IntroductionLinking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set.MethodsIn this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s (n=771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB).ResultsWe achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n=3,876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement.DiscussionIn conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.

The PAD (Property Address Directory) file contains additional geographic information at the tax lot level not found in the PLUTO files. This data includes alias addresses and Building Identification Numbers (BINs). It consists of two ASCII, comma delimited files: a tax lot file and an address file. All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE.

This catalog contains Property ID numbers with address and Plat, Lot, Unit data, as of November 24, 2020. Us this file to look up your address and find the corresponding Property ID, needed to file your Homestead Recertification.

Here are the short keys and their descriptions for each feature present in this dataset: PropertyID: unique identifier for each property xrCompositeLandUseID: code representing the type of land use for the property xrBuildingTypeID: code representing the type of building on the property ParcelID: unique identifier for the parcel of land LocationStartNumber: starting number of the property's street address ApartmentUnitNumber: unit number of the property's apartment, if applicable StreetNameAndWay: name of the street where the property is located xrPrimaryNeighborhoodID: code representing the primary neighborhood where the property is located LandSF: land square footage of the property TotalFinishedArea: total finished square footage of the property LivingUnits: number of living units in the property OwnerLastName: last name of the property's owner OwnerFirstName: first name of the property's owner PrimaryGrantor: name of the primary grantor of the property SaleDate: date when the property was sold SalePrice: sale price of the property TotalAppraisedValue: total appraised value of the property LegalReference: legal reference of the property xrSalesValidityID: code representing the sales validity of the property xrDeedID: code representing the type of deed for the property

This file contains the National Statistics UPRN Lookup (NSUL) for Great Britain as at February 2025. The NSUL relates the Unique Property Reference Number (UPRN) for each GB address from AddressBase® Epoch 116 to a range of current statutory administrative, electoral, health and other statistical geographies via 'best-fit' allocation from 2021 Census output areas (National Parks and Workplace Zones are exempt from 'best-fit' and use 'exact-fit' allocations). The NSUL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSUL is issued every 6 weeks and is designed to complement the Ordnance Survey AddressBase® product. For further technical information about this file, please refer to the User Guide document contained within the downloadable zip file. Please note that this product contains Royal Mail, Gridlink, Ordnance Survey and ONS Intellectual Property Rights. (File Size – 478 MB)

This repository is the second updated version of the attribute-linked residential property price dataset in UK Data Service ReShare 854240 (https://reshare.ukdataservice.ac.uk/854240/). As with the first updated version (ReShare 855033 https://reshare.ukdataservice.ac.uk/855033/) in 2021, this updated dataset contains individual property transactions and associated variables from both Land Registry Price Paid Dataset (LR PPD) and the Ministry for Housing, Communities and Local Government (MHCLG) Domestic Energy Performance Certificate (EPC) data. This is a linked result by address matching between LR-PPD data (1/1/1995-27/6/2022) and Domestic EPCs data (the twelfth version: ending with 30/6/2022). It is the whole of the 2022 update house price per square metre dataset published in the Greater London Authority (GLA) London Datastore (https://data.london.gov.uk/dataset/house-price-per-square-metre-in-england-and-wales). The linked dataset in this repository is the uncorrected version, recording almost 20 million transactions with 106 variables in England and Wales between 1/1/1995 and 27/6/2022. We have offered technical validation and data cleaning code in UKDA ReShare 854240 to help users to evaluate the representation and to clean up the data. There is no unique way to clean this raw linked dataset, so we suggest users develop their own clean-up process based on their research requirements. In addition, this repository covers the original LR PPD and Domestic EPCs for the linked data (house price per square metre dataset). Similar to the first updated version, a field header has been added in LR PPD. Six variables (individual lodgement identifier, address, address 1, address 2, address 3, postcode) in Domestic EPCs are removed. A newly created unique identifier (id) is added in Domestic EPCs, this id is newly created for Version 12 Domestic EPCs. It is not the same id as in the Domestic EPCs from UK Data Service ReShare 854240 and ReShare 855033. Since November 2021 DLUCH has published Domestic EPCs with the Unique Property Reference Number (UPRN) hence the dataset in this repository contains the UPRN information from the Domestic EPCs.

Our dataset features comprehensive housing market data, extracted from 250,000 records sourced directly from Redfin USA. Our Crawl Feeds team utilized proprietary in-house tools to meticulously scrape and compile this valuable data.

Key Benefits of Our Housing Market Data:

• In-Depth Market Analysis: Gain insights into the real estate market with up-to-date data on recently sold properties.
  
  
• Price Trend Identification: Track and analyze price trends across different cities.
  
  
• Accurate Price Estimation: Estimate property values based on key factors such as area, number of beds and baths, square footage, and more.
  
  
• Detailed Real Estate Statistics: Access detailed statistics segmented by zip code, area, and state.

