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A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?
Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.
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Dataset Description: This dataset contains information about various housing properties in Vietnam. It includes detailed attributes of each property, such as its location, physical characteristics, and legal and furnishing status, along with the price. This dataset can be useful for real estate analysis, price prediction models, and market trend analysis.
Data Collection: The data was crawled from batdongsan.vn
Column Descriptions: - Address: The complete address of the property, including details such as the project name, street, ward, district, and city. - Area: The total area of the property, measured in square meters. - Frontage: The width of the front side of the property, measured in meters. - Access Road: The width of the road providing access to the property, measured in meters. - House Direction: The cardinal direction the front of the house is facing (e.g., East, West, North, South). - Balcony Direction: The cardinal direction the balcony is facing. - Floors: The total number of floors in the property. - Bedrooms: The number of bedrooms in the property. - Bathrooms: The number of bathrooms in the property. - Legal Status: Indicates the legal status of the property, such as whether it has a certificate of ownership or is under a sale contract. - Furniture State: Indicates the state of furnishing in the property, such as fully furnished, partially furnished, or unfurnished. - Price: The price of the property, represented in billions of Vietnamese Dong (VND).
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TwitterAfter 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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Vijayawada House Price Dataset (2020-2024) Predict real estate prices using location, size, and property type
** Key Variables** Area: Neighborhood name (e.g., Benz Circle, Patamata) Region_Type: Premium/Commercial/Residential (shows price tier) BHK: Number of bedrooms (1 to 5+) Size (sqft): Property area (800–5,000 sqft range) Price_Per_Sqft: Cost per sqft (₹2,500–₹7,000) Total_Price: Calculated price (Size × Price_Per_Sqft) Projects You can prepare using this data set : Ideal for real estate price prediction, geospatial analysis, and market trends. Predict house prices using linear regression or XGBoost Explore price trends by region or BHK count Train a classification model to categorize properties into budget/mid-range/premium Create a price heatmap of Vijayawada (if extended with geo-coordinates)
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Single Family Home Prices in the United States increased to 415200 USD in October from 412300 USD in September of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.
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TwitterThe 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_20_03_24" class="govuk-link">create your own bespoke reports.
Datasets are available as CSV files. Find out about republishing and making use of the data.
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:
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-prices-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average_price&utm_term=9.30_20_03_24" class="govuk-link">Average price (CSV, 9.4MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-prices-Property-Type-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average_price_property_price&utm_term=9.30_20_03_24" class="govuk-link">Average price by property type (CSV, 28MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Sales-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=sales&utm_term=9.30_20_03_24" class="govuk-link">Sales (CSV, 5MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Cash-mortgage-sales-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=cash_mortgage-sales&utm_term=9.30_20_03_24" class="govuk-link">Cash mortgage sales (CSV, 7MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/First-Time-Buyer-Former-Owner-Occupied-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=FTNFOO&utm_term=9.30_20_03_24" class="govuk-link">First time buyer and former owner occupier (CSV, 6.3MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/New-and-Old-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=new_build&utm_term=9.30_20_03_24" class="govuk-link">New build and existing resold property (CSV, 17MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Indices-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=index&utm_term=9.30_20_03_24" class="govuk-link">Index (CSV, 6.1MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Indices-seasonally-adjusted-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=index_season_adjusted&utm_term=9.30_20_03_24" class="govuk-link">Index seasonally adjusted (CSV, 209KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-price-seasonally-adjusted-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average-price_season_adjusted&utm_term=9.30_20_03_24" class="govuk-link">Average price seasonally adjusted (CSV, 218KB)
<a rel="external" href="https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Repossession-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=repossession&utm_term=9.30_20_03_24" class
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TwitterThe statistic displays a **** year forecast for house price growth in the United Kingdom (UK) from 2020 to 2024, revised with the coronavirus (covid-19) impact on the market. According to the forecast, 2020 and 2021 will likely see a slower to no increase in house prices followed by a gradual recovery between 2022 and 2024. North West, North East, Yorkshire & the Humber, and Scotland prices are forecast to bounce back quicker than other UK regions with higher **** year price increase.
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Housing Index in Germany increased to 220.43 points in October from 219.91 points in September of 2025. This dataset provides the latest reported value for - Germany House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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Housing Index in the United States decreased to 435.40 points in September from 435.60 points in August 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.
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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.
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Key information about House Prices Growth
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Housing Index in Hong Kong increased to 143.46 points in November 23 from 142.49 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Housing Index in Sweden increased to 959 points in the third quarter of 2025 from 945 points in the second quarter of 2025. This dataset provides - Sweden House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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TwitterAccording to the forecast, the North West and Yorkshire & the Humber are the UK regions expected to see the highest overall growth in house prices over the five-year period between 2025 and 2029. Just behind are the North East and West Midlands. In London, house prices are expected to rise by **** percent.
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TwitterFinland's house prices grew by about **** percent in 2021, but according to the forecast the growth is expected to slow down in the following years. In 2023, the average house price is forecast to decrease by **** percent and in 2024, the trend is to reverse, with an annual growth of ***** percent. The average square meter price of apartments in Finland's largest cities ranged between ***** euros and ***** euros in 2022.
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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
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TwitterEl Paso is to experience the highest price growth among the largest metros in Texas in 2024, according to a November 2023 forecast. Due to the more affordable home prices in El Paso, it is also forecast to see further growth in home sales volumes. Conversely, prices in Austin-Round Rock, are forecast to decline by over ** percent.
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Key information about House Prices Growth
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The most accurate home price forecast in the U.S. Housing Market with 72% correlation coefficient in predicting value growth from April 2024 to 2025.
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Case Shiller Home Price Index in the United States decreased to 338.25 points in September from 339.99 points in August of 2025. This dataset provides the latest reported value for - United States S&P Case-Shiller Home Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg" alt="">
A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?
Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.