International Real Estate Investment Analysis

International Real Estate Investment Analysis The use of asset specific criteria when investing in non-listed funds.

P5 2014 Real Estate Management &

Real Estate Economics Graduation research

PTS van Alstede

International Real Estate Investment Analysis

1

Personal Information Title of research project

International real estate investment analysis and the use of asset specific criteria when investing in non-listed funds. A tool for identifying and assessing investment opportunities in international real estate portfolios suited for institutional investors. Topics International non-listed fund investments Underlying asset analysis US commercial real estate funds Name of student Photo of student

Phillip-Jan van Alstede Student number

1533444 Address

Julius Pergerstraat 85. Amsterdam. Phone (+31) 655758301 E-mail address

[email protected] , Date proposal

1-10-2014 MSc Laboratories

Real Estate Management (REM) Real Estate Economics (Fundamentals) Educational mentors:

1st Philip Koppels 2nd Hans de Jonge Graduation Company

Syntrus Achmea Real Estate and Finance Company mentor:

Victor Hagenbeek, senior research analyst

mailto:[email protected]


2

Preface This thesis was written as a final assignment to obtain the MSc degree at the Delft University of

Technology, faculty of Architecture, department of Real Estate and Housing. The research process

was supervised by the first mentor Philip Koppels, second mentor, Hans de Jonge and Graduation

company mentor, Victor Hagenbeek.

The initial objective of this research was to determine how asset specific criteria can be incorporated

into the international private fund investment methodology. Theoretical research has been done into the subjects of:

International real estate investments

Stakeholders in international real estate investments.

The Macro and Meso level aspects as decision making criteria

Asset specific criteria analysis of real estate as a decision making criteria

The methods used in this research are Hedonic pricing models incorporating macro, meso, fund and

asset specific criteria in order to research how these influence financial performances of underlying

real estate assets in international portfolios

Quantitative research has been done at the graduation company Syntrus Achmea Real Estate &

Finance for 8 underlying funds containing 400 assets separated over the 3 commercial real estate

sectors; Retail, Offices and Industrial

Writing this thesis has been a very intense learning process, requiring me to face, understand and

overcome my personal shortcomings; but also helping me to identify my personal strengths.

First of all I would like to thank my mentors from the TU Delft, Philip Koppels and Hans de Jonge for

guiding my research. The educational feedback, their enthusiasm and motivation to reach an optimal

result led to a pleasant collaboration. I would also like to thank my mentor at Syntrus Achmea, Victor

Hagenbeek, who was a vital influence on the process. The scheduling of meetings, gathering of data

and information and the overall research of such large investments could not have been achieved

without his help. I would also like to thank the business unit Strategy & Research for the opportunity to

conduct my graduation research in such a professional and friendly environment.

Also I would like to thank my family, friends and loved ones for supporting me during the entire

research process of graduation.

Phillip van Alstede

Delft, 16 december 2014


3

Management Summary

The goal of this research paper is to develop a tool which aims to improve the profitability of investing

in international real estate funds. This is done by evaluating asset specific criteria (ASC) and

determining which ASC are relevant and easily determined by institutional investors within a certain

time period. These ASC should be analyzed alongside macro and meso level financial performance

indicators to make a better informed decision when investing in new international real estate funds or

performing hold/sale analysis on existing funds.

Problem analysis

Chapter one is the problem analysis. Here the problem is analyzed and the relevance of the problem

is discussed. In this research paper the problem analysis starts by giving an introduction and some

facts about past and present situations. The demand for more indirect real estate was stimulated by

the need for the relatively quicker growth of real estate portfolios in the 60‘s due to the increase in

office labour work force, standardisation of office use and office projects. Indirectly investing in real

estate has always been a challenge. While stocks are primarily priced based on market risk and bonds

on default risk and interest rate, the pricing mechanism for real estate is more complicated. When

pricing real estate, both residual risks as well as non-risk factors such as taxes, marketability costs

and information costs have to be accounted for. Its determined that Dutch institutional investors are

currently invested in international real estate for around 70 billion Euro, this amount is substantially

larger than the 20 billion invested in national real estate. One can safely say that a large portion of

funded ratios is dependent on the performance of these investments. In common practice, real estate

investors often solely study macro and meso-economic aspects as decision making criteria to

determine a funds ability to perform according to or above a certain benchmark.

