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Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].
According to the forecast, house prices in the UK prime property market are expected to increase by almost **** percent by 2029. Growth is expected to accelerate over the five-year period, with 2025 expecting the lowest increase and 2029, the highest.
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House price prediction dataset
This dataset comprises housing data for various metropolitan cities of India. It includes: - Collection of prices of new and resale houses - The amenities provided for each house
This housing dataset is useful for a range of stakeholders, including real estate agents, property developers, buyers, renters, and researchers interested in analyzing housing markets and trends in metropolitan cities across India. It can be used for market analysis, price prediction, property recommendations, and various other real estate-related tasks.
Shape of dataset : (6207, 40)
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Github Link : https://github.com/TusharPaul01/House-Price-Prediction
For more such dataset & code check : https://www.kaggle.com/tusharpaul2001
Problem Statement 👉 Download the case studies here Investors and buyers in the real estate market faced challenges in accurately assessing property values and market trends. Traditional valuation methods were time-consuming and lacked precision, making it difficult to make informed investment decisions. A real estate firm sought a predictive analytics solution to provide accurate property price forecasts and market insights. Challenge Developing a real estate price prediction system involved… See the full description on the dataset page: https://huggingface.co/datasets/globosetechnology12/Real-Estate-Price-Prediction.
House prices in Spain are forecast to fall in 2024, after increasing by 1.2 percent in 2023. Nevertheless, prices are expected to pick up in 2025, with an increase of one percent. The Portuguese housing market, on the other hand, grew by 5.5 percent in 2023, but was forecast to contract in the next two years.
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Housing Index in the United States decreased to 434.90 points in April from 436.70 points in March of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
After a period of rapid increase, house price growth in the UK has moderated. In 2025, house prices are forecast to increase by ****percent. Between 2025 and 2029, the average house price growth is projected at *** percent. According to the source, home building is expected to increase slightly in this period, fueling home buying. On the other hand, higher borrowing costs despite recent easing of mortgage rates and affordability challenges may continue to suppress transaction activity. Historical house price growth in the UK House prices rose steadily between 2015 and 2020, despite minor fluctuations. In the following two years, prices soared, leading to the house price index jumping by about 20 percent. As the market stood in April 2025, the average price for a home stood at approximately ******* British pounds. Rents are expected to continue to grow According to another forecast, the prime residential market is also expected to see rental prices grow in the next five years. Growth is forecast to be stronger in 2025 and slow slightly until 2029. The rental market in London is expected to follow a similar trend, with Outer London slightly outperforming Central London.
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Key information about House Prices Growth
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License information was derived automatically
Housing Index in Hong Kong decreased to 136.44 points in July 13 from 136.68 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
According to the forecast, house prices in London are expected to continue to increase until 2029. During the five-year period from 2025 to 2029, the house prices for mainstream properties are forecast to rise by **** percent. In 2023, the average house price in London ranged between ******* British pounds and *** million British pounds, depending on the borough. Barking and Dagenham, Bexley, Newham, and Croydon were some of the most affordable boroughs to buy a house.
Residential Real Estate Market Size 2025-2029
The residential real estate market size is forecast to increase by USD 485.2 billion at a CAGR of 4.5% between 2024 and 2029.
The market is experiencing significant growth, fueled by increasing marketing initiatives that attract potential buyers and tenants. This trend is driven by the rising demand for housing solutions that cater to the evolving needs of consumers, particularly in urban areas. However, the market's growth trajectory is not without challenges. Regulatory uncertainty looms large, with changing policies and regulations posing a significant threat to market stability. Notably, innovative smart home technologies, such as voice-activated assistants and energy-efficient appliances, are gaining traction, offering enhanced convenience and sustainability for homeowners.
