Median price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

The median house price of residential real estate in California has increased notably since 2012. After a brief correction in property prices in 2022, the median price reached ******* U.S. dollars in December 2023.

In 2022, house price growth in the UK slowed, after a period of decade-long increase. Nevertheless, in June 2025, prices reached a new peak, with the average home costing ******* British pounds. This figure refers to all property types, including detached, semi-detached, terraced houses, and flats and maisonettes. Compared to other European countries, the UK had some of the highest house prices. How have UK house prices increased over the last 10 years? Property prices have risen dramatically over the past decade. According to the UK house price index, the average house price has grown by over ** percent since 2015. This price development has led to the gap between the cost of buying and renting a property to close. In 2023, buying a three-bedroom house in the UK was no longer more affordable than renting one. Consequently, Brits have become more likely to rent longer and push off making a house purchase until they have saved up enough for a down payment and achieved the financial stability required to make the step. What caused the recent fluctuations in house prices? House prices are affected by multiple factors, such as mortgage rates, supply, and demand on the market. For nearly a decade, the UK experienced uninterrupted house price growth as a result of strong demand and a chronic undersupply. Homebuyers who purchased a property at the peak of the housing boom in July 2022 paid ** percent more compared to what they would have paid a year before. Additionally, 2022 saw the most dramatic increase in mortgage rates in recent history. Between December 2021 and December 2022, the **-year fixed mortgage rate doubled, adding further strain to prospective homebuyers. As a result, the market cooled, leading to a correction in pricing.

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.

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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

The United States Residential Real Estate Market is Segmented by Property Type (Apartments and Condominiums, and Villas and Landed Houses), by Price Band (Affordable, Mid-Market and Luxury), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Region (Northeast, Midwest, Southeast, West and Southwest). The Market Forecasts are Provided in Terms of Value (USD)

Graph and download economic data for All-Transactions House Price Index for Seattle-Bellevue-Kent, WA (MSAD) (ATNHPIUS42644Q) from Q4 1975 to Q2 2025 about Seattle, WA, appraisers, HPI, housing, price index, indexes, price, and USA.

The China Residential Real Estate Market is Segmented by Property Type (Apartments & Condominiums and Villas & Landed Houses), Price Band (Affordable, Mid-Market and Luxury), Mode of Sale (Primary and Secondary), Business Model (Sales and Rental) and Key Cities (Shenzhen, Beijing, Shanghai, Hangzhou, Guangzhou, and Other Key Cities). The Market Forecasts are Provided in Terms of Value (USD).

This dataset is an engineered version of the original Ames Housing dataset from the "House Prices: Advanced Regression Techniques" Kaggle competition. The goal of this engineering was to clean the data, handle missing values, encode categorical features, scale numeric features, manage outliers, reduce skewness, select useful features, and create new features to improve model performance for house price prediction.

The original dataset contains information on 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, with the target variable being SalePrice. This engineered version has undergone several preprocessing steps to make it ready for machine learning models.

Preprocessing Steps Applied

1. Missing Value Handling: Missing values in categorical columns with meaningful absence (e.g., no pool for PoolQC) were filled with "None". Numeric columns were filled with median, and other categorical columns with mode.
2. Correlation-based Feature Selection: Numeric features with absolute correlation < 0.1 with SalePrice were removed.
3. Encoding Categorical Variables: Ordinal features (e.g., quality ratings) were encoded using OrdinalEncoder, and nominal features (e.g., neighborhoods) using OneHotEncoder.
4. Outlier Handling: Outliers in numeric features were detected using IQR and capped (Winsorized) to IQR bounds to preserve data while reducing extreme values.
5. Skewness Handling: Highly skewed numeric features (|skew| > 1) were transformed using Yeo-Johnson to make distributions more normal-like.
6. Additional Feature Selection: Low-variance one-hot features (variance < 0.01) and highly collinear features (|corr| > 0.8) were removed.
7. Feature Scaling: Numeric features were scaled using RobustScaler to handle outliers.
8. Duplicate Removal: Duplicate rows were checked and removed if found (none in this dataset).

