County of Riverside Continuum of Care (CoC)
CoC Strategic Planning (All Committees) in lieu of Housing Committee June 13, 2017 | 2:00 – 3:30 p.m. DPSS: Banning Children/Adult Services 901 E Ramsey Street, Rm.1, Banning, CA 92220
Facilitator: Donyielle Holley, CoC Planner
1. Call to Order: Welcome & Introductions
2. Unfinished Business: a. Strategic Planning Discussion & Workgroups ALL
3. Committee Member Comments
4. Call for Agenda Items for Next Meeting
5. Next Meeting: TBD
6. Adjournment
7. Next Meetings:
CES Oversight Committee: June 8, 2017, 1 p.m. – 3 p.m. Banning DPSS Self Sufficiency, 63 S. 4th Street, Banning, CA 92220 Standards & Evaluation Committee: June 15, 2017, 2 p.m. – 4 p.m. Banning DPSS Self Sufficiency, 63 S. 4th Street, Banning, CA 92220 CoC Membership Meeting: June 28, 2017, 10 a.m. – 12 p.m. Banning City Council Chambers, 99 E. Ramsey Street, Banning CA 92220 Housing Committee: July 11, 2017, 2 p.m. – 3:30 p.m. Banning DPSS Children/Adult Services Office, 901 E. Ramsey St., Banning, CA 92220 Planning Committee: July 20, 2017, 3 p.m. – 4 p.m. Banning DPSS Self Sufficiency, 63 S. 4th Street, Banning, CA 92220
CoC Meeting – Strategic Planning Session May 24, 2017 10:00 a.m. - 12:00 p.m. City of Banning Council Chambers 99 E. Ramsey Street, Banning CA 92220
Donyielle Holley, CoC Planner, presented with a PowerPoint slide on the continuation of the March 22, 2017, Strategic Planning Session. Slides presented at the meeting can be viewed on the DPSS website.
This session focused on thinking outside of the box to find ways to accomplish our goals in Riverside County to come up with a draft plan by summer 2017.
1. Strategic Planning Cycle Update • For the May 24 meeting, to finalize vision, mission, values and SWOT • Goals and objectives by June • Develop and approve strategic plan document by July 2. Community Engagement • Inadequate amount of housing landlords, suggestion to engage with different resources: o Realtors/Management companies o Adult protective services o Children protective services o Youth opportunity centers o Universities and colleges. Stephan McPeace, Office of Education, is a liaison with the school districts o Rotary clubs and different services groups o Neighborhoods and mentorships • To engage these resources o Linda barrack suggested to keep providers engaged, change our meeting formats to incorporate more of what is relevant to them in housing. o Susan Larkin suggested to provide more networking and educational pieces in our meetings o Angelina Coe suggested to provide service projects and to educate social service agencies, fraternities and sororities with educational information (i.e.: PIT count participation) to get them active and engaged o Florence White, Alternatives to Domestic Violence, suggested carving out time to welcome the business community that attend our meetings, evaluate them to know what they’re interests are. Be friendly and change how we do business with them. 3. SWOT Analysis • Powerful in the business world • When we met with our consultant Margaret McFaddin, this SWOT Analysis with all our strengths, weaknesses, opportunities and threats listed is what we came up with. Please refer to the SWOT Analysis and note any additions or changes you would like to make. 4. Review Mission & Vision 5. Goals & Objectives • Sustaining veteran homelessness • Chronic homelessness o Concerns with having enough affordable housing a. Are we maximizing the full capacity of our PSH? (Open Discussion & Commentary) • Jill Kowalski noted bed utilization is one of the performance measures, this is what CES is set up for, when beds become vacant, they are making matches and putting them into vacant beds • Linda Barrack, but are we using them at full capacity? Are we making our resources aware to all people in need? • Jill Kowalski, some locations have low utilization. • Linda Barrack, how are we going to address this? • Kristii MacEwen noted Housing Committee is focusing on bed utilization. Before we seek more funding and beds, we want to make sure we are using all of our resources. Every month utilization rates are tracked and CES did help raise bed utilization rates. Next step is to graduate people out to stable housing and self-sufficiency, not all clients need navigators. • Tanya shared that Housing Authority has successfully received referrals from the CES. The biggest barrier is the lack of bridge housing. There is a lapse in time before outreach can connect with the individual;it can take weeks. Can we speed up this process by looking at individuals in PSH and graduate them to affordable housing programs. • Damien said it would be helpful to see a list as to why we are not using our full capacity? Why beds are not being used? Or is the location a problem? Then we can address those concerns. • Susan Larkin suggested engaging more cities in this process; some cities may have more vacant properties that can be used for housing. She suggested looking into alternative options, i.e.: Tiny Houses. Are there barrier restrictions or requirements at certain locations we may not see? • Lynne Brockmeier said they are trying to work with identifying gaps, with individuals who are really chronically homeless and families that are not showing up in the system. HomeConnect is giving out bracelets with numbers on them, and it appears to be working since homeless individuals have been calling the number to get help. Barriers are widespread; hopefully HMIS can address that in the future. b. Donyielle Holley asked if the federal goal of ending chronic homelessness by 2017 is a realistic goal. We will look more at the data and CoC to set a new goal. • Christian Jelmberg, Founder of Street Life Project in the Coachella Valley, has been working with Tiny Homes, has seen a lot of success in cities that has Tiny Homes. They are self-managed communities, at each site there are full time case management. Partnered with different agencies and will have a partner that will provide free land for these communities, the cost of Tiny Homes is a lot less than other housing budgets. It will eventually centralize our service efforts as well. • Housing stability • Mainstream Benefits
The CoC will finish the rest of Strategic Planning Session on June 13, at the Housing Committee Meeting. 2017 Riverside County Homeless Continuum of Care STRATEGIC PLANNING
MISSION The Riverside County CoC will prevent and end homelessness for persons experiencing housing crises in Riverside County by serving as coordinated, collaborative, community stakeholders who provide housing, services and planning solutions.
VISION By 2027, our community will be one where homelessness is rare, brief and non-recurring. Our coordinated and collaborative network prevents homelessness. No one in our community is forced to live on the streets. We have the required resources available to ensure that everyone in need has a fair and equitable opportunity for affordable and safe housing. Our priorities are so compelling that funding is readily available and people are investing in solutions. Our performance is at a level that builds confidence among Contact: stakeholders, funders, and citizens. Our reputation as a community is enhanced because Donyielle Holley of our work, and we are known for practical innovations that help people and CoC Planner organizations. (951) 358-4087 [email protected]
2017 RIVERSIDE COUNTY HOMELESS COUNT SUMMARY
2017 TOTAL HOMELESS POPULATION 2017 OPEN DOOR SUBPOPULATIONS (Unsheltered) 1
91 341 Veterans Chronically Homeless 768 (32% ) 1638 (68%) (Met Veteran Goal: 2016) (Goal: 2016) Sheltered Unsheltered (HMIS) (Street & Service-Based)
2 The veteran persons enumerated in 2017 PIT total # of homeless persons Count decreased by 9% since 2016. The 2,406 chronically homeless persons enumerated in Homelessness has increased by 11% since 2016 2017 PIT Count increased by 14% since 2016.
REASONS FOR BECOMING HOMELESS 2017 HOMELESSNESS BY HOUSEHOLD TYPE (Unsheltered)
Count Percentage Primary Reasons for Becoming Homeless % 74% 44% 52% 33% 3 Unemployment 347 Lack of income for housing 288 27% Fleeing domestic violence 44 4% 1454 3 178 Discharged from medical institution 31 3% Discharged from jail or prison 79 8% Single Adults Families Unaccompanied Mental Illness 84 8% Youth (under 25) Runaway/left home 69 7% Other 368 35% 4
40 50% 70%
COMPANY NAME 2017 Riverside CoC Strategic Planning Worksheet
CoC Name: ______
CoC Leaders: ______
______
1. What are the major goals for your CoC to end all homelessness?
2. Which one new partner would your CoC engage to play a major role in working with your CoC to end homelessness?
3. How will your CoC recruit and engage your members to create a powerful team to end homelessness?
4. What are the strategies for ending chronic homelessness?
5. What are the strategies for sustaining veteran homelessness?
6. What are the strategies for ending family/youth homelessness?
7. How will your CoC prevent individuals from becoming homeless?
8. How will your CoC promote housing stability to minimize returns to homelessness?
NOTES
2017 Unsheltered Homeless Count by City
2017 PIT Count Total Unsheltered Percent City or Unincorporated Area 2015 2016 2017 Count Change Interview Observational Change Count Percent Count Percent Count Percent City Banning 26 6 30 2.0% 25 1.9% 32 2.0% 7 28.0% Beaumont 14 4 14 0.9% 10 0.7% 18 1.1% 8 80.0% Blythe 31 28 52 3.4% 63 4.7% 59 3.6% -4 -6.3% Calimesa 0 0 0 0.0% 0 0.0% 0 0.0% 0 - Canyon Lake 0 0 0 0.0% 0 0.0% 0 0.0% 0 - Cathedral City 37 7 38 2.5% 27 2.0% 44 2.7% 17 63.0% Coachella 61 20 41 2.7% 59 4.4% 81 4.9% 22 37.3% Corona 39 17 69 4.5% 83 6.1% 56 3.4% -27 -32.5% Desert Hot Springs 33 10 21 1.4% 28 2.1% 43 2.6% 15 53.6% Eastvale 0 0 0 0.0% 0 0.0% 0 0.0% 0 - Hemet 65 54 117 7.6% 107 7.9% 119 7.3% 12 11.2% Indian Wells 0 0 0 0.0% 0 0.0% 0 0.0% 0 - Indio 57 32 92 6.0% 70 5.2% 89 5.4% 19 27.1% Jurupa Valley 101 28 168 11.0% 113 8.4% 129 7.9% 16 14.2% La Quinta 1 1 10 0.7% 8 0.6% 2 0.1% -6 -75.0% Lake Elsinore 53 9 51 3.3% 53 3.9% 62 3.8% 9 17.0% Menifee 10 4 16 1.0% 20 1.5% 14 0.9% -6 -30.0% Moreno Valley 54 19 61 4.0% 61 4.5% 73 4.5% 12 19.7% Murrieta 5 2 2 0.1% 0 0.0% 7 0.4% 7 - Norco 3 1 5 0.3% 12 0.9% 4 0.2% -8 -66.7% Palm Desert 21 7 16 1.0% 19 1.4% 28 1.7% 9 47.4% Palm Springs 90 48 118 7.7% 83 6.1% 138 8.4% 55 66.3% Perris 27 2 52 3.4% 65 4.8% 29 1.8% -36 -55.4% Rancho Mirage 0 0 6 0.4% 3 0.2% 0 0.0% -3 -100.0% Riverside 231 158 399 26.1% 258 19.1% 389 23.7% 131 50.8% San Jacinto 23 8 12 0.8% 18 1.3% 31 1.9% 13 72.2% Temecula 67 18 42 2.7% 37 2.7% 85 5.2% 48 129.7% Wildomar 4 2 10 0.7% 13 1.0% 6 0.4% -7 -53.8% Subtotal 1053 485 1442 94.2% 1235 91.4% 1538 93.9% 303 24.5% Unincorporated Area Anza 0 0 0 0.0% 2 0.1% 0 0.0% -2 -100.0% Bermuda Dunes 10 0 8 0.5% 2 0.1% 10 0.6% 8 400.0% Cabazon 1 0 3 0.2% 6 0.4% 1 0.1% -5 -83.3% Cactus City 1 0 0 0.0% 0 0.0% 1 0.1% 1 - Cherry Valley 1 0 0 0.0% 3 0.2% 1 0.1% -2 -66.7% Coronita 0 5 0 0.0% 0 0.0% 5 0.3% 5 - Highgrove 1 4 0 0.0% 25 1.9% 5 0.3% -20 -80.0% Home Gardens 0 1 1 0.1% 0 0.0% 1 0.1% 1 - Homeland 0 0 1 0.1% 2 0.1% 0 0.0% -2 -100.0% Idyllwild 8 2 10 0.7% 12 0.9% 10 0.6% -2 -16.7% Lakeland Village 9 0 3 0.2% 14 1.0% 9 0.5% -5 -35.7% Mead Valley 12 2 1 0.1% 0 0.0% 14 0.9% 14 - Meadowbrook 0 0 2 0.1% 3 0.2% 0 0.0% -3 -100.0% Mecca 13 2 22 1.4% 21 1.6% 15 0.9% -6 -28.6% Mesa Verde 0 0 1 0.1% 2 0.1% 0 0.0% -2 -100.0% Romoland 4 1 1 0.1% 3 0.2% 5 0.3% 2 66.7% Temescal Valley 0 1 0 0.0% 0 0.0% 1 0.1% 1 - Thermal 1 0 0 0.0% 8 0.6% 1 0.1% -7 -87.5% Thousand Palms 12 5 4 0.3% 6 0.4% 17 1.0% 11 183.3% Valle Vista 2 2 1 0.1% 0 0.0% 4 0.2% 4 - White Water 0 0 3 0.2% 1 0.1% 0 0.0% -1 -100.0% Woodcrest 0 0 28 1.8% 6 0.4% 0 0.0% -6 -100.0% Subtotal 75 25 89 5.8% 116 8.6% 100 6.1% -16 -13.8% Grand Total 1128 510 1531 100.0% 1351 100.0% 1638 100.0% 287 21.2% *Note: 2017 PIT comprehensive data. Only unincorporated communities with a 2016/2017 unsheltered count greater than zero are included.
Riverside DPSS ASD HPU Preliminary PIT Count Data 6/13/2017 2017 Unsheltered Homeless Count - Subpopulation Data by City versus Unincorporated Area
Total Victims of Unsheltered Chronically Families with Youth (24 or Mental Health Physical Developmental Jail (within 12 City or Unincorporated Veterans Alcohol Use Drug Use PTSD Brain Injury Domestic AIDS or HIV Homeless Homeless Children younger) Conditions Disability Disability months) Area Violence Individuals Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent City Banning 32 2.0% 12 1.1% 0 0.0% 4 0.4% 1 0.1% 11 1.0% 11 1.0% 7 0.7% 12 1.1% 9 0.8% 3 0.3% 6 0.6% 8 0.7% 1 0.1% 5 0.5% Beaumont 18 1.1% 2 0.2% 0 0.0% 0 0.0% 0 0.0% 2 0.2% 2 0.2% 1 0.1% 1 0.1% 1 0.1% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 3 0.3% Blythe 59 3.6% 11 1.0% 0 0.0% 4 0.4% 4 0.2% 7 0.6% 11 1.0% 6 0.6% 13 1.2% 12 1.1% 6 0.5% 7 0.7% 12 1.1% 0 0.0% 5 0.5% Calimesa 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Canyon Lake 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Cathedral City 44 2.7% 15 1.3% 0 0.0% 5 0.4% 3 0.2% 9 0.8% 24 2.1% 7 0.7% 9 0.8% 6 0.5% 6 0.5% 6 0.6% 7 0.6% 2 0.2% 18 1.7% Coachella 81 4.9% 20 1.8% 0 0.0% 5 0.4% 1 0.1% 11 1.0% 12 1.1% 10 1.0% 11 1.0% 24 2.1% 4 0.4% 8 0.8% 11 1.0% 1 0.1% 6 0.6% Corona 56 3.4% 12 1.1% 0 0.0% 1 0.1% 6 0.4% 6 0.5% 16 1.4% 8 0.8% 11 1.0% 15 1.3% 9 0.8% 9 0.9% 7 0.6% 2 0.2% 8 0.8% Desert Hot Springs 43 2.6% 5 0.4% 0 0.0% 1 0.1% 6 0.4% 7 0.6% 11 1.0% 15 1.4% 6 0.5% 13 1.2% 2 0.2% 11 1.0% 17 1.5% 0 0.0% 5 0.5% Eastvale 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Hemet 119 7.3% 15 1.3% 0 0.0% 8 0.7% 21 1.3% 26 2.3% 27 2.4% 20 1.9% 19 1.7% 15 1.3% 4 0.4% 17 1.6% 15 1.3% 0 0.0% 13 1.2% Indian Wells 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Indio 89 5.4% 26 2.3% 0 0.0% 5 0.4% 7 0.4% 13 1.2% 29 2.6% 16 1.5% 10 0.9% 19 1.7% 8 0.7% 11 1.0% 17 1.5% 1 0.1% 9 0.9% Jurupa Valley 129 7.9% 30 2.7% 0 0.0% 6 0.5% 6 0.4% 25 2.2% 47 4.2% 27 2.6% 27 2.4% 27 2.4% 6 0.5% 21 2.0% 20 1.8% 1 0.1% 14 1.3% La Quinta 2 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lake Elsinore 62 3.8% 15 1.3% 0 0.0% 0 0.0% 2 0.1% 15 1.3% 24 2.1% 13 1.2% 15 1.3% 17 1.5% 8 0.7% 13 1.2% 10 0.9% 1 0.1% 16 1.5% Menifee 14 0.9% 0 0.0% 0 0.0% 0 0.0% 3 0.2% 2 0.2% 5 0.4% 0 0.0% 1 0.1% 4 0.4% 1 0.1% 1 0.1% 1 0.1% 0 0.0% 4 0.4% Moreno Valley 73 4.5% 14 1.2% 0 0.0% 3 0.3% 18 1.1% 16 1.4% 22 2.0% 11 1.0% 20 1.8% 15 1.3% 6 0.5% 7 0.7% 13 1.2% 5 0.4% 10 1.0% Murrieta 7 0.4% 1 0.1% 0 0.0% 0 0.0% 1 0.1% 3 0.3% 3 0.3% 1 0.1% 1 0.1% 1 0.1% 1 0.1% 1 0.1% 1 0.1% 0 0.0% 3 0.3% Norco 4 0.2% 3 0.3% 0 0.0% 0 0.0% 2 0.1% 3 0.3% 2 0.2% 0 0.0% 2 0.2% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Palm Desert 28 1.7% 7 0.6% 0 0.0% 3 0.3% 3 0.2% 7 0.6% 9 0.8% 4 0.4% 7 0.6% 14 1.2% 7 0.6% 5 0.5% 7 0.6% 0 0.0% 0 0.0% Palm Springs 138 8.4% 20 1.8% 1 0.1% 12 1.1% 11 0.7% 19 1.7% 29 2.6% 29 2.8% 25 2.2% 35 3.1% 9 0.8% 20 1.9% 26 2.3% 4 0.4% 13 1.2% Perris 29 1.8% 5 0.4% 0 0.0% 1 0.1% 1 0.1% 4 0.4% 9 0.8% 9 0.9% 8 0.7% 10 0.9% 5 0.4% 8 0.8% 5 0.4% 0 0.0% 3 0.3% Rancho Mirage 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Riverside 389 23.7% 73 6.5% 0 0.0% 21 1.9% 77 4.7% 66 5.9% 107 9.5% 48 4.6% 83 7.4% 75 6.6% 37 3.3% 42 4.0% 65 5.8% 3 0.3% 32 3.0% San Jacinto 31 1.9% 8 0.7% 0 0.0% 1 0.1% 5 0.3% 1 0.1% 12 1.1% 4 0.4% 5 0.4% 3 0.3% 5 0.4% 5 0.5% 5 0.4% 0 0.0% 5 0.5% Temecula 85 5.2% 21 1.9% 0 0.0% 6 0.5% 9 0.5% 17 1.5% 25 2.2% 20 1.9% 10 0.9% 18 1.6% 3 0.3% 8 0.8% 17 1.5% 0 0.0% 19 1.8% Wildomar 6 0.4% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 2 0.2% 0 0.0% 0 0.0% Sub Total 1538 93.9% 316 28.0% 1 0.1% 86 7.6% 187 11.4% 271 24.0% 438 38.8% 257 24.5% 296 26.2% 335 29.7% 130 11.5% 206 19.6% 267 23.7% 21 1.9% 191 18.2% Unincorporated Area Aguanga 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Alberhill 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Anza 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Belltown 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Bermuda Dunes 10 0.6% 6 0.5% 0 0.0% 1 0.1% 0 0.0% 2 0.2% 4 0.4% 0 0.0% 2 0.2% 5 0.4% 2 0.2% 2 0.2% 2 0.2% 0 0.0% 3 0.3% Cactus City 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 0 0.0% Cabazon 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Cherry Valley 1 0.1% 1 0.1% 0 0.0% 1 0.1% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% Chiriaco Summit 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Coronita 5 0.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Crestmore Heights 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Desert Beach 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Desert Center 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Desert Edge 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Desert Palms 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% East Blythe 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Eagle Mountain 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Eagle Valley 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% East Hemet 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% El Cerrito 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% El Sobrante 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% French Valley 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Garnet 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Good Hope 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Green Acres 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Highgrove 5 0.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Home Gardens 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Homeland 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Idyllwild 10 0.6% 3 0.3% 0 0.0% 0 0.0% 1 0.1% 3 0.3% 2 0.2% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 1 0.1% 0 0.0% 0 0.0% 1 0.1% Indio Hills 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lake Mathews 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0%
Riverside DPSS ASD HPU Preliminary PIT Count Data 6/12/2017 Lake Riverside 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lake Tamarisk 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lakeland Village 9 0.5% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 1 0.1% 2 0.2% 1 0.1% 1 0.1% 2 0.2% 0 0.0% 0 0.0% Lakeview 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lost Lake 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% March ARB 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Mead Valley 14 0.9% 7 0.6% 1 0.1% 0 0.0% 2 0.1% 1 0.1% 7 0.6% 6 0.6% 4 0.4% 4 0.4% 0 0.0% 1 0.1% 5 0.4% 0 0.0% 3 0.3% Meadowbrook 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Mecca 15 0.9% 0 0.0% 1 0.1% 0 0.0% 2 0.1% 6 0.5% 3 0.3% 0 0.0% 1 0.1% 2 0.2% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 2 0.2% Mesa Verde 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Mountain Center 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% North Shore 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Nuevo 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Oasis 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Pine Cove 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Pinyon Pines 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Rancho Capistrano 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Ripley 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Romoland 5 0.3% 2 0.2% 0 0.0% 0 0.0% 0 0.0% 2 0.2% 1 0.1% 1 0.1% 1 0.1% 4 0.4% 0 0.0% 0 0.0% 2 0.2% 0 0.0% 0 0.0% Sage 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Sky Valley 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Temescal Valley 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Thermal 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Thousand Palms 17 1.0% 3 0.3% 0 0.0% 3 0.3% 1 0.1% 3 0.3% 3 0.3% 2 0.2% 2 0.2% 4 0.4% 1 0.1% 0 0.0% 2 0.2% 0 0.0% 2 0.2% Valle Vista 4 0.2% 2 0.2% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 1 0.1% 1 0.1% 2 0.2% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Vista Santa Rosa 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Warm Springs 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Whitewater 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Winchester 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Woodcrest 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Sub Total 100 6.1% 25 2.2% 2 0.2% 5 0.4% 6 0.4% 20 1.8% 23 2.0% 11 1.0% 13 1.2% 27 2.4% 5 0.4% 6 0.6% 15 1.3% 0 0.0% 11 1.0% Grand Total 1638 100.0% 341 30.2% 3 0.3% 91 8.1% 193 11.8% 291 25.8% 461 40.9% 268 25.5% 309 27.4% 362 32.1% 135 12.0% 212 20.2% 282 25.0% 21 1.9% 202 19.2% *Note: 2017 PIT comprehensive data. Unsheltered count and Youth(24 or younger) includes total interview and observational collected data; percent of total unsheltered field and Youth(24 or younger) field calculated on total collected (1638). Families with Children, PTSD and Brain Injury field are unique to the general PIT questionnaire; percent of total calculated based on persons interviewed (1050). All remaining fields percentage calculated on total persons interviewed (1128).
