The Real Estate Conundrum The Real Estate Conundrum in CEE Markets: in CEE Markets:

Thinking Too big?Thinking Too big?

1

Annual ERES Conference June 25, 2010 - Milan Italy

Frédéric Laurin, Ph.D.

Professor in Economics

Université du Québec à Trois-Rivières

Trois-Rivières (QC), Canada

Research Fellow, International School of Economics at Tbilisi State University, Georgia

frederic.laurin@uqtr.ca

John-John D’Argensio, M.Sc.

Director, Economic Research & Investment Strategy

SITQ- Caisse de dépôt et placement du Québec

Montreal (QC), Canada

john-john.dargensio@sitq.com

ISETInternational School of Economics at TSU

MotivationsMotivations

● Average decline of 7.4 p.p. in total between 2000 and 2007. 2

3

MotivationsMotivations1 Lima Peru 15(…)17 Kiev Ukraine 9.2518 Cleveland USA 919 St.Louis USA 9(…)24 Belgrade Serbia 8.525 Istanbul Turkey 8.5(…)35 Philadelphia USA 7.5(…)37 Atlanta USA 7.438 Dallas/Ft.Worth USA 7.4(…)42 Ft.Lauderdale USA 7.143 Vilnius Lithuania 744 Zagreb Croatia 745 Kansas City USA 746 Auckland N. Zealand 747 Shanghai China 748 Budapest Hungary 6.8549 Las Vegas USA 6.750 Athens Greece 6.551 Riga Latvia 6.552 Tallinn Estonia 6.553 Montreal Canada 6.5(…)

63 Portland USA 6.264 Singapore Singapore 6.1465 Bratislava Slovakia 666 Bucharest Romania 667 Eindhoven Netherlands 668 Warsaw Poland 669 Houston USA 6(…)72 Washington USA 5.9673 San Francisco USA 5.8(…)77 Toronto Canada 5.778 Boston USA 5.679 Seoul South Korea 5.680 London Docklands UK 5.581 Prague Czech Rep. 5.582 Chicago USA 5.583 Vancouver Canada 5.5(…)111 Frankfurt Germany 4.17112 Düsseldorf Germany 4.1113 Paris France 4

Capitalization rates in 2007 (Source: Colliers International)

MotivationsMotivations

● D’Argensio and Laurin (2008): ►Interesting results for EU accession transition countries:

o Cap rates higher in average (1.63 p.p.) than Western countries;

o BUT: sharp decrease in cap rate from the first year of official entry in the European Union (in average, 2.42 p.p. lower relatively to their pre-accession level).

4

MotivationsMotivations

Factors explaining the cap rate compression in CEE property markets:◦A lower cap rate in Bucharest than in Dallas?!

Was this capitalization rate compression rational or irrational? o Do the cap rate levels reflect their “true” risk level?

Factors explaining the evolution of property prices in Europe.

5

ObjectivesObjectivesInvestigate the evolution of

office markets in Central and Eastern European (CEE) cities vs Western European (WE) cities;

Identify the determinants of property prices and rents;

Estimate a “predicted” property price and capitalization rate for CEE property markets.

6

Summary of ResultsSummary of ResultsInvestors’ valuation of property prices are not to far apart from the predicted property prices;

Macroeconomic factors have a greater impact on property prices in CEE than in WE.

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Table of contentTable of content

1. Review of Literature2. Data and Statistical Analysis3. Methodology4. Empirical Model5. Price Equation: Results6. Conclusion

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1. Review of Literature1. Review of Literature Evolution of real estate market in

CEE markets◦ Ghanbari Parsa (1997), Watkins and Merrill (2003),

McGreal et al. (2002), Adair et al. (2006) and Mansfield and Royston (2007)

Risk perception in CEE property markets

◦ Keivani et al. (2000), McGreal et al. (2002)

Impacts of globalization on CEE property markets.

