Income Vola ili y and Residen ial Mo gage
Delinquency: E idence om 12 EU coun ies
Luis Diaz-Se ano
Na ional Uni e si y o I eland Maynoo h
IZA Bonn, CREB Ba celona
Co esponding au ho :
Luis Diaz-Se ano
Na ional Uni e si y o I eland Maynoo h
Depa men o Economics (Rhe o ic House)
Maynoo h, Co. Kilda e, I eland
Tel: +353 1 7083793
Fax: +353 1 7083934
Email: [email protected]
1
Abs ac :
We in es iga e he socio-economic de e minan s o mo gage delinquency in 12
EU coun ies and obse e ha income ola ili y signi ican ly inc eases he mo gage
delinquency isk. This pa e n e en holds o bo owe s wi h highe -income p o iles i
ola ili y in income is high enough. F om his esul we can d aw he ollowing
conclusions: i) mo gage p o ec ion insu ance policies migh be ailing o co e hose
bo owe s mos in need; ii) he exis ence o c edi ma ke impe ec ions, and; iii) he
inabili y o a numbe o bo owe s mos a income isk o accumula e p ecau iona y
sa ings in o de o mee mo gage paymen s when shocks in income a ise.
JEL classi ica ion: D1, R0, J0
Keywo ds: Income ola ili y, mo gage delinquency, mo gage insu ance,
homeowne ship, paymen - o-income a io, c edi ma ke impe ec ions, p ecau iona y
sa ings.
2
1. In oduc ion
Du ing he second hal o he 1990s and ea ly 2000s he a es o mo gage
delinquency ha e allen d ama ically in mos o he EU-151 coun ies. These downwa d
ends coincide wi h alling in e es a es and imp o ing pe o mances in mos o he
EU-15 economies. Pa adoxically, his decline in he mo gage delinquency a es has
also coincided wi h upwa d ends in he p ices o housing, which in coun ies as
I eland, Spain, UK o The Ne he lands has been d ama ic. Besides he a o able
economic condi ions men ioned abo e, a g ea e e o by he lending indus y o make
mo gage ake-up mo e a o dable has been necessa y o mi iga e such a d ama ic
inc ease in housing p ices.
Despi e he decline in mo gage delinquency a es, his phenomenon s ill exis and
aises issues no only o lende s bu also o bo owe s. Fo he o me g oup, apa
o m he ob ious well documen ed economic losses ha a de aul would suppose, he e
a e some s udies ha epo heal h p oblems associa ed wi h he unsus ainable housing
and he mo gage a ea s (see e.g. Bu ows, 1998; Ne le on and Bu ows, 1998, in he
UK; Be y e al., 1999, in Aus alia; Doling and Ruona aa a, 1996, in Finland).
In andem wi h he upwa d ends in mo gage ake-up he e exis a g owing
indus y de o ed o p o iding sa e y-ne s o mo gage bo owe s ia he same lending
ins i u ions o insu ance companies. The objec i e o he mo gage insu ance policies is
o coun e ac he po en ially de as a ing e ec o he un o eseen e en s ha cause
luc ua ions in he mo gago ’s income. The e o e, in he e en o in olun a y
unemploymen , sickness o o he unexpec ed shock in he mo gago ’s income, he
mo gage paymen is co e ed. Howe e , he g owing li e a u e in his issue, mainly
1 We e e o EU-15 as he 15 EU coun ies be o e he ex ension o 25 coun ies execu ed in May-2004.
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ocused on he UK mo gage ma ke , p o ide e idence on he ine iciency and
inadequa eness o he p i a e mo gage p o ec ion insu ance (MPI) policies in bo h he
low ake-up om hose bo owe s mos a income isk and he poo co e age o he
isks (see P yce and Keoghan, 2002; Fo d and Quilga s, 2001).
In his pape we examine he de e minan s o mo gage delinquency in 12 o he
EU-15 coun ies. We ocus on he socio-economic ac o s a he han hose ega ding
he cha ac e is ics o he mo gage o he mo gaged p ope y. In his con ex , we
belie e ha ola ili y in household’s income is he a iable ha , in a isky wo ld, bes
p oxies he wide ange o un o eseen e en s ha migh cause mo gage delinquency o
mo gage a ea s. Addi ionally, o he socio-economic ac o s such as di e en ypes o
employmen , unemploymen and income a e conside ed.
To some ex en , we belie e ha examining he e ec o all hese ac o s on he
esiden ial mo gage delinquency isk is also a plausible es on he pe o mance o he
mo gage insu ance indus y, he po en ial exis ence o capi al ma ke impe ec ions, o
he (in)abili y o hose households mos a income isk o accumula e p ecau iona y
sa ings o mee he mo gage paymen s when a shock in income a ises. On he one
hand, an e icien mo gage insu ance ma ke would be ha capable o emo ing he
e ec o mos o hese ac o s om he mo gage delinquency p opensi ies by p o iding
bo h a sui able co e age o he isk as well as co e ing hose mos in need. On he o he
hand, in he absence o a mo gage insu ance households suscep ible o expe ience
shocks in income a e supposed o sa e om posi i e shocks o ace he nega i e ones,
o o bo ow i he e exis a pe ec capi al ma ke . Unde his scena io we should expec
income ola ili y o exe an insigni ican e ec on he likelihood o mo gage
delinquency once we con ol o he le el o income.
4
T adi ionally, he econome ic analysis o he de e minan s o he p obabili y o
mo gage delinquency o de aul has been ca ied ou using he s anda d logi o p obi
model. Howe e , i he p e ious enu e choice p ocess is no accoun ed o , es ima es
coming om his model a e biased. To sol e his we es ima e a bi a ia e model wi h
sample selec ion whe e bo h he homeowne ship and mo gage delinquency p opensi ies
a e conside ed simul aneously.
The pape is s uc u ed as ollows: Sec ion 2 o e s and o e iew o he li e a u e
on mo gage delinquency and de aul In sec ion 3 we b ie ly discuss some o he main
de e minan s o mo gage delinquency. In sec ion 4 we desc ibe he da ase and he
empi ical amewo k. Sec ion 5 shows ou main indings. And sec ion 6 summa izes
and concludes.
