100+ datasets found
  1. 🏡 Global Housing Market Analysis (2015-2024)

    • kaggle.com
    zip
    Updated Mar 18, 2025
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    Atharva Soundankar (2025). 🏡 Global Housing Market Analysis (2015-2024) [Dataset]. https://www.kaggle.com/datasets/atharvasoundankar/global-housing-market-analysis-2015-2024
    Explore at:
    zip(18363 bytes)Available download formats
    Dataset updated
    Mar 18, 2025
    Authors
    Atharva Soundankar
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

    📑 Column Descriptions

    Column NameDescription
    CountryThe country where the housing market data is recorded 🌍
    YearThe year of observation 📅
    Average House Price ($)The average price of houses in USD 💰
    Median Rental Price ($)The median monthly rent for properties in USD 🏠
    Mortgage Interest Rate (%)The average mortgage interest rate percentage 📉
    Household Income ($)The average annual household income in USD 🏡
    Population Growth (%)The percentage increase in population over the year 👥
    Urbanization Rate (%)Percentage of the population living in urban areas 🏙️
    Homeownership Rate (%)The percentage of people who own their homes 🔑
    GDP Growth Rate (%)The annual GDP growth percentage 📈
    Unemployment Rate (%)The percentage of unemployed individuals in the labor force 💼
  2. Factors Affecting USA National Home Prices Dataset

    • kaggle.com
    zip
    Updated Oct 30, 2023
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    Madhur Pant (2023). Factors Affecting USA National Home Prices Dataset [Dataset]. https://www.kaggle.com/madhurpant/factors-affecting-usa-national-home-prices
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    zip(28864 bytes)Available download formats
    Dataset updated
    Oct 30, 2023
    Authors
    Madhur Pant
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United States
    Description

    Factors Affecting USA National Home Prices:

    Overview:

    This dataset contains a comprehensive collection of indicators which dictate the housing prices in the United States.

    1. US Mortgage Rates:

    • The average interest rates on mortgage loans in the United States.
    • Used to track the cost of borrowing for housing and its impact on the real estate market.

    2. Gross Domestic Product (GDP):

    • The total monetary value of all goods and services produced within the United States during a specified period.
    • A fundamental measure of economic performance, reflecting the overall economic health and growth trends of the country.

    3. Unemployment Rates:

    • The percentage of the labor force that is currently unemployed and actively seeking employment.
    • A crucial indicator of labor market health and economic stability, influencing government policies and social welfare programs.

    4. FED Funds Rate:

    • The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, as set by the Federal Reserve.
    • This rate is a primary tool for monetary policy, influencing borrowing costs and, subsequently, overall economic activity.

    5. Population Growth:

    • The annual rate at which the U.S. population is changing, reflecting births, deaths, and migration.
    • Offers insights into demographic trends, which have implications for labor force, consumer markets, and social services planning.

    6. Consumer Price Index (CPI):

    • A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
    • A key indicator for assessing inflation or deflation, influencing consumer spending behavior and economic policy decisions.

    S&P Case-Shiller Housing Price Index (USA):

    • Measures changes in the prices of residential real estate properties over time, offering insight into the health and trends of the housing market in the United States.
    • Crucial for assessing the state of the housing market, including property values, trends, and their impact on the broader economy.
  3. U

    United States Home Construction Market Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Apr 23, 2025
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    Market Report Analytics (2025). United States Home Construction Market Report [Dataset]. https://www.marketreportanalytics.com/reports/united-states-home-construction-market-92174
    Explore at:
    pdf, doc, pptAvailable download formats
    Dataset updated
    Apr 23, 2025
    Dataset authored and provided by
    Market Report Analytics
    License

