Abstract

Title: Cotality Loan-Level Market Analytics (LLMA)

Cotality Loan-Level Market Analytics (LLMA) for primary mortgages contains detailed loan data, including origination, events, performance, forbearance and inferred modification data. This dataset may not be linked or merged with any of the other datasets we have from Cotality.

Formerly known as CoreLogic Loan-Level Market Analytics (LLMA).

Methodology

Cotality sources the Loan-Level Market Analytics data directly from loan servicers. Cotality cleans and augments the contributed records with modeled data. The Data Dictionary indicates which fields are contributed and which are inferred.

The Loan-Level Market Analytics data is aimed at providing lenders, servicers, investors, and advisory firms with the insights they need to make trustworthy assessments and accurate decisions. Stanford Libraries has purchased the Loan-Level Market Analytics data for researchers interested in housing, economics, finance and other topics related to prime and subprime first lien data.

Cotality provided the data to Stanford Libraries as pipe-delimited text files, which we have 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

Per the End User License Agreement, the LLMA Data cannot be commingled (i.e. merged, mixed or combined) with Tax and Deed Data that Stanford University has licensed from Cotality, or other data which includes the same or similar data elements or that can otherwise be used to identify individual persons or loan servicers.

The 2015 major release of Cotality Loan-Level Market Analytics (for primary mortgages) was intended to enhance the Cotality servicing consortium through data quality improvements and integrated analytics. See **Cotality_LLMA_ReleaseNotes.pdf **for more information about these changes.

For more information about included variables, please see Cotality_LLMA_Data_Dictionary.pdf.

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For more information about how the database was set up, please see LLMA_Download_Guide.pdf.

Bulk Data Access

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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.

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.

Graph and download economic data for Interest Rates and Price Indexes; Owner-Occupied Real Estate CoreLogic National Seasonally Adjusted by FRB Staff (SA), Level (BOGZ1FL075035243A) from 1975 to 2024 about real estate, interest rate, interest, price index, rate, indexes, price, and USA.

Proprietary property trends data and analytics for Australia and NZ from Corelogic.

Parcel level data from county assessment records and real estate transactions

Python script used to examine how the marketing of properties explains neighborhood racial and income change using historical public remarks in real estate listings from Multiple Listing Services (MLS) collected and curated by CoreLogic.The primary dataset used for this research consists of 158,253 geocoded real estate listings for single-family homes in Mecklenburg County, North Carolina between 2001 and 2020. The historical MLS data which include public remarks is proprietary and can be obtained through purchase agreement with CoreLogic. The MLS is not publicly available and only available for members of the National Association of Realtors. Public remarks for homes currently listed for sale can be collected from online real estate websites such as Zillow, Trulia, Realtor.com, Redfin, and others.Since we cannot share this data, users need to, before running the script provided here, run the script provided by Nilsson and Delmelle (2023) which can be accessed here: https://doi.org/10.6084/m9.figshare.20493012.v1. This in order to get a fabricated/mock dataset of classified listings called classes_mock.csv. The article associated with Nilsson and Delmelle's (2023) script can be accessed here: https://www.tandfonline.com/doi/abs/10.1080/13658816.2023.2209803The user can then run the code together with the data provided here to estimate the threshold models together with data derived from the publicly available HMDA data. To compile a historical data set of loan/application records (LAR) for the user's own study are, the user will need to download data from the following websites:https://ffiec.cfpb.gov/data-publication/snapshot-national-loan-level-dataset/2022 (2017-forward)https://www.ffiec.gov/hmda/hmdaproducts.htm (2007-2016)https://catalog.archives.gov/search-within/2456161?limit=20&levelOfDescription=fileUnit&sort=naId:asc (for data prior to 2007)

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.

Graph and download economic data for S&P CoreLogic Case-Shiller CA-Los Angeles Home Price Index (LXXRSA) from Jan 1987 to Aug 2025 about Los Angeles, CA, HPI, housing, price index, indexes, price, and USA.

The Property Intelligence Platform market is experiencing robust growth, driven by increasing demand for data-driven decision-making in the real estate sector. Technological advancements, such as AI and machine learning, are enhancing the capabilities of these platforms, providing more accurate and insightful property data analysis. This allows real estate professionals to make informed decisions regarding investments, valuations, risk assessment, and portfolio management. The market size in 2025 is estimated at $5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several factors, including the increasing adoption of cloud-based solutions, the growing need for efficient property management, and the expansion of the global real estate market. The rise of PropTech and the integration of various data sources, such as public records, transactional data, and market analytics, are further contributing to this expansion. The competitive landscape is highly fragmented, with a mix of established players and emerging startups. Key players like Yardi, VTS, and CoreLogic are leveraging their existing market presence and expertise to maintain their market share. However, agile startups are innovating with advanced analytical tools and specialized solutions, catering to niche market segments. Geographical expansion, particularly in emerging economies with rapidly growing real estate sectors, presents significant opportunities for both established and new entrants. The market's future growth will likely be shaped by the ongoing integration of data analytics, the development of more sophisticated predictive models, and the increasing adoption of these platforms by smaller real estate firms. The continued focus on enhancing data security and privacy will also play a crucial role in shaping the market's trajectory.

