The number of mortgage possessions spiked in the first quarter of 2023, followed by ***** quarters of decline. Possession actions occur when a borrower fails to repay their loan on time and the lender takes possession of the property. In the fourth quarter of 2023, there were *** possessions of properties occupied by homeowners and *** possessions of buy-to-let properties.

In 2024, the average age of recent first-time buyers in London was slightly higher than the England average. Across the UK, first-time buyers accounted for approximately ******* home sales. First-time buyer prices and mortgages In London, the average value of a mortgage for first-time buyers was far higher than all other regions in the UK. Apart from the initial cost of a down payment, those that can afford to, see monthly payment savings against those renting. In certain parts of the country, annual savings of buying against renting saw first time buyers amounted to over ************ British pounds. Help to buy To encourage first-time buyers, the UK government started the "Help to buy" scheme. The scheme sees people saving for a first-time home receive a ***********bonus to their savings when purchasing a house valued at ******* British pounds (******* British pounds in London). Between December 2015 and March 2018, the North West of England saw the highest number of Help to buy ISA bonuses paid.

This statistic displays the number of first time home buyers in the United Kingdom (UK) in 2016, by country. England had the highest number of first time buyers in 2016 with almost *** thousand first time buyers.

The United Kingdom Home Insurance Market is Segmented by Coverage (Building, Content, Combined Building and Content), Customer Type (Homeowner, Tenants and More), Property Type (Detached, Semi-Detached, Terraced, Flat and Apartments), Distribution Channel (Direct, Bancassurance, Brokers, Aggregators and More), and Region (England, Scotland, Wales, Norther Ireland). The Market Forecasts are Provided in Terms of Value (USD)

Financial cost was the major obstacle preventing homeowners in the United Kingdom (UK) from improving the energy sustainability of their primary property, a survey conducted in the second quarter of 2023 found. About ** percent of the ***** homeowners surveyed were not planning to renovate their home in the next 10 years. The majority of them, about ** percent, cited the costs as the main reason. Additionally, ** percent feared the disruption that the renovation works would cause.

For the past decade, buying a home in the UK has been more affordable than renting one, when only considering the monthly costs. The renting versus buying gap fluctuated during the period and in 2016, it reached its highest value of 131 British pounds. In 2023, the monthly costs for a first-time buyer were 1,231 British pounds, compared to 1,258 British pounds for renters. Rental growth vs house price growth Housing costs in the UK have been on an uprise, with both renting and buying a home increasingly unreachable. Though the monthly costs of buying have consistently been lower in the past decade, house price growth has been much stronger than rental growth since the beginning of the pandemic. Additionally, buyers have been affected by the aggressive mortgage rate hikes, making acquiring their first home even less affordable. Barriers to homeownership Buying a home is not straightforward. For younger (18-40) potential first-time buyers, there are a number of barriers. Approximately one in three first-time buyers point out that raising a deposit was the main obstacle. Other reasons stopping buyers were not being able to take out a mortgage on their current income and poor credit ratings. Unsurprisingly, the highest share of people who buy a home with a mortgage was in the age group of 45 to 55-year-olds.

United Kingdom House Price: Qtr: Former Owner Occupier: UK data was reported at 252,145.738 GBP in Sep 2018. This records an increase from the previous number of 249,890.350 GBP for Jun 2018. United Kingdom House Price: Qtr: Former Owner Occupier: UK data is updated quarterly, averaging 96,616.959 GBP from Mar 1983 (Median) to Sep 2018, with 143 observations. The data reached an all-time high of 252,145.738 GBP in Sep 2018 and a record low of 30,398.853 GBP in Mar 1983. United Kingdom House Price: Qtr: Former Owner Occupier: UK data remains active status in CEIC and is reported by Nationwide. The data is categorized under Global Database’s United Kingdom – Table UK.P001: House Price: Nationwide.

United Kingdom House Price: Qtr: Former Owner Occupier: North data was reported at 156,788.316 GBP in Jun 2018. This records an increase from the previous number of 155,157.670 GBP for Mar 2018. United Kingdom House Price: Qtr: Former Owner Occupier: North data is updated quarterly, averaging 63,095.967 GBP from Mar 1983 (Median) to Jun 2018, with 142 observations. The data reached an all-time high of 157,684.989 GBP in Sep 2007 and a record low of 26,061.468 GBP in Mar 1983. United Kingdom House Price: Qtr: Former Owner Occupier: North data remains active status in CEIC and is reported by Nationwide. The data is categorized under Global Database’s UK – Table UK.P001: House Price: Nationwide.

This page is no longer being updated. Please use the UK House Price Index instead. Mix-adjusted house prices, by new/pre-owned dwellings, type of buyer (first time buyer) and region, from February 2002 for London and UK, and average mix-adjusted prices by UK region, and long term Annual House Price Index data since 1969 for London. The ONS House Price Index is mix-adjusted to allow for differences between houses sold (for example type, number of rooms, location) in different months within a year. House prices are modelled using a combination of characteristics to produce a model containing around 100,000 cells (one such cell could be first-time buyer, old dwelling, one bedroom flat purchased in London). Each month estimated prices for all cells are produced by the model and then combined with their appropriate weight to produce mix-adjusted average prices. The index values are based on growth rates in the mix-adjusted average house prices and are annually chain linked. The weights used for mix-adjustment change at the start of each calendar year (i.e. in January). The mix-adjusted prices are therefore not comparable between calendar years, although they are comparable within each calendar year. If you wish to calculate change between years, you should use the mix-adjusted house price index, available in Table 33. The data published in these tables are based on a sub-sample of RMS data. These results will therefore differ from results produced using full sample data. For further information please contact the ONS using the contact details below. House prices, mortgage advances and incomes have been rounded to the nearest £1,000. Data taken from Table 2 and Table 9 of the monthly ONS release. Download from ONS website

