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Riverside County DPSS ASD HPU 2017 Point-In-Time Homeless Count Report

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

TABLE OF CONTENTS

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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 All Saints Episcopal Church Atrium Sober Living Banning Family Community Health Center Banning Police Department Banning Wellness City – RI International Barton CPA Beaumont Police Department Be Remarkable Today Bridge Church California Baptist University California Children's Services California Rural Legal Assistance California State University, San Bernardino Calimesa Code Enforcement Calvary Murrieta Church Calvary Presbyterian Church Catholic Charities Church of Jesus Christ Latter Day Saints City of Banning City of Beaumont City of Blythe City of Calimesa City of Cathedral City City of Coachella City of Corona City of Desert Hot Springs City of Eastvale City of Hemet City of Hemet Parks and Recreation City of Indian Wells City of Indio City of Jurupa Valley

City of Lake Elsinore City of La Quinta City of Menifee City of Moreno Valley City of Murrieta City of Norco City of Palm Desert City of Palm Springs City of Perris City of Rancho Mirage City of Riverside City of San Jacinto City of Temecula City of Wildomar Coachella Valley Assoc. of Governments (CVAG) Coachella Valley Economic Partnership Coachella Valley Rescue Mission Coachella Police Department Commission on Disabilities, Inland Regional Center Common Good Community Development Community Access Center Community Action Partnership Riverside County Community Good Deeds Nonprofit Organization Community of Mecca Community Mission of Hope Community of Reformed Evangelical Churches Community Resource and Educational Center Congregational Church of Menifee Corona Code Enforcement Corona Fire Safety Foundation Corona Police Department Corona Settlement House

ACKNOWLEDGEMENTS

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Corona Vet Center Courage Campaign Create a Bright Life Crime Watch Desert Arc Desert Best Friend's Closet Desert Hot Springs Family Resource Center Desert Hot Springs Neighborhood Group Dios Vive Dominium Down Syndrome Association of Inland Empire Eastvale Bible Church Eye of a Needle Foundation First Presbyterian Church of Hemet Foothill AIDS Project GIA Community Outreach Good Works Movement Gregory Barton & Associates Green Coalition of San Jacinto Valley GUMBO First Baptist Church of Riverside Habitat for Humanity Inland Valley Harmony Kitchen Harvest Church HD Supply White Cap Health to Hope Clinics Healthy Jurupa Helping Hands Group Hemet Code Enforcement Hemet Community Pantry Hemet Library Hemet Police Department Hemet/San Jacinto United for Change Hemet Unified School District Homeless Task Force Sub‐Committee Hope Lutheran Church Human Rights Commission Idyllwild HELP Center Indio Police Department Jewish Family Service of the Desert Joe Gray’s Plumbing Journey TAY Program Jubilee Sober‐Living Homes Jurupa Valley Code Enforcement Jurupa Valley Safety Committee JustServe.org La Quinta Police Department La Sierra University La Sierra University Church

Lake Menifee Women’s Club Larios LDS Church Lesbian Gay Center Life in His Hands Lifeline Street Ministry LightHouse Social Service Centers LLLeindecker & Associates Loma Linda Veteran Affairs Hospital Love and Loyalty Lutheran Social Services Magnolia Presbyterian Church Manzanita Ranch Martha’s Village & Kitchen Mecca Family & Farmworkers Service Center Mecca Family Resource Center Men and Women Ministry on the Move Menifee Buzz Newspaper Menifee Valley Chamber of Commerce Menifee Valley Community Cupboard Menifee Crime Watch Menifee Kay Ceniceros Senior Center Menifee Police Department Menifee Union School District Menifee Valley Community Church Mission Outreach Project Monteleone Meadows Moreno Valley College Moreno Valley Police Department My Butterfly House National Alliance for Women National Alliance on Mental Illness Norco Community College ONE‐PS Operation SafeHouse Osher Lifelong Learning Institute Palm Springs Homeless Task Force Palm Springs Human Rights Commission Palm Springs Police Department Palm Springs Public Library Palm Springs Rotary Club Palm Springs Unified School District Path of Life Ministries Peace Lutheran Church Perris Code Enforcement Perris Police Department Power Speaks Louder Press Enterprise

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Project Riley Provident Bank PW Enhancement Center Reaching New Heights Foundation Realty One Group ResCare Workforce Services Riverside County Registrar of Voters RI International/ NAMI Signature Programs Riverside Access Center Riverside City College Riverside Univ. Health Sys. Behavioral Health Riverside County Code Enforcement Riverside County Dept. of Animal Services Riverside County Dept. of Child Support Services Riverside County Dept. of Public Health Riverside County Dept. of Public Social Services Riverside County Dept. of Veterans Services Riverside County Economic Dev. Agency Riverside County District Attorney's Office Riverside County Emergency Mgmt. Dept. Riverside County Executive Office Riverside County Flood Control Riverside County Habitat Conservation Agency Riverside County Health Administration Riverside County Housing Authority Riverside County Human Resources Riverside County Probation Dept. Riverside County Sheriff’s Dept. Riverside County Supervisor Chuck Washington Riverside County Supervisor John J. Benoit Riverside County Supervisor John F. Tavaglione Riverside County Supervisor Kevin Jeffries Riverside County Supervisor Marion Ashley Riverside County TMLA Harmony Kitchen San Bernardino County San Jacinto Code Enforcement San Jacinto Police Dept. Santa Ana Ministry Group Santa Ana Watershed Project Authority

