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.

Update 10/31/2023: Sales are no longer filtered out of this data set based on deed type, sale price, or recency of sale for a given PIN with the same price. If users wish to recreate the former filtering schema they should set sale_filter_same_sale_within_365, sale_filter_less_than_10k, and sale_filter_deed_type to False.

Parcel sales for real property in Cook County, from 1999 to present. The Assessor's Office uses this data in its modeling to estimate the fair market value of unsold properties.

When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded.

Sale document numbers correspond to those of the Cook County Clerk, and can be used on the Clerk's website to find more information about each sale.

NOTE: These sales are filtered, but likely include non-arms-length transactions - sales less than $10,000 along with quit claims, executor deeds, beneficial interests are excluded. While the Data Department will upload what it has access to monthly, sales are reported on a lag, with many records not populating until months after their official recording date.

Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time.

For more information on the sourcing of attached data and the preparation of this dataset, see the Assessor's Standard Operating Procedures for Open Data on GitHub.

Read about the Assessor's 2025 Open Data Refresh.

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

This dataset represents real estate assessment and sales data made available by the Office of the Real Estate Assessor. This dataset contains information for properties in the city, including acreage, square footage, GPIN, street address, year built, current land value, current improvement value, and current total value. The information is obtained from the Office of the Real Estate Assessor ProVal records database. This dataset is updated daily on weekdays.

For data about this dataset, please click on the below link: https://data.norfolk.gov/Real-Estate/Property-Assessment-and-Sales-FY24/9gmp-9x4c/about_data

Abstract

Title: Cotality Smart Data Platform (SDP): Historical Property

Historical tax assessment data for all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C. Each table represents a previous edition of Cotality's tax assessment data.

Formerly known as CoreLogic Smart Data Platform: Historical 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

Each table contains an archived snapshot of the property data, roughly corresponding to the following assessed years:

• Historical Property 1 = 2022-2023
• Historical Property 2 = 2021-2022
• Historical Property 3 = 2020-2021
• Historical Property 4 = 2019-2020
• Historical Property 5 = 2018-2019
• Historical Property 6 = 2017-2018
• Historical Property 7 = 2016-2017
• Historical Property 8 = 2015-2016
• Historical Property 9 = 2014-2015
• Historical Property 10 = 2013-2014
• Historical Property 11 = 2012-2013
• Historical Property 12 = 2011-2012
• Historical Property 13 = 2010-2011
• Historical Property 14 = 2009-2010
• Historical Property 15 = 2008-2009

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Users can check theASSESSED_YEAR variable to confirm the year of assessment.

Roughly speaking, the tables use the following census geographies:

• 2020 Census Tract: Historical Property 1-2
• 2010 Census Tract: Historical Property 3 – 12
• 2000 Census Tract: Historical Property 13 – 15

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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.

For more information about included variables, please see **cotality_sdp_historical_property_data_dictionary_2024.txt **and Historical Property_v3.xlsx.

Under Supporting files, users can also find record counts per FIPS code for each edition of the Historical Property data.

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

Bulk Data Access

Data access is required to view this section.

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.

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.

Abstract

Title: Cotality Smart Data Platform (SDP): Owner Transfer and Mortgage

The Owner Transfer and Mortgage data covers over 450 million properties, and includes over 50 years of sales history. The tables were generated in June 2024, and cover all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C.

Formerly known as CoreLogic Smart Data Platform: Owner Transfer & Mortgage.

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 Owner Transfer and Mortgage data covers over 450 million properties, and includes over 50 years of sales history. The tables were generated in June 2024, and cover all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C. The Owner Transfer data provides historical information about property sales and ownership-related transactions, including full, nominal, and quitclaim transactions (involving a change in title/ownership). It contains comprehensive property and transaction information, such as property characteristics, current ownership, transaction history, title company, cash purchase/foreclosure/resale/short sale indicators, and buyer information.

The Mortgage data provides historical information at the mortgage level, including purchase, refinance, equity, as well as details associated with each transaction, such as lender, loan amount, loan date, interest rate, etc. Mortgage details include mortgage amount, type of loan (conventional, FHA, VHA), mortgage rate type, mortgage purpose (cash out first, consolidation, standalone subordinate), mortgage ARM features, and mortgage indicators such as fixed-rate, conforming loan, construction loan, and private party. The Mortgage data also includes subordinate mortgage types, rate details, and lender details (NMLS ID, Loan Company, Loan Officers).

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.

Mortgage records can be linked to a transaction using the MORTGAGE_COMPOSITE_TRANSACTION_ID.

For more information about included variables, please see:

• cotality_sdp_owner_transfer_data_dictionary_2024.txt
• cotality_sdp_mortgage_data_dictionary_2024.txt
• Mortgage_v3.xlsx
• Owner Transfer_v3.xlsx

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For a count of records per FIPS code, please see cotality_sdp_owner_transfer_counts_2024.txt and cotality_sdp_mortgage_counts_2024.txt.

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

Bulk Data Access

Data access is required to view this section.

This application shows comprehensive data for properties in the City of Winchester, Virginia. This data includes school district information, fire and rescue first due area, voting information, refuse and recycling and zoning information. It also shows the tax card information for each property queried.

Context

This dataset is a record of every building or building unit (apartment, etc.) sold in the New York City property market over a 12-month period.

Content

This dataset contains the location, address, type, sale price, and sale date of building units sold. A reference on the trickier fields:

• BOROUGH: A digit code for the borough the property is located in; in order these are Manhattan (1), Bronx (2), Brooklyn (3), Queens (4), and Staten Island (5).
• BLOCK; LOT: The combination of borough, block, and lot forms a unique key for property in New York City. Commonly called a BBL.
• BUILDING CLASS AT PRESENT and BUILDING CLASS AT TIME OF SALE: The type of building at various points in time. See the glossary linked to below.