One of the goals of this research is to make clear that the third scale aspect, micro economic aspects,

is a very important determinant of profitable international real estate investment. From the financial

crisis in 2008-2009 and the debt crisis of 2011-2012 investors have learned that the way in which

institutional investors were allocating funds towards real estate had to be changed. More insight was

needed in the financial performance and risks of real estate as an asset class. Two of the main reason

for the losses that were made in the past is focused on in this paper. The loss on income due to

vacancy and lower rental prices and the loss on value due to market failure and changing demand will

be elaborated further. Currently more and more capital is being allocated in the indirect asset class by

Dutch pension funds post-crisis. Another interesting observation is that the Dutch economy has been

preforming below the European average in recent years and this is expected to stay this way in the

near future, although the gap is becoming increasingly smaller. On the other hand, the US economy

has been growing. One of the most important recent trends is more transparency due to advances in

the real estate sector: more platforms for documentation of real estate indices are constantly

developing to document and benchmark real estate performance and related aspects. This gives all

stakeholders, mainly investors, more insight and this increase in available data makes research like

the one preformed in this paper possible to conduct. Still obtaining all relevant micro level data was a

difficult process requiring the cooperation of fund managements so there is still a vast amount of

transparency needed. In the third paragraph the possible aspects and reasons for an investment in a

private real estate fund to underperform or outperform a certain benchmark are discussed. It‘s

challenging to identify which factors were responsible for it and to what extent. The main problem is a

lack of transparency and proper data regarding the three scale levels. Only 20% of professional

investors include property specific criteria, the micro scale, into investment methodology. Because of

this problem fund strategy and investment analysis becomes less reliable and more difficult to fully

conduct.


4

In the fourth paragraph the research question and the sub questions are formulated. The main

problems to be solved in this research paper are the improvement of the investment methodology

used by institutional investors when making their international indirect real estate investment strategy

decisions by adding underlying asset analysis, the determination of which asset specific criteria are

influential in regards to indirect real estate performances based on historical performances of indirect

funds and translating these asset specific criteria of existing international real estate portfolios into

asset specific investment criteria for future investments. These problems lead to the formulation of the

main research question as:

―How can asset specific analysis improve International Real Estate fund investment analysis?”

In order to make answering this research question a bit easier, four sub questions were formulated.

These sub questions will be answered in chapter 2 and 4 and are the following:

1. How do the different forms of international private fund investments affect investor criteria?

2. How do the relationships between stakeholders affect investor criteria?

3. To which extent do macro and meso economic aspects influence commercial real estate

performance?

4. Which asset specific criteria can be used for underlying asset analysis and what is their

relative influence on the financial performance of commercial real estate?

In the fifth paragraph of chapter one the objectives, intended end result and scope of the research are

stated. The objective of the research paper is examining underlying assets of existing private real

estate funds in order to determine relevant asset specific criteria based on their historical

performances. This will then determine their influence on the assets performance. With the answering

of the questions mentioned above and the subsequent research the following end result is intended,

the research‘s intended end result is to give institutional investors and fund management

professionals‘ insights into the influence of asset specific criteria and how to incorporate these into

their investment methodology. This research is also intended to be a tool for making investment

decisions concerning their investments in non-listed international real estate funds. The research

primarily focuses on commercial real estate but is limited to Retail, Office and Industrial. As for the

variables, the main focus of the research is on the Micro level of assets as a determinant for

performance, however to increase reliability and decrease the missing variable bias in the hedonic

pricing study, the meso, macro and fund manager influence are needed to single out their effects. In

the sixth paragraph the design of the research comes forward. The design consists of five steps; first

the problem analysis is discussed. Next the research will elaborate on past literature on the topic and

