1. Housing supply and price reaction: A comparative approach between Spanish and Italian markets Laura Gabrielli Paloma Taltavull

2. Agenda Introduction: housing supply evolution and the role on the economies of Italy and Spain Cicles comparison Housing supply estimation Conclusions

3. Construction sector in Italian Economy GDP includes investment in constructions (residential, non residential and civil engineering works), transaction costs and rents and housing services In 2010 this sector represented the 10,24% of GPD, with a strong reduction in construction investments Rents and imputed rents are growing: that figure overcame investment in constructions in the last two years Istat; Conti economici annuall real value

4. Relavance of Constructions in GDP

5. Building permits  Building permits fell sharply towards the end of 2005 – 2006 (- 50%) going back below to the level at the beginning of the last market cycle  This is associated with the end of the cycles, the oversupply, the limited number of developing area, despite a constant grow of new families Istat, yearly data Istat and Banca d’Italia

6. Construction Cycle in Spain Volver

7. Type of dwellings  The average size of dwelling is increasing (104 sqm in comparison to 102 sqm of 2008), while the median value is constant at 90 sqm;  Half of the Italian families live in a dwelling of 60 – 100 sqm, while 14,5% and 18,9% live, respectively, in houses smaller than 60 sqm and bigger than 120 smq.  The average size of dwelling is positively correlated with the income: the families with a smaller income ( 45.000 €) live in a house with more than 145 sqm  On average, every person has 41 sqm (but that figure drops to 27 sqm for immigrants) showing a high overcrowding rate for those families  Spain is one of the Eu countries where the overcrowding rate among the population at-risk- of-poverty is below 6% (very low) Eurostat, 2010 Banca d’Italia, Indagini sui bilanci delle famiglie italiane

8. Destinándose la mayor parte a viviendas principales. Supply

9. Resultado: Stock de viviendas e intensidad de edificación en España, 1962-2010

10. Prices

11. Aim of this paper Describe the housing cycle and price dynamics in both countries Approach the supply elasticity for comparison purposes Controlling by region

12. Pre-view results Stronger housing cycle in Spain rather than in Italy – New supply – Price increase during 2004-2008 Similar responses from supply side – Both elastic responses to price signal – Stronger in Spain (2,5) than in Italy (0,91) for 1996-2010.

13. Fundamentals of housing supply Different experience (Meen, 2003, Barker review, 2003, Pryce, 1999, Malpezzi & Maclennan, 2001, Bramley, 2003) : – Long run elasticities in USA are >1 – Long run elasticities in Europe are < 1 Reasons are the difficulties to define and estimate the whole supply function (Hanusheck & Quigley, 1979), because: Starts are not the only source for housing supply The existing houses supplied as a source is difficult to be observed (Goodman et al, 2005) Supply function is local and specific to different regions (Glaesser, Gyurko & Sacks, 2005, DiPasquale, 1999) How to measure the supply? – By the stock (DiPasquale & Wheaton, 1994, Whitehead, 2004, Mayer & Somerville, 2000, Meen, 2001) – By new units arriving to the market or starts (Mason, 1977, Malpezzi & Maclennan, 2001, Meen et al, 1998; Bramley, 2003) Result… estimations of elasticities difficult to be compared

14. Principles of housing supply Housing supply theory elements – Supply could not be fixed (Meen, 2001) – It is changing on time (Pryce, 1999, Goodman, 2005) – Dependent of territorial factors, climate (Fergus, 1999) or the geographical situation (Goodman & Thibodeau, 1998). – Different market-control situations: Quasi-monopoly or monopolistic competition basis…land ownership, reduced number of building firms, land uses under control, restrictive permit system (Green & Malpezzi, 2003, Barker Review, 2003) – Control on the production process from developer, to adapt the supply to changes in the cycle (Coulson, 1999) – Others supply restrictions coming from its inputs (land available, materials, labour) – Public intervention… Housing Policy. (Murray, 1999, Malpezzi & Vandel, 2002, Whitehead, 2003). Asymmetric and disparates responses from the supply curve (Goodman, 2005, Pryce, 1999, Glaeser & Gyourko, 2005) VERY RELEVANT..

15. Literature  H =  (p, ccost, ir), Gs[land, mpower],  [Adm, HP] Where, ‘p = housing prices (new) ‘ccost= construction costs ‘it = financial costs Gs= Spatial differences Land= availability of land Mpover= development structure, market power Adm= effect of administrative processes HP= Housing policy impacts Gs and  are not observables - impose restrictions

16. Literature  H =  p +  ccost+  ir+  Under – Gs –  ‘  is the price elasticity of supply

17. 17 Relevance of housing supply… Prices Housing starts

18. 18 NEW HOUSING SUPPLY ‘MOVES’ when there is no restrictions Housing starts Housing prices  <1  =0  =1  >1

19. Empirical analysis Estimate housing supply elasticity of new units Market oriented focus: Prices are the signal… afecting starts Share of the market explained by the model Theres is no ‘intervention’ on the market as: – Market power – Escarcity of land – Administrative limits – Monopoly or oligopoly in development

20. Model Definition of new housing supply model according to Malpezzi & Maclenan, 2000 and Glaeser & Gyourko, 2005, Hanusheck & Quigley, 1979, DiPasquale, 1999, Malpezzi & Vandel, 2002, Goodman et al, 2005, Meen, 2001, 2003, Goodman & Thibodeau, 1998, Whitehead, 1974, Mayes, 1979, Bramley, 1996, 2003, Pryce, 1999, Swank et al. 2002, Mayo & Sheppard, 1991… (1)Q t s  = f(P H,t, C t,H t-1, G t k,  H ) = =  1 P H,t  Cm t  Cs t  i t  H t-1   [  k G t k ]    H e   t

21. Model Ln (Q t sn in,t  1 +  2 ln P H,t +  3 ln Cm t +  4 ln Cs t +  5 ln i t +  6 G t k  t with G t k  measured in full model (fix effects) and considering to be constant at regional level -  2 represents the new supply elasticities (1)> 1 …. Elastic (2)< 1 …. Inelastic (2)Adjust R 2 represents how the model explains the new supply, that is: (1)R 2 closer to 1 … the model capture the market performance (2)R 2 far from 1 … there are another drivers for new housing supply (construction decissions) other than the market ones.

22. Data Secondary source data: National institutes of statistics 1995-2010 (last available) Yearly data By region (14 and 17) Pool

23. HOUSE BUILDING PERMISSIONS

24. Prices

25. Methodology Pooled least squares Fixed effect estimator Non common root, adjusted by an AR(1) process at regional level White crossection standard errors and covarianze

26. Results

27. Fixed effects

28. Conclusions (1) Similar cycles with stronger house building in Spain than in Italy – Higher house price growth also in Spain but during 2004-2008 Similar market reacions Very market oriented (adjR 2 >0,93)

29. Conclusions (2) Labour costs has negative effects – Stronger in Spain Material costs increase prices – Stronger in Italy Interest rates are not stat significant in Spain – It does in Italy, small elasticity Elastic reactions of house-building to market signals… during 1997-2010 Close than 1 in Italy (  =0,911) Close to 3 in Spain (  =2,9)

30. THANKS FOR YOUR ATTENTION