Predictive regression analysis of housing price in Iowa

Zhida Guo

Master's thesis

Abstract:

This thesis presents an analysis of predictive modeling techniques and outlier detection methods in the context of real estate sale price prediction. The study aims to find an optimal regression model by integrating variable selection procedures, influential observation removal, and regression outlier detection, and understand the impacts of different variable selection methods. The investigation, conducted on a real-world housing dataset, evaluates the models using metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

Abstract:

This thesis presents an analysis of predictive modeling techniques and outlier detection methods in the context of real estate sale price prediction. The study aims to find an optimal regression model by integrating variable selection procedures, influential observation removal, and regression outlier detection, and understand the impacts of different variable selection methods. The investigation, conducted on a real-world housing dataset, evaluates the models using metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

Keywords

regression analysis, prediction, cross validation, empirical analysis

Keywords

cross validation, prediction, empirical analysis, Regression analysis

Language used: English

Date on which the thesis was submitted / produced: 4. 12. 2023

Thesis defence

• Date of defence: 21. 8. 2024
• Supervisor: Karel Helman
• Reader: Adam Čabla

Vysoká škola ekonomická v Praze

Master programme / field:
Economic Data Analysis / Data Analysis and Modeling