by Ashwin Parulkar
Senior Research Specialist


In 2022, 5,645 people experienced homelessness in Las Vegas and 51% were unsheltered (City of Las Vegas, 2023b; HHH, 2022). Las Vegas has the lowest number of affordable and available rental units  for low-income renters (14 per 100) and one of the highest rates of “cost-burdened” poor renters among U.S. cities: 75% of renters with incomes 80% or less area median income (AMI) levels pay more than 30% of their earnings on rent (National Low Income Housing Coalition, 2020). Extreme poverty (7.8%) and unemployment rates (6.0% in April 2023) are also consistently higher than national rates (U.S. Bureau Labor statistics, n.d U.S. Census Bureau, n.d.).  

This study examined causes of homelessness in Las Vegas through a survey of unsheltered clients of a homeless services center in the city.   It applied a Latent Class Analysis (LCA) to  measures of primary drivers of homelessness (1 variable), substance use & health problems (7 variables), adverse childhood experiences (ACEs) (7 variables) and incarceration (1 variable)to identify patterns of entering homelessness.  LCA is a probabilistic model that identifies different groups  of people, or “classes”, in a sample based on patterns of individual responses to multiple “categorical” questions (Tsai et al, 2013; Weller et al, 2020).  The final model yielded three “classes”. The largest group, class 1 (n=239; 66%), became homeless due to unemployment and had the lowest rates of ACEs and health and substance problems. Class 2 (n=40; 11%) entered homelessness due to family problems and had high rates of multiple ACEs. Class 3 (n=81; 23%) were most likely to enter homelessness due to health problems or the loss of housing (e.g., eviction), and had higher rates of substance use and mental health problems. 

Unemployment — the major cause of homelessness – is linked to the dynamics of extreme poverty and housing unaffordability in the city. This study has three recommendations.  First, The Courtyard should implement a robust employment and job placement program to rapidly stabilize clients who entered homelessness due to a job loss but had fewer additional needs (class 1). Second, the city should increase the number of full-serviced transitional housing facilities, which can facilitate housing placements of clients with complex needs (classes 2 and 3). Finally, to reduce cost-burdens among low-income renters, the city should enable multi-year leases and implement tenant protection policies.


Maurice Cloutier and Chibudom Yanez  of the Las Vegas government assembled volunteers to administer this study’s survey. They also tested the survey and provided feedback that proved critical to its final design.  Survey volunteers included employees of the Neighborhood Services department and several local nonprofits that work with people experiencing homelessness.

Shamonique Lawes, Executive Director or HELP USA’s Las Vegas programs,  also assembled survey volunteers. Members of HELP USA’s research department,  Kathryn Cardone, Data Analyst, and Steven McFeely, Performance Measurement Analyst, administered surveys.  

Kathi Thomas Gibson, Director of Neighborhood Services, Las Vegas, and Daniel C. Farrell, Chief Operating Officer, HELP USA, made this work possible. 

  1. Introduction

Since the late 20th century, the shortage of affordable rental units for the poorest Americans  and the “deep” national poverty rate have dramatically increased. Both factors  contributed to the onset, then rise of homelessness in the country (Aldridge et al., 2017; Borchard, 1997, 2005; Fargo et al., 2013; Filer et al., 1993; Hanratty, 2017; Hopper et al., 1985; HUD, 2017; Hwang, 2018; Lee, 2016; McChesney, 1990; 1995; O’Flaherty, 2004; Schuetz & Ring, 2021; Shlay & Rossi, 1992;Shinn & Khadduri, 2020).  

The number of affordable rentals for extremely low-income renters dwindled from 42 to 33 units for every 100 poor renters from the late 1990s to 2022  (National Low Income Housing Coalition, 2023.,Nelson et al., 2003; Shinn and Khadduri, 2020: 47). The share of households in “deep poverty” – those with incomes less than 50% of the federal poverty line –  increased from 29% in 1968 to 47% in 2017 (Fontenot, 2018; Haveman et al, 2015; Shinn & Khadduri, 2020:40). In 2022, approximately 582,000 people experienced homelessness in the United States (HUD, 2023).

Nationally, housing prices surged beyond median income growth after the Great Recession (2009 to 2019) (Harvard, 2022:5).  Many higher income earners could not afford to own homes and were forced into the rental market (Harvard, 2022:5).  This shift increased the size of the rental market by 70%.  The share of high-income earners to the total rental population also rose, from 20% to 26%.  Lastly,  rental prices in large cities hiked beyond poor households’ abilities to afford even low-end market rates  (Harvard, 2022:5).

In this context, researchers have found that higher rates of homelessness occur in America’s largest cities with the priciest rental markets, such as New York and Los Angeles (Colburn & Aldern, 2022; Hanratty, 2017; Harvard, 2017; Shinn & Khadduri, 2020). Yet, the problems of unaffordable rents, high extreme poverty levels,  and higher incidences of homelessness are concentrated in western cities that  are undergoing rapid population growth (Watson et al., 2017; Shinn & Khadduri, 2020:48; Woetzl et al., 2016; Harvard, 2022; Seymour and Akers, 2021). In 2015, only 30 affordable units were available for poor renters in the west ( 38 to 43 units were available in other regions) (Watson et al., 2017; Shinn & Khadduri, 2020: 48). Homelessness increased by 42% in California between 2014 and 2020, even as it declined nationally by 9% in these years (Streeter, 2022)

In Las Vegas, homelessness declined from 9,949 persons in 2010 (a 0.51% rate of homelessness) to 5,283 persons in 2020 (0.25%) (HUD, 2022). During this time, the national rate of homelessness declined from 0.21 to 0.18. In this context, Las Vegas is similar to some western cities, like San Diego and Santa Ana, that have (1) high rent, poverty and population growth levels and (2) consistently high rates of homelessness, relative to the national rate. 

Las Vegas’ median one-bedroom rental price was 60% higher than the national average in 2017, its extreme poverty rate (7.8%) was higher than the national rate (5.5%) in 2021, and its population grew by 8% from 2010 to 2017 (U.S. Bureau of Labor Statistics, n.d., U.S. Census Bureau; HUD, n.d;). 

The unaffordability of rental housing, the shortage in the supply of this type of housing, and the high rate of extreme poverty in Las Vegas comprise the major risks that poor people in the city face  to homelessness in the city. These inequalities are partly the outcome of policy choices that curtailed housing and viable employment options for poor households, and particularly single adults.  

