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

%3C!-- --%3E

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.

Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q2 2025 about homeownership, housing, rate, and USA.

This data publication contains 250 meter raster data depicting the spatial distribution of forest ownership types in the conterminous United States. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local; three types of private: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, and unincorporated partnerships and associations); as well as Native American tribal lands. The most up-to-date data available were used in creating this data publication. A plurality of the ownership data were from 2014, but some data were as old as 2004.

United States Home Ownership Rate: West data was reported at 60.200 % in Sep 2018. This records an increase from the previous number of 59.700 % for Jun 2018. United States Home Ownership Rate: West data is updated quarterly, averaging 59.900 % from Mar 1964 (Median) to Sep 2018, with 219 observations. The data reached an all-time high of 65.300 % in Sep 2006 and a record low of 57.200 % in Dec 1983. United States Home Ownership Rate: West data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB008: Housing Vacancy and Home Ownership Rate.

These data were created from existing federal fishing permit databases maintained by GARFO. Fishing permit numbers are assigned an affiliation identification number which combines permits into owner groups depending on who is listed as the permit owner and what other permits these individuals may own (or partially own). Revenue information is also included.

United States Homeownership Rate: 35 to 39 Years data was reported at 56.400 % in 2017. This records an increase from the previous number of 55.300 % for 2016. United States Homeownership Rate: 35 to 39 Years data is updated yearly, averaging 63.500 % from Dec 1982 (Median) to 2017, with 36 observations. The data reached an all-time high of 67.600 % in 1982 and a record low of 55.300 % in 2016. United States Homeownership Rate: 35 to 39 Years data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB008: Housing Vacancy and Home Ownership Rate.

The Skilled Nursing Facility (SNF) Change of Ownership (CHOW) - Owner Information dataset provides information on individual and organizational ownership interest and managerial control associated with the buyer and seller organizations, role of the owner, association date, address of the organizational owner and other ownership details.

All property addresses for City of Kitchener, which includes business names.All addresses are provided and only addresses not covered under MFIPPA are shown. MFIPPA is Ontario Municipal Freedom of Information and Protection of Privacy Act - The Act requires that local government institutions protect the privacy of an individual's personal information existing in government records. Only the owner of a business (or number company) or government agency can be shown, and all other addresses are marked as private.

Parcels and Land Ownership dataset current as of unknown. York County, South Carolina Parcel Database.

description: Parcels and Land Ownership dataset current as of 2008. Parcel and Ownership Data.; abstract: Parcels and Land Ownership dataset current as of 2008. Parcel and Ownership Data.

This dataset provides values for HOME OWNERSHIP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Russia Fixed Capital Investment: Annual: Domestic Ownership data was reported at 15,056.900 RUB bn in 2018. This records an increase from the previous number of 13,426.800 RUB bn for 2017. Russia Fixed Capital Investment: Annual: Domestic Ownership data is updated yearly, averaging 3,385.168 RUB bn from Dec 1993 (Median) to 2018, with 26 observations. The data reached an all-time high of 15,056.900 RUB bn in 2018 and a record low of 26.461 RUB bn in 1993. Russia Fixed Capital Investment: Annual: Domestic Ownership data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Investment – Table RU.OB002: Fixed Capital Investment: by Structure, Ownership.

Following an Order Instituting Rulemaking initiated in October 2005, amendments adopted by the Energy Commission and approved by California's Office of Administrative Law in July 2007 created two articles: Article 1, known as Quarterly Fuel and Energy Report (QFER) directed at current California energy information, and Article 2 directed at the forecast and assessment of energy loads and resources. The regulations under QFER provide for the collection of energy data relating to electric generation, control area exchanges, and natural gas processing and deliveries. The reports are submitted on forms specified by the Energy Commission's executive director.

The statistics presented here are derived from the QFER CEC-1304 Power Plant Owner Reporting Form. The CEC-1304 reporting form collects data from power plants with a total nameplate capacity of 1MW or more that are located within California or within a control area with end users inside California. The information includes gross generation, net generation, fuel use by fuel type for each generator, as well as total electricity consumed on site and electricity sales for the plant as a whole. Power plants with nameplate capacity of 20 megawatts or more also provide environmental information related to water supply and water/wastewater discharge.

Database and Source Files updated: June 07, 2017

ForestOwn_v1 is a 250-meter spatial resolution raster geospatial dataset of forest ownership of the conterminous United States (CONUS). The dataset was prepared by the Forest Inventory and Analysis (FIA) program, Northern Research Station, Forest Service, United States Department of Agriculture (USDA), and differentiates forest from non-forest land and water, public and private ownership, and the percent of private forest land in corporate ownership. The forest/non-forest land/water classification is derived from the USDA Forest Service's CONUS Forest/Nonforest dataset. Public and private land ownership class is derived from the Protected Areas Database of the United States, Version 1.1 (CBI Edition). Corporate ownership of private forest land is derived from the Forest Service's 2007 Resources Planning Act (RPA) dataset, summarized over the Environmental Protection Agency's Original Environmental Monitoring & Assessment Program (EMAP) grid 648 square kilometer hexagon dataset.The ForestOwn_v1 dataset is designed for conducting geospatial analyses and for producing cartographic products over regional to national geographic extents.A corresponding Research Map (RMAP) has been produced to cartographically portray this dataset.

Original metadata date was 02/09/2011. Minor metadata updates were made on 05/10/2013, 04/16/2014, 12/21/2016, and 02/06/2017. Additional minor metadata updates were made on 04/20/2023.

On 07/23/2020 a newer version of these data became available (Sass et al. 2020).

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Northwest Territories, Yukon, and Nunavut (representing approximately 0.3 percent of the Canadian population) were excluded.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Canada is 1007.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Data and code to reproduce all tables and figures

This dataset contains the owner information for all the accounts listed in the Business License Dataset, and is sorted by Account Number. To identify the owner of a business, you will need the account number or legal name, which may be obtained from theBusiness Licenses dataset: https://data.cityofchicago.org/dataset/Business-Licenses/r5kz-chrr. Data Owner: Business Affairs & Consumer Protection. Time Period: 2002 to present. Frequency: Data is updated daily.

This dataset tracks the updates made on the dataset "Ownership Payment Data – Detailed Dataset 2014 Reporting Year" as a repository for previous versions of the data and metadata.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Congo, Rep. is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Kazakhstan is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.