Facebook
TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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 Name | Description |
|---|---|
Country | The country where the housing market data is recorded 🌍 |
Year | The 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 💼 |
Facebook
TwitterIn 2021, Allegheny County Economic Development (ACED), in partnership with Urban Redevelopment Authority of Pittsburgh(URA), completed the a Market Value Analysis (MVA) for Allegheny County. This analysis services as both an update to previous MVA’s commissioned separately by ACED and the URA and combines the MVA for the whole of Allegheny County (inclusive of the City of Pittsburgh). The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies. This MVA utilized data that helps to define the local real estate market. The data used covers the 2017-2019 period, and data used in the analysis includes: Residential Real Estate Sales Mortgage Foreclosures Residential Vacancy Parcel Year Built Parcel Condition Building Violations Owner Occupancy Subsidized Housing Units The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources. Please refer to the presentation and executive summary for more information about the data, methodology, and findings.
Facebook
Twitterhttps://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy
Residential Real Estate Market is Segmented by Property Type (Apartments & Condominiums, and Landed Houses & Villas), by Price Band (Affordable, Mid-Market, and Luxury/Super-prime), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Region (North America, South America, Europe, Asia-Pacific, and Middle East & Africa). The Market Forecasts are Provided in Terms of Value (USD).
Facebook
Twitterhttps://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required
Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Oct 2025 about median and USA.
Facebook
Twitterhttps://www.technavio.com/content/privacy-noticehttps://www.technavio.com/content/privacy-notice
Residential Real Estate Market Size 2025-2029
The residential real estate market size is valued to increase USD 485.2 billion, at a CAGR of 4.5% from 2024 to 2029. Growing residential sector globally will drive the residential real estate market.
Major Market Trends & Insights
APAC dominated the market and accounted for a 55% growth during the forecast period.
By Mode Of Booking - Sales segment was valued at USD 926.50 billion in 2023
By Type - Apartments and condominiums segment accounted for the largest market revenue share in 2023
Market Size & Forecast
Market Opportunities: USD 41.01 billion
Market Future Opportunities: USD 485.20 billion
CAGR : 4.5%
APAC: Largest market in 2023
Market Summary
The market is a dynamic and ever-evolving sector that continues to shape the global economy. With increasing marketing initiatives and the growing residential sector globally, the market presents significant opportunities for growth. However, regulatory uncertainty looms large, posing challenges for stakeholders. According to recent reports, technology adoption in residential real estate has surged, with virtual tours and digital listings becoming increasingly popular. In fact, over 40% of homebuyers in the US prefer virtual property viewings. Core technologies such as artificial intelligence and blockchain are revolutionizing the industry, offering enhanced customer experiences and streamlined processes.
Despite these advancements, regulatory compliance remains a major concern, with varying regulations across regions adding complexity to market operations. The market is a complex and intriguing space, with ongoing activities and evolving patterns shaping its future trajectory.
What will be the Size of the Residential Real Estate Market during the forecast period?
Get Key Insights on Market Forecast (PDF) Request Free Sample
How is the Residential Real Estate Market Segmented and what are the key trends of market segmentation?
The residential real estate industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
Mode Of Booking
Sales
Rental or lease
Type
Apartments and condominiums
Landed houses and villas
Location
Urban
Suburban
Rural
End-user
Mid-range housing
Affordable housing
Luxury housing
Geography
North America
US
Canada
Mexico
Europe
France
Germany
UK
APAC
Australia
Japan
South Korea
South America
Brazil
Rest of World (ROW)
By Mode Of Booking Insights
The sales segment is estimated to witness significant growth during the forecast period.
Request Free Sample
The Sales segment was valued at USD 926.50 billion in 2019 and showed a gradual increase during the forecast period.
Request Free Sample
Regional Analysis
APAC is estimated to contribute 55% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.
