This dataset provides information on Tempe's subsidized housing program. Tempe has a fixed number of Housing Choice Vouchers (HCVs) based on our HUD contract, which represents the maximum number of families that the Housing Authority could assist. Congress and HUD do not fund the program to assist all of the families we are allotted to assist. We can only assist the number of families we have the budget to assist. HUD provides an initial funding amount based on what they anticipate they will allocate to housing assistance payments. The actual amount of funding received is subject to change depending on Federal Budget priorities, Congressional approval and many other factors. Expenditures are reported monthly, as HUD requires expenses to be posted in the month they were incurred rather than the month the expense was paid. The performance measure dashboard is available at 3.05 Subsidized Housing.Additional InformationSource: Manually maintained data, Housing Pro and QuickbooksContact: Irma Hollamby CainContact Phone: 480-858-2264Data Source Type: ExcelPreparation Method: Monthly values are calculated by determining the month each of the expenditures was for and retroactively accruing the funding use to the appropriate period. There are multiple, multistep excel worksheets that are used to balance between the specialty Housing Software, City Financial System and the HUD mandated reporting system. Additionally, it is important to note that Funding is allocated by Congress on the Federal Fiscal Year (October - September), the City operates on a Fiscal Year (July - June) and HUD provides funding on the Housing Authority in Calendar Year (January - December) funding increments. Therefore, the City must cross balance between three funding years.Publish Frequency: AnnuallyPublish Method: ManualData Dictionary

Analysis of ‘Social Housing Construction Status Report Q4 2018’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-usmart-io-org-ae1d5c14-c392-4c3f-9705-537427eeb413-dataset-viewdiscovery-datasetguid-f09c3be9-dca4-46a2-b345-e520367999a4 on 16 January 2022.

--- Dataset description provided by original source is as follows ---

The Construction Status Q4 report shows that 4,992 homes were under construction at end Q4 2018; and some 2,569 homes had been approved and were about to go on site. The full programme listed in the report now includes 1,299 schemes (or Phases), delivering 19,134 homes — a substantial increase on the 13,424 homes which were in the programme at the end of Q4 2017.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Housing and social housing in EPCI ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/6170ae10981edd7b132f28a0 on 13 January 2022.

--- Dataset description provided by original source is as follows ---

As part of its task of financing social housing in France, CDC, through the Banque des Territoires, monitors and provides useful data for stakeholders and observers seeking to analyse developments in the territories, construction activity and the social housing sector.

The available dataset presents background indicators on the housing stock and information on the social housing stock for the 8 main EPCI in each region.

These data come from the INSEE, the Sit@del2 database, the social rental park (RPLS) directory and the CDC. They are valued in the annual publication of the Atlas of Housing and Territories (Land banks), see reference below.

The data refer to the years N-2 compared to the year of publication indicated, except for the unemployment rate (Q4 N-1) and the poverty rate (N-3).

--- Original source retains full ownership of the source dataset ---

In 2017, the County Department of Economic Development, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for Allegheny County. A similar MVA was completed with the Pittsburgh Urban Redevelopment Authority in 2016. The Market Value Analysis (MVA) offers an approach for community revitalization; it recommends applying interventions not only to where there is a need for development but also in places where public investment can stimulate private market activity and capitalize on larger public investment activities. The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies.

The 2016 Allegheny County MVA does not include the City of Pittsburgh, which was characterized at the same time in the fourth update of the City of Pittsburgh’s MVA. All calculations herein therefore do not include the City of Pittsburgh. While the methodology between the City and County MVA's are very similar, the classification of communities will differ, and so the data between the two should not be used interchangeably.

Allegheny County's MVA utilized data that helps to define the local real estate market. Most data used covers the 2013-2016 period, and data used in the analysis includes:

•Residential Real Estate Sales; • Mortgage Foreclosures; • Residential Vacancy; • Parcel Year Built; • Parcel Condition; • Owner Occupancy; and • Subsidized Housing Units.

The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources.

During the research process, staff from the County and Reinvestment Fund spent an extensive amount of effort ensuring the data and analysis was accurate. In addition to testing the data, staff physically examined different areas to verify the data sets being used were appropriate indicators and the resulting MVA categories accurately reflect the market.

Please refer to the report (included here as a pdf) for more information about the data, methodology, and findings.