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COUNTY OF RIVERSIDE 2017 POINT-IN-TIME HOMELESS COUNT REPORT
May 10, 2017
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TABLE OF CONTENTS
1. Acknowledgements...... 3 2. Executive Summary ...... 11 3. Introduction ...... 14 4. Methodology ...... 15 a. Overview ...... 15 b. City Engagement ...... 16 c. Survey Development ...... 17 d. Data Entry & Deduplication Method ...... 18 e. Volunteer Recruitment ...... 20 f. Unsheltered Homeless Count ...... 20 i. Street-Based Count ...... 20 ii. Service-Based Count ...... 21 iii. Behavioral Health Outreach Count ...... 21 iv. Follow-up Count During Seven Day Window ...... 22 v. Youth Count ...... 22 g. Sheltered Homeless Count ...... 23 5. Unsheltered Homeless Count Findings ...... 24 a. Overall by City & Type of Data ...... 24 b. Reasons Contributing to Homelessness ...... 26 c. Overall by Gender ...... 26 d. Overall by Race and Ethnicity ...... 27 e. Overall by Age ...... 30 f. Overall by Veterans ...... 31 g. Overall by Youth...... 31 h. Overall by Subpopulations ...... 32 i. Subpopulations Disabling Conditions Preventing Housing ...... 34 j. Subpopulation Data by City ...... 35 6. Sheltered Homeless Count Findings ...... 37 a. Overall by Housing Type ...... 37 b. Subpopulations ...... 37 7. Conclusion ...... 38 8. Challenges, Limitations, & Recommendations ...... 39 9. Appendix A: Survey Instruments ...... 41 a. Traditional PIT Count Survey Tool (Interview & Observational combined) ...... 42 b. Youth PIT Count Survey Interview Tool ...... 44 c. Youth PIT Count Survey Observational Tool ...... 47 10. Appendix B: City Summary Tables ...... 48 11. Appendix C: County Summary Tables ...... 77 12. Appendix D: Misconceptions about PIT Counts ...... 94 13. Appendix E: HUD Youth Homeless Definition ...... 96
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ACKNOWLEDGEMENTS
The County of Riverside Department of Public Social Services (DPSS) Director, Susan von Zabern, and Assistant Director, Lisa Shiner, along with the 2017 Point-In-Time Homeless Count (PIT Count) Planning Team would like to thank all of the individuals and agencies who contributed their time, money, staff, and effort toward this important event. Coordinating the PIT Count is a massive undertaking. We would not have been able to conduct this count and survey without the participation of many community- based organizations and agencies.
We extend our sincerest appreciation to our public volunteers and the agencies that allowed their staff to volunteer for the PIT Count:
ABC Recovery Center City of Lake Elsinore All Saints Episcopal Church City of La Quinta Atrium Sober Living City of Menifee Banning Family Community Health Center City of Moreno Valley Banning Police Department City of Murrieta Banning Wellness City – RI International City of Norco Barton CPA City of Palm Desert Beaumont Police Department City of Palm Springs Be Remarkable Today City of Perris Bridge Church City of Rancho Mirage California Baptist University City of Riverside California Children's Services City of San Jacinto California Rural Legal Assistance City of Temecula California State University, San Bernardino City of Wildomar Calimesa Code Enforcement Coachella Valley Assoc. of Governments (CVAG) Calvary Murrieta Church Coachella Valley Economic Partnership Calvary Presbyterian Church Coachella Valley Rescue Mission Catholic Charities Coachella Police Department Church of Jesus Christ Latter Day Saints Commission on Disabilities, Inland Regional City of Banning Center City of Beaumont Common Good Community Development City of Blythe Community Access Center City of Calimesa Community Action Partnership Riverside County City of Cathedral City Community Good Deeds Nonprofit Organization City of Coachella Community of Mecca City of Corona Community Mission of Hope City of Desert Hot Springs Community of Reformed Evangelical Churches City of Eastvale Community Resource and Educational Center City of Hemet Congregational Church of Menifee City of Hemet Parks and Recreation Corona Code Enforcement City of Indian Wells Corona Fire Safety Foundation City of Indio Corona Police Department City of Jurupa Valley Corona Settlement House
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Corona Vet Center Lake Menifee Women’s Club Courage Campaign Larios Create a Bright Life LDS Church Crime Watch Lesbian Gay Center Desert Arc Life in His Hands Desert Best Friend's Closet Lifeline Street Ministry Desert Hot Springs Family Resource Center LightHouse Social Service Centers Desert Hot Springs Neighborhood Group LLLeindecker & Associates Dios Vive Loma Linda Veteran Affairs Hospital Dominium Love and Loyalty Down Syndrome Association of Inland Empire Lutheran Social Services Eastvale Bible Church Magnolia Presbyterian Church Eye of a Needle Foundation Manzanita Ranch First Presbyterian Church of Hemet Martha’s Village & Kitchen Foothill AIDS Project Mecca Family & Farmworkers Service Center GIA Community Outreach Mecca Family Resource Center Good Works Movement Men and Women Ministry on the Move Gregory Barton & Associates Menifee Buzz Newspaper Green Coalition of San Jacinto Valley Menifee Valley Chamber of Commerce GUMBO First Baptist Church of Riverside Menifee Valley Community Cupboard Habitat for Humanity Inland Valley Menifee Crime Watch Harmony Kitchen Menifee Kay Ceniceros Senior Center Harvest Church Menifee Police Department HD Supply White Cap Menifee Union School District Health to Hope Clinics Menifee Valley Community Church Healthy Jurupa Mission Outreach Project Helping Hands Group Monteleone Meadows Hemet Code Enforcement Moreno Valley College Hemet Community Pantry Moreno Valley Police Department Hemet Library My Butterfly House Hemet Police Department National Alliance for Women Hemet/San Jacinto United for Change National Alliance on Mental Illness Hemet Unified School District Norco Community College Homeless Task Force Sub-Committee ONE-PS Hope Lutheran Church Operation SafeHouse Human Rights Commission Osher Lifelong Learning Institute Idyllwild HELP Center Palm Springs Homeless Task Force Indio Police Department Palm Springs Human Rights Commission Jewish Family Service of the Desert Palm Springs Police Department Joe Gray’s Plumbing Palm Springs Public Library Journey TAY Program Palm Springs Rotary Club Jubilee Sober-Living Homes Palm Springs Unified School District Jurupa Valley Code Enforcement Path of Life Ministries Jurupa Valley Safety Committee Peace Lutheran Church JustServe.org Perris Code Enforcement La Quinta Police Department Perris Police Department La Sierra University Power Speaks Louder La Sierra University Church Press Enterprise
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Project Riley Seams of Gold Provident Bank SEIU 721 PW Enhancement Center Seller & Keever Foundation Reaching New Heights Foundation Senior Advocates of the Desert Realty One Group Serve-One, Inc. ResCare Workforce Services Southwest County Detention Center Riverside County Registrar of Voters Speak Life RI International/ NAMI Signature Programs StarWriter Enterprises Riverside Access Center St. Carries Center Riverside City College St. Catherine’s of Alexandria Riverside Univ. Health Sys. Behavioral Health St. Thomas Church Riverside County Code Enforcement State of California’s Employment Dev. Dept. Riverside County Dept. of Animal Services Step Up On Second Riverside County Dept. of Child Support Services Sunrise Christian Fellowship Riverside County Dept. of Public Health Sunrise Park Neighborhood Assoc. Palm Springs Riverside County Dept. of Public Social Services Temecula Community Pantry Riverside County Dept. of Veterans Services Temecula Police Department Riverside County Economic Dev. Agency Temecula Rotary Club Riverside County District Attorney's Office Temecula Valley Atheists Riverside County Emergency Mgmt. Dept. Temecula Veterans Center Riverside County Executive Office The Gathering Church Riverside County Flood Control The Grove Community Church Riverside County Habitat Conservation Agency The Hole In Wall, Inc. Riverside County Health Administration The Well in the Desert Riverside County Housing Authority United Church of God Riverside County Human Resources University of California Riverside Riverside County Probation Dept. University of Phoenix Riverside County Sheriff’s Dept. U.S. Dept. of Housing and Urban Dev. Riverside County Supervisor Chuck Washington U.S. Vets Riverside County Supervisor John J. Benoit VA Loma Linda Healthcare System Riverside County Supervisor John F. Tavaglione Val Verde Unified School District Riverside County Supervisor Kevin Jeffries Valley Restart Shelter Riverside County Supervisor Marion Ashley VCC Vista Community Clinic Lake Elsinore Riverside County TMLA Veterans Community Services Harmony Kitchen Veterans of Foreign Wars San Bernardino County VitalCare America San Jacinto Code Enforcement Western Riverside Council of Gov. (WRCOG) San Jacinto Police Dept. Women’s Veterans Unity Group Santa Ana Ministry Group 2YouMusic Santa Ana Watershed Project Authority
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Youth Count Volunteer Organizations
California Family Life Center - Rubidoux YOC Operation SafeHouse Riverside California Family Life Center - Empower Youth Operation SafeHouse Riverside - Cup of Happy City of Menifee Power Speaks Louder City of Murrieta Riverside University Health Systems - Behavioral City of Temecula Health TAY Journey Program City of Riverside Romoland School District County of Riverside Dept. of Public Social Srvcs. Safe Schools Desert Cities Oasis Community Services Victor Community Support Services Operation SafeHouse of the Desert
We would also like to thank all the following cities and agencies who permitted City Leaders, Law Enforcement Leaders and Site Coordinators to promote, develop, and coordinate the street-based count in their respective areas and to use their facilities as Deployment Centers. Deployment Centers were used as a place for volunteers to gather, receive instructions, safety reminders, supplies, incentives, maps, and team assignments:
City/Community Key Leader(s) Deployment Site CITIES Debbie Franklin City of Banning Banning Banning Police Department Police Chief Alex Diaz Banning Police Department Sergeant George Walter Beaumont Police Department Beaumont Beaumont Police Department Deputy Michael Granada Beaumont Police Department Blythe Barbara Burrow Blythe City Hall City of Blythe Sergeant Richard Beatty Riverside County Sheriff’s Calimesa Riverside County Sheriff’s Department Cabazon Station Deputy Jacob Jenson Riverside County Sheriff’s Department Cathedral City Deputy Dwayne Hodges Cathedral City Police Station Cathedral City Police Department Alex Alarcon City of Coachella Coachella Coachella City Hall Sergeant Robert Castro Riverside County Sheriff’s Department Lieutenant Mark Johnson Corona Deputy Jon Rosenblum Corona Police Department Corona Police Department Desert Hot Springs Joe McKee DHS Family Resource Center Desert Hot Springs City Councilman
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City/Community Key Leader(s) Deployment Site Eastvale Deputy Steve Lycopolus Riverside County Sheriff’s Riverside County Sheriff’s Department Jurupa Valley Station Carla Callahan City of Hemet Susan Larkin Valley Restart Lieutenant Eric Dickson Valley Wide Recreation Hemet Hemet Police Department Community Center
Sergeant Dan Reinbolt
Hemet Police Department Detective A. Paez Hemet Police Department Chief Dave Brown Hemet Police Department Indian Wells David Gassaway Indian Wells City Hall City of Indian Wells Indio Christie Curtis Martha’s Village & Kitchen Martha’s Village & Kitchen Jesus Gomez City of Indio Deputy Jose Ibarra Indio Police Department Deputy Brandon Haworth Indio Police Department Jurupa Valley Deputy Steve Lycopolus Riverside County Sheriff’s Riverside County Sheriff’s Department Jurupa Valley Station Deputy Arnoldo Iniguez La Quinta Sam’s Club, Palm Desert Riverside County Sheriff’s Department Lake Elsinore (including Audrey Young Riverside County Sheriff’s Lakeland Village) City of Lake Elsinore Lake Elsinore Station Nicole Dailey City of Lake Elsinore Deputy Alejandro Salcedo Riverside County Sheriff’s Department Deputy Kevin Whitford Riverside County Sheriff’s Department Maria Machuca Mecca Mecca Family Resource Center Department of Public Social Services Jason Hendrix Menifee City of Menifee Kay Ceniceros Senior Center Deputy Karla Girard Riverside County Sheriff’s Department Deputy Robert Wilson Riverside County Sheriff’s Department Moreno Valley Isa Rojas Moreno Valley City Hall City of Moreno Valley
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City/Community Key Leader(s) Deployment Site Mark Houck Lighthouse Social Services Centers Corporal Dave Clark Riverside County Sheriff’s Department Murrieta Brian Ambrose Temecula Community Center City of Murrieta Deputy Steve Lycopolus Norco Riverside County Sheriff’s Department Riverside County Sheriff’s Deputy Arturo Mendez Jurupa Valley Station Riverside County Sheriff’s Department Palm Desert Amy Lawrence Palm Desert City Hall City of Palm Desert Palm Springs Dale Cook Palm Springs City Hall City of Palm Springs Kristii MacEwen Perris Path of Life Ministries Perris Police Department Deputy James Updike Riverside County Sheriff’s Department Rancho Mirage Britt Wilson Rancho Mirage City Hall City of Rancho Mirage Riverside Monica Sapien City of Riverside Access Center City of Riverside Access Center St. Michael's Episcopal Church Renee Yarnall Valley Wide Recreation San Jacinto City of San Jacinto Community Center Sergeant Robert Himmelberg
Riverside County Sheriff’s Department Robin Gilliland City of Temecula Temecula Deputy Todd Johnson Temecula Community Center Riverside County Sheriff’s Department Deputy Jeret McClellan Riverside County Sheriff’s Department Wildomar Keith Ross Wildomar City Hall City of Wildomar COUNTY AREAS Sergeant Richard Beatty Cabazon RSO Station Area Riverside County Sheriff’s Department Riverside County Sheriff’s Deputy Jacob Jenson Cabazon Station Riverside County Sheriff’s Department Sergeant Tom Velarde Colorado River RSO Station Riverside County Sheriff’s Department Riverside County Sheriff’s Area Deputy Joe Aguilar Colorado River Station Riverside County Sheriff’s Department
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City/Community Key Leader(s) Deployment Site Deputy Frank James Valley Wide Recreation Hemet (County) RSO Riverside County Sheriff’s Department Community Center Station Area Deputy Marc Searl
Riverside County Sheriff’s Department Deputy Steve Lycopolus Riverside County Sheriff’s Department Jurupa Valley (County) RSO Deputy Aaron Avila Riverside County Sheriff’s Station Area Riverside County Sheriff’s Department Jurupa Valley Station
Deputy Bridgette Recksiek Riverside County Sheriff’s Department Deputy Alejandro Salcedo Riverside County Sheriff’s Lake Elsinore (County) RSO Riverside County Sheriff’s Department Lake Elsinore Station Station Area Deputy Kevin Whitford
Riverside County Sheriff’s Department Sergeant Zamora Riverside County Sheriff’s Department Palm Desert (County) RSO Deputy Mike Troyer Riverside County Sheriff’s Station Area Riverside County Sheriff’s Department Palm Desert Station Deputy Roberto Loza Riverside County Sheriff’s Department Deputy Todd Johnson Southwest RSO Station Riverside County Sheriff’s Department Temecula Community Center Area Deputy Jeret McClellan Riverside County Sheriff’s Department Sergeant Robert Castro Riverside County Sheriff’s Department Sergeant John Gaw Riverside County Sheriff’s Department Thermal RSO Station Area Sam Club, Palm Desert Deputy Arnoldo Iniguez Riverside County Sheriff’s Department Deputy Maggie Lopez Riverside County Sheriff’s Department
We also would like to provide a special acknowledgement to Riverside Sheriff’s Department Chief Jason Horton and Jurupa Valley Station’s Lieutenant Robert Spivacke and the Riverside County Sheriff’s Homeless Outreach Team, Deputy Aaron Avila and Deputy Bridgette Recksiek, for their leadership, partnership and contributions for coordinating and providing technical support to the Riverside Sheriff’s Homeless Liaisons and other police department liaisons during the planning phase preceding the Count.
