THE INTERNATIONAL FINANCIAL CRISIS AND REAL ESTATE VALUE:

THE SPANISH CASE

Ramon Ausina

A Thesis Submitted to the University of North Carolina Wilmington in Partial Fulfillment

of the Requirements for the Degree of Master of Business Administration

Cameron School of Business

University of North Carolina Wilmington

2010

Approved by

Advisory Committee

Clay Moffett Robert Burrus

J. Edward Graham Chair

Accepted by

_____________________________ Dean, Graduate School

ii

TABLE OF CONTENTS

ABSTRACT ........................................................................................................................iii

AKNOWLEDGMENTS ...................................................................................................... iv

DEDICATION...................................................................................................................... v

LIST OF TABLES............................................................................................................... vi

LIST OF FIGURES ............................................................................................................vii

NOMENCLATURE ..........................................................................................................viii

CHAPTER 1: INTRODUCTION AND THEORETICAL CONTEXT .................................. 1

CHAPTER 2: LITERATURE REVIEW ............................................................................... 5

CHAPTER 3: DESCRIPTION OF THE DATA.................................................................. 11

CHAPTER 4: CONCEPTUAL MODEL, RESEARCH OBJECTIVE, RESEARCH

QUESTIONS AND HYPOTHESIS .................................................................................... 16

TABLES............................................................................................................................. 27

FIGURES ........................................................................................................................... 38

BIBLIOGRAPHY............................................................................................................... 50

APPENDICES .................................................................................................................... 53

iii

ABSTRACT

Purpose: Analyze the different factors that could affect the Spanish Real Estate crisis

in Spain, especially those factors coming from abroad, the international factors and variables

that helped the Real Estate Bubble in Spain.

Design/Methodology/Approach: regression analyses were performed using ordinary

least squares (OLS), which produced coefficient estimates of the independent variables.

Findings: We conclude that international factors are important and have helped the

Real Estate Bubble in Spain. Some of them affected in the way we predicted, like FDI, while

others, like the globalisation index, affected in a negative way to the real estate prices

evolution.

Originality/Value: many studies evaluating the Real Estate markets in different

countries haven’t analyzed the importance of international factors like Globalisation and FDI

Audience: Government, International/National Corporations, Real Estate Investment

Trusts (REIT’s), Shareholders, Homeowners, Investors and academics.

Keywords: Real Estate, Price per Square Meter, International Factors.

iv

AKNOWLEDGEMENTS

First and foremost, I thank my family for their continuous support and encouragement.

I also acknowledge the hard work and guidance of Professor Edward Graham as my primary

committee chairman and advisor. I also thank the other members of my thesis committee,

Professors Clay Moffett and Robert Burrus. They each contributed their personal expertise

and invaluable input to help shape this thesis.

My study could not have been performed without the help and support of the Cameron

School of Business, so I thank to everyone for providing students with invaluable resources

and motivation.

v

DEDICATION

This thesis is absolutely dedicated to my family in general, and especially to my

cousin Jaume. His support from Spain has been key, making possible my studies here in

Wilmington.

vi

LIST OF TABLES

Table Page

1. Correlation Matrix between two US REITs ..................................................................... 27

2. Dependant Variables. Codes, description and sources ..................................................... 27

3. Independent Variables. Codes, description and sources .................................................. 27

4. Correlation Matrix........................................................................................................... 29

5. Descriptive Statistics ....................................................................................................... 30

6. Determinants in the price per Sq Meter: 1995­2009......................................................... 31

7. Determinants in the Price per Sq Meter. International Factors: 1995­2007....................... 32

8. Determinants in the Price per Sq Meter. International Factors: 1995­1998....................... 32

9. Determinants in the Price per Sq Meter. International Factors: 1999­2009....................... 33

10. Determinants in the Price per Sq Meter. International Factors: 1998­2007, with FTSE .. 33

11. Determinants in the Price per Sq Meter. International Factors: 1998­2007, with IBEX .. 34

12. Determinants in the Price per Sq Meter. International Factors: 1998­2007, with SP500 . 34

13. Determinants in the Price per Sq Meter. National Factors: 1995­2009 ........................... 35

14. Determinants in the Price per Sq Meter. National Factors: 1995­1998 ........................... 35

15. Determinants in the Price per Sq Meter. National Factors: 1999­2009 ........................... 36

16. Determinants in the Price per Sq Meter. National Factors: 1998­2007 ........................... 36

17. Simple Regressions: 1995­2009..................................................................................... 37

vii

LIST OF FIGURES

Figure Page

1. KOF – Globalisation Index evolution in Spain: 1970­2009............................................. 38

2. Foreign Direct Investment in Spain in Real Estate (th Euros).......................................... 38

3. Foreign Direct Investment in Spain per country. ............................................................. 38

4. Price per Square Meter: 1985­2009. ............................................................................... 39

5. Percentual evolution of CPI and Average Salary in Spain. .............................................. 39

6. House prices evolution UK ­ Spain................................................................................. 40

7. Population evolution versus Houses Completed............................................................. 40

8. Plots of the most relevant national variables. .................................................................. 40

9. Plots of the most relevant external variables. .................................................................. 46

viii

NOMENCLATURE

RIE: REGISTRO DE INVERSIONES EXTERIORES INE: INSTITUTO NACIONAL DE ESTADÍSTICA (STATISTICAL SPANISH OFFICE) BE: BANCO DE ESPAÑA (SPANISH BANK) IMF: INTERNATIONAL MONETARY FUND REIT: REAL ESTATE INVESTMENT TRUST FDI: FOREIGN DIRECT INVESTMENT GDP: GROSS DOMESTIC PRODUCT US: UNITED STATES UK: UNITED KINGDOM USD: US DOLLAR GBP: GREAT BRITAIN POUND

CHAPTER 1: INTRODUCTION AND THEORETICAL CONTEXT

The importance of housing for the wider economy, the financial system, labour

market, and construction industry justifies this study. House prices are therefore of great

interest to real estate developers, banks, policy makers or, in short, the general public as well

as to actual and potential home owners (Schulz & Werwatz, 2004).

Since the dictator Franco died in November 1975 1 , Spain has experienced many

periods of instability, both economically and politically in the most recent history. In fact,

after the advent of democracy, years later, on the 23 rd of February, 1981, there was an

attempted coup d’état by senior military officers that ultimately did not work. In the other

hand, Spain had the problem of the Basque Fatherland and Liberty (ETA) 2 terrorist

organization. The government continues to battle ETA nowadays, but we see in the evolution

of the number of terrorist attacks that the impact is much lower.

These trust issues in Spanish politics were eliminated with the addition of the same in

different international organisms. Spain joined the North Atlantic Treaty Organization

(NATO) in 1982, and the Organization for Economic Cooperation and Development; and the

European Union in 1986 (Accession Treaty of Spain and Portugal, 1985), among others.

In the early 90s, the problem was economic, with rates of unemployment reaching

23% (and not beginning to recover until 1993, when it fell to 15%). In addition, between

September 1992 and May 1993, the peseta suffered three devaluations, which was followed

by a final one in March 1995 (Calvo, 2008). These monetary policies did not give too much

credit to potential foreign investors in Spain.

With compliance with the provisions of the Maastricht Treaty (1992), Spain began a

1 https://www.cia.gov/library/publications/the­world­factbook/geos/sp.html 2 https://www.cia.gov/library/publications/the­world­factbook/geos/sp.html

2

new period of lower economic uncertainty, given that it was obliged to maintain certain

convergence criteria in order to enter the Euro zone. This Convergence criteria were

(TRATADO DE LA UNION EUROPEA, 1992):

• The relationship between the government deficit and gross domestic product (GDP) must

not exceed 3%;

• The ratio of government debt to GDP must not exceed 60%;

• A degree of lasting stability in prices, and average inflation rate (observed over a period of

one year before the examination) which must not exceed by more than 1.5% of the three

Member States to provide the best results in terms of price stability;

• An average rate of long­term nominal interest rate should not exceed 2% over that of the

three Member States to provide the best results in terms of price stability;

• The normal fluctuation margins provided for by the mechanism of exchange rates European

Monetary System must be respected without severe tensions known for at least the last two

years preceding the examination.

Spain accomplished the criteria, so with the entrance in the Euro area, the exchange

currency risk was totally eliminated for investors of the Euro area, and the currency risk was

lower for foreign investors due to the high expectative of the new currency, supported by

strong countries like Germany and France.

The disappearance of the currency uncertainty, together with the complete elimination

of political risks, and the improvement on the ease of doing business and other international

rankings, led Spain to be a focus for foreign investors.

If we pay attention to the Globalization Indices (Figure 1), we see the evolution of

Spain in the last years, from 1970 to 2009. This positive evolution has meant a progressive

opening to foreign markets and investors.

3

The motive of this study is to see how this has affected foreign investment in Real

Estate in Spain. Specifically, I want to see how the prices per square meter on the Spanish

real estate have fluctuated according to not only national variables (as many researchers have

tried to explain) but international ones. An example of this point is the evolution of the

foreign direct investment in the last years. As we see in the Figure 2, the Foreign Direct

Investments in Spain in Real Estate have increased sharply between the periods of 1998 to

2009. To say that some authors (Fernandez­Kranz & Hon, 2006) consider 1998 as the starting

year of the Real Estate bubble in Spain.

If we compare the evolution of foreign direct investments with the evolution of the

price per square meter 3 (Figure 2), we see a correlation between both variables.

One important point on this analysis is the Foreign Direct Investors’ currencies

exchange rates compared with the Euro. In many coastal cities like Valencia, many tourists

and retirees from Spain and the United Kingdom are leaving Spain due to the sharp

fluctuation of the exchange rate between British pound and euro. This has made Spain a more

expensive place to live for them. In fact, attending to the data of the Spanish Registro de

Inversiones, United Kingdom investments in Spain, in Real Estate, dropped down from

€167.290.860 in 2006, to €63.231.110 in 2.009. In fact, as Gibler et al. (2009) point out in his

study about groups of retirees en Alicante (Spain), many of the British retirees express their

desire to remain in Spain for the rest of their lives. They have sold their homes in the UK and

may not necessarily return to a concentration of family and friends. British immigrants show

more interest in retirement housing than others, like Germans.

As we see in figure 3, the FDI can be explained between a 65% and an 88%,

depending on the year, by 7 countries.