Las Vegas is one of the most “cost-burdened” cities in the U.S. That is, it has one of the highest rates of people with low incomes who pay between 30% to 50% of their earnings on rent. In Clark Country, for example, 75% of renter households with incomes 80% or less than the area median income (AMI) pay more than 30% of their earnings on rent; 40% of this group pay more than half of their income on rent (National Low Income Housing Coalition, 2020; Seymour and Akers, 2021). While Nevada has the lowest number of affordable rental stock available for its poorest renters (18 per 100), Las Vegas has even less — only 14 units for 100 such renters (ibid).  Only 4% of renters received HUD rental assistance (Harvard, 2022: 36)

Two factors partly explain the city’s high extreme poverty levels. First, over one-fourth (28%) of the workers are  “precarious[ly] employed” in “low-skilled” jobs in the hospitality and leisure industry (the city’s dominant economic sector) (Seymour and Akers, 2021). Second, these jobs are  subject to stiff competition by a large migrant workforce (Borchard, 1997; Seymour & Akers, 2021). Unemployment in Las Vegas (6.0%) is, indeed, higher than the national rate (3.5%) (U.S. Bureau Labor statistics, n.d.).

The Great Recession impaired Las Vegas’ housing and rental markets. The city had the highest foreclosure rate in the country at the peak of the crisis (12 percent, five times the national average), which was concentrated in Black and Latinx communities (Bocian et al, 2010; Maycock and Malacrida, 2018; Seymour and Akers, 2022; Wargo, 2010).  The city’s homeownership rate (54%) continues to lag behind the national rate (~64%) (U.S. Bureau of Labor Statistics, 2021; Schuetz, 2019).

Increasingly, people who lost their homes to foreclosures entered the rental market (Mallach, 2014; Schuetz, 2019). In 2009, private investors also began seizing foreclosed properties and converting them into market-rate rental units (Andrews & Sisson, 2018; Semuels, 2019; Seymour & Akers, 2021). These events spiked rental prices (which reduced access to housing for poorer renters) and increased evictions (and the housing insecurity of poor renters too) (ibid). 

In this context, researchers found that the trend in homelessness in Las Vegas conformed to macroeconomic conditions induced by the Great Recession and its recovery (Schuetz & Ring, 2021). That is, the total number of persons experiencing homelessness increased from 2008 to 2010, then declined throughout the decade (Schuetz & Ring, 2021). Yet, the city’s rate of homelessness remained higher than the national average from 2010 to 2020 (HUD, 2022). As well, unsheltered homelessness increased in Las Vegas, in numerous western cities, and nationally throughout during that period (Batko et al, 2020).  In Las Vegas, and other western cities, Black and Latinx people are overrepresented in unsheltered populations and nearly one-third are women (ibid).  In Last Vegas, the share of unsheltered to total number of persons experiencing homelessness increased from 30% in 2010 to 66% in 2020 (HUD, 2022). The total number of persons experiencing homelessness rose to 5,645 in 2022 as the share unsheltered persons decreased to 51% (City of Las Vegas, 2023b). The rise in unsheltered homelessness in Las Vegas and other western cities correlated with three housing and shelter supply constraints: higher rents and lower rental unit vacancy rates; lower numbers of affordable rental units per 100 extremely low-income households; and lower bed capacities in shelter systems (Batko et al, 2020).  In 2018, 8.9% of rental units were vacant in Vegas (0.8% decline from 2010) (ibid). From 2009 to 2019, Las Vegas eradicated 71% of its shelter beds (4080 beds) (ibid). The shortage of beds in Las Vegas shelters undermines access for the single homeless population, which comprise 90% of homeless population (Cohen et al, 2019; Schuetz & Ring, 2021). 

We can now summarize the trend of homelessness in Las Vegas that frames this study’s approach to understanding its causes.

The total number of persons experiencing homelessness in the city declined last decade ( 2010-2020) as the share of unsheltered persons in this population increased. These trends occurred under the following conditions:

  • Staggering shortages of affordable rental units  for the poorest individuals and families
  • scarce federal rental subsidies
  • high risks, among the city’s working poor , to job insecurity and unemployment
  •  increasingly limited capacities of shelters to serve the homeless, as the number of beds in these facilities have dwindled. 

We also need to also understand the mental and physical health burdens, substance use challenges, and histories of foster care involvement and incarceration in this population.  That is because unsheltered persons exhibit higher incidences of  these travails than the sheltered homeless population.   Mental health burdens, substance problems, and overdose deaths are especially high in homeless populations in western cities, where rates of total and unsheltered homelessness are also high and shelter beds and psychiatric facilities are few (Montgomery et al., 2016; Batko et al, 2020; Liu & Hwang, 2021; Liu et al, 2021; Batko et al, 2020; Nicholas et al, 2021; Streeter, 2022). Unsheltered persons with mental illness and substance use disorders in Las Vegas endure more difficulties than others in navigating the city’s local homeless service system (Cohen et al, 2019).

In this context, adverse childhood experiences (ACEs) –  abuse and deaths of parents, for example –  are more prevalent among people experiencing homelessness than in the general population (Liu and Hwang,2021; Lieu et al, 2021). These traumas are potentially associated with higher levels of substance use, mental and physical health illnesses in this population (ibid; Danese & McEwen, 2012; Estevez et al, 2020; Lupien et al, 2009 ). ACEs can directly impair long-term health because traumas “disrupt functional brain development,…activate inflammatory pathways, and shorten telomere length”, resulting in both “disease” and “cognitive deficits” (Lieu et al, 2021).  Outcomes from ACEs that are linked to homelessness include broken family bonds and limited social support (Herman et al, 1997; Shelton et al, 2009; Lieu et al, 2021). One review of 29 studies, comprising 16,942 individuals from the US, Canada and the UK, found that the lifetime prevalence of one or more ACEs among the homeless was 89.8% (Liu et al, 2021). By comparison, the global general population had 38-39% exposure to one or more ACEs (ibid).

In 2017, Las Vegas created The Courtyard Homeless Resource Center to address the needs of the unsheltered population. The Neighborhood Services department manages the center. In 2022, officials provided medical, transportation, social, and housing & shelter placement services to over 6,081 visitors (City of Las Vegas, 2023a). It also provides a place for clients to sleep that has an awning, air-conditioning, bedding and sanitation facilities.  In 2022, 1216 individuals accessed medical services, 1121 received assistance to secure identity documents, 798 received benefits or income assistance, and 242 received mental health or substance abuse services. Additionally, 302 people accessed housing services, including referrals to community housing programs (124), homelessness prevention services (87),  placements in flexible housing programs (54), and rental assistance support (37).  Lastly, 139 people were referred to non-congregate shelters (City of Las Vegas, 2023a)

2. Research Question

Three socio-economic contexts, discussed in the introduction, frame the research question. They are (i) potential structural drivers of homelessness in Las Vegas (rent-burden and extreme poverty) (ii) (bi-directional) risk factors of health burdens and substance use problems associated with unsheltered homelessness and (iii) adverse childhood experiences (ACEs) associated with homelessness.  In this context, this study’s research question was:

What are the major causes of homelessness in Las Vegas ?