See How Residential Real Estate Market Demand is Rising in APAC Request Free Sample
The market in the Asia Pacific (APAC) region holds a significant share and is projected to lead the global market growth. Factors fueling this expansion include the region's rapid urbanization and increasing consumer spending power. Notably, residential and commercial projects in countries like India and China are experiencing robust development. The residential real estate sector in China plays a pivotal role in the economy and serves as a major growth driver for the market.
With these trends continuing, the APAC the market is poised for continued expansion during the forecast period.
Market Dynamics
Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.
In the Residential Real Estate Market, understanding the impact property tax rates home values and effect interest rates mortgage affordability is essential for buyers and investors. Key factors affecting home price appreciation and factors influencing housing affordability shape market trends, while the importance property due diligence process and requirements environmental site assessment ensure informed decisions. Investors benefit from methods calculating rental property roi, process home equity loan application, and benefits real estate portfolio diversification. Tools like property management software efficiency and techniques effective property marketing help tackle challenges managing rental properties. Additionally, strategies successf
Facebook
Twitterhttps://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy
Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.
Key Features:
Who Can Benefit From This Dataset:
Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.
Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data
Facebook
Twitterhttps://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy
The US Office Real Estate Market Report is Segmented by Building Grade (Grade A, Grade B, and More), by Transaction Type (Rental and Sales), by End Use (Information Technology (IT & ITES), BFSI (Banking, Financial Services and Insurance), and More) and by States (Texas, California, Florida and More). The Report Offers Market Size and Forecasts in Value (USD) for all the Above Segments.
Facebook
TwitterIn 2025, India was the country with the highest increase in house prices since 2010 among the Asia-Pacific (APAC) countries under observation. In the second quarter of the year, the nominal house price index in India reached over 359 index points. This suggests an increase of 259 percent since 2010, the baseline year when the index value was set to 100. It is important to note that the nominal index does not account for the effects of inflation, meaning when adjusted for inflation, price growth in real terms was slower.
Facebook
Twitterhttps://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy
The US Commercial Real Estate Market Report is Segmented by Property Type (Offices, Retail, Logistics, Others), by Business Model (Sales, Rental), by End-User (Individuals/Households, Corporates & SMEs, Others), and by Geography (Texas, California, Florida, New York, Illinois, Rest of US). The Market Forecasts are Provided in Terms of Value (USD).
Facebook
Twitterhttps://straitsresearch.com/privacy-policyhttps://straitsresearch.com/privacy-policy
The global residential real estate market size is projected to grow from USD 11.619 trillion in 2025 to USD 23.493 trillion by 2033, exhibiting a CAGR of 9.2%.
Report Scope:
| Report Metric | Details |
|---|---|
| Market Size in 2024 | USD 10.64 Trillion |
| Market Size in 2025 | USD 11.619 Trillion |
| Market Size in 2033 | USD 23.493 Trillion |
| CAGR | 9.20% (2025-2033) |
| Base Year for Estimation | 2024 |
| Historical Data | 2021-2023 |
| Forecast Period | 2025-2033 |
| Report Coverage | Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends |
| Segments Covered | By Budget,By Size,By Region. |
| Geographies Covered | North America, Europe, APAC, Middle East and Africa, LATAM, |
| Countries Covered | U.S., Canada, U.K., Germany, France, Spain, Italy, Russia, Nordic, Benelux, China, Korea, Japan, India, Australia, Taiwan, South East Asia, UAE, Turkey, Saudi Arabia, South Africa, Egypt, Nigeria, Brazil, Mexico, Argentina, Chile, Colombia, |
Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.
| Field Name | Description | Type |
|---|---|---|
| PropertyID | A unique identifier for each property. | text |
| PropType | The type of property (e.g., Commercial or Residential). | text |
| taxkey | The tax key associated with the property. | text |
| Address | The address of the property. | text |
| CondoProject | Information about whether the property is part of a condominium | text |
| project (NaN indicates missing data). | ||
| District | The district number for the property. | text |
| nbhd | The neighborhood number for the property. | text |
| Style | The architectural style of the property. | text |
| Extwall | The type of exterior wall material used. | text |
| Stories | The number of stories in the building. | text |
| Year_Built | The year the property was built. | text |
| Rooms | The number of rooms in the property. | text |
| FinishedSqft | The total square footage of finished space in the property. | text |
| Units | The number of units in the property | text |
| (e.g., apartments in a multifamily building). | ||
| Bdrms | The number of bedrooms in the property. | text |
| Fbath | The number of full bathrooms in the property. | text |
| Hbath | The number of half bathrooms in the property. | text |
| Lotsize | The size of the lot associated with the property. | text |
| Sale_date | The date when the property was sold. | text |
| Sale_price | The sale price of the property. | text |
Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].
Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].
Facebook
Twitterhttps://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy
The Housing Data Extracted from Homes.com (USA) dataset is a comprehensive collection of 2 million real estate listings sourced from Homes.com, one of the leading real estate platforms in the United States. This dataset offers detailed insights into the U.S. housing market, making it an invaluable resource for real estate professionals, investors, researchers, and analysts.
The dataset contains extensive property details, including location, price, property type (single-family homes, condos, apartments), number of bedrooms and bathrooms, square footage, lot size, year built, and availability status. Organized in CSV format, it provides users with easy access to structured data for analyzing trends, developing investment strategies, or building real estate applications.
Key Features:
Facebook
Twitterhttps://www.expertmarketresearch.com/privacy-policyhttps://www.expertmarketresearch.com/privacy-policy
The United States real estate market was valued at USD 3.43 Trillion in 2024. The industry is expected to grow at a CAGR of 2.80% during the forecast period of 2025-2034 to reach a value of USD 4.52 Trillion by 2034. The market growth is mainly driven by the rising corporate investment, particularly in addressing the nation’s affordable housing shortage.
Major corporations are actively investing to integrate housing stability with social responsibility, supporting both new construction and the preservation of existing homes. In September 2024, UnitedHealth Group surpassed USD 1 billion in investments for affordable and mixed-income housing through direct capital and tax credits. These projects span 31 states and have delivered over 25,000 homes, simultaneously improved community health and providing secure housing for low- and moderate-income households.
Such corporate involvements are reshaping trends in United States real estate market by expanding the supply of affordable housing, reducing barriers for renters and homeowners, and stimulating development in high-demand urban and suburban areas. By aligning financial resources with strategic planning, corporations are enabling scalable solutions that meet social and economic objectives while enhancing overall market efficiency.
Facebook
TwitterThis dataset contains prices of New York houses, providing valuable insights into the real estate market in the region. It includes information such as broker titles, house types, prices, number of bedrooms and bathrooms, property square footage, addresses, state, administrative and local areas, street names, and geographical coordinates.
- BROKERTITLE: Title of the broker
- TYPE: Type of the house
- PRICE: Price of the house
- BEDS: Number of bedrooms
- BATH: Number of bathrooms
- PROPERTYSQFT: Square footage of the property
- ADDRESS: Full address of the house
- STATE: State of the house
- MAIN_ADDRESS: Main address information
- ADMINISTRATIVE_AREA_LEVEL_2: Administrative area level 2 information
- LOCALITY: Locality information
- SUBLOCALITY: Sublocality information
- STREET_NAME: Street name
- LONG_NAME: Long name
- FORMATTED_ADDRESS: Formatted address
- LATITUDE: Latitude coordinate of the house
- LONGITUDE: Longitude coordinate of the house
- Price analysis: Analyze the distribution of house prices to understand market trends and identify potential investment opportunities.
- Property size analysis: Explore the relationship between property square footage and prices to assess the value of different-sized houses.
- Location-based analysis: Investigate geographical patterns to identify areas with higher or lower property prices.
- Bedroom and bathroom trends: Analyze the impact of the number of bedrooms and bathrooms on house prices.
- Broker performance analysis: Evaluate the influence of different brokers on the pricing of houses.