2. O e iew o he li e a u e: Mo gage delinquency, de aul and insu ance
The li e a u e on mo gage delinquency is qui e sca ce and mos o he s udies
ocus on mo gage de aul . Howe e , gi en ha mo gage delinquency in i sel usually
is he p ecu so o he ul ima e de aul , i seems in e es ing o s udy his aspec o he
mo gage ma ke . Also mos o he p e ious s udies ocus on he cha ac e is ics o he
mo gage and he mo gaged p ope y as he de e minan s o de aul , and lea e
bo owe ’s socio-economic cha ac e is ics as a esidual elemen . Two s udies ha
explici ly examine mo gage delinquency in he US a e G een and Fu s enbe g (1975)
and Sp inge and Walle (1993). The i s obse ed ha in neighbo hoods wi h
inc easing black popula ion he p opensi y o mo gage delinquency is highe . The
second ocused on he lende ’s side and examined he ac o s de e mining he iming o
he lende ’s o eclosu e decision wi h delinquen mo gages. They obse ed ha he

5
du a ion o he delinquency pe iod and he inal lende ’s o eclosu e decision depend on
he bo owe ’s equi y posi ion. Ande son and Vande Ho (1999) also ocused on he
bo owe ’s ace and obse ed ha black mo gago s a e mo e likely o de aul .
Also in he con ex o he US housing ma ke Kau and Keenan (1999) s udied he
p obabili y o mo gage de aul and he se e i y loss o his de aul , and obse ed ha
he dis ibu ion o he de aul se e i y is c i ical in de e mining he bo owe ’s decision
o de aul . Vandell and Thibodeau (1985) examined he likelihood o mo gage de aul
using US indi idual loan his o y da a. These au ho s obse ed ha bo h he loan- o-
alue (LTV) a io and he di e ence be ween he ma ke alue o he mo gaged
p ope y and he pa alue o he mo gage a e posi i ely ela ed o he p obabili y o
de aul , and su p isingly, he paymen - o-income (PTI) a io is nega i ely ela ed. They
jus i y such a s iking esul by a guing ha highe PTI a ios a e associa ed wi h
bo owe s who ha e ample addi ional esou ces o o e come a de aul . Deng e al.
(1996) also ound e idence o he ele ance o he LTV a io on he p obabili y o
mo gage de aul . They also obse ed ha un o eseen si ua ions as unemploymen and
di o ce ac as igge e en s on he p obabili y o de aul . Ross (2000) s udied de aul
p opensi ies con olling o he sample selec ion caused by he app o al p ocess
p e ious o he mo gage ake-up.
Ou side he US he numbe o s udies examining he de e minan s o he mo gage
de aul isk a e mo e limi ed. Chinloy (1995) ea s de aul as a h ee s age sequen ial
p ocess, ini ial delinquency, long- e m non-paymen and ul ima e de aul . He applied
his mul is a e mo gage de aul model o he UK and ound ha income and liquidi y
cons ain s de e mine he bo owe ’s decision o keep a mo gage e en when he home
equi y becomes nega i e. Eichhol z (1995) in es iga ed he e ec o egional economic
6
s abili y in he egional a es o de aul in he Ne he lands. This au ho concludes ha
he egional employmen cha ac e is ics a e good p edic o s o he egional le els o
mo gage de aul .
The li e a u e on mo gage insu ance is apidly g owing, hough he e a e s ill
ela i ely ew heo e ical s udies. B ueckne (1985) cons uc ed a wo-pe iod model o
analyze he bo owe ’s choice o he op imal ime pa e n o mo gage paymen s
assuming u u e house alues as unce ain. The amoun o he p emium will depend on
he iskiness o he mo gage and he ini ial down paymen . P yce (2002) de eloped a
heo e ical model o he mo gage p o ec ion insu ance decision aking in o accoun he
wel a e sys em and he consump ion los in a o o he insu ance p emiums.
The e a e many mo e empi ical s udies, hough mos o hem ocus on he UK
mo gage ma ke . All o hese s udies emphasize he inadequa eness and he ailu e o
he MPI policies. P yce and Keoghan (2001, 2002) and Fo d and Quilga s (2001) ound
e idence ha he main consume s o such an insu ance policies we e no hose
bo owe s mos a income isk. Bu cha d and Hill (1997, 1998) ound ha MPI-holde s
did no ha e signi ican ly g ea e unemploymen isks han he uninsu ed mo gago s.
Fo d e al. (1995) indeed ound ha only a qua e o he insu ed mo gago s in a ea s
ied o claim. Kempson e al. (1999) obse ed ha hose bo owe s wi h uns able wo k
his o ies and ill-heal h a e sys ema ically p ecluded om being eligible o he MPI
policies.
3. The de e minan s o mo gage delinquency
Mo gage delinquency ep esen s a ele an p oblem o lende s. As men ioned
ea lie , hough mo gage delinquency does no sys ema ically lead o a de aul ,
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undoub edly i ends o be he p ecu so o he ul ima e de aul . Hence, he de e minan s
o he ini ial delinquency will also in luence he inal mo gage de aul . Howe e ,
analyzing he p obabili y o de aul is somewha mo e complica ed since he o eclosu e
p ocess can be sys ema ically delayed because o go e nmen egula ions o he lende s
olun a ily allowing he leng hen o he delinquency pe iod. Al hough a iables such as
he LTV a io is one o he p ima y ac o s a ec ing he isk o de aul and hence also
he ini ial mo gage delinquency, in his s udy we a he ocus mo e on he bo owe ’s
socio-economic ac o s. The mo gago ’s le el o income and he a iables de e mining
his income (occupa ion, educa ion, he ype o employmen , e c.) a e expec ed o exe
a signi ican e ec on he mo gage delinquency isk.