Inherently, the study also sought to understand specific pathways into homelessness, such as places of origin,  types of residences they lived in before homelessness,  and experiences in institutions, such as incarceration. In this context, a sub-question was:

What are the pathways into homelessness among clients of The Courtyard?

3. Methods

3.1.1 survey design

In January 2023, HELP USA agreed to conduct a study on the causes of homelessness in Las Vegas for local officials.

We designed a survey based on national and regional survey models and best practices.     HUD’s “combined” “public places” and “service-based” method focuses on surveying unsheltered populations in areas where non-shelter organizations serve persons experiencing homeless (HUD, 2008:45-50). Its survey applies “screener” questions to identify persons whose circumstances meet the federal definition of homelessness. Screeners also prevent duplicated counts, which hinder an accurate analysis of survey results. Major HUD questions are on causes of homelessness, disabilities, substance abuse, and health related issues.  HUD also recommends a “brief” “training for all volunteers…immediately before the count” (HUD, 2008:31).

A San Jose survey used  HUDs questions, but added two more on mental health (Smith and Castaneda Tinoco, 2019).  Its volunteers underwent a rigorous two month research methods training. 

A Calgary Homeless Foundation’s  Rehousing Triage and Assessment Survey (RTAS) was based on studies on unsheltered homelessness, particularly in Boston (Calgary Homeless Foundation, 2009).  This survey emphasized demographics, housing & homelessness histories, physical & mental health burdens,  and substance use problems and recommended assigning a planning committee to identify volunteers to conduct the survey and map out the area beforehand.

Lastly, a case study analysis on formerly homeless persons in New York with serious mental illnesses and substance abuse histories examined “adverse life events” to understand the “cumulative influence” of traumas on entering homelessness (Padgett et al, 2012: 421).   

Our survey incorporated each source. It contained an “informed consent” clause and  “screeners”, followed by questions on  demographics, family histories & adverse childhood experiences, primary causes of homelessness, institutionalization, and histories with substance use problems, mental health burdens, and treatment (Calgary Homeless Foundation; HUD, 2008; Padgett et al, 2012; Smith and Castaneda Tinoco, 2019).  

3.1.2 survey training

Three of these sources also informed our three-step training program (HUD, 2008; Smith and Castaneda Tinoco, 2019; and Calgary Homeless Foundation).

First,  two members in the Neighborhood Services Department anchored the planning committee. These officials assembled 20 local government and NGO survey volunteers, based on prior experiences that these individuals had in working with the city’s unsheltered homeless population.

Second, HELP trained the planning committee and volunteers in two remote sessions in February 2023.  Trainings covered the survey questions and pre-survey planning needs. These planning tasks included screening the Courtyard and informing the clients and residents about the survey;  designating a room near The Courtyard for HELP and Las Vegas teams to meet before and coordinate during the survey; acquiring tablets for volunteers to administer the online survey; and securing gift cards, as incentives, for respondents.

Third, volunteers tested the survey among unsheltered persons near The Courtyard in mid-February. Planning committee members’ feedback from the test round focused on reordering and rephrasing some questions. Several questions, particularly on adverse childhood experiences, covered sensitive issues so the committee’s “client-centered” feedback was intended to avoid “triggering” respondents. We accepted all feedback. For example, we added a “refuse to answer” choice for each question.

3.2 Administering the survey

3.2.1 participants: understanding The Courtyard clients

Over 20 volunteers conducted the survey in The Courtyard on February 23, 2022. Participants included employees from the following agencies:

  • Las Vegas Neighborhood Services Department
  • local community-based organizations

The team administered 400 administered surveys. In total, 360 (90%)were completed and useable for analysis.  We disqualified 40 surveys in cases where respondents:

  • did not meet the federal definition of homelessness (qualifier questions)
  • selected “refuse to answer” in several fields or
  • did not answer questions (missing data). 

In 2022, Courtyard clients represented 4.1% (n=233) of the federal “point in time” count (PIT) of persons experiencing homelessness in Las Vegas  (n= 5,645) and 8% of the unsheltered population (Help Hope Home (HHH), 2022).

There are key similarities between the PIT survey and our survey on single adult homeless persons (90%, HHH 2022; 89.43% our survey),  mental health burdens (33%, HHH, 2022; 32.2% our survey), and persons that have experienced domestic violence (3%, HHH, 2022; 1.6% our survey).

There were also critical differences. 

In the 2022 PIT count, adults (over 24) represented 88% of the homeless population. Children (under 18) comprised 6% of that surveys(PIT, 2019; 2022). The Courtyard is an adult space. In our sample, 98.89% of respondents were over 24 (all were above 18). Their average age was 50.1 years.

In the 2022 PIT count, 51% of the population was unsheltered.    The Courtyard caters to the unsheltered population. One of our survey questions asked “Where are you sleeping tonight?” Over half of our respondents (53.06%) said they were sleeping at the Courtyard that night. One-quarter (25.28%) stated they would sleep in an emergency shelter,  13.61% in an unsheltered location that was not the Courtyard, and 8% at “other” locations.

3.3 Measures

The following sections provide details on the measures of this survey. They include questions on the primary drivers of homelessness, substance use & health problems, adverse childhood experiences and incarceration

3.3.1 primary drivers of homelessness

This measure identified the issue that most directly caused homelessness. The question was “What is the primary cause of your homelessness (the issue that immediately precipitated your first instance of homelessness)?” 

It contained eighteen  unique answers, which included “other (please specify)” and “refuse to answer”). Sixteen individual answers (in quotes below) were related to eight factors (in bold).

  • employment (“job loss/couldn’t maintain housing payment”; “unable to get a job”; “had/have a job but couldn’t afford housing”)
  • institutional (“left state foster care system”; “released from an institution”) 
  • benefits access  (“loss of public assistance/aid”)
  • family problems (“death or loss of a household member”; “family discord”)
  • health & substance use problems (“medical/health problems”; “mental health problems”; “drug or alcohol problems”)
  • housing instability (“lost home I owned through foreclosure”; “lost home I rented through landlord foreclosure”)
  • violence (“domestic abuse/violence”; “communal/political/social violence before becoming homeless”) and
  • citizenship (“deportation/immigration/seeking asylum”).  

