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.

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

This table contains the information about the land including land sizes (square feet & acres) and land property type for properties within Fairfax County. There is a one to many relationship to the parcel data. Refer to this document for descriptions of the data in the table.

General Services Administration Owned PropertiesThis 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 DCData 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.comNGDA Data SetThis 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 National Property Administration handles the bidding for state-owned and non-public real estate and sets up the results of land use rights auctions.

REdistribute modernizes real estate data accessibility by providing access to fresh, reliable listings from trusted MLS sources.

Public sector and research teams can leverage REdistribute to: - Monitor affordability across housing markets - Track up-to-date housing supply and market trends

Key features: • Flexible Delivery: Available via a bulk data API or directly through Snowflake • Residential or Multi-Class: Choose a residential-only dataset or full MLS coverage across all property types, including residential, multi-family, land, commercial, rentals, farm and more • Comprehensive Field Access: Explore 800+ fields providing a complete view of both residential and non-residential property data • Fast & Fresh: Stay current with daily updates sourced directly from trusted MLSs partners

The sample data covers one listing in JSON format. For access to a broader set of sample listings (10,000+), reach out to the REdistribute sales contact.

ABOUT REDISTRIBUTE

REdistribute aims to modernize real estate data accessibility, fostering innovation and transparency through direct access to the most reliable MLS data. Our commitment to data integrity and direct MLS involvement guarantees the freshest, most accurate insights, empowering businesses across industries to drive innovation and make informed decisions.

Last updated on 22 Feb 2025

Introduction

This dataset provides comprehensive information on property sales in England and Wales, sourced from the UK government's HM Land Registry. Although the government site claims to update on the same day each month, actual updates can vary. To bridge this update variation gap, our fully automated ETL pipeline retrieves the official government data on a daily basis. This ensures that the dataset always reflects the most current transaction data available.

ETL Process

Our ETL (Extract, Transform, Load) process is designed to automate the data update and publishing workflow: 1. Extract:
The pipeline uses web scraping to retrieve the latest data from the official government website. This step is necessary as the site does not offer an API. 2. Transform:
Before loading the data, the ETL pipeline processes the dataset to ensure consistency and usability. As part of the transformation stage, the first column (Transaction_unique_identifier) is removed. This column is dropped during staging to focus on the most relevant transactional information. The column removal successfully reduces the data file size from almost 6GB to 3.1GB, and therefore will greatly increase the data analysis efficiency, and reduces the chance of kernal error/restart. 3. Load:
Finally, the transformed data is loaded into the dataset.

The transformed data is loaded into the dataset in two parts: - Complete Data (pp-complete.csv): This file encompasses all records from January 1995 to the present. The complete data file is replaced during each update to reflect any corrections or additional historical data. The first column is price. - Monthly Data: A separate monthly file is amended each month. This monthly archive ensures a complete record of updates over time, allowing users to track changes and trends more granularly.

Summary of Results

The dataset (pp-complete.csv) contains records of property sales dating back to January 1995, up to the most recent monthly data. It covers various types of transactions—from residential to commercial properties—providing a holistic view of the real estate market in England and Wales.

Column Descriptions

The original data includes the following columns: - Transaction_unique_identifier
- price
- Date_of_Transfer
- postcode
- Property_Type
- Old/New
- Duration
- PAON
- SAON
- Street
- Locality
- Town/City
- District
- County
- PPDCategory_Type
- Record_Status - monthly_file_only

Note: As part of the transformation process, the Transaction_unique_identifier column is removed from the final published pp-complete.csv data file. Therefore the first column of the pp-complete.csv file is price.

Address data Explanation - Postcode: The postal code where the property is located. - PAON (Primary Addressable Object Name): Typically the house number or name. - SAON (Secondary Addressable Object Name): Additional information if the building is divided into flats or sub-buildings. - Street: The street name where the property is located. - Locality: Additional locality information. - Town/City: The town or city where the property is located. - District: The district in which the property resides. - County: The county where the property is located. - Price Paid: The price for which the property was sold.

