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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

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Our proposed methodology
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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
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What about existing real estate research?

• Not many global products as such
• different consultancies focussing on different
  regions
• e.g. Global Insight Moodys 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 Funds (IMF) World
  Economic Outlook, 2008 OECD Economic Outlook
  No.78, 2005

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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 2005To address
  overvaluation it is necessary to relate these
  prices to their putative underlying determinants

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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
• EIUs 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

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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 EIUs 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.

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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
  analysisdrawing on the country expertise of our
  specialistsand the best in forecasting, grounded
  in tested models, carefully vetted data and a
  qualitycontrol process that ensures both
  accuracy and consistency.

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Our methodology variables to test
Price equation variables
Dependent variable
Change in real residential/commercial property price
Explanatory variables Explanation / Hypothesis
Lagged change in real price Persistence effect
Price divided by personal income per capita Reversion effect or affordability indicator
Growth in personal income per capita Reflects growing wealth and propensity to buy property
Income and corporation tax rates Act as downward pressures on the propensity to buy real estate
Short-term interest rate (real and nominal current and lagged) To reflect cost of borrowing for home-owners
Long-term interest rate (real and nominal current and lagged) Reflects long-term financing costs for commercial property development
Change in stockmarket prices Potential substitute for speculative investment
Population growth Creating higher demand and upward pressure on prices
Growth in the number of households Creating higher demand and upward pressure on prices
Population aged 20-39 divided by total population Reflecting pool of potential first-time buyers of property
Growth in supply of credit as percentage of GDP To account for credit conditions which influence ability to finance property acquisition
Unemployment Business cycle indicator and potential pool of consumers/labour force
Residential/commercial rental yield To account for buy-to-let investors also to account for rental market substitute
Global /regional real estate prices Relative domestic price to global prices, reflecting decision to buy/sell in other regions
Dummy variables 'On or off' variables, including 9/11 factor, Dot com burst, Banking crises
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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?
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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
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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
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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

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Our proposed research products
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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?

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What will our research provide?
There are numerous benefits arising from
subscribing to our research service

• Access key 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
• Download exclusive forecast data for residential
  and commercial property prices to 2020
• Understand the key economic fundamentals driving
  real estate market prices around the world

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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 Units 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

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Geographical coverageCountries over 50
Our Residential Property Forecasting Service
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Geographical coverageCities 65
Our Residential Property Forecasting Service
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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 Units 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

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Geographical coverageCountries 46
Our Commercial Property Forecasting Service
composite average of main cities principal/capi
tal city only
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Geographical coverageCities 75
Our Commercial Property Forecasting Service
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Fees and project team
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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/US16,000
• The annual fee for a subscription to our
  Commercial Property Forecasting Service with
  quarterly updates of the forecasts will be
  10,000/US16,000
• The annual fee for subscriptions to both services
  with quarterly updates of the forecasts will be
  16,000/US25,500
• For more information, please contact Catherine
  Wallen at catherinewallen_at_economist.com

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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

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Our economic forecasting record
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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.
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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.
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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.
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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.
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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.