The Zoopla Dataset provides a detailed repository of information covering property listings available on the Zoopla platform. Tailored to support businesses, researchers, and analysts in the real estate sector, this dataset delivers valuable insights into market trends, property valuations, and consumer preferences within the real estate market.

With key attributes such as property details, pricing data, location information, and listing history, users can conduct thorough analyses to refine property investment strategies, assess market demand, and identify emerging trends.

Whether you're a real estate agent seeking to enhance your property listings, a researcher investigating trends in the housing market, or an analyst aiming to refine investment strategies, the Zoopla Dataset serves as an essential resource for unlocking opportunities and driving success in the competitive landscape of real estate

In 2022, house price growth in the UK slowed, after a period of decade-long increase. Nevertheless, in June 2025, prices reached a new peak, with the average home costing ******* British pounds. This figure refers to all property types, including detached, semi-detached, terraced houses, and flats and maisonettes. Compared to other European countries, the UK had some of the highest house prices. How have UK house prices increased over the last 10 years? Property prices have risen dramatically over the past decade. According to the UK house price index, the average house price has grown by over ** percent since 2015. This price development has led to the gap between the cost of buying and renting a property to close. In 2023, buying a three-bedroom house in the UK was no longer more affordable than renting one. Consequently, Brits have become more likely to rent longer and push off making a house purchase until they have saved up enough for a down payment and achieved the financial stability required to make the step. What caused the recent fluctuations in house prices? House prices are affected by multiple factors, such as mortgage rates, supply, and demand on the market. For nearly a decade, the UK experienced uninterrupted house price growth as a result of strong demand and a chronic undersupply. Homebuyers who purchased a property at the peak of the housing boom in July 2022 paid ** percent more compared to what they would have paid a year before. Additionally, 2022 saw the most dramatic increase in mortgage rates in recent history. Between December 2021 and December 2022, the **-year fixed mortgage rate doubled, adding further strain to prospective homebuyers. As a result, the market cooled, leading to a correction in pricing.

***Overview: Dataset contains recent properties sold in England from Zoopla listings.

***Data Science Applications and tasks possible * Analyse average pricing and trends * Geo- Spatial analysis * Feature extraction/Engineering *Data Cleaning

***Ethically mined data Data has been mined ethically using API for Publicly available data only.

***Main Data Columns: bathrooms ,bedrooms ,country ,currentEnergyRating ,floorAreaSqM ,fullAddress ,historicListings/0/date ,historicListings/0/price historicListings/1/date ,historicListings/1/price ... .saleEstimate/ingestedAt ,saleEstimate/lowerPrice ,saleEstimate/upperPrice .saleEstimate/valueChange/numericChange ,saleEstimate/valueChange/percentageChange ,saleEstimate/valueChange/saleDate , soldPricesDataSource ,tenure ,uprn .

***Note ---(Requires Data Cleaning to remove unwanted columns)

These Kaggle datasets provide downloaded real-estate listings from the UK real estate market, capturing data from a leading platform in the UK (Zoopla), reminiscent of the approach taken for the US dataset from Redfin and French dataset from Seloger. It encompasses detailed property listings, pricing, and market trends across UK, stored in weekly CSV snapshots. The cleaned and merged version of all the snapshots is named as UK_clean_unique.csv.

The cleaning process mirrored that of the US and French datasets, involving removing irrelevant features, normalizing variable names for dataset consistency with the USA and France, and adjusting variable value ranges to get rid of extreme outliers. To augment the dataset's depth, external factors like inflation rates, stock market volatility, and macroeconomic indicators have been integrated, offering a multifaceted perspective on the UK's real estate market drivers.

For exact column descriptions, see columns for UK_clean_unique.csv and my thesis.

Table 2.6 and Section 2.2.2, which I refer to in the column descriptions, can be found in my thesis; see University Library. Click on Online Access->Hlavni prace.

If you want to continue generating datasets yourself, see my Github Repository for code inspiration.

Let me know if you want to see how I got from raw data to France_clean_unique.csv. There are multiple steps, including cleaning Tableau Prep and R, downloading and merging external variables to the dataset, removing duplicates, and renaming some columns.

London is one of the major cities in the world. It is also known as the centre of education and research where some of the most reputable universities in the world are located. Every year international students coming from many countries come to London in hoping not only to pursue university degrees but also to experience the vibes London has to offer.

Out of all matters they need to solve before coming to London, housing is on the top of the list. Choosing a property such as flats or houses involves a thorough research and precise consideration of the availability, ease of access and, of course, the price,

This inspires me to identify the listed properties that are available in London provided by Zoopla.co.uk. This dataset covers various information including the price (PCM and per week), nearby stations, the number of bedrooms, and the agent details. The update is scheduled to be done on weekly basis.

That's it. Happy exploring!

These Kaggle datasets provide downloaded real estate listings from the French real estate market, capturing data from a leading platform in France (Seloger), reminiscent of the approach taken for the US dataset from Redfin and UK dataset from Zoopla. It encompasses detailed property listings, pricing, and market trends across France, stored in weekly CSV snapshots. The cleaned and merged version of all the snapshots is named as France_clean_unique.csv.

The cleaning process mirrored that of the US dataset, involving removing irrelevant features, normalizing variable names for dataset consistency with USA and UK, and adjusting variable value ranges to get rid of extreme outliers. To augment the dataset's depth, external factors like inflation rates, stock market volatility, and macroeconomic indicators have been integrated, offering a multifaceted perspective on France's real estate market drivers.

For exact column descriptions, see columns for France_clean_unique.csv and my thesis.

Table 2.5 and Section 2.2.1, which I refer to in the column descriptions, can be found in my thesis; see University Library. Click on Online Access->Hlavni prace.

If you want to continue generating datasets yourself, see my Github Repository for code inspiration.

Let me know if you want to see how I got from raw data to France_clean_unique.csv. There are multiple steps, including cleaning Tableau Prep and R, downloading and merging external variables to the dataset, removing duplicates, and renaming some columns.