This paper attempts to describe the drivers of housing prices in Cumming, Georgia, a rapidly growing suburban area in the southeast of the US. Data from123 single family homes were collected and analyzed using multiple regression methodology. The findings from correlation matrix indicate that the price of the house is positively associated with the number of bedrooms, number of bathrooms, square footage of the house, the lot size, and the number of parking spaces available in the house, and negatively associated with the age of the house. The results from regression analysis suggest that number of bathrooms, square footage, parking spaces, and the dummy variables for Denmark High School, Forsyth Central High School, and North Forsyth High School are statistically significant predictors of the price of the house for Cumming, Georgia. Finally, about 80% of the variation in the prices of the houses is accounted for by our regression model. These findings may have important implications for decision-making by residents, real-estate agents, house buyers and sellers, financial institutions, policymakers, and scholars alike.