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The Real Estate Conundrum in CEE Markets: Thinking Too big?

The Real Estate Conundrum in CEE Markets: Thinking Too big?

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The Real Estate Conundrum in CEE Markets: Thinking Too big?

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  1. The Real Estate Conundrum in CEE Markets: Thinking Too big? Annual ERES Conference June 25, 2010 - Milan Italy ISET International School of Economics at TSU 1

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

  3. Motivations Capitalization rates in 2007 (Source: Colliers International) 3

  4. Motivations • D’Argensio and Laurin (2008):►Interesting results for EU accession transition countries: • Cap rates higher in average (1.63 p.p.) than Western countries; • 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

  5. Motivations • 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? • Do the cap rate levels reflect their “true” risk level? • Factors explaining the evolution of property prices in Europe. 5

  6. Objectives • Investigate the evolution of office markets in Central and Eastern European (CEE) cities vs Western European (WE) cities; • Identify the determinants of property pricesand rents; • Estimate a “predicted”property price and capitalization rate for CEE property markets. 6

  7. Summary of Results Investors’ 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. 7

  8. Table of content Review of Literature Data and Statistical Analysis Methodology Empirical Model Price Equation: Results Conclusion 8

  9. 1. 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

  10. 1. Review of Literature 10

  11. 2. Data and Statistical Analysis • Panel 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; 11

  12. 2. Data and Statistical Analysis 12

  13. 2. Data and Statistical Analysis Evolution of Office Stock within CEE • 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.

  14. 2. Data and Statistical Analysis • Spreads reached historical lows against WE in 2007: • 60 bps for CE; • 260 bps for EE.

  15. 2. Data and Statistical Analysis • WE shows a cyclical pattern. • Hyper supply applied downward pressure on rents since coverage inception. Prime Office Real Rents for Budapest, Prague and Warsaw (Index 2000=100) 15

  16. 3. 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… 16

  17. 3. Methodology • Solution? • 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! 17

  18. 3. 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: 18

  19. 4. Empirical Model • Price of a property: • The price should exactly reflect the sum of present value cash: • Cash flows can be approximated by rents: • Hence: Where: - d is an appropriate discount rate. - g is the expected rate of growth of cash flows 19

  20. 4. Empirical Model • Growth 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 (+). 20

  21. 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. 21

  22. 4. Empirical Model • 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 Both price and rent equations can be estimated in a Seemingly Unrelated Regression (SURE) system to solve for endogeneity. 22

  23. 5. Results Simple OLS results for the price equation 23

  24. 5. Results SURE results (constrained coefficients) 24

  25. 5. Results SURE 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. 25

  26. 5. Results Predicted property price Predicted cap rate 26

  27. 5. Results Predicted property price Predicted cap rate 27

  28. 5. Results Predicted property price Predicted cap rate 28

  29. 5. Results

  30. 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

  31. The Real Estate Conundrum in CEE Markets: Thinking Too big? ISET International School of Economics at TSU Thank you! 31