How predictive analytics can help identify people at risk of homelessness

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Jade Alsop, Commercial Director and Louise Murphy, Senior Policy Analyst with Policy in Practice, were invited to speak at the Homelessness Conference in Leeds on the topic of How predictive analytics can help identify people at risk of homelessness. For further information visit www.policyinpractice.co.uk, call 0330 088 9242 or email hello@policyinpractice.co.uk

Regierungs- und gemeinnützige Organisationen

Jade Alsop
Louise Murphy
Policy in Practice
How predictive
analytics can help
identify people at risk
of homelessness

About Policy in Practice
Jade Alsop
Commercial Director
Louise Murphy
Policy and Data Analyst

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

Around 5,000
rough sleepers
in England and
8,000 in the UK
A 15% increase
on a year ago,
and the 7th year
numbers have
risen
Rough sleeping

The NAO concluded that
government efforts to tackle
homelessness could not
demonstrate value for money
The government needs to:
• Evaluate effectiveness
• Help local authorities share best
practice
• Help ensure housing supply
meets housing need
• Monitor the impacts of policies
and interventions on
homelessness
NAO: A change in approach

111111
Ask the audience:
Do you use data to inform your
homelessness strategy?

We work with household-level data
Housing Benefit / Council Tax data, household
level arrears / debt data from local authorities
Data is processed by our Benefit and
Budgeting Calculator
Detailed view of household-level financial circumstances
now and in the future
Councils identify and engage households at risk before
a crisis occurs

Our analytics
We analyse the living
standards of households
now, and model their
predicted living
standards in 2023
This allows us to identify
households who are
coping now, but will be
struggling in the future

151515
How Croydon Council are using their data
to uncover vulnerability

EngageIdentify Track
people who need your
support the most
the impact of policy and
effectiveness of
interventions
your residents with
targeted support

Step 1.
34,186
low-income
households in
Croydon
www.policyinpractice.co.uk

Step 2.
6,108
are
unemployed
with children
www.policyinpractice.co.uk

Step 3.
316
live in the
Thornton
Heath ward
www.policyinpractice.co.uk

Step 4.
93 are on
Universal
Credit,
15 will be
worse off
www.policyinpractice.co.uk

Step 7. Track the impact of your intervention
778 households (10.5% of 7,413) moved into work

Growing financial resilience
Activity
Analysed Housing Benefit
and Council Tax Reduction
data
Identified £20 million in
unclaimed benefits
Outreach programme to
encourage benefit take up
Data insights Return on investment
Food bank usage +1% in
Greenwich vs +20%
benchmark.
Better use of limited
support
Measurable increase in
living standards
Services designed around
prevention
“LIFT has given us the opportunity to do targeted take-up work to
increase the income of our residents.”
Learn more

Prevention and reducing homelessness
Activity
HRA puts new
responsibilities on
councils… but 56 days is
too late.
Luton use their data to
identify those e.g. in
arrears, in shortfall, worse
off on UC.
Pooled with other data to
enhance predictive
capability
“It makes no sense at all to wait until someone is in crisis. Interventions
become less effective & engagement harder.”
Data insights Return on investment
78 households identified
initially
Proactive outreach
engaged 52 people in the
first wave of calls.
42% of people took up the
offer of support to increase
their income.
Fewer homelessness
applications (£7.5k) and
10% fall in TA.
Learn more

www.policyinpractice.co.uk
Thank you
Jade Alsop
jade@policyinpractice.co.uk
07551 165172
Louise Murphy
louise@policyinpractice.co.uk
0330 088 9242
hello@policyinpractice.co.uk

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How predictive analytics can help identify people at risk of homelessness

1. Jade Alsop Louise Murphy Policy in Practice How predictive analytics can help identify people at risk of homelessness

2. About Policy in Practice Jade Alsop Commercial Director Louise Murphy Policy and Data Analyst

3. 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

4. 444 Homelessness - what do we know?

6. Our policy work

7. Universal Credit roadmap

8. The drivers of homelessness

9. Around 5,000 rough sleepers in England and 8,000 in the UK A 15% increase on a year ago, and the 7th year numbers have risen Rough sleeping

10. The NAO concluded that government efforts to tackle homelessness could not demonstrate value for money The government needs to: • Evaluate effectiveness • Help local authorities share best practice • Help ensure housing supply meets housing need • Monitor the impacts of policies and interventions on homelessness NAO: A change in approach

11. 111111 Ask the audience: Do you use data to inform your homelessness strategy?

12. We work with household-level data Housing Benefit / Council Tax data, household level arrears / debt data from local authorities Data is processed by our Benefit and Budgeting Calculator Detailed view of household-level financial circumstances now and in the future Councils identify and engage households at risk before a crisis occurs

13. Our analytics We analyse the living standards of households now, and model their predicted living standards in 2023 This allows us to identify households who are coping now, but will be struggling in the future

14. 151515 How Croydon Council are using their data to uncover vulnerability

15. EngageIdentify Track people who need your support the most the impact of policy and effectiveness of interventions your residents with targeted support

16. Step 1. 34,186 low-income households in Croydon www.policyinpractice.co.uk

17. Step 2. 6,108 are unemployed with children www.policyinpractice.co.uk

18. Step 3. 316 live in the Thornton Heath ward www.policyinpractice.co.uk

19. Step 4. 93 are on Universal Credit, 15 will be worse off www.policyinpractice.co.uk

20. Step 5. Focus on one household

21. Step 6. Engage through tailored support

22. Step 7. Track the impact of your intervention 778 households (10.5% of 7,413) moved into work

23. 242424 Some other examples

24. Growing financial resilience Activity Analysed Housing Benefit and Council Tax Reduction data Identified £20 million in unclaimed benefits Outreach programme to encourage benefit take up Data insights Return on investment Food bank usage +1% in Greenwich vs +20% benchmark. Better use of limited support Measurable increase in living standards Services designed around prevention “LIFT has given us the opportunity to do targeted take-up work to increase the income of our residents.” Learn more

25. Prevention and reducing homelessness Activity HRA puts new responsibilities on councils… but 56 days is too late. Luton use their data to identify those e.g. in arrears, in shortfall, worse off on UC. Pooled with other data to enhance predictive capability “It makes no sense at all to wait until someone is in crisis. Interventions become less effective & engagement harder.” Data insights Return on investment 78 households identified initially Proactive outreach engaged 52 people in the first wave of calls. 42% of people took up the offer of support to increase their income. Fewer homelessness applications (£7.5k) and 10% fall in TA. Learn more

26. Target support: DHPs

27. 282828 Thank you Questions?

28.

29.

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31. www.policyinpractice.co.uk Thank you Jade Alsop jade@policyinpractice.co.uk 07551 165172 Louise Murphy louise@policyinpractice.co.uk 0330 088 9242 hello@policyinpractice.co.uk

Hinweis der Redaktion

So before we get into how we have done that – it would be good to get a show of hands on whether you are currently using a data led approach and whether you are looking for ways to improve that. No hands- OK so So I walk you through our data led approach and what data we use to do this
Louise has mentioned what we do and how we do it but whats important here today is how this has supported organisations to address current homelessness and prevent future homelessness

How predictive analytics can help identify people at risk of homelessness

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Hinweis der Redaktion