◦ Keogh and D’Arcy (1994), Adair et al. (1999), Keivani et al. (2000)

9

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1. Review of Literature1. Review of LiteratureStages in the establishment of real estate markets in central Europe

Stage 1

Transformation period (1989-1991)

• Sharp rise in real estate prices• Liberalization of state prices and rents

Stage 2

Entry of foreign firms (1992-1994)

• Shortage of internationally acceptable office property• Commencement of major developments• High capital growth• High rental growth• Increase in demand

Stage 3

1995-1998 • Substantial increase in supply of office property• Entry of domestic investment and development firms• Decreasing gap between demand and supply

Source: Parsa (1997) , reprinted in Adair et al. (2009).

2. Data and Statistical 2. Data and Statistical AnalysisAnalysisPanel data

◦Data on 30 WE office markets + Budapest, Prague and Warsaw: between 1990 and 2009

◦14 CEE office markets, with data ranging between 1998 and 2009 (with missing values)

Rents◦Office prime rent (in nominal terms) by city;

Property Prices◦Nominal Price index (100=2004) by city;

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2. Data and Statistical 2. Data and Statistical AnalysisAnalysis

Variables

Interest rates

10-year Bond Yields (or equivalent long-term rate) at country level

Economic Variables

Office-Using Employment Data at city level

GDP (at constant $US prices) at country level

CPI (2005=100) at country level

Other variablesForeign Direct Investments (Inward; US Dollars at current prices and current exchange rates in millions) at country level

Real estate variablesInventory by city (sqm/yr)Rents by city (€/sqm/yr)Price index (2004=100)Absorption by city (in sqm)Completions in city (in sqm)

Vacancy rate by city (in %)Capitalization rate by city (in %)

Cambridge Econometrics

Property and Portfolio Research, Cushman and Wakefield, CB Richard Ellis, Colliers Office Global Insights and Ober Haus Real Estate Adivsors

Source

Global Insight; Bloomberg; Eurostat.

IMF

World Economic Outlook, Global Insight

UNTACD and Economist and Intelligence Unit

12

Sources: CBRE, PMA, PPR and authors' own calculations

0

5,000

10,000

15,000

20,000

25,000

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000Eastern Europe (in '000 of sqm)Central Europe (in '000 of sqm)Change in stock (y-o-y; in '000 of sqm) _RHS

70% of the new supply was delivered during the latest commercial real estate boom.

Since 2003, stock increased by 121% in CE and 301% in EE.

2. Data and Statistical 2. Data and Statistical AnalysisAnalysis

Evolution of Office Stock within CEE

Spreads reached historical lows against WE in 2007:◦ 60 bps for CE;◦ 260 bps for EE.

Sources: CBRE, PMA, PPR, authors own calculations

Evolution of capitalisation rates for the Western Europe, Central and Eastern Europe

0%

5%

10%

15%

20%

25%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Western EuropeCentral EuropeEastern Europe

2. Data and Statistical 2. Data and Statistical AnalysisAnalysis

2. Data and Statistical 2. Data and Statistical AnalysisAnalysis

Sources: CBRE, PMA, C&W, PPR, authors' own calculations

Prime Office Real Rents in CEE and WE (EUR/sqm/pa)

0 €

100 €

200 €

300 €

400 €

500 €

600 €

700 €

800 €

900 €

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

300.00 €

350.00 €

400.00 €

450.00 €

500.00 €

550.00 €

Sofia Zagreb Prague TallinBudapest Riga Vilnius WarsawBucharest Moscow Belgrade BratislavaKiev WE avg. (RHS)

● WE shows a cyclical pattern.

● Hyper supply applied downward pressure on rents since coverage inception.

Source: Property Market Analayis (PMA)

50

100

150

200

250

300

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Prague

Budapest

Warsaw

Prime Office Real Rents for Budapest, Prague and Warsaw (Index 2000=100)

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3. Methodology3. Methodology

How to measure over or under valuation of an asset?

◦What benchmark to use?◦Techniques to identify asset price bubbles: need long time series…

◦We have short time series for CEE and many missing values…

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3. Methodology3. MethodologySolution?

Use WE cities as a benchmark!

◦We have longer time series (from 1990);

◦Economic and regulatory convergence because of European integration;

◦Makes more sense to compare European cities together, than with other regions;

◦WE cities not fully mature in the 90s;

◦No other satisfying solutions!