Legal and Ethical Considerations

Ownership and Attribution This dataset is the property of HM Land Registry and is released under the Open Government Licence (OGL). If you use or publish this dataset, you are required to include the following attribution statement:

>"Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0."

Usage Guidelines

The data can be used for both commercial and non-commercial purposes.

The OGL does not cover third-party rights, which HM Land Registry is not authorized to license. For any other use of the Address Data, you must contact Royal Mail.

Suggested Usages

Market Trend Analysis: Understand the ups and downs of the property market over time. Investment Research: Identify potential areas for property investment. Academic Studies: Use the data for economic research and studies related to the housing market. Policy Making: Assist government agencies in making informed decisions regarding housing policies. Real Estate Apps: Integrate the data into apps that provide property price information services.

By using this dataset, you agree to abide by the terms and conditions as specified by HM Land Registry. Failure to do so may result in legal consequences.

Property Asset Management Software Market Outlook

In 2023, the global property asset management software market size was estimated at USD 2.5 billion and is projected to reach USD 6.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.3% from 2024 to 2032. This growth is primarily driven by the increasing need for effective property management solutions, digital transformation in real estate, and rising real estate investments globally.

The surge in real estate investments across the globe has been a significant catalyst for the expansion of the property asset management software market. As investors diversify their portfolios and seek higher returns, effective management of property assets becomes critical. The software solutions offer comprehensive tools for managing various aspects such as leasing, maintenance, and compliance, thereby enhancing operational efficiency and profitability. Furthermore, the integration of advanced technologies like artificial intelligence (AI) and machine learning (ML) into property management software has revolutionized the industry, enabling predictive analytics and advanced reporting capabilities.

Another major growth factor is the increasing adoption of cloud-based solutions. As businesses move towards digital transformation, cloud deployment offers flexibility, scalability, and cost savings. It allows property managers and real estate investors to access data and manage assets remotely, which is particularly beneficial in a post-pandemic world where remote work and decentralized operations have become the norm. Additionally, cloud solutions help in reducing the total cost of ownership by eliminating the need for hefty infrastructure investments and maintenance costs.

The growing need for data-driven decision-making in property management is also fueling the market's growth. Property asset management software provides real-time data and insights, which aids in making informed decisions. These insights can range from tenant behavior patterns to property performance metrics, allowing managers to optimize operations and enhance tenant satisfaction. Moreover, the integration of Internet of Things (IoT) devices in properties further contributes to the accumulation of valuable data, which can be analyzed to predict maintenance needs and reduce operational costs.

Land Management Software is increasingly becoming an integral part of the property asset management landscape. As the real estate industry continues to evolve, there is a growing need for comprehensive solutions that can handle the complexities of land management. This software provides tools for managing land records, tracking land use, and ensuring compliance with zoning regulations. By integrating land management capabilities, property managers can optimize land utilization, streamline operations, and enhance decision-making processes. Additionally, the use of Land Management Software can facilitate better collaboration between stakeholders, including property developers, government agencies, and community organizations, thereby promoting sustainable land development practices.

Regionally, North America is expected to hold the largest market share due to the high adoption rate of advanced technologies and significant investments in real estate. Europe follows closely, driven by stringent regulations and the need for efficient property management solutions. The Asia Pacific region is anticipated to exhibit the highest growth rate, attributed to rapid urbanization, increasing real estate development, and growing awareness of the benefits of property asset management software.

Component Analysis

The property asset management software market is segmented by component into software and services. The software segment is expected to dominate the market, driven by the increasing demand for comprehensive property management solutions that offer functionalities ranging from tenant management to financial reporting. These software solutions are designed to streamline operations, reduce administrative burdens, and enhance overall efficiency. Additionally, advancements in software technology, such as AI and ML integrations, are making these solutions more powerful and user-friendly, thereby driving their adoption in the market.

