無(wú)抵押金融科技貸款對(duì)抵押貸款市場(chǎng)的溢出效應(yīng)（英）

HomeIsWhereYourFinTechLoanIs?

TamannaSinghDubey?

FirstDraft:October,2024ThisDraft:March,2025

Abstract

IstudythespillovereffectofunsecuredFinTechlendingonmortgagemarkets.Usingquasi-exogenousvariationinLendingClubloanactivityduetoitspartnershipwiththeBancAllianceconsortiumofcommunitybanksinFebruary2015,IpresentcausalevidencethatincreaseinunsecuredpersonalloansbyLendingClubresultedinasignificantincreaseinoverallmortgageactivityinanarea.Thisspilloverismorepronouncedfornewhomepurchaseactivityversusmortgagerefinancings,andforborrowerswhofacelargerinformationfrictionsinthemortgagemarket.Despitetheincreaseinmortgagelending,Ishowthatmortgagedefaultratesdidnotincrease.Overall,myfindingsuncoveranewbenefitofFinTechlendingandprovideimportantinputstoongoingpolicydebatesintheunsecuredFinTechlendingmarket.

Keywords:FinTechlending,Mortgagelending,Informationfrictions

JELClassification:G20,G21,G23

?Iamespeciallygratefultomydissertationchair,AmiyatoshPurnanandam,forguidanceandsupport.I

amalsogratefultomycommitteemembers,EdwardKim,JeremyKress,UdayRajan,andMelvinStephens.IthankSnehalBanerjee,SugatoBhattacharya,PulakGhosh,AnkitKalda,GeorgeKorniotis,MPNarayanan,PaoloPasquariello,SrinivasaraghavanSriram,BorisVallee,andparticipantsattheFederalReserveBoardofGovernors,IndianSchoolofBusiness,TexasA&MUniversity,UniversityofHongKong,UniversityofMiami,UniversityofMichigan,andWarwickUniversityfortheirhelpfulcommentsonthepaper.IthanktheMitsuiLifeFinancialResearchCenterforprovidingfinancialsupportforthisresearch.

?RossSchoolofBusiness,UniversityofMichigan;email:

tsdubey@.

PAGE

39

Oneoftheprimaryobjectivesoffinancialintermediariesistoreduceinformationfrictionsincreditmarkets.Traditionally,theyhaveachievedthisbyusingavarietyofscreeningdevicessuchasrequiringborrowerstopostcollateral,orbyofferingthemamenuofcontracts.Whilethesedevicesallowlenderstodistinguishbetweenborrowerswhoarecreditworthyandthosewhoarenot,somestillfacefinancingfrictionsandaredeniedcredit(

Gortonand

Winton

,

2003

).Mechanismsandinstitutionalarrangementsthatalleviatethesefrictionshaveafirstorderimpactontheefficiencyofcreditmarkets,aswellasonthewelfareofborrowers.Canrecentadvancesinlendingtechnologies,suchasthesignificantriseofFinTechlenders,improvesubsequentoutcomesincreditmarketsfortheseborrowers?

Toaddressthislargerquestion,IexaminetheimpactofunsecuredFinTechlendingonborrowers’accesstotheU.S.mortgagemarket.WhileseveralstudieshaveexploredtheimpactofFinTechlendingwithinitsownmarket,mypaperisamongthefirsttoexamineitsspillovereffectsonthemortgagemarketinacausalmanner.Ononehand,FinTechlenderscanalleviatecreditmarketfrictionsbyusingsuperiorscreeningtechnologyandalternativedata,therebyimprovingcreditaccessfornewandexistingborrowersthroughcheaperdebtconsolidationandtheprovisionofnewloans(

Berg,Burg,Gombovi′c,andPuri

,

2020

;

DiMaggio,Ratnadiwakara,andCarmichael

,

2022

).Ontheotherhand,ifFinTechlendersaremerely“skimming”thebestborrowersfromtheexistingpool,orifborrowersuseFinTechloansforconspicuousconsumption,theirpresencemightnotleadtoanysubstantialimprovementinborroweroutcomes(

DiMaggioandYao

,

2021

).Therefore,mystudyshedslightonanimportantandunresolvedempiricalquestion.

