Hauck, Lukas; S alde , Nicola; Büchle , Simon; on Eh lich, Maximilian
Wo king Pape
Incidence, alloca ion, and e iciency cos s o enancy en
con ol
Discussion Pape s, No. 25-07
P o ided in Coope a ion wi h:
Depa men o Economics, Uni e si y o Be n
Sugges ed Ci a ion: Hauck, Lukas; S alde , Nicola; Büchle , Simon; on Eh lich, Maximilian (2025) :
Incidence, alloca ion, and e iciency cos s o enancy en con ol, Discussion Pape s, No. 25-07,
Uni e si y o Be n, Depa men o Economics, Be n
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Facul y o Business, Economics
and Social Sciences
Depa men o
Economics
Incidence, Alloca ion, and E iciency Cos s o
Tenancy Ren Con ol
Lukas Hauck, Nicola S alde , Simon Büchle ,
Maximilian on Eh lich
25-07
Augus , 2025
Schanzenecks asse 1
CH-3012 Be n, Swi ze land
h p://www. wi.unibe.ch
DISCUSSION PAPERS
Incidence, Alloca ion, and E iciency Cos s o Tenancy Ren
Con ol
P elimina y e sion – Augus 15, 2025
Lukas Hauck
○b, Nicola S alde
○b,c, Simon B¨uchle
○a,b, Maximilian . Eh lich
○b
aFa me School o Business, Miami Uni e si y, MIT Cen e o Real Es a e
bUni e si y o Be n
cIAZI AG – CIFI SA
Abs ac
Tenancy en con ol limi s en inc eases o si ing enan s while allowing ma ke e-
se s a acancy. When demand g ows o household composi ion di e s ac oss segmen s,
spillo e s aise en s in he un egula ed ma ke . We s udy i s gene al equilib ium e ec s
in Swi ze land, whe e a na ionwide egime mee s la ge spa ial a ia ion. Linking ad-
minis a i e eco ds on all households om 2010–2022 o de ailed uni da a and ma ke
en s, we es ima e a s uc u al so ing model wi h he e ogeneous p e e ences, co ec ing
o selec ion and p ice endogenei y. Coun e ac ual simula ions show un egula ed en s
would be 8–21 pe cen lowe , wi h he la ges d ops in supply-inelas ic ci ies. Olde ,
lowe -income, and less educa ed households gain mos , while newcome s ace highe en-
y en s. The policy educes mobili y and induces space o e consump ion, gene a ing
e iciency losses.
Keywo ds: Ren Con ol, Residen ial Mobili y, Inequali y.
JEL-codes: H7, H72, R23, R31, R38.
⋆We hank Ma hias Amb¨uhl o his suppo in implemen ing he Hunga ian algo i hm. We bene i ed
om nume ous commen s by discussan s and pa icipan s a UEA Eu opean (2024, 2025) and No h
Ame ican (2024) mee ings; AREUEA In e na ional Con e ence (2024), ARES 2024; ERES 2024; SSES
Annual Cong ess (2024, 2025); he 2025 Lisbon U ban and Public Economics Wo kshop; he 2024 Swiss
Wo kshop on Local Public Finance and Regional Economics; he 2024 CRED-MIT Wo kshop on U ban
Economics and he Economics B own Bag Semina a he Uni e si y o Be n. We hank he Swiss
Fede al S a is ic O ice and IAZI AG o da a access and ela ed suppo .
Email add esses: [email p o ec ed] (Lukas Hauck
○), [email p o ec ed] (Nicola
S alde
○), [email p o ec ed] (Simon B¨uchle
○), [email p o ec ed]
(Maximilian . Eh lich
○)
1. In oduc ion
Housing a o dabili y and a ailabili y a e pe sis en conce ns wo ldwide. In esponse
o sha ply ising en s, many ju isdic ions ha e adop ed o expanded en con ol. Al-
hough poli ically popula and o en mo i a ed by dis ibu ional goals, en con ol can
dis o p ice signals (Olsen, 1972; Sims, 2007; Mon as and Ga cia-Mon al o, 2023) and
lead o ine icien alloca ion o housing (Suen, 1989; Glaese and Lu me , 2003; Bulow
and Klempe e , 2012; Fa ilukis e al., 2023; Diamond e al., 2019a). A common o m is
enancy en con ol1which limi s en inc eases o si ing enan s, o en ying hem o
in la ion, while allowing ma ke ese s a acancy. This widesp ead egula ion applies in
oughly 55% o OECD coun ies (OECD, 2024). Al hough o en pe cei ed as a milde
in e en ion, a de ining ea u e is he gap i c ea es be ween incumben and ma ke en s.
Quan i ying i s dis ibu ional consequences and he scale o he esul ing misalloca ion is
challenging. I equi es de ailed mic oda a and a s uc u al amewo k o eco e he ull
coun e ac ual ealloca ion: how he e ogeneous households would choose loca ions and
uni s wi hou egula ion. These policies gene a e p ice spillo e s be ween egula ed and
un egula ed segmen s and al e he composi ion o households ac oss neighbo hoods and
ma ke ie s. Because such e ec s shape equi y and e iciency ou comes, unde s anding
hem is cen al o in o med housing policy deba es.
This pape de elops a amewo k o e alua e he gene al-equilib ium e ec s o en
egula ion and o quan i y i s incidence ac oss households, loca ions, and uni ypes. We
implemen he amewo k in a ep esen a i e se ing, combining a s uc u al demand
model wi h comp ehensi e household–uni mic oda a co e ing he en i e en al ma ke
o Swi ze land.
Swi ze land is an ideal se ing o ou analysis. O e he pas decade, s ong housing
demand has gene a ed sizable and geog aphically he e ogeneous gaps be ween incum-
ben and ma ke en s. As As Figu e 1 shows, hese gaps a e la ge in many g owing
ci ies wo ldwide: Zu ich s ands ou wi h a gap o almos 40%, while he na ional a -
e age is abou 20%, compa able o es ima es o Vancou e . Compa able measu es a e
no sys ema ically a ailable in mos coun ies, and na ionwide mic oda a ha pe mi
decomposing hese gaps ac oss households and loca ions a e excep ional. Ou da a make
his possible. We mo e beyond a e ages o examine he ull dis ibu ion o gaps. Fig-
u e 2 shows ha he gaps widen mechanically wi h enancy du a ion (Panel A). The
desc ip i e pa e ns e eal s ong he e ogenei y. Olde households (Panel B) and lowe -
1This o m o en con ol policy is also e med hi d gene a ion en con ol (A no , 2003).
