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

%3C!-- --%3E

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): 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

%3C!-- --%3E

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

%3C!-- --%3E

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 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, rate, price index, indexes, price, and USA.

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.

**

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

Bulk Data Access

Data access is required to view this section.

The table Historical Property 11 is part of the dataset Cotality Smart Data Platform: Historical Property, available at https://stanford.redivis.com/datasets/e9sx-cn4k3cyva. It contains 147163876 rows across 220 variables.

The Property Intelligence Platform (PIP) market is experiencing robust growth, driven by the increasing need for data-driven decision-making in the real estate sector. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, advancements in big data analytics, and the growing demand for accurate property valuations and risk assessments. Technological innovations like AI and machine learning are enhancing the capabilities of PIPs, providing more sophisticated insights into property values, market trends, and investment opportunities. This translates into improved efficiency for real estate professionals, enabling faster deal closures, better risk management, and more informed investment strategies. The competitive landscape is dynamic, with established players like Yardi and CoreLogic alongside innovative startups constantly striving for market share. This competition fosters innovation and drives down costs, benefiting end-users across the real estate spectrum. We estimate the current market size to be approximately $5 billion in 2025, growing at a compound annual growth rate (CAGR) of 15% through 2033. This growth is expected across various segments including commercial real estate, residential real estate, and property management, with North America and Europe representing the largest market shares initially, followed by a steady expansion into Asia-Pacific and other emerging markets. The significant growth trajectory of the PIP market is further reinforced by the increasing complexity of real estate transactions and the need for comprehensive due diligence. Accurate and timely property information is critical for investors, lenders, and developers to mitigate risk and make sound investment choices. The integration of various data sources, including public records, satellite imagery, and market analytics, is empowering PIPs to deliver comprehensive, actionable intelligence. This holistic approach is transforming how real estate professionals operate, moving away from traditional, less efficient methods. The ongoing adoption of these platforms is expected to continue across different property types and geographical regions, further solidifying their role in the future of the real estate industry. The presence of numerous companies underscores a competitive and innovative environment, promising continuous improvements in platform capabilities and accessibility.

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

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

The table Historical Property 04 is part of the dataset Cotality Smart Data Platform: Historical Property, available at https://stanford.redivis.com/datasets/e9sx-cn4k3cyva. It contains 151968692 rows across 220 variables.

Graph and download economic data for Interest Rates and Price Indexes; Owner-Occupied Real Estate CoreLogic National Seasonally Adjusted by FRB Staff (SA), Level (BOGZ1FL075035243Q) from Q4 1975 to Q1 2025 about real estate, interest rate, interest, rate, price index, indexes, price, and USA.

CoreLogic Dwelling Prices MoM in Australia remained unchanged at 0.60 percent in July. This dataset includes a chart with historical data for Australia CoreLogic Dwelling Prices MoM.

The Property Intelligence Platform market is experiencing robust growth, driven by increasing demand for data-driven decision-making in the real estate sector. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering scalability and accessibility; the increasing need for sophisticated analytics to optimize investment strategies amongst both SMEs and large enterprises; and the proliferation of readily available data sources enriching the insights generated by these platforms. Technological advancements, such as AI and machine learning integration, further enhance the market's capabilities, enabling predictive analytics and improved risk assessment. While the on-premises segment still holds a significant market share, the cloud-based segment is witnessing faster growth, driven by its flexibility and cost-effectiveness. Competition is fierce, with established players like Yardi and VTS vying for market share alongside numerous innovative startups offering specialized solutions. Geographic expansion continues, with North America currently dominating the market, followed by Europe and Asia-Pacific regions exhibiting promising growth potential. However, challenges such as data security concerns, high implementation costs, and the need for skilled professionals to effectively utilize these platforms can act as potential restraints to market expansion. Looking forward, the market is projected to maintain a strong growth trajectory, with a Compound Annual Growth Rate (CAGR) estimated at 15% between 2025 and 2033. This continued expansion will be driven by increased adoption in emerging markets, further technological innovation, and the ongoing integration of these platforms into core real estate business processes. The focus will increasingly shift towards providing more comprehensive and integrated solutions, encompassing not only property-level data but also market trends, economic indicators, and regulatory information. This evolution will lead to a more sophisticated and holistic approach to real estate investment and management, further solidifying the importance of property intelligence platforms in the industry. The competitive landscape is anticipated to become even more dynamic, with mergers and acquisitions likely to shape the market's consolidation.

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

The table Mortgage is part of the dataset Cotality Smart Data Platform: Owner Transfer and Mortgage, available at https://stanford.redivis.com/datasets/rz0v-1mcs4sreb. It contains 427740655 rows across 146 variables.

Case Shiller Home Price Index in the United States increased to 342.97 points in May from 341.55 points in April of 2025. This dataset provides the latest reported value for - United States S&P Case-Shiller Home Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The table Historical Property 14 is part of the dataset Cotality Smart Data Platform: Historical Property, available at https://stanford.redivis.com/datasets/e9sx-cn4k3cyva. It contains 137860124 rows across 220 variables.

*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.

Graph and download economic data for S&P CoreLogic Case-Shiller TX-Dallas Home Price Index (DAXRNSA) from Jan 2000 to May 2025 about Dallas, TX, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for S&P CoreLogic Case-Shiller NV-Las Vegas Home Price Index (LVXRSA) from Jan 1987 to May 2025 about Las Vegas, NV, HPI, housing, price index, indexes, price, and USA.

The Information Broker Service market is experiencing robust growth, driven by the increasing demand for accurate and timely data across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of data-driven decision-making across industries like BFSI, retail, and manufacturing; the growing need for efficient risk management and fraud prevention; and the increasing complexity of regulatory compliance. Furthermore, technological advancements, including the proliferation of big data analytics and artificial intelligence, are enhancing the capabilities and efficiency of information broker services, leading to higher demand. The subscription-based model dominates the market, offering predictable revenue streams for providers, but pay-per-use and hybrid models are gaining traction as companies seek greater flexibility in their data acquisition strategies. North America currently holds a significant market share, attributable to the high concentration of established players and advanced data infrastructure. However, Asia-Pacific is projected to witness significant growth in the coming years due to rapid digitalization and expanding internet penetration. Despite the promising growth trajectory, the market faces certain challenges. Data privacy concerns and increasing regulatory scrutiny represent significant restraints. The high cost of data acquisition and maintenance, along with the need for continuous investment in technological advancements, also pose obstacles for market players. Furthermore, competition is intensifying as new players enter the market and established companies expand their offerings. To thrive, information brokers must prioritize data security, comply with evolving regulations, and continuously innovate to provide value-added services, such as advanced analytics and customized data solutions. This will enable them to cater to the evolving needs of clients across various sectors and maintain a competitive edge in a rapidly transforming market landscape. We estimate a 2025 market size of $150 billion, based on observable trends in related data industries and acknowledging a logical CAGR within the typical range of this sector.