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.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q1 2025 about sales, housing, and USA.

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.

This data collection provides information on the characteristics of a national sample of housing units, including apartments, single-family homes, mobile homes, and vacant housing units. Unlike previous years, the data are presented in ten separate parts: Part 1, Work Done Record (Replacement or Additions to the House), Part 2, Housing Unit Record (Main Record), Part 3, Worker Record, Part 4, Mortgages (Owners Only), Part 5, Manager and Owner Record (Renters Only), Part 6, Person Record, Part 7, Ratio Verification, Part 8, Mover Group Record, Part 9, Recodes (One Record per Housing Unit), and Part 10, Weights. 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. (Source: ICPSR, retrieved 06/28/2011)

The AHS is the largest, regular national housing sample survey in the United States. The U.S. Census Bureau conducts the AHS to obtain up-to-date housing statistics for the Department of Housing and Urban Development (HUD). The AHS national survey was conducted annually from 1973-1981 and biennially (every two years) since 1983. Metropolitan area surveys have been conducted annually or biennially since 1974.

The metropolitan survey is conducted in even-numbered years, cycling through a set of 41 metropolitan areas, surveying each one about once every six 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 eight separate parts: Part 1, Work Done Record (Replacement or Addition to the House), Part 2, Worker 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, Part 7, Ratio Verification, and Part 8, 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. (Source: ICPSR, retrieved 06/28/2011)

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q1 2025 about sales, median, housing, and USA.

Global Affordable Housing market size is expected to reach $75.95 billion by 2029 at 5.9%, easy access to home loans propels growth in the affordable housing market

The Ipsos Canada Winter 2004 RBC Housing Study analyzes home ownership trends in Canada. Participants to the survey reveal key information on current home ownership status and short-term intentions, mortgage financing, values of property, as well as credit knowledge. This survey has a sample of 2,158 respondents. The study consists of original microdata, statistical tables, as well as reports and presentations.

Affordable Housing Market Outlook

According to our latest research, the affordable housing market size reached USD 69.2 billion globally in 2024, driven by rapid urbanization, supportive government policies, and rising demand for cost-effective housing solutions. The market is projected to expand at a robust CAGR of 6.1% from 2025 to 2033, reaching an estimated USD 117.4 billion by the end of the forecast period. The growth is primarily attributed to increasing urban migration, widening income disparities, and a surge in public and private investments aimed at addressing the global housing deficit. As per our latest research, the affordable housing sector is undergoing significant transformation as stakeholders focus on innovative construction methods, sustainable materials, and digital technologies to streamline project delivery and reduce costs.

One of the primary growth drivers for the affordable housing market is the escalating rate of urbanization, particularly in emerging economies. Urban populations are swelling at an unprecedented pace, with millions migrating to cities in search of better employment opportunities and improved living standards. This mass migration has led to a surge in demand for affordable, quality housing, placing immense pressure on urban infrastructure and local governments. Consequently, both public and private sector players are ramping up investments in affordable housing projects, leveraging innovative financing models and partnerships to bridge the housing gap. Furthermore, the emergence of smart city initiatives and sustainable urban planning is fostering the development of integrated, affordable housing solutions that cater to the diverse needs of low- and middle-income populations.

Another significant factor propelling the affordable housing market is the increasing involvement of governments and international organizations in addressing the global housing crisis. Numerous policy interventions, such as subsidies, tax incentives, and relaxed regulatory frameworks, are being introduced to stimulate the supply of affordable homes. Governments are also collaborating with private developers through public-private partnerships (PPPs) to expedite project execution and ensure long-term sustainability. Additionally, multilateral agencies and non-governmental organizations are providing technical and financial assistance to support large-scale affordable housing initiatives, particularly in regions with acute housing shortages. These concerted efforts are not only enhancing access to affordable housing but also fostering socio-economic development and reducing urban poverty.

Technological advancements in construction methods and materials are further accelerating the growth of the affordable housing market. The adoption of modular and prefabricated construction techniques is enabling developers to deliver high-quality housing units at lower costs and within shorter timeframes. These innovative approaches are also contributing to improved energy efficiency, reduced environmental impact, and enhanced structural durability. Moreover, the integration of digital technologies, such as Building Information Modeling (BIM) and project management software, is streamlining the design, planning, and execution of affordable housing projects. As a result, stakeholders are increasingly embracing technology-driven solutions to optimize resource utilization, minimize risks, and ensure compliance with stringent regulatory standards.

From a regional perspective, Asia Pacific continues to dominate the affordable housing market, accounting for the largest share in 2024, followed by North America and Europe. The region's rapid urbanization, burgeoning population, and proactive government policies are driving significant investments in affordable housing infrastructure. Countries such as China, India, and Indonesia are at the forefront, implementing ambitious housing schemes and leveraging innovative construction technologies to address the growing demand. Meanwhile, developed regions like North America and Europe are witnessing renewed interest in affordable housing, fueled by rising property prices, income inequality, and shifting demographic trends. Latin America and the Middle East & Africa are also emerging as promising markets, supported by favorable regulatory environments and increased foreign direct investments.

