A Data-Driven Analysis of Housing Consumption Patterns: Evidence from King County

Housing is one of the biggest expenses for most households, so changes in how people consume housing can tell us a lot about broader trends in consumption and upgrading. This paper uses a dataset of housing sales in King County, Washington, to explore what factors influence housing prices and what these patterns say about consumer behaviour. The analysis uses descriptive statistics, correlation analysis, and multiple linear regression to identify the key drivers of housing prices. The results show that living area (r = 0.702) and waterfront status are the strongest predictors of price, followed by property grade and view quality. However, the year the house was built does not have a strong effect on price once other factors are accounted for, which suggests that consumers care more about quality and location than about whether a property is brand new. These findings suggest that housing consumption upgrading is less about buying newer houses and more about buying better houses, which has more space, better views, and higher quality construction.