The final dataset has fewer columns than the original (reduced from 81 to approximately 250 after one-hot encoding, then further reduced by feature selection), with improved quality for modeling.

New Features Created

To add more predictive power, the following new features were created based on domain knowledge: 1. HouseAge: Age of the house at the time of sale. Calculated as YrSold - YearBuilt. This captures how old the house is, which can negatively affect price due to depreciation. - Example: A house built in 2000 and sold in 2008 has HouseAge = 8. 2. Quality_x_Size: Interaction term between overall quality and living area. Calculated as OverallQual * GrLivArea. This combines quality and size to capture the value of high-quality large homes. - Example: A house with OverallQual = 7 and GrLivArea = 1500 has Quality_x_Size = 10500. 3. TotalSF: Total square footage of the house. Calculated as GrLivArea + TotalBsmtSF + 1stFlrSF + 2ndFlrSF (if available). This aggregates area features into a single metric for better price prediction. - Example: If GrLivArea = 1500 and TotalBsmtSF = 1000, TotalSF = 2500. 4. Log_LotArea: Log-transformed lot area to reduce skewness. Calculated as np.log1p(LotArea). This makes the distribution of lot sizes more normal, helping models handle extreme values. - Example: A lot area of 10000 becomes Log_LotArea ≈ 9.21.

These new features were created using the original (unscaled) values to maintain interpretability, then scaled with RobustScaler to match the rest of the dataset.

Data Dictionary

• Original Numeric Features: Kept features with |corr| > 0.1 with SalePrice, such as:
  • OverallQual: Material and finish quality (scaled, 1-10).
  • GrLivArea: Above grade (ground) living area square feet (scaled).
  • GarageCars: Size of garage in car capacity (scaled).
  • TotalBsmtSF: Total square feet of basement area (scaled).
  • And others like FullBath, YearBuilt, etc. (see the code for the full list).
• Ordinal Encoded Features: Quality and condition ratings, e.g.:
  • ExterQual: Exterior material quality (encoded as 0=Po to 4=Ex).
  • BsmtQual: Basement quality (encoded as 0=None to 5=Ex).
• One-Hot Encoded Features: Nominal categorical features, e.g.:
  • MSZoning_RL: 1 if residential low density, 0 otherwise.
  • Neighborhood_NAmes: 1 if in NAmes neighborhood, 0 otherwise.
• New Engineered Features (as described above):
  • HouseAge: Age of the house (scaled).
  • Quality_x_Size: Overall quality times living area (scaled).
  • TotalSF: Total square footage (scaled).
  • Log_LotArea: Log-transformed lot area (scaled).
• Target: SalePrice - The property's sale price in dollars (not scaled, as it's the target).

Total columns: Approximately 200-250 (after one-hot encoding and feature selection).

License

This dataset is derived from the Ames Housing...

The Indonesia Residential Real Estate Market is Segmented by Property Type (Apartments & Condominiums and Villas & Landed Houses), Price Band (Affordable, Mid-Market and Luxury), Mode of Sale (Primary and Secondary), Business Model (Sales and Rental) and Region (Java, Sumatra, Kalimantan, Sulawesi and Rest of Indonesia). The Market Forecasts are Provided in Terms of Value (USD).

Abstract

The Residential Property Price Index (RPPI) for South Africa was compiled by Statistics South Africa in partnership with the South African Reserve Bank and with the support of the International Monetary Fund. The source data for the RPPI are the records of property transactions registered with the Office of the Chief Registrar of Deeds (Deeds office). The RPPI is compiled using internationally accepted methods as outlined in Eurostat's Handbook on Residential Property Price Indices and the IMF's Residential Property Price Index Practical Compilation Guide. These documents are provided with the data. The indices are calculated using a rolling window time dummy hedonic regression model. The purpose of RPPIs is to measure changes in the price of residential properties, such as houses, townhouses and flats, purchased by households. Both new and existing dwellings are covered, independently of their final use and their previous owners. Only market prices are considered, including the price of the land on which residential buildings are located.

