This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q4 2025 about sales, housing, and USA.

Statistical data on prices, rents, market yields, number of transactions, completions, forecast completions, stock, vacancy and take-up of various types of properties.

Dataset

This dataset was created by Wen Li

Released under Apache 2.0

Contents

Single Family Home Prices in the United States increased to 398000 USD in February from 395000 USD in January of 2026. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Discover the latest trends and insights into the booming US residential real estate market. This comprehensive analysis forecasts growth, examines key segments (apartments, condos, houses, villas), and identifies leading companies shaping the future of homeownership. Learn more about market size, CAGR, and regional variations. 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.

The year-end value of the S&P Case Shiller National Home Price Index amounted to 321.45 in 2024. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given year, for example, it means that the house prices increased by 30 percent since 2000. S&P/Case Shiller U.S. home indices – additional informationThe S&P Case Shiller National Home Price Index is calculated on a monthly basis and is based on the prices of single-family homes in nine U.S. Census divisions: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain and Pacific. The index is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The index illustrates the trend of home prices and can be helpful during house purchase decisions. When house prices are rising, a house buyer might want to speed up the house purchase decision as the transaction costs can be much higher in the future. The S&P Case Shiller National Home Price Index has been on the rise since 2011.The S&P Case Shiller National Home Price Index is one of the indices included in the S&P/Case-Shiller Home Price Index Series. Other indices are the S&P/Case Shiller 20-City Composite Home Price Index, the S&P/Case Shiller 10-City Composite Home Price Index and twenty city composite indices.

Real Estate Prices Dataset This dataset comprises information on 4,600 real estate transactions, providing a detailed snapshot of the housing market in various locations. Each record captures the characteristics of a house, its surroundings, and transaction details from transactions that occurred around May 2, 2014. The dataset includes the following fields:

date: The date of the transaction. price: The sale price of the property (in USD). bedrooms: The number of bedrooms. bathrooms: The number of bathrooms, represented in half-baths (e.g., 1.5 indicates one full bath and one half bath). sqft_living: The square footage of the home's living area. sqft_lot: The square footage of the lot. floors: The number of floors. waterfront: A binary indicator for whether the property is on the waterfront (1) or not (0). view: An index from 0 to 4 indicating the quality of the view. condition: An index from 1 to 5 on the condition of the property. sqft_above: The square footage of the house apart from the basement. sqft_basement: The square footage of the basement. yr_built: The year the property was built. yr_renovated: The year of the last renovation. street: The street address of the property. city: The city in which the property is located. statezip: The state and ZIP code. country: The country of the property.

This dataset can be particularly useful for projects involving real estate market analysis, price prediction models, and economic research related to housing trends. Researchers and enthusiasts can explore aspects such as the impact of property characteristics on price, trends over time, and geographical price variations.

This dataset includes real estate market data collected from Redfin.com, a popular U.S. housing platform.

📂files format:

| 🇺🇸 State | 🔠 Abbreviation | | 🇺🇸 State | 🔠 Abbreviation | | -------------- | ------------------- | - | -------------- | ------------------- | | Alabama | AL | | Montana | MT | | Alaska | AK | | Nebraska | NE | | Arizona | AZ | | Nevada | NV | | Arkansas | AR | | New Hampshire | NH | | California | CA | | New Jersey | NJ | | Colorado | CO | | New Mexico | NM | | Connecticut | CT | | New York | NY | | Delaware | DE | | North Carolina | NC | | Florida | FL | | North Dakota | ND | | Georgia | GA | | Ohio | OH | | Hawaii | HI | | Oklahoma | OK | | Idaho | ID | | Oregon | OR | | Illinois | IL | | Pennsylvania | PA | | Indiana | IN | | Rhode Island | RI | | Iowa | IA | | South Carolina | SC | | Kansas | KS | | South Dakota | SD | | Kentucky | KY | | Tennessee | TN | | Louisiana | LA | | Texas | TX | | Maine | ME | | Utah | UT | | Maryland | MD | | Vermont | VT | | Massachusetts | MA | | Virginia | VA | | Michigan | MI | | Washington | WA | | Minnesota | MN | | West Virginia | WV | | Mississippi | MS | | Wisconsin | WI | | Missouri | MO | | Wyoming | WY |

Column Explanations

💡 This dataset is suitable for: - Machine learning projects (house price prediction, market segmentation) - Time series forecasting - Exploratory data analysis and dashboards - Understanding trends in different housing markets - Comparative studies between cities or states

📦 Files: Over 80 CSV files representing major cities from over 40 U.S. states.

