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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

Introduction

Description of the demand/supply drivers in housing market in Madrid

Model

Results

Conclusions

2

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

3

Introduction

EVOLUCIÓN DE LA CAPACIDAD DE COMPRA DE LOS SALARIOS EN MADRID

(En % de crecimiento anual)

40,00

35,00

30,00

25,00 inflación Madrid salarios REALES inflación en el precio de las viviendas. Madrid inflación en precio de las viviendas. España

20,00

15,00

10,00

5,00

0,00

9

199

0

199

1

199

2

199

3

199

4

199

5

199

6

199

7

199

8

199

9

200

0

200

1

200

2

200

3

200

4

200

5

200

6

-10,00

-15,00

Fte. INE y Ministerio de Vivienda

4

Introduction

With positive demand factors:

Strong increase on GDP, the lowest interest rates in the Spanish history

Enough flow of

Affordability

mortgages

gains

Strong growth on housing prices in other areas

Reasons for the less dynamism in Madrid housing prices?

Market factors?

Public intervention?

5

Introduction

180.000

160.000

140.000

120.000

CONCESIONES DE HIPOTECAS PARA FINCAS URBANAS. VIVIENDAS

(

En número de operaciones)

18,00

16,00

Madrid

TIPOS DE INTERÉS NOMINALES APLICADOS AL MERCADO HIPOTECARIO Y SU EVOLUCIÓN

(En % ) tipos de interés hipotecarios (eje izquierdo)

% anual de variación

100.000

14,00

80.000

12,00

60.000

10,00

40.000

8,00

6,00

20.000

-

19

62

19

64

19

66

19

68

19

70

19

72

19

74

19

76

4,00

Fte. INE.

19

80

19

82

19

84

19

86

1.

988

Fte. Banco de España

1.

990

1.

992

1.

994

1.

996

1.

998

2.

000

2.

002

2.

004

2.

006

2.

008

0,00

1 4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 4 3

19

87Q

19

87Q

19

88Q

19

89Q

19

90Q

19

90Q

19

91Q

19

92Q

19

93Q

19

93Q

19

94Q

19

95Q

19

96Q

19

96Q

19

97Q

19

98Q

19

99Q

19

99Q

20

00Q

20

01Q

20

02Q

20

02Q

20

03Q

20

04Q

20

05Q

20

05Q

20

06Q

20

07Q

2

10

0

-10

-20

-30

-40

50

40

30

20

6

Introduction

100,00

90,00

80,00

INDICADORES DE ACCESIBILIDAD EN MADRID

(En % )

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

70,00

60,00

R. Crédito/valor .. 80%

50,00

40,00

30,00

20,00

R. Accesibilidad... 30%

10,00

Fte. INE, MFOM, BDE y Elaboración propia

R. Solvencia.... 3-4 o menos

0,00

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

7

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...

8

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

9

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

10

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

11

Description of driver: total population

12

Description of driver: population mobility

(number of arrivals and departures)

ALTAS Y BAJAS RESIDENCIALES DESDE Y HACIA MADRID

(En número de individuos/mes)

45000

40000

35000

30000

25000

20000

15000

ALTAS

BAJAS

10000

5000

0

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

13

Description of driver: population mobility

(Spanish and foreigners –all arrivals)

ALTAS RESIDENCIALES EN MADRID

(En número de individuos/mes)

30000

ESPAÑOLES

EXTRANJEROS

25000

20000

15000

10000

5000

Fte. EVR, microdatos1988-

2006

0

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

14

Description of driver: population mobility

(Spanish and foreigners –all departures)

BAJAS DESDE MADRID A OTRAS LOCALIZACIONES

(En número de personas)

25000 españoles

Extranjeros

20000

Fte. INE. EVR microdatos

15000

10000

5000

0

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

15

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?

16

Description of drivers: Spatial demographic movements

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: Impact on prices?

27

Description of drivers: migration and prices

28

Description of drivers: Supply reactions

60000

50000

HOUSEBUILDING CYCLE IN MADRID

(num starts a year)

Source: Housing Ministery

PROTEGIDAS iniciadas iniciadas libres

40000

30000

20000

10000

0

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

29

Description of drivers: Supply reactions.. Enough??