Analysis unit

Other

Kind of data

Administrative records

Mode of data collection

Other

Based on the RWI-GEO-RED data that base on the data provided by ImmobilienScout24 hedonic housing price indices are estimated. The indices are on the grid level, LMR, district/county and municipality level. We conduct a hedonic price regression that covers characteristics of the object as well as regional fixed effects. The hedonic regression is estimated separately for houses for sale as well as apartments for rent and for sale. We also offer a combined index which combines the individual housing types into one index. There are three different specifications: First, the overall time development from 01/2008 to 05/2024 on grid level given yearly and quaterly; Second, cross-regional differences for each year separately and time development within one region from 01/2018 to 05/2024 (municipality, district, LMR, and grid level); third, the time-region fixed effect between 2008 and 2024, which is used to determine the price changes for all three region types to the base year of 2008. Sampled Universe: The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and May 2024. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level Sampling: Stratified random sampling Collection Mode: Other Unit Type: GeographicUnit Numer of Units: 1047014

The Mexico Residential Real Estate Market Report is Segmented by Business Model (Sales, Rental), by Property Type (Apartments & Condominiums, Villas & Landed Houses), by Price Band (Affordable, Mid-Market, Luxury), by Mode of Sale (Primary New-Build, Secondary Existing-Home Resale), and by States (Mexico City CDMX, Nuevo León, Jalisco, Querétaro, Rest of Mexico). The Market Forecasts are Provided in Terms of Value USD.

Quarterly median house prices for metropolitan Adelaide by suburb

ImmobilienScout24 is the largest real estate internet platform in Germany. Properties for private as well as commercial use are offered on the website. However, the data only cover residential properties. The dataset covers most characteristics collected on the platform like price, size and characteristics of the housing unit but also automatically generated items like the duration of the advertisement spell. Sampled Universe: All houses for sale at ImmobilienScout24 Sampling: Full sample Collection Mode: Observation.ComputerBased Unit Type: HousingUnit Number of Units: 18317160

Iowa Housing Data

The original Ames data that is being used for the competition House Prices: Advanced Regression Techniques and predicting sales price is edited and engineered to suit a beginner for applying a model without worrying too much about missing data while focusing on the features.

Contents

The train data has the shape 1460x80 and test data has the shape 1458x79 with feature 'SalePrice' to be predicted for the test set. The train data has different types of features, categorical and numerical.

A detailed info about the data can be obtained from the Data Description file among other data files.

Transformations

a. Handling Missing Values: Some variables such as 'PoolQC', 'MiscFeature', 'Alley' have over 90% missing values. However from the data description, it is implied that the missing value indicates the absence of such features in a particular house. Well, most of the missing data implies the feature does not exist for the particular house on further inspection of the dataset and data description.

Similarly, features which are missing such as 'GarageType', 'GarageYrBuilt', 'BsmtExposure', etc indicated no garage in that house but also corresponding attributes such as 'GarageCars', 'GarageArea','BsmtCond' etc are set to 0.

A house on a street might have similar front lawn area to the houses in the same neighborhood, hence the missing values can be median of the values in a neighborhood.

Missing values in features such as 'SaleType', 'KitchenCond', etc have been imputed with the mode of the feature.

b. Dropping Variables: 'Utilities' attribute should be dropped from the data frame because almost all the houses have all public Utilities (E,G,W,& S) available.

c. Further exploration: The feature 'Electrical' has one missing value. The first intuition would be to drop the row. But on further inspection, the missing value is from a house built in 2006. After the 1970's all the houses have Standard Circuit Breakers & Romex 'SkBrkr' installed. So, the value can be inferred from this observation.

d. Transformation: There were some variables which are really categorical but were represented numerically such as 'MSSubClass', 'OverallCond' and 'YearSold'/'MonthSold' as they are discrete in nature. These have also been transformed to categorical variables.

e. X Normalizing the 'SalePrice' Variable: During EDA it was discovered that the Sale price of homes is right skewed. However on normalizing the skewness decreases and the (linear) models fit better. The feature is left for the user to normalize.

Finally the train and test sets were split and sale price appended to train set.

Acknowledgements

The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.

