Скачать или смотреть Evidence that Racial Discrimination occurs in Mortgage Lending; HMDA dataset

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Varian (2014) revisits the classic mortgage lending discrimination study introduced by Munnell, Tootell, Browne, and McEneaney (1996) of the Boston Federal Reserve. Varian (chief economist at Google) applied machine learning algorithms to model the lending behaviour using the same dataset. Munnell et al (1996) were motivated by availability of new data on mortgage applications, showing that African American and Hispanic, "black" applicants were two to three times as likely to be turned down for mortgages as all other applicants. Munnell et al (1996) gathered all the variables known to be missing from the usual HMDA analysis, such as the applicants' debt burdens and credit histories, to determine if economic factors explained this disparity in mortgage denial rates for 'black' relative to all others. This involved incurring some considerable cost to collate the additional information and this additional data did explain some difference. Munnell et al (1996) found that after taking account of economic factors, the applicant's race still very significantly affected the probability of getting a mortgage.

The "tidyverse" suite assembles some of the most versatile R packages such as ggplot2 for visualization and dplyr for data transformation. The packages work in harmony to clean, process, pivot, explore and present data. Above, we perform some exploratory data analysis to get a feel for important attributes and properties in the Munnell, Tootell, Browne, and McEneaney (1996) dataset. To get background to the Varian (2014) HMDA example please follow link:

   • Background to Boston HMDA dataset from - M...