70 datasets found
  1. Number of home sales in the U.S. 2014-2024 with forecast until 2026

    • statista.com
    • ai-chatbox.pro
    Updated Jun 20, 2025
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    Statista (2025). Number of home sales in the U.S. 2014-2024 with forecast until 2026 [Dataset]. https://www.statista.com/statistics/275156/total-home-sales-in-the-united-states-from-2009/
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    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The number of home sales in the United States peaked in 2021 at almost ************* after steadily rising since 2018. Nevertheless, the market contracted in the following year, with transaction volumes falling to ***********. Home sales remained muted in 2024, with a mild increase expected in 2025 and 2026. A major factor driving this trend is the unprecedented increase in mortgage interest rates due to high inflation. How have U.S. home prices developed over time? The average sales price of new homes has also been rising since 2011. Buyer confidence seems to have recovered after the property crash, which has increased demand for homes and also the prices sellers are demanding for homes. At the same time, the affordability of U.S. homes has decreased. Both the number of existing and newly built homes sold has declined since the housing market boom during the coronavirus pandemic. Challenges in housing supply The number of housing units in the U.S. rose steadily between 1975 and 2005 but has remained fairly stable since then. Construction increased notably in the 1990s and early 2000s, with the number of construction starts steadily rising, before plummeting amid the infamous housing market crash. Housing starts slowly started to pick up in 2011, mirroring the economic recovery. In 2022, the supply of newly built homes plummeted again, as supply chain challenges following the COVID-19 pandemic and tariffs on essential construction materials such as steel and lumber led to prices soaring.

  2. F

    All-Transactions House Price Index for the United States

    • fred.stlouisfed.org
    json
    Updated May 27, 2025
    + more versions
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    (2025). All-Transactions House Price Index for the United States [Dataset]. https://fred.stlouisfed.org/series/USSTHPI
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    jsonAvailable download formats
    Dataset updated
    May 27, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q1 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

  3. A

    Affordable Housing Market Report

    • promarketreports.com
    doc, pdf, ppt
    Updated Feb 11, 2025
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    Pro Market Reports (2025). Affordable Housing Market Report [Dataset]. https://www.promarketreports.com/reports/affordable-housing-market-26535
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    ppt, doc, pdfAvailable download formats
    Dataset updated
    Feb 11, 2025
    Dataset authored and provided by
    Pro Market Reports
    License

    https://www.promarketreports.com/privacy-policyhttps://www.promarketreports.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    Affordable Housing Market Analysis The global affordable housing market is projected to reach $1,983.52 billion by 2033, exhibiting a CAGR of 4.71% from 2025 to 2033. The rising population, urbanization, affordability crisis, and supportive government policies are the primary drivers fueling market growth. The increasing demand for affordable single-family homes, multi-family units, and townhouses, coupled with the adoption of innovative construction methods like prefabrication, 3D printing, and sustainable construction, are key trends shaping the market. The market faces restraints such as escalating land and construction costs, regulatory challenges, and the shortage of skilled labor. Nevertheless, the emergence of crowdfunding platforms and non-profit organizations providing financial assistance, as well as government subsidies and tax incentives, are expected to mitigate these constraints. The market is segmented based on housing type, funding source, construction method, and target demographics. D.R. Horton, Taylor Morrison, PulteGroup, Zillow, Hovnanian Enterprises, and Lennar Corporation are notable companies in the global affordable housing market, with operations in key regions like North America, Europe, and Asia Pacific. Recent developments include: Recent developments in the Affordable Housing Market have highlighted the urgent need for innovative housing solutions as governments and organizations strive to address the growing housing crisis exacerbated by economic challenges and population growth. Various nations are prioritizing policies that encourage public-private partnerships to stimulate investment in affordable housing initiatives. Additionally, the integration of sustainable building practices and smart technologies is gaining traction as stakeholders aim to improve energy efficiency while reducing construction costs. Recent collaborations among international entities and local governments focus on leveraging funding for housing projects, particularly in urban areas where demand is surging. Moreover, rising material costs and labor shortages are prompting stakeholders to explore alternative building materials and methods, including modular construction and 3D printing, to streamline processes. These trends underscore a collective commitment to creating equitable housing opportunities while navigating the complexities of market dynamics, aiming for significant progress by 2032. Overall, this evolving landscape reflects a concerted effort to promote affordability, sustainability, and accessibility in housing worldwide.. Key drivers for this market are: Green building technologies adoption Public-private partnerships expansion Innovative financing solutions development Urban regeneration projects implementation Digital platforms for housing access. Potential restraints include: rising urbanization, government initiatives; increasing housing demand; socioeconomic disparities; affordable financing options.

