When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

In 2023, the estimated number of homeless people in the United States was highest in California, with about ******* homeless people living in California in that year.

In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

Between 2022 and 2023, New Hampshire had the highest positive percentage change in the estimated number of homeless people in the United States, with the number of homeless people living in New Hampshire increasing by **** percent within this time period.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

This statistic shows the estimated number of chronically homeless people in the United States in 2020, by state. In 2020, there were about ****** chronically homeless people living in California.

In the period from 2012 to 2013, discoveries in shale oil and advances in drilling techniques created an oil boom in North Dakota. Migrant workers from across the continent flocked to the rural prairie state in search of plentiful and well-paying jobs. The state now boasts high economic indexes across the board, including the lowest unemployment rate in the country. But the boom has put a strain on North Dakota's infrastructure. As some cities nearly double their populations, housing has been unable to keep pace with the growth. Employed and healthy individuals are forced to brave the frigid northern conditions in cars and tents.The three maps in this web application paint a picture of the homeless problem in North Dakota by showing how the state's homeless counts, percentages, and change compare to the rest of the United States. While North Dakota's total homeless population is relatively low, the population is high for its size and growing at a tremendous rate.

The most recent homelessness rate is calculated using ACS population estimates from the previous year, unless otherwise noted. The 2020 homelessness rate is based on 2020 PIT count and the average of the 2019 and 2021 ACS population estimates.

Data Source: HUD's Annual Homeless Assessment Report (AHAR) Point-in-Time (PIT) Estimates by State and American Community Survey (ACS) 1-Year Estimates

Why this MattersSafe, adequate, and stable housing is a human right and essential for the health and well-being of individuals, families, and communities.People who experience homelessness also struggle to maintain access to healthcare, employment, education, healthy relationships, and other basic necessities in life, according to the DC Interagency Council on Homelessness Strategic Plan.BIPOC populations are disproportionately affected by homelessness due to housing discrimination, mass incarceration, and other policies that have limited socioeconomic opportunities for Black, Latino, and other people of color.

The District's Response Strategic investments in proven strategies for driving down homelessness, including the Career Mobility Action Plan (Career MAP) program, operation of non-congregate housing, and expansion of the District’s shelter capacity.Homelessness prevention programs for at-risk individuals and families, such as emergency rental assistance, targeted affordable housing, and permanent supporting housing.Programs and services to enhance resident’s economic and employment security and ensure access to affordable housing.

In 2023, there were about ****** homeless youth living in California, the most out of any U.S. state. New York had the second-highest number of homeless youth in that year, at *****.

Context

This database contains the data reported in the Annual Homeless Assessment Report to Congress (AHAR). It represents a point-In-time count (PIT) of homeless individuals, as well as a housing inventory count (HIC) conducted annually.

The data represent the most comprehensive national-level assessment of homelessness in America, including PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

These data can be trended over time and correlated with other metrics of housing availability and affordability, in order to better understand the particular type of housing resources that may be needed from a social determinants of health perspective.

HUD captures these data annually through the Continuum of Care (CoC) program. CoC-level reporting data have been crosswalked to county levels for purposes of analysis of this dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_hud_pit_homelessness

Sample Query

What has been the change in the number of homeless veterans in the state of New York’s CoC Regions since 2012? Determine how the patterns of homeless veterans have changes across the state of New York

homeless_2018 AS ( SELECT Homeless_Veterans AS Vet18, CoC_Name
FROM bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc WHERE SUBSTR(CoC_Number,0,2) = "NY" AND Count_Year = 2018 ),

veterans_change AS ( SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange FROM homeless_2018 JOIN homeless_2012 ON homeless_2018.CoC_Name = homeless_2012.CoC_Name )

SELECT * FROM veterans_change

In 2024/25, 13,231 people who were seen to be sleeping rough in London compared with 11,993 in the previous reporting year, and the most reported during this time period. The number of people reported to be sleeping rough has steadily increased throughout this time period, with the dip in 2020/21, and 2022/23, likely related to the COVID-19 pandemic. Demographics of London's homeless As of the most recent reporting year, over 2,000 of London's rough sleepers were in the borough of Westminster, the most of any London borough. In terms of gender, the majority of rough sleepers are male, with more than 10,000 men seen to be sleeping rough, compared with 2,149 women, and 18 non-binary people. The most common age group was among those aged between 36 and 45 years old, at more than 3,900, compared with 1,411 25 and under, 3,580 aged between 26 and 34, 2,860 aged 45 and 55, and around 1,578 over 55s. Homelessness in the U.S. Homelessness is also an important social issue in several other countries. In the United States, for example, there were estimated to be approximately 653,104 people experiencing homelessness in 2023. This was a noticeable increase on the previous year, and the highest number between 2007 and 2023. When looking at U.S. states, New York had the highest homelessness rate, at 52 individuals per 10,000 population, followed by Vermont at 51.

