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.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Description:

This dataset contains prices of New York houses, providing valuable insights into the real estate market in the region. It includes information such as broker titles, house types, prices, number of bedrooms and bathrooms, property square footage, addresses, state, administrative and local areas, street names, and geographical coordinates.

Key Features:

• BROKERTITLE: Title of the broker
• TYPE: Type of the house
• PRICE: Price of the house
• BEDS: Number of bedrooms
• BATH: Number of bathrooms
• PROPERTYSQFT: Square footage of the property
• ADDRESS: Full address of the house
• STATE: State of the house
• MAIN_ADDRESS: Main address information
• ADMINISTRATIVE_AREA_LEVEL_2: Administrative area level 2 information
• LOCALITY: Locality information
• SUBLOCALITY: Sublocality information
• STREET_NAME: Street name
• LONG_NAME: Long name
• FORMATTED_ADDRESS: Formatted address
• LATITUDE: Latitude coordinate of the house
• LONGITUDE: Longitude coordinate of the house

Potential Use Cases:

• Price analysis: Analyze the distribution of house prices to understand market trends and identify potential investment opportunities.
• Property size analysis: Explore the relationship between property square footage and prices to assess the value of different-sized houses.
• Location-based analysis: Investigate geographical patterns to identify areas with higher or lower property prices.
• Bedroom and bathroom trends: Analyze the impact of the number of bedrooms and bathrooms on house prices.
• Broker performance analysis: Evaluate the influence of different brokers on the pricing of houses.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂 Thank you

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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.

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.

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.

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.

Average House Prices in Canada increased to 688800 CAD in October from 687600 CAD in September of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

Housing Index in Saudi Arabia decreased to 103.90 points in the third quarter of 2025 from 105 points in the second quarter of 2025. This dataset provides - Saudi Arabia Housing Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset Overview

This dataset comprises detailed real estate listings scraped from Realtor.com, providing a snapshot of various property types across Chicago. It includes 2,000 entries with information on property characteristics such as type, size, age, price, and features. This dataset was ethically collected using an API provided by Apify, ensuring all data scraping adhered to ethical standards.

Data Science Applications

This dataset is ideal for a variety of data science applications, including but not limited to: - Predictive Modeling: Forecast property prices based on various features like location, size, and age. - Market Analysis: Understand trends in real estate, including the types of properties being sold, pricing trends, and the influence of property features on market value. - Natural Language Processing: Analyze the textual descriptions provided for each listing to extract additional features or perform sentiment analysis. - Anomaly Detection: Identify unusual listings or potential outliers in the data, which could indicate errors in data collection or unique investment opportunities.

Column Descriptors

1. type: The type of property (e.g., single-family home, condo).
2. text: A textual description of the property.
3. year_built: The year in which the property was constructed.
4. beds: The number of bedrooms.
5. baths: Total number of bathrooms (including full and half).
6. baths_full: Number of full bathrooms.
7. baths_half: Number of half bathrooms.
8. garage: Garage capacity (number of cars).
9. lot_sqft: Size of the lot in square feet.
10. sqft: Living area size in square feet.
11. stories: Number of stories/floors in the property.
12. lastSoldPrice: The price at which the property was last sold.
13. soldOn: The date on which the property was last sold.
14. listPrice: The listing price of the property at the time of data collection.
15. status: The current status of the listing (e.g., for sale, sold).

Ethically Mined Data

This dataset was responsibly and ethically mined, adhering to all legal standards of data collection. The use of Apify's API ensures that the data collection process respects privacy and the platform's terms of service.

Acknowledgements

We thank Realtor.com for maintaining a comprehensive and accessible database, and Apify for providing the tools necessary for ethical data scraping. Their contributions have been invaluable in the creation of this dataset. Credits to Dall E3 for thumbnail image.

Usage Policy

This dataset is provided for non-commercial and educational purposes only. Users are encouraged to use this data to enhance learning, contribute to academic or personal projects, and develop skills in data science and real estate market analysis.