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 BGS PSA dataset provides insurers and homeowners access to a better understanding of the shrink-swell hazard at both the individual property and/or postcode level for Great Britain. It builds upon the GeoSure shrink-swell data by mapping the hazard to the individual building polygon and considering the other susceptibility factors of building type, foundation depth, and drainage and tree proximity. The user receives GIS building polygons with an overall susceptibility to subsidence score between 1-100. Scores are also classified from non-plastic to very high. Each building polygon is also scored from 1-10 for each subsidence factor (geology, foundation, drainage, building type, building storey and tree proximity). Postcode data is also available as a table and shapefiles showing the ‘average’ PSA score for all buildings within the postcode. The identification of shrink-swell related subsidence prone areas, alongside the inclusion of potential sources to exacerbate this phenomena, can better inform insurers and homeowners and form the basis to make decisions concerning prevention and remediation. The product enhances geological information obtained from GIP and GeoSure via the inclusion of the crucial shrink-swell susceptibility factors (proximity to trees and foundation depth). This therefore allows the derivation of a risk element for the housing stock at Building level, which is then generalised to Postcode level.

This data set contains Help to Buy: Equity Loan statistics at post code sector level.

The figures cover the launch of the scheme on 1 April 2013 until 31 October 2014.

Information on the allocation of completed sales to postcode sectors is derived using the latest available information on the full postcode for each scheme. Figures have been attributed to an individual postcode sector by reconciling data against the ONS Postcode Directory (May 2014) where possible. Figures may be subject to revision later in the year.

For sales before 31 March 2014, properties are included under the local authority district to which they were initially allocated. In some cases, this differs from latest information, which forms the basis of the first column of local authority district figures. Figures for some local authorities may be subject to revisions later in the year. Although local authority information is validated against other geographic data at the time of data entry, detailed reconciliation of the data, conducted twice a year, may result in a small number of changes to these monthly releases, for example where a new development crosses a local authority boundary.

An equity loan is Government financial assistance given to eligible applicants to purchase an eligible home through a Government equity mortgage secured on the home. The Government equity mortgage is ranked second in priority behind an owner’s main mortgage lender.

This scheme offers up to 20 per cent of the value as Government assistance to purchasers buying a new build home. The buyer must provide a cash deposit of at least 5 per cent and a main mortgage lender must provide a loan of at least 75 per cent.

The Government assistance to buy is made through an equity loan made by the Homes and Communities Agency (HCA) to the purchaser.

Help to Buy equity loans are only available on new build homes and the maximum purchase price is £600,000. Equity loan assistance for purchasers is paid via house builders registered with the HCA to participate in the Help to Buy equity loan initiative. The payment is made to builders (via solicitors) at purchaser legal completion.

The equity loan is provided without fees for the first five years of ownership.

The property title is held by the home owner who can therefore sell their home at any time and upon sale should provide the government the value of the same equity share of the property when it is sold.

For further information see
Help to Buy (equity loan) scheme monthly statistics.

Summary of UK House Price Index (HPI) price statistics covering England, Scotland, Wales and Northern Ireland. Full UK HPI data are available on GOV.UK.

This dataset contains qualitative data collected using semi-structured interviews and a structured survey at four time points during the REFIT field trial of smart home technologies which involved 20 households. A related data collection containing survey data is available via Related Resources /Related Data collections. This was also part of the part of the REFIT project. A national survey was conducted to measure perceptions of smart homes. The survey instrument was developed and tested by the project team. The survey was implemented online during September - October 2015 by a market research company using a representative sample of UK homeowners. A total of 1054 responses were collected.

These datasets were collected as part of the REFIT project (`Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’). The REFIT project ran from 2012 - 2015 as a consortium of three universities - Loughborough, Strathclyde and East Anglia - and ten industry stakeholders.

During the REFIT project, twenty households were recruited into a field trial of smart home technologies in Loughborough, UK. The field trial ran from October 2013 - October 2015. Smart home technologies were installed in participating households from May - August 2014.

During the field trial, the REFIT project team collected quantitative data on real-time electricity and gas usage (from smart meters) and qualitative data on perceptions and usage of smart home technologies. This dataset contains the qualitative data.

The qualitative data were collected using semi-structured interviews, surveys, and video ethnography. The video files are not shareable (as they cannot be anonymised). This dataset contains transcripts, notes and responses from the semi-structured interviews and surveys.

The interview and survey data were collected at four time points or cross-sections during the two year field trial. All twenty households participated at time point one (on agreeing to participate in the field trial) and time point two (prior to installation of the smart home technologies). A subset of ten households participated at time point three (immediately after installation of the smart home technologies) and time point four (after installation of the smart home technologies).

Annual house price inflation, simple and mix-adjusted average house prices, by dwelling, type of buyer, number of transactions, mortgage advances, distribution of borrowers' ages/incomes, interest rates, land prices, average valuations, Land Registry data

Affordability ratios calculated by dividing house prices by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

This statistic illustrates the time taken for homeowners in the United Kingdom (UK) to decide to purchase their property in 2015. The most typically quoted time for decision about purchase was: after viewing the whole property, reported by ** percent of the sample. Interestingly, ***** percent of homeowners surveyed said that they decided to buy their house before even setting their foot in the property.