Product Overview

You’re a few short steps away from accessing the largest and most comprehensive Pre-Foreclosure and Foreclosure database in the country. Whether you want to conduct property research, data analysis, purchase distressed properties, or market your services, licensing Pre-Foreclosure and Foreclosure Data provides in-depth intelligence on distressed properties across the country that will inform your next move.

What is Foreclosure?

Foreclosure is the legal process of taking possession of a mortgaged property when the borrower fails to keep up with mortgage payments. The foreclosure process varies from state to state, depending on whether the state has a judicial or nonjudicial process. Judicial process requires court action on a foreclosed property, where a nonjudicial process does not.

Foreclosure and Pre-Foreclosure Data Includes:

• 9 Different types of Judicial vs Non-Judicial
• Auctions
• Public Notices
• Lis Pendens
• Releases
• Defendant and Plaintiff Names
• Recording Dates, Published Dates, and Auction Dates
• Original Mortgage Information

The foreclosure rate in the United States has experienced significant fluctuations over the past two decades, reaching its peak in 2010 at **** percent following the financial crisis. Since then, the rate has steadily declined, with a notable drop to **** percent in 2021 due to government interventions during the COVID-19 pandemic. In 2024, the rate stood slightly higher at **** percent but remained well below historical averages, indicating a relatively stable housing market. Impact of economic conditions on foreclosures The foreclosure rate is closely tied to broader economic trends and housing market conditions. During the aftermath of the 2008 financial crisis, the share of non-performing mortgage loans climbed significantly, with loans 90 to 180 days past due reaching *** percent. Since then, the share of seriously delinquent loans has dropped notably, demonstrating a substantial improvement in mortgage performance. Among other things, the improved mortgage performance has to do with changes in the mortgage approval process. Homebuyers are subject to much stricter lending standards, such as higher credit score requirements. These changes ensure that borrowers can meet their payment obligations and are at a lower risk of defaulting and losing their home. Challenges for potential homebuyers Despite the low foreclosure rates, potential homebuyers face significant challenges in the current market. Homebuyer sentiment worsened substantially in 2021 and remained low across all age groups through 2024, with the 45 to 64 age group expressing the most negative outlook. Factors contributing to this sentiment include high housing costs and various financial obligations. For instance, in 2023, ** percent of non-homeowners reported that student loan expenses hindered their ability to save for a down payment.

Abstract

The CoreLogic 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. CoreLogic 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.

The CoreLogic Smart Data Platform (SDP) Pre-Foreclosure data was formerly known as the CoreLogic Pre-Foreclosure data. The CoreLogic SDP Property data contains almost all of the variables that were included in the CoreLogic Pre-Foreclosure data.

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 CoreLogic. 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 CoreLogic 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 **core_logic_sdp_preforeclosure_data_dictionary_2024.txt **and Pre-Foreclosure_v2.xlsx.

For a count of records per FIPS code, please see core_logic_sdp_preforeclosure_counts_2024.txt.

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

Bulk Data Access

Data access is required to view this section.

Gain unmatched access to data on all stages of the pre-foreclosure and foreclosure process from a single source.

The number of properties with foreclosure filings in the United States declined in 2024, but remained below the pre-pandemic level. Foreclosure filings were reported on approximately 322,100 properties, which was about 34,900 fewer than in 2023. Despite the decrease, 2024 saw one of the lowest foreclosure rates on record.

Graph and download economic data for Nonfarm Real Estate Foreclosures for United States (M09075USM476NNBR) from Jan 1934 to Mar 1963 about real estate, nonfarm, and USA.

Graph and download economic data for Large Bank Consumer Mortgage Balances: 30 or More Days Past Due: Including Foreclosures Rates: Balances Based (RCMFLBBALDPDPCT30P) from Q3 2012 to Q1 2025 about 30 days +, FR Y-14M, large, balance, mortgage, consumer, banks, depository institutions, rate, and USA.

Provides monthly totals of Notices of Intent (NOI), Notices of Foreclosure (NOF), and Foreclosure Property Registrations (FPR) for Maryland Counties as reported to the Office of Financial Regulation (OFR).

For additional information and definitions, please see the OFR's Foreclosure Data Tracker: https://www.labor.maryland.gov/finance/consumers/frforeclosuredatatracker.shtml.

NOTE: The data provided is for informational and research purposes only and is not intended to guide policy or provide specific outreach targets. The data provided is compiled from third-party filings with the Office of Financial Regulation (OFR) pursuant to applicable law. These third-party filings may contain duplicates and other errors and the OFR cannot guarantee the accuracy and quality of the submissions upon which the data is based. The data does not constitute foreclosure case records and may differ from the official foreclosure records contained in the court records of the State of Maryland. OFR makes no express or implied warranties or representations concerning the data contained in this report.

