General Services Administration Owned Properties This National Geospatial Data Asset (NGDA) dataset, shared as a General Services Administration (GSA) feature layer, displays federal government owned properties in the United States, Puerto Rico, Northern Mariana Islands, U.S. Virgin Islands, Guam and American Samoa. Per GSA, it is “the nation’s largest public real estate organization, provides workspace for over one million federal workers. These employees, along with government property, are housed in space owned by the federal government and in leased properties including buildings, land, antenna sites, etc. across the country.” Federally owned buildings in downtown DC Data currency: Current federal service (FC_IOLP_BLDG))NGDAID: 133 (Inventory of Owned and Leased Properties (IOLP))OGC API Features Link: Not AvailableFor more information: Real EstateFor feedback please contact: Esri_US_Federal_Data@esri.com NGDA Data Set This data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes." For other NGDA Content: Esri Federal Datasets

The Property Digital Market was valued at USD 21.1 Billion in 2025 and is projected to grow to USD 45 Billion by 2035, at a CAGR of 7.9%. Property Digital Market Overview: The Property Digital Market Size was valued at 19.6 USD Billion in 2024. The Property Digital Market is expected to grow from 21.1 USD Billion in 2025 to 45 USD Billion by 2035. The Property Digital Market CAGR (growth rate) is expected to be around 7.9% during the forecast period (2025 - 2035). Key Property Digital Market Trends Highlighted The Global Property Digital Market is experiencing significant shifts driven by technology advancements and evolving consumer preferences. One of the key market drivers is the increasing adoption of digital platforms in real estate transactions, which enhances transparency and efficiency. Property owners and buyers now prefer online services for facilitating virtual tours, digital documentation, and online payments, streamlining the user experience. Moreover, the rise in adoption of artificial intelligence and blockchain technology is transforming how property records are maintained and transactions are conducted, increasing trust and security in the process.Opportunities are emerging for companies that harness data analytics to offer value-added services, such as predictive property price trends or personalized property recommendations, catering to the unique needs of consumers. The growing trend of remote work and digital nomadism has also created demand for flexible property solutions, pushing businesses to innovate in the rental and sales sectors. In recent times, the shift towards sustainability in real estate has gained momentum, with more consumers seeking eco-friendly properties. This trend has prompted property developers to integrate green building practices and energy-efficient technologies into new projects, responding to a global push for environmental responsibility.Additionally, government regulations and incentives aimed at promoting digitization in the property sector are fostering growth, paving the way for a more interconnected real estate market. As digital transformations continue, the Global Property Digital Market is poised for sustained growth, with expectations of achieving a significant revenue milestone in the coming years. Source: Primary Research, Secondary Research, WGR Database and Analyst Review Property Digital Market Segment Insights: Property Digital Market Regional Insights The Regional segmentation of the Global Property Digital Market reveals significant insights into its dynamics across various regions. North America is the leading sector, holding a predominant position in the market, with a valuation of 8 USD Billion in 2024 and an anticipated growth to 18 USD Billion in 2035. This region benefits from advanced technology adoption and a strong emphasis on digital transformation in property management. Europe shows steady expansion, driven by increasing digitalization in the real estate sector and regulatory support for technology integration.In the APAC region, there is moderate increase in market activity as emerging economies invest in digital property solutions, enhancing efficiency and market penetration. South America reflects positive trends as local businesses recognize the value of digital platforms for property transactions, even though growth is more gradual compared to other regions. The MEA region is experiencing a significant rise in investment towards property digitalization, as demand for innovative technology solutions continues to grow. The diverse growth trends across various regions highlight the importance of tailoring strategies to local market conditions and leveraging opportunities presented by the ongoing digital revolution in real estate. Source: Primary Research, Secondary Research, WGR Database and Analyst Review North America : The North American Property Digital Market is propelled by advancements in AIoT and urban surveillance technologies, enhancing home automation and security. The real estate sector increasingly utilizes AI for predictive analytics, with the Digital Infrastructure Act

Title: Locked in the Ledger: Legal Identity, Colonial Persistence, and the Politics of Real Estate Reform in Latin America
Authors:

• Scott M. Brown, University of Puerto Rico (scott.brown@upr.edu)

• Daniel J. Hall, Texas Tech University (halldanielj@gmail.com)

• Stefan Holgersson, Linköping University (stefan.holgersson@liu.se)

Description:
This dataset accompanies the article “Locked in the Ledger: Legal Identity, Colonial Persistence, and the Politics of Real Estate Reform in Latin America”, which examines how legacy legal structures, notarial monopolies, and institutional exclusion impede property system reform in Latin America. Focusing on Puerto Rico and Mexico, and comparing them with advanced cadastral systems in Sweden and Germany, the paper argues that effective real estate governance hinges less on legal origin than on inclusive political institutions and administrative openness.

The dataset includes merged panel data from the Varieties of Democracy (V-Dem), the International Property Rights Index (IPRI), the World Governance Indicators (WGI), and the World Bank’s Ease of Doing Business (EODB) indicators. These are used to test hypotheses related to democratic institutions, legal formalism, and property system performance.

Included are cleaned .xlsx files used for statistical modeling, with variables such as judicial constraints (v2x_jucon), freedom of expression (v2x_freexp), and polyarchy (v2x_polyarchy), alongside outcomes such as “Registering Property” (EODB), “Registering Process” (IPRI), and political stability scores (WGI). A reproducible Python script in Google Colab is also provided for OLS regression modeling and variance inflation diagnostics.

Citation:
Brown, S. M., Hall, D. J., & Holgersson, S. (2025). Locked in the Ledger: Legal Identity, Colonial Persistence, and the Politics of Real Estate Reform in Latin America [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15072375

Keywords:
Property Rights, Land Governance, Legal Reform, Notarial Monopoly, Institutional Theory, Cadastral Systems, Real Estate Markets, Puerto Rico, Mexico, Comparative Law, Postcolonial Institutions

License: CC BY 4.0

Contents:

• merged_vdem_ipri_2024.xlsx – V-Dem + IPRI merged panel

• vdem_rankings_2020_merged_WB_EODB.xlsx – V-Dem + EODB merged panel

• pv.xlsx – V-Dem + WGI panel

• cadastre_regression_script.ipynb – Python (Google Colab) script for regression models and VIF analysis

OverviewThis Feature Service provides detailed geographic information on state-owned lands within Tennessee. It represents parcels of land managed by various departments and divisions of the Tennessee state government.Feature LayersStateOwnedLands Points: This layer contains point locations of state-owned land parcels, including key attributes such as property name, managing jurisdiction, and legislative district information. The points also provide latitude and longitude coordinates which represent parcel centroids.StateOwnedLands Polygons: This polygon layer depicts the spatial extent of state-owned land parcels, allowing for analysis of property boundaries and calculated land areas. Attributes include property names, jurisdictional management, stream protection status, and legislative district names.AttributionProperty ID: Unique identifier for each record.GISLINK: An ID to match records to specific parcels. property_name: Should match the language used by the organization that manages the property.dv_id_juris: The department of Tennessee government that manages the property.stream_status: Determined by the team that manages the property. Higher education property is not assessed by STREAM.Legal Interest: Owned or LeasedCounty_Name: The county the center of the property falls within.House Name: The representative whose district the center of the property falls within.Senate Name: The senator whose district the center of the property falls within.Geographic CoverageThe dataset exclusively covers the state of Tennessee, incorporating parcel data sourced from county tax assessors and internal state databases. It excludes easements, but contains a growing number of leases and rights-of-way objects.Purpose and Use CasesThis data supports asset management and planning for Tennessee state land holdings.It is useful for environmental management, policy making, and legislative referencing for state properties.The layers can be applied in mapping projects, public information systems, and spatial analyses related to state land use and jurisdiction.CreditDepartment of General Services, State of Tennessee Real Estate Asset Management, Portfolio and Performance Analytics