relevant related topics to the research question. In the third step the research method, research type

and the research concept will be explained. The conceptual model will be based on the hypotheses

derived from the literature research. Afterwards the analysis method will be conducted, which is

followed by the results. The last step includes the conclusion and a recommendation. The seventh

paragraph discussed the scientific and social relevance of the research question. In general there is a

lot of scientific information available on international real estate investments and international real

estate investors. This is especially the case for macro related aspects; however there is no specific

scientific information available on international investment methodology for institutional investors

available concerning asset specific criteria. Doing research into asset specific measurement aspects

into a decision making framework for institutional investors to increase the efficiency of international

real estate investments could help pension funds increase their total return on real estate investments

and eventually their funded ratios. In the eight paragraphs the utilization potential and economic

valorization is questioned. It can be concluded that this research can be used as a tool or guideline for

institutional investors doing international project and property investments. By comparing one‘s own

company strategy and that of the proposed international real estate investment, one can determine if


5

such an investment is a proper fit or profitable endeavor for their company. In the last paragraph the

personal motivation behind the research question is given. The main point of interest lies in the fact

that there are some many possibilities when envisioning projects and investing across international

borders. These possibilities and opportunities should be given more attention to increase profits.

Theoretical framework findings:

Chapter two is the theoretical framework. The theoretical framework demonstrates an understanding

of theories and concepts that are relevant to understand international real estate investment and that

relate to the broader areas of knowledge being considered. The sub questions are answered in four

main paragraphs.

Real estate as an investment asset class has been growing in the past couple of decades and has

become the third largest investment category after stocks and bonds. A large part of invested capital is

allocated to international real estate. As investment in real estate is growing, the use of financial

concepts that originate from scientific theories has increased as well. In order to understand and prove

such theories, insights into the performance measurement techniques of real estate are needed.

Investors always prefer a high as possible performance from their investments and use various

analytical tools and methods to predict and secure profits. These profits are generally measured on an

annual basis by the total returns. In the first paragraph these returns and what they entail are

described. In this paragraph it became clear that NOI and EV are underlying asset based performance

figures, which are of great influence on the fund and asset returns. The NOI and EV are therefore

used as financial performance measure. Their averages per square footage will be used as the

dependent variables for the statistical analysis in this research paper. Additionally the risks that come

with investing in international real estate are explained. One of the focus points of this research is

reducing the risks of international private fund investments, so it is important to understand risk.

Describing these types of risks has given us two important insights. Firstly it is obvious that most of the

relevant risks for international private real estate fund investments are micro level based risks.

Secondly country selection is clearly a key determinant of performance. The last point discussed in the

first paragraph is the different investment types and the implications these hold. From this discussion it

became clear that each type of investment has different implications for the risks and return of the

fund. These risks and returns must be evaluated and weighted carefully in order to make the right

investment decision. As a result, an investor in an international real estate fund must first decide in

which type of fund it has to invest in order to reach its desired goals.

In the second paragraph the way investors and fund managers manage indirect real estate fund

investments is discussed and how these stakeholders have an influence on each other. The main

conclusions are that the investors should be aware of the needs of the users and how well these

needs are being satisfied by fund management and the assets in the fund, when selecting a fund.

Secondly the fee structure influences the returns. It is important to make sure the management fees

are not higher than the added value of a better portfolio. Finally it is clear that the quality and structure

of management is of influence on the NOI and estimated values. This should therefore be controlled

for in the regression models. This is done by adding a fund category variable for each asset, next to

the micro macro and meso variables in the regression model. This will in turn measure if there is a

fund specific difference on NOI‘s and EV‘s of commercial real estate assets.

The third paragraph describes macro and meso economic aspects in relations to the investment

objectives for international real estate investors. First the macro economic variables and their effect on

real estate values are described. . Each of these variables tells us something about the state the

economy is in at a certain point in time. We can therefore conclude that macro-economic aspects

influence commercial real estate performance trough time related variables. Then the meso economic

variables and their influence on real estate values are explained. From this explanation we can


6

conclude that these variables can be bundled up into two variables to be used in the regression

analysis, ‗region‘ and ‗Gateway City‘.