Our foreclosure data offering provides an extensive suite of real-time real estate data, available through both API integration and bulk data delivery. This rich dataset is designed to meet the needs of a variety of users, from real estate investors to foreclosure prevention services and market analysts. With over 31 data points available, this dataset covers multiple aspects of foreclosure processes, including auction details, loan information, foreclosure status, and trustee data. Below is a detailed description of the data points and their potential use cases.

Data Points Overview for Foreclosure Data:

1. Auction Data (9+ Data Points) Auction Location, Auction Time, Case Number, Bid Parameters

2. Loans/Lender Data (9+ Data Points) Lender Name, Original Loan Details, Unpaid Balances, Pre-Foreclosure Flags, Related Documents

3. Foreclosure Status Data (7+ Data Points) Recording Date, Release Date, Status Indicators and Codes

4. Trustee Data (6+ Data Points) Trustee Name, Trustee Address, Trustee Phone Number, Sale Number

Top Use Cases

1. Surface Investment Opportunities Websites and Applications: Integrate our foreclosure data into real estate platforms to provide users with up-to-date information on potential investment properties. This can enhance search functionality and deliver greater value by identifying promising foreclosure opportunities.

2. Foreclosure Prevention Services Sales and Marketing: Leverage foreclosure data to target homeowners in distress with tailored marketing efforts. By identifying properties in pre-foreclosure status, you can focus your outreach to offer services designed to prevent foreclosure, such as financial counseling or loan modification programs.

3. Market Analysis and Predictive Analytics Data-Driven Insights: Utilize the comprehensive dataset to perform in-depth market analysis and develop predictive models. This can help forecast foreclosure trends, assess market conditions, and make informed decisions based on historical and current foreclosure activity.

Access and Delivery

Our foreclosure data is accessible through two primary methods: - API Integration: Seamlessly integrate the data into your applications or platforms with our robust API, offering real-time access and automated updates. - Bulk Data Delivery: Obtain large datasets for offline analysis or integration into internal systems through bulk delivery options, providing flexibility in how you utilize the information.

This comprehensive data listing is designed to empower users with detailed and actionable foreclosure data, facilitating a range of applications from investment analysis to foreclosure prevention and market forecasting.

Foreclosed rental properties registered with the Chicago Department of Housing under the Keep Chicago Renting ordinance.

Prior to 12/12/2022, Owner and Owner Management Agent addresses could not be registered through the registration site so no City, State, or ZIP columns were present in this dataset. Because all previously existing records had Chicago addresses for Owner and Owner Agent, the City and State columns were populated when added to this dataset but ZIP values are only available from 12/12/2022 forward.

The Property Address is always in Chicago.

Abstract

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

The CoreLogic Smart Data Platform (SDP) Property data was formerly known as the CoreLogic Tax data. The CoreLogic SDP Property data is an enhanced version of the CoreLogic Tax data. The CoreLogic SDP Property data contains almost all of the variables that were included in the CoreLogic Tax data, and its records are augmented with additional property-level characteristics.

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, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic 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 CoreLogic’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 CoreLogic 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 **core_logic_sdp_property_data_dictionary_2024.txt **and Property_v3.xlsx.

For a count of records per FIPS code, please see core_logic_sdp_property_counts_2024.txt.

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

Bulk Data Access

Data access is required to view this section.

This data includes filings related to mortgage foreclosure in Allegheny County. The foreclosure process enables a lender to take possession of a property due to an owner's failure to make mortgage payments. Mortgage foreclosure differs from tax foreclosure, which is a process enabling local governments to take possession of a property if the owner fails to pay property taxes. As Pennsylvania is a judicial foreclosure state, a lender files for foreclosure through the court system. Foreclosure data in the court system is maintained by the Allegheny County Department of Court Records. Data included here is from the general docket, and a mortgage foreclosure docket created to help homeowners maintain ownership of their property following an initial filing. Several different types of legal filings may occur on a property involved in the foreclosure process. At this time, only the most recent filing in a case is included in the data found here, but we hope to add all filings for a case in the coming months. After a property enters the foreclosure process, several potential outcomes are possible. Some of the more common outcomes include: borrowers may come to an agreement with the lender for unpaid debt; borrowers may sell the property to satisfy part or all of the debt; borrowers may voluntarily relinquish ownership to the lender; lenders may decide not to pursue the foreclosure any further; and the property may proceed all the way through a sheriff sale, where it is sold to a new owner. Before September 2022, the data presented here included only the final filing for the month in which each case (represented by Case ID) is opened; since then the feed has changed so we now have a new last_activity field, which gets updated whenever there is a new filing in the case with the date of the last filing for the month. The last_activity value gives some indication of which cases are still ongoing. (However, the new feed does not include the docket_type field, so these are blank for cases started after August 2022.) To view the detailed mortgage foreclosure filings for each property represented in this dataset, please visit the Department of Court Records Website, and enter the Case ID for a property to pull-up detailed information about each foreclosure case, including parties, docket entries, and services. Changelog 2022-12-14: Loaded data back to September (which had been missing due to the schema migration). Added a new last_activity field. Data since September 2022 is missing the docket_type value, for now those new values will be set to '' (empty string). Visualizations

