HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 06/2025

The rental housing developments listed below are among the thousands of affordable units that are supported by City of Chicago programs to maintain affordability in local neighborhoods. The list is updated periodically when construction is completed for new projects or when the compliance period for older projects expire, typically after 30 years. The list is provided as a courtesy to the public. It does not include every City-assisted affordable housing unit that may be available for rent, nor does it include the hundreds of thousands of naturally occurring affordable housing units located throughout Chicago without City subsidies. For information on rents, income requirements and availability for the projects listed, contact each property directly. For information on other affordable rental properties in Chicago and Illinois, call (877) 428-8844, or visit www.ILHousingSearch.org.

The Official Housing Authority is a leading provider of housing solutions, dedicated to addressing the shortage of affordable housing in the country. With a strong focus on community development, the organization works closely with local governments and stakeholders to create sustainable and equitable housing options for all.

The company's extensive database provides valuable insights into the housing market, including statistics on rental and ownership rates, demographic data on households, and information on government regulations and initiatives. Additionally, the Official Housing Authority's database offers details on various types of housing, such as apartments, condominiums, and single-family homes, as well as data on the economic and social implications of housing on local communities.

Graph and download economic data for Government subsidies: Federal: Housing (L312051A027NBEA) from 1960 to 2023 about subsidies, federal, government, housing, GDP, and USA.

Graph and download economic data for Government current expenditures: Federal: Housing and community services (G160651A027NBEA) from 1959 to 2023 about community, expenditures, federal, government, services, housing, GDP, and USA.

The NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available. The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.

The DC Housing Authority provides quality affordable housing to extremely low- through moderate-income households, fosters sustainable communities, and cultivates opportunities for residents to improve their lives. The following is a subset of the District Government Land (Owned, Operated, and or managed) dataset that include buildings with a "public housing" use type.

Government Housing Expenditure Hungary

Financing Social Inclusion, 1990-2023

By [source]

About this dataset

This dataset provides a detailed overview of the public expenditures on housing in Hungary since 1990, in order to better illustrate how the government has been tackling housing poverty through their resources. The sources of this data include the Budget Acts, Closing Accounts Acts and other public sources, allowing researchers and others to examine how much money is being used to reduce housing poverty and increase access to competitive housing. This data includes considerable information about each expenditure such as the name and code of each expenditure, its source, financial groupings and purpose (including who it benefits), as well as the amount of money spent in both Hungarian Forint currency (HUF_MIO_CP) and 2021 prices (HUF_MIO_2021P). Furthermore, this data also outlines whether or not a particular expenditure is part of any official national Housing Strategy or not. With all this valuable insight at hand, we can easily see where our money is going - so researchers, policy makers and private citizens alike can use this openly available data for a more informed decision making!

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How to use the dataset

This dataset provides a comprehensive overview of public expenditures on housing in Hungary from 1990 to 2023. It compiles publicly available data from the Budget Acts, Closing Accounts Acts and other sources. This dataset can be used to explore the relationships between government spending on housing, societal change, and public welfare initiatives.

In order to use this dataset effectively it is important to understand what information each of the columns provide. The Y column identifies the year of expenditure while HUF_MIO_CP and HUF_MIO_2021P provide the amount spent both in millions of Hungarian Forints (HUF) at current prices and 2021 prices respectively. OUTTURN indicates the actual amount spent on housing while PLANNED shows how much money was budgeted for spending in that year (and hence how much was over or underspent). NAME gives an indication of what type of expenditure is being reported while CODE helps identify more specific expenses within wider categories such as infrastructure development or residential construction. SOURCE identifies where the data has been sourced from, with NAME_HU providing further detail when available in Hungarian language sources such as budget documents or reports from local government authorities etc. GROUP_AR_HU describes which group have benefitted most from particular investments, for example 'Low Income Households', 'Social Housing Providers' etc., with similar detail also given by BENEFITORIES showing who specifically has benefitted either directly (end-users) or indirectly (construction companies etc.). GROUP_FIN provides information concerning which funds are being drawn upon when making expenditure decisions - whether it be regional grants etc., with SOURCE providing further context into these funding decisions if necessary indicating whether they relate to Europe Union regulations etc.. YR indicates which calendar year a particular contribution relates to - so that patterns may be studied - whilst CP & FP indicate how much money has been spent directly out government coffers (Central/Local respectively).
Finally HS-YESNO refers to whether this contribution is part of an ongoing housing strategy i:e: planned medium-term interventions aimed at meeting society's long term goals such as meeting social needs or enhancing community cohesion via urban renewal projects etc., whilst HS-SINCE & HS-UNTIL help map out longer term trends – suggesting any recent changes/developments occurring within a particular sector – allowing us greater temporal awareness and understanding when making judgements as to why certain outcomes are prevalent today over others by looking back upon historical data collected previously yet held within this source document

