data on key factors such as stock age and stock height would advance on previously published results. We are now pleased to share this analysis with our stakeholders.
In summary we found evidence that:
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The inclusion of new data on stock characteristics in the analysis, does advance our understanding of the range of factors influencing value for money performance across the sector.
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Around 70% of providers’ cost variation across England can be explained by factors including building height, proportions of supported housingfootnote 5 and regional wages. This compares to around half the variation in 2018. Measurable factors influencing performance variation across all other VFM metrics included in the analysis have also increased the explanatory power of the model but to a lesser extent.
- The analysis goes some way to explaining the headline cost associated with providing homes in different types of buildings. Homes in a block of more than six storeys are associated with a cost of £10,710 per unitfootnote 6, compared to a house or bungalow which will expect to have a cost of £2,850 per unitfootnote 7. The cost of a home in a block fewer than six storeys is also associated with significantly higher costs compared to the cost of a house or bungalow.
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Providers with older stock are associated with higher levels of reinvestment and gearing and will expect to have lower operating margins. The age of stock has no statistically significant relationship with the cost of running a social housing home.
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The relationship between regional wages and new supply and between deprivation and reinvestment were both found to be statistically significant for the first time. Whilst the average wage gap between London and the national average has narrowed slightly since 2018, London basedfootnote 8 providers are associated with lower levels of reinvestment and new supply and continue to have higher costs. Meanwhile, providers operating in the most deprived local authorities in England were found to have lower levels of reinvestment compared to providers operating in the least deprived local authorities in England.
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Evidence suggests that cross-subsidisation into new social homes is being delivered by providers with higher proportions of income derived from non-social housing activities. However, these providers are associated with higher costs and lower operating margins and higher levels of gearing.
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LSVT providers less than 12 years old continue to be associated with higher levels of reinvestment, lower gearing and lower operating margins.
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There is less clear evidence that the size of a provider impacts VFM metric performance. There is a statistically significant relationship between headline costs and size of provider; costs are expected to decrease by £13 per 1,000 unit increase in total social stock. This slight inverse relationship is being driven by providers with fewer than 40,000 homes. On the other hand, for providers with greater than 40,000 homes, the headline cost begins to rise. No other statistically significant relationship was found between total social stock and any other VFM metric.
The analysis concludes that while the range of observable factors included in the model can account for much of the variation between landlords, especially on headline cost, there is still potential for the sector to operate more efficiently within the context of their organisation. There is still a significant proportion of cost variation that cannot be explained by the model, and organisations with similar characteristics still demonstrate a range of reported performance on their VFM metrics.
More fundamentally, it is important that boards consider not just the costs of running their businesses, and how they use their assets, but also what outcomes they achieve from those inputs. At the heart of our new consumer framework, introduced in April 2024, is a suite of Tenant Satisfaction Measures (TSMs)footnote 9 which provide valuable insight into the range of outcomes delivered by different landlords for their tenants. Organisations with similar stock and cost profiles may have very different outcomes. Boards should consider the full range of their performance data to ensure that they have a rounded view of the economy, efficiency, and the effectiveness of their business.
In combination, the VFM and TSM measures should help focus the important debate about operational and strategic choices that boards make. We encourage management to use this information to drive greater value for money and build a stronger evidence base in that helps them go further in delivering their organisation’s strategic objectives. Management must recognise that boards can’t act on information they don’t have. Performance data is a pivotal tool, it helps to focus attention where it needs to be and shines a light on the opportunities available to generate greater efficiencies and improve on overall service quality and outcomes.
Introduction
The primary purpose of this report is to provide an overview of our latest regression analysis. The report provides information and detail relating to the most statistically significant factors influencing VFM performance across the sector.
Crucially, the analysis in this report is a helpful tool that will enable providers to extend their understanding of VFM performance with a particular focus on the outcomes achieved compared to the range of resources available to them.
The analysis aims to improve transparency and accountability in relation to value for money performance and we expect providers to capitalise on the opportunities that this report provides to explain their performance against their strategic objectives to stakeholders.