Unlock the Power of Redfin Data for Real Estate Professionals

Leveraging our Redfin properties dataset allows real estate professionals to make data-driven decisions. With detailed insights into property listings, sales history, and pricing trends, agents and investors can identify opportunities in the market more effectively. The data is particularly useful for comparing neighborhood trends, understanding market demand, and making informed investment decisions.

Enhance Your Real Estate Research with Custom Filters and Analysis

Our Redfin dataset is not only extensive but also customizable, allowing users to apply filters based on specific criteria such as property type, listing status, and geographic location. This flexibility enables researchers and analysts to drill down into the data, uncovering patterns and insights that can guide strategic planning and market entry decisions. Whether you're tracking the performance of single-family homes or exploring multi-family property trends, this dataset offers the depth and accuracy needed for thorough analysis.

Looking for deeper insights or a custom data pull from Redfin?
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🇬🇧 영국 English The OS Linked Identifiers API allows you to access the valuable relationships between properties, streets and OS MasterMap identifiers (TOIDs). Benefit from the valuable relationship found in OS Premium products. Link together datasets that are using different identifiers; for example, linking a property address (UPRN) to the street that it’s on (USRN). The OS Linked Identifiers API allows users to access the valuable relationships between properties, streets and OS MasterMap identifiers for free. An identifier is a unique reference assigned to a specific thing, so when you are talking to someone else you can use it to ensure you're talking about the same thing. They are used all the time, such as telephone numbers, postcodes and customer reference numbers. OS is striving to make its identifiers more accessible and useful for its customers. The OS Linked Identifiers API takes this further by enabling the linking together of datasets that are using different identifiers; for example, linking a property address (UPRN - Unique Property Reference Number) to the street that it is on (USRN - Unique Street Reference Number).

The Local Land and Property Gazetteer is the master address dataset maintained by Wyre Council.

The dataset is made of UPRN's (Unique Property Reference Numbers) which provides a reference key to join related address records across different datasets.

Each LLPG feeds into a central dataset called the NLPG (National Land and Property Gazetteer).

The NLPG was initiated in 1999 to become the master address dataset for England and Wales.

Abstract

Historical tax assessment data for all U.S. states, the U.S. Virgin Islands, Guam, and Washington, D.C. Each table represents a previous edition of CoreLogic's tax assessment data.

The CoreLogic Smart Data Platform (SDP) Historical Property data was formerly known as the CoreLogic Tax History data. The CoreLogic SDP Historical Property data is an enhanced version of the CoreLogic Tax History data. The CoreLogic SDP Historical Property data contains almost all of the variables that were included in the CoreLogic Tax History data, as well as 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 thousands of counties and county equivalents in the U.S.; laws and regulations governing real estate/property sales vary by state. Counties and county equivalents also have inconsistent approaches to archiving historical parcel data.

To fill researchers’ needs for uniform parcel data, CoreLogic collects, cleans, and normalizes public records that they collect from U.S. County Assessor and Recorder offices. CoreLogic augments this data with information gathered from other public and non-public sources (e.g., loan issuers, real estate agents, landlords, etc.). The Stanford Libraries has purchased bulk extracts from CoreLogic’s parcel data, including mortgage, owner transfer, pre-foreclosure, and historical and contemporary tax assessment data. Data is bundled into pipe-delimited text files, which are uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see CoreLogic 2024 GitLab.

Usage

Each table contains an archived snapshot of the property data, roughly corresponding to the following assessed years:

• Historical Property 1 = 2022-2023
• Historical Property 2 = 2021-2022
• Historical Property 3 = 2020-2021
• Historical Property 4 = 2019-2020
• Historical Property 5 = 2018-2019
• Historical Property 6 = 2017-2018
• Historical Property 7 = 2016-2017
• Historical Property 8 = 2015-2016
• Historical Property 9 = 2014-2015
• Historical Property 10 = 2013-2014
• Historical Property 11 = 2012-2013
• Historical Property 12 = 2011-2012
• Historical Property 13 = 2010-2011
• Historical Property 14 = 2009-2010
• Historical Property 15 = 2008-2009

%3C!-- --%3E

Users can check theASSESSED_YEAR variable to confirm the year of assessment.

Roughly speaking, the tables use the following census geographies:

• 2020 Census Tract: Historical Property 1-2
• 2010 Census Tract: Historical Property 3 – 12
• 2000 Census Tract: Historical Property 13 – 15

%3C!-- --%3E

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.

For more information about included variables, please see **core_logic_sdp_historical_property_data_dictionary_2024.txt **and Historical Property_v3.xlsx.

Under Supporting files, users can also find record counts per FIPS code for each edition of the Historical Property data.

For more information about how the CoreLogic Smart Data Platform: Historical Property data compares to legacy data, please see core_logic_legacy_content_mapping.pdf.

