These National Statistics provide monthly estimates of the number of residential and non-residential property transactions in the UK and its constituent countries. National Statistics are accredited official statistics.

England and Northern Ireland statistics are based on information submitted to the HM Revenue and Customs (HMRC) Stamp Duty Land Tax (SDLT) database by taxpayers on SDLT returns.

Land and Buildings Transaction Tax (LBTT) replaced SDLT in Scotland from 1 April 2015 and this data is provided to HMRC by https://www.revenue.scot/" class="govuk-link">Revenue Scotland to continue the time series.

Land Transaction Tax (LTT) replaced SDLT in Wales from 1 April 2018. To continue the time series, the https://gov.wales/welsh-revenue-authority" class="govuk-link">Welsh Revenue Authority (WRA) have provided HMRC with a monthly data feed of LTT transactions since July 2021.

LTT figures for the latest month are estimated using a grossing factor based on data for the most recent and complete financial year. Until June 2021, LTT transactions for the latest month were estimated by HMRC based upon year on year growth in line with other UK nations.

LTT transactions up to the penultimate month are aligned with LTT statistics.

Go to Stamp Duty Land Tax guidance for the latest rates and information.

Go to Stamp Duty Land Tax rates from 1 December 2003 to 22 September 2022 and Stamp Duty: rates on land transfers before December 2003 for historic rates.

The latest release was published 09:30 31 July 2025 and was updated with provisional data from completed transactions during June 2025.

The next release will be published 09:30 29 August 2025 and will be updated with provisional data from completed transactions during July 2025.

https://webarchive.nationalarchives.gov.uk/ukgwa/20240320184933/https://www.gov.uk/government/statistics/monthly-property-transactions-completed-in-the-uk-with-value-40000-or-above" class="govuk-link">Archive versions of the Monthly property transactions completed in the UK with value of £40,000 or above are available via the UK Government Web Archive, from the National Archives.

INSPIRE Cadastral Parcels is a dataset maintained and produced by the Registers of Scotland to comply with the INSPIRE Directive. It is a sub-set of the Cadastral Map and contains the location of ownership polygons at ground level in Scotland. The polygons contained within the dataset are shapes that show the position and indicative extent of ownership of the earth's surface for each registered property. Each cadastral parcel has a unique identifier called the inspire id that relates to a registered title on Scotland's Land Register. The extent of rights and land contained within a title registered in the land register cannot be established from the cadastral parcel. This service provides access to each of the 33 Registration Counties as a pre-defined dataset in csv format or as an ATOM feed. For more detailed information on land and property data in Scotland you can search free at https://scotlis.ros.gov.uk/.

Public transit routes in San Diego County managed by the San Diego County Metropolitan Transit System (MTS) and the North County Transit District (NCTD). Bus, commuter and light rail, and trolley routes managed and developed from the General Transit Feed Specification (GTFS) data available from the transitland feed registry (formerly from GTFS Data Exchange). Routes are developed from the GTFS data available through the transitland feed registry (https://transit.land/feed-registry/), formerly from the GTFS Data Exchange. GTFS data is provided to the exchange by the transit agencies and processed by SanGIS to create a consolidated GIS layer containing routes from both systems. SanGIS uses a publicly available ESRI ArcToolbox tool to create the GIS data layer. The toolbox can be found at http://www.arcgis.com/home/item.html?id=14189102b795412a85bc5e1e09a0bafa. This data set is created using the ROUTES.txt and SHAPES.txt GTFS data files.Routes layers for MTS and NCTD are created separately and combined into a single layer using ArcGIS tools.Please note that this data was reprojected for use in the San Diego Ocean Planning Partnership, a collaborative pilot project between the California State Lands Commission and the Port of San Diego. For more information about the Partnership, please visit: https://www.sdoceanplanning.org/

Public transit stops in San Diego County serviced by the San Diego County Metropolitan Transit System (MTS) and the North County Transit District (NCTD). Bus, commuter and light rail, and trolley stops developed from the General Transit Feed Specification (GTFS) data available from the transitland/transitfeeds feed registries (and formerly the GTFS Data Exchange). Stops are developed from the GTFS data available through the transitland feed registry (https://transit.land/feed-registry/) or transitfeed (http://transitfeeds.com) depending on which is most current, and formerly from the GTFS Data Exchange (http://www.gtfs-data-exchange.com/). GTFS data is provided to the exchange by the transit agencies and processed by SanGIS to create a consolidated GIS layer containing stops for both MTS and NCTD systems. SanGIS uses built-in ArcGIS tools to develop the stops from the STOPS.txt data file.Stops layers for MTS and NCTD are created separately and combined into a single layer using ArcGIS tools.

Patent data is aggregated across multiple Intellectual Property (IP) registries, including USPTO, CIPO, EUIPO and WIPO (USA, Canada, Europe). Our complete dataset of active patent records is updated weekly. Customized reports available based on company lists, or full dataset via raw feed or one-off reports. Full bibliographic data provided for each IP record; including filing date, grant date, expiry date, inventor(s), IPC, full text abstract, title, etc. Ownership/entity relationship mapping, ticker mapping, ISIN mapping, Crunchbase uuid mapping, Crunchbase domain mapping. We also provide our proprietary IP Activity Score for each owner, which can assist to compare recent innovation activity amongst owners, as reflected in their Intellectual Property filings.

