This dataset represents affordable housing development/preservation that is supported by the State of Connecticut Department of Housing. It includes units that have been completed under the current administration and when, or are actively under construction. Completion dates/percentages for HTCC only developments are based upon the most recently submitted quarterly statements. Projects labeled as "Under Construction" include those that have had reported greater than 0% completed. Projects labeled as "In Progress" include those that have received funding commitments but have not yet reported a % complete. * In some cases the Total DOH & CHFA funding exceeds Total Project Cost due to CHFA construction financing. Only projects with a Board Approval (or application date if Board Approval N/A), Initial Close, Final Close or Construction Completion % date after 1/1/19 are to be included on list Any Subtotal/Total amounts may be inflated due to multiple applications being captured for a single project All projects specified as Family in the "Family or Elderly" column are non-age restrictive Projects that have Final Closed prior to 2019 are not shown Projects that were approved for funding or under construction but not yet complete, prior to 2019 are included *Total Equity is an aggregate of 4%, 9%, SHPO, & HTCC equities

This dataset contains more detailed information on the recommendations and resolutions identified by the Office of the City Auditor in their special request report on gentrification and displacement. See report at: To complete this special request project, we categorized recommendations and resolutions based on the following definitions:

Directly related to displacement or gentrification ("direct"): Clearly and immediately impacts the population experiencing gentrification and displacement (e.g., creation of affordable housing units).

Indirectly related to displacement or gentrification ("indirectly"): An initiative designed to support direct efforts (such as monitoring or increasing funding for staffing). Alternatively, an indirect initiative may be one that is designed to affect the broad population (e.g., overall tax decrease), or the language is so vague or broad that the precise intention is unclear.

Actionable recommendations and resolutions ("actionable"): Recommendations that are specific and implementable as written; recommendations that detail the specific site of an action.

Not actionable recommendations and resolutions ("not actionable"): Things that do not appear to be legal; recommendations that are not implementable as worded or that would require significant additional clarification/research.

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.

Comprehensive dataset of 23,148 Housing complexes in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Abstract Student housing was discussed in the literature under the aspects of residential preference and satisfaction; inducing factors of behavior and demand in the real estate market; and influencing variables in choosing the habitation. However, the works that approach topics about real estate meant for students and the new products of the Brazilian real estate, the ones named Student Housing (SH), are scarce. Within this context, the objective of this research is to propose guidelines to orient the development process of SH buildings. For this purpose, the work proposed, initially, through a comparative study of seven SH real states, to investigate the main characteristics of this product model. Such information, added to the bibliographic review of the literature, allowed the exploration, through a survey, of university students’ opinion about the importance of four pre-established requirements. The results revealed that many attributes present in new Brazilian SH buildings are not considered important or are indifferent from the students' point of view, which shows additional and unnecessary costs for investors. In contrast, the study identified several requirements valuable to users. Based on thist, twelve guidelines were suggested to orient the student market designers, developers, and investors in the development of their products.

The Rochester 2018 Housing Market Study was commissioned to aid the City in understanding the patterns of housing investment and disinvestment since 2007 and formulate actionable strategies to include in the comprehensive plan, Rochester 2034. The study drew upon a wide range of quantitative sources, including U.S. Census Bureau and local data and qualitative sources including stakeholder focus groups and expert interviews. The study contains four parts:Part 1: Rochester's Housing MarketPart 2: Stretegic Direction for Rochester's Housing MarketPart 3: Housing Market InterventionsPart 4: Recommendations

Community Development - Birmingham Housing Study web site

Section 62 of Public Act 21-2, June Special Session, as modified by Section 71 of Public Act 23-204, required the Office of Policy and Management (OPM) to conduct a “Housing and Segregation Study”. This dataset is one of the products of the Housing and Segregation Study. This dataset shows all the data collected and analyzed for the Housing and Segregation Study, including housing data, population and socioeconomic data from the Census, and segregation/economic indices for various Connecticut geographies.

Comprehensive dataset of 43,216 Housing developments in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Government Code section 65400 requires that each city, county, or city and county, including charter cities, prepare an annual progress report (APR) on the status of the housing element of its general plan and progress in its implementation. This dataset includes information reported to the Department of Housing and Community Development (HCD) by local jurisdictions on their APR form. Additional information about annual progress reports (APR), including the form, instructions, and definition can be found on HCD’s website here: https://www.hcd.ca.gov/planning-and-community-development/annual-progress-reports.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.

The metropolitan survey is conducted in even-numbered years, cycling through a set of 41 metropolitan areas, surveying each one about once every 6 years. This data collection provides information on the characteristics of a metropolitan sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. The data are presented in seven separate parts: Part 1, Work Done Record (Replacement or Addition to the House), Part 2, Journey to Work Record, Part 3, Mortgages (Owners Only), Part 4, Housing Unit Record (Main Record), Recodes (One Record per Housing Unit), and Weights, Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, and Part 7, Mover Group Record. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy.

Comprehensive dataset of 3,491 Low income housing programs in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 433 Low income housing programs in California, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Section 62 of Public Act 21-2, June Special Session, as modified by Section 71 of Public Act 23-204, required the Office of Policy and Management (OPM) to conduct a “Housing and Segregation Study”. This dataset is one of the products of the Housing and Segregation Study. This dataset shows "Income Segregation (Gini Index)" for various Connecticut geographies.

Comprehensive dataset of 58 Low income housing programs in Arizona, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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.

In late 2016, the URA, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for the City of Pittsburgh. 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 neighborhood 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. Pittsburgh’s 2016 MVA utilized data that helps to define the local real estate market between July, 2013 and June, 2016: • Median Sales Price • Variance of Sales Price • Percent Households Owner Occupied • Density of Residential Housing Units • Percent Rental with Subsidy • Foreclosures as a Percent of Sales • Permits as a Percent of Housing Units • Percent of Housing Units Built Before 1940 • Percent of Properties with Assessed Condition “Poor” or worse • Vacant Housing Units as a Percentage of Habitable 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 URA 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.

​​​​This Annual Housing Report​ (Report) describes the affordable housing activities of the Enterprises and meets the reporting requirements of the Federal Housing Enterprises Financial Safety and Soundness Act of 1992

The Federal Housing Finance Agency (FHFA) was established by the Housing and Economic Recovery Act of 2008 (HERA) and is responsible for the supervision, regulation, and housing mission oversight of the Federal National Mortgage Association (Fannie Mae), the Federal Home Loan Mortgage Corporation (Freddie Mac), and the Federal Home Loan Bank (FHLBank) System, which includes 11 FHLBanks and the Office of Finance. FHFA’s mission is to ensure its regulated entities fulfill their mission by operating in a safe and sound manner to serve as a reliable source of liquidity for equitable and sustainable housing finance and community investment throughout the economic cycle. Since 2008, FHFA has also served as conservator of Fannie Mae and Freddie Mac (collectively, the Enterprises).