bre client report - housing essex · bre dwelling level housing stock modelling and database for...

BRE Client Report

BRE Dwelling Level Housing Stock Modelling and Database for Thames Gateway South Essex

Prepared for: Thames Gateway South Essex Group

Date: 20 September 2017

Report Number: Issue: 1

BRE

Watford, Herts

WD25 9XX

Customer Services 0333 321 8811

From outside the UK:

T + 44 (0) 1923 664000

F + 44 (0) 1923 664010

E [email protected]

www.bre.co.uk

Prepared for:

Alastair Pollock

Strategy Coordinator

Opportunity South Essex

Castle Point Borough Council Office

Kiln Road

Thundersley

Benfleet

Essex

SS7 1TF

Housing Stock Modelling and Database for Thames Gateway South Essex

Commercial in Confidence

Template Version V2-082014

© Building Research Establishment Ltd

Page 1 of 64

This report is made on behalf of Building Research Establishment Ltd. (BRE) and may only be distributed

in its entirety, without amendment, and with attribution to BRE to the extent permitted by the terms and

conditions of the contract. BRE’s liability in respect of this report and reliance thereupon shall be as per

the terms and conditions of contract with the client and BRE shall have no liability to third parties to the

extent permitted in law.

Prepared by

Name Fiona Mackenzie

Position Senior Consultant and Technical Editor

Date 20 September 2017

Signature

Authorised by

Name Ian Watson

Position Principal Consultant, Housing & Health

Date 20 September 2017

Signature





Page 2 of 64

A number of local authorities within the Thames Gateway South Essex (TGSE) area commissioned

BRE to undertake a series of modelling exercises on their housing stock1 – Basildon, Castle Point,

Rochford and Southend-on-Sea. Separate reports were produced for each authority describing the

modelling work and providing more granular details of the results obtained from the dwelling level

models and database. Each authority was also provided with a database enabling them to obtain

specific information whenever required. This current report provides a high level summary for TGSE

broken down by local authority and, for completion, including figures for Thurrock.

This project also took advantage of the huge amount of locally held data, integrating thousands of

records into the BRE Housing Stock Models2. For example, 3 of the TGSE local authorities supplied

data from over 95,000 Energy Performance Certificates (EPCs). This way, information on the thermal

characteristics from ‘actual’ observed inputs of 49% of the stock in these authorities, or 32% across

the whole region, were used in preference to modelled or imputed inputs. Not only does it therefore

improve the quality of data used in the stock models for those properties covered by an EPC, but it

also helps improve or confirm the data used; for example, on the property age or wall type of a

significant number of adjoining properties.

The main aims of this work were to provide estimates of:

o The percentage of dwellings meeting each of the key indicators3 for TGSE overall and broken

down by tenure and then mapped by ward (private sector stock only)

o Information relating to Local Authority Housing Statistics (LAHS) reporting for the private

sector stock – Energy Performance Certificate (EPC) ratings and category 1 hazards4

BRE Housing Stock Models were used to provide such estimates at dwelling level with a focus on

private sector housing. The key indicators provide the local authorities within the TGSE area with

detailed information on the likely condition of the stock and the geographical distribution of properties

of interest.

1 The exception was Thurrock, however high level results for this local authority have been included in this report to

enable the provision of area-wide results and comparisons across the local authorities.

2 The exception was Rochford which did not have any local data integrated into the model.

3 Housing Health and Safety Rating System (HHSRS) category 1 hazards, excess cold hazards, fall hazards,

disrepair, fuel poverty, low income households.

4 3 of the 4 commissioning TGSE local authorities were also supplied with information on Houses in Multiple

Occupation (HMOs) and various energy efficiency and energy planning variables. As this exercise was not carried out

for the whole region, this information has not been included in this summary report.

Executive summary





Page 3 of 64

A stock modelling approach has been developed and used by BRE for many years and the models

used for this project have been updated to make use of the results of the 2012 English Housing

Survey (EHS)5 and additionally now incorporate a technique known as geomodelling6 which makes

use of Ordnance Survey (OS) data. These dwelling level models are used to estimate the likelihood

of a particular dwelling meeting the criteria for each of the key indicators. These outputs can then be

mapped to provide the authority with a geographical distribution of each of the key indicators which

can then be used to target resources for improving the housing stock.

In addition to the stock models, the 4 local authorities commissioned BRE to produce a Health Impact

Assessment (HIA) to better understand the effect of private sector housing hazards and intervention

strategies on the health of residents in their area. An HIA is a formal method of assessing the impact

of a project, procedure or strategy on the health of a population. The HIA draws on evidence of the

health impact of hazards identified using the Housing Health and Safety Rating System (HHSRS) and

a methodology developed by the BRE Trust and published in “The Real Cost of Poor Housing”7 and

in the more recent “The Full Cost of Poor Housing”8. The HHSRS is the method by which housing

condition is now assessed in accordance with the Housing Act 2004. A dwelling with a category 1

HHSRS hazard is considered to fail the minimum statutory standard for housing and is classified as

“poor housing”. This report summarises the findings of the HIAs carried out for the 4 commissioning

local authorities, and combines this with HIA estimates for Thurrock to provide a complete

assessment for the region.

The headline results for the stock modelling and the HIA analysis are shown overleaf.

5 2012 is the latest available data when the models were produced.

6 The OS data has been used to update a number of the model inputs – the main value of the OS data is the ability to

determine the dwelling type with much greater confidence – see Appendix B for more information.

7 The Real Cost of Poor Housing, M Davidson et al., HIS BRE Press, February 2010

8 The Full Cost of Poor Housing, Roys M, Nicol S, Garrett H and Margoles S, HIS BRE Press, 2016





Page 4 of 64

Headline results for Thames Gateway South Essex (TGSE)

TGSE has a higher percentage of private sector dwellings containing fall hazards

compared to England. See the full results

Energy efficiency of the private housing stock is, however, better than the England

average (SimpleSAP of 59 rather than 55). Excess cold hazards for the region are below

national average. Overall housing hazards are at a level lower than the national average.

See the full results

14% of private sector stock in the region is estimated to have a category 1 hazard. The

greatest proportion is in Southend-on-Sea (20%) and the lowest are in Basildon (11%).


2% of private sector stock is estimated to have an excess cold hazard. The highest proportions are in Southend-on-Sea (4%) and the lowest are in Castle Point (1%). See the

full results

11% of private sector stock is estimated to have a falls hazard. The highest proportions are in Southend-on-Sea (15%) and the lowest are in Rochford (8%). See the full results

3% of private sector stock is estimated to be in disrepair. The highest proportions are in

Southend-on-Sea (4%) and the other local authorities are all lower at 2%. See the full

results

For the private sector stock, Southend-on-Sea has the highest proportions of fuel

poverty under both the 10% and Low Income High Costs definitions – 15% and 12%,

respectively. See the full results

11% of the private sector stock in the region is estimated to be occupied by low income

households. The highest proportions are in Southend-on-Sea and Thurrock (both 14%)

and the lowest are in Rochford (6%). See the full results

For the private sector stock, Basildon has the highest average SimpleSAP rating (62)

and Southend-on-Sea has the lowest (55). The average SimpleSAP rating for TGSE is 59.


The percentage of private rented stock with an EPC rating below band E for TGSE is

6.6%. Southend-on-Sea has the highest percentage falling below band E

(9.4%) and Basildon has the lowest (3.8%). See the full results

Maps are provided throughout the report for the above variables.

There are an estimated 48,598 category 1 hazards in the private stock across TGSE with

an estimated 2,275 harmful events requiring medical treatment annually. The estimated

cost of mitigating all these hazards is £87.6m and the total savings to society are

estimated to be nearly £74m annually. See the full results





Page 5 of 64

Key illustrations of headline results

The chart below shows the percentage of private sector dwellings in TGSE failing each of the key

indicators compared to the percentage for England as a whole (2012 EHS data). TGSE has a higher

percentage of private sector dwellings with fall hazards. For all other key indicators, TGSE performs

better than England – particularly for low income households, excess cold and disrepair.

Comparison of TGSE with England (EHS 2012), % of private sector dwellings failing each of the indicators

All HHSRS cat1 hazards

Excess cold Fall hazards DisrepairFuel poverty

(10%)Fuel poverty

(LIHC)Low incomehouseholds

TGSE 14% 2% 11% 3% 11% 9% 11%

2012 EHS 15% 5% 8% 5% 14% 11% 14%

0%

2%

4%

6%

8%

10%

12%

14%

16%

% o

f p

riva

te s

ect

or

dw

elli

ngs

Under the Energy Act 2011, new rules mean that from 2018 landlords must ensure that their properties

meet a minimum energy efficiency standard - which has been set at band E - by 1 April 2018. The

following table shows the percentage of private rented stock falling into each of the EPC ratings bands

(based on SimpleSAP) for TGSE overall and by local authority. Whilst the majority of properties in the

private rented sector fall in bands D and E, 6.6% overall are estimated to fall into bands F and G.

Southend-on-Sea has the highest estimated proportion of private rented stock falling into these bands at

9.4%.





Page 6 of 64

Percentage of private rented stock falling into each of the EPC ratings bands (based on SimpleSAP) by local authority

BasildonCastle

PointRochford

Southend-

on-SeaThurrock TGSE

(92-100) A 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

1.1% 0.7% 0.4% 0.9% 0.9% 0.9%

35.3% 22.8% 27.6% 13.2% 34.4% 25.0%

45.8% 49.9% 44.3% 43.1% 43.7% 44.3%

14.1% 21.9% 18.4% 33.5% 16.6% 23.3%

3.0% 3.6% 7.3% 8.0% 4.0% 5.6%

0.7% 1.1% 2.0% 1.4% 0.5% 1.0%

EPC rating band

Distribution of EPC rating bands by local authority - private rented sector

(1-20) G

(81-91) B

(69-80) C

(55-68) D

(39-54) E

(21-38) F

The map below shows the distribution of dwellings with a category 1 hazard across TGSE, indicating that

this hazard is generally concentrated in rural areas where there are fewer flats and there tend to be more

properties off the gas network. The greatest numbers of hazards are concentrated in Southend-on-Sea,

to the east of Rochford and southern wards of Thurrock. Wards to the north of Basildon and the East of

Rochford have the lowest levels of hazards

Percentage of private sector dwellings in TGSE estimated to have a HHSRS category 1 hazard





Page 7 of 64

The table below provides a summary of the HIA results at local authority level and for TGSE. The greatest

number of hazards are in Southend-on-Sea and the lowest number is in Castle Point.

