Questions about homelessness

Homelessness fundamentals

  • Homelessness takes many forms - including many that are “invisible”. Through Title 24 of the Code of Federal Regulations (CFR), Section 578.3, the federal government defines homelessness in four parts: Those who are “literally homeless”, those who are at imminent risk of experiencing literal homelessness, those fleeing or attempting to flee domestic violence, those who are defined as homeless in other federal statutes

  • Homelessness is not always visible or easily recognizable. People experiencing homelessness may be living in shelters, staying temporarily with others, sleeping in vehicles, or moving between unstable situations. Many do not fit common public perceptions. They are part of our communities: neighbors, coworkers, and family members, and their experiences often go unseen.

  • A home is more than a physical structure like an apartment or a house. At its core, a home is a place that offers stability, safety, and a sense of security—where someone can keep their belongings, rest without fear, and maintain dignity in daily life. A home does not have to follow a single architectural or legal model; it can take many forms as long as it provides privacy, safety, and some degree of personal control. Equally important is whether a person’s living situation allows them to belong and participate in their community. From this perspective, housing is about creating the conditions for stability and well-being, not just meeting a technical definition.

  • Homelessness often unfolds in stages rather than happening all at once. People may start by being unstably housed, then experience short-term or transitional homelessness. If homelessness happens repeatedly, it can become episodic, and in some cases it becomes chronic—meaning long-term or recurring homelessness, often alongside health or disability challenges that make stability harder to regain. People can move between these stages depending on their circumstances and the support available to them. Early intervention is critical because helping people access stable, affordable housing sooner can prevent homelessness from becoming more frequent, prolonged, and difficult to resolve. Acting early not only improves individual outcomes, but also strengthens community resilience and reduces long-term costs.

Measuring & counting homelessness

  • A Point-in-Time (PIT) count is a snapshot taken on a single night each year to estimate how many people are experiencing homelessness. While it provides useful information, it has important limitations. Because it captures only one moment in time, it often misses people who experience homelessness temporarily or episodically—such as those who are couch surfing, staying briefly with friends, or entering and exiting homelessness throughout the year. As a result, PIT counts tend to underestimate the true scale of homelessness and may overlook less visible forms of housing instability.

  • Key metrics include the total number of people experiencing homelessness, the subcategories of vulnerable populations (such as minors, veterans, survivors of domestic violence), and the rate at which people become homeless or exit homelessness. Tracking these helps identify trends and areas for intervention.

  • “Per capita” allows us to compare places with different population sizes in a consistent way. For example, the same number of people experiencing homelessness can represent very different conditions depending on the size of the community. By standardizing the measure (typically per 100,000 people), we can better understand and compare rates across regions. This helps make differences between places more visible and easier to interpret.

  • HMIS is a database system used by service agencies to track people who receive assistance related to housing instability. It helps provide a more accurate count of homelessness over time but raises privacy issues and still has challenges like avoiding double-counting individuals.

Data quality & methodology

  • Lunous uses publicly available data from sources such as the U.S. Department of Housing and Urban Development (HUD) and the Washington State Department of Commerce.

    This includes the annual Point-in-Time (PIT) count, which estimates the number of people experiencing homelessness on a single night each year. While this provides a consistent baseline, it is widely understood to undercount the full scale of homelessness, particularly less visible forms.

    Additional systems, such as the Homeless Management Information System (HMIS), offer more detailed data over time, though access is often limited due to privacy protections.

    Lunous prioritizes transparency about data sources, methods, and limitations to support clearer understanding and decision-making.

  • Homelessness and housing are complex, and without reliable data it’s hard to know what’s actually happening, or what’s working. Trustworthy data that is accurate, transparent, and up to date helps everyone work from the same understanding of the problem. It allows communities to see how many people are experiencing homelessness, what housing is available, and where the biggest gaps are. Good data also makes it possible to track progress over time, learn from what works, and adjust strategies so efforts have the greatest impact.