Analysis of ‘Social Housing Construction Status Report Q1 2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-usmart-io-org-ae1d5c14-c392-4c3f-9705-537427eeb413-dataset-viewdiscovery-datasetguid-e4bdcd53-8a99-4649-a5cb-0be2ef1845d6 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

The Construction Status Q1 report shows that 5,598 homes were under construction at end Q1 2019; and some 2,180 homes had been approved and were about to go on site. The full programme listed in the report now includes 1,416 schemes (or phases), delivering 20,324 homes – a substantial increase on the 14,813 homes which were in the programme at the end of Q1 2018

--- Original source retains full ownership of the source dataset ---

Author:Buro HappoldCreation date:November 2024Date of source data harvest:Multiple inputsTemporal coverage of source data:Multiple inputsSpatial Resolution:Lower Super Output Area (LSOA)Geometry:PolygonSource data URL:MultipleData terms of use:Dataset can be shared openly for reuse for non-commercial purposes, with appropriate attribution.Data attribution:- Dataset created by Buro Happold as part of the CIEN & South London sub-regional LAEPs, 2024. Contains data derived from the London Building Stock Model (v2).- Contains OS data © Crown copyright and database right 2025.- Office for National Statistics licensed under Open Government Licence v3.0.Workflow Diagram:N/A - Analysis layerComments:The data and analysis developed for the sub-regional LAEP was undertaken using data available at the time and will need to be refined for a full Phase 2 LAEP. Please check here for more detailed background on the data.Whilst every effort has been made to ensure the quality and accuracy of the data, the Greater London Authority is not responsible for any inaccuracies and/or mistakes in the information provided.

HUD furnishes technical and professional assistance in planning, developing and managing these developments. Public Housing Developments are depicted as a distinct address chosen to represent the general location of an entire Public Housing Development, which may be comprised of several buildings scattered across a community. The building with the largest number of units is selected to represent the location of the development. 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 Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Developments Date Updated: Q1 2025

This dataset contains the amount of social housing units of the Netherlands per urban area, as well as the intensity of their spatial autocorrelation and its proportion compared to the total housing stock, for the year 2023. Original data comes from Statistics Netherlands (Centraal Bureau voor de Statistiek) released for 100 m x 100 m grid cells covering a large share of the Dutch territory. Grid cells with missing values were excluded from the analysis. The spatial autocorrelation of social housing was calculated with urban area-level and U-style computations of Global Moran's I based on the share of social housing units compared to the total housing stock of every grid cell. Limits and definition of urban areas are extracted from the OECD. Data show considerable variation in the levels of social housing and its spatial concentration among Dutch urban areas.

This project combines data extraction, predictive modeling, and geospatial mapping to analyze housing trends in Mercer County, New Jersey. It consists of three core components: Census Data Extraction: Gathers U.S. Census data (2012–2022) on median house value, household income, and racial demographics for all census tracts in the county. It accounts for changes in census tract boundaries between 2010 and 2020 by approximating values for newly defined tracts. House Value Prediction: Uses an LSTM model with k-fold cross-validation to forecast median house values through 2025. Multiple feature combinations and sequence lengths are tested to optimize prediction accuracy, with the final model selected based on MSE and MAE scores. Data Mapping: Visualizes historical and predicted housing data using GeoJSON files from the TIGERWeb API. It generates interactive maps showing raw values, changes over time, and percent differences, with customization options to handle outliers and improve interpretability. This modular workflow can be adapted to other regions by changing the input FIPS codes and feature selections.

Abstract This work reflects on the vertical growth by self-construction of the favela Nova Jaguaré, as result of the irregular appropriation of undeveloped spaces, in the interstices of social housing projects implemented in the period from 2014 to 2019. Through surveys carried out locally, it presents cartographic information that identifies new occupations and uses located in areas of interstices of social housing projects already implanted in the place. It presents a qualitative study, using bibliographic analysis based on the works of authors such as Reinhard Goethert, Pedro Abramo, Samuel Jaramillo, Nabil Bonduki and Rem Koolhaas among others, in addition to data obtained through on-site surveys and interviews conducted with community leaders. It is based on the concept of Incremental Housing, understood as the progressive housing construction, built in phases, as one of the elements that facilitate the occupation and real estate exploitation of degraded areas. As a result, it points out that such occupations are reflection of several factors, among them: socioeconomic factors, including precarious labor markets; the need to enable better access to urban infrastructure; seamless negotiations in an informal market in addition to the need / possibility to generate an increase in the family budget through self-construction.

these are the Replication files for: How Global is the Affordable Housing Crisis? accepted by the International Journal of Housing Markets and Analysis

Abstract This article discusses, based on interviews with several specialists, the main factors that enable/hinder the production of social housing in downtown areas of large urban centers. The transformation process of real estate into global financial assets and the analysis of housing production by the State in Brazilian metropolises are references for an extended reflection on the theme. The city of São Paulo is the main object of the discussions, researches and data presented in this work. At the end of the article, some notes are presented in order to contribute to the maturation of the debate on the development of housing policies for downtown areas of Brazilian metropolises.