3 1 Square meter = 10.7 Square feet = 1.549 Sq. inches

4

In conclusion, we want to analyze how foreign direct investment and other

international variables have affected the housing prices per square meter. As we will see in

the Literature Review, there are many studies about how the market efficiency on the real

estate, on the variables affecting on the housing demand, but only few studies try to analyze

international factors, being the most important topic when introducing international variables

the real estate market efficiency.

CHAPTER 2: LITERATURE REVIEW

About the studies about real estate, we see some interesting ones that try to explain the

bubble in some countries, especially in United States, United Kingdom, and Spain.

Fernandez­Kranz & Hon (2006) analyze the bubble by comparing the behaviour of the

real estate demand before and after the hypothetic bubble, while others like Ayuso & Restoy

(2006) analyze the bubble by comparing the real estate prices per square meter with the ratio

of house price­to­rent ratio.

Another point that state Fernandez­Kranz & Hon (2006) is the existence of a previous

real estate boom in Spain, in the late 80’s. If we see the evolution of prices on those years, we

see that the price per square meter increased 100% from 1985 to 1990.

And of course, all those studies were not only predicting but affirming the Real Estate

bubble. Zhou & Sornette (2005) suggested there was a real estate bubble in US earlier than

most authors. In Spain, Fernandez­Kranz & Hon (2006) showed that the rate of growth in the

price of houses in Spain between 1998 and 2003 was consistent with the existence of a real

estate bubble in that country; that house prices were between 24% and 34% above their long­

term equilibrium level.

Ayuso & Restoy (2006) study the relationship between housing prices per square

meter (about 10.7 square feet), and the house price­to­rent ratio for the US, the United

Kingdom, and Spanish markets. the authors state that the increases in the last few years have

taken house price­to­rent ratios above their equilibrium values. Even so, in Spain there are

only 1.791.475 houses available for rent, serving less than 9% of the population (OEVA,

2007). This data, and the fact that the rental house market in Spain has a huge black economy

and not reilable data, will drive my work to avoid this variable. In fact, we see other papers

6

explaining the housing demand variable that do not include the renting price variable

(Fernandez­Kranz & Hon, 2006), probably because of this lack of information.

Several papers analyzed tell us that our story, that’s to say, the oppening of the

Spanish economy to the globalisation phenomenon by decreasing political and economical

risk, works. And it works even more for the real estate. In fact, international diversification

works better for property shares than it does for stocks and for bonds (Eichholtz, 1996).

We found more examples in countries like Czech Republic, Hungary and Poland.

They have received substantial levels of FDI due to an environment of substantial

restructuring of the legal framework concerning taxation, financial status, and the repatriation

of profits in foreign exchange (Adair, Berry, McGreal, Sykora, Ghanbari Parsa, & Redding,

1999).

The housing market can be influenced by macro­economic variables, spatial

differences, characteristics of community structure, and environmental amenities (Kim &

Park, 2005). In previous research on house prices, we see that some variables that at first

sight could be significant are not. For example, we found that the size of the MSA,

population growth, employment (unemployment rates in our models), and per capita incomes

drive house prices in the expected directions, at least in the early years of our observation

period, through 2003 (Coleman IV, LaCour­Little, & Vandell, 2008). In the other hand,

mortgage interest rates were not found to have a significant relationship with house prices

when other factors were taken into account (Coleman IV, LaCour­Little, & Vandell, 2008).

The construction cost index for housing, proxied in this study by the CPI index, was found to

have a marginally significant positive influence on house prices, but only during regime I

(1998–2003) when economic fundamentals were most influential (Coleman IV, LaCour­

Little, & Vandell, 2008). Other analysis of Barot (2002) on house prices and housing

investment points out that both nominal and real interest rates matter for house prices in

7

Sweden and the UK, indicating the results that Sweden has stronger interest rate effects both

on the short and the long term. To say the analysis was made for the period 1970­1998,

previous to the huge financial crisis.

Mikhed & Zemcik (2009) studied the effect of fundamental variables, including real

house rent, mortgage rate, personal income, building cost, stock market wealth, and

population. They wanted to test whether recently high and consequently rapidly decreasing

U.S. house prices were justified by fundamental factors. Results: the house price does not

align with the fundamentals in sub­samples prior to 1996 and from 1997 to 2006. It appears

that the real estate prices take long swings from their fundamental value and it can take

decades before they revert to it.

The evidence suggests that in the process of rising prices, the reason investment has

played a major role (Garcia Montalvo, 2005). In the previous boom (until 1996), key factors

in price increases were real, while in the beginning in '98 were determinants of a financial

nature: interest rates, credit conditions, and evolution the stock market. Here we see a good

argument to do our research; authors suggesting that there are not only national economical

factors like many researchers were analyzing, but other factors like investment, and probably

FDI.

Other papers talk about other factors affecting to house prices like credit restrictions.

Relaxation of credit restrictions contained either in the growth of housing credit or the

decrease in the nominal interest rate applied to this, explains an important part of the growth

of housing prices (Martinez Pages & Angel Maza, 2003). Elíasson & Pétursson (2009)

explain how in the case of Iceland the structural changes in the domestic mortgage market led

to a lowering of real mortgage interest rates, and easier access to credit, having significant

effects on household behaviour and led to a substantial rise in house prices, which fuelled a

domestic spending spree and contributed to an overheating of the economy.

8

Are real estate trends predictable by fundamental factors in the economy? Yes, even

they do not explain most of the variation in the property prices in the short run (Quigley,

1999).

Can exogenous trends in real estate prices – really bubbles in this market – affect

economic fundamentals? Bubbles in Asian markets had real consequences for the course of

national and regional economic conditions during the late 1990’s.

About the Foreign Direct Investments (FDI), Departamento de Balanza de Pagos

(2007) considers that FDI has important potential effects, both on the host and the receiving

economy, which not only derived from its magnitude, but also its more stable and long­term

funding and other positive externalities on the production and dissemination of technology

associated with this type of investment.

However, this conventional view of FDI has recently received much criticism, see

Razin (2002). It has been suggested that the preponderance of FDI may be a symptom of

institutional weakness. On the other hand, the stability of FDI during the Asian crisis could

be conditioned by the acquisition of low­cost companies, see Krugman (1998).

In Spain there are two sources of FDI transaction data. The first, developed by the

Bank of Spain, is the balance of payments, and follows the methodological criteria specified

in the Fifth Balance of Payments Manual of the IMF. The second is developed by the

Ministry of Industry, Tourism and Trade, based on information compiled by the Registry of

Foreign Investments (RIE 4 ). In a paper from the Spanish Bank, Inversión Exterior Directa en

España, Comparación de las Fuentes nacionales, while the evolution of the data from both

sources is broadly similar, the value of direct investment liabilities of the balance of

payments is significantly higher. These differences are due in large part to methodological

differences, highlighting the different coverage concepts, which is far more extensive in the

4 RIE: Registro de Inversiones Exteriores (Registry of Foreign Investments)

9

case of the Balance of Payments data. In fact, data from the RIE only consider the shares in

the capital. However, while building a homogeneous series, i.e. restricted to shares in the

capital, since 2000 appears again a significant gap in FDI data in Spain, though, this once, to

the contrary, that is, data from the RIE outnumber those published by the balance of

payments. This gap is due to compensation with other items included in the Balance of

Payments and the more intense decrease in the balance in the data of the RIE of the shares in

the capital. Other methodological differences concerning the time of recording and the

criteria of geographical and sector allocation would not affect much the overall level, but the

data held to a lower level of disaggregating. Both sources provide information relevant to

analyze the evolution of FDI, in fact, the RIE is one of the sources of information in the

balance, although the latter source data are internationally comparable, due to compliance

with the standards set by BPM5 (See Appendix A).

Other papers:

Englund & Ioannides (1997) compare the dynamics of housing prices in 15 OECD

countries. The data reveal a remarkable degree of similarity across countries and suggest rich

dynamics for the first­differenced real house prices, with a significant structure of

autocorrelation. They estimate a highly significant first­order autocorrelation coefficient at

around 0.45 and obtain signs of negative autocorrelation for lags up to the fifth order. These

results imply oscillatory behaviour around a trend for house prices.

Schreyer (2009) shows how by combining the short­run and long­run formulation,

unobserved risk premia and asset price expectations cancel out except for the long­term

change in an overall price index such as the CPI.

The methodology will be based in Graham & Hall (2001) and Burrus et al. (2009), base on

Ordinary Least Squares as we will see in the methodology section. In fact, as we only have a

dummy variable, ECU, we don’t need to use analysis of variance techniques. As Cody &

10

Smith (1997) states, only if all of your independent variables are categorical (or most of

them) you may be better off using analysis of variance techniques.

CHAPTER 3: DESCRIPTION OF THE DATA

The data used in this study are extracted from the following sources: Bank of Spain,

Instituto Nacional de Estadística (INE), Ministerio de la Vivienda, Sociedad de Tasación, and

Bloomberg databases.

The dependant variable will be the price per square meter, obtained from the Sociedad

de Tasación; that’s to say, we will get the property valuation model based on the prudent

valuation certified by authorised appraiser, as used by Catte, P. et al. (2004).

I initially exclude some variables like the rental rate. It could be an important determinant

of the price, but I exclude it because of two main reasons. First of all, the lack of reliability of

the data on renting matter. Second, less than 9% of households in Spain live in rented

properties while the percentage of families that live in their own property has increased

steadily since 1970: from 70% in 1970 to 85% in 1991, and to 88.5% in 2001 (Bank of Spain,

2006). This may be attributed to the rigid legal system protecting the rights of tenants who

rent (resulting in owners sithdrawing their properties from the rental market) and tax

incentives for home ownership.

Analyzing the different graphs of the national variables, see for example that the

evolution of the price per square meter and the IBEX maintains several stages, taking the

IBEX as an indicator of the economic cycle in Spain, we see that when the IBEX was

increasing its value between 1994 and 1999, the price development was much more moderate.

However, with the change of cycle in 1999, the price began to rise significantly, probably

because people began to see the active housing shelter their investments because the price of

housing in Spain, historically, has always had a positive development. In mid 2002, with the

change of the economic cycle, housing prices remain unchanged; explanation for this

phenomenon is given by the values that made up the IBEX. Given the exponential growth

12

given in the price of housing, construction companies and real estate in Spain also grew

exponentially, so much of the big companies in the sector will begin trading on the index

(remember that the IBEX 35 is an index composed of the 35 Spanish companies with greater

market capitalization). Thus, major companies like Sacyr­Vallehermoso, Metrovacesa,

Huarte Lain, Fadesa, and Colonial began to form part of the index. The same happened with

infrastructure management companies as many urban projects were required. In addition,

service companies and banks began to multiply their benefits because of their involvement in

the urban layout. This means that the evolution of a sector began to direct the development of

the IBEX, and therefore the economic cycle.

