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).

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

The average price of detached and duplex houses in the biggest cities in Germany varied between approximately ***** euros and 10,000 euros per square meter in 2024. Housing was most expensive in Munich, where the square meter price of houses amounted to ***** euros. Conversely, Berlin was most affordable, with the square meter price at ***** euros. How have German house prices evolved? House prices maintained an upward trend for more than a decade, with 2020 and 2021 experiencing exceptionally high growth rates. In 2021, the nominal year-on-year change exceeded 10 percent. Nevertheless, the second half of 2022 saw the market slowing, with the annual percentage change turning negative for the first time in 12 years. Another way to examine the price growth is through the house price index, which uses 2015 as a base. At its peak in 2022, the German house price index measured about *** percent, which means that a house bought in 2015 would have appreciated by ** percent. Is housing affordable in Germany? Housing affordability depends greatly on income: High-income areas often tend to have more expensive housing, which does not necessarily make them unaffordable. The house price to income index measures the development of the cost of housing relative to income. In the first quarter of 2024, the index value stood at ***, meaning that since 2015, house price growth has outpaced income growth by about ** percent. Compared with the average for the euro area, this value was lower.

After soaring for three years, house price in England declined in 2023. In London, the most expensive regional market, the average house price fell to 508,000 British pounds in December 2023, down from 529,000 British pounds a year ago. According to the UK regional house price index, the North West saw the highest price increase since 2015.

According to the forecast, the North East and Wales are the regions in the United Kingdom estimated to see the highest overall growth in house prices over the five-year period between 2024 and 2028. Just behind are North West, Yorkshire & the Humber, and Scotland, which are forecast to see house prices increase by **** percent over the five-year period. 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)

Geospatial data on real estate price development in the Netherlands is a strategic asset for informed decision-making in the property market. This data, at 6-digit postal code level, reveals regional price trends, market dynamics, and development opportunities, allowing real estate professionals and investors to optimize their strategies. Whether assessing risk, valuing properties, or identifying growth areas, geospatial insights play a crucial role.

Mean and median property prices from all price paid data published on Land Registry website. Number of property sales also included.

Number of Census 2011 dwellings has been included for context. The Census dwellings figure takes no account of tenure of dwellings in an area ie the actual housing stock that could be sold.

Data for all years from 1995-2014.

LSOA and MSOA 2011 boundaries and codes used. (Lower and Middle Super Output Areas).

2014 ward boundaries only affect three boroughs in which wards changed in 2014 - Hackney, Tower Hamlets, and Kensington and Chelsea. All other ward boundaries in London remain unchanged.

Postcode Districts and sectors - postcode districts with fewer than ten sales in the last three years have been excluded, postcode sectors with fewer than an average of five sales in the last three years have been excluded.

Postcode data converted into boundary codes using GIS Entries in the land registry database marked with a 'D' have been removed from calculations. No other entries have been removed. Entries without a postcode have been excluded from the calculations at ward, MSOA and LSOA level - though are left in at local authority level.

This data has been calculated directly from Land Registry files and figures at borough level will not match official published figures for house prices. Borough figures are provided here for comparison purposes.

Caution should be used when analysing figures based on a low number of sales, particularly in the LSOA dataset.

Download from Land Registry

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

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.

In 2024, one square meter of greenfield land cost an average of 1,116 Australian dollars in Melbourne, Australia. This remained around the same level compared to the green land cost per square meter in the previous year.