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.

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.

Prices for prime residential real estate in Central London were expected to decline slightly in 2024, followed by a gradual increase until 2028, according to a *********** forecast. During the five-year period, the prices are forecast to rise by **** percent. In comparison, regional prime property prices and Outer London prime property prices are forecast to grow at a lower rate.

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.

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

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 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.

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.

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.

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...

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.

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.

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.

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

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. ...

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

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.

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

Prices for prime residential real estate in Outer London are expected to grow year-on-year, achieving a cumulative increase of over **** percent until 2029. Growth is expected to be slower at first but accelerate toward the end of the period. Meanwhile, Central London prime property prices are projected to experience a slower growth rate.