About this Dataset

Class materials for REE 6315 in Fall 2017. We will be using this data as an ongoing example throughout the course. Students will need this data to complete in class quizzes and out of class assignments. Please also download the free real estate listing data also required for the course: https://www.dataandsons.com/categories/sales_&_transactions/u.s._real_estate_inventory

Data was sourced by combining open data sources with instructors original content.

Category

Classroom Datasets

Keywords

housing,equity,realestate,transactions,sales

Row Count

929

Price

$75.00

Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

BatchData's Deed Dataset - Real Estate Transaction Data + Property Transaction Data

Unlock a wealth of historical real estate insights with BatchData's Deed Dataset. This premium offering provides detailed real estate transaction data, including comprehensive property transaction records with over 15 critical data points. Whether you're analyzing market trends, assessing investment opportunities, or conducting in-depth property research, this dataset delivers the granular information you need.

Why Choose BatchData?

At BatchData, we are committed to delivering the most accurate and comprehensive datasets in the industry. Our Deed Dataset exemplifies our dedication to quality and precision:

• Comprehensive Datasets: As a single-vendor provider, we offer an extensive array of data including property, homeowner, mortgage, listing, valuation, permit, demographic, foreclosure, and contact information. All this is available from one reliable source, streamlining your data acquisition process.

• Technical Excellence: Our dataset comes with clear documentation, purpose-built APIs, and extensive developer resources. Our technical teams are supported by robust engineering resources to ensure seamless integration and utilization.

• Tailor-Fit Pricing and Packaging: We understand that different businesses have different needs. That’s why we offer flexible pricing models and practical API metering. You only pay for the data you need, making our solutions scalable and aligned with your business objectives.

• Unmatched Contact Information Accuracy: We lead the industry with superior right-party contact rates, ensuring you get multiple accurate contact points, including highly reliable phone numbers.

Choose BatchData for your real estate data needs and experience unparalleled accuracy and flexibility in data solutions.

Our dataset features comprehensive housing market data, extracted from 250,000 records sourced directly from Redfin USA. Our Crawl Feeds team utilized proprietary in-house tools to meticulously scrape and compile this valuable data.

Key Benefits of Our Housing Market Data:

• In-Depth Market Analysis: Gain insights into the real estate market with up-to-date data on recently sold properties.
  
  
• Price Trend Identification: Track and analyze price trends across different cities.
  
  
• Accurate Price Estimation: Estimate property values based on key factors such as area, number of beds and baths, square footage, and more.
  
  
• Detailed Real Estate Statistics: Access detailed statistics segmented by zip code, area, and state.

Unlock the Power of Redfin Data for Real Estate Professionals

Leveraging our Redfin properties dataset allows real estate professionals to make data-driven decisions. With detailed insights into property listings, sales history, and pricing trends, agents and investors can identify opportunities in the market more effectively. The data is particularly useful for comparing neighborhood trends, understanding market demand, and making informed investment decisions.

Enhance Your Real Estate Research with Custom Filters and Analysis

Our Redfin dataset is not only extensive but also customizable, allowing users to apply filters based on specific criteria such as property type, listing status, and geographic location. This flexibility enables researchers and analysts to drill down into the data, uncovering patterns and insights that can guide strategic planning and market entry decisions. Whether you're tracking the performance of single-family homes or exploring multi-family property trends, this dataset offers the depth and accuracy needed for thorough analysis.

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

Dataset Overview

This dataset provides a detailed snapshot of real estate properties listed in Dubai, UAE, as of August 2024. The dataset includes over 5,000 listings scraped using the Apify API from Propertyfinder and various other real estate websites in the UAE. The data includes key details such as the number of bedrooms and bathrooms, price, location, size, and whether the listing is verified. All personal identifiers, such as agent names and contact details, have been ethically removed.

Data Science Applications

Given the size and structure of this dataset, it is ideal for the following data science applications:

• Price Prediction Models: Predicting the price of properties based on features like location, size, and furnishing status.
• Market Analysis: Understanding trends in the Dubai real estate market by analyzing price distributions, property types, and locations.
• Recommendation Systems: Developing systems to recommend properties based on user preferences (e.g., number of bedrooms, budget).
• Sentiment Analysis: Extracting and analyzing sentiments from the property descriptions to gauge the market's tone.

This dataset provides a practical foundation for both beginners and experts in data science, allowing for the exploration of real estate trends, development of predictive models, and implementation of machine learning algorithms.

# Column Descriptors

• title: The listing's title, summarizing the key selling points of the property.
• displayAddress: The public address of the property, including the community and city.
• bathrooms: The number of bathrooms available in the property.
• bedrooms: The number of bedrooms available in the property.
• addedOn: The timestamp indicating when the property was added to the listing platform.
• type: Specifies whether the property is residential, commercial, etc.
• price: The listed price of the property in AED.
• verified: A boolean value indicating whether the listing has been verified by the platform.
• priceDuration: Indicates if the property is listed for sale or rent.
• sizeMin: The minimum size of the property in square feet.
• furnishing: Describes whether the property is furnished, unfurnished, or partially furnished.
• description: A more detailed narrative about the property, including additional features and selling points.

