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Consumer credit debug #104

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Consumer credit debug #104

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6747220
Units bug in credit market clearing was erroneously limiting credit s…
t-youngman Aug 19, 2026
774bf86
Make default parameters for consumer loans and mortgages more sensibl…
t-youngman Aug 19, 2026
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10 changes: 5 additions & 5 deletions macromodel/configurations/bank_configuration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,11 +37,11 @@ class BankParameters(BaseModel):
firm_loans_debt_to_equity_ratio: float = Field(ge=0, le=1, default=0.03)
firm_loans_return_on_equity_ratio: float = Field(ge=0, le=1, default=0.05)
firm_loans_return_on_assets_ratio: float = Field(ge=0, le=1, default=0.05)
household_consumption_loans_loan_to_income_ratio: float = Field(ge=0, le=1, default=0.05)
mortgage_loan_to_income_ratio: float = Field(ge=0, le=1, default=0.05)
mortgage_loan_to_value_ratio: float = Field(ge=0, le=1, default=0.05)
mortgage_debt_service_to_income_ratio: float = Field(ge=0, le=1, default=0.05)
household_consumption_loan_maturity: int = Field(ge=0, default=1)
household_consumption_loans_loan_to_income_ratio: float = Field(ge=0, le=1, default=0.5)
mortgage_loan_to_income_ratio: float = Field(ge=0, le=10, default=3)
mortgage_loan_to_value_ratio: float = Field(ge=0, le=1.1, default=0.8)
mortgage_debt_service_to_income_ratio: float = Field(ge=0, le=1, default=0.3)
household_consumption_loan_maturity: int = Field(ge=0, default=12)
long_term_firm_loan_maturity: int = Field(ge=0, default=60)
mortgage_maturity: int = Field(ge=0, default=120)
short_term_firm_loan_maturity: int = Field(ge=0, default=20)
Expand Down
24 changes: 12 additions & 12 deletions macromodel/markets/credit_market/func/clearing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -260,19 +260,19 @@ def clear(
- banks.ts.current("total_outstanding_loans")
- new_credit_by_bank,
)
max_supply_based_on_preferences_firms = banks.ts.initial("new_loans_fraction_firms") * np.exp(
max_supply_based_on_preferences_firms = max_car * banks.ts.initial("new_loans_fraction_firms") * np.exp(
-self.credit_supply_temperature * current_npl_firm_loans
)
max_supply_based_on_preferences_hh_cons = banks.ts.initial("new_loans_fraction_hh_cons") * np.exp(
max_supply_based_on_preferences_hh_cons = max_car * banks.ts.initial("new_loans_fraction_hh_cons") * np.exp(
-self.credit_supply_temperature * current_npl_hh_cons_loans
)
max_supply_based_on_preferences_mortgages = banks.ts.initial("new_loans_fraction_mortgages") * np.exp(
max_supply_based_on_preferences_mortgages = max_car * banks.ts.initial("new_loans_fraction_mortgages") * np.exp(
-self.credit_supply_temperature * current_npl_mortgages
)
current_sum = (
max_supply_based_on_preferences_firms * max_car
+ max_supply_based_on_preferences_hh_cons * max_car
+ max_supply_based_on_preferences_mortgages * max_car
max_supply_based_on_preferences_firms
+ max_supply_based_on_preferences_hh_cons
+ max_supply_based_on_preferences_mortgages
)
scale = np.divide(
max_car,
Expand Down Expand Up @@ -999,19 +999,19 @@ def clear(
- banks.ts.current("total_outstanding_loans")
- new_credit_by_bank,
)
max_supply_based_on_preferences_firms = banks.ts.initial("new_loans_fraction_firms") * np.exp(
max_supply_based_on_preferences_firms = max_car * banks.ts.initial("new_loans_fraction_firms") * np.exp(
-self.credit_supply_temperature * current_npl_firm_loans
)
max_supply_based_on_preferences_hh_cons = banks.ts.initial("new_loans_fraction_hh_cons") * np.exp(
max_supply_based_on_preferences_hh_cons = max_car * banks.ts.initial("new_loans_fraction_hh_cons") * np.exp(
-self.credit_supply_temperature * current_npl_hh_cons_loans
)
max_supply_based_on_preferences_mortgages = banks.ts.initial("new_loans_fraction_mortgages") * np.exp(
max_supply_based_on_preferences_mortgages = max_car * banks.ts.initial("new_loans_fraction_mortgages") * np.exp(
-self.credit_supply_temperature * current_npl_mortgages
)
current_sum = (
max_supply_based_on_preferences_firms * max_car
+ max_supply_based_on_preferences_hh_cons * max_car
+ max_supply_based_on_preferences_mortgages * max_car
max_supply_based_on_preferences_firms
+ max_supply_based_on_preferences_hh_cons
+ max_supply_based_on_preferences_mortgages
)
scale = np.divide(
max_car,
Expand Down
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