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

House prices in England have increased notably in the last 10 years, despite a slight decline in 2023. In December 2024, London retained its position as the most expensive regional market, with the average house price at ******* British pounds. According to the UK regional house price index, Northern Ireland saw the highest increase in house prices since 2023.

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

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).

Art Presence & Property Prices in London

Quantifying the Relationship with Online Data

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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...

Annual mean and median property prices calculated by the GLA from Price Paid Data published on Land Registry website. Number of property sales also included. Data has been aggregated to Borough, Ward, MSOA, LSOA, Postcode Districts and Postcode Sectors. Caution should be used when analysing figures based on a low number of sales. Price Paid Data provides information on every residential property sale in England and Wales that has been lodged with HM Land Registry for registration. Download full price paid data from the Land Registry. Click here to access an interactive dashboard using some of the data available from this page.

According to the forecast, the North West and Yorkshire & the Humber are the UK regions expected to see the highest overall growth in house prices over the five-year period between 2025 and 2029. Just behind are the North East and West Midlands. In London, house prices are expected to rise by **** percent.

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. ...

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.

Median monthly rental prices for the private rental market in England by bedroom category, region and administrative area, calculated using data from the Valuation Office Agency and Office for National Statistics.

This CSV table shows a breakdown of the dwelling stock down to a lower geographic level Lower layer Super Output Area or LSOA, categorised by the property build period and property type. Counts in the tables are rounded to the nearest 10 with those below 5 recorded as negligible and appearing as -. Data on build period, or age of property, has been used to create 12 property build period categories: Pre-1900, 1900-1918, 1919-1929, 1930-1939, 1945-1954, 1955-1964, 1965-1972, 1973-1982, 1983-1992, 1993-1999, 2000-2009, and 2010-2015. Data on property type includes breakdown by bungalow, terraced, flat/maisonette, semi-detached and detached, and by the number of bedrooms. The counts are calculated from domestic property data for England and Wales extracted from the Valuation Office Agencys administrative database on 31 March 2015, and on 1 August 2012 and 31 March 2014. The VOA have published data that shows homes by period built, or type, and council tax band down to MSOA and LSOA level. Rounding: Small differences between the rounding conventions are applied to the 2014 and 2015 statistics. For 2014 The rounding convention applied to the tables: Counts are rounded to the nearest 10 dwellings and counts less than 5 are reported as negligible (-). For 2015 The rounding convention applied to the tables: Counts are rounded to the nearest 10 with counts of zero being reported as "0" and counts fewer than 5 reported as negligible and denoted by "-". National Statistics Postcode Lookup file (NSPL): Different NSPL files have been used for 2014 and 2015 statistics (February 2014 NSLP used February 2015 NSLP used). As a results, postcodes can be moved in different OAs. Further information on NSPL can be found at ONS Property attributes: As part of the day to day VOA work, attributes information can be added (where no information is recorded) and/or changed (existing information is updated). This would result in counts in categories changing. New entries and deletions: New entries into the CT List together with deletions from the CT List will result in changes to counts. New entries could be as a result from splits, mergers, new build but also entries which were not previously in the CT List i.e. a shop is converted in a domestic property. Similarly, deletions could be as a result from splits, mergers, demolitions but also entries no longer domestic properties i.e. a house is converted into a shop (non-domestic property). The map below was created to show the average age of properties at MSOA level.

Context

A multivariate time series has more than one time-dependent variable. Each variable depends not only on its past values but also has some dependency on other variables.

We have accumulated property sales data for the 2007-2019 period for one specific region. The data contains sales prices for houses and units with 1,2,3,4,5 bedrooms. These are the cross-depended variables. The chart illustrates these variables for houses:

Raw Data: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2858272%2F94d5f8d79c60d468f1c50b29d6ae7527%2Fdownload.png?generation=1565753055319944&alt=media" alt="">

Data re-sampled at quarterly intervals using a median aggregator: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2858272%2Fb8b5c3bee9442a25cb71659176ca8d20%2Fdownload1.png?generation=1565753032380625&alt=media" alt="">

The data can be summarised as:

• date of sale
• price
• property type: unit or house
• number of bedrooms: 1,2,3,4,5 (these are the multi-variables we are targeting)
• 4digit postcode (this is for reference only, we don't expect that this data point will be useful)

Content

• raw_sales.csv contains house sales data ranging from 2007-2019
• ma_lga_12345.csv transforms the raw sales data, re-sampling it at quarterly intervals with median price aggregation and step 4 moving average smoothing

Inspiration

What model would you use to forecast 8 future quarters for each property type and # of bedrooms? Would the traditional multi-variate forecasting models from the VARMAX family or multi-variate regression outperform LSTM, DLM, RNN networks for this problem?

In 2024, Sydney had the highest price per square meter of land across major cities in Australia. Lot buyers expected to pay a premium of ***** Australian dollars per square meter in the capital of New South Wales. Conversely, lot buyers in Adelaide expected to spend around *** Australian dollars per square meter of land. Prices through the roof Over the past decade, the surge in land and housing costs has been attributed to rapid population growth, driving up median prices for property and land, particularly in cities. In Sydney, the per square meter price of land has almost tripled since 2010, while the number of new property listings has declined over the years. A shortage of residential land available to build on has exacerbated the housing affordability crisis in Australia. Will lending rates continue to climb? The homeownership dream is out of reach for the average Australian without a housing loan. Nevertheless, Australia's high mortgage interest rates for both owner-occupiers and investors have impacted current and aspiring mortgage holders, with the value of household lending trending downwards over the past two years. While rates remained high in the first half of 2024, they likely reached their peak, as shown by the gradual plateau in the second half of the year. This stabilization should, in turn, accelerate buying, selling, and lending activities.

Context

The dataset is originated from Sydney Suburb Reviews. It includes 30 features over 421 suburbs in Sydney. The features are:

Name Region Population (rounded)* Postcode Ethnic Breakdown 2016 Median House Price (2020) Median House Price (2021) % Change Median House Rent (per week) Median Apartment Price (2020) Median Apartment Rent (per week) Public Housing % Avg. Years Held Time to CBD (Public Transport) [Town Hall St] Time to CBD (Driving) [Town Hall St] Nearest Train Station Highlights/Attractions Ideal for Traffic Public Transport Affordability (Rental) Affordability (Buying) Nature Noise Things to See/Do Family-Friendliness Pet Friendliness Safety Overall Rating Review Link

Quarterly summary of median private rent in South Australia by: suburb, postcode, State Government regions and Local Government Areas. The information relates to bonds lodged with Consumer and Business Services for private rental properties in South Australia.