# Ethically Mined Data

This dataset was ethically scraped using the Apify API, ensuring compliance with data privacy standards. All personal data such as agent names, phone numbers, and any other sensitive information have been omitted from this dataset to ensure privacy and ethical use. The data is intended solely for educational purposes and should not be used for commercial activities.

# Acknowledgements

This dataset was made possible thanks to the following:

• Apify: For providing the API to ethically scrape the data.
• Propertyfinder and various other real estate websites in the UAE for the original listings.
• Kaggle: For providing the platform to share and analyze this dataset.

-**Photo by** : Francesca Tosolini on Unsplash

Use the Data Responsibly

Please ensure that this dataset is used responsibly, with respect to privacy and data ethics. This data is provided for educational purposes.

In 2021, Allegheny County Economic Development (ACED), in partnership with Urban Redevelopment Authority of Pittsburgh(URA), completed the a Market Value Analysis (MVA) for Allegheny County. This analysis services as both an update to previous MVA’s commissioned separately by ACED and the URA and combines the MVA for the whole of Allegheny County (inclusive of the City of Pittsburgh). The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies.

This MVA utilized data that helps to define the local real estate market. The data used covers the 2017-2019 period, and data used in the analysis includes:

• Residential Real Estate Sales
• Mortgage Foreclosures
• Residential Vacancy
• Parcel Year Built
• Parcel Condition
• Building Violations
• Owner Occupancy
• Subsidized Housing Units

The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources.

Please refer to the presentation and executive summary for more information about the data, methodology, and findings.

Real estate transactions - annual version corresponds to the dataset describing transactions of real rights on real estate property such as recorded by the FPS Finance for registration purposes. This dataset is composed of five classes. The first class shows, at the national level, for each cadastral nature and for each type of transaction, the number of real estate property concerned by a transaction as well as market values of these transactions. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities. The dataset can be freely downloaded as a zipped CSV.

This dataset provides comprehensive real estate transaction records, including detailed property attributes, sale prices, listing information, and anonymized participant identifiers. It enables robust market price estimation, trend analysis, and portfolio management for investors, analysts, and real estate professionals. The dataset's granular structure supports property valuation modeling and geographic market comparisons.

In the cadastre information system, for the purposes of cadastral valuation, the SLS maintains the Real Estate Market Database, which is larger in Latvia. Transactions with real estate, which are registered in the State Unified Computerised Land Register, are accumulated in the database. Real estate market data are published as denormalised text data (*.csv and *xlsx format files) containing information on real estate transactions registered in the Real Estate Market Information System from 2012 onwards. Data are updated once a month, on the 10th day of the calendar month (or on the next working day if it falls on a public day or a holiday More information about the opening of the Real Estate Market Database is available on the VZD website: https://www.vzd.gov.lv/lv/NITIS-datu-atversana

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

Success.ai’s Commercial Real Estate Data and B2B Contact Data for Global Real Estate Professionals is a comprehensive dataset designed to connect businesses with industry leaders in real estate worldwide. With over 170M verified profiles, including work emails and direct phone numbers, this solution ensures precise outreach to agents, brokers, property developers, and key decision-makers in the real estate sector.

Utilizing advanced AI-driven validation, our data is continuously updated to maintain 99% accuracy, offering actionable insights that empower targeted marketing, streamlined sales strategies, and efficient recruitment efforts. Whether you’re engaging with top real estate executives or sourcing local property experts, Success.ai provides reliable and compliant data tailored to your needs.

Key Features of Success.ai’s Real Estate Professional Contact Data

• Comprehensive Industry Coverage Gain direct access to verified profiles of real estate professionals across the globe, including:

1. Real Estate Agents: Professionals facilitating property sales and purchases.
2. Brokers: Key intermediaries managing transactions between buyers and sellers.
3. Property Developers: Decision-makers shaping residential, commercial, and industrial projects.
4. Real Estate Executives: Leaders overseeing multi-regional operations and business strategies.
5. Architects & Consultants: Experts driving design and project feasibility.

• Verified and Continuously Updated Data

AI-Powered Validation: All profiles are verified using cutting-edge AI to ensure up-to-date accuracy. Real-Time Updates: Our database is refreshed continuously to reflect the most current information. Global Compliance: Fully aligned with GDPR, CCPA, and other regional regulations for ethical data use.

• Customizable Data Delivery Tailor your data access to align with your operational goals:

API Integration: Directly integrate data into your CRM or project management systems for seamless workflows. Custom Flat Files: Receive detailed datasets customized to your specifications, ready for immediate application.

Why Choose Success.ai for Real Estate Contact Data?

• Best Price Guarantee Enjoy competitive pricing that delivers exceptional value for verified, comprehensive contact data.

• Precision Targeting for Real Estate Professionals Our dataset equips you to connect directly with real estate decision-makers, minimizing misdirected efforts and improving ROI.

• Strategic Use Cases

  Lead Generation: Target qualified real estate agents and brokers to expand your network. Sales Outreach: Engage with property developers and executives to close high-value deals. Marketing Campaigns: Drive targeted campaigns tailored to real estate markets and demographics. Recruitment: Identify and attract top talent in real estate for your growing team. Market Research: Access firmographic and demographic data for in-depth industry analysis.