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

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

    The United States home construction market, valued at approximately $700 billion in 2025, is experiencing robust growth, projected to maintain a compound annual growth rate (CAGR) exceeding 3% through 2033. This expansion is fueled by several key factors. Firstly, a persistent housing shortage, particularly in desirable urban areas like New York City, Los Angeles, and San Francisco, continues to drive demand. Secondly, favorable demographic trends, including millennial household formation and an increasing preference for homeownership, are bolstering the sector. Furthermore, low interest rates (though this is subject to change depending on economic conditions) have historically made mortgages more accessible, stimulating construction activity. However, the market isn't without its challenges. Rising material costs, labor shortages, and supply chain disruptions continue to exert upward pressure on construction prices, potentially impacting affordability and slowing growth in certain segments. The market is segmented by dwelling type (apartments & condominiums, villas, other), construction type (new construction, renovation), and geographic location, with significant activity concentrated in major metropolitan areas. The dominance of large national builders like D.R. Horton, Lennar Corp, and PulteGroup highlights the industry's consolidation trend, while the growth of multi-family construction reflects shifting urban preferences. Looking ahead, the market's trajectory will depend on macroeconomic factors, interest rate fluctuations, government policies impacting housing affordability, and the ability of the industry to address supply-chain and labor challenges. Innovation in construction technologies, sustainable building practices, and prefabricated homes are also emerging trends expected to significantly influence market dynamics over the forecast period. The competitive landscape is characterized by a mix of large publicly traded companies and smaller regional builders. While established players dominate the market share, opportunities exist for smaller firms specializing in niche markets, such as sustainable or luxury home construction, or those focused on specific geographic areas. The ongoing expansion of the market signifies significant potential for investment and growth, despite the hurdles currently impacting the sector. Addressing supply chain disruptions and labor shortages will be crucial for sustained growth. Continued demand in key urban centers and evolving consumer preferences toward specific dwelling types will be critical factors determining the market's future trajectory. Recent developments include: June 2022 - Pulte Homes - a national brand of PulteGroup, Inc. - announced the opening of its newest Boston-area community, Woodland Hill. Offering 46 new construction single-family homes in the charming town of Grafton, the community is conveniently located near schools, dining, and entertainment, with the Massachusetts Bay Transportation Authority commuter rail less than a mile away. The collection of home designs at Woodland Hill includes three two-story floor plans, ranging in size from 3,013 to 4,019 sq. ft. with four to six bedrooms, 2.5-3.5 baths, and 2-3 car garages. These spacious home designs feature flexible living spaces, plenty of natural light, gas fireplaces, and the signature Pulte Planning Center®, a unique multi-use workstation perfect for homework or a family office., December 2022 - D.R. Horton, Inc. announced the acquisition of Riggins Custom Homes, one of the largest builders in Northwest Arkansas. The homebuilding assets of Riggins Custom Homes and related entities (Riggins) acquired include approximately 3,000 lots, 170 homes in inventory, and 173 homes in the sales order backlog. For the trailing twelve months ended November 30, 2022, Riggins closed 153 homes (USD 48 million in revenue) with an average home size of approximately 1,925 square feet and an average sales price of USD 313,600. D.R. Horton expects to pay approximately USD 107 million in cash for the purchase, and the Company plans to combine the Riggins operations with the current D.R. Horton platform in Northwest Arkansas.. Notable trends are: High-interest Rates are Negatively Impacting the Market.

  4. k

    Data from: Labor Market Improvement and the Use of Subsidized Housing...

    • kansascityfed.org
    pdf
    Updated Oct 4, 2021
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    (2021). Labor Market Improvement and the Use of Subsidized Housing Programs [Dataset]. https://www.kansascityfed.org/research/economic-review/4q17-slyjohnson-labor-market-improvement-subsidized-housing/
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    pdfAvailable download formats
    Dataset updated
    Oct 4, 2021
    Description

    Local and demographic-specific labor market conditions are more useful than national aggregates in explaining subsidized housing use.

  5. Housing Prices Dataset

    • kaggle.com
    zip
    Updated Jan 12, 2022
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    M Yasser H (2022). Housing Prices Dataset [Dataset]. https://www.kaggle.com/datasets/yasserh/housing-prices-dataset
    Explore at:
    zip(4740 bytes)Available download formats
    Dataset updated
    Jan 12, 2022
    Authors
    M Yasser H
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg" alt="">

    Description:

    A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

    Acknowledgement:

    Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

    Objective:

    • Understand the Dataset & cleanup (if required).
    • Build Regression models to predict the sales w.r.t a single & multiple feature.
    • Also evaluate the models & compare thier respective scores like R2, RMSE, etc.
  6. 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
    Explore at:
    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.

