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  • Thumbnail for EFFECTS OF ENERGY STAR RATINGS ON COMMERCIAL OFFICE SPACE RENT
    EFFECTS OF ENERGY STAR RATINGS ON COMMERCIAL OFFICE SPACE RENT by Pollack, Andrew Rae

    Energy Star rated commercial office space has been well researched in the United States with national level data. This research takes a step back from this aggregate data and investigates the impact that an Energy Star rating has on the rent of commercial office space in the Denver, Colorado market. Rent data from the Denver commercial office space market was collected and analyzed using the framework of an existing model from a previous research study. The value of an Energy Star Rated building is considered through both the risk and return of an investment. While the results do not yield a rent premium for Energy Star rated office space in the Denver market, the risk is shifted, reducing exposure to the energy industry which could prove as an equally valuable asset for institutional investors and developers.

  • Thumbnail for Colorado Springs Public Park Amenities: Relating Park Types to Housing Prices With a Spatial Context
    Colorado Springs Public Park Amenities: Relating Park Types to Housing Prices With a Spatial Context by Suszynski, Gabriel Joseph

    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.

  • Thumbnail for Residential real estate : leading market factors
    Residential real estate : leading market factors by Levy, Saul Moses

    This thesis examines multiple variables believed to have a relationship with residential housing price. The sales price of 6,464 homes sold from 18 Colorado Springs zip codes from January 1, 2010 to December 31, 2010 serves as the dependent variable. By examining the multiple variables that are hypothesized to have a statistically significant impact on housing prices, this thesis attempts to answer the question, “What are the major factors that dictate the development of a residential neighborhood?”

  • Thumbnail for ECONOMIC TRANSFORMATION OF DETROIT THROUGH REAL ESTATE
    ECONOMIC TRANSFORMATION OF DETROIT THROUGH REAL ESTATE by Gooch, Trevor John

    An exploratory study of Detroit’s economic trends with a focus on Real Estate with relationship to crime rates. This paper looks not only at the past but the present and future of Detroit’s success and what the city needs to do in order to ensure that it is a top destination city in America. Their population has been on a steady decline since the deindustrialization of the United States, while crime rates have sky rocketed since the start of the 21st Century. Their real estate market has been sub-par compared to similar Rust Belt cities but is showing signs of increasing which will hopefully separate Detroit from its’ gloomy past.

  • Thumbnail for HOUSE PRICES: FACTORS THAT INFLUENCE THE VALUE OF A HOME
    HOUSE PRICES: FACTORS THAT INFLUENCE THE VALUE OF A HOME by Ockey, Tanner

    The Housing market in the United States is beginning to change due to millennial lifestyle, aging Gen Xer’s and inflation. It is important for a consumer to understand what makes a housing unit valuable. This study aims to compare 33 variables and the influence each of them have on house value. Using the 2013 data from the American Housing Survey, I found that location is the most influential variable in terms of prices. However, demographics play a larger role than anticipated but in a different context than location. These finding have an important role in determining house prices.