1. PIT 2017 Point-In-Time Count Data Review & Analysis 2017
County of Riverside Continuum of Care (CoC)
2017 Point-In-Time Count
Data Review & Analysis
PIT
2017
April 6th, 2017

2. Today’s Objectives
Gain understanding of point-in-time count methodology
Gain understanding of the data cleaning and deduplication process
Review 2017 PIT Data
Discuss PIT Count observations
Analyze the data
Discuss rationale for the numbers
Discuss ways for using the PIT data to make decisions in our County
Discuss D

3. 6 9 5 1 8
THE GOAL IS TO TURN DATA INTO INFORMATION, AND INFORMATION INTO INSIGHT. 3 4 5 2 7 8 4 D

4. The Point-In-Time Count
HUD PIT count is the main data source used for measuring progress in meeting the goals in Opening Doors and collects important data on the general homeless population and subpopulations of homeless persons, including Veterans, families, chronically homeless individuals, and youth.
We count those persons who are living on the streets and in shelter or transitional housing. D

5. Why Is the Data So Important?
A PIT count is the best standardized way method to get an accurate picture of the number of people who are homeless, particularly in an urban area, however, a PIT Count is not without its limitations:
May miss some who do not appear to be homeless, who are well- hidden, or who are actively avoiding being counted.
Even though a PIT Count is a carefully executed scientific process, all PIT counts (by their inherent limitations) undercount the homeless population.
PIT Count is not a measure of all need in a community. D

6. 2017 PIT COUNT METHODOLOGY
Methodology did not change from 2016 to 2017, but we got better at implementing our Count
Street-based Count
Service-based Count
Sheltered Count
Complete Census Count
Deployment sites covering every City (except Canyon Lake)
Volunteers
Veteran, Youth and Family Services D

7. Day of the PIT Count
Increased law enforcement engagement and participation (RSO, local PDs, Code, Probation)
Committed leadership and volunteers
Bad weather conditions
Vacated encampments and identified locations
Large number of volunteer no-shows D

8. Why Is the Data So Important?
A PIT count is the best standardized way method to get an accurate picture of the number of people who are homeless, particularly in an urban area, however, a PIT Count is not without its limitations:
May miss some who do not appear to be homeless, who are well- hidden, or who are actively avoiding being counted.
Even though a PIT Count is a carefully executed scientific process, all PIT counts (by their inherent limitations) undercount the homeless population.
PIT Count is not a measure of all need in a community. D

9. Data Cleaning & Deduplication Process
Total Surveys Returned
Rejected Surveys (Not Entered)
New Sub Total
Rejected Surveys (Duplicates)
Final Sample Size
1,892 (1,149 interview, 222 youth surveys, 521 observational)
242 (91 interview, 140 youth, 11 observational)
1650 (1058 interview, 82 youth surveys, 510 observational) 
8 interview, 4 youth, 0 observational 
1638 (1050 interview, 78 youth surveys, 510 observational D

10. How will the data review process work?
Present the Data
Questions about the Data
Structured Questions about the Count and the data
Open-ended Questions & Discussion as time permits
Brief follow-up survey to provide feedback D

11. Total Counts
2017
Total
1638
2016 Unsheltered Count
1351
2017 Unsheltered Count
1638 %∆
+21.2 %
Interview
1128
Observational
510 J

12. 775 2017 Sheltered Count Breakdown 594 2017 Total 181
Emergency Shelters
594
Transitional Housing
181
2017 Total
775 J

13. Unsheltered Count Comparison
Interview
Observational
2016 Total
2016 (% of Total)
2017 Total
2017 (% of Total)
City
Banning 26 6 25
1.9% 32
2.0%
Beaumont 14 4 10
0.7% 18
1.1%
Blythe 31 28 63
4.7% 59
3.6%
Calimesa
0.0%
Canyon Lake
Cathedral City 37 7 27 44
2.7%
Coachella 61 20
4.4% 81
4.9%
Corona 39 17 83
6.1% 56
3.4%
Desert Hot Springs 33
2.1% 43
2.6%
Eastvale
Hemet 65 54
107
7.9%
119
7.3%
Indian Wells
Indio 57 70
5.2% 89
5.4%
Jurupa Valley 95
113
8.4%
6.9%
La Quinta 1 8
0.6% 2
0.1%
Lake Elsinore 62 9 53
3.9% 71
4.3%
Menifee
1.5%
0.9%
Moreno Valley 58
4.5% 78
4.8%
Murrieta 5
0.4%
Norco 3 12
0.2%
Palm Desert 21 19
1.4%
1.7%
Palm Springs 90 48
138
Perris 29
1.8%
Rancho Mirage
Riverside
233
167
258
19.1%
400
24.4%
San Jacinto 23
1.3%
Temecula 67 85
Wildomar 13
1.0%
City Total
1062
485
1235
91.4%
1547
94.4% J

