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 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 (21^oC in the living room, and between 18^oC and 21^oC 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.

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.

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.

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.

"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.

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.

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%.

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.

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. 

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.

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.