NHMF best practice

These two new best practice sections will help social landlords to maintain quality homes in an increasingly challenging economic environment. The Compliance Section has a set of topical guides summarising social landlords’ responsibilities for meeting essential statutory health and safety requirements and explain how to do this economically and efficiently. The Fuel Saving Section explains how to include a fuel saving strategy as a key aspect of an organisation’s business plan. The Guide then sets out how to develop a practical improvement strategy as an integral part of the asset management programme and how to successfully deliver the improvements and manage the risks. There are also best practice training courses which relate to this guide.

Compliance Fuel saving improvements

Chapter 2: Establishing energy-efficiency targets

Housing Stock Data

The ability of a housing organisation to develop a comprehensive and effective fuel saving (retrofit) strategy that incorporates appropriate targets is dependent on accurate knowledge of the energy performance of its housing stock. It is necessary to understand the current energy performance of the stock, what standards might be technically attainable and affordable, the improvement options that would have to be implemented in order to meet proposed targets, and what they would cost. Without this knowledge the organisation is working in the dark. 

Most housing organisations hold some form of energy data about their housing stock. In many cases these are low-precision (also known as "Level 0") Standard Assessment Procedure (SAP) energy-rating data, collected during housing stock condition surveys. Often these data have only been collected from a sample of dwellings, and have been copied ("cloned") to the records for other dwellings that are thought to be similar. Many housing organisations fail to record changes to their stock that would change the rating – usually for the better – making these data even less useful. The provenance of low-precision energy data is often unclear, and their accuracy is frequently poor.

The longstanding requirement that landlords must make Energy Performance Certificates (EPCs) available when dwellings are offered for tenancy has resulted in more accurate Reduced Data SAP (RDSAP) energy-rating data being collected by the Domestic Energy Assessors (DEAs) who carry out the assessments required for EPCs. Some housing organisations only procure the actual EPCs or the associated SAP energy ratings and A-G efficiency bands from their DEAs; others also collate the RDSAP data into their databases in order to have more accurate information about the energy efficiency of their dwellings. If the RDSAP data are collated by the housing organisation, then, as the number of dwellings for which EPCs have been issued increases, so do the scope and accuracy of the energy data. It is therefore important that housing organisations always obtain the underlying RDSAP energy-rating data, and collate them into their housing stock databases, when they procure EPCs. At present some housing organisations hold RDSAP data for only about 20% of their stock, while others have data for 80%, but overall the proportion is steadily increasing. The more RDSAP data a housing organisation holds the more accurate its housing stock assessment and improvement option evaluation will be.

Asset management data

The demands placed on housing organisations’ asset management data are continually changing but the approach to surveys and data collection has not kept pace. 

Recently, stock condition surveys have focussed on the state of repair of building elements with an emphasis on components with defined lives – windows and doors, kitchens, bathrooms, roofs, etc. Fuel saving investments, however, require a new level of information: surface areas of floors, walls, roofs and glazing, heat loss perimeters, installed ventilation, external wall finishes, verge and eaves overhangs, externally mounted utilities and rain water goods, porches, canopies, extensions, means of access, etc. Much of this can be drawn from RDSAP data but the approach to surveys and to wider information storage and management should reflect these new needs.

The current approach is often inefficient, involving multiple visits: a stock condition survey one year, an EPC a few years later, then a contractor being paid for a pre-work survey. Integrating information related to energy efficiency and fuel saving measures into stock condition surveys and databases should be a priority.   

The Standard Assessment Procedure (SAP) energy rating

The SAP energy rating of a dwelling is based on the estimated annual fuel cost for heating, hot water and fixed lighting, per unit of floor area, under a standard occupancy pattern and heating regime. The SAP is expressed on a scale of 1 (very inefficient) to 100+ (very efficient). SAP incorporates a version of the Building Research Establishment Domestic Energy Model (BREDEM), which takes into account the thermal characteristics of the building fabric, the efficiency and responsiveness of the building services and the interactions between them, as well as the contributions of any renewable energy technologies. The procedure estimates annual fuel use, fuel costs and carbon dioxide emissions using weather data for a standard year, and national average fuel costs and carbon dioxide emissions factors. All SAP assessments assume that the dwellings being assessed are located in the East Midlands region.