Data is provided in .xlsx format within this repository. The data is taken from publicly available datasets published annually by the Regulator of Social Housing, including:

Global Accounts and Value for Money metrics data (accessed at https://www.gov.uk/government/collections/global-accounts-of-housing-providers) Statistical Data Return (accessed at https://www.gov.uk/government/collections/private-registered-provider-social-housing-stock-in-england)

The data covers the financial years from 2016/17 to 2020/21. R scripts to replicate the analysis are available from a separate repository (doi: 10.15131/shef.data.23523471).

Abstract This paper presents a method to evaluate the perceived value expected by technicians from public institutions and received by users of Brazilian social housing. The development of the method involved the structuring of a questionnaire, a sample design, data collection and analysis. The method was implemented and enhanced during three studies that evaluated social housing projects from the Programa Integrado Entrada da Cidade (PIEC) in Porto Alegre, RS, Brazil. The main contribution of the study is the proposition of an evaluation method, which is operated in three stages. The first stage aims to identify the levels that make up a hierarchical mapping of expected value and the customization of the data collection tool. The second stage aims to build a protocol for the collection, analysis and processing of data. The third stage consists of a combined analysis research data and the comparison between the value expected by technicians and that perceived by the users through a hierarchical value mapping, and the dissemination of the results to improve future projects. The main results of this study include the identification of benefits perceived by users but not expected by technicians, as well as an explanation of the more abstract levels present in users' perceptions, which translate the objective used in Brazilian housing programs.

Update 29-04-2020: The data is now split into two files based on the variable collection frequency (monthly and yearly). Additional variables added: area size in hectares, number of jobs in the area, number of people living in the area.

Context

I have been inspired by Xavier and his work on Barcelona to explore the city of London! 🇬🇧 💂

Content

The datasets is primarily centered around the housing market of London. However, it contains a lot of additional relevant data: - Monthly average house prices - Yearly number of houses - Yearly number of houses sold - Yearly percentage of households that recycle - Yearly life satisfaction - Yearly median salary of the residents of the area - Yearly mean salary of the residents of the area - Monthly number of crimes committed - Yearly number of jobs - Yearly number of people living in the area - Area size in hectares

The data is split by areas of London called boroughs (a flag exists to identify these), but some of the variables have other geographical UK regions for reference (like England, North East, etc.). There have been no changes made to the data except for melting it into a long format from the original tables.

Acknowledgements

The data has been extracted from London Datastore. It is released under UK Open Government License v2 and v3. The underlining datasets can be found here: https://data.london.gov.uk/dataset/uk-house-price-index https://data.london.gov.uk/dataset/number-and-density-of-dwellings-by-borough https://data.london.gov.uk/dataset/subjective-personal-well-being-borough https://data.london.gov.uk/dataset/household-waste-recycling-rates-borough https://data.london.gov.uk/dataset/earnings-place-residence-borough https://data.london.gov.uk/dataset/recorded_crime_summary https://data.london.gov.uk/dataset/jobs-and-job-density-borough https://data.london.gov.uk/dataset/ons-mid-year-population-estimates-custom-age-tables

Cover photo by Frans Ruiter from Unsplash

Inspiration

The dataset lends itself for extensive exploratory data analysis. It could also be a great supervised learning regression problem to predict house price changes of different boroughs over time.

Abstract In Brazil, recent research has pointed out resident retention as one of the problems in social housing projects. This study proposes to measure the sense of community. Based on systematizing existing tools, a questionnaire was sent to 274 housing units with 95% confidence level (α) and 5 (five) margin of error (E) to evaluate socioeconomic, behavioral and spatial data. Two other methods were also adopted: observation in loco and mapping neighbourhood relations. The study overlaps the mapped data as a multidimensional analysis to compare the variables. The results reveal spatial organisation guidelines to develop a sense of community and shows evidence of areas where there is a movement of people and social interactions. They also point out socioeconomic and behavioral aspects associated with constructing a sense of community, such as a greater number of residents, the satisfaction of living in the area and the expectation of remaining in the neighbourhood. This research contributes to synthesising existing tools, empirical studies in a case study, discussing spatial organisation of social housing developments and measuring factors considered subjective of the built environment linked to the quality of life.