  4. Approximated hazard rate.

    • plos.figshare.com
    xls
    Updated Sep 6, 2024
    + more versions
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    Kwangwon Ahn; Minhyuk Jeong; Jinu Kim; Domenico Tarzia; Ping Zhang (2024). Approximated hazard rate. [Dataset]. http://doi.org/10.1371/journal.pone.0309483.t005
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    xlsAvailable download formats
    Dataset updated
    Sep 6, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Kwangwon Ahn; Minhyuk Jeong; Jinu Kim; Domenico Tarzia; Ping Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Housing markets are often characterized by price bubbles, and governments have instituted policies to stabilize them. Under this circumstance, this study addresses the following questions. (1) Does policy tightening change expectations in housing prices, revealing a regime change? (2) If so, what determines the housing market’s reaction to policy tightening? To answer these questions, we examine the effects of policy tightening that occurred in 2016 on the Chinese housing market where a price boom persisted in the post-2000 period. Using a log-periodic power law model and employing a modified multi-population genetic algorithm for parameter estimation, we find that tightening policy in China did not cause a market crash; instead, shifting the Chinese housing market from faster-than-exponential growth to a soft landing. We attribute this regime shift to low sensitivity in the Chinese housing market to global perturbations. Our findings suggest that government policies can help stabilize housing prices and improve market conditions when implemented expediently. Moreover, policymakers should consider preparedness for the possibility of an economic crisis and other social needs (e.g., housing affordability) for overall social welfare when managing housing price bubbles.

  5. Residential Real Estate in China - Market Research Report (2015-2030)

    • ibisworld.com
    Updated Apr 1, 2025
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    IBISWorld (2025). Residential Real Estate in China - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/china/market-research-reports/residential-real-estate-industry/
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    Dataset updated
    Apr 1, 2025
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2015 - 2030
    Area covered
    China
    Description

    Revenue for the Residential Real Estate industry in China is expected to decrease at a CAGR of 9.8% over the five years through 2025. This trend includes an expected decrease of 9.6% in the current year.Since August 2020, the People's Bank of China and the China Banking and Insurance Regulatory Commission have proposed three debt indicators for real estate development and management companies through which the company's financial health can be rated. This new policy has exacerbated the company's debt pressure, making it unable to repay old debts by borrowing new debt. Some real estate companies faced a liquidity crisis.In 2022, the city's lockdown and laying-off caused by COVID-19 epidemic led to the pressure of delaying the delivery of houses. The industry's newly constructed and completed areas decreased significantly throughout the year. In addition, the epidemic has impacted sales in the industry, and some sales offices have been forced to close temporarily. In 2022, the residential sales area decreased by 26.8%, and the residential sales decreased by 31.2%.Industry revenue will recover at an annualized 0.7% over the five years through 2030. Over the next five years, the industry's drag on GDP will weaken, and industry growth will stabilize. However, high housing prices have become a major social problem in China. Under the measures on the principle that residential real estate is used for living, not speculation, the financial attributes of real estate will gradually weaken, and housing prices will tend to stabilize.

  6. U.S. metro areas at highest risk of a housing downturn in recession 2019

    • statista.com
    Updated Sep 14, 2021
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    Statista (2021). U.S. metro areas at highest risk of a housing downturn in recession 2019 [Dataset]. https://www.statista.com/statistics/1091659/housing-market-metro-highest-risk-downturn-recession-usa/
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    Dataset updated
    Sep 14, 2021
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    United States
    Description

    In a 2019 analysis, Riverside, California was the most at risk of a housing downturn in a recession out of the 50 largest metro areas in the United States. The Californian metro area received an overall score of 72.8 percent, which was compiled after factors such as home price volatility and average home loan-to-value ratio were examined.

  7. Affordable Housing Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Jun 30, 2025
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    Growth Market Reports (2025). Affordable Housing Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/affordable-housing-market-global-industry-analysis
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    pdf, pptx, csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    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.



  8. f

    Descriptive statistics.

