EST Home Analytics - Energy Saving Trust

EST Home Analytics
home: “the physical structure of the dwelling and the person, family or household who reside
within”
analytics: “the application of statistical models, data and technology to solve problems and
inform decisions”
Contact details:
Will Rivers
Housing Data Manager
Energy Saving Trust
T: 0207 654 2633
E: will.rivers@est.org.uk

Understanding the market for low carbon retrofit
The Energy Saving Trust has 20 years of experience targeting customers to deliver low carbon retrofit. During this time we
have developed extensive datasets and a unique range of knowledge on the UK housing stock. We also have an in-depth
insight in to UK householders, their attitudes to retrofit measures and how to target them effectively.
To help other organisations target retrofit activity the Energy Saving Trust has develop EST Home Analytics.
EST Home Analytics provides address level information on the UK housing stock, householders and the opportunities for low
carbon retrofit. This data can be used to assess the size and location of opportunities for retrofit helping organisations to
deliver measures through CERT, CESP, the Green Deal, Feed in Tariffs, the Renewable Heat Incentive and the open market.
The data provided by EST Home Analytics seeks to answer the following questions;
Which homes are suitable for which measures
What is the overall potential for retrofit in an area
Homes
People
Which householders are more likely to undertake retrofit
Which householders are eligible for funding
What marketing channels and messages will have the biggest impact
The Energy Saving Trust is also the UK’s leading organisation providing robust information on the impact of measures:
What are the costs, savings and payback periods
How will different packages of measures affect different homes
Improvements
How will a retrofit scheme impact on the local economy and jobs
Case Study: Targeting the
Green Deal in Exeter
The Energy Saving Trust worked with the Exeter
Low Carbon Task Force to understand the
potential for retrofit measures in the central
Exeter housing stock. Wall type, loft insulation,
glazing type, fuel type and boiler type were
modelled alongside data on property tenure, age
and type. As an additional step, SAP modelling
was undertaken to understand the likely payback
periods for Green Deal measures.

EST Home Analytics data variables
Availability Dataset Scale Coverage
Pack 1. Energy efficiency and property pack (all 10 datasets below)
Now Cavity wall insulation / wall type Address Great Britain
Now Loft insulation level
Address Great Britain
Now Glazing type
Address Great Britain
Now Gas mains present Y/N + off gas fuel type analysis
Address United Kingdom
Now Boiler type and age
Address Great Britain
Now Hard to fill cavities data
Address Great Britain
Now Property type
Address United Kingdom
Now Property age
Address United Kingdom
Now Property tenure
Address United Kingdom
Now Property size Address United Kingdom
Renewable energy and fuel switching
June 2012 Proximity to gas grid Address United Kingdom
June 2012 Solar PV and thermal potentials data
Address United Kingdom
June 2012 Ground source heat pump potential (garden size and off gas)
Address United Kingdom
June 2012 Domestic wind potential
Address United Kingdom
June 2012 Distance to pavement (external wall insulation access)
Address United Kingdom
June 2012 Distance to nearest other dwelling (planning permission barrier)
Address United Kingdom
June 2012 Proximity to biomass supplier Address United Kingdom
June 2012 m2 footprint of dwelling
Address United Kingdom
Funding eligibility and income
June 2012 CERT PG and SPG eligibility Address Great Britain
Now Experian household income
Address United Kingdom
Household energy use
June 2012 Estimated energy use, CO 2 emissions and fuel bills Address Great Britain

Case Study – Targeting an external solid wall insulation programme
In this case study, an organisation was interested in finding properties suitable for external wall insulation in the social
housing sector. Dwellings suitable for external wall insulation tend to be solid walls that were built after 1919 so a
combination of wall type, property tenure and property age datasets were used. The four maps below show the analysis
results at address level for an example Lower Super output Area (LSOA). The map in the bottom right corner shows the
combination of Social Housing, post 1920 solid wall properties that were likely to be the most suitable for the scheme.