VIVIENDAS A CONSTRUIR EN MADRID RELATIVAS AL TOTAL NACIONAL

En %

40,0

35,0

30,0

25,0

20,0

15,0

Madrid/España

10,0

5,0

Fte. Ministerio de Fomento

0,0

1990

Ene

1991

Ene

1992

Ene

1993

Ene

1994

Ene

1995

Ene

1996

Ene

1997

Ene

1998

Ene

1999

Ene

2000

Ene

2001

Ene

2002

Ene

2003

Ene

2004

Ene

2005

Ene

2006

Ene

2007

Ene

30

Description of drivers: Supply reactions

..

Spatial segmentation

31

Description of drivers: Supply reactions

..

Spatial segmentation

32

Model: aggregate definition

Qv d t

Qv o t

=

=

f

[

A

(pop, y,f) t

,

B

(Pv t

, ti t

, tr t

, cu t

)]

g

[Pv t

, Cm t

, ti t

, Otros t

]

Pv t

=

G

[ k

(pop, y,f, h t

) t

, m

( ti t

, tr t

, cu t

)]

Where:

Qv d t

pop

is housing demand, is population,

y

, income

f

mortgages funds

Pv t

, housing prices

ti t

, interest rate

(1)

(2)

(3

)

tr t

, transactions

cu t

housing use cost

Qv o t

Housing supply

Cm t

, construction costs

Otros t

other components, like land, developers market size, market power, restrictions, administrative housing policy, regional differences

33

Model: Demand equation

(See Andrew and Meen, 2003,

DiPascuale and

Wheaton, 1996 and many others references)

Ph d

(w) t

* t

= a

6 a

1

+

(uc) t a

+

2 a

(pop) t

6

(ff) t

+

+e t a

3

(ry) t

– a

4

(h) t

+ a

5

Identifying the role of different components of population dynamic

Pop =

D

p + IR + OI

34

Model: empirical exercise (ECM model)

D ln(P

Ht

)

+

+ d

4 d

= L

0

+ l

3

[

X lnFF

1

] + L t-1

+ l

4

1

1

D

D lnP lnFF

H,t-i t-i

+ d

+

5 d

2

[

ln(P

Ht-1

) + l

1 lnInf t-1

D lnRY

D lnInf t-i

+

+ l d t-i

6

5

+ lnRY t-1 lnri d

D lnri

3 t-i t-1

D

+

+

+ l

2 l

6 ln

D

POB d

7 ln

D

POB t-1+ ln

D

H t-i

D ln

D

H t-i t-1

+

] +

+ m t

P

Ht

RY t

Housing prices in the moment t real income

POB t

Existing population in the Madrid region.

FF t

Inf t ri t

Mortgage finance flows

Madrid inflation rate

Real interest rate.

H t

[

X

1

L

0,

Housing stock.

]

L matrix of exogenous variables

1, l i, d 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

35

HOUSING DEMAND MODELS FOR MADRID MARKET

Variable dependiente

Mod 1

Long term relationship

l t

D(LRPRV)

Mod. 2

1988-2007 l t

D(LRPRV)

Mod.3

l t

LRPRV(-1)

LRY(-1)

t-stud

LDPOB(-1)

t-stud

LEVRAL(-1)

t-stud

LEVRALEXT(-1)

t-stud

LDEVR(-1)

t-stud

LFF(-1)

t-stud

LINF(-1)

t-stud

LRI(-1)

t-stud

LDH(-1)

t-stud

C

1

-0,75

[-2,64182]

-0,13

[-2,33740]

-0,36

[-4,58717]

[3,20846]

-0,06

[-1,54677]

0,42

[7,10577]

1

-1,53

[-6,63478]

0,31

[4,81938]

-0,19

[-2,67231]

0,24

[-3,86900]

0,18

[4,76728]

0,04

[0,72742]

1

-1,99

[-4,27342]

0,42

[5,25553]

0,56

[3,47261]

-0,22

[-4,95648]

0,63

[6,39406]

-0,64

[-4,63171]

D(LRPRV)

Mod. 4 l t

1

1,22

[1,72930]

D(LRPRV)

-0,11

[-3,01786]

0,02

[0,14267]

-0,55

[3,66203]

0,049

[0,56357]

0,37

[4,21846]

-9,76

[-3,39184]