Seams of Gold SEIU 721 Seller & Keever Foundation Senior Advocates of the Desert Serve‐One, Inc. Southwest County Detention Center Speak Life StarWriter Enterprises St. Carries Center St. Catherine’s of Alexandria St. Thomas Church State of California’s Employment Dev. Dept. Step Up On Second Sunrise Christian Fellowship Sunrise Park Neighborhood Assoc. Palm Springs Temecula Community Pantry Temecula Police Department Temecula Rotary Club Temecula Valley Atheists Temecula Veterans Center The Gathering Church The Grove Community Church The Hole In Wall, Inc. The Well in the Desert United Church of God University of California Riverside University of Phoenix U.S. Dept. of Housing and Urban Dev. U.S. Vets VA Loma Linda Healthcare System Val Verde Unified School District Valley Restart Shelter VCC Vista Community Clinic Lake Elsinore Veterans Community Services Veterans of Foreign Wars VitalCare America Western Riverside Council of Gov. (WRCOG) Women’s Veterans Unity Group 2YouMusic

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Riverside County DPSS ASD HPU 2017 Point‐In‐Time Homeless Count Report

Youth Count Volunteer Organizations

California Family Life Center ‐ Rubidoux YOC

California Family Life Center ‐ Empower Youth

City of Menifee

City of Murrieta

City of Temecula

City of Riverside

County of Riverside Dept. of Public Social Srvcs.

Oasis Community Services

Operation SafeHouse of the Desert

Operation SafeHouse Riverside

Operation SafeHouse Riverside ‐ Cup of Happy

Power Speaks Louder

Riverside University Health Systems ‐ Behavioral

Health TAY Journey Program

Romoland School District

Safe Schools Desert Cities

Victor Community Support Services

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

Banning

Debbie Franklin City of Banning

Police Chief Alex Diaz Banning Police Department

Banning Police Department

Beaumont

Sergeant George Walter Beaumont Police Department Deputy Michael Granada

Beaumont Police Department

Beaumont Police Department

Blythe

Barbara Burrow City of Blythe

Blythe City Hall

Calimesa

Sergeant Richard Beatty Riverside County Sheriff’s Department

Deputy Jacob Jenson Riverside County Sheriff’s Department

Riverside County Sheriff’s Cabazon Station

Cathedral City

Deputy Dwayne Hodges Cathedral City Police Department

Cathedral City Police Station

Coachella

Alex Alarcon City of Coachella

Sergeant Robert Castro Riverside County Sheriff’s Department

Coachella City Hall

Corona

Lieutenant Mark Johnson Deputy Jon Rosenblum

Corona Police Department

Corona Police Department

Desert Hot Springs

Joe McKee Desert Hot Springs City Councilman

DHS Family Resource Center

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Eastvale

Deputy Steve Lycopolus Riverside County Sheriff’s Department

Riverside County Sheriff’s Jurupa Valley Station

Hemet

Carla Callahan City of Hemet Susan Larkin Valley Restart

Lieutenant Eric Dickson Hemet Police Department Sergeant Dan Reinbolt

Hemet Police Department Detective A. Paez

Hemet Police Department Chief Dave Brown

Hemet Police Department

Valley Wide Recreation Community Center

Indian Wells

David Gassaway City of Indian Wells

Indian Wells City Hall

Indio

Christie Curtis Martha’s Village & Kitchen

Jesus Gomez City of Indio

Deputy Jose Ibarra Indio Police Department Deputy Brandon Haworth Indio Police Department

Martha’s Village & Kitchen

Jurupa Valley



La Quinta Deputy Arnoldo Iniguez

Riverside County Sheriff’s Department Sam’s Club, Palm Desert

Lake Elsinore (including Lakeland Village)

Audrey Young City of Lake Elsinore

Nicole Dailey City of Lake Elsinore

Deputy Alejandro Salcedo Riverside County Sheriff’s Department

Deputy Kevin Whitford Riverside County Sheriff’s Department

Riverside County Sheriff’s Lake Elsinore Station

Mecca Maria Machuca

Department of Public Social Services Mecca Family Resource Center

Menifee

Jason Hendrix City of Menifee

Deputy Karla Girard Riverside County Sheriff’s Department

Deputy Robert Wilson Riverside County Sheriff’s Department

Kay Ceniceros Senior Center

Moreno Valley

Isa Rojas City of Moreno Valley

Moreno Valley City Hall

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Mark Houck Lighthouse Social Services Centers