    • figshare.com
    xls
    Updated Sep 6, 2024
    + more versions
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    Kwangwon Ahn; Minhyuk Jeong; Jinu Kim; Domenico Tarzia; Ping Zhang (2024). Descriptive statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0309483.t001
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    xlsAvailable download formats
    Dataset updated
    Sep 6, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Kwangwon Ahn; Minhyuk Jeong; Jinu Kim; Domenico Tarzia; Ping Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Housing markets are often characterized by price bubbles, and governments have instituted policies to stabilize them. Under this circumstance, this study addresses the following questions. (1) Does policy tightening change expectations in housing prices, revealing a regime change? (2) If so, what determines the housing market’s reaction to policy tightening? To answer these questions, we examine the effects of policy tightening that occurred in 2016 on the Chinese housing market where a price boom persisted in the post-2000 period. Using a log-periodic power law model and employing a modified multi-population genetic algorithm for parameter estimation, we find that tightening policy in China did not cause a market crash; instead, shifting the Chinese housing market from faster-than-exponential growth to a soft landing. We attribute this regime shift to low sensitivity in the Chinese housing market to global perturbations. Our findings suggest that government policies can help stabilize housing prices and improve market conditions when implemented expediently. Moreover, policymakers should consider preparedness for the possibility of an economic crisis and other social needs (e.g., housing affordability) for overall social welfare when managing housing price bubbles.

  9. Global Financial Crisis: Fannie Mae stock price and percentage change...

    • statista.com
    Updated Sep 2, 2024
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    Statista (2024). Global Financial Crisis: Fannie Mae stock price and percentage change 2000-2010 [Dataset]. https://www.statista.com/statistics/1349749/global-financial-crisis-fannie-mae-stock-price/
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    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

  10. f

    Data from: Mitigating housing market shocks: an agent-based reinforcement...

    • tandf.figshare.com
    bin
    Updated Jul 10, 2024
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    Sedar Olmez; Alison Heppenstall; Jiaqi Ge; Corinna Elsenbroich; Dan Birks (2024). Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support [Dataset]. http://doi.org/10.6084/m9.figshare.26232214.v1
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    binAvailable download formats
    Dataset updated
    Jul 10, 2024
    Dataset provided by
    Taylor & Francis
    Authors
    Sedar Olmez; Alison Heppenstall; Jiaqi Ge; Corinna Elsenbroich; Dan Birks
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns.

  11. Great Recession: real house price index in Europe's weakest economies...

    • statista.com
    Updated Sep 2, 2024
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    Statista (2024). Great Recession: real house price index in Europe's weakest economies 2005-2011 [Dataset]. https://www.statista.com/statistics/1348857/great-recession-house-price-bubbles-eu/
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    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2005 - 2011
    Area covered
    Europe
    Description

    Portugal, Italy, Ireland, Greece, and Spain were widely considered the Eurozone's weakest economies during the Great Recession and subsequent Eurozone debt crisis. These countries were grouped together due to the similarities in their economic crises, with much of them driven by house price bubbles which had inflated over the early 2000s, before bursting in 2007 due to the Global Financial Crisis. Entry into the Euro currency by 2002 had meant that banks could lend to house buyers in these countries at greatly reduced rates of interest.

    This reduction in the cost of financing contributed to creating housing bubbles, which were further boosted by pro-cyclical housing policies among many of the countries' governments. In spite of these economies experiencing similar economic problems during the crisis, Italy and Portugal did not experience housing bubbles in the same way in which Greece, Ireland, and Spain did. In the latter countries, their real housing prices (which are adjusted for inflation) peaked in 2007, before quickly declining during the recession. In particular, house prices in Ireland dropped by over 40 percent from their peak in 2007 to 2011.

  12. T

    United Kingdom House Price Index

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +12more
    csv, excel, json, xml
    Updated Jun 15, 2025
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    TRADING ECONOMICS (2025). United Kingdom House Price Index [Dataset]. https://tradingeconomics.com/united-kingdom/housing-index
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    json, excel, xml, csvAvailable download formats
    Dataset updated
    Jun 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 1983 - Jun 30, 2025
    Area covered
    United Kingdom
    Description

    Housing Index in the United Kingdom decreased to 511.60 points in June from 511.80 points in May of 2025. This dataset provides - United Kingdom House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  13. f

    The best fit of log-periodic power law parameters.