These datasets are published as part of the requirements on data transparency and are refreshed on the first of the month. This dataset provides information on the government estate, including various property related characteristics such as: location, ownership, size, tenure and type of property. The scope of the data includes land and property information for UK central government departments and their arms length bodies including non-ministerial departments, executive agencies, non-departmental public bodies and special health authorities. Whilst these assets are primarily located in the UK,some are located overseas. Some properties may have more than one entry in the data extract as the government has more than one ‘interest’ in that property. For example, there may be two or more government occupiers in the same property. It also provides information about the ‘holding’ government department and, if relevant, the arm’s length body of the department responsible for the property. This dataset contains non sensitive information on the government estate e.g. commercially sensitive contract data is not published. The dataset also excludes property records that are classed as sensitive e.g. for national security purposes. All data provided via these data sets are as reported to the Cabinet Office by the holding departments. Property and Contracts This dataset covers properties and their associated contracts. A property may have more than one contract associated with it. This data set includes information such as Ownership, Location, Size, Usage, Asset type (Building or Land), Contract Name and Contracted Organisation. Building Properties can be made up of one or more buildings and are linked to the property via a property reference. Characteristics such as Building Ownership, Location, Floor Area, Usage, Size and Construction Date are recorded and this entity is linked to the property via the property reference. Land Whilst properties can be made up of Building(s) and Land they can also refer exclusively to Land only. Land records include information on Ownership, Location, Size and Usage and this entity is linked to the property via the property reference. Occupation Occupations highlight which organisations reside within a given property. The following types of information about occupying organisations is recorded: organisation, location, asset type(e.g. Land, Building), size of the occupation (floor area), type of agreement (e.g. sub-let) and the usage (e.g. Office, Court). Surplus Property When a property is no longer required for the purposes of the organisation that currently holds the asset, it is then designated as being Surplus. These can then be made available for disposal which involves the transfer of a freehold or leasehold by way of sale or other agreement. Data such as Ownership, Location, Size, Usage and Contact Information is recorded for surplus property. Vacant Space To facilitate better utilisation of the estate; where space is available in properties these can be marked as such and made available to other government departments for co-location purposes. This data set contains Ownership, Location, Size, Information about the Space, and Contact Details.

General Services Administration Owned Properties This National Geospatial Data Asset (NGDA) dataset, shared as a General Services Administration (GSA) feature layer, displays federal government owned properties in the United States, Puerto Rico, Northern Mariana Islands, U.S. Virgin Islands, Guam and American Samoa. Per GSA, it is “the nation’s largest public real estate organization, provides workspace for over one million federal workers. These employees, along with government property, are housed in space owned by the federal government and in leased properties including buildings, land, antenna sites, etc. across the country.” Federally owned buildings in downtown DC Data currency: Current federal service (FC_IOLP_BLDG))NGDAID: 133 (Inventory of Owned and Leased Properties (IOLP))OGC API Features Link: Not AvailableFor more information: Real EstateFor feedback please contact: Esri_US_Federal_Data@esri.com NGDA Data Set This data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes." For other NGDA Content: Esri Federal Datasets

The statistical analysis of cases of improvement and utilization of state-owned non-public immovable properties by the target project authority in the administrative area and the area of state-owned land (statistical cut-off date: December 111)."

Abstract: The processes starting with the identification and registration of treasury properties have an essential place in the cadastral systems. Spatial data modelling studies were conducted in 2002 to establish a common standard structure on the fundamental similarities of land management systems. These studies were stated as a beginning named Core Cadastral Domain Model (CCDM), since 2006, it has been started to be made under the name of LADM. This model was accepted in 2012 as a standard model in the field of land administration by the International Organization for Standardization (ISO). In this study, an external model class is proposed for LADM’s transactions related to Treasury’s real estates properties which are related National Property Automation Project (MEOP). In order to determine the deficiency of this current external model, databases containing records related to spatial data and property rights were examined, and the deficiencies related to transactions on treasury properties were determined. The created external class is associated with the LADM’s LA_Party, LA_RRR, LA_SpatialUnit and LA_BAUnit master classes. Herewith the standardization of the external data model is ensured. If the external model is implemented by the responsible standardization of the archiving processes will be more comfortable and faster to register.