Another important point to notice here when we analyze the IBEX is the fact that its

evolution does not seem to be accompanied in the late 90's and early 2000 by other factors

that are affected by the economic cycle. In fact, we see that the GDP and Unemployment

Rates in the first case are constant increases and decreases in the latter case, indicating that

Spain was not really in a negative cycle. By contrast, production was increasing and

unemployment decreasing. In fact, other factors shaped the crisis in the Madrid stock

exchange, such as technological crisis among others. Thus, dismissing the IBEX variable as

an indicator of the economic cycle so as not to influence other factors in our analysis, as was

the tech bubble.

Finally remark that, since the Spanish stock market is strongly correlated with stock

markets in the United Kingdom and the United States because of the relationships studied in

this project is the influence of international markets in the Spanish property market We

believe the index should include either British FTSE100, with a correlation of 0.8601 on the

IBEX, or the U.S. index, S & P500, with a correlation of 0.9062, since this way include one

of the international variables model, which would be affected as the price of housing to

changes in international business cycles. In fact, the S & P500 and the FTSE are strongly

13

correlated with each other (0.8212) as shown in table 4, and they are two indices that in a

globalized world eliminate any possible effect of an internal crisis in Spain, as was the tech

bubble. In fact, as we see in tables 10, 11 and 12, where we use FTSE, SP500 or IBEX, we

see very similar results, while if we apply two or all of them, autocorrelation problems

appears.

These data are reinforced by the variable Houses completed (figure 8, panel 2). We

see continued growth of it, explaining the effect of shelter in the same asset, not only

benefited from interest rates to fall, but through the credit facility, which in our analysis

pointed out the difference between Interest Rates and Mortgage the Interest rates. In Fact,

The Relationship between Interest mortgage rates, the stock market and house price index is

less clear­cut, as Fernández­Kranz and Hon (2006) pointed out. The Mortgage Interest Rates

(both in real and nominal terms) were high firs during the housing boom in Spain, in the last

years of the 80s and 91 (at an average of 15% and 10% for the nominal and the real Interest

rate respectively. It Both Decreased sharply in real and nominal Terms Between 1991 and

1997 (when decreased real estate prices in real terms), and will continue declining in the

second housing boom. So, as we pointed out in the literature review, and as it is noted in the

graphs, both variables are not significant explaining the variations of prices on real estate.

However, it is relevant the ease of credit (Pagés & Maza, 2003), so we will consider this

variable for our model.

Regarding strongly correlated variables such as population, population, GDP, and

Unemployment Rates, will choose the latter, because in a country like Spain, with a historical

structural unemployment rates, its evolution has been used as a fairly reliable indicator of the

economic situation in the country. In fact, in recent times, with rates of adverse evolution of

GDP, the UR follows the same steps. But as we see in the correlation matrix (table 4), this

14

variable is highly correlated with the EASY credit; as unemployment rate decreases, the

banks are more confident when giving credit to the people.

Taking on account the salaries, we will take the Personal Income per capita, because

includes not only the income from labour force but the income of other activities made by

citizens, like renting assets. In fact, as we see in the figure (put number), we see a very

similar evolution between the Average Salary and the CPI, so doesn’t look a determinant

factor at first sight.

Other indices which have considered the possibility of incorporating in the model are

the Real Estate Investment Funds (REIT) in the U.S. and Europe. We found great variety of

them in the U.S. since 1995, date when we start our data collection, but not so with REITs in

Europe. Indeed, using Bloomberg as a database, we only found an index that had the

longevity required in Europe, SIIC de Paris 8ème (rents of commercial property leases Such

as supermarkets, offices, warehouses, restaurants, hotels, hospitals and industrial space. The

properties are rented or leased in France, Primarily in the Paris region; Bloomberg code:

BSHO FP), given that only works the market of Paris, we see that is not a representative

indicator. Among them, shall elect the American index Residenti EQUITY (Bloomberg code:

EQR U.S.), since not only is one of the Real Estate in U.S. with greater market capitalization

(first one of the REIT Apartments and fourth of the total REIT's), but because it is a trust

That Acquires, develops and manages apartment complexes in the U.S., so it is fairly

representative of the market. About the US REIT’s, we see high correlation between the two

we collected data about (See table 1), what give us confidence on how representative is the

REIT selected.

In Spain two series of financial transaction data of FDI are published monthly. One of

them is released by the Bank of Spain, as part of the Balance of Payments statistics and the

other published by the State Department for Trade of the Ministry of Industry, Tourism and

15

Trade (RIE). As the Departamento de Balanza de Pagos (2007) states, both sources provide

information relevant to analyze the evolution of FDI, in fact, the RIE is one of the sources of

information in the balance, although the latter source data are internationally comparable, due

to compliance with the standards set by BPM5. As our goal is not to compare the data with

other countries, we will use the data from the RIE as it is disaggregated by sector, so we can

take real estate investments only (code of activity 68).

Concerning the exchange rates, the two exchange rates that are most interesting for

our study are the U.S. Dollar exchange rate versus ECU, and the British Pound (GBP) versus

XEU (ECU). Both of them are highly correlated, 0.7819. We will use in our model the GBP

due to the importance of the FDI of United Kingdom residents compared with the US ones

(See Figure 3). To say that we will use the Euro (EUR) before called European Currency Unit

(ECU) in our exchange rates (see table 3 for more explanations about the ECU and Euro).

CHAPTER 4: CONCEPTUAL MODEL, RESEARCH OBJECTIVE, RESEARCH QUESTIONS AND HYPOTHESIS

Hypothesis.

Null Hypothesis: House prices are not significantly associated to macro­economic

variables and international factors.

Research Question: How international variables are related to the house prices in Spain?

Data.

Quarterly data from March 1.995 till September 2.009; some data is not available

quarterly, like the Corruption Perception Index, the Globalization Index (KOF), and the

Population; the data of those variables is annual.

In a first draft of our analysis, we converted the quarterly data into monthly data. In the

case of variables like the GDP, that is presented quarterly, I made the summation of three

months, dividing the quarterly amount between three months in order to share between those

months. In data like price per square meter (Y), I put in the lacking­of­data months the

previous quarter data amount plus the difference between the current quarter and the previous

quarter divided by three. Even so, when we run the regressions with monthly data adjusted,

the Durbin­Watson test used to show autocorrelation in all of them.

Once seen this problem, the following step was to use only quarterly data, what gave us

significant regressions, with no autocorrelation or heteroskedasticity. In order to test them,

we used Durbin­Watson test and the specification test.

The empirical model.

We consider that the price per square meter on housing will depend on a series of

international and national variables, like international and national stock exchange indices

(FTSE, SP500, IBEX); exchange rates (ER$, ER£); national macroeconomic variables, like

17

population, consumer price index, oil prices, interest rates, mortgage interest rates, gross

domestic product, houses completed, unemployment rate, active population, trade of balance,

foreign direct investments on Spain; and other international variables, like the globalisation

index (see tables 2 and 3 to see the codes description).

Y = D (FTSE, SP500, IBEX, ER$, ER£, POP, CPI, OIL, IR, HLOANS, EASY, FDI,

FDIAD, GDP, REIT$, REIT€, IX, HCOMP, UR, POPAC, PI, MG, KOF, CORRPI, ECU,

HPUK)

Relationships to Analyze:

• House Prices & Spanish macro­economic variables (GDP, Interest Rates,

Mortgage rates, Unemployment, IBEX, CPI, Population, Unemployment Rate)

• House Prices & International factors (S&P500, FTSE100, ER, KOF, REIT’s,

House Prices in UK)

• House Prices & International factors affecting directly to Spain (FDI’s, ECU, IX,

ER$, ER£)

The data I will use in this study will be extracted principally from the following

sources: Instituto Nacional de Estadística (INE), Bank of Spain, Sociedad de Tasación,

Registro de Inversiones, and Bloomberg data base. (See Variables and Sources of Data for a

more detailed breakdown).

In order to evaluate the relationships described above, a series of regression analyses were

performed using ordinary least squares (OLS), which produced coefficient estimates of the

independent variables. Additional regressions were performed to determine whether or not

the different independent variables analyzed were more strongly related to real estate value

during the bubble and crisis years than the years previous to the bubble very beginning. This

conclusion was based on F­statistics, which were analyzed for their significance.

The following conceptual model was employed in the OLS regression:

18

Yi = ß0 + ß1FTSEi + ß2SP500i + ß3IBEXi + ß4ER$i + ß5ER£i + ß6POPi + ß7CPIi + ß8OILi +

ß9IRi + ß10HLOANSi + ß11EASYi + ß12FDIi + ß13FDIADi + ß14GDPi + ß15REIT$i +

ß16REIT€i + ß17IXi + ß18HCOMPi + ß19URi + ß20POPACi + ß21PIi + ß22MGi + ß23KOFi +

ß24CORRPIi + ß25ECUi + ß26HPUKi +

About the dependant variable, we take the difference between periods in order to have a

normal distribution. If we have a look to the table 2, if we take only the price per square

meter, the skewness is higher than zero, 0.228, so the distribution is right skewed, being the

Media higher than the mode. As we want the dependant variable to have a normal distribution,

we take the difference between periods of Yi, which give as a skew value close to zero, and

the mode and the median are almost the same.

As we have quite a big number of dependant variables, one problem we have to take on

account is the multicollinearity. Also known as collinearity between variables occurs when

two or more x variables can be expressed as linear combinations of one another.

If we want to predict with our model, multicollinearity is not a big issue, but as we are

interested in testing hypothesis about the effect/influence of the dependant variables on Yi,

we have to take on account the evidence of potential multicollinearity. That’s to say, we have

to see first if we have high correlation coefficients between variables; and second we have to

see if relatively high R 2 couple with relatively low t­statistics, or we have unexpected signs.

