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How does the market react to cooling measures?The case of Singapore
T. Xie1 S.-K. Chao2 R. Schulz3 W. K. Hardle4
1Singapore Management University
2Purdue University
3University of Aberdeen
4Humboldt University of Berlin
February 22, 2017
Macro-prudential Policies Post-crisis
I Monetary policy became a “blunt tool”I Central banks turned to macro-prudential policies
I e.g. loan-to-value ratio, stamp duties, etcI mostly aimed at housing market
I Objectives of MPPs
1. to promote the resilience of the financial system by mandatinghigher levels of liquidity, capital and collateralisation
2. to restrain the build-up of financial imbalances by slowingcredit and asset price growth
Movements in house prices
-.50
.51
1.5
Cha
nge
in lo
g pr
ice
sinc
e 20
07Q
1
2007q1 2009q3 2012q1 2014q3 2017q1
London Hong Kong SingaporeSydney Vancouver
Figure: House price movements in major cities since 2007.
How does macro-prudential policies work?
I Lean against the wind (Zhang and Zoli 2016)
I Discretion vs rule (Kuttner and Shim 2016)
I House price indices still show upward trends
Monetary vs macro-prudential policies
I Interest rate affects consumption and investmentsI hence the inflation
I LTV, DSTI ratios affect demand for loansI affect the demand for housing only indirectlyI hence the house price more indirectly
Inelastic housing demand
8010
012
014
016
0Pr
ice
inde
x (2
009=
100)
050
0010
000
1500
020
000
No.
of d
eals
2007q1 2009q3 2012q1 2014q3 2017q1
No. of deals HDB PrivateSource: CEIC Database.
Figure: No. of deals and property price indices.
Singapore’s housing market
I Public housing sector is dominant (Phang 2001)I Singapore citizens can afford their first homes
I Prone to foreign speculation (Chow and Xie 2016)I Free mobility of capital in and out of the real estate sectorI Foreign investors have freedom in acquiring private properties
in Singapore
I Challenge to policy makersI Housing market needs to remain attractive to investorsI At the same time affordable to local residents and
fundamentally healthy
Data
I SRX data for private and HDB transactions spanning fromJan 1, 2007 to Jun 30, 2016
I PriceI SizeI Geo-locationI Property type
I Monthly consumer price index from the CEIC Database
I We calculate the log real price per square foot
Sampling periods
Table: Sampling periods.
s Period Date
0 Before cooling measures Jan 1, 2007 – Sep 13, 20091 During cooling measures Sep 14, 2009 – Dec 9, 20132 After cooling measures Dec 10, 2013 – Jun 30, 2016
Hypothesis testing
I For a total of S sampling periods, we define τ the conditionalquantile Qs (τ |x) of the log real house price psf Ys at period son the geo-location X = x:
P [Ys ≤ Qs (τ |x) |X = x] = τ (1)
I Null hypothesis:
H0 : Qs (τ |x) = Qs′ (τ |x) (2)
for all s 6= s ′.
I Acceptance of the null hypothesis implies stable house pricesacross the two periods
Uniform confidence bands (Chao et al. 2017)
I Bootstrapped simultaneous confidence bands
I For each period, and at location x, we compute an estimatorof Qs (τ |x) by
Qs (τ |x) = arg minq∈R
ns∑i=1
Khs (x− X si ) ρτ (Y s
i − q) (3)
Uniform confidence bands
I Hardle, Ritov, and Wang 2015 and Chao et al. 2017
I The simultaneous confidence set with nominal level α:
Cs (X0) :={
(x, q) ∈ X0 × R : q ∈[Qs (τ |x)± cτ,nξ
∗α
]}(4)
where cτ,n is the scaling factor
cτ,n :=
√√√√ τ(1− τ)
ns |hs |fX s (x)f 2Y s |X s
(Qs(τ |x)|x
) (5)
