This study seeks to evaluate the effects of public parks on housing prices in Colorado Springs. I expand on previous hedonic housing price models by adding a spatial metric as well as characterizing public parks into four categories: neighborhood, community, open space, and regional. I do this to differentiate the types of parks and provide better data to urban planners about consumer preferences. It is an ordinary least squares estimation and the variable of interest is distance. Contrary to the hypothesis that housing prices will increase with proximity to parks, for every mile closer to a neighborhood park a house is estimated to be worth $15,080 less. A house is estimated to be worth $16,900 more for every mile closer to an open space. When the tests are run on a subset of the data using only condominium sales, the original hypothesis proves true, value increases with proximity to all park types. Urban planners in the United States can use this as valuable insight when developing plans for public parks in the future.