# Code to estimate default probability of CRE Loans
# By

data <- read.csv("CREData.csv", header=TRUE, sep=",", stringsAsFactors=FALSE)
dim(data)
# 41932 x 18
names(data)
# [1] "ID"                   "DEFAULTFLAG"          "Status"             "Coupon"             "Original.Balance"    "Occupancy.Rate"
# [7] "LTV"                "DSCR"                "NOI"                "Net.Cash.Flow"       "Origination.Dt"       "Original..Amortization"
# [13] "Master.Servicer"    "Special.Servicer"    "State"                "Property.Type"       "State.Unemployment" "Per.Capita.Income"
# Summarize Data

dd2 <- summary(data)
show(dd2)

data$LTV <- as.numeric(data$LTV)
#       ID       DEFAULTFLAG          Status       Coupon    Original.Balance Occupancy.Rate    LTV          DSCR          NOI
# Min. : 245 Min. :0.0000 Perform :26340 Min. : 0.000 Min. : 100000 Min. :0.0400 0.58 : 5549 Min. : 0.400 Min. : 9071
# 1st Qu.:12426 1st Qu.:0.0000 Perform(w): 8263 1st Qu.: 5.590 1st Qu.: 1904600 1st Qu.:0.9300 0.8 : 1278 1st Qu.: 1.250 1st Qu.: 223280
# Median :24741 Median :0.0000 Grace : 1635 Median : 5.920 Median : 4000000 Median :1.0000 0.799 : 563 Median : 1.380 Median : 440292

In a clinical trial of a new drug designed to alleviate some of the...