The solution we will adopt if we have or we suspect multicollinearity will be to remove

one of the variables, estimating previously two models, one with each of the variables. This

way, in order to avoid the multicollinearity, we have divided the previous model in the

following ones:

General Model:

Yi = ß0 + ß1FTSEi + ß2ER£i + ß3CPIi + ß4OILi + ß5EASYi + ß6FDIADi + ß7REIT$i + ß8IXi

+ ß9URi + ß10PIi + ß11MGi + ß12KOFi + ß13CORRPIi + ß14HPUKi +

19

Foreign Factors Model:

Yi = ß0 + ß1FTSEi + ß2ER$i + ß3ER£i + ß4FDIADi + ß5REIT$i + ß6IXi + ß7KOFi +

ß8HPUKi +

National Factors Model:

Yi = ß0 + ß1CPIi + ß2OILi + ß3EASYi + ß4HCOMPi + ß5URi + ß6PIi + ß7MGi +

ß8CORRPIi +

CHAPTER 5: RESULTS AND DISCUSSION

The approach chosen is, as mentioned above, essentially empirical. It is not intended

to estimate or identify structural relationships, but only to analyze the dynamic relationship

between housing prices and their main determinants in Spain, including external factors. To

do this, we estimate equation’s error correction mechanism (ECM), using quarterly series

since 1995 until 2009. We have calculated these quarterly series as well, and separated into

two periods: from 1995 to 1998, the period previous to the real estate boom, and from 1999

to 2009, the period when the boom began, consolidated, and melted. We tried to calculate the

meltdown period per separate, but we had only data from three years, 1995 and 1997 both

inclusive, and the results were not conclusive.

Running the different regressions, we saw that some variables were causing

autocorrelation problems, as we previously expected. That’s to say, highly correlated

variables like FTSE, IBEX and S&P500 were expected to cause autocorrelation on the model.

One of the first valid regressions is the one shown in table 6. Although we have a significant

regression with no autocorrelation nor heterokedasticity, only two variables appear to be

significant, and both of them are international variables: the globalisation index (KOF), and

the house prices in the UK (HPUK).

In the first regression, by mixing international and national factors (table 6), we see

very highly significant variables, like EASY, KOF and HPUK, significant at 1% level, and

MG and FDIAD significant at 10% level.

By separating the national and international factors we obtain quite relevant

information about which variables are affecting the prices, and which are insignificant. We

found that, depending on the period of time for which we ran the regression, the different

variables gave different effects on the house prices.

21

National Factors (tables 11 to 14).

We see that UR is a relevant variable when we make the regression between 1995­

2009, and 1999­2009. When people start feeling some job security due to the decrease in

unemployment rates, it moves them to buy more houses. This decline in unemployment also

led banks to facilitate access to credit. When the bubble started, banks took no interest in

determining a borrower’s level of job security when granting a loan. We see this in the table

14; analyzing only the boom period, we see that UR is not significant at all.

As happens with the UR, MG is a strong variable in our models but not in the period

1995­1998. When the housing boom started, the residential mortgages increased

exponentially, reinforced as well by the EASY variable, as we will see later.

A particular issue in the residential mortgages is that, as we see in the Figure 8 panel 4,

the number of residential mortgages goes up all the time, even in 2009. Some news gives us

an idea about why this is happening. As Money & Investing points out 5 , home loans are

getting easier for Spaniards. Banks, under the weight of an estimated €59.7 billion ($73.8

billion) in real­estate assets on their books, and (under) the pressure to make further

markdowns on the assets by their main regulator, the Bank of Spain, many banks are now

scrambling to unload the properties as quickly as possible.

PI is not a significant variable in our models. This result contrast with previous studies

about the Spanish market for houses, which implied that personal income is a variable that

has been assuming a weak role in real estate value (Fernández­Kranz & Hon, 2006).

One important observation of our national regressions is the significance of the EASY

variable. In fact, new products like Bridging loans or mortgages 6 , and the wrong assumption

5 Money & Investing (June 21, 2010), Home Loans Get Easier For Spaniards, by Sara Schaefer Muñoz & Christopher Bjork. 6 Crédito o hipoteca puente (bridging loan or mortgage): This is a rather curious form as the bank or financial institution makes a loan to us for our financing, and with the guarantee that it will enter later with interest. The mortgage bridge the often ask people who need to acquire a new property and also do not have time to sell your present home.

22

that prices would increase permanently, reinforced this easy credit. We see in our regressions

that it wasn’t a relevant factor before the boom, but gets important during it, obtaining

significance in our model at 5% level.

CORRPI is not a relevant variable and it only appears to be important in our

regression on the period 1995­1998 (table 14). Nevertheless when the index gets worst in

many cases, we learn of the corrupted event that occurred in prior years. Therefore, the index

takes the corruption cases of the present and the previous years (see appendix C) and we

show that while prices were going up, the corruption index was improving, because we still

were unaware of the corruption. This is when corruption cases began to appear in the news,

and prices were still going up.

In this model, we see several variables which are not relevant, confirming some of the

results of previous researchers. That’s the case of the CPI variable, insignificant in all our

models, and the IR and HLOANS variable, historically insignificant as shown by our analysis

and previous works. Because of the historical decrease of the IR, moved householders to

increase the demand of houses, pushing the prices up.

International Factors (tables 7 to 10).

In all our models, KOF is strongly significant, and the coefficient is negative, so when

KOF increases, prices decrease. In fact, when globalisation increases, not only does capital

comes to Spain, but it leaves as well. That’s to say, the index accounts for not only the

foreign inflows but the outflows as well. We have cases of big Spanish multinational

corporations that in the last years have been investing abroad, not only in South America as it

had been in the first years of the nineties, but all around the world. One particular case is the

23

Bank of Santander 7 , which after selling real estate actives by the amount of 4.398 million

Euros between November 2007 and January 2008, began to acquire international banks that

were having problems because of the financial crisis, like the US bank Sovereign, and the

British B&B and Alliance & Leicester 8 .

The negative coefficient of the KOF variable is due as well to an increased

competition among construction and real estate companies. In fact, as the index shows,

globalization is also accompanied by increased flows of information, so the transparency of

information increases, making the potential abnormal returns decline.

FDIAD is a barely relevant variable. As the FDIAD increases, the price per square

meter increases. Obviously, if international investors invest in Spain, the real estate demand

increases, increasing the prices. This variable, at first sight seems to be one of the more

logical explanatory ones, but even so, we found it has a significance of 10%. The explanation

can be the way the data is collected. As we can see in appendix A, 2.F, the RIE are

introduced the acquisition of properties in Spain when the total exceeds 500 million pesetas

(equivalent to €3,005,060). If the investment comes from countries listed in the Royal Decree

1080/1991, of 5 July, countries considered as tax heavens, then RIE consider all the

investments. That’s to say, many personal investments like the British and Germans that want

to retire in Spain and buy a house, are not considered by the RIE.

About the other component of the Trade of Balance, the Net Exports variable (IX), results

show us that are barely significant, like the FDIAD. In this case, the coefficient is negative,

so when exports increase, the price per Sq. meter increases. The coefficient has a low value

due to the poor logical connection we find on it. If the Net Exports increase the Trade of

Balance increases as well, increasing the output of the economy and the money coming to the

7 http://elinformadorinmobiliario.wordpress.com/2008/01/30/banco­santander­alcanza­un­ acuerdo­para­la­venta­de­la­ciudad­financiera­por­1900­millones­de­euros/ 8 Diario Expansión (06/13/2009), Olga Grau.

24

country. As there is more money in the economy, people want to spend more money, which

drives the country to have inflation.

HPUK is a relevant variable in all the models we have analyzed. In fact, when UK

real estate prices goes up, the Spanish real estate prices goes up. This shows us not only the

high correlation of both markets, but how higher increases on the housing prices in UK

brought some investors to Spain, principally people looking for a cheap place to retire. That’s

to say, as prices on UK were going up, some investors where deciding to invest in Spain,

which increased the demand for Spanish houses and the housing prices.

The effect of the variable HPUK is reinforced by another statistically significant

variable, the British Pound exchange rate versus the Euro. This variable has a positive

coefficient, which tells us that as the GBP gets more value against the Euro, the price per sq.

meter in Spain increases. It has a logical consistency from the point of view that is cheaper

for them to buy a house in Spain with their currency. Let’s see a numerical example of this: a

British investor in January 2000 wants to retire and to buy a house. The average cost of a

house in UK is £98.221. If he buys a house in Spain of 100 Sq. meters, the price will be 100 x

€1,200 (price per sq. meter), which equals €120,000. Taking the exchange rate at the time,

the Spanish house value in GBP would be £72,240. That means £25,000, a 35.96% difference.

That’s why people used to sell their houses in UK in order to buy a house in Spain and retire.

Even though, when analyzing only the bubble period, 1998­2007, we found out that the

variable exchange rate is not that relevant. In fact, along the bubble years, the value of the

GBP was quite stable against the EUR, but with the financial crisis, UK was one of the first

countries to pay the consequences, so for British residents in Spain, a majority of whom were

retired, began to have problems because of the cost of living in Spain increased with the new

exchange rate.

25

The British Stock Market Index (FTSE) has significance and a positive coefficient.

The logic behind these results is that as stock market goes up, investors have more capital

gains, so they have more disposable income to diversify in other kind of investments; and as

we saw previously, Spain was a good investment because of the evolution of the HPUK and

the ER£.

In this model we see a few variables which are not relevant, like the dummy variable

ECU. Although ECU is not relevant, the financial bubble begins with the entry of Spain into

the European Currency. The launch of the European currency in 2000 considerably improved

the globalisation index, raising it from 77.91 in 1997 to 85.49 in 2000. From 2000 on, the

index hasn’t changed that much, achieving the maximum value (85.71) in 2007. Furthermore,

both variables, KOF and ECU, have a correlation of 0.8969, what bring us the conclusion that

the effect of the ECU is included on KOF.

Finally, in reference to this model, we see the differences we obtain when analyzing

the data before the year the bubble started, during the bubble, and the crisis period. In fact,

we see that between 1995 and 1998 the only international factor that was affecting the prices

was the FTSE, while the others were not significant. This point is very important from the

view that one of the main reasons of the price increases was the increase of investments,

which is due to national and foreign investors, who could be either speculators or people

looking for a home or a second home. As we see in figure 7, the increase in population

wasn’t enough to cover all the houses completed. If we have a quick look at the numbers,

doing the summation of all the houses completed, and we compare with the increase of

population, we have 12.7 million houses completed against 6.4 million new habitants in the

country. Without attempting to analyze this data, we see that the speculation is an important

factor on the demand for houses; and as we obtain from the regressions, one important

component is the international demand.