The SAP standard occupancy pattern was derived from English House Condition Survey data during the 1990s and assumes that the number of occupants is related to the floor area (bigger dwellings have more occupants) and that the demand for hot water is related to the number of occupants. The demand for fixed lighting depends on the floor area, the area and orientation of glazed openings, and any external shading. It is assumed that on weekdays dwellings are unoccupied during the day, when all occupants are at work or at school, so they are only heated in the mornings and evenings; at weekends dwellings are assumed to be heated throughout the day. It can be argued that this results in under-estimation of fuel use, fuel costs and carbon dioxide emissions associated with energy use in social housing, because many homes are occupied and heated during the day (by parents with young children, or by other adults who are unemployed). However, another view is that low income households do not heat their homes to the standards assumed by SAP (21oC in the living room, and between 18oC and 21oC in the rest of the house, during occupied periods), resulting in over-estimation of fuel use, fuel costs and carbon dioxide emissions. In practice these two effects seem to cancel out. Because of the variation between households (energy use has been shown to vary by a factor of five, between different households in identical dwellings) it is necessary to adopt some form of standard occupancy pattern for analytical purposes, and there is little or no evidence to support any alternative to SAP standard occupancy.

Overall, SAP provides a reasonably accurate prediction of the average fuel use, fuel costs and carbon dioxide emissions associated with a dwelling, accurate to about ±10%. SAP is not a good predictor of the fuel use, fuel costs or carbon dioxide emissions associated with an individual household occupying a home in a particular location.

Reduced Data SAP (RDSAP)

RDSAP is a "cut down" version of the SAP energy rating in which ‘least unlikely’ default values are used to replace data items that are too difficult or time-consuming for energy surveyors to collect on site (e.g. ground floor insulation and window areas).  This facilitates rapid and inexpensive energy surveys but reduces the accuracy of the predicted energy performance.  However, an experienced SAP Assessor can convert RDSAP data to Full SAP data, to support detailed analysis of energy performance, and improvement option evaluation.

Low Precision (Level 0) SAP energy ratings

A Full SAP energy rating requires hundreds of items of data about the dwelling, and an RDSAP assessment requires almost as many. By contrast, low-precision energy ratings use only the eighteen items of data for houses (twenty for flats) to which the calculation is most sensitive. "Least unlikely" default data are used for all other characteristics of the dwelling.

This approach was developed to support the incorporation of energy data into stock condition surveys, and for calculating stock key performance indicators (KPIs). Consequently low precision energy ratings of individual dwellings are very inaccurate (the SAP energy rating is predicted to an accuracy of approximately ±10). However, when low precision energy ratings are calculated for a stock of dwellings (more than a hundred) then the errors cancel out (the energy performance of some dwellings is underestimated, but for other dwellings it is over-estimated), so that very accurate average ratings can be calculated (SAP ±1)  Therefore low precision energy ratings should only be used for the purpose for which they are intended: the preparation of housing stock profiles and the calculation of stock-level KPIs: average SAP and average fuel use, fuel costs and carbon dioxide emissions. Low precision energy ratings of individual dwellings are very inaccurate and quoting them is essentially meaningless.

Measuring Energy Performance

The process of assessing the energy efficiency of a housing stock starts with calculation of the SAP energy ratings of all dwellings, using data at the best level of precision that is available. Often this means combining data at different levels of precision. 

In practice, in order to make reasonably accurate assessments, it is usually necessary to use not only the housing stock condition database but also other databases such as the rental database (which will reveal the number of bedrooms, from which numbers of rooms can be derived) and the gas safety database (which will reveal which dwellings have gas-fired heating systems, and often the types of boilers installed). Once the SAP assessments have been made and recorded in the database, housing stock profiles can be prepared and stock-level key performance indicators (KPIs) can be calculated.

Housing Stock Profiles

Housing stock profiles are bar charts showing the distribution of performance indicators such as average and minimum SAP energy ratings, and average and maximum fuel costs and carbon dioxide emissions across the stock. Figures 3.1 to 3.3 show housing stock profiles (of SAP, fuel costs and carbon dioxide emissions) for a UK housing association.

