The Future of Global Real Estate

1. The Future of Global Real Estate A subscription service uncovering the future of global property values Economist Intelligence Unit Country and Economic Research Winter 2009

2. Our proposed methodology

3. A new dawn for real estate? • Economic boom of the last six years was largely characterised by: • huge increase in credit and liquidity • high demand for assets – equities, bonds, commodities, property • Nevertheless, cheap credit was not the only driver of property prices • demographic trends • changes in incomes • pace of urbanisation • macroeconomic environment • But in many markets property prices rose far above a level which could be justified by these long-term drivers, i.e. above “valuation based on fundamentals” • Recent credit crunch accompanied by a steep decline in property prices Long-term “fundamentals”

4. What about existing real estate research? • Not many ‘global’ products as such • different consultancies focussing on different regions • e.g. Global Insight & Moody’s for US, Jones Lang LaSalle for separate regions • coverage mostly for developed / OECD economies • Many survey based forecasts • short-term forecasts; limited country coverage • e.g. PwC “Emerging Trends in Real Estate” • Modelling based on macroeconomic fundamentals seems restricted to academic research and international organisation working papers • e.g. International Monetary Fund’s (IMF) World Economic Outlook, 2008; OECD Economic Outlook No.78, 2005

5. Our methodology • Theoretical background: • IMF, WEO 2004: “House prices in Australia, UK, Ireland and Spain exceeded their predicted values by 20 pc” • IMF, WEO 2007: “During 1997 to 2007 […] house prices were [up to] 30 pc higher than justified by the fundamentals” • OECD, Economic Outlook 2005:”To address [overvaluation] it is necessary to relate these prices to their putative underlying determinants”

6. Our methodology • Econometric analysis to arrive at a real estate ‘true value' price equation • based on a regression which best explains past price fluctuations given historical economic and financial data • determine what should have happened to prices given the path of economic fundamentals in the past and determine the positive or negative 'price gap‘ • Forecasts: calculate price equation based on our robust in-house macroeconomic forecasts • determine the future path of ‘true value' prices of real estate in light of future macroeconomic conditions • EIU’s forecasting approach will combine long-term economic forecasting with property specific factors and will ensure that price forecasts take appropriate account of the state of the economy and income levels

7. Why the Economist Intelligence Unit? Independent, long-run perspective required Some property specialists will forecast property prices based on historic trends and industry specific factors (such as availability of planning permits etc). But a truly insightful long run property forecast requires much more than this - it needs to be rooted in a deep understanding of the broader national and international economic context. This is an area in which the EIU has a proven track record. Therefore the EIU’s forecasting approach, which combines long-term economic forecasting with property specific factors, is designed to ensure that our forecasts take appropriate account of the state of the economy and income levels. Many of the mistakes in forecasting property prices in the past have arisen because these factors were not taken sufficiently into account.

8. Why the Economist Intelligence Unit? World leader in country analysis and forecasting. For over 60 years we have provided business intelligence that corporate executives, government officials and academics require to understand developments around the world. We cover more than 200 countries, providing economic forecasts on the world's 150 largest markets. A truly insightful long run property forecast needs to be rooted in a deep understanding of the broader national and international economic context. This is an area in which the EIU has a proven track record. It is our analytical framework and forecasting methodology that gives us our competitive edge. Our approach combines the best in analysis–drawing on the country expertise of our specialists–and the best in forecasting, grounded in tested models, carefully vetted data and a quality–control process that ensures both accuracy and consistency.

9. Our methodology – variables to test Price equation variables

10. Our methodology – UK residential case study We are already able to accurately model quarterly UK residential property prices: Real house price growth (Source: DCLG) Model 1 drivers: • Income growth • Previous growth in price (speculator effect) • Interest rates • Population growth • Growth in domestic credit • Labour market conditions EIU model estimate But what would have happened if prices were driven only by economic fundamentals?

11. Our methodology – UK residential case study Annual UK property prices based on ‘fundamentals’: Real house price growth (Source: DCLG) Model 2 drivers: • Income growth • Interest rates • Population growth • Economic development • Labour market conditions EIU fair price model estimate Actual prices rose faster than the economic fundamentals since 1997 But undervalued from 1990 to 1996

12. Our methodology – Spain residential case study Again, controlling for fundamentals, residential prices in Spain rose above the price level explained by the fundamental drivers from 2003. During the economic downturn, we expect actual prices to converge towards these “correct” levels and even undershoot based on past trends. Model 3 drivers: • Income growth • Interest rates • Population growth • Labour market conditions Price gap Source: Banco de Espana; Economist Intelligence Unit estimates

13. Our methodology – UK commercial case study We have also applied our approach to commercial property values. The preliminary results are shown below. Changes in key economic variables are able to explain much of the change in commercial property prices. Model 4 drivers: • Income growth • Interest rates • Population growth • Labour market conditions • Residential prices

14. Our proposed research products

15. A new dawn for real estate? Individual forecasting models of residential and commercial property prices in a comprehensive group of countries and cities that ascertains the underlying price level based on long-term fundamentals for each market. An exciting research service that will provide subscribers with insight into the real estate market around the world. • In which countries is real estate overvalued and how low are prices likely to fall? • When can we expect a recovery? • Which markets are relatively undervalued and where will the next investment opportunities occur?