Summary of HIA results for all HHSRS category 1 hazards, split by local authority and for TGSE as a

whole

Savings to NHSSavings to

society

Basildon 9,199 442 £15,501,958 £1,587,890 £13,544,727

Castle Point 5,363 249 £8,340,369 £923,470 £7,640,494

Rochford 5,147 214 £10,478,543 £872,640 £8,400,624

Southend-on-Sea 17,985 856 £34,113,903 £3,096,330 £28,029,623

Thurrock 10,903 514 £19,127,299 £1,877,250 £16,382,000

TGSE 48,598 2,275 £87,562,073 £8,357,580 £73,997,467

Local authority

Numbers of

hazards

(total private

sector stock)

Estimated

number of

instances

requiring

medical

intervention

Cost of

mitigating all

hazards

Potential annual savings from

mitigating hazards





Page 8 of 64

Table of Contents

1 Introduction 11

1.1 Project aims 12

2 Overview of the BRE Dwelling Level Housing Stock Modelling approach 13

3 Summary results for TGSE from the BRE Dwelling Level Housing Stock Models and

Database 17

3.1 Overview of Thames Gateway South Essex 18

3.2 Comparison of TGSE with England 19

3.3 Comparison of the key indicators across the TGSE local authorities 20

3.4 Comparison of information relating to LAHS reporting 36

4 Health Impact Assessment (HIA) summary 39

4.1 Introduction 39

4.2 Housing hazards 40

4.3 Methodology 43

4.4 Summary of findings 45

5 Conclusion 49

5.1 Conclusion 49

Appendix A Definitions of the key indicators 50

Appendix B Methodology for the BRE Dwelling Level Housing Stock Modelling approach 53

Appendix C Policy background 59

Glossary of terms 62





Page 9 of 64

List of Tables

Table 1: Key indicators split into categories 11

Table 2: Integrated data summary (note that Rochford did not have an integrated database model) 14

Table 3: Overview of TGSE housing stock by tenure for each local authority 19

Table 4: Percentage of dwellings estimated to have a HHSRS category 1 hazard by local authority and

private sector tenure 20

Table 5: Percentage of dwellings estimated to have a HHSRS category 1 hazard for excess cold by local

authority and private sector tenure 22

Table 6: Percentage of dwellings estimated to have a HHSRS category 1 hazard for falls by local

authority and private sector tenure 24

Table 7: Percentage of dwellings estimated to be in disrepair by local authority and private sector tenure26

Table 8: Percentage of dwellings estimated to be in fuel poverty by local authority and by private sector

tenure – Low Income High Costs (LIHC) definition 28


tenure – 10% definition 29

Table 10: Percentage of dwellings estimated to be occupied by low income households by local authority

and by private sector tenure 32

Table 11: Average SimpleSAP rating by local authority and by private sector tenure 34

Table 12: Percentage of private sector stock falling into each of the EPC ratings bands (based on

SimpleSAP) by local authority 37

Table 13: Percentage of private rented stock falling into each of the EPC ratings bands (based on

SimpleSAP) by local authority 37

Table 14: The 29 hazards covered by the HHSRS (those highlighted in bold are covered in this HIA,

although the 3 infection hazards and electrical hazards are not covered in the cost benefit analysis due to

insufficient information) 41

Table 15: Summary of the main hazards, their effects, vulnerable groups affected and potential mitigation

actions 42

Table 16: Summary of HIA results for all HHSRS category 1 hazards, split by local authority and for

TGSE as a whole 46

Table 17: Summary of HIA results for category 1 excess cold hazards, split by local authority and for

TGSE as a whole 46

Table 18: Summary of HIA results for category 1 fall hazards affecting older people (i.e. falls associated

with baths, falls on the level and falls on stairs), split by local authority and for TGSE as a whole 47





Page 10 of 64

List of Figures

Figure 1: Simplified flow diagram of overall BRE housing stock modelling approach (N.B. the EHS data is

only used to inform the mathematical algorithms of the model – it does not provide data) 16

Figure 2: Comparison of TGSE with England (EHS 2012), % of private sector dwellings failing each of the

indicators 19

Figure 3: Relating housing hazards to health, adapted from CIEH publication 40

Figure 4: Breakdown of hazard types by tenure for TGSE 43

Figure 5: Payback periods for mitigating all hazards 48

List of Maps

Map 1: The local authorities in the Thames Gateway South Essex area 18

Map 2: Percentage of private sector dwellings in TGSE estimated to have a HHSRS category 1 hazard 21

Map 3: Percentage of private sector dwellings in TGSE estimated to have a HHSRS category 1 hazard

for excess cold 23


for falls 25

Map 5: Percentage of private sector dwellings in TGSE to be in disrepair 27

Map 6: Percentage of private sector dwellings in the TGSE estimated to be occupied by households in

fuel poverty – Low Income High Costs definition 30

Map 7: Percentage of private sector dwellings in the TGSE estimated to be occupied by households in

fuel poverty -10% definition 31

Map 8: Percentage of private sector dwellings in TGSE estimated to be occupied by low income

households 33

Map 9: Average SimpleSAP rating - private sector dwellings in TGSE 35

Map 10: EPC rating F or G - private rented dwellings in TGSE 38





Page 11 of 64

1 Introduction

Four of the five local authorities within the Thames Gateway South Essex (TGSE) area commissioned

BRE to undertake a series of modelling exercises on their housing stock9 – Basildon, Castle Point,

Rochford and Southend-on-Sea. Separate reports were produced for each authority describing the

modelling work and providing details of the results obtained from the dwelling level models and database.

Each authority was also provided with a dwelling level database enabling them to obtain specific

information whenever required. This current report provides a high level summary for the Thames

Gateway South Essex area. Three of the commissioning authorities – Basildon, Castle Point and

Southend-on-Sea - chose to also have local data integrated into the BRE Model.

The stock models and database provide each authority with dwelling level information on various key

housing indicators, focussing on private sector housing. The key indicators provide each authority with

detailed information on the likely condition of the stock and the geographical distribution of properties of

interest. These properties are likely to be suitable targets for energy efficiency improvements or other

forms of intervention, such as mitigating Housing Health and Safety Rating System (HHSRS) hazards.

The key indicators are split into categories related to house condition, energy efficiency and household

vulnerability as shown in Table 1 (see Appendix A for full definitions):

Table 1: Key indicators split into categories

Indicator House

condition

indicators

Energy

efficiency

indicators

Household

vulnerability

indicators

Presence of HHSRS cat 1 hazard

Presence of Cat 1 hazard for excess cold

Presence of Cat 1 hazard for falls

Dwellings in disrepair

Fuel Poverty (10% and Low Income, High cost

definitions)

Dwellings occupied by low income households

SimpleSAP rating

N.B. Presence of category 1 hazard for falls does NOT include the hazard of falling between levels


enable the provision of area-wide results and comparisons across the local authorities.





Page 12 of 64

The single indicators shown in Table 1 can also be combined within each database to provide powerful

information on the housing stock, for example dwellings suffering from excess cold and also occupied by

households on a low income. The true potential of the databases lies in their ability to produce combined

indicators such as this, as it allows council officers to explore the stock and to assess the likely scope of

any programmes they might wish to implement.

It is also possible to extract other information from the database which is of use to local authorities. This

information includes estimates relating to the Department for Communities and Local Government’s

(DCLG) Local Authority Housing Statistics (LAHS) reporting of Energy Performance Certificate (EPC)

ratings and costs of mitigating hazards.

The key indicators and other information are derived from the Housing Stock Database which is made up

of a series of dwelling level stock models. The BRE Dwelling Level Stock Models have been used for

many years to provide key housing indicators to local authorities. The most recent 2014 models have

been updated to make use of the results of the 2012 English Housing Survey (EHS)10 and additionally

now incorporate a technique known as geomodelling11 which makes use of Ordnance Survey (OS) data.

The models also make significant use of the Experian UK Consumer Dynamics Database of dwelling and

household indicators as inputs to the models.

The information in each of the databases can be used to ensure councils meet various policy and

reporting requirements. For example, local housing authorities are required to review housing conditions

in their districts in accordance with the Housing Act 200412.

Furthermore, having this information available will also facilitate the delivery of housing strategies. It will

enable a targeted intervention approach to improving housing; therefore allowing councils to concentrate

their resources on housing in the poorest condition or with the greatest health impact.

1.1 Project aims

The main aim of this project was to provide data on key private sector housing indicators for each of the

separate local authorities included in the Thames Gateway South Essex area. The main aims of this work

were therefore to provide estimates of:

The percentage of dwellings meeting each of the key indicators for TGSE overall and broken down by

tenure and then mapped by ward (private sector stock only)

Information relating to LAHS reporting for the private sector stock - EPC ratings and category 1

hazards

Information on the policy background to this work is included in Appendix C and the remainder of this

report focusses on a description of the overall stock modelling approach, followed by the modelling

results for TGSE covering each of the main aims above.

10 2012 is the latest available data when the models were produced

11 The OS data has been used to update a number of the model inputs – the main value of the OS data is the ability

to determine the dwelling type with much greater confidence – see Appendix B for more information.

12 http://www.legislation.gov.uk/ukpga/2004/34/contents





Page 13 of 64

2 Overview of the BRE Dwelling Level Housing Stock Modelling approach

This section provides a simplified overview of the BRE dwelling level housing stock modelling approach.

More detail on the methodology is provided in Appendix B.

A stock modelling approach has been developed and used by BRE for many years and dwelling level

models are used to estimate the likelihood of a particular dwelling meeting the criteria for each of the key

indicators (and other outputs of interest). These outputs can then be mapped to provide the authority with

a geographical distribution of each of the key indicators which can then be used to target resources for

improving the housing stock. The process itself is actually made up of a variety of data sources,

calculations and models.

The models are principally informed by the Department for Communities and Local Government’s (DCLG)

English Housing Survey (EHS)13. The survey is not used to supply data for the database, but rather it

allows the identification of patterns in the housing stock, so that this knowledge can be applied, in the

form of mathematical algorithms, to impute key indicators and energy characteristics from other data

available at the national level. The particular approach for these local authorities, however, makes

significant use of the Experian UK Consumer Dynamics Database of dwelling and household indicators

as inputs to the models. One example is the BRE SimpleCO2 Model which is based on dwelling level

inputs from Experian and expands on these using imputation techniques to provide sufficient information

to calculate the likely energy efficiency of each dwelling in the stock. Some of the key housing indicators,

such as HHSRS excess cold category 1 hazards and BRE’s SimpleSAP14, can be directly inferred from

this data.

Furthermore, 3 of the 4 commissioning local authorities provided additional sources of local data which

were then incorporated into the BRE Housing Stock Model and Database to produce an integrated

housing stock model and database. The additional data provided and how it was used is as follows:

EPC data – EPCs contain data on key dwelling energy characteristics (e.g. energy demand, excess

cold, SimpleSAP) and where these were available they were used in preference to the modelled data.

It should be noted that to comply with bulk EPC data licencing requirements the EPC data is only

used to inform the energy efficiency aspects of the model.

