What is a list status?

By-name data list statuses are part of the Built for Zero methodology to identify how someone is showing up in the homeless response system. List statuses differ from population and subpopulation status, which are described in further detail here. Identifying and tracking the list status of each individual can help your community have a complete and accurate view of who is experiencing homelessness and how they are entering and exiting the system. Key terms related to tracking list statuses are listed below and can be found in more detail in the Built for Zero Reporting Reference Guide. 

Active: Individuals who are experiencing literal homelessness in your system as of the last day of the month. 

Inflow: The statuses that identify an individual who entered your homeless response system during a specific month or who was newly identified as part of a population or subpopulation during the month. Includes newly identified, returned from housing, and returned from inactive.

Newly Identified: The status used to identify any person who first began experiencing homelessness in your community* during the month or who met the criteria for a specific population during the month (i.e., become chronically homeless after entering homelessness as not chronic).

Returned to Active from Housed: The status used to identify any person who first began experiencing homelessness during the month and who was previously housed within your community’s geographic region.*

Returned to Active from Inactive: The status used to identify any person who first began experiencing homelessness during the month and who was previously experiencing homelessness within your community’s geographic region, but became inactive.* 

Outflow: The statuses that identify an individual as no longer active within your community’s geographic region, or as no longer part of a population or subpopulation during a specific month. Includes housed, inactive, or no longer meeting the population criteria of a specific population or subpopulation.

Housed: The status used to identify any person who was experiencing homelessness and was permanently housed during the reporting month. This includes people who were housed through the homeless response system as well as people who self-resolved their housing crisis. We recommend that communities identify which of the HMIS exit destinations (Universal Data Element 3.12 – Destination) indicates a permanent housing destination locally.

Inactive: The status used to identify any person who was experiencing homelessness but as of the last day of the month is no longer known to be experiencing homelessness in the area, per the community’s inactive policy. Read more about inactivity here.

No Longer Meets Population Criteria: This status is used to identify when a person’s population or subpopulation status changed over the course of the month and often indicates a change to their unique characteristics or housing situation. Read more about tracking population and subpopulation status here.

*Communities often establish a “lookback period” to determine the timeframe to consider if someone is  “Newly identified” or a “Return” to active homelessness, meaning that only those with a previous episode of homelessness that ended within the lookback period would be considered a return. While two years is a commonly used lookback period, to align with the 24-month HUD System Performance Measure, this is not prescriptive, and communities may adopt a timeframe that is most appropriate for their specific system, including a substantially longer period of time.

Why does tracking list statuses matter in the homeless response system?

Making sure each person is correctly identified in your by-name dataset is important for creating a trustworthy system and is a key step toward using data to improve the system. Tracking list statuses supports system coordination to get people housed quickly and to get them connected to the resources they need. Analyzing historical list statuses can shed light on the actual journey of people experiencing homelessness in your system and can highlight areas to be strengthened. 

Tracking list statuses helps a community to better understand how people are entering and exiting homelessness and can support system improvement. 

Example: ABC City can see that the number of veterans returning to active homelessness from housing has increased each month during the last six months. The ABC City team uses this data to convene a group of housing providers and people who have returned to homelessness after a period of housing to develop strategies to keep veterans successfully housed. This group reviews the data monthly to see if the strategies that they have put in place are making a difference in helping veterans maintain their housing.

It helps build a community’s trust in the homeless response system data.

Example: XYZ County has been monitoring and reporting homeless response system data for a few years and recently started a workgroup to focus on ending chronic homelessness. After noticing a decline in housing placements for people experiencing chronic homelessness, they decided to bring in more experts to gain a better understanding of the last six months of data indicating this downward trend. They invited the Lived Experience Advisory Board, housing providers, a medical services center, and others to the workgroup, and presented the data. The Lived Experience Advisory Board reported that the chronic verification process is confusing for people in the community, and the paperwork is difficult to obtain. The housing providers shared that the number of move-ins for people experiencing chronic homelessness has decreased because they aren’t able to get the necessary documentation to verify chronic status. The medical services provider reports that five months ago they lost their only physician who can complete disability verification forms and have struggled to fill that position. By bringing in diverse experts and perspectives, the workgroup was able to gain greater insight into what’s driving the reduction in housing placements for people experiencing chronic homelessness and build trust in the community. They were able to brainstorm new strategies to remove barriers to housing and support more individuals experiencing chronic homelessness into housing.

It helps to understand how a person’s previous experiences and episodes of homelessness impact their current status.

Example: MNOP Balance of State region just started to include a person’s historical list status in their current by-name dataset. Before, they were not able to see if someone had a previous experience of homelessnessness until they completed an assessment. With the additional information, the MNOP Balance of State team can identify how many individuals became inactive across the Balance of State, only to return to homelessness less than a year later. The team used this data to improve their inactive tracking processes and develop services to keep in contact with unhoused neighbors to ensure that folks were connected to resources quicker, before they became inactive. 

Methods for tracking list statuses 

Tracking list statuses can seem complex, but most communities already have the necessary data to get started. The section below is primarily for the people in your community tasked with creating data-tracking systems.

The mechanics of tracking list statuses

Start with HMIS data (Homeless Management Information System), focusing on key data elements, including, but not limited to:

• Program enrollments (focusing specifically on programs that serve literally homeless individuals)
• Entry and exit dates
• Veteran and disability status
• Approximate date homelessness started 
• Exit destination

This data helps identify who is experiencing homelessness and who is inflowing and outflowing. If your HMIS lacks comprehensive data, you may need to combine multiple data sources to track statuses.

Manual or automated approach?

Smaller communities may prefer to manually track list status

• Review the list monthly and identify inflow and outflow based on changes in the active list using HMIS or other enrollment data to identify inflow and outflow categories.

Larger communities may opt for a more automated process

• Excel Programs: Track inflow/outflow with HMIS program enrollment entry and exit data
• HMIS Reports: Leverage reports available in your HMIS system that show who is active during a specific period of time
• Custom Dashboards: Work with your HMIS team to develop a report or dashboard that tracks list status monthly
• Data Cleaning Tools: Combine datasets with Tableau Prep or similar tools and develop flows for cleaning and organizing the data

Each community will approach this process differently to develop a method specific to their system.

How does this relate to data reliability? 

Data reliability is a primary component of Quality Data and is strongly related to a community’s ability to track list statuses accurately. Data Reliability measures how far off your data is from being balanced — i.e., can you account for changes in the number of people on your active list in the inflow and outflow.