2
Figu e 1: Gap be ween p i a e-sec o en -con olled (si ing- enan ) and ma ke en s (new leases)
0 10 20 30 40
Ren gap (%)
Pa is
Be n
Swi ze land
Vancou e
New Yo k
To on o
Los Angeles
S ockholm
Zu ich
No es: Sou ces: S ockholm: Donne and Kopsch (2023); Los Angeles: Diamond e al. (2019a); To on o
and Vancou e : Canada Mo gage and Housing Co po a ion (2022); New Yo k: NYC Depa men o
Housing P ese a ion and De elopmen (2021); I eland: Residen ial Tenancies Boa d (2023); Pa is:
Obse a oi e des Loye s de l’Agglom´e a ion Pa isienne (OLAP) (2022); Swi ze land: own calcula ions.
See Appendix A o de ails on he me hodologies o each sou ce.
income households (Panel C) pay en s a below cu en ma ke le els. These sizable
gaps make i essen ial o unde s and bo h he dis ibu ional and e iciency e ec s o
enancy en con ol. C ucially, such an assessmen mus accoun o spillo e e ec s
om con olled o uncon olled ma ke s (Geo ge Fallis, 1984; Ea ly, 2000; Au o e al.,
2014; Hahn e al., 2024). Inco po a ing hese spillo e s, his pape uses ich mic oda a
and a no el empi ical app oach o es ima e he ull incidence o enancy en egula ion.
We s udy enancy en con ol unde Swi ze land’s s able ede al egime. I allows
ee nego ia ion o ini ial en s bu caps wi hin- enancy inc eases o mo gage a e pass-
h ough, pa ial in la ion adjus men s, and alue-enhancing in es men s. These ules
shield si ing enan s om ma ke p essu es while newcome s pay ma ke en s, c ea ing
3
Figu e 2: Gap be ween en -con olled and ma ke en s ac oss household cha ac e is ics
Panel A: Gap o e Tenancy Du a ion
0 20 40 60
Occupa ion Bonus (as % o cu en en )
0 10 20 30 40
Tenancy Du a ion
Panel B: Gap o e Age
10 20 30 40
Occupa ion Bonus (as % o cu en en )
20 40 60 80
Age
Panel C: Gap o e Income Pe cen ile
5 10 15 20 25 30
Occupa ion Bonus (as % o cu en en )
0 2 4 6 8 10
Income Pe cen ile
No es: Da a om he S uc u al Su ey (see Sec ion 4). Obse ed en s a e a ailable o all
households. We es ima e a hedonic en model using he subsample o households ha mo ed
wi hin he su ey yea (“mo e s”) and use he es ima ed coe icien s o impu e ma ke en s
o uni s occupied by households ha did no mo e (“non-mo e s”). Panels A–C epo he
di e ence be ween impu ed ma ke en and ac ual en paid, wi h 95% boo s ap con idence
in e als. See Appendix B o de ails on me hodology.
4
a pe sis en en gap. Because he egula ion applies na ionwide, ye ma ke condi ions
a y sha ply ac oss space, he se ing p o ides ich a ia ion o iden i ying gene al-
equilib ium e ec s.
Ou empi ical analysis combines adminis a i e mic oda a on he uni e se o Swiss
households om 2010–2022 wi h de ailed housing uni cha ac e is ics and p ecise geolo-
ca ion. We link he Popula ion and Households S a is ics o annual income eco ds om
he social secu i y egis y, s uc u al dwelling a ibu es om he Fede al Regis e o
Buildings and Dwellings, and sel - epo ed en s om he S uc u al Su ey. This link-
age allows us o obse e household demog aphics, incomes, and exac en al paymen s
o he egula ed sec o , and o impu e ma ke en s o hese uni s.
We build a s uc u al esiden ial choice model ollowing McFadden (1978), Baye
e al. (2003), and Baye e al. (2007). Households choose among a ini e se o a ailable
uni s wi hin hei labo ma ke egion. Indi ec u ili y depends on housing a ibu es,
p ice, and a lexible measu e o dis ance om he cu en esidence, which in e ac s wi h
li e-cycle indica o s such as ha ing child en o being e i ed. We allow o ich he e o-
genei y in as es and p ice sensi i i y ac oss households. To a oid bias om egula ed
“s aye s,” we es ima e p e e ences using only mo e s, co ec ing o selec ion wi h a
Heckman-s yle i s s age. Using a Ba ik shi –sha e ins umen based on ini ial munic-
ipal employmen sha es and subsequen sec o al shocks, we add ess p ice endogenei y
om unobse ed quali y.
The es ima ed model yields household-le el demand elas ici ies and willingness- o-
pay measu es o each uni . We hen simula e he coun e ac ual wi hou enancy con ol
by ma ching households o uni s h ough a Hunga ian auc ion algo i hm, which adjus s
p ices un il ma ke s clea . This p ocedu e accoun s o demand spillo e s om egula ed
o un egula ed segmen s, a key channel in he incidence o en con ol. Compa ing ob-
se ed and coun e ac ual alloca ions gi es he implici subsidies acc uing o incumben s,
he co esponding bu dens on newcome s, and he agg ega e deadweigh loss om mis-
alloca ion. We decompose hese ou comes by income, age, educa ion, and geog aphy,
and s udy how egula ion a ec s mobili y and housing consump ion.
Ou indings e eal sizable and une en e ec s o enancy en con ol. The model
es ima es show la ge a ia ion in households’ p ice elas ici ies and willingness o pay.
Low-income and less-educa ed households a e mo e sensi i e o en changes. In he
coun e ac ual wi hou egula ion, and accoun ing o spillo e s, ma ke en s in he un-
egula ed segmen would be 8 o 21% lowe han obse ed. The e ec s di e sha ply
ac oss space. U ban labo ma ke s wi h inelas ic supply see he la ges en educ ions.
Ru al a eas expe ience smalle changes. Composi ion e ec s, whe e households wi h
5
low p ice elas ici y clus e in he un egula ed sec o , ampli y p ice p essu es in ci ies.