As such, companies seeking to capitalize on the opportunities presented by the growing the market must navigate these challenges with agility and foresight. The residential construction industry's expansion is driven by urbanization and the rising standard of living in emerging economies, including India, China, Thailand, Malaysia, and Indonesia. By staying abreast of regulatory changes and implementing innovative marketing strategies, they can effectively meet the evolving needs of consumers and maintain a competitive edge. These regulatory shifts can impact everything from property prices to financing options, making it crucial for market players to stay informed and adapt quickly.
What will be the Size of the Residential Real Estate Market during the forecast period?
Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic housing market analysis, small flats continue to be a popular choice for both investors and first-time homebuyers, driven by affordability and urban growth. International investment in housing projects, including apartments and condominiums, remains strong, offering attractive investment returns. Real estate syndication and property management software facilitate efficient property ownership and management. Real estate loans, property insurance, and urban planning are essential components of the housing market, ensuring the development of affordable housing and addressing the needs of the middle class and upper middle class. Property disputes, property tax assessments, and real estate litigation are ongoing challenges, requiring careful attention from stakeholders.
Property search engines streamline the process of finding the perfect property, from studio apartments to luxury homes. Real estate auctions, land banking, and nano apartments are innovative solutions in the market, while property flipping and short sales provide opportunities for savvy investors. Urban growth and community development are key trends, with a focus on sustainable, planned cities and the integration of technology, such as real estate blockchain, into the industry. Developers secure building permits, review inspection reports, and manage escrow accounts during real estate transactions. Key services include contract negotiation, dispute resolution, and tailored investment strategies for portfolio management. Financial aspects cover tax implications, estate planning, retirement planning, taxdeferred exchanges, capital gains, tax deductions, and maintaining positive cash flow for sustained returns.
How is this Residential Real Estate Industry segmented?
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. The sales segment dominates the global residential real estate market and will continue to dominate during the forecast period. The sales segment includes the sale of any property that is majorly used for residential purposes, such as single-family homes, condos, cooperatives, duplexes, townhouses, and multifamily residences. With the growing population and urbanization, the demand for homes is also increasing, which is the major factor driving the growth of the sales segment. Moreover, real estate firms work with developers to sel
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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)
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Key information about US Nominal Residential Property Price Index
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China Property Price: YTD Avg: Overall data was reported at 9,510.153 RMB/sq m in Mar 2025. This records a decrease from the previous number of 9,547.228 RMB/sq m for Feb 2025. China Property Price: YTD Avg: Overall data is updated monthly, averaging 5,157.474 RMB/sq m from Dec 1995 (Median) to Mar 2025, with 352 observations. The data reached an all-time high of 11,029.538 RMB/sq m in Feb 2021 and a record low of 599.276 RMB/sq m in Feb 1996. China Property Price: YTD Avg: Overall data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Price – Table CN.PD: NBS: Property Price: Monthly.
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Single Family Home Prices in the United States increased to 435300 USD in June from 423700 USD in May of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.
Real Estate Market Size 2025-2029
The real estate market size is forecast to increase by USD 1,258.6 billion at a CAGR of 5.6% between 2024 and 2029.
The market is experiencing significant shifts and innovations, with both residential and commercial sectors adapting to new trends and challenges. In the commercial realm, e-commerce growth is driving the demand for logistics and distribution centers, while virtual reality technology is revolutionizing property viewings. Europe's commercial real estate sector is witnessing a rise in smart city development, incorporating LED lighting and data centers to enhance sustainability and efficiency. In the residential sector, wellness real estate is gaining popularity, focusing on health and well-being. Real estate software and advertising services are essential tools for asset management, streamlining operations, and reaching potential buyers. Regulatory uncertainty remains a challenge, but innovation in construction technologies, such as generators and renewable energy solutions, is helping mitigate risks.
What will be the Size of the Real Estate Market During the Forecast Period?
