Thesis (M.A.)--University of Hong Kong, 2008. / Includes bibliographical references (p. 96-99).

published_or_final_version / Geography / Master / Master of Arts in China Development Studies

Thesis (M.B.A.)--Globe University/Minnesota School of Business, 2008. / Includes bibliographical references (leaves v-xxii).

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract, appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on March 19, 2010). Thesis advisor: Dr. Charles Davis. Includes bibliographical references.

Thesis (B.A.)--Haverford College, Dept. of Economics, 2009. / Includes bibliographical references.

Despite being governed by several laws aimed at preventing racial inequality in access to housing and credit resources, the mortgage lending market remains a contributor to racial and place-based disparities in homeownership rates, wealth, and access to high-quality community resources. Scholarship has identified persistent disparities in mortgage loan approval rates and subprime lending between white borrowers and those from other racial and ethnic groups, and between white neighborhoods and neighborhoods with high levels of non-white residents. Against this backdrop, the mortgage lending industry is undergoing rapid, technology-driven changes in risk assessment and application processing. Traditional borrower risk-assessment methods including face-to-face discussions between lenders and applicants and the prominent use of FICO credit scores have been replaced or supplemented by automated decision-making tools at a new generation of institutions known as fintech lenders. Little is known about the relationship between lenders using these new tools and the racial and spatial disparities that have long defined the wider mortgage market. Given the well-documented history of discrimination in lending along with findings of technology-driven racial inequality in other economic sectors, fintech lending’s potential for racial discrimination warrants increased scrutiny. This dissertation compares the lending outcomes of traditional and fintech mortgage lenders in the United States depending on applicant and neighborhood racial characteristics. Using data from the Home Mortgage Disclosure Act, an original dataset classifying lenders as fintech or traditional, and an array of complimentary administrative data sources, it provides an assessment of the salience of race and place in the rates at which mortgage loans from each lender type are approved and assigned subprime terms. Results from a series of regression-based quantitative analyses suggest fintech mortgage lenders, like traditional mortgage lenders, approve and deny loans and distribute subprime credit at disparate rates to white borrowers and neighborhoods relative to nonwhite borrowers and neighborhoods. Findings suggest that policymakers and regulators must increase their oversight of fintech lenders, ensuring that further advances in lending technology do not concretize longstanding racial and spatial disparities.

Thesis (MBA)--Stellenbosch University, 2012. / The use of leading indicators provides a valuable method to predict changes in macro-economic variables. However, the accuracy of the various models using leading indicators is a topic of constant debate. This study aimed to identify whether leading indicator models predicting residential house price changes performed as well during the recent global financial crisis (fourth quarter 2007 to second quarter 2012) as during the period directly before the crisis. Several potential drivers of the South African property market were identified with the help of previous studies on this topic. Following that, a quantitative analysis was done and single leading indicator models were built using regression analysis to evaluate the importance of each independent variable. This information was used to create a composite leading index for the South African housing market. The accuracy of these models were then compared to predict the changes in house prices during the period preceding the recent global economic crisis.It was found that the ability of these leading indicator models to predict house price changes during the recent global economic crisis decreased significantly.