Supply esponses mi iga e bu do no o se hese p essu es. The dis ibu ional impac s
a e p onounced. Incumben enan s in egula ed uni s cap u e la ge implici subsidies.
These subsidies a e highes o olde , lowe -income, and less-educa ed households. New-
come s and mobile households bea he cos s h ough highe en s. Ac oss egions and
demog aphic g oups, he policy eshapes housing alloca ion and wel a e dis ibu ion.
E iciency losses a e concen a ed in high-demand u ban a eas.
This pape con ibu es o a g owing body o li e a u e analyzing he causal impac s o
en con ol policies on a ious ou comes.2Diamond e al. (2019a) show ha San F an-
cisco’s 1994 en con ol law bene i ed incumben enan s bu imposed cos s on u u e
en e s and un egula ed uni s, e ec i ely ans e ing weal h o long- e m esiden s. Sim-
ila ly, Ahe n and Giacole i (2022) ind ha S . Paul’s 2021 en con ol policy sha ply
educed p ope y alues, wi h weal hie enan s gaining he mos , con a y o he pol-
icy’s in ended edis ibu i e goals. Mense e al. (2023) documen ha Ge many’s en
cap lowe ed egula ed en s bu aised un egula ed ones, educed mobili y, and led o
ine icien ede elopmen . Using a quasi-na u al expe imen , Ce quei o e al. (2024) ind
ha en con ol emo al disp opo iona ely ha ms low-income wo ke s, pushing hem o
ci y ou ski s wi h highe en s and lowe -quali y jobs. By capping en s below ma ke
le els, egula ion may also discou age new cons uc ion and main enance (Downs, 1988)
ul ima ely exace ba ing housing sho ages in he long un (Asqui h, 2019). Ou s udy
ex ends his li e a u e by quan i ying how enancy en con ol edis ibu es housing
consump ion ac oss income, educa ion, and age g oups, and by measu ing i s e ec s on
mobili y, composi ion o demand, and u al–u ban p ice di e en ials. S udies by Nagy
(1995); Gyou ko and Linneman (1989); Aul e al. (1994); Munch and S a e (2002)
ound educed mobili y among enan s in en -con olled uni s. Fu he , his pape e-
la es o he li e a u e examining en egula ion ex e nali ies, such as he e ec s on land
p ices (Mense e al., 2019), housing quali y (Olsen e al., 2004; Moon and S o sky, 1993),
c ime (Au o e al., 2019), labo ma ke s (Jiang e al., 2025), and gen i ica ion (Au o
e al., 2017).
Ou pape makes se e al con ibu ions o he exis ing li e a u e. Fi s , we de elop
a s uc u al amewo k ha combines a esiden ial so ing model wi h an assignmen
algo i hm o eco e gene al-equilib ium p ices and alloca ions absen en con ol. Sec-
ond, we es ima e he model using linked household–uni mic oda a co e ing he en i e
2Malpezzi (2003) p o ides a concise li e a u e e iew on he cos s and bene i s o en con ol up o
he ea ly 2000s, while Kholodilin and Kohl (2023) p esen a mo e ecen su ey.
6
Swiss en al ma ke om 2010 o 2022, yielding household-speci ic demand elas ici ies
and willingness- o-pay measu es. Thi d, we quan i y spillo e s om egula ed o un-
egula ed segmen s, inding ha un egula ed en s would be 8–21 pe cen lowe in he
coun e ac ual, wi h e ec s a ying by loca ion, demand g ow h, and supply elas ic-
i y. Fou h, we documen la ge and une en dis ibu ional impac s, as olde and lowe -
income households cap u e he la ges subsidies. Fi h, we show ha egula ion educes
mobili y and induces space o e consump ion, gene a ing subs an ial misalloca ion and
deadweigh loss.
The emainde o he pape is s uc u ed as ollows. Sec ion 2 in oduces he concep-
ual amewo k mo i a ing ou analysis. Sec ion 3 de i es he esiden ial choice model.
Sec ion 4 desc ibes he Swiss en - egula ion sys em and he household-le el da a. Sec-
ion 5 epo s he es ima ion esul s, and Sec ion 6 p esen s he coun e ac ual analysis.
Sec ion 7 concludes.
2. Concep ual amewo k
Ren con ol policies a y in design, pa icula ly in e ms o which ma ke segmen s
hey a ge . The li e a u e commonly dis inguishes be ween wo gene a ions o en
con ol (Basu and Eme son, 2000; A no , 2003; Malpezzi, 2003). Fi s -gene a ion en
con ol policies apply b oadly ac oss he en i e en al ma ke and ypically in ol e a
s ic en eeze, se ing en s below ma ke -clea ing le els (A no , 1995). In con as ,
second-gene a ion policies egula e only speci ic segmen s o he ma ke , lea ing o he
pa s una ec ed (Geo ge Fallis, 1984; A no , 1995; Basu and Eme son, 2000).
We ocus on enancy en con ol, a p e alen o m o second-gene a ion egula ion
(Basu and Eme son, 2000; A no , 2003). This app oach es ic s en inc eases wi hin
an exis ing enancy bu pe mi s landlo ds o ese en s o ma ke le els be ween en-
ancies. I is widely used in ju isdic ions like Ge many, F ance, I eland, Spain, Sweden,
and se e al US and Canadian ci ies, including New Yo k, San F ancisco, To on o, and
Vancou e . As A no (2003) no es, enancy en con ol ep esen s a comp omise be-
ween hea y-handed egula ion and un e e ed ma ke mechanisms, c ea ing a dis inc
ma ke equilib ium ha balances educed e iciency wi h inc eased enu e secu i y.
Tenancy en con ol e ec i ely segmen s he housing ma ke in o wo dis inc sec-
o s: a egula ed sec o , in which incumben enan s pay below-ma ke en s (p ), and
an un egula ed sec o , in which new enan s ace ma ke -clea ing en s (pu ). This seg-
men a ion gene a es spillo e e ec s and leads o housing misalloca ion, such ha pu
exceeds he uni ied ma ke -clea ing p ice p ha would p e ail in he absence o en
7
The a e age u ili y componen o housing uni hcan be u he decomposed as
δh=
U
X
u=1
α0uxh,u −β0ph+ϕh,(4)
whe e xh,u a e he obse able cha ac e is ics o he uni , phis i s en , and ϕhis an
unobse ed uni -speci ic e o e m. The coe icien s α0ucap u e how each a ibu e
con ibu es o u ili y o he a e age household, while β0measu es he a e age p ice
sensi i i y.
