Using predictive analytics to prevent homelessness
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This document summarizes a workshop about using predictive analytics to identify people at risk of homelessness. The workshop discusses how predictive analytics works, identifying early predictors of homelessness using household data, engaging with at-risk households early through programs like in Luton and Croydon Councils, and measuring the success of prevention efforts by tracking outcomes. Attendees then broke into groups to discuss challenges and opportunities around early identification, engagement, and measuring impact.
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Using predictive analytics to prevent homelessness
- 1. Jade Alsop Ben Fell Policy in Practice How predictive analytics can help you identify people at risk of homelessness
- 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. About today’s workshop: Using predictive analytics to identify people at risk of homelessness
- 4. 444 About Policy in Practice
- 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. 666 Homelessness - what do we know?
- 8. 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
- 9. • Around half of all homelessness applications are found to be in priority need • Under the new responsibilities, demand could double The challenge of HRA
- 10. 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
- 11. 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
- 12. 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.
- 13. 141414 Section 1 Identifying early predictors
- 14. 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
- 15. Identifying those at risk of homelessness
- 16. 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?
- 17. 181818 Section 2 Early engagement: how do you engage early?
- 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
- 21. Target support where most needed and most effective
- 22. 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
- 23. 5 minute breakout Early engagement 1. What challenges have you encountered when getting people to engage? 2. What has worked for you?
- 24. 252525 Section 3 Measuring successful prevention
- 25. Tracking outcomes and evidencing prevention
- 26. 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?
- 27. 282828 What are your next steps?
- 28. 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
Editor's Notes
- Session outline - Discussing challenges and solutions for early prevention Intended outcome - Come away with ideas for how to overcome these challenges What's worked for us - What's worked for you DOES ANYONE HAVE ANYTHING THEY WOULD LIKE TO ADD TO THIS
- Analytics – Software – Policy
- A poll on a webinar of around 60 councils this is what they said Priority for many authorities particularly due to the introduction of HRA HRA: The Homelessness Reduction Act 2017 is one of the biggest changes to the rights of homeless people in England for 15 years. It effectively bolts two new duties to the original statutory rehousing duty: Duty to prevent homelessness Duty to relieve homelessness https://assets.publishing.service.gov.uk/media/5a969da940f0b67aa5087b93/Homelessness_Code_of_Guidance.pdf
- 2018 stats INCREASE AND UNKNOWN DEMAND= PREVENTION OF THOSE WE DO KNOW
- NATIONAL AUDIT OFFICE = PREVENTION BETTER THAN CURE- PREDICTIVE ANALYTICS
- (million possible predictors, which to choose, what is covered by the data? what has the biggest effect?
- DEMO DB- ---- IN CRISIS PRIVATE RENT Governments may know how one policy affects many people. We can show how all policies combined affect one person. We work with household level data from over 40 different local authorities to Welfare reforms we model, and how accurate we are.
- Traditional approach doesn’t always work. engage around the problems that ARE relevant e.g., benefit take up, childcare, DHPs REF TO BEN TAKE UP SCREEN/DHP SCREEN AND CALC
- THEN DEMO BEN TAKE UP SCREEN DEMO
- THE RESULTS FOR CROYDON AND MENTION HUBS
- How do you track cases, evidence a successful service, record 'preventions' for statutory requirements of the Homelessness Reduction Act Demo of tracking screen Download the cohort from step 1 and put in the sankey/import pre-prepared cohort
- How do you track cases, evidence a successful service, record 'preventions' for statutory requirements of the Homelessness Reduction Act Demo of tracking screen Download the cohort from step 1 and put in the sankey/import pre-prepared cohort