Subprime Housing Loans Case Study Example | Topics and Well Written Essays - 1000 words

Subprime house Loans - Case Study ExampleThe selective information sources will come from vi facilitys of selective information. The aim of the selective information is to construct a set of borrower characteristics, lend characteristics, property characteristics, lender characteristics and macroeconomic variables. The commencement ceremony data series is the Home Mortgage Disclosure Act (HMDA) data from 2000 to 2007. The aim is to stick individual loan level data (such as whether a loan isbeing accepted or rejected, loan amount, income, race and gender of the borrower, etc). The HMDA data is also utilize to derive measures of lender characteristics, the Herfindahl-Hirschmann Index of the numerate brochure and whether the lender is a bank. The second data set is the Department of Housing andUrban Developments (HUD) bring up of lenders that change in the subprime market to code each loan as being subprime or not. The thirda data set is the U.S. numerate data to derive Cen sus footpath level demographic, property and borrower characteristics. The Census data is matched to HMDA by state, county and Census pamphlet number. The fourth data set is from a major credit authority for tract median FICO score (MEDFICO) and debt-to-income ratio (DTI), which are widely accepted borrower risk variables used by owe bankers and brokers in their lending decision. The credit bureau data is also matched to HMDA data by state, county and Census tract number. ... The first data series is the Home Mortgage Disclosure Act (HMDA) data from 2000 to 2007. The aim is to obtain individual loan level data (such as whether a loan isbeing accepted or rejected, loan amount, income, race and gender of the borrower, etc). The HMDA data is also used to derive measures of lender characteristics, the Herfindahl-Hirschmann Index of the Census tract and whether the lender is a bank. The second data set is the Department of Housing and Urban Developments (HUD) list of lenders that spe cialize in the subprime market to code each loan as being subprime or not. The thirda data set is the U.S. Census data to derive Census tract level demographic, property and borrower characteristics. The Census data is matched to HMDA by state, county and Census tract number. The fourth data set is from a major credit bureau for tract median FICO score (MEDFICO) and debt-to-income ratio (DTI), which are widely accepted borrower risk variables used by mortgage bankers and brokers in their lending decision. The credit bureau data is also matched to HMDA data by state, county and Census tract number. Fifth, I match the House Price Index (HPI) data from the Office Federal Housing Enterprise Oversight (OFHEO) to HMDA data by year and Metropolitan Statistical Area (MSA). This data is used to construct neighborhood house price appreciation rate, which is used to calculate the loan-to-value ratio (LTV). The sixth data set is the macroeconomic data from the Federal Reserve Bank of San Franci sco to control for macroeconomic risk.The methodology to be used is the single equation Probit regression.