OverviewThis Feature Service provides detailed geographic information on state-owned lands within Tennessee. It represents parcels of land managed by various departments and divisions of the Tennessee state government.Feature LayersStateOwnedLands Points: This layer contains point locations of state-owned land parcels, including key attributes such as property name, managing jurisdiction, and legislative district information. The points also provide latitude and longitude coordinates which represent parcel centroids.StateOwnedLands Polygons: This polygon layer depicts the spatial extent of state-owned land parcels, allowing for analysis of property boundaries and calculated land areas. Attributes include property names, jurisdictional management, stream protection status, and legislative district names.AttributionProperty ID: Unique identifier for each record.GISLINK: An ID to match records to specific parcels. property_name: Should match the language used by the organization that manages the property.dv_id_juris: The department of Tennessee government that manages the property.stream_status: Determined by the team that manages the property. Higher education property is not assessed by STREAM.Legal Interest: Owned or LeasedCounty_Name: The county the center of the property falls within.House Name: The representative whose district the center of the property falls within.Senate Name: The senator whose district the center of the property falls within.Geographic CoverageThe dataset exclusively covers the state of Tennessee, incorporating parcel data sourced from county tax assessors and internal state databases. It excludes easements, but contains a growing number of leases and rights-of-way objects.Purpose and Use CasesThis data supports asset management and planning for Tennessee state land holdings.It is useful for environmental management, policy making, and legislative referencing for state properties.The layers can be applied in mapping projects, public information systems, and spatial analyses related to state land use and jurisdiction.CreditDepartment of General Services, State of Tennessee Real Estate Asset Management, Portfolio and Performance Analytics

General Services Administration Owned Properties This National Geospatial Data Asset (NGDA) dataset, shared as a General Services Administration (GSA) feature layer, displays federal government owned properties in the United States, Puerto Rico, Northern Mariana Islands, U.S. Virgin Islands, Guam and American Samoa. Per GSA, it is “the nation’s largest public real estate organization, provides workspace for over one million federal workers. These employees, along with government property, are housed in space owned by the federal government and in leased properties including buildings, land, antenna sites, etc. across the country.” Federally owned buildings in downtown DC Data currency: Current federal service (FC_IOLP_BLDG))NGDAID: 133 (Inventory of Owned and Leased Properties (IOLP))OGC API Features Link: Not AvailableFor more information: Real EstateFor feedback please contact: Esri_US_Federal_Data@esri.com NGDA Data Set This data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes." For other NGDA Content: Esri Federal Datasets

Discover the booming US real estate industry solutions market! Our analysis reveals a projected $30-35 billion market by 2033, driven by PropTech innovation, AI, and increasing demand for efficient property management tools. Learn key trends and growth projections now! Recent developments include: January 2022: CBRE Group announced it acquired Buildingi, a leading provider of occupancy planning and technology services, to meet growing occupier demand for holistic occupancy management services. Buildingi will fully integrate with CBRE's Occupancy Management team and initially transition to Buildingi from CBRE. Buildingi provides space utilization data management and Computer-Aided Design (CAD) services that help to underpin CBRE's occupancy management offering., January 2022: Long & Foster Real Estate expanded its market-leading presence in Richmond, joining forces with local franchise Dew Realty. Founded in 1978 and currently led by Bob Flanagan, Trey Flanagan, Lou Flanagan, and Sharon Coleman, Dew Realty specializes in residential resale, new construction, land, relocation, and commercial sales throughout Central Virginia.. Notable trends are: Increase in Demand for Facility Management.

