Abstract A number of research studies have analysed the production of social housing (SH) in Brazil, verifying its poor performanc, and its inability to satisfy residents’ needs, particularly if we consider the total time of permanence in the housing units. The designs lack both diversity and flexibility, having serious functional problems due to the lack of small dimensions of the different areas. Hence, the aim of this study is to develop a practical method to support decision making in SH projects. The investigation adopted the Design Science Research approach and was structured in five main steps: (1) identifying the problem, (2) understanding the theme, (3) proposition of tools, (4) evaluation of tools, and (5) organisation of the contributions. The conceptual method is oriented to decision making in projects developed with the BIM (Building Information Modeling), focusing on functionality and flexibility. The method consists of a set of articulated instruments that allow the evaluation and guidance of the designers so that they comply with the criteria of functionality and flexibility. The device demonstrated to be effective in use when applied in a design workshop with professionals. It was also evaluated by researchers specialised in the subject. The research contributed to a review and systematisation of functionality and flexibility requirements and, in the practical field, to a set of operational instruments intended for the use of social housing architecture professionals.

The governments statistical data on social housing leasing and management over the years

In 2021, Allegheny County Economic Development (ACED), in partnership with Urban Redevelopment Authority of Pittsburgh(URA), completed the a Market Value Analysis (MVA) for Allegheny County. This analysis services as both an update to previous MVA’s commissioned separately by ACED and the URA and combines the MVA for the whole of Allegheny County (inclusive of the City of Pittsburgh). 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. This MVA utilized data that helps to define the local real estate market. The data used covers the 2017-2019 period, and data used in the analysis includes: Residential Real Estate Sales Mortgage Foreclosures Residential Vacancy Parcel Year Built Parcel Condition Building Violations Owner Occupancy 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. Please refer to the presentation and executive summary for more information about the data, methodology, and findings.

Asset management practices for provincially, territorially, regionally and municipally owned social and affordable housing assets for all provinces and territories. Values are presented for 2016.

IntroductionMeasuring long-term housing outcomes is important for evaluating the impacts of services for individuals with homeless experience. However, assessing long-term housing status using traditional methods is challenging. The Veterans Affairs (VA) Electronic Health Record (EHR) provides detailed data for a large population of patients with homeless experiences and contains several indicators of housing instability, including structured data elements (e.g., diagnosis codes) and free-text clinical narratives. However, the validity of each of these data elements for measuring housing stability over time is not well-studied.MethodsWe compared VA EHR indicators of housing instability, including information extracted from clinical notes using natural language processing (NLP), with patient-reported housing outcomes in a cohort of homeless-experienced Veterans.ResultsNLP achieved higher sensitivity and specificity than standard diagnosis codes for detecting episodes of unstable housing. Other structured data elements in the VA EHR showed promising performance, particularly when combined with NLP.DiscussionEvaluation efforts and research studies assessing longitudinal housing outcomes should incorporate multiple data sources of documentation to achieve optimal performance.

To revitalize and utilize existing private residential housing, conduct rental housing matching, and provide rental assistance to low-income families, vulnerable individuals, and those in need of housing for employment or education purposes at rents lower than those in the market through leasing or management arrangements.

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.

Property management of the state's social housing assets including modifications and maintenance.

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

Graph and download economic data for Data Processing and Other Purchased Computer Services for Home Health Care Services, All Establishments, Employer Firms (DISCONTINUED) (EXPDPSEF6216ALLEST) from 2012 to 2017 about employer firms, computers, purchase, processed, establishments, health, expenditures, services, housing, and USA.

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.

Challenges seen in the implementation of public policies in Brazil have fostered a series of debates, among which we highlight the ones related to the theoretical models of policy analysis and their limitations in face of a polycentric and fractal perspective of public policies. Starting from a specific thematic policy (social housing), this paper sustains a critical point of view about mono-disciplinary studies. However, one cannot deny the complexity of this task. In fact, understand the advances of Brazilian social housing in the last decade, in a multi-disciplinary and integrated way, is a challenge. With the objective to advance in this area, this paper aims to evaluate theoretical models of public policies and, from these elements, propose constructs to support future theoretical models to investigate social housing policy in Brazil.

Information system(s) used for management of municipally owned social and affordable housing assets for all provinces and territories, by urban and rural and population size. Values are presented for 2016.

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.

Affordable Housing - This indicator shows the percentage of housing units sold that are affordable on the median teacher’s salary. Affordable housing can improve health by providing greater stability and reducing stress. Having affordable housing can allow family resources to be used for other needs like healthy food and healthcare. Link to Data Details

This is the R script used for the analysis for Socio-spatial stratification of housing tenure trajectories in Sweden – A longitudinal cohort study. Note that we cannot share the micro-data used for this analysis because it belongs to the SCB, Statistics Sweden. To arrange access to Swedish micro-data go to: https://www.scb.se/en/services/ordering-data-and-statistics/ordering-microdata/

Abstract Housing transformations have been studied over time. Numerous Post-Occupancy-Evaluation (POE) studies reveal user-initiated alterations occurring in Brazilian Social Housing (SH). These transformations have specific objectives, such as additional space, refurbishment, and improvement, or upgrading. However, results of transformations are not always positive, and may affect dwellers’ well-being through losses in environmental comfort. A critical investigation using the Systematic Literature Review (SLR) method analysed forty-seven studies to understand what needs and requirements stimulate SH projects' upgrading and how transformations impact well-being. In addition, an in-depth analysis was made to assess aspects of environmental comfort, safety, design, layout and economic aspects that affect people’s well-being, and improve their quality of life. Results contribute to supporting upgrading processes of existing SH and to guide the improved design of new SH projects based on the desires and well-being requirements of low-income families.