Housing supply and price reaction: A
comparative approach between
Spanish and Italian markets
Laura Gabrielli
Paloma Taltavull
Agenda
• Introduction: housing supply evolution and
the role on the economies of Italy and Spain
• Cicles comparison
• Housing supply estimation
• Conclusions
Construction sector in Italian Economy
• GDP includes investment in constructions (residential, non residential and
civil engineering works), transaction costs and rents and housing services
• In 2010 this sector represented the 10,24% of GPD, with a strong reduction
in construction investments
• Rents and imputed rents are growing: that figure overcame investment in
constructions in the last two years
Istat; Conti economici annuall real value
Relavance of Constructions in GDP
Años
s/VAB
real
s/FBCF
Población
ocupada
s/total
Productividad s/total
(pib por ocupado)
Edificaci
ón.
1985
1995
2000
2005
2006
2007
2008
2009
2010
6,7
8,1
8,3
9,5
9,7
9,6
9,2
9.0
8.4
61,7
7,1
100,2
63,5
57,4
58.4
58.5
58,1
57,5
59,5
56,7
8,9
10,9
12,0
12,3
12,6
11,7
9.4
8.4
73,92
67,72
81,30
84,13
81,28
89,09
102,43
104,52
En % del total de
producción del sector
construcción a
Obra
Edific.
Resto
civil
construcc residencial
ión
FBKF en % del PIB
Sector construcción
11,29
(e)
4,4
6,1
8,9
9,3
9,2
8,0
5,8
4,7
7,9
7,2
8,3
8,6
8,6
8,5
8,5
8,1
32,7 b
40,9 b
34,8
40,7
62,7
52,1(c)
51,7(c)
48,5(c)
40,6(c)
36,7(c)
43,4
36,1
37,3
47,9(d)
48,0(d)
51,5(d)
59,3(d)
63,2(d)
Building permits


Building permits fell
sharply towards the end
of 2005 – 2006 (- 50%)
going back below to the
level at the beginning of
the last market cycle
This is associated with
the end of the cycles,
the oversupply, the
limited number of
developing area,
despite a constant
grow of new families
Istat, yearly data
Istat and Banca d’Italia
Construction Cycle in Spain
EVOLUCIÓN DEL CICLO DE EDIFICACIÓN EN ESPAÑA
90,00
Fte. Ministerio de Fomento
(En número de viviendas iniciadas por mes)
80,00
VISADAS
70,00
TERMINADAS
INICIADAS
60,00
50,00
40,00
30,00
20,00
10,00
0,00
Volver
Type of dwellings





The average size of dwelling is increasing (104 sqm in comparison to 102 sqm of 2008), while
the median value is constant at 90 sqm;
Half of the Italian families live in a dwelling of 60 – 100 sqm, while 14,5% and 18,9% live,
respectively, in houses smaller than 60 sqm and bigger than 120 smq.
The average size of dwelling is positively correlated with the income: the families with a
smaller income (< 20.000 €/year) live in a 70 sqm flat, while the families with a higher income
(>45.000 €) live in a house with more than 145 sqm
On average, every person has 41 sqm (but that figure drops to 27 sqm for immigrants)
showing a high overcrowding rate for those families
Spain is one of the Eu countries where the overcrowding rate among the population at-riskof-poverty is below 6% (very low)
Banca d’Italia, Indagini sui bilanci delle famiglie italiane
Eurostat, 2010
Supply
• Destinándose la mayor parte a viviendas principales.
STOCK DE VIVIENDAS EN ESPAÑA
(En número de viviendas y personas)
5,8%
41000000
Fte. INE
3,17%
36000000
31000000
Censo 2001
Censo 1991
26000000
21000000
Censo 1981
20,90%
16,6%
16000000
21,6%
12,5%
3,3%
11000000
13,7%
6000000
17%
54%
1000000
-4000000
NUM. TOTAL
PRINCIPALES
SECUNDARIAS
desocupadas (vacantes)
Población
Resultado: Stock de viviendas e intensidad de
edificación en España,
1962-2010
Período
Stock de viviendas a
Millones de unidades
(al final del período)
1962-1967
1968-1971
1972-1978
1979-1984
1985-1991
1992-1993
1994-1999
2000-2004
2005-2007
2008-2010*
9,89
11,39
14,20
15,59
17,2
17,7
19,7
22,4
24,3
25,5
Tasa de
variación
en % (bruta en el
período)
21,3
15,2
24,7
9,8
9,0
3,0
11,3
13,4
8,5
5,2
Intensidad en edificación
Viviendas iniciadas Iniciadas/stock
por año b
en %
Media del
período
(en miles)
269,9
318,6
351,2
223,1
240,1
203,9
344,0
581,9
665,3
198,9
2,73
2,80
2,47
1,43
1,40
1,15
1,74
2,60
2,74
0,78
Precios
residenciales
Variación anual
acc. nominal en
%c
15,2
1,0
23,1
2,0
30,2
-0,8
4,6
13,6
7,4
-4,6
20,00
Prices
HOUSE PRICES DYNAMICS IN ITALY AND SPAIN
% annual change
15,00
ITALY
SPAIN
10,00
5,00
0,00
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
-5,00
-10,00
Aim of this paper
• Describe the housing cycle and price dynamics
in both countries
• Approach the supply elasticity for comparison
purposes
• Controlling by region
Pre-view results
• Stronger housing cycle in Spain rather than in
Italy
– New supply
– Price increase during 2004-2008
• Similar responses from supply side
– Both elastic responses to price signal
– Stronger in Spain (2,5) than in Italy (0,91) for
1996-2010.