Bulk Data Access

Data access is required to view this section.

The relationship between a property and an address is many-to-many. In DC a SSL (Square, Suffix, Lot) is used to identify a property. One SSL can have multiple addresses located on it. This often includes garden-style apartment complexes as well as corner buildings with separate addresses facing each adjacent street. One address can also sit upon multiple SSLs. One single family residence can sit upon multiple lots. The cross reference table contains the many-to-many relationship between address IDs and SSLs. [A small percentage of addresses do not have an associated SSL (such as metro entrances or many addresses on Federal property.] Use this cross reference table to relate the District's address points in the Master Address Repository (MAR) with the SSLs and vice versa.

This essential dataset is tailored for real estate investors, home service providers, and Proptech companies, offering in-depth information that drives strategic decision-making and market analysis for Property Owner Data.

The dataset includes detailed address data, owner data, and mailing address data, providing a thorough understanding of each property’s profile. Real estate investors can leverage this data to identify high-potential investment opportunities and analyze market trends with greater accuracy. Home service providers can utilize the mailing address data to target specific properties and optimize their outreach efforts. For Proptech companies, this dataset enhances the development of innovative solutions and data-driven platforms.

Powered by BatchData, a leader in high-quality, up-to-date property information, this dataset ensures you receive the most accurate and current data available. Explore BatchService’s USA Property Owner Data to gain a competitive edge and make informed decisions in the dynamic real estate market.

Basic Property Data Includes: - Property ID - Address City - Address County - Address County FIPS Code - Address Hash - Address House Number - Address Latitude - Address Longitude - Address State - Address Street - Address Zip - Address Zip+4 Code - APN (Assessor's Parcel Number) - Property Owner Full Name - Property Owner First Name - Property Owner Middle Name - Property Owner Last Name - Property Owner Mailing Address City - Property Owner Mailing Address County - Property Owner Mailing Address State - Property Owner Mailing Address Street - Property Owner Mailing Address Zip - Property Owner Mailing Address Zip+4 code

BatchService also has 700+ additional datapoints available ranging from listing information, property characteristics, mortgage data, contact information and more.

This file contains the ONS UPRN Directory (ONSUD) for Great Britain as at January 2022. The ONSUD relates the Unique Property Reference Number (UPRN) for each GB address from AddressBase® Epoch 89 to a range of current statutory administrative, electoral, health and other statistical geographies. The ONSUD is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The ONSUD is issued every 6 weeks and is designed to complement the Ordnance Survey AddressBase® product. For further technical information about this file, please refer to the User Guide document contained within the downloadable zip file. Please note that this product contains Royal Mail, Gridlink, Ordnance Survey and ONS Intellectual Property Rights. (File Size - 511 MB)

WARNINGThis data is provided here for specialist users only. The same information is available in a much more user-friendly format in the Services near me page on the main council website, and anyone interested in enrolling a child in school should see the page on Enrolment in Primary and Secondary Schools in DundeePurposeThese files are intended to be downloaded and used within systems such as SEEMIS that have access to a separate source of address information such as the Council's Corporate Address Gazetteer (CAG), One Scotland Gazetteer (OSG) or Ordnance Survey AddressBase.DescriptionThis collection includes the following files:Dundee_UPRN_SEED_lookup_all.csvDundee_UPRN_SEED_lookup_all_primary.csv - required for use in SEEMISDundee_UPRN_SEED_lookup_all_secondary.csv - required for use in SEEMISDundee_UPRN_SEED_lookup_denom_primary.csvDundee_UPRN_SEED_lookup_denom_secondary.csvDundee_UPRN_SEED_lookup_non_denom_primary.csvDundee_UPRN_SEED_lookup_non_denom_secondary.csvSchema.ini - explicitly sets UPRN datatype to text (with preceding zeroes) for use in excelOnly files 2 & 3 are required for use in SEEMIS. The other files are produced by the same process and are provided here in case they are useful. The combined files (1-3) will contain multiple records for each address. The remaining files (4-7) contain just one record for each address. Each file contains just two columnsUPRN - the Unique Property Reference Number of each address in DundeeSEED - the seedcode of the school catchment that the address is within Both columns are qualified with double quotes and separated with a comma. UPRNs are Unique Property Reference Numbers, as used in the Council's Corporate Address Gazetteer (CAG), the One Scotland Gazetteer (OSG) Or the Ordnance Survey AddressBase products.. For more information please see www.osg.scotSeedcodes are unique identifiers for Scottish Schools. A full list can be found at https://www.gov.scot/publications/school-contact-details/This collection is updated each night with the latest UPRNs from the Council's Corporate Address Gazetteer (CAG). SEED codes are taken from the Council's current catchment boundaries layer ("SchoolsAndCatchments - Current") which is updated as required. This typically happens around October or November before enrolment starts for the next school year each year. The data may therefore reflect the catchments that are due to take effect at the start of the next school year. For more details on the catchment data please see the the Council's current catchment boundaries layer ("SchoolsAndCatchments - Current") This data can be combined with Ordnance Survey OpenUPRN data to visualise it on a map in GIS software such as ArcGIS or QGIS. Or alternatively see the separate "UPRNs with school catchments" layer.

