Construction Activity and Real Estate Market Trend: Evidence from Albania

1
Construction Activity and Real Estate Market
Trend Evidence from Albania

• Delina Ibrahimaj Gianluca Mattarocci
•  
• e-mail dibrahimaj_at_bankofalbania.org

2
Index

• Introduction
• Literature review
• Sample
• Methodology
• Results
• Conclusions
• References

3
Introduction

• The construction sector produce assets that are
  offered to both individual and firms in the
  private sector and to the public sector
• GDP is one of the key drivers of the Construction
  sector output
• The role of the GDP in explaining the
  construction sector dynamics is higher for not
  developed economies while for other stage of
  development the role could be lower or even
  negative (Crosthwaite, 2000).
• Production in Construction is defined on the
  basis of public demand and new orders from the
  private sector

4
Introduction

• The volume of orders is strictly affected by the
  real estate market dynamics
• More profitable conditions for the real estate
  investment are expected to lead to an increase of
  the construction sector output
• The paper analyses this gap introducing variables
  on the efficiency and profitability of the real
  estate sector and evaluating their contribution
  to the construction industry.
• The relationship between the construction sector
  and the main macroeconomic determinants. (GDP)
  ,(INT), (CRE), (RPI), (HPI) (No LICENSES), (REM)
  and Tobin Q on the construction sector production
  in the economy (CONS).

5
Introduction

• Understand the direction of the relationships by
  testing the variables through the granger
  causality tests.
• Analyse the effect of these macroeconomic
  variables in the construction sector.
• Trace out the time path of the effect in the
  construction activity of structural shocks on the
  macroeconomic variables.
• Than we will try to understand how much of a
  change in a variable is due to its own shock and
  how much due to shocks to other variables.

6
Literature Review

• The size of the construction sector is
  significantly affected by the overall trend of
  the economy due to the fact that the increase of
  wealth available for citizen will increase the
  demand for real estate investments
• Crosthwaite (2000) There exists a non linear
  relationship between Construction and GDP for
  different levels of development ( S shape)
• Bon (1992) The different relevance of the
  construction sector for different level of
  economic development is linked to the demand of
  housing and real estate
• Bon and Pietroforte (1993) The growth of the
  construction sector can be affected differently
  by different economic sector. For both new and
  restructured building the higher demand is
  related to the retail and the wholesale industry
  .
• Lean (2001) The degree to which different
  sectors of GDP affect the construction sector
  depend on the time horizon. (ST services, LT
  Manufacturing, Transportation and Communications)

7
Literature Review

• Ball (1965) The construction sector dynamics can
  be explained on the basis of the differences
  between current prices and construction costs
  (Tobin Q market prices/construction prices)
• Barriot and Yang (2002) In the ST if the ratio
  is gt1 the construction products supply will
  increase.
• Dorward et al (1998) In the LT supply is not
  affected by building costs.
• McGough and Tsolacos (1999) Rent market dynamics
  affect the supply of Real Estate for investment
  purposes.
• Ng, Fan and Wong (2011) The change in the
  lending rate can affect the development of the
  construction sector in the future
• Ozcelebi (2011) Independently with respect to
  the cost of lending an excess supply of credit
  will have a positive effect on the construction
  sector development
• Osili (2004) In less developed countries the
  construction demand is affected by the amount of
  remittances

8
Sample

• Empirical analysis will be computed by using data
  from the first quarter of 2004 to the second
  quarter of 2013.
• All variables are expressed as natural logarithms
  in order to analyse their elasticity (except for
  interest rate).
• Unit root test using (ADF) wore performed. ADF
  test suggests that the indicators are integrated
  in different orders.
• Stationary data should be used in econometric
  analysis because non stationary data may cause
  spurious regressions.
• Literature argue against differencing time
  series (even if they may contain unit
  roots)because differencing throws away
  cointegrating relationships
• Enders (1995) explains that a VAR in differences
  may cause loss of information on the co-movement
  among the variables.
• Following the approach proposed by Ozcelebi
  (2011) we will estimate the VAR model in levels.
  

9
Sample

• CONS is the production in construction series
  published quarterly by the Albanian Statistical
  Institute (INSTAT) ( Real, SA)
• GDP is the real gross domestic product (without
  construction sector) that is published quarterly
  by INSTAT. ( Real, SA)
• INT is the weighted average interest rate on
  domestic currency deposits published monthly by
  the Bank of Albania. (Quarterly average)
• CRE is the credit to individuals and it refers to
  the stock of the credit published monthly by the
  Bank of Albania. (Last month of quarter value)
• RPI and HPI are the Rent and House Price Index
  published on a quarterly basis by the Bank of
  Albania.
• No_Lic is the number of approved building
  licenses published by INSTAT on a quarterly
  basis.
• REM is the Remittances published by the Bank of
  Albania on quarterly basis.
• T_Q is the Tobin Q computed as the ratio of House
  Price Index over the Cost of Building Materials
  Index calculated by the authors.