Inaddition,mystudyisimportantfortwootherreasons.First,unsecuredFinTechlendinghasgrowntenfoldoverthepastdecade,whilethemortgagemarketrepresentsasubstantialshareofhouseholddebtintheU.S.,makingthisaneconomicallysignificantsetting.

1

,

2

Second,

1Source:Transunion

2

Iacoviello

(

2011

)findthattwo-thirdsofthewealthofamedianU.S.householdconsistsofhousingwealth.Forthebottom90%wealthgroup,

Smith,Zidar,andZwick

(

2021

)showthat62%ofnon-pensionwealthiscomposedofhousing.

asFinTechloansaretypicallysmallerthanmortgages,mystudyprovidesinsightintowhethersmall,cash-flow-basedlendingcanhelpfacilitateaccesstolarger,collateral-basedloans.Thiscanbeparticularlyrelevantwhencollateralaloneisinsufficienttoaddressinformationfrictionsbetweenlendersandborrowers(

BesankoandThakor

,

1987

;

Benmelech

,

2024

).Thus,improvementsintraditionalcreditmetrics,suchasrepaymenthistory,debt-to-incomeratio,andcreditscoresresultingfromcash-flow-basedlending,couldfurtherenhanceborrowers’accesstocollateral-basedloans.

IempiricallyexaminetheeffectofunsecuredFinTechloanoriginations,measuredusingLendingClubdata,ontheoutcomesinthemortgagemarket,measuredusingHMDAdata,during2010to2019.Icombinethesetwodatasetsandaggregatethemat3-digitzipcodelevel,orzip3level,toanalyzehowthegrowthofunsecuredFinTechlendinginfluencesmortgagemarketoutcomesacrossover900zip3areasintheUS.Sinceborrowersoftenreceivepreliminaryindicationsfromlendersabouttheirlikelihoodofobtainingamortgagebeforetheyformallyapply,Iuseboththenumberofmortgageapplicationsandthenumberofloansoriginatedasmeasuresofmortgageactivityineacharea.Tocontrolforanytime-invariantlocalcharacteristics,Iemployzip3areafixedeffects.Additionally,Iincludetheinteractionofstateandyearfixedeffectstoaccountforanytime-varyingchangesinstate-levelfactors,suchaseconomicgrowth,fiscalpolicy,regulation,anddemographics.Furthermore,byregressingmortgageactivityonlaggedvaluesofLendingClubloanactivity,Iaddresspotentialconcernsaboutreversecausalitybetweenthetwomarkets.IfindapositivecorrelationbetweenLendingClubloanactivityandoverallmortgageactivityinanareainthisbaselinespecification.

Isthisrelationshipcausal?Unobservedtime-varyingfactors,suchaseconomicgrowthandjobopportunity,maysimultaneouslydrivebothFinTechlendingandmortgageactivitywithinanarea.Toaddressthisendogeneityconcern,IemployanovelidentificationstrategybasedontheFebruary2015partnershipbetweenLendingClubandtheBancAlliancenetwork,oneofthelargestconsortiumofcommunitybanksintheUS.Akeyaspectofthispartnership

wasthatBancAlliancememberbankswouldredirecttheirunsecuredpersonalloancustomerstotheLendingClubplatform,enablingLendingClubtosignificantlyexpanditsborrowerbaseinareaswherethesebanksoperated.SincethepartnershipwiththeBancAlliancenetworkwasestablishedatthenational-level,LendingClubcouldnotselectivelytargetspecificareastoexpanditsborrowerbase.ThismakestheincreaseinLendingClubloanactivityexogenoustomortgageoutcomesandgrowthatthezip3level.Furthermore,thisarrangementalsopreventedLendingClubfromsellingmortgageorotherproductstobankcustomers.