Next, we moved relevant “write-in” choices  from the “other (please specify)” field, into appropriate fields or created new fields. We then combined relevant individual answer choices together, which  yielded a final list of 10 answer choices  (Table 1). We will elaborate on the definitions of answer choices variables in findings section.

primary causen%
family problems (discord or loss/death of household member)4713.10%
health problems (physical, mental and substance related)5013.90%
loss of housing (foreclosure, eviction, burned down, affordability)4111.40%
loss of public assistance or identification documents133.60%
relocation/deportation/immigration/seeking asylum143.90%
released from institution123.30%
personal issues92.50%
other or refused to answer215.80%

Table 1: primary causes of homelessness

3.3.2 health & substance use problems

Substance use and health problems increase risks to entering homelessness and risks to mortality during homelessness (Calgary Homeless Foundation, 2009; Hwang, et al, 1998; O’Connell et al, 2005).  According to the 2020 Las Vegas point in time count, 49% of persons experiencing homeless had a substance use disorder and 44% had a mental health burden These are bi-directional risk factors (Doran et al, 2022). 

Eight questions on substance use and health problems measured the extent to which respondents ever had:

  • a physical disability (n=118; 32.8%)
  • abused drugs or alcohol (n=156; 43.3%)
  • used injection drugs (n=25 6.9%)
  • received treatment for alcohol or drug abuse (n=83; 23.1%)
  • ever experienced psychotic symptoms, such as hallucinations or delusions (n=54; 14.7%)
  • received treatment for mental health issues (n=116; 32.2%)
  • been hospitalized for mental health reasons against their will (n=50; 13.9%)

Rates of substance use and mental health challenges are significantly higher than in the general population. For example,

3.3.3 adverse childhood experiences (ACE)

The 2020 Las Vegas had established that 99.3% of the population was above the age of 18 (Las Vegas PIT, 2020). Researchers have established that persons experiencing homelessness have had significantly higher ACEs (before age 18) than the general population (Lieu et al, 2021). ACEs in the adult homeless population is also associated with major depressive disorders, substance use problems (ibid).

Seven binary ACE questions (Padgett et al, 2012) measured the extent to which respondents:

  • had ever failed a grade or dropped out of school (n=133; 36.9%)
  • grew up while one or both parents experienced unemployment (n=115; 31.9%)
  • had ever been in the foster care system (n=53; 14.7%)
  • were abandoned by one or both parents  (n=88; 24.4%)
  • experienced the death of a mother or stepmother (n=46; 12.8%)
  • experienced the death of a father or stepfather (n=56; 15.6%)
  • were ever physically abused by a parent or guardian (n=82; 22.8%)

In total, 73.3% (n=264) of respondents experienced at least one ACE.

3.3.4 incarceration

Persons with serious mental illnesses have significantly high lifetime rates of arrests (between 42%-50%) (Shinn & Khadduri 2020:64).  Incarcerated persons have four to five times the rate of serious psychological distress than non-incarcerated persons (14% for prison inmates,  26% for jail inmates, and 4-5% in general population)  (Bronson & Berzofsky, 2017; Shinn & Khadduri 2020:64). Lastly,  “employer.. biases… against former offenders” also “make it harder for persons with arrest records to … earn enough to avoid homelessness” (Shinn & Khadduri, 2020: 64). 

In this context, one survey question measured the extent to which The Courtyard population had ever been incarcerated in a jail or prison (n=229; 63.6%).

3.4 Method of Analysis: Latent Class Analysis (LCA)

This study applied Latent Class Analysis (LCA) to the above measures (section 3.3) to locate distinct patterns of entering homelessness.  LCA is a probabilistic model that identifies different groups  of people, or “classes”, in a sample based on patterns of individual responses to multiple “categorical” questions (Tsai et al, 2013; Weller et al, 2020). The responses are “observed” measures. That is, one can quantify each indicator  because each respondent has answered each question that was directly posed to them.  The model’s “classes”, however,  represent “unobserved” – or, “latent – measures because  they arise from statistically significant combinations of the “observed” measures (B.O. Muthen & L.K Muthen, 2000; Wolke et al., 2013; Weller et al, 2020). The “latent classes”, therefore, represent the sample’s heterogeneity (Hagenaars & McCutcheon, 2002; Weller et al, 2020).  In this context, the LCA assumes that one can locate at least two classes in a sample (Barile, 2018). The model also assumes that “membership” in classes can explain patterns of responses across a survey (Barile, 2018; B.O. Muthen & Muthen, 2000; Wolke et al., 2013; Weller et al, 2020). This study employed the LCA to identify specific patterns of the observed measures (section 3.3) associated with entering homelessness in Las Vegas. Each observed measure pertained to the following major categories: adverse experiences, incarceration, primary drivers of homelessness, substance use & health problems, .

The optimal number of classes in an LCA – the best model fit  – is determined by the following recommended statistical criterion: 

  • low Bayesian Information Criterion (BIC) (Killian et al, 2019; Schwarz, 1978; Weller et al, 2020)
  • low Akaike Information Criterion (AIC) (Akaike, 1974; Weller et al, 2020)
  • low sample size adjusted Bayesian Information(Weller et al 2020)
  • significant Vuong-Lo-Mendell-Rubin (VLMR)  adjusted likelihood test values (p<0.05)
  • significant bootstrapped likelihood (BL) test values (p<0.05)  (Lo et al., 2001; McLachlan & Peel, 2000; Nyland et al., 2007; Vuong, 1989; Weller et al., 2020).
  • High entropy values (Tsai et al., 2013; Weller et al., 2020; Wang, 2017)
  • Robust class size, in which the smallest class should contain at least 10% of the total sample (Tsai et al., 2013)

Two conditions can yield a best fitting model. One condition is the class solution that yields the lowest BIC, AIC and sample-size adjusted BIC (hereafter adjust BIC), the highest entropy values (optimally between 0.8 and 1.0), significant levels of both likelihood test statistics (p-values < 0.05) (Celeux & Soromenho, 1996; Weller et al, 2020). 

Another permissible condition is the class solution that yields the lowest SABC and AIC, highest entropy values and significant levels of likelihood test statistics (Weller et al, 2020). The SABC is “superior” to the “combination of…AIC and BIC” (Nylund, Asparouhov, & B.O. Muthen, 2007:537)

3. Findings

The two-class solution produced the lowest BIC (6896.37) with  significant Vuon Lo Mendell Rubin (VLMR) and bootstrapped likelihood ratio test values (p<0.05). The three-class solution produced the lowest adjusted BIC (6716.01) and AIC (6663.19), significant likelihood test values (p<0.05), and highest entropy (0.867).  We therefore concluded that the three-class solution was the best fitting LCA model for this sample  (Table 1 in Appendix).  No viable class solution was found above the three-class trial.

The three-class solution’ two-tailed tests (in the LCA’s probability scale) also revealed statistically significant differences (p<0.01) between each class’s and the sample’s endorsement of the seventeen variables.  

Respondents of class 1 (n=239; 66%) exhibited the highest probabilities among members of all classes that unemployment (45.8%)  and violence (11%)  were the primary drivers of their homelessness (see Figures 1-4 in Appendix). 