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3. Methodology3. Methodology

Solution: new methodology• Design an equation explaining the evolution

of property prices in time;

• Estimate this equation for a sample of WE cities only;

• From the estimated coefficients, compute a predicted values for property prices for CEE cities.

• Compute a predicted cap rate, using the following proxy: t

t

Price Predicted

Rent Actual

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4. Empirical ModelPrice of a property:

The price should exactly reflect the sum of present value cash:

T

tt

t

tt

d

CFP

1 1

ttt g1γRENTCF

T

tt

t

tt

ttd

gRENTP

1 1

1

o Cash flows can be approximated by rents:

:Hence ס

Where:

- d is an appropriate discount rate.

- g is the expected rate of growth of cash flows19

4. Empirical ModelGrowth expectation of cash flows:

RENTS: past real growth of rents (+); GROWTH: past real GDP growth country-wide (+); FDI: new demand for local assets: real FDI inflows country-

wide (+).

Discount rate (market risk): SPREAD: spread in the 10-year government bond yield

relative to the US (-); OCCEMP: Depth of property market: total annual occupied

space divided by office-using employment city-wide (+) CREDIT (liquidity measure): gross volume of domestic credit

as a percentage of GDP country-wide (+);

European trend: TREND: average annual property prices across sample (+).

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4. Empirical Model

So, the empirical equation:

in first-difference: we are interested in the evolution in time of property prices;

in logs for variables not in % or ratios;

growth expectation variables: lags greater than two periods never significant.

132211 )log()log(log itititit SPREADrentrentprice

tEuropeit

itititit

TRENDGROWTH

FDIFDICREDITOCCEMP

817

1651514 )log()log()log()log(

21

141321 )log()log( ititititit EMPNETCOMPLETIONABSORBrents

Europeitit TRENDGROWTHEMP 71625 )log(

We also model rents since this variable might be endogenous in the price equation:

where: ABSORB: absorbtion at the city level; COMPLETION: completion at the city level; NET: absorbtion - completion (past demand not fullfilled at

time t); EMP: office-using employment. GROWTH: real GDP growth country-wide TREND: average evolution of rents in the sample

4. Empirical Model

Both price and rent equations can be estimated in a Seemingly Unrelated Regression (SURE) system to solve for endogeneity.

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5. Results

Table 6 : Results for the Price Equation

1 2 3 4 5 6

∆logRENT(t-1) 0,6733 0,5945 0,6757 0,5925 0,6343 0,3478 7,240*** 6,610*** 7,130*** 6,430*** 6,660*** 4,550***

∆logRENT(t-2) -0,4250 -0,3276 -0,4013 -0,3684 -0,4076 -0,1578 -6,520*** -5,330*** -6,380*** -5,720*** -6,140*** -2,950***

∆SPREAD(t-1) - -0,0347 - - - -0,0085 -5,340*** -1,730*

∆logOCCEMP(t-1) - 0,1354 - - - 0,0780 0,960 0,680

∆logCREDIT(t-1) - - -0,2528 - - -0,1034 -2,310** -1,930*

∆logFDI - - - 0,1938 - -0,0336 6,940*** -1,490

∆logFDI(t-1) - - - 0,1685 - -0,0182 5,880*** -0,710

GROWTH(t-1) - - - - 0,0065 0,0050 1,770* 1,730*

∆TREND - - - - - 0,9096 15,510***

Constant -0,0076 -0,0098 0,0028 -0,0111 -0,0269 -0,0189 -1,060 -1,510 0,400 -1,610 -2,310** -2,100**

Nb of Obs, 623 600 617 623 623 600

R² 0,1526 0,2125 0,1660 0,2111 0,1594 0,5055

Notes: Estimation using White heteroscedasticity robust standard errors. Below coefficient: t-statistics, * = significant at 10%; **=significant at 5%; ***= significant at 1%.

Simple OLS results for the price equation

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5. ResultsSURE results (constrained coefficients)

Table 8: SURE Results – Restricted Regressions

1 2 3 4

Total/WE CEE Total/WE CEE Total/WE CEE Total/WE CEE

∆logRENT(t-1) -0,0255 0,2452 0,0095 - 0,0260 - 0,0120 - -0,570 2,480** 0,230 0,620 0,280

∆logRENT(t-2) -0,0523 -0,1501 -0,0747 - -0,0684 - -0,0711 - -1,260 -1,650* -1,950* -1,770* -1,820*

∆logSPREAD(t-1) -0,0068 - -0,0029 -0,0092 -0,0079 - -0,0082 - -2,030** -0,560 -2,230** -2,380** -2,460**

∆logOCCEMP(t-1) 0,0325 - 0,0964 -1,2591 0,0426 - 0,0403 - 0,320 0,920 -2,790*** 0,410 0,390

∆logCREDIT(t-1) -0,0615 - -0,0570 - -0,0510 - -0,0453 -0,0991 -1,270 -1,190 -1,060 -0,900 -0,570

∆logFDI -0,0076 - -0,0042 - -0,0067 0,9100 -0,0082 - -0,350 -0,190 -0,310 2,510** -0,380

∆logFDI(t-1) -0,0046 - -0,0033 - -0,0026 0,1417 -0,0074 - -0,210 -0,150 -0,120 0,350 -0,340

GROWTH(t-1) 0,0102 - 0,0114 - 0,0093 - 0,0114 0,0095 4,460*** 4,890*** 4,100*** 3,690*** 3,650***

∆TREND 0,8965 - 0,8766 - 0,8735 - 0,8837 - 19,970*** 19,190*** 19,330*** 19,470***

Constant -0,0386 - -0,0400 - -0,0375 - -0,0419 - -4,710*** -4,880*** -4,590*** -4,590***

Nb of Obs, 599 599 599 599 R² 0,4734 0,4784 0,4750 0,4715

Notes: SURE estimated with small sample adjustment for the variance-covariance matrix. Below coefficient: t-statistics, * = significant at 10%; **=significant at 5%; ***= significant at 1%.

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5. ResultsSURE results.

For CEE: three cities: Budapest, Prague and Warsaw.

For WE: estimated on a random sample of 3 WE cities out of 30. This is repeated 10 000 times.

Average coefficients and t-students across 10 00 samples shown.

Comparison of t-stats with the same nb of cities.

Table 9 : OLS Results for the Price Equation – Unrestricted Regressions

Warsaw, Budapest,

Prague

WE

Variable Average

value Std, Dev, Min Max ∆logRENT(t-1) Coefficient 0,6370 0,2992 0,2745 -0,4680 1,3915

t-student 2,100** 1,298 1,245 -1,829 7,382 ∆logRENT(t-2) Coefficient -0,2711 -0,1711 0,2092 -1,0862 0,4671

t-student -1,010 -1,053 1,234 -5,624 2,313 ∆SPREAD(t-1) Coefficient 0,0011 -0,0091 0,0271 -0,0991 0,1170

t-student 0,150 -0,476 0,965 -3,395 2,921 ∆logOCCEMP(t-1) Coefficient 3,6268 0,1383 0,7202 -5,1943 3,1507

t-student 2,800*** 0,314 1,105 -3,584 4,092 ∆logCREDIT(t-1) Coefficient 0,3234 -0,1945 0,2471 -1,3198 1,1560

t-student 0,610 -0,885 0,968 -5,437 3,427 ∆logFDI Coefficient 1,1622 -0,0336 0,1282 -1,0866 1,0764

t-student 2,020** -0,289 1,172 -4,271 3,644 ∆logFDI(t-1) Coefficient 0,8077 0,0105 0,1368 -0,6107 1,9049

t-student 1,670* 0,049 1,260 -3,534 6,472 GROWTH(t-1) Coefficient 0,0024 0,0058 0,0161 -0,0917 0,0581

t-student 0,220 0,528 1,185 -4,894 3,774 ∆TREND Coefficient 0,8984 0,8025 0,2825 0,1480 1,6977

t-student 4,340*** 4,078*** 1,422 0,820 10,578 Constant Coefficient -0,1029 -0,0083 0,0494 -0,1078 0,3313

t-student -1,530 -0,447 1,297 -4,199 6,506

Nb of Obs, 38 10000 - - - R² 0,6875 0,5852 0,0970 0,2959 0,8463

Notes: Estimation using White heteroscedasticity robust standard errors. Below coefficient: t -statistics, * = significant at 10%; **=significant at 5%; ***= significant at 1%.

