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 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

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.

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)."

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

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

This data includes the relocated public institution, property location, and purchase price of previously purchased real estate by the Korea Asset Management Corporation to support the relocation of public institutions to local areas. The base year allows users to view asset status at a specific time, and the relocated institution name is used to understand the ownership transfer process. Address information down to the town/village level allows for location-based analysis, and land use zoning allows for review of the development potential and regulatory requirements of the property. Area data is provided separately for the site and building, making it highly useful. Purchase price, contract, and takeover dates allow for tracking the actual transaction process.

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

This table contains information about the parcel including livable units, land use code, zoning, and utility description for properties in Fairfax County. There is a one to many relationships to parcels data. Refer to this document for descriptions of the data in the table.

The actual information of real estate transaction cases in Taipei City is reported and recorded for the production of data at that time. Subsequent system maintenance may result in data changes. The latest transaction real price information status can be checked using the real estate transaction real price inquiry function provided by the Taipei Land Cloud (https://cloud.land.gov.taipei/), which will synchronize the real price data of buying and selling, pre-sale houses, and leasing transactions.

This table contains the assessed values for current tax year and prior tax year for land and building for properties in Fairfax County. There is a one to one relationship to the parcel data. Refer to this document for descriptions of the data in the table.

General Services Administration Leased Properties This National Geospatial Data Asset (NGDA) dataset, shared as a General Services Administration (GSA) feature layer, displays federal government leased 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.”GSA Leased Properties in Washington D.C. Data currency: Current federal service (FC IOLP Lease)NGDAID: 133 (Inventory of Owned and Leased Properties (IOLP))OGC API Features Link: Not AvailableFor more information: Inventory of GSA Owned and Leased Properties; Real EstateFor feedback, please contact: ArcGIScomNationalMaps@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

Develop Louisville Focuses on the full range of land development activities, including planning and design, vacant property initiatives, advanced planning, housing & community development programs, permits and licensing, land acquisition, public art and clean and green sustainable development partnerships.Data Dictionary:“LBA” is the abbreviation for the Louisville and Jefferson County LBA Authority, Inc."Parcel ID" is an identification code assigned to a piece of real estate by the Jefferson County Property Valuation Administration. The Parcel ID is used for record keeping and tax purposes.“IMPROV” stands for whether or not the real estate parcel had an “improvement” (i.e., a structure) situated on it at the time it was sold. “1” indicates that a structure existed when the parcel was sold and “0” indicates that the parcel was an empty, piece of land.“APPLICANT” is the individual(s) or active business entity that submitted an Application to Purchase the real estate parcel and whose application was presented to and approved by the LBA’s Board of Directors. The Board of Directors must approve each application before a transfer deed is officially recorded with the Office of the County Clerk of Jefferson County, Kentucky.“SALE DATE” is the date that the Applicant signed the transfer deed for the respective real estate parcel.“SALE AMOUNT” is the amount that the Applicant paid to purchase the respective real estate parcel.“SALE PROGRAM” is the LBA’s disposition program that the Applicant participated in to acquire the real estate parcel.The Office of Community Development defines each “Sale Program” as follows:Budget Rate (“Budget Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land.Cut It Keep It - Applicant requested to maintain the empty piece of land situated on the same block as a real estate parcel owned by the Applicant. Applicant must retain ownership of the lot for three (3) years before the Applicant can sell it.Demo for Deed (“Last Look – Demo for Deed”) – Applicant requested to demolish the structure situated on the real estate parcel and retain the land for a future use.Flex Rate (“Flex Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land but did not have proof of funding or a timeline as to when the project would be completed.Metro Redevelopment – The real estate parcel was part of a redevelopment project being considered by Metro Government.Minimum Pricing Policy – The pricing policy that was approved by the LBA’s Board of Directors and in effect as of the real estate parcel’s sale date.RFP (“Request for Proposals”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Save the Structure (“Last Look – Save the Structure”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Side Yard – The Applicant requested to acquire the LBA’s adjoining piece of land to make the Applicant’s occupied, real estate parcel larger and more valuable.SOI (“Solicitation of Interest”) – The LBA assembled two (2) or more real estate parcels and the Applicant submitted a redevelopment project for the subject parcels.For more information about each of the current disposition programs that the LBA offers, please refer to the following website pages:https://louisvilleky.gov/government/community-development/vacant-lot-sales-programshttps://louisvilleky.gov/government/community-development/vacant-structures-saleContact:Connie Suttonconnie.sutton@louisvilleky.gov

