Initial commit

__init__.py

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+import numpy as np
+from multipolyfit import multipolyfit as mpf
+
+GROSS_RENT_YIELD: float = 0.03686
+RISK_POOL_ALLOCATION: float = 0.01
+LOSS_SEVERITY: float = 0.19
+RECOVERY_RATE: float = 0.9
+
+
+def get_default_risk_by_fico(consumer_fico: int, loan_to_value: float) -> float:
+    # remember that the credit score is the first variable and the ltv is the second
+    initial_fico_ltv_risks = [
+        [620, 0.01],
+        [620, 0.70],
+        [620, 0.85],
+        [620, 0.90],
+        [620, 0.95],
+        [660, 0.01],
+        [660, 0.70],
+        [660, 0.85],
+        [660, 0.90],
+        [660, 0.95],
+        [700, 0.01],
+        [700, 0.70],
+        [700, 0.85],
+        [700, 0.90],
+        [700, 0.95],
+        [740, 0.01],
+        [740, 0.70],
+        [740, 0.85],
+        [740, 0.90],
+        [740, 0.95]
+    ]
+
+    default_rates_by_credit_score = {
+        620: 0.0303,
+        660: 0.0201,
+        700: 0.0150,
+        740: 0.0094
+    }
+    risk_factors_by_credit_score = {
+        620: [0.20, 0.61, 1.22, 1.48, 1.80],
+        660: [0.20, 0.62, 1.22, 1.48, 1.82],
+        700: [0.20, 0.62, 1.22, 1.49, 1.83],
+        740: [0.20, 0.63, 1.21, 1.47, 1.81]
+    }
+
+    # We should now fill out the table of default rates with the LTV risk factors included
+    initial_risk_factors_by_ltv_credit_score = []
+    for fico in risk_factors_by_credit_score:
+        initial_risk_factors_by_ltv_credit_score += list(np.multiply(default_rates_by_credit_score[fico],
+                                                                     risk_factors_by_credit_score[fico]))
+        
+    # Compute a line of best fit
+    lobf_coefficients = mpf(initial_fico_ltv_risks, initial_risk_factors_by_ltv_credit_score, 1)
+
+    # the first coeffient is a constant offset, the second is the credit score factor, and the third is the ltv factor
+    const_offset = lobf_coefficients[0]
+    consumer_fico_factor = (lobf_coefficients[1] * consumer_fico)
+    loan_to_value_factor = (lobf_coefficients[2] * loan_to_value)
+    # now we just sum these up to get the result
+    result = consumer_fico_factor + loan_to_value_factor + const_offset
+    return result
+
+def get_risk_pool_health() -> float:
+    return 1.0
+
+
+def compute_mre(home_value: float,
+                down_payment: float,
+                consumer_fico: int) -> float:
+
+    investor_value = home_value - down_payment
+    loan_to_value = float(investor_value/home_value)
+    # the monthly payment is made on the value from the investors
+    monthly_payment = investor_value * (GROSS_RENT_YIELD) / 12.0
+    # how much is at risk if the occupant "defaults"
+    at_risk_value = home_value * LOSS_SEVERITY
+    # how much is expected to be recovered?
+    recovery_value = at_risk_value * RECOVERY_RATE
+    # get the default rate with the risk-factors dealt with
+    default_rate = get_default_risk_by_fico(consumer_fico, loan_to_value)
+    # get the risk pool allocation
+    risk_pool_allocation = RISK_POOL_ALLOCATION * home_value
+    # we should add the 4 month buffer that effectively halves the default rate,
+    # this is already included in the get_default_risk_by_fico method.
+    income_interruption_buffer = 4.0 * monthly_payment
+    default_rate = get_default_risk_by_fico(consumer_fico, loan_to_value)
+    # the expected loss to the risk pool
+    default_rate_risk_pool_loss = default_rate * risk_pool_allocation
+    # adjust the risk pool loss by the risk pool's health
+    risk_pool_factor = get_risk_pool_health() * default_rate_risk_pool_loss
+    # since we don't actually lose anything in the risk pool, it does not 
+    # get counted toward the loss
+
+
+
+    at_risk_value -= risk_pool_factor
+    # start putting together the pieces
+    mre = income_interruption_buffer
+    mre += at_risk_value - recovery_value
+    # make the mre a percentage of the home value
+    mre /= home_value
+    return mre
+
+    
+
+
+#if __name__ == '__main__':
+#    print('Computing risk factor for consumer with 660 FICO and 70% LTV')
+#    print(f'Deault Rate: {get_default_risk_by_fico(660, 0.7)}\n')
+#    print('Comuting MRE for 742,500 home with 2.5% down payment and a 680 fico')
+#    print(f' MRE: {compute_mre(742500.0, 0.025*742500.0, 680)}')
+
+

requirements.txt

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+multipolyfit==0.0.1
+numpy==2.2.4