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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 Alcalá Carlos Manuel Fernández-Otheo, Universidad Complutense Julio Rodríguez, Universidad de Alcalá. Index.

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

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  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

  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)

  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

  33. Model: aggregate definition • Qvdt = f[A(pop, y,f) t, B(Pvt, tit, trt, cut)] (1) • Qvot = g[Pvt, Cmt, tit, Otrost] (2) • Pvt = G[k(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

  34. Model: Demand equation (See Andrew and Meen, 2003, DiPascuale and Wheaton, 1996 and many others references) • Phd*t = 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] + L1[ln(PHt-1) +l1 lnRYt-1 +l2 lnDPOBt-1+ +l3 lnFFt-1 +l4 lnInft-1 +l5 lnrit-1+l6 lnDHt-1] + + d1DlnPH,t-i +d2DlnRYt-i + d3D lnDPOBt-i + + d4 D lnFFt-i+d5 DlnInft-i+ d6 Dlnrit-i+ d7DlnDHt-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

  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 • 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] • 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

  38. Model: fundamentals’ effect Madrid

  39. Model: fundamental effects in all Spain

  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 , Gtk , pH) = • =ea1 PH,ta2 Cmta3 Csta4 ita5 pta6 Ht-1a6 [hk Gtk ]a7pHe 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 • - pHeinflation 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 + a8hk Gtk + nt • Looking for the supply elasticity a2 • Method: 2 stages regression 2SLS • 1988-2007

  45. Model: Supply Elasticity

  46. Model: Supply Elasticity and model explanation capacity

  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

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