Real Estate Development Due Diligence via Satellite Market Outlook

According to our latest research, the global market size for Real Estate Development Due Diligence via Satellite stood at USD 1.48 billion in 2024, reflecting the sector’s rapid digital transformation. The market is projected to expand at a CAGR of 13.6% from 2025 to 2033, reaching an estimated USD 4.74 billion by 2033. This robust growth is driven by the increasing adoption of satellite-based geospatial technologies in real estate development processes, aiming to enhance accuracy, efficiency, and compliance in due diligence practices worldwide.

The primary growth factor for the Real Estate Development Due Diligence via Satellite market is the increasing demand for high-precision, real-time data in property assessment and land development. As urbanization accelerates and land becomes a premium resource, real estate developers and investors are seeking innovative ways to mitigate risks and ensure regulatory compliance. Satellite imagery and geospatial mapping solutions provide comprehensive data on land use, environmental conditions, and infrastructure, enabling stakeholders to make informed decisions quickly. This shift toward data-driven due diligence is further bolstered by regulatory requirements for environmental and land use assessments, particularly in regions with stringent sustainability mandates. The integration of artificial intelligence and machine learning with satellite data analytics is also enhancing the accuracy and predictive capabilities of these solutions, making them indispensable in modern real estate development.

Another significant driver is the expansion of cloud-based deployment models, which are democratizing access to advanced satellite analytics for organizations of all sizes. Cloud-based platforms allow real estate developers, government agencies, and financial institutions to access and process vast volumes of satellite imagery and geospatial data without the need for substantial upfront investments in IT infrastructure. This scalability and flexibility are particularly beneficial for small and medium enterprises (SMEs) and emerging markets, where resource constraints have traditionally limited the adoption of sophisticated due diligence solutions. As a result, cloud-based offerings are expected to capture a growing share of the market, supporting the broader digital transformation of the real estate industry.

Furthermore, the increasing frequency and severity of climate-related risks are prompting real estate stakeholders to adopt satellite-based environmental assessment tools. With climate change impacting land stability, flood risk, and vegetation cover, developers and investors are leveraging satellite data to assess and mitigate environmental risks before committing to new projects. This trend is especially pronounced in regions prone to natural disasters, where traditional due diligence methods may fall short. The ability to monitor land use changes, detect unauthorized developments, and assess compliance with environmental regulations in near-real time is positioning satellite-based due diligence solutions as a critical component of sustainable real estate development.

From a regional perspective, North America remains the largest market, accounting for over 37% of global revenues in 2024, driven by the presence of major technology providers and a mature real estate sector. Europe follows closely, supported by strong regulatory frameworks for environmental protection and urban planning. The Asia Pacific region is expected to register the fastest CAGR of 16.2% through 2033, fueled by rapid urbanization, infrastructure investments, and government initiatives to digitize land records and property assessments. Meanwhile, Latin America and Middle East & Africa are emerging as promising markets as governments and private sector players increasingly recognize the value of satellite-based due diligence in supporting sustainable development and risk management.

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.

The number of first-time registered real estate properties, the number of building units, and the building area statistics for each administrative district of this city.

This series comprises Torrens system title deeds relating to properties purchased by the Board of Land and Works for use as public offices such as police stations, court houses or government office buildings.

Background

An innovative system of land registration began in the 19th century with the passing of the Real Property Act 25 Vic, No.140 (1862) and the Transfer of Land Statute 29 Vic., No.301 (1866). This system of registering and transferring titles to land is colloquially known as the "Torrens System of Land Registration", named after Robert Torrens who was influential in the adoption of the system in South Australia in 1858. The Torrens system provided a single title document which was registered. Subsequent transfers of land were all recorded on the same title document. This system was still in use as of April 1994.