I The null hypothesis is rejected when two confidence sets(s 6= s ′) are disjoint:
Cs (X0) ∩ Cs′ (X0) = ∅ (6)
Price dynamics: West to east5
67
8Lo
g re
al p
rice
psf
0 5 10 15 20 25West -> East
(a) s = 1, τ = 20%5
67
8Lo
g re
al p
rice
psf
0 5 10 15 20 25West -> East
(b) s = 1, τ = 50%
56
78
Log
real
pric
e ps
f
0 5 10 15 20 25West -> East
(c) s = 1, τ = 80%
56
78
Log
real
pric
e ps
f
0 5 10 15 20 25West -> East
(d) s = 2, τ = 20%
56
78
Log
real
pric
e ps
f
0 5 10 15 20 25West -> East
(e) s = 2, τ = 50%5
67
8Lo
g re
al p
rice
psf
0 5 10 15 20 25West -> East
(f) s = 2, τ = 80%
Price dynamics: South to north4
68
1012
Log
real
pric
e ps
f
0 5 10 15 20 25South -> North
(g) s = 1, τ = 20%4
68
1012
Log
real
pric
e ps
f
0 5 10 15 20 25South -> North
(h) s = 1, τ = 50%
46
810
12Lo
g re
al p
rice
psf
0 5 10 15 20 25South -> North
(i) s = 1, τ = 80%
46
810
12Lo
g re
al p
rice
psf
0 5 10 15 20 25South -> North
(j) s = 2, τ = 20%
46
810
12Lo
g re
al p
rice
psf
0 5 10 15 20 25South -> North
(k) s = 2, τ = 50%4
68
1012
Log
real
pric
e ps
f
0 5 10 15 20 25South -> North
(l) s = 2, τ = 80%
Price dynamics: EW line
2.5
5.0
7.5
10.0
Joo
Koo
nP
ione
erB
oon
Lay
Lake
side
Chi
nese
Gar
den
Juro
ng E
ast
Cle
men
tiD
over
Buo
na V
ista
Com
mon
wea
lthQ
ueen
stow
nR
edhi
llT
iong
Bah
ruO
utra
m P
ark
Tanj
ong
Pag
arR
affle
s P
lace
City
Hal
lB
ugis
Lave
nder
Kal
lang
Alju
nied
Pay
a Le
bar
Eun
osK
emba
ngan
Bed
okTa
nah
Mer
ahS
imei
Tam
pine
sP
asir
Ris
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.2 QR, public+private
(m) τ = 20%
2.5
5.0
7.5
10.0
Joo
Koo
nP
ione
erB
oon
Lay
Lake
side
Chi
nese
Gar
den
Juro
ng E
ast
Cle
men
tiD
over
Buo
na V
ista
Com
mon
wea
lthQ
ueen
stow
nR
edhi
llT
iong
Bah
ruO
utra
m P
ark
Tanj
ong
Pag
arR
affle
s P
lace
City
Hal
lB
ugis
Lave
nder
Kal
lang
Alju
nied
Pay
a Le
bar
Eun
osK
emba
ngan
Bed
okTa
nah
Mer
ahS
imei
Tam
pine
sP
asir
Ris
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.5 QR, public+private
(n) τ = 50%
2.5
5.0
7.5
10.0
Joo
Koo
nP
ione
erB
oon
Lay
Lake
side
Chi
nese
Gar
den
Juro
ng E
ast
Cle
men
tiD
over
Buo
na V
ista
Com
mon
wea
lthQ
ueen
stow
nR
edhi
llT
iong
Bah
ruO
utra
m P
ark
Tanj
ong
Pag
arR
affle
s P
lace
City
Hal
lB
ugis
Lave
nder
Kal
lang
Alju
nied
Pay
a Le
bar
Eun
osK
emba
ngan
Bed
okTa
nah
Mer
ahS
imei
Tam
pine
sP
asir
Ris
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.8 QR, public+private
(o) τ = 80%
Price dynamics: NS line
2.5
5.0
7.5
10.0
Juro
ng E
ast
Buk
it B
atok
Buk
it G
omba
kC
hoa
Chu
Kan
gYe
w T
eeK
ranj
iM
arsi
ling
Woo
dlan
dsA
dmira
ltyS
emba
wan
gY
ishu
nK
hatib
Yio
Chu
Kan
gA
ng M
o K
ioB
isha
nB
radd
ell
Toa
Pay
ohN
oven
aN
ewto
nO
rcha
rdS
omer
set
Dho
by G
haut
City
Hal
lR
affle
s P
lace
Mar
ina
Bay
Mar
ina
Sou
th P
ier
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.2 QR, public+private
(p) τ = 20%
2.5
5.0
7.5
10.0
Juro
ng E
ast
Buk
it B
atok
Buk
it G
omba
kC
hoa
Chu
Kan
gYe
w T
eeK
ranj
iM
arsi
ling
Woo
dlan
dsA
dmira
ltyS
emba
wan
gY
ishu
nK
hatib
Yio
Chu
Kan
gA
ng M
o K
ioB
isha
nB
radd
ell
Toa
Pay
ohN
oven
aN
ewto
nO
rcha
rdS
omer
set
Dho
by G
haut
City
Hal
lR
affle
s P
lace
Mar
ina
Bay
Mar
ina
Sou
th P
ier
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.5 QR, public+private
(q) τ = 50%
2.5
5.0
7.5
10.0
Juro
ng E
ast
Buk
it B
atok
Buk
it G
omba
kC
hoa
Chu
Kan
gYe
w T
eeK
ranj
iM
arsi
ling
Woo
dlan
dsA
dmira
ltyS
emba
wan
gY
ishu
nK
hatib
Yio
Chu
Kan
gA
ng M
o K
ioB
isha
nB
radd
ell
Toa
Pay
