This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].

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.

📝 Dataset Description: This synthetic dataset contains 3,000 residential property listings modeled after real U.S. house sales data (in a Zillow-style format). It is designed for use in real estate analysis, machine learning, data visualization, and web scraping practice.

Each row represents a unique property and includes 16 key features commonly used by real estate agents, investors, and analysts. The data spans multiple U.S. states and cities, with realistic values for price, square footage, bedroom/bathroom count, property type, and more.

✅ Included Fields: Price – Listing price (in USD)

Address, City, State, Zipcode – U.S. formatted property location

Bedrooms, Bathrooms, Area (Sqft) – Core home specs

Lot Size, Year Built, Days on Market

Property Type, MLS ID, Listing Agent, Status

Listing URL – Mock Zillow-style property link

⚙️ Use Cases: Exploratory data analysis (EDA)

Regression/classification model training

Feature engineering and preprocessing

Real estate dashboards and web app mockups

Practice with BeautifulSoup, Pandas, or Power BI

Context and Acknowledgements This dataset is inspired by and improves upon the City of New York's NYC Property Sales dataset. The dataset contains a record of every property sold in the New York City property market since 2003 (the first year sales data was first listed on the public record) and updates monthly to include rolling sales.

Please upvote if you found the dataset or additional resources helpful. 👍

Content This dataset contains the location, address, type, sale price, tax category, and sale date of properties sold.

• BOROUGH: Manhattan (1), Bronx (2), Brooklyn (3), Queens (4), and Staten Island (5).
• TAX CLASSES:
  • 1: Most Residential Properties up to Three Units, Vacant Lots Zoned for Residential, Condominiums Less Than Three Stories.
  • 2: All Other Residential Properties.
  • 3: Property with equipment owned by a gas, telephone, or electric company.
  • 4: All other Properties (Garages, Factories, Warehouses...)
• EASEMENT: A right that allows an entity to make limited use of another's real property.
• $0 Sales Prices: Indicates a transfer of ownership without a cash consideration.

For further reference on the fields in this dataset see the City of New York Department of Finance's Glossary of Terms and Building Codes.

<div></div>

This table contains property sales information including sale date, price, and amounts for properties within Fairfax County. There is a one to many relationship to the parcel data. Refer to this document for descriptions of the data in the table.

This dataset represents real estate assessment and sales data that is updated on a quarterly basis by the Real Estate Assessor’s Office. This dataset contains information for properties in the city including: Acreage, Square footage, GPIN, Street Address, year built, current land value, current improvement values, and current total value. The information is obtained from Real Estate Assessor’s Office ProVal records database.

A polygon depiction of property sales from 2010 to the present that occurred in Stark County, Ohio. The Stark County Auditor's Office (SCAO) maintains records of property sales using a Computer-Assisted Mass Appraisal (CAMA) Database. This layer is a SQL view combining the sales records from the CAMA database with the Stark County parcel layer. A new view is created every morning through a combination of python scripts and SQL stored procedures. The data always reflects the most-recent information available from the previous day for both sources.

Property Sales information sourced from Assessor records. This data is updated regularly.

Residential property sales to foreign buyers in the United States between 2010 and 2024 peaked in 2017 at a value of *** billion U.S. dollars. Since 2017, the value of property sales to foreign buyers dramatically decreased to pre-2010 values. The dramatic decline in 2021 was due to the economic effect of the coronavirus (COVID-19) pandemic both in the U.S. and globally. By March 2025, property sales to foreign buyers in the United States amounted to ** billion U.S. dollars.

NYC Real Estate Sales, January 2016 – September 2025 (DuckDB Format) This dataset contains records of real estate transactions in New York City from January 2016 through September 2025, stored as a single DuckDB database file. It includes details such as sale date, price, property address, borough, and other relevant attributes. The data is suitable for property market analysis, data science projects, and urban studies research. Data for each borough is in a database table named as such, eg Manhattan data is in the manhattan table, Staten Island data in the staten_island table, etc.

Malaysia Number of Property Sales: Residential data was reported at 68,032.000 Unit in Dec 2024. This records a decrease from the previous number of 70,520.000 Unit for Sep 2024. Malaysia Number of Property Sales: Residential data is updated quarterly, averaging 53,235.500 Unit from Mar 2002 (Median) to Dec 2024, with 92 observations. The data reached an all-time high of 73,630.000 Unit in Jun 2011 and a record low of 28,284.000 Unit in Jun 2020. Malaysia Number of Property Sales: Residential data remains active status in CEIC and is reported by Valuation and Property Services Department, Ministry of Finance. The data is categorized under Global Database’s Malaysia – Table MY.EB006: Property Sales: Unit: by Type of Property.

