1. New England Regional Point in Time Report How Data Can Inform a Regional Approach to Preventing & Ending Homelessness Matthew D. Simmonds, Simtech Solutions Inc. Dr. Dennis Culhane, PhD, University of Pennsylvania

2. PIT Data Collection – New England

3. Limitations of Data (Overall) Point of Origin Vs. Where Served Point of Origin Vs. Where Served Different Counting Methods Different Counting Methods Different Collection Dates Different Collection Dates Missing Data Missing Data Does not reflect changes in housing inventory Does not reflect changes in housing inventory Unanswered often shown as zeros Unanswered often shown as zeros

4. Limitations of Data (by year) 2006 6 did not do a count in 2006 and used 2005 data. 6 did not do a count in 2006 and used 2005 data. 2 did not collect # of Households with Dependent Children. 2 did not collect # of Households with Dependent Children.2007 Overcount by 930 due to double counting of 1 CoC. Overcount by 930 due to double counting of 1 CoC. Figures derived from CoC data from submitted chart Ks are different than last year’s for 5 of 6 states. Figures derived from CoC data from submitted chart Ks are different than last year’s for 5 of 6 states.2008 Missing all data for 2 CoCs. Used 2007 data which constituted 1121 homeless to derive a total figure. Missing all data for 2 CoCs. Used 2007 data which constituted 1121 homeless to derive a total figure. Missing subpopulation detail data for 20 CoCs. Missing subpopulation detail data for 20 CoCs. Missing chronic count for 3 CoCs. Missing chronic count for 3 CoCs. Missing # of people in the household for 5 CoCs. Used 3.3 as a multiplier to extrapolate this to “create” 1443 homeless individuals. Missing # of people in the household for 5 CoCs. Used 3.3 as a multiplier to extrapolate this to “create” 1443 homeless individuals.

5. Very Preliminary Regional Count

6. Feedback from Each State 1) Best practices: Was there anything in particular that helped make your Point-in-Time Count successful? 1) Best practices: Was there anything in particular that helped make your Point-in-Time Count successful? 2) Lessons learned: Was there anything in particular that hindered you from making your Point-in-Time Count successful? 2) Lessons learned: Was there anything in particular that hindered you from making your Point-in-Time Count successful? 3) Trends: Is there any supplemental information about your state or locality that you believe should be highlighted when discussing the data. 3) Trends: Is there any supplemental information about your state or locality that you believe should be highlighted when discussing the data. 4) Next Steps: What next steps for the regional PIT would you suggest, and/or what are you doing on the state/local level to improve the count for next year? 4) Next Steps: What next steps for the regional PIT would you suggest, and/or what are you doing on the state/local level to improve the count for next year?

7. PIT Data Collection – Quincy/Weymouth’s Approach CoC Description - Quincy-Weymouth Point in Time Count –256 homeless persons on 1/30/08 General Population Count - 142,013 Source: 2000 US Census Figures

8. Best Practices: Collection of Raw Data in Excel

9. Best Practices: Leveraging the Point In Time Count o Excel tools allowed for generation of Chart K within 1 day. o Simple process helped persuade agencies to implement a successful “dry run” count. o Excel enables us to collect data from non-HUD funded agencies. o Serves as an effective auditing tool of our HMIS data. o Sharing point in time info with others around New England will enable a regional count and help us benchmark our performance with similar communities.

10. Best Practices: Community Defined PIT Report o Counted doubled up and at risk. o Counted young adults as this was a growing subpopulation in the past.

11. Best Practices: Mapping Street Count Locations Street Count Map Legend Point in Time Street Count Map for January 30, 2008

12. Using PIT Data for Trend Analysis 2+ years ahead of pace on the 10 year plan goal to build up 100- 120 housing units for the chronically homeless with 52 new units 2+ years ahead of pace on the 10 year plan goal to build up 100- 120 housing units for the chronically homeless with 52 new units Quincy beats housing goal: City reports 20% drop in chronic homelessness (Source Patriot Ledger) Quincy beats housing goal: City reports 20% drop in chronic homelessness (Source Patriot Ledger) Quincy beats housing goal: City reports 20% drop in chronic homelessness Quincy beats housing goal: City reports 20% drop in chronic homelessness o We are now seeing more vets than ever. more vets than evermore vets than ever

13. Lessons Learned HMIS is not always the ideal originating source of Point in Time data. HMIS is not always the ideal originating source of Point in Time data. Some people are incompetent. Some people are incompetent. Enforcement of data collection by techies alone is difficult. Enforcement of data collection by techies alone is difficult. Housing Inventory goes hand in hand. Housing Inventory goes hand in hand.

14. Next Steps: Compile Data via a Secure Web App