  7. Housing Developers in the US - Market Research Report (2015-2030)

    • ibisworld.com
    Updated Aug 25, 2024
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    IBISWorld (2024). Housing Developers in the US - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-states/market-research-reports/housing-developers-industry/
    Explore at:
    Dataset updated
    Aug 25, 2024
    Dataset authored and provided by
    IBISWorld
    License

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

    Time period covered
    2015 - 2030
    Description

    Housing developers have navigated pronounced economic swings over the past five years, as borrowing environments and Federal Reserve rate policy have dictated industry growth and contraction. Early pandemic-era interest rate cuts and remote work fueled a boom in home building, especially in suburban and affordable regions, but subsequent rate hikes sharply reversed momentum. Developers enjoyed robust sales from projects initiated during the low-rate period, even as new housing starts declined under pressure from rising mortgage costs and weakening consumer demand. The struggle has been particularly acute for small and medium-sized housing developers, which continue to close their doors or merge as cost pressures mount and competition from large developers intensifies. Persistent labor shortages and escalating input costs, driven partly by tariffs, have prevented profit growth, boosting the market share and pricing power of prominent developers able to pass costs to buyers or access strategic partners. Overall, industry revenue has been increasing at a CAGR of 5.2% over the past five years to total an estimated $324.2 billion in 2025, including an estimated decrease of 0.7% in 2025. Single-family construction marked a bright spot in 2024, with leading developers like DR Horton capitalizing on demand for space and affordability. However, the pipeline for single-family projects has been hindered by high rates and tariff uncertainty that persisted throughout most of 2025. Multifamily development endured deeper contractions, particularly in 2023 and 2024, with vacancy rates and losses intensifying among even the largest developers before rebounding in 2025 as starts and demand recovered. Continued rate cuts by the Federal Reserve will set the stage for housing developers to regain growth momentum. Developers are poised to benefit from pent-up demand, housing shortages and renewed construction activity, particularly in the single-family segment, where affordability remains critical. However, rising material and labor costs will continue to pose operational challenges, leading developers to seek efficiencies or pass costs downstream. The expiration of federal green building credits in 2026 will prompt a rush to complete qualifying projects, but may curb longer-term investment in sustainable construction unless new incentives emerge. Expansions near newly announced manufacturing hubs are expanding, with developers acquiring land and prepping communities to meet workforce housing needs as the national focus on domestic manufacturing spurs regional population inflows and rising housing demand. Overall, industry revenue is forecast to climb at a CAGR of 1.8% to total an estimated $354.7 billion through the end of 2030.

  8. U.S. housing: national home price index 1967-2024

    • statista.com
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    Statista, U.S. housing: national home price index 1967-2024 [Dataset]. https://www.statista.com/statistics/208492/national-home-price-index-for-the-us/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 1967 - Nov 2024
    Area covered
    United States
    Description

    In November 2024, the Bureau of Labor national home price index (HPI) in the United States exceeded *** index points. The years 1982 to 1984 were chosen as base years, which means that house prices had tripled since then.

  9. d

    Replication Data for: Replication data for: Commuting, Labor, and Housing...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Sep 25, 2024
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    Severen, Christopher (2024). Replication Data for: Replication data for: Commuting, Labor, and Housing Market Effects of Mass Transportation: Welfare and Identification [Dataset]. http://doi.org/10.7910/DVN/SWCGSP
    Explore at:
    Dataset updated
    Sep 25, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Severen, Christopher
    Description

    Severen, C. (2023). “Commuting, Labor, and Housing Market Effects of Mass Transportation: Welfare and Identification.” Review of Economics and Statistics 105:5, 1073–1091.

