This statistic shows the foreclosure filings in the United States as of June 2017, by state. South Dakota had the lowest rate with only *** in every 24,583 housing units being subject to foreclosure.

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.

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/.

Financial institutions that have filed an exemption for participation in foreclosure mediation in 2017, pursuant to SB 558.

The percentage of properties where the lending company or loan servicer has filed a foreclosure proceeding with the Baltimore City Circuit Court out of all residential properties within an area. This is not a measure of actual foreclosures since not every property that receives a filing results in a property dispossession. Source: Baltimore City Circuit Court Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020

2017 Foreclosure Properties registered with the LAHD from January 1, 2017 through December 31, 2017.

The portion of the homes and condominiums sold that were identified as being owned by the bank (REO) out of all residential properties sold in a calendar year. Source: RBIntel Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

The percentage of properties where the lending company or loan servicer has filed a foreclosure proceeding with the Baltimore City Circuit Court out of all residential properties within an area. This is not a measure of actual foreclosures since not every property that receives a filing results in a property dispossession. Source: Baltimore City Circuit Court Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020

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.

The data presented here is in beta form because only the most recent filing type is included as of September 2017. Monthly updates are forthcoming. It is also our intent to include a record for each filing type for each docket if the data becomes available.

Between 2015 and 2017, Memphis, Tennessee had the highest eviction rate at *** percent. The metropolitan areas with the next highest eviction rates were Phoenix (Arizona), Atlanta (Georgia), Indianapolis (Indiana) and Dallas (Texas) in that period.

Why do evictions occur? Eviction rate refers to the share of renters who are legally removed from a rental property by their landlord, because rent is overdue, the tenant has breached a condition of the rental agreement or for other legally permitted reasons.

Higher rates in the South and Midwest Eviction rates tend to be higher in the South and Midwest of the country, because median incomes are low and foreclosure rates are high. Vacancy rates are consistently higher in the South and Midwest than in the Northeast and West, which means that landlords cannot afford to be as picky when choosing a tenant in the South and Midwest. Tenants who struggle to pay their rent have a much lower chance of being chosen as tenant in the more competitive rental markets, which also keeps the eviction rates lower in those areas.

Thailand EXIM: Assets: Properties Foreclosed: Net data was reported at 804.743 THB mn in 2017. This records a decrease from the previous number of 845.480 THB mn for 2016. Thailand EXIM: Assets: Properties Foreclosed: Net data is updated yearly, averaging 860.292 THB mn from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 1,215.928 THB mn in 2009 and a record low of 804.743 THB mn in 2017. Thailand EXIM: Assets: Properties Foreclosed: Net data remains active status in CEIC and is reported by Export-Import Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KB039: Balance Sheet: Export-Import Bank of Thailand.

Thailand Sec Co: Assets: OA: Properties Foreclosed data was reported at 65.392 THB mn in Mar 2018. This stayed constant from the previous number of 65.392 THB mn for Dec 2017. Thailand Sec Co: Assets: OA: Properties Foreclosed data is updated quarterly, averaging 78.611 THB mn from Mar 2011 (Median) to Mar 2018, with 29 observations. The data reached an all-time high of 110.270 THB mn in Sep 2011 and a record low of 65.392 THB mn in Mar 2018. Thailand Sec Co: Assets: OA: Properties Foreclosed data remains active status in CEIC and is reported by Securities and Exchange Commission. The data is categorized under Global Database’s Thailand – Table TH.Z021: Securities Company Statistics.

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.

The are several factors that can accumulate in the repossession of a home, the most common reason for being mortgage arrears. This occurs when borrowers can no longer make the mortgage repayments. Mortgage lenders will repossess the home to sell to recover the money owed. In 2023, between *** and *** homes in England were repossessed monthly. In Wales, this figure ranged between ** and **. Which regions saw the most repossessions? The North West recorded the highest number of repossessions in 2023. Conversely, the East of England, South West, East Midlands, and Wales had the lowest number of repossessions. London and South East, the regions with the highest average earnings, ranked in the middle. Mortgage arrears on the rise Mortgage arrears in the UK have increased quarter-on-quarter since the third quarter of 2022, showing that homebuyers are increasingly struggling to meet their monthly obligations. Borrowers who missed a mortgage payment were highly likely to also fall behind on other financial commitments, with credit card debt being the most common one.