Other Local data

o Local Land and Property Gazetteer (LLPG) data – the Unique Property Reference Number

(UPRN) from the LLPG is used to uniquely identify all properties, while the address details

from the LLPG are used to merge the BRE models and EPC data using address matching.

o Benefits data – any households listed in this data are set as households in receipt of

benefits in the database.

13 The most recent survey used in the housing stock models is 2012.

14 A Simplified version of the SAP model that produces an output broadly comparable to SAP. The SimpleSAP model

is distinct from both full SAP and RD SAP in that uses a smaller, simplified set of inputs.





Page 14 of 64

o HHSRS category 1 hazards data – the data provided lists the hazards which have been

resolved, therefore if these dwellings originally had a hazard in the database that hazard is

removed from that dwelling.

o HMO data –list of properties recorded as being HMOs or licensed HMOs. The UPRNs were

matched to the LLPG data and these HMOs were then added to the modelled outputs.

The number of records integrated and the impact it had on the modelling process is summarised in Table

2 below.

Table 2: Integrated data summary (note that Rochford did not have an integrated database model)

Data source Total no. of records

No. (and %) of addresses matched

Notes / impact on the modelling process

EPC data Basildon Castle Point Southend-on-Sea

40,912 18,935 48,474

30,279 (88%) 13,857 (88%) 30,017 (87%)

Several duplicate cases where the same property has multiple EPC surveys. Additionally, further dwellings had their age or wall type data improved as a result of being in the same block or terrace as dwelling with an EPC.

LLPG data Basildon Southend-on-Sea

88,186 82,553

78,421 (89%) 80,061 (97%)

Used to form backbone of address list

Benefit data Basildon

15,475

13,839 (89%)

Used to update low income model

HMO data Basildon

485

415 (99% of de-duplicated)

Used to compare with BRE model estimates

Energy data Basildon

11,056

10,871 (98%)





Page 15 of 64

Figure 1 shows a simplified flow diagram of the overall BRE housing stock modelling approach. The

process is made up of a series of data sources and models which, combined with various imputation and

regression techniques and the application of other formulae, make up the final database. The database is

essentially the main output of the modelling and provides information on the key indicators and other data

requirements (e.g. energy efficiency variables). More detailed information on the data sources and

models is provided in Appendix B but to summarise:

The data sources are:

EHS, Experian, Ordnance Survey (OS) MasterMap other local data (if available).

The Models are:

SimpleSAP, Fuel Poverty, HHSRS (all hazards, falls hazards and excess cold), Disrepair and Low

Income Households.

The data sources and models are linked as shown in the flow diagram and the modelling process itself

can be divided into “energy inputs” and “other inputs”, which are summarised as follows:

Energy inputs - are developed from Experian. The EHS data is used to impute (using cold deck

imputation15) and interpolate where there are gaps in the data. The “energy inputs” are then fed into the

SimpleCO2 Model to produce the “energy outputs” for the database plus information on excess cold for

the HHSRS Model and information on energy costs for the Fuel Poverty Model.

Other inputs – are developed from Experian, OS MasterMap and other local data sources. The EHS

data is used to impute (using cold deck imputation15) and interpolate where there are gaps in the data.

The “other inputs” are then fed into the HHSRS, Disrepair, and Low Income Models (note that tenure data

is fed directly into the database). Information from the EHS also feeds into the Fuel Poverty, HHSRS,

Disrepair and Low Income Models.

15 Cold deck imputation is a process of assigning values in accordance with their known proportions in the stock.





Page 16 of 64

Figure 1: Simplified flow diagram of overall BRE housing stock modelling approach (N.B. the EHS data is

only used to inform the mathematical algorithms of the model – it does not provide data)

BRE housing stock modelling process Data

Integration of additional data Imputed (cold deck)

Data used for imputation & interpolation Information

Outputs

Experian data

Model inputs

Experian data

EHS data Energy inputs

BRE SimpleCO2 Model

(BREDEM-based model)

Energy outputs:

SimpleCO2

SimpleSAP

Excess cold

Energy demand

Energy cost

Other inputs

Local data

OS MasterMap data EPC data

Fuel Poverty Model

HHSRS ‘Falls’ & ‘Other’ Model

Disrepair Model

Low Income Model

BRE Integrated Housing Stock Database

(dwelling level)

Key indicators:

House condition

Energy efficiency

Household vulnerability

+

EPC, Cat 1 hazards, HMOs

Energy efficiency variables and energy

planning variables

tenure

Loft & wall

insulation

formula

formulae





Page 17 of 64

3 Summary results for TGSE from the BRE Dwelling Level Housing Stock Models and Database

As described in the previous section, the housing stock modelling process consists of a series of different

stock models with the main output being the database. The results provided in this section are a high

level summary for TGSE as a whole. The individual reports and databases provided to each authority are

at a more detailed level.

The first sub-section below provides a map of the local authorities making up the TGSE area and a table

summarising the tenure split for each local authority. The second section provides a comparison of TGSE

with England based on the 2012 English Housing Survey (EHS).

The results are then displayed in the remaining sub-sections and include maps of a number of variables,

thus enabling quick observation of the geographical distribution of areas of interest. The maps show the

percentages of dwellings by ward for each local authority that are estimated to have met the requirements

for each of the variables. The ranges shown in the map keys are defined based on the Jenks’ Natural

Breaks algorithm of the COA statistics16. The outputs in the lightest and darkest colours on the maps

show the extreme ends of the range, highlighting the best and the worst areas. The maps also highlight

the differences between areas, showing that the results for some areas are much worse than for others

and these are the specific areas which might warrant attention. The maps also show that within local

authorities there can be large differences between the results at ward level.

The sub-sections are as follows:

Comparison of the key indicators across the local authorities within TGSE (see Appendix A for full definitions):

o Presence of HHSRS category 1 hazard o Presence of category 1 hazard for excess cold o Presence of category 1 hazard for falls o Dwellings in disrepair o Fuel poverty (10% and Low Income High Costs definitions) o Dwellings occupied by low income households o SimpleSAP rating

Comparison of information relating to LAHS reporting17:

o EPC ratings o Cost of mitigating category 1 hazards

16 Natural breaks classes are based on natural groupings inherent in the data.

17 3 of the 4 commissioning TGSE local authorities were also supplied with information on Houses in Multiple

Occupation (HMOs) and various energy efficiency and energy planning variables. As this exercise was not carried out

for the whole region this information has not been included in this summary report.





Page 18 of 64

3.1 Overview of Thames Gateway South Essex

Map 1 below shows the 5 local authorities making up the Thames Gateway South Essex area. The majority of data in this report is shown as a high level summary separated into local authorities; however, each commissioning local authority has been provided with a database which provides data at more disaggregated levels, e.g. ward, Census Output Area (COA).

Table 3 provides an overview of the housing stock in TGSE broken down by local authority and tenure.

Map 1: The local authorities in the Thames Gateway South Essex area





Page 19 of 64

Table 3: Overview of TGSE housing stock by tenure for each local authority

Owner occupied Private rented

Basildon 49,555 10,279 18,587 78,421

Castle Point 31,094 3,111 2,262 36,467

Rochford 29,886 3,217 1,554 34,657

Southend-on-Sea 49,372 20,083 10,599 80,054

Thurrock 46,129 13,660 5,300 65,089

TGSE 206,036 50,350 38,302 294,688

No. of dwellings

Private sector stockSocial TOTAL

Local authority

3.2 Comparison of TGSE with England

Figure 2 shows the percentage of private sector dwellings in TGSE failing each of the key indicators

compared to the percentage for England as a whole (2012 EHS data). TGSE has a higher percentage of

private sector dwellings with fall hazards compared to England. It does however, perform better for all

other indicators; particularly excess cold, fuel poverty (10% definition) and low income households.

Looking at SimpleSAP ratings for the private sector stock - TGSE has an average rating of 59 compared

to 55 for England as a whole.

Figure 2: Comparison of TGSE with England (EHS 2012), % of private sector dwellings failing each of the indicators

All HHSRS cat1 hazards

Excess cold Fall hazards DisrepairFuel poverty

(10%)Fuel poverty

(LIHC)Low incomehouseholds

TGSE 14% 2% 11% 3% 11% 9% 11%

2012 EHS 15% 5% 8% 5% 14% 11% 14%

0%

2%

4%

6%

8%

10%

12%

14%

16%

% o

f p

riva

te s

ect

or

dw

elli

ngs





Page 20 of 64

3.3 Comparison of the key indicators across the TGSE local authorities

The following sub-sections provide the results for each of the key indicators.

3.3.1 Presence of a HHSRS category 1 hazard

Table 4 shows the percentage of dwellings estimated to have a HHSRS category 1 hazard by local authority and tenure, and for TGSE overall. The greatest proportions of all stock with hazards is in Southend-on-Sea (18%) and the lowest are in Basildon and Rochford (both 11%). The figure for TGSE overall is 14%.

For the private sector stock, again the highest proportion is in Southend-on-Sea (20%) and the lowest is in Basildon (11%) with the figure for TGSE overall being 14%.

Overall, the social stock has a lower proportion of dwellings with hazards compared to the other tenures.

The commissioning local authorities were provided with maps at Census Output Area level. However, given this report is presenting a picture of the whole region, the maps within this report are produced at ward level. Using the first map below (Map 2) as an example, it can be seen that all 5 local authorities across the region are split into wards, and, in this instance there are 7 wards that have 22 - 30% of private sector dwellings estimated to have the presence of a category 1 hazard. The ranges shown in the map are based on the Jenks’ Natural Breaks algorithm of the ward level statistics. The outputs in the lightest and darkest colours on the maps show the extreme ends of the range.

Map 2 shows that the greatest numbers of hazards are concentrated in Southend-on-Sea, to the east of Rochford and southern wards of Thurrock. Wards to the north of Basildon and the east of Rochford have the lowest levels of hazards

The maps also highlight the differences between areas, showing that the results for some areas are much worse than for others, and these are specific areas which might warrant attention.

Table 4: Percentage of dwellings estimated to have a HHSRS category 1 hazard by local authority and private sector tenure

% of private

sector

% of owner

occupied

% of private

rented

Basildon 11% 11% 14% 8% 11%Castle Point 12% 12% 14% 6% 12%Rochford 12% 11% 15% 8% 11%Southend-on-Sea 20% 17% 26% 8% 18%

Thurrock 14% 14% 15% 8% 13%

TGSE 14% 13% 19% 8% 14%

Local authority

% of dwellings with HHSRS category 1 hazards

Private sector stock

% of social % of all stock

Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

21 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex






Page 22 of 64

3.3.2 Presence of category 1 hazard for excess cold

The percentage of dwellings estimated to have the presence of a category 1 hazard for excess cold is

shown in Table 5, for TGSE overall and broken down by local authority and tenure. The greatest

proportions of all stock with the presence of an excess cold hazard are in Rochford and Southend-on-Sea

(both 3%).