As men ioned ea lie , in a pe ec wo ld we e households do no ace bo owing
cons ain s o can sa e du ing posi i e shocks o mee he nega i e ones, we should
expec income ola ili y o ha e an insigni ican e ec on mo gage delinquency once
we con ol o income le el. Howe e , he p e ious in e na ional empi ical e idence on
hese issues does no allow us o admi he exis ence o “such a pe ec wo ld”. One he
one hand, he exis ence o c edi ma ke impe ec ions is well documen ed. Fo ins ance,
Jappelli and Pagano (1989) obse ed o a selec ed g oup o OECD coun ies (Sweden,
USA, UK, Japan, I aly, Spain and G eece) ha he sensi i i y o consump ion o cu en
income luc ua ions was e y high. They ound his esul o be caused by he exis ence
o capi al ma ke impe ec ions. On he o he hand, con adic ing he p ecau iona y
mo i e o sa ings assumed in he heo e ical li e a u e, he e exis li le empi ical
e idence suppo ing ha weal h accumula ion and sa ings a ise as a p ecau ion agains
u u e income isk. (Guiso e al. ,2002, in I aly; A ondel ,2002, in F ance; Skinne
,1988, and Lusa di ,1998, in he US). In his con ex , we belie e ha income ola ili y,
8
a he han income le el, migh be mo e sui able o cap u ing he bo owe ’s abili y o
ace he pe iodical mo gage paymen s. The e o e, we ocus on his a iable as he main
de e minan o mo gage delinquency.
Unexpec ed si ua ions such as in olun a y unemploymen o job mobili y,
sickness, demand shocks o any o he un o eseen e en ha cause luc ua ions in
household’s income may also aise he isk o mo gage delinquency. To cap u e hese
e ec s we conside he unemploymen his o y o he household head du ing he las 5
yea s p e ious o he su ey and his/he sel - epo ed heal h s a us du ing las 12 mon hs
p e ious o he su ey. As men ioned abo e, in o de o a oid mo gage delinquency all
hese isks migh be co e ed by he mo gago ’s own inancial esou ces. The e o e, we
also es he ole o sa ings as a de e minan o mo gage delinquency.
Addi ionally o he LTV a io, he loan- o-income (LTI) a io is also an impo an
de e minan o mo gage delinquency isk. Clea ly, i he LTV and LTI a ios a e oo
high his may signi ican ly a ec he paying capaci y o he bo owe . Howe e , he
e ec o hese a iables can be smoo hed h oughou ime by con ac ing a mo gage
wi h longe du a ion. This would be he case o some no hwes e n EU coun ies as
Denma k o The Ne he lands whe e high LTV (abo e 80-90 pe cen ) and high LTI
(abo e 3) a ios a e combined wi h longe mo gage du a ions (30-35 yea s). These
igu es con as wi h o he EU coun ies as I aly, Belgium o Aus ia whi LTV a ios
bellow 50 pe cen and LTI alues bellow 1 combined wi h mo gage du a ions ha
ange o m he 10-15 yea s in I aly o he 15-20 yea s in Belgium o Aus ia2. The e o e,
since he a loan wi h a gi en size migh become mo e a o dable wi h a longe paymen
pe iod, he paymen - o-income (PTI) a io is p obably he a iable ha bes measu es
2 See Neu eboom (2003) o an ex ensi e analysis o he isks associa ed o he LTV and LTI a ios in a
selec ed g oup o EU coun ies.
15
incomes han homeowne s, and only in I eland and Aus ia a e he le els o income
ola ili y simila be ween bo h enu e ypes. Ma ked di e ences a e also obse ed in
mos o he coun ies conce ning he le els o household income ola ili y be ween
mo gage delinquen s and non-delinquen s. Excep in F ance and Finland, income
ola ili y ends o be subs an ially la ge o mo gage delinquen s. These esul s sugges
ha bo h he homeowne ship and mo gage delinquency pa e ns migh be in luenced by
his a iable.
Inse able 2 a ound he e
Table 3 epo s he econome ic es ima ion o he bi a ia e p obi wi h sample
selec ion on mo gage delinquency. Recall ha gi en he nonlinea na u e o he
econome ic model, he es ima ed coe icien s lack o any economic in e p e a ion and
a e jus used o de e mine he di ec ion o he ela ionship. Howe e , since we a e no
in e es ed in compa ing he magni ude o he es ima ed e ec s ac oss coun ies, he sign
and signi icance o he es ima ed coe icien s a e enough o d aw ou conclusions. I is
wo h no ing ha excep o Belgium, he co ela ion be ween bo h equa ions is highly
signi ican ( 0
ρ
≠). This esul indica es ha con olling o sample selec ion is c i ical
o ob ain unbiased es ima es in he mo gage delinquency equa ion.
Fo he sake o simplici y we will ocus on he es ima es conce ning he a iables
we conside as impo an de e minan s o he mo gage p o ec ion insu ance ake-up.
These a iables indica e which households a e mos a income isk, and hence mos in
need o he MPI. These a e household income, income ola ili y, sa ings, household’s

16
head unemploymen his o y and his/he sel - epo ed heal h s a us. We assess
signi icance a 5 pe cen .
As expec ed, household income is highly signi ican in bo h he homeowne ship
and he mo gage delinquency equa ion, and wi h he expec ed sign in all coun ies. Ou
key a iable, income ola ili y, u ns ou o be signi ican in bo h he homeowne ship
and he mo gage delinquency p opensi ies o mos o he coun ies. We obse e a
nega i e e ec on homeowne ship5 and a posi i e one on mo gage delinquency.
Excep ions o his gene al esul a e Po ugal and Aus ia whe e he e ec on
homeowne ship is insigni ican , Luxembou g whe e he e ec on mo gage delinquency
is insigni ican , and I eland wi h an insigni ican e ec on bo h he homeowne ship and
he mo gage delinquency p opensi ies.
Household heads ha we e unemployed a leas once du ing he i e yea s be o e
he su ey also shows a signi ican nega i e e ec on homeowne ship, and posi i e on
mo gage delinquency in mos o he coun ies. An insigni ican e ec on
homeowne ship is only obse ed in Spain and Luxembou g, while an insigni ican
e ec on mo gage delinquency is obse ed in he UK, Po ugal and Luxembou g. The
sel - epo ed bad-heal h o he household head also e eals i sel as an impo an
a iable, hough in I eland, Spain and Aus ia he e ec is no signi ican in ei he he
homeowne ship o mo gage delinquency equa ions, and no signi ican o Belgium in
he mo gage delinquency equa ion. We obse e ha in all coun ies he a iable
sa ings exe s a signi ican nega i e e ec on he p obabili y o mo gage delinquency,
while he e ec is posi i e on homeowne ship o all coun ies excep o F ance, I aly
and Finland.
5 This esul coincides wi h he p e ious empi ical e idence analyzing he e ec o income unce ain y on
he p obabili y o homeowne ship (Hau in, 1991; Robs e al., 1999, in he US; Diaz-Se ano, 2004a, in
Ge many and Spain; Diaz-Se ano, 2004b, in I aly).
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Consis en wi h he p e ious e idence in he US, we also obse e ha in “bad
neighbo hoods”, p oxied as he exis ence o c ime o andalism, homeowne ship is less
likely and he e exis a g ea e p opensi y o mo gage delinquency. Howe e , in
con as wi h wha G een and Fu s enbe g (1975) obse ed, we ind li le e idence han
mo gage delinquency is less likely in he ea lie yea s o he mo gage ake-up and wi h
inc easing p obabili y a la e s ages. This e ec is only obse ed in Belgium, while he
opposi e holds o Denma k, Luxembou g, UK, I eland and Finland. In he es o
coun ies he e ec o he age o he mo gage on he mo gage delinquency isk has
u n ou o be unimpo an .
Inse able 3 a ound he e
We ha e also ca ied ou sepa a e es ima es o ou bi a ia e p obi model wi h
sample selec ion including he PTI a io as explana o y a iable in he mo gage
delinquency equa ion. Resul s a e epo ed in able 4. We ollow his p ocedu e in o de
o a oid he po en ial inconsis ency ha he endogenous na u e o his a iable migh
cause in ou es ima es. Howe e , in all he coun ies examined hese new es ima es a e
simila o he ones shown in able 3 conce ning he sign, he size and he signi icance o
he o he explana o y a iables. In all coun ies excep in Denma k, UK, I eland and
Aus ia, he PTI a io exe s a signi ican and posi i e e ec on he mo gage
delinquency isk. This esul con as s Vendell and Thibodeau (1985) in he US, who
obse ed he opposi e.
Inse able 4 a ound he e
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Finally, we shall poin ou ha o a sui able unde s anding o he e ec o income
ola ili y on bo h he homeowne ship and mo gage delinquency p opensi ies, his
a iable should be ela ed wi h he le el o income i sel . One may be emp ed o
associa e mo e ola ile incomes o low-income p o iles, howe e , i is no necessa ily
ue. The e a e a numbe o s udies in he labo economics li e a u e whe e he isk-
e u n ade-o in indi iduals’ income is well documen ed6. To es o wha ex en his
inding i s ou da a, we ha e ca ied ou eg essions aking ou measu e o income
ola ili y (CV) as he endogenous a iable. The esul s a e shown in able 5.
A e con olling o a numbe o ac o s we obse e ha mo e ola ile incomes
a e associa ed o highe le els o income in all coun ies excep in he UK and he
sou he n EU coun ies (I aly, Spain and Po ugal). This esul is qui e e ealing since i
indica es ha highe -income p o iles a e also suscep ible o incu in mo gage
delinquency i he le el o ola ili y in income is high enough. The coun ies we e his
pa e n does no hold a e hose ha epo ed he highe le els o income ola ili y (see
able 2).
Inse able 5 a ound he e
6. Discussion and concluding ema ks
In his pape we examine he p obabili y o mo gage delinquency in 12 EU
coun ies. To a oid he bias caused by he homeowne ship selec ion p ocess we use he
bi a ia e p obi model wi h sample selec ion. To examine he de e minan s o he
mo gage delinquency isk we ocus on he mo gago ’s socio-economic cha ac e is ics
6 King, 1975; McGold ick (1995), Ha og and Vi ej e g (2002), in he US and Ha og e . al. (2003), o a
selec ed g oup o EU coun ies obse ed ha mo e a iable ea nings dis ibu ions end o possess also a
highe mean.
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a he han on he mo gaged p ope y and he cha ac e is ics o he mo gage, as usual
in he de aul isk li e a u e. We pay special a en ion o hose a iables ha a e likely o
cause shocks in he mo gago ’s income, and hence a e also likely as de e minan s o
he mo gage insu ance ake-up. Ou key a iable is household’s income ola ili y
p oxied as he coe icien o a ia ion o ne annual household income, which u ns ou
o be c ucial in explaining bo h he homeowne ship and he mo gage delinquency
pa e ns. O he a iables as unemploymen , sa ings and he ill-heal h s a us o he
household head also ha e signi ican e ec s. To some ex en , one migh ind su p ising
ha he same esul pe sis en ly holds o such di e en coun ies in e ms o bo h hei
housing and hei mo gage ma ke s7. Howe e , i sugges s ha hough he e a e ma ked
di e ences among hem, unsus ainable homeowne ship and lende ’s a i ude owa ds
he mo gage delinquency isk is a common elemen ac oss mos o he coun ies
examined in his s udy.
The ac ha income ola ili y signi ican ly inc eases he mo gage delinquency
isk sugges s he exis ence o c edi ma ke impe ec ions and he low abili y o hose
households mos a income isk o accumula e p ecau iona y sa ings. Hence, when a
nega i e shock in income a ise mo gage delinquen s eac s educing housing
consump ion i he size o shock is big enough. Addi ionally, he signi icance o he
o he s ac o s lis ed abo e also sugges s ha MPI policies a e no adequa e in co e ing
hose households mos in need o he ange o isks co e ed. As obse ed in he UK, a
la ge p opensi y o a low-income mo gago p o ile o be a mo gage delinquen
sugges s ha p obably o his popula ion s a um he low MPI ake-up is d i en by he
non-a o dabili y o he p emiums. Howe e , in his pape we no only obse e ha
7 See Me ce Oli e Wyman’s (2003) epo o an ex ensi e analysis o he mo gage ma ke s in a
selec ed g oup o EU-15 coun ies.