CHAPTER 6: CONCLUSIONS

In this paper, we have argued that the real estate price overvaluation was due to, not

only national factors like the decrease of the unemployment rate, the decrease between the

mortgage interest loans and the Euribor, and the increase of the mortgages signed, but also

international factors. In fact, we see how poor the influence of international factors was on

the real estate prices before the housing boom, and how relevant they were from 1999 to

2009. As a result, we can assume that the boom was pushed by international factors mainly,

like the decrease on the globalization index (KOF), increase of foreign direct investments on

Real Estate (FDIAD), and the evolution of the international stock markets. All of them, with

the exception of the globalization index, show a positive relationship with the price evolution.

Some of the variables, like the exchange rate British Pound (BPD) against Euro (EUR), show

some significance depending on the period analyzed. This is an example of the positive

relationship shown with the dependent variable as with other variables analyzed.

TABLES

Table 1: Correlation Matrix between two US REIT:

REIT$ REIT$ REIT$ PEI 1 REIT$ EQR US 0,860097 1

Table 2: Dependant Variables. Codes, description, and sources. Code Variable Description Source

Y Price per square meter. The price per square meter of newly built houses in Spain. 1995­ 2009.

Sociedad de Tasación

Table 3: Independent Variables. Codes, description, and sources. Code Variable Description Source

CORRPI Corruption Perception Index: Since 1995, Transparency International has published it annually ordering the countries of the world according to the degree to which corruption is perceived to exist among public officials and politicians. The organization defines corruption as "the abuse of entrusted power for private gain". A higher score means less (perceived) corruption.

International Transparency

CPI Consumer Price Index: statistical measure of the evolution of the prices of goods and services consumed by the population that reside in family dwellings in Spain. The combination of goods and services in the shopping basket is basically obtained from the consumption of families, and the importance of each one of these within the calculation of the CPI is determined by said consumption.

Instituto Nacional de Estadística (INE)

EASY Easy of Credit: Difference between the variables HLOANS (Mortgage Interest Rate) and IR (Interest Rates)

ECU Dummy variable of Euro: 1 when Spain entered to the Euro area, 0 before it.

ER* (ER$) Exchange Rate USD­XEU 9 : Exchange rate between US Dollar and the European Currency Unit (ECU), called EURO in 1995.

Bloomberg

ER0 (ER£) Exchange Rate GBP­XEU: Exchange rate between British Pound and the European Currency Unit (ECU), called EURO in 1995.

Bloomberg

FDI Foreign Direct Investment: sum of absolute values of inflows and outflows of foreign direct investment recorded in the balance of payments financial account. It includes equity capital, reinvestment of earnings, other long­term capital, and short­term capital.

Bank of Spain

9 The ECU (European Currency Unit, Spanish European Currency Unit) was a unit of account used in the European Community, later European Union (EU) ­ monetary purposes. The ISO 4217 code was XEU. At its meeting in Madrid in December 1995, we decided to call the single currency Euro community, having a 1:1 parity with the ECU.

28

FDIAD FDI adjusted 10 : Foreign Direct Investment on Real Estate.

Registro de Inversiones

FTSE FTSE100. British Stock Market Index Bloomberg GDP* Gross Domestic Product. Current prices. Bank of Spain HCOMP Number of Houses Completed Sociedad de tasación HLOANS* Mortgage Interest Rate (National). Average of

mortgage interest rates applied by Spanish Banks and “Cajas”.

Bank of Spain

IBEX* IBEX 35. Spanish Stock Market Index Bloomberg IR* Interest Rates – EURIBOR 1 year. Instituto Nacional de

Estadística (INE) IX Trade Balance: Exports minus Imports Bank of Spain KOF KOF globalisation index: measures how open an

economy is to the international markets. http://globalization.kof.ethz.ch/

MG Financial Balance Sheet for Households & NPISHs Residential Mortgages. NPISHs are Non Profit institutions whose principal earnings come from donations and contributions of Public Administration. Households include wage earners, those who earn through property rental, pensioners, recipients of other transfer payments and individual businessmen whose business is not conducted as in a corporation.

Bank of Spain

OIL Oil Prices: price of the North Sea Brent oil. Spot price in USD per barrel.

Bank of Spain

PI Personal Disposable Income Eurostat POP Population Instituto Nacional de

Estadística (INE) POPAC* Active Population: people working or looking for a

job. Instituto Nacional de Estadística (INE)

REIT (REIT$)

US Real Estate Investment Trusts – Residenti EQUITY (Bloomberg code: EQR U.S.)

Bloomberg

REIT0* (REIT€)

European Real Estate Investment Trust ­ SIIC de Paris 8ème

Bloomberg

SP500* S&P 500 Bloomberg T Time variable.

UR Unemployment Rate Instituto Nacional de Estadística (INE)

* Indicates variable was eliminated from sample due to avoid multicollinearity

10 The Ministry of Industry, Tourism and Trade offers in its website, a search tool for the Statistics of Foreign Investment in Spain: DataInvex, which contains official data and updated since 1993 (historical and comparative reports of foreign investment by country and its most important groupings, countries past, industries and communities).

29

Table 4: Correlation Matrix.

Y HCOMP

GDP POP

UR POPAC

EASY PI

HLOANS IR

MG OIL

CPI CORRPI

ER ER0

REIT REIT0

ECU KOF

FDI FDIAD FTSE

SP500 IBEX

HPUK IX

Y 1

HCOMP 0,1416

1 GDP

0,1263 0,2158

1 POP

0,2201 ­0,0381 0,0114

1 UR

­0,3060 ­0,1176 ­0,0972 0,2615

1 POPAC

0,0809 0,2364 0,0780 0,2439 0,2607

1 EASY

­0,1531 ­0,1122 0,2372 0,0134 ­0,2886

0,2307 1

PI 0,0683

0,1325 0,9860 0,0079 ­0,0642 0,0731 0,2662

1 HLOANS

0,2059 0,1683 0,1023 0,2748 ­0,2847

0,3114 0,5070 0,0954 1

IR 0,2355

0,2046 ­0,0738 0,0070 ­0,2153 0,1201 0,1189 ­0,1149

0,4719 1

MG 0,5032

0,3384 0,1851 0,3840 ­0,1987 0,4166 0,1504 0,1350

0,4636 0,2587 1

OIL 0,1618

0,2030 0,0393 0,0004 ­0,0227 0,1400 ­0,2714 0,0154

0,1084 0,3298 0,1714 1

CPI 0,2811

0,2148 ­0,2226 0,1233 0,1899 ­0,1094 ­0,9002 ­0,2597

­0,0810 0,1010 0,0603 0,3685 1

CORRPI ­0,0176

0,1173 0,0505 ­0,3310 ­0,2943 ­0,0882 ­0,0085 0,0238

­0,0812 0,0861 ­0,2252 ­0,0795 ­0,0312 1

ER 0,1096

0,1564 ­0,1142 ­0,1143 ­0,4357 ­0,2232 ­0,1100 ­0,1446

0,0271 0,2752 0,1822 0,1630 0,1409 0,0601

1 ER0

0,2314 0,1733 ­0,0879 ­0,1891 ­0,5413

­0,0973 ­0,0433 ­0,1091 0,1287 0,3407 0,1293 0,2792 0,1151

0,1148 0,7515 1

REIT ­0,0446

­0,2620 ­0,3298 ­0,1020 0,0165 ­0,1319 ­0,0678 ­0,2984

­0,1185 ­0,0239 ­0,2066 ­0,3208 0,0186 0,1164 ­0,1371 ­0,1734

1 REIT0

0,0900 ­0,2277 0,0684 0,0692 ­0,0455

0,0254 0,1008 0,0819 0,0014 0,0273 0,0832 ­0,1657 ­0,1158

­0,0452 0,0375 ­0,1421 0,1358 1

ECU 0,2752

0,0469 0,0070 0,7546 0,1974 0,2797 0,0715 ­0,0136

0,2904 0,1283 0,4950 0,0779 0,0641 ­0,4181 ­0,0528 ­0,1218 ­0,0919 0,0831

1 KOF

­0,4521 0,0211 ­0,0642 ­0,5797 ­0,0554

­0,1871 ­0,0326 ­0,0696 ­0,1298 0,0215 ­0,4685 ­0,0923 ­0,0279

0,5189 ­0,0678 ­0,0132 0,1637 ­0,1136 ­0,4626 1

FDI ­0,0348

­0,2298 0,2181 0,0439 0,1166 ­0,0582 ­0,0388 0,2374

­0,2316 ­0,3130 ­0,3464 ­0,0200 ­0,0722 ­0,0204 ­0,3926 ­0,2224 ­0,0070 ­0,1046 ­0,0298 ­0,0665

1 FDIAD

0,1651 ­0,0646 0,1811 ­0,0264 0,0688

0,0330 ­0,0047 0,1983 0,2056 0,1318 ­0,0052 ­0,1091 0,1093

0,0297 ­0,0624 ­0,0489 0,1315 0,1790 ­0,0519 0,0183 0,0962 1

FTSE 0,2212

­0,0268 0,1655 ­0,2115 ­0,0418 ­0,1371 ­0,1143 0,1626

­0,0017 0,0608 0,0555 0,0280 0,1313 0,2144 ­0,0348 ­0,1815 0,2423 0,2901 ­0,1920 0,1478 ­0,1653 0,1044

1 SP500

0,1234 ­0,0473 0,0159 ­0,2720 ­0,0423

­0,1271 ­0,1559 0,0252 ­0,0855 0,0360 ­0,1067 0,0169 0,1370

0,1542 ­0,0571 ­0,0984 0,3208 0,1890 ­0,2296 0,3335 ­0,1329 0,0337 0,8212 1

IBEX 0,1275

­0,0376 ­0,0237 ­0,2221 ­0,0811 ­0,2441 ­0,1677 ­0,0294

­0,1276 ­0,0355 0,0114 ­0,1006 0,1294 0,1602 0,1402 0,0353 0,3459 0,2806 ­0,2587 0,1899 ­0,1219 0,0970 0,6892 0,5897

1 HPUK

0,6236 ­0,1632 0,0719 0,0034 ­0,3986

0,0819 0,1456 0,0610 0,2255 0,2217 0,3999 0,2864 ­0,0544

0,0650 0,0915 0,2944 ­0,1211 0,0794 0,2326 ­0,2384 0,0876 0,0182 0,1854 0,1411 0,0627 1

IX ­0,1419

0,0598 0,0490 ­0,1417 0,0802 ­0,1621 0,0303 0,0552

­0,1132 ­0,0104 ­0,0250 ­0,0432 ­0,0922 ­0,0152 0,0748 0,1091 ­0,0954 ­0,1358 ­0,1752 0,0726 ­0,1032 0,2427 0,0741 0,1547 0,2075 ­0,0961

1

30

Table 5: Descriptive Statistics.