Inspiration

The data after the transformation done by me can easily be fitted on to a model after label encoding and normalizing features to reduce skewness. The main variable to be predicted is 'SalePrice' for the TestData csv file.

Snapshot img

Myanmar Residential Real Estate Market Size 2025-2029

The Myanmar residential real estate market size is forecast to increase by USD 233.2 million at a CAGR of 4.7% between 2024 and 2029.

The market is experiencing significant growth, driven by increasing urbanization and a burgeoning middle class population. Technological advancements are transforming the residential real estate industry, with digital platforms and mobile applications becoming essential tools for property listings, transactions, and customer engagement. However, regulatory uncertainty remains a major challenge, as the government implements new policies and regulations to govern the sector. This instability can impact investor confidence and hinder market growth. To capitalize on opportunities and navigate these challenges effectively, companies should closely monitor regulatory developments and adapt their strategies accordingly.
Additionally, leveraging technology to streamline operations and enhance customer experience will be crucial in a competitive market. Overall, the market presents both risks and rewards for investors and industry players, requiring a strategic and agile approach to succeed.

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The residential real estate market continues to evolve, shaped by various factors influencing urban areas worldwide. Essential services and infrastructure, including transportation systems and functional infrastructure, remain crucial elements driving demand for urban living. Urban sustainability and the development of new metropolises and cities are gaining momentum, with a focus on tall structures and affordable housing solutions. Economic growth and living levels are key factors influencing the market's size and direction. Despite the overall positive trend, economic headwinds and poor management in some areas can lead to imbalances in the demand-supply equation. First-time buyers face challenges in securing real estate loans due to rising mortgage rates and transactional taxes.
Central banks and governments implement measures to stabilize the market, including adjusting mortgage interest rates and promoting inexpensive housing schemes. The industrial regions' growth and the establishment of new urban areas contribute to increasing transaction volumes, with a growing emphasis on urban planning and efficient decision-making processes. However, the market's dynamics are complex, with various factors influencing property values and the homeownership rate. Informal settlements and poor management in some areas can hinder the market's growth and stability.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Landed houses and villas
  Apartments and condominiums


Mode Of Booking

  Sales
  Rental/Lease


Geography

  Myanmar

By Type Insights

The landed houses and villas segment is estimated to witness significant growth during the forecast period.

The market is primarily driven by the demand for landed houses and villas. These properties, which accounted for the largest market share in 2024, offer a unique blend of community and privacy. Villas, specifically, are standalone houses with a veranda or yard, typically located in exclusive areas. They provide a sense of community while maintaining privacy, distinguishing them from flats. In contrast, landed houses are stand-alone dwellings that can be constructed on any type of land. Property tax implications, investor confidence, and housing affordability significantly impact the residential real estate market. Property value fluctuations, home sellers, and housing supply also play crucial roles.

Urban planning strategies, such as sustainable housing development and urban regeneration, are essential to address affordability and urban mobility concerns. Real estate investment trends, including home renovation, property management services, and data analysis, are shaping the market. Smart home technology and urban design are also influencing housing demand. City branding, competitiveness, and resilience are key factors in urban development and planning. Infrastructure development, sustainable urbanism, and economic diversification are essential for creating smart cities and addressing urban sprawl.

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The Landed houses and villas segment was valued at USD 566.90 million in 2019 and showed a gradual increase 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 compan

Global Real Estate Rental market size is expected to reach $3862.88 billion by 2029 at 7.4%, segmented as by type, residential buildings and dwellings rental services, non-residential buildings rental services

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).

The Europe Residential Real Estate Market is Segmented by Property Type (Apartments & Condominiums and Villas & Landed Houses), Price Band (Affordable, Mid-Market and Luxury), Mode of Sale (Primary and Secondary), Business Model (Sales and Rental) and Country (Germany, United Kingdom, France, Spain, Italy, Netherlands, Sweden, Denmark, Norway and Rest of Europe). The Market Forecasts are Provided in Terms of Value (USD).

Real Estate Tokenization Market size and share is estimated to reach USD 21,821.9 Million by 2035, with a CAGR of 21.2% during the forecast period