Thank you to all city leaders for working to increase public awareness about the Riverside County PIT Homeless Count. We express our deep gratitude to the individuals and organizations who donated money, personal care items, and services to the county-wide PIT Homeless Count, including, but not limited to:
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HomeAid Inland Empire Sage Dentistry Valley Restart Power Speaks Louder Starbucks McDonalds Restaurants (Shalhoub Family) Menifee Valley Community Cupboard San Gorgonio Girl Scout Troop 176 San Gorgonio Girl Scout Troup 284 Anna Hause Elementary School, Beaumont, CA - Early Act Club & Friday Night Live Kids Club
These generous donations contributed to 2,200 incentive bags which included a pair of socks, a bar of soap, a resource card, a knit cap, a pair of gloves and additional incentives targeted to unaccompanied youth. These incentive bags were given to homeless individuals who completed our survey during the unsheltered street-based and service-based counts on January 24, 2017.
Finally, we would like to thank: Members of the Continuum of Care (CoC) for their insightful comments and feedback regarding the preliminary PIT Count data; The Institute for Urban Initiatives for serving as an ad hoc consultant throughout the PIT Count process; Riverside County Information Technology for their incredible turnaround time generating maps that were distributed to volunteers during the PIT street-based count; and Riverside County DPSS Purchasing Department for quickly processing all PIT Count-related purchase requests.
The 2017 PIT Count was planned, coordinated and implemented by the Riverside County DPSS, Adult Services Division, Homeless Programs Unit.
The County of Riverside, Department of Public Social Services Adult Services Division – Homeless Programs Unit PIT Count Planning Team
Joshua Coda Rowena Concepcion Erika Gonzalez Administrative Services Assistant Administrative Services Officer Office Assistant II (TAP)
Elizabeth Hernandez Donyielle Holley Jill Kowalski Program Specialist II Social Service Planner Administrative Services Manager II
Patricia McMicheaux Tiffany Nelson Evelyn Pham Administrative Services Analyst II Administrative Services Assistant Office Assistant III
Veronica Ramirez Linda Salas George Solis Administrative Services Assistant Program Specialist II Administrative Services Analyst II
Tammy Trinh Ryoko Yamasaki Amie St. Criox UCR Intern Administrative Services Analyst II Secretary II (ASD)
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EXECUTIVE SUMMARY
The total The 2017 Point-In-Time Homeless Count and Survey (PIT Count), Point-In-Time conducted on January 24, 2017, identified 1,638 unsheltered and Homeless Count 768 sheltered homeless people (total = 2,406) in Riverside County, increased by 11.5% California. The total Point-In-Time Count increased by 11% from 2016 to 2017. Data collected during the count reflects a snapshot of from 2016 to 2017 Riverside County’s homeless population at one particular point-in-time.
The 2017 unsheltered count methodology consisted of a street-based count, service-based count, and a homeless outreach count conducted by the Riverside University Health System The total - Behavioral Health homeless outreach teams. A total of 498 volunteers unsheltered were deployed across Riverside County to successfully count and survey homeless people the unsheltered homeless population. PIT Count data was deduplicated counted increased using an algorithm to generate a unique ID for each individual counted by 21.2% in 2017 which utilized several fields of information from the survey. This process to 1,638 allowed data analysts to easily identify and eliminate duplicate records using a statistical database program. Compared to the 2016 PIT Count, the number of unsheltered homeless people increased by 21.2% in 2017 to 1,638. Among the 1,638 unsheltered individuals counted in 2017, 1,128 were interviewed and 510 were observed.
The sheltered count methodology consisted of data extracted from the Homeless Management Information System (HMIS) to identify sheltered homeless individuals on the night of January 23, 2017. Compared to the 2016 PIT Count, the number of sheltered homeless people decreased by 5.7% in 2017 to 768. The increase in the overall unsheltered 2017 PIT Count reflects an actual increase in our homeless population and a The total sheltered countywide commitment to conducting a more collaborative and rigorous homeless people count. Based on feedback provided to the County of Riverside Department counted decreased of Public Social Services (DPSS), the increase may be attributed to the by 5.7% in 2017 to following: 768
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In 2017, following HUD’s new requirements to have a separate youth count, efforts to obtain a more accurate count of youth ages 24 and under that are experiencing homelessness were increased. Youth friendly partners were invited to assist in implementing the youth count. These partners included: youth opportunity centers, transitional age youth programs, youth emergency shelters, city leaders, and other public, private, and non-profit agencies focused on the goal to end homelessness for youth. The knowledge and experience that each collaborative partner brought to the table helped us better identify homeless youth during both the traditional and the youth counts. By having youth friendly volunteers, youth experiencing homelessness were more willing to participate in the PIT Survey. The total 2017 count for youth ages 24 and under is 193 (interview and observational); a 103% increase from last year’s count. This increase is likely due to an increased collaboration and focus on youth experiencing homelessness.
For the 2017 Point in Time Count, there was a targeted countywide effort to significantly increase city engagement activities and the participation of law enforcement agencies working on the frontlines to end homelessness. This enhanced partnership included the Riverside County Sheriff’s Department, various City Police Departments, Riverside County and City Code Enforcement agencies, Riverside County Probation Department, DPSS Homeless Programs Unit, Housing Authority of the County of Riverside, The Riverside University Health System – Behavioral Health (formerly Department of Mental Health), the U.S. Department of Veterans’ Affairs (VA), Loma Linda Medical Center, and other key nonprofit housing providers. These coordination efforts helped to improve planning activities through the pre-canvassing of communities to identify more accurate homeless locations. There was also an increased commitment from community partners to conduct a more accurate count by conducting follow up counts during the seven day window following the PIT Count date.
Riverside County was under a severe thunderstorm warning by the National Weather Service in the weeks and days leading up to the PIT Count. In addition, the region received amounts of rain that were record-breaking in the days before the PIT Count, including the day before. As the majority of Riverside County’s unsheltered homeless population resides in unsheltered settings, a partnership with first responders to raise awareness for the PIT Count and to promote the outreach services for homeless veterans, youth and families taking place on the day of the Count had a significant impact. This message was layered onto an existing safety advisory initiated by first responders to warn homeless individuals of the dangers of residing in riverbeds, flood control channels and areas prone to mudslides during thunderstorms. This countywide effort between partnering county agencies helped to:
Raise awareness and educate the homeless population of the benefits of participating in the Count; Educate the most service-resistant individuals residing in hard to reach areas; and Reinforce a common purpose to bring homeless individuals out of the shadows and promote a common message not to fear law enforcement personnel escorting PIT Count volunteers into encampments.
Homeless individuals that heeded the law enforcement/emergency management warning either sought shelter in drier areas or felt safe to participate in the survey, thus increasing the number of individuals surveyed during the entire PIT Count.
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During the 2017 PIT Count, there was a 65% (201) increase in the number of observational surveys utilized compared to 2016. The U.S. Department of Housing and Urban Development (HUD) makes provisions for using an observational survey instead of an interview survey in the following situations: PIT Count volunteer is unable to enter the location to conduct a survey. PIT Count volunteer cannot conduct a survey (person refused to answer questions, language or other issues). PIT Count volunteer does not wish to disturb people who are sleeping.
However, due to the limited amount of information collected on the observational survey and the fact that it includes the volunteer’s perspective as opposed to the homeless individual’s response, it makes the deduplication process challenging. Even though every effort is made to remove duplicates, there is no guarantee that some of the individuals observed may or may not have been interviewed at another time during the PIT Count.
Riverside County’s Department of Public Social Services (DPSS), in partnership with the County of Riverside Continuum of Care continues to explore new and innovative strategies for conducting a successful PIT Count in 2017 and beyond.
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INTRODUCTION
The Point-In-Time (PIT) Homeless Count is a count and survey of Riverside County’s sheltered and unsheltered homeless population. Riverside County’s Department of Public Social Services (DPSS), in partnership with County of Riverside Continuum of Care, is required to conduct this biennial count during the last 10 days in January. The Continuum of Care (CoC) is a network of private- and public- sector homeless service providers, designed to promote community-wide planning and the strategic use of resources to address homelessness.
The PIT Count is federally-mandated by the U.S. Department of Housing and Urban Development (HUD) to count and survey the homeless population in cities and counties throughout the nation, including Riverside County. Each count is planned, coordinated, and carried out locally. Data collected during the Count reflects a snapshot of Riverside County’s homeless population at one particular point-in-time and is required in order to: continue receiving federal funding for homeless assistance programs, develop strategies to end homelessness, help homeless individuals and families who are in need, and produce a full report to HUD regarding the homeless individuals in Riverside County. In 2016, Riverside City and County received a total of $9.9 million by HUD for the Continuum of Care Program for homeless programs and services.
HUD defines a person who is homeless as: (a) living in a place not designed or ordinarily used as a regular sleeping accommodation for humans (i.e., abandoned buildings, cars, parks, under bridges, bus stops, etc.); and (b) living in emergency shelters and transitional housing projects1. It is important to note that HUD’s definition does not include households that are below the poverty line or those individuals that are near-homeless. In addition, HUD identifies people to exclude from the count2:
(a) Persons counted in any location not listed on the CoC’s Housing Inventory Count (HIC);
(b) Persons residing in permanent housing (PH) programs, including persons housed using HUD Veterans Affairs Supportive Housing (VASH) vouchers;
(c) Persons temporarily staying with family or friends (i.e., “doubled-up” or “couch surfing”);
(d) Persons residing in housing they rent or own (i.e., permanent housing), including persons residing in rental housing with assistance from a RRH project on the night of the count; and
(e) Persons residing in institutions (e.g., jails, juvenile correction facilities, foster care, hospital beds, detox centers).
1 2014 Point-In-Time HUD Methodology Guide, p. 4 2 2014 Point-In-Time HUD Methodology Guide, p. 30
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METHODOLOGY
Overview
The 2017 Point-In-Time (PIT) Homeless Count unsheltered methodology consisted of the following primary components:
a) Street-based count on January 24, 2017 between 5:30 a.m. to 9:30 a.m. b) Service-based count on January 24, 2017 between 7:00 a.m. to 2:00 p.m. c) Riverside University Health System - Behavioral Health (RUHS-BH) Homeless Outreach between January 24, 2017 – January 30, 2017 (seven-day period of time to cover unincorporated areas and perform a sweep of city areas to count and survey homeless individuals missed on January 24, 2017.) d) Follow-up Count and Survey during the seven day post-count window (City and County staff, volunteers and Riverside County Sheriff’s deputies (seven day period between January 24, 2017 – January 30, 2017 to count and survey homeless individuals in cities and unincorporated areas missed on January 24, 2017.) e) Unaccompanied Youth (age 24 or under) street-based and service-based counts between January 24, 2017 – January 30, 2017 (seven-day period of time to cover targeted unaccompanied youth homeless locations.)
The 2017 PIT Count unsheltered methodology was similar to the 2016 PIT Count unsheltered methodology, with the exception of the HUD mandated separate Youth Count conducted in 2017. Although a separate Youth Count was not conducted in 2016, youth were identified and counted during the street-based, service-based, and sheltered counts. See Table 1 below for a comparison of PIT Count methodology.
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Table 1: Comparison of PIT Count Unsheltered Methodology 2016 2017 Street-based count Street-based count
Service-based count Service-based count Riverside University Health System - Riverside University Health System – Behavioral Health Homeless Outreach Behavioral Health Homeless Outreach (seven-day period of time to cover the (seven-day period of time to cover the unincorporated areas and perform sweep of unincorporated areas and perform sweep of cities to count and survey missed people) cities to count and survey missed people) Follow-up Count and Survey during the seven day post-count window (City and County staff, volunteers and Riverside County Sheriff officers (seven day period between January 24, 2017 – January 30, 2017 to count and survey homeless individuals in cities and unincorporated areas missed on January 24, 2017.) This was due to the unprecedented rainstorms in the region prior to the Count. Youth Count (seven day period to cover targeted homeless unaccompanied youth locations)
Subsequent sections will further compare the 2016 and 2017 PIT Count unsheltered methodologies. In both 2016 and 2017, specific attention was given to city engagement and survey development as opportunities to increase the accuracy of the PIT Count.
City Engagement
The methods of city engagement used in 2017 paralleled the methods used in 2016. DPSS made efforts to engage city leaders, law enforcement personnel, and establish deployment centers in each city. A thorough list of unincorporated communities was incorporated into the street-based count. City leaders who requested further assistance were assigned a DPSS Site Coordinator to support PIT Count activities on the day of the PIT Count. These DPSS Site Coordinators worked in tandem with city leaders. Their role was primarily to assist the city leaders who were in charge at each deployment center during the street-based count. It is important to note that some city leaders elected not to use a DPSS Site Coordinator as they felt confident with their abilities and available resources to conduct the count. See Table 2 below for a comparison of city engagement.
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Table 2: Comparison of City Engagement
City Engagement 2016 2017 27 out of 28 cities participated in the Count 27 out of 28 cities participated in the Count and had deployment centers in the city or a and had deployment centers in the city or a neighboring city neighboring city Did NOT designate red zones, but rather Did NOT designate red zones, but rather attempted to engage every city attempted to engage every city Included 53 census designated places Included 53 census designated places (unincorporated areas) (unincorporated areas) 20% of the deployment centers had an 30% of the deployment centers had an assigned DPSS Site Coordinator assigned DPSS Site Coordinator
Survey Development
Interview Survey Tools
The survey that was selected for implementation was a slightly modified version of the 2016 Count and Survey tool based on the feedback from volunteers to improve the previous version. The survey was pilot tested to assess whether prospective volunteers knew how to navigate the survey tool comfortably and whether prospective interviewees clearly understood the questions that were asked.
In 2017, an additional interview survey was developed for the Youth Count. The Youth Interview Survey incorporates most of the information included on the traditional PIT Count Survey and additional questions targeted specifically to homeless youth.
A total of 40 people piloted the survey tools including: DPSS Adult Services Division Administration staff, community members, and sheltered homeless individuals. In addition, general survey feedback was given by the RUHS-BH. The version that received the most positive feedback was selected for interviewing homeless individuals during the street-based count and the service-based count.
Observational Survey Tools
The primary goal for the PIT Count was to interview each homeless person. However, it was understood interviewing was not feasible under certain circumstances. Thus, an observational survey tool was developed to allow interviewers to record observational data (perceived gender, ethnicity, age, physical description, etc.) for individuals encountered during the street-based count who were suspected to be homeless and were unable or unwilling to be engaged. This observational technique is acceptable according to HUD guidelines3.
3 2014 Point-In-Time HUD Methodology Guide, p. 52
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The following are reasons that volunteers used an observational tool:
there were barriers to enter the site the site was unsafe to enter the person refused to participate the person was sleeping there was a language barrier the person was unable to respond (i.e., mental issues, intoxication, etc.)
This observational tool was used during the general PIT street-based count. In 2017, an additional observational survey was developed for the Youth Count. The youth observational survey tool incorporates most of the information included on the traditional PIT Count survey tool along with additional questions targeted specifically toward homeless youth.
Data Entry and Deduplication Method
A Microsoft Access application was created in order to consolidate and store the unsheltered homeless count survey data. Separate applications were created for the street-based interview surveys and street-based observational surveys. Five data entry volunteers entered this unsheltered homeless data into the Microsoft Access application during the month of February.
Since the 2017 PIT Count involved both street-based and service-based counts, a deduplication method was implemented to account for any individuals who may have been interviewed multiple times. The deduplication method for the interview surveys consisted of creating a unique identifier using several fields of information from the survey, such as: first initial, last initial, birth month, birth year, city born, state born, gender, race, and veteran status. The deduplication method for the observational surveys used the following fields to create a unique identifier: reason for observational tool, type of location, age observed, perceived gender, and perceived race. The creation of the unique identifier was conducted in a statistical database program (Microsoft Access), using an algorithm with the identified demographic information to generate a unique ID for each individual counted. This process allowed data analysts to easily identify and eliminate duplicate records.
Table 3 below shows that the total number of street-based (interview and observational tools) and service-based interview surveys returned to DPSS was 1,892 (1,149 interview, 222 youth surveys, 521 observational). During the data entry process, several survey errors were discovered.
Surveys were rejected prior to data entry for the following reasons: (a) the survey was completely blank except for interviewer name and location; (b) the homeless individual indicated that they stayed in a place the night before that was not considered unsheltered according to HUD (house, apartment, emergency shelter, motel, etc.); or (c) the homeless individual indicated they had already been surveyed. There were 242 surveys that were rejected prior to being entered into the database. These criteria resulted in a new subtotal of 1,650 surveys. The PIT Count’s analytic rationale was to include all
Riverside County DPSS ASD HPU 2017 Point-In-Time Homeless Count Report P a g e | 19 records until there was clarity and certainty that they should be excluded. Once all data was entered and the deduplication method implemented, twelve surveys were rejected as duplicates, resulting in a final sample size of 1,638 individuals. See Table 3 below for the numeric breakdown and filtering process.
Table 3: Breakdown of PIT Count Surveys – Data Cleaning and Deduplication
Final Sample Size Total Surveys Rejected Surveys New Sub Total Rejected Surveys (total number of Returned (Not Entered) (Duplicates) people)
1,892 (1,149 242 (91 interview, 1650 (1058 12 (8 interview, 4 1638 (1050 interview, 222 140 youth, 11 interview, 82 youth youth, 0 interview, 78 youth youth surveys, 521 observational) surveys, 510 observational) surveys, 510 observational) observational) observational
Since the observational surveys contained less information and therefore fewer fields to use in the deduplication process, it was challenging to rationalize the drop of any potential duplicates in this data. For example, if two different volunteers were instructed to record their observations regarding the same scenario, they would likely describe the scenario in two completely different ways. Thus, there would be no certainty that the individuals observed in the scenario were the same individual or different individuals. Therefore, it was decided to include all observational surveys. Throughout the rest of the report, the results will be discussed in terms of interview survey data versus observational survey data. It is important to make this distinction as the interview survey data is based on the respondent’s own self-identification, while the observational survey data is based on the volunteer’s perceptions.
Extrapolation techniques are not incorporated into this report. The 2017 PIT Count reflects only individuals who were counted (through direct observation or interviewed), rather than estimated based on tents or belongings.
