126 lines
4.6 KiB
Python
126 lines
4.6 KiB
Python
import numpy as np
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from multipolyfit import multipolyfit as mpf
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GROSS_RENT_YIELD: float = 0.03686
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RISK_POOL_ALLOCATION: float = 0.01
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LOSS_SEVERITY: float = 0.19
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RECOVERY_RATE: float = 0.9
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MIN_MRE: float = 0.05
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def get_default_risk_by_fico(consumer_fico: int, loan_to_value: float) -> float:
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# remember that the credit score is the first variable and the ltv is the second
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initial_fico_ltv_risks = [
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[620, 0.01],
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[620, 0.70],
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[620, 0.85],
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[620, 0.90],
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[620, 0.95],
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[660, 0.01],
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[660, 0.70],
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[660, 0.85],
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[660, 0.90],
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[660, 0.95],
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[700, 0.01],
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[700, 0.70],
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[700, 0.85],
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[700, 0.90],
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[700, 0.95],
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[740, 0.01],
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[740, 0.70],
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[740, 0.85],
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[740, 0.90],
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[740, 0.95]
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]
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default_rates_by_credit_score = {
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620: 0.0303,
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660: 0.0201,
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700: 0.0150,
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740: 0.0094
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}
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risk_factors_by_credit_score = {
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620: [0.20, 0.61, 1.22, 1.48, 1.80],
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660: [0.20, 0.62, 1.22, 1.48, 1.82],
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700: [0.20, 0.62, 1.22, 1.49, 1.83],
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740: [0.20, 0.63, 1.21, 1.47, 1.81]
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}
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# We should now fill out the table of default rates with the LTV risk factors included
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initial_risk_factors_by_ltv_credit_score = []
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for fico in risk_factors_by_credit_score:
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initial_risk_factors_by_ltv_credit_score += list(np.multiply(default_rates_by_credit_score[fico],
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risk_factors_by_credit_score[fico]))
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# Compute a line of best fit
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lobf_coefficients = mpf(initial_fico_ltv_risks, initial_risk_factors_by_ltv_credit_score, 1)
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# the first coeffient is a constant offset, the second is the credit score factor, and the third is the ltv factor
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const_offset = lobf_coefficients[0]
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consumer_fico_factor = (lobf_coefficients[1] * consumer_fico)
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loan_to_value_factor = (lobf_coefficients[2] * loan_to_value)
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# now we just sum these up to get the result
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result = consumer_fico_factor + loan_to_value_factor + const_offset
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return result
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def get_risk_pool_health() -> float:
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return 1.0
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def compute_mre(home_value: float,
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down_payment: float,
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consumer_fico: int) -> float:
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if down_payment < 0 or down_payment > 1:
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raise ValueError('The down_payment must be between 0 and 1')
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if consumer_fico < 620 or consumer_fico > 850:
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raise ValueError('The consumer_fico must between 620 and 850')
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down_payment *= home_value
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investor_value = home_value - down_payment
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loan_to_value = float(investor_value/home_value)
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# the monthly payment is made on the value from the investors
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monthly_payment = investor_value * (GROSS_RENT_YIELD) / 12.0
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# how much is at risk if the occupant "defaults"
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at_risk_value = home_value * LOSS_SEVERITY
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# how much is expected to be recovered?
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recovery_value = at_risk_value * RECOVERY_RATE
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# get the default rate with the risk-factors dealt with
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default_rate = get_default_risk_by_fico(consumer_fico, loan_to_value)
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# get the risk pool allocation
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risk_pool_allocation = RISK_POOL_ALLOCATION * home_value
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# we should add the 4 month buffer that effectively halves the default rate,
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# this is already included in the get_default_risk_by_fico method.
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income_interruption_buffer = 4.0 * monthly_payment
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default_rate = get_default_risk_by_fico(consumer_fico, loan_to_value)
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# the expected loss to the risk pool
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default_rate_risk_pool_loss = default_rate * risk_pool_allocation
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# adjust the risk pool loss by the risk pool's health
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risk_pool_factor = get_risk_pool_health() * default_rate_risk_pool_loss
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# since we don't actually lose anything in the risk pool, it does not
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# get counted toward the loss
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at_risk_value -= risk_pool_factor
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# start putting together the pieces
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mre = income_interruption_buffer
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mre += at_risk_value - recovery_value
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# make the mre a percentage of the home value
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mre /= home_value
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mre = max(mre, MIN_MRE)
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return mre
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def compute_from_target_dti(consumer_income: float,
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home_price: float,
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comsumer_fico: int,
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target_dti: float) -> float:
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return None
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#if __name__ == '__main__':
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# print('Computing risk factor for consumer with 660 FICO and 70% LTV')
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# print(f'Deault Rate: {get_default_risk_by_fico(660, 0.7)}\n')
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# print('Comuting MRE for 742,500 home with 2.5% down payment and a 680 fico')
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# print(f' MRE: {compute_mre(742500.0, 0.025*742500.0, 680)}')
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