• Data Highlights 170M+ Verified Professional Profiles 50M Work Emails 30M Company Profiles 700M Global Professional Profiles

• Powerful APIs for Enhanced Functionality

  Enrichment API Ensure your contact database remains relevant and up-to-date with real-time enrichment. Ideal for businesses seeking to maintain competitive agility in dynamic markets.

Lead Generation API Boost your lead generation with verified contact details for real estate professionals, supporting up to 860,000 API calls per day for robust scalability.

• Use Cases for Real Estate Contact Data

1. Targeted Outreach for New Projects Connect with property developers and brokers to pitch your services or collaborate on upcoming projects.

2. Real Estate Marketing Campaigns Execute personalized marketing campaigns targeting agents and clients in residential, commercial, or industrial sectors.

3. Enhanced Sales Strategies Shorten sales cycles by directly engaging with decision-makers and key stakeholders.

4. Recruitment and Talent Acquisition Access profiles of highly skilled professionals to strengthen your real estate team.

5. Market Analysis and Intelligence Leverage firmographic and demographic insights to identify trends and optimize business strategies.

• What Makes Us Stand Out? >> Unmatched Data Accuracy: Our AI-driven validation ensures 99% accuracy for all contact details. >> Comprehensive Global Reach: Covering professionals across diverse real estate markets worldwide. >> Flexible Delivery Options: Access data in formats that seamlessly fit your existing systems. >> Ethical and Compliant Data Practices: Adherence to global standards for secure and responsible data use.

Success.ai’s B2B Contact Data for Global Real Estate Professionals delivers the tools you need to connect with the right people at the right time, driving efficiency and success in your business operations. From agents and brokers to property developers and executiv...

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

Context This dataset is a record of every building or building unit (apartment, etc.) sold in the California property market along with the customer data.

Content Real estate is property consisting of land and the buildings on it, along with its natural resources such as crops, minerals or water; immovable property of this nature; an interest vested in this (also) an item of real property, (more generally) buildings or housing in general.

Inspiration

What can you discover about California real estate by looking at a year's worth of raw transaction records? Can you spot trends in the market, or build a model that predicts sale value in the future?

Single Family Home Prices in the United States increased to 435300 USD in June from 423700 USD in May of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides comprehensive, transaction-level real estate data, including property attributes, sale prices, geolocation, and transaction details. It is ideal for training automated valuation models (AVMs), optimizing appraisals, and conducting in-depth investment and market analytics. The dataset's granularity and breadth make it valuable for both industry professionals and data scientists.

The reports are updated once a month, taking into account the current market information collected in the database, which is updated daily. Each report is offered for three different time periods — annual, half-year and quarter. The choice of “year” means that the report is presented for the previous two full years, for the “half year” for the two previous half-years and for the previous two quarters. The years change on 1 February, half-years change on 1 February and 1 August, and quarters on 1 February, 1 May, 1 August and 1 November. _ The statistical indicators of the Latvian real estate market are composed of:_ * Apartment transaction prices; * Transaction prices of individual residential houses; * Individual building land transaction prices; * Commercial building land transaction prices; * Prices of industrial enterprises building land transactions; * Agricultural land transaction prices; * Forestry land transaction prices; * Breakdown of transactions by purpose of real estate use of the transaction object; * Breakdown of transactions by transaction object. Statistic indicators: * Number of transactions used; * Minimum value; * Maximum value; * Average value; * Cut-off mean (5 %); * Cut-off mean (10 %); * Moda (adjusted the most common value based on the breakdown of data in intervals and frequencies within the specified intervals); * Median; * Price level. _Data sets with the breakdown of transactions by transaction object NIP and the breakdown of transactions by object of the transaction _ are reflected in one of the statistical indicators (the title specifies which): * Number of transactions; * Total land area; * Total amount of transactions. Projects of transactions: * Apartments; * Buildings; * Buildings and engineering structures; * Engineering structures; * Groups of premises; * Land; * Land with buildings; * Land with buildings and engineering structures; * Land with engineering structures. The tables show only areas where the number of transactions to be used to determine values is at least three. Published data are informative in nature and are produced automatically without manual transaction analysis. The SLS assumes no legal or financial responsibility for the consequences of using the published information and making the relevant conclusions. All transaction amounts and prices are in EUR. Information on the possibility to obtain data on real estate market transactions and transaction objects is available on the VZD website.

Real estate transactions corresponds to the dataset describing transactions of real rights on real estate property such as recorded by the FPS Finance for registration purposes.This dataset is composed of seven classes. The first class shows, at the national level, for each cadastral nature and for each type of transaction, the number of parcels concerned by a transaction as well as market values of these transactions. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities, cadastral divisions and statistical sectors. The dataset can be freely downloaded as a zipped CSV.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that includes real property transactions data. The dataset consists of real property transactions reported by Local Authorities that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year. Authorities are required to report real property transactions having an estimated fair market of more than $15,000.

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more