If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂 Thank you
Facebook
Twitterhttps://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy
United States Luxury Residential Real Estate Market Report is Segmented by Property Type (Apartments and Condominiums, and Villas and Landed Houses), by Business Model (Sales and Rental), by Mode of Sale (Primary (New-Build) and Secondary (Existing-Home Resale)), and by Region (Northeast, Midwest, Southeast, West and Southwest). The Market Forecasts are Provided in Terms of Value (USD).
Facebook
TwitterRedfin is a real estate brokerage and publishes the US housing market data on a regular basis. Using this dataset, you can analyze and visualize housing market data for US cities. Timeline: Starting from February 2012 until the present time (Data is refreshed and updated on a monthly basis)
The dataset has the following columns:
- period_begin
- period_end
- period_duration
- region_type
- region_type_id
- table_id
- is_seasonally_adjusted. (indicates if prices are seasonally adjusted; f represents False)
- region
- city
- state
- state_code
- property_type
- property_type_id
- median_sale_price
- median_sale_price_mom (median sale price changes month over month)
- median_sale_price_yoy (median sale price changes year over year)
- median_list_price
- median_list_price_mom (median list price changes month over month)
- median_list_price_yoy (median list price changes year over year)
- median_ppsf (median sale price per square foot)
- median_ppsf_mom (median sale price per square foot changes month over month)
- median_ppsf_yoy (median sale price per square foot changes year over year)
- median_list_ppsf (median list price per square foot)
- median_list_ppsf_mom (median list price per square foot changes month over month)
- median_list_ppsf_yoy. (median list price per square foot changes year over year)
- homes_sold (number of homes sold)
- homes_sold_mom (number of homes sold month over month)
- homes_sold_yoy (number of homes sold year over year)
- pending_sales
- pending_sales_mom
- pending_sales_yoy
- new_listings
- new_listings_mom
- new_listings_yoy
- inventory
- inventory_mom
- inventory_yoy
- months_of_supply
- months_of_supply_mom
- months_of_supply_yoy
- median_dom (median days on market until property is sold)
- median_dom_mom (median days on market changes month over month)
- median_dom_yoy (median days on market changes year over year)
- avg_sale_to_list (average sale price to list price ratio)
- avg_sale_to_list_mom (average sale price to list price ratio changes month over month)
- avg_sale_to_list_yoy (average sale price to list price ratio changes year over year)
- sold_above_list
- sold_above_list_mom
- sold_above_list_yoy
- price_drops
- price_drops_mom
- price_drops_yoy
- off_market_in_two_weeks (number of properties that will be taken off the market within 2 weeks)
- off_market_in_two_weeks_mom (changes in number of properties that will be taken off the market within 2 weeks, month over month)
- off_market_in_two_weeks_yoy (changes in number of properties that will be taken off the market within 2 weeks, year over year)
- parent_metro_region
- parent_metro_region_metro_code
- last_updated
Filetype: gzip (gz) Support for gzip files in Python: https://docs.python.org/3/library/gzip.html
Data Source & Credit: Redfin.com
Facebook
Twitterhttps://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.
Facebook
TwitterNorth America was home to the largest listed real estate market in 2024. The aggregate market size of the listed commercial real estate market in Canada and the United States amounted to *** trillion U.S. dollars as of December 2024. Listed real estate refers to real estate companies that are quoted on stock exchanges and receive income from real estate assets.
Facebook
TwitterInnsbruck was the most expensive Austrian city to buy an apartment in, with average values of 7,700 euros per square meter in the first quarter of 2025. The price of an apartment in Graz was significantly lower at 4,590 euros per square meter.
Facebook
Twitterhttps://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy
North America Luxury Residential Real Estate Market Report is Segmented by Property Type (Apartments & Condominiums, Villas & Landed Houses), by Business Model (Sales and Rental), by Mode of Sale (Primary (New-Build) and Secondary (Existing-Home Resale)), and by Geography (United States, Canada, Mexico). The Report Offers Market Size and Forecasts in Value (USD) for all the Above Segments.
Facebook
TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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 Name | Description |
|---|---|
Country | The country where the housing market data is recorded 🌍 |
Year | The 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 💼 |