    • figshare.com
    xls
    Updated Sep 6, 2024
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    Kwangwon Ahn; Minhyuk Jeong; Jinu Kim; Domenico Tarzia; Ping Zhang (2024). The best fit of log-periodic power law parameters. [Dataset]. http://doi.org/10.1371/journal.pone.0309483.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Sep 6, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Kwangwon Ahn; Minhyuk Jeong; Jinu Kim; Domenico Tarzia; Ping Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The best fit of log-periodic power law parameters.

  14. Global real estate bubble risk 2024, by market

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Global real estate bubble risk 2024, by market [Dataset]. https://www.statista.com/statistics/1060677/global-real-estate-bubble-risk/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Worldwide
    Description

    In 2024, Miami was the housing market most at risk, with a real estate bubble index score of ****. Tokyo and Zurich followed close behind with **** and ****, respectively. Any market with an index score of *** or higher was deemed to be a bubble risk zone.

  15. d

    Replication Data for: How Global is the Affordable Housing Crisis?...

    • search.dataone.org
    Updated Nov 22, 2023
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    Coupe, Tom (2023). Replication Data for: How Global is the Affordable Housing Crisis? International Journal of Housing Markets and Analysis [Dataset]. http://doi.org/10.7910/DVN/NVGSV7
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Coupe, Tom
    Description

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

  16. F

    All-Transactions House Price Index for Hartford-East Hartford-Middletown, CT...

    • fred.stlouisfed.org
    json
    Updated May 27, 2025
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    (2025). All-Transactions House Price Index for Hartford-East Hartford-Middletown, CT (MSA) [Dataset]. https://fred.stlouisfed.org/series/ATNHPIUS25540Q
    Explore at:
    jsonAvailable download formats
    Dataset updated
    May 27, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    Hartford County, Connecticut, Middletown, East Hartford
    Description

    Graph and download economic data for All-Transactions House Price Index for Hartford-East Hartford-Middletown, CT (MSA) (ATNHPIUS25540Q) from Q4 1977 to Q1 2025 about Hartford, CT, appraisers, HPI, housing, price index, indexes, price, and USA.

  17. Annual change of housing consumer price in U.S. cities 2000-2024

    • statista.com
    Updated Mar 20, 2025
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    Statista (2025). Annual change of housing consumer price in U.S. cities 2000-2024 [Dataset]. https://www.statista.com/statistics/196606/change-of-us-housing-consumer-price-index-since-2000/
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    Dataset updated
    Mar 20, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The consumer price of housing in urban areas of the United States increased by over four percent in 2024. 2022 and 2023 saw the largest price increases on a year-over-year basis since 2000. Meanwhile, 2010 was the only year in which housing prices decreased. One of the main reasons for that may have been the subprime mortgage crisis of 2007. During that period, the value of new residential construction put in place in the U.S. stagnated.

  18. F

    Real Residential Property Prices for China

    • fred.stlouisfed.org
    json
    Updated Jun 26, 2025
    + more versions
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    (2025). Real Residential Property Prices for China [Dataset]. https://fred.stlouisfed.org/series/QCNR628BIS
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    jsonAvailable download formats
    Dataset updated
    Jun 26, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required

    Description

    Graph and download economic data for Real Residential Property Prices for China (QCNR628BIS) from Q2 2005 to Q1 2025 about China, residential, HPI, housing, real, price index, indexes, and price.

  19. T

    Canada Average House Prices

    • tradingeconomics.com
    • ar.tradingeconomics.com
    • +8more
    csv, excel, json, xml
    Updated May 19, 2025
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    TRADING ECONOMICS (2025). Canada Average House Prices [Dataset]. https://tradingeconomics.com/canada/average-house-prices
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    json, csv, xml, excelAvailable download formats
    Dataset updated
    May 19, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 31, 2005 - May 31, 2025
    Area covered
    Canada
    Description

    Average House Prices in Canada decreased to 690900 CAD in May from 692400 CAD in April of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

  20. C

    China Commercial Real Estate Industry Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 7, 2025
    + more versions
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    Data Insights Market (2025). China Commercial Real Estate Industry Report [Dataset]. https://www.datainsightsmarket.com/reports/china-commercial-real-estate-industry-17458
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    doc, ppt, pdfAvailable download formats
    Dataset updated
    Mar 7, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    China
    Variables measured
    Market Size
    Description