Scope and approach
A fuller explanation of our analysis is provided in the accompanying VFM Regression Technical Reportfootnote 10. The regression analysis considers the statistical relationship between reported performance on some of the key VFM metrics and observable factors that are relevant to the social housing sector. The analysis focuses on the following VFM metrics which previouslyfootnote 11 had the highest explanatory power, including:
- Reinvestment %
- New Supply (social) %
- Gearing %
- Headline Social Housing Cost (HSHC) £
- Operating Margin (overall) %
- Return on Capital Employed (ROCE) %
Regression analysis is a useful statistical tool in estimating the relationship between a set of explanatory variables such as supported housing or building height and a dependent variable – in this case, providers’ VFM metrics, and is limited by the data that is available to test. Inevitably, there will be other important contextual information which has not been captured in our analysis and which may explain part of the remaining variation in VFM performance.
Regression analysis is also an important technique in data analysis and can be very effective in many scenarios. However, there are limitations to its use. As such, analysis results reported in this publication should be interpreted with care. While most of the sector provide similar types of services, some other providers employ complex business models which regression analysis cannot capture. As such, there is no right single value that applies to all providers. The values that the regression analysis produces should not be taken to be a target for providers to aim at.
Equally, the analysis is based on a single year’s data, which means it is only a representative snapshot of the sector’s activity for the year related to 2022footnote 12. This is an important point to consider given the cyclical nature of the sector’s activities including asset management cycles and ongoing investment towards meeting energy efficiency and decarbonisation targets. The timing of such programmes of works will vary between providers. Other considerations include merger activity in which providers seek to take advantage of synergies and economies of scale. Many planned objectives and outcomes of these mergers take time to be realised, for example, the replacement of high-cost resources such as IT infrastructure and therefore may not be evident in a single year’s data.
Data methodology and context
The analysis is based on a cross-sectional dataset of VFM metrics and explanatory variables. This is complete for the vast majority of providers who own and/or manage at least 1,000 homes. The analysis is based on financial data submitted by providers relating to the period ended March 2022 and previously published by the regulatorfootnote 13. It is also based on providers’ data collected as part of the Statistical Data Return (SDR)footnote 14 relating to the same period. Other related data underpinning the analysis is set out in the Technical Report that accompanies this publication.
Data is complete for 198 providers. For-profit providers have been removed from the analysis due to the nature of their different business models, while most also have different accounting periods. Outliers referred to in this publication relate to influential data points that can skew results and mislead findings. On that basis, some providers have been excluded from a small number of VFM metrics as a part of the analysis.
At each stage of the analysis, extensive quality assurance processes have been undertaken. The methodology and statistical process has also been verified and validated externally by government analysts.
Key factors influencing VFM performance
This section provides an overview of the explanatory variables that were tested in the analysis. A detailed description of their measurements are reported on further in the supporting Technical Report.
Since the principal focus of our analysis is the regulator’s suite of VFM measures, it is also worth noting the importance of the numerator and denominator when determining results. Specifically, the reinvestment metric and gearing metric which can both be influenced by the value of provider’s social housing assets. In such instances, the impact is explained in the report.
The explanatory power of the regression model is shown in Table 1 below and identifies the most influential variables that impact the VFM metrics. A positive figure represents a positive relationship, where an increase in the explanatory variable (a provider characteristic or regional factor) is associated with an increase in the dependent variable (VFM metric). For example, larger proportions of supported housing are associated with a higher headline social housing cost. A negative figure represents a negative relationship; an increase in the independent variable is associated with a decrease in the dependent variable. For example, larger proportions of houses or bungalows are associated with a lower headline social housing cost.
Geographical Location – regional wages and local authority deprivation
The regional wage index is a measure of the spread of annual salaries between regions, taken from the Annual Survey of Hours and Earnings (ASHE) in comparison to the national average. Providers in London operate in the region with the highest average annual salaries, this compares to the North East region which has the lowest. The North West and the Midlands regions have seen wages rise in relative terms and moved closer to the national average whilst in relative terms Yorkshire and The Humber and East of England regions have seen wages fall further behind the national average since 2018.
Figure 1: Regional Wage Index
| Region | Wage index |
|---|---|
| England | 1 |
| North East | 0.911 |
| North West | 0.959 |
| Yorkshire & the Humber | 0.923 |
| East Midlands | 0.952 |
| West Midlands | 0.96 |
| East of England | 0.974 |
| London | 1.236 |
| South East | 1.023 |
| South West | 0.942 |
The level of deprivation of the areas a provider operates in has been calculated by taking the local authority district summaries from the 2019 Index of Multiple Deprivation (IMD) and applying this to the proportion of homes owned in each local authority to give each provider a weighted deprivation percentile rank.
