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Completeness of homelessness data

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HomelessnessPoverty

What you should know about this indicator

  • Countries are given one point for each criterion satisfied under five key categories: Methodology, Timeliness, Definition, Geographic Scope, and Disaggregation.
  • These five categories are further broken down into 11 criteria, each of which is worth one point. Methodology comprises whether the methodology used to collect the homelessness data is listed by the source and whether the enumeration is from a primary data source. Timeliness includes whether the enumeration was conducted within the last four years and whether the enumeration was conducted at the same time of year or based on routinely updated data. Definition measures whether the definition of homelessness includes people without accommodation, living in emergency accommodation, and living in insecure or inadequate housing. Geographic Scope assesses whether the geographic scope of the data is listed by the source and disaggregation by region, city, or community. Disaggregation assesses whether the data is disaggregated by gender, age or two additional categories (disability status, income, race or ethnicity, migratory status, length of time homeless, and relevant health data)."
  • The scale is unweighted. In other words, the same score for two countries does not imply that both countries have satisfied the criteria in the same way.
  • We only consider the data from the source that is at most five years old.

How is this data described by its producer?

After gathering information related to countries' homelessness enumeration and data practices, we evaluated countries according to our Homeless Data Scorecard (HDS). The HDS is an 11-point scale that provides a rough assessment of the current state of a country's enumeration and data practices. Countries were given one point for each criterion satisfied under the five key categories discussed in the "Homeless Data Scorecard" section above - Methodology, Timeliness, Definition, Geographic Scope, and Disaggregation. The scale is unweighted. In other words, in awarding one point for disaggregation by gender and one point for enumerations occurring within the last four years, we are not implying that the satisfaction of one criterion is as important as the other. Furthermore, two countries being awarded a point for satisfying the same criteria should not be taken to imply that both countries have satisfied the criteria in the same way. For instance, whereas two countries may satisfy the criterion of "methodology listed," one country may simply state that data was produced through a street count, while the other country may issue comprehensive reports detailing their data collection tactics and statistical methods used to arrive at their estimate. In cases where the relative lack of contextual information regarding how a country's data was produced was insufficient to make a determination, countries were not scored according to the HDS.

Completeness of homelessness data
The Homeless Data Scorecard is an 11-point scale that provides an assessment of the current state of a country’s enumeration and data practices on homelessness. Higher values indicate better data on homelessness.
Source
Institute of Global Homelessness (2024) – with minor processing by Our World in Data
Last updated
July 5, 2024
Next expected update
May 2026
Date range
2001–2024

Sources and processing

This data is based on the following sources

Institute of Global Homelessness – Homelessness - Better Data Project

Mapping and measuring homelessness as a global phenomenon has never been done, due in part to differing definitions of homelessness and varying methods for data collection which render side-by-side comparisons impossible. In order to showcase the difference between definitions and methodologies and to spotlight gaps in the data, the Ruff Institute of Global Homelessness's Better Data Project reviewed publicly available information from government sources, nongovernmental organizations, intergovernmental organizations, and news media reports. We found that the data varied widely from country to country, with vast differences in the quality, transparency, accuracy, and reliability of the enumeration.

We have called this experiment the Better Data Project, as we recognize that much of global homelessness data is outdated, not collected best practices, and not comparable across countries. Without standardized definitions and consistent methodologies, current measures of homelessness are incomplete, and policy-makers lack adequate and timely information about the scale of the problem. The goal of the data visualization is to demonstrate the gaps and issues of the data and where and how countries can align on definition and methodologies, improving over time.

Only then will we be able to accurately track the effectiveness of strategies that address homelessness and answer the question, “how many people are experiencing homelessness globally?”

Retrieved on
July 5, 2024
Retrieved from
https://ighomelessness.org/global-homeless-data/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Institute of Global Homelessness (2024). Better Data Project Homeless Data

Mapping and measuring homelessness as a global phenomenon has never been done, due in part to differing definitions of homelessness and varying methods for data collection which render side-by-side comparisons impossible. In order to showcase the difference between definitions and methodologies and to spotlight gaps in the data, the Ruff Institute of Global Homelessness's Better Data Project reviewed publicly available information from government sources, nongovernmental organizations, intergovernmental organizations, and news media reports. We found that the data varied widely from country to country, with vast differences in the quality, transparency, accuracy, and reliability of the enumeration.

We have called this experiment the Better Data Project, as we recognize that much of global homelessness data is outdated, not collected best practices, and not comparable across countries. Without standardized definitions and consistent methodologies, current measures of homelessness are incomplete, and policy-makers lack adequate and timely information about the scale of the problem. The goal of the data visualization is to demonstrate the gaps and issues of the data and where and how countries can align on definition and methodologies, improving over time.

Only then will we be able to accurately track the effectiveness of strategies that address homelessness and answer the question, “how many people are experiencing homelessness globally?”

Retrieved on
July 5, 2024
Retrieved from
https://ighomelessness.org/global-homeless-data/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Institute of Global Homelessness (2024). Better Data Project Homeless Data

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At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Completeness of homelessness data”, part of the following publication: Bastian Herre and Pablo Arriagada (2024) - “Homelessness”. Data adapted from Institute of Global Homelessness. Retrieved from https://gfs-wave-2-new.owid.pages.dev:8789/20260304-094028/grapher/completeness-of-homelessness-data.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Institute of Global Homelessness (2024) – with minor processing by Our World in Data

Full citation

Institute of Global Homelessness (2024) – with minor processing by Our World in Data. “Completeness of homelessness data” [dataset]. Institute of Global Homelessness, “Homelessness - Better Data Project” [original data]. Retrieved April 10, 2026 from https://gfs-wave-2-new.owid.pages.dev:8789/20260304-094028/grapher/completeness-of-homelessness-data.html (archived on March 4, 2026).

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://gfs-wave-2-new.owid.pages.dev/grapher/completeness-of-homelessness-data.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://gfs-wave-2-new.owid.pages.dev/grapher/completeness-of-homelessness-data.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://gfs-wave-2-new.owid.pages.dev/grapher/completeness-of-homelessness-data.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://gfs-wave-2-new.owid.pages.dev/grapher/completeness-of-homelessness-data.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://gfs-wave-2-new.owid.pages.dev/grapher/completeness-of-homelessness-data.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://gfs-wave-2-new.owid.pages.dev/grapher/completeness-of-homelessness-data.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://gfs-wave-2-new.owid.pages.dev/grapher/completeness-of-homelessness-data.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://gfs-wave-2-new.owid.pages.dev/grapher/completeness-of-homelessness-data.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear