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. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

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 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.

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

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

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

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

ABOUT REDISTRIBUTE

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

Recorded transaction data including deed transfers, mortgage loans, and sales history. Sales history goes back to 1988 in some areas.

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.

1. Real estate transaction information includes location, area, total price, etc. 2. This dataset is updated every 10 days.

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.

ATTOM’s Home Sales Trends dataset delivers historical residential sales statistics across the United States, including sales counts, average sale prices, and median sale prices by geography.

Built from ATTOM’s nationwide database of recorded deed transactions spanning more than 2,700 counties, the dataset provides long-term market visibility with coverage generally extending back to 2005 and, in some areas, as far back as 2000.

Sales trends are aggregated across multiple geographic levels, ranging from state-level summaries to more granular tract-level insights, enabling analysis of market activity at both macro and hyperlocal scales. Statistics are typically delivered in quarterly increments, with monthly and annual options available based on customer needs.

To ensure accuracy and consistency, ATTOM applies a rigorous methodology that includes filtering for arm’s-length residential transactions, excluding non-market transfers, limiting analysis to valid residential property types, and removing price outliers. This approach delivers a reliable and standardized view of residential sales activity suitable for market analysis, pricing trends, and transaction research.

For every real estate property in Arlington which has been sold, this dataset includes property sales information and can be associated with other Real Estate datasets by the RPC (RealEstatePropertyCode).

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.

🔗 Related Datasets

• Property Assessment and Sales - FY26 — City of Norfolk
• Property Assessment and Sales - FY24 — City of Norfolk
• Property Assessment and Sales - FY23 — City of Norfolk
• Property Assessment and Sales - FY22 — City of Norfolk
• Property Assessment and Sales - FY21 — City of Norfolk

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.

In accordance with the Ministry of the Interior's policy, conduct dynamic analysis of the Taipei City real estate market every quarter.

Yearly Real Estate sales data by count and purchase price (median and average) from 2005 to 2018. All communities in the Keys to the Valley region are included.

Vermont Dataset Description

Purchase price - Average Sales Price based on listing price at time of purchase

Source – www.HousingData.org

NH Dataset Description

This data set provides an estimate of the median sale price of existing and new primary homes in New Hampshire. A primary home is defined as a single family home occupied by an owner household as their primary place of residence. Multi-family rental housing, seasonal or vacation homes and manufactured housing are not included in the analysis of this data.

Purchase price -
Median Sales Price

Data Collection Process - For the Period 1990 through 2014, the median purchase prices were calculated from data collected by the New Hampshire Department of Revenue Administration on the PA-34 Form through their vendor Real Data Corp. A PA-34 Form is filed by the buyer and seller at the time of sale of all real property in the State of New Hampshire. In 2015 this source of data was no longer available, and has been replaced by real estate transaction data supplied by The Warren Group and filtered and compiled by NHHFA. This change in data source is reflected in the charts by a break in the trend line.

Analysis - Median sale prices of all, new, existing, and condominium homes are calculated. The frequency of sales by $10,000 increment is also calculated for each of the above categories. Calculations based on sample sizes smaller than 50 are viewed as providing inconsistent and highly volatile results and are not typically released. Individual record level data is not released.

Limitations - The quality of this data at the higher geographic levels (statewide and counties) is consistent over the entire time series. For the larger LMAs and Municipalities the data is reasonably consistent with some holes in the data. For smaller LMAs and moderate sized municipalities the data is most consistent for existing homes since 1998. For the smallest municipalities this data set does not provide adequately consistent analysis.

Source - NHHFA Purchase Price Database; Source: 1990-2014 - NH Dept. of Revenue, PA-34 Dataset, Compiled by Real Data Corp. Filtered and analyzed by New Hampshire Housing.

https://www.nhhfa.org/publications-data/housing-and-demographic-data/

ATTOM’s Home Sales Trends dataset delivers reliable Real Estate Market Data by summarizing historical residential sales activity across the United States. Built from ATTOM’s proprietary database of verified deed transactions, it provides consistent House Price Data, Property Market Data, and Residential Real Estate Data across more than 2,700 counties.

What the Dataset Includes • Aggregated residential sales counts • Average sale prices • Median sale prices • Historical sales trends typically dating back to 2005 • Extended history to 2000 in select markets • Multi-level geographic aggregation from state to tract

How the Data Is Calculated • Derived from ATTOM’s verified property transaction database • Includes only arm’s-length residential transactions • Transaction types limited to: – Construction sales – Transfers and resales – Subdivision transfers • Residential property types only, including: – Single-family homes – Condo and townhome units • Sale price outliers removed to eliminate data errors

Why It Matters • Reflects true market-driven pricing and volume trends • Removes distressed and non-market transactions • Enables accurate comparison across markets and time periods • Supports consistent residential market analysis nationwide

Delivery & Cadence • Statistics typically delivered quarterly • Monthly or annual delivery available depending on use case

ATTOM’s Home Sales Trends dataset provides a clean, consistent, and historically rich foundation for analyzing residential market activity, price movement, and long-term housing trends across the U.S.

China Land Transaction Amount: Year to Date data was reported at 916,596.960 RMB mn in Dec 2022. This records an increase from the previous number of 759,069.090 RMB mn for Nov 2022. China Land Transaction Amount: Year to Date data is updated monthly, averaging 369,907.220 RMB mn from Jan 2004 (Median) to Dec 2022, with 228 observations. The data reached an all-time high of 1,775,627.670 RMB mn in Dec 2021 and a record low of 21,898.000 RMB mn in Feb 2009. China Land Transaction Amount: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Real Estate Sector – Table CN.RKA: Real Estate Investment: Monthly: Land Transaction: By Province.

Real Estate Sales 2001-2020 State of Connecticut

The provided dataset contains information about real estate transactions in Connecticut for the year 2020. Each row in the dataset represents a single real estate transaction, and the columns provide details about each transaction.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6286349%2Ffb5411354da1bff9948ada8097dc597b%2FScreenshot%202023-09-27%20103117.png?generation=1695803697508615&alt=media" alt="">

This dataset appears to contain information about various real estate transactions in Connecticut, including details about the properties, their assessed values, sale prices, and additional remarks or notes. The data can be used for various purposes, including real estate market analysis, property assessment accuracy assessment, and more.

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Please note that this dataset may require cleaning and preprocessing before performing any data analysis or visualization. Additionally, specific analyses or insights can be derived from this data depending on your research or analytical objectives.

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.

Real estate transaction information under the jurisdiction of this office is registered at actual transaction prices.

## # Dataset Overview#####

This is a synthetic OLTP dataset containing 1 million transaction records spanning over one year. Unlike standard training sets like AdventureWorks, this dataset is structured to provide the complexity required for advanced DWBI tasks:Scale: 1,000,000 primary transaction records.Format: Distributed across two source types (Relational Database tables and Flat Files) to test multi-source ETL capabilities.Schema: 8 distinct tables designed to be transformed into a Star Schema with at least 5 dimensions and 1 fact table.

Data Structure & Sources

Source A: Relational Database (OLTP Tables) These tables represent the operational core of the real estate business:db_transactions: The central transaction log containing SalePrice, event_time (creation), and complete_time.db_properties: Details of 100,000 unique properties, including bedroom counts and street addresses.db_agents: Information on 500 agents, including their seniority levels (perfect for implementing Slowly Changing Dimensions - SCD).db_clients: Records of 20,000 buyers and sellers.db_offices: The branch offices where agents are stationed.db_status: A lookup table for transaction states (Pending, Completed, Cancelled).

Source B: External Flat Files (Enrichment) These files are used to demonstrate data merging and hierarchy creation during the ETL process:source_location_hierarchy.csv: Maps Suburbs to Postcodes and Regions, enabling the creation of Geographic Hierarchies.source_property_types.txt: A tab-separated file categorizing properties into Residential, Commercial, or Industrial types.