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income ola ili y inc eases signi ican ly he p obabili y o mo gage delinquency, bu
also ha mo e ola ile incomes a e associa ed o highe -income p o iles. This esul
sugges s ha e en being a o dable, he low MPI ake-up om he bo owe s wi h
highe income-p o iles and wi h la ge income ola ili y is p obably d i en by he
limi ed co e age o he isks associa ed wi h he mo gago ’s income.
Undoub edly, bo h lende s and insu e s a e in business, he e o e, e iciency and
adequa eness mus be sac i iced o he sake o p o i abili y. In he UK he e is e idence
ha MPI p emiums do ise du ing slumps and all du ing booms (Goodman, 1998). And
in Walke e al. (1995) he e a e lis ed a numbe o clauses in he MPI con ac s ha
p eclude a la ge numbe o claims. Hence, he numbe o e en s sensible o cause
shocks in mo gago ’s income co e ed by MPI policies is ce ainly limi ed. Ou esul s
sugges ha simila analyses o he EU coun ies examined he e using MPI ake-up
da a will p obably p o ide he same indings. I seems ha bo h insu e s and lende s
play mo e wi h mo gago ’s isk a e sion han wi h his/he needs. Al hough his
li e a u e is g owing, we ind his issue is s ill unde - esea ched. Gi en i s impo ance
and implica ions o bo h lende s and bo owe s mo e esea ch in hese lines would be
necessa y.

21
Re e ences
Ande son, R., Vande Ho , J., 1999. Mo gage de aul a es and bo owe ace. J. Real
Es a e Res. 18, 279-289.
A ondel, L., 2002. Risk managemen and weal h accumula ion beha io in F ance.
Econ. Le e s 74, 187-194.
Be y, M., Dal on, T., Engels, B., Whi hing, K., 1999. Falling ou o Home Owne ship.
Uni e si y o Queensland P ess, B isbane.
B ueckne , J.K., 1985. A simple model o mo gage insu ance. AREUEA J. 13, 129-
142.
Bu cha d , T., Hills, J., 1997. Mo gage paymen p o ec ion: eplacing s a e p o ision.
Housing Finance 33, 24-31.
Bu cha d , T., Hills, J., 1998. F om pubic o p i a e: he case o mo gage paymen
insu ance in G ea B i ain. Housing S ud. 13, 311-323.
Bu ows, R., 1998. Mo gage indeb edness in England: an ‘epidemiology’. Housing
S ud. 13, 5-22.
Chinloy, P., 1995. P i a ized de aul isk and eal es a e ecessions: he UK mo gage
ma ke . Real Es a e Econ. 23, 401-402.
Deng, Y., Quigley, J.M., Van O de , R., Mac, F., 1996. Mo gage de aul and low
downpaymen loans: he cos s o public subsidy. Reg. Sci. U ban Econ. 26, 263-
285.
Diaz-Se ano, L., 2004a. Labo Income Unce ain y, Skewness and Homeowne ship: a
Panel Da a S udy o Spain and Ge many. IZA Discussion Pape 1008, IZA, Bonn.
22
Diaz-Se ano, L., 2004b. On he nega i e ela ionship be ween income unce ain y and
homeowne ship: isk-a e sion s. c edi cons ain s. IZA Discussion Pape 1208,
IZA, Bonn.
Doling, J., Ruona aa a, H., 1996. Home owne ship unde mined: an analysis o he
Finnish case in he ligh o he B i ish expe ience. J. o Housing Buil En . 11, 31-
46.
Eichhol z, P.M.A., 1995. Regional economic s abili y and mo gage de aul isk in he
Ne he lands. Real Es a e Econ. 23 (4), 421-439.
Fo d, J., Kempson, E., Wilson, M., 1995. Mo gage a ea s and possessions;
pe spec i es om bo owe s, lende s and he cou s. Depa men o en i onmen ,
HMSO, London.
Fo d, J., Quilga s, D., 2001. Failing home owne s? The e ec i eness o public and
p i a e sa e y-ne s. Housing S ud. 16, 147-162.
Guiso, L., Jappelli, T., Te lizzese, D., 1992. Ea nings unce ain y and p ecau iona y
sa ings. J. Mone . Econ. 30, 307-338.
G een, R.J., Fus enbe g, G.M., 1975. The e ec s o ace and age o housing on
mo gage delinquency isk. U ban S ud. 12, 85-89.
Ha og, J., Plug, E.J.S., Diaz-Se ano, L., Viei a, A.J.C., 2003. Risk compensa ion in
wages: a eplica ion, Empi ical Econ. 28, 639-647.
Ha og, J., Vij e be g, W., 2002. Do wages eally compensa e o isk a e sion and
skewness a ec ion, IZA discussion pape #426, IZA, Bonn.
Hau in, D.R., 1991. Income a iabili y, homeowne ship, and housing demand. J.
Housing Econ. 1, 60-74.
23
Jappelli, T., Pagano, M., 1989. Consump ion and capi al ma ke impe ec ions: an
in e na ional compa ison. Ame . Econ. Re . 79, 1088-1105.
Lusa di, A., 1998. On he impo ance o he p ecau iona y sa ings mo i e. Ame . Econ.
Re . 88, 449-453.
Kau, J.B., Keenan, D.C., 1999. Pa e ns o a ional de aul . Reg. Sci. U ban Econ. 29,
765-785.
Kempson, E., Fo d, J., Quilga s, D., 1999. Unsa e sa e y ne s?. Cen e o Housing
Policy, Uni e si y o Yo k.
King, A., 1974. Occupa ional choice, isk a e sion, and weal h. Ind. Lab. Rela . Re .,
July, 586-596.
