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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Description:

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?

Acknowledgement:

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.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

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.

This data was collected from one of the famous websites in Egypt that specialized in publishing buying and selling ads, The data of the apartments for sale was extracted for the month of 8/2022.

this data was collected using web scrapping py Python.

I collected this data and wanted to analyze it and create a machine learning model to predict the price of a housing unit based on other variables in the data and would like to share this information with you.

This is a real dataset of house prices sold in Seattle, Washing, USA between August and December 2022. The task is to predict the house price in this area based on several features, which are described below.

Useful fact: * 1 acre = 43560 sqft

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.

The 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.

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

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

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.

Key information about House Prices Growth

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

• DATE : Starting from 1979 to 2022
• Average_Earning : Average Earning of the people of US
• GDP : GDP of the country US
• Mortgage : Mortgage
• Population : Population of US
• Unemployment_Rate : Unemployment rate of the country
• Average_Sales_Price : Average sales price of the Home

The U.S. housing market continues to evolve, with the median price for existing homes forecast to fall to ******* U.S. dollars by 2027. 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 a modest price increase of *** percent in 2024. 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 2025, with Rhode Island and West Virginia leading the packby home 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.

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.

This is the dataset of average housing prices in Ho Chi Minh City, Vietnam.

Data for districts 1 to 9.

Data collected from 2017 to 2022

Data in the form of Time Series Data for predicting house prices, holidays and weekends is not included in this dataset.

The Date column represents the transaction date Columns from District 1 to District 9 show the average transaction value per square meter.

With this data, data scientists can: - Compare house prices between districts by year - Forecast house prices in the future, can use singular spectrum analysis to analyze - Evaluate model quality with RMSE or equivalent measures

Update: HousePricingHCM_v2.csv is including holidays and weekends, We use replacing missing data by average transactions of the days before and after the holiday to fill in the missing data

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.

Dataset Description: Hyderabad City House Prices

Overview

The Hyderabad City House Prices dataset is a detailed collection of real estate data for residential properties across various localities in Hyderabad. This dataset is aimed at real estate analysts, data scientists, urban planners, and researchers who are interested in studying the housing market, price trends, and neighborhood dynamics within Hyderabad, one of India's rapidly growing metropolitan cities.

Features

The dataset includes the following features:

1. Title: The headline or main title of the property listing.
2. Location: Specific address or locality details within Hyderabad.
3. Price (L): The listed price of the property in Indian Lakhs.
4. Rate per Sqft: The cost per square foot of the property.
5. Area in Sqft: The total area of the property in square feet.
6. Building Status: The construction status of the property (e.g., Under Construction, Ready to Move).

Usage

This dataset can be utilized for various purposes, including: - Market Analysis: Understanding pricing trends, supply and demand, and market conditions in different localities of Hyderabad. - Price Prediction Models: Developing machine learning models to predict property prices based on the given features. - Investment Analysis: Identifying potential investment opportunities by analyzing location, property type, and price data. - Urban Planning: Assisting urban planners in understanding housing distribution and development trends across the city.

Data Collection

The data has been scraped from popular real estate websites such as Magicbricks, 99acres, and Housing.com using the Scrapy framework. The data was collected in [insert month/year] and represents a snapshot of the real estate market in Hyderabad at that time.

Sample Data

Contact

For more information or to access the dataset, please contact [Your Name] at [Your Email Address].

This dataset provides valuable insights into Hyderabad's diverse real estate market, helping stakeholders make informed decisions based on accurate and up-to-date data.

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.

In 2022, housing prices in Belgium rose. According to the forecast, 2023 and 2024 will follow with a slight increase of two percent. Consumers signal much uncertainty on, for example, development of unemployment, which can hamper the housing market.

Belgium’s housing prices development

For years, house prices in Belgium followed a similar growth pattern to the country’s economy. Residential property prices grew when Belgium's economy performed well but stagnated when the economy slowed down. Since 2020, however, growth has accelerated. In 2022, the average house price exceeded 319,000 euros, up from 298,000 euros the year before.

The Belgian economy faces an uncertain future

Belgium’s real estate market is closely connected to the economic performance of the country. According to a 2022 forecast, the Belgian economy was predicted to grow by 2.1 percent in 2023. This prediction reflected inflation, supply chain disruptions impacting domestic demand, as well as (a lack of) international trade impacting Belgian growth.

Real Estate Analysis Of the Seattle Market, For Investors, to predict appreciation for future acquisitions.

This is a real dataset of house prices sold in Seattle, Washington USA between August and December 2022. The task is to predict the house price in this area based on several features, which are described below.

Please click on Provenance, to get both links for sources.