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. 2 Index  Introduction  Description of the demand/supply drivers in housing market in Madrid  Model  Results  Conclusions

3. 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. 4 Introduction

5. 5  With positive demand factors:  Strong increase on GDP,  the lowest interest rates in the Spanish history  Enough flow of mortgagesmortgages  Affordability gainsgains  Strong growth on housing prices in other areas  Reasons for the less dynamism in Madrid housing prices?  Market factors?  Public intervention?

6. 6 Introduction

7. 7

8. 8 -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. 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. 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. 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. 12 Description of driver: total population

13. 13 Description of driver: population mobility (number of arrivals and departures)

14. 14 Description of driver: population mobility (Spanish and foreigners –all arrivals)

15. 15 Description of driver: population mobility (Spanish and foreigners –all departures)

16. 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. 17 Description of drivers: Spatial demographic movements

18. 18 Description of drivers: Spatial demographic movements

19. 19 Description of drivers: Spatial demographic movements

20. 20 Description of drivers: Spatial demographic movements

21. 21 Description of drivers: Spatial demographic movements

22. 22 Description of drivers: Spatial demographic movements

23. 23 Description of drivers: Spatial demographic movements

24. 24 Description of drivers: Spatial demographic movements

25. 25 Description of drivers: Spatial demographic movements

26. 26 Description of drivers: Spatial demographic movements

27. 27 Description of drivers: Impact on prices?

28. 28 Description of drivers: migration and prices

29. 29 Description of drivers: Supply reactions

30. 30 Description of drivers: Supply reactions.. Enough??

31. 31 Description of drivers: Supply reactions.. Spatial segmentation

32. 32 Description of drivers: Supply reactions.. Spatial segmentation

33. 33 Model: aggregate definition  Qv d t =  [  (pop, y,f) t,  (Pv t, ti t, tr t, cu t )](1)  Qv o t =  [Pv t, Cm t, ti t, Otros t ](2)  Pv t =  [  (pop, y,f, h t ) t,  ( ti t, tr t, cu t )](3 ) Where:  Qv d t is housing demand,  pop is population,  y, income  f mortgages funds  Pv t, housing prices  ti t, interest rate  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, administrative restrictions, housing policy, regional differences

34. 34 Model: Demand equation (See Andrew and Meen, 2003, DiPascuale and Wheaton, 1996 and many others references)  Ph d * t =  1 +  2 (pop) t +  3 (ry) t –  4 (h) t +  5 (w) t –  6 (uc) t +  6 (ff) t +e t  Identifying the role of different components of population dynamic  Pop =  p + IR + OI

35. 35 Model: empirical exercise (ECM model)   ln(P Ht )  0  X   1 [ln(P Ht-1 ) + 1 lnRY t-1 + 2 ln  POB t-1+ + 3 lnFF t-1 + 4  lnInf t-1 + 5 lnri t-1 + 6 ln  H t-1   +  1  lnP H,t-i +  2  lnRY t-i +  3  ln  POB t-i + +  4  lnFF t-i +  5  lnInf t-i +  6  lnri t-i +  7  ln  H t-i   t  P Ht Housing prices in the moment t  RY t real income  POB t Existing population in the Madrid region.  FF t Mortgage finance flows  Inf t Madrid inflation rate  ri t Real interest rate.  H t Housing stock.   X   matrix 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. 36 Model: demand equation results  HOUSING DEMAND MODELS FOR MADRID MARKET  Variable dependienteD(LRPRV)D(LRPRV)D(LRPRV)D(LRPRV)  Mod 1Mod. 2Mod.3Mod. 4  Long term relationship 1988-2007  t t t t   LRPRV(-1)1111  LRY(-1)-0,75-1,53-1,991,22  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)-0,36-0,190,560,02  t-stud[-4,58717][-2,67231][3,47261][0,14267]  LINF(-1)0,24-0,22-0,550,44  t-stud[3,20846][-3,86900][-4,95648][3,66203]  LRI(-1)-0,060,180,630,049  t-stud[-1,54677][4,76728][6,39406][0,56357]  LDH(-1)0,420,04-0,640,37  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. 37 Model: demand equation results

38. 38 Model: fundamentals’ effect Madrid

39. 39 Model: fundamental effects in all Spain

40. 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. 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. 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. 43 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,  H ) =  =e  1 P H,t  Cm t  Cs t  i t  p t  6 H t-1   [  k G t k ]    H e   t  Where:  - P H,t housing prices  - Cm t materials costs  - Cs t cost of salaries .- i t interest rates  - p t cpi  - H t-1  existing stock  -  k G t k regional caracteristics matrix  -  H e  inflation expectations in housing   t random component   1..8 estimated parameters

44. 44 Model: Supply equation  Ln (Viv in,t  1 +  2 ln P H,t +  3 ln Cm t +  4 ln Cs t +  5 ln i t +  6 ln p t +  8  k G t k  t  Looking for the supply elasticity  2  Method: 2 stages regression 2SLS  1988-2007

45. 45 Model: Supply Elasticity

46. 46 Model: Supply Elasticity and model explanation capacity

47. 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. 48 Conclusions