The fourth paragraph describes the influential asset specific criteria for the location aspects: distance

to central business district, distance to transportation nodes, accessibility, walkability and surrounding

amenities. The building aspects include: construction year/ age, last Renovation, climate systems,

ceiling height, number of stories, size / total floor area, building amenities, type classification,

sustainability labels & certifications, energy, water and waste consumption. The influences of these

variables on NOI and EV per sector were hypothesized. These variables will be then operat ionalized,

transformed and ultimately used as de independent variables in the regressions.

Methodology:

The main goal of performing quantitative research methods in the form of linear mixed models is to

determine to which extent all the influential factors found in the theoretical framework have impact on

the financial performance of commercial real estate assets.

The statistical analysis will be performed with panel data out of the funds that are part of the North

America Fund of Syntrus Achmea Real Estate & Finance (SAREF). The commercial investments

studied in this fund consist out of office assets, retail assets and industrial assets, all geographically

dispersed throughout all parts of the United States.

The financial performance of each building is based upon the different independent variables delivered

to us by the each funds management. The outcome/dependent variables Net Operating Income (NOI)

and Estimated Value (EV) are measured over a time-frame of 4 consecutive years. The first

measurements are those of Q4-2010 and go on until Q4-2013. All dependent variables are reported in

real terms according to the Q4 price level. The independent variables were transformed and

operationalized in order to be incorporated into the models

Hedonic pricing studies were conducted for each sector, retail, office and industrials and for both the

NOI and the EV. This research uses panel data and is based upon empirical analysis using linear

mixed models (LMM). Based upon a predetermined set of variables the financial performance of the

funds is analyzed by analyzing the separate underlying assets in the fund on the basis of 2 types of

dependent/outcome variables. The set of independent/predictor variables that is used as input for the

hedonic pricing models are categorized in three different groups: location features, building features

and sustainability features. An overview of all variables that have been analyzed is provided in

paragraph 3.2. During the several modeling phases, a final model is constructed that reflects the

effects of individual variables on the dependent variables for each of the 2 dependent variables for

each commercial property sector.

Statistical findings:

Note that the other variables which didn‘t make the final model might have an influence but weren‘t

found significant or applicable in this research. The following variables can be seen as the influential

asset specific criteria found on the basis of the hedonic pricing studies for the 2 different dependent

performance variables NOI and EV per sector:

Retail Assets

Significant and influential variables on NOI Significant and influential variables on EV

Macro Year (timing) Macro: Year (timing)

Meso: Region and Gatew ay City Meso: Region and Gatew ay City

Micro / ASC: Google Walk Score, Retail Type, Size

Micro / ASC: Google Walk Score, Retail Type, Size, Tenant Density and Age


7

All these significant variables can be used as an indicator when looking at their corresponding financial performance figure. Retail type has the biggest influence on the NOI and EV. Being located in

a gateway city has a positive effect on the NOI and EV and so does an increase in the Google Walk score. Age and size have a negative influence; while an increase in tenant density increases EV.

Office Assets

For the office assets; Region has the biggest influence on the NOI and fund had the biggest influence on EV. An increase in age has a positive effect on NOI and so does being located in

a central business district. An increase in size has a negative effect on NOI. Google walk score is positively related to EV and not having a LEED certification negatively influences EV.

Industrial Assets

For the Industrial assets; Region has the biggest influence on the NOI and being located

within 1 mile of an airport had the biggest influence on EV. Not being located near an airport or in a gateway city also has a negative effect on NOI. An increase in size negatively influences NOI. An increase in Google Transit score increases the EV, while not being located near an airport has a

negative effect on EV too. Conclusions:

In order to answer the research question, the sub questions were answered first. These answers led to the final conclusion.