It was not feasible to deduplicate the interview and observational data together because, again, the interview data was based on self-identification while the observational data was based on volunteer perception. Therefore, none of the fields from these two different surveys were identical, making it nearly impossible to deduplicate across survey types.
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Volunteer Recruitment
Four hundred and ninety eight volunteers were recruited through the following methods:
email blasts outreach to faith-based organizations local colleges and universities; press releases published in several print and online newspapers throughout Riverside County volunteer trainings which ran from January 3, 2017 to January 20, 2017 printed flyers distributed at various agencies and collaborative meetings
A web-based survey (SurveyMonkey) was used to keep track of volunteer registrations. PIT Count volunteers provided assistance in the following ways:
Served as enumerators in the street-based count on January 24, 2017, between 5:30 a.m. and 9:30 a.m. Helped DPSS identify locations in Riverside County where homeless individuals and families were known to live and congregate Donated items to be used in the incentive bags given to homeless individuals who were surveyed
All volunteers were required to complete a 90-minute onsite or online volunteer orientation where they received instructions on how to conduct the count and on important safety guidelines.
Unsheltered Homeless Count
Street-Based Count
The street-based count took place on January 24, 2017, between 5:30 a.m. and 9:30 a.m. A total of volunteers were divided into small groups and deployed across Riverside County to pre-identified locations where homeless people were known to live and/or congregate. Volunteers served as team leaders, lookouts, or counters and met at one of the deployment centers across the county to receive their team assignments, surveys, supplies, and maps.
In 2017, the Riverside County Sheriff law enforcement teams worked with designated crime analysts to produce maps with the known locations of homeless individuals throughout Riverside County. Based on the analysis of the homeless locations identified and the general coverage areas, city and law enforcement leaders were able to estimate the effective number of volunteer count teams needed to address the entire coverage areas.
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Service-Based Count
The service-based count took place on January 24, 2017, between 7:00 a.m. and 2:00 p.m. Several weeks prior to the count, DPSS staff compiled a list of agencies that provide services to homeless people throughout Riverside County. The list was compiled using several resources, including:
a list of service agencies that the DPSS Homeless Programs Unit had on hand an internet search Riverside County’s Continuum of Care
Though efforts were taken to compile a comprehensive list, this list is by no means exhaustive. Once the final list was completed, contact was attempted with each organization. The goal of contacting each organization was to verify the agency name, contact information, operational hours, and to assess each agency’s interest level and how many estimated volunteers would be needed. All agencies that operated between the hours of 7:00 a.m. and 2:00 p.m. on Tuesdays were contacted or attempts were made. The remaining agencies were classified into four categories: information unconfirmed; willing to administer surveys with agency staff; willing to have volunteers administer surveys at facility; or not interested in participating.
Behavioral Health Homeless Outreach Count
Riverside University Health System – Behavioral Health Homeless Outreach (RUHS-BH) teams used the departments’ Housing Crisis Response teams (16 staff) to assist in the count to canvass non-city (unincorporated) areas throughout Riverside County. RUHS-BH performed their primary unsheltered counts of non-city areas on the day of the Count, and also performed a follow-up sweep during the seven-day period afterward, as allowed by the HUD methodology4
The RUHS-BH staff was also encouraged to count and survey homeless clients who were receiving services at Behavioral Health Clinics throughout the county. The coverage they provided to the non-city areas included both (a) reconnaissance prior to the PIT Count to assess if particular areas had homeless people; and (b) surveying the individuals they found during their reconnaissance at a later date (but within the seven-day window following the PIT Count. The Housing Crisis Response teams canvassed the areas to assess the presence of individuals living on the streets or in uninhabitable locations. The reconnaissance efforts began the month prior to the Count; increasing in frequency over the week before the Count; and then continued every day leading up to the Count. If no individuals living on the street were identified in a particular area, RUHS-BH teams returned for one last round of reconnaissance two days prior to the Count date. If RUHS-BH found, or knew of, suspected homeless individuals in a particular area, they returned on the day of the Count (or the seven day period following the Count date) to administer the survey. On the day of the Count, RUHS-BH teams surveyed
4A survey of people at various social service locations or other public or private locations to identify people who were unsheltered, but not counted, on the night of the count--either because CoC's did not use a night of the count approach or because unsheltered people were missed on the night of the count. Service-based counts may only be conducted within the seven-day period after the designated count night.
Riverside County DPSS ASD HPU 2017 Point-In-Time Homeless Count Report P a g e | 22 the locations with known homeless and then surveyed the remaining locations over the next seven days, ensuring that every known or encountered homeless individual had been counted and/or surveyed. The deduplication method was implemented to account for any individuals who may have been interviewed multiple times.
Follow-up Count During Seven Day Window
For weeks and days leading up to the PIT Count, there were severe thunderstorms which affected the riverbeds, flood control channels and areas prone to mudslides in which homeless individuals resided. On the day of the PIT Count, many of these locations were vacated due to the severe weather conditions. Subsequently, the PIT methodology was expanded to afford city leaders and law enforcement personnel the opportunity to conduct a follow up Count during the seven day window to count and survey homeless individuals that may have been missed on the PIT Count date. The deduplication method was implemented to account for any individuals who may have been interviewed multiple times.
Youth Count
Beginning in 2017, HUD mandated that all Continua of Care (CoCs) conduct a Youth Point in Time Count. A separate Count is required because youth experiencing homelessness congregate in different locations and at different times than older adults; youth often do not want to be found, and youth do not often think of themselves as being homeless.
The purpose of the Youth Count is to: Identify how many youth under age 24 are homeless on a given day in Riverside County; Determine the need for youth homeless services; and Guide decisions about how to use available resources.
The Youth Count will provide essential data for planning and developing community interventions to meet the goal of preventing and ending homelessness in families, children and youth by the year 2020, as established by the federal Opening Doors strategy.
Although youth experiencing homelessness or at-risk for homelessness were counted, only those that were “literally homeless” according to HUD guidelines are included in the official unsheltered counts within this report (See Appendix E).
The County of Riverside CoC conducted a Youth PIT Count and Survey in partnership with the Department of Public Social Services, Riverside University Health System – Behavioral Health TAY Program, and Operation Safehouse. The street-based and service-based counts took place during the seven-day window between January 24, - January 30, 2017. Partnering youth agencies led their own teams of trained, youth-friendly staff and volunteers to conduct the Youth Count and Survey at various locations throughout Riverside County.
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Youth Count Planning Team
California Family Life Center Rubidoux YOC Operation SafeHouse Transitional Living Prog. City of Riverside Riverside County Sheriff's Homeless Outreach DPSS Homeless Programs Unit Team Desert AIDS Project RUHS Behavioral Health HHOPE Desert Sands Unified School District RUHS Behavioral Health TAY Journey Program Housing Authority of the County of Riverside RUHS Behavioral Health Children’s Board Operation SafeHouse of the Desert Safe Schools Desert Cities Operation SafeHouse Riverside United Way of the Inland Valleys Operation SafeHouse Cup of Happy
Sheltered Homeless Count
As required by HUD, the sheltered Count involved counting homeless individuals and families who stayed in emergency shelters and transitional housing on the night of January 23, 2017. The sheltered Count was conducted in accordance with HUD’s counting standards and methodology guidance. The Homeless Management Information System (HMIS) data was used to generate the sheltered Count and subpopulation data for projects with one hundred percent of beds participating in HMIS.
In preparation for the 2017 Housing Inventory Count (HIC) and the Point-in-Time Count (PIT), the HMIS Administrators Council, HMIS participating agencies and users were provided with training and resources on data collection processes and requirements. The training emphasized the users’ responsibilities for ensuring data quality – accuracy, completeness and timeliness – that included entering entry and exit dates into HMIS prior to the assigned deadline.
For non-HMIS participating agencies and domestic violence shelter providers, a survey form was provided to fill in demographic and household data. This information was used to complete the population reporting requirement. The survey included a definition of terms and detailed instructions to ensure data verification and data quality standards were met.
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UNSHELTERED COUNT FINDINGS
Overview
There were a total of 1,638 individuals counted during the 2017 unsheltered homeless count. Table 4 displays the overall data by city or unincorporated area. When presenting any data below, please note that there is a distinction between observational data (based on volunteer perception) and interview data (based on respondent’s self-identification), as discussed earlier in the report.
The total column for 2017 represents a summation of the interview and observational data. Both the total count for each city and the respective percentage of the total number of unsheltered homeless people in Riverside County are provided. Table 4 also compares the 2017 city counts to the 2016 city counts. In the final column on the right of the table is the percent change between 2016 and 2017. It should be noted that this comparison does not take into account the total sample sizes for each year. The comparative data is presented this way in order to show the raw change in unsheltered homeless counts for each city or unincorporated area from 2016 to 2017.
Table 4: Unsheltered Homeless Count by City and Type of Data
2017 PIT Count Unsheltered Totals 2016 – 2017 2016 – 2017 City or Unincorporated 2016 2017 Count Percent Area Interview Observational Change Change Count Percent Count Percent City Banning 26 6 25 1.9% 32 2.0% 7 28.0% Beaumont 14 4 10 0.7% 18 1.1% 8 80.0% Blythe 31 28 63 4.7% 59 3.6% -4 -6.3% Calimesa 0 0 0 0.0% 0 0.0% 0 - Canyon Lake 0 0 0 0.0% 0 0.0% 0 - Cathedral City 37 7 27 2.0% 44 2.7% 17 63.0% Coachella 61 20 59 4.4% 81 4.9% 22 37.3% Corona 39 17 83 6.1% 56 3.4% -27 -32.5% Desert Hot Springs 33 10 28 2.1% 43 2.6% 15 53.6% Eastvale 0 0 0 0.0% 0 0.0% 0 - Hemet 65 54 107 7.9% 119 7.3% 12 11.2% Indian Wells 0 0 0 0.0% 0 0.0% 0 - Indio 57 32 70 5.2% 89 5.4% 19 27.1% Jurupa Valley 101 28 113 8.4% 129 7.9% 16 14.2% La Quinta 1 1 8 0.6% 2 0.1% -6 -75.0% Lake Elsinore 53 9 53 3.9% 62 3.8% 9 17.0% Menifee 10 4 20 1.5% 14 0.9% -6 -30.0% Moreno Valley 54 19 61 4.5% 73 4.5% 12 19.7% Murrieta 5 2 0 0.0% 7 0.4% 7 - Norco 3 1 12 0.9% 4 0.2% -8 -66.7%
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2017 PIT Count Unsheltered Totals 2016 – 2017 2016 – 2017 City or Unincorporated 2016 2017 Count Percent Area Interview Observational Change Change Count Percent Count Percent City Palm Desert 21 7 19 1.4% 28 1.7% 9 47.4% Palm Springs 90 48 83 6.1% 138 8.4% 55 66.3% Perris 27 2 65 4.8% 29 1.8% -36 -55.4% Rancho Mirage 0 0 3 0.2% 0 0.0% -3 -100.0% Riverside 231 158 258 19.1% 389 23.7% 131 50.8% San Jacinto 23 8 18 1.3% 31 1.9% 13 72.2% Temecula 67 18 37 2.7% 85 5.2% 48 129.7% Wildomar 4 2 13 1.0% 6 0.4% -7 -53.8% Subtotal 1053 485 1235 91.4% 1538 93.9% 303 24.5% Unincorporated Area Anza 0 0 2 0.1% 0 0.0% -2 -100.0% Bermuda Dunes 10 0 2 0.1% 10 0.6% 8 400.0% Cabazon 1 0 6 0.4% 1 0.1% -5 -83.3% Cactus City 1 0 0 0.0% 1 0.1% 1 - Cherry Valley 1 0 3 0.2% 1 0.1% -2 -66.7% Coronita 0 5 0 0.0% 5 0.3% 5 - Highgrove 1 4 25 1.9% 5 0.3% -20 -80.0% Home Gardens 0 1 0 0.0% 1 0.1% 1 - Homeland 0 0 2 0.1% 0 0.0% -2 -100.0% Idyllwild 8 2 12 0.9% 10 0.6% -2 -16.7% Lakeland Village 9 0 14 1.0% 9 0.5% -5 -35.7% Mead Valley 12 2 0 0.0% 14 0.9% 14 - Meadowbrook 0 0 3 0.2% 0 0.0% -3 -100.0% Mecca 13 2 21 1.6% 15 0.9% -6 -28.6% Mesa Verde 0 0 2 0.1% 0 0.0% -2 -100.0% Romoland 4 1 3 0.2% 5 0.3% 2 66.7% Temescal Valley 0 1 0 0.0% 1 0.1% 1 - Thermal 1 0 8 0.6% 1 0.1% -7 -87.5% Thousand Palms 12 5 6 0.4% 17 1.0% 11 183.3% Valle Vista 2 2 0 0.0% 4 0.2% 4 - White Water 0 0 1 0.1% 0 0.0% -1 -100.0% Woodcrest 0 0 6 0.4% 0 0.0% -6 -100.0% Subtotal 75 25 116 8.6% 100 6.1% -16 -13.8% Grand Total 1128 510 1351 100.0% 1638 100.0% 287 21.2% *Note: 2017 PIT comprehensive final data. There may be slight variations between the preliminary numbers and the data contained in this report. Only unincorporated communities with a 2016/2017 unsheltered count greater than zero are included.
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Reasons Contributing to Homelessness
In 2017, at the recommendation of city officials and representatives from the Riverside County Continuum of Care (CoC), a new question was added to the interview survey tool in order capture the primary reasons for individuals becoming homeless within Riverside County. Among the unsheltered homeless population, 33% selected unemployment as a primary reason for homelessness (n = 347); 27% selected lack of income for housing as a primary reason for homelessness (n = 288); 4% selected fleeing domestic violence as a primary reason (n = 44); 3% selected discharge from medical institution as a primary reason (n = 31); 8% selected discharged from jail or prison as a primary reason (n = 79); 8% selected mental illness as a primary reason (n = 84); 7% selected runaway and/or left home as a primary reason (n = 69). See Table 5 below for full details on these unsheltered subpopulations.
Table 5: 2017 Primary Reasons for Becoming Homeless
Primary Reasons for Becoming Homeless Count Percentage % Unemployment 347 33% Lack of income for housing 288 27% Fleeing domestic violence 44 4% Discharged from medical institution 31 3% Discharged from jail or prison 79 8% Mental Illness 84 8% Runaway/left home 69 7% Other 368 35%
Gender
Of the 1,638 unsheltered homeless people, a majority (69%) of these individuals were men (n = 1123) and (27%) of these individuals were female (n=445). See Table 6 below for full details. Table 6 also compares the 2016 to the 2017 gender subpopulation counts. In the final column on the right of the table is the percent change between 2016 and 2017. It should be noted that this comparison does not take into account the total sample sizes for each year. The comparative data is presented this way in order to show the raw change in unsheltered homeless gender counts from 2016 to 2017.
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Table 6: 2016-2017 Unsheltered Homeless Count Comparison – Breakdown of Gender
2017 General PIT Count 2016-2017 2016-2017 Unsheltered Totals
Gender 2016 2017 Count Percent Interview Observational Count Percent Count Percent Change Difference
Female 333 112 389 28.79% 445 27.17% 56 14.40% Male 777 346 940 69.58% 1123 68.56% 183 19.47% Transgender 8 0 1 0.07% 8 0.49% 7 700.00% Don't Know 2 45 13 0.96% 47 2.87% 34 261.54% or Refused (blank) 8 7 8 0.59% 15 0.92% 7 87.50% Total 1128 510 1351 - 1638 - 287 - *Note: Interview data is based on respondent's self-identification while observational data is based on volunteer perception.
Figure 1: 2017 Unsheltered Homeless Count – Breakdown of Gender
2016-2017 Unsheltered Homeless Count Comparison – Breakdown of Gender 1200
1000
800
600 2016 400 2017
200
0 Female Male Transgender Don't Know or (blank) Refused
Race and Ethnicity
A majority (57%) of the unsheltered homeless population either identified or were observed as White (n = 928); (11%) either identified or were observed as African American (n=186); (5%) either identified or were observed as American Indian or Alaskan Native (n=89); (1%) either identified or were observed as Native Hawaiian or Pacific Islander (n=20); (1%) either identified or were observed as Asian (n=10); (3%) either identified or were observed as Multiple Races (n=50); (See Table 7 below). Approximately (28%) of the population either identified as or were observed as Hispanic or Latino (n=465) (See Table 8
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below). Ethnicity was assessed in a separate question than race as HUD considers Hispanic to be an ethnicity, not a race. See Table 7 and 8 for full details regarding the 2016 to the 2017 race and ethnicity subpopulation comparison. In the final column on the right of the table is the percent change between 2016 and 2017. It should be noted that this comparison does not take into account the total sample sizes for each year. The comparative data is presented this way in order to show the raw change in unsheltered homeless race counts from 2016 to 2017.
Table 7: 2016-2017 Unsheltered Homeless Count Comparison – Breakdown of Race
Race 2017 PIT Count Unsheltered Totals 2016-2017
2016 2017 Count Percentage Interview Observational Count Percent Count Percent Change Change American Indian or 82 7 87 6.44% 89 5.43% 2 2.30% Alaska Native Asian 8 2 10 0.74% 10 0.61% 0 0.00%
Black or African 131 55 160 11.84% 186 11.36% 26 16.25% American Native Hawaiian, 19 1 12 0.89% 20 1.22% 8 66.67% Pacific Islander White 710 218 822 60.84% 928 56.65% 106 12.90%
Multiple Races 50 0 55 4.07% 50 3.05% -5 -9.09% Don't Know or 107 154 258 19.10% 261 15.93% 3 1.16% Refused (blank) 129 72 39 2.89% 201 12.27% 162 415.38%
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Figure 2: 2016-2017 Unsheltered Homeless Count – Breakdown of Race
2016 - 2017 Unsheltered Homeless Count – Breakdown of Race 1000 800 600 400 2016 200 0 2017 American Asian Black or Native White Multiple Don't (blank) Indian or African Hawaiian, Races Know or Alaska American Pacific Refused Native Islander
Table 8: 2016-2017 Unsheltered Homeless Count Comparison – Breakdown of Ethnicity
2017 General PIT Count Unsheltered Totals 2016-2017
Ethnicity 2016 2017 Count Percent Interview Observational Change Change Count Percent Count Percent Hispanic or Latino 360 105 372 20% 465 28% 25% 45% Not Hispanic or Latino 723 247 930 49% 970 59% 4% 21% Blank 45 158 49 3% 203 12% 314% 381% Total 1128 510 1351 71% 1638 100% 21% 41%
Figure 3: 2016-2017 Unsheltered Homeless Count – Breakdown of Ethnicity
Unsheltered Homeless Count Comparison – Breakdown of Ethnicity
1200 1000 800 2016 600 2017 400 200 0 Hispanic or Latino Not Hispanic or Latino Blank
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Age
The majority of the 2017 unsheltered homeless population was between the ages of 50-61 (n = 403) and 40-49 (n = 318). See Table 9 for full details regarding age. Table 9 also compares the 2016 to the 2017 age subpopulation counts. In the final column on the right of the table is the percent change between 2016 and 2017. It should be noted that this comparison does not take into account the total sample sizes for each year. The comparative data is presented this way in order to show the raw change in unsheltered homeless age counts from 2016 to 2017.
