Homeless Count Data
Point-in-time counts, shelter utilization, and service enrollment -- the homelessness data cities use to compete for $5B in HUD funding.
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Find Me This Data →Overview
What Is Homeless Count Data?
Homeless Count Data encompasses point-in-time (PIT) counts, shelter utilization metrics, and service enrollment records that cities and regions collect to document and address homelessness. These datasets are the foundation of HUD's annual homelessness estimation and are critical for allocating federal funding. Public homelessness systems use coordinated data-driven approaches to match support services to clients based on assessed needs, with data collected across multiple touchpoints including shelter services, healthcare encounters, and social services. The data captures both single-night snapshot counts and longitudinal enrollment patterns that help track individuals through the homelessness system over time.
Market Data
771,000 people
U.S. Homeless Population (2024 PIT Count)
Source: American Enterprise Institute
18% increase
Year-over-Year Increase (2023–2024)
Source: American Enterprise Institute
33% surge (historical, 2020)
Increase Since 2020 (Pre-Pandemic)
Source: American Enterprise Institute
46 of 50 states plus DC
States with Increased Homeless Count Since Pandemic
Source: American Enterprise Institute
682,612 people
Annual Homelessness Experiences (2022)
Source: National Alliance to End Homelessness
Who Uses This Data
What AI models do with it.do with it.
HUD Funding Competition
Cities and regions compete for approximately $5 billion in annual HUD funding using PIT counts and service enrollment data to justify resource allocation and demonstrate need for federal homelessness programs.
Service Coordination & Resource Allocation
Public homelessness systems use coordinated data-driven approaches to match clients to support services based on assessed needs, track individuals over time, and allocate available shelter and housing resources efficiently.
Policy Analysis & Housing Strategy
Researchers and policymakers analyze homelessness data to identify drivers of the crisis, correlate housing costs and rents with homelessness rates, and evaluate the effectiveness of housing and support interventions.
Population Health Surveillance
Health systems use administrative data algorithms to identify individuals experiencing homelessness, estimate population prevalence, track health outcomes, and evaluate the impact of housing and healthcare initiatives.
What Can You Earn?
What it's worth.worth.
Anonymized Aggregate PIT Data
Varies
Public datasets and research partnerships; pricing depends on granularity, time span, and exclusivity agreements with municipalities.
Detailed Client-Level Dataset
Varies
Longitudinal anonymized enrollment and service data; value increases with multi-year coverage and detailed demographic/outcome variables; subject to privacy validation.
Real-Time Shelter Utilization Feed
Varies
Daily or near-real-time occupancy and capacity data; premium pricing for institutional buyers (cities, nonprofits, federal agencies).
Health Administrative Integration
Varies
Cross-linked homelessness and healthcare encounter data; highest value for population health systems; requires robust de-identification and compliance.
What Buyers Expect
What makes it valuable.valuable.
Completeness & Longitudinal Continuity
Data must track individuals consistently over time across multiple service points (shelter, healthcare, social services). Gaps undermine resource allocation decisions and trend analysis.
Accurate Demographic & Need Assessment
Reliable age, family status, health condition, and vulnerability flags are essential for matching clients to appropriate services. Inaccurate assessments waste resources and harm vulnerable populations.
Robust De-Identification & Privacy Safeguards
Datasets must meet strict anonymization standards to prevent re-identification risk, especially for sensitive populations. Health administrative linkages require HIPAA or equivalent compliance.
Consistency Across Collection Points
Definitions and taxonomies must align across shelter, healthcare, law enforcement, and social services data to avoid duplication and ensure valid prevalence estimates for federal funding.
Timeliness & Currency
PIT counts and service enrollment must reflect current conditions; outdated data loses relevance for HUD funding cycles and policy decisions.
Companies Active Here
Who's buying.buying.
Collects and publishes annual point-in-time counts; uses data to allocate $5 billion in federal homelessness funding and track national homelessness trends.
Operate coordinated entry systems, collect client data across shelter and service providers, use AI tools for resource allocation and risk prediction, and report to federal funders.
Develop algorithms to identify homeless individuals in healthcare data, validate prevalence estimates, and integrate homelessness with population health surveillance and outcomes tracking.
Analyze HUD PIT data and administrative datasets to publish state-of-homelessness reports, correlate housing costs with homelessness rates, and inform policy recommendations.
Use shelter utilization and enrollment data to coordinate services, assess client needs, track outcomes, and report to municipal and federal funders.
FAQ
Common questions.questions.
What is a point-in-time (PIT) count and how often is it conducted?
A point-in-time count is a snapshot of homelessness on a single night, capturing the number of people experiencing homelessness at that moment. HUD conducts an annual PIT count, with the most recent 2024 estimate showing 771,000 people homeless on a single night in the United States—an 18% increase from 2023.
Why do cities need homeless count data to compete for HUD funding?
HUD allocates approximately $5 billion annually in federal homelessness funding based on documented need. Cities and regions must submit accurate PIT counts, shelter utilization data, and service enrollment records to justify their funding requests and demonstrate the scope of their homelessness crisis.
How do homelessness systems use data to help clients?
Public homelessness systems collect client data across shelter, healthcare, and social services to enable coordinated entry—matching individuals to appropriate support services based on their assessed needs. Data also tracks individuals over time to evaluate housing outcomes and prevent recurrence of homelessness.
What privacy and de-identification standards apply to homeless count data?
Datasets must meet strict anonymization standards to prevent re-identification and are subject to compliance frameworks like HIPAA when linked with health administrative data. Researchers and data providers must validate that de-identification is robust, especially for sensitive populations.
How has homelessness changed since the pandemic?
Since 2020, the U.S. homeless population has surged 33%, with homelessness increasing in 46 of 50 states plus DC. The 2024 PIT count of 771,000 represents a record high, driven largely by rising housing costs; California and New York account for nearly half of all homelessness nationally.
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