For the private sector stock, the highest proportion is also in Southend-on-Sea (4%), followed by Rochford (3%), the remaining local authorities have lower proportions of excess cold bringing the figure for the region as a whole to 2%.

Overall, and perhaps unsurprisingly, the social stock has a lower proportion of dwellings with excess cold

hazards compared to the other tenures.

Map 3 shows the geographical distribution of private sector dwellings in the TGSE area estimated to have

a category 1 hazard for excess cold. The greatest concentrations of excess cold are mainly to the

southern areas of Southend-on-Sea and Thurrock, and also in more rural areas where dwellings tend to

be older, there are fewer flats and there tend to be more properties off the gas network.

Table 5: Percentage of dwellings estimated to have a HHSRS category 1 hazard for excess cold by local authority and private sector tenure

% of private

sector

% of owner

occupied

% of private

rented


Thurrock 2% 2% 3% 2% 2%

TGSE 2% 2% 4% 1% 2%

Local authority

% of dwellings with category 1 hazard for excess cold



Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

23 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex

Map 3: Percentage of private sector dwellings in TGSE estimated to have a HHSRS category 1 hazard for excess cold





Page 24 of 64

3.3.3 Presence of category 1 hazard for falls

Table 6 shows the percentage of dwellings estimated to have a category 1 hazard for falls by local authority and tenure, and for TGSE overall. The greatest proportions of all stock with fall hazards is in Southend-on-Sea (13%) and the lowest is in Basildon and in Rochford (both 8%).

For the private sector stock, the highest proportion is again in Southend-on-Sea (14%) and the lowest is in Rochford (8%), with the figure for TGSE overall being 11%.

Overall, the social stock has a lower proportion of dwellings with fall hazards compared to the other tenures.

Map 4 shows the geographical distribution of private sector dwellings in TGSE estimated to have a

category 1 hazard for falls. The greatest numbers of hazards are concentrated in Southend-on-Sea and

the southern wards of Basildon. This may be a result of older, more terraced housing in urban areas

which tends to result in more falls hazards.

Table 6: Percentage of dwellings estimated to have a HHSRS category 1 hazard for falls by local authority and private sector tenure

% of private

sector

% of owner

occupied

% of private

rented


Thurrock 11% 11% 10% 4% 10%

TGSE 11% 10% 13% 4% 10%

Local authority

% of dwellings with category 1 hazard for falls



Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

25 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex

Map 4: Percentage of private sector dwellings in TGSE estimated to have a HHSRS category 1 hazard for falls





Page 26 of 64

3.3.4 Dwellings in disrepair

This indicator is based on the former Decent Homes Standard which states that a dwelling fails this

criterion if it is not found to be in a reasonable state of repair. This is assessed by looking at the age of

the dwelling and the condition of a range of building components (including walls, roofs, windows, doors,

electrics and heating systems).

Table 7 shows the percentage of dwellings estimated to be in disrepair by local authority and tenure and

for TGSE overall. The greatest proportion of all stock in disrepair is in Southend-on-Sea (4%) and the

other local authorities are all lower at 2%.

For the private sector stock, the highest proportion is again in Southend-on-Sea (4%) and the other local

authorities are all lower at 2%, with the figure for the region overall being 3%.

Overall the social stock for all 5 local authorities are the same (1%) and owner occupied stock have

similar proportions of dwellings in disrepair compared to the private rented stock which has double the

proportion for 3 local authorities.

Map 5 shows the geographical distribution of private sector dwellings in TGSE estimated to be in

disrepair. The greatest concentrations are generally in Southend-on-Sea.

Table 7: Percentage of dwellings estimated to be in disrepair by local authority and private sector tenure

% of private

sector

% of owner

occupied

% of private

rented


Thurrock 2% 2% 4% 1% 2%

TGSE 3% 2% 5% 1% 2%

Local authority

% of dwellings in disrepair



Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

27 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex

Map 5: Percentage of private sector dwellings in TGSE to be in disrepair





Page 28 of 64

3.3.5 Fuel poverty (Low Income High Costs and 10% definitions)

This report covers both the original definition and the more recent definition of fuel poverty. The original

definition states that a household is said to be in fuel poverty if it spends more than 10% of its income on

fuel to maintain an adequate level of warmth (defined as 21oC for the main living area, and 18oC for other

occupied rooms in the 2012 Hills Fuel Poverty Review)18. For the purposes of this report this is termed

“fuel poverty (10%)”.

Under the Low Income High Costs definition, a household is said to be in fuel poverty if they have

required fuel costs that are above average (the national median level) and were they to spend that

amount they would be left with a residual income below the official poverty line. For the purposes of this

report this is termed “fuel poverty (Low Income High Costs)”.

Table 8 and Table 9 show the results based on the LIHC and 10% definitions respectively. In general, the estimated percentages are lower under the LIHC definition. For the private sector stock, Southend-on-Sea has the highest proportions of fuel poverty under both definitions – 12% and 15%.

Map 6 and Map 7 show the results based on the LIHC and 10% definitions respectively and both indicate

that the highest concentrations tend to be in the Southend-on-Sea and southern wards of Thurrock.


tenure – Low Income High Costs (LIHC) definition

% of private

sector

% of owner

occupied

% of private

rented


Thurrock 10% 7% 19% 7% 10%

TGSE 9% 7% 18% 6% 9%

Local authority

% of dwellings in fuel poverty (LIHC definition)



18 Hills, J. Getting the measure of fuel poverty - Final Report of the Fuel Poverty Review, London: LSE., 2012





Page 29 of 64


tenure – 10% definition

% of private

sector

% of owner

occupied

% of private

rented


Thurrock 11% 9% 17% 8% 11%

TGSE 11% 9% 18% 8% 11%

Local authority

% of dwellings in fuel poverty (10% definition)



Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

30 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

ba

se

for T

ha

mes G

ate

wa

y S

outh

Essex

Map 6: Percentage of private sector dwellings in the TGSE estimated to be occupied by households in fuel poverty – Low Income High Costs

definition

Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

31 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

ba

se

for T

ha

mes G

ate

wa

y S

outh

Essex

Map 7: Percentage of private sector dwellings in the TGSE estimated to be occupied by households in fuel poverty -10% definition





Page 32 of 64

3.3.6 Dwellings occupied by low income households

Table 10 shows the percentage of dwellings estimated to be occupied by low income households by local

authority and tenure, and for TGSE overall. The greatest proportions of all stock occupied by households

on a low income are in Basildon and Southend-on-Sea (both 22%) and the lowest is in Rochford (9%).

For the private sector stock, the highest proportions are in Southend-on-Sea and Thurrock (both 14%)

and the lowest is in Rochford (6%) with the figure for TGSE overall being 11%.

Unsurprisingly, the social stock has a much higher proportion of dwellings on low income compared to the

other tenures.

Map 8 shows the geographical distribution of private sector dwellings in TGSE on low incomes. The

greatest numbers are generally concentrated in Southend-on-Sea, the southern wards of Thurrock and to

a lesser extent, the southern wards of Basildon.

Table 10: Percentage of dwellings estimated to be occupied by low income households by local authority

and by private sector tenure

% of private

sector

% of owner

occupied

% of private

rented


Thurrock 14% 9% 30% 70% 18%

TGSE 11% 7% 25% 68% 18%

Local authority

% of dwellings on low income



Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

33 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex

Map 8: Percentage of private sector dwellings in TGSE estimated to be occupied by low income households





Page 34 of 64

3.3.7 SimpleSAP rating

The average SimpleSAP ratings for TGSE overall and broken down by local authority and tenure are

shown in Table 11. For all stock, Basildon has the highest average SimpleSAP rating (62) and Southend-

on-Sea has the lowest average rating (57).

For the private sector stock, Basildon has the highest average SimpleSAP rating (62) and Southend-on-

Sea has the lowest (55). The average SimpleSAP rating for the region as a whole is 59 and the England

average from the 2012 EHS is 55. Overall the social stock has higher average ratings compared to the

other tenures.

Map 9 shows the geographical distribution of average SimpleSAP ratings. Rural areas with low

population densities tend to have the lowest ratings. The size of the home itself is not a factor in

SimpleSAP, and properties in these areas may be more likely to be semi-detached or detached, and

therefore have larger heat loss areas. As they are in a rural location, properties in these wards may also

not be connected to the mains gas network leading to a decrease in SimpleSAP score.

Table 11: Average SimpleSAP rating by local authority and by private sector tenure

Total private

sector Owner occupied Private rented

Basildon 62 61 63 65 62

Castle Point 59 59 60 64 59

Rochford 58 58 60 62 59

Southend-on-Sea 55 55 56 64 57

Thurrock 60 59 63 62 60

TGSE 59 59 60 64 59

Local authority

Average SimpleSAP rating


Social All stock

Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

35 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex

Map 9: Average SimpleSAP rating - private sector dwellings in TGSE





Page 36 of 64

3.4 Comparison of information relating to LAHS reporting

The following sub-sections show the results for EPC ratings and the cost of mitigating category 1

hazards.

3.4.1 EPC ratings

An Energy Performance Certificate (EPC) is required whenever a new building is constructed, or an

existing building is sold or rented out. An EPC is a measure of the energy efficiency performance of a

building and is rated from band A – G, with A representing the best performance. The EPC ratings

correspond to a range of SAP ratings from 1 – 100, with 100 being the best. It is possible, therefore, to

give a dwelling an EPC rating based on the SAP rating.

Table 12 shows the Bands A – G and corresponding SAP scores in brackets. The columns show the

percentage of private sector stock falling into each of the EPC rating bands for each local authority and

for TGSE overall.

The percentage of private sector dwellings with an EPC rating below Band E for TGSE overall is 4.3%.

Southend-on-Sea has the highest percentage falling below Band E (6.8%) and Castle Point has the

lowest (2.4%).

Under the Energy Act 2011, new rules mean that from 2018 landlords must ensure that their properties

meet a minimum energy efficiency standard - which has been set at band E - by 1 April 201819, 20.

Table 13 shows the breakdown of SimpleSAP results into the A – G Bands for the private rented stock

only. The percentage of private rented dwellings with an EPC rating below Band E for TGSE overall is

6.6%. Southend-on-Sea has the highest percentage falling below Band E (9.4%) and Basildon has the

lowest (3.8%). The distribution of dwellings with EPC ratings below band E is shown in Map 10. These

are for the private rented stock only, since this is affected by the new rules on minimum standards. Under

the legislation these properties would not be eligible to be rented out after 2018.

