The Power of Simple: Linear Regression to Predict Home Prices

The real estate sector is one of the most diverse and complex in the world. Understanding how property prices are determined can be a challenge as it depends on multiple factors such as the size of the house, the number of bedrooms, the location, the size of the garage (if you have one), among others. But will all these really be determining factors?

In this article, we will explore a simple but powerful model, Linear Regression, to help us not only predict house prices, but also identify whether some of the variables mentioned really are important or influential in the model.

Throughout the article, you will learn:

• How to prepare real estate data for analysis.
• The fundamentals of linear regression, including the assumptions that must be met (normality, homoscedasticity, among others) to obtain reliable results.
• The practical implementation of the model in Python.
• The interpretation of results, key metrics and next steps to consider.

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