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Demand pressure and housing market expansion under supply restrictions: Madrid housing market. Paloma Taltavull de La Paz,Universidad de Alicante Federico de Pablo Martí, Universidad de Alcalá Carlos Manuel Fernández-Otheo, Universidad Complutense Julio Rodríguez, Universidad de Alcalá. Index. - PowerPoint PPT Presentation
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Demand pressure and housing market
expansion under supply restrictions: Madrid
housing market Paloma Taltavull de La Paz,Universidad de Alicante
Federico de Pablo Martí, Universidad de AlcaláCarlos Manuel Fernández-Otheo, Universidad Complutense
Julio Rodríguez, Universidad de Alcalá
2
Index
Introduction Description of the demand/supply drivers
in housing market in Madrid Model Results Conclusions
3
Introduction
Between 1997 and 1999, housing prices in Madrid falled in real terms without the existence of any economic crisis. At the same time than a strong rise in other
Spanish areas
4
Introduction EVOLUCIÓN DE LA CAPACIDAD DE COMPRA DE LOS SALARIOS EN MADRID
-15,00
-10,00
-5,00
0,00
5,00
10,00
15,00
20,00
25,00
30,00
35,00
40,00
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
inflación Madrid
salarios REALES
inflación en el precio de las viviendas. Madrid
inflación en precio de las viviendas. España
(En % de crecimiento anual)
Fte. INE y Ministerio de Vivienda
5
Introduction
With positive demand factors: Strong increase on GDP, the lowest interest rates in the Spanish history Enough flow of mortgages Affordability gains
Strong growth on housing prices in other areas Reasons for the less dynamism in Madrid housing prices?
Market factors? Public intervention?
6
Introduction CONCESIONES DE HIPOTECAS PARA FINCAS URBANAS. VIVIENDAS
-
20.000
40.000
60.000
80.000
100.000
120.000
140.000
160.000
180.000
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1.9
88
1.9
90
1.9
92
1.9
94
1.9
96
1.9
98
2.0
00
2.0
02
2.0
04
2.0
06
2.0
08
Madrid
(En número de operaciones)
Fte. INE.
TIPOS DE INTERÉS NOMINALES APLICADOS AL MERCADO HIPOTECARIO Y SU EVOLUCIÓN
0,00
2,00
4,00
6,00
8,00
10,00
12,00
14,00
16,00
18,00
1987
Q1
1987
Q4
1988
Q3
1989
Q2
1990
Q1
1990
Q4
1991
Q3
1992
Q2
1993
Q1
1993
Q4
1994
Q3
1995
Q2
1996
Q1
1996
Q4
1997
Q3
1998
Q2
1999
Q1
1999
Q4
2000
Q3
2001
Q2
2002
Q1
2002
Q4
2003
Q3
2004
Q2
2005
Q1
2005
Q4
2006
Q3
2007
Q2
-40
-30
-20
-10
0
10
20
30
40
50
tipos de interés hipotecarios (eje izquierdo)
% anual de variación
Fte. Banco de España
(En % )
7
Introduction
INDICADORES DE ACCESIBILIDAD EN MADRID
0,00
10,00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
100,00
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
R.accesib 30 años.. 30%
R.accesib 20 años.. 30%
R.accesib 25 años.. 30%
R. Crédito/Valor (LVR).. 80%
R. Solvencia (IVR)..3-4
(En % )
Fte. INE, MFOM, BDE y Elaboración propia
R. Crédito/valor .. 80%
R. Solvencia.... 3-4 o menos
R. Accesibilidad... 30%
8
Introduction
- Capital of Spain
- Mayor city: 6 millions P
- 17% of Spanish GDP
- Main based on service activities and high quality jobs
- Financial center
- Decission center for business...
9
Introduction Years later, we were ask to develop a
research project to explain why Madrid housing prices rise more than in the rest of Spain
In only five years everything changed in Madrid housing market
We are witness of what have happened during the period,
from an static situation to a very dynamic process
10
Introduction – Methodology followed 1st. Explore statistics trying to describe what
has happened Different methodologies: Time series, Panel data,
GIS, combined. 2nd. Inside a theoretical framework
Economic intuition to define the hipothesis 3rd. Contrast the hipothesis 4th. Need for spatial analysis
11
Description of drivers evolution Agreement about the fundamental reasons to
explain housing prices last decade Meen, 2001, Andrew and Meen, 2003, Case and Shiller,
2003, Case, Quigley and Shiller, 2005, In dense cities... Gibb,and O’Sullivan, 2002, Wheaton,
1998..