Abstract

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 CoreLogic Smart Data Platform (SDP) Owner Transfer and Mortgage data was formerly known as the CoreLogic Deed data. The CoreLogic Deed data contained both owner transfer and mortgage information. In the CoreLogic Smart Data Platform (SDP), this data was separated into two tables: Owner Transfer and Mortgage. Between the two tables, the CoreLogic Smart Data Platform (SDP) Owner Transfer and Mortgage data contains almost all of the variables that were included in the CoreLogic Deed data. Further, each CoreLogic Smart Data Platform (SDP) table is augmented with additional owner transfer and mortgage characteristics.

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, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic 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 CoreLogic’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 CoreLogic 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.

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

For more information about included variables, please see:

• core_logic_sdp_owner_transfer_data_dictionary_2024.txt
• core_logic_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 core_logic_sdp_owner_transfer_counts_2024.txt and core_logic_sdp_mortgage_counts_2024.txt.

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

Bulk Data Access

Data access is required to view this section.

Monthly foreclosures in Connecticut by county, 2008 through the present. Data updated monthly by the Connecticut Housing Finance Authority and tracked in the following dashboard: https://www.chfa.org/about-us/ct-monthly-housing-market-dashboard/.

BatchData Marketing Prospects data are frequently used to find new leads to target. BatchData is committed to data quality, providing accurate, up-to-date marketing data through a suite of APIs, data delivery services, and self-service tools.

MARKETING LISTS WITH CONTACT INFORMATION BatchData uniquely provides emails and phone numbers of marketable contacts in your list. With industry-leading right party contact rates, you'll get the most out of your marketing and prospects lists. We've got you covered with the highest quality prospect data for your business - B2C Contact data including new homeowner data, phone number data, email address data, and more!

THE DATA DIFFERENCE: We start by sourcing data from multiple providers, cleaning and enriching the data with our in-house data science team, and leveraging user feedback to continually refine property and contact records.

ACCESS MARKETING PROSPECTS DATA INCLUDING: US Homeowners Marketing Prospect Data Updated daily using multiple public record sources including county assessors and recorders.

New Homeowner Data Market to homeowner as they move in and are looking for products and services. Includes MLS information and data compiled from deed recordings.

Distressed Property Owner Marketing Prospect Data Identify homeowners with financial, situational, or physical property distress factors. Including Defaults, lis Pendents, auctions, pre-foreclosures, vacant properties, out-of-state owners, and more.

Commercial Property Marketing Prospect Data Target 25M+ commercial, agricultural, and industrial marketing prospects with reliable entity resolution to uncover true commercial prospects behind LLCs and corporate veils.

INVESTOR MARKETING PROSPECT DATA Market to prospects who own multiple properties, or are actively making cash investments in specific types of real estate assets.

REAL ESTATE AGENT MARKETING PROSPECT DATA Target specific agents to market products and services to including the ability to focus on transactional volume, number of transactions, and the types of properties agents and specific MLS markets your ideal agents are representing.

Abstract

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 CoreLogic's tax assessment data.

The CoreLogic Smart Data Platform (SDP) Historical Property data was formerly known as the CoreLogic Tax History data. The CoreLogic SDP Historical Property data is an enhanced version of the CoreLogic Tax History data. The CoreLogic SDP Historical Property data contains almost all of the variables that were included in the CoreLogic Tax History data, as well as additional property-level characteristics.

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, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic 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 CoreLogic’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 CoreLogic 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 **core_logic_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 CoreLogic Smart Data Platform: Historical Property data compares to legacy data, please see core_logic_legacy_content_mapping.pdf.

Bulk Data Access

Data access is required to view this section.

Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up, but remained stable throughout 2024. In the first quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.