Abstract

Title: Cotality Smart Data Platform (SDP): Property

Tax assessment data for all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C., as of June 2024.

Formerly known as CoreLogic Smart Data Platform (SDP): Property.

Methodology

In the United States, parcel data is public record information that describes a division of land (also referred to as "property" or "real estate"). Each parcel is given a unique identifier called an Assessor’s Parcel Number or APN. The two principal types of records maintained by county government agencies for each parcel of land are deed and property tax records. When a real estate transaction takes place (e.g. a change in ownership), a property deed must be signed by both the buyer and seller. The deed will then be filed with the County Recorder’s offices, sometimes called the County Clerk-Recorder or other similar title. Property tax records are maintained by County Tax Assessor’s offices; they show the amount of taxes assessed on a parcel and include a detailed description of any structures or buildings on the parcel, including year built, square footages, building type, amenities like a pool, etc. There is not a uniform format for storing parcel data across the thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, Cotality collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. Cotality augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from Cotality's parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see Cotality 2024 GitLab.

Usage

The Property, Mortgage, Owner Transfer, Historical Property and Pre-Foreclosure data can be linked on the CLIP, a unique identification number assigned to each property.

Census tracts are based on the 2020 census.

For more information about included variables, please see **cotality_sdp_property_data_dictionary_2024.txt **and Property_v3.xlsx.

For a count of records per FIPS code, please see cotality_sdp_property_counts_2024.txt.

For more information about how the Cotality Smart Data Platform: Property data compares to legacy data, please see 2025_Legacy_Content_Mapping.pdf.

Bulk Data Access

Data access is required to view this section.

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.

The Department of Finance (DOF) maintains records for all property sales in New York City, including sales of family homes in each borough. This list is a summary of all neighborhood sales for Class 1-, 2- and 3-Family homes Citywide. This list includes all sales of 1-, 2-, and 3-Family Homes', whose sale price is equal to or more than $150,000. The Building Class Category for Sales is based on the Building Class at the time of the sale.

Here's an explanation of each column:

1. MSSubClass: The building class. This column represents the type of dwelling involved in the sale.

2. MSZoning: The general zoning classification of the sale. This column indicates the general zoning classification of the property.

3. LotArea: Lot size in square feet. This column represents the area of the land associated with the property.

4. LotConfig: Lot configuration. This column describes the shape of the property and how it is situated.

5. BldgType: Type of dwelling. This column indicates the type of building involved in the sale, such as single-family detached, duplex, etc.

6. OverallCond: Overall condition rating. This column represents the overall condition of the property, rated on a scale from 1 to 10.

7. YearBuilt: Original construction date. This column indicates the year when the property was originally built.

8. YearRemodAdd: Remodel date. This column represents the year when the property was remodeled or renovated.

9. Exterior1st: Exterior covering on the house. This column describes the primary exterior covering material of the property.

10. BsmtFinSF2: Type 2 finished square feet. This column represents the area of the basement that is finished with a secondary type of finishing material.

11. TotalBsmtSF: Total square feet of basement area. This column indicates the total area of the basement in square feet.

12. SalePrice: Sale price of the property. This column represents the sale price of the property in dollars.

These columns provide various attributes and characteristics of the properties in the dataset, which can be used to predict the sale price of houses.

The Office of the Assessor compiles property sales data to perform an annual property sales study to adjust calculated costs of property values based on local market conditions. This dataset includes property sales data obtained for annual sales studies from 2018 to the present. While only Valid Arm's Length transactions that occurred in the two years prior to when a given sales study is finalized are included in each study, this dataset includes all sales transactions obtained to perform the sales studies, whether or not the sales transactions met inclusion criteria for a study. More information about the Sales Study is available from the Office of the Assessor.Values in categorical fields such as 'Sales Instrument' are recorded based on State of Michigan CAMA standards at the time the value was recorded. Some variation in field value codes occurs over time as a related CAMA standard is updated. CAMA standards are available from the State of Michigan Department of Treasury State Tax Commission.Click here for the Analytics Hub visualization of Property Sales.

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

This data set is a listing of all property sales by NORA through the following disposition channels. - Auction: Properties put up for auction and sold to the highest bidder. - Development: Properties offered to development partners at a discounted rate to support the development of affordable housing. - Lot Next Door: Properties sold to adjacent parcel owners, with discount opportunities for eligible participants. - Alternative Land Use: Properties sold for development of green space and community gardens. Note: this dataset contains duplicate addresses, which likely represent reversions or quitclaims that NORA sold again.