The price of terraced housing in Bromley rose by *** percent year-on-year in May 2025, making it the borough with the highest price increase in London. At about ******* British pounds, the most affordable borough in London to purchase a terraced house was Barking and Dagenham. The same borough also had the cheapest average price for semi-detached homes in London.

More than **** of London's boroughs witnessed an annual decrease in residential property prices as of June 2024. The City of Westminster, one of the most expensive areas for housing in London, experienced the greatest decline in prices, amounting to ** percent year-on-year. In Bexley, the borough with the highest increase, the cost of buying a residential property rose by *** percent. The City of Westminster, Kensington and Chelsea, and the City of London, which happen to be

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.

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...

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.

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

This Power BI project analyzes London housing data to uncover patterns and trends in pricing, borough-wise distribution, affordability, and market growth over time. The dashboard is designed for anyone interested in property investment, urban planning, or simply understanding how the London housing market behaves.

✅ Key Features:

📍 Borough-wise Price Distribution Understand how average property prices vary across different London boroughs.

📈 Trend Analysis Visualize long-term price trends with dynamic line and area charts to observe how the market has evolved over time.

🧮 Affordability Index A calculated metric to measure housing affordability based on price vs income estimations.

🏘️ Property Type Breakdown Interactive visuals showing trends across Flats, Detached, Semi-Detached, and Terraced houses.

🗓️ Time Filters & Slicers Easily filter by year, month, or borough to explore specific time periods or locations.

This table shows the average House Price/Earnings ratio, which is an important indicator of housing affordability. Ratios are calculated by dividing house price by the median earnings of a borough.

The Annual Survey of Hours and Earnings (ASHE) is based on a 1 per cent sample of employee jobs. Information on earnings and hours is obtained in confidence from employers. It does not cover the self-employed nor does it cover employees not paid during the reference period. Information is as at April each year. The statistics used are workplace based full-time individual earnings.

Land Registry housing data are for the first half of the year only, so that they comparable to the ASHE data which are as at April.
Prior to 2006 data are not available for Inner and Outer London.

The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile.
The "lower quartile" property price/income is determined by ranking all property prices/incomes in ascending order.
The 'median' property price/income is determined by ranking all property prices/incomes in ascending order. The point at which one half of the values are above and one half are below is the median.

Regional data has not been published by DCLG since 2012. Data for regions has been calculated by the GLA. Data for 2014 has been calculated by the GLA.

Link to DCLG Live Tables

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

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.

Update 29-04-2020: The data is now split into two files based on the variable collection frequency (monthly and yearly). Additional variables added: area size in hectares, number of jobs in the area, number of people living in the area.

Context

I have been inspired by Xavier and his work on Barcelona to explore the city of London! 🇬🇧 💂

Content

The datasets is primarily centered around the housing market of London. However, it contains a lot of additional relevant data: - Monthly average house prices - Yearly number of houses - Yearly number of houses sold - Yearly percentage of households that recycle - Yearly life satisfaction - Yearly median salary of the residents of the area - Yearly mean salary of the residents of the area - Monthly number of crimes committed - Yearly number of jobs - Yearly number of people living in the area - Area size in hectares

The data is split by areas of London called boroughs (a flag exists to identify these), but some of the variables have other geographical UK regions for reference (like England, North East, etc.). There have been no changes made to the data except for melting it into a long format from the original tables.

Acknowledgements

The data has been extracted from London Datastore. It is released under UK Open Government License v2 and v3. The underlining datasets can be found here: https://data.london.gov.uk/dataset/uk-house-price-index https://data.london.gov.uk/dataset/number-and-density-of-dwellings-by-borough https://data.london.gov.uk/dataset/subjective-personal-well-being-borough https://data.london.gov.uk/dataset/household-waste-recycling-rates-borough https://data.london.gov.uk/dataset/earnings-place-residence-borough https://data.london.gov.uk/dataset/recorded_crime_summary https://data.london.gov.uk/dataset/jobs-and-job-density-borough https://data.london.gov.uk/dataset/ons-mid-year-population-estimates-custom-age-tables

Cover photo by Frans Ruiter from Unsplash

Inspiration

The dataset lends itself for extensive exploratory data analysis. It could also be a great supervised learning regression problem to predict house price changes of different boroughs over time.

