1. Santa Clara County Continuum of Care | Findings
DataDive
Santa Clara County Continuum of Care | Findings

2. Reminder: Goals of DataDive

3. To make homelessness rare, brief, and nonrecurring
Mission:
To make homelessness rare, brief, and nonrecurring
How do we predict housing demand?
How do we determine what resources are necessary based on likely homeless scenarios in coming years?

4. Approach

5. Look deeper into historical inflow, by SCC key sub- populations.
Estimate unmet demand by equating Emergency Shelter use to ‘wait times.’
Identify trends in unmet demand by key sub-populations.
Make predictions where possible....

6. Audience Sub-Populations (Over 7-year data set)
Size
% of Total
Veterans
10k
30%
Families with children 8k
20%
Youth not part of household 3k 8%
Chronically homeless
3.5k 9%
Criminal justice involvement
Disability but not chronically homeless
1.5k 4%
(Any of the above)
19.2k
52%
(Entire population)
37k
100%
Overlapping Populations

7. Analysis

8. Inflow Analysis and Next Steps

9. Inflow per Sub-population

10. After Inflow: How Clients Flow Through SCC Services
Not Shown: Wait Time Analysis...

11. Overall Consumption Trends

12. Services Effectiveness - Leading Edge of Analysis!

13. Services Effectiveness - Leading Edge of Analysis!

14. Follow-up Opportunities

15. Recommended Changes at SCC re: process / data collection / data management
Capture precisely the date from which clients qualify for a service rather than the date from which they receive service, so wait times can be calculated directly
Follow-up Modeling
Discrete event simulation modeling, given parameters generated this weekend
Look at transitions among services
Look at required resources
Cohort analysis of recidivists versus non-recidivists
Across all housing programs
From emergency shelters only
⇒ Complete recommendations for changes in housing resources investments

16. Questions?