McGold ick, K., 1995. Do women ecei e compensa ing wages o ea nings
unce ain y?, Sou he n Econ. J. 62, 210-222.
MERCER OLIVER WYMAN, 2003. S udy on he inancial in eg a ion o Eu opean
mo gage ma ke s.
Ne le on, S., Bu ows, R., 1998. Mo gage deb , insecu e home owne ship and heal h:
and explana o y analysis. In: Ba ley, M., Blane, D., Da ey-Smi h, g., (Eds.), The
Sociology o Heal h Inequali ies, Blackwell P ess, Ox o d.
P yce, G., Keoghan, M., 2001. De e minan s o mo gage p o ec ion insu ance ake-up.
Housing S ud. 16, 179-198.
P yce, G., 2002. Theo y and es ima ion o he mo gage paymen p o ec ion insu ance
decision. Sco . J. Poli . Economy 49, 216-234.
P yce, G., Keoghan, M., 2002. Unemploymen insu ance o mo gage bo owe s: is i
iable and does i co e hose mos in need?. Eu . J. Housing Policy 2, 87-114.
24
Robs , J., Dei z, R., McGold ick, R., 1999. Income a iabili y, unce ain y and housing
enu e choice. Reg. Sci. U ban Econ. 29, 219-229.
Ross, S.L., 2000. Mo gage lending, sample selec ion and de aul . Real Es a e Econ. 28,
581-621.
Ross, S.L., Too ell, G.M.B., 2004. Redlining he communi y ein es men ac , and
p i a e mo gage insu ance. J. U ban Econ. 55, 278-297.
Skinne , J., 1988. Risky income, li e cycle consump ion and p ecau iona y sa ings. J.
Mone . Econ. 22, 237-255.
Sp inge , T.M., Walle , N.G., 1993. Lende o ebea ance: e idence om mo gage
delinquency pa e ns. AREUEA J. 21 (1), 27-46.
Vandell, K.D., Thibodeau, T., 1985. Es ima ion o mo gage de aul s using disagg ega e
loan his o y da a. AREUEA J. 13 (3), 292-316.
31
Table 3 (con inua ion)
F ance UK I eland I aly
Owne ship Mo age del. Owne ship Mo age del. Owne ship Mo age del. Owne ship Mo age del.
Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al.
Cons an e m -3.747 -31.78 -2.071 -4.23 -1.019 -4.64 -4.714 -5.51 -2.179 -10.58 -0.197 -0.65 -2.491 -17.17 -0.774 -2.27
Income ola ili y -0.330 -6.69 0.261 2.54 -0.390 -3.43 0.716 2.29 -0.098 -0.90 0.215 1.81 -0.304 -5.37 0.489 4.24
Household income/1000 0.113 8.14 -0.138 -4.32 0.348 18.80 -0.164 -3.06 0.297 15.93 -0.172 -13.39 0.505 13.77 -0.138 -4.59
Sa ings 0.008 0.677 -0.710 -8.00 0.379 9.65 -0.644 -4.21 0.213 5.22 -0.505 -9.46 -0.002 -0.06 -0.452 -5.85
Household size 0.031 3.73 0.108 5.87 -0.050 -2.97 0.026 0.53 -0.136 -11.62 0.103 7.64 0.031 3.15 0.113 5.11
Job mobili y -0.787 -18.87 -0.622 -7.47 -0.749 -5.52 -0.463 -5.99
C ime in he a ea -0.252 -12.03 0.281 5.86 -0.436 -8.26 0.129 0.63 -0.408 -10.14 0.294 5.98 -0.121 -4.55 0.186 3.48
Mo gage age -0.008 -0.56 0.091 2.00 0.023 1.93 0.018 1.25
Mo gage age squa ed 0.001 1.48 -0.004 -1.68 -0.001 -2.26 -0.001 -1.02
Household head
Age 0.152 30.14 0.019 0.88 0.046 6.65 0.139 3.73 0.122 13.89 -0.038 -2.69 0.077 13.00 -0.033 -2.56
Age squa ed -0.002 -29.79 0.000 -0.80 -0.001 -8.63 -0.002 -3.65 -0.001 -13.68 0.000 2.93 -0.001 -14.01 0.000 2.78
Seconda y educa ion 0.185 8.46 -0.092 -1.79 0.275 4.60 -0.154 -0.79 0.328 8.55 -0.270 -6.30 0.288 10.98 -0.217 -4.14
Highe educa ion 0.108 3.40 -0.191 -2.69 0.217 4.70 -0.225 -1.44 0.394 6.30 -0.229 -3.38 0.122 2.57 -0.369 -3.40

32
Female -0.222 -8.11 0.163 2.11 -0.053 -1.33 -0.160 -1.12 -0.239 -5.39 0.273 5.00 -0.061 -1.63 0.189 2.43
Sel -employed 0.095 2.53 -0.053 -0.75 0.535 6.15 -0.037 -0.19 0.216 3.60 -0.019 -0.33 0.038 1.16 0.214 3.57
Public employee -0.144 -6.31 0.014 0.25 0.178 2.79 -0.501 -1.96 0.130 2.65 -0.150 -2.84 0.035 1.23 -0.001 -0.02
Occupa ion dummies Yes No Yes No Yes No Yes No
Unemployed las 5 yea s -0.369 -13.63 0.449 8.45 -0.179 -3.92 0.183 1.28 -0.523 -13.81 0.415 9.62 -0.249 -7.44 0.227 3.39
Bad Heal h -0.165 -4.91 0.219 2.96 -0.339 -5.51 0.573 2.81 -0.098 -1.28 0.181 1.78 -0.139 -3.73 0.305 4.22
Ma ied 0.708 30.71 0.014 0.23 0.359 7.80 -0.151 -1.50 0.965 17.73 0.276 3.43 0.273 6.93 -0.190 -2.44
Yea dummies Yes Yes Yes Yes
Rho 0.568 -0.146 0.906 0.516
Tes ho=0 186.638 8.922 53.163 122.877
Log-likelihood -15,027 -3,250 -4,061 -9,742
Sample size 27,639 6,571 9,426 16,125
33
Table 3 (con inua ion)
Spain Po ugal Aus ia Finland
Owne ship Mo age del. Owne ship Mo age del. Owne ship Mo age del. Owne ship Mo age del.
Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al. Coe . Z- al.
Cons an e m -1.245 -8.57 -0.981 -3.29 -1.515 -10.79 -0.989 -2.04 -3.225 -18.38 -5.137 -3.51 -2.190 -11.07 -1.686 -5.26
Income ola ili y -0.247 -3.97 0.556 5.15 -0.103 -1.54 0.521 3.07 -0.103 -1.38 0.555 1.98 -0.331 -3.41 0.399 2.99
Household income/1000 0.224 15.58 -0.244 -7.98 0.335 15.16 -0.205 -2.43 0.124 11.93 -0.254 -3.36 0.364 13.44 -0.154 -5.19
Sa ings 1.133 42.09 -0.324 -5.86 -0.196 -5.07 -0.612 -2.25 0.135 4.60 -0.576 -3.94 -0.032 -0.84 -0.618 -9.34
Household size 0.120 6.75 0.041 4.12 0.044 1.76 0.256 19.06 0.010 0.18 0.127 6.86 0.077 3.67
Job mobili y -0.158 -1.90 -1.336 -7.22 -0.805 -8.49
C ime in he a ea -0.637 -8.15 0.257 5.23 -0.060 -1.78 0.055 0.51 -0.615 -10.89 -0.298 -0.95 -0.460 -11.90 0.177 3.08
Mo gage age 0.000 -0.03 0.010 0.46 0.024 0.50 0.048 3.37
Mo gage age squa ed 0.001 0.79 -0.001 -0.75 -0.001 -0.60 -0.002 -2.87
Household head
Age 0.056 9.19 -0.022 -1.78 0.035 5.61 -0.030 -1.58 0.083 13.53 0.155 2.58 0.053 7.47 0.025 1.92
Age squa ed -0.001 -12.05 0.000 1.84 -0.001 -8.28 0.000 2.35 -0.001 -13.47 -0.002 -2.85 -0.001 -6.98 0.000 -1.66
Seconda y educa ion -0.086 -2.49 -0.179 -2.59 0.119 2.78 -0.098 -0.84 0.004 0.10 -0.203 -1.32 0.141 3.40 -0.220 -3.87
Highe educa ion -0.040 -1.04 -0.121 -1.66 0.062 0.87 -0.133 -0.60 -0.158 -2.62 0.494 2.32 0.355 6.79 -0.302 -4.58
34
Female 0.066 1.61 0.086 0.94 0.016 0.46 -0.117 -1.00 -0.009 -0.27 -0.112 -0.59 0.102 2.95 0.017 0.36
Sel -employed 0.102 2.83 0.068 1.12 0.320 8.64 -0.037 -0.36 0.018 0.28 0.176 1.00 0.210 3.30 0.066 0.96
Public employee -0.043 -1.15 -0.232 -2.70 0.234 6.90 -0.127 -1.27 0.104 2.75 -0.083 -0.48 -0.028 -0.64 0.022 0.39
Occupa ion dummies Yes No Yes No Yes No Yes No
Unemployed las 5 yea s -0.011 -0.38 0.208 3.96 -0.284 -7.82 -0.023 -0.18 -0.229 -5.97 0.515 3.41 -0.188 -5.17 0.240 4.76
Bad Heal h 0.072 1.83 0.090 1.22 -0.156 -4.19 0.255 2.31 -0.088 -1.71 0.094 0.45 0.063 1.14 0.079 1.07
Ma ied 0.471 11.21 -0.037 -0.47 0.352 8.80 -0.105 -0.79 0.143 2.89 -0.137 -1.01 0.203 4.70 0.050 0.81
Yea dummies Yes Yes Yes Yes
Rho 0.435 0.548 0.191 0.640
Tes ho=0 84.217 68.884 9.661 154.357
Log-likelihood -8,834 -7,477 -6,301 -4,464
Sample size 13,386 14,393 11,527 7,348
35
Table 4: Es ima es o he bi a ia e p obi model
Income ola ili y Paymen - o-income
Coe . z- alue Coe . z- alue
Denma k 0.6537 3.75 0.0598 0.50
The Ne he lands 0.5906 9.19 0.0140 6.26
Belgium 0.5771 4.02 0.6486 3.66
Luxembou g 0.2121 1.02 0.5642 3.60
F ance 0.2602 2.55 0.4946 2.71
UK 0.6860 2.15 0.0854 1.12
I eland 0.2081 1.74 0.3233 1.73
I alia 0.4758 3.88 0.4945 4.92
Spain 0.5321 4.70 0.6507 5.99
Po ugal 0.5170 2.96 0.3530 3.22
Aus ia 0.5512 1.96 0.0322 0.76
Finland 0.3855 2.83 0.1839 3.00
36
Table 5: OLS es ima ion o he de e minan s o income ola ili y (endogenous a iable: CV o household income).