3.1. Es ima ion
Selec ion s age: In he selec ion s age, we es ima e he p obabili y ha household
imo es wi h a logi model:
M∗
i=
K
X
k=1
γkzi
k+νi,(5)
whe e M∗
i= 1 i household imo es and 0 o he wise. The ec o ziincludes cu en
household cha ac e is ics and changes in hese cha ac e is ics since he las pe iod, and
νiis an i.i.d. e o e m.
The selec ion model implies
P(Mi= 1 |zi, wi) = expz⊤
iγ+π wi
1 + expz⊤
iγ+π wi,(6)
whe e wiis an excluded ins umen ha a ec s selec ion bu is no included in he
ou come equa ion and z⊤
iγ+π wi=PK
k=1 γkzi
k+π wi. F om he es ima ed model,
we compu e he gene alized IMR o household i:
IMRi=
ϕ(z⊤
iγ)
Φ(z⊤
iγ)i Mi = 1,
ϕ(z⊤
iγ)
1−Φ(z⊤
iγ)i Mi= 0,
(7)
whe e ϕ(·) and Φ(·) a e he p obabili y densi y unc ion and cumula i e dis ibu ion
unc ion o he logis ic dis ibu ion, espec i ely. We hen include he IMR as an addi-
ional household cha ac e is ic in he esiden ial choice model, ollowing Heckman e al.
(1998).
Fi s s age: We es ima e (2) using a condi ional logi model. Fo each household
i, we d aw n= 19 non-chosen al e na i es om uni s aca ed in he same yea and
14
labo ma ke egion, and add he ue choice o o m he choice se .8We hen es ima e
he pa ame e s in λi
h om (3) and he uni -speci ic ixed e ec s δh, maximizing he
p obabili y ha each household chooses i s obse ed uni h∗.9
The likelihood unc ion is
l=X
iX
h
Ii
hln Pi
hwhe e Pi
h=exp (Vi
h)
Pj∈Ciexp Vi
j,(8)
whe e Ii
hequals 1 i ichooses i s ue housing choice h∗and 0 o he wise, and Pi
his he
p obabili y o indi idual ichoosing uni h.
Following Baye e al. (2003, 2007), we apply con ac ion mapping o ensu e ha
demand o each uni does no exceed supply (see Be y e al., 1995). The ma ke -
clea ing condi ion is
X
iPi
h= 1 o all n(9)
is me a e e y i e a ion o e i ing he model. Fo he case a hand, he con ac ion
mapping ele an o he p esen ed applica ion is simply
δ +1
h=δ
h−ln X
i
ˆ
Pi
h!.(10)
Second s age: We decompose mean indi ec u ili ies δhbased on he se o hous-
ing cha ac e is ics xhand p ices ph. We es ima e 4 while add essing he endogenei y
o p ices. Unobse ed uni and neighbo hood a ibu es (ϕh) may co ela e wi h en s
ph. To add ess his, we ins umen phwi h a shi –sha e a iable based on sec o al
employmen in municipali y min 2000. We combine he municipali y’s sec o al employ-
men sha es wi h g ow h a es a he can onal sec o le el. This app oach uses nea ly
20-yea -old his o ical sec o al sha es ha in e ac ed wi h agg ega e sec o al shi s o
gene a e exogenous a ia ion in en s. Wo k on shi –sha e designs (Ad˜ao e al., 2018;
Goldsmi h-Pinkham e al., 2020; Bo usyak e al., 2022) shows ha alidi y equi es p e-
de e mined exposu e sha es o andom agg ega e shocks. We ely on he i s condi ion.
Ini ial sec o al sha es a e pe sis en , shaped by na u al ameni ies and ma ke access,
and unlikely o co ela e wi h ecen changes in local housing–supply shi e s.
8We es ic non-chosen al e na i es o he same labo ma ke egion, aking he decision o which
labo ma ke o mo e o as gi en.
9Baye e al. (2007) and Baye e al. (2003) p o ide a de ailed desc ip ion o he app oach.
15
δh=
U
X
u=1
α0uxh,u −β0bph+ϕh(11)
3.2. Willingness o pay
Wi h he es ima ed pa ame e s in (3) and (4), we compu e each household i’s will-
ingness o pay (WTP) o each housing op ion h. To ob ain he ma ginal WTP o a
speci ic a ibu e x1, we hold u ili y cons an and sol e o he en change ∆WTPi ha
o se s a change ∆x1. Fo he a e age household, WTP o uni hwi h all cha ac e is ics
iden ical o he a e age uni excep x1is:
WTPh= exp(¯p)×exp −αi
1
β0
(xh,1−¯x1),(12)
whe e (xh,1−¯x1) is he de ia ion o uni hin cha ac e is ic x1 om he mean, and ¯pis
he a e age en . I household idi e s om he a e age in cha ac e is ic z1by ∆z1, i s
WTP o uni h, again di e ing in x1 om he mean, is:
WTPi
h= exp(¯p)×exp −α01 +α11∆z1
β0+β1∆z1
(xh,1−¯x1)(13)
3.3. Ma ke clea ing p ices
We compu e equilib ium p ices ha clea he ma ke o mo ing households, so
ha Dmo e =Smo e.10 We use a Hunga ian auc ion algo i hm, ollowing Gilbe and
F ench (2024) and Demange e al. (1986). Households alue uni s acco ding o he WTP
pa ame e s om he esiden ial choice model. Households alue uni s acco ding o he
WTP pa ame e s om he esiden ial choice model. The auc ion begins wi h all p ices
se o ze o. Each household chooses he uni h ha maximizes hei u ili y, i.e., he uni
o which WTPih -phis la ges . An equilib ium occu s when e e y uni is alloca ed o
exac ly one household, maximizing ha household’s u ili y. I no such ma ching exis s,
he algo i hm aises he p ices o uni s wi h excess demand. I epea s his p ocess,
inc easing he p ices o sca ce uni s, un il i eaches an equilib ium alloca ion. The
Hunga ian auc ion is well-sui ed o his ask because i ensu es he inal alloca ion is
e icien and ma ke -clea ing. I inds a p ice ec o and a ma ching whe e no household-
uni pai would p e e o de ia e, and no uni emains unma ched.