0,44

Convergence coefficient

t-stud

-0,23

[-6,16575]

-0,11

[-3,22361]

-0,05

[-3,68685]

-0,085

[-6,00824]

Model: demand equation results

36

Model: demand equation results

Model 1 model 2 model 3 model 4

Adj,Rsquare d

0,62

0,35

0,38

Sumsq, S,E,eq resids

0,01

0,04

0,04 uation

0,02

0,02

0,02

FLoglikel statistic

4,51 ihood

224,2

5,9 174,24

6,54 175,9

0,62 0,01 0,02 6,46 153,03

37

Model: fundamentals’ effect Madrid

RELACIÓN DE LARGO PLAZO ENTRE LOS FACTORES DETERMINANTES DE LA DEMANDA

0,30

Relación de largo plazo

0,20

Efecto de corrección de las variables de corto plazo

0,10

0,00

1 4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1 4 3 2 1

19

87Q

19

87Q

19

88Q

19

89Q

19

90Q

19

90Q

19

91Q

19

92Q

19

93Q

19

93Q

19

94Q

19

95Q

19

96Q

19

96Q

19

97Q

19

98Q

19

99Q

19

99Q

20

00Q

20

01Q

20

02Q

20

02Q

20

03Q

20

04Q

20

05Q

20

05Q

20

06Q

20

07Q

20

08Q

20

08Q

4

-0,10

-0,20

-0,30

-0,40

38

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

0,1

0

-0,1

-0,2

-0,3

-0,4

-0,5

0,4

0,3

0,2

Serie2

39

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

40

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....

41

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.

42

Model: Supply equation

Goodman, 2005, Meen, 2003, Malpezzi y Maclenan, 2000 y Glaeser y Gyourko, 2005

Q t s

= f(P

H,t

, C t

,H t-1

, G t k

, p

H

) =

=e a

1

P

H,t a2

Cm t a3

Cs t a4 i t a5 p t a

6

H t-1 a6

[ h k

G t k

] a7 p

H e a8 e t

Where:

- P

H,t housing prices

- Cm t

- Cs t

.- i t materials costs cost of salaries interest rates

- p t cpi

-

-

- H t-1 p h

H e k

- e t

G existing stock t k regional caracteristics matrix inflation expectations in housing random component a

1..8

estimated parameters

43

Model: Supply equation

Ln (Viv in,t

) = a

1 a

4 ln Cs t

+ a

5

+ a

2 ln i t ln P

H,t

+ a

6

+ a ln p t

3 ln Cm t

+ a

8 h k

+

G t k

+ n t

Looking for the supply elasticity a

2

Method: 2 stages regression 2SLS

1988-2007

44

Model: Supply Elasticity

EVOLUCIÓN DE LA ELASTICIDAD PRECIO DE LA OFERTA DE VIVIENDAS NUEVAS EN MADRID

2,00

1,00

0,80

0,60

0,40

1,80

1,60

1,40

1,20

Elasticidad precio de la oferta nueva

Largo plazo corto plazo

0,20

0,00

1988Q1

2007Q3

1989Q1

2007Q3

1990Q1

2007Q3

1991Q1

2007Q3

1992Q1

2007Q3

1993Q1

2007Q3

1994Q1

2007Q3

1995Q1

2007Q3

1995Q1

2007Q3

1998Q1

2007Q3

1999Q1

2007Q3

2000Q1

2007Q3

2001Q1

2007Q3

2003Q1

2007Q3

45

Model: Supply Elasticity and model

ELASTICIDAD Y SIGNIFICATIVIDAD DE LA OFERTA DE VIVIENDAS NUEVAS EN MADRID

0,70

0,60

0,50

0,40

0,30

0,20

0,10

0,00

0,00 0,20 0,40

1988Q1 2007Q3

1989Q1 2007Q3

1991Q1 2007Q3

1990Q1 2007Q3

1992Q1 2007Q3

1993Q1 2007Q3

1994Q1 2007Q3

Serie1

2001Q1 2007Q3

1999Q1 2007Q3

1998Q1 2007Q3

2000Q1 2007Q3

2003Q1 2007Q3

1995Q1 2007Q3

0,60 0,80 1,00 1,20

Elasticidad precio de la oferta

1,40 1,60 1,80 2,00

46

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

47

Conclusions

48

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