14. Unsheltered Count Comparison…
Interview
Observational
2016 Total
2016 (% of Total)
2017 Total
2017 (% of Total)
Unincorporated
Anza 2
0.1%
0.0%
Bermuda Dunes 10
0.6%
Cabazon 1 6
0.4%
Cactus City
Cherry Valley 3
0.2%
Coronita 5
0.3%
Highgrove 4 25
1.9%
Home Gardens
Homeland
Idyllwild 8 12
0.9%
Lakeland Village 14
1.0%
Mead Valley
Meadowbrook
Mecca 13 21
1.6% 15
Mesa Verde
Romoland
Temescal Valley
Thermal
Thousand Palms 17
Valle Vista
White Water
Woodcrest
Unincorporated Total 66
116
8.6% 91
5.6%
Grand Total
1128
510
1351
100.0%
1638 J

15. Unsheltered Subpopulation Comparison
2016
2017
Subpopulation
Count
Percent
Percent Change
Chronically Homeless
299
28.7%
341
20.8%
14.0%
Families with Children 8
0.8% 3
0.2%
-62.5%
Veterans
100
9.6% 91
5.6%
-9.0%
Youth 24 or younger 95
9.1%
122
7.6%
31.6%
Alcohol Use
273
26.2%
291
17.8%
6.6%
Drug Use
32.7%
461
28.1%
35.2%
PTSD
200
19.2%
268
16.4%
34.0%
Mental Health Conditions
275
26.4%
309
18.9%
12.4%
Physical Disability
326
31.3%
362
22.1%
11.0%
Developmental Disability
128
12.3%
135
8.2%
5.5%
Brain Injury
201
19.3%
212
12.9%
Victim of Domestic Violence
265
25.4%
282
17.2%
6.4%
AIDS or HIV 11
1.1% 21
1.3%
90.9%
Jail (within 12 months)
223
21.4%
202
-9.4% J

16. Unsheltered Subpopulation ComparisonJ

17. DEFINITION What is Chronically Homeless?
Homeless continuously at least 1 year or homeless four or more times in the last 3 years where the cumulative time homeless is at least 1 year AND possess a disabling condition. J

18. 299 341 Unsheltered Chronically Homeless 14.1%
Percent Change
14.1% J

19. Unsheltered Chronically Homeless Comparison…
2016
2017
City
Count
Percent
Banning 5
0.4% 12
1.1%
Beaumont
0.0% 2
0.2%
Blythe 20
1.5% 11
1.0%
Calimesa
Canyon Lake
Cathedral City 7
0.5% 15
1.3%
Coachella 9
0.7%
1.8%
Corona 27
2.0%
Desert Hot Springs 3
Eastvale
Hemet 24
Indian Wells
Indio 16
1.2% 26
2.3%
Jurupa Valley 18 29
2.6%
La Quinta
0.1%
Lake Elsinore
1.4%
Menifee 1
Moreno Valley
0.9%
1.6%
Murrieta
Norco
0.3%
Palm Desert
0.6%
Palm Springs 21
Perris
Rancho Mirage
Riverside 48
3.6% 70
6.2%
San Jacinto 6 8
Temecula
1.9%
Wildomar
Sub Total
266
19.7%
317
28.1% J

20. Unsheltered Chronically Homeless Comparison
2016
2017
Unincorporated Area
Count
Percent
Alberhill 2
0.1%
0.0%
Anza
Belltown 4
0.3%
Bermuda Dunes 6
0.5%
Cherry Valley 1
Highgrove
0.4%
Homeland
Idyllwild 3
Lake Tamarisk
March ARB
0.2%
Mead Valley 7
0.6%
Mecca
Romoland
Thermal
Thousand Palms
Valle Vista
Whitewater
Woodcrest
Sub Total 33
2.4% 24
2.1%
Grand Total
299
22.1%
341
30.2% J

21. Unsheltered Disability Comparison
Number of respondents who stated they have experienced…
Number of respondents who stated it prevents them from obtaining housing or work.
2016
2017
Subpopulation
Count
Percent
Percent Change
Alcohol Use
273
26.2%
264
23.4%
-3.3% 61
5.9% 47
4.2%
-23.0%
Drug Use
341
32.7%
409
36.3%
19.9% 90
8.6%
109
9.7%
21.1%
PTSD
200
19.2%
268
23.8%
34.0%
106
10.2%
136
12.1%
28.3%
Mental Health Conditions
275
26.4%
277
24.6%
0.7%
144
13.8%
137
-4.9%
Physical Disability
326
31.3%
343
30.4%
5.2%
190
18.2%
181
16.0%
-4.7%
Developmental Disability
128
12.3%
121
10.7%
-5.5% 59
5.7% 58
5.1%
-1.7%
Brain Injury
201
19.3%
212
18.8%
5.5% 82
7.9% 72
6.4%
-12.2% J

22. 2017 Chronically Homeless Veterans 2017 Chronically Homeless Veterans
Unsheltered Veterans
2016 Veterans
100
2017 Chronically Homeless Veterans 33
2017 Veterans 91
2017 Chronically Homeless Veterans 37
Percent Change
-9.0% J