10We assume supply is limi ed o uni s aca ed by mo e s and do no allow o new cons uc ion.
16
We add ou side op ions wi h ze o u ili y o cap u e he possibili y ha households
lea e o do no en e he egional labo ma ke . We choose he numbe o ou side op ions
so ha he algo i hm’s mean ma ke -clea ing p ice ma ches he obse ed mean. In ou
p e e ed speci ica ion, abou 3.5% o op ions a e ou side he mo e ma ke , consis en
wi h a e age ne immig a ion. We keep his sha e cons an ac oss simula ions.
The algo i hm uses an I×Hma ix o WTP alues o all household-uni combina-
ions wi hin a labo ma ke egion. To speed compu a ion, we andomly sample 800 i–h
combina ions pe ma ke , yielding an 800 ×800 ma ix. We un he ou ine 20 imes
o each labo ma ke egion.
3.4. O e iden i ica ion check
We in e p e he p ices ob ained o mo e s as he un egula ed ma ke p ices, deno ed
pUR
h. To alida e he model, we compa e he p edic ed pUR
hwi h he obse ed en s o
newly con ac ed leases on each uni . Tes s o po en ial sampling bias ensu e s able
esul s. The panel s uc u e o he da ase enables a s onge back es ing amewo k.
The model is es ima ed o one yea and used o p edic pUR
h o he p eceding o ollowing
yea . Such ou -o -sample p edic ions p o ide a obus check on he alidi y and s abili y
o he es ima es.
4. Ins i u ional backg ound and Da a
4.1. Ins i u ional backg ound
Swi ze land, wi h o e 60% o households en ing, p o ides a pe inen se ing o
s udying he e ec s o enancy en con ol. Table 1 compa es enu e ypes in Swi ze -
land, Ge many, and he USA, highligh ing no able di e ences. Swi ze land’s high en al
sha e s ands in sha p con as o he USA, which has a simila GDP pe capi a, and o
Ge many, i s closes geog aphical and cul u al neighbo .11 Ren ing in Swi ze land spans
all income le els: nea ly 50% o households in he op income quin ile en , a pa e n un-
common in he USA and Ge many. P i a e households own almos hal (47%) o en ed
apa men s. Ins i u ional in es o s hold 34%, coope a i es own 8%, eal es a e i ms
accoun o 7%, and public housing ini ia i es manage only 4% o he en al ma ke .
Swiss en al law, es ablished in he 1980s and 1990s, builds on eme gency en al
egula ions in oduced du ing he wo Wo ld Wa s (Hausmann, 2016; Roh bach, 2014).
11We czbe ge (1997) and Bou assa and Hoesli (2010) discuss he d i e s o Swi ze land’s low home-
owne ship a e.
17
Table 1: Compa ison Tenu e Types 2020
Income Quin ile
All households Lowes 2nd 3 d 4 h Highes
Swi ze land
Ren e 60.8 68.9 65.8 63.1 57.4 49.0
Owne wi h mo gage 33.9 22.8 29.0 32.4 39.3 45.9
Owne ou igh 4.4 6.6 4.0 4.1 2.9 4.5
O he , unknown 0.9 1.6 1.2 0.4 0.4 0.6
Ge many
Ren e 51.0 68.7 58.8 50.7 42.2 34.7
Owne wi h mo gage 25.6 11.8 17.4 26.0 33.4 39.1
Owne ou igh 19.8 14.4 19.3 19.5 21.6 24.2
O he , unknown 3.6 5.1 4.5 3.8 2.8 1.9
Uni ed S a es
Ren e 32.1 52.6 37.9 29.8 23.2 17.0
Owne wi h mo gage 40.4 17.4 31.1 42.9 52.4 58.4
Owne ou igh 25.7 26.2 28.8 25.9 23.5 24.1
O he , unknown 1.7 3.7 2.1 1.4 0.9 0.6
Sou ce: OECD A o dable Housing Da abase.
While landlo ds and enan s can eely nego ia e ini ial en s, he law limi s en inc eases
du ing exis ing enancies.12
Ren s wi hin exis ing con ac s can ise only unde h ee condi ions: (i) highe e-
inancing cos s om ising mo gage a es, (ii) in la ion adjus men s, and (iii) passing
on cos s om alue-enhancing in es men s such as majo e u bishmen s o new ins al-
la ions. The Fede al Agency o Housing se s a qua e ly e e ence in e es a e, which
allows landlo ds o adjus en s when mo gage a es inc ease. Regula ions also pe mi
in la ion adjus men s, capped a 40% o he in la ion a e. Tenan s bene i om s ong
p o ec ions. Ren al ag eemen s a e ypically open-ended, and enan s can e mina e
wi h h ee mon hs’ no ice. Landlo ds ace s ic e ules and may e mina e only o
pe sonal use o when majo eno a ions equi e i .13
12Regula ions nominally es ic en inc eases be ween enancies, bu en o cemen is weak. No
cen al agency moni o s compliance; households mus appeal i hey belie e ini ial en s a e excessi e.
Wi h li le access o in o ma ion on p e ious en s, only 0.3% o new leases ace legal challenges (BWO
2022). Acco ding o he common p ac ice o a bi a ion cou s, en inc eases o up o 10% a e no
conside ed excessi e. E en i a ew appeals occu , egula ion could s ill ha e a disciplina y e ec on
landlo ds. In his case, we would expec o obse e bunching a ound he 10% cu o . I so, we would
expec en changes o bunch a ound he 10% cu o . Figu e B.11 shows no such bunching, sugges ing
he egula ion does no bind en inc eases be ween enancies.
13Can onal and communal au ho i ies can impose u he es ic ions, such as in Gene a, Basel, and
Vaud.
18
4.2. Da a sou ces
We combine indi idual-le el da a om mul iple adminis a i e egis ies o 2010–
2022. Ou co e da ase is he Popula ion and Households S a is ics (STATPOP), which
acks all indi iduals and households in Swi ze land o e he 13 yea s. We link hese
eco ds o yea ly labo incomes om he Old Age and Su i o s’ Insu ance (AHV). Each
indi idual is ma ched o hei apa men , enabling us o me ge s uc u al cha ac e is ics
om he Fede al Regis e o Buildings and Dwellings main ained by he Swiss Fede al
S a is ical O ice (FSO).