Let us elaborate on the ‘unemployment’ and ‘violence’ variables, which we briefly discussed in section 2.3. ‘Unemployment’ included two types of job precarity that caused homelessness. One type was the loss of a job that led to  the inability to make a housing payment. The other type was the inability to find a job. Both conditions (italicized) were listed as individual answer choices in the survey’s question on the primary driver of homelessness (section 3.3.1). As part of our coding process, we combined both answers into one “unemployment” choice under the “homelessness causes” variable for the LCA analysis.  

‘Violence’ entailed domestic violence as well as  communal, political or social violence that took place before the respondent became homeless.  Similarly, both types of violence (italicized) were listed as individual responses under the question we have just discussed. We combined both types into one “violence” choice in the same question.

Class 1 also had the lowest rates of all adverse childhood experiences and health and substance problems compared to other classes. Lower prevalence rates of adverse experiences included foster care system involvement (7.4%); and being abandoned (9.5%) or abused  (7.8%) by a parent (Table 4). This group had significantly lower rates (p<0.01) of ever having had a disability (30%), a history of substance use problems (28.3%) , and experiences of treatment in a substance use (8.4%) or mental health program (12.1%).

Persons from this class were also least likely to have experienced psychosis (2.5%) or hospitalization for a mental health condition (1.7%). But these differences were not statistically significant (p=0.128 and p=0.216).  In summary, this class exhibited the highest likelihood of entering homelessness due to unemployment and violence with the least likelihood of encountering adverse experiences during childhood, or health and substance use problems before entering homelessness. We call this class Unemployment and violence for short.

Class 2 respondents (n=40; 11%) had the highest probability of being driven into homelessness by  family problems  (21.9%). They also  had a relatively high probability – compared to class 3 –  of experiencing a form of violence that caused homelessness (10%).

Class 2 also recorded the highest rate of ever having a physical disability (51.8%). Members of this group had the highest exposure to six adverse experiences:  ever having (i) failed a grade or dropped out of school (45.5%), (ii) endured a period of childhood in which a parent experienced unemployment (62.7%), (iii) been in the foster care system (49.8%), (iv) experienced the death of one’s mother before the age of 18 (22.5%); (v) been incarcerated (71.9%) and (v) experienced parental abuse (81%). 

Members of class 2 also had moderate levels of mental health burdens. Moderate, here, means significantly higher rates than the sample size but lower rates than at least one of the two other classes. For example,  28% of class 2  had been hospitalized for a mental health disorder against their will compared to 1.7% of members in class 1 and 40.2% members of class 3 ( total sample =13.9% ). Additionally, 55.9% of class 2 had received mental health treatment compared to 12.1% of those in class 1 and 75.3% in class 2 (total sample = 32.2% ).   In summary, this class exhibited the highest likelihood of entering homelessness due to family problems, with the highest likelihood of enduring multiple adverse childhood experiences and incarceration. We call this class  family problems, disabilities, and multiple adverse childhood experiences (ACEs) .

Class 3  (n=81;23%) members  were most likely to report that health problems (25.8%) and the loss of housing (13.9%) caused their homelessness. We must also briefly discuss how we defined this study’s “health challenges” and “loss of housing” indicators.  Answer choices to the survey’s causes of homelessness question included (i) “medical/health problems”, (ii)“drug or alcohol problems”, (iii) “mental health problems”, (iv) “had/have a job but can’t afford housing” (v) “lost home I rented through landlord foreclosure” (vi) “lost home I owned through foreclosure”. Additionally, several answers that were accompanied by the “other” option included write-in responses pertaining to (vii) an eviction and (viii) the loss of housing due to a fire.  We recoded responses (i) to (iii) into one “health problems” field. We recoded responses (iv) to (viii) into one “loss of housing” field. The “loss of housing” indicator implies that an external forced caused the  exit from home, which differs from other types of exits captured by the “family problems” or, even, “personal reasons” fields.  

Respondents of class 3 also had a much higher likelihood of being abandoned by a guardian (30.4%) than their class 1 counterparts (class 2’s 100% probability of being abandoned was not significant).  This group had the highest rate of  experiencing the death of a father during childhood (23.2%).  It also had higher rates of all substance use and mental health-related problems. These include histories of a substance use problem (85%), using injection drugs (25.8%), psychosis (47.3%),  being treated in a substance use  or mental health program (72.4% and 75.3%), and ever having been hospitalized in a mental health facility against one’s will (40.2%). In summary, this class exhibited the highest likelihood of entering homelessness due to health problems and the loss of housing (due to external forces; e.g. eviction) with the highest likelihood of substance use and mental health problems. We call this class health and substance use problems with histories of abandonment and paternal death

Now, let’s discuss critical differences between classes across demographics:  gender, race & ethnicity,  primary language, places of origin,   regions and the types of residences respondents had lived in before entering homelessness. “Places of origin” refer to eight categories. Four categories are regions of the United States grouped by the states in that location (midwest, northeast, southwest, and southeast) . The west was denoted by three categories. “West (not Nevada)” refers to all western states excluding Nevada.  “Nevada” refers to persons who grew up only in Nevada. “West (not Nevada) + Nevada” refers to persons who grew up in Nevada and in another western state.  “Outside the U.S.” refers to persons who grew up in a country other than the United States .

The classes represent discrete combinations of immediate causes, early life traumas, and health burdens associated with entering homelessness.  It is necessary to analyze racial dynamics with respect to these economic and social “patterns” for two reasons. First, Latinx and Black persons are overrepresented in the city’s unsheltered homeless populations (Batko, Oneto & Shroyer, 2020). Second, both communities were disproportionately affected by the foreclosure crisis (Schuetz, 2019). It is also  necessary to locate geographic and mobility dynamics across classes.  Only 14.49% of the survey’s respondents grew up in Nevada.   We need to identify relationships between economic & social drivers of homelessness  and the regions & housing types that people lived in before they became homeless (Table 2 – Appendix).

In this context, people  who entered homelessness due to family problems after multiple early life traumas (class 2) were more likely to be female (37.5%), non-Hispanic White (43.24%), and to have lived with friends immediately before entering homelessness.  They were much less likely to be Latinx (10.81%).

People in class 3 (health and substance problems with histories of abandonment and paternal death) were also more likely to be non-Hispanic White (42.5%; p<0.01). They were  less likely to have grown up outside the United States (5.13%; p<0.05)  but more likely to have resided in a U.S. state outside Nevada (45.68%) immediately before becoming homeless.  They were also less likely to claim Spanish as their primary language (4.94%; p<0.01).

