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

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

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

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

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

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

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

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

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

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

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

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

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

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

Unsheltered Subpopulation Comparison 2016 2017 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

Unsheltered Subpopulation Comparison J

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

299 341 Unsheltered Chronically Homeless 14.1% 2016-2017 Percent Change 14.1% J

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

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

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 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

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 2016-2017 Percent Change -9.0% J

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

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

Unsheltered Race/Ethnicity Comparison 2017 PIT Count 2016 2017 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

Unsheltered Race/Ethnicity Comparison

Unsheltered Age Comparison 2016 2017 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

Unsheltered Gender Comparison 2016 2017 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

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

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

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

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

2016 Total Homeless Count Comparison D

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

THANK YOU RIVERSIDE COUNTY!