ohN
oven
aN
ewto
nO
rcha
rdS
omer
set
Dho
by G
haut
City
Hal
lR
affle
s P
lace
Mar
ina
Bay
Mar
ina
Sou
th P
ier
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.8 QR, public+private
(r) τ = 80%
Price dynamics: NE line
2.5
5.0
7.5
10.0
Har
bour
fron
t
Out
ram
Par
k
Chi
nato
wn
Cla
rke
Qua
y
Dho
by G
haut
Littl
e In
dia
Farr
er P
ark
Boo
n K
eng
Pot
ong
Pas
ir
Woo
dlei
gh
Ser
ango
on
Kov
an
Hou
gang
Bua
ngko
k
Sen
gkan
g
Pun
ggol
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.2 QR, public+private
(s) τ = 20%
2.5
5.0
7.5
10.0
Har
bour
fron
t
Out
ram
Par
k
Chi
nato
wn
Cla
rke
Qua
y
Dho
by G
haut
Littl
e In
dia
Farr
er P
ark
Boo
n K
eng
Pot
ong
Pas
ir
Woo
dlei
gh
Ser
ango
on
Kov
an
Hou
gang
Bua
ngko
k
Sen
gkan
g
Pun
ggol
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.5 QR, public+private
(t) τ = 50%
2.5
5.0
7.5
10.0
Har
bour
fron
t
Out
ram
Par
k
Chi
nato
wn
Cla
rke
Qua
y
Dho
by G
haut
Littl
e In
dia
Farr
er P
ark
Boo
n K
eng
Pot
ong
Pas
ir
Woo
dlei
gh
Ser
ango
on
Kov
an
Hou
gang
Bua
ngko
k
Sen
gkan
g
Pun
ggol
Locations
Rea
l pric
e pe
r sq
uare
foot
period
07−09
10−13
14−16
Uniform confidence bands α =0.05 for τ = 0.8 QR, public+private
(u) τ = 80%
Findings
I Cooling measures are more likely to suppress demand forloans, not for housing
I We observe a pattern of substitution effect down the pricedistribution
I Prices of the high-end houses are cooled first
I Prices at the lower percentiles respond to cooling measureswith lags
Upcoming plan
I Use different independent variables:I E.g. distance from MRT stations
I Use specific policy tools as independent variables:I LTV ratioI Debt servicing ratioI Stamp duties
References I
Chao, Shih-Kang et al. (2017). “Confidence Corridors forMultivariate Generalized Quantile Regression”. In: Journal ofBusiness & Economic Statistics 35.1, pp. 70–85. issn:0735-0015. doi: 10.1080/07350015.2015.1054493. url:http://dx.doi.org/10.1080/07350015.2015.1054493.
Chow, Hwee Kwan and Taojun Xie (2016). “Are House PricesDriven by Capital Flows? Evidence from Singapore”. In: Journalof International Commerce, Economics and Policy 07.01,p. 1650006. doi: 10.1142/S179399331650006X. url:http://www.worldscientific.com/doi/abs/10.1142/
S179399331650006X.
References II
Hardle, Wolfgang Karl, Ya’acov Ritov, and Weining Wang (2015).“Tie the straps: Uniform bootstrap confidence bands forsemiparametric additive models”. In: Journal of MultivariateAnalysis 134, pp. 129–145. issn: 0047-259X. doi:10.1016/j.jmva.2014.11.003. url:http://www.sciencedirect.com/science/article/pii/
S0047259X14002395.Kuttner, Kenneth N. and Ilhyock Shim (2016). “Can non-interest
rate policies stabilize housing markets? Evidence from a panel of57 economies”. In: Journal of Financial Stability 26, pp. 31–44.issn: 1572-3089. doi: 10.1016/j.jfs.2016.07.014. url:https://www.sciencedirect.com/science/article/pii/
S1572308916300705.
References III
Phang, Sock-Yong (2001). “Housing Policy, Wealth Formation andthe Singapore Economy”. In: Housing Studies 16.4,pp. 443–459. issn: 0267-3037. doi:10.1080/02673030120066545. url:http://dx.doi.org/10.1080/02673030120066545.
Zhang, Longmei and Edda Zoli (2016). “Leaning against the wind:Macroprudential policy in Asia”. In: Journal of Asian Economics42, pp. 33–52. issn: 1049-0078. doi:10.1016/j.asieco.2015.11.001. url:http://www.sciencedirect.com/science/article/pii/
S1049007815001025.
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