Homelessness Statistics User Group 07 November 2014 Housing Access and Scottish Welfare Fund Statistics, Communities Analytical Services Division

Explaining the differences in rates of securing settled accommodation An introduction to statistical modelling

A famous statistician once said … 'Essentially all models are wrong, but some are useful‘ George Box, Statistician

Warnings Some of this material is quite complex and might blow your mind But, I am [hopefully] going to make it as simple as possible so please bear with me And, there will be audience participation (not singing or role playing).

Predicting who will get settled accommodation Aims: –To predict likelihood of homeless applicants securing settled accommodation and determine factors that provide the best predictors To do this: built a statistical model from HL1 data Method: used a technique called logistic regression –Potential predictors: age, gender, ethnicity, assessment, reason etc. –Initially we put all potential predictors into model – some are successfully chosen as actual predictors –Outcome: binary (secure / don’t secure settled accommodation) Hence, we predict who will get settled accommodation from the demographics and characteristics of applicants –Only includes applications with outcome dates since 1st April 2007 –138,000 applications

Quiz What factors do you think are predictive of securing settled accommodation ? In theory there is a whole spectrum of potential candidate factors: 1.Information we do not collect, maybe qualitative That couldn’t be predictive That could be predictive 2.Information we collect, more quantitative That couldn’t be predictive That is slightly predictive That is moderately predictive That is very predictive – we focus on this

…and the answer is…

Predictive characteristics The following nine characteristics are predictive of getting settled accommodation –In order of their predictive power –“Effect entered” is statistical term for HL1 field name –“Variable Label” is definition from HL1 data spec.

Notes The technique requires a baseline – we chose Glasgow because it is the largest authority and would aid stability. –Some other authorities also predicted likelihood in the same way as Glasgow and so they were grouped with it. Certain characteristics found to be predictive of outcome –Not predictive included age, gender, ethnicity & no. of children Actual Predictors: –Assessment decision, –Temporary accomm. occupied between app’n & duty discharge, –Whether a form of support been provided e.g. housing support A stable reliable model has been constructed

Who is most likely to get settled accommodation The table on the right shows the odds ratios and this lets us make conclusion about who is most likely to get settled accommodation Its easier to see this in a chart (next few slides)

Local authority effects

Assessment effects

Support effects

Housing support effects

Accommodation effects

Other effects

How good is the model ? Random Even better model A useful model

Thoughts ? What do you think ? Does it meet your expectations ? Is anything missing ? Are you concerned about your authority ?

Summary Our aims were to: –Predict likelihood of homeless applicants securing settled accomm. –Determine factors that provide the best predictors. We built a statistical model that identified –some variables that weren’t predictive, and –some that were predictive. Statutory assessment decision most predictive With all other things being equal: –[As expected, according to the legislation] Applicants assessed as non-priority (prior to December 2012) or intentionally homeless are much less likely to secure settled accommodation compared to those who are unintentionally homeless. –Applicants in Highland, Dundee, East Ayrshire & West Dunbartonshire over twice as likely to secure settled accommodation compared to Glasgow. –Applicants in South Ayrshire less likely to secure settled accommodation compared to Glasgow. –Applicants at least twice as likely to secure settled accommodation if they occupied a local authority, RSL or Private Sector Leased property as temporary accommodation, compared to those not occupying those forms of temporary accommodation.

Contact Details Dr. Andrew Waugh Ian Morton