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

1
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

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
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
7
Introduction
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

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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 OSullivan, 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)
14
Description of driver population mobility
(Spanish and foreigners all arrivals)
15
Description of driver population mobility
(Spanish and foreigners all departures)
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
30
Description of drivers Supply reactions..
Enough??
31
Description of drivers Supply reactions..Spatial
segmentation
32
Description of drivers Supply reactions..Spatial
segmentation
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Model aggregate definition

• Qvdt fA(pop, y,f) t, B(Pvt, tit, trt,
  cut) (1)
• Qvot gPvt, Cmt, tit, Otrost (2)
• Pvt Gk(pop, y,f, ht) t, m( tit, trt,
  cut) (3)
• Where
• Qvdt is housing demand,
• pop is population,
• y, income
• f mortgages funds
• Pvt, housing prices
• tit, interest rate

• trt, transactions
• cut housing use cost
• Qvot Housing supply
• Cmt, construction costs
• Otrost other components, like land, developers
  market size, market power, administrative
  restrictions, housing policy, regional differences

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Model Demand equation

• (See Andrew and Meen, 2003, DiPascuale and
  Wheaton, 1996 and many others references)
• Phdt a1 a2 (pop)t a3 (ry)t a4 (h)t
  a5 (w)t a6 (uc)t a6 (ff)t et
• Identifying the role of different components of
  population dynamic
• Pop Dp IR OI

35
Model empirical exercise (ECM model)

• Dln(PHt) L0 X1 L1ln(PHt-1) l1 lnRYt-1
  l2 lnDPOBt-1
• l3 lnFFt-1 l4 lnInft-1 l5
  lnrit-1 l6 lnDHt-1
• d1 DlnPH,t-i d2 DlnRYt-i d3 D
  lnDPOBt-i
• d4 D lnFFt-i d5 DlnInft-i d6 Dlnrit-i
  d7 DlnDHt-i mt
• 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.
• X1 matrix of exogenous variables
• L0, L1, li, di 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
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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
• lt   lt   lt lt
• 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
  

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Model demand equation results
38
Model fundamentals effect Madrid
39
Model fundamental effects in all Spain
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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 , Gtk , pH)
• ea1 PH,ta2 Cmt a3 Cst a4 ita5 pt a6 Ht-1 a6
  hk Gtk a7 pHe a8 et
• Where
• - PH,t housing prices
• - Cmt materials costs
• - Cst cost of salaries
• .- it interest rates
• - pt cpi
• - Ht-1 existing stock
• - hk Gtk regional caracteristics matrix
• - pHe inflation expectations in housing
• - et random component
• a1..8 estimated parameters

44
Model Supply equation

• Ln (Vivin,t) a1 a2 ln PH,t a3 ln Cmt
• a4 ln Cst a5 ln it a6 ln pt a8
  hk Gtk nt
• Looking for the supply elasticity a2
• Method 2 stages regression 2SLS
• 1988-2007

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Model Supply Elasticity
46
Model Supply Elasticity and model explanation
capacity
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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