Key information about House Prices Growth

• Mexico house prices grew 8.9% YoY in Sep 2025, following an increase of 8.7% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Sep 2025, with an average growth rate of 10.2%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

CEIC calculates House Price Growth from quarterly House Price Index. Federal Mortgage Society provides House Price Index with base 2017=100.

CompCurve is proud to partner with Nickle5 to be a distirbution partner of the Nextplace home valuation dataset.

Welcome to the future of real estate data with the Nickel5 Home Value Estimations Dataset, an innovative and groundbreaking product from Nickel5, a company dedicated to pushing the boundaries of data science through AI and Decentralized AI. This dataset provides meticulously crafted estimations of home values for properties currently on the market, powered by the cutting-edge Bittensor network and our proprietary Subnet 48, also known as Nextplace. By leveraging the unparalleled capabilities of Decentralized AI, Nickel5 delivers a dataset that redefines how real estate professionals, investors, and analysts understand property values in an ever-changing market.

The Power of AI and Decentralized AI at Nickel5

At Nickel5, we believe that AI is the cornerstone of modern data solutions, but we take it a step further with Decentralized AI. Unlike traditional centralized AI systems that rely on a single point of control, Decentralized AI taps into a global, distributed network of intelligence. This is where Bittensor comes in—a decentralized AI network that connects thousands of nodes worldwide to collaboratively train and refine Machine Learning Models. With Nickel5 leading the charge, our Decentralized AI approach ensures that the Nickel5 Home Value Estimations Dataset is not just accurate but also adaptive, drawing from diverse data sources and real-time market signals processed through Subnet 48 (Nextplace).

The team at Nickel5 has deployed sophisticated Machine Learning Models within Bittensor’s ecosystem, specifically on Subnet 48 (Nextplace), to analyze property data, market trends, and economic indicators. These Machine Learning Models are the backbone of our dataset, providing estimations that are both precise and forward-looking. By harnessing AI and Decentralized AI, Nickel5 ensures that our clients receive insights that are ahead of the curve, powered by a system that evolves with the real estate landscape.

What is Bittensor? A Breakdown of the Decentralized AI Network

To truly appreciate the Nickel5 Home Value Estimations Dataset, it’s essential to understand Bittensor—the decentralized AI network that fuels our innovation. Bittensor is an open-source protocol designed to democratize AI development by creating a peer-to-peer marketplace for Machine Learning Models. Unlike traditional AI frameworks where a single entity controls the data and computation, Bittensor distributes the workload across a global network of contributors. Each node in the Bittensor network provides computational power, data, or model refinements, and in return, participants are incentivized through a cryptocurrency called TAO.

Within Bittensor, subnets like Subnet 48 (Nextplace) serve as specialized ecosystems where specific tasks—like real estate value estimation—are tackled with precision. Nickel5 has carved out Subnet 48 (Nextplace) as our domain, optimizing it for real estate data analysis using Decentralized AI. This subnet hosts our Machine Learning Models, which are trained collaboratively across the Bittensor network, ensuring that Nickel5’s dataset benefits from the collective intelligence of a decentralized system. By integrating Bittensor’s infrastructure with Nickel5’s expertise, we’ve created a synergy that delivers unmatched value to our users.

Why Decentralized AI Matters for Real Estate

The real estate market is complex, dynamic, and influenced by countless variables—location, economic conditions, buyer demand, and more. Traditional AI systems often struggle to keep pace with these shifts due to their reliance on static datasets and centralized processing. That’s where Decentralized AI shines, and Nickel5 is at the forefront of this revolution. By utilizing Bittensor and Subnet 48 (Nextplace), our Decentralized AI approach aggregates real-time data from diverse sources, feeding it into our Machine Learning Models to produce home value estimations that reflect the market as it stands today, March 12, 2025, and beyond.

With Nickel5’s Decentralized AI, you’re not just getting a snapshot of home values—you’re accessing a living, breathing dataset that evolves with the industry. Our Machine Learning Models on Subnet 48 (Nextplace) are designed to learn continuously, adapting to new patterns and anomalies in the real estate market. This makes the Nickel5 Home Value Estimations Dataset an indispensable tool for anyone looking to make informed decisions in a competitive landscape.

