A number of studies have explored the relationship between public housing policy, poverty, and crime. This Commentary discusses the results of a recent study, which investigated the effects of closing large public housing developments on crime. To see if the demolitions—and the associated deconcentration of poverty—reduced crime or merely displaced it, researchers examined the case of Chicago. They found that closing large public housing developments and dispersing former residents throughout a wider portion of the city was associated with net reductions in violent crime, at the city level.

Abstract copyright UK Data Service and data collection copyright owner.

The English Housing Survey (EHS) Fuel Poverty Datasets are comprised of fuel poverty variables derived from the EHS, and a number of EHS variables commonly used in fuel poverty reporting. The EHS is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England.

Safeguarded and Special Licence Versions
Similar to the main EHS, two versions of the Fuel Poverty dataset are available from 2014 onwards. The Special Licence version contains additional, more detailed, variables, and is therefore subject to more restrictive access conditions. Users should check the Safeguarded Licence (previously known as End User Licence (EUL)) version first to see whether it meets their needs, before making an application for the Special Licence version.

The English Housing Survey: Fuel Poverty Dataset, 2022: Special Licence is the outcome of analysis conducted to produce estimates of fuel poverty in England in 2022 undertaken by the Department for Energy Security and Net Zero (DESNZ).

Fuel poverty in England is measured using the Low Income Low Energy Efficiency (LILEE) indicator, which considers a household to be fuel poor if:

• it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) Methodology; and
• its disposable income (income after housing costs (AHC) and energy costs) would be below the poverty line. The poverty line (income poverty) is defined as an equivalised disposable income of less than 60 per cent of the national median in Section 2 of the ONS publication 'Persistent poverty in the UK and EU: 2017'.

The Low Income Low Energy Efficiency model is a dual indicator, which allows us to measure not only the extent of the problem (how many fuel poor households there are), but also the depth of the problem (how badly affected each fuel poor household is). The depth of fuel poverty is calculated using the fuel poverty gap. This is the reduction in fuel costs needed for a household to not be in fuel poverty. This is either the change in required fuel costs associated with increasing the energy efficiency of a fuel poor household to a Fuel Poverty Energy Efficiency Rating (FPEER) of band C or reducing the costs sufficiently to meet the income threshold.

The fuel poverty dataset is derived from the English Housing Survey, 2022 database created by the MHCLG. This database is constructed from fieldwork carried out between April 2021 and March 2023. The midpoint of this period is April 2022, which can be considered as the reference date for this dataset.

A brief summary of each of the variables included in the English Housing Survey: Fuel Poverty Dataset, 2022: Special Licence dataset is included in the study documentation. The variables can be grouped into the following categories:

• Low Income Low Energy Efficiency fuel poverty indicator variables
• income and fuel costs variables
• 10 per cent affordability indicator variables
• additional fuel poverty variables
• English Housing Survey variables
• policy eligibility flags
• income split variables
• energy cost variables
• energy use variables
• weights
• variables introduced in 2015 data release (first published 2017)

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

Severe housing deprivation rate is defined as the percentage of population living in the dwelling which is considered as overcrowded, while also exhibiting at least one of the housing deprivation measures.

Housing deprivation is a measure of poor amenities and is calculated by referring to those households with a leaking roof, no bath/shower and no indoor toilet, or a dwelling considered too dark.

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. This translates into the following equation: Where i represents census tracts, () is the metropolitan/micropolitan (CBSA) mean tract poverty rate, is the ith tract poverty rate, () is the non-Hispanic white population in tract i, and Pop is the population in tract i.While this definition of R/ECAP works well for tracts in CBSAs, place outside of these geographies are unlikely to have racial or ethnic concentrations as high as 50 percent. In these areas, the racial/ethnic concentration threshold is set at 20 percent.

Data Source: American Community Survey (ACS), 2009-2013; Decennial Census (2010); Brown Longitudinal Tract Database (LTDB) based on decennial census data, 1990, 2000 & 2010.

Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17. Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.

References:Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

To learn more about R/ECAPs visit:https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 11/2017

The English Housing Survey (EHS) Fuel Poverty Datasets are comprised of fuel poverty variables derived from the EHS, and a number of EHS variables commonly used in fuel poverty reporting. The EHS is a continuous national survey commissioned by the Ministry of Housing, Community and Local Government (MHCLG) that collects information about people's housing circumstances and the condition and energy efficiency of housing in England.

