Trying to place ‘most of the homeless’ into different categories of deserving and undeserving poor is a common element in virtually every conversation or debate about homelessness, poverty and poverty survivors. The numbers are often sliced, diced and presented in a dozen different ways, making comparative analysis and logical conclusions difficult (at best).

This is my attempt to collect current data (available freely through the internet) and present the numbers in a reasonably easy-to-understand manner.

The primary question being answered: Who are the homeless?

Data Differences

I’ve included a list of links to key sources of data on Homelessness at the end of this post. All of these resources are academically respected and frequently cited in articles and other forms of research. Unfortunately, the data presented often contains inconsistencies that must be identified and addressed before completing a truly effective analysis or a clear presentation of high-level data. These inconsistencies do not negate the quality of the data or the effectiveness of the research, they are simply the natural outcome of a data-collection survey that (literally) examines millions of people.

For the purposes of this post, I have decided to focus on presenting the data contained within a single data source: HUD Exchange.

Step 1: Analysis of Data Collection Techniques

It’s important to begin the analysis by getting an understanding of the methods used during collection. An examination of the HUD Point in Time (PIT) Count Implementation Tools provides the following key details:

Data Collection Personnel consist of average people (volunteers), professionals in the ‘Helping Services’ (e.g.: homeless shelter workers), formerly homeless people and currently homeless people. According to the Tips For Including People Experiencing Homelessness (PDF) reference sheet, formerly and currently homeless are employed as Subject Matter Experts (SME) and may or may not be paid for their assistance.

Pre-Selected Data Categories outlined in the PIT Count Planning Worksheet (PDF) are detailed, extensive and specific. The data that collection personnel are expecting to find and, therefore, seeking out is clearly defined. The data outlined in the Sub-population Crosswalk (PDF) survey instrument is limited and specific. The data collection personnel are expecting to find and, therefore, seeking out is both clearly defined and restrictive, presenting the possibility of missed data points (e.g.: people not included in the count because they are not ‘real’ homeless) or inflated/inaccurate data points (e.g.: placing people in non-applicable categories for the sole purpose of including the data somewhere). The data points contained within the Sub-population Crosswalk (PDF) survey instrument are as follows:

• Chronically Homeless Individuals or Families (based on family head of household)
• Veteran
• Adults with a Serious Mental Illness
• Adults with a Substance Use Disorder
• Adults with HIV/AIDS
• Victims of Domestic Violence

Database Affected Data Points are a possibility due to the nature of the data contained with the Homeless Management Information System (HMIS), as outlined in the Sheltered PIT Count and HMIS Data Element Crosswalk (PDF) guide. Based on the categories and subcategories of data contained within the database, the expectations surrounding the situations of all homeless are clearly defined. This presents the possibility of inaccurate or inflated data points resulting from workers trying to find a way to enter data into the database.

Flexibility of Toolkit in Data Collection suggests that more extensive and (potentially) accurate data is being collected than may (or may not) be found within the databases. The Point In Time (PIT) Survey Tools (HTML) include forms that specifically address situations where the individuals conducting the survey are unable to talk to the individuals being counted and the answers include ‘unsure’ or ‘unknown.’ In other words, a data point may consist of a family that is found sleeping outside, but the collector is unable to access the location or communicate with the individuals in question (e.g.: does not want to wake them up, cannot speak their language, etc.) so it is not possible to verify whether the family is truly homeless or dealing with some other situation.

Inherent Data Collection Problems are centered around evaluating the entirety of a poverty survivors situation through distant observation or a single face-to-face interaction. Accurately identifying an age or race can be extremely difficult under these circumstances. Correctly evaluating mental health and assessing whether or not an individuation meets the ‘chronically homeless’ definition are nearly impossible.

Inherent Data Collection Strengths are in the total count of human bodies. The PIT count provides a total number of people who are living on the street or in shelters during a specific period of time. While exact ages are difficult to pinpoint, total numbers of individuals falling within per-defined age ranges are reasonably reliable. Total numbers of family groups and children, teens, adults and the elderly who are trying to survive the streets alone are also reasonably reliable. Therefore, the strength is in the reasonable reliability of the high-level total counts.

Step 2: General Examination of Raw Data

Positive: The HUD data is clear and easy to decipher. It provides total counts for high-level categories, divided by state and geographic region.

Negative: The revisions tab lists changes to historic data that have occurred since 2007 (earliest available data). The changes listed are significant. However, the changes are also limited to select portions of historic data and do not indicate that equally significant changes will be made throughout all bodies of data.

Step 3: Data Analysis

Based on my analysis of the data collection methods, I focused on the strongest data points available.

Percentages: All percentages are a comparison to the total number of homeless people in the United States during the 2016 PIT count. Because the totals change, depending on the data being presented, the specific totals used to generate the percentages are included at the top of each chart followed by 100%.

Children and Youth: The data provided by HUD does not provide a high-level total count of all children and/or youth included in the PIT count. There are several subcategories focusing on children and youth and I have summed these categories to create a rough total, but I suspect this number represents BOTH overlap in data categories and a a significantly deflated total. Without knowing the total number of children and youth that are included in the total number of people ‘In Families’ it’s impossible to calculate the total number of children and youth.

Data Sources

• US Census Bureau:
  • Poverty Data Tools
  •  National Survey of Homeless Assistance Providers and Clients (NSHAPC)
• National Alliance to End Homelessness:
  • The State of Homelessness in America 2015
• World Bank:
  • Open Data Catalog
  •  Data Bank
• Quandl.com: Provides access to global raw data provided by government agencies and similarly data-heavy and highly reputable sources.
  • USA Economic Data
  • USA Labor, Employment and Productivity Data and Government Data.
• Local Studies (every state and most towns in the USA have one):
  • Minnesota Homelessness Study
• Veterans Administration (VA)
  • Homelessness Initiative
  • Statistics
• The U.S. Department of Housing and Urban Development (HUD)
  • Point In Time (PIT) and HIC Guides and Tools
  • CoC Homeless Populations and Subpopulations Reports
  • Homelessness Data Exchange
• The United States Inter-agency Council on Homelessness (USICH) uses the HUD PIT reports in their annual report to congress

Data Provided by Homeless Services Providers (smaller scale)

• National Coalition for Homeless Veterans: Statistics
• Covenant House: Teen Homelessness Statistics
• Homeless World Cup: Global Homelessness Statistics