FOCUSONLONDON2011: HOUSING:AGROWINGCITY With the highest average incomes in the country but the least space to grow, demand for housing in London has long outstripped supply, resulting in higher housing costs and rising levels of overcrowding. The pressures of housing demand in London have grown in recent years, in part due to fewer people leaving London to buy homes in other regions. But while new supply during the recession held up better in London than in other regions, it needs to increase significantly in order to meet housing needs and reduce housing costs to more affordable levels. This edition of Focus on London authored by James Gleeson in the Housing Unit looks at housing trends in London, from the demand/supply imbalance to the consequences for affordability and housing need. PRESENTATION: How much pressure is London’s popularity putting on housing provision in the capital? This interactive presentation looks at the effect on housing pressure of demographic changes, and recent new housing supply, shown by trends in overcrowding and house prices. Click on the start button at the bottom of the slide to access. View Focus on London - Housing: A Growing City on Prezi FACTS: Some interesting facts from the data… ● Five boroughs with the highest proportion of households that have lived at their address for less than 12 months in 2009/10:

📹Project Video available on YouTube - https://youtu.be/q-Omt6LgRLc

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

London Housing Price Dataset

The dataset contains housing market information for different areas of London over time. It includes details such as average house prices, the number of houses sold, and crime statistics. The data spans multiple years and is organized by date and geographic area.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

Using this dataset, we answered multiple questions with Python in our Project.

Q. 1) Convert the Datatype of 'Date' column to Date-Time format.

Q. 2.A) Add a new column ''year'' in the dataframe, which contains years only.

Q. 2.B) Add a new column ''month'' as 2nd column in the dataframe, which contains month only.

Q. 3) Remove the columns 'year' and 'month' from the dataframe.

Q. 4) Show all the records where 'No. of Crimes' is 0. And, how many such records are there ?

Q. 5) What is the maximum & minimum 'average_price' per year in england ?

Q. 6) What is the Maximum & Minimum No. of Crimes recorded per area ?

Q. 7) Show the total count of records of each area, where average price is less than 100000.

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These are the main Features/Columns available in the dataset :

1) Date – The month and year when the data was recorded.

2) Area – The London borough or area for which the housing and crime data is reported.

3) Average_price – The average house price in the given area during the specified month.

4) Code – The unique area code (e.g., government statistical code) corresponding to each borough or region.

5) Houses_sold – The number of houses sold in the given area during the specified month.

6) No_of_crimes – The number of crimes recorded in the given area during the specified month.

FOCUSON**LONDON**2011: HOUSING:A**GROWING**CITY

With the highest average incomes in the country but the least space to grow, demand for housing in London has long outstripped supply, resulting in higher housing costs and rising levels of overcrowding. The pressures of housing demand in London have grown in recent years, in part due to fewer people leaving London to buy homes in other regions. But while new supply during the recession held up better in London than in other regions, it needs to increase significantly in order to meet housing needs and reduce housing costs to more affordable levels.

This edition of Focus on London authored by James Gleeson in the Housing Unit looks at housing trends in London, from the demand/supply imbalance to the consequences for affordability and housing need.

REPORT:

Read the report in PDF format.

https://londondatastore-upload.s3.amazonaws.com/fol/fol11-housing-cover-thumb.jpg" alt="">

PRESENTATION:

How much pressure is London’s popularity putting on housing provision in the capital? This interactive presentation looks at the effect on housing pressure of demographic changes, and recent new housing supply, shown by trends in overcrowding and house prices. Click on the start button at the bottom of the slide to access.

View Focus on London - Housing: A Growing City on Prezi

HISTOGRAM:

This histogram shows a selection of borough data and helps show areas that are similar to one another by each indicator.

Histogram

MOTION CHART:

This motion chart shows how the relationship, between key housing related indicators at borough level, changes over time.