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The market continues to exhibit strong activity, driven by rising population growth and increasing demand for personal household space. Both residential and commercial sectors have experienced a rebound in home sales and leasing activity. The trend towards live-streaming rooms and remote work has further fueled demand for housing and commercial real estate. Economic conditions and local market dynamics influence the direction of the market, with interest rates playing a significant role in investment decisions. Fully furnished, semi-furnished, and unfurnished properties, as well as rental properties, remain popular options for buyers and tenants. Offline transactions continue to dominate, but online transactions are gaining traction.
The market encompasses a diverse range of assets, including land, improvements, buildings, fixtures, roads, structures, utility systems, and undeveloped property. Vacant land and undeveloped property present opportunities for investors, while the construction and development of new housing and commercial projects contribute to the market's overall growth.
How is this Real Estate Industry segmented and which is the largest segment?
The 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.
Type
Residential
Commercial
Industrial
Business Segment
Rental
Sales
Manufacturing Type
New construction
Renovation and redevelopment
Land development
Geography
APAC
China
India
Japan
South Korea
North America
Canada
US
Europe
Germany
UK
South America
Brazil
Middle East and Africa
By Type Insights
The residential segment is estimated to witness significant growth during the forecast period.
The market encompasses the buying and selling of properties designed for dwelling purposes, including buildings, single-family homes, apartments, townhouses, and more. Factors fueling growth in this sector include the increasing homeownership rate among millennials and urbanization trends. The Asia Pacific region, specifically China, dominates the market due to escalating homeownership rates. In India, the demand for affordable housing is a major driver, with initiatives like Pradhan Mantri Awas Yojana (PMAY) spurring the development of affordable housing projects catering to the needs of lower and middle-income groups. The commercial real estate segment, consisting of office buildings, shopping malls, hotels, and other commercial properties, is also experiencing growth.
Furthermore, economic and local market conditions, interest rates, and investment opportunities in fully furnished, semi-furnished, unfurnished properties, and rental properties influence the market dynamics. Technological integration, infrastructure development, and construction projects further shape the real estate landscape. Key sectors like transportation, logistics, agriculture, and the e-commerce sector also impact the market.
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The Residential segment was valued at USD 1440.30 billion in 2019 and showed a gradual increase during the forecast period.
Regional Analysis
APAC is estimated to contribute 64% 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 Asia Pacific region holds the largest share of The market, dr
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The global residential real estate market size was valued at approximately $9.7 trillion in 2023 and is projected to reach an astounding $15.4 trillion by 2032, growing at a compound annual growth rate (CAGR) of 5.2%. This growth is driven by several factors, including increasing urbanization, rising disposable incomes, and the ongoing global shift towards homeownership as a stable investment. Demographic shifts, such as the growing number of nuclear families and millennials entering the housing market, also contribute significantly to this upward trend.
One of the primary growth factors for the residential real estate market is the increasing urbanization across the globe. As more people migrate to urban areas in search of better job opportunities and a higher standard of living, the demand for residential properties in cities continues to rise. This trend is particularly pronounced in developing countries, where rapid economic growth is accompanied by significant rural-to-urban migration. Additionally, the trend of urban redevelopment and the creation of smart cities are further fueling the demand for modern residential properties.
Another crucial growth factor is the rise in disposable incomes and improved access to financing options. With strong economic growth in many parts of the world, individual incomes have been rising, allowing more people to afford homeownership. Financial institutions are also playing a critical role by offering a variety of mortgage products with attractive interest rates and flexible repayment terms. This increased access to capital has enabled a broader section of the population to invest in residential real estate, thereby expanding the market.
Technological advancements and the digital transformation of the real estate sector are also contributing to market growth. The proliferation of online platforms and real estate technology (proptech) solutions has made the process of buying, selling, and renting properties more efficient and transparent. Virtual tours, online mortgage applications, and blockchain for property transactions are some of the innovations revolutionizing the industry. These technological advancements not only improve the customer experience but also attract tech-savvy millennials and Gen Z buyers.
