Paper Title : Leveraging Data Analytics Methods For Statistical (ANOVA) Analysis of Factors Influencing US Housing Prices
ISSN : 2394-2231
Year of Publication : 2022
10.5281/zenodo.7339460
MLA Style: Leveraging Data Analytics Methods For Statistical (ANOVA) Analysis of Factors Influencing US Housing Prices " Amlan Jyoti Patnaik" Volume 9 - Issue 6 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: Leveraging Data Analytics Methods For Statistical (ANOVA) Analysis of Factors Influencing US Housing Prices " Amlan Jyoti Patnaik" Volume 9 - Issue 6 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
Abstract
REITs (Real Estate Investment Trusts) own approximately $3.5 trillion in gross real estate assets, with more than $2 trillion of that total from public listed and non-listed REITs and the remainder from privately held REITs. The economic and investment impact of those assets is felt by millions of Americans all across the country. REITs focus on income-producing and highly appreciating commercial and residential real estate in order to provide high returns to their investors. REITs assess several factors for determining the true value and the future growth potential of the investment properties. Investment decisions are made based on the outcomes of the research and analysis of the factors influencing the returns on the investment. In the journey to model housing prices, two approaches have been widely used. The first approach is the monocentric model which assumes that the housing price is a function of its proximity to a single employment center or workplace. The relative housing prices then reflect the relative savings in commuting costs associated with different locations. However, unlike other consumer goods, the housing market is unique because it manifests the characteristics of durability, heterogeneity, and spatial fixity. Thus, to model this differentiation effectively, the second approach of the hedonic price model has been introduced. The hedonic price model posits that goods are typically sold as a package of inherent attributes (Rosen 1974). Therefore, the price of one house relative to another will differ with the additional unit of the different attributes inherent in one house relative to another house. The relative price of a house is then the summation of all its marginal or implicit prices estimated through the regression analysis. The hedonic price model, derived from Lancaster’s (1966) consumer theory and Rosen’s theoretical (1974) model, has been used extensively in the scientific investigation of various aspects of housing markets. Most of the previous researches used regression analysis and were mainly focused on the structural, locational and neighborhood factors and their impact on housing prices. Studies have revealed that the number of rooms and bedrooms (Fletcher, et al. 2000; Li & Brown 1980), the number of bathrooms (Garrod & Willis 1992; Linneman 1980), and the floor area (Carroll, Clauretie, & Jensen 1996; Rodriguez & Sirmans 1994) are positively related to the sale price of houses. This is because buyers are willing to pay more for more functional space. Researchers also proved that building age is negatively related to property prices (Clark & Herrin 2000; Kain & Quigley 1970). Ketkar (1992) observed that whites in New Jersey tended to be sensitive about the proportion of non-whites in their neighborhood. Proximity to shopping centers and the size of shopping centers have both been found to exert an influence on the value of the surrounding residential properties (Des Rosiers, et al. 1996). While all the factors determined in the previous researches have a significant influence on the housing prices and the expected future return on the investments, the research of the state graduation attainment level factor and its impact on housing prices has not been studied earlier. This research will utilize ANOVA to analyze the impact of the state graduation attainment levels on the housing prices. The contribution of this research to the field of Data Analytics is to explore whether the graduation attainment levels of the states lead to a significant statistical difference in housing prices which will help the Real Estate Investment Trusts to make informed property investment decisions by considering the state graduation attainment level as one of the factors for identifying and investing in properties in the states that offer a possibility of higher real estate price appreciation. The state graduation attainment level is selected for analysis as it may be an indicator of the availability of high paying job opportunities and lower unemployment rates in the state leading to higher housing prices due to higher affordability.
Reference
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