The average price per square foot of floor space in new single-family housing in the United States decreased after the great financial crisis, followed by several years of stagnation. Since 2012, the price has continuously risen, hitting 168 U.S. dollars per square foot in 2022. In 2024, the average sales price of a new home exceeded 500,000 U.S. dollars. Development of house sales in the U.S. One of the reasons for rising property prices is the gradual growth of house sales between 2011 and 2020. This period was marked by the gradual recovery following the subprime mortgage crisis and a growing housing sentiment. Another significant factor for the housing demand was the growing number of new household formations each year. Despite this trend, housing transactions plummeted in 2021, amid soaring prices and borrowing costs. In 2021, the average construction cost for single-family housing rose by nearly 12 percent year-on-year, and in 2022, the increase was even higher, at close to 17 percent. Financing a house purchase Mortgage interest rates in the U.S. rose dramatically in 2022 and remained elevated until 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under three percent, whereas in 2024, the average rate for the same mortgage type was more than twice higher. That has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in the United States (MEDLISPRIPERSQUFEEUS) from Jul 2016 to Feb 2025 about square feet, listing, median, price, and USA.

Single Family Home Prices in the United States increased to 398400 USD in February from 393400 USD in January of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

In the United States, Hawaii was the state with the most expensive housing, with the typical value of single-family homes in the 35th to 65th percentile range exceeding 981,000 U.S. dollars. Unsurprisingly, Hawaii also ranked top as the state with the highest cost of living. Meanwhile, a property was the least expensive in West Virginia, where it cost under 167,000 U.S. dollars to buy the typical single-family home. Single-family home prices increased across most states in the United States between December 2023 and December 2024, except in Louisiana, Florida, and the District of Colombia. According to the Federal Housing Association, house appreciation in 13 states exceeded nine percent in 2023.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in New York (MEDLISPRIPERSQUFEENY) from Jul 2016 to Feb 2025 about square feet, NY, listing, median, price, and USA.

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

Housing Index in Portugal increased to 228.89 points in the third quarter of 2024 from 220.74 points in the second quarter of 2024. This dataset provides - Portugal House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in Arkansas (MEDLISPRIPERSQUFEEAR) from Jul 2016 to Feb 2025 about AR, square feet, listing, median, price, and USA.

Average House Prices in Canada decreased to 712400 CAD in February from 718500 CAD in January of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

This table contains data on the number, living area, and assessment value per square foot of residential properties, by property type and period of construction, for the provinces of Nova Scotia, Ontario and British Columbia, their census metropolitan areas (CMAs) and census subdivisions (CSDs).

What Makes Our Data Unique? We do not buy and resell other provider's data. We aggregate our housing data, which we source ourselves, to ensure the highest quality.

Our real estate data encompasses a wide range of comprehensive information on homeowners and properties.

• Current listings as well as properties for rent.
• Current and past property owners
• Current and past building permits
• Property attributes such as number of rooms, square footage, roof type, and many more.
• Zoning details
• Businesses at location
• Forclosure details
• Current and historical property values
• Tax details

Use cases and verticals.

• Developers looking to create applications around real estate data tailored towards real estate investors or agents.
• Agents or investors looking to acquire new leads within a geographical territory.
• Market analysts looking to uncover trends within the housing sector.

Overview

The House Price Prediction Challenge, will test your regression skills by designing an algorithm to accurately predict the house prices in India. Accurately predicting house prices can be a daunting task. The buyers are just not concerned about the size(square feet) of the house and there are various other factors that play a key role to decide the price of a house/property. It can be extremely difficult to figure out the right set of attributes that are contributing to understanding the buyer's behavior as such. This dataset has been collected across various property aggregators across India. The dataset provides the 12 influencing factors your role as a data scientist is to predict the prices as accurately as possible.

You will get a lot of room for feature engineering and mastering advanced regression techniques such as Random Forest, Deep Neural Nets, and various other ensembling techniques.

Data Description:

Train.csv - 29451 rows x 12 columns Test.csv - 68720 rows x 11 columns Sample Submission - Acceptable submission format. (.csv/.xlsx file with 68720 rows)

Explore the Redfin Canada Properties Dataset, available in CSV format and extracted in April 2022. This comprehensive dataset offers detailed insights into the Canadian real estate market, including property listings, prices, square footage, number of bedrooms and bathrooms, and more. Covering various cities and provinces, it’s ideal for market analysis, investment research, and financial modeling.

Key Features:

• Property Details: Includes crucial data such as listing price, property type, square footage, number of bedrooms and bathrooms, and more.
• Geo-Location Data: Provides geographical coordinates, allowing for spatial analysis and mapping.
• Market Trends: Offers historical data to analyze price trends and market fluctuations.

Who Can Use This Dataset:

• Real Estate Professionals: Evaluate market trends and property values to better advise clients or guide investment decisions.
• Investors: Analyze the Canadian housing market to identify investment opportunities and potential returns.
• Data Analysts and Researchers: Use this dataset to study market dynamics, urban development, or economic factors influencing the real estate sector.
• Financial Analysts: Incorporate the data into financial models to forecast market behavior and investment outcomes.

Download the Redfin Canada Properties Dataset to access valuable information on the Canadian housing market, perfect for anyone involved in real estate, finance, or data analysis.

This dataset represents real estate assessment and sales data made available by the Office of the Real Estate Assessor. This dataset contains information for properties in the city, including acreage, square footage, GPIN, street address, year built, current land value, current improvement value, and current total value. The information is obtained from the Office of the Real Estate Assessor ProVal records database. This dataset is updated daily on weekdays.

For data about this dataset, please click on the below link: https://data.norfolk.gov/Real-Estate/Property-Assessment-and-Sales-FY24/9gmp-9x4c/about_data

This dataset uses data provided from Washington State’s Housing Market, a publication of the Washington Center for Real Estate Research (WCRER) at the University of Washington.