By comparison, class 1 members (unemployment & violence) – the reference group –  were more likely to hail from Latinx communities (28.39%). While they were more likely to have immigrated to the United States (17.37%) than members of other classes, they were also more likely to have resided in Clarke County, Nevada, immediately before entering homelessness (68.62%). 

4. Discussion

Two-thirds of the population (66%) entered homelessness due to immediate economic distress, such as  unemployment  (class 1),  and had fewer early life traumas (ACEs) and  chronic health maladies.   This finding implies that major structural inequalities in Las Vegas, which  disproportionately harm poor people, can cause homelessness. Let’s discuss the interplay of these structural risk factors.

First, the lack of formal benefits associated with low-skilled jobs in Las Vegas and the  stiff competition for these gigs among migrant workers potentially leads to  high rates of unemployment and extreme poverty in the city (Borchard, 1997; Seymore & Akers, 2021; U.S. Bureau Labor statistics, n.d.; U.S. census bureau, n.d.). As discussed, members of class 1 were more likely to hail from immigrant and Latinx communities that had lived in Clarke Country immediately before entering homelessness. These patterns are, potentially, linked to the Great Recession’s higher foreclosure rates among minority communities.

Second, unemployment among the working class can greatly increase the risk to homelessness amid the city’s vast shortage of affordable rental units for poor renters. As discussed, Las Vegas has the highest such   shortage among all major U.S. cities (Seymore & Akers, 2021).  The city’s “financialized” rental market prices have surged beyond income growth while policymakers’ have failed to produce enough affordable units for poor people or ensure that existing renters can access federal rental subsidies (Harvard, 2022; Schuetz, 2019; Seymore & Akers, 2021; Seymore, 2022). The loss of a job under such instability can, as respondents indicated, immediately prevent one from making a housing payment or lead to chronic unemployment. Further, the dwindling supply of beds  in Las Vegas’ shelter system can lead to unsheltered homelessness (Batko et al, 2019; Cohen et al, 2019 )

About one-fourth of respondents (23%) – class 3 –  were more likely (i) to enter homelessness due to health problems; (ii) have higher levels of substance use and mental health burdens; and (iii) to have been abandoned by a parent and experienced the death of their father during childhood (two of this study’s ACEs). In this context, substance use & mental health burdens and homelessness are potentially bi-directional risks in these respondents’ lives  (Doran et al, 2022).  As discussed, a higher lifetime prevalence of ACEs among adults experiencing homelessness positively correlate incidences of severe mental health and substance use problems in this population (Doran et al, 2022; Liu and Hwang,2021; Lieu et al, 2021).

 These respondents were also more likely than others to report that (i) the loss of housing caused homelessness, (ii) they had lived in another US state immediately before entering homelessness, and (iii) they had grown up in the United States itself. It is therefore possible that conditions of housing instability that led to homelessness among this group occurred in another state and influenced their migration.  About 38.5% of members of class 3 hailed from a western state other than Nevada. By comparison, 28.8% of class 1 members and 29.0% of class 2 members were from this region. The influx of people from LA and Orange Country to Nevada during 2000 to 2010 accounted for 56% of the state’s in-migration during those years but “only wealthier people” were able to comfortably secure housing (Seymore & Akers, 2021).  Members of class 3 may be acutely vulnerable to unsheltered homelessness compared to those in other classes. As discussed, people with mental illnesses and substance use disorders face more difficulties than others in  accessing the local homeless service system in Las Vegas (Cohen et al, 2019).

About one-tenth ( 11%) of respondents were more likely to (i)  report that family problems caused their homelessness, (ii) to have had a physical disability, and  (iii) to have experienced more, and – aggregately – greater degrees of traumatic childhood events (ACEs) than other classes (class 2).  The prevalence of ACEs among adults experiencing homelessness are associated with higher rates of mental health and substance burdens in the population (observed in class 3) as well as physical health maladies and family problems (that lead to homelessness) (observed in class 2) (Lieu et al 2021).  Members of this group were also more likely to be female and to have lived with friends immediately before entering homelessness. This potentially indicates that these respondents’ experiences of traumas (childhood abuse) and poverty (parental unemployment) in childhood was linked to lack of social support in adulthood preceding or associated with becoming homeless (Herman et al, 1997; Shelton et al, 2009; Lieu et al, 2021).

Lastly, class two was more likely than other classes to have experienced incarceration. But two points are worth mentioning. First, the incarceration rate of the sample was extremely high (63.6%). Second, previous studies found no significant correlation between ACEs and incarceration in the homeless population  (Lieu et al, 2021).  This was potentially because both factors are high risk factors for homelessness (ibid).

5. Conclusions & Recommendations

This study has shown that most (66%) of the Courtyard population entered homelessness due to unemployment, with fewer  early life traumas (ACEs) and  chronic health maladies. The dependence of  poor people, especially migrants, on low-skilled jobs, for income, and limited and expensive private rental stock, for housing, are major drivers of homelessness in Las Vegas.  The affordable rental shortage is a problem for “cost-burdened” families and individuals,  persons experiencing homelessness and local officials trying to solve the affordable crisis. This study has three recommendations for the city of Las Vegas.

First, the Courtyard should focus on employment and job placement services. This may help people who became homelessness due, primarily, to unemployment (class 1) to secure and retain housing.  Second,  the city should increase the number of transitional housing sites in the city. This model should cater to the single adult homeless population and provide employment and job placement, housing placement, and harm reduction-based substance abuse services. Case management services should also link clients to benefits and healthcare services.  The other mentioned services  can address the mental health, substance use and trauma  that afflict over one third of this surveyed population (classes 2 and 3). The Courtyard currently links clients to health and substance use services. This study recommends that the city fund trauma-informed care models  in The Courtyard and transitional housing sites (Lieu et al, 2021).

Finally, the city needs to reduce the deficit in affordable rental units for the poorest renters. This is a huge task. It will require (i) addressing the volatility of the local housing market, (ii) increasing housing ownership rates, particularly among members of the Black and Latinx communities, and (iii) reducing the cost-burden or poor renters.  Items (i) and (ii) are beyond the scope of this study.  On reducing cost-burden, this study recommends enabling multi-year leases – that include pre-determined annual rent increases – and policies that provide tenant protections, such as help with mediating landlord disputes to prevent evictions  (Schuetz, 2019).