The Rural Real Estate Appraisal Service Market was valued at USD 2024.5 Million in 2025 and is projected to grow to USD 3000 Million by 2035, at a CAGR of 4%. Rural Real Estate Appraisal Service Market Overview: The Rural Real Estate Appraisal Service Market Size was valued at 1,946.6 USD Million in 2024. The Rural Real Estate Appraisal Service Market is expected to grow from 2,024.5 USD Million in 2025 to 3,000 USD Million by 2035. The Rural Real Estate Appraisal Service Market CAGR (growth rate) is expected to be around 4.0% during the forecast period (2025 - 2035). Key Rural Real Estate Appraisal Service Market Trends Highlighted The Global Rural Real Estate Appraisal Service Market is experiencing significant growth driven by various factors. One of the key market drivers is the increasing demand for accurate property valuation as rural areas undergo transformation with new developments. The continued migration of urban populations to rural settings, alongside the growth of agricultural investments, is further enhancing the need for professional appraisal services to cater to diverse clientele. Moreover, government initiatives aimed at promoting rural development and improving infrastructure are creating a conducive environment for property transactions, thus fueling the demand for appraisal services.There are opportunities to be explored, particularly in utilizing technology for appraisal processes. The integration of advanced data analytics, remote sensing, and geographic information systems can improve the accuracy and efficiency of rural property assessments. As rural economies evolve, there is a untapped potential for developing tailored appraisal methodologies that cater specifically to unique rural characteristics and market conditions, offering significant competitive advantages for service providers. Recent trends indicate a shift towards more sustainable practices and green building assessments in the rural real estate market. Stakeholders are increasingly recognizing the importance of environmental considerations in property appraisals.Furthermore, the rise of online platforms and mobile applications is transforming how rural real estate appraisals are conducted, making access easier for users while streamlining the appraisal process. This digitization trend aligns well with global advancements in technology, propelling the industry into a more efficient future. Overall, these trends collectively work towards enhancing service quality and improving market dynamics in the Global Rural Real Estate Appraisal Service Market. Source: Primary Research, Secondary Research, WGR Database and Analyst Review Rural Real Estate Appraisal Service Market Segment Insights: Rural Real Estate Appraisal Service Market Regional Insights The Global Rural Real Estate Appraisal Service Market showcases a diverse regional segmentation, where North America dominates with a valuation of 800 USD Million in 2024 and is projected to reach 1,100 USD Million by 2035. This significant presence can be attributed to a robust real estate market and increasing demand for accurate appraisals in rural areas. Europe follows with steady expansion, driven by regulatory frameworks aiming for improved valuation standards. Meanwhile, the APAC region is experiencing moderate increase as developing economies focus on rural development and land valuation services.South America is witnessing gradual growth as investment in rural infrastructure enhances demand for appraisal services. The Middle East and Africa (MEA) also show potential with emerging markets starting to recognize the importance of accurate land valuations, thus leveraging opportunities in agricultural and rural sectors. Overall, these trends reflect a dynamic landscape where regional demands and challenges shape the Global Rural Real Estate Appraisal Service Market, ultimately contributing to its sustained relevance and growth prospects. Source: Primary Research, Secondary Research, WGR Database and Analyst Review North America : The North American Rural Real Estate Appraisal Service Market is experiencing growth due to advances in AIoT technologies

The Real Estate Asset Management and Consulting industry faces a challenging environment, shaped by limited housing supply, elevated financing costs and profound structural shifts in commercial property markets. Existing home sales stagnated at a 30‑year low of 4.06 million units in 2025, as elevated mortgage rates and high home prices kept buyers sidelined and owners locked into low‑rate loans. This prolonged freeze constrained brokerage and transaction-based revenue but reinforced the appeal of income-driven strategies such as multifamily, single‑family rentals and build‑to‑rent platforms. Asset managers increasingly recalibrate their portfolios toward steady cashflow assets and credit structures that monetize resilient renter demand. At the same time, consultants emphasize operator quality, policy-risk awareness and rate‑sensitive scenario planning. Through the end of 2026, industry revenue has climbed at a CAGR of 1.1% to reach $98.3 billion, including a 1.4% gain in 2026 alone. In commercial real estate, a persistent office glut exerts pressure. National office vacancy climbs above 20.0% as hybrid work models become entrenched, eroding demand for non‑prime buildings and depressing market values. Asset managers are responding with a decisive flight to quality, trimming exposure to Class B and C properties and reallocating capital toward higher‑performing sectors such as data centers and premium office assets. Alternative investments such as private equity, REITs and data‑center‑backed strategies gained traction, generating new fee streams and boosting profit. Concurrently, technology adoption is accelerating, and consultants are deploying AI, AVMs and IoT systems to track valuations, forecast risk and improve property performance, positioning data analytics as a core competency across management teams. Asset managers and consultants will prioritize operational efficiency, property management and disciplined leverage while maintaining a barbell approach between resilient rental markets and selective for‑sale opportunities. Compliance complexity will strengthen with disclosure laws like the Corporate Transparency Act and expanding state housing regulations, spurring investment in legal, digital and compliance infrastructure. Through the five years to 2031, revenue will climb at a CAGR of 2.0% to reach $108.7 billion in 2031.