How Nickel5 Uses Bittensor and Subnet 48 (Nextplace)

At Nickel5, we’ve tailored Subnet 48 (Nextplace) within the Bittensor network to focus exclusively on real estate analytics. This subnet acts as a hub for our Decentralized AI efforts, where Machine Learning Models process vast amounts of property data—square footage, listing prices, historical sales, and more—to generate accurate value estimations. The decentralize...

Overview

This dataset provides a comprehensive snapshot of the Texas real estate market as of 2024, featuring a curated selection of 500 property listings. It encompasses a wide array of properties, reflecting the diverse real estate landscape across Texas. This dataset serves as a foundational tool for understanding market dynamics, property valuations, and regional housing trends within the state.

Data Science Application of Dataset

Given its breadth and depth, this dataset is poised to facilitate a multitude of data science applications. Researchers and analysts can leverage this dataset for exploratory data analysis (EDA) to identify patterns, trends, and anomalies within the Texas real estate market. It is particularly suited for regression analyses to predict property prices based on various features, classification tasks to categorize properties into different market segments, and geographical data analysis to understand regional market dynamics. Despite the dataset's modest size, it offers a rich source for machine learning models aimed at providing insights into price determinants and market trends, ensuring practical applications remain within realistic and achievable bounds.

Full Column Descriptors

• url: Web address for the property listing on Realtor.com.
• status: Current status of the listing, indicating availability.
• id: Unique identifier for each property listing.
• listPrice: The asking price for the property.
• baths: Total number of bathrooms, including partials.
• baths_full: Number of full bathrooms.
• baths_full_calc: Calculated number of full bathrooms, for consistency.
• beds: Number of bedrooms in the property.
• sqft: Total square footage of the property.
• stories: Number of levels or floors in the property.
• sub_type: Specific sub-category of the property, if applicable.
• text: Descriptive narrative provided for the property listing.
• type: General category of the property (e.g., single-family, condo).
• year_built: Year the property was constructed.

Ethically Mined Publicly Available Data Only

This dataset has been meticulously compiled, adhering to ethical standards and ensuring all data is sourced from publicly available information. It respects privacy and copyright considerations, utilizing data that is openly accessible and intended for public consumption.

Acknowledgments

Gratitude is extended to Realtor.com for serving as an invaluable resource in the compilation of this dataset. The platform's commitment to providing comprehensive and accessible real estate data has significantly contributed to the depth and quality of this dataset.

Image Acknowledgment

The dataset thumbnail image is credited to Realtor.com, as featured on their official Facebook page. The image serves as a visual representation of the diverse and dynamic nature of the Texas real estate market, captured in this comprehensive dataset. View Image

Key information about House Prices Growth

• India house prices grew 2.5% YoY in Jun 2025, following an increase of 5.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2011 to Jun 2025, with an average growth rate of 5.1%.
• House price data reached an all-time high of 30.6% in Mar 2011 and a record low of -11.4% in Sep 2020.

CEIC calculates House Prices Growth from quarterly House Price Index. National Housing Bank provides House Price Index with base 2017-2018=100. House Prices Growth covers Mumbai only. House Prices Growth prior to Q2 2014 is calculated from House Price Index with base 2007=100.

House Price Index YoY in Ireland increased to 7.60 percent in September from 7.50 percent in August of 2025. This dataset includes a chart with historical data for Ireland Residential Property Prices YoY.

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.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.

House Price Index YoY in the United Kingdom increased to 1.90 percent in October from 1.30 percent in September of 2025. This dataset includes a chart with historical data for the United Kingdom House Price Index YoY.

Graph and download economic data for All-Transactions House Price Index for Connecticut (CTSTHPI) from Q1 1975 to Q3 2025 about CT, appraisers, HPI, housing, price index, indexes, price, and USA.