Tables and Figures

Table 2:  fit statistic criteria for LCA analysis

class solutionBICSABIC (low)AIC (low)VLMR valueVLMR pBLR pentropy
class 26896.376740.916705.95364.7170.000.000.784
class 36950.776716.016663.19592.130.0480.000.867

Figure 1: primary causes of homelessness by class

Figure 2: health and substance use measures by class

Figure 3: incarceration rates by class

Figure 4: adverse childhood experiences (ACE) by class

Table 2: significant demographic characteristics by class

GenderUnemployment & violence (class 1)Family conflict & multiple ACEs (class 2)Health & substance use burdens/abandonment and paternal death (class 3)Sample
race & ethnicity    
White (non-Hispanic)27.12%43.24%*0.425^32.29%
primary language    
place of origin (region)    
Outside US17.37%5.26%0.0513^12.78%
region before homelessness    
Clarke County68.62%62.50%0.5185^64.17%
Nevada (outside CC)2.51%5.00%2.47%2.78%
out of State28.87%32.50%0.4568*33.06%
accommodation before homelessness    
staying w friends9.21%0.225*7.41%10.28%
* = p<0.05; ^ = p<0.01


H. Akaike. (1974).  A new look at the statistical model identification, in IEEE Transactions on                             Automatic Control, vol. 19, no. 6, December, pp. 716-723.

Aldridge, R., Story, A., Hwang, S., Nordentoft, M., Luchenski, S., Hartwell, G., Tweed, E., Lewer, D., Katikireddi, S., & Hayward, A. (2017). Morbidity and mortality in homeless individuals, prisoners, sex workers, and individuals with substance use disorders in high-income countries: A systematic review and meta-analysis. The Lancet. 391. doi: 10.1016/S0140-6736(17)31869-X

Andrews, J. & Sisson, P. (2018). Wall Street’s new housing frontier: Single-family homes. Curbed, 18 May. Retrieved:

Barile, J.P., Smith Pruitt, A., & Parker, J. (2018). A latent class analysis of self-identified reasons for experiencing homelessness: Opportunities for prevention. J Community Soc Psychol; 28: 94-107.

Batko, S., Oneto, A.D., & Shroyer, A. (2020). Unsheltered Homelessness: Trends, Characteristics, and Homeless Histories. Urban Institute

Bocian, D.G., Wei Li, & Ernst, K. (2010). Foreclosures by Race and Ethnicity: The demographics of a crisis. Center for Responsible Lending report. Retrieved:

Borchard, Kurt B. (1997). An ethnography of homeless men in Las Vegas”. UNLV Retrospective Theses & Dissertations. 3037. doi: 10.25669/7b4w-7ccn

Borchard, Kurt. (2005). The word on the street: Homeless men in Las Vegas. University of Nevada Press.

Bronson, J. & Berzofsky, M. (2017). Indicators of mental problems reported by prisoners and jail inmates, 2011-2012 (NCJ250612). U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics. Retrieved:

Calgary Homeless Foundation. (2009).  Rehousing Triage and Assessment Survey Toolkit. Calgary Homeless Foundation.

Celeux G., Soromenho G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195-212.

Cohen, R., Yetvin, W., & Khadduri, J. (2019). Understanding Encampments of People Experiencing Homelessness and Community Responses: Emerging Evidence as of Late 2018. SSRN Electronic Journal. doi: 10.2139/ssrn.3615828.

City of Las Vegas, Nevada. (2023a). Courtyard Homeless Resource Center: January – December 2022. (the city provided this data for this study)

City of Las Vegas, Nevada. (2023b). HOME-ARP Allocation Plan. Retrieved:

Danese A. & McEwen B.S.(2012) Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiol Behav ; 106: 29–39.

Doran, K.M., Elswick Fockele, C., & Maguire, M. (2022). Overdose and Homelessness – Why We Need to Talk About Housing. JAMA Netw Open. 5(1):e2142685.

Dunlap, E., & Johnson, B.D. (1992). Setting for the Crack Era: Macro Forces, Microconsequences (1960-1992) (author manuscript). Available at National Institute of Health Public Access, pp 1-21; Published in final edited form in Journal of Psychoctic  Drugs, 24(4):307-32.

Esteves KC, Jones CW, Wade M, et al.(2020). Adverse childhood experiences: implications for offspring telomere length and psychopathology. Am J Psychiatry; 177: 47–57.

Fargo, J., Munley, E., Byrne, T., Montgomery, A., & Culhane, D. (2013). Community-Level Characteristics Associated With Variation in Rates of Homelessness Among Families and Single Adults. American Journal of Public Health. doi: 10.2105/AJPH.2013.301619 

Filer, Randall & Honig, Marjorie. (1993). Causes of Intercity Variation, 1993 in Homelessness. American Economic Review. 83. 248-55.

Fontentot, K. Semega, J., & Kollar, M. (2018). Income and poverty in the United States. (PGO-263). Washington, DC: United States Census Bureau

Hagenaars J. A., McCutcheon A. L. (2002). Applied latent class analysis. Cambridge University Press.

Hanratty, Maria. (2017). Do Local Economic Conditions Affect Homelessness? Impact of Area Housing Market Factors, Unemployment, and Poverty on Community Homeless Rates. Housing Policy Debate. doi: 10.1080/10511482.2017.1282885

Harvard University, Joint Center for Housing Studies. (2017). America’s Rental Housing 2017

Harvard University, Joint Center for Housing Studies. (2022). America’s Rental Housing 2017

Haveman, R., Blank, R., Moffitt, R., Smeeding, T., & Wallace, G. (2015). The war on poverty :50 years later. Journal of Policy Analysis and Management, 34(3), 593-638.

Help Hope Home. (2022). Homelessness in Southern Nevada: 2020 Homeless Point-in-Time Count & Survey. Retrieved:

Herman D.B., Susser E.S., Struening E.L., & Link BL. (1997). Adverse childhood experiences: are they risk factors for adult homelessness?Am J Public Health 1997; 87: 249–55.

Hopper, K., Susser, E., & Conover, S. (1985). Economies of Makeshift: Deindustrialization and Homelessness in New York City. Urban Anthropology. 14:183-236.

Hwang, Stephen. (2018). Permanent Supportive Housing: Evaluating the Evidence for Improving Health Outcomes Among People Experiencing Chronic Homelessness.

Killian M. O., Cimino A. N., Weller B. E., Hyun Seo C. (2019). A systematic review of latent variable mixture modeling research in social work journals. Journal of Evidence-Based Social Work, 16(2), 192-210.

Lee, Barrett. (2016). Determinants of Homelessness in Metropolitan Areas. Journal of Urban Affairs. doi: 10.1111/1467-9906.00168

Liu M, Hwang SW. Health care for homeless people. (2021). Nat Rev Dis Primers; 7: 5

Liu, M., Linh, L., Lachaud, J., Edalati, H., Reeves, A., Hwang, S.W. (2021). Adverse childhood experiences and related outcomes among adults experiencing homelessness: a systematic review and meta-analysis. Lancet Public Health, 6:e836-47

Lo Y., Mendell N., Rubin D. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767-778.