The sector as a whole is concentrated in relatively deprived areas in England, with the median group of providers operating in local authorities that are in the 38% most deprived local authorities in England according to the IMD.
Supported housing and housing for older people
Supported housing (as defined here) and housing for older people (policy statement on rents for social housing) providers are defined as having greater than 30% supported housing or housing for older people homes as a proportion of total stock. Providers with higher proportions of supported housing and housing for older people will typically have different reported performance outcomes compared to the rest of the sector due to the wide range of specialist services and activities they offer. Depending on the range of services and activities provided, these providers are often associated with higher headline costs.
Stock characteristics
Stock height is a new variable and assesses the impact that the proportion of different stock types have on VFM metric performance. The measure is split into three discrete categories, with homes in a block fewer than six storeys excluded from the final model:
- House or bungalow
- Homes in a block fewer than six storeysfootnote 15
- Homes in a block of six or more storeys in heightfootnote 16.
The proportion of homes in a block fewer than six storeys has been excluded from the analysis to avoid interference with the overall results. Additional information on the methodology and the statistical reason for excluding this variable is set out in the accompanying regression Technical Report. However, the impact of this type of stock height is reported on in relevant parts of this reportfootnote 17.
The average stock age [LINK TO TECHNICAL NOTE] is a new variable which assesses the impact average stock age has on the VFM metrics. The median age of stock in the sector is 48 years old. Stock age is calculated using the midpoint of the categories defined in the SDR, with the oldest category classed as pre-1919 and newest as post-2020. Calculations in these categories have used the years 1918 and 2021 due to midpoints not being available. As such, homes may have been constructed prior to 1918 and more recently than 2021.
The average property size is also a new variable and assesses the impact that the average size of a provider’s property has on the VFM metrics. The measurement is based on average bedroom size [LINK TO TECHNICAL NOTE] - the median number of bedrooms in the sector is 2.04.
Large-Scale Voluntary Transfers (LSVT)
An LSVT is defined as a provider where over 50% of their stock has been obtained through a transfer from a local authority. A provider which is not classified as an LSVT is defined as a traditional provider unless otherwise stated.
The previously published analysis in 2018 had three categories for LSVTs: less than seven years old, seven to 12 years old and greater than 12 years old. The new analysis found providers greater than 12 years exhibit similar characteristics to a traditional provider and are now classified as such. There are no remaining LSVTs less than seven years old in the sector, as the most recent stock transfers from the local authority sector took place in early 2015. Only LSVTs that are less than 12 years old are included in our analysis, of which there were eight providers in 2022.
Income generated from Non-Social Housing Lettings (SHL) activities
The percentage of income generated from non-SHL activity is a new variable tested to measure whether there is any statistical relationship with VFM performance. It is also tested to assess the impact of cross-subsidisation on the VFM metrics. While the vast majority of providers are designated as not-for-profit organisations, which covers all providers included in this piece of analysis, most large providers will generate income from non-social housing activities which enables them to cross subsidise their core social housing activities. These activities can include for example, student accommodation, or homes built for outright sales. It can also include the provision of nursing homes and non-social support services. The calculation can be found in the Technical Report. The median percentage of income generated from non-SHL activity is 16%.
Organisational Size
The size of a providerfootnote 18 is an important variable which assesses the variability of performance between different size bands. The size of groups ranges between 1,020 to 109,800 homes in the analysis, with a median of 6,830 homes. The impact of the total social stock variable is reported on under Additional analysis section in this report.
Table 1: Overview of regression analysis
| VFM Metric | Final Model | Most influential significant factors* | ||
|---|---|---|---|---|
| % of variation explained by the variables** | First | Second | Third | |
| Reinvestment | 36% | Average stock age + |
Wage index - |
LSVT <12 Years + |
| New Supply (Social) | 32% | % Non-SHL income + |
Wage index - |
IMD - |
| Gearing | 30% | % SH - |
% Non-SHL income + |
IMD - |
| Headline Social Housing Cost | 70% | % SH + |
% Houses or bungalow - |
% Homes in blocks of six or more storeys + |
| Operating Margin (Overall) | 48% |
IMD - |
Average Property Size + |
% Non-SHL income - |
| ROCE | 26% | Wage Index - |
Average property size + |
IMD - |
*Most influential factors from the final model using standardised coefficients which are statistically significant at 90%+. These are the variables with the most positive or negative standardised coefficient and therefore considered the most influential.