Safeguarded and Special Licence Versions
Similar to the main EHS, two versions of the Fuel Poverty dataset are available from 2014 onwards. The Special Licence version contains additional, more detailed, variables, and is therefore subject to more restrictive access conditions. Users should check the Safeguarded Licence (previously known as End User Licence (EUL)) version first to see whether it meets their needs, before making an application for the Special Licence version.

Fuel Poverty Statistics, 2016

Fuel Poverty Energy Efficiency Rating (FPEER) figures for 2016 are based on improved RdSAP assumptions, introduced in November 2017. Users are therefore advised that there will be a step-change between 2016 data and data published in previous years. Please see section Chapter 1 of the fuel poverty publication for further information on RdSAP changes: https://www.gov.uk/government/statistics/annual-fuel-poverty-statistics-report-2018.

The fuel poverty dataset is comprised of fuel poverty variables derived from the English Housing Survey (EHS), and a number of EHS variables commonly used in fuel poverty reporting. The fieldwork for the EHS is carried out each financial year (between April and March). The fuel poverty datasets combine data from two consecutive financial years. The midpoint of this period is April 2016, which can be considered as the reference date for the fuel poverty dataset. Guidance on use of EHS data provided by DCLG should also be applied to the fuel poverty dataset. Full information on the EHS survey is available at the Ministry of Housing, Communities and Local Government (MHCLG) EHS website. Fuel Poverty Statistics are also available from gov.uk.

The majority of fuel poverty variables are included in the dataset deposited at the UK Data Archive under the standard End User Licence (SN 8393). To comply with the data disclosure control guidance issued by the Government Statistical Service, supplementary fuel poverty variables are released under this Special Licence version, which is subject to more restrictive access conditions (see Access section below). Users are advised to obtain SN 8393 to see whether it is suitable for their needs before making an application for the Special Licence version.

Besides the information contained in SN 8393, the Special Licence dataset also includes the following: more detailed income information, the amount of energy (kWh/year) used for space heating, water heating, cooking, light and appliances, as well as the annual cost for each of these, the Building Research Establishment Domestic Energy Model (BREDEM) floor area and the boiler efficiency after control adjustment. More information about the extra variables can be found in the Fuel Poverty Special Licence Dataset Documentation.

At-risk-of-poverty rate after deducting housing costs by degree of urbanisation

The indicator measures the share of population living in households that spend 40 % or more of the household disposable income on housing ('net' of housing allowances). Housing costs include rental or mortgage interest payments but also the cost of utilities such as water, electricity, gas or heating.

The indicator measures the share of population living in households that spend 40 % or more of the household disposable income on housing ('net' of housing allowances). Housing costs include rental or mortgage interest payments but also the cost of utilities such as water, electricity, gas or heating.

This map shows the percent of families with an income at or below the poverty level within the last 12 months, and the percent of occupied housing built before 1950, by block group, based on the US Census American Community Survey, 2009-13.

At-risk-of-poverty rate after deducting housing costs by age and sex

NATSEM estimates of housing stress (2006 and 2010) and estimates of poverty variables (2006) of SLAs, excluding SLAs in Brisbane and Canberra, in Australia. These data were derived from spatial microsimulation using 2006 Census benchmarks (SPATIALMSM08b) applied to Australian Bureau of Statistics (ABS) Confidentialised Unit Record File data. For housing stress, the indicator is based on a commonly used measure of housing stress known as the 30/40 rule. Using this definition, a household is said to be in housing stress if it spends more than 30 per cent of its gross income on housing costs and if it also falls into the bottom 40 per cent of the equivalised disposable household income distribution. The poverty indicator represents the percentage of people in households where income is below the poverty line. The poverty line has been set at half the median OECD equivalised household disposable income.