Table 9: 2016-2017 Unsheltered Homeless Count Comparison – Breakdown of Age
Age 2017 PIT Count Unsheltered Totals 2016-2017 Range 2016 2017 Count Percentage Interview Observational Change Difference Count Percent Count Percent 0 0 1 0.07% 0 0.00% -1 -100.00% 0-5 17 or 11 4 13 0.96% 15 0.92% 2 15.38% under 107 71 82 6.07% 178 10.87% 96 117.07% 18-24 91 112 101 7.48% 203 12.39% 102 100.99% 25-29 203 92 256 18.95% 295 18.01% 39 15.23% 30-39 250 68 305 22.58% 318 19.41% 13 4.26% 40-49 340 63 396 29.31% 403 24.60% 7 1.77% 50-61 70 34 94 6.96% 104 6.35% 10 10.64% 62-69 9 4 22 1.63% 13 0.79% -9 -40.91% 70-79 6 0 4 0.30% 6 0.37% 2 50.00% 80+ 41 62 78 5.77% 103 6.29% 25 32.05% (blank) Total 1128 510 1351 - 1638 - 287 -
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Figure 4: 2016-2017 Unsheltered Homeless Count – Breakdown of Age
2016-2017 Unsheltered Homeless Count – Breakdown of Age 500 400 300 200 2016 100 2017 0 0-5 17 or 18-24 25-29 30-39 40-49 50-61 62-69 70-79 80+ (blank) under
Veterans
Most of the unsheltered homeless individuals were not veterans. Table 10 compares the 2016 to the 2017 veteran and chronically homeless veterans that were counted.
Table 10: 2017 Unsheltered Homeless Count – Veterans
2016 2017 2016-2017 Veterans Count Percentage Count Percent Count Percent Change Difference Unsheltered Homeless 100 7.40% 91 5.56% -9 -9.00% Veterans Unsheltered Chronically 36 2.66% 37 2.26% 1 2.78% Homeless Veterans
Youth
Among the unsheltered homeless population, 12% were considered homeless youth under the age of 25 (n = 193) according to HUD’s “literally homeless” definition. Unaccompanied homeless youth must not be in the physical custody of a parent or guardian. Table 11 provides a breakdown of these unsheltered youth subpopulations.
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Table 11: 2017 Unsheltered Youth Count – Age 24 and Under
2016 2017 2016-2017 Count Percentage Count Percent Count Percent Change Difference Unaccompanied Minor 1 0.07% 11 0.67% 10 1000.00% Youth (17 and Under) Unaccompanied Transitional 81 6.06% 171 10.43% 90 111.11% Age Youth (24 and Under) Total Minor Youth (17 and 13 0.96% 15 1.00% 2 15.38% Under) Total Transitional Age Youth 82 6.06% 178 10.87% 96 117.07% (24 and Under) *Note: This information was obtained from the interview and observational surveys that meet the HUD definition of homeless youth. Additional details regarding youth population will be published in a separate Youth Count Addendum.
Subpopulations
Among the unsheltered homeless population, 30% were considered chronically homeless (n = 341); 18% were recently released from jail or prison (n = 202); and 25% were victims of domestic violence (n =282). See Table 12 below for full details on these unsheltered subpopulations. Table 12 also compares the 2016 to the 2017 subpopulation counts. In the final column on the right of the table is the percent change between 2016 and 2017. It should be noted that this comparison does not take into account the total sample sizes for each year. The comparative data is presented this way in order to show the raw change in unsheltered homeless subpopulation counts from 2016 to 2017.
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Table 12: 2016-2017 Unsheltered Homeless Count Comparison – Breakdown of Subpopulations
2016 2017 2016-2017
Count Percent Subpopulation Count Percent Count Percent Change Change Chronically Homeless 299 25.95% 341 30.23% 42 14.05% Families with Children 8 0.69% 3 0.29% -5 -62.50% Veterans 100 8.68% 91 8.07% -9 -9.00% Youth 24 or younger 95 8.25% 193 17.11% 98 103.16% Alcohol Use 273 23.70% 291 25.80% 18 6.59% Drug Use 341 29.60% 461 40.87% 120 35.19% PTSD 200 17.36% 268 25.52% 68 34.00% Mental Health Conditions 275 23.87% 309 27.39% 34 12.36% Physical Disability 326 28.30% 362 34.48% 36 11.04% Developmental Disability 128 11.11% 135 11.97% 7 5.47% Brain Injury 201 17.45% 212 20.19% 11 5.47% Victim of Domestic Violence 265 23.00% 282 25.00% 17 6.42% AIDS or HIV 11 0.95% 21 1.86% 10 90.91% Jail (within 12 months) 223 19.36% 202 17.91% -21 -9.42% *Note: All subpopulations with the exception of Youth 24 or younger are obtained from the interview surveys (not observational surveys). Youth 24 or younger count is obtained from interview and observational surveys. Chronically homeless was defined according to HUD's definition if the person had been living in emergency shelter and/or on the streets (a) for the past year or more or (b) four or more times during the last three years and who may have a disability (HUD HEARTH Act Interim Rule, pgs. 53-54). Jail was defined as someone who was released from jail or prison within the past 12 months. An individual could potentially be categorized into multiple or all subpopulations.
Among the unsheltered homeless population, 26% reported using alcohol (n = 291), 41% reported using drugs (n = 461), 26% reported suffering from Post-Traumatic Stress Disorder (PTSD) (n = 268), 27% reported having a mental health condition (n = 309), 34% reported having a physical disability (n = 362), 12% reported having a developmental disability (n = 135), and 20% reported suffering from a brain injury (n = 212). Among those individuals who reported experiencing these situations, almost half of them indicated that PTSD (51%), physical disabilities (50%), mental health conditions (44%), and developmental disabilities (43%) prevented them from obtaining stable housing or a steady job. It is important to note that an individual could potentially be categorized into multiple or all subpopulations. Table 13 also compares the 2016 to the 2017 subpopulation counts.
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Table 13: 2016-2017 Unsheltered Homeless Count Comparison – Breakdown of Disabling Conditions
Number of respondents Number of respondents who Disabling who stated they have stated it prevents them from Conditions experienced… obtaining housing or work. 2016 2017 2016 2017
Subpopulation Count Percent Count Percent Count Percent Count Percent
Alcohol Use 273 26.20% 291 25.80% 61 5.90% 47 4.20% Drug Use 341 32.70% 461 40.87% 90 8.60% 109 9.70% PTSD 200 19.20% 268 25.52% 106 10.20% 136 12.10% Mental Health 275 26.40% 309 27.39% 144 13.80% 137 12.10% Conditions Physical Disability 326 31.30% 362 34.48% 190 18.20% 181 16.00% Developmental 128 12.30% 135 11.97% 59 5.70% 58 5.10% Disability Brain Injury 201 19.30% 212 20.19% 82 7.90% 72 6.40%
Table 14 below displays subpopulation data by city versus unincorporated area. This table provides a highlight of subpopulation information. A count for each city is provided, as well as its corresponding percentage of the total unsheltered sample that had subpopulation data available (n = 1,128). It is important to note that subpopulation information was not collected on the observational tool. Additional subpopulation information for the overall total amount of unsheltered homeless can be found in Table 12.
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Table 14: Unsheltered Homeless Count – Subpopulation Data by City versus Unincorporated Area 2017 Unsheltered Homeless Count - Subpopulation Data by City versus Unincorporated Area
Total Victims of Unsheltered Chronically Families with Youth (24 or Mental Health Physical Developmental Jail (within 12 City or Unincorporated Veterans Alcohol Use Drug Use PTSD Brain Injury Domestic AIDS or HIV Homeless Homeless Children younger) Conditions Disability Disability months) Area Violence Individuals Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent City Banning 32 2.0% 12 1.1% 0 0.0% 4 0.4% 1 0.1% 11 1.0% 11 1.0% 7 0.7% 12 1.1% 9 0.8% 3 0.3% 6 0.6% 8 0.7% 1 0.1% 5 0.5% Beaumont 18 1.1% 2 0.2% 0 0.0% 0 0.0% 0 0.0% 2 0.2% 2 0.2% 1 0.1% 1 0.1% 1 0.1% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 3 0.3% Blythe 59 3.6% 11 1.0% 0 0.0% 4 0.4% 4 0.2% 7 0.6% 11 1.0% 6 0.6% 13 1.2% 12 1.1% 6 0.5% 7 0.7% 12 1.1% 0 0.0% 5 0.5% Calimesa 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Canyon Lake 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Cathedral City 44 2.7% 15 1.3% 0 0.0% 5 0.4% 3 0.2% 9 0.8% 24 2.1% 7 0.7% 9 0.8% 6 0.5% 6 0.5% 6 0.6% 7 0.6% 2 0.2% 18 1.7% Coachella 81 4.9% 20 1.8% 0 0.0% 5 0.4% 1 0.1% 11 1.0% 12 1.1% 10 1.0% 11 1.0% 24 2.1% 4 0.4% 8 0.8% 11 1.0% 1 0.1% 6 0.6% Corona 56 3.4% 12 1.1% 0 0.0% 1 0.1% 6 0.4% 6 0.5% 16 1.4% 8 0.8% 11 1.0% 15 1.3% 9 0.8% 9 0.9% 7 0.6% 2 0.2% 8 0.8% Desert Hot Springs 43 2.6% 5 0.4% 0 0.0% 1 0.1% 6 0.4% 7 0.6% 11 1.0% 15 1.4% 6 0.5% 13 1.2% 2 0.2% 11 1.0% 17 1.5% 0 0.0% 5 0.5% Eastvale 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Hemet 119 7.3% 15 1.3% 0 0.0% 8 0.7% 21 1.3% 26 2.3% 27 2.4% 20 1.9% 19 1.7% 15 1.3% 4 0.4% 17 1.6% 15 1.3% 0 0.0% 13 1.2% Indian Wells 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Indio 89 5.4% 26 2.3% 0 0.0% 5 0.4% 7 0.4% 13 1.2% 29 2.6% 16 1.5% 10 0.9% 19 1.7% 8 0.7% 11 1.0% 17 1.5% 1 0.1% 9 0.9% Jurupa Valley 129 7.9% 30 2.7% 0 0.0% 6 0.5% 6 0.4% 25 2.2% 47 4.2% 27 2.6% 27 2.4% 27 2.4% 6 0.5% 21 2.0% 20 1.8% 1 0.1% 14 1.3% La Quinta 2 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lake Elsinore 62 3.8% 15 1.3% 0 0.0% 0 0.0% 2 0.1% 15 1.3% 24 2.1% 13 1.2% 15 1.3% 17 1.5% 8 0.7% 13 1.2% 10 0.9% 1 0.1% 16 1.5% Menifee 14 0.9% 0 0.0% 0 0.0% 0 0.0% 3 0.2% 2 0.2% 5 0.4% 0 0.0% 1 0.1% 4 0.4% 1 0.1% 1 0.1% 1 0.1% 0 0.0% 4 0.4% Moreno Valley 73 4.5% 14 1.2% 0 0.0% 3 0.3% 18 1.1% 16 1.4% 22 2.0% 11 1.0% 20 1.8% 15 1.3% 6 0.5% 7 0.7% 13 1.2% 5 0.4% 10 1.0% Murrieta 7 0.4% 1 0.1% 0 0.0% 0 0.0% 1 0.1% 3 0.3% 3 0.3% 1 0.1% 1 0.1% 1 0.1% 1 0.1% 1 0.1% 1 0.1% 0 0.0% 3 0.3% Norco 4 0.2% 3 0.3% 0 0.0% 0 0.0% 2 0.1% 3 0.3% 2 0.2% 0 0.0% 2 0.2% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Palm Desert 28 1.7% 7 0.6% 0 0.0% 3 0.3% 3 0.2% 7 0.6% 9 0.8% 4 0.4% 7 0.6% 14 1.2% 7 0.6% 5 0.5% 7 0.6% 0 0.0% 0 0.0% Palm Springs 138 8.4% 20 1.8% 1 0.1% 12 1.1% 11 0.7% 19 1.7% 29 2.6% 29 2.8% 25 2.2% 35 3.1% 9 0.8% 20 1.9% 26 2.3% 4 0.4% 13 1.2% Perris 29 1.8% 5 0.4% 0 0.0% 1 0.1% 1 0.1% 4 0.4% 9 0.8% 9 0.9% 8 0.7% 10 0.9% 5 0.4% 8 0.8% 5 0.4% 0 0.0% 3 0.3% Rancho Mirage 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Riverside 389 23.7% 73 6.5% 0 0.0% 21 1.9% 77 4.7% 66 5.9% 107 9.5% 48 4.6% 83 7.4% 75 6.6% 37 3.3% 42 4.0% 65 5.8% 3 0.3% 32 3.0% San Jacinto 31 1.9% 8 0.7% 0 0.0% 1 0.1% 5 0.3% 1 0.1% 12 1.1% 4 0.4% 5 0.4% 3 0.3% 5 0.4% 5 0.5% 5 0.4% 0 0.0% 5 0.5% Temecula 85 5.2% 21 1.9% 0 0.0% 6 0.5% 9 0.5% 17 1.5% 25 2.2% 20 1.9% 10 0.9% 18 1.6% 3 0.3% 8 0.8% 17 1.5% 0 0.0% 19 1.8% Wildomar 6 0.4% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 2 0.2% 0 0.0% 0 0.0% Sub Total 1538 93.9% 316 28.0% 1 0.1% 86 7.6% 187 11.4% 271 24.0% 438 38.8% 257 24.5% 296 26.2% 335 29.7% 130 11.5% 206 19.6% 267 23.7% 21 1.9% 191 18.2% *Note: 2017 PIT comprehensive data. Unsheltered count and Youth(24 or younger) includes total interview and observational collected data; percent of total unsheltered field and Youth(24 or younger) field calculated on total collected (1638). Families with Children, PTSD and Brain Injury field are unique to the general PIT questionnaire; percent of total calculated based on persons interviewed (1050). All remaining fields percentage calculated on total persons interviewed (1128).
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Table 14: 2017 Unsheltered Homeless Count – Subpopulation Data by City versus Unincorporated Area (Continued)
2017 Unsheltered Homeless Count - Subpopulation Data by City versus Unincorporated Area
Total Victims of Unsheltered Chronically Families with Youth (24 or Mental Health Physical Developmental Jail (within 12 City or Unincorporated Veterans Alcohol Use Drug Use PTSD Brain Injury Domestic AIDS or HIV Homeless Homeless Children younger) Conditions Disability Disability months) Area Violence Individuals Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Count Percent Unincorporated Area Aguanga 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Alberhill 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Anza 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Belltown 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Bermuda Dunes 10 0.6% 6 0.5% 0 0.0% 1 0.1% 0 0.0% 2 0.2% 4 0.4% 0 0.0% 2 0.2% 5 0.4% 2 0.2% 2 0.2% 2 0.2% 0 0.0% 3 0.3% Cactus City 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 0 0.0% Cabazon 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Cherry Valley 1 0.1% 1 0.1% 0 0.0% 1 0.1% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% Chiriaco Summit 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Coronita 5 0.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Crestmore Heights 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Desert Beach 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Desert Center 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Desert Edge 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Desert Palms 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% East Blythe 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Eagle Mountain 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Eagle Valley 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% East Hemet 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% El Cerrito 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% El Sobrante 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% French Valley 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Garnet 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Good Hope 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Green Acres 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Highgrove 5 0.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Home Gardens 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Homeland 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Idyllwild 10 0.6% 3 0.3% 0 0.0% 0 0.0% 1 0.1% 3 0.3% 2 0.2% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 1 0.1% 0 0.0% 0 0.0% 1 0.1% Indio Hills 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lake Mathews 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lake Riverside 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lake Tamarisk 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lakeland Village 9 0.5% 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 1 0.1% 2 0.2% 1 0.1% 1 0.1% 2 0.2% 0 0.0% 0 0.0% Lakeview 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Lost Lake 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% March ARB 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Mead Valley 14 0.9% 7 0.6% 1 0.1% 0 0.0% 2 0.1% 1 0.1% 7 0.6% 6 0.6% 4 0.4% 4 0.4% 0 0.0% 1 0.1% 5 0.4% 0 0.0% 3 0.3% Meadowbrook 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Mecca 15 0.9% 0 0.0% 1 0.1% 0 0.0% 2 0.1% 6 0.5% 3 0.3% 0 0.0% 1 0.1% 2 0.2% 0 0.0% 0 0.0% 1 0.1% 0 0.0% 2 0.2% Mesa Verde 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Mountain Center 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% North Shore 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Nuevo 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Oasis 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Pine Cove 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Pinyon Pines 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Rancho Capistrano 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Ripley 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Romoland 5 0.3% 2 0.2% 0 0.0% 0 0.0% 0 0.0% 2 0.2% 1 0.1% 1 0.1% 1 0.1% 4 0.4% 0 0.0% 0 0.0% 2 0.2% 0 0.0% 0 0.0% Sage 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Sky Valley 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Temescal Valley 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Thermal 1 0.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Thousand Palms 17 1.0% 3 0.3% 0 0.0% 3 0.3% 1 0.1% 3 0.3% 3 0.3% 2 0.2% 2 0.2% 4 0.4% 1 0.1% 0 0.0% 2 0.2% 0 0.0% 2 0.2% Valle Vista 4 0.2% 2 0.2% 0 0.0% 0 0.0% 0 0.0% 1 0.1% 1 0.1% 1 0.1% 1 0.1% 2 0.2% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Vista Santa Rosa 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Warm Springs 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Whitewater 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Winchester 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Woodcrest 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Sub Total 100 6.1% 25 2.2% 2 0.2% 5 0.4% 6 0.4% 20 1.8% 23 2.0% 11 1.0% 13 1.2% 27 2.4% 5 0.4% 6 0.6% 15 1.3% 0 0.0% 11 1.0% Grand Total 1638 100.0% 341 30.2% 3 0.3% 91 8.1% 193 11.8% 291 25.8% 461 40.9% 268 25.5% 309 27.4% 362 32.1% 135 12.0% 212 20.2% 282 25.0% 21 1.9% 202 19.2% *Note: 2017 PIT comprehensive data. Unsheltered count and Youth(24 or younger) includes total interview and observational collected data; percent of total unsheltered field and Youth(24 or younger) field calculated on total collected (1638). Families with Children, PTSD and Brain Injury field are unique to the general PIT questionnaire; percent of total calculated based on persons interviewed (1050). All remaining fields percentage calculated on total persons interviewed (1128).
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SHELTERED COUNT FINDINGS
Total Emergency Transitional Sheltered Shelters Housing Count 587 181 768
Figure 5: 2017 Shelter Total Count Breakdown
A total of 768 homeless individuals were counted during the sheltered PIT Count. These individuals stayed in emergency shelters (including domestic violence shelters and hotel/motel vouchers) and transitional housing. This represents a 5.7% decrease from the 2016 sheltered count (n = 814). See Table 15 below regarding the 2016 to the 2017 sheltered subpopulation comparison. In the final column on the right of the table is the percent change between 2016 and 2017. The comparative data is presented this way in order to show the raw change in unsheltered homeless race counts from 2016 to 2017.