CoreLogic Inc. Business Operations, Opportunities, Challenges and Risk (SWOT, PESTLE and Porters Five Forces Analysis); Corporate and ESG Strategies; Competitive Intelligence; Financial KPI’s; Operational KPI’s; Recent Trends: “ Read More

Beijing Corelogic Communication Co Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Graph and download economic data for S&P CoreLogic Case-Shiller IL-Chicago Home Price Index (CHXRSA) from Jan 1987 to Sep 2025 about Chicago, WI, IN, IL, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for S&P CoreLogic Case-Shiller CA-San Francisco Home Price Index (SFXRSA) from Jan 1987 to Sep 2025 about San Francisco, CA, HPI, housing, price index, indexes, price, and USA.

CoreLogic Dwelling Prices MoM in Australia decreased to 1 percent in November from 1.10 percent in October of 2025. This dataset includes a chart with historical data for Australia CoreLogic Dwelling Prices MoM.

Abstract

Title: Cotality Smart Data Platform (SDP): Pre-Foreclosure

The Cotality Pre-Foreclosure data documents over 35 million property transactions representing pre-foreclosure events. These transactions occurred in U.S. states (excluding Vermont), the U.S. Virgin Islands and Washington, D.C. Cotality has been collecting pre-foreclosure data since 2000.

Transaction events include Notice of Default, Lis Pendens, Release of Lis Pendens and Final Judgment. Transactions illustrate the pre-foreclosure events leading up to a foreclosure or sale at auction. Transaction data can include property address, default date, default amount, document type (Notice of Default, Lis Pendens, etc.), court filing details, attorney, beneficiary or plaintiff name, borrower name, lender, trustee, final judgment amount and any relevant auction information. Transactions also include a subject transaction, which identifies the original transaction (usually Deed of Trust or another prior activity) to which a transaction applies. Activities recorded and delivered support transactions within both judicial and non-judicial states.

Formerly known as CoreLogic Smart Data Platform: Pre-Foreclosure.

Methodology

Pre-foreclosure data comes from four types of documents:

• Final Judgment of Foreclosure
• Lis Pendens
• Notices of Default
• Release of Lis Pendens

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These documents are sourced from U.S. County Assessor and Recorder offices, and newspapers. The data is collected, cleaned and normalized by Cotality. Data is bundled together in a pipe-delimited text file, which has been 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.

For more information about included variables, please see **cotality_sdp_preforeclosure_data_dictionary_2024.txt **and Pre-Foreclosure_v2.xlsx.

For a count of records per FIPS code, please see cotality_sdp_preforeclosure_counts_2024.txt.

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

Bulk Data Access

Data access is required to view this section.

Thames-Coromandel District Councils (TCDC) Property Information is derived from external datasets and internal databases. This Feature Service is made up of 5 Datasets:Address is derived from LINZand enhanced by CoreLogic.Building Footprints is derived from the 2007 LiDAR data capture undertaken by Waikato Regional Council (WRC)Property data is derived from LINZ Parcel Informationwhich is enhanced by Corelogic. TCDC add Property Information from our Property Database and is final merged into the Property layer based on Property Key ID and Parcel ID.Parcel data is derived from LINZ Parcel Informationwhich is enhanced by CorelogicRoads is derived from LINZ and enhanced by CoreLogic

Discover the booming Property Intelligence Platform market! This comprehensive analysis reveals key trends, growth drivers, and leading companies shaping this dynamic sector. Learn about market size, CAGR, regional insights, and future predictions for 2025-2033.

*The CEC purchased property and parcel boundary data from CoreLogic, Incorporated that includes information on parcel location, ownership, tax assessment, and property characteristics. This data was used to estimate home charging barriers and likeliness of not having a home charger. In general, tribal lands are exempt from local and state taxation, including property taxes. Therefore, property data to assess barriers to having a home charger may be sparse in federally recognized tribal lands. CoreLogic, Inc. and/or its subsidiaries retain all ownership rights in the data, which end user agree is proprietary to CoreLogic. All Rights Reserved. The data is provided AS IS; end user assumes all risk on any use or reliance on the data.

Abstract

Title: Cotality Multiple Listing Service (MLS)

A multiple listing service (MLS) is an exchange where real estate brokers share information about properties they are selling. Other real estate brokers review the listings, and are compensated if they can identify a buyer for a property. Multiple listing services promote cooperation and mutual benefit for real estate brokers representing buyers and sellers. The Cotality Multiple Listing Service data contains listings from 135 real estate boards utilizing Cotality's multiple listing service software. The data was produced in August 2024.

Formerly known as CoreLogic Multiple Listing Service (MLS).

Methodology

The data consists of listings from 135 real estate boards that use Cotality listing software. The data DOES NOT cover listings from all real estate boards in the United States. The National Association of Realtors maintains the most complete and up-to-date list of real estate boards; however, this information is only available to members of the National Association of Realtors.

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

Usage

Quick Search (QS) contains the most recent listing data (as of August 2024). In order to see the entire listing history of a property/record, you will need to search the Quick History (QH) table on the SysPropertyID, which is a unique key for a listing across multiple listing boards. You can use the variable FA_PostDate to see when updates occurred. Updates include name changes, price changes, etc.

During upload to Data Farm, a small number of invalid records were dropped from the Quick History (QH) table. For more information, see Cotality 2024 GitLab. To access the complete data (including invalid records), please see Bulk Data Access instructions, below.

Bulk Data Access

Data access is required to view this section.