    The China commercial real estate market, valued at $890 million in 2025, is projected to experience steady growth, exhibiting a compound annual growth rate (CAGR) of 3.49% from 2025 to 2033. This growth is fueled by several key drivers. Increasing urbanization and a burgeoning middle class are driving demand for modern office spaces, retail outlets, and logistics facilities. Government initiatives focused on infrastructure development and sustainable urban planning further contribute to the sector's expansion. The hospitality segment, while susceptible to fluctuations in tourism, is also expected to witness moderate growth, driven by increasing domestic and international travel. However, the market faces certain headwinds. Stringent regulatory policies, particularly concerning land acquisition and environmental concerns, could potentially constrain growth. Furthermore, fluctuating economic conditions and potential oversupply in certain segments could impact profitability and investment. The market is segmented into office, retail, industrial (logistics), and hospitality, each displaying unique growth trajectories. Office spaces are expected to see consistent demand driven by expansion of tech companies and service sectors. Retail is experiencing a shift towards experience-based retail and online-to-offline (O2O) models, while the industrial (logistics) segment benefits from e-commerce growth and improved supply chain infrastructure. Key players like China Aoyuan Group, Longfor, CapitaLand, and Wanda Group are actively shaping the market landscape through strategic acquisitions, developments, and operational efficiencies. The market's future trajectory will depend on the government’s regulatory approach, macroeconomic stability, and the ability of developers to adapt to evolving consumer preferences and technological advancements. The competitive landscape is characterized by both established giants and emerging players, leading to intensified competition and innovation. The concentration of development activity in major metropolitan areas like Beijing, Shanghai, and Guangzhou indicates regional disparities in growth. Despite challenges, the long-term outlook remains positive, driven by China's continued economic growth and urbanization. Strategic partnerships and technological integration are expected to become increasingly crucial for success within this dynamic market. Understanding these factors is vital for both domestic and international investors seeking opportunities in this lucrative sector. This report provides a detailed analysis of the China commercial real estate market, covering the period from 2019 to 2033. With a base year of 2025 and a forecast period extending to 2033, this study offers invaluable insights into the industry's dynamics, trends, and future prospects. It examines key segments including office, retail, industrial (logistics), and hospitality, providing crucial data for investors, developers, and industry professionals. This research incorporates high-impact events such as the recent acquisition of the Beijing Suning Life Plaza by CapitaLand. Recent developments include: May 2023: The Beijing Suning Life Plaza mixed-use complex was recently purchased from Suning for about USD 400 million by CapitaLand Investment Private Fund with the help of Cushman & Wakefield's Greater China Capital Markets division., April 2023: AIA put US$1.3 billion into a Shanghai office-retail complex, while Ping An paid about US$7 billion for industrial and office assets in Shanghai and Beijing. Insurers, including AIA and Ping An Life Insurance, are investing billions of dollars in mainland China properties, which are expected to remain an attractive asset class for insurers despite the property market downturn.. Key drivers for this market are: Foreign Investments driving the market, Implementation of government policies driving the market. Potential restraints include: Oversupply of commercial real estate, Increasing property prices affecting the growth of the market. Notable trends are: Technology and Innovation Driving the Market.

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Close
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Statista (2025). Number of home sales in the U.S. 2014-2024 with forecast until 2026 [Dataset]. https://www.statista.com/statistics/275156/total-home-sales-in-the-united-states-from-2009/
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Number of home sales in the U.S. 2014-2024 with forecast until 2026

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Dataset updated
Jun 20, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States
Description

The number of home sales in the United States peaked in 2021 at almost ************* after steadily rising since 2018. Nevertheless, the market contracted in the following year, with transaction volumes falling to ***********. Home sales remained muted in 2024, with a mild increase expected in 2025 and 2026. A major factor driving this trend is the unprecedented increase in mortgage interest rates due to high inflation. How have U.S. home prices developed over time? The average sales price of new homes has also been rising since 2011. Buyer confidence seems to have recovered after the property crash, which has increased demand for homes and also the prices sellers are demanding for homes. At the same time, the affordability of U.S. homes has decreased. Both the number of existing and newly built homes sold has declined since the housing market boom during the coronavirus pandemic. Challenges in housing supply The number of housing units in the U.S. rose steadily between 1975 and 2005 but has remained fairly stable since then. Construction increased notably in the 1990s and early 2000s, with the number of construction starts steadily rising, before plummeting amid the infamous housing market crash. Housing starts slowly started to pick up in 2011, mirroring the economic recovery. In 2022, the supply of newly built homes plummeted again, as supply chain challenges following the COVID-19 pandemic and tariffs on essential construction materials such as steel and lumber led to prices soaring.

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