Research Ideas

• It can be used to create a visualization of public housing spending in Hungary over the years, showing changes in trends as well as identifying key areas for future investment and expenditure.
• It can be used to identify locations in Hungary that have higher levels of housing poverty and focus efforts on these areas by using predictive analytics to suggest potential policies or programs that target these specific areas.
• Researchers could use this dataset to better understand how public funds are allocated towards reducing housing poverty in Hungary, ultimately helping them inform g...

DC government is making historic investments in affordable housing, home ownership programs and housing preservation. We're working to ensure that people of all backgrounds and at all stages of life can live and thrive in Washington, DC.

As of August 2024, the Indonesian government built nearly *** thousand houses as part of the One Million Houses program. The One Million Houses program (Program Sejuta Rumah) aims to build *********** houses each year to lower the number of housing backlog in Indonesia. Due to rising urbanization and population growth, the housing demand in Indonesia was expected to keep growing in the coming years.

The United States Department of Housing and Urban Development had outlays of about 55.19 billion U.S. dollars in 2023. By 2029, the outlays of the Department of Housing and Urban Development are expected to increase to about 76.6 billion U.S. dollars.

In 2023, the Indonesian government funded around ******* housing units under the housing financing liquidity facility (Fasilitas Likuiditas Pembiayaan Perumahan/FLPP) program. The housing financing liquidity facility is a program from the Ministry of Public Works and Public Housing to help low-income citizens buy their own homes.

The data provided herein denotes the estimated service areas for all Public Housing Authorities (PHA) receiving assistance through the U.S. Department of Housing and Urban Development (HUD) (excluding Guam, the Marshall Islands, and the U.S. Virgin Islands). HUD’s Office of Policy Development and Research (PD&R) developed this dataset in response to repeated requests from HUD staff, researchers, and external partners. This is an experimental dataset that is designed to aid researchers in studying the HUD-funded Public Housing and Housing Choice Voucher programs. The methodology and the service areas themselves have not been validated by HUD’s Office of Public and Indian Housing (PIH) or the Public Housing Agencies. PD&R welcomes engagement from internal and external stakeholders on the continued refinement and development of this dataset. Please send any comments or questions to GISHelpDesk@hud.gov. Standards used to estimate PHA primary service areas are as follows: State-Level Public Housing Authorities:For the purposes of this dataset State-Level PHAs are identified through either their name, or their PHA Code also known as the Participant Code. Any PHA whose name contains the word “State”, or whose PHA Code begins with a ‘9’ (not including the two-character state code that begins the PHA code) is considered a State-Level PHA, and the service area therefore includes the entirety of that state. County-Level Public Housing Authorities:For the purposes of this dataset County-Level PHAs are identified as any PHA containing the word ‘County’ or ‘COUNTY’ in the organization’s formal name. The service area of a County-Level PHA includes the entire county after which the PHA is named, or the county which contains the majority of the units (combined low-rent and voucher) administered by the PHA. Moreover, a PHA that administers units located in jurisdictions outside the county for which the PHA is named, or the county which contains the majority of the units administered by the PHA, does not include those extraterritorial jurisdictions as part of its service area . Subsequently, the estimated service areas of housing authorities operating at a regional level, that is operating in multiple counties (contiguous or otherwise), are relegated to a single county. Local-Level Public Housing Authorities:For the purposes of this dataset Local-Level PHAs are identified as any PHA that does not fall into the category of State-Level or County-Level Public Housing Authority as described above. The service area for a Local-Level PHA is first defined as the primary Unit of General Local Government (UGLG) served by the PHA. The primary local government jurisdiction is defined as the UGLG that contains the largest share of total units (combined low-rent and voucher) administered by that PHA. However, in cases where greater than 20% of units administered by that PHA are located outside of the primary local government jurisdiction served by the PHA, the PHA’s service area is defined as the entirety of the county that the primary local government is located in.Please note, that the methods used to compile the estimated local PHA service areas illustrated in this dataset remain the same regardless of a state’s allowance for state-wide voucher portability.