Graph and download economic data for 30-Year Fixed Rate FHA Mortgage Index (OBMMIFHA30YF) from 2017-01-03 to 2025-07-10 about FHA, 30-year, fixed, mortgage, rate, indexes, and USA.

Guyana Google Search Trends: Economic Measures: Mortgage Loan data was reported at 2.000 Score in 14 May 2025. This records an increase from the previous number of 1.000 Score for 13 May 2025. Guyana Google Search Trends: Economic Measures: Mortgage Loan data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 51.000 Score in 27 Jun 2022 and a record low of 0.000 Score in 11 May 2025. Guyana Google Search Trends: Economic Measures: Mortgage Loan data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Guyana – Table GY.Google.GT: Google Search Trends: by Categories.

Online searches for mortgages have dramatically increased during the coronavirus pandemic as many people in the United States are struggling to make payments on their debts amidst record levels of unemployment. Between March and August 2020, "Rocket mortgage" was the most-searched for financial institution in regard to mortgages in the United States, generating an average of 591,000 monthly searches.

Japan Google Search Trends: Economic Measures: Mortgage Loan data was reported at 21.000 Score in 14 May 2025. This records a decrease from the previous number of 24.000 Score for 13 May 2025. Japan Google Search Trends: Economic Measures: Mortgage Loan data is updated daily, averaging 24.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 91.000 Score in 19 Mar 2024 and a record low of 0.000 Score in 31 May 2023. Japan Google Search Trends: Economic Measures: Mortgage Loan data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Japan – Table JP.Google.GT: Google Search Trends: by Categories.

Explore the historical Whois records related to mortgage-partners.net (Domain). Get insights into ownership history and changes over time.

Mortgage rates increased at a record pace in 2022, with the 10-year fixed mortgage rate doubling between March 2022 and December 2022. With inflation increasing, the Bank of England introduced several bank rate hikes, resulting in higher mortgage rates. In May 2025, the average 10-year fixed rate interest rate reached **** percent. As borrowing costs get higher, demand for housing is expected to decrease, leading to declining market sentiment and slower house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold declined in 2023, reaching just above *** million. Despite the number of transactions falling, this figure was higher than the period before the COVID-19 pandemic. The falling transaction volume also impacted mortgage borrowing. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans fell year-on-year for five straight quarters in a row. How are higher mortgages affecting homebuyers? Homeowners with a mortgage loan usually lock in a fixed rate deal for two to ten years, meaning that after this period runs out, they need to renegotiate the terms of the loan. Many of the mortgages outstanding were taken out during the period of record-low mortgage rates and have since faced notable increases in their monthly repayment. About **** million homeowners are projected to see their deal expire by the end of 2026. About *** million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026.

The Pre-1990 HMDA Aggregation Data were prepared annually during this period by the FFIEC on behalf of institutions reporting HMDA data. The Aggregation Data consists of home purchase and home improvement loans that a depository institution originated or purchased during each calendar year. The collected HMDA data were individually aggregated up to the tract level by the reporting depository institution and submitted accordingly to the FFIEC. Individual records are the summary of loan activity for the specified respondent for the indicated census tract except when the census tract numbers were either 888888 or 999999. The 888888 tract records are the sum of all loan activity by the reporter outside of the MSA being reported, but not appearing in any other MSA report. The 999999 tract records are the consolidated county summary data for loans made in untracted counties or counties with 1980 total population less than 30,000. The 1988 and 1989 Aggregation Data files include aggregated data from nondepository institutions, specifically mortgage banking subsidiaries of bank holding companies.

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

Iran Google Search Trends: Economic Measures: Mortgage Loan data was reported at 43.000 Score in 15 May 2025. This records a decrease from the previous number of 49.000 Score for 14 May 2025. Iran Google Search Trends: Economic Measures: Mortgage Loan data is updated daily, averaging 40.000 Score from Dec 2021 (Median) to 15 May 2025, with 1262 observations. The data reached an all-time high of 100.000 Score in 11 Apr 2023 and a record low of 0.000 Score in 07 Apr 2023. Iran Google Search Trends: Economic Measures: Mortgage Loan data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Iran – Table IR.Google.GT: Google Search Trends: by Categories.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company MLD-Mortgage-Inc.

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.

Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name Mortgage Bankers Association of America.

Russia Google Search Trends: Economic Measures: Mortgage Loan data was reported at 18.000 Score in 14 May 2025. This records a decrease from the previous number of 19.000 Score for 13 May 2025. Russia Google Search Trends: Economic Measures: Mortgage Loan data is updated daily, averaging 14.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 77.000 Score in 15 Aug 2023 and a record low of 0.000 Score in 23 Mar 2023. Russia Google Search Trends: Economic Measures: Mortgage Loan data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Russian Federation – Table RU.Google.GT: Google Search Trends: by Categories.

Explore the historical Whois records related to mortgage-comparison-7531214.zone (Domain). Get insights into ownership history and changes over time.

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address online-home-mortgage.net@domainsbyproxy.com..

We use nationwide deed-level records on home foreclosures to examine the effects of economic distress on electoral outcomes and individual voter turnout. County-level difference-in-differences estimates show that counties that suffered larger increases in foreclosures did not punish or reward members of the incumbent president’s party more than less affected counties. Linking the Ohio voter file to individual foreclosures, difference-in-differences estimates show that individuals whose homes were foreclosed on were less likely to turn out, rather than being mobilized. However, in 2016 counties more exposed to foreclosures supported Trump at substantially higher rates. Taken together, the evidence suggests that the effect of local economic distress on incumbent performance is generally close to zero and only becomes substantial in unusual circumstances.

Mexico Google Search Trends: Economic Measures: Mortgage Loan data was reported at 15.000 Score in 14 May 2025. This records a decrease from the previous number of 16.000 Score for 13 May 2025. Mexico Google Search Trends: Economic Measures: Mortgage Loan data is updated daily, averaging 11.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 32.000 Score in 19 Jan 2025 and a record low of 0.000 Score in 16 Mar 2023. Mexico Google Search Trends: Economic Measures: Mortgage Loan data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Mexico – Table MX.Google.GT: Google Search Trends: by Categories.

Lithuania Google Search Trends: Economic Measures: Mortgage Loan data was reported at 12.000 Score in 14 May 2025. This records an increase from the previous number of 9.000 Score for 13 May 2025. Lithuania Google Search Trends: Economic Measures: Mortgage Loan data is updated daily, averaging 5.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 31.000 Score in 02 Nov 2022 and a record low of 0.000 Score in 31 Dec 2024. Lithuania Google Search Trends: Economic Measures: Mortgage Loan data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Lithuania – Table LT.Google.GT: Google Search Trends: by Categories.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.