Denma k The Ne he lands Belgium Luxembou g F ance UK
Coe . - alue Coe . - alue Coe . - alue Coe . - alue Coe . - alue Coe . - alue
Cons an 0.545 42.12 0.308 16.85 0.149 7.80 0.226 14.01 0.542 52.12 0.455 27.04
Household Income/1040.090 11.62 0.163 19.96 0.074 13.23 0.075 11.51 0.103 20.44 -0.054 -5.23
Household size -0.027 -20.44 -0.005 -3.98 -0.014 -9.11 -0.005 -3.57 -0.012 -12.84 0.002 0.98
Age -0.011 -21.71 -0.003 -5.45 0.004 5.62 0.002 3.21 -0.008 -20.68 -0.003 -4.98
Age squa ed 0.000 18.00 0.000 4.25 -0.000 -4.66 -0.000 -4.34 0.000 13.51 0.000 1.53
Seconda y educa ion 0.020 6.37 0.003 1.19 0.015 4.01 -0.013 -3.81 -0.024 -9.99 0.008 1.27
Highe educa ion 0.020 5.41 -0.030 -7.25 -0.001 -0.24 -0.003 -0.57 0.000 0.13 0.004 0.92
Female 0.014 5.32 0.011 3.20 0.028 6.24 0.039 9.09 0.020 6.58 0.034 7.95
Sel -employed 0.175 30.64 0.180 31.72 0.197 33.34 0.067 10.31 0.126 28.48 0.054 6.66
Public employee -0.018 -5.35 -0.025 -6.69 -0.047 -9.35 -0.019 -4.45 -0.052 -17.41 -0.011 -1.66
P o essionals -0.030 -6.35 -0.038 -8.18 -0.028 -4.31 -0.037 -5.87 -0.050 -10.66 -0.033 -3.93
Technicians -0.024 -5.53 -0.044 -9.81 -0.020 -2.98 -0.023 -4.27 -0.051 -13.62 -0.068 -8.06
Cle ks -0.034 -6.44 -0.015 -2.57 -0.030 -4.73 -0.015 -2.39 -0.041 -8.51 -0.034 -4.68
Se ices and sales -0.022 -3.83 0.002 0.30 -0.017 -1.98 -0.031 -4.16 -0.036 -6.56 -0.023 -3.11
Skilled p ima y sec o 0.054 4.73 0.030 1.86 0.070 3.61 0.019 1.66 -0.020 -2.82 -0.014 -0.59

37
C a and ade -0.049 -9.09 -0.003 -0.65 -0.027 -3.76 -0.002 -0.32 -0.051 -13.57 -0.074 -8.05
Ope a o s -0.035 -5.81 -0.033 -5.07 -0.050 -5.50 -0.031 -4.72 -0.060 -14.43 -0.078 -8.66
Unemployed 0.013 4.20 0.038 9.32 0.026 5.72 0.100 6.09 0.000 0.14 0.027 5.09
Ma ied -0.015 -4.67 -0.038 -10.09 0.001 0.23 -0.015 -3.54 -0.022 -7.87 -0.048 -9.84
C ime in he a ea 0.010 2.38 0.003 1.05 -0.003 -0.65 0.125 12.44 0.002 0.83 0.037 6.55
Job mobili y 0.065 9.01 0.021 2.14 0.030 2.29 0.085 3.66 0.057 11.65 0.036 3.84
R-squa ed 0.246 0.175 0.203 0.146 0.199 0.211
Sample size 18,783 26,944 20,174 18,296 43,125 9,113
38
Table 5 (con inua ion)
I eland I aly Spain Po ugal Aus ia Finland
Coe . - alue Coe . - alue Coe . - alue Coe . - alue Coe . - alue Coe . - alue
Cons an 0.068 4.49 0.231 18.31 0.093 7.02 0.086 5.68 0.245 14.82 0.502 34.81
Household Income/1040.025 9.37 -0.050 -20.10 -0.038 -4.33 -0.251 -15.59 0.029 3.16 -0.003 -0.28
Household size 0.004 4.48 0.015 17.42 0.020 24.54 0.010 10.56 0.001 0.95 -0.019 -12.44
Age 0.007 13.13 0.002 5.08 0.006 12.40 0.010 19.34 0.001 2.41 -0.009 -13.74
Age squa ed -0.000 -14.37 -0.000 -6.50 -0.000 -13.78 -0.000 -21.40 -0.000 -4.12 0.000 8.24
Seconda y educa ion 0.001 0.38 -0.022 -8.69 0.001 0.24 -0.005 -0.93 -0.008 -2.08 -0.002 -0.42
Highe educa ion -0.010 -2.26 -0.009 -2.00 -0.009 -2.56 0.014 1.78 0.001 0.12 -0.022 -4.66
Female 0.021 5.75 0.023 6.71 0.010 2.69 0.012 3.10 0.005 1.42 -0.008 -2.28
Sel -employed 0.112 26.48 0.156 53.91 0.199 65.19 0.124 34.70 0.160 23.38 0.097 16.78
Public employee -0.041 -10.74 -0.044 -14.87 -0.054 -14.44 -0.060 -14.37 -0.036 -7.72 -0.015 -3.24
P o essionals -0.022 -3.67 -0.045 -7.89 -0.051 -9.39 -0.025 -2.64 -0.031 -3.18 -0.015 -2.45
Technicians -0.018 -3.04 -0.064 -13.98 -0.040 -8.37 -0.068 -10.36 -0.018 -3.04 -0.018 -3.10
Cle ks -0.013 -1.95 -0.061 -15.39 -0.037 -6.34 -0.064 -9.38 -0.043 -6.47 -0.020 -2.72
Se ices and sales 0.015 2.28 -0.031 -7.03 -0.018 -3.79 -0.027 -5.03 -0.014 -2.17 -0.018 -2.56
Skilled p ima y sec o -0.097 -18.43 -0.004 -0.68 0.019 4.12 0.012 1.26 0.018 2.18
39
C a and ade 0.009 1.81 -0.053 -15.62 -0.046 -10.83 -0.054 -9.48 -0.028 -4.19
Ope a o s 0.009 1.70 -0.069 -14.46 -0.029 -6.89 -0.072 -12.70 -0.038 -4.92 -0.030 -3.94
Unemployed 0.036 10.16 0.095 30.62 0.083 31.60 0.029 6.65 0.016 3.67 -0.003 -0.85
Ma ied -0.020 -5.51 -0.007 -2.20 -0.039 -11.16 -0.037 -9.85 -0.034 -8.78 -0.022 -5.55
C ime in he a ea -0.008 -2.18 0.015 5.85 -0.003 -1.35 0.016 4.20 0.007 1.13 -0.007 -1.74
Job mobili y 0.088 5.70 -0.018 -2.00 0.068 7.49 -0.010 -0.86 0.083 5.77 0.103 11.46
R-squa ed 0.196 0.242 0.291 0.219 0.179 0.255
Sample size 19,211 46,708 39,741 34,598 18,907 12,552
40