Fu he , we supplemen his wi h he S uc u al Su ey (SE), which co e s a yea ly
sample o abou 200,000 households. The SE p o ides socio-economic in o ma ion such
as educa ion, esiden ial s a us, and he ne en paid. To impu e ma ke en s o
he un egula ed segmen (new con ac s), we use he IAZI da abase on o e ed en s
om 2004–2022.14 This da ase includes de ailed housing cha ac e is ics, p ecise geo-
coo dina es, and asking en s. Finally, we use a ich se o enancy con ac s cu a ed by
IAZI o back- es ou esul s and assess model pe o mance.
Figu e 4 shows he spa ial dis ibu ion o a e age en s ac oss Swiss municipali ies.
La ge ci ies such as Zu ich and Gene a, as well as hei su ounding a eas, ha e he
highes en s. High en s also appea in municipali ies nea lakes and ou is des ina ions
like In e laken and G indelwald.
14Appendix B de ails he impu a ion o ma ke en s o he un egula ed segmen .
19
Figu e 4: Spa ial Dis ibu ion o Ren s on Un egula ed Ma ke Segmen
20
5. Resul s o he es ima ion o housing demand pa ame e s
We es ima e he e ogeneous housing p e e ences in h ee s eps using (2) o (4). Fi s ,
we model selec ion be ween mo e s and s aye s and compu e he In e se Mills Ra io
(IMR) o he nex s ep. Second, we sol e a condi ional choice model by con ac ion
mapping o eco e he e ogenei y in housing demand ac oss household ypes. Thi d,
we use he s age- wo alua ions o es ima e in e se demand o he a e age household.
These s eps deli e willingness- o-pay (WTP) by household ype and uni .
We implemen ou speci ica ions ha a y housing cha ac e is ics xhand household
cha ac e is ics zk. The comp ehensi e model uses se en household and 12 uni dimen-
sions. The benchma k uses six and nine. The minimal uses ou and se en. A da a-based
model e ains only s a is ically signi ican a iables om he i s s age, excluding he
IMR as a household cha ac e is ic.
5.1. Selec ion s age es ima es
Table 2 epo s selec ion-model coe icien s. Figu e 5 plo s IMR sco es o mo e s
and non-mo e s. The speci ica ion includes all household cha ac e is ics zk om (5) and
indica o s o changes in ma i al s a us (newly ma ied o newly sepa a ed).
The es ima es show ha ansi ions om single o ma ied and ma ied o di o ced
signi ican ly aise mo ing p opensi y. O he coe icien s align wi h expec a ions. The
coe icien on e i ed may look su p ising, bu age ully iden i ies i .
The g ouped his og ams e eal appa en IMR di e ences be ween mo e s and non-
mo e s. These di e ences a e consis en wi h unobse ed shocks ied o expec a ions,
ne wo ks, o cons ain s. To add ess selec ion on unobse ables when es ima ing he
esiden ial choice model on mo e s, we include he es ima ed IMR as a household cha -
ac e is ic in he indi ec u ili y unc ion, ollowing Heckman (1979). The subs an ial
o e lap in IMR dis ibu ions p o ides common suppo o his con ol.
21
Figu e 5: Dis ibu ions o in e se-Mills a io Table 2: Selec ion model es ima es
Mo ed = T ue
∆ Sepa a ion 1.399∗∗∗
(0.012)
∆ Ma iage 0.313∗∗∗
(0.006)
Child en U18 −0.042∗∗∗
(0.004)
HH Income −0.044∗∗∗
(0.001)
HH Age −1.332∗∗∗
(0.004)
HH Size −0.084∗∗∗
(0.004)
Re i ed 0.091∗∗∗
(0.005)
Ma ied −0.079∗∗∗
(0.003)
Obse a ions 115,933
No e: ∗p < 0.1; ∗∗ p < 0.05; ∗∗∗ p < 0.01
Figu e 6: P edic ed a e age alua ions ac oss models
5.2. Fi s -s age es ima es
Table B.7 in Appendix B epo s i s -s age coe icien s om (5). These coe icien s
a e no di ec ly in e p e able in le els, bu s a is ical signi icance e eals he di ec ion
22
and ele ance o uni and household cha ac e is ics. Highe -income households, house-
holds wi h child en, and la ge households demand mo e li ing space, ce e is pa ibus.
P ice elas ici y is la ge o households wi h child en and smalle o highe -income
and olde households. Mul icollinea i y cau ions agains s ic causal in e p e a ion.
Fo example, he posi i e link be ween ha ing child en and high-income neighbo hoods
weakens once we accoun o he nega i e link be ween household size and neighbo hood
income.
Figu e 6 compa es eco e ed uni -le el mean u ili ies δh om he baseline o h ee
al e na i es. The le panel uses he “da a-based” model wi h only s a is ically signi -
ican in e ac ions. The middle panel uses he minimal he e ogenei y pa ame e iza ion.
The igh panel uses he comp ehensi e he e ogenei y speci ica ion. The δhalign igh ly
ac oss panels. Co ela ions a e high, and slopes a e close o one. Uni ankings emain
s able ac oss speci ica ions. This suppo s δhas a c edible app oxima ion o he la en
mean indi ec u ili y o he a e age household. Figu e C.12 in Appendix B shows
addi ional s abili y checks ac oss speci ica ions.
5.3. Second-s age es ima es
Table 3 decomposes he eco e ed δhin o obse able uni cha ac e is ics. Column
(1) epo s OLS es ima es, which su e om en endogenei y. Column (2) shows he
i s s age o he 2SLS in (11), ins umen ing en wi h a shi -sha e measu e. Column
(3) p esen s he second-s age 2SLS es ima es. The ins umen is s ong ( i s -s age
F= 612). The 2SLS p ice semi-elas ici y is −7.9, so a 1% en inc ease lowe s demand
by abou 8%.
Combining he p ice coe icien wi h he li ing-space coe icien yields an a e age
subs i u ion a e o 4.2/7.9≈0.53. Households pay abou 0.53% highe en o a 1%
inc ease in li ing space.
