Improvement Prophet
Improvement Prophet
A tool for designing optimal housing programmes and fuel-poverty ‘snapshots’
CSE is currently developing the Improvement Prophet tool on behalf of a coalition of funders (see box, right). The tool will bring together the results of work previously undertaken to create housing models based on the English Housing Condition Survey (EHCS), namely.
- Fuel prices model – Developed for the Energy Efficiency Partnership for Homes (EEPfH) from an original model for the National Right to Fuel Campaign (funded by EEPfH and Unison, energywatch & NEA)
- Energy improvement model (Funded by Eaga PCT and the Government Office for the South West)
In themselves, these models have built upon the work carried out for the development of the Association for the Conservation of Energy (ACE)’s Fuel Prophet tool.
The project seeks to update, integrate and rationalise the two models to create a resource that will enable policy makers to assess the impact of potential energy efficiency improvements on the housing stock.
The tool will be delivered through a stand-alone on-line interface (either installed locally or accessed via the internet) that will enable non-technical users (e.g. policy makers and energy professionals) to generate their own statistics depending upon the scenarios they choose to explore. This could be, for example, the measures required and the associated cost of achieving a target SAP 70 for the fuel poor, or, say, the feasibility of reducing carbon emissions by 80%.
In order to limit development costs, Improvement Prophet will make use of open-access software platforms.
CSE has extensive experience in statistical modelling and analysis around housing, energy consumption and fuel poverty
The final version of the tool will consist of three interlinked modules:
1. Fuel prices module
2. Income module
3. Energy improvement module (including an insulation sub-module and a demographics sub-module)
1. Fuel prices module
The team will use data from the 2007 EHCS and the official 2007 fuel poverty model to determine the separate energy requirements (GJ/yr) needed to achieve satisfactory space heating, water heating, cooking and adequate lights and appliance use.
Depending on the particular fuels and tariff types (e.g. off-peak electricity) used for each component, the updated fuel prices data is applied to each energy requirement and any additional standing charge to give updated fuel costs for space heating, water heating, cooking and lights and appliance use. These separate costs are then summed to give the new total fuel cost resulting from the various fuel price rises. The team plan to use the Consumer Focus funding to help determine the influence of switching energy suppliers and energy tariffs on current energy bills between 2007 and 2010.
2. Income model
The team has re-built and enhanced the EHCS income model. The model first determines the individual components of the respondent’s income and then transforms these to the current date through the use of up-rating data from the Department for Work and Pensions, the Annual Survey of Hours and Earnings and the Treasury Pre-Budget Report. The future projections of incomes are based on standard Treasury assumptions for income growth to 2020.
The research will improve the existing income model by modelling incomplete benefit data to match recipient numbers at the time of the last house condition survey (HCS) and by developing the modelling used in the recent Consumer Focus project, Cutting the energy bills of the fuel poor.
3. Energy improvement model
The energy improvement aspect of the model represents the most complex part of the Improvement Prophet tool. The team is developing a SAP based calculation engine to determine the current energy performance and also the future energy potential of existing dwellings.
The team used a prototype version of the calculation engine to examine the measures required to achieve a target SAP 81 standard for the Consumer Focus-funded research Raising the SAP. The study found that 83% of fuel poor households would be removed from fuel poverty as a result of improving dwellings to a target SAP 81 standard. An updated version of the prototype found that 78% of households were removed from fuel poverty after the installation of measures. The lower success rate for the updated prototype is a result of more accurate projections of fuel costs and incomes.
a) Insulation sub-module
The team has created a database stored procedure to randomly select properties for insulation and heating improvements carried out between 2006 and 2010. The number of measures is defined by national installation rates for loft insulation, cavity wall insulation and heating measures. The allocation process itself is informed by both scheme eligibility criteria (e.g. priority group) and the respondents reported levels of thermal satisfaction and comfort.
Following the application of measures the energy improvement model (see below) is used to determine the change in energy performance of dwellings. The current assumptions regarding the application of measures will be improved through analysis of the EHCS longitudinal data that relates to households who installed energy measures between 2004 and 2007, modified by information on subsequent trends.
b) Demographic model
The predictive fuel poverty model used in the previous studies assesses changes in fuel prices, household incomes and energy efficiency levels. However, other than allowing for new population totals, it does not systematically allow for demographic changes within the household population or for any new housing or demolition. The tool will address these omissions in the following ways:
- By using the latest EHCS longitudinal data and other information, such as CLG household projections, the research will model demographic change to take account of changes in household numbers and composition and occupancy of the housing stock.
- The research will use the longitudinal data and CLG housing starts and completions data to model new building and demolitions and their impact on the distribution of fuel cost since the last HCS (the addition of private sector new build households will be based on four standard occupancy patterns). CSE will use the JRF Study ‘Why do people buy new build housing’ as a starting point for this research.
CSE's fuel-poverty websites
www.fuelpovertyindicator.org.uk
The Fuel Poverty Indicator is designed to help target fuel poverty programmes and inform research. Includes downloadable maps and data. Read the project profile here.
www.energyefficiencywales.org.uk
A sister site to the above, providing maps and data for Wales
www.ruralfuelpoverty.org.uk
The third in the suite, this one focuses on rural fuel poverty particularly in relation to 'hard-to-treat' homes.