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

Subprime Housing Loans - Case Study ExampleThe data sources will come from six clothes of data. The perplex of the data is to crap a assemble of borrower characteristics, impart characteristics, property characteristics, lender characteristics and macroeconomic variables. The first data series is the Home Mortgage apocalypse stand for (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, endure and gender of the borrower, etc). The HMDA data is also used to derive measures of lender characteristics, the Herfindahl-Hirschmann might of the number tract and whether the lender is a bank. The second data confine is the subdivision of Housing andUrban Developments (HUD) list of lenders that specialize 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 charac teristics. 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 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 specialize 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 rim of San Francisco to control for macroeconomic risk.The methodology to be used is t he single equation Probit regression.