Mean Standard Error Median Standard

Deviation Range Minimum Maximum

Y 6,90 1,34 7,30 10,33 47,67 ­20,13 27,53 HCOMP 2.355 1.101 3.392 8.458 64.233 ­53.917 10.317 GDP 808 485 0 3.724 15.789 ­7.317 8.473 POP 35.984 4.653 40.981 35.742 145.646 ­65.888 79.757 UR ­0,0337 0,0363 ­0,0667 0,2787 1,9767 ­0,8267 1,1500 POPAC 19,38 2,31 21,33 17,76 111,17 ­68,83 42,33 EASY ­0,1380 0,0453 ­0,1420 0,3479 1,3740 ­0,8780 0,4960 PI 792 606 0 4.657 18.888 ­8.181 10.707 HLOANS ­0,0567 0,0198 ­0,0640 0,1520 0,7680 ­0,5030 0,2650 IR ­0,0271 0,0250 ­0,0210 0,1924 1,2650 ­0,8980 0,3670 MG 4,0546 0,4262 3,4240 3,2736 12,3990 ­0,6260 11,7730 OIL 0,3907 0,6011 0,5200 4,6168 27,3400 ­16,1100 11,2300 CPI 0,0814 0,0392 0,1000 0,3008 1,2000 ­0,5000 0,7000 CORRPI 0,1254 0,0598 0,0000 0,4592 1,9900 ­0,4000 1,5900 ER ­0,0063 0,0034 ­0,0010 0,0264 0,1525 ­0,1044 0,0481 ER0 ­0,0041 0,0048 ­0,0024 0,0370 0,2397 ­0,1851 0,0546 REIT ­0,0027 0,2135 0,1000 1,6397 8,4300 ­5,0400 3,3900 REIT0 0,0476 0,0283 0,0020 0,2172 1,3080 ­0,3980 0,9100 ECU 0,7288 0,0584 1 0,4484 1 0 1 KOF 0,6277 0,0958 0,5225 0,7362 2,3230 ­0,4269 1,8961 FDI ­182.198 387.137 ­119.687 2.973.659 21.944.367 ­11.605.483 10.338.883 FDIAD 4.233 40.023 8.852 307.425 1.622.312 ­756.079 866.233 FTSE ­2,12 28,51 30,40 219,00 1.160,80 ­734,10 426,70 SP500 4,73 6,42 10,34 49,31 252,54 ­120,38 132,16 IBEX 8,72 66,03 114,20 507,17 2.863,70 ­1.554,70 1.309,00 HPUK 822 160 914 1.230 6.436 ­3.602 2.834 IX ­299.973 97.422 ­392.149 748.314 3.907.474 ­1.528.466 2.379.008

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Table 6: Determinants in the Price per Sq. Meter (Y): 1995­2009.

TOTAL PERIOD: 1995­2009 R Square 0,684057 Adjusted R Square 0,583530 Significance F 0,000000 F 6,804700

Coefficients Standard Error t Stat P‐value Intercept 0,907062 2,776412 0,326703 0,745442 UR ­5,465144 4,611143 ­1,185204 0,242299 EASY*** ­10,694019 3,432546 ­3,115477 0,003229 PI ­0,000084 0,000276 ­0,304970 0,761827 MG* 0,667772 0,333340 2,003277 0,051331 OIL ­0,374793 0,278465 ­1,345924 0,185221 CPI 1,478784 1,890774 0,782105 0,438341 CORRPI 1,618269 2,435666 0,664405 0,509898 ER0 9,027930 33,257218 0,271458 0,787309 REIT ­0,127544 0,665271 ­0,191717 0,848846 KOF*** ­4,475028 1,611358 ­2,777178 0,008027 FDIAD* 0,000006 0,000003 1,959765 0,056376 FTSE 0,006164 0,004890 1,260568 0,214109 HPUK*** 0,003654 0,000942 3,880175 0,000345 IX ­0,000001 0,000001 ­1,093813 0,279989 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

32

Table 7: Determinants in the Price per Sq. Meter (Y). International Factors: 1995­2009.

TOTAL PERIOD: 1995­2009 R Square 0,68505 Adjusted R Square 0,58484 Significance F 0,0000004 F 6,83612

Coefficients Standard Error t Stat P‐value Intercept 6,93878 1,64148 4,22714 0,00010 ER$ ­50,78034 58,42786 ­0,86911 0,38894 ER£* 77,53720 45,54663 1,70237 0,09490 REIT ­0,00421 0,62246 ­0,00676 0,99464 KOF*** ­5,47751 1,39499 ­3,92657 0,00026 FDIAD* 0,00001 0,00000 1,90929 0,06197 FTSE** 0,01150 0,00504 2,28346 0,02669 HPUK*** 0,00333 0,00094 3,54916 0,00085 IX ­0,0000022 0,0000014 ­1,61171 0,11332

* denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

Table 8: Determinants in the Price per Sq. Meter (Y). International Factors: 1995­1998.

PERIOD: 1995­1998 R Square 0,66285 Adjusted R Square 0,27754 Significance F 0,24444 F 1,72030

Coefficients Standard Error t Stat P‐value Intercept ­2,91177 3,28533 ­0,88630 0,40488 ER$ ­167,47845 213,95586 ­0,78277 0,45942 ER£ 193,81920 105,48099 1,83748 0,10875 REIT$ ­3,19879 2,31753 ­1,38026 0,20997 KOF 6,94420 4,20860 1,65000 0,14293 FDIAD ­0,00002 0,00002 ­1,45111 0,19004 FTSE* 0,03725 0,01653 2,25317 0,05892 HPUK ­0,01371 0,01457 ­0,94123 0,37793 IX 0,0000051 0,0000044 1,16113 0,28365 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

33

Table 9: Determinants in the Price per Sq. Meter (Y). International Factors: 1999­2009.

PERIOD: 1999­2009 R Square 0,68819 Adjusted R Square 0,61482 Significance F 0,000001 F 9,37998

Coefficients Standard Error t Stat P‐value Intercept 9,17637 1,85738 4,94050 0,00002 ER$ ­77,16761 60,19559 ­1,28195 0,20853 ER£* 99,96748 50,20874 1,99104 0,05456 REIT$ 0,33239 0,61990 0,53620 0,59531 KOF*** ­8,12343 1,82402 ­4,45359 0,00009 FDIAD* 0,0000060 0,0000032 1,88702 0,06772 FTSE** 0,01516 0,00543 2,79486 0,00847 HPUK** 0,00258 0,00100 2,57471 0,01456 IX* ­0,0000024 0,0000014 ­1,74331 0,09032 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

Table 10: Determinants in the Price per Sq. Meter (Y). International Factors: 1998­2007, with FTSE.

PERIOD: 1998­2007 R Square 0,537541 Adjusted R Square 0,418197 Significance F 0,001044 F 4,504129

Coefficients Standard Error t Stat P‐value Intercept 11,768185 2,844401 4,137316 0,000249 ER ­43,979248 56,360963 ­0,780314 0,441124 ER0 48,305881 52,245231 0,924599 0,362316 REIT 0,624773 0,693349 0,901095 0,374486 KOF*** ­5,975448 1,449299 ­4,122991 0,000259 FDIAD* 0,000006 0,000003 1,822639 0,078017 FTSE** 0,013167 0,005271 2,497742 0,018015 HPUK 0,001434 0,001767 0,811127 0,423477 IX ­0,000002 0,000002 ­1,207508 0,236369 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

34

Table 11: Determinants in the Price per Sq. Meter (Y). International Factors: 1998­2007, with IBEX.

PERIOD: 1998­2007 R Square 0.567595 Adjusted R Square 0.437873 Significance F 0.001029 F 4.375488

Coefficients Standard Error t Stat P­value Intercept 10.802786 2.841232 3.802148 0.000656 ER ­28.248067 60.173860 ­0.469441 0.642149 REIT 0.460739 0.691616 0.666178 0.510388 IBEX*** 0.006376 0.002191 2.909866 0.006752 FDIAD* 0.000006 0.000003 1.851987 0.073892 KOF*** ­5.548818 1.434556 ­3.867970 0.000548 FDI 0.000000 0.000000 0.302762 0.764161 HPUK 0.001978 0.001754 1.127467 0.268482 IX* ­0.000003 0.000002 ­1.725685 0.094692 ER0 13.294149 52.997089 0.250847 0.803643 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

Table 12: Determinants in the Price per Sq. Meter (Y). International Factors: 1998­2007, with SP500.

PERIOD: 1998­2007 R Square 0.546961 Adjusted R Square 0.411049 Significance F 0.001847 F 4.024383

Coefficients Standard Error t Stat P­value Intercept 12.491954 2.890455 4.321794 0.000157 ER ­44.497491 62.134628 ­0.716146 0.479439 REIT 0.377417 0.722825 0.522141 0.605406 SP500** 0.062769 0.024222 2.591389 0.014622 FDIAD** 0.000007 0.000003 2.173222 0.037790 KOF*** ­7.478204 1.643445 ­4.550323 0.000083 FDI 0.000000 0.000000 0.038914 0.969217 HPUK 0.000959 0.001788 0.536525 0.595552 IX ­0.000002 0.000002 ­1.409328 0.169021 ER0 51.821128 54.599832 0.949108 0.350149 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

35

Table 13: Determinants in the Price per Sq. Meter (Y). National Factors: 1995­2009.

TOTAL PERIOD: 1995­2009 R Square 0,405215 Adjusted R Square 0,310049 Significance F 0,000592 F 4,258000

Coefficients Standard Error t Stat P‐value Intercept ­2,003385 2,722843 ­0,735769 0,465309 HCOMP ­0,000165 0,000154 ­1,068897 0,290249 UR** ­11,231636 4,700115 ­2,389651 0,020676 EASY** ­11,179231 4,268335 ­2,619108 0,011640 PI 0,000165 0,000304 0,541906 0,590290 MG*** 1,691530 0,404668 4,180044 0,000117 OIL ­0,054285 0,312720 ­0,173589 0,862889 CPI 0,481286 2,323112 0,207173 0,836716 CORRPI 0,512795 2,772899 0,184931 0,854031 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

Table 14: Determinants in the Price per Sq. Meter (Y). National Factors: 1995­1998.