19 http://www.legislation.gov.uk/uksi/2015/962/contents/made

20 Although landlords will still be able to rent out F and G rated properties after this date they will not be able to renew

or sign a new contract.

http://www.legislation.gov.uk/uksi/2015/962/contents/made





Page 37 of 64

Table 12: Percentage of private sector stock falling into each of the EPC ratings bands (based on SimpleSAP) by local authority

BasildonCastle

PointRochford

Southend-

on-SeaThurrock TGSE

(92-100) A 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

0.4% 0.1% 0.1% 0.4% 0.3% 0.3%

23.0% 12.9% 15.8% 10.3% 20.0% 16.6%

56.7% 60.8% 55.2% 45.2% 53.6% 53.2%

17.3% 23.9% 23.9% 37.3% 22.3% 25.6%

2.2% 2.0% 4.2% 6.0% 3.3% 3.7%

0.4% 0.4% 0.7% 0.8% 0.4% 0.5%

EPC rating band

Distribution of EPC rating bands by local authority - private sector

(39-54) E

(21-38) F

(1-20) G

(81-91) B

(69-80) C

(55-68) D

Table 13: Percentage of private rented stock falling into each of the EPC ratings bands (based on SimpleSAP) by local authority

BasildonCastle

PointRochford

Southend-

on-SeaThurrock TGSE

(92-100) A 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

1.1% 0.7% 0.4% 0.9% 0.9% 0.9%

35.3% 22.8% 27.6% 13.2% 34.4% 25.0%

45.8% 49.9% 44.3% 43.1% 43.7% 44.3%

14.1% 21.9% 18.4% 33.5% 16.6% 23.3%

3.0% 3.6% 7.3% 8.0% 4.0% 5.6%

0.7% 1.1% 2.0% 1.4% 0.5% 1.0%

EPC rating band

Distribution of EPC rating bands by local authority - private rented sector

(1-20) G

(81-91) B

(69-80) C

(55-68) D

(39-54) E

(21-38) F

Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

38 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex

Map 10: EPC rating F or G - private rented dwellings in TGSE





Page 39 of 64

4 Health Impact Assessment (HIA) summary

4.1 Introduction

The Thames Gateway South Essex partnership has recognised that poor housing has an important effect

on health as most occupiers spend longer in their own home than anywhere else. Additional information

is also required concerning private sector housing to inform the Joint Strategic Needs Assessment

(JSNA).

BRE was commissioned to produce housing stock models to help understand the condition of the private

sector housing within local authority areas (as described in the previous sections). The housing stock

model is based on data gathered from a number of sources (including the English Housing Survey (EHS))

and includes an assessment of dwelling hazards using the Housing Health and Safety Rating System

(HHSRS). It also integrates various data sources provided by local authorities, as discussed in Section 2.

This data from the integrated housing stock model has then been used as a basis for this Health Impact

Assessment (HIA) to better understand the effect of private sector housing hazards and intervention

strategies on the health of residents across the region.

A Health Impact Assessment (HIA) is a formal method of assessing the impact of a project, procedure or

strategy on the health of a population. This HIA draws on evidence of the health impact of hazards

identified using the Housing Health and Safety Rating System (HHSRS21) and a methodology developed

by the BRE Trust and published in the “Real Cost of Poor Housing”22. The HHSRS is the method by

which housing condition is now assessed in accordance with the Housing Act 2004. A dwelling with a

category 1 hazard is considered to fail the minimum statutory standard for housing and is classified as

“poor housing”.

21 Housing Health and Safety Rating System Operating Guidance, Housing Act 2004, Guidance about Inspections

and Assessments given under Section 9, ODPM, 2006

22 The Real Cost of Poor Housing, M Davidson et al., IHS BRE Press, February 2010





Page 40 of 64

4.2 Housing hazards

The links between hazards found in dwellings and the health issues that may arise are shown in Figure

3.

Figure 3: Relating housing hazards to health, adapted from CIEH publication23

Falls

Access problems

Pneumonia &

heart attacks

Stress & worry

Sleep deprivation

Rubbish dumping

Lack of educational

achievement

Colds

Asthma

Lack of money

23 Good Housing Leads to Good Health, A toolkit for environmental health practitioners CIEH, September 2008





Page 41 of 64

Of the total 25 HHSRS hazards covered in the EHS, there are 15 hazards within TGSE which there is

sufficient information to quantify the number of dwellings affected. The hazards are divided into those

likely to cause:

Physiological conditions

Psychological illness

Infection

Accidents

Table 14: The 29 hazards covered by the HHSRS (those highlighted in bold are covered in this HIA,

although the 3 infection hazards and electrical hazards are not covered in the cost benefit analysis due to

insufficient information)

Physiological conditions Psychological illness Infection Accidents

Damp & mould growth Crowding & space Domestic hygiene, pests

& refuse

Falls associated with

baths etc.

Excessive cold Entry by intruders Food safety Falling on level surfaces

Excessive heat Lighting Personal hygiene,

sanitation & drainage

Falling on stairs etc.

Asbestos Noise Water supply Falling between levels

Biocides Electrical hazards

CO & fuel combustion

productions

Fire

Lead Flames, hot surfaces etc.

Radiation Collision & entrapment

Un-combusted fuel gas Explosions

Volatile organic

compounds

Position & operability of

amenities

Structural collapse & falling

elements

Italics = not covered in any further detail in this report as there is insufficient data for these purposes.





Page 42 of 64

Table 15 shows the main health conditions caused by each of the hazard types, the vulnerable groups

most affected and the mitigation actions which could be taken

Table 15: Summary of the main hazards, their effects, vulnerable groups affected and potential mitigation

actions

Housing Hazard type

Main health conditions Vulnerable groups Mitigating the hazard

Excess cold Respiratory diseases, chronic obstructive pulmonary disease (COPD), cardio-

vascular conditions

Increased risk of falls

Worsening of symptoms of rheumatoid arthritis and leg ulcers

Excess winter deaths

Work and school days lost, reduction in educational attainment (Marmot report)

Older people

People in fuel poverty

Families

Improving heating and thermal efficiency

measures

Damp and mould growth

Asthma exacerbation, lower respiratory infections

Social isolation

Children

Adults

Improved heating, ventilation and addressing any

structural problems

Entry by intruders Fear of burglary

Emotional stress All

Window and door locks, security lighting

and key safes

Falls in baths, on stairs, trips and

slips

Accidents

Fractures to older people and subsequent loss of independence

General health deterioration

Older people Stair rails, balustrades,

grab rails, repair to paths

Accidents affecting children

(falling between levels, flames & hot surfaces, electrical hazards, collision &

entrapment)

Physical injury, falls, electrocution, severe burns and scalds

Children

Identifying hazards, provide more space,

education of professionals





Page 43 of 64

4.3 Methodology

4.3.1 Number of hazards and estimated instances requiring medical intervention

Maps provided in the previous sections of this report show the distribution of all HHSRS category 1

hazards in TGSE and the two most common hazards of excess cold hazards and fall hazards.

Figure 4 below shows the estimated number of category 1 hazards by private tenure. The tenure split is

important because dwellings that are privately rented should have any category 1 hazard mitigated by the

landlord at their expense to comply with the Housing Act 2004. The chart shows the figures for all

hazards and it is clear that many of the hazards are not present in sufficient numbers to be considered

further in this HIA.

Figure 4: Breakdown of hazard types by tenure for TGSE

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000

Domestic hygiene, Pests and Refuse

Electrical hazards

Crowding and space

Collision and entrapment

Personal hygiene, Sanitation and Drainage

Food safety

Entry by intruders

Flames, hot surfaces etc

Damp and mould growth

Fire

Falling between levels

Falls associated with baths etc

Excess cold

Falling on level surfaces etc

Falling on stairs etc

Private rented Owner occupied

For these hazards, it is then possible to estimate the number of harm outcomes requiring medical

intervention resulting from these hazards. Table 16 shows the total number of category 1 hazards is

48,598 and the estimated number of instances requiring medical intervention expected across the region

is 2,275. The most common category 1 hazard is falling on stairs etc. and the second most common is

falling on level surfaces, etc., followed by excess cold. The most common category 1 hazards do not

necessarily mean the greatest number of medical interventions as the HHSRS scoring system requires

an assessment of likelihood and the extent of harm outcome. For example, it could be the case that the

hazard of damp and mould causes a greater number of incidents than excess cold although the severity

of harm outcome would be less.





Page 44 of 64

4.3.2 Cost of prospective interventions to reduce the number of hazards

The cost of work necessary to mitigate the hazards is representative of local (at a regional level) costs

based on information from the English Housing Survey (EHS) and consistent with “The Cost of Poor

Housing to the NHS24” briefing paper produced by BRE as an update to “The Real Cost of Poor

Housing22” report. The cost is based on mitigating the hazard and bringing the dwelling up to the standard

for an “average dwelling”.

4.3.3 Costs to the NHS and wider society of treating these health issues

Costs to the NHS are based on real estimates of the costs of incidents occurring as a result of the

hazards and have been developed by looking at typical health outcomes and first year treatment costs

that can be attributed to selected HHSRS hazards. This information is published in ‘The Real Cost of

Poor Housing’25 and as updated in ‘The Cost of Poor Housing to the NHS’24. The NHS costs do not take

into account the more complex nature of the wider cost impacts to society. ‘The Full Cost of Poor

Housing’26 report provides the most up-to-date estimates for the costs to society and provides a different

cost for each harm outcome. These costs to society are greater than the costs to the NHS and cover a

variety of costs, which could include, but are not limited to, the following:

Social services costs following discharge from hospital

Capital value of the dwelling

Loss of future earnings

Increased spending on benefits

Cost of moving

Cost of enforcement action by councils

Table 16 summarises the estimated costs to the NHS and society of poor housing for each of the TGSE

local authorities. If category 1 hazards in the private sector across the region were mitigated, it is

estimated this would save the NHS over £8 million, with just under £74 million savings to wider society.

Table 17 summarises the estimated costs to the NHS and wider society, and potential savings if the

hazard of excess cold was mitigated across the private stock. It is estimated that the NHS could save just

under £1 million, with savings to wider society of nearly £19 million. This can be compared with the three

fall hazards (defined as falls involving stairs and steps, associated with baths and trips and slips) where

the vulnerable group is persons over 60 years of age. The potential saving to the NHS is £6 million, and

to society is £46 million, as shown in Table 18.

24 BRE Briefing Paper: The cost of poor housing to the NHS, Nicol S, Roys M and Garrett H, BRE Trust, 2015 -

http://www.bre.co.uk/filelibrary/pdf/87741-Cost-of-Poor-Housing-Briefing-Paper-v3.pdf

25 The Real Cost of Poor Housing, M Davidson et al., IHS BRE Press, February 2010

26 The Full Cost of Poor Housing, Roys M, Nicol S, Garrett H and Margoles S, HIS BRE Press, 2016

http://www.bre.co.uk/filelibrary/pdf/87741-Cost-of-Poor-Housing-Briefing-Paper-v3.pdf





Page 45 of 64

4.3.4 Health cost benefit scenarios

Taking this a step further it is possible to look at a cost benefit analysis and determine the likely payback

periods of mitigating hazards. The payback period is the time taken to break even on an investment and,

in this case, is based on the cost of mitigating the hazard and the savings achieved from carrying out the

mitigation work to an individual dwelling.

