Median price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

House prices in England have increased notably in the last 10 years, despite a slight decline in 2023. In December 2024, London retained its position as the most expensive regional market, with the average house price at ******* British pounds. According to the UK regional house price index, Northern Ireland saw the highest increase in house prices since 2023.

Quarterly house price data based on a sub-sample of the Regulated Mortgage Survey.

Summary statistics for housing transactions by local authority in England and Wales, on an annual basis, updated quarterly using HM Land Registry Price Paid Data. Select values from the Year and Month dimensions for data for a 12-month period ending that month and year (e.g. selecting June and 2018 will return the twelve months to June 2018).

According to the forecast, the North West and Yorkshire & the Humber are the UK regions expected to see the highest overall growth in house prices over the five-year period between 2025 and 2029. Just behind are the North East and West Midlands. In London, house prices are expected to rise by **** percent.

This dataset contains a cleaned and enhanced version of publicly available UK housing transaction data, sourced from HM Land Registry. It covers housing sales across England, Wales, Scotland, and Northern Ireland from 2000 to 2023.

The dataset is preprocessed for immediate use in machine learning, statistical analysis, and data storytelling tasks. Here’s your UK Housing Dataset Column Descriptor, followed by where and how to apply it in Hugging Face or Kaggle:

Column Descriptions (for cleaned_uk_housing_prices.csv)

| price_per_sqm | Float | Estimated price per square meter (if available) | | log_price | Float | Log-transformed sale price (useful for ML models) |

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.

This house price per square metre dataset is created through complex address-based matching between the Land Registry’s Price Paid Data (LR-PPD) and property size information from the Domestic Energy Performance Certificates (EPC) data published by the Department for Levelling Up, Housing and Communities (DLUHC, formerly MHCLG). Details of the data linkage are published in the UCL Open: Environment along with the related linkage code via the UK Data Service ReShare repository. During this data linkage process, the transactions assigned as category B (Additional Price Paid entry) and other property types are removed. Here we publish our latest limited attribute version of the uncorrected house price per square metre dataset in England and Wales with the LR-PPD data (1/1/1995-26/2/2021) and Domestic EPCs data (the sixth version, up to 20/9/2020) downloaded on 1/4/2021 for non-commercial purpose. This uncorrected version of house price per square metre dataset records over 18 million transactions with 16 variables in England and Wales since 1995. Unlike in our published article, in this uncorrected version we have not removed transactions with any improbable price per square metre values - i.e. where either the transaction price or total floor area values are null, 0 or too low to be realistic. This uncorrected version of the data will offer the most flexibility for researchers. We offer technical validation and data cleaning code via the UKDA ReShare repository to help users evaluate the representation of the linked data for a given time period. The data cleaning code shows our methods for cleaning up unlikely floor size records before using this data in analysis. Users can create their own rules and undertake this clean-up process based on their own experience and research aims. This limited attribute version is published by local authority (2021 version). Details of the 16 variables are described in the explanation file. The National Statistics Postcode Lookup NSPL (May 2021 version) is used to assign the local authority unit for your production of area-based statistics. Users can match historical changes in LA boundaries by choosing appropriate aggregations using, for instance ONSPD, and the postcode variable in our dataset. An extended version of this dataset containing additional variables is available from UK Data Service Reshare service. Users can directly access this full version dataset (tranall_link_01042021.zip) via the following link: https://reshare.ukdataservice.ac.uk/855033/ . Accompanying LR-PPD and EPC data are also supplied through the ReShare service. Users who would like to attach their own additional variables from the LR-PPD data are advised to use the transactionid variable to link to the LR-PPD (LRPPD_01042021.zip). Users who would like to attach additional variables from the EPC data are advised to use the id variable to link to the sixth version Domestic EPCs (epc6_id.zip). The 2024 update

Latest property prices Source: Land Registry Publisher: Land Registry Geographies: Local Authority District (LAD), Postcode Geographic coverage: England

Annual mean and median property prices calculated by the GLA from Price Paid Data published on Land Registry website. Number of property sales also included. Data has been aggregated to Borough, Ward, MSOA, LSOA, Postcode Districts and Postcode Sectors. Caution should be used when analysing figures based on a low number of sales. Price Paid Data provides information on every residential property sale in England and Wales that has been lodged with HM Land Registry for registration. Download full price paid data from the Land Registry. Click here to access an interactive dashboard using some of the data available from this page.