In the United States in 2023, **** percent of the homeless population living in El Dorado County, California were unsheltered.

In the United States, California had the highest amount of homeless children and youths enrolled in their schools for the school year 2019-20 at ******* students. North Carolina had ****** recorded homeless students for the same year. A homeless child or youth is someone who lacks a fixed residence or is forced to share housing due to economic hardship.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

This statistic depicts the rate of homeless individuals in the United States in 2017, by metropolitan area. In 2017, the rate of homelessness per 10,000 individuals was highest in New York City, at ****.

Abstract

The central focus of the National Baseline Household Survey for the year 2009 (NBHS 2009) is to provide indicators and reference statistics for the living condition of all Sudanese. The NBHS 2009 was conducted in all the 25 States of Sudan in a uniform way whereas CBS collected and processed the data for the 15 Northern States and the Southern Sudan Centre for Census, Statistics and Evaluation (SSCCSE) had similar responsibility for the 10 Southern States. The current report covers the 15 Northern States of Sudan.

The objective with the present document is to summarize the findings from all parts of the extensive NBHS 2009 survey questionnaire. Hopefully this will inspire for further and deeper special analyses of this vast dataset.

Geographic coverage

This survey is representative for the 15 states of Northern Sudan, including urban and rural areas.

Analysis unit

Households and individuals.

Universe

The survered covered all private households and their memebers. It did not cove special types of households such as institutional households (hostels, hospitals etc), refugee camps, IDP camps, cattle camps, and homeless people.

Kind of data

Données échantillonées [ssd]

Sampling procedure

The sample selected for the NBHS2009 was based on a stratified two stage sampling procedure. The preliminary count of households per enumeration area (EA) as well as the cartographic work from the 2008 National Population and Housing Census were used as the sampling frame. The EAs from the census constituted the primary sampling units. For the NBHS2009, the Census EAs were stratified by urban and rural in each State. Some of the sample EAs could not be covered because of security or other problems, in which case they were replaced by EAs within the same geographical areas. In addition, the sample did not include nomadic population due to lack of proper sampling frame for them and problems of accessibility. Also institutional households, camps etc as well as the homeless part of the population were excluded from the sample.

A second sampling stage was conducted by listing all households within the selected EAs in the sample and thereafter selecting a fixed random sample of 12 households to be interviewed. A total sample size of 528 households in each State was distributed into 44 primary sampling units (PSUs).

The sample size was designed to obtain reliable estimates for key survey variables at the State level and for urban and rural domains at the national level, the State 15 level and the State 10 level.

Mode of data collection

Interview face à face [f2f]

Research instrument

The questionnaire was designed in English and translated into Arabic with the same wording and modules. It was distributed to the respondents in Arabic only.

In addition a comprehensive field manual (English) was prepared to assist the fieldworkers in filling out each section of the questionnaire. A summary manual was translated to Arabic and used for the training in the 15 Northern States.

The questionnaire was designed for Optical Character Recognition (OCR) using a commonly available software. It was printed on standard 80 grams A4 paper and stapled to a booklet.

A technical working group was established in July 2008. This group oversaw the development of the questionnaire, with inputs from different stakeholders and technical consultants.

Cleaning operations

The questionnaires for the 15 Northern states were scanned centrally at CBS premises in Khartoum. A high capacity scanner and optical character recognition (OCR) software were used. Approximately 96-97% of all characters filled in was automatically interpreted and entered into the software internal database. The scanning procedure included manual on-screen verification of remaining data that could not be automatically interpreted. Finally, the scanned data were exported as ASCII files with corresponding digital images of each questionnaire. The data files were converted, further processed/edited and also tabulated using the software SPSS/PASW.

The NBHS2009 was edited as a combination of post-scanning automated edits and manual back-checks on electronic images (TIF-files) stored for each questionnaire. The latter mainly used for verifying outliers due to possible scanning or fieldworker errors.

The automated edits were pre-programmed to identify and correct consistency errors within each thematic section of the questionnaire and, especially for age related variables (marital status, education and work), also across section checks were applied.

Outliers were defined as outside the range of MEAN +/- 3 x STDV of actual variable in stratum. Outliers were listed and, unless manual intervention from subject matter specialist, the outliers were automatically imputed to MEDIAN value of stratum. However, for the very thorough edits of the questionnaire section M (purchase and consumption) additional information on local market prices were used to correct the raw data.

If skip was missing or inconsistent with responses given in the related detailed question, the detailed question response overruled the skip and the skip was adjusted.

The difficulties with achieving consistency between age and level of current school attending was approached by introducing a predefined acceptable age range with upper and lower cut-off for each level of school from Primary 1 to University. People defined too old for a certain school level reported, was corrected to “not currently attending” and the initially reported school level was imputed in the “highest ever school level” variable.

