Being able to accurately calculate the maximum price that the market is prepared to pay in real estate can be crucial to analyze if a certain house price is over or under priced. Using this dataset, one can train a regression model to calculate the optimal price of a house. This can help an investment fund to discover new opportunities or discard expensive deals, or an agent to understand if a managed property is under or over priced.
This dataset contains historical data of 134,000 real estate transactions in the UK from 1st January 1995 to 29th June 2017. The target variable (Price) indicates the price paid in the given transaction, and there are 9 explainable variables to predict this column, that contain information about the transaction (date and price) and the sold property (location, property type, whether is new construction, tenure type, etc).
The objective is to train a ML regression model that generates more accurately the price of the property based on the rest of features. Being a regression model problem, metrics such as the coefficient of determination (R2) and the mean squared error (MAE) are used to evaluate the model.
Although this dataset can make a huge difference on real estate business' performance, it has some problems that complicate its usage. Luckily, Synthesized can solve these problems in a fast and intuitive way.
This dataset is publicly available in "UK Housing Prices Paid" Kaggle Dataset