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].

Just as in many other countries, the housing market in the UK grew substantially during the coronavirus pandemic, fueled by robust demand and low borrowing costs. Nevertheless, high inflation and the increase in mortgage rates has led to house price growth slowing down. According to the forecast, 2024 is expected to see house prices decrease by ***** percent. Between 2024 and 2028, the average house price growth is projected at *** percent. A contraction after a period of continuous growth In June 2022, the UK's house price index exceeded *** index points, meaning that since 2015 which was the base year for the index, house prices had increased by ** percent. In just two years, between 2020 and 2022, the index surged by ** index points. As the market stood in December 2023, 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 years. Growth is forecast to be stronger in 2024 and slow down in the period between 2025 and 2028. The rental market in London is expected to follow a similar trend, with Central London slightly outperforming Greater London.

The US residential real estate market, a cornerstone of the American economy, is projected to experience steady growth over the next decade. While the provided CAGR of 2.04% is a modest figure, it reflects a market maturing after a period of significant expansion. This sustained growth is driven by several key factors. Firstly, population growth and urbanization continue to fuel demand for housing, particularly in densely populated areas and emerging suburban markets. Secondly, low interest rates (historically, though this can fluctuate) have made mortgages more accessible, stimulating buyer activity. Thirdly, a robust construction sector, though facing challenges in material costs and labor shortages, is gradually increasing the housing supply, mitigating some of the upward pressure on prices. However, challenges remain. Rising inflation and potential interest rate hikes pose a risk to affordability, potentially dampening demand. Furthermore, the ongoing evolution of remote work is reshaping residential preferences, with a shift toward larger homes in suburban or exurban locations. This trend impacts the relative demand for various property types, potentially increasing the appeal of landed houses and villas compared to apartments and condominiums in certain regions. The segmentation of the market into apartments/condominiums and landed houses/villas provides crucial insights into consumer preferences and investment strategies. High-density urban areas will continue to see strong demand for apartments and condos, while suburban and rural areas are likely to experience a greater increase in landed property sales. Major players like Simon Property Group, Mill Creek Residential, and others are strategically adapting to these trends, focusing on both development and management across various property types and geographic locations. Analyzing regional data within the US (e.g., comparing growth in the Northeast versus the Southwest) will highlight market nuances and potential investment opportunities. While the global data provided is valuable for understanding broader market forces, focusing the analysis on the US market allows for a more granular understanding of the specific drivers, trends, and challenges within this significant segment of the real estate sector. The forecast period (2025-2033) suggests continued, albeit measured, expansion. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Key drivers for this market are: Investment Plan Towards Urban Rail Development. Potential restraints include: Italy’s Fragmented Approach to Tenders. Notable trends are: Existing Home Sales Witnessing Strong Growth.

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)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11965067%2F75861c40e86a4d2d10c044be79542436%2FCapture.JPG?generation=1704918894425981&alt=media" alt="">

Github Link : https://github.com/TusharPaul01/House-Price-Prediction

For more such dataset & code check : https://www.kaggle.com/tusharpaul2001

House prices in Norway fell by *** percent and, according to the forecast, are expected to continue to fall until 2024. In 2023, properties were forecast to experience a decline in prices of ** percent. In 2025, growth is projected to recover, rising to **** percent.

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.

Single Family Home Prices in the United States increased to 422800 USD in May from 414000 USD in April of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Snapshot img

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

The U.S. housing market continues to evolve, with the median home price forecast to reach ******* U.S. dollars by the second quarter of 2026. This projection comes after a period of significant growth and recent fluctuations, reflecting the complex interplay of economic factors affecting the real estate sector. The rising costs have not only impacted home prices, but also down payments, with the median down payment more than doubling since 2012. Regional variations in housing costs Home prices and down payments vary dramatically across the United States. While the national median down payment stood at approximately ****** U.S. dollars in early 2024, homebuyers in states like California, Massachusetts, and Hawaii faced down payments exceeding ****** U.S. dollars. This disparity highlights the challenges of homeownership in high-cost markets and underscores the importance of location in determining housing affordability. Market dynamics and future outlook The housing market has shown signs of cooling after years of rapid growth, with more modest price increases of *** percent in 2022 and *** percent in 2023. This slowdown can be attributed in part to rising mortgage rates, which have tempered demand. Despite these challenges, most states continued to see year-over-year price growth in the fourth quarter of 2023, with Rhode Island and Vermont leading the pack at over ** percent appreciation. As the market adjusts to new economic realities, potential homebuyers and investors alike will be watching closely for signs of stabilization or renewed growth in the coming years.

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.

Housing Index in Spain increased to 2033 EUR/SQ. METRE in the first quarter of 2025 from 1972.10 EUR/SQ. METRE in the fourth quarter of 2024. This dataset provides the latest reported value for - Spain House Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset

This dataset was created by Fahad QureXhi

Released under Apache 2.0

Contents

Dataset

This dataset was created by Kat Hernandez

Contents

S&P/Case-Shiller home price index and 12 demographic and macroeconomic factors in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco (SF) data were collected from the Federal Reserve Bank, FBI, and Freddie Mac. https://fred.stlouisfed.org; http://www.freddiemac.com/pmms/; https://www.philadelphiafed.org/surveys-and-data/community-development-data/consumer-credit-explorer; https://ucr.fbi.gov/crime-in-the-u.s/2005;

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.

Housing Index in Sweden decreased to 936 points in the first quarter of 2025 from 937 points in the fourth quarter of 2024. This dataset provides - Sweden House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The California Housing dataset is based on 1990 US census and is widely used for machine learning and statistics. It was published in 1990 by Pace, R. Kelley and Ronald Barry, and can be found in the UCI Machine Learning Repository. The California Data set gives the information about Economic and Geographic values of the Houses,and also the economic status of the people present in the California.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Understanding the Dynamics and Implications of a Housing Market Recession

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data