Demand pressure and housing market expansion under supply restrictions: Madrid housing market

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. 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


19


20


21


22


23


24


25


26


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

Demand pressure and housing market expansion under supply restrictions: Madrid housing market

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