  10. P

    Prefabricated Housing Market Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 7, 2025
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    Data Insights Market (2025). Prefabricated Housing Market Report [Dataset]. https://www.datainsightsmarket.com/reports/prefabricated-housing-market-17234
    Explore at:
    doc, pdf, pptAvailable 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
    US
    Variables measured
    Market Size
    Description

    Discover the booming prefabricated housing market! Learn about its $134.57 million valuation in 2025, 6.67% CAGR, key drivers, trends, and leading companies. Explore market segmentation, regional analysis (North America, Europe, Asia-Pacific), and future growth projections until 2033. Key drivers for this market are: Increase FDI in construction in Asia-Pacific, Minimized Construction Wastage. Potential restraints include: Availability of Skilled Labor. Notable trends are: Expansion Of Prefabricated Housing To Drive The Market.

  11. T

    Vital Signs: Home Prices - Bay Area (2022)

    • data.bayareametro.gov
    csv, xlsx, xml
    Updated Oct 26, 2022
    + more versions
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    (2022). Vital Signs: Home Prices - Bay Area (2022) [Dataset]. https://data.bayareametro.gov/Economy/Vital-Signs-Home-Prices-Bay-Area-2022-/2uf4-6aym
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    xlsx, xml, csvAvailable download formats
    Dataset updated
    Oct 26, 2022
    Area covered
    San Francisco Bay Area
    Description

    VITAL SIGNS INDICATOR
    Home Prices (EC7)

    FULL MEASURE NAME
    Home Prices

    LAST UPDATED
    December 2022

    DESCRIPTION
    Home prices refer to the cost of purchasing one’s own house or condominium. While a significant share of residents may choose to rent, home prices represent a primary driver of housing affordability in a given region, county or city.

    DATA SOURCE
    Zillow: Zillow Home Value Index (ZHVI) - http://www.zillow.com/research/data/
    2000-2021

    California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
    2000-2021

    US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
    2000-2021

    Bureau of Labor Statistics: Consumer Price Index - http://data.bls.gov
    2000-2021

    US Census ZIP Code Tabulation Areas (ZCTAs) - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html
    2020 Census Blocks

    CONTACT INFORMATION
    vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator)
    Housing price estimates at the regional-, county-, city- and zip code-level come from analysis of individual home sales by Zillow based upon transaction records. Zillow Home Value Index (ZHVI) is a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. ZHVI is computed from public record transaction data as reported by counties. All standard real estate transactions are included in this metric, including REO sales and auctions. Zillow makes a substantial effort to remove transactions not typically considered a standard sale. Examples of these include bank takeovers of foreclosed properties, title transfers after a death or divorce and non arms-length transactions. Zillow defines all homes as single-family residential, condominium and co-operative homes with a county record. Single-family residences are detached, which means the home is an individual structure with its own lot. Condominiums are units that can be owned in a multi-unit complex, such as an apartment building. Co-operative homes are slightly different from condominiums in that the homeowners own shares in the corporation that owns the building, not the actual units themselves.

    For metropolitan area comparison values, the Bay Area metro area’s median home sale price is the population-weighted average of the nine counties’ median home prices. Data is adjusted for inflation using Bureau of Labor Statistics metropolitan statistical area (MSA)-specific series. Inflation-adjusted data are presented to illustrate how home prices have grown relative to overall price increases; that said, the use of the Consumer Price Index (CPI) does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of the CPI itself.

  12. a

    Labor Market Engagement Index

    • hub.arcgis.com
    • data.lojic.org
    • +2more
    Updated Jul 5, 2023
    + more versions
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    Department of Housing and Urban Development (2023). Labor Market Engagement Index [Dataset]. https://hub.arcgis.com/datasets/HUD::labor-market-engagement-index
    Explore at:
    Dataset updated
    Jul 5, 2023
    Dataset authored and provided by
    Department of Housing and Urban Development
    Area covered
    Description

    LABOR MARKET ENGAGEMENT INDEXSummary

    The labor market engagement index provides a summary description of the relative intensity of labor market engagement and human capital in a neighborhood. This is based upon the level of employment, labor force participation, and educational attainment in a census tract (i). Formally, the labor market index is a linear combination of three standardized vectors: unemployment rate (u), labor-force participation rate (l), and percent with a bachelor’s degree or higher (b), using the following formula:

    Where means and standard errors are estimated over the national distribution. Also, the value for the standardized unemployment rate is multiplied by -1.

