Gallery Guide: Seventeenth-Century Italian, French, and Spanish Art
Housing supply and price reaction : A comparative approach between Spanish and Italian markets
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Transcript of Housing supply and price reaction : A comparative approach between Spanish and Italian markets
Housing supply and price reaction: A comparative approach between Spanish and
Italian markets
Laura GabrielliPaloma 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 yearsIstat; Conti economici annuall real value
Relavance of Constructions in GDPAños s/VAB
real s/FBCF Población
ocupada s/total
Productividad s/total (pib por ocupado)
FBKF en % del PIB Sector construcción
En % del total de producción del sector construcción a
Edificación.
Resto construcción
Edific. residencial
Obra civil
1985 6,7 61,7 7,1 100,2 11,29 (e)
32,7 b 40,9 b
1995 8,1 63,5 8,9 73,92 4,4 7,9 34,8 43,4 2000 8,3 57,4 10,9 67,72 6,1 7,2 40,7 36,1 2005 9,5 58.4 12,0 81,30 8,9 8,3 62,7 37,3 2006 9,7 58.5 12,3 84,13 9,3 8,6 52,1(c) 47,9(d) 2007 9,6 58,1 12,6 81,28 9,2 8,6 51,7(c) 48,0(d) 2008 9,2 57,5 11,7 89,09 8,0 8,5 48,5(c) 51,5(d) 2009 9.0 59,5 9.4 102,43 5,8 8,5 40,6(c) 59,3(d) 2010 8.4 56,7 8.4 104,52 4,7 8,1 36,7(c) 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 Volver
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EVOLUCIÓN DEL CICLO DE EDIFICACIÓN EN ESPAÑA
VISADAS
TERMINADAS
INICIADAS
(En número de viviendas iniciadas por mes)Fte. Ministerio de Fomento
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-risk-of-poverty is below 6% (very low)
Eurostat, 2010Banca d’Italia, Indagini sui bilanci delle famiglie italiane
• Destinándose la mayor parte a viviendas principales. Supply
-4000000
1000000
6000000
11000000
16000000
21000000
26000000
31000000
36000000
41000000
NUM. TOTAL PRINCIPALES SECUNDARIAS desocupadas (vacantes) Población
STOCK DE VIVIENDAS EN ESPAÑA
Censo 2001Censo 1991Censo 1981
(En número de viviendas y personas)
Fte. INE
20,90%
13,7% 17%
5,8%
21,6%
3,17%
3,3%
54%
12,5%
16,6%
Resultado: Stock de viviendas e intensidad de edificación en España, 1962-2010
Período Stock de viviendas a Intensidad en edificación Viviendas iniciadas
por año b Iniciadas/stock en %
Precios residenciales
Millones de unidades (al final del período)
Tasa de variación en % (bruta en el período)
Media del período (en miles)
Variación anual acc. nominal en % c
1962-1967 9,89 21,3 269,9 2,73 15,2 1968-1971 11,39 15,2 318,6 2,80 1,0 1972-1978 14,20 24,7 351,2 2,47 23,1 1979-1984 15,59 9,8 223,1 1,43 2,0 1985-1991 17,2 9,0 240,1 1,40 30,2 1992-1993 17,7 3,0 203,9 1,15 -0,8 1994-1999 19,7 11,3 344,0 1,74 4,6 2000-2004 22,4 13,4 581,9 2,60 13,6 2005-2007 24,3 8,5 665,3 2,74 7,4 2008-2010* 25,5 5,2 198,9 0,78 -4,6
Prices
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10,00
15,00
20,00
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
HOUSE PRICES DYNAMICS IN ITALY AND SPAIN% annual change
ITALY
SPAIN
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..
LiteratureD H = f(p, ccost, ir), Gs[land, mpower], G[Adm, HP]Where,
‘p = housing prices (new)‘ccost= construction costs
‘it = financial costsGs= Spatial differencesLand= availability of landMpover= development structure, market powerAdm= effect of administrative processesHP= Housing policy impacts
Gs and G are not observables- impose restrictions
Literature• DH = a + bp + g ccost+ dir+ m
• Under– Gs– G‘ b is the price elasticity of supply
17
Relevance of housing supply…Prices
Housing starts
18
NEW HOUSING SUPPLY ‘MOVES’ when there is no restrictions
Housing starts
Housing prices
e<1e=0
e=1
e>1
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 , Gt
k , pH) =
• = a1 PH,ta2 Cmt a3 Cst a4 it
a5 Ht-1 a6 [hk Gtk ] a7 pH
e a8 et
Model Ln (Qt
sn in,t) = a1 + a2 ln PH,t +a3 ln Cmt + a4 ln Cst + a5 ln it
+ a6 Gt
k + mt
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
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
1996 1998 2000 2002 2004 2006 2008 2010 2012
HAND HARA HASTHBAL HCANA HCANTHCATAL HCLE HCMANHCVAL HEXTR HGALHMAD HMUR HNAVHPVASC HRIOJ
0
10,000
20,000
30,000
40,000
50,000
60,000
1996 1998 2000 2002 2004 2006 2008 2010 2012
HAB HBAS HCALHCAM HEMIROM HFRIUVGIHLAZ HLIGU HLOMBHMARC HMOL HPIEMHPUGL HSARD HSICIHTOSC HTREN HUMBRHVALLE HVEN
ITALY
Prices
80
120
160
200
240
280
1996 1998 2000 2002 2004 2006 2008 2010 2012
RPHAB RPHBAS RPHCALRPHCAM RPHEMIROM RPHFRIUVGIRPHLAZ RPHLIGU RPHLOMBRPHMARC RPHMOL RPHPIEMRPHPUGL RPHSARD RPHSICIRPHTOSC RPHTREN RPHUMBRRPHVALLE RPHVEN
ITALY
0
500
1,000
1,500
2,000
2,500
3,000
3,500
1996 1998 2000 2002 2004 2006 2008 2010 2012
RPHAND RPHARA RPHASTRPHBAL RPHCANA RPHCANTRPHCATAL RPHCLE RPHCMANRPHCVAL RPHEXTR RPHGALRPHMAD RPHMUR RPHNAVRPHPVASC RPHRIOJ
SPAIN
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
ResultsDependent 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
SPAIN
Variable Coefficient t-
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
Log likelihood 77,10
-20,38
F-statistic 221,44***
91,98***
DWt 1,77
1,85
Fixed effects
Fixed Effects (Cross) ITALY
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