Corporal Dave Clark Riverside County Sheriff’s Department

Murrieta

Brian Ambrose City of Murrieta

Temecula Community Center

Norco


Deputy Arturo Mendez Riverside County Sheriff’s Department


Palm Desert

Amy Lawrence City of Palm Desert

Palm Desert City Hall

Palm Springs

Dale Cook City of Palm Springs

Palm Springs City Hall

Perris

Kristii MacEwen Path of Life Ministries Deputy James Updike

Riverside County Sheriff’s Department

Perris Police Department

Rancho Mirage

Britt Wilson City of Rancho Mirage

Rancho Mirage City Hall

Riverside

Monica Sapien City of Riverside Access Center

City of Riverside Access Center St. Michael's Episcopal Church

San Jacinto

Renee Yarnall City of San Jacinto

Sergeant Robert Himmelberg Riverside County Sheriff’s Department


Temecula

Robin Gilliland City of Temecula

Deputy Todd Johnson Riverside County Sheriff’s Department

Deputy Jeret McClellan Riverside County Sheriff’s Department


Wildomar

Keith Ross City of Wildomar

Wildomar City Hall

COUNTY AREAS

Cabazon RSO Station Area

Sergeant Richard Beatty Riverside County Sheriff’s Department

Deputy Jacob Jenson Riverside County Sheriff’s Department

Riverside County Sheriff’s Cabazon Station

Colorado River RSO Station Area

Sergeant Tom Velarde Riverside County Sheriff’s Department

Deputy Joe Aguilar Riverside County Sheriff’s Department

Riverside County Sheriff’s Colorado River Station

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Hemet (County) RSO Station Area

Deputy Frank James Riverside County Sheriff’s Department

Deputy Marc Searl Riverside County Sheriff’s Department


Jurupa Valley (County) RSO Station Area


Deputy Aaron Avila Riverside County Sheriff’s Department

Deputy Bridgette Recksiek Riverside County Sheriff’s Department


Lake Elsinore (County) RSO Station Area

Deputy Alejandro Salcedo Riverside County Sheriff’s Department

Deputy Kevin Whitford Riverside County Sheriff’s Department

Riverside County Sheriff’s Lake Elsinore Station

Palm Desert (County) RSO Station Area

Sergeant Zamora Riverside County Sheriff’s Department

Deputy Mike Troyer Riverside County Sheriff’s Department

Deputy Roberto Loza Riverside County Sheriff’s Department

Riverside County Sheriff’s Palm Desert Station

Southwest RSO Station Area

Deputy Todd Johnson Riverside County Sheriff’s Department

Deputy Jeret McClellan Riverside County Sheriff’s Department


Thermal RSO Station Area

Sergeant Robert Castro Riverside County Sheriff’s Department

Sergeant John Gaw Riverside County Sheriff’s Department

Deputy Arnoldo Iniguez Riverside County Sheriff’s Department

Deputy Maggie Lopez Riverside County Sheriff’s Department

Sam Club, Palm Desert

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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P a g e | 12


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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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

INTRODUCTION

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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.

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 ‐ Behavioral Health Homeless Outreach (seven‐day period of time to cover the

unincorporated areas and perform sweep of cities to count and survey missed people)

Riverside University Health System – Behavioral Health Homeless Outreach (seven‐day period of time to cover the

unincorporated areas and perform sweep of 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.

P a g e | 17


Table 2: Comparison of City Engagement

City Engagement

2016 2017

27 out of 28 cities participated in the Count and had deployment centers in the city or a

neighboring city

27 out of 28 cities participated in the Count and had deployment centers in the city or a

neighboring city

Did NOT designate red zones, but rather attempted to engage every city

Did NOT designate red zones, but rather attempted to engage every city

Included 53 census designated places (unincorporated areas)

Included 53 census designated places (unincorporated areas)

20% of the deployment centers had an assigned DPSS Site Coordinator

30% of the deployment centers had an 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

P a g e | 18


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

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

Total Surveys

Returned

Rejected Surveys

(Not Entered)

New Sub Total

Rejected Surveys

(Duplicates)

Final Sample Size

(total number of

people)

1,892 (1,149

interview, 222

youth surveys, 521

observational)

242 (91 interview,

140 youth, 11

observational)

1650 (1058

interview, 82 youth

surveys, 510

observational)

12 (8 interview, 4

youth, 0

observational)

1638 (1050

interview, 78 youth

surveys, 510

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.

P a g e | 20


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.

P a g e | 21


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.

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.

P a g e | 23


Youth Count Planning Team

California Family Life Center Rubidoux YOC City of Riverside DPSS Homeless Programs Unit Desert AIDS Project Desert Sands Unified School District Housing Authority of the County of Riverside Operation SafeHouse of the Desert Operation SafeHouse Riverside Operation SafeHouse Cup of Happy

Operation SafeHouse Transitional Living Prog. Riverside County Sheriff's Homeless Outreach Team RUHS Behavioral Health HHOPE RUHS Behavioral Health TAY Journey Program RUHS Behavioral Health Children’s Board Safe Schools Desert Cities United Way of the Inland Valleys

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.

P a g e | 24


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

City or Unincorporated Area

2017 PIT Count Unsheltered Totals 2016 – 2017

Count Change

2016 – 2017

Percent Change Interview Observational

2016 2017

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%

UNSHELTERED COUNT FINDINGS

P a g e | 25


City or Unincorporated Area

2017 PIT Count Unsheltered Totals 2016 – 2017

Count Change

2016 – 2017

Percent Change Interview Observational

2016 2017


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.

P a g e | 26


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.

