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.
Prior studies that estimate the impact of amenity accessibility on residential property prices have largely treated housing as a homogenous commodity. Yet there is strong evidence that differentiation in metropolitan housing sub-markets matter (Goodman and Thibodeau, 1998; Hoesli and Peng, 2002). Using an hedonic pricing approach and controlling for spatial effects, this paper examines the preferences of house and apartment buyers regarding amenity accessibility in Brooklyn, NY, for the period 2008 to 2013. We find that the preferences for amenities between the two types of home-buyers are indeed different. More specifically, our findings show that the marginal implicit value, as evidenced through home sales prices, of accessibility to cultural amenities (e.g. proximity to monuments, beaches, parks and cafes) is greater for apartment than house buyers. On the other hand, the marginal implicit value of workplace amenity accessibility (e.g. proximity to central business district and subway stations) is greater for house than apartment buyers. The result illustrates the importance of differentiating housing sub-markets when estimating these impacts. Urban policy makers and real estate developers can also use the result to inform land use planning in metropolitan areas aimed at further increasing residential property values.
The 21rst century will be marked by an ever increasing urban world. Projections predict this trend to be largest for developing nations in which formal housing markets are inefficient at meeting the increasing demand for urban housing. This unmet housing demand will continue to exacerbate the housing crisis and necessitate sustainable solutions. Past policies of slum clearance, modernist apartment projects, housing provision, self-help, sites and services, and in-situ upgrading have not been effective at solving the crisis. This thesis considers the central role that architectural elements plays in slum housing communities. Considering architectural elements in addition to the conventional elements of financing mechanisms and land tenure augments an understanding of what a successful housing project is. Analyzing six successful international slum housing projects for both conventional and architectural elements, this thesis highlights the importance of vernacular architecture as a determinant of a successful project. Appropriate, vernacular architecture will best serve the beneficiary community's built environment needs and lead to sustainable housing solutions. Central in this process is the inclusion of slum communities in the design process of housing projects.
Exponential growth bias (EGB) is a largely unnoticed bias that plagues the financial decision making of most individuals in the United States. It is characterized as the tendency to linearize exponential compound saving and interest rates, and it shows itself through poor decision making around wrong estimates and/or no understanding of how money grows through time. Based on the theory that education and valid experience might tame EGB, a model was built to measure variable drivers of individual EGB related to exposure. Based on previous theory that more extreme situations demand more incentive for participation, it was hypothesized that the government dictated interest rates at times of individual’s first home purchases could subconsciously influence EGB, for two main reasons. First, a more expensive payment plan carries greater incentive to fully understand, and second, a first home purchase is a fundamentally monumental financial decision with potential to positively or negatively shape bias. A variable for interest rate at the time of a first home purchase was created a combined with more lifetime-housing-exposure variables theorized to influence EGB, to model overall effects of individual housing exposure on EGB. The results showed that government set interest rates hold no statistically significant influence on an individual’s current EGB, however, the marginal coefficients showed the correlations consistent with the theory. The model statistically significantly determined that the variables for number of homes purchased in a lifetime and price paid for a first home are inversely correlated with current EGB. In addition, income and education levels were statistically proven to be inversely correlated with current EGB.