Table 15: Sheltered Homeless Count – Subpopulation Data
2016 2017 2016-2017
Count Percentage Subpopulation Count Percentage Count Percentage Change Change
Chronically Homeless Individuals 70 8.60% 77 10.03% 7 10.00% HIV Positive/AIDS 6 0.74% 10 1.30% 4 66.70% Mental Health Problems 209 25.68% 206 26.82% -3 -1.40% Substance Users (Alcohol or Drug Abuse) 166 20.39% 128 16.67% -38 -22.90% Minor Youth (under 18) 166 20.39% 165 21.48% -1 -0.60% Accompanied Minor Youth (under 18) 150 18.43% 146 19.01% -4 -2.70% Unaccompanied Minor Youth (under 18) 16 1.97% 19 2.47% 3 18.80% Youth (18-24) 72 8.85% 67 8.72% -5 -6.90% Veterans 111 13.64% 57 7.42% -54 -48.60% Chronically Homeless Veterans 8 0.98% 8 0.00% 0 -100.00% Victims of Domestic Violence 108 13.27% 115 14.97% 7 6.50%
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CONCLUSION
The 2017 Point-In-Time Homeless Count unsheltered methodology consisted of a street-based count, and a service-based count. The sheltered count methodology consisted of data extracted from the Homeless Management Information System (HMIS) to count sheltered homeless individuals on the night of January 23, 2017. A total of 1,638 unsheltered homeless individuals and a preliminary sheltered count of 768 (total homeless = 2,406) were identified in Riverside County during the 2017 Point-In-Time Homeless Count on January 24, 2017. In contrast, during the 2016 PIT Count, 1,351 unsheltered and 814 sheltered homeless individuals (total homeless = 2,165) were identified. Therefore, there was an 11% increase in the overall total number of homeless people counted from 2016 to 2017 (21.2% increase in unsheltered homeless; 5.7% decrease in sheltered homeless). Among the 1,638 unsheltered individuals counted in 2017, 1,128 were interviewed and 510 were observed. The increase in the overall unsheltered 2017 Point-in-Time (PIT) Count reflects an actual increase in our homeless population and a countywide commitment to conducting a more collaborative and rigorous count.
The increase in the size of the general homeless population and most subpopulations may obscure the fact that many individuals’ lives have been changed for the better through Riverside County’s homeless initiatives. It is important to consider the results of the 2017 Count within the context of local efforts to move individuals and families out of homelessness through the provision of housing and supportive services. Through innovative and effective housing programs and services, Riverside County remains committed to moving persons experiencing homelessness into permanent housing.
The completion of the 2017 Homeless Point-in-Time Count provides required data for federal funding for the Riverside County Continuum of Care (CoC). The data presented in the 2017 Homeless Point-in-Time Count and Survey report will continue to be used by the CoC planning bodies and the City and County of Riverside and other organizations to inform additional outreach, service planning, and policy decision-making over the next year as they continue to address homelessness.
Figure 6. 2017 Total Homeless Count Comparison – Year-Over-Year
PIT Count Numbers 2005 - 2017 6203 4785 4508 3366 1113 2406 2978 2467 2165 1622 1733 1323 5090 1090 880 768 3163 2775 814 1638 2043 1888 1587 1351 2005 2007 2009 2011 2013 2015 2016 2017
Unsheltered Sheltered
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CHALLENGES, LIMITATIONS AND RECOMMENDATIONS
Street Based Count
Riverside County was under a severe thunderstorm warning by the National Weather Service in the weeks and days leading up to the PIT Count. As the majority of Riverside County’s unsheltered homeless population resides in unsheltered settings, we partnered with first responders to raise awareness for the PIT Count and to promote the outreach services for homeless veterans, youth and families taking place on the day of the Count. This message was layered onto an existing safety advisory initiated by first responders to warn homeless individuals of the dangers of residing in riverbeds, flood control channels and areas prone to mudslides during thunderstorms. This countywide effort between partnering county agencies helped to:
Raise awareness and educate the homeless population of the benefits of participating in the Count;
Educate the most service resistant individuals residing in hard to reach areas; and Reinforce common purpose to bring homeless individuals out of the shadows and promote a common message not to fear law enforcement personnel escorting PIT Count volunteers into encampments.
Homeless individuals that heeded the law enforcement/emergency management warning either sought shelter in drier areas or felt safe to participate in the survey, thus increasing the number of individuals surveyed during the entire PIT Count.
Increase in the Number Observational Surveys
During the 2017 PIT Count, there was a 65% (201) increase in the number of observational surveys utilized compared to 2016. The U.S. Department of Housing and Urban Development (HUD) makes provisions for using an observational survey instead of an interview survey in the following situations:
PIT Count volunteer is unable to enter the location to conduct a survey. PIT Count volunteer cannot conduct a survey (person refused to answer questions, language or other issues). PIT Count volunteer does not wish to disturb people sleeping.
However, due to the limited amount of information collected on the observational survey and the fact that it includes the volunteer’s perspective as opposed to the homeless individual’s response, it makes the deduplication process challenging. Even though every effort is made to remove duplicates, there is no guarantee that some of the individuals observed may or may not have been interviewed at another time during the PIT Count. The PIT Count Planning Team recommends placing a greater emphasis on using the interview survey instead of the observational surveys as much as possible.
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Service-Based Count
Several challenges were encountered with the service-based count. First, the list of service agencies was not comprehensive because this task was initiated in late December and thus not enough time was permitted to create an exhaustive list. Significant time and staff resources are needed to create such a comprehensive list. The next PIT Count Planning Team should consider eliciting assistance from DPSS Continuum of Care community partners in researching and creating a more comprehensive list of homeless service agencies in Riverside County. Additionally, a more thorough outreach to agencies well in advance of the PIT Count should be conducted.
Youth Count
The PIT Count Planning Team, and participating youth partner agencies lacked adequate resources to conduct a separate Youth Count to cover the entire geography within Riverside County. Unaccompanied homeless youth are often difficult to find because they tend to: (a) congregate at different times and places than older adults, (b) refuse (or are unaware of) social service programs and agencies, and (c) do not want to be found as they may be fleeing abuse or the foster care system.
Although a separate Youth Count was conducted in 2017, the PIT Count Planning Team met with youth service agencies to identify locations of homeless youth. For future Youth Counts, the PIT Count Planning Team recommends early and ongoing collaboration and planning with youth service agencies to facilitate the planning and implementation of a more comprehensive Youth PIT Count.
Shelter Count
The sheltered count primarily consisted of extracting data from the HMIS database. However, not all shelters participate in HMIS (such as, providers for victims of domestic violence and hotel/motel vouchers). Some non- HMIS participating agencies do not use a database system to track their data. Instead, they rely on daily written logs and sign-in-sheets to provide the count demographics and household information. Additionally, there have been challenges in collecting the required information from the several non-HMIS agencies in a timely manner. The PIT Count Planning Team recommends that non-HMIS participating agencies continue to be encouraged to utilize the HMIS (except for the Domestic Violence agencies) database in order to collect the required information in a timely manner.
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Appendix A: Survey Instruments
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Appendix B: City Summary Tables
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - BANNING
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 3 0 3 9% Asian 0 0 0 0% Black or African American 2 0 2 6% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 11 6 17 50% Multiple Races 1 0 1 3% Don't Know/ Refused to Answer 11 0 11 32% Blank 0 0 0 0% TOTAL 28 6 34 100% Hispanic or Latino 10 0 10 31% Not Hispanic or Latino 16 6 22 69% Ethnicity Blank 0 0 0 0% TOTAL 26 6 32 100% Female 8 3 11 34% Male 18 3 21 66% Transgender 0 0 0 0% Gender Does Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 26 6 32 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 1 0 1 3% 25-29 1 0 1 3% 30-39 9 3 12 38% 40-49 8 1 9 28% Age 50-61 7 1 8 25% 62-69 0 1 1 3% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 26 6 32 100% Chronically Homeless 12 0 12 13% Families with Children 0 0 0 0% Jail (Within 12 Months) 5 0 5 6% Veterans 4 0 4 4% Alcohol Use 11 0 11 12% Drug Use 11 0 11 12% PTSD 7 0 7 8% Subpopulations Mental Health Conditions 12 0 12 13% Physical Disability 9 0 9 10% Developmental Disability 3 0 3 3% Brain Injury 6 0 6 7% Victim of Domestic Violence 8 0 8 9% AIDS or HIV 1 0 1 1% TOTAL 89 0 89 278% Total Unsheltered Homeless Individuals 26 6 32 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - BEAUMONT
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 1 1 2 9% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 16 1 17 77% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 1 2 3 14% TOTAL 18 4 22 100% Hispanic or Latino 3 0 3 17% Not Hispanic or Latino 10 2 12 67% Ethnicity Blank 1 2 3 17% TOTAL 14 4 18 100% Female 3 1 4 22% Male 11 2 13 72% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 1 1 6% TOTAL 14 4 18 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 1 1 6% 30-39 4 2 6 33% 40-49 2 0 2 11% Age 50-61 3 0 3 17% 62-69 1 0 1 6% 70-79 1 0 1 6% 80+ 0 0 0 0% Blank 3 1 4 22% TOTAL 14 4 18 100% Chronically Homeless 2 0 2 15% Families with Children 0 0 0 0% Jail (Within 12 Months) 3 0 3 23% Veterans 0 0 0 0% Alcohol Use 2 0 2 15% Drug Use 2 0 2 15% PTSD 1 0 1 8% Subpopulations Mental Health Conditions 1 0 1 8% Physical Disability 1 0 1 8% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 1 0 1 8% AIDS or HIV 0 0 0 0% TOTAL 13 0 13 72% Total Unsheltered Homeless Individuals 14 4 18 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - BLYTHE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 4 0 4 7% Asian 0 0 0 0% Black or African American 2 6 8 13% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 19 7 26 43% Multiple Races 1 0 1 2% Don't Know/ Refused to Answer 1 5 6 10% Blank 5 10 15 25% TOTAL 32 28 60 100% Hispanic or Latino 7 8 15 25% Not Hispanic or Latino 22 17 39 66% Ethnicity Blank 2 3 5 8% TOTAL 31 28 59 100% Female 9 9 18 31% Male 21 16 37 63% Transgender 0 0 0 0% Gender Do Not Identify 0 1 1 2% Blank 1 2 3 5% TOTAL 31 28 59 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 3 3 5% 25-29 2 4 6 10% 30-39 3 3 6 10% 40-49 10 4 14 24% Age 50-61 13 8 21 36% 62-69 2 5 7 12% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 1 1 2 3% TOTAL 31 28 59 100% Chronically Homeless 11 0 11 12% Families with Children 0 0 0 0% Jail (Within 12 Months) 5 0 5 5% Veterans 4 0 4 4% Alcohol Use 7 0 7 7% Drug Use 11 0 11 12% PTSD 6 0 6 6% Subpopulations Mental Health Conditions 13 0 13 14% Physical Disability 12 0 12 13% Developmental Disability 6 0 6 6% Brain Injury 7 0 7 7% Victim of Domestic Violence 12 0 12 13% AIDS or HIV 0 0 0 0% TOTAL 94 0 94 159% Total Unsheltered Homeless Individuals 31 28 59 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - CALIMESA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 0 0 0 0% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 0 0 0% TOTAL 0 0 0 0% Female 0 0 0 0% Male 0 0 0 0% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 0 0 0 0%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - CANYON LAKE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 0 0 0 0% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 0 0 0% TOTAL 0 0 0 0% Female 0 0 0 0% Male 0 0 0 0% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 0 0 0 0%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - CATHEDRAL CITY
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 2 0 2 5% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 25 3 28 64% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 3 4 7 16% Blank 7 0 7 16% TOTAL 37 7 44 100% Hispanic or Latino 12 3 15 34% Not Hispanic or Latino 22 1 23 52% Ethnicity Blank 3 3 6 14% TOTAL 37 7 44 100% Female 10 1 11 25% Male 27 5 32 73% Transgender 0 0 0 0% Gender Do Not Identify 0 1 1 2% Blank 0 0 0 0% TOTAL 37 7 44 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 1 2 3 7% 25-29 6 2 8 18% 30-39 4 0 4 9% 40-49 11 0 11 25% Age 50-61 13 1 14 32% 62-69 2 0 2 5% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 2 2 5% TOTAL 37 7 44 100% Chronically Homeless 15 0 15 13% Families with Children 0 0 0 0% Jail (Within 12 Months) 18 0 18 16% Veterans 5 0 5 4% Alcohol Use 9 0 9 8% Drug Use 24 0 24 21% PTSD 7 0 7 6% Subpopulations Mental Health Conditions 9 0 9 8% Physical Disability 6 0 6 5% Developmental Disability 6 0 6 5% Brain Injury 6 0 6 5% Victim of Domestic Violence 7 0 7 6% AIDS or HIV 2 0 2 2% TOTAL 114 0 114 259% Total Unsheltered Homeless Individuals 37 7 44 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - COACHELLA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 8 0 8 8% Asian 0 0 0 0% Black or African American 1 0 1 1% Native Hawaiian, Pacific Islander 1 0 1 1% Race White 45 8 53 56% Multiple Races 6 0 6 6% Don't Know/ Refused to Answer 4 10 14 15% Blank 10 2 12 13% TOTAL 75 20 95 100% Hispanic or Latino 31 8 39 48% Not Hispanic or Latino 26 6 32 40% Ethnicity Blank 4 6 10 12% TOTAL 61 20 81 100% Female 24 8 32 40% Male 37 9 46 57% Transgender 0 0 0 0% Gender Do Not Identify 0 3 3 4% Blank 0 0 0 0% TOTAL 61 20 81 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 1 0 1 1% 25-29 5 5 10 12% 30-39 8 9 17 21% 40-49 22 2 24 30% Age 50-61 18 1 19 23% 62-69 4 1 5 6% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 3 2 5 6% TOTAL 61 20 81 100% Chronically Homeless 20 0 20 16% Families with Children 0 0 0 0% Jail (Within 12 Months) 6 0 6 5% Veterans 5 0 5 4% Alcohol Use 11 0 11 9% Drug Use 12 0 12 10% PTSD 10 0 10 8% Subpopulations Mental Health Conditions 11 0 11 9% Physical Disability 24 0 24 20% Developmental Disability 4 0 4 3% Brain Injury 8 0 8 7% Victim of Domestic Violence 11 0 11 9% AIDS or HIV 1 0 1 1% TOTAL 123 0 123 152% Total Unsheltered Homeless Individuals 61 20 81 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - CORONA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 2 0 2 3% Asian 0 0 0 0% Black or African American 3 3 6 10% Native Hawaiian, Pacific Islander 1 0 1 2% Race White 25 7 32 53% Multiple Races 2 0 2 3% Don't Know/ Refused to Answer 3 7 10 17% Blank 7 0 7 12% TOTAL 43 17 60 100% Hispanic or Latino 17 6 23 41% Not Hispanic or Latino 21 6 27 48% Ethnicity Blank 1 5 6 11% TOTAL 39 17 56 100% Female 10 5 15 27% Male 29 11 40 71% Transgender 0 0 0 0% Gender Do Not Identify 0 1 1 2% Blank 0 0 0 0% TOTAL 39 17 56 100% 0-5 0 0 0 0% 17 or under 0 1 1 2% 18-24 2 3 5 9% 25-29 4 1 5 9% 30-39 4 4 8 14% 40-49 7 1 8 14% Age 50-61 16 2 18 32% 62-69 4 1 5 9% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 2 4 6 11% TOTAL 39 17 56 100% Chronically Homeless 12 0 12 12% Families with Children 0 0 0 0% Jail (Within 12 Months) 8 0 8 8% Veterans 1 0 1 1% Alcohol Use 6 0 6 6% Drug Use 16 0 16 15% PTSD 8 0 8 8% Subpopulations Mental Health Conditions 11 0 11 11% Physical Disability 15 0 15 14% Developmental Disability 9 0 9 9% Brain Injury 9 0 9 9% Victim of Domestic Violence 7 0 7 7% AIDS or HIV 2 0 2 2% TOTAL 104 0 104 186% Total Unsheltered Homeless Individuals 39 17 56 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - DESERT HOT SPRINGS
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 2 0 2 5% Asian 0 0 0 0% Black or African American 2 2 4 9% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 20 7 27 63% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 3 0 3 7% Blank 6 1 7 16% TOTAL 33 10 43 100% Hispanic or Latino 12 3 15 35% Not Hispanic or Latino 20 5 25 58% Ethnicity Blank 1 2 3 7% TOTAL 33 10 43 100% Female 13 2 15 35% Male 19 8 27 63% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 1 0 1 2% TOTAL 33 10 43 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 6 0 6 14% 25-29 1 5 6 14% 30-39 4 1 5 12% 40-49 7 1 8 19% Age 50-61 11 2 13 30% 62-69 3 0 3 7% 70-79 1 0 1 2% 80+ 0 0 0 0% Blank 0 1 1 2% TOTAL 33 10 43 100% Chronically Homeless 5 0 5 5% Families with Children 0 0 0 0% Jail (Within 12 Months) 5 0 5 5% Veterans 1 0 1 1% Alcohol Use 7 0 7 8% Drug Use 11 0 11 12% PTSD 15 0 15 16% Subpopulations Mental Health Conditions 6 0 6 6% Physical Disability 13 0 13 14% Developmental Disability 2 0 2 2% Brain Injury 11 0 11 12% Victim of Domestic Violence 17 0 17 18% AIDS or HIV 0 0 0 0% TOTAL 93 0 93 216% Total Unsheltered Homeless Individuals 33 10 43 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - EASTVALE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 0 0 0 0% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 0 0 0% TOTAL 0 0 0 0% Female 0 0 0 0% Male 0 0 0 0% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 0 0 0 0%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - HEMET
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 5 0 5 4% Asian 0 0 0 0% Black or African American 10 4 14 11% Native Hawaiian, Pacific Islander 2 0 2 2% Race White 38 15 53 42% Multiple Races 3 0 3 2% Don't Know/ Refused to Answer 8 11 19 15% Blank 6 24 30 24% TOTAL 72 54 126 100% Hispanic or Latino 18 12 30 25% Not Hispanic or Latino 46 27 73 61% Ethnicity Blank 1 15 16 13% TOTAL 65 54 119 100% Female 14 9 23 19% Male 48 40 88 74% Transgender 0 0 0 0% Gender Do Not Identify 0 4 4 3% Blank 3 1 4 3% TOTAL 65 54 119 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 10 11 21 18% 25-29 2 18 20 17% 30-39 14 9 23 19% 40-49 18 3 21 18% Age 50-61 14 3 17 14% 62-69 3 0 3 3% 70-79 1 0 1 1% 80+ 0 0 0 0% Blank 3 10 13 11% TOTAL 65 54 119 100% Chronically Homeless 15 0 15 8% Families with Children 0 0 0 0% Jail (Within 12 Months) 13 0 13 7% Veterans 8 0 8 4% Alcohol Use 26 0 26 15% Drug Use 27 0 27 15% PTSD 20 0 20 11% Subpopulations Mental Health Conditions 19 0 19 11% Physical Disability 15 0 15 8% Developmental Disability 4 0 4 2% Brain Injury 17 0 17 9% Victim of Domestic Violence 15 0 15 8% AIDS or HIV 0 0 0 0% TOTAL 179 0 179 150% Total Unsheltered Homeless Individuals 65 54 119 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - INDIAN WELLS
