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

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).

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

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.

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) |

London Property Prices Dataset 200k+ records Overview This dataset offers a comprehensive snapshot of residential properties in London, capturing both historical and current market data. It includes property-specific information such as address, geographic coordinates, and various price estimates. Data spans from past transaction prices to present estimates for sale and rental values, making it ideal for real estate analysis, investment modeling, and trend forecasting.

Key Columns fullAddress: Complete address of the property. postcode: Postal code identifying specific areas in London. outcode: First part of the postcode, grouping properties into broader geographic zones. latitude & longitude: Geographic coordinates for mapping or location-based analysis. property details: Includes bathrooms, bedrooms, floorAreaSqM, livingRooms, tenure (e.g., leasehold or freehold), and propertyType (e.g., flat, maisonette). energy rating: Current energy rating, indicating the property’s energy efficiency. Pricing Information Rental Estimates: Ranges for estimated rental values (rentEstimate_lowerPrice, rentEstimate_currentPrice, rentEstimate_upperPrice). Sale Estimates: Current sale price estimates with confidence levels and historical changes. saleEstimate_currentPrice: Current estimated sale price. saleEstimate_confidenceLevel: Confidence in the sale price estimate (LOW, MEDIUM, HIGH). saleEstimate_valueChange: Numeric and percentage change in sale value over time. Transaction History: Date-stamped sale prices with historic price changes, providing insight into property appreciation or depreciation. Potential Applications This dataset enables a variety of analyses:

Market Trend Analysis: Track how property values and rents have evolved over time. Investment Insights: Identify high-growth areas and property types based on historical and estimated price changes. Geospatial Analysis: Use location data to visualize price distributions and trends across London. Usage Recommendations This dataset is well-suited for machine learning projects predicting property values, rent estimations, or analyzing urban property trends. With rich details spanning multiple facets of the real estate market, it’s an essential resource for data scientists, analysts, and investors exploring the London property market.

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 on the image below to access an interactive dashboard using some of the data available from this page.

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

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

Art Presence & London Property Prices

Quantifying Visual Environment at Scale

By [source]

About this dataset

Explore a new and different way to measure the relationship between art presence and property prices in Inner London neighbourhoods. By quantifying the visual environment at scale with geotagged Flickr photos containing the word “art,” this dataset can help us garner an understanding of how aesthetic values translate into its economic value. Using data from the Land Registry of England and Wales, this dataset allows users to spot correlations between property values and art presence through visual analysis of postcode districts plotted against rank change in prices and proportion of “art” photos. Investigate whether aesthetics, particularly within urban neighbourhoods, have a bearing on local house pricing markets – adding a valuable insight into London’s ever-changing social landscape

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How to use the dataset

This dataset provides a useful tool for determining the correlation between the visual environment in a given neighbourhood and its associated property values. This dataset can be used to gain insights into how art presence in an area affects housing prices.

To work with this dataset, you will first need to download it as a csv file or as an XML file. Once you have downloaded your desired version of the data, open it in your favorite spreadsheet program or text editor for further manipulation and analysis.

The two key columns you will want to focus on are Rank Mean Change and Proportion Art Photos. The Rank Mean Change column indicates how each neighbourhood ranked based on its mean property price change from Jan 1995 to Mar 2017, while Proportion Art Photos denotes the proportion of photographs taken within these areas containing the word “art”. You may also want to take note of Postcode Districts as this indicates which neighbourhood each row corresponds to making it easier for contextualizing results at a place-based level.

From here you can conduct linear regression analysis using Rank Mean Change and Proportion Art Photos as independent variables, allowing you to determine whether there is indeed any correlation between art presence in London neighbourhoods and their property values over time

Research Ideas

• Correlating the value of properties with art presence to inform investment decisions in residential real estate.
• Utilizing Photographs from Flickr as a tool to monitor changes in art presence and creative expression over time.
• Investigating the effects of art preservation/creation initiatives on property values to determine their potential effectiveness

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: London_Prices_Flickr_Art_Agg.csv | Column name | Description | |:--------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Postcode.District | This column indicates the postcode district of each neighbourhood in Inner London. (String) | | Rank.Mean.Change | This column indicates the rank of each neighbourhood based on its mean change in property prices over time. (Integer) | | Proportion.Art.Photos | This column captures the proportion of photographs containing “art” within each postcode district during a given time period, allowing us to measure art presence at scale across inner London neighbourhoods. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. ...