10
Methodology

• The causal relationship analysis is computed
  considering the bi-lateral causality between each
  explaining variable and the production in the
  construction sector.
• The causality is tested using the Granger
  causality approach (Granger, 1969), in formulas
• If ßi2 is statistically significant while is not
  significant ?i2, the Factor used in the formula
  granger cause the Construction sector.
• If both ßi2 and ?i2 are statistically
  significant, the granger causality test does not
  allow to identify the sign and the strength of
  the causal relationship because both factors
  influence each other.

11
Methodology

• In order to consider the interrelationship among
  explaining variables, we perform a Vector
  Autoregression Analysis treating every endogenous
  variable in the system as a function of the
  lagged values and uncorrelated with all the other
  variables.
• A shock to the i-th variable not only directly
  affects the i-th variable but it is also
  transmitted to all of the other endogenous
  variables through dynamic lag structure of the
  VAR.
• We use impulse response functions for tracing the
  effects of a shock to one endogenous variable on
  the construction sector
• We use also the Variance decomposition (FVED) in
  order to measure the different degree of
  importance of different variable in influencing
  the construction sector activity in a time
  horizon of 10 quarters.

12
Results Granger Causality

• Results of Granger Causality
• The (Granger) causes of the Construction sector
  dynamics are the credit to individuals (demand
  side theory), the amount of remittances (
  increase in investments).
• The construction sector granger cause the
  interest rate on deposits, the number of
  licenses, the Tobin Q ( economies of scale) and
  the Rent Price Index.

13
Results VAR Impulse response analysis
14
Results VAR Impulse Response Functions

• Shock to variables like gross domestic product
  (GDP), interest rates (INT), real estate prices
  (HPI) and efficiency (TOBIN_Q) do not show a
  clear reaction to a construction sector output
  shock.
• The missing economic significance is due to the
  low quality of data
• High degree of informality
• Short data series
• Existence of structural breaks.
• The lag length of the endogenous variables is
  usually determined using the Schwarz Information
  Criteria. Like Afonso et al in 2011, we used 2
  lags given the low number of observations. A
  higher number of observations would allow
  estimating VAR model with 4 lags suggested by
  the tests, and this could give a better reaction
  of the construction sector to the shock in the
  above mentioned macro variables.

15
Results Forecast Error Variance Decomposition

• The FVED shows how much of this variance of CONS
  is determined by each of the macroeconomic
  variables.
• Construction has the highest explanatory power
  over itself.
• Shocks to No of Licenses and Rent Price Index
  explain constantly nearly 20 and 5 of the
  variation in the construction sector.
• The importance of the credit to individuals
  increase gradually so their impact should be
  analysed in the longer term.
• FEVD analysis implies that the Remittances gain
  importance in the variance forecasting power in
  the long run.

  Period Period Period Period Period Period Period Period Period Period
  1 2 3 4 5 6 7 8 9 10
CONS 100 75.58 72.79 69.09 67.79 66.80 65.93 65.30 68.47 64.55
CRE 0.00 0.89 1.49 5.03 6.63 7.56 8.44 9.00 9.35 9.61
No_Lic 0.00 18.48 20.27 20.45 19.99 19.68 19.32 19.03 18.86 18.74
REM 0.00 0.61 0.77 0.93 1.16 1.60 1.96 2.28 2.53 2.71
RPI 0.00 4.44 4.67 4.48 4.43 4.36 4.36 4.38 4.39 4.40
16
Conclusions

• The purpose of this study was to analyse the
  effects of some Macroeconomic variables on the
  Construction activity in Albania
• The analysis of the main drivers of the
  construction sector demonstrates that the credit
  market and the amount of remittances are the main
  sources of construction changes. ( Capital
  intensive)
• Looking at real estate sector specific
  determinant, the main drivers identified by the
  analysis are the number of construction
  permissions and the real estate rent index.
• The article provide evidence that a construction
  sector is significantly affected by credit market
  variable but also real estate dynamics matters
• In a not fully developed economy the growth of
  the market is also related to the opportunity of
  creating real estate market conditions that
  support the demand for investing in that asset
  class.

17
References

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•  

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• Thank you for your attention!