Thailand GHB: Assets: Properties Foreclosed: Net data was reported at 7,707.536 THB mn in 2017. This records an increase from the previous number of 5,177.444 THB mn for 2016. Thailand GHB: Assets: Properties Foreclosed: Net data is updated yearly, averaging 5,617.511 THB mn from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 9,812.770 THB mn in 2008 and a record low of 3,921.743 THB mn in 2014. Thailand GHB: Assets: Properties Foreclosed: Net data remains active status in CEIC and is reported by Government Housing Bank. The data is categorized under Global Database’s Thailand – Table TH.KB038: Balance Sheet: Government Housing Bank.

PT Bank Chinatrust Indonesia: Non-Productive Asset: Foreclosed Assets data was reported at 312.963 IDR bn in Mar 2017. PT Bank Chinatrust Indonesia: Non-Productive Asset: Foreclosed Assets data is updated monthly, averaging 312.963 IDR bn from Mar 2017 (Median) to Mar 2017, with 1 observations. The data reached an all-time high of 312.963 IDR bn in Mar 2017 and a record low of 312.963 IDR bn in Mar 2017. PT Bank Chinatrust Indonesia: Non-Productive Asset: Foreclosed Assets data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Banking Sector – Table ID.KBI005: Joint Venture: Assets and Liabilities: PT Bank Chinatrust Indonesia.

Indonesia PT Bank Multi Arta Sentosa: Foreclosed Assets data was reported at 0.375 IDR bn in Sep 2017. This stayed constant from the previous number of 0.375 IDR bn for Aug 2017. Indonesia PT Bank Multi Arta Sentosa: Foreclosed Assets data is updated monthly, averaging 1.976 IDR bn from Jan 2010 (Median) to Sep 2017, with 93 observations. The data reached an all-time high of 5.107 IDR bn in Mar 2011 and a record low of 0.375 IDR bn in Sep 2017. Indonesia PT Bank Multi Arta Sentosa: Foreclosed Assets data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Banking Sector – Table ID.KBD015: Non Foreign Exchange Bank: Assets and Liabilities: PT Bank Multi Arta Sentosa.

Thailand FIDF: Assets: Properties Foreclosed data was reported at 2.600 THB mn in 2017. This stayed constant from the previous number of 2.600 THB mn for 2016. Thailand FIDF: Assets: Properties Foreclosed data is updated yearly, averaging 646.810 THB mn from Sep 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 3,446.190 THB mn in 2004 and a record low of 2.400 THB mn in 2015. Thailand FIDF: Assets: Properties Foreclosed data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KB035: Balance Sheet: Financial Institutions Development Fund.

Thailand SME: Assets: Properties Foreclosed: Net data was reported at 1,332.015 THB mn in 2017. This records a decrease from the previous number of 1,370.459 THB mn for 2016. Thailand SME: Assets: Properties Foreclosed: Net data is updated yearly, averaging 1,107.508 THB mn from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 1,370.459 THB mn in 2016 and a record low of 349.188 THB mn in 2008. Thailand SME: Assets: Properties Foreclosed: Net data remains active status in CEIC and is reported by Small and Median Enterprise Development Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KB040: Balance Sheet: Small and Medium Enterprise Development Bank.

Thailand CB: MB: Assets: Properties Foreclosed data was reported at 1,331.000 THB mn in Mar 2018. This records an increase from the previous number of 1,322.000 THB mn for Dec 2017. Thailand CB: MB: Assets: Properties Foreclosed data is updated quarterly, averaging 2,542.000 THB mn from Mar 2011 (Median) to Mar 2018, with 29 observations. The data reached an all-time high of 4,867.000 THB mn in Mar 2011 and a record low of 1,102.000 THB mn in Sep 2016. Thailand CB: MB: Assets: Properties Foreclosed data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KB072: Assets and Liabilities: Average: Commercial Bank: Local.