PERIOD: 1995­1998 R Square 0,73076 Adjusted R Square 0,42306 Significance F 0,13567 F 2,37491

Coefficients Standard Error t Stat P‐value Intercept 5,57508 3,80098 1,46675 0,18588 HCOMP 0,00081 0,00114 0,70648 0,50272 UR 28,95670 22,65059 1,27841 0,24186 EASY 4,30687 13,11182 0,32847 0,75216 PI* ­0,00115 0,00042 ­2,73130 0,02928 MG 1,04515 1,83385 0,56992 0,58655 OIL** ­5,71647 1,98065 ­2,88616 0,02344 CPI 12,31196 13,53768 0,90946 0,39334 CORRPI* ­5,76939 2,85931 ­2,01775 0,08341 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

36

Table 15: Determinants in the Price per Sq. Meter (Y). National Factors: 1999­2009.

PERIOD: 1999­2009 R Square 0,460466 Adjusted R Square 0,333517 Significance F 0,003780 F 3,627169

Coefficients Standard Error t Stat P‐value Intercept 0,123010 3,521893 0,034927 0,972342 HCOMP ­0,000187 0,000168 ­1,114141 0,273033 UR* ­11,164977 5,811785 ­1,921093 0,063137 EASY** ­11,041784 4,780022 ­2,309986 0,027090 PI 0,000544 0,000407 1,335819 0,190482 MG*** 1,629656 0,516040 3,158001 0,003324 OIL 0,072198 0,343970 0,209897 0,835001 CPI ­1,542657 2,984772 ­0,516842 0,608612 CORRPI 7,397250 6,379242 1,159581 0,254298 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

Table 16: Determinants in the Price per Sq. Meter (Y). National Factors: 1998­2007.

PERIOD: 1998­2007 R Square 0,467284 Adjusted R Square 0,329808 Significance F 0,006471 F 3,399038

Coefficients Standard Error t Stat P‐value Intercept 10,967682 3,214648 3,411783 0,001814 HCOMP* ­0,000510 0,000271 ­1,881462 0,069329 UR 4,776172 5,613200 0,850882 0,401360 EASY*** ­10,676280 3,709633 ­2,877988 0,007187 PI 0,000326 0,000306 1,064451 0,295346 MG 0,681732 0,447613 1,523041 0,137886 OIL ­0,469597 0,343716 ­1,366234 0,181695 CPI ­1,863306 2,328703 ­0,800148 0,429715 CORRPI ­5,655368 5,395832 ­1,048099 0,302696 * denotes significance at the 10% level ** denotes significance at the 5% level *** denotes significance at the 1% level

37

Table 17: Simple Regressions: 1995­2009.

R Square Coefficients Error típico t Stat P­value Confidence Level

HCOMP 0,020045 0,000173 0,000160 1,079794 0,284782 GDP 0,015941 0,000350 0,000364 0,960918 0,340652 POP 0,048434 0,000064 0,000037 1,703311 0,093957 10% UR 0,093618 ­11,340300 4,673711 ­2,426402 0,018436 5% EASY 0,023433 ­4,544592 3,885907 ­1,169506 0,247067 PI 0,004665 0,000151 0,000293 0,516885 0,607236 ER 0,012020 42,861798 51,471129 0,832735 0,408470 REIT 0,001986 ­0,280697 0,833460 ­0,336785 0,737516 OIL 0,026190 0,362024 0,292393 1,238144 0,220738 CORRPI 0,000311 ­0,396492 2,978474 ­0,133119 0,894568 ECU 0,075739 6,338905 2,933023 2,161219 0,034893 5% SP500 0,015236 0,025854 0,027531 0,939080 0,351652 FDIAD 0,027259 0,000006 0,000004 1,263852 0,211427 FTSE 0,048922 0,010431 0,006092 1,712313 0,092275 10% KOF 0,204411 ­6,342731 1,657414 ­3,826884 0,000325 1% FDI 0,001211 0,000000 0,000000 ­0,262892 0,793582 IBEX 0,016252 0,002596 0,002675 0,970391 0,335951 HPUK 0,388886 0,005235 0,000869 6,022654 0,000000 1% REIT0 0,008106 4,280254 6,271393 0,682504 0,497684 MG 0,253192 1,587485 0,361120 4,396008 0,000049 1% POPAC 0,006541 0,047035 0,076778 0,612608 0,542572 HLOANS 0,042408 13,991150 8,806093 1,588803 0,117637 IR 0,055444 12,638815 6,909639 1,829157 0,072609 10% IX 0,020125 ­0,000002 0,000002 ­1,081993 0,283812 CPI 0,078998 9,648790 4,363731 2,211133 0,031053 5% ER0 0,053526 64,663388 36,015700 1,795422 0,077888 10%

FIGURES

Figure 1: KOF­Globalisation Index evolution in Spain: 1970­2009

0

20

40

60

80

100

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

Figure 2: Foreign Direct Investment in Spain in Real Estate (thousand Euros)

0

500.000

1.000.000

1.500.000

2.000.000

2.500.000

3.000.000

1.993

1.994

1.995

1.996

1.997

1.998

1.999

2.000

2.001

2.002

2.003

2.004

2.005

2.006

2.007

2.008

2.009

Source: Registro de Inversiones de España 11

Figure 3: Foreign Direct Investment in Spain per country.

0,00%

20,00%

40,00%

60,00%

80,00%

100,00%

120,00%

1.993

1.994

1.995

1.996

1.997

1.998

1.999

2.000

2.001

2.002

2.003

2.004

2.005

2.006

2.007

2.008

2.009

OTHERS U.S. UNITED KINGDOM LUXEMBOURG SWITZERLAND FRANCE GERMANY NETHERLAND

11 Graph: Own elaboration.

39

Data Source: Registro de Inversiones de España 12

Figure 4: Price per Square Meter: 1985­2009

0

500

1000

1500

2000

2500

3000

3500

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Data Source: Sociedad de Tasación, S.A.

Figure 5: Percentual evolution of CPI and Average Salary in Spain.

­3,00%

­2,00%

­1,00%

0,00%

1,00%

2,00%

3,00%

4,00%

5,00%

6,00%

1.997

1.998

1.999

2.000

2.001

2.002

2.003

2.004

2.005

2.006

2.007

CPI

Average Salary

Source: INE 13

12 Graph: own elaboration. 13 Graph: Own elaboration.

40

Figure 6: House prices evolution UK ­ Spain

­20,00%

0,00%

20,00%

40,00%

60,00%

80,00%

100,00%

120,00%

140,00%

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

Y HPUK

Figure 7: Population evolution (thousand Euros) versus Houses Completed

0

20.000

40.000

60.000

80.000

100.000

120.000

1995

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2006

2007

2008

2009

HCOMP POP

Figure 8: Plots of the most relevant national variables:

Figure 8, Panel 1

Price per Sq. Meter: 1994­2009

0 500

1.000

1.500 2.000 2.500 3.000 3.500

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

41

Figure 8, Panel 2

Houses Completed: 1994­2009

0 200.000 400.000 600.000 800.000

1.000.000 1.200.000 1.400.000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

42

Figure 8, Panel 3

GDP: 1995­2009

0 200.000 400.000 600.000 800.000

1.000.000 1.200.000

1995

1997

1999

2001

2003

2005

2007

2009

Figure 8, Panel 4

Personal Disposable Income: 1994­2009

0

50.000

100.000

150.000

200.000

250.000

300.000

1994 1995 1996 1997

1998 1999 2000 2001 2002 2003 2004 2005

2006 2007 2008 2009

Figure 8, Panel 5

IBEX 35: 1994­2009

0 2.000 4.000 6.000 8.000 10.000 12.000 14.000 16.000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

43

Figure 8, Panel 6

CPI: 1992­2009

0 1 2 3 4 5 6

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 8, Panel 7

Active Population: 1994­2009

0

5.000

10.000

15.000

20.000

25.000

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 8, Panel 8

Population: 1994­2009

36.000.000

38.000.000

40.000.000

42.000.000

44.000.000

46.000.000

48.000.000

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

44

Figure 8, Panel 9

Unemployment Rate: 1994­2009

0,00 5,00 10,00 15,00 20,00 25,00

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 8, Panel 10

Corruption Perception Index (Score): 1995­2009

0 1 2 3 4 5 6 7 8

1995 1996

1997 1998

1999 2000

2001 2002

2003

2004 2005

2006 2007

2008 2009

Figure 8, Panel 11

Mortgage Interest Rates: 1994­2009

0 2 4 6 8 10 12

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 2009

45

Figure 8, Panel 12

Interest Rates, EURIBOR (1 year): 1994­ 2009

0 2 4 6 8

1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 8, Panel 13

Easy of giving credit (Mortgage rages minus Interest rates): 1994­2009

0

1 2 3 4 5 6 7

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 8, Panel 14

Residential Mortgages: 1994­2009

0 100 200 300 400 500 600 700 800

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

46

Figure 9: Plots of the most relevant external variables:

Figure 9, Panel 1

FDI, Spanish Bank: 1995­2009

0

100.000.000

200.000.000

300.000.000

400.000.000

500.000.000

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 9, Panel 2

FDI ad, Invest in Spain (Registro de Inversiones): 1995­2009

0 2.000.000 4.000.000 6.000.000 8.000.000 10.000.000

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 9, Panel 3

Exchange Rates US Dollar ­ ECU: 1994­2009

0

0,2

0,4

0,6

0,8

1

1,2

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

47

Figure 9, Panel 4

Exchange Rates British Pound ­ ECU: 1994­2009

0,0000

0,5000

1,0000

1,5000

2,0000

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004 2005

2006

2007

2008

2009

Figure 9, Panel 5

FTSE 100: 1994­2009

0 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000

1994 1995 1996

1997 1998 1999 2000 2001 2002 2003

2004 2005

2006 2007 2008 2009

Figure 9, Panel 6

S&P 500: 1994­2009

0 200 400 600 800

1.000 1.200 1.400 1.600

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

48

Figure 9, Panel 7

North Sea Brent (Oil), spot price $US per Barrel: 1994­2009

0,00

20,00

40,00

60,00

80,00

100,00

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 9, Panel 8

Net Exports: 1994­2009

­120.000.000 ­100.000.000 ­80.000.000 ­60.000.000 ­40.000.000 ­20.000.000

0

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 9, Panel 9

UK Average House Price: 1994­2009

£0

£50.000

£100.000

£150.000

£200.000

£250.000

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

49

Figure 9, Panel 10

KOF Globalization Index: 1994­2009

72,00 74,00 76,00 78,00 80,00 82,00 84,00 86,00 88,00 90,00

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Figure 9, Panel 11

REITs: 1994­2009

0,00

10,00

20,00

30,00

40,00

50,00

60,00

ene­95

ene­96

ene­97

ene­98

ene­99

ene­00

ene­01

ene­02

ene­03

ene­04

ene­05

ene­06

ene­07

ene­08

ene­09

REIT$ REIT$1 REIT€

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APPENDICES

APPENDIX A: Items included in the Spanish FDI statistics 1. Balance of Payments Bank of Spain