16. What will our research provide? There are numerous benefits arising from subscribing to our research service: • Accesskey price, economic and financial data for over 50 countries and 75 cities delivered through functional Microsoft Excel workbooks • Identify which markets are over- or undervalued and target your investments effectively • Downloadexclusive forecast data for residential and commercial property prices to 2020 • Understand the key economic fundamentals driving real estate market prices around the world

17. Our Residential Property Forecasting Service 1. Real estate database Access comprehensive data on residential real estate prices for 53 countries and 65 cities, annual and quarterly, including latest available data and historical time series 2. ‘Drivers’ database Access the Economist Intelligence Unit’s premium economic and financial indicator and forecasts database, updated quarterly through the Excel workbooks 3. Forecasts and scenario testing Interactive forecasting models in Excel format with residential price projections to 2020 with adjustable parameters for various forecast scenarios 4. Briefing papers Textual analysis on the economic and political outlook for each country that guide our overall residential property forecasts

18. Our Residential Property Forecasting Service Geographical coverageCountries – over 50

19. Our Residential Property Forecasting Service Geographical coverageCities – 65

20. Our Commercial Property Forecasting Service 1. Real estate database Access comprehensive data on commercial property real estate prices for 46 countries and 75 cities, annual and quarterly, including latest available data and historical time series 2. ‘Drivers’ database Access the Economist Intelligence Unit’s premium economic and financial indicator and forecasts database, updated quarterly through the Excel workbooks 3. Forecasts and scenario testing Interactive forecasting models in Excel format with residential price projections to 2020 with adjustable parameters for various forecast scenarios 4. Briefing papers Textual analysis on the economic and political outlook for each country that guide our overall commercial property forecasts

21. Our Commercial Property Forecasting Service Geographical coverageCountries – 46 *composite average of main cities **principal/capital city only

22. Our Commercial Property Forecasting Service Geographical coverageCities – 75

23. Fees and project team

24. Fees • Subscriptions to our Residential Property Forecasting Service and our Commercial Property Forecasting Service will be available from December • The annual fee for a subscription to our Residential Property Forecasting Service with quarterly updates of the forecasts will be £10,000/US$16,000 • The annual fee for a subscription to our Commercial Property Forecasting Service with quarterly updates of the forecasts will be £10,000/US$16,000 • The annual fee for subscriptions to both services with quarterly updates of the forecasts will be £16,000/US$25,500 For more information, please contact Catherine Wallen at catherinewallen@economist.com

25. The team Project management team • Andrew Williamson, Global Director Economic Research • Gavin Jaunky, Senior Economist • Robert Metz, Senior Economist • John McNamara, Senior Economist • Harald Langer, Economist Economics team • Robin Bew, Editorial Director and Chief Economist • Robert Ward, Director, Global Forecasting • Chris Pearce, Director, Economics Unit; Director, Data Services Regional teams • Charles Jenkins, Regional Director, Western Europe • Pat Thaker, Regional Director, Africa • Laza Kekic, Regional Director, Central & Eastern Europe; Director, Country Forecasting Services • Justine Thody, Regional Director, Latin America • Gerard Walsh, Regional Director, Asia • David Butter, Regional Director, MENA

26. Our economic forecasting record

27. Predicting 2007 GDP growth in the US Average forecasting error Root mean squared error, forecasts made in 2006/07 for 2007 annual real GDP growth figure. Root mean squared error is a measure of average forecasting error and a commonly used standard in assessing forecasting accuracy It is calculated by taking the square root of the sum squared of each deviation of the forecast from the actual of each observation divided by the number of observations to arrive at a standardised score.

28. Predicting 2007 GDP growth in the Euro area Average forecasting error Root mean squared error, forecasts made in 2006/07 for 2007 annual real GDP growth figure. Root mean squared error is a measure of average forecasting error and a commonly used standard in assessing forecasting accuracy It is calculated by taking the square root of the sum squared of each deviation of the forecast from the actual of each observation divided by the number of observations to arrive at a standardised score.

29. Predicting 2007 GDP growth in Asia Average forecasting error (Malaysia, Thailand, Indonesia, Taiwan) Root mean squared error, forecasts made in 2006/07 for 2007 annual real GDP growth figure. Root mean squared error is a measure of average forecasting error and a commonly used standard in assessing forecasting accuracy It is calculated by taking the square root of the sum squared of each deviation of the forecast from the actual of each observation divided by the number of observations to arrive at a standardised score.

30. Predicting 2008 global GDP growth Average forecasting error Root mean squared error, forecasts made in 2007 for 2008 annual real GDP growth figure. Root mean squared error is a measure of average forecasting error and a commonly used standard in assessing forecasting accuracy It is calculated by taking the square root of the sum squared of each deviation of the forecast from the actual of each observation divided by the number of observations to arrive at a standardised score.

31. APPENDIX I Our long-run forecasting methodology Growth projections The main building blocks for the long-term forecasts of key market and macroeconomic variables are long-run real GDP growth projections. We have estimated growth regressions (based on cross-section, panel data for 86 countries for the 1970-2000 period) that link real growth in GDP per head to a large set of growth determinants. The sample is split into three decades: 1971-80, 1981-90 and 1991-2000. This gives a maximum of 258 observations (86 countries for each decade); given missing values for some countries and variables, the actual number of observations is 246. The estimation of the pooled, cross-section, panel data is conducted on the basis of a statistical technique called Seemingly Unrelated Regressions. (SUR) to allow for different error variances in each decade and for correlation of these errors over time. The regressions, which have high explanatory power for growth, allow us to forecast the long-term growth of real GDP per head for sub-periods up to 2030, on the basis of demographic projections and assumptions about the evolution of policy variables and other drivers of long-term growth.