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.

This dataset contains various characteristics and price information about houses in London. Consisting of 1000 entries, it reflects many aspects of each house, from location to interior design. In addition to physical features such as the address, neighborhood, number of rooms, and square footage, it also includes more specific details like the age of the building, garage availability, and balcony presence. Furthermore, the price of each house provides valuable insights into its market value.

Columns;

Address: The address of the house.

Neighborhood: The neighborhood or district where the house is located.

Bedrooms: The number of bedrooms in the house.

Bathrooms: The number of bathrooms in the house.

Square Meters: The total size of the house in square meters.

Building Age: The age of the building, indicating how long ago it was constructed.

Garden: Indicates whether the house has a garden ("Yes" or "No").

Garage: Indicates whether the house has a garage ("Yes" or "No").

Floors: The total number of floors in the house.

Property Type: The type of property, such as "Apartment" or "House."

Heating Type: The type of heating system used in the house (e.g., "Central Heating," "Gas").

Balcony: Indicates whether the house has a balcony ("Yes" or "No").

Interior Style: The interior design style of the house (e.g., "Modern," "Contemporary").

View: The type of view from the house (e.g., "City View," "Sea View").

Materials: The materials used in the construction of the house (e.g., "Brick," "Wood").

Building Status: The current condition of the building (e.g., "New," "Renovated," "Old").

Price (£): The sale price of the house, in British pounds (£).

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:

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.

The Greater London Authority's ‘Housing in London’ report sets out the evidence base for the Mayor's housing policies, summarising key patterns and trends across a wide range of topics relevant to housing in the capital. The report is the evidence base for the Mayor’s London Housing Strategy, the latest edition of which was published in May 2018. The 2024 edition of Housing in London can be viewed here. It includes monitoring indicators for the London Housing Strategy, and five thematic chapters: Demographic, economic and social context Housing stock and supply Housing costs and affordability Housing needs, including homelessness and overcrowding Mobility and decent homes

Number of residential property sales in England and Wales, by property type and administrative geographies. 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.

Through reading this publication you will: • gain an understanding of how house prices are set in economics terms, how they are measured, and why the cost of housing matters for London’s economy and its residents • see whether incomes and earnings in London have kept pace with the costs of home ownership in London, and see how affordability may be affected by future changes in interest rates • find out about the drivers of demand for residential property in London, and how the supply of homes has responded to changing conditions

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

This dataset provides information about the number of properties, residents, and average property values for May Lene Drive cross streets in London, KY.

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.

📹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.

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...

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.