The direct investment capital that records the balance of payments is grouped under the following categories: shares, other forms of participation, inter­company financing and real estate. a) Actions: subscriptions and sale of shares where the amount of the investor's share is less than 10% of the equity of the issuing company. It also includes purchases of rights to subscribe for investors. b) Other forms of participation: procurement and sale of securities representing the capital, other than shares, the allocations to branches or establishments and, in general, any form of participation in companies that did not materialize into action. Also included in this section the contributions of capital to companies in the process of incorporation or on account of increased capital and operating expenditure allocations to branches or facilities that lack of equity, where such capital is not a loan, nor is there repayment obligation. c) Funding from related companies: includes, in general, lending operations between parent companies and their affiliates or subsidiaries and between subsidiaries of the same group, provided that it is not crédito37 entities. Specifically, under this heading include the loans granted by parent companies to their affiliates and subsidiaries, and refundable advances granted to subsidiaries or establishments, unless the loans in reverse, i.e. those granted by branches or subsidiaries their own investors. Also included are loans between firms within the same group, although these are not direct loans from parent to subsidiaries, or vice versa. In accordance with the provisions in the Fifth Manual, the loans granted by resident subsidiaries of a non resident enterprise to other non­resident companies of the group, other than the parent, are included in Spanish direct investment abroad, while the amounts received by resident subsidiaries of a non resident enterprise as a result of loans granted by other non­resident subsidiaries are included in foreign direct investment in Spain. Additionally, include changes in account balances Intercompany. For Intercompany accounts mean the accounts between subsidiaries and parent companies or between companies of the group, in which transactions are settled with each other, group or business transaction with third parties. These transactions result in changes in account balances inter­company, which constitute a loan or received from the parent company or the company that manages the treasury. Such credit may be included within the inter­company financing, foreign investment in Spain, where the resident company is a subsidiary or branch of non­resident parent, and Spanish investment abroad where the resident company is a direct investor. Finally, are excluded from this item loans to direct investors resident FVC established in countries which are considered tax havens. These loans are included within the range of liabilities, under the other heading investment. d) Property: This includes the acquisition of property or other real rights over immovable property, including the purchase of undivided shares of such immovable property for your enjoyment part­time, and the acquisition of property by financial leasing. 2. Registration of Foreign Investment, Ministry of Industry, Tourism and Trade

In accordance with Article 4 of Royal Decree 664/1999 of 23 April, on foreign investments, foreign investments in Spain, and its settlement will be declared to the Register of Investment (RIE) of the Ministry of Finance for administrative, statistical or economic. According to Article 3, foreign investments in Spain may be effected through any of the following: a. Participation in Spanish companies: means covered under this scheme both the constitution of society, as underwriting and acquisition of all or part of its shares or ownership of shares. Also, are also covered in this section the purchase of securities such as rights to subscribe for shares, debentures convertible into shares or similar securities which by their nature give right to participation in the capital as well as any legal transaction under political rights which are acquired. b. The constitution and opening of additional branches. c. The subscription and acquisition of securities representing borrowing by residents. d. Participation in investment funds, registered in the records of the National Securities Market. e. The acquisition of properties in Spain, where the total exceeds 500 million pesetas, or the equivalent in Euros or if, regardless of the amount and appropriate tax havens, which include, countries and territories listed in article Only the Royal Decree 1080/1991, of 5 July. f. The constitution, formalizing contracts or participation in venture accounts, foundations, economic interest groups, cooperatives and community properties, where the total value corresponding to the

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participation of foreign investors is more than 500 million pesetas, or its equivalent in Euros or when, irrespective of its amount, comes from tax havens, which include countries and territories listed in the single article of the Royal Decree 1080/1991, of 5 July.

Symmetrically, under Article 7 of that decree, the Spanish foreign investments and its settlement will be declared to the RIE for administrative, statistical or economic purposes. And according to Article 6, the Spanish foreign investments may be carried out through symmetric operations described above, but replacing the threshold of 500 million pesetas per 250 million pesetas in points e) and f).

Not all operations are issued as required statement of foreign direct investment flows from abroad in Spain. In particular, the number of FDI in the RIE publishes the following concepts as foreign direct investment in Spain: a) Investments in companies resident in Spain not publicly traded. b) Investments in companies’ resident in Spain to publicly trade and in which non­resident investor acquires at least 10% of the capital, which is conventionally considered to achieve a permanent relationship in the management of it. c) Constitution and extension of additional branches in Spain. d) Other forms of investment or contracts to entities registered abroad (foundations, cooperatives, economic interest groups).

The concepts published as foreign direct investment flows abroad in Spain are: a) Investment of non­Spanish resident in Spain who are not listed on stock exchange or organized markets. b) Investments in companies not resident in Spain listed on stock exchange or organized market and where the resident investor acquires at least 10% of the capital, which is conventionally considered to achieve a permanent relationship management itself. c) Constitutional and extensions of additional branches abroad. d) Other forms of investment: the constitution, formalizing contracts or participation in venture accounts, foundations, economic interest groups, cooperatives or community property when the value corresponding to the participation of resident investors, by themselves or in conjunction of the previously existing exceed EUR 1,502,530.26, or if they have as a destination territories or countries considered tax havens.

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APPENDIX B: 2010 KOF Index of Globalization

The KOF Index of Globalization was introduced in 2002 (Dreher, 2006) and is updated and described in detail in Dreher, Gaston and Martens (2008). The overall index covers the economic, social and political dimensions of globalization. Following Clark (2000), Norris (2000) and Keohane and Nye (2000), it defines globalization to be the process of creating networks of connections among actors at multi­continental distances, mediated through a variety of flows including people, information and ideas, capital and goods. Globalization is conceptualized as a process that erodes national boundaries, integrates national economies, cultures, technologies and governance and produces complex relations of mutual interdependence.

More specifically, the three dimensions of the KOF index are defined as: • economic globalization, characterized as long distance flows of goods, capital and

services as well as information and perceptions that accompany market exchanges; • political globalization, characterized by a diffusion of government policies; and • Social globalization, expressed as the spread of ideas, information, images and people. The 2010 index introduces an updated version of the original index, employing more recent

data than has been available previously.

In constructing the indices of globalization, each of the variables introduced above is transformed to an index on a scale of one to hundred, where hundred is the maximum value for a specific variable over the period 1970 to 2007 and one is the minimum value. Higher values denote greater globalization. The data is transformed according to the percentiles of the original distribution. The weights for calculating the sub­indices are determined using principal components analysis for the entire sample of countries and years. 2 The analysis partitions the variance of the variables used in each sub­group. The weights are then determined in a way that maximizes the variation of the resulting principal component, so that the indices capture the variation as fully as possible. The same procedure is applied to the sub­indices in order to derive the overall index of globalization.

Data are calculated on a yearly basis. In calculating the indices, all variables are linearly interpolated before applying the weighting procedure. Instead of linear extrapolation, missing values at the border of the sample are substituted by the latest data available. When data are missing over the entire sample period, the weights are readjusted to correct for this. When observations with value zero do not represent missing data, they enter the index with weight zero. Data for sub­indices and the overall index of globalization are not calculated, if they rely on a small range of variables in a specific year and country. Observations for the index are reported as missing if more than 40 percent of the underlying data are missing or at least two out of the three sub­indices cannot be calculated. The indices on economic, social and political globalization as well as the overall index are calculated employing the weighted individual data series instead of using the aggregated lower­level globalization indices. This has the advantage that data enter the higher levels of the index even if the value of a sub­index is not reported due to missing data.

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APPENDIX C: Corruption Perception Index. Short methodological note.

1. The 2009 Corruption Perceptions Index (CPI) gathers data from sources that cover the past two years. For the 2009 CPI, this includes surveys from 2008 and 2009. 2. The 2009 CPI is calculated using data from 13 sources from 10 independent institutions. All sources measure the overall extent of corruption (frequency and/or size of bribes) in the public and political sectors, and all sources provide a ranking of countries, i.e., include an assessment of multiple countries. 3. For CPI sources that are surveys, and where multiple years of the same survey are available, data for the past two years is included to provide a smoothing effect. 4. For sources that are scores provided by experts (risk agencies/country analysts), only the most recent iteration of the assessment is included, as these scores are generally peer reviewed and change very little from year to year. 5. Evaluation of the extent of corruption in countries/territories is done by two groups: country experts, both residents and non­residents, and business leaders. In the 2009 CPI, the following seven sources provided data based on expert analysis: African Development Bank, Asian Development Bank, Bertelsmann Foundation, Economist Intelligence Unit, Freedom House, Global Insight and the World Bank. Three sources for the 2009 CPI reflect the evaluations by resident business leaders of their own country, IMD, Political and Economic Risk Consultancy, and the World Economic Forum. 6. To determine the mean value for a country, standardisation is carried out via a matching percentiles technique. This uses the ranks of countries reported by each individual source. This method is useful for combining sources that have a different distribution. While there is some information loss in this technique, it allows all reported scores to remain within the bounds of the CPI, i.e., to remain between 0 and 10. 7. A beta­transformation is then performed on scores. This increases the standard deviation among all countries included in the CPI and avoids the process by which the matching percentiles technique results in a smaller standard deviation from year to year. 8. All of the standardised values for a country are then averaged, to determine a country's score. 9. The CPI score and rank are accompanied by the number of sources, high­low range, standard deviation and confidence range for each country. 10. The confidence range is determined by a bootstrap (non­parametric) methodology, which allows inferences to be drawn on the underlying precision of the results. A 90 per cent confidence range is then established, where there is a five per cent probability that the value is below and a five per cent probability that the value is above this confidence range.