Within the software segment, there are various types of software solutions available, including lease management, property maintenance, and financial management softwa

The Inventory of Owned and Leased Properties (IOLP) allows users to search properties owned and leased by the General Services Administration (GSA) across the United States, Puerto Rico, Guam and American Samoa.

The Owned and Leased Data Sets include the following data except where noted below for Leases:

• Location Code - GSA’s alphanumeric identifier for the building
• Real Property Asset Name - Allows users to find information about a specific building
• Installation Name - Allows users to identify whether a property is part of an installation, such as a campus
• Owned or Leased - Indicates the building is federally owned (F) or leased (L)
• GSA Region - GSA assigned region for building location
• Street Address/City/State/Zip Code - Building address
• Longitude and Latitude - Map coordinates of the building (only through .csv export)
• Rentable Square Feet - Total rentable square feet in building
• Available Square Feet - Vacant space in building
• Construction Date (Owned Only) - Year built
• Congressional District - Congressional District building is located
• Senator/Representative/URL - Senator/Representative of the Congressional District and their URL
• Building Status (Owned Only) - Indicates building is active
• Lease Number (Leased Only) - GSA’s alphanumeric identifier for the lease
• Lease Effective/Expiration Dates (Leased Only) - Date lease starts/expires
• Real Property Asset Type - Identifies a property as land, building, or structure

Like other Assessor and Recorder data sets from First American, BlackKnight, ATTOM or HouseCanary, we provide both residential real estate and commercial restate data on homes, properties and pracels nationally.

Over 250M parcels, updated daily.

Access detailed property and tax assessment records with our extensive nationwide database. This robust dataset provides comprehensive information about residential and commercial properties, including detailed ownership, valuation, and transaction history. Core Data Elements:

Complete property identification (APNs, Tax IDs) Full property addresses with geocoding Precise latitude/longitude coordinates FIPS codes and Census tract information School district assignments

Property Characteristics:

Detailed lot dimensions and size Building square footage breakdowns Living area measurements Basement and attic specifications Garage and parking information Year built and effective year Number of bedrooms and bathrooms Room counts and configurations Building class and condition codes Construction details and materials Property amenities and features

Valuation Information:

Current AVM (Automated Valuation Model) values Confidence scores and value ranges Market valuations with dates Assessed values (land and improvements) Tax amounts and years Tax rate codes and districts Various tax exemption statuses

Transaction History:

Current and previous sale details Recording dates and document numbers Sale prices and price codes Buyer and seller information Multiple mortgage records including:

Loan amounts and terms Lender information Recording dates Interest rates Due dates Loan types and positions

Ownership Details:

Current owner information Corporate ownership indicators Owner-occupied status Mailing addresses Care of names Foreign address indicators

Legal Information:

Complete legal descriptions Subdivision details Lot and block numbers Zoning information Land use codes HOA information and fees

Property Status Indicators:

Vacancy flags Pre-foreclosure status Current listing status Price ranges Market position

Perfect For:

Real Estate Professionals

Property researchers Title companies Real estate attorneys Appraisers Market analysts

Financial Services

Mortgage lenders Insurance companies Investment firms Risk assessment teams Portfolio managers

Government & Planning

Urban planners Tax assessors Economic developers Policy researchers Municipal agencies

Data Analytics

Market researchers Data scientists Economic analysts GIS specialists Demographics experts

Data Delivery Features:

Multiple format options Regular updates Bulk download capability Custom field selection Geographic filtering API access available Standardized formatting Quality assured data

Quality Assurance:

Verified against public records Regular updates Standardized formatting Address verification Geocoding validation Duplicate removal Data normalization Quality control processes

This comprehensive property database provides unprecedented access to detailed property information, perfect for industry professionals requiring in-depth property data for analysis, research, or business development. Our data undergoes rigorous quality control processes to ensure accuracy and completeness, making it an invaluable resource for real estate professionals, financial institutions, and government agencies. Updated continuously from authoritative sources, this dataset offers the most current and accurate property information available in the market. Custom data extracts and specific geographic coverage options are available to meet your exact needs.