**This is the r-squared value, which is a measure of the percentage of variation in the VFM metrics that can be explained by the independent variables.
Key findings from the analysis
The baseline results referenced in this section are based on median values of all explanatory variables and a provider with 100% GN units. These outputs should be interpreted with care and must not be used as a precise calculation for business planning purposes. Outputs with references to the baseline are intended as a demonstration to show the impact a variable’s relationship has with the VFM metric only.
Baseline definitions
The baseline definition for results are as follows:
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Traditional provider (non-LSVT)
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Median total social stock units (6,830 homes)
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100% general needs stock
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Operating in a median deprivation level based on the dataset of providers (median percentile: 0.63)
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Operating in a region of the England average wage (index: 1.00)
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Holding a median proportion of house/bungalows (58%)
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Holding a median proportion of homes in blocks six or more storeys (1%)
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Holding a median proportion of homes in blocks less than six storeys (41%)
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Holding a median average property age (48 years)
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Holding a median average property size (2.04 bedrooms)
*Holding a median proportion of non-SHL income (16%)
Reinvestment
Around 36% of the variation in reinvestment can be explained by observable factors compared to 23% in 2018. The analysis found that the average stock age has a significant impact on levels of reinvestment. Providers with an older average stock age are associated with higher levels of reinvestment which is driven by providers with older housing stock who have considerably lower Net Book Values (NBVs)footnote 19 – the denominator of the reinvestment measure. A provider with an average age of 62 years will have a reinvestment level which will be 63% higher compared to a provider with an average stock age of 33 years. However, there is no significant difference in the reinvestment spend on a per unit basis between providers with younger housing stock and those with older housing stock.
Regional wage is now also a factor that explains part of the variation in reinvestment across England – this variable had no statistically significant relationship with the reinvestment measure in 2018. A provider with 100% of its stock based in the North East of England will expect a reinvestment level that is 110% higher compared to a London based provider. However, the relationship is driven by a higher average NBV per unit, leading to a larger denominator which significantly lowers the result for London. London based providers spend more on reinvestment into homes on a per unit basis compared to the rest of the country, which is reflective of their stock profile, specifically higher proportions of buildings that are seven or more storeys in height. The impact of the denominator is more significant however, leading to London based providers being associated with lower levels of reinvestment.
There are only a small minority of LSVT organisations who are defined as less than 12 years old. This group of providers continue to be associated with higher levels of reinvestment. LSVTs would expect to have reinvestment levels that are 74% higher compared to a traditional provider. These providers are required to invest more into existing stock post transfer. They also have a lower NBV which also impacts the denominator and further drives this higher result.
Housing for older people and supported housing providers were also found to be statistically significant factors for reinvestment. A housing for older people provider and a supported housing provider would each expect to have reinvestment levels that are 20% lower compared a provider with 100% general needs homes. This is driven by lower levels of reinvestment in new development activity rather than reinvestment into existing stock. The level of reinvestment expected for providers with higher proportions of housing for older people is in stark contrast to previous analysis when the expected level of reinvestment was reported to be 15% higher than the sector average.
New Supply
Around 33% of the variation in new supply can be explained by key factors – this compares to just 16% in 2018. The proportion of non-SHL income has a significant impact on levels of new supply. Providers with higher proportions of income from non-core activities are associated with higher levels of new supply. Whilst the vast majority of providers are designated as not-for-profit, most large providers will generate income from non-social housing activities including student accommodation and the sale of market homes. This model enables providers to cross-subsidise into social housing activities. It is expected that for every 10-percentage point increase in non-social housing income there is a 0.3 percentage point increase in new supply. This provides evidence of the continuing role that cross-subsidy from non-social housing activity plays in supporting new supply.
