1.
Peter Carter
Megan McLean
Policy in Practice
Universal Credit's hidden
hardships: case studies of
councils intervening early

2.
Agenda
1. Introduction to Policy in Practice
2. Universal Credit – Who is falling off the cliff?
3. Homelessness
• What is predictive analytics?
• Case studies
4. Measuring successful preventions

3.
333
About Policy in Practice

4.
A team of
professionals
with extensive
knowledge of the
welfare system
who are
passionate about
making social
policy work
We help local
authorities use
their household
level data to
identify
vulnerable
households,
target support
and track their
interventions
We develop
software that
engages people.
We identify the
actions people can
take to increase
their income,
lower their costs
and build their
financial
resilience

5.
555
Universal Credit:
Identifying households who are falling off
the cliff

6.
National case study
"The worst is yet to come
for those who will be most
severely hit, namely low
income families with
children. Local authorities
need to plan ahead for the
impact."
Professor Christina Beatty,
Centre for Regional
Economic and Social
Research
source: Centre for Regional Economic
and Social Research

7.
A new Living Standards Index for London tracks
the financial security of 550,000 low-income
families to reveal the pockets of poverty in our
nation’s capital.
• Study of 550,000 families shows 15% of
London’s low-income households can’t pay
the bills week to week
• 42% of low income families will be worse off
under Universal Credit – this represents
231,000 households across 18 London
Boroughs
• 15% of low income households face a cash
shortfall. This is 82,500 of 550,000
households.
• This has increased by 21% since 2016 and is
expected to increase by a further 61% by
2021
Living Standards Index for London

8.
Drill down to one local authority

9.
Easy segmentation

10.
Household level insights

11.
Transition to Universal Credit

12.
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Homelessness: what do we know?

13.
The triggers that immediately
precede a homelessness
application are shown in the
table opposite.
Causes include:
• An increase in demand for
affordable homes, not
matched by supply
• The impact of welfare reforms
• Personal factors that can
cause homelessness
The drivers of homelessness

14.
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Predictive analytics:
Advanced analytics which are used to make
predictions about unknown future events.
Our predictive analytics uses many techniques such as
data mining, statistics, modelling, to analyse current
data in order to make predictions about the future.

15.
Identifying those at risk of homelessness

16.
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Early engagement: how do you engage
early?

17.
Identifying early predictors
Effective use of data can identify struggling
households and offer proactive, tailored
support to help boost their incomes before
they hit crisis point.
This can accelerate employment, prevent
homelessness, improve health and alter the
trajectory of low-income families.
Mark Fowler is director of community solutions at Barking
& Dagenham LBC and
Sue Nelson is director for revenues, benefits and customer
services at Luton BC Article in Municipal Journal 27 March 2019

18.
Luton Council: context
• Luton Council has received funding for the
Homelessness Trailblazer
• As part of a service re-design, a new
homelessness prevention team has been
created
• The use of household level data a key feature
of new team
• Identifying homelessness risk early through
data to drive prevention at 56+ days
• New service estimated to be three times as
effective in preventing homelessness

19.
How is Luton Council using data?
• Tracking pathways into homelessness to
understand biggest local risk factors
• 78 households identified as at risk but not
yet presenting as homeless
• All households contacted early and offered
support through Early Action Network; 22
agreed
• Early support included benefits/budgeting
support, rent deposits, income maximisation
etc.
• Prevention team staff designed a manual for
process post data-led identification
• Outcomes of 22 households tracked through
data to determine impact

20.
Engaging households early

21.
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Examples of councils intervening early

22.
Target support where most needed and most
effective
Corin Hammersley of The Royal Borough of Greenwich has led a benefits take-
up campaign using this screen. Her strategy has been to focus on those who are
eligible but are not yet claiming Severe Disability Premiums. This benefit is
worth £64.30 per week for a single claimant and £128.60 for a couple who are
both eligible, which could make a significant impact on household finances.

23.
Target support where most needed and most
effective

24.
Croydon Council
• Croydon’s award winning Gateway programme
focused on using data tackle hardship
proactively
• As a result of the programme, 2,003 families at
high risk of homelessness have avoided being
moved into temporary accommodation.
• 217 of these were previously unemployed
households helped into stable work
• Overall, Croydon Council estimates that using
the LIFT Dashboard to guide early intervention
has achieved costs avoidance savings of over
£4m in one year

25.
Swansea City Council action plan

26.
Smaller scale projects
• Cornwall Council: Undertaking a small scale project to support fewer
than 100 families across the private rented sector identified as being at
risk or in crisis. Support provided by Cornwall Housing and external
employment support partners.
• Exeter Council: Preventing households that are currently in rent arrears
from accruing further arrears when they move to Universal Credit. 66
households in Exeter that are council tenants with rent arrears and
would be worse-off under Universal Credit.
• Islington Council: 113 lone parent households with their youngest
child turning 5 within 6 months. Fliers sent to these households
informing them about the support network available for UC through
the council and the voluntary sector.

27.
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Measuring successful prevention

28.
Tracking outcomes and evidencing prevention

29.
Tracking outcomes

30.
www.policyinpractice.co.uk
Thank you
Peter Carter
peter@policyinpractice.co.uk
07805 254371
Megan McLean
megan@policyinpractice.co.uk
0330 088 9242
hello@policyinpractice.co.uk