Weekly/Quarterly/Annual and One-time options are available for sale.

See our sample

The National Property Administration recently held a public announcement of land use rights for state-owned non-public real estate through bidding.

Abstract

Tax assessment data for all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C., as of August 2022.

The CoreLogic Smart Data Platform (SDP) Property data was formerly known as the CoreLogic Tax data. The CoreLogic SDP Property data is an enhanced version of the CoreLogic Tax data. The CoreLogic SDP Property data contains almost all of the variables that were included in the CoreLogic Tax data, and its records are augmented with additional property-level 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 3,006 counties in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties 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 have purchased bulk extracts from CoreLogic’s public records data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which we upload to Redivis for preview, extraction and light analysis.

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 Core_Logic_SDP_Property_Codebook.xlsx (under Supporting files).

For a count of records per FIPS code, please see ***property_counts.txt ***(under** Supporting files**).

For more information about how the CoreLogic Smart Data Platform: Property data compares to legacy data, please see ***Legacy_Content_Mapping.pdf ***(under Supporting files).

For more information about the terms of use, please see 2022_corelogic_sdp_end_user_license_agreement.pdf (under Supporting files).

Bulk Data Access

Data access is required to view this section.

This table contains the legal description information including legal address (site address), deeded land area, and tax district for properties within Fairfax County. There is a one to one relationship to the parcels data. Refer to this document for descriptions of the data in the table.

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.

1. Real estate pre-sale case actual transaction registration information, including the location, area, total price, etc.2. This dataset is updated every 10 days.

Unlock the potential of blockchain in land registry and asset tracking. Explore market trends, growth forecasts (2025-2033), key players (Accenture, IBM, etc.), and regional insights. Discover how blockchain technology is revolutionizing property management and supply chain efficiency.

introduction

This dataset provides comprehensive information on property sales in England and Wales, as sourced from the UK government's HM Land Registry. It offers valuable insights into property transactions, including sale prices, locations, and types of properties sold. This dataset is particularly useful for analysts, researchers, and businesses looking to understand market trends, property valuations, and investment opportunities in the real estate sector of England and Wales.

Summary of Results

The dataset contains records of property sales dating back to January 1995, up to the most recent monthly data. It covers various types of transactions, from residential to commercial properties, providing a holistic view of the real estate market in England and Wales.

Column Descriptions

colnames=['Transaction_unique_identifier', 'price', 'Date_of_Transfer', 'postcode', 'Property_Type', 'Old/New', 'Duration', 'PAON', 'SAON', 'Street', 'Locality', 'Town/City', 'District', 'County', 'PPDCategory_Type', 'Record_Status - monthly_file_only' ]

Address data Explaination Postcode: The postal code where the property is located. PAON (Primary Addressable Object Name): Typically the house number or name. SAON (Secondary Addressable Object Name): Additional information if the building is divided into flats or sub-buildings. Street: The street name where the property is located. Locality: Additional locality information. Town/City:The town or city where the property is located. District: The district in which the property resides. County:The county where the property is located. Price Paid:The price for which the property was sold.

Legal and Ethical Considerations

Ownership and Attribution

This dataset is the property of HM Land Registry and is released under the Open Government Licence (OGL). If you use or publish this dataset, you are required to include the following attribution statement:

"Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0."

Usage Guidelines

The data can be used for both commercial and non-commercial purposes.

The OGL does not cover third-party rights, which HM Land Registry is not authorized to license. For any other use of the Address Data, you must contact Royal Mail.

##Suggested Usages Market Trend Analysis: Understand the ups and downs of the property market over time. Investment Research: Identify potential areas for property investment. Academic Studies: Use the data for economic research and studies related to the housing market. Policy Making: Assist government agencies in making informed decisions regarding housing policies. Real Estate Apps: Integrate the data into apps that provide property price information services.

By using this dataset, you agree to abide by the terms and conditions as specified by HM Land Registry. Failure to do so may result in legal consequences.