Regional wages and deprivation also have a significant relationship with the level of new supply. A provider operating in London will expect to deliver 1.2-percentage point fewer homes (as a proportion of their existing housing stock) compared to a provider operating in the North East of the country. This is due to higher land values and practical challenges associated with developing at scale in London. In terms of deprivation, providers solely operating in the 1% most deprived local authority will deliver on average 1.3 percentage point fewer homes compared to a provider solely operating in the 1% least deprived local authority. Providers operating from the most deprived local authorities tend to have lower levels of rental income. Social rent in more deprived local authorities is usually aligned closer to market rent compared to less deprived local authorities, which can result in less demand for social homes in these areas.
Figure 2: New supply and deprivation (assuming 100% GN and the median of all other values).
| New supply (social) % | |
|---|---|
| Baseline | 1.50% |
| 1% most deprived local authority | 1.02% |
| 1% lease deprived local authority | 2.30% |
Gearing
A baseline provider will expect to have a gearing level of 48% (which is measured as net debtfootnote 20 as a proportion of total to social housing assets). Providers with higher proportions of homes defined as supported housing are associated with lower levels of gearing. A supported housing provider will expect to have a gearing level that is 36% lower compared to a provider with 100% general needs homes. This is driven by a small group of supported housing providers holding low levels of debt and a higher average net book value of social housing assets on a per unit basis. These providers face greater challenges due to the nature of their business models – supported housing providers hold less debt as they face significantly higher costs which can give these providers less flexibility to raise new finance to support the delivery of new homes.
A key change to previous analysis shows that higher proportions of non-SHL income is associated with higher levels of gearing. A provider which increases its proportion of non-SHL income by 10 percentage-points is associated with a 3.6 percentage point increase in gearing. Providers with higher proportions of non-SHL income are associated with higher levels of net debt per unit. Providers operating in the most deprived local authorities are also associated with lower levels of gearing - these providers average less net debt per home.
Other statistically significant but less influential relationships found that LSVT organisations will expect a gearing level of 33% lower compared to a traditional provider. Providers with a higher stock age are associated with a higher level of gearing, with a one-year increase in the average stock age associated with a 0.2 percentage point increase in the level of gearing. Providers with a higher average stock age average lower NBVs, the denominator, leading to these providers being associated with higher levels of gearing.
Headline Social Housing Cost
The explanatory power of the headline cost model has increased significantly which means we can explain around 70% of the causes of variation relating to providers’ costs. This compares to 2018 when only half of the variation could be explained. The main reason for this increased explanatory power is the availability of the new data on building height. The baseline headline cost for a social housing unit is £3,850 in the analysisfootnote 21. The most influential factors include the proportion of supported housing, the proportion of homes in a block more than six storeys in height and the proportion of houses or bungalows held by a provider. Regional wages, the proportion of non-SHL income, and the proportion of housing for older people are also shown to be statistically significant.
Each unit of supported housing is associated with costs of £8,340 above the baseline cost. Challenges facing these providers have arguably never been greater, including the cost of delivering key essential services to those most vulnerable in our society. The analysis also shows that providers with higher proportions of supported housing are associated with higher costs relating to charges for support services and service charges. The estimate relating to the cost of providing a supported housing home is sensitive to the inclusion and removal of outliers. With outliers included, the estimated additional cost compared of a supported housing home increases to £9,840 per unit compared to a general needs home.
In comparison, each unit of housing for older people is associated with an additional cost of £3,420 which is 89% higher compared to a general needs home. Providers with larger proportions of housing for older people homes are associated with higher service charge costs. With the inclusion of outliers, the additional cost of a housing for older people home is £6,620 above a general needs home.
Figure 3: Cost comparison of a supported housing and housing for older people unit vs a General Needs unit
| Supported housing | Housing for older people | |
|---|---|---|
| Analysis | +£8,340 | +£3,420 |
| Outliers included | +£1,500 | +£3,200 |
The new stock height factors have a significant impact on cost. A home classed as a house or bungalow is expected to cost £2,850, assuming the remaining baseline assumptions. Providers with higher proportions of houses and bungalows are associated with lower service charge costs.
A baseline home in a block of six storeys or more is associated with a cost of £10,710 which is 275% above the cost of a house or bungalow. These homes are associated with significantly higher maintenance and major repair costs (sometimes related to fire remediation costs) as well as higher service charge costs.
Homes in a block with fewer than six storeys are also associated with costs of £5,130, and less than half the cost of a home in a block of six or more storeys.