The associated KPIs derived from the assessments and profiles (and calculated under SAP standard occupancy) are shown in Table 3.1. It should be noted that the shape of each profile is often as informative as the numerical KPIs such as stock averages. Housing stock profiles are useful tools, both for asset management and for presenting information to Boards and regulators. They focus attention on the least energy efficient dwellings (at the left hand side of each profile) where the return on investment in improvements is greatest. If profiles and KPIs are updated regularly (at least annually) they can be used to track the progress of an improvement strategy: the bars will move to the right and the KPIs will improve. Multiple years can be plotted on a single chart to illustrate progress. Under various Governments' fuel poverty strategies housing organisations may be monitored on their progress in reducing fuel poverty and such charts and KPIs will enable them to present strong evidence of progress.

Figure 3.1 A SAP energy rating profile for a housing association’s stock

SAP Energy Rating Profile

Figure 3.2 A fuel cost profile for a housing association’s stock

Fuel cost profile

 

Figure 3.3 A carbon dioxide emissions profile for a housing association’s stock

Carbon dioxide emissions profile

Assessing external funding potential

The final stage of the assessment process is to consider the scope for external funding from schemes such as the Energy Company Obligation (ECO), the Feed in Tariff (FiT) or the Renewable Heat Incentive (RHI), as well as private finance options such as energy performance contracting. 

"Do it yourself pay as you save" (DIY PAYS) schemes, in which improvements are paid for by borrowing against predicted fuel cost savings, are sometimes also considered. There are many funding schemes, at both national and European levels, each scheme has its own rules, and most schemes are politically volatile and short-lived. Therefore it is not possible to estimate the scope for external funding over a thirty-year asset management programme, but it is possible to estimate what the schemes available at the time of the analysis could contribute, as an indicator of external funding potential. This is done by applying the rules of each scheme to each of the eligible improvements in each package, and calculating how much funding each scheme might contribute. The results are rarely encouraging, leading inevitably to the conclusion that housing improvement to appropriate standards will require significant internal investment by landlords. 

However, defining medium-term improvement plans and assessing the potential for external funding does help housing organisations to be well prepared with ‘shovel ready’ proposals, either when short-term funding becomes available or when there is an opportunity to bid for longer-term funding (e.g. from the EU).

Once the stock assessment and improvement option evaluation have been completed, and the results have been summarised, the implications for the asset management strategy should be considered (see Chapter 3). Different energy standards will have different overall costs, and require different patterns of investment. The required investment should be considered alongside the investment required in broader housing management activity, such as cyclical maintenance, other types of improvements (e.g. new kitchens and bathrooms) and maintaining the Decent Homes standard.

Housing improvement programmes are often driven by the objective of alleviating fuel poverty. Estimates of fuel use and of the fuel cost savings arising from proposed improvements are useful for assessing the extent to which those improvements will help to deliver affordable warmth. Fuel poverty is the result of the combination of a low-income household with an inefficient dwelling. Different households have different levels of income, so a particular type of household (e.g. a single parent or a pensioner couple) may be fuel poor in one type of dwelling (whether it has been improved or not) but not in another type. 

It can be useful to create an ‘affordable warmth matrix’, i.e. a tool in which household types (with known ‘worst case’ benefit incomes) are tabulated against dwelling types. The national definition of fuel povertycan then be used to calculate whether each type of household (with worst case income) would be in fuel poverty in each dwelling type.

Affordable warmth matrices can be prepared using fuel costs for unimproved or improved dwellings, and with fuel costs and benefits inflated in accordance with official projections for future years (possibly as far as 2025). Such matrices can be used to investigate the likely effects of proposed improvement programmes on fuel poverty across the stock, as well as to identify ‘excluded combinations’ of household types and dwelling types (especially prior to improvement). This information can be used to prioritise improvements and to support the development of allocation and transfer policies, as well as the specification of new developments.

 

Table 3.1 Key performance indicators for a housing association’s stock

Key Performance Indicator Value
Average SAP 65 (band D)
Minimum SAP  
Average annual fuel cost £469/yr
Maximum annual fuel cost >£1000/yr (189 dwellings)
Average carbon dioxide emissions 4,554 kg/yr
Maximum carbon dioxide emissions >10,000 kg/yr (223 dwellings)

 

Setting Improvement Targets

Housing stock profiles and KPIs also provide an initial indication of what might be realistic improvement targets, expressed in terms of KPIs.

