homelessness statistics user group 07 november 2014 housing access and scottish welfare fund...

Homelessness Statistics User Group07 November 2014

Housing Access and Scottish Welfare Fund Statistics,Communities Analytical Services Division

Explaining the differences in rates of securing settled

accommodationAn 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.

Effect VariableEntered LabelASSESS Statutory assessment decisionSPTPRV1 Housing support providedLACODE Local authorityTEMP4 Temporary accommodation occupied - LA ordinary dwellingTEMP11 Temporary accommodation occupied - Private sector leasePNCAT15 Household member discharged from armed forces, hospital or prisonTEMP5 Temporary accommodation occupied - Housing association / RSL dwellingSPTNDS6 Support needs identified for basic housing management / independent living skillsSUPPORT Whether any kind of support had been provided

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)

LACODE Aberdeen City vs Other incl. Glasgow 1.248LACODE Aberdeenshire vs Other incl. Glasgow 1.638LACODE Angus vs Other incl. Glasgow 1.076LACODE Argyll & Bute vs Other incl. Glasgow 1.864LACODE Dumfries & Galloway vs Other incl. Glasgow 1.253LACODE Dundee City vs Other incl. Glasgow 2.478LACODE East Ayrshire vs Other incl. Glasgow 2.565LACODE East Dunbartonshire vs Other incl. Glasgow 1.433LACODE East Lothian vs Other incl. Glasgow 1.296LACODE Edinburgh vs Other incl. Glasgow 1.756LACODE Eilean Siar vs Other incl. Glasgow 1.672LACODE Fife vs Other incl. Glasgow 1.692LACODE Highland vs Other incl. Glasgow 2.441LACODE Moray vs Other incl. Glasgow 1.336LACODE North Ayrshire vs Other incl. Glasgow 1.124LACODE North Lanarkshire vs Other incl. Glasgow 1.077LACODE Orkney vs Other incl. Glasgow 1.453LACODE Perth & Kinross vs Other incl. Glasgow 1.672LACODE Renfrewshire vs Other incl. Glasgow 1.659LACODE Scottish Borders vs Other incl. Glasgow 1.82LACODE South Ayrshire vs Other incl. Glasgow 0.857LACODE South Lanarkshire vs Other incl. Glasgow 1.549LACODE West Dunbartonshire vs Other incl. Glasgow 2.693LACODE West Lothian vs Other incl. Glasgow 1.716ASSESS Homeless - priority intentional vs Homeless - priority unintentional 0.13ASSESS Non-priority vs Homeless - priority unintentional 0.056ASSESS Potentially homeless - priority intentional vs Homeless - priority unintentional 0.107ASSESS Potentially homeless - priority unintentional vs Homeless - priority unintentional 0.795PNCAT15 1 vs 0 0.445SPTNDS6 1 vs 0 0.624SUPPORT Support not provided vs Some form of support has been provided 0.569SPTPRV1 2 vs Blank 1.623SPTPRV1 3 vs Blank 0.751SPTPRV1 0 vs Blank 0.925SPTPRV1 1 vs Blank 2.334TEMP4 1 vs 0 2.001TEMP5 1 vs 0 2.072TEMP11 1 vs 0 4.559

Odds Ratio EstimatesPoint

EstimateEffect

Local authority effects

Assessment effects

Support effects

Housing support effects

Accommodation effects

Other effects

How good is the model ?

Even bettermodel

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 andrew.waugh@scotland.gsi.gov.uk0131 244 7232

• Ian MortonIan.morton@scotland.gsi.gov.uk 0131 244 7235