The monthly house price index in London has increased since 2015, albeit with fluctuation. In August 2025, the index reached 99.1, which is a slight decrease from the same month in 2024. Nevertheless, prices widely varied in different London boroughs, with Kensington and Chelsea being the priciest boroughs for an apartment purchase.

The house price index in London reached 99.1 index points in May 2025, which was an increase of 2.2 percent year on year. The house price index (HPI) is an easy way of illustrating trends in the house sales market and help simplify house purchase decisions. By using hedonic regression, the index models property price data for all dwellings and shows how much the price has changed since January 2023. Average house prices in Londnon boroughs Location plays a huge role in the price of a home. Kensington and Chelsea and City of Westminster are undoubtedly the most expensive boroughs in London, with an average house price that can exceed one million British pounds. In comparison, a house in Barking and Dagenham cost approximately one third. Nevertheless, the housing market is the busiest in the boroughs with average house prices. How have regional house prices in the UK developed? House prices in other UK regions have risen even more than in London. In Northern Ireland, the house price index reached nearly 120 index points in May 2025, ranking it among the regions with the highest property appreciation. The UK house price index stood at 103 index points, suggesting an increase of 51 percent since 2015.

Housing Index in the United Kingdom increased to 517.10 points in October from 514.20 points in September of 2025. This dataset provides - United Kingdom House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The average mix-adjusted house price in London, England, peaked in August 2022, followed by a slight correction in 2023. In June 2024, the average house price amounted to about ******* British pounds, up from ******* British pounds a year ago. These recent fluctuations have also been observed by other measures, such as the house price index. The house price index is an important measure for the residential real estate market and is used to show changes in the value of residential properties.

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.

According to the forecast, house prices in London are expected to continue to increase until 2029. During the five-year period from 2025 to 2029, the house prices for mainstream properties are forecast to rise by **** percent. In 2023, the average house price in London ranged between ******* British pounds and *** million British pounds, depending on the borough. Barking and Dagenham, Bexley, Newham, and Croydon were some of the most affordable boroughs to buy a house.

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.

The house price dataset generated with 100 samples of houses in London. Each row of the DataFrame represents a single house and its features. The features of the houses in the dataset include:

Address: The address of the house, including the street name and number, as a string Square Footage: The total square footage of the house, as an integer Bedrooms: The number of bedrooms in the house, as an integer Bathrooms: The number of bathrooms in the house, as an integer Has Garden: A binary feature indicating whether the house has a garden or not, represented as 0 or 1 Has Garage: A binary feature indicating whether the house has a garage or not, represented as 0 or 1 Has Pool: A binary feature indicating whether the house has a pool or not, represented as 0 or 1 Has Gym: A binary feature indicating whether the house has a gym or not, represented as 0 or 1 Has Elevator: A binary feature indicating whether the house has an elevator or not, represented as 0 or 1 Has Fireplace: A binary feature indicating whether the house has a fireplace or not, represented as 0 or 1 Is Waterfront: A binary feature indicating whether the house is waterfront or not, represented as 0 or 1 Has Central Air: A binary feature indicating whether the house has central air or not, represented as 0 or 1 Is Renovated: A binary feature indicating whether the house is renovated or not, represented as 0 or 1 Has View: A binary feature indicating whether the house has a view or not, represented as 0 or 1 Price: The estimated price of the house, calculated based on the square footage, number of bedrooms, and number of bathrooms.

All of the features in the dataset are randomly generated, and the price is calculated based on simple formula that is not necessarily representative of the real world.

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

Average House Prices in the United Kingdom increased to 299862 GBP in October from 298215 GBP in September of 2025. This dataset includes a chart with historical data for the United Kingdom Average House Prices.

Graph and download economic data for Real Residential Property Prices for United Kingdom (QGBR628BIS) from Q2 1968 to Q2 2025 about United Kingdom, residential, HPI, housing, real, price index, indexes, and price.

House prices in the UK rose dramatically during the coronavirus pandemic, with growth slowing down in 2022 and turning negative in 2023. The year-on-year annual house price change peaked at 14 percent in July 2022. In April 2025, house prices increased by 3.5 percent. As of late 2024, the average house price was close to 290,000 British pounds. Correction in housing prices: a European phenomenon The trend of a growing residential real estate market was not exclusive to the UK during the pandemic. Likewise, many European countries experienced falling prices in 2023. When comparing residential property RHPI (price index in real terms, e.g. corrected for inflation), countries such as Germany, France, Italy, and Spain also saw prices decline. Sweden, one of the countries with the fastest growing residential markets, saw one of the largest declines in prices. How has demand for UK housing changed since the outbreak of the coronavirus? The easing of the lockdown was followed by a dramatic increase in home sales. In November 2020, the number of mortgage approvals reached an all-time high of over 107,000. One of the reasons for the housing boom were the low mortgage rates, allowing home buyers to take out a loan with an interest rate as low as 2.5 percent. That changed as the Bank of England started to raise the base lending rate, resulting in higher borrowing costs and a decline in homebuyer sentiment.

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

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

Median price paid for residential property in England and Wales, for all property types by lower layer super output area. Annual data..

Median price paid for residential property in England and Wales by property type and electoral ward. Annual data.

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.

Graph and download economic data for All-Transactions House Price Index for New London County, CT (ATNHPIUS09011A) from 1977 to 2024 about New London County, CT; Norwich; CT; HPI; housing; price index; indexes; price; and USA.

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.

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. ...