Regionally, the Asia-Pacific region is experiencing significant growth in the residential real estate market. Countries like China and India, with their large populations and rapid urbanization, are at the forefront of this expansion. Government initiatives aimed at providing affordable housing and improving infrastructure are also playing a pivotal role. In contrast, mature markets like North America and Europe are witnessing steady growth driven by economic stability and continued investment in housing. Meanwhile, regions like Latin America and the Middle East & Africa are also showing promise, albeit at a slower pace, due to varying economic conditions and market maturity levels.
The residential real estate market is segmented by property type, including single-family homes, multi-family homes, condominiums, townhouses, and others. Single-family homes are the most traditional and widespread type of residential property. They are particularly popular in suburban areas where space is more abundant. The demand for single-family homes continues to be driven by the desire for privacy, larger living spaces, and the ability to customize the property. These homes appeal especially to families with children and those looking to invest in a long-term residence.
Multi-family homes, which include duplexes, triplexes, and apartment buildings, are gaining traction, particularly in urban settings. These properties are attractive due to their potential for generating rental income and their ability to house multiple tenants. Investors find multi-family homes appealing as they offer a higher return on investment (ROI) compared to single-family homes. Additionally, the increasing trend of co-living and shared housing arrangements has bolstered the demand for multi-family properties in cities.
Condominiums, or condos, are another significant segment within the residential real estate market. Condos are particularly popular in urban areas where land is scarce and expensive. They offer a balance between affordability and amenities, making them an attractive option for young professionals and small families. Condominiums often come with added benefits such as maintenance services, security, and shared facilities like gyms and swimmin
The UK House Price Index is a National Statistic.
Download the full UK House Price Index data below, or use our tool to https://landregistry.data.gov.uk/app/ukhpi?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=tool&utm_term=9.30_18_08_21" class="govuk-link">create your own bespoke reports.
Datasets are available as CSV files. Find out about republishing and making use of the data.
Google Chrome is blocking downloads of our UK HPI data files (Chrome 88 onwards). Please use another internet browser while we resolve this issue. We apologise for any inconvenience caused.
This file includes a derived back series for the new UK HPI. Under the UK HPI, data is available from 1995 for England and Wales, 2004 for Scotland and 2005 for Northern Ireland. A longer back series has been derived by using the historic path of the Office for National Statistics HPI to construct a series back to 1968.
Download the full UK HPI background file:
If you are interested in a specific attribute, we have separated them into these CSV files:
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-prices-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average_price&utm_term=9.30_18_08_21" class="govuk-link">Average price (CSV, 9.2MB)
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-prices-Property-Type-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average_price_property_price&utm_term=9.30_18_08_21" class="govuk-link">Average price by property type (CSV, 28MB)
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Sales-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=sales&utm_term=9.30_18_08_21" class="govuk-link">Sales (CSV, 4.7MB)
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Cash-mortgage-sales-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=cash_mortgage-sales&utm_term=9.30_18_08_21" class="govuk-link">Cash mortgage sales (CSV, 6.1MB)
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/First-Time-Buyer-Former-Owner-Occupied-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=FTNFOO&utm_term=9.30_18_08_21" class="govuk-link">First time buyer and former owner occupier (CSV, 5.9MB)
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/New-and-Old-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=new_build&utm_term=9.30_18_08_21" class="govuk-link">New build and existing resold property (CSV, 17MB)
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Indices-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=index&utm_term=9.30_18_08_21" class="govuk-link">Index (CSV, 6MB)
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Indices-seasonally-adjusted-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=index_season_adjusted&utm_term=9.30_18_08_21" class="govuk-link">Index seasonally adjusted (CSV, 192KB)
http://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-price-seasonally-adjusted-2021-06.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average-price_season_adjusted&utm_term=9.30_18_08_21" class="govuk-link">Average price seasonally adjusted</a
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Key information about House Prices Growth
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Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q1 2025 about sales, median, housing, and USA.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].