Figure 4: Cost comparison of stock height units, assuming 100% GN and median of all other explanatory variables
| House or bungalow | £2,850 |
|---|---|
| Home in a block of less than six storeys | £5,129 |
| Home in a block of six or more storeys | £10,707 |
Geographical location also has an impact on headline costs. London based providers continue to be associated with higher costs compared to providers based in all other regions in England. However, regional cost differences have reduced since 2018 once all factors are controlled for. A provider with 100% of their stock based in London has an average cost of £1,100 above an equivalent provider based in the North East, and £900 above an equivalent provider based in the North West or West Midlands. The expected cost difference of a unit depending on the region that a provider is based in compared to the national average is shown in figure 5.
Figure 5: Regional difference compared to national average, assuming median of all other explanatory variables
| North East | -£298 |
|---|---|
| North West | -£136 |
| Yorkshire & the Humber | -£258 |
| East Midlands | -£160 |
| West Midlands | -£133 |
| East of England | -£85 |
| London | £799 |
| South East | £80 |
| South West | -£194 |
Providers with higher proportions of non-SHL income are also associated with higher costs. These providers typically have higher development costs, service charges and ‘other social housing activities’ costs.
Operating Margin (Overall)
Operating margins are closely associated with rental income. Providers operating in the most deprived areas tend to have lower rents and are therefore associated with lower operating margins. A provider solely operating in the 1% least deprived local authority will expect to have an overall operating margin that is 60% higher than an equivalent provider solely operating in the most deprived local authority. Providers with an older average stock age and LSVTs were also found to be associated with lower operating margins, which is also driven by lower levels of rental income per unit.
Equally, providers with larger properties are associated with a higher overall operating margin. Properties with a higher average number of bedrooms are associated with higher levels of rental income. A one-room increase in the average number of bedrooms is associated with a 7.5 percentage point increase in the overall operating margin for a provider.
Providers with higher proportions of non-SHL income are associated with lower overall operating margins. These providers average a higher turnover per unit which increases the denominator.
Return on Capital Employed (ROCE)
Regional wages are the most influential factor impacting a provider’s ROCE. Providers based in London are associated with a lower return on capital employed because they have higher net book assets on a per unit basis. Average property size also influences ROCE. Larger properties tend to have higher rents, which are associated with higher operating surpluses, and therefore a higher ROCE. Providers operating in the most deprived areas of the country are also associated with a lower ROCE, for similar reasons.
Additional analysis
Organisational size
A key objective of the analysis was also to assess empirically whether there is evidence of economies of scale with respect to social housing providers. Overall, the results of this analysis show limited evidence that the size of a provider impacts VFM performance.
Total social stock (000s) is only found to have a statistically significant relationship with headline social housing costs, albeit the relationship is minimal. The analysis shows that a £13 reduction per 1,000 units is expected according to the model. There was no other statistical relationship found between the variable total social stock (000s) and any other VFM metric.
A snapshot of the 2022 headline cost data mapped against total social stock demonstrates that large providers up to around 40,000 homes exhibit lower costs. However, large providers with more than 40,000 homes exhibit higher costs, with only two of the ten largest providers in the sector exhibiting a headline cost lower than the sector median. A similar pattern was also followed in 2023.
Overall, the results of this analysis do not support simple assumptions about the relationship between provider size and efficiency. The overall picture is very complex, and many other factors must be taken into account when considering the relation between scale and performance as set out in earlier sections of this report. The landscape for the largest providers has changed significantly since the previously published regression analysis in 2018. The number of providers with greater than 40,000 social homes has increased by 129% (16 providers) between 2018 and of 2022. The number of providers with greater than 40,000 social homes and with homes located across different regions and are categorised as ‘Mixed’ has also increased from four providers to ten providers over the same period.
As part of a wider default model, two merger dummy variables were also included to test for economies of scale given the number of mergers in recent years. First, mergers that took place less than five years ago to test for short-term impacts of a merger taking place, and mergers that took place between five and ten years ago to test for longer-term impacts. There was weak evidence found of any statistically significant across the range VFM metrics.
How should the data in this report be used?