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5. ResultsPredicted property price Predicted cap rate

26

Property prices - Budapest

0

1000

2000

3000

4000

5000

6000

7000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Cap rates - Budapest

0%

5%

10%

15%

20%

25%

30%

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Property prices - Prague

0

1000

2000

3000

4000

5000

6000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Cap rates - Prague

0%

2%

4%

6%

8%

10%

12%

14%

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Property prices - Warsaw

0

1000

2000

3000

4000

5000

6000

7000

2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Cap rates - Warsaw

0%

2%

4%

6%

8%

10%

12%

2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

5. ResultsPredicted property price Predicted cap rate

27

Property prices - Kiev

0

500

1000

1500

2000

2500

3000

3500

2002 2003 2004 2005 2006 2007 2008

Actual

Predicted

Cap rates - Kiev

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

2002 2003 2004 2005 2006 2007 2008

Actual

Predicted

Property prices - Bratislava

0

500

1000

1500

2000

2500

3000

3500

4000

4500

2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Cap rates - Bratislava

0%

2%

4%

6%

8%

10%

12%

14%

2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Property prices - Sofia

0

500

1000

1500

2000

2500

3000

2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Cap rates - Sofia

0%

2%

4%

6%

8%

10%

12%

14%

2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

5. ResultsPredicted property price Predicted cap rate

28

Property prices - Riga

0

500

1000

1500

2000

2500

3000

3500

2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Cap rates - Riga

0%

2%

4%

6%

8%

10%

12%

14%

2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Property prices - Tallinn

0

500

1000

1500

2000

2500

3000

2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Cap rates - Tallinn

0%

2%

4%

6%

8%

10%

12%

14%

16%

2001 2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Property prices - Vilnius

0

500

1000

1500

2000

2500

3000

3500

2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

Cap rates - Vilnius

0%

2%

4%

6%

8%

10%

12%

14%

2002 2003 2004 2005 2006 2007 2008 2009

Actual

Predicted

5. Results

Table A4: Results for Bucharest and ZagrebProperty prices Cap rates

year Actual Predicted Actual PredictedBucharest 2008 2518,69 2086,43 8,50% 10,26%

2009 1985,30 2267,92 9,50% 8,32%

Zagreb 2007 2864,88 2374,41 6,70% 8,08%2008 2486,31 1941,66 7,50% 9,60%2009 2039,32 1733,31 8,50% 10,00%

6. Conclusion

Predicted property prices tend to follow more or less closely their actual values : Even using only WE coefficients!

Predicted cap rates not too far apart from their actual values: Warsaw, Kiev, Bratislava, Tallinn and Zagreb: predicted cap

rates should have been higher than actual values in specific period (especially the last 4 years);

Otherwise : actual cap rates are somewhat over valuating the “true” risk;

OVERALL: Investors may not have been as short-sighted as expected by the rapid decline of cap rates in CEE.

Determinants of property price: The macroeconomic environment + general risk assessment:

stronger effect on property prices in CEE than in WE. 30

The Real Estate Conundrum The Real Estate Conundrum in CEE Markets: Thinking in CEE Markets: Thinking

Too big?Too big?

31

Frédéric Laurin, Ph.D.

Professor in Economics

Université du Québec à Trois-Rivières

Trois-Rivières (QC), Canada

Research Fellow, International School of Economics at Tbilisi State University, Georgia

frederic.laurin@uqtr.ca

John-John D’Argensio, M.Sc.

Director, Economic Research & Investment Strategy

SITQ- Caisse de dépôt et placement du Québec

Montreal (QC), Canada

john-john.dargensio@sitq.com

Thank you!

ISETInternational School of Economics at TSU