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The London Homelessness Conference 2019

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Policy in Practice's Commercial Director, Jade Alsop, hosted an interactive workshop about how predictive analytics can help tackle homelessness at the London Homelessness Conference. Delegates took part in discussions about how administrative datasets can be interrogated for social good and used, by local authorities, to identify vulnerability, target support and track change.

For more details visit www.policyinpractice.co.uk, email hello@policyinpractice.co.uk or call 0330 088 9242.

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The London Homelessness Conference 2019

  1. 1. Jade Alsop Ben Fell Policy in Practice How predictive analytics can help you identify people at risk of homelessness
  2. 2. Agenda 1. Introduction • to our workshop • to Policy in Practice 2. Homelessness - what do we know? 3. What is predictive analytics? 4. Section 1: Identifying early predictors 5. Section 2: Early engagement 6. Section 3: Measuring and Tracking successful prevention 7. Take away action plan
  3. 3. About today’s workshop: Using predictive analytics to identify people at risk of homelessness
  4. 4. 444 About Policy in Practice
  5. 5. 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
  6. 6. 666 Homelessness - what do we know?
  7. 7. 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
  8. 8. • Around half of all homelessness applications are found to be in priority need • Under the new responsibilities, demand could double The challenge of HRA
  9. 9. There are around 5,000 rough sleepers in England and 8,000 across the UK. A 15% increase on a year ago, and the seventh year numbers have been rising. Rough sleeping
  10. 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. 11. 131313 Predictive analytics: Advanced analytics which are used to make predictions about unknown future events. Predictive analytics uses many techniques such as data mining, statistics, modeling, machine learning and artificial intelligence, to analyse current data in order to make predictions about the future.
  12. 12. 141414 Section 1 Identifying early predictors
  13. 13. How 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
  14. 14. Identifying those at risk of homelessness
  15. 15. 5 minute breakout Identifying early predictors of homelessness 1. How are you doing it now? 2. What measures are you using? 3. If you’re not doing it how could a data approach help?
  16. 16. 181818 Section 2 Early engagement: how do you engage early?
  17. 17. 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
  18. 18. 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
  19. 19. Engaging households early
  20. 20. Target support where most needed and most effective
  21. 21. 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
  22. 22. 5 minute breakout Early engagement 1. What challenges have you encountered when getting people to engage? 2. What has worked for you?
  23. 23. 252525 Section 3 Measuring successful prevention
  24. 24. Tracking outcomes and evidencing prevention
  25. 25. 5 minute breakout Measuring successful prevention 1. What impact would tracking have on how you deliver your service? 2. Have you had challenges with this? 3. Could a data approach help you overcome them?
  26. 26. 282828 What are your next steps?
  27. 27. www.policyinpractice.co.uk Thank you Jade Alsop jade@policyinpractice.co.uk 07551 165172 Dr Ben Fell Ben@policyinpractice.co.uk 0330 088 9242 hello@policyinpractice.co.uk

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