Art Presence & Property Prices in London

Quantifying the Relationship with Online Data

By [source]

About this dataset

This dataset explores the potential relationship between art presence and property prices in London neighborhoods. We conducted an analysis to investigate this by measuring the proportion of Flickr photographs with the keyword ‘art’ attached. We then compared that data to residential property price gains for each Inner London neighborhood, seeking out any associations or correlations between art presence and housing value. Our findings demonstrate the impact of aesthetics on neighborhoods, illustrating how visual environment influences socio-economic conditions. With this dataset, we aim to show how online platforms can be leveraged for quantitative data collection and analysis which can visualize these relationships so as to better understand our urban settings

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How to use the dataset

This dataset can be used to investigate the relationship between art presence and property prices in London neighborhoods. The dataset includes three columns – Postcode.District, Rank.Mean.Change, and Proportion.Art.Photos – which provide quantitative analyses of the association between art presence and price gains for London neighborhoods.

To use this dataset, first identify the postcode district for which you wish to access data by referencing a street list or PostCodeSearcher website that outlines postcodes for each neighborhood in London(http://postcodesearcher.com/london). This will allow you to easily find properties within each neighborhood as there are specific postcode districts that demarcate boundaries of particular areas (for example W2 covers Bayswater).

Once you have identified a postcode district of interest, review the ‘Rank.Mean Change’ column to explore how residential property prices have changed relative to other areas in Inner London since 2010-13 using fractions (1 = highest gain; 25 = lowest gain). Focusing on one particular location will also provide an idea about their current pricing level compared with others in order to evaluate whether further investment is worthwhile or not based on its past history of growth rates . It is important to note that higher rank numbers indicate higher price gains while lower rank numbers indicate lower price gains relative with respect from 2010-13 timeframe therefore comparing these values across many neighborhoods gives an indication as what area offers more value growth wise over given time period..

Finally pay attention how much did art contributes as far change in property price goes? To answer this question , review ‘Proportion Art Photos’ column which provides ratio of Flickr photographs associated with keyword 'art' attached within given regions helps identify visual characteristics within different localities.. Comparing proportions across various locations provide detail information regarding how much did share visual aesthetic characterstics impacts change in pricings accross different region.. For example it can give us further understandings if majority photographs are made up of urban landscape , abstracts or simply portrait presences had any role play when we look at relativity gains over past few years? Such comparisons help inform our understanding about potential impact art presence can have on changes stay relatively stable even during volatile market times..

By combining this data with other datasets related to demographics, infrastructure and socioeconomics present within londons different areas we can gain further insight which then allows us making informed decisions when it comes investing particular locations .

Research Ideas

• Use this dataset to develop a predictive analytics model to identify areas in London most likely to experience an increase in residential property prices associated with the presence of art.
• Use this dataset to develop strategies and policies that promote both artistic expression and urban development in Inner London neighborhoods.
• Compare the presence of art across inner London boroughs, as well as against other cities, to gain insight into the socio-economic conditions related to the visual environment of a city and its impact on life quality for citizens

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**License: [CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication](https://creativecommons.org/publicd...

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.

London Property Listings Dataset

Description

This dataset expands upon the original London Property Listings by including additional attributes to facilitate deeper analysis of rental properties in London. It is ideal for research and projects related to real estate trends, price categorization, and area-wise analysis in one of the world's busiest markets.

Dataset Features

• Price: Monthly rental price in GBP.
• Property Type: Classification of the property (e.g., Apartment, Flat).
• Bedrooms: Number of bedrooms in the property.
• Bathrooms: Number of bathrooms.
• Size: Property size in square feet (where available).
• Postcode: Postal code of the property location.
• Area: General area or neighborhood information.
• Price_Category: Categorization of prices into predefined ranges (e.g., Low, Medium, High).
• Area_Avg_Price: Average price of properties within the same area.

Potential Use Cases

• Price Analysis: Study how property attributes impact rental prices.
• Price Prediction Models: Utilize the dataset for machine learning tasks like regression and classification.
• Regional Insights: Compare rental trends across different neighborhoods.
• Categorical Analysis: Investigate trends within predefined price categories.

Data Summary

• Total Records: 29,537
• Total Attributes: 9
• Data Completeness: No missing values. All columns are fully populated.

Attribution

This dataset was prepared and uploaded by Mehmet Emre Sezer. It is intended for educational and non-commercial use.

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

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 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.