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.

2024 Foreclosure Properties registered with the LAHD from January 1, 2024 through December 31, 2024.

In the second quarter of 2024, the share one-to-four family residential mortgage loans entering the foreclosure process in the U.S. was **** percent. Following the coronavirus pandemic outbreak in 2020, mortgage delinquency rates surged, followed by a gradual decline. Between the second quarter of 2020 and the first quarter of 2022, foreclosures remained at record low levels due to The Coronavirus Aid, Relief, and Economic Security Act (CARES Act).

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 ******* properties, which was about ****** fewer than in 2023. Despite the decrease, 2024 saw one of the lowest foreclosure rates on record.

Active foreclosure properties that are currently on the market (includes Pre-foreclosure Auction and REO properties). This matches the active listings shown on RealtyTrac. Does not include historical foreclosure data.

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.

Financial institutions that have filed an exemption affidavit in 2024 pursuant to HB 2009.

In the second quarter of 2025, the share of mortgage loans in the foreclosure process in the U.S. decreased slightly to **** percent. Following the outbreak of the coronavirus crisis, the mortgage delinquency rate spiked to the highest levels since the subprime mortgage crisis (2007-2010). To prevent further impact on homeowners, Congress passed the CARES Act, which provides foreclosure protections for borrowers with federally backed mortgage loans. As a result, the foreclosure rate fell to historically low levels.

The numbers of foreclosures refer to the year 2024 and are based on data from Swiss cantons.

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.

Abstract

Data describes property foreclosures that occurred between 2012 - June 2024. Includes bidder information, auction announcements, target asset information and auction guidelines.

Methodology

The raw data were wrangled for inclusion in Data Farm. For more information, please see CnOpenData GitLab.

This document contains links to an official list of current-month foreclosure notices in Bexar County, as well at to Bexar County's Interactive Foreclosure Map.PDF LIST OF FORECLOSURE NOTICESINTERACTIVE FORECLOSURE MAP

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 second 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.

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.

The share of non-performing mortgage loans in the United States has declined significantly since the subprime mortgage crisis in 2008. After the burst of the housing bubble, the share of loans which were ** to *** days past due date climbed to *** percent. The fourth quarter of 2010 witnessed the highest rate of loans in foreclosure, bankruptcy, or deed-in-lieu, amounting to **** percent. In the third quarter of 2024, the foreclosure rate stood at *** percent - the lowest figures on record. Meanwhile, the ** to ** days delinquency rate rose to *** percent and the ** to *** days delinquency rate rose to *** percent, showing an uptick in the late mortgage payments.

Repossessions occur when a borrower fails to repay their loan on time or a tenant is late on their rent, and the lender takes possession of the property. To avoid a spike in repossessions during the coronavirus (COVID-19) crisis, the Financial Conduct Authority (FCA) introduced measures for renters and mortgage borrowers. As a result, the number of repossessions fell to a record low in 2020. In the second quarter of 2024, there were *** repossessions of mortgaged homes and ***** repossessions of rental properties by landlords.

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 Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q2 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, commercial, residential, domestic, banks, depository institutions, rate, and USA.

Graph and download economic data for Delinquency Rate on Commercial Real Estate Loans (Excluding Farmland), Booked in Domestic Offices, All Commercial Banks (DRCRELEXFACBS) from Q1 1991 to Q2 2025 about farmland, domestic offices, delinquencies, real estate, commercial, domestic, loans, banks, depository institutions, rate, and USA.