To keep track of the amount and type of edits done, all variables with automated or manual intervention were flagged.

Two cleaned data master files are produced from the NBHS2009. One file with individuals distributed (section B-D) and one file with households distribute (E-O). In addition special files are produced for commodities (section M) used for poverty and food security calculation and for the agriculture (section N) concerning crop production and structures.

There were some challenges encountered in the implementation of the survey: · Change from Quick Baseline Poverty Survey (QBPS) to the NBHS concept resulted in addition of other modules that inflated the questionnaire which involved much more work and additional funds were required to conduct the survey · Delay of transfer of filed work budget to the CBS statistical offices at the states to almost one month had delayed the start of data collection stage from April to May 2009. · Due to insecurity situations in some parts of Darfur region; six clusters in South Darfur, three in North Darfur and one in West Darfur were replaced in the same geographical areas. In addition, due to respondents refusal to cooperate with the field work teams in two EAs (clusters) one in each of Blue Nile and Nahr Elnil states, these selected EAs were replaced and the field work was completed. · The collection of consumption information for some items was made especially hard by the lack of standardized units of measurement in North Sudan. Because, consumption of these items is sourced in non-standardized units (such as heaps, cups, bundles, rubu etc.), it is hard to calculate consumption in standardized comparable units (such as kilograms and litres). Accordingly, the questionnaire allowed respondents to report consumption in non-standardized units. A market survey, conducted at state level, provided specific conversion factors for the non-standardized measurement units. While this was the only feasible solution, it may still be prone to non-trivial measurement errors.

Response rate

The response rate for the NBHS 2009 15 Northern States, including replacements, is 99.9.

Abstract

The Household Living Conditions Survey has been carried out annually since 1999 by the State Statistics Service of Ukraine (formerly the State Statistics Committee of Ukraine). The survey is based on generally accepted international standards and depicts social and demographic situation in Ukraine.

From 2002, items of consumer money and aggregate expenditures have been developed in line with the International Classification of Individual Consumption of Goods and Services (COICOP-HBS), recommended by Eurostat.

From 2004, the State Statistics Service of Ukraine has been implementing a new system of household sample survey organization and delivery. A unified interviewer network was established to run simultaneously three household surveys: Household Living Conditions Survey, households' economic activity survey and the survey of household farming in rural areas. A new national territorial probability sampling was introduced to deliver the three sampling surveys in 2004-2008.

Geographic coverage

National, except some settlements within the territories suffered from the Chernobyl disaster.

Analysis unit

• Households,
• Individuals.

A household is a totality of persons who jointly live in the same residential facilities of part of those, satisfy all their essential needs, jointly keep the house, pool and spend all their money or portion of it. These persons may be relatives by blood, relatives by law or both, or have no kinship relations. A household may consist of one person (Law of Ukraine "On Ukraine National Census of Population," Article 1). As only 0.50% households have members with no kinship relations (0.65% total households if bachelors are excluded), the contemporary concepts "household" and "family" are very close.

Universe

Whole country, all private households. The survey does not cover collective households, foreigners temporarily living in Ukraine as well as the homeless.

Kind of data

Sample survey data [ssd]

Sampling procedure

12,977 households representing all regions of Ukraine (including 8,975 in urban areas and 4,002 in rural areas) are selected for this survey. Grossing up sample survey results to all households of Ukraine is done by the statistic weighting method.

Building a territorial sample, researchers excluded settlements located in the excluded zone (Zone 1) and unconditional (forced) resettlement zone (Zone 2) within the territories suffered from the Chernobyl disaster.

Computing the number of population subject to surveying, from the number of resident population researchers excluded institutional population - army conscripts, persons in places of confinement, residents of boarding schools and nursing homes, - and marginal population (homeless, etc).

The parent population was stratified so that the sample could adequately represent basic specifics of the administrative and territorial division and ensure more homogeneous household populations. To achieve this objective, the parent population was divided into strata against the regions of Ukraine. In each stratum three smaller substrata were formed: urban settlements (city councils) having 100,000 or more inhabitants (big cities), urban settlements (city councils) having less than 100,000 inhabitants (small towns) and all districts (except city districts), i.e. administrative districts in rural areas. Sample size was distributed among strata and substrata in proportion to their non-institutional resident population.

Detailed information about selecting primary territorial units of sampling (PTUS) and households is available in the document "Household Living Conditions Survey Methodological Comments" (p. 4-7).

Mode of data collection

Face-to-face [f2f]

Research instrument

The HLCS uses the following survey tools:

1) Main interviews

Main interview questionnaires collect general data on households, such as household composition, housing facilities, availability and use of land plots, cattle and poultry, characteristics of household members: anthropometric data, education, employment status. Interviewing of households takes place at the survey commencement stage. In addition, while interviewing, the interviewer completes a household composition check card to trace any changes during the entire survey period.

