diff --git a/macromodel/configurations/bank_configuration.py b/macromodel/configurations/bank_configuration.py
index c0e27cd2..edb8dbe9 100644
--- a/macromodel/configurations/bank_configuration.py
+++ b/macromodel/configurations/bank_configuration.py
@@ -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)
diff --git a/macromodel/markets/credit_market/func/clearing.py b/macromodel/markets/credit_market/func/clearing.py
index a4e41552..4f4d1d8c 100644
--- a/macromodel/markets/credit_market/func/clearing.py
+++ b/macromodel/markets/credit_market/func/clearing.py
@@ -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,
@@ -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,