The borough with the highest property prices in London, Kensington and Chelsea, had an average price for a flat exceeding *** million British pounds. London is the most populous metropolitan area in the UK, and living in it comes with a price tag. Unsurprisingly, the most expensive boroughs in terms of real estate prices are located in the heart of the metropolis: Kensington and Chelsea, the City of Westminster, and the City of London. In Kensington and Chelsea, home to several museums such as the Natural History Museum, the Victoria and Albert Museum, and the Science Museum, as well as galleries and theaters, the average price of apartments was over a million British pounds. How have residential property prices developed in recent years? The average house price in England have risen notably over the past decade, despite a slight decline in 2023. While London continues to be the hottest market in the UK, price growth in the capital has moderated. Conversely, prices in the more affordable cities, such as Belfast and Liverpool, have started to rise at a faster pace. Are residential property prices in London expected to grow in the future? Despite property prices declining in 2024, the market is forecast to continue to grow in the next five years, according to a October 2024 forecast. Some of the reasons for this are the robust demand for housing, the chronic shortage of residential properties, and the anticipated decline in mortgage interest rates.

This dataset contains detailed information about rental properties across various locations in the UK. The data was collected by scraping Rightmove, a popular real estate platform. Each entry in the dataset includes the property's address, subdistrict code, rental price, deposit amount, letting type, furnish type, council tax details, property type, number of bedrooms and bathrooms, size in square feet, average distance to the nearest train station, and the count of nearest stations.

Researchers and analysts interested in the UK rental market can utilize this dataset to explore rental trends, pricing variations based on location and property type, amenities preferences, and more. The dataset provides a valuable resource for machine learning models, statistical analysis, and market research in the real estate sector.

Metadata: Source: The data was collected by scraping the Rightmove real estate platform, a leading source for property listings in the UK. Date Range: The dataset covers rental property listings available during the scraping period. Geographical Coverage: Primarily focused on various locations across the UK, providing insights into regional rental markets. Data Fields: Address: The location of the rental property. Subdistrict Code: A code representing the subdistrict or area of the property. Rent: The monthly rental price in GBP (£) for the property. Deposit: The deposit amount required for renting the property. Let Type: Indicates whether the property is available for short-term or long-term rental. Furnish Type: Describes the furnishing status of the property (e.g., furnished, unfurnished, or flexible options). Council Tax: Information about the council tax associated with the property. Property Type: Specifies the type of property, such as apartment, flat, maisonette, etc. Bedrooms: The number of bedrooms in the property. Bathrooms: The number of bathrooms in the property. Size: The size of the property in square feet (sq ft). Average Distance to Nearest Station: The average distance (in miles) to the nearest train station from the property. Nearest Station Count: The count of nearest train stations within a certain distance from the property. Data Quality: The data may contain missing values or "Ask agent" placeholders, which require direct inquiry with agents or landlords for specific information. Potential Uses: The dataset can be used for market analysis, rental price prediction models, understanding property preferences, and exploring the impact of location and amenities on rental properties in the UK.

London was the most expensive city to buy an apartment in the United Kingdom, with an average value of ****** euros per square meter in the first quarter of 2025. The price of an apartment in Leeds was significantly lower at approximately ***** euros per square meter.

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 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.

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.

This statistic shows the average price of flats and maisonettes in the Greater London (United Kingdom) from the first quarter of 2012 to the fourth quarter of 2015, according to the Halifax house price index. In the fourth quarter of 2014, the average price of a flat or maisonettes in Greater London was 329.9 thousand British pounds (GBP). By the end of the fourth quarter of 2015, the price increased to 385.3 thousand GBP.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

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

Context

My first attempt to created dataset using Octoparse (data scraping).

Content

Dataset contains listing type (apartment, house, villa etc), price, link, location.

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.

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.

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 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

Canada Construction Price Index: Residential: Apartment: London data was reported at 107.800 2023=100 in Mar 2025. This records an increase from the previous number of 105.200 2023=100 for Dec 2024. Canada Construction Price Index: Residential: Apartment: London data is updated quarterly, averaging 101.800 2023=100 from Mar 2023 (Median) to Mar 2025, with 9 observations. The data reached an all-time high of 107.800 2023=100 in Mar 2025 and a record low of 98.700 2023=100 in Mar 2023. Canada Construction Price Index: Residential: Apartment: London data remains active status in CEIC and is reported by Statistics Canada. The data is categorized under Global Database’s Canada – Table CA.EA011: Construction Price Index: 2023=100.

The average price of houses bought by first-time buyers was notably lower than houses purchased by repeat buyers in London in 2024. Homebuyers spent on average 480,000 British pounds when purchasing their first property in 2024. For repeat buyers, this figure amounted to 850,000 British pounds in that year. In London, the average house price was about 630,000 British pounds in 2024.

The London house prices dataset contains details for property sales and contains around 1.38 million observations.

Dataset Overview

This dataset provides a snapshot of properties listed for sale in London, sourced from the Rightmove website. It includes various property details such as the number of bedrooms, bathrooms, type of property, and price. The dataset is designed for educational purposes, offering insights into real estate trends and allowing data science enthusiasts to apply their skills in the context of property analysis.

Data Science Applications

This dataset is a valuable resource for students and researchers to practice various data science and analytics techniques. Potential applications include: - Exploratory Data Analysis (EDA): Understanding property distribution across London, price trends, and property types. - Price Prediction Models: Building machine learning models to estimate property prices based on available features. - Real Estate Trend Analysis: Analyzing trends in London’s real estate market, such as price fluctuations or differences in property features by neighborhood. - Text Analysis: Using the property descriptions for natural language processing (NLP) to extract keywords or sentiment related to property value or appeal.

Column Descriptions

• addedOn: Date when the property listing was added or updated on the website.
• title: Brief listing title describing the property, typically including the number of bedrooms and the location.
• descriptionHtml: Detailed description of the property, including features and potentially some promotional language.
• propertyType: Type of property, such as House, Terraced, or Detached.
• sizeSqFeetMax: Maximum size of the property in square feet, if provided.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• listingUpdateReason: Reason for updating the listing (e.g., new listing, price reduction).
• price: Price at which the property is listed for sale.

Ethically Mined Data

This dataset was ethically mined from a publicly accessible website using the APIFY API. All data in this dataset reflects publicly available information about properties listed for sale, with no Personally Identifiable Information (PII) included. The dataset does not include any data that could infringe on individual privacy.

Acknowledgements

• Data Source: Rightmove for providing publicly accessible real estate listings.
• Image Credit: Photo by Douglas Sheppard on Unsplash.

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.

Graph and download economic data for All-Transactions House Price Index for Norwich-New London, CT (MSA) (ATNHPIUS35980Q) from Q3 1985 to Q2 2025 about Norwich, CT, appraisers, HPI, housing, price index, indexes, price, and USA.