👨‍💻 Author: MR. Soulaimane 📅 Updated: July 2025

Annual house price data based on a sub-sample of the Regulated Mortgage Survey.

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Feb 2026 about median and USA.

This comprehensive dataset, titled "US Housing Market and Economic Indicators (2024 Forecast Data)", serves as a robust analytical tool for professionals, researchers, and enthusiasts interested in understanding and forecasting trends in the United States housing market and broader economic landscape as they pertain to the year 2024. The collection is meticulously curated to provide a holistic view of the interplay between real estate dynamics and key economic indicators.

Key Components:

CPIAUCSL.csv: Tracks the Consumer Price Index for All Urban Consumers, a critical measure of inflation. CSUSHPINSA.csv: Contains the S&P/Case-Shiller U.S. National Home Price Index data, offering insights into national home value trends. EXHOSLUSM495S.csv: Provides data on existing home sales, reflecting housing market activity. FEDFUNDS.csv: Includes data on the Federal Funds Rate, an important economic indicator influencing mortgage rates. HPI_master (1).csv: The comprehensive House Price Index from FHFA, detailing price changes in single-family homes. MORTGAGE30US.csv & PMMS_history.csv: These files present historical data on 30-year fixed mortgage rates, essential for understanding financing trends. historicalweeklydata (1).xlsx: A compilation of weekly data providing a more granular view of market fluctuations. Applications: This dataset is invaluable for:

Conducting predictive analysis and forecasting in real estate. Understanding the impact of economic indicators on housing markets. Academic research focusing on economic and housing trends. Real estate market analysis for investment and policy-making. Advantages:

Comprehensive Coverage: Spanning various critical economic and housing market indicators. Historical Context: Offering a historical perspective essential for trend analysis and forecasting. Diverse Applications: Suitable for a wide range of analyses, from academic research to investment strategies. This dataset is a one-stop resource for anyone looking to delve deep into the dynamics of the US housing market and its correlation with broader economic trends, particularly with an eye towards 2024 projections.

Summary of UK House Price Index (HPI) price statistics covering England, Scotland, Wales and Northern Ireland. Full UK HPI data are available on GOV.UK.

Dataset Introduction

This dataset contains property listings from various cities across Bangladesh, specifically including Dhaka, Chattogram, Cumilla, Narayanganj City, and Gazipur, with prices listed in Bangladeshi Taka (৳). The dataset provides valuable insights into various features of the properties, including the number of bedrooms, bathrooms, floor number, floor area in square feet, and their respective prices. The data has been collected from a real estate website, offering a comprehensive view of the housing market across these key cities in Bangladesh.

Feature Description

Title: The title or description of the property listing.

Bedrooms: The number of bedrooms in the property.

Bathrooms: The number of bathrooms in the property.

Floor_no: The floor number on which the property is located.

Occupancy_status: Indicates whether the property is vacant or occupied.

Floor_area: The total floor area of the property in square feet.

City: The city where the property is located. This dataset includes listings from Dhaka, Chattogram, Cumilla, Narayanganj City, and Gazipur.

Price_in_taka: The listing price of the property in Bangladeshi Taka (৳).

Location: The specific location or address within the city.

Usages and Applications

This dataset can be utilized in several ways:

Market Analysis: Understanding the pricing trends across different cities in Bangladesh. It can help identify which cities or neighborhoods are more expensive and which are more affordable.

Investment Decisions: Investors can use this data to evaluate potential real estate investments by comparing properties in terms of price, size, and location across different cities.