    Interpretation

    Values are percentile ranked nationally and range from 0 to 100. The higher the score, the higher the labor force participation and human capital in a neighborhood.

    Data Source: American Community Survey, 2011-2015Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 9.

    To learn more about the Labor Market Engagement Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

  13. C

    China City Labor Market: Demand: Real Estate

    • ceicdata.com
    Updated Sep 15, 2020
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    CEICdata.com (2020). China City Labor Market: Demand: Real Estate [Dataset]. https://www.ceicdata.com/en/china/city-labor-market-demand-of-labour-by-industry/city-labor-market-demand-real-estate
    Explore at:
    Dataset updated
    Sep 15, 2020
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Jun 1, 2011 - Mar 1, 2014
    Area covered
    China
    Variables measured
    Job Market Indicators
    Description

    China City Labor Market: Demand: Real Estate data was reported at 172.415 Person th in Mar 2014. This records an increase from the previous number of 146.166 Person th for Dec 2013. China City Labor Market: Demand: Real Estate data is updated quarterly, averaging 107.422 Person th from Mar 2001 (Median) to Mar 2014, with 53 observations. The data reached an all-time high of 186.311 Person th in Jun 2010 and a record low of 20.571 Person th in Mar 2001. China City Labor Market: Demand: Real Estate data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GJ: City Labor Market: Demand of Labour: by Industry.

  14. H

    The Economic Impact of Remote Work on Urban Housing Markets

    • dataverse.harvard.edu
    Updated Feb 22, 2024
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    Lemuel Kenneth David (2024). The Economic Impact of Remote Work on Urban Housing Markets [Dataset]. http://doi.org/10.7910/DVN/MKDKS6
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 22, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Lemuel Kenneth David
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This data was use to explore the economic repercussions of remote work on urban housing prices, investment patterns, and demographic shifts across thirty countries from 2006 to 2023

  15. G

    Manufacturing Houses Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Aug 29, 2025
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    Growth Market Reports (2025). Manufacturing Houses Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/manufacturing-houses-market-global-industry-analysis
    Explore at:
    csv, pdf, pptxAvailable download formats
    Dataset updated
    Aug 29, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Manufacturing Houses Market Outlook



    According to our latest research, the global manufacturing houses market size reached USD 156.7 billion in 2024, reflecting robust expansion fueled by evolving construction technologies and a growing need for efficient, sustainable housing solutions. The market is anticipated to witness a CAGR of 6.2% from 2025 to 2033, projecting a value of USD 269.8 billion by 2033. The primary growth driver for this market is the increasing adoption of prefabricated and modular construction methods, which significantly reduce construction time and costs while enhancing quality and sustainability.




    One of the most significant growth factors for the manufacturing houses market is the rapid urbanization occurring worldwide, particularly in emerging economies. As urban populations swell, the demand for affordable, quickly constructed, and high-quality housing solutions has surged. Modular and prefabricated housing options are increasingly favored by both governments and private developers as they can be manufactured off-site, transported, and assembled rapidly, minimizing disruption and labor costs. Furthermore, the ability to customize these homes to meet local regulations and preferences adds to their appeal, driving their adoption in regions facing acute housing shortages.




    Another crucial factor propelling the manufacturing houses market is the rising emphasis on sustainability and environmental responsibility within the construction sector. Traditional construction techniques are often resource-intensive and generate significant waste. In contrast, modern manufacturing houses—especially those utilizing steel, composite materials, and energy-efficient designs—offer a greener alternative. These houses are often designed with energy-saving features, use recycled or renewable materials, and produce less waste during the building process. As regulatory bodies enforce stricter environmental standards, and as consumers become more eco-conscious, the demand for sustainable manufacturing houses is expected to climb steadily.



    Manufactured Housing Community Finance is becoming an increasingly important aspect of the modular and prefabricated housing sectors. As the demand for these housing solutions rises, financial institutions are developing specialized financing options to support both developers and buyers. These financial products are designed to accommodate the unique characteristics of manufactured housing, such as their rapid construction timelines and off-site assembly processes. By providing tailored financing solutions, lenders are facilitating greater accessibility to manufactured housing, thereby driving market growth. This financial support is crucial for expanding the reach of modular homes, particularly in regions with high demand for affordable and sustainable housing solutions.