Motion Chart

MAP:

These interactive borough maps help to geographically present a range of housing data within London, as well as presenting trend data where available.

MAP

DATA:

All the data contained within the Housing: A Growing City report as well as the data used to create the charts and maps can be accessed in this spreadsheet.

FACTS:

Some interesting facts from the data…

● Five boroughs with the highest proportion of households that have lived at their address for less than 12 months in 2009/10:

1. Westminster – 19 per cent
2. Wandsworth – 17 per cent
3. Camden – 16 per cent
4. Lambeth – 14 per cent
5. Southwark – 13 per cent

-31. Harrow – 6 per cent

-32. Havering – 5 per cent

● Five boroughs with the highest percentage point increase between 2004 and 2009 of households in the ‘private rented’ sector:

1. Newham – 17 per cent
2. Greenwich – 11 per cent
3. Enfield – 10 per cent
4. Camden – 9 per cent
5. Harrow – 8 per cent

-32. Islington – 1 per cent

-33. Bexley – 1 per cent

● Five boroughs with the highest percentage difference in median house prices between 2007 Q4 and 2010 Q4:

1. Kensington & Chelsea – 29 per cent
2. Westminster – 19 per cent
3. Camden – 15 per cent
4. Islington – 14 per cent
5. Southwark – 10 per cent

-31. Newham – down 9 per cent

-32. Barking & D’ham – down 9 per cent

The average price of a semi-detached house in Camden, London's third most expensive borough, increased by *** percent year-on-year in May 2025. Despite a decline of ****percent, Hammersmith and Fulham saw some of the most expensive semi-detached homes prices in London. At the other end of the spectrum was Barking and Dagenham - the most affordable and only borough where homebuyers could purchase a property for under ******* British pounds. Semi-detached homes are those that were built as one of two properties that share a common wall.

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 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.

The London Borough Profiles help paint a general picture of an area by presenting a range of headline indicator data in both spreadsheet and map form to help show statistics covering demographic, economic, social and environmental datasets for each borough, alongside relevant comparator areas. The London Borough Atlas does the same but provides further detailed breakdowns and time-series data for each borough. The full datasets and more information for each of the indicators are usually available on the London Datastore. A link to each of the datasets is contained in the spreadsheet and map. London Borough Profiles On opening the Microsoft Excel version, a simple drop down box allows you to choose which borough profile you are interested in. Selecting this will display data for that borough, plus either Inner or Outer London, London and a national comparator (usually England where data is available). To see the full set of data for all 33 local authorities in London plus the comparator areas in Excel, click the 'Data' worksheet. A chart and a map are also available to help visualise the data for all boroughs (macros must be enabled for the Excel map to function). The data is set out across 11 themes covering most of the key indicators relating to demographic, economic, social and environmental data. Sources are provided in the spreadsheet. Notes about the indicator are provided in comment boxes attached to the indicator names. For a geographical and bar chart representation of the profile data, choose the InstantAtlas version. Choose indicators from the left hand side. Click on the comparators to make them appear on the chart and map. Sources, links to data, and notes are all contained in the box in the bottom right hand corner. These profiles include data relating to: Population, Households (census), Demographics, Migrant population, Ethnicity, Language, Employment, NEET, DWP Benefits (client group), Housing Benefit, Qualifications, Earnings, Volunteering, Jobs density, Business Survival, Crime, Fires, House prices, New homes, Tenure, Greenspace, Recycling, Carbon Emissions, Cars, Public Transport Accessibility (PTAL), Indices of Multiple Deprivation, GCSE results, Children looked after, Children in out-of-work families, Life Expectancy, Teenage conceptions, Happiness levels, Political control, and Election turnout. London Borough Atlas To access even more data at local authority level, use the London Borough Atlas. It contains data about the same topics as the profiles but provides further detailed breakdowns and time-series data for each borough. There is also an InstantAtlas version available. The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster. You may also find our small area profiles useful - Ward, LSOA, and MSOA.

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. ...