Fundamentals of housing supply
• Different experience (Meen, 2003, Barker review, 2003, Pryce, 1999,
Malpezzi & Maclennan, 2001, Bramley, 2003) :
– Long run elasticities in USA are >1
– Long run elasticities in Europe are < 1
• Reasons are the difficulties to define and estimate the whole supply
function (Hanusheck & Quigley, 1979), because:
• Starts are not the only source for housing supply
• The existing houses supplied as a source is difficult to be observed
(Goodman et al, 2005)
• Supply function is local and specific to different regions (Glaesser,
Gyurko & Sacks, 2005, DiPasquale, 1999)
• How to measure the supply?
– By the stock (DiPasquale & Wheaton, 1994, Whitehead, 2004, Mayer
& Somerville, 2000, Meen, 2001)
– By new units arriving to the market or starts (Mason, 1977, Malpezzi
& Maclennan, 2001, Meen et al, 1998; Bramley, 2003)
• Result… estimations of elasticities
difficult to be compared
Principles of housing supply
• Housing supply theory elements
– Supply could not be fixed (Meen, 2001)
– It is changing on time (Pryce, 1999, Goodman, 2005)
– Dependent of territorial factors, climate (Fergus, 1999) or the
geographical situation (Goodman & Thibodeau, 1998).
– Different market-control situations: Quasi-monopoly or monopolistic
competition basis…land ownership, reduced number of building firms,
land uses under control, restrictive permit system (Green & Malpezzi,
2003, Barker Review, 2003)
– Control on the production process from developer, to adapt the supply to
changes in the cycle (Coulson, 1999)
– Others supply restrictions coming from its inputs (land available,
materials, labour)
– Public intervention… Housing Policy. (Murray, 1999, Malpezzi & Vandel,
2002, Whitehead, 2003).
Asymmetric and disparates responses from the supply curve (Goodman,
2005, Pryce, 1999, Glaeser & Gyourko, 2005)
VERY RELEVANT..
Literature
 H = f(p, ccost, ir), Gs[land, mpower], G[Adm, HP]
Where,
‘p = housing prices (new)
‘ccost= construction costs
‘it = financial costs
Gs= Spatial differences
Land= availability of land
Mpover= development structure, market power
Adm= effect of administrative processes
HP= Housing policy impacts
Gs and G are not observables
- impose restrictions
Literature
• H = a + bp + g ccost+ dir+ m
• Under
– Gs
– G
 b is the price elasticity of supply
Relevance of housing supply…
Prices
Housing starts
17
NEW HOUSING SUPPLY ‘MOVES’ when there is no
restrictions
Housing
prices
e=0
e<1
e=1
e>1
Housing starts
18
Empirical analysis
• Estimate housing supply elasticity of new
units
• Market oriented focus:
• Prices are the signal… afecting starts
• Share of the market explained by the model
• Theres is no ‘intervention’ on the market as:
–
–
–
–
Market power
Escarcity of land
Administrative limits
Monopoly or oligopoly in development
•
Model
Definition of new housing supply model
according to Malpezzi & Maclenan, 2000 and Glaeser &
Gyourko, 2005, Hanusheck & Quigley, 1979, DiPasquale, 1999, Malpezzi
& Vandel, 2002, Goodman et al, 2005, Meen, 2001, 2003, Goodman &
Thibodeau, 1998, Whitehead, 1974, Mayes, 1979, Bramley, 1996, 2003,
Pryce, 1999, Swank et al. 2002, Mayo & Sheppard, 1991…
(1) Qts = f(PH,t, Ct ,Ht-1 , Gtk , pH) =
•
= a1 PH,ta2 Cmt a3 Cst a4 ita5 Ht-1 a6 [hk Gtk ] a7 pHe a8 et
Ln (Qtsn in,t) = a1 + a2
Gtk + mt
Model
ln P +a ln Cm
H,t
3
t
+ a4 ln Cst + a5 ln it + a6
with Gtk measured in full model (fix effects) and
considering to be constant at regional level
-
(1) a2 represents the new supply elasticities
(1) > 1 …. Elastic
(2) < 1 …. Inelastic
(2) Adjust R2 represents how the model explains the new
supply, that is:
(1) R2 closer to 1 … the model capture the market performance
(2) R2 far from 1 … there are another drivers for new housing supply
(construction decissions) other than the market ones.