    Technological advancements are also playing a pivotal role in shaping the manufacturing houses market. Innovations in digital design, automation, and material science have enabled manufacturers to produce highly customizable, durable, and cost-effective housing units. Building Information Modeling (BIM), 3D printing, and advanced robotics are streamlining the design and assembly processes, making it possible to meet diverse customer requirements while maintaining stringent quality standards. These technological improvements not only enhance the efficiency of production but also open up new possibilities for integrating smart home features and energy management systems, further boosting market growth.




    From a regional perspective, Asia Pacific is emerging as the dominant force in the global manufacturing houses market, driven by rapid economic development, government initiatives to provide affordable housing, and large-scale urban migration. North America and Europe also hold substantial market shares, thanks to their established construction industries and growing interest in sustainable building practices. Meanwhile, Latin America and the Middle East & Africa are witnessing increasing investments in modular housing solutions, primarily to address urban housing shortages and infrastructure development. Each region presents unique opportunities and challenges, influenced by local regulations, economic conditions, and cultural preferences.



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  16. House Prices US Shiller

    • kaggle.com
    zip
    Updated Oct 28, 2025
    + more versions
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    yedige ashmet (2025). House Prices US Shiller [Dataset]. https://www.kaggle.com/datasets/yedigeashmet/house-prices
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    zip(51570 bytes)Available download formats
    Dataset updated
    Oct 28, 2025
    Authors
    yedige ashmet
    License

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

    Area covered
    United States
    Description

    Case-Shiller Index of U.S. Residential House Prices

    This dataset provides comprehensive insights into U.S. residential house prices through the S&P Case-Shiller Home Price Index, which includes both the national index and indices for 20 metropolitan regions. The data is derived from the S&P Case-Shiller Index, a widely recognized and reliable measure of U.S. housing price movements. It is updated monthly and utilizes the "repeat sales method" to track the price changes of the same properties over time, making it an accurate reflection of housing appreciation.

    Data Overview

    The dataset includes: - S&P/Case-Shiller U.S. National Home Price Index: A composite index of single-family home prices across nine U.S. Census divisions. - Indices for 20 Metropolitan Regions: Regional indices that highlight housing price trends in major U.S. cities.

    Key Methodology

    The index uses a "repeat sales" approach, which tracks properties that have been sold at least twice to capture changes in their value over time. This method minimizes biases from changes in housing stock or individual property characteristics. The index originated in the 1980s through the work of Karl E. Case and Robert J. Shiller, pioneers in developing the repeat sales technique. It remains one of the most trusted tools for measuring U.S. housing market trends.

    The indices are used widely by policymakers, economists, and analysts to gauge housing market conditions and make informed decisions.

    Use Cases

    This dataset can be used for: - Housing Market Analysis: Track trends in national and metropolitan housing prices. - Econometric Modeling: Analyze the relationship between housing prices and macroeconomic factors. - Forecasting: Build models to predict future housing market movements.

    Source

    Data sourced from: https://github.com/datasets/house-prices-us Original source: https://datahub.io/core/house-prices-us

  17. F

    Other Financial Information: Estimated Market Value of Owned Home by...

    • fred.stlouisfed.org
    json
    Updated Sep 25, 2024
    + more versions
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    (2024). Other Financial Information: Estimated Market Value of Owned Home by Quintiles of Income Before Taxes: Highest 20 Percent (81st to 100th Percentile) [Dataset]. https://fred.stlouisfed.org/series/CXU800721LB0106M
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 25, 2024
    License

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

    Description

    Graph and download economic data for Other Financial Information: Estimated Market Value of Owned Home by Quintiles of Income Before Taxes: Highest 20 Percent (81st to 100th Percentile) (CXU800721LB0106M) from 1984 to 2023 about owned, market value, information, percentile, estimate, tax, financial, income, housing, and USA.