Dataset Description:

This dataset has been meticulously pre-processed from the official UK government’s Price Paid Data, available for research purposes. The original dataset contains millions of rows spanning from 1995 to 2024, which posed significant challenges for machine learning operations due to its large size. For this project, we focused on house price predictions and filtered the data to only include transactions from 2015 to 2024. The final dataset contains 90,000 randomly sampled records, which should be ideal for training machine learning models efficiently. The goal of this dataset is to provide a well-structured, pre-processed dataset for students, researchers, and developers interested in creating house price prediction models using UK data. There are limited UK house price datasets available on Kaggle, so this contribution aims to fill that gap, offering a reliable dataset for dissertations, academic projects, or research purposes. This dataset is tailored for use in supervised learning models and has been cleaned, ensuring the removal of missing values and encoding of categorical variables. We hope this serves as a valuable resource for anyone studying house price prediction or real estate trends in the UK. In the future, I plan to provide an even larger dataset for more detailed and comprehensive predictions.

Features:

Feature Name - Description - Price - Sale price of the property (target variable). - Date - Date of the property transaction. Converted to datetime format for easier handling. - Postcode - Postcode of the property, offering location-based information. - property_type - Type of property (Detached, Semi-detached, Terraced, Flat, etc.). - new_build - Indicator whether the property was newly built at the time of sale (Yes or No). - freehold - Indicator whether the property was sold as freehold or leasehold (Freehold, Leasehold). - Street - Street name of the property location. - Locality - Locality of the property. - Town - Town or city where the property is located. - District - Administrative district of the property. - County - County where the property is located.

File Information:

The dataset is saved as a CSV file with 90,000 records, each representing a property transaction in the UK from 2015 to 2024. Feel free to explore this dataset and use it for any academic, research, or machine learning projects related to housing price predictions!

This repository is the fourth updated version of the attribute-linked residential property price dataset in the UK Data Service ReShare (854240) (https://reshare.ukdataservice.ac.uk/854240/). This dataset contains individual property transactions and associated variables from both Land Registry Price Paid Dataset (LR PPD) and the Department for Levelling Up, Housing and Communities (DLUHC, formerly MHCLG) Domestic Energy Performance Certificate (EPC) data. It is a linked dataset produced by address matching between LR PPD data (1/1/1995–31/10/2024) and Domestic EPC data (up to 31/10/2024). It is the full version of the 2024 update of the 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 (tranall_link_26122024) provided here is the initial, uncleaned version, intended to offer maximum flexibility for users to clean the data according to their research purposes. This linked dataset records over 22 million transactions with 106 variables across England and Wales, covering the period from 01/01/1995 to 31/10/2024. We have provided technical validation and data cleaning code in UKDA ReShare 854240 to help users evaluate the data structure and perform their own cleaning. There is no single way to clean this raw linked dataset, so we encourage users to develop their own cleaning process based on their research needs. This repository also includes the original Land Registry Price Paid Data (LR PPD) and Domestic EPCs used to create the linked dataset (house price per square metre dataset). Unlike previous versions, this updated dataset no longer includes the id variable (created by the authors). Instead, for the first time, both the Domestic EPCs and the linked dataset retain the LMK_KEY variable, which originates from the Domestic EPCs dataset. This change was made because LMK_KEY serves as a unique identifier, with no duplicate records since 2024. Five address-related variables from the original Domestic EPCs dataset(ADDRESS1, ADDRESS2, ADDRESS3, POSTCODE, and ADDRESS) have been removed from the EPC data in this repository. The priceper and classt variables were created by the authors and can be found in the linked dataset (tranall_link_26122024.zip). A detailed explanation of these fields is available on the GLA London Datastore (https://data.london.gov.uk/dataset/house-price-per-square-metre-in-england-and-wales). The lad23cd field originates from the NSPL dataset. Since November 2021, DLUHC has published Domestic EPCs with the Unique Property Reference Number (UPRN). As a result, both the EPC and the full linked dataset in this repository include UPRN information from the Domestic EPCs

