1. 2.01 Driving Change with System Performance Measures NAEH 2017

2. Driving Change with SPMs
HUD Systems Performance Measures
Length of Time Homeless- average and median
Numbers of Homeless People and Newly Homeless
Returns to Homelessness
Increases in Earned and Benefit Income
Exits to and Retention in Permanent Housing
Requirement - not just an exercise
What are the numbers telling us?
Why collect all this data?
What do we do with it? How do we make it meaningful?
Address Gaps, Priorities, Capacity, Emerging Needs, P&P

3. Drive Change with System Performance Measures NAEH 2017
Tess Colby
Manager Community Services Programs
July 18, 2017

4. Pierce County, WA COC COC has about 2,400 permanent and temporary beds
$11.3 million in 2017
COC - $3.2 million
ESG - $390,000
WA State - $3.5 million
Local - $4.2 million
PIT (2017): 1,321 persons
Increase in unsheltered and chronic
In ,838 unduplicated persons sheltered/housed (60% increase over 2012)
Coordinated Entry, Prioritization
Population = 832,000
City of Tacoma = 205,000
HUD Median Income = $74,500
13.1% below poverty level
Market Rent 2br = $1,360
FMR 2 br = $1,142
Vacancy rate = below 3%
Walk thru COC data
Increase in persons served since 2010 – used data to analyze our system performance, especially TH - conversion increased system capacity to serve
7/18/17

5. Performance Measures - Sample
Performance Based Contracts Outcome Measures
System Performance Measures
All Hearth Measures
Coordinated Entry Measures, e.g., referral acceptance rates, length of time to
Expenditure of Contract Rate
Big Three
Length of Time in Program
Exits to Permanent Housing
Returns to Homelessness
7/18/17

6. Sample dashboard – tracks project outcomes against universal outcomes for the intervention type – in this case, RRH
Picks up main HEARTH measures, plus the our local ones like contract expenditure
CLICK Draw attention to two measures – two line graphs on left show how long it takes to move into housing; bar graph on right shows how long people remain in the RRH program, and how many people exit the program and retain permanent housing.
Examples of why context and overall system goals are important to keep in mind when looking at data.
Rents have increased by over 25% in the last year, so it’s much harder to find units – thus, longer search times
We implemented CES with prioritization in late 2016, so programs are receiving referrals of persons with much higher housing barriers, so time in program has increased a little, but retention of permanent housing upon exit has really taken a dive. Tells us we need to rethink our provider training and RRH policies now that RRH is serving a more vulnerable population.
7/10/17

7. This is the 2nd page of the dashboard – sharing a bit of additional detail.
CLICK – we wanted to take a few of the outcomes and disaggregate by race – in particular, exits to perm housing and returns to homelessness – and where there are discrepancies, as there are in this instance (lower Perm housing exit rate and higher return rate for African Americans).
In both of these examples, the data doesn’t really give us an answer – it tells us the questions to ask. That’s what ultimately leads to change and improved performance.
7/10/17

8. So Much Data, So Little Time!
Data paralysis: we can extract it… now what??
Data Story Telling: translating numbers to words
Data Sharing: helping providers use their own data
Finally, Data Challenges – so many!
Our top three are: data paralysis. HMIS is a wealth of data, which can be slied and diced so many different ways. So how do we figure out where to focus our time?
It depends on what story you want to tell. We’ve found that we are spending way more time now talking about messaging, and selecting data that helps support that messaging… even if the data shows a need for improvement. Because that’s always part of the story.
And third, when we started sharing the dashboards with provider agencies, we realized that we needed to teach many of them how to read them, how to interpret them and what they could do with them. SO that’s a whole new set of skills we are developing! AND, we also got a lot of suggestions about what OTHER reports we could create for them, so now we’re helping agencies learn how to do their own data analysis.
Since sharing the data with providers we’ve found that agencies are more invested in understanding their outcomes and improving them.
7/18/17

9. Kevin Finn, MSW, LISW-S
President/CEO

10. Roles within our CoC
HUD Continuum of Care (OH-500)
Unified Funding Agency
HUD Emergency Solutions Grant
HUD Housing Opportunities for Person with AIDS (HOPWA)
State of Ohio
Housing Crisis Response Program (HCRP)
Hamilton County –
Indigent Care Levy funds
City of Cincinnati –
Human Services funding
Collaborative Funding Model -
Privately Raised Funding for Community Initiatives
$ 18.5 million
$ 1.2 million
$ ,000
$ ,000
$ 2 million
$ ,000
$ ,000
Outcomes Data + Community Input = Funding Allocation

11. Cincinnati/Hamilton County CoC (OH 500)
Population within our CoC ,631
Population of “Greater Cincinnati” 2,114,580
U.S. Census Bureau's formal name for the area is the Cincinnati–Middletown, OH–KY–IN Metropolitan Statistical Area.
“Greater Cincinnati” encompasses our CoC, & portions of 3 Balance of State CoCs (Ohio, Indiana, Kentucky)

12. System Performance: What we are doing well…
Following the Data
Allocation processes use System Performance measures
Using data analytics company to analyze HMIS data & compare data to other data sets
Working Groups (PSH, RRH, Street Outreach, etc.) compare outcomes data quarterly; identify best practices.
Use data to target Shelter Diversion resources.
Using data to forge partnerships (PHA, across CoCs, etc.)
Signs of improvement
Returns to homelessness- Street Outreach returns down from 25% to 13% (with Coordinated Entry & CABHI).
12% increase in Street Outreach exits to PH.
Reductions in the number of people becoming homeless for the first time

13. System Performance: Where we suck…
Length of Time People Remain Homeless
Has actually increased by 2 days (???)
Fits & starts with the RRH model
Landlords, landlords, landlords…
Employment & Income measures
Almost all measures have actually decreased
May be due to implementation of Coordinated Entry system- housing our most chronic clients
Homelessness as a Regional Issue
We are helping run Coordinated Entry in BOS KY CoC
People moving across the river: impacts recidivism, returns to homelessness, documentation of CH status

14. Coalition for the Homeless
Eva Thibaudeau, LCSW
Coalition for the Homeless
Greater Houston, TX

15. The Way Home CoC is:
City of Houston (see right)
Harris County
Population 4.59M
1777 sq. miles
Houston
Pasadena
Fort Bend County
Population 741,237
885 sq. miles
Montgomery County
Population 1M
507 sq. miles
Conroe

17. % Successful Exits to PSH & RRH*
Eva’s Thought Bubble:
WHAT???!!!!!
Our data analysis shows success!
The PIT is steadily dropping!
HELP!!!
Houston—we have a problem!
FY2016
SO = 16%
ES/TH/PH = 29%
*Related factors:
Auto-exits from day/overnight shelter = no data
High volume of shelter users = volume of auto exits
High volume of light touch users = volume of auto exits
Outreach team comfort w/ HMIS in the field = training opps

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19. Discussion and Q&A Driving Change with System Performance Measures