If income and financial growth process do not create restrictions Demografics? .... Where?
Spatial effects
12
Description of driver: total population
13
Description of driver: population mobility (number of arrivals and departures)
ALTAS Y BAJAS RESIDENCIALES DESDE Y HACIA MADRID
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
ALTAS
BAJAS
(En número de individuos/mes)
14
Description of driver: population mobility (Spanish and foreigners –all arrivals)
ALTAS RESIDENCIALES EN MADRID
0
5000
10000
15000
20000
25000
30000
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
ESPAÑOLES
EXTRANJEROS
(En número de individuos/mes)
Fte. EVR, microdatos1988-2006
15
Description of driver: population mobility (Spanish and foreigners –all departures)
BAJAS DESDE MADRID A OTRAS LOCALIZACIONES
0
5000
10000
15000
20000
25000
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
españoles
Extranjeros
(En número de personas)
Fte. INE. EVR microdatos
16
Description of drivers evolution These behaviour show a double shock in
basic demand of houses From foreigners From increase on internal mobility
Located from 2001 Increasing the size of the housing market Along the territory?
17
Description of drivers: Spatial demographic movements
18
Description of drivers: Spatial demographic movements
19
Description of drivers: Spatial demographic movements
20
Description of drivers: Spatial demographic movements
21
Description of drivers: Spatial demographic movements
22
Description of drivers: Spatial demographic movements
23
Description of drivers: Spatial demographic movements
24
Description of drivers: Spatial demographic movements
25
Description of drivers: Spatial demographic movements
26
Description of drivers: Spatial demographic movements
27
Description of drivers: Impact on prices?
28
Description of drivers: migration and prices
29
Description of drivers: Supply reactions
HOUSEBUILDING CYCLE IN MADRID
0
10000
20000
30000
40000
50000
60000
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
PROTEGIDAS iniciadas
iniciadas libres
(num starts a year)
Source: Housing Ministery
30
Description of drivers: Supply reactions.. Enough??
VIVIENDAS A CONSTRUIR EN MADRID RELATIVAS AL TOTAL NACIONAL
0,0
5,0
10,0
15,0
20,0
25,0
30,0
35,0
40,0
1990Ene
1991Ene
1992Ene
1993Ene
1994Ene
1995Ene
1996Ene
1997Ene
1998Ene
1999Ene
2000Ene
2001Ene
2002Ene
2003Ene
2004Ene
2005Ene
2006Ene
2007Ene
Madrid/España
Fte. Ministerio de Fomento
En %
31
Description of drivers: Supply
reactions..Spatial segmentation
32
Description of drivers: Supply
reactions..Spatial segmentation
33
Model: aggregate definition Qvd
t =[(pop, y,f) t, (Pvt, tit, trt, cut)] (1) Qvo
t = [Pvt, Cmt, tit, Otrost] (2)
Pvt = [(pop, y,f, ht) t, ( tit, trt, cut)](3)
Where: Qvd
t is housing demand, pop is population, y, income f mortgages funds Pvt, housing prices
tit, interest rate
trt, transactionscut housing use costQvo
t Housing supplyCmt, construction costs Otrost other components, like land, developers market size, market power, administrative restrictions, housing policy, regional differences
34
Model: Demand equation
(See Andrew and Meen, 2003, DiPascuale and Wheaton, 1996 and many others references)
Phd*t = 1 + 2 (pop)t + 3 (ry)t – 4 (h)t + 5 (w)t – 6 (uc)t + 6 (ff)t +et
Identifying the role of different components of population dynamic
Pop = p + IR + OI
35
Model: empirical exercise (ECM model) ln(PHt)0 X1[ln(PHt-1) +1 lnRYt-1 +2 lnPOBt-1+
+3 lnFFt-1 +4 lnInft-1 +5 lnrit-1 + 6 lnHt-1
+ 1 lnPH,t-i +2 lnRYt-i + 3 lnPOBt-i + + 4 lnFFt-i
+ 5lnInft-i +6lnrit-i
+ 7 lnHt-i t
PHt Housing prices in the moment t RYt real income POBt Existing population in the Madrid region. FFt Mortgage finance flows Inft Madrid inflation rate rit
Real interest rate.