RQ1 When determining how the different forms of international private fund investment affect investor criteria it became clear that NOI and EV are underlying asset based performance figures

which are of great influence on the fund and asset returns. The NOI and EV were therefore used as financial performance measures. Another conclusion taken from this paragraph is that most of the relevant risks for international private real estate fund investments are micro level based risks.

RQ2 Next the relationships between stakeholders and how these affect investor criteria were evaluated. First of all it is interesting to conclude that the different stakeholder groups each earn a

profit in a different manner and can have positive or negative influences on investor criteria. This can sometimes lead to a conflict of interest between stakeholders. This means the investors should be aware of the needs of the users and how well these needs are being satisfied by fund management

and the assets in the fund, when selecting a fund. A second important conclusion is that the fee structure influences the returns. It is important to make sure the management fees are not higher than the added value of a better portfolio. Finally it is clear that the quality and structure of management is

of influence on the NOI and estimated values. To research the possible influence and control for the effect different fund management has on NOI‘s and EV‘s the variable ‗fund‘ was added to the regression model.

RQ3 For the third sub question it is determined to which extent macro and meso economic criteria influence commercial real estate performance. The macro level indicators such as GDP, employment

growth, building output etc. are all time related. This was ultimately controlled for by a time variable. This was incorporated into the multi-level mixed linear model by means of measuring 4 consecutive years of real estate transactions. The meso level indicators were all related to the specific region an


Macro - Macro: Year (timing)

Meso: Region Meso: Region

Micro / ASC: Age, Size, Office Type Micro / ASC: Google Walk, Office class, and LEED.

Fund: Fund management Fund: Fund management


Macro Year (timing) Macro: Year (timing)

Meso: Region and Gatew ay City Meso: -

Micro / ASC: Airport property, Google transit score and size

Micro / ASC: Airport property and Google Transit.


8

asset is located in or their presence in a gateway city. From this we can conclude that these variables can be bundled up into two variables to be used in the regression analysis. The first is ‗region‘ and the

second is ‗Gateway City‘. RQ4 For the last sub question it was evaluated which asset specific criteria can be used for

underlying asset analysis of international private real estate portfolios and to which extent they influence commercial real estate performance. The ability to use certain asset specific criteria (ASC) was dependent on a few factors throughout the course of this research. The ASC have passed the

following stages; scientific evidence, time related aspects, data limitations and statistical analysis. For the retail sector, walk score, retail type. Size, tenant density and age were significant and influential. For the office sector walk score, office type and class, LEED certification and age were significant and

influential. For the industrial sector transit score, airport property and size were significant and influential. When it comes to the return figures the macro and meso criteria seem to be the only significant variables for return. When we examine the NOI‘s and the EV‘s the asset specific criteria

become significant. The variable fund was also added to research the effects of different funds on asset performances. This was also relevant for each sector. So ‗fund‘ is neither a macro meso nor micro aspect but is definitely of influence. So this should also be taken into consideration when

performing an investment analysis. After this analysis the main research question can be answered. In paragraph 2.1 the different

influences on investment criteria of institutional investors (risk and return) were examined. It became clear that Investment methodology is different for each type of fund. Paragraph 2.2 states that the type of fund is also an influential factor on returns regarding the fee structure. Paragraph 2.3 about the

macro (time) and meso (region) level determinants showed us the different underlying how and to which extent they can affect asset performance. This, the process of determining influences on the financial performance of assets, this research paper wants to improve, is done by adding ASC that

also affect asset performance. The outcomes of the statistical research presented in chapter 4 showed us that the time variable; year (macro level aspects) proved to be significant for each dependent financial performance variable. The different regions have shown a different impact in each different

sector for the different dependent financial performance variables. In regards to this, geographical spread amongst submarkets needs to be examined. The final step was the addition of asset specific criteria. If assets seem to satisfy the asset specific criteria this could result in a profitable investment,

since these ASC have proven to be of positive influence for the NOI‘s and EV‘s. This is because being aware of and evaluating ASC gives investors the ability to make better decisions and thus improve the NOI‘s and EV‘s. If the price of a share is less than it‘s estimated worth calculated by the investment

tool, the investor is able to invest with decreased information risk. So by using the investment tool investors can improve their risk-return ratio. This confirms the hypothesis: Analyzing indirect real estate investments with added underlying asset specific criteria will give better insight into profits of a

proposed investment. Recommendation:

By using the historical performances of 9 different non-listed international real estate funds of Syntrus Achmea Real Estate & Finance (SAREF) over the past 4 years, relevant, obtainable and researchable

asset specific variables have been found which have proven to be of significant influence on the Net Operating Incomes (NOI) and Estimated Values (EV) of commercial real estate assets.

All separate variables are part of a hedonic pricing function for the sector of which the variable is relevant the final percentage influence is calculated by adding all separate influences for that sector. The relevant variables for each sector are calculated differently for each sector. For each variable a

proper method and formula is given to calculate the funds (weighted) average or percentage for that variable. This tool was put together to help investors make better investment decision by reducing the information risk. With this tool investors can examine the underlying asset of a real estate fund and

this gives them an idea of the NOI and EV influencing qualities of the assets. This can protect an investor from buying into a fund with bad assets or aid an investor in choosing the fund with better asset specific criteria. This tool can amongst fund comparison be used for identifying underpriced or

overpriced funds, comparing sectors and comparing NOI‘s to EV‘s.


9

Contents

Personal Information ............................................................................................................................................................ 1

Preface .................................................................................................................................................................................. 2

Management Summary........................................................................................................................................................ 3

1. Problem Analysis.......................................................................................................................................................11

1.1 Introduction............................................................................................................................................................11

1.2 The past and current situation............................................................................................................................12

1.3 Problem analysis ..................................................................................................................................................14

1.4 Study questions and research questions .........................................................................................................15

1.5 Objective, intended end result and Scope .......................................................................................................16

1.6 The research design ............................................................................................................................................16

1.7 Scientific and societal relevance........................................................................................................................18

1.8 The utilization potential and economic valorization ........................................................................................19

1.9 Personal motivation..............................................................................................................................................20

2. Theoretical framework ...................................................................................................................................................21

2.1 International Real Estate Investments ..............................................................................................................22

2.1.1 Introduction ..................................................................................................................................................22

2.1.2 Returns .........................................................................................................................................................23

2.1.3 Risks .............................................................................................................................................................26

2.1.4 Investment types.........................................................................................................................................28

2.1.5 Conclusion ...................................................................................................................................................32

2.2 Stakeholders in international real estate investments....................................................................................33

2.2.1 Introduction ..................................................................................................................................................33

2.2.2 Institutional investors..................................................................................................................................34

2.2.3 Fund Managers ...........................................................................................................................................35

2.2.4 Users ...........................................................................................................................................................36

2.2.5 Conclusion ...................................................................................................................................................37

2.3 Macro / Meso level investment decision making criteria................................................................................38

2.3.1 Introduction .................................................................................................................................................38

2.3.2 Macro scale performance indicators........................................................................................................38

2.3.3 Meso scale performance indicators .........................................................................................................40

2.3.4 Conclusion ...................................................................................................................................................42

2.4 Asset specific criteria analysis, the Micro level decision making criteria ...................................................43

2.4.1 Introduction ..................................................................................................................................................43

2.4.2 Locational features .....................................................................................................................................44

2.4.3 Building level aspects.................................................................................................................................47

2.4.4 Conclusion ...................................................................................................................................................55

3. Methods ......................................................................................................................................................................57

3.1 Introduction............................................................................................................................................................57


10

3.2 Chosen methods ..................................................................................................................................................58

3.3 Variables ................................................................................................................................................................60

3.4 Correlation analyses ............................................................................................................................................62

4. Statistical analysis .........................................................................................................................................................63

4.1 Retail Assets .........................................................................................................................................................64

4.1.1 Descriptive Statistics for Retail models ...................................................................................................64

4.1.2 Exploratory Analysis for Retail models....................................................................................................66