Real Estate Valuation: Property developers and agents can assess the market value of similar properties, enabling them to set competitive prices for new developments or resale properties in various regions.

Machine Learning Opportunities

This dataset presents several opportunities for applying machine learning techniques:

Price Prediction: Using features such as floor area, number of bedrooms, and location, machine learning models can be trained to predict the price of a property. This can be especially useful for buyers and sellers looking for price guidance across different cities.

Clustering: By clustering properties based on features like location, size, and price, one could identify distinct property segments or neighborhoods in Dhaka, Chattogram, Cumilla, Narayanganj City, and Gazipur with similar characteristics.

Demand Forecasting: Analyzing trends in the dataset over time can help predict future demand for housing in these cities, which could be valuable for both real estate developers and policymakers.

Anomaly Detection: Identifying properties that are significantly over- or under-priced compared to similar properties, which could indicate potential issues or opportunities in the market.

ATTOM’s Home Sales Trends dataset delivers reliable Real Estate Market Data by summarizing historical residential sales activity across the United States. Built from ATTOM’s proprietary database of verified deed transactions, it provides consistent House Price Data, Property Market Data, and Residential Real Estate Data across more than 2,700 counties.

What the Dataset Includes • Aggregated residential sales counts • Average sale prices • Median sale prices • Historical sales trends typically dating back to 2005 • Extended history to 2000 in select markets • Multi-level geographic aggregation from state to tract

How the Data Is Calculated • Derived from ATTOM’s verified property transaction database • Includes only arm’s-length residential transactions • Transaction types limited to: – Construction sales – Transfers and resales – Subdivision transfers • Residential property types only, including: – Single-family homes – Condo and townhome units • Sale price outliers removed to eliminate data errors

Why It Matters • Reflects true market-driven pricing and volume trends • Removes distressed and non-market transactions • Enables accurate comparison across markets and time periods • Supports consistent residential market analysis nationwide

Delivery & Cadence • Statistics typically delivered quarterly • Monthly or annual delivery available depending on use case

ATTOM’s Home Sales Trends dataset provides a clean, consistent, and historically rich foundation for analyzing residential market activity, price movement, and long-term housing trends across the U.S.

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.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q3 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

According to ValuStrat, the average residential prices in Dubai reached around 1,448 United Arab Emirates dirhams (AED) in December 2024, rising from around 894 AED in 2020. Real estate market in Dubai Despite the impact of the global COVID-19 pandemic on the real estate market, Dubai's real estate sector continues to show resilience and remains a lucrative investment option. In the first quarter of 2021, the real estate transactions in Dubai amounted to approximately 25 billion U.S. dollars in value. With its emphasis on the goal of transforming into a high-end tourist destination, Dubai has become an appealing choice for real estate investors. In 2019, investment villas made up most urban buildings in the emirate, with around 72,000 units. Residential market outlook The residential market in Dubai has experienced substantial growth in recent years. In 2022, it was projected to witness the addition of approximately 45 thousand new apartments and seven thousand new villas. These additions contribute to the existing supply of 743,000 residential units in the emirate for that year. According to the same source, in December 2022, the capital prices of residential apartments in Jumeirah Beach Residence, Dubai, stood at approximately 2.5 million United Arab Emirates dirhams. This represented a 5.7 percent growth in capital values compared to the prior year. With its strong market presence and attractive investment opportunities, Dubai's residential market remains a key player in the region.

Local housing market statistics for Portland, Tennessee

Analyze European housing market trends with this clean, beginner-friendly dataset featuring quarterly price indices for 35 countries and regions from Q4 2022 to Q3 2025. Sourced directly from Eurostat's official statistics, this dataset provides reliable economic indicators for real estate analysis, forecasting, and comparative studies.