Ht Housing stock. Xmatrix of exogenous variables 0, 1, i, i parameters to be estimated T time
Identifying the impact of different demographics component:
DPop is population in differences
EVRAL is household arrivals with house
EVRALEXT those coming from foreign countries
DEVR is arrivals in differences
36
Model: demand equation results
HOUSING DEMAND MODELS FOR MADRID MARKET Variable dependiente D(LRPRV) D(LRPRV) D(LRPRV) D(LRPRV) Mod 1 Mod. 2 Mod.3 Mod. 4 Long term relationship 1988-2007 t t t t LRPRV(-1) 1 1 1 1 LRY(-1) -0,75 -1,53 -1,99 1,22 t-stud [-2,64182] [-6,63478] [-4,27342] [1,72930] LDPOB(-1) -0,13 t-stud [-2,33740] LEVRAL(-1) 0,31 t-stud [4,81938] LEVRALEXT(-1) 0,42 t-stud [5,25553] LDEVR(-1) -0,11 t-stud [-3,01786] LFF(-1) -0,36 -0,19 0,56 0,02 t-stud [-4,58717] [-2,67231] [3,47261] [0,14267] LINF(-1) 0,24 -0,22 -0,55 0,44 t-stud [3,20846] [-3,86900] [-4,95648] [3,66203] LRI(-1) -0,06 0,18 0,63 0,049 t-stud [-1,54677] [4,76728] [6,39406] [0,56357] LDH(-1) 0,42 0,04 -0,64 0,37 t-stud [7,10577] [0,72742] [-4,63171] [4,21846] C -9,76 [-3,39184] Convergence coefficient -0,23 -0,11 -0,05 -0,085 t-stud [-6,16575] [-3,22361] [-3,68685] [-6,00824]
37
Model: demand equation results
Adj,R-squared
Sumsq,resids
S,E,equation
F-statistic
Loglikelihood
Model 1 0,62 0,01 0,02 4,51 224,2model 2 0,35 0,04 0,02 5,9 174,24model 3 0,38 0,04 0,02 6,54 175,9model 4 0,62 0,01 0,02 6,46 153,03
38
Model: fundamentals’ effect Madrid
RELACIÓN DE LARGO PLAZO ENTRE LOS FACTORES DETERMINANTES DE LA DEMANDA
-0,40
-0,30
-0,20
-0,10
0,00
0,10
0,20
0,30
1987
Q1
1987
Q4
1988
Q3
1989
Q2
1990
Q1
1990
Q4
1991
Q3
1992
Q2
1993
Q1
1993
Q4
1994
Q3
1995
Q2
1996
Q1
1996
Q4
1997
Q3
1998
Q2
1999
Q1
1999
Q4
2000
Q3
2001
Q2
2002
Q1
2002
Q4
2003
Q3
2004
Q2
2005
Q1
2005
Q4
2006
Q3
2007
Q2
2008
Q1
2008
Q4
Relación de largo plazo
Efecto de corrección delas variables de cortoplazo
39
Model: fundamental effects in all Spain
EFECTO DE LOS FACTORES FUNDAMENTALES EXPLICATIVOS DE LA VARIACIÓN DE LOS PRECIOS RESIDENCIALES EN ESPAÑA. 1988-2007
-0,5
-0,4
-0,3
-0,2
-0,1
0
0,1
0,2
0,3
0,4
0,5
1988
Q4
1989
Q2
1989
Q4
1990
Q3
1991
Q2
199
1Q4
199
2Q3
1993
Q2
1993
Q4
1994
Q3
199
5Q1
199
5Q3
199
6Q1
1996
Q3
1997
Q1
199
7Q3
1998
Q1
1998
Q3
199
9Q1
199
9Q3
200
0Q1
2000
Q3
2001
Q1
2001
Q3
200
2Q1
200
2Q3
2003
Q1
2003
Q3
2004
Q1
200
4Q3
2005
Q1
2005
Q3
200
6Q1
200
6Q3
200
7Q1
2007
Q3
Serie2
40
Model: demand equation results Long term components explain the price
evolution, in the general model (all population)
Negative impact of income, finance, changes on population and interest rates (increase on prices, reduces the demand) Short run impacts of income (2 lags) and changes on
population, finance and interest rates (3 lags) Positive impact on prices from inflation and available
stock Short run effects for stock (4 lags) and inflation (3 lags)
Strong dynamic relationships
41
Model: demand equation results Migration models:
Higher sensibility to changes on income Migration is positive correlated with changes on prices:
arrivals stress the prices (both cases, total and foreign) Total inmigration is positivelly correlated with interest rates
but not with existing stock, so, household movements could stress construction outside Madrid
Foreign inmigration is positive correlated with finance and interest rates, and negativelly with housing availability. These could suggest that their arrival depends of income but
also of the existent stock available, purchase capacity and the availability to have finance.