Analysis of ‘Current Tax Sale list’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0c5b8262-1651-4344-a459-9d4f9fa8417f on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains all properties that are eligible for tax sale as of May 6, 2021. Note: properties may be removed from the sale daily and this dataset only represents a snapshot in time as of May 6. This dataset does not constitute an official copy of the list.

The data include owner-occupied properties. On May 3rd, 2021, Mayor Scott announced that tax lien certificates on these properties would not be sold, however they are included in these data for reference. Use the field "BEING_REMO" to filter out properties that will no longer be sold based on Mayor Scott’s announcement on May 3.

Data Dictionary

Field Name Description

BLOCK The block number for the property.

LOT The lot number for the property.

OWNERSHIP INDICATOR Indicator for type of ownership on property. H = Owner occupied principal residence D = Dual use N Not owner occupied

LAND USE CODE The land use code for the parcel. R = residential C = commercial I = Industrial

OWNER NAME The name of the owner of the property.

TAX BASE The value of the property.

CITY TAX The annual city property tax based on the assesed value of the property.

STATE TAX The annual state property tax based on the assesed value of the property.

TOTAL TAXES The sum of the "City Tax" and "State Tax" columns.

TOTAL 3 YEAR TAXES DUE The total remaining taxes owed for the property.

TOTAL LIENS DUE The sum of the columns "Total 3 Year Taxes Due" and "Total Lien". This is the current total amount of money owed including liens and past due taxes.

TOTAL LIEN The total amount of liens on the property.

YEARS ELIGIBLE FOR SALE The number of years the property has been eligible for tax sale in the past.

DEED DATE The date that ownership of the property was transferred to the owner.

COUNCIL DISTRICT The city council district where the property is located.

NEIGHBORHOOD The neighborhood where the property is located.

WHEN SOLD The last time the tax lien certificate on the property was sold. Street Address The street number and street name of the property. City The city the property is in (Baltimore). State The state the property is in (Maryland). ZIP The ZIP code of the property. Latitude The latitude of the property. Longitude The longitude of the property.

--- Original source retains full ownership of the source dataset ---

Barrow Council's Local Land and Property Gazetteer is a database containing addresses of property and Land within the Borough. This local database is combined at a national level to form a national database. A key identifier in the database is the Unique Property Reference Number (UPRN) which is a unique identifier for every addressable location in Great Britain.

Access to the data is organised through Geoplace a public sector limited liability partnership between the Local Government Association and Ordnance Survey.

https://www.geoplace.co.uk

*** THIS DATA IS UPDATED DAILY *** A property is defined as a separate unit, comprising an area of land with or without buildings. The ‘Property’ dataset provides information about the location, size, tenure and type of property. Certain properties may have more than one entry in the data extract as government has more than one ‘interest’ in that property. For example, there may be two leases in the same property. It also provides information about the ‘owning’ government department and the ‘property centre’, i.e. that part of the government department responsible for that property. In addition, it has a property reference (the ‘ePIMS Property Ref’) that allows it to be linked to the other datasets. The scope of the data includes land and property information for those government departments, together with any arms’ length bodies for which they are responsible, including their non-departmental public bodies (NDPBs), which fall under the responsibility of English Ministers. These assets are primarily located in England, but are also located in the devolved administrations of Northern Ireland, Scotland and Wales as well as overseas. Also, some Local Authorities have chosen to publish their property data as part of our transparency exercise. X

This dataset is a supplement to the statewide dataset of Real Property Assessments, at https://opendata.maryland.gov/d/ed4q-f8tm, which shows all properties in the state and assessment data from SDAT and MDP.

This file contains the National Statistics UPRN Lookup (NSUL) for Great Britain as at May 2022. The NSUL relates the Unique Property Reference Number (UPRN) for each GB address from AddressBase® Epoch 92 to a range of current statutory administrative, electoral, health and other statistical geographies via 'best-fit' allocation from 2011 Census output areas (National Parks and Workplace Zones are exempt from 'best-fit' and use 'exact-fit' allocations). The NSUL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSUL is issued every 6 weeks and is designed to complement the Ordnance Survey AddressBase® product. For further technical information about this file, please refer to the User Guide document contained within the downloadable zip file. Please note that this product contains Royal Mail, Gridlink, Ordnance Survey and ONS Intellectual Property Rights. (File Size – 521 MB)