Ipqwery's Patent data is also available as a combined dataset with our Trademark dataset, enabling full IP profiles for corporate entities.

The ATOM Feed Download Service for the non-residential building construction years in Wuppertal provides a dataset for download, which is created by a 1x per year intersection of the non-residential buildings from the building file of the Wuppertal Statistical Office with the addresses of the real estate register. There is also a classification of the building construction year in 11 ages, most of which are 10-year intervals. The resulting data set models the buildings with the house number coordinates of the property register as point-shaped objects. The attributes include, among other things, the address (road name and house number), the building year and the age level from the above classification. The building file is based on the results of the 1987 census, it is continuously updated via the statistical survey sheets from the building application documents and status reports from the building approval process on the approval, start of construction and completion of the building. In 2015, the building file was systematically improved by comparisons with other data sources (Zensus 2011, data from the GWG, etc.). The annual intersection with the addresses of the property register takes place from 2017 onwards in the first half of each year. The overlapping results in the formats ESRI-Shapefile, KML, GeoJSON and CSV, to which the download service is accessed, are provided by the City of Wuppertal as Open Data under the CC BY 4.0 license.

This data includes filings related to mortgage foreclosure in Allegheny County. The foreclosure process enables a lender to take possession of a property due to an owner's failure to make mortgage payments. Mortgage foreclosure differs from tax foreclosure, which is a process enabling local governments to take possession of a property if the owner fails to pay property taxes.

As Pennsylvania is a judicial foreclosure state, a lender files for foreclosure through the court system. Foreclosure data in the court system is maintained by the Allegheny County Department of Court Records. Data included here is from the general docket, and a mortgage foreclosure docket created to help homeowners maintain ownership of their property following an initial filing. Several different types of legal filings may occur on a property involved in the foreclosure process. At this time, only the most recent filing in a case is included in the data found here, but we hope to add all filings for a case in the coming months.

After a property enters the foreclosure process, several potential outcomes are possible. Some of the more common outcomes include: borrowers may come to an agreement with the lender for unpaid debt; borrowers may sell the property to satisfy part or all of the debt; borrowers may voluntarily relinquish ownership to the lender; lenders may decide not to pursue the foreclosure any further; and the property may proceed all the way through a sheriff sale, where it is sold to a new owner.

Before September 2022, the data presented here included only the final filing for the month in which each case (represented by Case ID) is opened; since then the feed has changed so we now have a new last_activity field, which gets updated whenever there is a new filing in the case with the date of the last filing for the month. The last_activity value gives some indication of which cases are still ongoing. (However, the new feed does not include the docket_type field, so these are blank for cases started after August 2022.) To view the detailed mortgage foreclosure filings for each property represented in this dataset, please visit the Department of Court Records Website, and enter the Case ID for a property to pull-up detailed information about each foreclosure case, including parties, docket entries, and services.

CHANGELOG

2022-12-14: Loaded data back to September (which had been missing due to the schema migration). Added a new last_activity field. Data since September 2022 is missing the docket_type value, for now those new values will be set to '' (empty string).

Agricultural land resources – a global suitability evaluation (v3.0)

Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature.

Determining Agricultural Suitability

Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach.

Agricultural Suitability

The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km² at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops.

Food, feed, fiber and first-generation bioenergy crops

Barley	Potato	Sugarbeet
Cassava	Rapeseed	Sugarcane
Groundnut	Rice	Sunflower
Maize	Rye	Summer wheat
Millet	Sorghum	Winter wheat
Oilpalm	Soybean

Second-generation bioenergy crops

Jatropha	Reed canary grass
Miscanthus	Eucalyptus
Switchgrass	Willow

Growing Season Adaptation

The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account.

Most Suitable Crop

The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability.

Further information

Detailled information are available in the following publications:

Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: 10.1371/journal.pone.0107522

Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: 10.1111/gcbb.12697

Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: 10.1038/s41597-022-01632-8

Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: 10.5194/hess-22-1119-201

Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Syst., 8, 41– 65, doi: 10.1002/2015MS000526.

Improvements in v3.0

Compared to the previous version (v2.0), this version (v3.0) uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops.

Contact

Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department of Geography, LMU München (www.geografie.uni-muenchen.de)

Trademark data aggregated across multiple Intellectual Property (IP) registries, including USPTO, CIPO, EUIPO and WIPO (USA, Canada, Europe). Full dataset updated weekly, available via customized reports, raw feed, or one-off reports. Full bibliographic data provided for each trademark record; filing date, registration date, NICE classification, Trademark name, type, etc. Ownership/entity relationship mapping, ticker mapping, ISIN mapping, Crunchbase uuid mapping, Crunchbase domain mapping. We also provide our proprietary IP Activity Score for each owner, which can assist to compare recent innovation activity amongst owners, as reflected in their intellectual property filings.

Ipqwery's Trademark dataset is also available as a combined dataset with our Patent dataset, enabling full IP profiles for corporate entities.