Predicting the U.S. Housing Prices Using Time Series Forecasting Techniques
Session Title
Government and Policy
Faculty Mentor
Anna Romanova, Ph.D.
College
College of Business Administration
Department
Computer Science & Quantitative Methods
Abstract
Real estate and the housing market play an integral role in the U.S. economy. Home ownership is the main source of wealth and savings for many Americans, and residential construction provides widespread employment for a significant number of economic agents in the country. As evidenced by the 2008 crisis, housing prices can impact residential investment and, as a result, affect the overall economic growth. This study is an attempt to better understand the driving forces behind the changes in the housing market and to envision what the future of the U.S. housing market could look like. We use monthly time series data from Kaggle that describe several economic indicators between 1987 and 2021 including the housing price index, stock price index, unemployment rate, mortgage rate, real disposable income, and GDP. We employ various data visualization tools and time series regression modeling techniques to examine the factors that affect the housing market in the U.S. Using a set of model selection criteria, we identify the best time series regression model and use it to create short-term forecasts for the housing price index. We also contrast our times series regression results with exponential smoothing techniques and identify the overall best model for forecasting future values of the housing price index.
Course Assignment
BADM 571 – Romanova
Previously Presented/Performed?
Winthrop University Showcase of Undergraduate Research and Creative Endeavors, Rock Hill, SC, April 2023.
Type of Presentation
Oral presentation
Start Date
15-4-2023 12:00 PM
Predicting the U.S. Housing Prices Using Time Series Forecasting Techniques
Real estate and the housing market play an integral role in the U.S. economy. Home ownership is the main source of wealth and savings for many Americans, and residential construction provides widespread employment for a significant number of economic agents in the country. As evidenced by the 2008 crisis, housing prices can impact residential investment and, as a result, affect the overall economic growth. This study is an attempt to better understand the driving forces behind the changes in the housing market and to envision what the future of the U.S. housing market could look like. We use monthly time series data from Kaggle that describe several economic indicators between 1987 and 2021 including the housing price index, stock price index, unemployment rate, mortgage rate, real disposable income, and GDP. We employ various data visualization tools and time series regression modeling techniques to examine the factors that affect the housing market in the U.S. Using a set of model selection criteria, we identify the best time series regression model and use it to create short-term forecasts for the housing price index. We also contrast our times series regression results with exponential smoothing techniques and identify the overall best model for forecasting future values of the housing price index.