Median sales prices represent that price at which half the sales in a county (or the state) took place at higher prices, and half at lower prices. Since WCRER does not receive sales data on individual transactions (only aggregated statistics), the median is determined by the proportion of sales in a given range of prices required to reach the midway point in the distribution. While average prices are not reported, they tend to be 15-20 percent above the median.

Movements in sales prices should not be interpreted as appreciation rates. Prices are influenced by changes in cost and changes in the characteristics of homes actually sold. The table on prices by number of bedrooms provides a better measure of appreciation of types of homes than the overall median, but it is still subject to composition issues (such as square footage of home, quality of finishes and size of lot, among others).

There is a degree of seasonal variation in reported selling prices. Prices tend to hit a seasonal peak in summer, then decline through the winter before turning upward again, but home sales prices are not seasonally adjusted. Users are encouraged to limit price comparisons to the same time period in previous years.

The average transaction price of new housing in Europe was the highest in Norway, whereas existing homes were the most expensive in Austria. Since there is no central body that collects and tracks transaction activity or house prices across the whole continent or the European Union, not all countries are included. To compile the ranking, the source weighed the transaction prices of residential properties in the most important cities in each country based on data from their national offices. For example, in Germany, the cities included were Munich, Hamburg, Frankfurt, and Berlin. House prices have been soaring, with Sweden topping the ranking Considering the RHPI of houses in Europe (the price index in real terms, which measures price changes of single-family properties adjusted for the impact of inflation), however, the picture changes. Sweden, Luxembourg and Norway top this ranking, meaning residential property prices have surged the most in these countries. Real values were calculated using the so-called Personal Consumption Expenditure Deflator (PCE), This PCE uses both consumer prices as well as consumer expenditures, like medical and health care expenses paid by employers. It is meant to show how expensive housing is compared to the way of living in a country. Home ownership highest in Eastern Europe The home ownership rate in Europe varied from country to country. In 2020, roughly half of all homes in Germany were owner-occupied whereas home ownership was at nearly 97 percent in Romania or around 90 percent in Slovakia and Lithuania. These numbers were considerably higher than in France or Italy, where homeowners made up 65 percent and 72 percent of their respective populations.For more information on the topic of property in Europe, visit the following pages as a starting point for your research: real estate investments in Europe and residential real estate in Europe.

Housing Index in Bosnia and Herzegovina increased to 3066 BAM/SQ. METRE in the fourth quarter of 2024 from 2906 BAM/SQ. METRE in the third quarter of 2024. This dataset provides - Bosnia And Herzegovina Completed Residential Construction - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

Overview Welcome to the House Price Prediction Challenge, you will test your regression skills by designing an algorithm to accurately predict the house prices in India. Accurately predicting house prices can be a daunting task. The buyers are just not concerned about the size(square feet) of the house and there are various other factors that play a key role to decide the price of a house/property. It can be extremely difficult to figure out the right set of attributes that are contributing to understanding the buyer's behavior as such. This dataset has been collected across various property aggregators across India. In this competition, provided the 12 influencing factors your role as a data scientist is to predict the prices as accurately as possible.

Also, in this competition, you will get a lot of room for feature engineering and mastering advanced regression techniques such as Random Forest, Deep Neural Nets, and various other ensembling techniques.

Content

Train.csv - 29451 rows x 12 columns Test.csv - 68720 rows x 11 columns Sample Submission - Acceptable submission format. (.csv/.xlsx file with 68720 rows)

Columns Description

POSTED_BY - Category marking who has listed the property UNDER_CONSTRUCTION - Under Construction or Not RERA - Rera approved or Not BHK_NO - Number of Rooms BHK_OR_RK - Type of property SQUARE_FT - Total area of the house in square feet READY_TO_MOVE - Category marking Ready to move or Not RESALE - Category marking Resale or not ADDRESS - Address of the property LONGITUDE - Longitude of the property LATITUDE - Latitude of the property

Evaluation

What is the Metric In this competition? How is the Leaderboard Calculated ?? The submission will be evaluated using the RMSLE (Root Mean Squared Logarithmic Error) metric. One can use np.sqrt(mean_squared_log_error( actual, predicted)) This hackathon supports private and public leaderboards The public leaderboard is evaluated on 30% of Test data The private leaderboard will be made available at the end of the hackathon which will be evaluated on 100% Test data

Acknowledgements

This is a data Shared by Machine Hack you can participate in Hackathon and submit your own submissions Link to Machine Hack, Hackathon- https://www.machinehack.com/hackathons/house_price_prediction_beat_the_benchmark/overview

Property currently or historically owned and managed by the City of Chicago. Information provided in the database, or on the City’s website generally, should not be used as a substitute for title research, title evidence, title insurance, real estate tax exemption or payment status, environmental or geotechnical due diligence, or as a substitute for legal, accounting, real estate, business, tax or other professional advice. The City assumes no liability for any damages or loss of any kind that might arise from the reliance upon, use of, misuse of, or the inability to use the database or the City’s web site and the materials contained on the website. The City also assumes no liability for improper or incorrect use of materials or information contained on its website. All materials that appear in the database or on the City’s web site are distributed and transmitted "as is," without warranties of any kind, either express or implied as to the accuracy, reliability or completeness of any information, and subject to the terms and conditions stated in this disclaimer.

The following columns were added 4/14/2023:

• Sales Status
• Sale Offering Status
• Sale Offering Reason
• Square Footage - City Estimate
• Land Value (2022) -- Note: The year will change over time.

The following columns were added 3/19/2024:

• Application Use
• Grouped Parcels
• Application Deadline
• Offer Round
• Application URL