P a g e | 27


Table 6: 2016‐2017 Unsheltered Homeless Count Comparison – Breakdown of Gender

*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

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

0

200

400

600

800

1000

1200

Female Male Transgender Don't Know orRefused

(blank)

2016‐2017 Unsheltered Homeless Count Comparison – Breakdown of Gender

2016

2017

2017 General PIT Count

Unsheltered Totals 2016‐2017

2016‐2017

Gender 2016 2017 Count Change

Percent Difference Interview Observational Count Percent Count Percent

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 or Refused

2 45 13 0.96% 47 2.87% 34 261.54%

(blank) 8 7 8 0.59% 15 0.92% 7 87.50%

Total 1128 510 1351 ‐ 1638 ‐ 287 ‐

P a g e | 28


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 Change

Percentage Change Interview Observational Count Percent Count Percent

American Indian or

Alaska Native 82 7 87 6.44% 89 5.43% 2 2.30%

Asian 8 2 10 0.74% 10 0.61% 0 0.00%

Black or African American

131 55 160 11.84% 186 11.36% 26 16.25%

Native Hawaiian,

Pacific Islander 19 1 12 0.89% 20 1.22% 8 66.67%

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 Refused

107 154 258 19.10% 261 15.93% 3 1.16%

(blank) 129 72 39 2.89% 201 12.27% 162 415.38%

P a g e | 29


0

200

400

600

800

1000

1200

Hispanic or Latino Not Hispanic or Latino Blank

Unsheltered Homeless Count Comparison –Breakdown of Ethnicity

2016

2017

Figure 2: 2016‐2017 Unsheltered Homeless Count – Breakdown of Race

Table 8: 2016‐2017 Unsheltered Homeless Count Comparison – Breakdown of Ethnicity

Figure 3: 2016‐2017 Unsheltered Homeless Count – Breakdown of Ethnicity

0

200

400

600

800

1000

AmericanIndian orAlaskaNative

Asian Black orAfricanAmerican

NativeHawaiian,PacificIslander

White MultipleRaces

Don'tKnow orRefused

(blank)

2016 ‐ 2017 Unsheltered Homeless Count –Breakdown of Race

2016

2017

Ethnicity

2017 General PIT Count Unsheltered Totals 2016‐2017

Interview Observational2016 2017 Count

Change Percent Change


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%

P a g e | 30


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 Range

2017 PIT Count Unsheltered Totals 2016‐2017

Interview Observational 2016 2017 Count

Change

Percentage Difference


0‐5 0 0 1 0.07% 0 0.00% ‐1 ‐100.00%

17 or under

11 4 13 0.96% 15 0.92% 2 15.38%

18‐24 107 71 82 6.07% 178 10.87% 96 117.07%

25‐29 91 112 101 7.48% 203 12.39% 102 100.99%

30‐39 203 92 256 18.95% 295 18.01% 39 15.23%

40‐49 250 68 305 22.58% 318 19.41% 13 4.26%

50‐61 340 63 396 29.31% 403 24.60% 7 1.77%

62‐69 70 34 94 6.96% 104 6.35% 10 10.64%

70‐79 9 4 22 1.63% 13 0.79% ‐9 ‐40.91%

80+ 6 0 4 0.30% 6 0.37% 2 50.00%

(blank) 41 62 78 5.77% 103 6.29% 25 32.05%

Total 1128 510 1351 ‐ 1638 ‐ 287 ‐

P a g e | 31


Figure 4: 2016‐2017 Unsheltered Homeless Count – Breakdown of Age

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

Veterans

2016 2017 2016‐2017

Count Percent Count Percent Count Change


Unsheltered Homeless Veterans

100 7.40% 91 5.56% ‐9 ‐9.00%

Unsheltered Chronically Homeless Veterans

36 2.66% 37 2.26% 1 2.78%

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.

0

100

200

300

400

500

0‐5 17 orunder

18‐24 25‐29 30‐39 40‐49 50‐61 62‐69 70‐79 80+ (blank)

2016‐2017 Unsheltered Homeless Count –Breakdown of Age

2016

2017

P a g e | 32


Table 11: 2017 Unsheltered Youth Count – Age 24 and Under

2016 2017 2016‐2017



Unaccompanied Minor Youth (17 and Under)

1 0.07% 11 0.67% 10 1000.00%

Unaccompanied Transitional Age Youth (24 and Under)

81 6.06% 171 10.43% 90 111.11%

Total Minor Youth (17 and Under)

13 0.96% 15 1.00% 2 15.38%

Total Transitional Age Youth (24 and Under)

82 6.06% 178 10.87% 96 117.07%

*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.

P a g e | 33


Table 12: 2016‐2017 Unsheltered Homeless Count Comparison – Breakdown of Subpopulations

Subpopulation

2016 2017 2016‐2017


Percent 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.

P a g e | 34


Table 13: 2016‐2017 Unsheltered Homeless Count Comparison – Breakdown of Disabling Conditions

Disabling Conditions

Number of respondents who stated they have experienced…

Number of respondents who stated it prevents them from obtaining housing or work.

2016 2017 2016 2017

Subpopulation Count Percent 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 Conditions

275 26.40% 309 27.39% 144 13.80% 137 12.10%

Physical Disability 326 31.30% 362 34.48% 190 18.20% 181 16.00%

Developmental Disability

128 12.30% 135 11.97% 59 5.70% 58 5.10%

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.