Land Registry Price Paid Data (PPD) have been published as open data since 2013. These data have been transformative for house price variation research in the UK as they are a comprehensive record of residential transactions at address level and cover the whole of England and Wales over a period dating back to 1995. Despite the utility of these data, a lack of attribute information relating to the properties, such as total floor area information, is identified as one of the major shortcomings of the PPD data. This means that the impacts of stock mix on broader price patterns cannot be fully accounted for. This research outlines one approach which addresses this deficiency by combining transaction information from the official open Land Registry Price Paid Data (PPD) with property size information form the official open Domestic Energy Performance Certificates (EPCs). A four-stage data linkage is created to generate a new linked dataset, representing 79% of the full market sales in the Land Registry PPD. This new linked dataset details 5,732,838 transactions in England and Wales between 2011 and 2019, along with each property's total floor area and the number of habitable rooms. Codes for other commonly used spatial units from Output Area to Local Authority are also included in the dataset. This offers greater flexibility for the exploration of house price variation in England and Wales at different spatial scales. The data collection includes the scripts used for linkage, as well as the resulting dataset.

Current residential house price variation research in the UK is limited by lack of an open and comprehensive house price database that contains both transaction price alongside dwelling attributes such as size. This research outlines one approach which addresses this deficiency in England and Wales through combining transaction information from the official open Land Registry Price Paid Data (PPD) and property size information form the official open Domestic Energy Performance Certificates (EPCs). A four-stage data linkage is created to generate a new linked data, representing 79% of the full market sales in Land Registry PPD. This new linked dataset offers greater flexibility for the exploration of house price (house price per square metre) variation in England and Wales at different spatial scales over postcode unit between 2011 and 2019.

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.

The Council is required to publish this information under the Local Government Transparency Code 2015 set out by the Department of Local Government (DCLG), now the Ministry of Housing, Communities and Local Government (MHCLG). The Council has followed this guidance on data protection when compiling this information. The guidance requires the data for individual postcode sectors to be merged so that each postcode sector or merged postcode sectors contains at least 2,500 households. It also requires the data for valuation bands to be combined when the number of occupied social housing properties in any valuation band is less than 10. The data is merged with the next lowest valuation band so that no individual valuation band contains the data for less than 10 properties. This guidance requires Councils to explain the difference between the tenanted sale value of dwellings within the Housing Revenue Account and their market sale value and to include assurance that the publication of this information is not intended to suggest that tenancies should end to realise the market value of properties. The market value is the Council’s estimate of the total sum that it would receive if all the dwellings were sold on the open market. The existing use value for social housing value (EUV-SH) is calculated on the basis of rents receivable on existing tenancies. These are less than the rent that would be obtainable on the open market, and the EUV-SH value is therefore lower than the market value. The difference between the two values therefore shows the economic cost of providing housing at less than market value.

The data in this data set was provided by HM Treasury and details mortgage completions on properties supported by Help to Buy: mortgage guarantee completions, by local authority, England. The data set covers the period 8 October 2013 to 30 June 2014.

A dataset highlighting properties affected by the recent flooding in the Leeds region broken down by postcode sector. Please note Properties described as flats may have partially flooded but each property would be logged individually. Some locations do not have a postcode sector, this may be due to the property being a parent shell, playing field, sports ground, allotment or the land is used for storage. A parent shell is a 'wrapper' used for a building that contains a number of properties such as a block of flats or group of offices. The parent shell represents the shared or communal feature within a building such as corridors, stairwells or lifts. Priority Descriptions 1 - Internal flooding (inc.occupied cellar /multi storey lower level car park) 2 - Imminent threat of internal flooding (e.g. underdrawings) 3 - Multiple flooding incidents / Area flooding / Repeat flooding 3A - Multiple Occupancy Property 4 - Nuisance to highway network / Environment 5 - Other internal flooding (e.g.cellar, outbuilding)