4.1.3 Final models for Retail Assets. ................................................................................................................71

4.1.4 Statistical conclusions for Retail Assets. ...............................................................................................75

4.2 Office Assets .........................................................................................................................................................76

4.2.1 Descriptive statistics for office models ....................................................................................................76

4.2.2 Exploratory analysis for office models.....................................................................................................78

4.2.3 Final models for Office assets. .................................................................................................................83

4.2.4 Statistical conclusions for Office Assets. ...............................................................................................86

4.3 Industrial Assets ...................................................................................................................................................87

4.3.1 Descriptive statistics for industrial models..............................................................................................87

4.3.2 Exploratory analysis for industrial models ..............................................................................................89

4.3.3 Final models for Industrial assets. ..........................................................................................................94

4.3.4 Statistical conclusions for Industrial Assets. .........................................................................................96

5. Conclusion ..................................................................................................................................................................97

5.1 Introduction....................................................................................................................................................................97

5.2 Answering the research questions ............................................................................................................................97

6. Recommendation ................................................................................................................................................... 101

6.1 Introduction......................................................................................................................................................... 101

6.2 Methodological framework ............................................................................................................................... 102

6.3 Analysis of retail assets .................................................................................................................................... 103

6.4 Analysis of office assets ................................................................................................................................... 106

6.5 Analysis of industrial assets............................................................................................................................. 107

6.6 Analysis results and fund comparison ........................................................................................................... 108

6.7 Limitations and recommendations for future research ........................................................................................ 110

7. Reflection .......................................................................................................................................................................... 112

Literature and other sources ............................................................................................................................................... 113

APPENDIX I – Explenetory information................................................................................................................... 118

APPENDIX II – Return models.................................................................................................................................. 121

APPENDIX III – EFE tables SPSS per Model ........................................................................................................ 124

APPENDIX IV – CORRELATION ANALYSES ....................................................................................................... 139

APPENDIX V – Z-Value Estimates tables............................................................................................................... 142

APPENDIX VI – Syntaxes for LMM .......................................................................................................................... 147


11

1. Problem Analysis

1.1 Introduction

The demand for more indirect real estate was stimulated by the need for the relatively quicker growth of real estate portfolios in the 60‘s due to the increase in office labour work force, standardisation of

office use and office projects. This shift was then strengthened by the increasingly larger supply of investment funds to choose from. This made it possible for investors to rapidly invest in properties on a larger scale. This was later on adopted by other real estate sectors.

Within indirect investments a shift over the past decade can be identified from national towards international real estate investments. After years of getting used to the investment class of real estate

and coping with 5 years of recession in the private equity markets, larger institutional investors are prepared and willing to internationally diversify in real estate again. What we need to ask ourselves is how different investment strategies will affect our investment performances in the future. We all want

our pension funds‘ funded ratios to increase but we need to know as of today what we have to do to achieve this in the future.

Indirectly investing in real estate has always been a challenge. While stocks are primarily priced based on market risk and bonds on default risk and interest rate, the pricing mechanism for real estate is more complicated. When pricing real estate, both residual risk as well as non-risk factors such as

taxes, marketability costs and information costs have to be accounted for (Ibbotson, R. G., & Siegel, L. B., 1984). The added value of analyzing markets and real estate assets alongside research in the field of real estate is giving us insight into what might happen in the future, how that could affect our

investment performances, what we can do to prevent losses and mitigate certain risks. Its determined that Dutch institutional investors are currently invested in international real estate for

around 70 billion Euro, this amount is substantially larger than the 20 billion invested in national real estate. One can safely say that a large portion of funded ratios is dependent on the performance of these investments. (Vastgoedmarkt)

International real estate funds are comprised out of multiple underlying assets. Whether it‘s for retail, office, or industrial use, the asset fulfills its purpose under certain economic circumstances providing

the investor with rental income and a given value. These commonly increase or decrease depending on its desirability in the market. This is often done through comparison with other assets. The assets financial performance and the risks associated with that performance are then influenced by its ability

to outperform other assets. In common practice, real estate investors often solely study macro and meso-economic aspects as

decision making criteria to determine a funds ability to perform according to or above a certain benchmark. In addition to this, a third scale level referred to as the micro level or asset specific criteria, also influences a fund‘s performance. The asset specific criteria contain the technical and physical

aspects of an individual assets location and building. These criteria are commonly based on what tenants and investors consider being value increasing aspects of a property.