For further reference on individual fields see the Glossary of Terms. For the building classification codes see the Building Classifications Glossary.

Note that because this is a financial transaction dataset, there are some points that need to be kept in mind:

• Many sales occur with a nonsensically small dollar amount: $0 most commonly. These sales are actually transfers of deeds between parties: for example, parents transferring ownership to their home to a child after moving out for retirement.
• This dataset uses the financial definition of a building/building unit, for tax purposes. In case a single entity owns the building in question, a sale covers the value of the entire building. In case a building is owned piecemeal by its residents (a condominium), a sale refers to a single apartment (or group of apartments) owned by some individual.

Acknowledgements

This dataset is a concatenated and slightly cleaned-up version of the New York City Department of Finance's Rolling Sales dataset.

Inspiration

What can you discover about New York City 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?

This dataset has been published by the Office of the Real Estate Assessor of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

High-quality, free real estate dataset from all around the United States, in CSV format. Over 10.000 records relevant to Real Estate investors, agents, and data scientists. We are working on complete datasets from a wide variety of countries. Don't hesitate to contact us for more information.

This dataset offers a holistic view of a real estate agency's operations. It is divided into five Excel sheets, each representing a key component of the business:

🏘️ Properties Details of available properties.

Columns:

• PropertyID: Unique identifier for each property

• PropertyType: Type of property (e.g., Villa, Retail, Warehouse)

• Location: City where the property is located

• Size_sqm: Size of the property in square meters

• PriceUSD: Listing price in USD

👤 Clients Information about potential buyers or renters.

Columns:

• ClientID: Unique identifier for each client

• Name: Client's full name

• Email: Contact email address

• Phone: Contact phone number

• PreferredLocation: Desired location for property

• BudgetUSD: Budget in USD

🧑‍💼 Agents Details of the real estate agents.

Columns:

• AgentID: Unique identifier for each agent

• Name: Agent's name

• Email: Contact email

• Phone: Phone number

• YearsExperience: Number of years in real estate

• PropertiesSold: Total properties sold

💰 Sales Transactional records of property sales.

Columns:

• SaleID: Unique transaction identifier

• PropertyID: Linked property

• ClientID: Buyer client

• AgentID: Responsible agent

• SaleDate: Date of transaction

• SalePriceUSD: Final sale price

📅 Visits Records of client visits to properties.

Columns:

• VisitID: Unique identifier for the visit

• ClientID: Visiting client

• PropertyID: Visited property

• VisitDate: Date of the visit

• InterestLevel: Client's interest (e.g., High, Medium, Low)

This dataset is ideal for projects involving predictive modeling, real estate price estimation, agent performance tracking, client segmentation, and sales funnel analysis. Its clean structure and multiple relational tables make it suitable for machine learning, business intelligence dashboards, and educational use.

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.

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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.

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment.

Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019.

The table below showcases the total number of homes sold for each zip code in Keshena, Wisconsin. It's important to understand that the number of homes sold can vary greatly and can change yearly.

General UsagePublic users who enter the portal will land at the Parcel Search page by default. The four (4) buttons located on the top navigation bar are used to initiate searches for land parcel and tax information in the system. Note that Sales History is currently a premium feature of the Ascent Land Records Portal and be not be available every county. When the user hovers over a particular button with the mouse pointer, that button will change to the color red.If the user clicks the left mouse button while the button is red, the user will be navigated the specific search screen and will be able to enter search criteria and view the search results. A detailed explanation of each search is provided in the help topics that follow. An overview is provided below and each bullet provides a link to more detail.Parcel Search: Allows a user to locate a real estate tax parcel using one or more search criteria. A search will return the user zero or more candidate results that satisfy the search criteria. The user may then choose a specific real estate tax parcel in order to investigate it in more detail. Survey Search: A survey is an element in the Ascent Land Records System that is always related to one or more parcels. Any parcel created within the Ascent Land Records System must have an associated survey that describes what circumstances resulted in the parcel's creation. Parcels that existed prior to the county's transition to the Ascent Land Records System may not have an associated survey element.Sales History: This search provides the capability to search for property sales for a single municipality within a specified date range. It analyzes and combines data from both the county's property listing database and the county's Register of Deeds database.Plat & Condo Directory: This provides a listing of any subdivision, condominium, cemetery, and transportation plats maintained in this system by the county. Note that this information will only be available if the county department responsible for property listing records manages maintains this information in the Ascent Land Records System.

During the COVID-19 pandemic, the number of house sales in the UK spiked, followed by a period of decline. In 2023 and 2024, the housing market slowed notably, and in January 2025, transaction volumes fell to 46,774. House sales volumes are impacted by a number of factors, including mortgage rates, house prices, supply, demand, as well as the overall health of the market. The economic uncertainty and rising unemployment rates has also affected the homebuyer sentiment of Brits. How have UK house prices developed over the past 10 years? House prices in the UK have increased year-on-year since 2015, except for a brief period of decline in the second half of 2023 and the beginning of 2024. That is based on the 12-month percentage change of the UK house price index. At the peak of the housing boom in 2022, prices soared by nearly 14 percent. The decline that followed was mild, at under three percent. The cooling in the market was more pronounced in England and Wales, where the average house price declined in 2023. Conversely, growth in Scotland and Northern Ireland continued. What is the impact of mortgage rates on house sales? For a long period, mortgage rates were at record-low, allowing prospective homebuyers to take out a 10-year loan at a mortgage rate of less than three percent. In the last quarter of 2021, this period came to an end as the Bank of England rose the bank lending rate to contain the spike in inflation. Naturally, the higher borrowing costs affected consumer sentiment, urging many homebuyers to place their plans on hold and leading to a decline in sales.