The Value for Money Standard expects providers to understand their own performance and compare their performance to their peers. The analysis in this report should help management improve their understanding of the factors influencing their organisation’s performance and to identify a comparable peer group who exhibit similar characteristics in the future. The VFM data relating to all large providers is available on our website and included in our benchmarking tool. It is important that providers consider the evidence from the VFM metrics alongside other sources of performance information; including for example the Tenant Satisfaction Measures; and their own management data.
The analysis can only provide a partial picture of the factors impacting VFM metrics performance. Headline social housing cost has the highest explanatory power but still leaves around a third of variation unexplained. The regression analysis is limited to testing what data is available, meaning there are uncaptured variables which may explain variation in the performance of VFM metrics. We expect management and board to consider the analysis in this report to assess whether the outputs and outcomes achieved are commensurate to the resources invested and to take effective action towards improving their organisation’s management strategies and performance in the context of their strategic objectives.
Annex A: VFM metrics definitions
Metric 1 – Reinvestment %
The Reinvestment metric looks at the investment in properties (existing stock and new supply) as a percentage of the value of total properties heldfootnote 22.
Measurement of VFM cost chain – efficiency
[Development of new properties (Total housing properties)
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Newly built properties acquired (Total housing properties)
-
Works to existing (Total housing properties)
-
Capitalised interest (Total housing properties)
-
Schemes completed (Total housing properties)]
Divided byfootnote 23.
[Tangible fixed assets: Housing properties at cost (Current period)
OR Tangible fixed assets: Housing properties at valuation (Current period)].
Metric 2 – New Supply delivered %
The New Supply metric sets out the number of new social housing units that have been newly constructed (acquired or developed) in the year as a proportion of total social housing units owned at period end.
Measurement of VFM cost chain – effectiveness
A. New supply delivered (Social housing units) %
[Total social housing units developed, or newly built units acquired in-year (owned)
(Social rent general needs housing (excluding Affordable Rent), Affordable Rent general needs housing, social rent supported housing and housing for older people (excluding Affordable Rent), Affordable Rent supported housing and housing for older people, Low-Cost Home Ownership, care homes, other social housing units, Social leasehold)]
Divided by
Total social housing units owned at period end (‘social units’ as defined in numerator).
Metric 3 – Gearing %
The Gearing metric assesses how much of the adjusted assets are made up of debt and the degree of dependence on debt finance. It is often a key indicator of a registered provider’s appetite for growth.
Note: Registered providers can be restricted by lenders’ covenants and therefore may not have the ability in which to increase the loan portfolio despite showing a relatively average gearing result.
Measurement of VFM cost chain – efficiency
[Short-term loans
-
Long-term loans
-
Cash and cash equivalents
-
Amounts owed to group undertakings
-
Finance lease obligations]
Divided byfootnote 24
[Tangible fixed assets: Housing properties at cost (Current period)
OR Tangible fixed assets: Housing properties at valuation (Current period)].
Metric 4 – Earnings Before Interest, Tax, Depreciation and Amortisation, Major Repairs Included (EBITDA MRI Interest Cover %)
The EBITDA MRI Interest Cover measure is a key indicator for liquidity and investment capacity. It seeks to measure the level of surplus that a registered provider generates compared to interest payable; the measure avoids any distortions stemming from the depreciation charge.
Note: Grants related to capitalised major repairs expenditure must be excluded.
Measurement of VFM cost chain – efficiency
[Operating surplus / (deficit) (overall)
- Gain/(loss) on disposal of fixed assets (housing properties)
-
Gain/(loss) on disposal of other fixed assets
- Amortised government grant
- Government grants taken to income
- Interest receivable
- Capitalised major repairs expenditure for period
- Total depreciation charge for period]
Divided by
[Interest capitalised - Interest payable and financing costs].
Metrics 5 – Headline social housing cost per unit
The Headline Social Housing Cost per unit metric assesses the headline social housing cost per unit as defined by the regulator. It is a proxy cash measure of a social housing cost per unit. This means it excludes non-cash items such as depreciation, amortisation and write downs.
Note: Grants related to capitalised major repairs expenditure must be excluded.