This data layer shows census tracts in Florida that are designated as Racially and Ethnically Concentrated Areas of Poverty (RECAPs) by Florida Housing Finance Corporation (Florida Housing). RECAP-designated tracts have more than 50% of the population who identify as other than non-Hispanic white and a poverty rate greater than or equal to 40%. Data used in the RECAP designation process is derived from the American Community Survey (ACS). The survey is conducted by the Census Bureau on an ongoing basis. It is the most complete and reliable source of information about the American people. The Census Bureau releases ACS data in 1-year, 3-year, and 5- year averages. One-year data is the most current; however, the 3-year and 5-year averages are more reliable because they are based on a larger sample size. Florida Housing has used the most recent and available 5-year estimates from the ACS, which includes survey data from (2019-2023). In addition, Florida Housing has discarded high margin of error values. Applying these rigorous standards, Florida Housing has based the RECAP designations on accurate data that reflect long-term trends.

A growing body of research suggests that housing eviction is more common than previously recognized and may play an important role in the reproduction of poverty. The proportion of children affected by housing eviction, however, remains largely unknown. We estimate that 1 in 7 children born in large American cities in 1998–2000 experienced at least one eviction for nonpayment of rent or mortgage between birth and age 15. Rates of eviction were substantial across all cities and demographic groups studied, but children from disadvantaged backgrounds were most likely to experience eviction. Among those born into deep poverty, we estimate that about 1 in 4 were evicted by age 15. Given prior evidence that forced moves have negative consequences for children, we conclude that the high prevalence and social stratification of housing eviction are sufficient to play an important role in the reproduction of poverty and warrant greater policy attention.

This indicator is defined as the percentage of the population living in a household where the total housing costs (net of housing allowances) represent more than 40% of the total disposable household income (net of housing allowances) presented by poverty status.

In France in 2024, more than 2.7 million households applied for public housing. The number of applicants for this specific kind of housing has continuously increased since 1984. In France, public housing is built with the help of the State and is subject to precise construction, management, and allocation rules. Moreover, public housing is reserved for low-income households.

NEW MEXICO RURAL v URBAN HOUSING MEASURES, CENSUS TRACTS, AVERAGES

MEASURE RURAL URBAN New Mexico USA

Tracts 294 204

Median Value of Owner Occupied Housing Units $
148,363 $
199,546 $
193,500 $
163,900

Percent Occupied Housing Units 79.6 90.1 83.0 87.8

Percent Renter Occupied Housing Units 22.9 37.5 29.5 34.3

Percent Owner Occupied Housing Units 73.3 60.9 67.9 63.8

Percent Vacant Housing Units 20.4 9.9 17.0 12.2

Percent Housing Units Built before 1980 63.9 66.9 63.4 68.3

Percent Owner Occupied Housing Units with No Plumbing 1.9 0.4 1.002 0.396

Percent Households with Broadband Subscription 51.2 67.3 58.0 68.6

Percent Renter Occupied with Rent 35% or More of Income (GRAPI) 37 42.9 41.4 41.5

Percent Owner Occupied with Mortgage 35% or More of Income (SMOCAPI) 23.5 25.2 23.9 22.3

Percent Persons (Age 1 and Over) Living at the Same Residence as 1 Year Ago 87.9 83.8 86.2 89.7

Source - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=3e3aeabc04ac4672994e25a1ec94df83Methods for Urban-Rural - https://www2.census.gov/geo/pdfs/reference/ua/2010ua_faqs.pdf

Adults aged between 25 and 59 years by age group according to at-risk-of-poverty status and tenure status of the housing when they were teenagers. National.

This indicator is defined as the percentage of the population living in a household where the total housing costs (net of housing allowances) represent more than 40% of the total disposable household income (net of housing allowances) presented by poverty status.

Designed to collect new data related to housing, poverty, and urban life, the Milwaukee Area Renters Study (MARS) is an in-person survey of 1,086 households in Milwaukee. One person per household, usually an adult leaseholder, was interviewed. The MARS instrument was comprised of more than 250 unique items and administered in-person in English and Spanish. The University of Wisconsin Survey Center supervised data collection, which took place between 2009 and 2011. The MARS sample was limited to renters. Nationwide, the majority of low-income families live in rental housing, and most receive no federal housing assistance. Except in exceptional cities with very high housing costs, the rental population is comprised of some upper- and middle-class households who prefer renting and most of the cities’ low-income households who are excluded both from public housing and homeownership. To focus on urban renters in the private market, then, is to focus on the lived experience of most low-income families living in cities. MARS was funded by the John D. and Catherine T. MacArthur Foundation, through its “How Housing Matters” initiative.