23. Unsheltered Veteran Comparison…
2015
2016
2017
City
Banning 6 4
Beaumont 2 1
Blythe 8
Calimesa
Canyon Lake
Cathedral City 5
Coachella 11
Corona
Desert Hot Springs
Eastvale
Hemet 7
Indian Wells
Indio 3
Jurupa Valley
Lake Elsinore
La Quinta
Menifee
Moreno Valley
Murrieta
Norco
Palm Desert
Palm Springs 9 12
Perris
Rancho Mirage
Riverside 27 15 20
San Jacinto
Temecula
Wildomar
City Subtotal 99 90 86 J

24. Unsheltered Veteran Comparison
2015
2016
2017
Unincorporated
Anza
Bermuda Dunes 1
Cabazon 2
Cherry Valley
Highgrove
Lakeland Village
Mead Valley
Meadowbrook
Mecca
Mesa Verde
Thousand Palms 3
Valle Vista
White Water
Unincorporated Subtotal 10 5
Grand Total
102
100 95 J

25. Unsheltered Race/Ethnicity Comparison
2017 PIT Count
2016
2017
Race
Interview
Observational
Count
Percent
Percent Change
American Indian or Alaska Native 82 7 87
6.4% 89
5.4%
2.3%
Asian 8 2 10
0.7%
0.6%
0.0%
Black or African American
131 55
160
11.8%
186
11.4%
16.3%
Native Hawaiian, Pacific Islander 19 1 12
0.9% 20
1.2%
66.7%
White
710
218
822
60.8%
928
56.7%
12.9%
Don't Know or Refused
107
154
258
19.1%
261
15.9%
(blank)
129 72 39
2.9%
201
12.3%
415.4%
Hispanic (ethnicity)
360
105
372
27.5%
465
28.4%
25.0% E

26. Unsheltered Race/Ethnicity Comparison

27. Unsheltered Age Comparison
2016
2017
Count
Percent
Percent Difference
0-5 1
0.1%
0.0%
-0.1%
17 or under 13
1.0% 15
1.3%
0.4%
18-24 82
6.1%
107
9.5%
3.4%
25-29
101
7.5%
7.3%
-0.2%
30-39
256
18.9%
203
18.0%
-1.0%
40-49
305
22.6%
250
22.2%
-0.4%
50-61
396
29.3%
340
30.1%
0.8%
62-69 94
7.0% 70
6.2%
-0.8%
70-79 22
1.6% 9
80+ 4
0.3% 6
0.5%
0.2%
(blank) 78
5.8% 46
4.1%
-1.7% E

28. Unsheltered Gender Comparison
2016
2017
Count
Percent
Percent Difference
Female
389
28.8%
445
27.2%
-1.6%
Male
940
69.6%
1123
68.6%
-1.0%
Transgender 1
0.1% 8
0.5%
0.4%
Don't Know or Refused 13
1.0% 47
2.9%
1.9%
(blank)
0.6% 15
0.9%
0.3% E

29. Youth PIT Count Methodology
2017 is the first year HUD has mandated that CoCs conduct a separate Youth Count with the intention to better capture unaccompanied youth age 24 and under
Youth Count was led by DPSS, RUHS-BH TAY Programs, and Operation Safe House
Youth partnering agencies were recruited and lead their own teams of trained, youth-friendly volunteers on their day or days of choice within the 7-day period of the youth PIT count
While it was recommended that partnering agencies conduct their counts between the hours of 2 p.m. – 8 p.m., partnering agencies were able to choose their own days, times, and locations to conduct the counts of the homeless youth in their area
Youth PIT Count numbers are combined with traditional PIT numbers from a data perspective T

30. Lessons learned will be included in final report
Youth PIT Count Outcomes & Challenges
OUTCOMES
Increased collaboration with youth partner agencies
Established a foundation for future Youth PIT Counts that will be expanded
Included formerly homeless youth in the Count
CHALLENGES
Limited county-wide coverage
Vacated homeless locations
Lessons learned will be included in final report T

31. 2017 Youth PIT Count Totals 15 107 D 2016 2017 2016-2017
Youth Count
Count
Percent
Percent Change
Youth 24 or younger 95
9.1%
122
7.6%
31.6%
Ages 17 or under 15
Ages 18-24
107 D

32. Questions Did the weather impact the results of our Count? If so, HOW?
Were the homeless more visible or less visible in the 2017 PIT Count?
What are the reasons for the overall increase in our unsheltered population?
Most unsheltered subpopulations experienced an increase (except veterans, families and jail). Why?
What else do we need to know to effectively explain our INCREASE to HUD and our communities?
Are there any specific regional concerns we should be aware of? D

33. 2016 Total Homeless Count ComparisonD

34. The Next Steps… COMPLETE DATA ANALYSIS April 5-20, 2017 D
DATA RELEASED TO MEDIA
April 10, 2017
PRESENT FINAL DATA TO CoC
April 26, 2017
SUBMIT DATA TO HUD
May 1, 2017
FINAL REPORT PUBLISHED
May 10, 2017 D

35. THANK YOU RIVERSIDE COUNTY!