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 0 0 0 0% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 0 0 0% TOTAL 0 0 0 0% Female 0 0 0 0% Male 0 0 0 0% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 0 0 0 0%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - INDIO
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 2 0 2 2% Asian 0 0 0 0% Black or African American 5 0 5 6% Native Hawaiian, Pacific Islander 1 0 1 1% Race White 33 12 45 50% Multiple Races 1 0 1 1% Don't Know/ Refused to Answer 12 20 32 36% Blank 4 0 4 4% TOTAL 58 32 90 100% Hispanic or Latino 33 17 50 56% Not Hispanic or Latino 24 6 30 34% Ethnicity Blank 0 9 9 10% TOTAL 57 32 89 100% Female 18 6 24 27% Male 39 26 65 73% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 57 32 89 100% 0-5 0 0 0 0% 17 or under 0 1 1 1% 18-24 1 5 6 7% 25-29 6 5 11 12% 30-39 7 6 13 15% 40-49 15 5 20 22% Age 50-61 23 6 29 33% 62-69 1 4 5 6% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 4 0 4 4% TOTAL 57 32 89 100% Chronically Homeless 26 0 26 16% Families with Children 0 0 0 0% Jail (Within 12 Months) 9 0 9 10% Veterans 5 0 5 3% Alcohol Use 13 0 13 8% Drug Use 29 0 29 18% PTSD 16 0 16 10% Subpopulations Mental Health Conditions 10 0 10 6% Physical Disability 19 0 19 12% Developmental Disability 8 0 8 5% Brain Injury 11 0 11 7% Victim of Domestic Violence 17 0 17 10% AIDS or HIV 1 0 1 1% TOTAL 164 0 164 184% Total Unsheltered Homeless Individuals 57 32 89 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - JURUPA VALLEY
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 9 0 9 6% Asian 1 0 1 1% Black or African American 11 0 11 8% Native Hawaiian, Pacific Islander 1 0 1 1% Race White 69 14 83 59% Multiple Races 4 0 4 3% Don't Know/ Refused to Answer 7 7 14 10% Blank 10 7 17 12% TOTAL 112 28 140 100% Hispanic or Latino 34 2 36 28% Not Hispanic or Latino 65 11 76 60% Ethnicity Blank 2 13 15 12% TOTAL 101 26 127 100% Female 34 2 36 28% Male 66 20 86 67% Transgender 1 0 1 1% Gender Do Not Identify 0 5 5 4% Blank 0 1 1 1% TOTAL 101 28 129 100% 0-5 0 0 0 0% 17 or under 0 1 1 1% 18-24 3 2 5 4% 25-29 7 3 10 8% 30-39 18 6 24 19% 40-49 28 4 32 25% Age 50-61 37 0 37 29% 62-69 8 2 10 8% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 10 10 8% TOTAL 101 28 129 100% Chronically Homeless 30 0 30 12% Families with Children 0 0 0 0% Jail (Within 12 Months) 14 0 14 11% Veterans 6 0 6 2% Alcohol Use 25 0 25 10% Drug Use 47 0 47 19% PTSD 27 0 27 11% Subpopulations Mental Health Conditions 27 0 27 11% Physical Disability 27 0 27 11% Developmental Disability 6 0 6 2% Brain Injury 21 0 21 8% Victim of Domestic Violence 20 0 20 8% AIDS or HIV 1 0 1 0% TOTAL 251 0 251 195% Total Unsheltered Homeless Individuals 101 28 129 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - LA QUINTA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 1 1 2 100% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 1 1 2 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 1 1 2 100% Ethnicity Blank 0 0 0 0% TOTAL 1 1 2 100% Female 0 0 0 0% Male 1 1 2 100% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 1 1 2 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 1 1 50% 40-49 0 0 0 0% Age 50-61 1 0 1 50% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 1 1 2 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 1 0 1 50% Drug Use 1 0 1 50% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 2 0 2 100% Total Unsheltered Homeless Individuals 1 1 2 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - LAKE ELSINORE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 6 0 6 9% Asian 1 0 1 1% Black or African American 3 1 4 6% Native Hawaiian, Pacific Islander 1 0 1 1% Race White 39 6 45 66% Multiple Races 3 0 3 4% Don't Know/ Refused to Answer 2 2 4 6% Blank 4 0 4 6% TOTAL 59 9 68 100% Hispanic or Latino 10 1 11 18% Not Hispanic or Latino 43 6 49 79% Ethnicity Blank 0 2 2 3% TOTAL 53 9 62 100% Female 15 0 15 24% Male 38 9 47 76% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 53 9 62 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 5 3 8 13% 30-39 15 1 16 26% 40-49 7 1 8 13% Age 50-61 12 0 12 19% 62-69 9 0 9 15% 70-79 1 1 2 3% 80+ 1 0 1 2% Blank 3 3 6 10% TOTAL 53 9 62 100% Chronically Homeless 15 0 15 10% Families with Children 0 0 0 0% Jail (Within 12 Months) 16 0 16 26% Veterans 0 0 0 0% Alcohol Use 15 0 15 10% Drug Use 24 0 24 16% PTSD 13 0 13 9% Subpopulations Mental Health Conditions 15 0 15 10% Physical Disability 17 0 17 12% Developmental Disability 8 0 8 5% Brain Injury 13 0 13 9% Victim of Domestic Violence 10 0 10 7% AIDS or HIV 1 0 1 1% TOTAL 147 0 147 237% Total Unsheltered Homeless Individuals 53 9 62 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - MENIFEE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 1 0 1 7% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 2 0 2 14% Race White 4 3 7 50% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 3 1 4 29% Blank 0 0 0 0% TOTAL 10 4 14 100% Hispanic or Latino 2 0 2 14% Not Hispanic or Latino 8 2 10 71% Ethnicity Blank 0 2 2 14% TOTAL 10 4 14 100% Female 3 2 5 36% Male 6 2 8 57% Transgender 1 0 1 7% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 10 4 14 100% 0-5 0 0 0 0% 17 or under 1 0 1 7% 18-24 2 0 2 14% 25-29 1 2 3 21% 30-39 3 0 3 21% 40-49 2 0 2 14% Age 50-61 1 1 2 14% 62-69 0 1 1 7% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 10 4 14 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 4 0 4 29% Veterans 0 0 0 0% Alcohol Use 2 0 2 11% Drug Use 5 0 5 26% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 1 0 1 5% Physical Disability 4 0 4 21% Developmental Disability 1 0 1 5% Brain Injury 1 0 1 5% Victim of Domestic Violence 1 0 1 5% AIDS or HIV 0 0 0 0% TOTAL 19 0 19 136% Total Unsheltered Homeless Individuals 10 4 14 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - MORENO VALLEY
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 3 0 3 4% Asian 3 0 3 4% Black or African American 19 7 26 33% Native Hawaiian, Pacific Islander 2 0 2 3% Race White 20 5 25 32% Multiple Races 2 0 2 3% Don't Know/ Refused to Answer 5 4 9 12% Blank 5 3 8 10% TOTAL 59 19 78 100% Hispanic or Latino 13 4 17 23% Not Hispanic or Latino 38 12 50 68% Ethnicity Blank 3 3 6 8% TOTAL 54 19 73 100% Female 15 3 18 25% Male 38 15 53 73% Transgender 1 0 1 1% Gender Do Not Identify 0 1 1 1% Blank 0 0 0 0% TOTAL 54 19 73 100% 0-5 0 0 0 0% 17 or under 3 0 3 4% 18-24 11 4 15 21% 25-29 3 3 6 8% 30-39 9 4 13 18% 40-49 9 3 12 16% Age 50-61 17 5 22 30% 62-69 2 0 2 3% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 54 19 73 100% Chronically Homeless 14 0 14 10% Families with Children 0 0 0 0% Jail (Within 12 Months) 10 0 10 14% Veterans 3 0 3 2% Alcohol Use 16 0 16 11% Drug Use 22 0 22 15% PTSD 11 0 11 8% Subpopulations Mental Health Conditions 20 0 20 14% Physical Disability 15 0 15 11% Developmental Disability 6 0 6 4% Brain Injury 7 0 7 5% Victim of Domestic Violence 13 0 13 9% AIDS or HIV 5 0 5 4% TOTAL 142 0 142 195% Total Unsheltered Homeless Individuals 54 19 73 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - MURRIETA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 1 0 1 11% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 5 2 7 78% Multiple Races 1 0 1 11% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 7 2 9 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 5 1 6 86% Ethnicity Blank 0 1 1 14% TOTAL 5 2 7 100% Female 0 0 0 0% Male 5 2 7 100% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 5 2 7 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 1 0 1 14% 25-29 1 0 1 14% 30-39 2 0 2 29% 40-49 1 0 1 14% Age 50-61 0 2 2 29% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 5 2 7 100% Chronically Homeless 1 0 1 6% Families with Children 0 0 0 0% Jail (Within 12 Months) 3 0 3 43% Veterans 0 0 0 0% Alcohol Use 3 0 3 19% Drug Use 3 0 3 19% PTSD 1 0 1 6% Subpopulations Mental Health Conditions 1 0 1 6% Physical Disability 1 0 1 6% Developmental Disability 1 0 1 6% Brain Injury 1 0 1 6% Victim of Domestic Violence 1 0 1 6% AIDS or HIV 0 0 0 0% TOTAL 16 0 16 229% Total Unsheltered Homeless Individuals 5 2 7 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - NORCO
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 1 0 1 25% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 2 0 2 50% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 1 1 25% Blank 0 0 0 0% TOTAL 3 1 4 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 3 0 3 75% Ethnicity Blank 0 1 1 25% TOTAL 3 1 4 100% Female 0 0 0 0% Male 3 1 4 100% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 3 1 4 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 2 0 2 50% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 1 0 1 25% 62-69 0 0 0 0% 70-79 0 1 1 25% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 3 1 4 100% Chronically Homeless 3 0 3 27% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 3 0 3 27% Drug Use 2 0 2 18% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 2 0 2 18% Physical Disability 1 0 1 9% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 11 0 11 275% Total Unsheltered Homeless Individuals 3 1 4 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - PALM DESERT
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 1 0 1 4% Asian 0 0 0 0% Black or African American 2 0 2 7% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 17 7 24 86% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 1 0 1 4% Blank 0 0 0 0% TOTAL 21 7 28 100% Hispanic or Latino 4 2 6 21% Not Hispanic or Latino 17 5 22 79% Ethnicity Blank 0 0 0 0% TOTAL 21 7 28 100% Female 4 0 4 14% Male 17 7 24 86% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 21 7 28 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 2 1 3 11% 25-29 3 2 5 18% 30-39 3 1 4 14% 40-49 4 1 5 18% Age 50-61 7 2 9 32% 62-69 2 0 2 7% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 21 7 28 100% Chronically Homeless 7 0 7 10% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 3 0 3 4% Alcohol Use 7 0 7 10% Drug Use 9 0 9 13% PTSD 4 0 4 6% Subpopulations Mental Health Conditions 7 0 7 10% Physical Disability 14 0 14 20% Developmental Disability 7 0 7 10% Brain Injury 5 0 5 7% Victim of Domestic Violence 7 0 7 10% AIDS or HIV 0 0 0 0% TOTAL 70 0 70 250% Total Unsheltered Homeless Individuals 21 7 28 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - PALM SPRINGS
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 10 0 10 6% Asian 0 0 0 0% Black or African American 11 2 13 8% Native Hawaiian, Pacific Islander 1 0 1 1% Race White 59 32 91 58% Multiple Races 7 0 7 4% Don't Know/ Refused to Answer 18 12 30 19% Blank 4 2 6 4% TOTAL 110 48 158 100% Hispanic or Latino 28 5 33 24% Not Hispanic or Latino 62 39 101 73% Ethnicity Blank 0 4 4 3% TOTAL 90 48 138 100% Female 29 9 38 28% Male 61 32 93 67% Transgender 0 0 0 0% Gender Do Not Identify 0 6 6 4% Blank 0 1 1 1% TOTAL 90 48 138 100% 0-5 0 0 0 0% 17 or under 2 0 2 1% 18-24 8 1 9 7% 25-29 4 7 11 8% 30-39 11 12 23 17% 40-49 23 9 32 23% Age 50-61 33 6 39 28% 62-69 5 5 10 7% 70-79 0 2 2 1% 80+ 2 0 2 1% Blank 2 6 8 6% TOTAL 90 48 138 100% Chronically Homeless 20 0 20 8% Families with Children 1 0 1 0% Jail (Within 12 Months) 13 0 13 9% Veterans 12 0 12 5% Alcohol Use 19 0 19 8% Drug Use 29 0 29 12% PTSD 29 0 29 12% Subpopulations Mental Health Conditions 25 0 25 10% Physical Disability 35 0 35 14% Developmental Disability 9 0 9 4% Brain Injury 20 0 20 8% Victim of Domestic Violence 26 0 26 11% AIDS or HIV 4 0 4 2% TOTAL 242 0 242 175% Total Unsheltered Homeless Individuals 90 48 138 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - PERRIS
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 1 0 1 3% Asian 0 0 0 0% Black or African American 6 1 7 24% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 9 1 10 34% Multiple Races 2 0 2 7% Don't Know/ Refused to Answer 2 0 2 7% Blank 7 0 7 24% TOTAL 27 2 29 100% Hispanic or Latino 9 0 9 31% Not Hispanic or Latino 14 2 16 55% Ethnicity Blank 4 0 4 14% TOTAL 27 2 29 100% Female 3 0 3 10% Male 24 2 26 90% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 27 2 29 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 1 0 1 3% 25-29 4 1 5 17% 30-39 6 0 6 21% 40-49 5 0 5 17% Age 50-61 4 1 5 17% 62-69 4 0 4 14% 70-79 1 0 1 3% 80+ 1 0 1 3% Blank 1 0 1 3% TOTAL 27 2 29 100% Chronically Homeless 5 0 5 7% Families with Children 0 0 0 0% Jail (Within 12 Months) 3 0 3 10% Veterans 1 0 1 1% Alcohol Use 4 0 4 6% Drug Use 9 0 9 13% PTSD 9 0 9 13% Subpopulations Mental Health Conditions 8 0 8 12% Physical Disability 10 0 10 15% Developmental Disability 5 0 5 7% Brain Injury 8 0 8 12% Victim of Domestic Violence 5 0 5 7% AIDS or HIV 0 0 0 0% TOTAL 67 0 67 231% Total Unsheltered Homeless Individuals 27 2 29 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - RANCHO MIRAGE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 0 0 0 0% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 0 0 0% TOTAL 0 0 0 0% Female 0 0 0 0% Male 0 0 0 0% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 0 0 0 0% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 0 0 0 0%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - RIVERSIDE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 14 4 18 4% Asian 1 2 3 1% Black or African American 34 20 54 13% Native Hawaiian, Pacific Islander 4 1 5 1% Race White 145 63 208 51% Multiple Races 10 0 10 2% Don't Know/ Refused to Answer 12 54 66 16% Blank 33 14 47 11% TOTAL 253 158 411 100% Hispanic or Latino 77 24 101 26% Not Hispanic or Latino 141 69 210 54% Ethnicity Blank 13 65 78 20% TOTAL 231 158 389 100% Female 74 40 114 29% Male 149 99 248 64% Transgender 4 0 4 1% Gender Do Not Identify 2 0 2 1% Blank 2 19 21 5% TOTAL 231 158 389 100% 0-5 0 0 0 0% 17 or under 2 1 3 1% 18-24 46 28 74 19% 25-29 21 32 53 14% 30-39 36 22 58 15% 40-49 40 22 62 16% Age 50-61 60 14 74 19% 62-69 12 11 23 6% 70-79 2 0 2 1% 80+ 1 0 1 0% Blank 11 28 39 10% TOTAL 231 158 389 100% Chronically Homeless 73 0 73 11% Families with Children 0 0 0 0% Jail (Within 12 Months) 32 0 32 8% Veterans 21 0 21 3% Alcohol Use 66 0 66 10% Drug Use 107 0 107 16% PTSD 48 0 48 7% Subpopulations Mental Health Conditions 83 0 83 13% Physical Disability 75 0 75 12% Developmental Disability 37 0 37 6% Brain Injury 42 0 42 6% Victim of Domestic Violence 65 0 65 10% AIDS or HIV 3 0 3 0% TOTAL 652 0 652 168% Total Unsheltered Homeless Individuals 231 158 389 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - SAN JACINTO
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 2 0 2 6% Asian 0 0 0 0% Black or African American 2 1 3 9% Native Hawaiian, Pacific Islander 1 0 1 3% Race White 14 1 15 45% Multiple Races 1 0 1 3% Don't Know/ Refused to Answer 1 0 1 3% Blank 4 6 10 30% TOTAL 25 8 33 100% Hispanic or Latino 3 4 7 23% Not Hispanic or Latino 18 2 20 65% Ethnicity Blank 2 2 4 13% TOTAL 23 8 31 100% Female 5 1 6 19% Male 17 7 24 77% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 1 0 1 3% TOTAL 23 8 31 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 4 1 5 16% 25-29 3 4 7 23% 30-39 6 1 7 23% 40-49 3 2 5 16% Age 50-61 5 0 5 16% 62-69 1 0 1 3% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 1 0 1 3% TOTAL 23 8 31 100% Chronically Homeless 8 0 8 15% Families with Children 0 0 0 0% Jail (Within 12 Months) 5 0 5 16% Veterans 1 0 1 2% Alcohol Use 1 0 1 2% Drug Use 12 0 12 22% PTSD 4 0 4 7% Subpopulations Mental Health Conditions 5 0 5 9% Physical Disability 3 0 3 6% Developmental Disability 5 0 5 9% Brain Injury 5 0 5 9% Victim of Domestic Violence 5 0 5 9% AIDS or HIV 0 0 0 0% TOTAL 54 0 54 174% Total Unsheltered Homeless Individuals 23 8 31 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - TEMECULA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 3 1 4 4% Asian 0 0 0 0% Black or African American 7 2 9 10% Native Hawaiian, Pacific Islander 1 0 1 1% Race White 46 6 52 58% Multiple Races 2 0 2 2% Don't Know/ Refused to Answer 4 7 11 12% Blank 9 2 11 12% TOTAL 72 18 90 100% Hispanic or Latino 12 2 14 16% Not Hispanic or Latino 51 8 59 69% Ethnicity Blank 4 8 12 14% TOTAL 67 18 85 100% Female 21 4 25 29% Male 46 13 59 69% Transgender 0 0 0 0% Gender Do Not Identify 0 1 1 1% Blank 0 0 0 0% TOTAL 67 18 85 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 7 2 9 10% 25-29 8 6 14 16% 30-39 21 2 23 26% 40-49 9 3 12 13% Age 50-61 14 5 19 21% 62-69 6 0 6 7% 70-79 1 0 1 1% 80+ 1 0 1 1% Blank 4 0 4 4% TOTAL 71 18 89 100% Chronically Homeless 21 0 21 13% Families with Children 0 0 0 0% Jail (Within 12 Months) 19 0 19 21% Veterans 6 0 6 4% Alcohol Use 17 0 17 10% Drug Use 25 0 25 15% PTSD 20 0 20 12% Subpopulations Mental Health Conditions 10 0 10 6% Physical Disability 18 0 18 11% Developmental Disability 3 0 3 2% Brain Injury 8 0 8 5% Victim of Domestic Violence 17 0 17 10% AIDS or HIV 0 0 0 0% TOTAL 164 0 164 184% Total Unsheltered Homeless Individuals 71 18 89 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - WILDOMAR
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 4 0 4 67% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 2 2 33% Blank 0 0 0 0% TOTAL 4 2 6 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 2 0 2 33% Ethnicity Blank 2 2 4 67% TOTAL 4 2 6 100% Female 2 0 2 33% Male 2 2 4 67% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 4 2 6 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 1 0 1 17% 30-39 1 1 2 33% 40-49 0 0 0 0% Age 50-61 1 1 2 33% 62-69 1 0 1 17% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 4 2 6 100% Chronically Homeless 1 0 1 20% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 1 0 1 20% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 1 0 1 20% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 2 0 2 40% AIDS or HIV 0 0 0 0% TOTAL 5 0 5 83% Total Unsheltered Homeless Individuals 4 2 6 100%
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Appendix C: County Unincorporated Communities Summary Tables (PIT Count greater than zero)
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - BERMUDA DUNES
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 10 0 10 100% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 10 0 10 100% Hispanic or Latino 1 0 1 10% Not Hispanic or Latino 8 0 8 80% Ethnicity Blank 1 0 1 10% TOTAL 10 0 10 100% Female 1 0 1 10% Male 9 0 9 90% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 10 0 10 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 3 0 3 30% 40-49 1 0 1 10% Age 50-61 6 0 6 60% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 10 0 10 100% Chronically Homeless 6 0 6 21% Families with Children 0 0 0 0% Jail (Within 12 Months) 3 0 3 10% Veterans 1 0 1 3% Alcohol Use 2 0 2 7% Drug Use 4 0 4 14% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 2 0 2 7% Physical Disability 5 0 5 17% Developmental Disability 2 0 2 7% Brain Injury 2 0 2 7% Victim of Domestic Violence 2 0 2 7% AIDS or HIV 0 0 0 0% TOTAL 29 0 29 290% Total Unsheltered Homeless Individuals 10 0 10 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - CABAZON