3

BancAlliancememberbanks,whowereprimarilysmallcommunitybanks,enteredthepartnershiptoinvestinsecuritizedLendingClubloansinordertodiversifytheircreditportfolioswithunsecuredconsumerloansratherthanexpandmortgagelending.Thisquasi-exogenousexpansionofLendingClubloanactivityprovidesauniqueopportunitytocausallyidentifytheimpactofunsecuredFinTechlendingonmortgagemarketoutcomesinanarea.

Atthetime,BancAlliancehadover200memberbanksacross39states,withmemberbanksbeingsmallcommunitybankswithassetsbetween$200millionand$10billion.Toappreciatethescaleofthisnetwork,itishelpfultonotethatifallitsmemberbanksformedasingleentity,theBancAlliancenetworkwouldrankfourthinthecountrybybranchcountandseventeenthbyassetsize.

4

AreaswhereBancAlliancememberbanksoperatedduringthe2015partnershipwithLendingClubaredesignatedastreatedareas,whileallotherareasintheU.S.serveascontrolareas.Iuseastandarddifference-in-differencesspecificationtocompareoutcomesinLendingClubandmortgageactivitybeforeandafter2015acrosstreatedandcontrolareas.IfindthatthenumberofLendingClubloansincreasedby9.25%intreatedareascomparedtocontrolareasafterthe2015BancAlliancepartnership,highlightingthesignificanteffectofthispartnershiponLendingClubloanactivity.Inmymaintestusingthesamedifference-in-differencesspecification,Ishowthatmortgageactivityintreatedareasincreasedbynearly3%relativetocontrolareasafter2015.Moreover,Ishowthatbefore

3

LendingClubAndSmallerBanksInUnlikelyPartnership,WallStreetJournal,June2015

4

LendingClub’sNewestDealFuelsInvestorExcitement,WallStreetJournal,February2015

2015,treatedandcontrolareasexhibitedparalleltrendsinbothmortgageapplicationsandloanoriginations.

TodirectlylinkLendingClubloanstomortgages,Iuseatwo-stageregressionspecificationusinginstrumenteddifference-in-differences(DDIV)approach(

Duflo

,

2001

;

Hudson,Hull,

andLiebersohn

,

2017

).Thefirststageisastandarddifference-in-differencesspecification,asdescribedearlier,whereIestimatetheimpactoftheBancAlliancepartnershiponLendingClubloanactivityinanarea.Inthesecondstage,IassessthesensitivityofmortgageactivitytounsecuredFinTechloanactivity,instrumentedbythe2015BancAlliancepartnershipfromthefirststage.Iobservetwokeyfindings.First,thereisastatisticallysignificantpositivespillovereffectfromLendingClubloanactivitytomortgageactivitywithinanarea.Onaverage,a1%increaseinLendingClubloansledtoa30-40basispointincreaseinmortgageapplicationsandloans.Second,thiseffectissmallestimmediatelyaftertheincreaseinLendingClubloansandgrowslargerovertime.Thispatternisconsistentwiththegradualimprovementinborrowers’traditionalcreditmetrics,drivenbyFinTechlenders’abilitytoidentifycreditworthyborrowersandalleviateinformationfrictionsevenbeyondtheunsecuredloanmarket.Additionally,usingthesamedifference-in-differencesspecification,Ifindthatmortgageinterestratesdeclinedby1.62%intreatedareascomparedtocontrolareasafter2015.Thisresult,combinedwiththeobservedincreaseinmortgagequantity,suggestsanoutwardshiftinthecreditsupplycurveinthemortgagemarketfollowingthequasi-exogenousincreaseinLendingClubloanactivity,asinstrumentedthroughits2015partnershipwiththeBancAlliancenetwork.

Whatisthemagnitudeofthisspillovereffect?IntheDDIVspecification,IfindthataonepercentincreaseinLendingClubloansresultsinapproximatelya30-40basispointincreaseinmortgageactivityinanarea.GiventhattheaveragenumberofLendingClubloansis335,andtheaveragenumberofmortgageloansis10,570inanareainmysample,theDDIVspecificationsuggeststhatthreeadditionalLendingClubloansleadtoabout32extramortgageloans.ThisindicatesamultipliereffectinthespilloverfromLendingClub

loanactivitytomortgageactivity.

Therearefourpossibleexplanationsforthismultipliereffect.First,borrowersvettedandapprovedbytheLendingClubplatformmayusetheloantermstheyreceiveasleveragetoobtainanunsecuredloanfromabank.

5

Second,banksmayinterpretaFinTechplatform’sdecisiontoofferaloanasapositivesignal,promptingthemtoextendanunsecuredloantotheseborrowers(

Balyuk

,

2023

).Third,onceaLendingClubborrowersecuresamortgage,theymaydecidetorefinanceit—sometimesmorethanonce—resultinginmultipleloansforthesameborrowerinthemortgagemarketdata.Finally,theIVestimateshererepresenttheeffectofthetreatmentonthesubsetofareasthatareaffectedbytheinstrument,i.e.thecompliers.ThisgroupmayrespondmorestronglytothetreatmentthantheaverageareacapturedbythebaselineOLSspecification,makingtheIVestimatelarger.Consequently,thelargevalueofelasticityhereisinlinewiththeinterpretationthatbecausethecomplierareashaveahighersensitivitytotreatment(

Jiang

,

2017

).

Whatistheeconomicmechanismbehindmyfindings?FinTechlenderscanusealternativedataandtheirsuperiorprocessingtechnologytoidentifycreditworthyborrowersintheunsecuredloanmarket,andconsequentlyarmthemwithcheaperdebtconsolidationloansthathelpthemimprovetheirtraditionalcreditmetrics,whichiscrucialtofacilitatethespillovereffectfromunsecuredloanmarkettosecuredmortgagemarket.Icheckthismechanismusingtwotests.Inthesetests,Iexaminethevaryinglevelsofinformationasymmetryacrossbothborrowertypesandmortgageproductsinthemortgagemarket.First,differentmortgageproductsalsopresentvaryingdegreesofinformationbarriers.Forexample,newhomepurchasestypicallyinvolvegreaterinformationasymmetryforborrowerstoovercome,whereasrefinancingsgenerallycarrylowerinformationfrictionsduetotheborrower’sabilitytosecureamortgageinthepast.Second,someborrowers,suchasmiddle-incomeindividuals,aremorelikelytofacehigherinformationfrictionscomparedtootherborrowers.

5ObtainingloantermsfromLendingClubwouldnotonlyimprovetheinformativenessofborrowersbut

alsoreducetheirsearchcosts.Thiswouldfurtheraidborrowerstogetfavourableunsecuredloancontractsfromtraditionallenders(

Argyle,Nadauld,andPalmer

,

2023

).

Inthefirsttest,Iconsiderthevaryinglevelsofinformationasymmetryinthemortgagemarketbasedonthetypeofmortgageproduct.Fornewhomepurchases,theinformationgapbetweenlendersandborrowersshouldbelargercomparedtorefinancings,whereborrowersarealreadyrepayinganexistingmortgage.Therefore,IcomparethesensitivityofnewhomepurchaseversusrefinancingstoLendingClubloanactivityinanarea.IfindthatnewhomepurchasesaremoresensitivetoLendingClubloanactivitythanrefinancingactivity.Usingatripledifferencesregressionspecificationtocomparetreatedversuscontrolareasbeforeandafterthe2015BancAlliancepartnership,Ishowthat,whencomparedtorefinancings,newhomepurchaseapplicationsincreasedby3.5%andloanoriginationsincreasedby4.6%intreatedareaspost2015,versuscontrolareas.

Thesecondtestexaminestheimpactofalleviatinginformationasymmetryacrossdifferentincomebandsinthemortgagemarket.Ifindthat,conditionalonborrowersapplyingforamortgage,middle-incomeborrowersarethemostsensitivetoincreasesinLendingClubloanactivityinanarea.Thisresultalignswiththeinformationfrictionsmechanism.Thisisbecauseifthespillovereffectweresolelydrivenbyaccesstocheaperdebtconsolidationloans,low-incomeborrowerswouldbeexpectedtoshowthegreatestsensitivity.However,thehigherrelativesensitivityamongmiddle-incomeborrowerssuggeststhatFinTechlenderseffectivelyidentifycreditworthyindividuals,whostandtolosethemostifdeniedaccesstounsecuredFinTechloans.

ArethenewmortgagesfacilitatedbyLendingClubloanactivityriskierthanaverage?Toassessthis,IstudytheperformanceofmortgagesoriginatedasaresultofincreasedunsecuredLendingClubloans,usingFannieMaeandFreddieMacdata.EmployingaDDIVregressiondesign,withthe2015LendingClubpartnershipwithBancAllianceastheinstrument,IprovidecausalevidencethattheriseinmortgageloansduetoincreasedLendingClubactivitydoesnotleadtohighermortgagedelinquencies.ThisfindingsuggeststhatthespilloverfromunsecuredFinTechlendingtothemortgagemarketisdrivenbyFinTechlenders’abilitytousealternativedataandsuperiorscreeningtechnologytoidentifypreviouslyexcludedbut

creditworthyborrowers.

Tosupportmymainfindings,Iconducttworobustnesstests.First,IreplicatemymainfindingsusingHMDAmortgagedata,excludingallmortgageactivitygeneratedbyFinTechlendersinthemortgagemarket.ThistestshowssimilarresultstomymainfindingsandhelpseliminatethepossibilitythattheimpactofunsecuredFinTechloanactivityonmortgageactivityisdrivenbyFinTechlendersinthemortgagemarket.Second,IreplicatemymainresultsusingFannieMaeandFreddieMacmortgagedata.Theresultsaresimilartomymainfindings,furthersubstantiatingtheuseofFannieMaeandFreddieMacdatatostudymortgageperformance.

Myfindingscarryimportantimplicationsforborrowersinthemortgagemarket.DespitenumerouspolicyinitiativesandregulatoryeffortsintheU.S.overthepastfewdecadestoincreaseaccesstothemortgagemarket,significantfrictionsexistincreditmarketsformarginalborrowers.Mystudyshowsthattechnologyenabledmarket-basedmechanismscanbeapromisingtooltoalleviatethesefrictions.AlthoughmystudydoesnotdirectlyassessthewelfareeffectsofFinTechlending,ithighlightsasignificantbenefit:FinTechlendingcanimprovemortgageaccess.Thisfindingiscrucialforcomprehensivewelfareevaluations,asrecentconcernsfromregulatorsandpolicyadvocateshaveraisedquestionsabouttheuseofalternativedatabyunsecuredFinTechlendersanditspotentialimpactonfairlendingpractices(

DiMaggioetal.

,

2022

),aswellasonprivacy.MystudyprovidesanessentialinputtothisbroaderwelfareanalysisbydocumentingoneofFinTechlending’spositiveeffectsoncreditmarketaccess.

RelatedLiterature

ThispaperisamongthefirsttoprovidecausalevidenceofthespillovereffectsofunsecuredFinTechlendingonsecuredmortgageloans.Threerelatedpapersherearestudiesby

Chava,

Ganduri,Paradkar,andZhang

(

2021

),

Balyuk

(

2023

),and

DoreandMach

(

2022

),eachof

whichcontributestotheunderstandingofthebroadroleofunsecuredFinTechlendingonborrowers,butwithdifferentfocusesandfindings.First,

Chavaetal.

(

2021

)comparethecreditscoresanddefaultratesofborrowersofananonymousFinTechmarketplacelender(MPL)withbankborrowers,aswellaswithborrowerswhoweredeniedunsecuredpersonalcreditbyboththeMPLandbanks.TheyfindthatMPLborrowersinitiallyexperienceariseandeventuallyexperiencedeclinesincreditscores.Moreover,theyexperiencehigherdefaultratesintheunsecuredpersonalloanmarket.

