Counting People Experiencing Homelessness with Animals: Using the PIT Count and HMIS as Tools

 The HUD required Point-In-Time Count occurs at least once every other year in an effort to enumerate individuals and households experiencing homelessness. Photo Credit: From the archived Medium account of  Julián Castro  - the former 16th U.S. Secretary of Housing and Urban Development.

The HUD required Point-In-Time Count occurs at least once every other year in an effort to enumerate individuals and households experiencing homelessness. Photo Credit: From the archived Medium account of Julián Castro - the former 16th U.S. Secretary of Housing and Urban Development.

The simplest and perhaps most difficult question that plagues advocates of people experiencing homeless with animals is “How big is the population?” An estimate given by Pets of the Homeless, a national organization dedicated to feeding and providing veterinary care to companion animals of the homeless, is 5-10%, and up to 24% in certain areas of the country (Pets of the Homeless, n.d.). Our direct experience and observation generally puts My Dog is My Home in agreement with this estimate. However, we also caution and admit that there is little quantitative research which can support our claim. A rigorous method for counting this population is sorely needed in our field. Accurately gaging how many people experiencing homelessness are caring for animals creates a basis for proving and quantifying need - in other words, understanding the scope of the problem helps us inquire where, how much, and why our resources should be allocated to serving this population. 

The recent uptick in interest in counting people who are homeless with animals has manifested in certain communities' Point-In-Time (PIT) Counts. PIT Counts are required by the US Department of Housing and Urban Development and are designed to enumerate homeless individuals. In 2016, Coalition for Homelessness Intervention and Prevention (CHIP), the Continuum of Care (CoC) to cover Indianapolis, IN, was the first known CoC to ask a question about pet ownership to all unsheltered individuals surveyed during their annual count. Since then, both the Los Angeles Homeless Services Authority (LAHSA) and the Toledo-Lucas County Homelessness Board (TLCHB) have also included a question about animal caretaking in their surveys.

In 2016, CHIP counted ten homeless households with a total of twenty-two companion animals between them, which accounted for 7.7% of the unsheltered homeless households surveyed within the vicinity of Indianapolis (Indiana University Public Policy Institute, 2016). In this year's count results, CHIP reported an increase to nineteen homeless households who are caring for a total of twenty-eight animals, constituting 15% of the homeless households surveyed (personal communication, June 7, 2017).

TLCHB reported lower numbers of homeless animal guardians, counting only one unsheltered household caring for an animal in the Toledo-Lucas County area (personal communication, February 14, 2017). However, this is a deceptively low result from the count. There were ten households with animals who attended a service fair for companion animals of the homeless, although none of the households fit the PIT Count outreach/engagement criteria - the households were neither street homeless nor utilizing a shelter program. 

The numbers for Los Angeles’ 2017 count have yet to be released. 

The buy-in already attained from these three large CoCs is a victory for advocates of people experiencing homelessness with animals. The PIT Count, however, only provides us with a starting point on which to continue our assessment of need; it is a convenient way to take a snapshot of homelessness and animal companionship, but that is wherein the limitation resides. It is simply a snapshot which relies on how many individuals are engaged by volunteers on a single date at the coldest time of the year. 

PIT Count estimates are often criticized for being an under-representation of homelessness, as the count of unsheltered individuals are designed to capture those who are street-dwelling. This can exclude those who are hiding in hard-to-survey places such as ATM vestibules, 24 hour restaurants, abandoned buildings, or underground tunnels. But there are few other reliable methods for estimating population size. Difficulty with counting can be attributed to unclear definitions of homelessness, transience, and the cyclical nature of homelessness (Institute of Medicine, 1988).

However, after decades of developing and redeveloping strategies, researchers have come to a consensus that “service-based methods” produce the most accurate enumeration of homeless individuals. Service-based methods refer to survey techniques that sample from or count people who are homeless in a variety of service system locations, such as shelters, soup kitchens, and day programs (Peressini, McDonald, & Hulchanski, 2009).   

In one study, researchers employed a service-based method to specifically study the population of people experiencing homelessness who were caring for animals in Knoxville, TN (Cronley, Strand, Patterson, & Gwaltney, 2009). They achieved this through their use of Homeless Management Information Systems (HMIS), an electronic database meant to facilitate the collection of accurate and streamlined data and the coordination of services among homeless service providers. Since the enactment of the HEARTH Act in 2009, all communities are now required operate and consistently participate in an HMIS (US Department of Housing and Urban Development [HUD], 2009). While using HMIS to quantify homelessness and animal companionship may provide a more accurate number, it may still provide an under-representation of homeless animal guardians due to its inability to capture information on individuals who do not engage with service providers. 

For example, researchers in Toledo, OH who interviewed human attendees of a service fair for companion animals of the homeless found that some participants had foreclosed on the idea of accessing services due to an anticipation that their pets would not be welcomed (Tscherne, Kim, & Hoy, 2017). Thus, the interview participants and the uncounted number of human-animal families like them are invisible to HMIS and to the homeless service system in general.

The study from Knoxville which used HMIS reported that 5.5% of the sampled individuals were caring for an animal. The study also found certain common qualities among people experiencing homelessness who were caring for an animal, characterizing this population in Knoxville as more likely to be female, Euro-American, married, experiencing homelessness for the first time, and possibly being homeless due to domestic violence. Chronically homeless individuals were the second most likely type of person to be caring for an animal. The study suggests that in light of this information, organizations serving the homeless should have intake questions which inquire about animal companionship if clients present any of the identified characteristics which make them more likely to be an animal guardian (Cronley et al., 2009).

The same study explores the significance of the population size. While 5.5% may seem like a small percentage, it does indicate that clinicians and other social service workers in the field of homelessness will encounter clients with animals at some point in their practice (Cronley et al., 2009). What’s more, therapists, substance use counselors, and others who specialize in certain areas who are working with homeless clients with companion animals may find their work on hold for longer, as their clients face additional barriers to eating, protecting themselves from the elements, and having a safe place to sleep. 

Year round, we advocate for better quantification of homelessness and animal companionship through HMIS - the most accurate way we know how to collect numbers on this population. But especially in the winter, we ask providers and the public to push for an accessible way to count people experiencing homelessness with animals - the PIT Count. The PIT Count is an existing tool than needs only one question to be added in order to begin collecting the data needed to better understand this issue. And the process of collecting such crucial data can come in the simple shape of a single check-box.