1. Real estate pre-sale case real price registration information, including target location, area, total price, and other information. 2. This dataset is updated every 10 days. (Shenkeng District)

Overview

These data contain city-owned properties from the eProperties Plus database. The data describe basic aspects of the property (square footage, location, identification, etc.) and its geographically based assignment of various components of city infrastructure (police zone, council district, etc.).

Motivation

These data are useful for anyone (e.g., the URA) in identifying properties that are unused throughout the city.

Collection

These data are taken directly from the city's property management system (EProperty) database, one of several independent systems used by the City to track property-level data.

Preprocessing

The latitude and longitude for these properties have to be geocoded from the parcel identifier (that is, the latitude and longitude are not contained in the source data). The various geographic zones (police zone, etc.) are then reverse geocoded from the derived coordinates. The parcel identifiers in the source data can follow several syntax conventions. The original city-formatted parcel identifier is in the parc field, and the normalized version (using a uniform 16-character format) is in the pin field. The id and census_tract fields are categorical/not intended for mathematical operations and are therefore converted to strings.

Known Uses

The URA uses this data for vacant-property identification.

Recommended Uses

Users can analyze the location of city-owned vacant properties and the property size/classification. These can be used to understand zonal conditions (e.g., neighborhood conditions), concentrations of vacant land spanning zones (across two police zones), etc.

Care should be taken, however, when inferring socioeconomic attributes, city resource allocation, or other higher level variables of interest. There may be a link between vacant land and these factors, but the complexities of city operations and high-order variables can be influenced by lurking/hidden/latent variables. It could be advantageous to consider multivariate models and/or construction of component variables (e.g., using PCA or Factor Analysis).

The city is in the process of publishing other property-based datasets (all city-owned properties, treasury sale properties, condemned properties, and tax-delinquent properties). The city also publishes information on PLI code enforcement violations/permit applications which could be informative of property conditions in a given area.

Known Limitations/Biases

These data describe only city-owned properties (not privately held properties). The city is currently (2023) overhauling its property management databases. These data represent the best descriptions of city-owned vacant properties that are currently available.

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.

This dataset provides a comprehensive list of council owned land and building assets which are deemed corporately owned. Some assets such as investment properties, void properties, and office accommodation are managed on behalf of the whole council by Asset Management (corporate landlord), whilst others which generally support front line services are managed by the Services themselves (tenants).

Definitions and information

• “Lease in” and “Leased in” means a property which the council rents from any external company or organisation
• “Lease out” and “Leased out” means a council property rented out to an external company or organisation
• “Surplus” means that the council has determined that it no longer requires the asset and whilst usually applied to vacant properties this is not necessarily the case
• “ Vacant” means not currently used and it’s future has yet to be determined
• “Vacant temporarily” means one council user has vacated and another council user will be moving in
• “Vacant surplus” means one council user has vacated and it has been determined that the asset is no longer required
• There are around 200 properties without a status, this is because the information was not available at the time when the data was prepared. It is likely that these properties are “Council occupied”
• Improved descriptions and omissions will be addressed at the next revision

In the spirit of openness and transparency, this is a first attempt at producing this list and it is possible that there may be some errors and omissions, the aim however, is to continuously improve on the quality of the data where necessary, in future editions.

Local Government Transparency Code 2015

• This is a key dataset which the government wants local authorities to publish.
• https://www.gov.uk/government/publications/local-government-transparency-code-2015

Real estate transaction case real price registration information, including target location, area, total price and other information.