2) Observation of household expenditures and incomes

For the observation, two tools are used: - Weekly diary of current expenditures. It is completed directly by a household twice a quarter. In the diary respondents (households) record all daily expenditures in details (e.g. for purchased foodstuffs - product description, its weight and value, and place of purchase). In addition, a household puts into the diary information on consumption of products produced in private subsidiary farming or received as a gift.

Households are evenly distributed among rotation groups, who complete diaries in different week days of every quarter. Assuming that the two weeks data are intrinsic for the entire quarter, the single time period of data processing (quarter) is formed by means of multiplying diary data by ratio 6.5 (number of weeks in a quarter divided on the number of weeks when diary records were made). Inclusion of foodstuffs for long-time consumption is done based on quarterly interview data.

• Quarterly questionnaire. It is used to interview households in the first month following the reporting quarter. Researchers collect data on large and irregular expenditures, in particular those relating to the purchase of foodstuffs for long-time consumption (e.g. sacks, etc.), and also data on household incomes. Since recalling all incomes and expenditures made in a quarter is uneasy, households make records during a quarter in a special quarterly expenditures log.

The major areas for quarterly observation are the following: - structure of consumer financial expenditures for goods and services; - structure of other expenditures (material aid to other households, expenditures for private subsidiary farming, purchase of real estate, construction and major repair of housing facilities and outbuildings, accumulating savings, etc); - importance of private subsidiary farming for household welfare level (receipt and use of products from private subsidiary farming for own consumption, financial income from sales of such products, etc.); - structure of income and other financial sources of a household. We separately study the income of every individual household member (remuneration of labor, pension, scholarship, welfare, etc.) and the income in form payments to a household as a whole (subsidies for children, aid of relatives and other persons, income from - sales of real estate and property, housing and utility subsidies, use of savings, etc.).

3) Single-time topical interviews

Single-time topical interviews questionnaires are used quarterly and cover the following topics: - household expenditures for construction and repair of housing facilities and outbuilding - availability of durable goods in a household - assessment by households members of own health and accessibility of selected medical services - self-assessment by a household of adequacy of its income - household's access to Internet

Response rate

10,622 households took part in the 2008 survey (83.3% sampled addresses excluding nonresidential buildings). The response rate of rural households (95.5%) was higher than the similar parameter in urban areas (77.5%).

The highest response rate of the 2008 survey was in Trans-Carpathians (98.4%), Chernivtsi (98.3%), Rivno (97.6%), Sumy (96.3%), Volyn (96.0%), Cherkassy (95.4%), Ternopil and Chernivtsi (95.1% in each) regions, the lowest rate - in Odessa region (59.8%), Kiev City (61.1%) and Donetsk region (63.5%). In most regions this parameter fluctuated from 73.4% to 94.7%.

Rao-Scott chi-square statistic and p-values for group comparisons on prevalence of mental disorder.

In 2022, about ****** veterans living in California were homeless, the most out of all U.S. states.

Homelessness data Official homelessness data is produced by local authorities through the Pathway Accommodation and Support System (PASS). PASS was rolled-out nationally during the course of 2013. The Department’s official homelessness statistics are published on a monthly basis and refer to the number of homeless persons accommodated in emergency accommodation funded and overseen by housing authorities during a specific count week, typically the last full week of the month. The reports are produced through the Pathway Accommodation & Support System (PASS), collated on a regional basis and compiled and published by the Department. Homelessness reporting commenced in this format in 2014. The format of the data may change or vary over time due to administrative and/or technology changes and improvements. The administration of homeless services is organised across nine administrative regions, with one local authority in each of the regions, “the lead authority”, having overall responsibility for the disbursement of Exchequer funding. In each region a Joint Homelessness Consultative Forum exists which includes representation from the relevant State and non-governmental organisations involved in the delivery of homeless services in a particular region. Delegated arrangements are governed by an annually agreed protocol between the Department and the lead authority in each region. These protocols set out the arrangements, responsibilities and financial/performance data reporting requirements for the delegation of funding from the Department. Under Sections 38 and 39 of the Housing (Miscellaneous Provisions) Act 2009 a statutory Management Group exists for each regional forum. This is comprised of representatives from the relevant housing authorities and the Health Service Executive, and it is the responsibility of the Management Group to consider issues around the need for homeless services and to plan for the implementation, funding and co-ordination of such services. In relation to the terms used in the report for the accommodation types see explanation below: PEA - Private Emergency Accommodation: this may include hotels, B&Bs and other residential facilities that are used on an emergency basis. Supports are provided to services users on a visiting supports basis. STA - Supported Temporary Accommodation: accommodation, including family hubs, hostels, with onsite professional support. TEA - Temporary Emergency Accommodation: emergency accommodation with no (or minimal) support