You are presented with the following data for 4 fixed-rate 30 year mortgages in your loan sample. MSA State Loan ID FICO 123456 OLTV 650 725 DTI 80 85 90 750 356 CA 425 MD . MD 225 CA 356 CA 425 MD 680 125789 135095 147653 123456 125789 135095 147653 83 80 650 725 85 750 90 . MD All loans were originated in Q12010 Origination FHFA HPI Quarter/Year Loan Amount ($) Index Q1/2010 $ 525,000 100 Q1/2010 $ 350,000 125 Q1/2010 $ 250,000 150 Q1/2010 $ 450,000 200 Q2/2010 $ 520,000 102 Q2/2010 $ 347,000 129 Q2/2010 $ 248,000 155 Q2/2010 $ 445,000 203 Q3/2010 $ 515,000 108 Q3/2010 $ 345,000 135 Q3/2010 $ 245,000 157 Q3/2010 $ 443,000 210 Q4/2010 $ 510,000 130 04/2010 $ 342,000 120 04/2010 $ 240,000 158 04/2010 $ 440,000 225 680 Loan Status Loan Purpose 30 Current Cashout Refi 35 Current Purchase 40 Current Rate & Term Refi 32 Current Purchase 30 Current Cashout Refi 35 30 Purchase 40 Current Rate & Term Refi 32 30 Purchase 30 Current Cashout Refi 35 60 Purchase 40 30 Rate & Term Refi 32 90 Purchase 30 Current Cashout Refi 35 90 Purchase 40 60 Rate & Term Refi 32 60 Purchase 83 123456 80 650 725 750 85 90 225 CA 356 CA 425 MD . MD 225 CA 356 CA 425 MD 680 125789 135095 147653 123456 125789 135095 147653 83 650 80 85 725 750 90 . MD 680 83 225 CA Question 1 Using the table above, comment on whether the organization of the data is structured in a manner that your team was to use in estimating your survival model in SAS used by your team If it is, please describe what this data structure is called; and if not provide provide a suitable data table along with what this data structure is called that your team was instructed to use for estimating your models. Question 2 Provide a new column (paste it below) of data for the loans above creating a suitable dependent variable for your default model assuming a default event is defined as a loan that ever becomes 90 days past due or worse Question 2 Provide a new column (paste it below) of data for the loans above creating a suitable dependent variable for your default model assuming a default event is defined as a loan that ever becomes 90 days past due or worse Question 3 Using the data above, what variable would you use to best represent the borrowers' equity stake at the time of default? Full credit requires providing a column of data for each loan below. Question 4 You have conducted an analysis of your data and found the following results OLTV (%) 0-60 60-80 80-85 85-90 90-95 >95 Loan Count 100,000 25000 2000 30000 20000 10000 # D90+ Loans 2000 550 50 2000 1500 850 Using the data above provide a suitable transformation of the variable and its projected effect on default in a survival model of default Full credit requires showing how the OLTV variable would appear in the model (e.g., linear effect, etc.) Provide a new column (paste it below) of data for the loans above creating a suitable dependent variable for your default model assuming a default event is defined as a loan that ever becomes 90 days past due or worse You are presented with the following data for 4 fixed-rate 30 year mortgages in your loan sample. MSA State Loan ID FICO 123456 OLTV 650 725 DTI 80 85 90 750 356 CA 425 MD . MD 225 CA 356 CA 425 MD 680 125789 135095 147653 123456 125789 135095 147653 83 80 650 725 85 750 90 . MD All loans were originated in Q12010 Origination FHFA HPI Quarter/Year Loan Amount ($) Index Q1/2010 $ 525,000 100 Q1/2010 $ 350,000 125 Q1/2010 $ 250,000 150 Q1/2010 $ 450,000 200 Q2/2010 $ 520,000 102 Q2/2010 $ 347,000 129 Q2/2010 $ 248,000 155 Q2/2010 $ 445,000 203 Q3/2010 $ 515,000 108 Q3/2010 $ 345,000 135 Q3/2010 $ 245,000 157 Q3/2010 $ 443,000 210 Q4/2010 $ 510,000 130 04/2010 $ 342,000 120 04/2010 $ 240,000 158 04/2010 $ 440,000 225 680 Loan Status Loan Purpose 30 Current Cashout Refi 35 Current Purchase 40 Current Rate & Term Refi 32 Current Purchase 30 Current Cashout Refi 35 30 Purchase 40 Current Rate & Term Refi 32 30 Purchase 30 Current Cashout Refi 35 60 Purchase 40 30 Rate & Term Refi 32 90 Purchase 30 Current Cashout Refi 35 90 Purchase 40 60 Rate & Term Refi 32 60 Purchase 83 123456 80 650 725 750 85 90 225 CA 356 CA 425 MD . MD 225 CA 356 CA 425 MD 680 125789 135095 147653 123456 125789 135095 147653 83 650 80 85 725 750 90 . MD 680 83 225 CA Question 1 Using the table above, comment on whether the organization of the data is structured in a manner that your team was to use in estimating your survival model in SAS used by your team If it is, please describe what this data structure is called; and if not provide provide a suitable data table along with what this data structure is called that your team was instructed to use for estimating your models. Question 2 Provide a new column (paste it below) of data for the loans above creating a suitable dependent variable for your default model assuming a default event is defined as a loan that ever becomes 90 days past due or worse Question 2 Provide a new column (paste it below) of data for the loans above creating a suitable dependent variable for your default model assuming a default event is defined as a loan that ever becomes 90 days past due or worse Question 3 Using the data above, what variable would you use to best represent the borrowers' equity stake at the time of default? Full credit requires providing a column of data for each loan below. Question 4 You have conducted an analysis of your data and found the following results OLTV (%) 0-60 60-80 80-85 85-90 90-95 >95 Loan Count 100,000 25000 2000 30000 20000 10000 # D90+ Loans 2000 550 50 2000 1500 850 Using the data above provide a suitable transformation of the variable and its projected effect on default in a survival model of default Full credit requires showing how the OLTV variable would appear in the model (e.g., linear effect, etc.) Provide a new column (paste it below) of data for the loans above creating a suitable dependent variable for your default model assuming a default event is defined as a loan that ever becomes 90 days past due or worse