O he coe icien s show clea ade-o s. Households pay abou 0.14% mo e en o
a 1% inc ease in neighbo hood income. They pay abou 0.11% mo e en o a 1%
educ ion in building age, consis en wi h a p emium o newe s ock. The small and
s a is ically insigni ican e ec o dis ance o public anspo e lec s measu emen
limi s ha omi se ice equency, a el imes, and high baseline accessibili y.
These elas ici ies align wi h p io wo k. The es ima ed ax elas ici y o en s is 0.28,
wi hin he Swiss ange epo ed by Bas en e al. (2017).
5.4. Willingness o pay es ima es
Table 4 epo s willingness o pay (WTP) om he esiden ial choice model. Column
(1) gi es he a e age household’s WTP o uni and neighbo hood a ibu es in mon hly
23
15% o Be ne Obe land. Resul s om he second exe cise e eal e en bigge spillo e s
anging up o 28% in Be ne Obe land.
O e all, he esul s o bo h exe cises indica e sizable spillo e s om egula ion: by
segmen ing he ma ke and p o ec ing incumben enan s, egula ion pushes up en s in
he un egula ed segmen by oughly 10% o 21%. Compa ing his o he obse ed eg-
ula ed–un egula ed gap (pu /p ≈1.2) implies ha , unde a uni ied ma ke , egula ed-
segmen households would ace 8-16% highe en s, while un egula ed-segmen house-
holds would pay 10% - 21% less. Fu he mo e, he enancy du a ion equi ed o be an
a e age ne bene icia y o he egula ion anges be ween 8 and 12 yea s.
Table 5: Median Simula ed P ices and Rela i e Di e ences: Municipali y A achmen
Median pu 1−(p′/pu ) 1 −(p′/pu )
Only Uni s om All Uni s
Un egula ed Ma ke
Aa eland 1 885 5% 10%
Zen alschweiz 2 305 9% 12%
Zu ich 2 477 7% 10%
Sop acene i 1 680 2% 7%
So ocene i 1 638 0% 5%
Bodensee egion 1 961 5% 6%
Os alpen 2 054 6% 4%
Neuenbu g 1 460 9% 9%
F eibu g 1 916 5% 10%
Biel-Ju a 1 697 7% 10%
Be n 1 757 12% 11%
Wes alpen 1 840 2% 1%
Basel∗1 982 7% 9%
Be ne Obe land 1 726 15% 16%
Swiss Median 2 088 8% 10%
No e: The labo ma ke egions o Gene a and Vaud a e omi ed because hey ha e s ic e en
egula ions a he can onal le el. ∗The ci y o Basel in oduced a s ic e o m o en egula ion in
2021; ou analysis ocuses on he yea 2018.
In he ollowing, we explo e he incidence o enancy con ol in mo e de ail ac oss
di e en household ypes and compu e he wel a e cos s caused by misalloca ion induced
by enancy con ol.
6.1. Incidence
To be comple ed: Incidence in di e en dimensions: a wha age, wha enu e du a-
ion, e c. We show o di e en labo ma ke egions he incidence o enancy con ol,
i.e., a which du a ion o enancy, wha age, le el o income, e c., households end o be
30
Table 6: Median Simula ed P ices and Rela i e Di e ences: Neighbou hood A achmen
Median pu 1−(p′/pu ) 1 −(p′/pu )
Only Uni s om All Uni s
Un egula ed Ma ke
Aa eland 2371 17% 21%
Zen alschweiz 2856 22% 22%
Zu ich 3071 24% 24%
Sop acene i 2406 15% 18%
So ocene i 2296 8% 11%
Bodensee egion 2544 23% 23%
Os alpen 2794 20% 18%
Neuenbu g 2005 24% 19%
F eibu g 2506 19% 21%
Biel-Ju a 2173 22% 22%
Be n 2397 22% 20%
Wes alpen 2496 23% 17%
Basel∗2843 13% 14%
Be ne Obe land 2178 27% 28%
Swiss Median 2731 21% 21%
No e: The labo ma ke egions o Gene a and Vaud a e omi ed because hey ha e s ic e en
egula ions a he can onal le el. ∗The ci y o Basel in oduced a s ic e o m o en egula ion in
2021; ou analysis ocuses on he yea 2018.
winne s o lose s o enancy con ol. This is displayed in g aphs simila o Figu e 2 17
6.2. Misalloc ion
To be comple ed: We ollow wo ways o compu e misalloca ion: Fi s , we measu e
he e iciency loss om enancy con ol by quan i ying he deg ee o o e consump ion
(e.g., in squa e me e s) o egula ed households compa ed o he coun e ac ual. Mo e
speci ically, we compu e x−x′and mul iply wi h 1/2×(pUR −pR). Second, we com-
pu e a ull u ili y based measu e accoun ing o all dimensions o uni and households
he e ogenei y by compu ing he consume su plus – es ima es o WTP minus p ices i.e.
WTPi−pu ,WTPi−p ,WTPi−p′. – in he obse ed and he coun e ac ual scena io.
The by, we quan i y he ull misalloca ion by labo ma ke egion, household ype, and
uni ype.
17Essen ially, he y-axis in Figu e 2 panel (a) - (c) would be shi ed down by 10-20 pe cen age poin s.
Indica ing ha , e.g., households wi h a enancy du a ion below 10 yea s a e ne lose s o he egula ion.
31
7. Conclusion
Housing a o dabili y and a ailabili y emain p essing conce ns wo ldwide. Ren
con ol policies con inue o gene a e deba e o e hei e ec i eness and unin ended con-
sequences. This pape de elops and implemen s a s uc u al amewo k o quan i y he
gene al-equilib ium incidence o enancy en con ol. We combine adminis a i e mi-
c oda a on all Swiss households wi h a esiden ial so ing model and a ma ke -clea ing
assignmen algo i hm o eco e coun e ac ual p ices and alloca ions absen egula ion.
Accoun ing o spillo e s, we ind ha un egula ed en s would be 8–21 pe cen lowe
wi hou egula ion. Reduc ions a e la ges in u ban a eas wi h inelas ic supply. Incum-
ben enan s cap u e la ge implici subsidies, concen a ed among olde , lowe -income,
and less-educa ed households. Mo ing households ace highe en s han hey would in
he absence o egula ion. The policy educes mobili y and induces o e consump ion o
space, gene a ing misalloca ion and deadweigh loss, especially in igh ma ke s.
