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Influence Factor Analysis and Forecast of US House Prices Based on Linear Regression and Time Series
- 1 Sichuan University
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Abstract
In the United States, house prices remain a perennial topic of interest for every resident. Beyond influencing individual housing choices, fluctuations in house prices play a pivotal role in shaping pleasant lifestyles and ensuring the nation's stable economic growth. The housing market, in fact, underpins the entire U.S. economic system. This study employed a multifaceted approach, utilizing both multiple linear regression models and time series analysis, to delve deeply into the factors influencing U.S. house prices. The research identified that residential construction spending and GDP are the most significant drivers leading to an increase in the American House Price Index (HPI). Additionally, through time series analysis, various graphical representations were constructed to visualize the quarterly trends in the American HPI, rendering the findings more comprehensible. In essence, this research offers a scientific foundation for future adjustments in housing price policies. It holds substantial relevance for all stakeholders in the real estate market, from buyers and sellers to developers and policymakers.
Keywords
US house price index, multiple linear regression, time series analysis, influence factors
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Cite this article
He,Q. (2023). Influence Factor Analysis and Forecast of US House Prices Based on Linear Regression and Time Series. Advances in Economics, Management and Political Sciences,64,251-262.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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