P a g e | 35


Table 14: Unsheltered Homeless Count – Subpopulation Data by City versus Unincorporated Area

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%

Jail (within 12

months)Brain Injury

Victims of

Domestic

Violence

AIDS or HIV

*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).

City or Unincorporated

Area

Total

Unsheltered

Homeless

Individuals

Chronically

Homeless

Families with

ChildrenVeterans

Youth (24 or

younger)Alcohol Use Drug Use PTSD

Mental Health

Conditions

Physical

Disability

Developmental

Disability

2017 Unsheltered Homeless Count ‐ Subpopulation Data by City versus Unincorporated Area

P a g e | 36


Table 14: 2017 Unsheltered Homeless Count – Subpopulation Data by City versus Unincorporated Area (Continued)

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%

Brain Injury

*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).

Alcohol Use Drug Use PTSDMental Health

Conditions

Physical

Disability

Developmental

Disability

Victims of

Domestic

Violence

AIDS or HIVJail (within 12

months)

2017 Unsheltered Homeless Count ‐ Subpopulation Data by City versus Unincorporated Area

City or Unincorporated

Area

Total

Unsheltered

Homeless

Individuals

Chronically

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09 25.68

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50 18.43

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26.82%

16.67%

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19.01%

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8.72%

7.42%

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S

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se individuals

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7

Total Sheltered Count

768

a g e | 37

Count Report

s stayed in

ousing. This

he 2016 to

ent change

change in

2017

ercentage Change

10.00%

66.70%

‐1.40%

‐22.90%

‐0.60%

‐2.70%

18.80%

‐6.90%

‐48.60%

‐100.00%

6.50%

P a g e | 38


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

3163 2775 2043

5090

1888 1587 13511638

1622 17331323

1113

1090 880 814 768

4785 4508

3366

6203

2978 2467 21652406

2005 2007 2009 2011 2013 2015 2016 2017

PIT Count Numbers 2005 ‐ 2017

Unsheltered Sheltered

CONCLUSION

P a g e | 39


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.

CHALLENGES, LIMITATIONS AND RECOMMENDATIONS

P a g e | 40


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.

P a g e | 41


Appendix A: Survey Instruments

P a g e | 42


P a g e | 43


P a g e | 44


P a g e | 45


P a g e | 46


P a g e | 47


P a g e | 48


Appendix B: City Summary Tables

P a g e | 49


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%

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%

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%

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%

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%

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%

Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ BANNING

Field Response OptionsGeneral PIT Count Total 2017 Unsheltered

Race

P a g e | 50




Asian 0 0 0 0%



White 16 1 17 77%



Blank 1 2 3 14%

TOTAL 18 4 22 100%



Blank 1 2 3 17%

TOTAL 14 4 18 100%

Female 3 1 4 22%

Male 11 2 13 72%


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%

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%




Veterans 0 0 0 0%


Drug Use 2 0 2 15%

PTSD 1 0 1 8%







TOTAL 13 0 13 72%


2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ BEAUMONT


Race

Ethnicity

Gender

Age

Subpopulations

P a g e | 51




Asian 0 0 0 0%



White 19 7 26 43%



Blank 5 10 15 25%

TOTAL 32 28 60 100%



Blank 2 3 5 8%

TOTAL 31 28 59 100%

Female 9 9 18 31%

Male 21 16 37 63%



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%

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%




Veterans 4 0 4 4%


Drug Use 11 0 11 12%

PTSD 6 0 6 6%







TOTAL 94 0 94 159%


2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ BLYTHE


Race

Ethnicity

Gender

Age

Subpopulations

P a g e | 52




Asian 0 0 0 0%



White 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%

Female 0 0 0 0%

Male 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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ CALIMESA


Race

Ethnicity

Gender

Age

Subpopulations

P a g e | 53




Asian 0 0 0 0%



White 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%

Female 0 0 0 0%

Male 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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ CANYON LAKE