  18. Importance of different issues for the real estate development market U.S....

    • statista.com
    Updated Jun 20, 2025
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    Statista (2025). Importance of different issues for the real estate development market U.S. 2025 [Dataset]. https://www.statista.com/statistics/1282361/importance-of-development-issues-for-real-estate-us/
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    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    United States
    Description

    Construction labor costs are the development issues that industry experts in the United States expect to have the highest importance in real estate in 2025. Respondents ranked construction labor costs as having an importance score of **** out of five. On the other hand, health and safety related policies are expected to be of the least importance in the industry come 2025.

  19. F

    Other Financial Information: Estimated Market Value of Owned Home by Age:...

    • fred.stlouisfed.org
    json
    Updated Sep 25, 2024
    + more versions
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    (2024). Other Financial Information: Estimated Market Value of Owned Home by Age: from Age 25 to 34 [Dataset]. https://fred.stlouisfed.org/series/CXU800721LB0403M
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 25, 2024
    License

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

    Description

    Graph and download economic data for Other Financial Information: Estimated Market Value of Owned Home by Age: from Age 25 to 34 (CXU800721LB0403M) from 1984 to 2023 about owned, age, market value, information, 25 years +, estimate, financial, housing, and USA.

  20. m

    Python code for the estimation of missing prices in real-estate market with...

    • data.mendeley.com
    Updated Dec 12, 2017
    + more versions
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    Iván García-Magariño (2017). Python code for the estimation of missing prices in real-estate market with a dataset of house prices from Teruel city [Dataset]. http://doi.org/10.17632/mxpgf54czz.2
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    Dataset updated
    Dec 12, 2017
    Authors
    Iván García-Magariño
    License

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

    Area covered
    Teruel
    Description

    This research data file contains the necessary software and the dataset for estimating the missing prices of house units. This approach combines several machine learning techniques (linear regression, support vector regression, the k-nearest neighbors and a multi-layer perceptron neural network) with several dimensionality reduction techniques (non-negative factorization, recursive feature elimination and feature selection with a variance threshold). It includes the input dataset formed with the available house prices in two neighborhoods of Teruel city (Spain) in November 13, 2017 from Idealista website. These two neighborhoods are the center of the city and “Ensanche”.

    This dataset supports the research of the authors in the improvement of the setup of agent-based simulations about real-estate market. The work about this dataset has been submitted for consideration for publication to a scientific journal.

    The open source python code is composed of all the files with the “.py” extension. The main program can be executed from the “main.py” file. The “boxplotErrors.eps” is a chart generated from the execution of the code, and compares the results of the different combinations of machine learning techniques and dimensionality reduction methods.

    The dataset is in the “data” folder. The input raw data of the house prices are in the “dataRaw.csv” file. These were shuffled into the “dataShuffled.csv” file. We used cross-validation to obtain the estimations of house prices. The outputted estimations alongside the real values are stored in different files of the “data” folder, in which each filename is composed by the machine learning technique abbreviation and the dimensionality reduction method abbreviation.

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Atharva Soundankar (2025). 🏡 Global Housing Market Analysis (2015-2024) [Dataset]. https://www.kaggle.com/datasets/atharvasoundankar/global-housing-market-analysis-2015-2024
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🏡 Global Housing Market Analysis (2015-2024)

Understanding Housing Market Trends Across Countries

Explore at:
zip(18363 bytes)Available download formats
Dataset updated
Mar 18, 2025
Authors
Atharva Soundankar
License

Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically

Description

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Column NameDescription
CountryThe country where the housing market data is recorded 🌍
YearThe year of observation 📅
Average House Price ($)The average price of houses in USD 💰
Median Rental Price ($)The median monthly rent for properties in USD 🏠
Mortgage Interest Rate (%)The average mortgage interest rate percentage 📉
Household Income ($)The average annual household income in USD 🏡
Population Growth (%)The percentage increase in population over the year 👥
Urbanization Rate (%)Percentage of the population living in urban areas 🏙️
Homeownership Rate (%)The percentage of people who own their homes 🔑
GDP Growth Rate (%)The annual GDP growth percentage 📈
Unemployment Rate (%)The percentage of unemployed individuals in the labor force 💼
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