Update dead url of the paper to a working one

README.md

@@ -10,5 +10,5 @@ bank regulation policy.
 
 The model incorporates owner-occupiers, renters, buy-to-let investors, a housing market, a rental market, banks, a
 central bank and a government. A more detailed description of the model can be found at this
-[Bank of England Working Paper](http://www.bankofengland.co.uk/research/Pages/workingpapers/2016/swp619.aspx) and at the
+[Bank of England Working Paper](https://www.inet.ox.ac.uk/files/swp619.pdf) and at the
 ModelDescriptionFeb16.pdf file.

src/main/java/collectors/CollectorBase.py

@@ -0,0 +1,9 @@
+class CollectorBase:
+    def __init__(self):
+        self.active = False
+
+    def isActive(self):
+        return self.active
+
+    def setActive(self, active: bool):
+        self.active = active

src/main/java/collectors/CoreIndicators.py

@@ -0,0 +1,115 @@
+from .CollectorBase import CollectorBase
+
+import housing.Config
+import housing.Model
+import utilities.MeanAboveMedian
+
+#**************************************************************************************************
+#* Class to collect the information contained in the Bank of England "Core Indicators" set for LTV
+#* and LTI limits, as set out in the Bank of England's draft policy statement "The Financial policy
+#* committee's power over housing tools" (Feb 2015). See Table A for a list of these indicators and
+#* notes on their definition.
+#*
+#* @author danial, Adrian Carro
+#*
+#*************************************************************************************************/
+class CoreIndicators(CollectorBase):
+    #------------------#
+    #----- Fields -----#
+    #------------------#
+    def __init__(self):
+        self.config = Model.config  # Passes the Model's configuration parameters object to a private field
+
+    #-------------------#
+    #----- Methods -----#
+    #-------------------#
+
+    #----- Getter/setter methods -----#
+
+    # Note that some of these methods are just wrappers around methods contained in other classes with the purpose of
+    # storing here a coherent set of core indicators getters
+
+    def setActive(self, active: bool) -> None:
+        super().setActive(active)
+        Model.creditSupply.setActive(active)
+        Model.housingMarketStats.setActive(active)
+        Model.householdStats.setActive(active)
+
+    # Owner-occupier mortgage LTI ratio (mean above the median)
+    def getOwnerOccupierLTIMeanAboveMedian(self) -> float:
+        if Model.creditSupply.oo_lti.getN() > 0:
+            return Model.creditSupply.oo_lti.apply(MeanAboveMedian())
+        else:
+            return 0.0
+
+    # Owner-occupier mortage LTV ratio (mean above the median)
+    def getOwnerOccupierLTVMeanAboveMedian(self) -> float:
+        if Model.creditSupply.oo_ltv.getN() > 0:
+            return Model.creditSupply.oo_ltv.apply(MeanAboveMedian())
+        else:
+            return 0.0
+
+    # Buy-to-let loan-to-value ratio (mean)
+    def getBuyToLetLTVMean(self) -> float:
+        if Model.creditSupply.btl_ltv.getN() > 0:
+            return Model.creditSupply.btl_ltv.getMean()
+        else:
+            return 0.0
+
+    # Annualised household credit growth (credit growth: rate of change of credit, current month new credit divided by
+    #  new credit in previous step)
+    def getHouseholdCreditGrowth(self) -> float:
+        return Model.creditSupply.netCreditGrowth * 12.0 * 100.0
+
+    # Household mortgage debt to income ratio (%)
+    def getDebtToIncome(self) -> float:
+        denominator = (Model.householdStats.getOwnerOccupierAnnualisedTotalIncome() + Model.householdStats.getActiveBTLAnnualisedTotalIncome() + Model.householdStats.getNonOwnerAnnualisedTotalIncome())
+        return 100.0 * (Model.creditSupply.totalBTLCredit + Model.creditSupply.totalOOCredit) / denominator
+
+    # Household debt to income ratio (owner-occupier mortgages only) (%)
+    def getOODebtToIncome(self) -> float:
+        return 100.0 * Model.creditSupply.totalOOCredit / Model.householdStats.getOwnerOccupierAnnualisedTotalIncome()
+
+    # Number of mortgage approvals per month (scaled for 26.5 million households)
+    def getMortgageApprovals(self) -> int:
+        return int(Model.creditSupply.nApprovedMortgages * config.getUKHouseholds() / Model.households.size())
+
+    # Number of houses bought/sold per month (scaled for 26.5 million households)
+    def getHousingTransactions(self) -> int:
+        return int(Model.housingMarketStats.getnSales() * config.getUKHouseholds() / Model.households.size())
+
+    # Number of advances to first-time-buyers (scaled for 26.5 million households)
+    def getAdvancesToFTBs(self) -> int:
+        return int(Model.creditSupply.nFTBMortgages * config.getUKHouseholds() / Model.households.size())
+
+    # Number of advances to buy-to-let purchasers (scaled for 26.5 million households)
+    def getAdvancesToBTL(self) -> int:
+        return int(Model.creditSupply.nBTLMortgages * config.getUKHouseholds() / Model.households.size())
+
+    # Number of advances to home-movers (scaled for 26.5 million households)
+    def getAdvancesToHomeMovers(self) -> int:
+        return self.getMortgageApprovals() - self.getAdvancesToFTBs() - self.getAdvancesToBTL()
+
+    # House price to household disposable income ratio
+    # TODO: ATTENTION ---> Gross total income is used here, not disposable income! Post-tax income should be used!
+    def getPriceToIncome(self) -> float:
+        # TODO: Also, why to use HPI*HPIReference? Why not average house price?
+        return (Model.housingMarketStats.getHPI() *
+                config.derivedParams.getHPIReference() *
+                (Model.households.size() - Model.householdStats.getnRenting() - Model.householdStats.getnHomeless()) /
+                (Model.householdStats.getOwnerOccupierAnnualisedTotalIncome() +
+                 Model.householdStats.getActiveBTLAnnualisedTotalIncome()))
+        # TODO: Finally, for security, population count should be made with nActiveBTL and nOwnerOccupier
+
+    # Wrapper around the HouseHoldStats method, which computes the average stock gross rental yield for all currently
+    # occupied rental properties (%)
+    def getAvStockYield(self) -> float:
+        return 100.0 * Model.householdStats.getAvStockYield()
+
+    # Wrapper around the HousingMarketStats method, which computes the quarter on quarter appreciation in HPI
+    def getQoQHousePriceGrowth(self) -> float:
+        return Model.housingMarketStats.getQoQHousePriceGrowth()
+
+    # Spread between mortgage-lender interest rate and bank base-rate (%)
+    def getInterestRateSpread(self) -> float:
+        return 100.0 * Model.bank.interestSpread