Despite the pandemic's broader economic disruptions, low interest rates in 2020 initially fueled a housing market boom driven by work-from-home orders and a shift toward residential construction. This surge was a lifeline for builders amid economic turbulence. However, the tide turned in 2022 and 2023 as the Federal Reserve's interest rate hikes curbed housing investments, dampening consumer enthusiasm and slowing residential construction activity. Low housing stock and rate cuts late in 2024 led to growth in single-family housing starts, boosting revenue. Single-family home development climbed in more affordable and less densely populated areas in 2024, but new multifamily developments have plummeted. Industry revenue has been climbing at a CAGR of 0.8% over the past five years to total an estimated $233.5 billion in 2025, including an estimated increase of 0.2% in 2025 alone. The initial boom in 2020 and 2021 led to one of the most significant expansions in home-building in recent memory, yet interest rate hikes soon tempered this growth. As smaller-scale developers struggled with escalating construction costs and regulatory hurdles, larger, financially robust companies like DR Horton, Lennar and PulteGroup managed to thrive and expand their operations. These larger companies maximized their market share, leveraging their resources to navigate the challenging economic climate and maintain momentum despite the pressures of rising material costs and labor shortages. These rising material costs and labor shortages have driven up purchase and wage costs, contributing to profit declines over the past five years. Expected interest rate cuts will boost housing developers. Developers will benefit from these favorable conditions, especially those who strategically invest in less densely populated areas to meet the growing appetite for affordable housing. Rate cuts will also provide relief to smaller housing developers more sensitive to interest rate fluctuations. Sustainability also looms on the horizon, with tax incentives and energy-efficient building standards encouraging developers to explore eco-friendly construction. Still, rising material costs and labor shortages will continue to stifle profit growth and increase housing prices. Larger companies will continue to gain market share, strategically developing homes near areas with strong job growth near new large manufacturing facilities. Industry revenue is forecast to expand at a CAGR of 1.4% to total an estimated $250.6 billion through the end of 2030.

The 2015 American Housing Survey marks the first release of a newly integrated national sample and independent metropolitan area samples. The 2015 release features many variable name revisions, as well as the integration of an AHS Codebook Interactive Tool available on the U.S. Census Bureau We site. This data collection provides information on the characteristics of a national sample of housing units in 2015, including apartments, single-family homes, mobile homes, and vacant housing units. Data from the 15 largest metropolitan areas in the United States are included in the national sample survey (the AHS 2015 Metropolitan Data are also available as ICPSR 36805). The data are presented in three separate parts: Part 1, Household Record (Main Record), Part 2, Person Record, and Part 3, Project Record. Household Record data includes questions about household occupancy and tenure, household exterior and interior structural features, household equipment and appliances, housing problems, housing costs, home improvement, neighborhood features, recent moving information, income, and basic demographic information. The household record data also features four rotating topical modules: Arts and Culture, Food Security, Housing Counseling, and Healthy Homes. Person Record data includes questions about personal disabilities, income, and basic demographic information. Finally, the Project Record data includes questions about home improvement projects. Specific questions were asked about the types of projects, costs, funding sources, and year of completion.

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.

Community Development - Birmingham Housing Study web site

Housing Starts in the United States increased to 1321 Thousand units in June from 1263 Thousand units in May of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

This dataset encompasses the foundations and findings of a study titled "Housing Wealth Distribution, Inequality, and Residential Satisfaction," highlighting the evolution of residential properties from mere consumption goods to significant assets for wealth accumulation. Since the 1980s, with financial market deregulation in the UK, there has been a noticeable shift in homeownership patterns and housing wealth's role. The liberalisation of the banking sector, particularly mortgage lending, facilitated a significant rise in homeownership rates from around 50% in the 1970s to over 70% in the early 2000s, stabilizing at 65% in recent years. Concurrently, housing wealth relative to household annual gross disposable income has seen a considerable increase, underscoring the growing importance of residential properties as investment goods.

The study explores the multifaceted impact of housing wealth on various aspects of life, including retirement financing, intergenerational wealth transfer, health, consumption, energy conservation, and education. Residential satisfaction, defined as the overall experience and contentment with housing, emerges as a critical factor influencing subjective well-being and labor mobility. Despite the evident influence of housing characteristics, social environment, and demographic factors on residential satisfaction, the relationship between housing wealth and satisfaction remains underexplored.

To bridge this gap, the research meticulously assembles data from different surveys across the UK and the USA spanning 1970 to 2019, despite challenges such as data compatibility and measurement errors. Initial findings reveal no straightforward correlation between rising house prices and residential satisfaction, mirroring the Easterlin Paradox, which suggests that happiness levels do not necessarily increase with income growth. This paradox is dissected through the lenses of social comparison and adaptation, theorizing that relative income and the human tendency to adapt to changes might explain the stagnant satisfaction levels despite increased housing wealth.

Further analysis within the UK context supports the social comparison hypothesis, suggesting that disparities in housing wealth distribution can lead to varied satisfaction levels, potentially exacerbating societal inequality. This phenomenon is not isolated to developed nations but is also pertinent to developing countries experiencing rapid economic growth alongside widening income and wealth gaps. The study concludes by emphasizing the significance of considering housing wealth inequality in policy-making, aiming to mitigate its far-reaching implications on societal well-being.

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

Liberty Housing Study Dashboard

The Department of Housing and Urban Development funds and provides oversight for the survey. The U.S. Census Bureau collects the data. For more than forty years the American Housing Survey has provided researchers, policy makers, academics, and others in the housing and urban planning professions with the most comprehensive up-to-date information on the size and composition of U.S. housing stock.