The annual Point-in-Time count of homelessness is an event that invites coverage from local outlets. It is an opportunity to report on the state of homelessness in a community and efforts to address it. This guidance is intended to equip journalists with background and questions to explore those topics more deeply.
Every year, communities that receive federal funding for homelessness conduct an annual census of people experiencing homelessness on a single night, called the Point-in-Time count. Annually, communities are required to do a count of people experiencing homelessness who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. Every other year, communities must conduct a count of unsheltered people experiencing homelessness.
These counts are conducted by a Continuum of Care, a local or regional planning organization that receives federal funding and works to coordinate efforts to end homelessness. Continuums of Care rely on groups of volunteers to conduct the count, which often occurs in late January. Within most communities, this is often a period of high media visibility and interest around the important issue of homelessness.
The period of conducting the Point-in-Time count, and when a community releases its results, is one moment where journalists explore questions like: what is the state of homelessness in our community, is the problem improving, what solutions are required to make progress?
It is important to understand the insights that the Point-in-Time Count can and cannot provide to answer these questions, and explore opportunities to incorporate other sources of information to support a comprehensive, accurate, and meaningful understanding of homelessness within a community.
How should the Point-in-Time Count results be used to paint a picture of homelessness within our community?
The Point-in-Time count is the only mandated count of all people experiencing homelessness that happens annually across the country. With that, journalists and community stakeholders have been equipped with yearly estimates to track trends at a large scale — both locally and nationally — over periods of time. The event is often a significant undertaking in terms of coordination and execution for any community, which involves staff within the homeless response system and the local Continuum of Care as well as volunteers.
Journalists should be cautious in framing the Point-in-Time count results as the complete and definitive picture of the scale of homelessness in a community. They should also explore whether the community has additional data sources to help paint a more comprehensive and accurate picture. The Point-in-Time Count is meant to serve as a snapshot of homelessness in a community and to provide a sense of the overall scope of homelessness in America. On its own, the Point-in-Time Count should not be referred to or used as a full and accurate picture of homelessness in a community.
The Point-in-Time (PIT) count relies on self-reported data and studies have found that its results can miss large segments of the homeless population. A 2017 report from the National Law Center on Homelessness found that most methodologies miss unsheltered homeless people and youth experiencing homelessness. In a 2019 study, researchers in El Paso, Texas, developed a Point-in-Time count methodology hypothesized to better estimate the number of persons experiencing homelessness. This was run parallel to the formal 2013 PIT count and the researchers identified nearly twice the number of unsheltered individuals in El Paso than what was reported through the formal Point-in-Time count.
To get that full and accurate picture, many communities are often also collecting higher quality data on a more frequent basis that reflects the dynamic nature of homelessness and is based on more meaningful interactions with unhoused neighbors. Due to very different methodological approaches, please be aware that it is not uncommon for there to be differences between a community’s quality data and what is reported as a result of the Point-in-Time count. In those cases, connect with local leaders to better understand why those differences might exist.
Questions to explore
1. How do local homeless response leaders feel the results should be contextualized?
2. How is the Point-in-Time count used by the local homeless response system to understand the scale and features of homelessness within the community?
3. Can those results be supplemented or complemented with any other data sources to increase confidence and meaning in what it is telling us?
4. Does the community have more comprehensive, real-time, verified, quality data on who is experiencing homelessness at any given time? More on this will be explored in the following section.
Journalists should understand the risks of making any comparisons of counts from one community to another, or from one year to the next, and either caveat or avoid making those comparisons when possible. Studies have raised that the Point-in-Time Count results are unreliable across Continuums of Care or over time within a community due to different methodologies utilized to conduct the counts. HUD offers different methodologies that communities can use to conduct the Point-in-Time count (for example, a census count, sampling and extrapolation, or a combination). These methodologies, and how they are implemented, are subject to high variability across local contexts and from one year to the next in the same community. As one example, one study conducted in 2016 looked across three cities (Houston, Denver, and New York City) and found that the amount of resources and volunteers invested in the Point-in-Time count varied widely, significant variation in how the count was conducted, and the use of different survey instruments.
During 2020, the United States Government Accountability Office reviewed efforts to measure homelessness and recommended quality assurance checks on the PIT count methodology data that HUD requires Continuums of Care to submit and take actions as appropriate to ensure that HUD’s standards for conducting valid and reliable PIT counts are met. The report also noted that some communities’ total and unsheltered PIT counts have large year-over-year fluctuations, which raise questions about data accuracy.