Because it is not possible to account for building type, local knowledge of the particular characteristics will

be important for making decisions on whether a particular dwelling type is more cost effective for priority

spending or whether the decision should be made in accordance with occupier need.

The HIA reports provided to the individual TGSE local authorities provide a range of cost benefit

analyses. However, the general results for each hazard assessed are depicted in Figure 5 which shows

that the lowest payback periods are always achieved for the society analysis because the savings are

greater than for the NHS but the mitigation costs are the same. The hazards with the shortest payback

periods are generally falls associated with baths and falls on the level since mitigation of these hazards

can be achieved at relatively low cost. The hazards with the longest payback periods are those which are

more complex and therefore more costly to mitigate, such as damp and mould, excess cold and fire.

4.4 Summary of findings

This section provides a summary of the HIA results at local authority level and for TGSE as a whole. A

summary is provided for all HHSRS category 1 hazards and for the two most common hazards – excess

cold and fall hazards.

Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

46 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex

Table 16: Summary of HIA results for all HHSRS category 1 hazards, split by local authority and for TGSE as a whole

Costs to NHS Costs to society Savings to NHSSavings to

society

Basildon 9,199 442 £15,501,958 £1,704,470 £13,554,090 £1,587,890 £13,544,727

Castle Point 5,363 249 £8,340,369 £992,340 £7,645,951 £923,470 £7,640,494

Rochford 5,147 214 £10,478,543 £941,290 £8,406,391 £872,640 £8,400,624

Southend-on-Sea 17,985 856 £34,113,903 £3,324,890 £28,048,434 £3,096,330 £28,029,623

Thurrock 10,903 514 £19,127,299 £2,016,350 £16,393,270 £1,877,250 £16,382,000

TGSE 48,598 2,275 £87,562,073 £8,979,340 £74,048,137 £8,357,580 £73,997,467

Local authority

Numbers of

hazards

(total private

sector stock)

Estimated

number of

instances

requiring

medical

intervention

Cost of

mitigating all

hazards

Potential annual costs of not

mitigating hazards


mitigating hazards

Table 17: Summary of HIA results for category 1 excess cold hazards, split by local authority and for TGSE as a whole


society

Basildon 917 5 £4,786,723 £154,140 £2,763,097 £138,730 £2,761,428

Castle Point 423 2 £2,208,052 £71,100 £1,274,580 £63,990 £1,273,810

Rochford 1,023 6 £5,340,041 £171,960 £3,082,495 £154,760 £3,080,634

Southend-on-Sea 2,611 15 £13,629,371 £438,900 £7,867,443 £395,010 £7,862,693

Thurrock 1,280 7 £6,681,576 £215,160 £3,856,885 £193,640 £3,854,556

TGSE 6,254 35 £32,645,763 £1,051,260 £18,844,499 £946,130 £18,833,121

Local authority

Numbers of

excess cold

hazards

(total private

sector stock)

Estimated

number of

instances

requiring

medical

intervention

Cost of

mitigating all

hazards


mitigating hazards


mitigating hazards

Com

me

rcia

l in C

onfid

ence

© B

uild

ing R

ese

arc

h E

sta

blis

hm

ent L

td

Repo

rt No. T

em

pla

te

Vers

ion V

2-0

82

014

Page

47 o

f 64

Housin

g S

tock M

odellin

g a

nd D

ata

base

for T

ha

mes G

ate

wa

y S

outh

Essex

Table 18: Summary of HIA results for category 1 fall hazards affecting older people (i.e. falls associated with baths, falls on the level and falls on

stairs), split by local authority and for TGSE as a whole


society

Basildon 7,360 293 £7,769,232 £1,354,050 £9,147,504 £1,254,720 £9,140,006

Castle Point 4,511 179 £4,762,204 £829,970 £5,607,025 £769,080 £5,602,429

Rochford 3,757 149 £3,966,352 £691,260 £4,669,988 £640,550 £4,666,160

Southend-on-Sea 13,384 531 £14,128,875 £2,462,440 £16,635,356 £2,281,810 £16,621,721

Thurrock 8,552 339 £9,028,255 £1,573,480 £10,629,879 £1,458,060 £10,621,166

TGSE 37,564 1,491 £39,654,918 £6,911,200 £46,689,753 £6,404,220 £46,651,483


mitigating hazards

Local authority

Numbers of fall

hazards affecting

older people

(total private

sector stock)

Estimated

number of

instances

requiring

medical

intervention

Cost of

mitigating all

hazards


mitigating hazards





Page 48 of 64

Figure 5: Payback periods for mitigating all hazards





Page 49 of 64

5 Conclusion

5.1 Conclusion

The majority of local authorities within TGSE commissioned BRE to undertake a series of modelling

exercises on their housing stock27 – Basildon, Castle Point, Rochford and Southend-on-Sea. Separate

reports were produced for each authority describing the modelling work and providing details of the

results obtained from the dwelling level models and database. Each authority was also provided with a

dwelling level database enabling them to obtain specific information whenever required. This current

report provides a high level summary for the Thames Gateway South Essex areas.

The stock models and databases for each of the separate local authorities have been used to provide the

following, with a focus on private sector housing:

The percentage of dwellings meeting each of the key indicators for TGSE overall and broken down

by tenure and then mapped by ward (private sector stock only)

Information relating to LAHS reporting for the private sector stock – EPC ratings and category 1

hazards28

Such information will facilitate the decision making process for targeting resources to improve the

condition of housing and to prevent ill health resulting from poor housing conditions. Furthermore, the

results of this project provide the local authorities in TGSE with information which will assist in health and

housing policy and strategy development whether these are inspired locally, arise from obligations under

the Housing Act 2004 or as responses to government initiatives such as DCLG’s Housing Strategy Policy

and ECO.

The project estimates that there are over 36,879 dwellings containing 48,598 serious (category 1

hazards) across the private stock. These hazards would require £87.5 million of improvements to

mitigate, but would save the NHS an estimated £8 million per year, with total savings to society of nearly

£74 million. Given the potential savings from investing in mitigating housing hazards, falls hazards have

some of the shortest payback periods.


enable the provision of county-wide results and comparisons across the local authorities.

28 The 4 commissioning TGSE local authorities were also supplied with information on Houses in Multiple Occupation

(HMOs) and various energy efficiency and energy planning variables. As this exercise was not carried out for the

whole region this information has not been included in this summary report.





Page 50 of 64

Appendix A Definitions of the key indicators

1. House condition indicators

a. The presence of a category 1 hazard under the Housing Health and Safety Rating

System (HHSRS29) – reflecting both condition and thermal efficiency

Homes posing a category 1 hazard under the HHSRS – the system includes 29 hazards in

the home categorised into category 1 (serious) or category 2 (other) based on a weighted

evaluation tool. Note that this includes the hazard of excess cold which is also included as

one of the energy efficiency indicators.

The 29 hazards are:

1 Damp and mould growth 16 Food safety

2 Excess cold 17 Personal hygiene, Sanitation and Drainage

3 Excess heat 18 Water supply

4 Asbestos 19 Falls associated with baths etc.

5 Biocides 20 Falling on level surfaces etc.

6 Carbon Monoxide and fuel combustion products 21 Falling on stairs etc.

7 Lead 22 Falling between levels

8 Radiation 23 Electrical hazards

9 Uncombusted fuel gas 24 Fire

10 Volatile Organic Compounds 25 Flames, hot surfaces etc.

11 Crowding and space 26 Collision and entrapment

12 Entry by intruders 27 Explosions

13 Lighting 28 Position and operability of amenities etc.

14 Noise 29 Structural collapse and falling elements

15 Domestic hygiene, Pests and Refuse

b. The presence of a category 1 hazard for falls (includes “falls associated with baths”,

“falling on the level” and “falling on stairs”)

The HHSRS Falls Model includes the 3 different falls hazards where the vulnerable person is

over 60 as listed above.

c. Dwellings in disrepair (based on the former Decent Homes Standard criteria for

Disrepair)

29 The Housing Health and Safety Rating System (HHSRS) is a risk-based evaluation tool to help local authorities

identify and protect against potential risks and hazards to health and safety from any deficiencies identified in

dwellings. It was introduced under the Housing Act 2004 and applies to residential properties in England and Wales.





Page 51 of 64

The previous Decent Homes Standard states that a dwelling fails this criterion if it is not found

to be in a reasonable state of repair. This is assessed by looking at the age of the dwelling

and the condition of a range of building components including walls, roofs, windows, doors,

electrics and heating systems).

2. Energy efficiency indicators:

a. The presence of a category 1 hazard for excess cold (using SAP ratings as a proxy

measure in the same manner as the English House Condition Survey)

This hazard looks at households where there is a threat to health arising from sub-optimal

indoor temperatures. The HHSRS assessment is based on the most low income group for

this hazard – persons aged 65 years or over (note that the assessment requires the hazard to

be present and potentially affect a person in the low income age group should they occupy

that dwelling. The assessment does not take account of the age of the person actually

occupying that dwelling at that particular point in time).

The English Housing Survey (EHS) does not measure the actual temperatures achieved in

each dwelling and therefore the presence of this hazard is measured by using the SAP rating

as a proxy. Dwellings with a SAP rating of less than 31.5 (SAP 2005 methodology) are

considered to be suffering from a category 1 excess cold hazard.

b. An estimate of the SAP rating which, to emphasise its origin from a reduced set of

input variables, is referred to as “SimpleSAP”

The Standard Assessment Procedure (SAP) is the UK Government’s standard methodology

for home energy cost ratings. SAP ratings allow comparisons of energy efficiency to be

made, and can show the likely improvements to a dwelling in terms of energy use. The

Building Regulations require a SAP assessment to be carried out for all new dwellings and

conversions. Local authorities, housing associations, and other landlords also use SAP

ratings to estimate the energy efficiency of existing housing. The version on which the

Average SAP rating model is based is SAP 2005.

The SAP ratings give a measure of the annual unit energy cost of space and water heating

for the dwelling under a standard regime, assuming specific heating patterns and room

temperatures. The fuel prices used are averaged over the previous 3 years across all regions

in the UK. The SAP takes into account a range of factors that contribute to energy efficiency,

which include:

Thermal insulation of the building fabric The shape and exposed surfaces of the dwelling Efficiency and control of the heating system The fuel used for space and water heating Ventilation and solar gain characteristics of the dwelling

3. Household vulnerability indicators:

a. Fuel poverty - 10% definition

This definition states that a household is said to be in fuel poverty if it spends more than 10%

of its income on fuel to maintain an adequate level of warmth (usually defined as 21oC for the

main living area, and 18oC for other occupied rooms). This broad definition of fuel costs also

includes modelled spending on water heating, lights, appliances and cooking.