Prior to the adoption of the Torrens system, each transaction of land (conveyance, mortgage etc.) resulted in the creation of a new document which rather than replacing the previous documents, was an addition to them. All the documents were required to ensure that ownership could be proven, as a missing title from the "chain" of documents meant that a person holding that document could challenge for the ownership of the property. This system was known as the "general law" system, or more commonly, the "old law" system.

Often, property titles were converted into the new Torrens system. When this occurred, the general law titles were annotated with a number indicating that they had been registered in the new Torrens System and the general law titles were no longer required once the new title was issued.

Provenance

Until 1985 the construction and maintenance of the State's public works and buildings was the primary responsibility of the Public Works Department (VA 669). These records formed part of the record keeping system of that department (numbers found on the documents tend to support this theory - see below "Evidence of Previous Numbering Systems").

The Summary Guide should be consulted for further information on this department. (See Inventory of Series for VA 669.)

The records appear to have been in the hands of the Public Works Department until 1985 when the property management function was transferred from the Public Works Department to the Department of Property and Services. The function of managing State owned and leased property assets was subsequently transferred to the Department of Finance.

Creation of an Artificial Series

These records were found in the basement of 35 Spring Street by the Records Manager of the Ministry of Finance in 1992. They are presumed to be a fragment of a much larger record keeping system which no longer exists. Because so little is known about the original record keeping system, an artificial series has been created for these documents, until further information comes to light.

Seven archive boxes were discovered, which contained title documents and leases. The non-current leases were approved for destruction in 1994. Some of the leases however, were still current - being for 999 years duration. These have been registered as a separate series - VPRS 8835 Agreements and Leases [Government Properties].

What all the documents appear to have in common is that they relate to properties that have been acquired for the Crown or Public Works Department / Board of Land and Works for public use, and the construction of public buildings.

Some of the deeds are annotated with numbers which are known to correspond to Public Works Department files. It is presumed that these files deal with the properties to which the title belongs.

The Crown Solicitor was involved with conveyancing of properties and some files bear numbers from the Crown Solicitor's record keeping system.

1. Real estate transaction case information, including the location, area, total price, and other information of the target property. 2. This dataset is updated every 10 days. (Wulai District)

People typically purchase residential properties for two reasons: to live in or invest. However, both purposes necessitate careful consideration before deciding because high financial costs are involved, and housing loans are typically considered necessary for this purpose. Customers’ demands are constantly changing, becoming more complicated with higher requirements. The focus of this research is on tourism real estate selection. This market in Vietnam is still new and emerging and has encountered numerous issues regarding government policy, finance, and land authorization for constructing, owning, and managing. Because the form of tourism real estate is still new, customers are hesitant about investing in or buying these properties. Hence, to compete in the current fiercely real estate industry, real estate firms must understand their customers’ expectations by frequently involving customer research in the company’s strategy. However, there is still a lack of research on the connection between these factors and individual expectations in the well-known philosophy of the Theory of Planned Behavior (TPB), leading to behavioral intentions. Therefore, to fulfill the gap in the previous literature, this paper aims to investigate the connection between these factors with core variables of TPB, hence, addressing the current problems in the real estate industry. 471 valid respondents in Vietnam were collected for data analysis through two survey approaches. PLS-SEM was used to test hypotheses due to the relationship complication in the conceptual models. The results show that government policy influences attitudes and perceived behavioral control, whereas social infrastructure affects social norms and perceived behavioral control. Moreover, Fengshui ambient condition also positively influences all three core factors: attitudes, social norms, and perceived behavioral control. Finally, these factors impact on intention to buy tourism real estate. Through results, this paper has developed a purchase intention model through social aspects of the tourism real estate industry. In addition, this paper demonstrates the connection between social factors and individuals’ expectations for a purchase intention, providing the importance of the government’s role, architecture style, and social infrastructure in the marketing literature of the real estate industry. As a result, managers and governments need to take advantage of new releases of government regulations in time to enhance customers’ positive attitudes toward purchasing tourism real estate. Moreover, social infrastructure and Fengshui conditions are crucial to establishing social norms and perceived control, aiming to leverage the intention to purchase tourism real estate. Thereby, recommendations of marketing strategies based on these findings were suggested to attain the optimal result for sales. Finally, this research also includes some limitations. Hence, suggestions for further research were also provided, such as possible moderation, possible mediating effects, or control of data bias.