Race

Ethnicity

Gender

Age

Subpopulations

P a g e | 54




Asian 2 0 2 5%



White 25 3 28 64%



Blank 7 0 7 16%

TOTAL 37 7 44 100%



Blank 3 3 6 14%

TOTAL 37 7 44 100%

Female 10 1 11 25%

Male 27 5 32 73%



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%

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%




Veterans 5 0 5 4%


Drug Use 24 0 24 21%

PTSD 7 0 7 6%







TOTAL 114 0 114 259%


2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ CATHEDRAL CITY


Race

Ethnicity

Gender

Age

Subpopulations

P a g e | 55




Asian 0 0 0 0%



White 45 8 53 56%



Blank 10 2 12 13%

TOTAL 75 20 95 100%



Blank 4 6 10 12%

TOTAL 61 20 81 100%

Female 24 8 32 40%

Male 37 9 46 57%



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%

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%




Veterans 5 0 5 4%


Drug Use 12 0 12 10%

PTSD 10 0 10 8%







TOTAL 123 0 123 152%


Race

Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ COACHELLA


P a g e | 56




Asian 0 0 0 0%



White 25 7 32 53%



Blank 7 0 7 12%

TOTAL 43 17 60 100%



Blank 1 5 6 11%

TOTAL 39 17 56 100%

Female 10 5 15 27%

Male 29 11 40 71%



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%

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%




Veterans 1 0 1 1%


Drug Use 16 0 16 15%

PTSD 8 0 8 8%







TOTAL 104 0 104 186%


Race

Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ CORONA


P a g e | 57




Asian 0 0 0 0%

Black or African American 2 2 4 9%Native Hawaiian, Pacific Islander 0 0 0 0%

White 20 7 27 63%



Blank 6 1 7 16%

TOTAL 33 10 43 100%



Blank 1 2 3 7%

TOTAL 33 10 43 100%

Female 13 2 15 35%

Male 19 8 27 63%



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%

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%




Veterans 1 0 1 1%


Drug Use 11 0 11 12%

PTSD 15 0 15 16%







TOTAL 93 0 93 216%


2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ DESERT HOT SPRINGS


Race

Ethnicity

Gender

Age

Subpopulations

P a g e | 58




Asian 0 0 0 0%



White 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%

Female 0 0 0 0%

Male 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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ EASTVALE


Race

Ethnicity

Gender

Age

Subpopulations

P a g e | 59




Asian 0 0 0 0%



White 38 15 53 42%



Blank 6 24 30 24%

TOTAL 72 54 126 100%



Blank 1 15 16 13%

TOTAL 65 54 119 100%

Female 14 9 23 19%

Male 48 40 88 74%



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%

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%




Veterans 8 0 8 4%


Drug Use 27 0 27 15%

PTSD 20 0 20 11%







TOTAL 179 0 179 150%


Gender

Age

Subpopulations

Ethnicity

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ HEMET


Race

P a g e | 60




Asian 0 0 0 0%



White 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%

Female 0 0 0 0%

Male 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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ INDIAN WELLS


Race

P a g e | 61




Asian 0 0 0 0%



White 33 12 45 50%



Blank 4 0 4 4%

TOTAL 58 32 90 100%



Blank 0 9 9 10%

TOTAL 57 32 89 100%

Female 18 6 24 27%

Male 39 26 65 73%



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%

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%




Veterans 5 0 5 3%


Drug Use 29 0 29 18%

PTSD 16 0 16 10%







TOTAL 164 0 164 184%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ INDIO


Race

P a g e | 62




Asian 1 0 1 1%



White 69 14 83 59%



Blank 10 7 17 12%

TOTAL 112 28 140 100%



Blank 2 13 15 12%

TOTAL 101 26 127 100%

Female 34 2 36 28%

Male 66 20 86 67%



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%

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%




Veterans 6 0 6 2%


Drug Use 47 0 47 19%

PTSD 27 0 27 11%







TOTAL 251 0 251 195%


Race

Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ JURUPA VALLEY


P a g e | 63




Asian 0 0 0 0%



White 1 1 2 100%



Blank 0 0 0 0%

TOTAL 1 1 2 100%



Blank 0 0 0 0%

TOTAL 1 1 2 100%

Female 0 0 0 0%

Male 1 1 2 100%



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%

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%




Veterans 0 0 0 0%


Drug Use 1 0 1 50%

PTSD 0 0 0 0%







TOTAL 2 0 2 100%


Race

Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ LA QUINTA


P a g e | 64




Asian 1 0 1 1%



White 39 6 45 66%



Blank 4 0 4 6%

TOTAL 59 9 68 100%



Blank 0 2 2 3%

TOTAL 53 9 62 100%

Female 15 0 15 24%

Male 38 9 47 76%



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%

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%




Veterans 0 0 0 0%


Drug Use 24 0 24 16%

PTSD 13 0 13 9%







TOTAL 147 0 147 237%


Race

Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ LAKE ELSINORE


P a g e | 65




Asian 0 0 0 0%



White 4 3 7 50%



Blank 0 0 0 0%

TOTAL 10 4 14 100%



Blank 0 2 2 14%

TOTAL 10 4 14 100%

Female 3 2 5 36%

Male 6 2 8 57%



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%

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%




Veterans 0 0 0 0%


Drug Use 5 0 5 26%

PTSD 0 0 0 0%







TOTAL 19 0 19 136%


Race

Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ MENIFEE


P a g e | 66




Asian 3 0 3 4%



White 20 5 25 32%



Blank 5 3 8 10%

TOTAL 59 19 78 100%



Blank 3 3 6 8%

TOTAL 54 19 73 100%

Female 15 3 18 25%

Male 38 15 53 73%



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%

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%




Veterans 3 0 3 2%


Drug Use 22 0 22 15%

PTSD 11 0 11 8%







TOTAL 142 0 142 195%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ MORENO VALLEY


Race

P a g e | 67




Asian 0 0 0 0%



White 5 2 7 78%



Blank 0 0 0 0%

TOTAL 7 2 9 100%



Blank 0 1 1 14%

TOTAL 5 2 7 100%

Female 0 0 0 0%

Male 5 2 7 100%



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%

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%




Veterans 0 0 0 0%


Drug Use 3 0 3 19%

PTSD 1 0 1 6%







TOTAL 16 0 16 229%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ MURRIETA


Race

P a g e | 68




Asian 0 0 0 0%



White 2 0 2 50%



Blank 0 0 0 0%

TOTAL 3 1 4 100%



Blank 0 1 1 25%

TOTAL 3 1 4 100%

Female 0 0 0 0%

Male 3 1 4 100%



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%

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%




Veterans 0 0 0 0%


Drug Use 2 0 2 18%

PTSD 0 0 0 0%







TOTAL 11 0 11 275%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ NORCO


Race

P a g e | 69




Asian 0 0 0 0%



White 17 7 24 86%



Blank 0 0 0 0%

TOTAL 21 7 28 100%



Blank 0 0 0 0%

TOTAL 21 7 28 100%

Female 4 0 4 14%

Male 17 7 24 86%



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%

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%




Veterans 3 0 3 4%


Drug Use 9 0 9 13%

PTSD 4 0 4 6%







TOTAL 70 0 70 250%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ PALM DESERT


