Empowering Home Buyers: A Data-Ddriven Approach to Real Estate Pricing in Ames, Iowa

Abstract

The demand for both housing and investment real estate has significantly increased due to rising urbanization and growing household savings. To address the need for operable real estate pricing models, this study explores potential variables affecting the sale prices of properties in Ames, Iowa, from 2006 to 2010. Utilizing data mining techniques and regression analysis, this study develops a model incorporating 12 independent variables that can be easily obtained during property visits. By offering an easily appliable tool, this research enables potential home buyers to estimate property sale prices, even without extensive expertise in Data Science, Investment, and Economics. The findings demonstrate that analyzing 12 variables directly related to the property itself such as interior finish of the garage, foundation material, and remodeling date can explain approximately 85.2% of the variance in sale prices. Empowering consumers with this knowledge can help reduce the information gap in the real estate market and promote informed decision-making in property purchases.