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

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.

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

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.

House price affordability for the Greater Sydney Region. Details on the methodology can be found here: http://blogs.unsw.edu.au/cityfutures/blog/2016/03/where-is-housing-affordable-in-sydney. For …Show full descriptionHouse price affordability for the Greater Sydney Region. Details on the methodology can be found here: http://blogs.unsw.edu.au/cityfutures/blog/2016/03/where-is-housing-affordable-in-sydney. For more information refer to UNSW City Futures Research Centre. Copyright attribution: University of New South Wales - City Futures Research Centre, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

Dataset Description:

This dataset has been meticulously pre-processed from the official UK government’s Price Paid Data, available for research purposes. The original dataset contains millions of rows spanning from 1995 to 2024, which posed significant challenges for machine learning operations due to its large size. For this project, we focused on house price predictions and filtered the data to only include transactions from 2015 to 2024. The final dataset contains 90,000 randomly sampled records, which should be ideal for training machine learning models efficiently. The goal of this dataset is to provide a well-structured, pre-processed dataset for students, researchers, and developers interested in creating house price prediction models using UK data. There are limited UK house price datasets available on Kaggle, so this contribution aims to fill that gap, offering a reliable dataset for dissertations, academic projects, or research purposes. This dataset is tailored for use in supervised learning models and has been cleaned, ensuring the removal of missing values and encoding of categorical variables. We hope this serves as a valuable resource for anyone studying house price prediction or real estate trends in the UK. In the future, I plan to provide an even larger dataset for more detailed and comprehensive predictions.

Features:

Feature Name - Description - Price - Sale price of the property (target variable). - Date - Date of the property transaction. Converted to datetime format for easier handling. - Postcode - Postcode of the property, offering location-based information. - property_type - Type of property (Detached, Semi-detached, Terraced, Flat, etc.). - new_build - Indicator whether the property was newly built at the time of sale (Yes or No). - freehold - Indicator whether the property was sold as freehold or leasehold (Freehold, Leasehold). - Street - Street name of the property location. - Locality - Locality of the property. - Town - Town or city where the property is located. - District - Administrative district of the property. - County - County where the property is located.

File Information:

The dataset is saved as a CSV file with 90,000 records, each representing a property transaction in the UK from 2015 to 2024. Feel free to explore this dataset and use it for any academic, research, or machine learning projects related to housing price predictions!

If you want to give feedback on this dataset, or wish to request it in another form (e.g csv), please fill out this survey here. We are a not-for-profit research organisation keen to see how others use our open models and tools, so all feedback is appreciated! It's a short form that takes 5 minutes to complete.

Important Note: Before downloading this dataset, please read the License and Software Attribution section at the bottom.

This dataset aligns with the work published in Centre for Net Zero's report "Hitting the Target". In this work, we simulate a range of interventions to model the situations in which we believe the UK will meet its 600,000 heat pump installation per year target by 2028. For full modelling assumptions and findings, read our report on our website.

The code for running our simulation is open source here.

This dataset contains over 9 million households that have been address matched between Energy Performance Certificates (EPC) data and Price Paid Data (PPD). The code for our address matching is here. Since these datasets are Open Government License (OGL), this dataset is too. We basically model specific columns from various datasets, as set out in our methodology section in our report, to simplify and clean up this dataset for academic use. License information is also available in the appendix of our report above.

The EPC data loaders can be found here (the data is here) and the rest of the schemas and data download locations can be found here.

Note that this dataset is not regularly maintained or updated. It is correct as of January 2022. The data was curated and tested using dbt via this Github repository and would be simple to rerun on the latest data.

The schema / data dictionary for this data can be found here.

Our recommended way of loading this data is in Python. After downloading all "parts" of the dataset to a folder. You can run:

import pandas as pd


data = pd.read_parquet("path/to/data/folder/")

Licenses and software attribution:

For EPC, PPD and UK House Price Index data:

For the EPC data, we are permitted to republish this providing we mention that all researchers who download this dataset follow these copyright restrictions. We do not explicitly release any Royal Mail address data, instead we use these fields to generate a pseudonymised "address_cluster_id" which reflects a unique combination of the address lines and postcodes, as well as other metadata. When viewing ICO and GDPR guidelines, this still counts as personal data, but we have gone to measures to pseudonymise as much as possible to fulfil our obligations as a data processor. You must read this carefully before downloading the data, and ensure that you are using it for the research purposes as determined by this copyright notice.

Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0.

Contains OS data © Crown copyright and database right 2022.

Contains Office for National Statistics data licensed under the Open Government Licence v.3.0.

The OGL v3.0 license states that we are free to:

copy, publish, distribute and transmit the Information;

adapt the Information;

exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application.

However we must (where we do any of the above):

acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence;

You can see more information here.

For XOServe Off Gas Postcodes:

This dataset has been released openly for all uses here.

For the address matching:

GNU Parallel: O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014

The data can be used to support a wide range of opportunities including location analysis, marketing planning and targeting and new product development, allowing you to create detailed views of specific areas of geography.

What is it? Enhance Geo contains all 1.8m PAF valid residential postcodes, with indicators across over 300 variables built using Sagacity’s trusted, comprehensive data asset repository.

This product is created utilising Open-Source data sets such as; The UK Census, ONS area level statistics, Postcode-level category spends, and aggregations of Sagacity's individual level data.

Use cases - Situations where marketing campaigns are required and further insights are needed, but there are legislation issues surrounding GDPR and non-PII data is required - Where data quality is poor and individual and address match rates are low - Planning business/shop front expansions, identifying the best areas to move into based on their characteristics - Where users want a holistic view of geographical areas rather than the individuals or households within them - Wide reaching campaigns, where engagement & impressions are the most important KPIs, identify the best areas that will have the best response rates

Additional Insights Enhance Geo, Core & Property can all be used modularly, allowing you understand the full picture of your customer base, considering not only their individual variance but also where they live & those around them.