Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q2 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.

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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.

REdistribute modernizes real estate data accessibility by providing access to fresh, reliable listings from trusted MLS sources.

For Market Insights & Analytics, this standardized bulk dataset enables: - Macro and micro-level housing market trend analysis - Competitive benchmarking and regional performance tracking - Consumer demand forecasting grounded in verified transaction activity

Key features: • Flexible Delivery: Available via a bulk data API or directly through Snowflake • Residential or Multi-Class: Choose a residential-only dataset or full MLS coverage across all property types, including residential, multi-family, land, commercial, rentals, farm and more • Comprehensive Field Access: Explore 800+ fields providing a complete view of both residential and non-residential property data • Fast & Fresh: Stay current with daily updates sourced directly from trusted MLSs partners

The sample data covers one listing in JSON format. For access to a broader set of sample listings (10,000+), reach out to the REdistribute sales contact.

ABOUT REDISTRIBUTE

REdistribute aims to modernize real estate data accessibility, fostering innovation and transparency through direct access to the most reliable MLS data. Our commitment to data integrity and direct MLS involvement guarantees the freshest, most accurate insights, empowering businesses across industries to drive innovation and make informed decisions.

What is Rental Data?

Rental data encompasses detailed information about residential rental properties, including single-family homes, multifamily units, and large apartment complexes. This data often includes key metrics such as rental prices, occupancy rates, property amenities, and detailed property descriptions. Advanced rental datasets integrate listings directly sourced from property management software systems, ensuring real-time accuracy and eliminating reliance on outdated or scraped information.

Additional Rental Data Details

The rental data is sourced from over 20,000 property managers via direct feeds and property management platforms, covering over 30 percent of the national rental housing market for diverse and broad representation. Real-time updates ensure data remains current, while verified listings enhance accuracy, avoiding errors typical of survey-based or scraped datasets. The dataset includes 14+ million rental units with detailed descriptions, rich photography, and amenities, offering address-level granularity for precise market analysis. Its extensive coverage of small multifamily and single-family rentals sets it apart from competitors focused on premium multifamily properties.

Rental Data Includes:

• Property Types
• Single-Family Rentals
• Small Multi-family Units
• Premium Apartments
• 16,000+ ZIP Codes
• 800+ MSAs
• Pricing Trends
• Lease Terms Amenities

Russia Residential Housing Stock: Area: Urban: State data was reported at 93.000 sq m mn in 2017. This records a decrease from the previous number of 116.000 sq m mn for 2016. Russia Residential Housing Stock: Area: Urban: State data is updated yearly, averaging 142.000 sq m mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 773.000 sq m mn in 1991 and a record low of 93.000 sq m mn in 2017. Russia Residential Housing Stock: Area: Urban: State data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.EE001: Residential Housing Stock: Area.

In 2023, there were *** million dwelling units in Tokyo Prefecture in Japan. The number of inhabited and vacant dwellings in Tokyo has constantly grown over the past decades.

Graph and download economic data for Existing Home Sales (EXHOSLUSM495S) from Oct 2024 to Oct 2025 about headline figure, sales, housing, and USA.

In 2024, residential housing prices in South Korea increased by around **** percent year-on-year. This was a tentative sign of recovery from the significant drops seen in the two years prior.

Russia Residential Housing Completed: Floor Area data was reported at 4,169.600 sq m th in Jan 2019. This records a decrease from the previous number of 17,133.300 sq m th for Dec 2018. Russia Residential Housing Completed: Floor Area data is updated monthly, averaging 3,400.000 sq m th from Jan 1993 (Median) to Jan 2019, with 313 observations. The data reached an all-time high of 19,700.000 sq m th in Dec 2014 and a record low of 400.000 sq m th in Jan 1997. Russia Residential Housing Completed: Floor Area data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.EA007: Residential Housing Completed: Floor Area.

House Price Index YoY in the United States decreased to 1.70 percent in September from 2.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

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.

Affordability ratios calculated by dividing house prices for existing dwellings, by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

Graph and download economic data for Residential Property Prices for Israel (QILN368BIS) from Q1 1995 to Q2 2025 about Israel, residential, housing, and price.

Residential Housing Completed: Floor Area: IF: SF: Republic of Crimea data was reported at 903.700 sq m th in 2023. This records an increase from the previous number of 705.500 sq m th for 2022. Residential Housing Completed: Floor Area: IF: SF: Republic of Crimea data is updated yearly, averaging 515.250 sq m th from Dec 2014 (Median) to 2023, with 10 observations. The data reached an all-time high of 903.700 sq m th in 2023 and a record low of 122.000 sq m th in 2016. Residential Housing Completed: Floor Area: IF: SF: Republic of Crimea data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Construction and Properties Sector – Table RU.EA009: Residential Housing Completed: Floor Area: Annual.

The Arlington Profile combines countywide data sources and provides a comprehensive outlook of the most current data on population, housing, employment, development, transportation, and community services. These datasets are used to obtain an understanding of community, plan future services/needs, guide policy decisions, and secure grant funding. A PDF Version of the Arlington Profile can be accessed on the Arlington County website.

The percentage of residential addresses for which the United States Postal Service has identified as being unoccupied (no mail collection) for a period of at least 90 days or longer. These properties may be habitable, but are not currently being occupied. It is important to note that a single residential property can contain more than one address. Source: U.S. Postal Service, U.S. Department of Housing and Urban Development Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

View monthly updates and historical trends for US Existing Home Sales. from United States. Source: National Association of Realtors. Track economic data w…

The global residential real estate market size is projected to grow from USD 11.619 trillion in 2025 to USD 23.493 trillion by 2033, exhibiting a CAGR of 9.2%.
Report Scope:

Since 2000, the number of homes bought by investors in the United States has fluctuated significantly. It experienced a decrease during the financial crises of 2008 hitting its bottom in the first quarter of 2009 with ****** purchases, and it slowly recovered the number of purchases in the following years, peaking in the third quarter of 2021 with ****** purchases. Due to inflation, current purchase numbers are similar to those of the pre-pandemic times.

This is the monthly trend data on apartment sales prices and average sales prices provided by the Korea Real Estate Board (formerly Korea Appraisal Board) from the National Housing Price Trend Survey.