Second,

Balyuk

(

2023

)usesloan-leveldatafromProspertodemonstratethatProsperborrowingincreasesaccesstorevolvingbankcreditbyalleviatinginformationfrictions.Whileherfindingsalignwithmyresultsregardingtheshort-termimprovementofcreditprofilesduetoProsperborrowing,herfocusisonthespilloverwithintheunsecuredloanmarket.Incontrast,thispaperprovidescausalevidenceofspilloverfromtheunsecuredFinTechlendingmarkettothesecuredmortgagemarket,highlightingabroaderandmoreimpactfulrelationship.Third,

DoreandMach

(

2022

)alsoshowincreasedsecuredlendingbyProsperborrowers,afindingclosetomyresults.However,theseresultsarenotcausal.MystudybuildsontheseresultsbyprovidingcausalevidenceofthespillovereffectfromunsecuredFinTechlendingtosecuredmortgagelending.

MystudycontributestotheliteratureoncreditmarketfrictionsbyshowinghowFinTechlendinginthesmall-ticketloanmarketcanalleviateinformationfrictionsthatborrowersfaceinlarger,collateral-basedloanmarkets.Whilesomestudiesemphasizetheroleofcash-flow-basedlendinginaddressingcreditfrictions(

HolmstromandTirole

,

1997

),othersdemonstratehowcollateralcanreduceborrowingcostsandincreasecreditlimits(

Bernanke,

Gertler,andGilchrist

,

1996

;

KiyotakiandMoore

,

1997

).Recentresearchhighlightsthegrowingsignificanceofcash-flow-basedlending,with

LianandMa

(

2021

)documentingthatnearly80%ofU.S.corporateloansarecash-flow-based,and

Benmelech,Kumar,andRajan

(

2020

)observingadeclineinsecuredlending.Thus,myfindingthatcash-flow-basedlendingcancomplementcollateral-basedlendingisparticularlyrelevantintoday’slendinglandscape.

ThispaperisalsorelatedtothevastandgrowingliteraturethatdiscussestheimpactofFinTechlenders.MostofthisliteraturefocusesontheimpactofFinTechlendingwithinagivencreditmarket.IcontributetothisliteraturebyprovidingcausalevidenceofimpactofunsecuredFinTechloanmarkettocreditmarketoutcomesinothersecuredcreditmar-kets.

AgarwalandZhang

(

2020

),

Berg,Fuster,andPuri

(

2022

),

Allen,Gu,andJagtiani

(

2021

),

BranzoliandSupino

(

2020

),and

Morse

(

2015

)provideexcellentoverviewoffrictionsaddressedbyFinTechlenders.Specifically,useofalternativedataforscreening,superiorscreeningalgorithms,improvedcustomerexperienceintermsofcompletelyonlineapplica-tionprocessandfasterprocessingtimes,andinnovativeproductsforeffectiveenforcementmechanisms,suchasrevenue-basedfinancing,havehelpedFinTechlendersgainanedgeovertraditionallendersinunsecuredpersonalcreditlending(

JagtianiandLemieux

,

2019

;

Avramidis,Mylonopoulos,andPennacchi

,

2022

;

DiMaggioetal.

,

2022

;

RajanandXiong

,

2024

;

ValleeandZeng

,

2019

),smallbusinesslending(

GopalandSchnabl

,

2022

;

Ereland

Liebersohn

,

2020

;

Beaumont,Tang,andVansteenberghe

,

2021

)andthemortgagemarket(

Fuster,Plosser,Schnabl,andVickery

,

2019

;

Buchak,Matvos,Piskorski,andSeru

,

2018

;

Fuster,Goldsmith-Pinkham,Ramadorai,andWalther

,

2022

).

Thispaperisalsorelatedtothebroadliteraturethatrecordstheroleofinnovationinfinancialintermediationtoreduceinformationfrictionsincreditmarkets.Theimpactofdigitalpaymentsisattheforefrontofthisliteratureastheyhelpincreasetheeconomicoutcomesofborrowers(

DubeyandPurnanandam

,

2023

;

JackandSuri

,

2014

)byalleviatinginformationasymmetryforhouseholdsandfirms,resultinginrelaxationofcreditconstraints(

Parlour,Rajan,andZhu

,

2022

;

Ghosh,Vallee,andZeng

,

2022

;

Bj¨orkegrenandGrissen

,

2018

).