The fuel poverty ratio is defined as:





Page 52 of 64

Fuel poverty ratio = Fuel costs (usage * price) Full income

If this ratio is greater than 0.1 then the household is in fuel poverty.

The definition of full income is the official headline figure and in addition to the basic income

measure, it includes income related directly to housing (i.e. Housing Benefit, Income Support

for Mortgage Interest (ISMI), Mortgage Payment Protection Insurance (MPPI), Council Tax

Benefit (CTB)).

Fuel costs are modelled, rather than based on actual spending. They are calculated by

combining the fuel requirements of the household with the corresponding fuel prices. The key

goal in the modelling is to ensure that the household achieves the adequate level of warmth

set out in the definition of fuel poverty whilst also meeting their other domestic fuel

requirements.

b. Fuel poverty - Low Income High Costs definition

The government has recently set out a new definition of fuel poverty which it intends to adopt

under the Low Income High Costs (LIHC) framework30. Under the new definition, a household

is said to be in fuel poverty if:

• They have required fuel costs that are above average (the national median level)

• Were they to spend that amount they would be left with a residual income below the official poverty line

c. Dwellings occupied by a low income household

A household in receipt of:

• Income support • Housing benefit • Attendance allowance • Disability living allowance • Industrial injuries disablement benefit • War disablement pension • Pension credit • Child tax credit • Working credit

For child tax credit and working tax credit, the household is only considered a low income

household if it has a relevant income of less than £15,050.

The definition also includes households in receipt of Council Tax benefit and income based Job

Seekers Allowance.

30 https://www.gov.uk/government/collections/fuel-poverty-statistics





Page 53 of 64

Appendix B Methodology for the BRE Dwelling Level Housing Stock Modelling approach

This Appendix provides a more detailed description of the models which make up the overall housing

stock modelling approach and feed into the database. The process is made up of a series of data sources

and models which, combined with various imputation and regression techniques and the application of

other formulae, make up the final database. The database is essentially the main output of the modelling

and provides information on the key indicators and other data requirements (e.g. energy efficiency

variables). An overview of the approach and a simplified flow diagram are provided in Section 2 of this

report.

The models making up the overall housing stock modelling approach are:

• SimpleCO2 Model

• Fuel Poverty Model

• HHSRS (All Hazards, Falls Hazards and Excess Cold) Models

• Disrepair Model

• Low Income Households Model

Figure B. 1 shows the data flows for the stock modelling approach, showing which models each of the

outputs in the database (split into the key indicators and other information) come from. The exception is

the Green Deal variables (if used) which come directly from the Energy inputs, and the tenure and HMO

data (if used) which come directly from the other inputs.

Section B.1 describes the SimpleCO2 Model in more detail, Section B.2 provides more information on the

other four models and Section B.3 gives details of the OS MasterMap/geomodelling approach.





Page 54 of 64

Figure B. 1: Simplified data flow for the housing stock modelling approach

EPC estimates

Energy efficiency

variables

Basic Green Deal variables

(wall and loft insulation)

Cost of mitigating Cat 1

Hazards

SimpleCO2 Presence of Cat 1 Excess cold

Hazard

Presence of Cat 1 Hazard

Dwellings occupied by Low

Income Household

Fuel poverty

HHSRS (‘Falls’

and ‘Other’)

Disrepair

Low Income

Households

Inputs

Tenure split

HMOs

Fuel Poverty

Dwellings in Disrepair

SimpleSAP rating

Presence of Cat 1 Falls Hazard

Inputs Model Outputs - Key indicators Outputs - Other information

Database

HHSRS Excess

Cold





Page 55 of 64

B.1 BRE SimpleCO2 Model

BRE have developed a variant of the BREDEM software, named ‘SimpleCO2’, that can calculate outputs

from a reduced set of input variables. These outputs are indicative of the full BREDEM outputs and the

minimum set of variables the software accepts is information on:

• Dwelling type

• Dwelling age

• Number of bedrooms

• Heating fuel

• Heating system

• Tenure

The Experian UK Consumer Dynamics Database is used as a source of these variables and they are

converted into a suitable format for the SimpleCO2 software. However, these variables alone are

insufficient for the software to calculate the ‘SimpleSAP’ rating or carbon emissions estimate (one of the

outputs of the SimpleCO2 model). Additional variables are required and as these values cannot be

precisely inferred then a technique known as cold deck imputation is undertaken. This is a process of

assigning values in accordance with their known proportions in the stock. For example, this technique is

used for predicting heating fuels as the Experian data only confirms whether a dwelling is on the gas

network or not. Fuel used by dwellings not on the gas network is unknown, so in most cases this

information will be assigned using probabilistic methods. The process is actually far more complex e.g.

dwellings with particular characteristics such as larger dwellings are more likely to be assigned with oil as

a fuel than smaller dwellings.

The reason for taking this approach is to ensure that the national proportions in the data source are the

same as those found in the stock nationally (as predicted by the EHS or other national survey). Whilst

there is the possibility that some values assigned will be incorrect for a particular dwelling (as part of the

assignment process has to be random) they ensure that examples of some of the more unusual types of

dwelling that will be present in the stock are included.

Whilst this approach is an entirely sensible and commonly adopted approach to dealing with missing data

in databases intended for strategic use, it raises issues where one of the intended uses is planning

implementation measures. It must therefore be kept in mind at all times that the data provided represents

the most likely status of the dwelling, but that the actual status may be quite different. That said, where

EPC data has been used, the energy models (which use EPC data) are likely to be more accurate.

It is important to note that some variables have been entirely assigned using cold decking imputation

techniques. These include presence of cavity wall insulation and thickness of loft insulation as there is no

reliable database with national coverage for these variables.

The ‘SimpleCO2’ software takes the combination of Experian and imputed data and calculates the

‘SimpleSAP’ rating for each dwelling in the national database. The calculated ‘SimpleSAP’ ratings are the

basis of the estimates of SAP and Excess cold. How the other key variables are derived is discussed later

in this Appendix.

Because the estimates of ‘SimpleSAP’ etc. are calculated from modelled data it is not possible to

guarantee the figures. They do, however, provide the best estimates that we are aware can be achieved

from a data source with national coverage and ready availability. The input data could, however, be

improved in its:

• accuracy for example through correcting erroneous values,





Page 56 of 64

• depth of coverage, for example by providing more detailed information on age of dwellings,

• breadth by providing additional input variables such as insulation.

Improving any of these would enhance the accuracy of the output variables and for this reason it is

always worth considering utilising additional information sources where they are available. Using EPC

data will go some way towards meeting these improvements by providing more accurate data.

B.2 Housing Condition and Household Vulnerability Models

This section provides further information on the remaining four models – Fuel Poverty, HHSRS, Disrepair

and Low Income Households. These models are discussed together since the approach used for each

one is broadly the same.

These models are not based solely on thermal characteristics of the dwelling, and in some cases are not

based on these characteristics at all. A top down methodology has been employed for these models,

using data from the EHS and statistical techniques, such as logistic regression, to determine the

combination of variables which are most strongly associated with failure of each standard. Formulae have

been developed by BRE to predict the likelihood of failure based on certain inputs. The formulae are then

applied to the variables in the national Experian dataset to provide a likelihood of failure for each dwelling.

Each individual case is then assigned a failure/compliance indicator based on its likelihood of failure and

on the expected number of dwellings that will fail the standard within a given geographic area. Thus if the

aggregate values for a census output area are that 60% of the dwellings in the area fail a particular

standard then 60% of the dwellings with the highest failure probabilities will be assigned as failures and

the remaining 40% as passes.

The presence of a category 1 hazard failure is the only exception to this as it is found by combining

excess cold, fall hazards and other hazards such that failure of any one of these hazards leads to failure

of the standard.

B.3 OS MasterMap information (‘geomodelling’)

The OS data has been used to update a number of the SimpleCO2 model inputs. The most valuable use

of the OS data is the ability to determine the dwelling type with much greater confidence.

The existing dwelling type is replaced with a new dwelling type derived from OS data. By looking at the

number of residential address points it can be inferred whether the building is a house or block of flats

(houses have one residential address point and blocks of flats have two or more).

Houses - where the dwelling is a house the number of other buildings it is attached to can be observed

and the following assumptions made:

• If there are no other dwellings attached, the house is detached

• If two dwellings are joined to one another, but not to any other dwellings, they are semi-detached

• If they are attached to two or more other dwellings, they are mid terraced

• If they are attached to only one dwelling, but that dwelling is a mid-terrace, they are an end-terrace

Flats - if the building is a block of flats, its exact nature is determined by its age and the number of flats in

the block and the following assumptions made:

• If there are between two and four flats in the block (inclusive) and the dwelling was built before

1980 then it is a conversion

• Otherwise it is purpose built





Page 57 of 64

This information can also be used to reconcile discrepancies within blocks of flats, terraced and semi-

detached houses. These discrepancies occur in variables such as dwelling age, location of flat in block,

number of storeys, loft insulation, wall insulation, wall type and floor area.

Looking at dwelling age, although the OS data does not itself provide any information on age, it does

allow reconciliation of age data within semi-detached, terraces and blocks of flats.

Where a group of buildings are all attached in some way, such as a terrace, it is logical to assume that

they were built at the same time. Therefore the age of each building is replaced with the most common

age among those present. Where the most common age occurs in equal numbers, this is resolved by

looking at the average age of houses in the same postcode.

If one dwelling has an age that is notably newer than its neighbours, then the age is not changed, as it is

assumed that the original dwelling was destroyed and rebuilt.

Figure B. 2 and Figure B. 3 below show how the initial base data is adjusted using the OS data to

produce more consistent and reliable results.

Considering the number of storeys and the location of a flat in its block, if the OS data reveals that the

dwelling type is significantly different from the original value – specifically if a house becomes a flat, or

vice versa then the variables are adjusted. If this is the case a new location for the flat within the block or

the number of storeys will be imputed using the same method as before, but taking into account the

revised dwelling type.

Similarly with floor area, loft insulation and wall type - if the dwelling type or location of a flat within a block

changes as a result of OS data then the variables are calculated using the same method of imputation as

the original models, but taking into account the new data.





Page 58 of 64

Figure B. 2: Dwelling level map showing the base data, prior to using the OS data

Figure B. 3: Dwelling level map showing the OS updated data





Page 59 of 64

Appendix C Policy background

The detailed housing stock information provided in this report will facilitate the delivery of local housing

strategies and enable a targeted intervention approach to improving housing. This strategy needs to be

set in the context of relevant government policy and legislative requirements. These polices either require

reporting of housing-related data by local authorities, or the use of such data to assist in meeting policy

requirements. The main policies and legislative requirements are summarised in the following sub-

sections.