Description of data variables
Energy efficiency and property characteristics
Cavity wall insulation
/ wall type
Over 4.5 million actual installations and survey records are used alongside modelled data to develop an
estimate of likely wall type at the address level. Modelled data is derived from a combination of
predictor variables (Property tenure, property type, property age, region, Index of Multiple Deprivation
and rural/urbn morphology) and national housing surveys (the English Housing Survey, Scottish House
Condition Survey and Living Wales survey).
Output: Solid wall, Solid wall insulated, Cavity wall, Cavity wall insulated
A factor between 0 and 1 is given showing the likelihood for each possible wall type
Loft insulation level
Over 4.4 million actual installations and survey records are used alongside modelled data to develop an
estimate of loft insulation level at the address level. Modelled data is derived from a combination of
predictor variables (Property tenure, property type, property age, region, Index of Multiple Deprivation
and rural/urbn morphology) and national housing surveys (the English Housing Survey, Scottish House
Condition Survey and Living Wales survey).
Output: No loft, Loft insulation 0 – 50mm, Loft insulation 50 – 150mm, Loft insulation 150mm+
A factor between 0 and 1 is given showing the likelihood for each possible loft insulation value
Glazing type
Over 7.3 million actual installations and survey records are used alongside modelled data to develop an
estimate of glazing type at the address level. Modelled data is derived from a combination of predictor
variables (Property tenure, property type, property age, region, Index of Multiple Deprivation and
rural/urban morphology) and national housing surveys (the English Housing Survey, Scottish House
Condition Survey and Living Wales survey).
Output: Less than 80% Double Glazing, More than 80% Double Glazing
A factor between 0 and 1 is given showing the likelihood for each glazing type
Main fuel type
Actual data on the presence of a gas meter at the address level forms the basis of the dataset. Where
gas meters are no present modelled data on the fuel type for off gas dwellings is provided using a
combination of actual and modelled data.
Output: Main fuel gas, Main fuel electric, Main fuel oil, Main fuel communal, Main fuel solid
The value of gas is given where a gas meter is present, a factor between 0 and 1 showing the likelihood
for each fuel type is given where a gas type is not present
Boiler type and age
Over 2.5 million actual installations and survey records are used alongside modelled data to develop an
estimate of glazing type at the address level. Modelled data is derived from a combination of predictor
variables (Property tenure, property type, property age, region, Index of Multiple Deprivation and
rural/urban morphology) and national housing surveys (the English Housing Survey, Scottish House
Condition Survey and Living Wales survey).
Output: No boiler, Standard boiler, Condensing boiler
A factor between 0 and 1 is given showing the likelihood for each Boiler type

Hard to fill cavities
data
The Energy Saving Trust worked with Consultants Inbuilt to build on a previous 2010 DECC study on
hard to fill cavity walls. The additional modelling we have undertaken provides a likelihood for
individual addresses to be one of several hard to fill cavity types.
Outputs: Narrow cavity, Concrete, metal or timber frame, Partial fill cavity, Too tall, Wall fault, Cavity
with issues (non-CERT eligible, partial cavity, conservatory), Random stone
A factor between 0 and 1 is given showing the likelihood for each hard to fill type
Property type
Property type data from Experian is used to provide an estimate of property type for every address in
the UK.
Outputs: Detached, Semi-detached, Bungalow, Terraced, Flat
An absolute single value is provided indicating the modelled property type
Property age
Property age data from Experian is used to provide an estimate of property age for every address in the
UK.
Outputs: Pre 1870, 1871 – 1919, 1920 – 1945, 1946 – 1955, 1956 – 1979, Post 1980
An absolute single value is provided indicating the modelled property age
Property tenure
Property tenure data from Experian is used to provide an estimate of property tenure for every address
in the UK.
Outputs: Owner occupied, Privately rented, Council/Housing Association
An absolute single value is provided indicating the modelled property tenure
Number of bedrooms
Number of bedrooms data from Experian is used to provide an estimate of property size for every
address in the UK.
Renewable energy data
Outputs: 1,2,3,4,5 bedrooms
An absolute single value is provided indicating the modelled number of bedrooms
Proximity to gas grid
data
The Energy Saving Trust can provide GIS modelled data on the proximity of every address to the
nearest other gas meter. This provides a valuable dataset for identifying properties that are likely to be
convertible to gas under the national grids standard connection charges.
Outputs: Distance (meters) to the nearest gas meter.
Solar potentials data
The Energy Saving Trust has undertaken sophisticated GIS modelling of data available from the
Ordnance Survey to ascertain likely roof orientation for every dwelling in the UK. Based on roof
orientation and the degree of latitude of the property, we have calculated the solar PV and solar
thermal potentials based on data held by the Energy Saving Trust of typical savings from solar, taking
in to account usage patterns and export to the grid.
Outputs: kWh Solar PV potential, kWh solar thermal potential, estimated kWh output (solar PV),
estimated kWh output (solar thermal), estimated electricity bill savings (Solar PV), estimated fuel bill
savings (solar thermal)