These esul s show ha enancy en con ol shapes bo h he dis ibu ion and e i-
ciency o housing consump ion. I p o ec s long- e m enan s and deli e s p og essi e
ans e s along some dimensions, ye i imposes subs an ial cos s on mobile households
and dis o s alloca ion in high-demand a eas. The p oposed amewo k p o ides a ans-
pa en ool o e alua e hese ade-o s and can be adap ed o assess al e na i e policy
designs o o he ins i u ional se ings.
32
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Appendix A. Da a Sou ces and Me hodologies o Ren Gap Es ima es
Figu e 1 shows es ima es o he en gap be ween ma ke (new-lease) en s and con-
olled (si ing- enan ) en s om epu able sou ces, each d awing on di e en da a and
me hods. Donne and Kopsch (2023) measu e he Swedish gap by compa ing egula ed
en s wi h es ima ed ma ke -clea ing en s using a hedonic eg ession ha con ols o
uni cha ac e is ics and loca ion. Diamond e al. (2019a) es ima e he Los Angeles gap as
he a e age en discoun o en -con olled enan s unde he ci y’s Ren S abiliza ion
O dinance, applying a hedonic eg ession o adjus o di e ences in uni s and neighbo -
hoods. Canada Mo gage and Housing Co po a ion (2022) epo he gaps o To on o
and Vancou e using a ixed-sample app oach ha compa es a e age en s o he same
se o uni s ac oss yea s, dis inguishing be ween u no e and non- u no e uni s. NYC
Depa men o Housing P ese a ion and De elopmen (2021) p o ide he New Yo k es-
ima e by epo ing median en s o en -s abilized and un egula ed uni s. Residen ial
Tenancies Boa d (2023) gi e he I eland igu e as median en s in exis ing e sus new
enancies in Ren P essu e Zones. Obse a oi e des Loye s de l’Agglom´e a ion Pa isi-
enne (OLAP) (2022) supply he Pa is igu e, epo ing a e age en pe squa e me e
o new e sus ongoing p i a e enancies, g ouped by uni size ca ego y wi hou hedonic
con ols. We calcula e he Swiss gap ou sel es om adminis a i e en al da a, compa -
ing he a e age en paid by si ing enan s o he es ima ed ma ke en o he same
uni s. These es ima es di e in me hodology, bu all e lec obus , locally accep ed ap-
p oaches. Reade s should keep his he e ogenei y in mind when compa ing gaps ac oss
loca ions.
38
Appendix B. Da a
Appendix B.1. Es ima ing en s in un egula ed segmen o uni s in egula ed segmen
Fi s , we es ima e how much a household bene i s om he Swiss en con ol policy.
To do so, we calcula e he gap be ween he ( egula ed) en a household pays unde i s
cu en lease ag eemen and he en i would ha e o pay in he un egula ed ma ke
segmen . The la e ep esen s he household’s eplacemen cos s (e.g., en ing i s uni
unde cu en condi ions). As he co esponding en on he un egula ed ma ke segmen
is unknown, o each obse ed en wi hin an exis ing con ac (pR
h ), we es ima e he
en on he un egula ed ma ke segmen (ph ) based on machine lea ning:
We ain a machine-lea ning model (XGBoos ) o p edic ma ke en s. Below, we
p esen a s ylized e sion o he model o con ey o he eade an in ui i e unde s anding:
PUR
h =β0+δ +θ′Xh +λ′Nh +κ′Mh +ϵh (B.1)
The model is ained o p edic he ma ke en (PUR
h ) o uni ha ime . I akes
in o conside a ion dwelling cha ac e is ics (X), neighbo hood cha ac e is ics (N), and
municipali y cha ac e is ics (M) as well as yea - ixed e ec s (δ). The model is ained on
o e 900,000 uni s ad e ised o en online. The IAZI aining da a is used o de e mine
pa ame e s (ˆ
β0,ˆ
δ ,ˆ
θ, ˆ
λand ˆκ). We use he ained model pa ame e s o p edic en s on
he un egula ed ma ke segmen o households wi hin an exis ing enancy:
GAPh =ˆ
PUR
h −PR
h (B.2)
GAPh ep esen s he bene i ha he household li ing in uni hin neighbou hood n
in municipali y ma ime d aws om he en egula ion. I is he di e ence be ween
he en he household would pay unde cu en condi ions a ime on he un egula ed
segmen and he en he household pays. To documen he dis ibu ional consequences
o he egula ion, we a e in e es ed in es ima ing he ollowing s ylized eg ession. We
eplace subsc ip h( o uni ) by i, indica ing he household li ing in uni h, whe e Zi
is he households dimension o in e es (e.g. income, age).
GAP∗
h =α+xh β+εh (B.3)
Howe e , we only ha e a noisy measu emen o GAP∗
i:
GAPh =GAP∗
h +uh (B.4)
39
Appendix E. Fu he Resul s
Table E.8: De e minan s o Mo ing P opensi y
(1) (2) (3)
GAP -0.662*** -0.394*** -0.263***
(20.02) (11.36) (7.08)
∆ Income 0.0581*** 0.0574*** 0.0362**
(5.53) (5.41) (3.21)
∆ Child en 0.498*** 0.400*** 0.375***
(12.39) (9.92) (9.25)
Age -0.0194*** -0.0127*** -0.0140***
(27.97) (17.11) (18.32)
Rooms -0.281*** -0.254*** -0.254***
(31.46) (28.32) (27.06)
Tenancy Du a ion -0.0478*** -0.0435***
(29.04) (26.07)
Labo Ma ke , Educa ion
and Residence Pe mi FE Yes
Obse a ions 120,761 120,761 120,761
No es: Robus -s a is ics in pa en heses. * p < 0.1, ** p < 0.05, ***
p < 0.01.
Based on speci ica ion (3): A he 25 h pe cen ile o he GAP a iable, he
p edic ed p obabili y o mo ing is 12.7%; a he 75 h pe cen ile, i dec eases
o 11.7%.
46
Figu e E.14: Model e alua ion – back es ing
Panel (a): P edic ed s. obse ed en s by uni Panel (b): P edic ed s. obse ed housing expendi u e
sha e by household
47
Appendix F. Mo e Desc ip i es
48