Lupien SJ, McEwen BS, Gunnar MR, Heim C. (2009). Effects of stress throughout lifespan on the brain, behaviour and cognition. Nat Rev Neurosci 2009; 10: 434–45.

Mallach, A. (2014). Lessons from Las Vegas: Housing markets, neighborhoods, and distressed single-family property investors. Housing Policy Debate, 24(4), 769-801.

Mayock, Tom and Rachel Malacrida. (2018). Socioeconomic and racial disparities in the financial returns to homeownership. Regional Science and Urban Economics 70: 80-96.

McChesney K.Y. (1995). A Review of the Empirical Literature on Contemporary Urban Homeless Families. Soc. Serv. Rev. 69:429-460. doi: 10.1086/604134.

McChesney K.Y.(1990).  Family Homelessness: A Systemic Problem. J. Soc. Issues. 46:191-205. doi: 10.1111/j.1540-4560.1990.tb01806.x.

McLachlan G., Peel D. (2000). Finite mixture models. Wiley.

Montgomery, A.E., Szymkowiak, D., Marcus, J., Howard, P., and Culhane,D.P. (2016). Homelessness, Unsheltered Status, and Risk Factors for Mortality: Findings from the 100,000 Homes Campaign. Public Health Reports 131 (6): 765–72.

Muthén B. O., Muthén L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical & Experimental Research, 24(6), 882-891

National Low Income Housing Coalition. (2020). The gap: A shortage of affordable homes. Washington, DC:

National Low Income Housing Coalition. (2022). The gap: a shortage of affordable homes. Washington, DC.

National Low Income Housing Coalition. (2023). The gap: a shortage of affordable homes. Washington, DC. Retrieved:,income%20(whichever%20is%20greater).

Nelson, K.P., Vandenbroucke, D.A., Lubell, J.M., Schroder, M.D., & Rieger, A. (2003). Trends in worst case needs for housing, 1978-1999: A report to congress needs. Retrieved:

Nicholas W, Greenwell L, Henwood BF, Simon P. (2021). Using Point-in-Time Homeless Counts to Monitor Mortality Trends Among People Experiencing Homelessness in Los Angeles County, California, 2015‒2019. Am J Public Health. Dec;111(12):2212-2222

Nylund K. L., Asparouhov T., Muthén B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535-569.

O’Flaherty, B. (2004). Wrong person and wrong place: for homelessness, the conjunction is what matters. Journal of Housing Economics. 13. 1-15. doi:10.1016/j.jhe.2003.12.0

Padgett, D. K., Smith, B. T., Henwood, B. F., & Tiderington, E. (2012). Life

course adversity in the lives of formerly homeless persons with serious

mental illness: Context and meaning. American Journal of Orthopsychiatry, 82(3), 421–430.

Schuetz, J. (2019). Should Las Vegas Bet on Homeownership? Trends in Housing Affordability and Ownership. The Brookings Institution.  Retrieved:

Schuetz, J., & Ring, M. (2021). How Did Homelessness Change During the Schuetz & Ring, 2021?. The Brookings Institution. 1-10. Retrieved:

Shwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2) 461-        463.

Semuels, A. (2019). When Wall Street is your landlord. The Atlantic, 13 February. Retrieved:

Seymore, E. (2022). Corporate Landlords and Pandemic and Prepandemic Evictions in Las Vegas, Housing Policy Debate

Seymour, E. & Akers, J. (2021). Our customer is America: Housing insecurity and eviction in Las Vegas, Nevada’s postcrisis rental markets. Housing Policy Debate, 31 (3-5), 516-539.

Shelton K.H., Taylor P.J., Bonner A, & van den Bree M. (2009). Risk factors for homelessness: evidence from a population-based study. Psychiatr Serv; 60: 465–72.

Shinn, M. and Khadduri, J. (2020). In the Midst of Plenty: Homelessness and What to Do About it. Hoboken: Wiley Blackwell

Shlay, Anne & Rossi, Peter. (1992). Social Science Research and Contemporary Studies of Homelessness. Annual Review of Sociology 18:129-60.

Smith, C. & Castaneda-Tinoco. (2018). Improving Homeless Point-In-Time Counts: Uncovering the Marginally Housed. Social Currents; 6(2):91-104

Tsai, J., Kasprow, W. J., & Rosenheck, R. A. (2013). Latent homeless risk profiles of a national sample of homeless veterans and

their relation to program referral and admission patterns. American Journal of Public Health, 103(S2), S239–S247.

Streeter, J.L. (2022). Homelessness in California: Causes and policy considerations. Stanford University Institute for Economic Policy Research (SIEPR), May, 1-13

U.S. Bureau of Labor Statistics. (n.d.). Economy at a Glance: Las Vegas – Paradise, NV. U.S. Bureau of Labor Statistics.

U.S. Census Bureau (n.d.). American Community Survey (2017-2021).  Retrieved:

U.S. Department of Housing and Urban Development (HUD). (2017). The 2016 Annual Homeless Assessment Report (AHAR) to Congress. United States, 2017. Washington, DC: HUD.

U.S. Department of Housing and Urban Development (HUD). (2008). A Guide to Counting Unsheltered Homeless People. Washington, DC: HUD

U.S. Department of Housing and Urban Development (HUD). (2022). 2021 AHAR: Part 1 – PIT Estimates of Homelessness in the U.S. Retrieved:

U.S. Department of Housing and Urban Development (HUD). (n.d.). 50th percentile rent estimates. Retrieved:

U.S. Interagency Council on Homelessness (USICH). (2022). ALL IN: The Federal Strategic Plan to Prevent and End Homelessness. Washington, DC. Retrieved from

Vuong Q. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307-333.

Wang M.-C., Deng Q., Bi X., Ye H., Yang W. (2017). Performance of the entropy as an index of classification accuracy in latent profile analysis: A Monte Carlo simulation study. Acta Psychologica Sinica, 49(11), 1473-1482.

Wargo, B. (2010). Las Vegas leads nation in foreclosures for 2009. Las Vegas Sun

Watson, N.E., Steffan, B.L., Martin, M., & Vandenbroucke, D.A. (2017). Worst case housing needs: 2017 report to Congress. Retrieved:

Weller, B.E., Bowen, N.K., & Flaubert, S.J. (2020). Latent Class Analysis: A Guide to Best Practice. Journal of Black Psychology. 46(4). Accessed online:

Woetzel, J., Mischke, J., Peloquin, S., & Weisfield, D. (2016). A tool kit to close California’s Housing Gap: 3.5 Million Homes by 2025. McKinsey Global Institute. Retrieved From:

Wolke D., Copeland W. E., Angold A., Costello E. J. (2013). Impact of bullying in childhood on adult health, wealth, crime, and social outcomes. Psychological Sciences, 24(10), 1958-1970.

Photo Inquiry