Ground source heat
pump potential
(garden size)
(requires a licence
for Ordnance Survey
MasterMap)
Domestic wind
potential
The Energy Saving Trust has undertaken sophisticated GIS modelling of Ordnance Survey’s Mastermap
layer to ascertain garden size for every dwelling in the UK. Garden size is a key indicator of the
potential for ground source heat pumps. This data can be combined with data on the presence of a gas
meter and data on the m2 footprint of the dwelling to gain an accurate picture on the potential for
ground source heat pumps.
Outputs: Front garden size (m2), Rear Garden Size (m2), Total plot size (m2)
The Energy Saving Trust has undertaken GIS analysis of the Met Office NOABL wind speed database
and mapped average wind speeds for km2 grids to individual addresses. This data is combined on data
with housing density in the surrounding area to provide an estimation of the dwelling’s suitability for a
domestic wind turbine.
Other variables
m2 footprint of
dwelling
Distance to
pavement (external
wall insulation
access)
Distance to nearest
other dwelling
(planning permission
barrier)
Proximity to biomass
supplier
CERT Priority Group
and Super Priority
Group eligibility
Experian household
income
The Energy Saving Trust has undertaken GIS analysis of Ordnance Survey data to ascertain an estimate
of the m2 footprint of the dwelling. This information is particularly valuable for undertaking SAP
modelling of properties or calculating the likely requirement for materials or size of a heating system.
Outputs: building footprint (m2)
Properties located immediately on the pavement can have access issues or potential planning
considerations when installing external solid wall insulation or erecting scaffolding. The Energy Saving
Trust has undertaken GIS analysis to ascertain the distance of each dwelling to the pavement.
Outputs: Distance to pavement (meters)
Planning permission for many measures such as air source heat pumps can be dependent on the
distance of the property to its nearest neighbour. The Energy Saving Trust has undertaken GIS analysis
to determine both the nearest point and furthest point of a dwelling away from its neighbour.
Outputs: Nearest distance to nearest dwelling (meters), Furthest distance to nearest dwelling (meters)
The Energy Saving Trust can provide GIS modelled data on the proximity of every address to the
nearest supplier of biomass fuel. Biomass suppliers and the types of fuel supplied are sourced from
resources including the Biomass Energy Centre and the Logpile website.
Output: Distance to nearest biomass supplied (miles) and the type of biomass fuel supplied
The Energy Saving Trust has developed an model to predict the likely level of eligibility for the CERT
Priority Group and the CERT Super Priority Group. The model is based on several datasets available to
the Energy Saving Trust as well as data from the Office of National Statistics. The Energy Saving Trust is
also working to predict the likely eligibility for the forthcoming Energy Companies Obligation (ECO).
Output: CERT Priority Group eligible, CERT Super Priority Group eligible
A factor between 0 and 1 is given showing the likelihood for each possible wall type
Experian provide income projections for households which can be used to indicate people’s likely
ability and willingness to pay for retrofit measures.
Estimated energy
use, CO2 emissions
and fuel bills
Based on the likely property and energy efficiency characteristics of the dwelling, the Energy Saving
Trust can model the likely energy use, CO 2 emissions and fuel bills of properties using SAP based
software.
Outputs: Estimated kWh energy use, fuel bills and tCO 2

Incorporating your data in to the analysis
Where organisations have collected data on their housing stock, we strongly encourage that this data is included in the
analysis to improve the accuracy of the model and the final dataset. The Energy Saving Trust carried out analysis for
Newcastle City Council that incorporated the Council’s data. This was used to support the business case for a green finance
retrofit scheme and will help to target priority areas for intervention. The Energy Saving Trust can provide a range of data
processing analysis and presentation tools to provide a full database management, update and analysis service. Please
contact us for further details.
A holistic approach to targeting - the House,
householder and suitability for measures
To understand the market for retrofit a combination of data is required. For example,
understanding the market for solar PV requires data on:
Roof orientation
Whether the dwelling could achieve a ‘D’ rating on an EPC
The attitudes of the householder towards green retrofit and their ability to
pay
Example data input: Energy Saving Trust trigger points research
The Energy Saving Trust recently undertook extensive
market research to understand a) what type of people are
most likely to undertake refurbishment activity b) at what
life stage do they undertake retrofit c) what energy saving
measures they would consider as part of the project and d)
how much would they extend their budgets to incorporate
green retrofit measures.
This analysis can be mapped to Experian’s consumer
segmentation analysis to enhance understanding of which
households are the best potential customers for green
retrofit.
Example output: Regional level
Greater London Authority – RE:NEW programme
targeting
The RE:NEW programme is London’s flagship programme for
reducing CO 2 emissions from domestic properties. In order to
target the scheme effectively, the GLA commissioned the Energy
Saving Trust to develop Ward level estimates of the remaining
potential for energy efficiency measures. This data will be used
to prioritise wards for door knocking and community
engagement.

Further information
For further information please contact
Will Rivers
Housing Data Manager
Energy Saving Trust
e: will.rivers@est.org.uk
p: 0207 654 2633

Energy Saving Trust, 21 Dartmouth Street, London SW1H 9BP
Tel: 0207 222 0101
energysavingtrust.org.uk
XXXXX © 2010. Energy Saving Trust. E&OE
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