Measurement of VFM cost chain – economy
[Management costs
-
Service charge costs
-
Routine maintenance costs
-
Planned maintenance costs
-
Major repairs expenditure
-
Lease costs
-
Capitalised major repairs expenditure for period
-
Other (social housing letting) costs
-
Charges for support services (Operating expenditure)
-
Development services (Operating expenditure)
-
Community / neighbourhood services (Operating expenditure)
-
Other social housing activities: Other (Operating expenditure)
Divided by
Total social housing units owned and/ or managed at period endfootnote 25
(Social rent general needs housing (excluding Affordable Rent), Affordable Rent general needs housing, social rent supported housing and housing for older people (excluding Affordable Rent), Affordable Rent supported housing and housing for older people, Low-Cost Home Ownership, care homes, other social housing units).
Metric 6 – Operating margin %
The Operating Margin demonstrates the profitability of operating assets before exceptional expenses are considered. Increasing margins are one way to improve the financial efficiency of a business. In assessing this ratio, it is important that consideration is given to registered providers’ purpose and objectives (including their social objectives). Further consideration should also be given to specialist providers who tend to have lower margins than average.
Measurement of VFM cost chain – efficiency
Operating margin (overall) %
[Operating surplus / (deficit) (overall)
-
Gain/(loss) on disposal of fixed assets (housing properties)]
-
Gain/(loss) on disposal of other fixed assets
Divided by
Turnover (overall).
Metric 7 – Return on capital employed %
The Return on Capital Employed (ROCE) compares the operating surplus to total assets less current liabilities and is a common measure in the commercial sector to assess the efficient investment of capital resources. The ROCE metric supports registered providers with a wide range of capital investment programmes.
Measurement of VFM cost chain – efficiency
[Operating surplus / (deficit) (overall)
(including gain / (loss) on disposal of fixed assets)
- Share of operating surplus/(deficit) in joint ventures or associates]
Divided by
Total assets less current liabilities.
1: Value for Money Standard and Code of Practice↩
2:) Value for Money metrics – Technical note guidance↩
3: Headline Social Housing Costs (HSHC)↩
4: The regression analysis is based on data for the period ended 31st March 2022 FVAs (PRPs electronic annual accounts).↩
5: Supported Housing providers are defined as providers with greater than 30% stock classed as supported housing as a proportion of total stock owned and or managed. The definition of supported housing can be found: https://www.legislation.gov.uk/ukpga/2008/17/section/69↩
6: This is based on a baseline provider with the median of all explanatory variables and 100% of their stock classed as homes in blocks of six or more storeys. A baseline provider definition is outlined in paragraph 36.↩
7: This is based on a baseline provider with the median of all explanatory variables and 100% of their stock classed as houses or bungalows. A baseline provider definition is outlined in paragraph 36.↩
8: Region of operation is based on where greater than 50% of total stock owned and or managed is based.↩
9: Tenant Satisfaction Measures 2023-24↩
10: Value for Money metrics – Technical note guidance↩
11: VfM metrics - technical regression report September 2018↩
12: The analysis focuses on 2022 data due to the delay of other important data relating to 2023 to help inform our analysis.↩
13: 2022 Global Accounts of private registered providers↩
14: PRP sector briefing notes↩
15: Since 2023, data relating to this stock category has changed to homes in blocks of less than 18 metres or fewer than seven storeys.↩
16: Since 2023, data relating to this stock category has changed to homes in a block of 18 metres or more or has at least seven storeys.↩
17: This is to avoid violating an assumption of linear regression.↩
18: Total social stock is inclusive of all owned and managed social homes. It also includes general needs affordable rent managed units.↩
19: In accounting, NBV refers to the historical value of an organisation’s assets. For the purpose of this report, it relates to a provider’s total social housing assets.↩
20: Short term loans plus Long-term loans less Cash↩
21: It should be noted that since 2022 costs have increased due to a challenging macroeconomic and operating environment for providers. Estimates in headline cost are intended to demonstrate the impact a variable has to headline costs.↩
22: This metric is not based on cashflow data given the limitations on data collected as part of the FVA return.↩
23: Providers should use the measure agreed in their Statement of Financial Position / Balance Sheet. The figure should be either historic cost or valuation.↩
24: Providers should use the measure agreed in their Statement of Financial Position / Balance Sheet. The figure should be either historic cost or valuation.↩
25: Leasehold units, which for example include Right to Buy and fully stair-cased shared ownership units where the provider retains the freehold, are excluded from the denominator of this metric.↩