For example, Figure 3.1 shows that the adoption of a target to raise the minimum SAP energy rating to 60 would involve improvement of 2139 dwellings, but raising the minimum SAP to 80 (a more realistic affordable warmth standard) would be much more challenging, requiring improvement of 9218 dwellings altogether – most of the stock!  Similarly, Figure 3.3 shows that reducing the maximum carbon dioxide emissions to less than 5 tonnes per year would involve the improvement of 2543 dwellings, but reducing it to 2 tonnes per year (more consistent with climate change targets) would involve improvement of 9204 dwellings. In both cases the dwellings to be improved can be identified from the database containing the assessment results. The database can also be used to establish other values such as the total carbon dioxide emissions associated with energy use in the stock (to which a percentage reduction target might be applied).

However, identifying and evaluating appropriate improvement (retrofit) standards is not straightforward. There are often overlapping objectives: delivering affordable warmth (to reduce fuel poverty); reducing overall energy use; and reducing the carbon dioxide emissions associated with energy use (to help mitigate climate change). In addition some landlords monitor minimum SAP energy ratings to assess HHSRS Category 1 risks. Housing organisations should be clear how all these objectives are prioritised, because without clear priorities it is difficult to define an optimum set of fuel saving improvements for any dwelling. Quite often standards are reduced to SAP energy ratings or EPC bands, but the SAP is based on fuel costs so measures that improve the SAP may not necessarily reduce emissions, and vice versa. EPC bands provide only a very crude measure of performance.

Affordable warmth standards designed to help eliminate fuel poverty are complicated by changes in the definition of fuel poverty (the definition used in England is different from those used by the devolved administrations), by changes in fuel tariffs and by changes in benefit levels. There is little point in investing in improvements that would deliver affordable warmth to residents today if by the end of the investment programme fuel costs will have risen by more than household incomes, so that the poorest residents remain in or return to fuel poverty. Northern Ireland saw its fuel poverty decrease between 2001 and 2004 due to energy efficiency improvements but then increase after 2005 when fuel prices increased sharply, some households fell back into fuel poverty and other households entered fuel poverty for the first time.

It is therefore necessary to consider both fuel cost and ‘worst case’ income scenarios, with horizons at ten, twenty or even thirty years, and to set energy efficiency standards with an eye to the future. Some housing organisations have considered a minimum SAP energy rating of 80 as a proxy affordable warmth standard for investment programmes that may not be completed until the end of the next decade or beyond.

Emissions reduction targets

Setting carbon dioxide emissions reduction targets is equally difficult. 

The Retrofit for the Future programme run by the Technology Strategy Board (now Innovate UK), involving eighty-six one-off ‘low carbon’ retrofit projects across the UK, showed that the cost of reducing emissions by 80% is of the order of £85,000 per dwelling. Even if economies of scale could reduce this to £50,000 per dwelling, it would still be unaffordable for most housing organisations. Fortunately there are reasons for believing that it will only be necessary to reduce emissions by between 50% and 60%, and that the balance of the 80% reduction required by our statutory national target will come from the supply side (‘decarbonisation’ of the electricity supply), from replacement of some of the worst performing dwellings with much more efficient new homes, and from ‘offsetting’ by local renewable energy schemes. Both the Retrofit for the Future programme and assessments of many dwelling types across a range of housing stocks suggest that reducing emissions by between 50% and 60% is much more affordable – of the order of £25,000 per dwelling on average (although the range of costs is wide). Therefore some housing organisations have been considering the implications of reducing the carbon dioxide emissions associated with energy use in their stocks by at least 50%.

Dwelling type analyses

Once a housing stock energy assessment has been completed, in order to explore the implications of possible energy efficiency standards in more detail, attention should turn to individual dwellings. The SAP energy rating data should be repetitively sorted by the variables to which energy performance is most sensitive: age, built form, construction type, heating system type and fuel, etc. This process will expose the thermally distinct dwelling types in the stock – usually around twenty of them, and every dwelling in the stock will be of one of the types. 

In practice the sorting process should be extended to take account of other characteristics (e.g. pre-fabricated construction) and of information that may not be included in low-precision data (e.g. the position of a flat in a block – ground, mid or top floor, middle or corner of block; or whether a house is a mid- or end-terrace unit). The number of dwelling types arrived at will be a compromise – too few will not adequately represent the stock, but too many will require excessive analytical resource. Typically, the stock is reasonably well represented by between twenty-five and thirty-five dwelling types. In order that the energy performance of each type can be assessed in detail (using Full SAP) it is important that there are RDSAP data (i.e. data from an EPC assessment) available for at least one representative example of each dwelling type. Google Streetview is a useful tool for checking that the proposed representative dwellings are indeed good examples of their types. Once this has been confirmed a Full SAP data set can be assembled for each dwelling type.