Questions to explore
1. What methodology is the community employing?
2. Has the same methodology always been used?
3. What methodology was used in any year or community that is being used as part of a comparison?
4. Can those results be supplemented or complemented with any other data sources to increase confidence and meaning in what it is telling us?
Journalists should be clear that the Point-in-Time count results are from one single night, which — depending on when the results are released — can often be dated back several months earlier. The Annual Homeless Assessment Report (AHAR), which contains a nationwide reporting of the Point-in-Time count, is often published by HUD nearly a year after the Point-in-Count time is conducted. The scale of homelessness changes night over and night, and organizations are working day to day to respond to the evolving problem. As such, it is valuable to remind readers that results reflect an estimate of homelessness from a time period that may be dated a few months, to a year, prior.
What data might we seek out to paint a deeper and more comprehensive understanding of the scale of homelessness in a community?
Homelessness is a dynamic problem that changes every night, like many other public health challenges. Solving it requires not only a clear picture of who is experiencing it at any given time, but details on who each of those individuals are and what supports they need to exit homelessness. This means that communities need both person-centered data and a dynamic view of the problem at a population level, and how it is changing.
Achieving this kind of real-time, comprehensive and quality data has long been proven possible in other fields like public health. In the past few years, communities across the country have proven that it is possible to achieve a more rigorous and meaningful understanding of homelessness in their community.
EXAMPLE: BY-NAME DATA
By-name data is a real-time, person-specific list of everyone experiencing homelessness — including those who are sheltered and unsheltered — which provides communities with a full and up-to-date view of homelessness in their geography.
By-name data is updated in real time (at least monthly) and helps communities answer important questions like: How many people became homeless for the first time this month? How many were people returning to homelessness? How many people exited from homelessness? Are the experiences of people moving through the system equitable?
Specifically, communities can look at the following data points:
- Inflow. When a person enters into homelessness
- Actively homeless. People who are currently experiencing homelessness
- Outflow. People who have exited homelessness
Using this data, communities are able to better serve individuals by providing tailored solutions to fit their individual needs, understand the broader patterns of homelessness in their area, and more effectively allocate resources and efforts toward ending homelessness locally.
Questions to explore
1. Does the community have more comprehensive, real-time, quality data on who is experiencing homelessness at any given time?
2. Does the community know how many people are experiencing homelessness at any given time? How has that number changed month over month? Is it going up or down?
3. Does the community understand why that number is what it is — how much if it is being impacted by inflow into homelessness and how much of it is being impacted by outflow?
4. Can the community disaggregate data by race and ethnicity to understand and respond to disparities?
5. Who is collaborating in service of achieving that goal?
6. Does the community have a goal to reduce homelessness?
7. Who is collaborating in service of achieving that goal?
8. How is the community measuring success toward its goal? Is it making progress and how would we know?
How might we use the Point-in-Time count to launch a deeper understanding of the community’s progress?
You can’t solve a problem that you can’t see, and getting a clear, dynamic understanding of homelessness is the first step to solving it. The Point-in-Time count is an opportunity to paint a broader picture of the community’s goals around solving homelessness, and the progress it is making toward that important aim.
The homeless response system is complex and operates across a system that can include any number of the following actors: the local continuum of care, non profits, city and county agencies or authorities, local elected leaders like mayors and county executives, shelter providers, faith-based organizations, and the local VA medical center.
Identifying whether the community has a shared aim that ensures effective collaboration across this system, and a measure for understanding success toward it — and what role the Point-in-Time count plays — can offer valuable context for readers.
The 2021 Menino Survey of Mayors, conducted by Boston University, found that a strong majority of mayors do not define policy success in terms of reducing homelessness, and a surprising number of mayors do not have clear definitions for success.
Some communities have a goal to measurably solve homelessness for an entire population, making it rare and brief. This may be measured using the functional zero definition, or by the federal government’s benchmarks and criteria, or both.
Questions to explore
1. What is the community’s goal in solving homelessness? Is it measurable and equitable?
2. Who is collaborating in service of achieving that goal?
3. Does the community have a goal to reduce homelessness?
4. How is the community measuring success toward its goal? Is it making progress and how would we know?