Cy's Data Lab

By Cy Seeley, M.S. Student, Applied Data Science, Syracuse University

Abstract

This paper explores the dynamics of the U.S. housing market using a large real estate dataset containing over two million records. The analysis delves into housing prices, property sizes, and temporal trends through comprehensive data preparation, statistical methods, and advanced visualization techniques. Findings highlight key insights such as the relationship between house size and price, significant regional variations in average housing prices, and clustering patterns in price per acre. This research serves as a foundation for understanding the housing market’s complexities and provides actionable insights for stakeholders such as buyers, sellers, and policymakers.

Introduction

The U.S. housing market plays a vital role in the nation’s economy, reflecting both regional and national trends in real estate. Housing prices, property sizes, and trends over time are key indicators of market dynamics that influence decisions made by buyers, sellers, investors, and policymakers. However, the market is complex, with diverse factors influencing property values, from house size and location to economic conditions and buyer preferences.

This study uses a comprehensive dataset to examine critical aspects of the housing market, such as the distribution of property values, correlations between house size and price, and temporal trends in housing prices. The analysis incorporates both statistical techniques and machine learning methods, such as clustering, to uncover patterns and actionable insights. By leveraging advanced visualizations, this paper provides a nuanced understanding of market trends and regional disparities, offering readers a well-rounded perspective on the U.S. housing market.

Key Findings

• Housing prices are highly skewed, with the majority of properties valued below $1 million but a long tail extending into the multi-million dollar range.
• Regional disparities are pronounced, with Hawaii and the District of Columbia displaying the highest average housing prices.
• Temporal trends reveal a consistent increase in housing prices over time, punctuated by economic fluctuations.
• Clustering analysis further highlighted differences in property valuations between urban and rural areas.
• Log transformations proved valuable for improving the interpretability of price and size distributions, enabling more accurate modeling and visualization.

Conclusion

The U.S. housing market is a complex ecosystem influenced by myriad factors, including property characteristics, location, and broader economic trends. This analysis provides insights into key dynamics, from regional variations to temporal price trends. By combining robust data preparation with advanced statistical and machine learning techniques, this study offers a detailed perspective on housing market patterns, empowering stakeholders to make informed decisions.

Link to the Paper