Race

P a g e | 70




Asian 0 0 0 0%



White 59 32 91 58%



Blank 4 2 6 4%

TOTAL 110 48 158 100%



Blank 0 4 4 3%

TOTAL 90 48 138 100%

Female 29 9 38 28%

Male 61 32 93 67%



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%

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%




Veterans 12 0 12 5%


Drug Use 29 0 29 12%

PTSD 29 0 29 12%







TOTAL 242 0 242 175%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ PALM SPRINGS


Race

P a g e | 71




Asian 0 0 0 0%



White 9 1 10 34%



Blank 7 0 7 24%

TOTAL 27 2 29 100%



Blank 4 0 4 14%

TOTAL 27 2 29 100%

Female 3 0 3 10%

Male 24 2 26 90%



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%

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%




Veterans 1 0 1 1%


Drug Use 9 0 9 13%

PTSD 9 0 9 13%







TOTAL 67 0 67 231%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ PERRIS


Race

P a g e | 72




Asian 0 0 0 0%



White 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 0 0 0%

Female 0 0 0 0%

Male 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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ RANCHO MIRAGE


Race

P a g e | 73




Asian 1 2 3 1%



White 145 63 208 51%



Blank 33 14 47 11%

TOTAL 253 158 411 100%



Blank 13 65 78 20%

TOTAL 231 158 389 100%

Female 74 40 114 29%

Male 149 99 248 64%



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%

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%




Veterans 21 0 21 3%


Drug Use 107 0 107 16%

PTSD 48 0 48 7%







TOTAL 652 0 652 168%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ RIVERSIDE


Race

P a g e | 74




Asian 0 0 0 0%



White 14 1 15 45%



Blank 4 6 10 30%

TOTAL 25 8 33 100%



Blank 2 2 4 13%

TOTAL 23 8 31 100%

Female 5 1 6 19%

Male 17 7 24 77%



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%

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%




Veterans 1 0 1 2%


Drug Use 12 0 12 22%

PTSD 4 0 4 7%







TOTAL 54 0 54 174%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ SAN JACINTO


Race

P a g e | 75




Asian 0 0 0 0%



White 46 6 52 58%



Blank 9 2 11 12%

TOTAL 72 18 90 100%



Blank 4 8 12 14%

TOTAL 67 18 85 100%

Female 21 4 25 29%

Male 46 13 59 69%



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%

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%




Veterans 6 0 6 4%


Drug Use 25 0 25 15%

PTSD 20 0 20 12%







TOTAL 164 0 164 184%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ TEMECULA


Race

P a g e | 76




Asian 0 0 0 0%



White 4 0 4 67%



Blank 0 0 0 0%

TOTAL 4 2 6 100%



Blank 2 2 4 67%

TOTAL 4 2 6 100%

Female 2 0 2 33%

Male 2 2 4 67%



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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 1 0 1 20%







TOTAL 5 0 5 83%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ WILDOMAR


Race

P a g e | 77


Appendix C: County Unincorporated Communities

Summary Tables

(PIT Count greater than zero)

P a g e | 78




Asian 0 0 0 0%



White 10 0 10 100%



Blank 0 0 0 0%

TOTAL 10 0 10 100%



Blank 1 0 1 10%

TOTAL 10 0 10 100%

Female 1 0 1 10%

Male 9 0 9 90%



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%

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%




Veterans 1 0 1 3%


Drug Use 4 0 4 14%

PTSD 0 0 0 0%







TOTAL 29 0 29 290%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ BERMUDA DUNES


Race

P a g e | 79




Asian 0 0 0 0%



White 1 0 1 100%



Blank 0 0 0 0%

TOTAL 1 0 1 100%



Blank 0 0 0 0%

TOTAL 1 0 1 100%

Female 0 0 0 0%

Male 1 0 1 100%



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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 1 0 1 100%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ CABAZON


Race

P a g e | 80




Asian 0 0 0 0%



White 1 0 1 100%



Blank 0 0 0 0%

TOTAL 1 0 1 100%



Blank 0 0 0 0%

TOTAL 1 0 1 100%

Female 0 0 0 0%

Male 1 0 1 100%



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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 4 0 4 400%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ CACTUS CITY


Race

P a g e | 81




Asian 0 0 0 0%



White 0 0 0 0%



Blank 0 0 0 0%

TOTAL 1 0 1 100%



Blank 0 0 0 0%

TOTAL 1 0 1 100%

Female 0 0 0 0%

Male 1 0 1 100%



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%

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%




Veterans 1 0 1 20%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 5 0 5 500%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ CHERRY VALLEY