Therestofthepaperisorganizedasfollows.Section

1

discussesdataconstructionanddescriptivestatistics.Section

2

explainstheresultsfromthebaselineordinaryleastsquares(OLS)specification.Section

3

explainstheidentificationstrategy,anddiscussesresultsusingthestandarddifference-in-differencesandtheinstrumenteddifference-in-differences

specifications.Section

4

presentspotentialeconomicmechanism,andpresentsresultsthatsupportthismechanism.Section

5

discussesimpactonmortgageloanperformanceduetounsecuredFinTechlending.Section

6

discussesrobustnessteststosupportmymainresultsandmechanism.Section

7

concludesthepaper.

DataandDescriptiveStatistics

IconstructthemainsampletostudythespillovereffectsfromunsecuredFinTechloanactivityonmortgageactivityusingloan-leveldatabyLendingClubandapplication-leveldatafromHMDA.Iaggregatethisdataat3-digitzipcodelevel,orzip3level,whereeachzip3isanareadefinedbyallzipcodessharingthesamefirstthreedigits.Consequently,azip3areaservesasboththeunitofobservationandtheleveloftreatmentintheempiricalsettingofthispaper.Thesamplehereconsistsof905zip3areasacross50USstatesandtheDistrictofColumbia.Eachzip3areahasapopulationofapproximately336,751,consistentwiththetypicalpopulationofazip3areaintheU.S.

LendingClubData:Figure

1

depictsthegeographicvariationofLendingClub’sloanactivityinanareaasaproportionofthetotalloanamountoriginatedbyLendingClubacrosstheU.S.inagivenyearduringthesampleperiod.From2010to2019,thisdatasetcoversabout3millionloans.Foreachoftheseloans,Iobserveloantermssuchasloanamount,interestrate,termperiod,purposeetc,reportedincomeoftheborrower,informationaboutcreditprofileoftheborrowersuchastheirdebt-to-incomeratio,creditscoreetc,andemploymentdetailsoftheborrower.Onaverage,LendingCluboriginated335loansinanareainayear.

AsshowninTable

1

,atypicalpersonalloanfromLendingClubhasanaverageloanamountof$14,047,aninterestrateof12.7%,andatermofeither3or5years.Seventypercentoftheloansinmysamplewereissuedfora3-yearterm,whiletheremaining30%

Figure1:VariationInLendingClubLoanActivity

Figure

1

presentstheheatmapshowingtheaverageshareofLendingClub’sunsecuredpersonalloanamountsineachzip3areaasapercentageofthetotalLendingClubloanamountacrosstheU.S.inagivenyear.Thevaluesrepresenttheaverageofthesepercentagesoverthesampleperiodfrom2010to2019.

(0.1356,1.2043]

(0.0513,0.1356]

(0.0223,0.0513]

[0.0000,0.0223]

hada5-yearterm.TheaverageLendingClubborrowerhasanannualincomeof$69,199.Intermsofcreditprofile,thetypicalborrowerhasaFICOscoreof696andadebt-to-incomeratioof18.2%.Additionally,80%oftheLendingClubloansinmysampleweretakenoutforthepurposeofdebtconsolidation,specificallytorefinanceexistinghigh-interestcreditcarddebtandotherunsecuredloansatalowercost.

HMDAMortgageData:Tostudymortgageactivityinanarea,IprimarilyusedatareportedbyU.S.mortgagelendersundertheHomeMortgageDisclosureAct(HMDA).Thisapplication-leveldata,providedannuallybytheFederalFinancialInstitutionsExaminationCouncil(FFIEC),representsthenearuniverseofmortgageapplicationsintheU.S.From2010to2019,Iobserveapproximately155millionmortgageapplications.Foreachapplication,Ihaveinformationontheloancharacteristics,suchastheloanamount,type,andpurpose,aswellasthegeographiclocationandsomeborrowercharacteristics,suchastheirreported

income.Notably,theaverageincomeofamortgageborrowerinanareais$90,480,whichishigherthantheaverageincomeofaLendingClubborrower,at$69,199.

Table

2

showsthat,onaverage,azip3areaintheU.S.saw17,095mortgageapplicationsperyearduringthisperiod.Of