  • Data on homelessness comes from many sources, and those sources don’t always measure the same things or tell the same story. Some data is based on a single day, some methods differ across regions, and some forms of homelessness are hard to see or count. This makes the issue complex and easy to misunderstand. Transparency—about where data comes from, how it’s analyzed, and what its limits are—helps build trust and shared understanding. Rather than relying on “black box” conclusions, transparent data allows people to see assumptions, compare sources, and ask better questions. Lunous uses this approach to support clearer analysis, accessible visualizations, and informed community conversations about housing needs and solutions.

  • Reliability refers to the consistency of data across different sources, how much agreement there is when the same question is asked more than once, while accuracy is about how close data is to the true value. Reliability does not guarantee accuracy and vice versa.

  • Completeness refers to whether the data set covers all relevant regions or groups, rather than just certain areas or segments. Incomplete data requires “stitching together” multiple sources to build a fuller picture, which can still leave important gaps.

  • Maintaining consistency prevents confusion and ensures that the story told by the data is coherent. If definitions like Area Median Income (AMI) change from one part of the data to another (for example, from individual to household AMI), it can cause contradictions that confuse users and weaken trust in the findings. Consistent definitions help users follow the narrative and make sense of the entire picture, instead of leaving them to speculate about discrepancies.

Housing market dynamics

  • Affordable housing means housing that costs no more than 30% of a household’s income. It isn’t limited to subsidized housing—it includes any home that people can realistically afford based on what they earn. Because incomes vary widely within a community, looking only at average numbers can hide who is actually being priced out. To truly understand affordability, it’s important to look at housing costs alongside different income levels, especially for those most at risk of homelessness.

  • Housing affordability is shaped by several interconnected factors, including the supply of available homes, the level of demand, household incomes, vacancy rates, and how much of a household’s income is spent on housing costs (often called “rent burden”). Understanding affordability also depends on how well these factors are measured and analyzed, since incomplete or inconsistent data can make it harder to see where housing pressures are greatest and who is most affected.

  • Area Median Income (AMI) is the income level where half of households in a community earn less and half earn more. Unlike average income, which can be skewed by very high earners, AMI provides a more realistic picture of what people in a specific area can actually afford. AMI is often broken into ranges—such as 100%, 80%, or 50% of AMI—to describe different income groups and help determine housing needs, program eligibility, and planning priorities. While AMI is a useful benchmark, Lunous looks beyond a single number by examining how incomes are distributed across the community. This broader view helps reveal who is most affected by housing costs and where affordability gaps truly exist.

  • Housing works like any other market: when there aren’t enough affordable homes and many people need them, some people are pushed out of housing altogether. This mismatch is one of the main drivers of homelessness. Vacancy rates help show how available housing really is, but when supply remains too low, even well-intended programs struggle to keep up. Increasing the number of affordable homes—and aligning housing options with what people can actually afford—is one of the most direct ways to reduce the risk of homelessness and create long-term stability for communities.

  • Housing supply refers to the total number of homes that exist, while demand refers to how many people need housing. Availability describes how many of those homes are actually open for people to move into at a given time. Availability alone can be misleading: a high number of available units might mean there’s plenty of housing, or it could mean fewer people can afford what’s available. Likewise, low availability could reflect strong demand, limited supply, or both. Looking at supply, demand, and availability together helps provide a clearer understanding of what’s really happening in a housing market.

Data quality & methodology

  • A causal loop is a system dynamics tool used to visualize how different factors influence one another. In the context of homelessness, it helps illustrate how variables like housing availability, barriers to exiting homelessness, and community health interact to affect the number of people experiencing homelessness.

  • DMAIC stands for Define, Measure, Analyze, Improve, and Control. It’s a Six Sigma process for continuous improvement. In this context, it’s used to assess the effectiveness of interventions in reducing homelessness, enabling data-driven decisions and improvements.