Task 1: Is there evidence of discrimination in mortgage lending? (50%).
You are given a dataset called Wldrg_Mortgage_App, with 1989 observations on approvals and rejections of mortgage applications in a US urban area. Description of all variables is given below.
1. occ occupancy
2. loanamt loan amt in thousands
3. action type of action taken
4. msa msa number of property
5. suffolk =1 if property in Suffolk County
6. race race of applicant
7. gender gender of applicant
8. appinc applicant income, $1000s
9. typur type of purchaser of loan
10. unit number of units in property
11. married =1 if applicant married
12. dep number of dependents
13. emp years employed in line of work
14. yjob years at this job
15. self self-employment dummy
16. atotinc total monthly income
17. cototinc coapp total monthly income
18. hexp propose housing expense
19. price purchase price
20. other other financing, $1000s
21. liq liquid assets
22. rep no. of credit reports
23. gdlin credit history meets guidelines
24. lines no. of credit lines on reports
25. mortg credit history on mortgage paym
26. cons credit history on consumer stuf
27. pubrec =1 if filed bankruptcy
28. hrat housing exp, % total inccome
29. obrat other oblgs, % total income
30. fixadj fixed or adjustable rate?
31. term term of loan in months
32. apr appraised value
33. prop type of property
34. inss PMI sought
35. inson PMI approved
36. gift gift as down payment
37. cosign is there a cosigner
38. unver unverifiable info
39. review number of times reviewed
40. netw net worth
41. unem unemployment rate by industry
42. min30 =1 if minority pop. > 30%
43. bd =1 if boarded-up val > MSA med
44. mi =1 if tract inc > MSA median
45. old =1 if applic age > MSA median
46. vr =1 if tract vac rte > MSA med
47. sch =1 if > 12 years schooling
48. black =1 if applicant black
49. hispan =1 if applicant Hispanic
50. male =1 if applicant male
51. reject =1 if action == 3
52. approve =1 if action == 1 or 2
53. mortno no mortgage history
54. mortperf no late mort. payments
55. mortlat1 one or two late payments
56. mortlat2 > 2 late payments
57. chist =0 if accnts deliq. >= 60 days
58. multi =1 if two or more units
59. loanprc amt/price
60. thick =1 if rep > 2
61. white =1 if applicant white
62. obwhte obrat*awhite
Wooldridge data sets: http://fmwww.bc.edu/ec-p/data/wooldridge/datasets.list.html
Please carry out the following tasks.
1. Use a non-linear estimators such as Probit or Logit (or both) to estimate a baseline model in which the probability of rejection depends only on racial and demographic characteristics of the applicant. Carry out post-estimation evaluation and interpret your findings. (15 marks)
2. Add to model (1) above a range of credit risk indicators associated with the applicant, re- estimate and run post-estimation tests as above. Interpret your findings and compare with those in (1) above. (15 marks).
3. Add to model (2) above a range of credit risk indicators associated with the property, re- estimate and run post-estimation tests as above. (15 marks).
4. Which of the models above is preferred and why? (5 marks).
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