C.1 Housing Act 2004

The Housing Act 2004 requires local housing authorities to review housing statistics in their district. The

requirements of the Act are wide-ranging and also refer to other legislation which between them covers

the following:

• Dwellings that fail to meet the minimum standard for housings (i.e. dwellings with HHSRS category

1 hazards)

• Houses in Multiple Occupation (HMOs)

• Selective licensing of other houses

• Demolition and slum clearance

• The need for provision of assistance with housing renewal

• The need to assistance with adaptation of dwellings for disabled persons

C.2 Key housing strategy policy areas and legislation

In the report ‘Laying the Foundations: A Housing Strategy for England’ Chapters 4 and 5 focus on the

private rented sector and empty homes.

Private rented sector

There has been significant growth in the private rented sector in recent years and new measures are

being developed to deal with rogue landlords and to encourage local authorities to make full use of

enforcement powers for tackling dangerous and poorly maintained dwellings. The report encourages

approaches which work closely with landlords whilst still operating a robust enforcement regime (e.g.

landlord forums and panels across the country).

Health inequalities

The government’s white paper ‘Choosing Health’ states that the key to success in health inequalities will

be effective local partnerships led by local government and the NHS working to a common purpose and

reflecting local needs. Housing is a key determinant of health, and poor housing conditions continue to

cause preventable deaths and contribute to health inequalities. An example in this area is the work

carried out by Liverpool City Council in partnership with Liverpool Primary Care Trust – the ‘Healthy

Homes Programme’. This has identified over 3,800 hazards and led to an estimated £4.8 million

investment by landlords, delivering sustainable health improvements and enhancing community

wellbeing.





Page 60 of 64

Integrated care

It has been recognised by central government that to fully address the health needs of the population,

services need to become more integrated and there needs to be better communication between different

providers. Housing is a key aspect of this:

“Many people with mental and physical disabilities, complex needs, long-term conditions and terminal

illness also need to access different health care, social care, housing and other services, such as

education, and often simultaneously” .

It is therefore essential that departments providing or regulating housing work with other council

departments and health organisations to provide services that are integrated and take full account of the

needs of the individual.

Public Health Outcomes Framework

The Public Health Outcomes Framework ‘Healthy lives, healthy people: Improving outcomes and

supporting transparency’ sets out desired outcomes for public health and how they will be measured.

Many of the measurements have links to housing, some of the more relevant being:

• Falls and injuries in over 65’s

• Fuel poverty

• Excess winter deaths

Joint Strategic Needs Assessment (JSNA) and Joint Health and Wellbeing Strategies

The JSNA and joint health and wellbeing strategy allow health and wellbeing boards to analyse the health

needs of their local population and to decide how to make best use of collective resources to achieve the

priorities that are formed from these. The Department of Health document ‘Joint Strategic Needs

Assessment and joint health and wellbeing strategies explained - Commissioning for populations’ says

“This will ensure better integration between public health and services such as housing and education

that have considerable impact on the wider determinants of health”.

Energy Act 2011

The Energy Act 2011 requires that from 2016 reasonable requests by tenants for energy efficiency

improvements will not be able to be refused. Furthermore, from 2018 it will be unlawful for landlords to

rent out properties that do not reach a minimum standard of energy efficiency (likely to be set at Energy

Performance Certificate rating E). While there will be various caveats to these powers, they will provide a

new minimum standard for rented accommodation. Part of this current project includes provision of a

private rented sector variable that should assist in identifying such dwellings.

Empty homes

Empty homes brought back into use will qualify for the New Homes Bonus where, for the following 6

years, the government will match fund the Council Tax on long term empty properties brought back into

use. In addition, from 2012-15, £100million of capital funding from within the Affordable Homes

Programme will be available to tackle problematic empty homes. Whilst the data provided by this project

cannot necessarily assist with the actual identification of empty homes, the database provided would be

the logical place for such information to be stored should it be gathered from other sources.

C.3 Other policy areas

The following policy areas, whilst not directly relating to environmental health services, will have an effect

on demand and local authorities will need to be aware of the possible impact in their area.





Page 61 of 64

Welfare Reform Act 2012

The key parts of this Act for environmental health services are the sections relating to the under

occupation of social housing, and the benefit cap. Whilst this will mainly affect tenants in the social rented

sector it will undoubtedly have an impact on private sector services. Social tenants may find themselves

being displaced into the private sector, increasing demand in this area, and the tenants of Registered

Providers (RP’s) and some private landlords may have greater trouble affording rent payments. If tenants

are in arrears on their rental payments then authorities may be met with reluctance from landlords when

requiring improvements to properties.

Localism Act 2011

The Localism Act allows social housing providers to offer fixed term, rather than secure lifetime,

tenancies. As with the Welfare Reform Act, this has a greater direct impact on the social rented sector,

however, there is some concern this may lead to greater turnover of tenancies meaning that some

‘traditional’ social tenants may find themselves in the private rented sector.

Both of these policy changes above may increase the number of vulnerable persons in private sector

properties. If this occurs any properties in this sector in poor condition are likely to have a far greater

negative impact on the health of those occupiers

Potential increase in private rented sector properties

Policies such as the Build to Rent and the New Homes Bonus are aimed at increasing the supply of

properties. As the private rented sector is already growing, it is reasonable to assume that many of the

new properties being built will be rented to private tenants. Local authorities will need to be aware of the

potential impact on the demand for their services and how their perception of their local area may have to

change if large numbers of properties are built.

C.4 Local Authority Housing Statistics (LAHS)

The purpose of these statistics is twofold – firstly to provide central government with data with which to

inform and monitor government strategies, policies and objectives as well as contributing to national

statistics on housing, secondly, to the local authorities themselves to help manage their housing stock.

Local authorities are required to complete an annual return which covers a wide range of housing-related

issues. Of particular relevance to this current project is ‘Section F: Condition of dwelling stock’ which,

amongst other things, requests the following information:

• Average EPC rating of the private sector stock and the proportion below a certain rating.

• Total number of dwellings and number of private sector dwellings with category 1 HHSRS hazards

and the estimated costs of mitigating these

• Estimates of the number of HMOs and the number of mandatory licensable HMOs





Page 62 of 64

Glossary of terms

BREDEM BRE Domestic Energy Model

Category 1 hazard Hazards with a HHSRS score of > 1,000. A dwelling with a category 1

hazard is considered to fail the minimum statutory standard for housing

CLG Department for Communities and Local Government

COA Census Output Area

Designed for statistical purposes, built from postcode units,

approximately 125 households

DCLG Department for Communities and Local Government

Disrepair Based on former Decent Homes Standard criteria which states that a

dwelling fails this if it is not in a reasonable state of repair – this is based

on the dwelling age and condition of a range of building components

including walls, roofs, windows, doors, electrics and heating systems

ECO Energy Companies Obligation

Places legal obligations on the larger energy suppliers to deliver energy

efficiency measures to domestic energy users

EHS English Housing Survey

A continuous national survey commissioned by the Department for

Communities and Local Government (DCLG). It collects information

about people’s housing circumstances and the condition and energy

efficiency of housing in England

EPC Energy Performance Certificate

Present the energy efficiency of domestic properties on a scale of A

(most efficient) to G (least efficient)

Fuel poverty The original definition of fuel poverty states that a household is in fuel

poverty if it needs to spend more than 10% of their income on fuel to

maintain an adequate level of warmth (10% definition). The new definition

now adopted by government is that a household is said to be in fuel

poverty if they have fuel costs that are above average and were they to

spend that amount they would be left with a residual income below the

official poverty line (Low Income High Costs definition)

GIS Geographic Information System

A system designed to capture, store, manipulate, analyse, manage and

present spatial or geographical data

HHSRS Housing Health and Safety Rating System

A risk assessment tool to help local authorities identify and protect

against potential risks and hazards to health and safety related

deficiencies in dwellings, covering 29 categories of hazards





Page 63 of 64

HIA Health Impact Assessment

A formal method of assessing the impact of a project, procedure or

strategy on the health of a population

HMO Houses in Multiple Occupation

An entire house or flat which is let to 3 or more tenants who form 2 or

more households and who share a kitchen, bathroom or toilet

A house which has been converted entirely into bedsits or other non-self-

contained accommodation and which is let to 3 or more tenants who form

two or more households and who share kitchen, bathroom or toilet

facilities

A converted house which contains one or more flats which are not wholly

self-contained (i.e. the flat does not contain within it a kitchen, bathroom

and toilet) and which is occupied by 3 or more tenants who form two or

more households

A building which is converted entirely into self-contained flats if the

conversion did not meet the standards of the 1991 Building Regulations

and more than one-third of the flats are let on short-term tenancies

In order to be an HMO the property must be used as the tenants’ only or

main residence and it should be used solely or mainly to house tenants.

Properties let to students and migrant workers will be treated as their only

or main residence and the same will apply to properties which are used

as domestic refuges

HSM Housing Stock Model

Desktop based modelling used to determine the condition of the housing

stock

Jenks’ Natural Breaks The natural breaks classification method is a data clustering method

determining the best arrangement of values into different classes. It is

achieved through minimising each class’s average deviation from the

class mean while maximising each class’s deviation from the means of

the other groups. The method seeks to reduce the variance within

classes and maximise variance between classes thus ensuring groups

are distinctive

JSNA Joint Strategic Needs Assessment

An assessment of the current and future health and social care needs of

the local community

LACORs Local Authority Coordinators of Regulatory Services – now renamed

Local Government Regulation

LAHS Local Authority Housing Statistics

National statistics on housing owned and managed by local authorities

LIHC Low Income High Cost





Report Ends

Page 64 of 64

Measure of fuel poverty, considers a household to be in fuel poverty if

required fuel costs are above average, or if they were to spend that

amount they would be left with a residual income below the official

poverty line

LLPG Local Land and Property Gazetteer

An address database maintained by local authorities

LSOA Lower Super Output Area

Designed for statistical purposes, built from census output areas,

approximately 400 households

MSOA Medium Super Output Area

Designed for statistical purposes, built from lower super output areas,

approximately 2,000 households

NHS National Health Service

Older people People over 65 for the excess cold hazard, people over 60 for the fire and

fall hazards (excl. falling between levels)

OS Ordnance Survey

Poor housing Dwellings where a category 1 hazard is present

Private sector housing Housing not owned by the local authority or a housing association

SAP Standard Assessment Procedure

Method system for measurement of energy rating of residential buildings.

SimpleSAP An estimate of a residential dwelling’s likely SAP score, it is not based on

the full required range of data for a SAP calculation or a reduced data

SAP calculation (RDSAP), it should only ever be considered an estimate

of the SAP score, and used as a guide

UPRN Unique Property Reference Number

A unique 12 digit number assigned to every unit of land and property

recorded by local authorities as part of their LLPG

Vulnerable persons Persons who are more likely to be affected by the particular hazard as

defined by the HHSRS Operating Guidance

bre client report - housing essex · bre dwelling level housing stock modelling and database for...

Documents