Data
• Secondary source data: National institutes of
statistics
• 1995-2010 (last available)
• Yearly data
• By region (14 and 17)
• Pool
HOUSE BUILDING PERMISSIONS
60,000
160,000
ITALY
140,000
50,000
120,000
40,000
100,000
30,000
80,000
60,000
20,000
40,000
10,000
20,000
0
1996
1998
2000
HAB
HCAM
HLAZ
HMARC
HPUGL
HTOSC
HVALLE
2002
2004
HBAS
HEMIROM
HLIGU
HMOL
HSARD
HTREN
HVEN
2006
2008
2010
HCAL
HFRIUVGI
HLOMB
HPIEM
HSICI
HUMBR
0
2012 1996
1998
2000
2002
HAND
HBAL
HCATAL
HCVAL
HMAD
HPVASC
2004
HARA
HCANA
HCLE
HEXTR
HMUR
HRIOJ
2006
2008
HAST
HCANT
HCMAN
HGAL
HNAV
2010
2012
Prices
280
3,500
ITALY
SPAIN
3,000
240
2,500
200
2,000
1,500
160
1,000
120
500
80
1996
1998
2000
RPHAB
RPHCAM
RPHLAZ
RPHMARC
RPHPUGL
RPHTOSC
RPHVALLE
2002
2004
2006
RPHBAS
RPHEMIROM
RPHLIGU
RPHMOL
RPHSARD
RPHTREN
RPHVEN
2008
2010
RPHCAL
RPHFRIUVGI
RPHLOMB
RPHPIEM
RPHSICI
RPHUMBR
0
2012 1996
1998
2000
RPHAND
RPHBAL
RPHCATAL
RPHCVAL
RPHMAD
RPHPVASC
2002
2004
2006
RPHARA
RPHCANA
RPHCLE
RPHEXTR
RPHMUR
RPHRIOJ
2008
2010
RPHAST
RPHCANT
RPHCMAN
RPHGAL
RPHNAV
2012
Methodology
• Pooled least squares
• Fixed effect estimator
• Non common root, adjusted by an AR(1)
process at regional level
• White crossection standard errors and covarianze
Results
Dependent Variable: LOG(H?)
Method: Pooled Least Squares
Sample (adjusted): 1996 2010
Cross-sections included: 20
(Italy), 17 (Spain)
Total pool (balanced) observations:
280/252
ITALY
Variable
SPAIN
tCoefficient Statistic
Prob.
Coefficient
t-Statistic
Prob.
C
15,08
12,78
0,00
25,12
12,99
0,00
LOG(CL)
-5,67
-11,17
0,00
-9,37
-10,00
0,00
LOG(CM)
3,16
5,66
0,00
1,30
1,45
0,15
LOG(IR)
0,09
2,95
0,00
0,05
0,33
0,74
LOG(RPH?)
0,91
3,88
0,00
2,94
12,11
0,00
TEST
R-squared
0,98
0,94
Adjusted R-squared
0,97
0,93
S.E. of regression
0,20
0,28
Sum squared resid
9,45
17,34
77,10
-20,38
221,44***
91,98***
1,77
1,85
Log likelihood
F-statistic
DWt
Fixed Effects
(Cross)
ITALY
Fixed
effects
SPAIN
AB--C
-0,24
AND--C
1,79
BAS--C
-1,53
ARA--C
-0,43
CAL--C
-0,16
AST--C
-0,47
CAM--C
0,36
BAL--C
-1,52
EMIROM--C
0,97
CANA--C
-0,10
FRIUVGI--C
-0,18
CANT--C
-1,28
LAZ--C
1,25
CMAN--C
0,46
LIGU--C
-1,01
CLE--C
0,43
LOMB--C
1,97
CATAL--C
1,45
MARC--C
-0,19
CVAL--C
1,40
MOL--C
-1,84
EXTR--C
0,83
PIEM--C
0,85
GAL--C
0,97
PUGL--C
0,85
MAD--C
-0,43
SARD--C
0,00
MUR--C
0,60
SICI--C
0,58
NAV--C
-0,83
TOSC--C
0,46
PVASC--C
-2,37
TREN--C
-0,40
RIOJ--C
-0,90
UMBR--C
-0,57
VALLE--C
-2,67
VEN--C
0,98
Conclusions (1)
• Similar cycles with stronger house building in
Spain than in Italy
– Higher house price growth also in Spain but during
2004-2008
• Similar market reacions
• Very market oriented (adjR2>0,93)
Conclusions (2)
• Labour costs has negative effects
– Stronger in Spain
• Material costs increase prices
– Stronger in Italy
• Interest rates are not stat significant in Spain
– It does in Italy, small elasticity
• Elastic reactions of house-building to market
signals… during 1997-2010
• Close than 1 in Italy (e=0,911)
• Close to 3 in Spain (e=2,9)
•THANKS FOR YOUR
ATTENTION