Modeling Real Estate Using Regression Analysis

The real estate market is a type of market where the sales and purchases between sellers and buyers refer to the exchange of real estate of any kind, such as housing, land, commercial premises, and so on. Real estate prices depend on a series of factors that make the asset more palatable for potential buyers. Regression analysis is the statistical process of studying the relationship between a set of independent variables (explanatory variables) and the dependent variable (response variable). Through this technique, it is possible to understand how the value of the response variable changes when the explanatory variable is varied. In this chapter, the real estate market will be modeled through a regression analysis.

In this chapter, we will cover the following topics:

• Defining a regression problem
• Creating a linear regression model
• Multiple linear regression concepts
• Neural networks for regression using Keras

By the end of this chapter, we will have learned about the different types of regression techniques. We will apply regression methods to your data and understand how the regression algorithm works. We will then understand the basic concepts that multiple linear regression methods use to fit equations to data using the Keras layers. We will also learn how to evaluate the model's performance and how to tune a model to improve the model's performance.