Race

P a g e | 82




Asian 0 0 0 0%



White 0 1 1 20%



Blank 0 0 0 0%

TOTAL 0 5 5 100%



Blank 0 4 4 80%

TOTAL 0 5 5 100%

Female 0 0 0 0%

Male 0 2 2 40%



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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ CORONITA


Race

P a g e | 83




Asian 0 0 0 0%



White 1 2 3 60%



Blank 0 0 0 0%

TOTAL 1 4 5 100%



Blank 0 0 0 0%

TOTAL 1 4 5 100%

Female 0 1 1 20%

Male 1 3 4 80%



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%

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%




Veterans 0 0 0 0%


Drug Use 1 0 1 100%

PTSD 0 0 0 0%







TOTAL 1 0 1 20%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ HIGHGROVE


Race

P a g e | 84




Asian 0 0 0 0%



White 0 0 0 0%



Blank 0 0 0 0%

TOTAL 0 1 1 100%



Blank 0 0 0 0%

TOTAL 0 1 1 100%

Female 0 0 0 0%

Male 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 0 0 0%

40‐49 0 1 1 100%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ HOME GARDENS


Race

P a g e | 85




Asian 0 0 0 0%



White 7 1 8 80%



Blank 0 0 0 0%

TOTAL 8 2 10 100%



Blank 0 1 1 10%

TOTAL 8 2 10 100%

Female 1 0 1 10%

Male 6 2 8 80%



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%

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%




Veterans 0 0 0 0%


Drug Use 2 0 2 17%

PTSD 0 0 0 0%







TOTAL 12 0 12 120%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ IDYLLWILD


Race

P a g e | 86




Asian 0 0 0 0%



White 7 0 7 78%



Blank 0 0 0 0%

TOTAL 9 0 9 100%



Blank 0 0 0 0%

TOTAL 9 0 9 100%

Female 4 0 4 44%

Male 5 0 5 56%



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%

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%




Veterans 0 0 0 0%


Drug Use 1 0 1 10%

PTSD 1 0 1 10%







TOTAL 10 0 10 111%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ LAKELAND VILLAGE


Race

P a g e | 87




Asian 0 0 0 0%



White 5 1 6 43%



Blank 0 0 0 0%

TOTAL 12 2 14 100%



Blank 1 0 1 7%

TOTAL 12 2 14 100%

Female 5 0 5 36%

Male 7 2 9 64%



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%

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%




Veterans 0 0 0 0%


Drug Use 7 0 7 18%

PTSD 6 0 6 15%







TOTAL 39 0 39 279%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ MEAD VALLEY


Race

P a g e | 88




Asian 0 0 0 0%



White 5 2 7 47%



Blank 5 0 5 33%

TOTAL 13 2 15 100%



Blank 0 0 0 0%

TOTAL 13 2 15 100%

Female 3 2 5 33%

Male 10 0 10 67%



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%

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%




Veterans 0 0 0 0%


Drug Use 3 0 3 19%

PTSD 0 0 0 0%







TOTAL 16 0 16 107%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ MECCA


Race

P a g e | 89




Asian 0 0 0 0%



White 3 1 4 80%



Blank 1 0 1 20%

TOTAL 4 1 5 100%



Blank 0 0 0 0%

TOTAL 4 1 5 100%

Female 1 0 1 20%

Male 3 1 4 80%



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%

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%




Veterans 0 0 0 0%


Drug Use 1 0 1 8%

PTSD 1 0 1 8%







TOTAL 13 0 13 260%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ ROMOLAND


Race

P a g e | 90




Asian 0 0 0 0%



White 0 1 1 100%



Blank 0 0 0 0%

TOTAL 0 1 1 100%



Blank 0 1 1 100%

TOTAL 0 1 1 100%

Female 0 1 1 100%

Male 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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ TEMESCAL VALLEY


Race

P a g e | 91




Asian 0 0 0 0%



White 1 0 1 100%



Blank 0 0 0 0%

TOTAL 1 0 1 100%



Blank 0 0 0 0%

TOTAL 1 0 1 100%

Female 0 0 0 0%

Male 1 0 1 100%



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%

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%




Veterans 0 0 0 0%


Drug Use 0 0 0 0%

PTSD 0 0 0 0%







TOTAL 0 0 0 0%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ THERMAL


Race

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Asian 0 0 0 0%



White 9 1 10 50%



Blank 0 0 0 0%

TOTAL 15 5 20 100%



Blank 0 1 1 6%

TOTAL 12 5 17 100%

Female 4 3 7 41%

Male 8 2 10 59%



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%

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%




Veterans 3 0 3 13%


Drug Use 3 0 3 13%

PTSD 3 0 3 13%







TOTAL 24 0 24 141%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ THOUSAND PALMS


Race

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Asian 0 0 0 0%



White 1 1 2 50%



Blank 0 0 0 0%

TOTAL 2 2 4 100%



Blank 0 0 0 0%

TOTAL 2 2 4 100%

Female 0 0 0 0%

Male 2 2 4 100%



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%

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%




Veterans 0 0 0 0%


Drug Use 1 0 1 13%

PTSD 1 0 1 13%







TOTAL 8 0 8 200%


Ethnicity

Gender

Age

Subpopulations

2017 Unsheltered Homeless Count Overall and Subpopulation Data ‐ VALLE VISTA


Race

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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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