Performance assessment and improvement option evaluation

Analysis of each representative dwelling using Full SAP will calculate an indicative SAP energy rating and estimate annual fuel use, fuel costs and carbon dioxide emissions for each dwelling type. This provides a ‘base case’ for the evaluation of improvement options and identification of ‘packages’ of measures that will bring the performance of each dwelling type to the proposed standards (e.g. minimum SAP 80 and/or 50% reduction of carbon dioxide emissions).

Each improvement measure or package should be evaluated in terms of its capital cost and the effect its implementation will have on fuel use, fuel bills and carbon dioxide emissions. This information will support the calculation of simple payback periods for each improvement option or package, or of more sophisticated investment indicators such as carbon cost effectiveness or net present value (NPV). 

To support this process, good knowledge of the capital costs of improvement measures is needed, in the form of rates that can be applied to the areas of walls, floors, roofs, windows, etc. in the SAP data files.

Housing organisations should therefore develop databases of improvement costs, based on the M3NHF schedule of rates and/or their own recent experience. Some consultancies also maintain their own databases of improvement costs, differentiated by geographical regions, etc. It is important when assembling improvement cost data to adopt a consistent approach to contractors’ preliminary costs, overheads, profit and VAT. Some housing organisations prefer total improvement costs to be used, others use only net costs for comparison.

When evaluating improvement options to identify appropriate packages of measures it is important to assume a ‘fabric first’ approach in which building fabric improvements (insulation and air tightness) are installed first, followed by improvements in the efficiency of building services (including installing heat pumps in off-gas network locations) and then finally by the installation of renewable energy technologies (e.g. solar water heating, solar photovoltaic systems) to ‘top up’ the performance of the dwelling to the required standard. This is because building fabric improvements are the most cost effective and long-lived measures; building services improvements are often equally cost effective but have much shorter service lives (typically the improved building fabric will outlast three heating systems); and renewable energy systems are expensive, short-lived and constrained by the available roof space. In practice the fabric first approach will usually be modified by the local factors, including the housing organisation’s own experience and consequent preferences for particular measures. 

 

Improvement plans for dwelling types

The outcome of the improvement option evaluation is the definition of a medium-term improvement plan for each dwelling type. These plans will identify the preferred package of improvement options for meeting each of the proposed retrofit standards. They should also include: 

the estimated capital cost of each package; its effect on the SAP energy rating; the associated reduction of annual fuel use, fuel bills and carbon dioxide emissions; and an appropriate cost effectiveness indicator such as simple payback, capital cost per tonne of carbon saved per year, carbon cost effectiveness or net present value. Medium term improvement plans can be prepared for any proposed energy, affordable warmth or emissions reduction target, but the analysis is time consuming and expensive, so in practice usually only two or three prioritised standards are investigated. The objective is not to provide a prescription for improvement of each dwelling type but to illustrate the technical and financial implications, for the asset management programme, of standards that the Board might be asked to adopt or that might be imposed on the housing organisation by regulators or Government. The evaluation work will also illustrate the relative effects of various improvement measures on different dwelling types, and how those measures contribute to improving the SAP energy ratings.

The whole-stock programme

The next stage of the assessment process is to scale-up the analyses to the level of the whole stock, by multiplying the capital cost of each improvement package, and the associated fuel use, fuel cost and carbon dioxide emissions savings, by the number of dwellings of that type, in each case. 

This estimates the total cost of bringing the whole of the housing stock to each of the standards, as well as the total reduction in fuel use, the total fuel cost savings to residents and the total reduction in emissions. All of these figures can be broken down by dwelling types, or by measures. The total improvement cost is usually an eye-watering number that becomes more palatable when it is divided by the number of years over which the improvement programme is to be delivered. In order to illustrate the scale of the challenge the required annual investment can then be compared with the current rate of investment, and with what the organisation must spend just to "stand still" (e.g. under Decent Homes). Projected housing stock profiles and KPIs, after improvement to each standard, can also be prepared.

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