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 1 0 1 100% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 1 0 1 100% Ethnicity Blank 0 0 0 0% TOTAL 1 0 1 100% Female 0 0 0 0% Male 1 0 1 100% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 1 0 1 100% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 1 0 1 100% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 1 0 1 100% Total Unsheltered Homeless Individuals 1 0 1 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - CACTUS CITY
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 1 0 1 100% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 1 0 1 100% Ethnicity Blank 0 0 0 0% TOTAL 1 0 1 100% Female 0 0 0 0% Male 1 0 1 100% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 0 0 0 0% 62-69 1 0 1 100% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 1 0 1 25% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 1 0 1 25% Physical Disability 1 0 1 25% Developmental Disability 0 0 0 0% Brain Injury 1 0 1 25% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 4 0 4 400% Total Unsheltered Homeless Individuals 1 0 1 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - CHERRY VALLEY
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 1 0 1 100% Race White 0 0 0 0% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% Hispanic or Latino 1 0 1 100% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 0 0 0% TOTAL 1 0 1 100% Female 0 0 0 0% Male 1 0 1 100% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 1 0 1 100% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% Chronically Homeless 1 0 1 20% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 1 0 1 20% Alcohol Use 1 0 1 20% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 1 0 1 20% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 1 0 1 20% AIDS or HIV 0 0 0 0% TOTAL 5 0 5 500% Total Unsheltered Homeless Individuals 1 0 1 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - CORONITA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 0 1 1 20% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 4 4 80% Blank 0 0 0 0% TOTAL 0 5 5 100% Hispanic or Latino 0 1 1 20% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 4 4 80% TOTAL 0 5 5 100% Female 0 0 0 0% Male 0 2 2 40% Transgender 0 0 0 0% Gender Do Not Identify 0 3 3 60% Blank 0 0 0 0% TOTAL 0 5 5 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 5 5 100% 30-39 0 0 0 0% 40-49 0 0 0 0% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 0 5 5 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 0 5 5 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - HIGHGROVE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 2 2 40% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 1 2 3 60% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 1 4 5 100% Hispanic or Latino 0 2 2 40% Not Hispanic or Latino 1 2 3 60% Ethnicity Blank 0 0 0 0% TOTAL 1 4 5 100% Female 0 1 1 20% Male 1 3 4 80% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 1 4 5 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 2 2 40% 30-39 0 0 0 0% 40-49 0 1 1 20% Age 50-61 1 1 2 40% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 1 4 5 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 1 0 1 100% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 1 0 1 20% Total Unsheltered Homeless Individuals 1 4 5 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - HOME GARDENS
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 0 0 0 0% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 1 1 100% Blank 0 0 0 0% TOTAL 0 1 1 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 0 1 1 100% Ethnicity Blank 0 0 0 0% TOTAL 0 1 1 100% Female 0 0 0 0% Male 0 0 0 0% Transgender 0 0 0 0% Gender Do Not Identify 0 1 1 100% Blank 0 0 0 0% TOTAL 0 1 1 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 0 1 1 100% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 0 1 1 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 0 1 1 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - IDYLLWILD
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 7 1 8 80% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 1 1 2 20% Blank 0 0 0 0% TOTAL 8 2 10 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 8 1 9 90% Ethnicity Blank 0 1 1 10% TOTAL 8 2 10 100% Female 1 0 1 10% Male 6 2 8 80% Transgender 1 0 1 10% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 8 2 10 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 1 1 10% 25-29 0 0 0 0% 30-39 3 0 3 30% 40-49 2 0 2 20% Age 50-61 2 0 2 20% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 1 1 2 20% TOTAL 8 2 10 100% Chronically Homeless 3 0 3 25% Families with Children 0 0 0 0% Jail (Within 12 Months) 1 0 1 8% Veterans 0 0 0 0% Alcohol Use 3 0 3 25% Drug Use 2 0 2 17% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 1 0 1 8% Developmental Disability 1 0 1 8% Brain Injury 1 0 1 8% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 12 0 12 120% Total Unsheltered Homeless Individuals 8 2 10 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - LAKELAND VILLAGE
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 1 0 1 11% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 7 0 7 78% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 1 0 1 11% Blank 0 0 0 0% TOTAL 9 0 9 100% Hispanic or Latino 4 0 4 44% Not Hispanic or Latino 5 0 5 56% Ethnicity Blank 0 0 0 0% TOTAL 9 0 9 100% Female 4 0 4 44% Male 5 0 5 56% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 9 0 9 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 1 0 1 11% 30-39 1 0 1 11% 40-49 5 0 5 56% Age 50-61 1 0 1 11% 62-69 1 0 1 11% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 9 0 9 100% Chronically Homeless 1 0 1 10% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 1 0 1 10% PTSD 1 0 1 10% Subpopulations Mental Health Conditions 1 0 1 10% Physical Disability 2 0 2 20% Developmental Disability 1 0 1 10% Brain Injury 1 0 1 10% Victim of Domestic Violence 2 0 2 20% AIDS or HIV 0 0 0 0% TOTAL 10 0 10 111% Total Unsheltered Homeless Individuals 9 0 9 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - MEAD VALLEY
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 4 1 5 36% Native Hawaiian, Pacific Islander 1 0 1 7% Race White 5 1 6 43% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 2 0 2 14% Blank 0 0 0 0% TOTAL 12 2 14 100% Hispanic or Latino 4 0 4 29% Not Hispanic or Latino 7 2 9 64% Ethnicity Blank 1 0 1 7% TOTAL 12 2 14 100% Female 5 0 5 36% Male 7 2 9 64% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 12 2 14 100% 0-5 0 0 0 0% 17 or under 1 0 1 7% 18-24 1 0 1 7% 25-29 0 0 0 0% 30-39 1 0 1 7% 40-49 4 1 5 36% Age 50-61 3 1 4 29% 62-69 1 0 1 7% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 1 0 1 7% TOTAL 12 2 14 100% Chronically Homeless 7 0 7 18% Families with Children 1 0 1 3% Jail (Within 12 Months) 3 0 3 8% Veterans 0 0 0 0% Alcohol Use 1 0 1 3% Drug Use 7 0 7 18% PTSD 6 0 6 15% Subpopulations Mental Health Conditions 4 0 4 10% Physical Disability 4 0 4 10% Developmental Disability 0 0 0 0% Brain Injury 1 0 1 3% Victim of Domestic Violence 5 0 5 13% AIDS or HIV 0 0 0 0% TOTAL 39 0 39 279% Total Unsheltered Homeless Individuals 12 2 14 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - MECCA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 5 2 7 47% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 3 0 3 20% Blank 5 0 5 33% TOTAL 13 2 15 100% Hispanic or Latino 13 1 14 93% Not Hispanic or Latino 0 1 1 7% Ethnicity Blank 0 0 0 0% TOTAL 13 2 15 100% Female 3 2 5 33% Male 10 0 10 67% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 13 2 15 100% 0-5 0 0 0 0% 17 or under 2 0 2 13% 18-24 0 0 0 0% 25-29 1 0 1 7% 30-39 4 0 4 27% 40-49 2 2 4 27% Age 50-61 4 0 4 27% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 13 2 15 100% Chronically Homeless 0 0 0 0% Families with Children 1 0 1 6% Jail (Within 12 Months) 2 0 2 13% Veterans 0 0 0 0% Alcohol Use 6 0 6 38% Drug Use 3 0 3 19% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 1 0 1 6% Physical Disability 2 0 2 13% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 1 0 1 6% AIDS or HIV 0 0 0 0% TOTAL 16 0 16 107% Total Unsheltered Homeless Individuals 13 2 15 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - ROMOLAND
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 3 1 4 80% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 1 0 1 20% TOTAL 4 1 5 100% Hispanic or Latino 1 0 1 20% Not Hispanic or Latino 3 1 4 80% Ethnicity Blank 0 0 0 0% TOTAL 4 1 5 100% Female 1 0 1 20% Male 3 1 4 80% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 4 1 5 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 1 1 2 40% Age 50-61 2 0 2 40% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 1 0 1 20% TOTAL 4 1 5 100% Chronically Homeless 2 0 2 15% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 2 0 2 15% Drug Use 1 0 1 8% PTSD 1 0 1 8% Subpopulations Mental Health Conditions 1 0 1 8% Physical Disability 4 0 4 31% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 2 0 2 15% AIDS or HIV 0 0 0 0% TOTAL 13 0 13 260% Total Unsheltered Homeless Individuals 4 1 5 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - TEMESCAL VALLEY
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 0 1 1 100% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 0 1 1 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 1 1 100% TOTAL 0 1 1 100% Female 0 1 1 100% Male 0 0 0 0% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 0 1 1 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 1 1 100% 40-49 0 0 0 0% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 0 1 1 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 0 4 4 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - THERMAL
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 0 0 0 0% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 1 0 1 100% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% Hispanic or Latino 1 0 1 100% Not Hispanic or Latino 0 0 0 0% Ethnicity Blank 0 0 0 0% TOTAL 1 0 1 100% Female 0 0 0 0% Male 1 0 1 100% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 0 0 0% 30-39 0 0 0 0% 40-49 1 0 1 100% Age 50-61 0 0 0 0% 62-69 0 0 0 0% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 1 0 1 100% Chronically Homeless 0 0 0 0% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 0 0 0 0% Drug Use 0 0 0 0% PTSD 0 0 0 0% Subpopulations Mental Health Conditions 0 0 0 0% Physical Disability 0 0 0 0% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 0 0 0 0% Total Unsheltered Homeless Individuals 1 0 1 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - THOUSAND PALMS
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 2 0 2 10% Asian 0 0 0 0% Black or African American 3 3 6 30% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 9 1 10 50% Multiple Races 1 0 1 5% Don't Know/ Refused to Answer 0 1 1 5% Blank 0 0 0 0% TOTAL 15 5 20 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 12 4 16 94% Ethnicity Blank 0 1 1 6% TOTAL 12 5 17 100% Female 4 3 7 41% Male 8 2 10 59% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 12 5 17 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 1 0 1 6% 25-29 0 0 0 0% 30-39 1 3 4 24% 40-49 2 0 2 12% Age 50-61 5 0 5 29% 62-69 2 2 4 24% 70-79 1 0 1 6% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 12 5 17 100% Chronically Homeless 3 0 3 13% Families with Children 0 0 0 0% Jail (Within 12 Months) 2 0 2 8% Veterans 3 0 3 13% Alcohol Use 1 0 1 4% Drug Use 3 0 3 13% PTSD 3 0 3 13% Subpopulations Mental Health Conditions 2 0 2 8% Physical Disability 4 0 4 17% Developmental Disability 1 0 1 4% Brain Injury 0 0 0 0% Victim of Domestic Violence 2 0 2 8% AIDS or HIV 0 0 0 0% TOTAL 24 0 24 141% Total Unsheltered Homeless Individuals 12 5 17 100%
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2017 Unsheltered Homeless Count Overall and Subpopulation Data - VALLE VISTA
General PIT Count Total 2017 Unsheltered Field Response Options Interview Observational Count Percent American Indian or Alaska Native 1 1 2 50% Asian 0 0 0 0% Black or African American 0 0 0 0% Native Hawaiian, Pacific Islander 0 0 0 0% Race White 1 1 2 50% Multiple Races 0 0 0 0% Don't Know/ Refused to Answer 0 0 0 0% Blank 0 0 0 0% TOTAL 2 2 4 100% Hispanic or Latino 0 0 0 0% Not Hispanic or Latino 2 2 4 100% Ethnicity Blank 0 0 0 0% TOTAL 2 2 4 100% Female 0 0 0 0% Male 2 2 4 100% Transgender 0 0 0 0% Gender Do Not Identify 0 0 0 0% Blank 0 0 0 0% TOTAL 2 2 4 100% 0-5 0 0 0 0% 17 or under 0 0 0 0% 18-24 0 0 0 0% 25-29 0 1 1 25% 30-39 0 0 0 0% 40-49 1 0 1 25% Age 50-61 1 0 1 25% 62-69 0 1 1 25% 70-79 0 0 0 0% 80+ 0 0 0 0% Blank 0 0 0 0% TOTAL 2 2 4 100% Chronically Homeless 2 0 2 25% Families with Children 0 0 0 0% Jail (Within 12 Months) 0 0 0 0% Veterans 0 0 0 0% Alcohol Use 1 0 1 13% Drug Use 1 0 1 13% PTSD 1 0 1 13% Subpopulations Mental Health Conditions 1 0 1 13% Physical Disability 2 0 2 25% Developmental Disability 0 0 0 0% Brain Injury 0 0 0 0% Victim of Domestic Violence 0 0 0 0% AIDS or HIV 0 0 0 0% TOTAL 8 0 8 200% Total Unsheltered Homeless Individuals 2 2 4 100%
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Appendix D: PIT Count Misconceptions Here are the 5 Most Common Misconceptions about PIT Count Estimates written by Sam Batko February 11, 2014
Are you releasing your Point-in-Time (PIT) Count numbers soon? We at the Alliance have noticed a number of inaccuracies in stories in the media having to do with the PIT Count estimates of homeless populations and we have noted five points in particular on which journalists may sometimes want clarification. The Alliance's Homeless Research Institute has put together a media resource, "5 Myths about PIT Counts" to help journalists. We encourage you to provide it to journalists when you announce the 2014 PIT Count estimate for your community. You can download it here.
5 MYTHS about PIT COUNTS
February 2014
Myth 1: Point-in-Time counts do not count every homeless person and therefore are inaccurate.
PIT counts are a solid count of people experiencing homelessness in shelters, transitional housing, and on the street, in cars, in abandoned buildings, and in other places not intended for human habitation.
People experiencing homelessness in unsheltered locations are particularly difficult to find and enumerate. The PIT counts do miss people, as do most censuses. Nevertheless, PIT counts are important. They are the ONLY measure that captures the scope of people experiencing homelessness who are unsheltered – living on the streets, in cars, in abandoned buildings, and other places not meant for human habitation. And, by using the same methodology every year, PIT counts allow a community to assess the relative size of the homeless population over time.
Myth 2: Other national statistics contradict the data presented in Point-in-Time counts.
There are several national data sources on homelessness, but they do not cover the same population over the same time period, and subsequently cannot be directly compared to the PIT count.
PIT counts cover all people sheltered in homeless programs and unsheltered on a given night. They are conducted in every community, and are required by the U.S. Department of Housing and Urban Development (HUD). HUD also requires communities to collect data on people in homeless programs over the course of a year via Homeless Management Information Systems (HMIS). The Department of Education (Education) counts children who are enrolled in school and living in shelter, unsheltered situations, hotels and motels, and doubled up with family or friends during the school year. The U.S. Census Bureau includes a measure for people experiencing homelessness in the decennial Census.
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These measures examine different populations over different time periods using different methodologies and cannot be directly compared. They have, however, tended to show similar trends over time. For example, between 2012 and 2013, both the PIT count and the Education data showed that the number of people who live in shelters, transitional housing, and unsheltered has gone down.
Myth 3: Point-in-Time counts are meant to provide data on everyone who is homeless during a year.
PIT counts are intended to provide a snapshot of how many people are homeless on a given night.
PIT counts provide the number of people living in emergency shelters, transitional housing, and unsheltered locations in a community. They provide little detailed information about the characteristics of who experiences homelessness over time in a community. Communities can glean more information on homelessness over the course of a year by examining the data collected in HMIS.
Data from HMIS, however, does not provide information about the scope of unsheltered homelessness. PIT counts are uniquely valuable in that respect.
Myth 4: Fluctuations in the Point-in-Time counts simply reflect fluctuations in the number of shelter beds – not in the number of homeless people.
The PIT counts reflect the interaction among people in beds, people unsheltered, and utilization of beds.
PIT counts can be affected by the number of beds, but they also count people who are not sheltered reflecting the entire population of people who are literally homeless. PIT counts also reflect the varying utilization of beds (i.e., they sometimes go unused). For example, in 2013 the number of shelter and transitional housing beds went down, but the number of people in those beds went up meaning vacancy was reduced.
Myth 5: If the Point-in-Time count is down, it means that there is enough affordable housing in the community.
The PIT describes how the homeless system is managing people’s experience of the shortage of affordable housing – not the availability of that housing.
The PIT count does not describe the affordable housing crisis. For that, a community should examine how many affordable units there are for the number of extremely low income households, the number of households that are doubled up, and the percentage of income that people pay for rent, among other things. Data on these measures is available from HUD and the U.S. Census Bureau.
A Point-in-Time count is a one-night, unduplicated count of people experiencing homelessness in a Continuum of Care (CoC). HUD requires that CoCs count their sheltered homeless population every year. They require that CoCs count their unsheltered homeless population every other year, on odd numbered calendar years. In 2012, despite not being required, 67 percent of CoCs conducted an unsheltered count. CoCs are expected to conduct their count during the last 10 days of January.
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Appendix E: HUD Youth Homeless Definition
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For questions regarding this report, please contact: Donyielle Holley, Social Service Planner at [email protected] or (951) 358-3844.
Please use the following citation when referencing this report: Riverside County Department of Public Social Services Adult Services Division Homeless Programs Unit. County of Riverside 2017 Point-in-Time Homeless Count Report. County of Riverside, CA May 2017.
Participating Riverside Cities and County Agencies
Riverside County DPSS ASD HPU 2017 Point-In-Time Homeless Count Report