A housing market prediction that many experts agree on is that it will be a seller’s market. Home prices are expected to rise for some time due to increased demand and limited supply. Millennials are at the age to start investing in the real estate market for the first time. Hence, the demand for residential and commercial projects is rising with every passing day. The future of real estate will witness a rise in demand and limited supply, resulting in it being a seller’s market.

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

Redfin 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

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.

Key information about House Prices Growth

• US house prices grew 3.3% YoY in Sep 2025, following an increase of 4.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2025, with an average growth rate of -12.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Snapshot img

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?

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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.

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The Sales segment was valued at USD 926.50 billion in 2019 and showed a gradual increase during the forecast period.

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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

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

The U.S. housing market has slowed, after ** consecutive years of rising home prices. In 2021, house prices surged by an unprecedented ** percent, marking the highest increase on record. However, the market has since cooled, with the Freddie Mac House Price Index showing more modest growth between 2022 and 2024. In 2024, home prices increased by *** percent. That was lower than the long-term average of *** percent since 1990. Impact of mortgage rates on homebuying The recent cooling in the housing market can be partly attributed to rising mortgage rates. After reaching a record low of **** percent in 2021, the average annual rate on a 30-year fixed-rate mortgage more than doubled in 2023. This significant increase has made homeownership less affordable for many potential buyers, contributing to a substantial decline in home sales. Despite these challenges, forecasts suggest a potential recovery in the coming years. How much does it cost to buy a house in the U.S.? In 2023, the median sales price of an existing single-family home reached a record high of over ******* U.S. dollars. Newly built homes were even pricier, despite a slight decline in the median sales price in 2023. Naturally, home prices continue to vary significantly across the country, with West Virginia being the most affordable state for homebuyers.

Description:

This 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.

Key Features:

• 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

Potential Use Cases:

• 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

House prices in the second most populous state in the United States, Texas, have increased more than two-fold since 2011. In 2023, the median house price reached ******* U.S. dollars, a decrease of *** percent from the previous year. Texas is one of the more affordable states for buying a home with house prices below the national average.

Description: This dataset provides historical housing prices scraped from Centaline Property Hong Kong, one of the largest real estate agencies in Hong Kong. The dataset includes information on the date of the transaction, the property address, floor plan, saleable area, unit rate, source, and district. The dataset covers a period of time spanning several years, allowing for analysis of trends and changes in the Hong Kong housing market.

Columns: Date: the date of the property transaction Address: the address of the property Floor Plan: -- Price: the price of the property Changes: any changes made to the property since the last transaction Saleable Area: the area of the property that can be sold to a buyer Unit Rate: the price per square foot of saleable area Source: the source of the data (Centaline Property Hong Kong/ Land Registry) District: the district in which the property is located in Hong Kong

House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

Key information about House Prices Growth

• Mexico house prices grew 8.9% YoY in Sep 2025, following an increase of 8.7% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Sep 2025, with an average growth rate of 10.2%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

House Price Index YoY in the United States decreased to 1.70 percent in September from 2.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

Discover the booming Latin American residential real estate market! This comprehensive analysis reveals a CAGR of 8.32%, driven by urbanization and economic growth. Explore market size, key players (JLL, CBRE, MRV Engenharia), and regional trends in Mexico, Brazil, Colombia, and beyond. Invest wisely with our insightful forecast to 2033. Recent developments include: November 2023: CBRE, a prominent global consultancy and real estate services firm, unveiled its latest initiative, the Latam-Iberia platform. The platform's primary goal is to reinvigorate the real estate markets in Europe and Latin America while fostering investment ties between the two regions. By enhancing business collaborations and amplifying the visibility of real estate solutions, CBRE aims to catalyze growth in the sector., May 2023: CJ do Brasil, a subsidiary of multinational firm CJ Bio, completed its USD 57 million plant expansion in Piracicaba, 160 km from Brazil's capital. CJ Bio is renowned for its expertise in amino acid production. The expansion is projected to create 650 new job opportunities, and the investment also encompasses the establishment of residential, research, and development centers.. Key drivers for this market are: Increase in Population is Boosting the Residential Real Estate Market, Rapid Growth in Urbanization. Potential restraints include: Increase in Population is Boosting the Residential Real Estate Market, Rapid Growth in Urbanization. Notable trends are: Increase in Urbanization Boosting Demand for Residential Real Estate.

The year-end value of the S&P Case Shiller National Home Price Index amounted to 321.45 in 2024. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given year, for example, it means that the house prices increased by 30 percent since 2000. S&P/Case Shiller U.S. home indices – additional informationThe S&P Case Shiller National Home Price Index is calculated on a monthly basis and is based on the prices of single-family homes in nine U.S. Census divisions: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain and Pacific. The index is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The index illustrates the trend of home prices and can be helpful during house purchase decisions. When house prices are rising, a house buyer might want to speed up the house purchase decision as the transaction costs can be much higher in the future. The S&P Case Shiller National Home Price Index has been on the rise since 2011.The S&P Case Shiller National Home Price Index is one of the indices included in the S&P/Case-Shiller Home Price Index Series. Other indices are the S&P/Case Shiller 20-City Composite Home Price Index, the S&P/Case Shiller 10-City Composite Home Price Index and twenty city composite indices.

About Dataset

Source

The source of this dataset is REDFIN Data Center. To download the latest dataset available, please go to: https://www.redfin.com/news/data-center/

They also provide a page with the definitions for each metric used here: https://www.redfin.com/news/data-center-metrics-definitions/

For more informaton on Data and Data Quality, please visit: https://www.redfin.com/about/data-quality-on-redfin Reading the Data

The data is a .tsv format and can be imported using pandas as follows:

df = pd.read_csv("weekly_housing_market_data_most_recent.tsv000", sep='\t')

MOST RECENT DATAPOINT: 2022-07-11

The Market Value Analysis (MVA) is a tool to help residents and policymakers identify and understand the elements of their local real estate markets. It is an objective, data-driven tool built on local administrative data and validated with local experts. With an MVA, public officials and private actors can more precisely target intervention strategies in weak markets and support sustainable growth in stronger markets.In 2023, Reinvestment Fund completed an update to the City of Dallas MVA. The first MVA study in the City of Dallas was conducted in 2018 and a new study was needed to update information on current housing market conditions in Dallas neighborhoods.This is a map of the 2023 Housing Market Value Analysis and Displacement Risk Ratio for the City of Dallas. The map displays affordability information related to housing such as household income and house prices within the context of determined market types A-I. The map also includes data variables related to displacement risk ratio, or the likelihood for residents in a housing area to be push out, or displaced. The analysis was completed by a contractor, Reinvestment Fund. The analysis and findings are provided on the 2023 Market Value Analysis storymap.

Housing Index in China remained unchanged at -2.20 percent in October. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.