From 2000, banks in Spain start to give mortgages masivelly to inmigrans with permanent job....
42
Model: demand equation results Migration models (cont):
Positive correlation among stock and prices in presence of foreign movers suggest that there is a lack on supply for this demand
Negative correlation in the case of all movers (most are previous residents, spanish and foreigners) suggest that they could decide move to other market in the case of good condicions. This also suggest that higher prices or other factors
expulse this demand to other housing markets.
43
Model: Supply equation Goodman, 2005, Meen, 2003, Malpezzi y Maclenan, 2000 y Glaeser y Gyourko, 2005
Qts = f(PH,t, Ct ,Ht-1 , Gt
k , H) =
=e1 PH,t Cmt Cst it
pt 6 Ht-1 [k Gt
k ] Het
Where: - PH,t housing prices - Cmt materials costs - Cst cost of salaries .- it
interest rates - pt cpi - Ht-1 existing stock - k Gt
k regional caracteristics matrix - H
einflation expectations in housing t random component
1..8 estimated parameters
44
Model: Supply equation
Ln (Vivin,t1 + 2 ln PH,t +3 ln Cmt +
4 ln Cst + 5 ln it + 6 ln pt
+ 8 k Gtk
t
Looking for the supply elasticity 2 Method: 2 stages regression 2SLS 1988-2007
45
Model: Supply Elasticity
EVOLUCIÓN DE LA ELASTICIDAD PRECIO DE LA OFERTA DE VIVIENDAS NUEVAS EN MADRID
0,00
0,20
0,40
0,60
0,80
1,00
1,20
1,40
1,60
1,80
2,00
1988Q12007Q3
1989Q12007Q3
1990Q12007Q3
1991Q12007Q3
1992Q12007Q3
1993Q12007Q3
1994Q12007Q3
1995Q12007Q3
1995Q12007Q3
1998Q12007Q3
1999Q12007Q3
2000Q12007Q3
2001Q12007Q3
2003Q12007Q3
Elasticidad precio de la oferta nueva
corto plazo
Largo plazo
46
Model: Supply Elasticity and model explanation capacity Gráfico 28
ELASTICIDAD Y SIGNIFICATIVIDAD DE LA OFERTA DE VIVIENDAS NUEVAS EN MADRID
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,00 0,20 0,40 0,60 0,80 1,00 1,20 1,40 1,60 1,80 2,00
Elasticidad precio de la oferta
R2
aju
stad
o
Serie11988Q1 2007Q3
2003Q1 2007Q3
2001Q1 2007Q3
2000Q1 2007Q3
1999Q1 2007Q3
1998Q1 2007Q3
1995Q1 2007Q3
1994Q1 2007Q3
1993Q1 2007Q31992Q1 2007Q3
1991Q1 2007Q3
1990Q1 2007Q3
1989Q1 2007Q3
47
Model: Supply Elasticity results High supply elasticity which suggest rapid reactions
of the developers when prices rise Low capacity of explanation, which suggest that the
share of the market performing as a market is small Also suggest that there are other variables affecting the
new supply decissions process, The existence of supply restrictions in Madrid markets Lack on supply... Expulse demand
And increase prices in a market segment
48
Conclusions