What's Inside: • 417 records spanning 12 quarters of housing market data • 35 European countries including all EU members, EEA nations, and key markets • Housing Price Index (baseline 2015=100) tracking property value changes • Quarterly and year-over-year percentage changes for trend analysis • Country classifications: EU membership and Eurozone participation status • Pre-calculated metrics including total price change since 2015

Ideal For: ✓ Data science students learning time series analysis ✓ Economists studying European real estate markets ✓ Analysts comparing housing trends across countries ✓ Forecasting models and predictive analytics ✓ Visualization projects and dashboard creation

Key Features: - Clean, structured CSV format ready for immediate analysis - Descriptive column names requiring no preprocessing - Multiple analytical perspectives (indices, growth rates, comparisons) - Documented data quality indicators - Comprehensive data dictionary included

Data Source: Official Eurostat database (TEICP270 - House price index, quarterly data), last updated January 9, 2026. All data validated through the European Statistical System.

Perfect for beginners exploring economic data analysis, intermediate analysts conducting comparative studies, or advanced users building sophisticated forecasting models. No complex cleaning required - download and start analyzing immediately!

KEYWORDS (Primary - High Search Volume): housing prices, european real estate, price index, eurostat data, housing market trends, quarterly data, eu housing, real estate analysis, economic indicators, time series data

KEYWORDS (Secondary - Long-tail SEO): european housing price index, eurostat housing data, eu real estate trends 2022-2025, housing market analysis europe, quarterly housing statistics, eurozone property prices, beginner data analysis dataset, clean economic data, housing price forecasting, european property market data

KEYWORDS (AEO - Answer Engine Optimization): what is european housing price index, how to analyze housing market trends, european real estate data for beginners, housing price comparison across eu countries, quarterly housing statistics europe, eurostat housing data explained

TAGS (Kaggle Platform - 20 max):

HousingPrices #RealEstate #Europe #Eurostat #EconomicIndicators #TimeSeries #PriceIndex #EU #Eurozone #QuarterlyData #BeginnerFriendly #CleanData #DataAnalysis #Economics #PropertyMarket #MarketTrends #Forecasting #EDA #FinancialData #OpenData

CATEGORY: Primary: Economics & Finance Secondary: Real Estate & Housing Tertiary: European Statistics

TARGET AUDIENCE: - Skill Level: Beginner to Intermediate - Roles: Data Scientists, Analysts, Students, Economists, Researchers - Use Cases: Learning, Analysis, Forecasting, Visualization, Research

SEARCH INTENT KEYWORDS (User Queries): "european housing data csv" "housing price index dataset" "eurostat data kaggle" "european real estate analysis dataset" "beginner economic data" "housing market trends europe" "quarterly economic indicators" "eu housing statistics" "real estate forecasting data" "clean economics dataset"

COMPETITIVE ADVANTAGES (Why choose this dataset): 1. Official Eurostat source - highly credible 2. Pre-cleaned and beginner-friendly format 3. Multiple analytical metrics in one dataset 4. Recent data through Q3 2025 5. Comprehensive documentation included 6. Country classification features (EU/Eurozone) 7. Ready for immediate analysis - no preprocessing needed 8. Suitable for multiple skill levels

DATA HIGHLIGHTS (For Featured Snippets): • Time Period: Q4 2022 to Q3 2025 (12 quarters) • Countries: 35 European nations and regions • Records: 417 data points • Metrics: 12 variables including price indices and growth rates • Format: Clean CSV with descriptive column names • Source: Eurostat (TEICP270) • Update Frequency: Quarterly • License: Open data with attribution

SOCIAL MEDIA SNIPPETS:

Twitter/X (280 characters): 📊 New Dataset: European Housing Price Index (2022-2025) ✅ 35 countries, 12 quarters ✅ Eurostat official data ✅ Beginner-friendly format Perfect for #DataScience learning & #RealEstate analysis!

OpenData #Economics

LinkedIn: Excited to share a comprehensive European Housing Price Index dataset perfect for analysts and students! Featuring quarterly data from 35 countries (Q4 2022 - Q3 2025), sourced from official Eurostat statistics. Clean, documented, and ready for immediate analysis. Ideal for trend analysis, forecasting, and comparative studies. #Da...