These three scale levels can give indications of the probability of a fund‘s assets being leased in the future and the amount of money an entity is willing to pay for its lease or its purchase. Therefore it is important for an investor to know that when comprising a prognosis and stating the risks of a certain

fund that these are partially in line with the asset specific criteria and they should analyze these aspects accordingly. When investing in international funds with a certain lack of local and asset specific knowledge we depend on a fund´s management to take these aspects into account when

allocating committed capital. However, some fund managers might in practice not do this for all criteria or not at all.

This research sheds light on which asset specific criteria are relevant and investigable within a convenient time period for investors and fund managers. It ultimately provides a methodology how fund management or investors can analyze these asset specific criteria alongside macro and meso


12

level indicators and use this as a decision making component in cooperation with their existing investment methodology when investing in new funds or performing hold/sale analysis.

1.2 The past and current situation

Learning from the past

The effects of the financial crisis in 2008-2009 and the debt crisis of 2011-2012 have been a wakeup

call for many real estate investors. The way in which institutional investors were allocating funds

towards real estate had to be changed. More insight was needed in the financial performance and

risks of real estate as an asset class.

As of 2014 Approximately 10,5% of the total Dutch pension capital is invested into real estate the

other 89,5% is divided over stocks, bonds, mortgages and other non-real estate related investments.

Dutch pension funds have been confronted with low coverage ratios due to the effects which the crisis

has had on real estate returns since 2008. In many cases their liabilities have even outgrown their

assets (van der Werf, 2013).

Direct real estate has proven to be a good shock absorber for mixed portfolios of these institutional investors. Large losses on stocks, bonds and (international) indirect real estate (Graph 1) were partially absorbed by stable performances of direct portfolios of real estate investors (van Gool, 2013).

However, to manage the ever increasing investment process and to achieve desired levels of diversification and risk reduction, both institutional investors and asset managers still choose real estate funds as the preferred vehicle in order to obtain their international exposure post -crisis. (Acosta)

The reasons for losses made by indirect funds can be categorized into four aspects;

The loss on income due to vacancy and lower rental prices;

The loss on value due to market failure and changing demand;

The loss due to the negative effects of gearing;

The overhead costs and costs of external resources needed for investing in indirect funds.

These four reasons for losses are either due to micro, macro of meso aspects. Micro aspects have to do with the real estate property itself and the condition that it is in. Macro aspects are related to timing e.g. GDP, spending power and other nation related variables. Meso aspects are more closely related

to a specific submarket region of the property and include variables such as regional income and competitors. These three aspects will be discussed fully later on in the paper.

Graph 1 - Global Total Returns on Indirect Real Estate Funds

Source: IPD Global quarterly Return Index Q1 2014


13

This research focuses on the first two points for losses made on indirect funds. Gearing and overhead costs are elaborated on in relation to the first two in terms of risk but ultimately remain factors for

which an investor is dependent on himself and/or the fund manager. Current affairs

In recent years we see a steady increase in indirect fund investments and a decrease in direct and

stock listed fund investments made by Dutch institutional investors. (Graph 2) This is partially due to

the relatively higher losses that publicly traded real estate has made in comparison to non-listed real

estate. This supports the notion that capital is increasingly being allocated in the indirect asset class

by Dutch pension funds post-crisis.

According to FGH banks annual statement; the investments of large institutional investors in Holland

have also increased in 2013. The growth is predominantly caused by the larger number of indirect

investments. Within these indirect investments the amount of international investments has strongly

increased.

In a recent report Ernst & Young Global limited (2014) states that the private equity real estate industry

is back after five years of recession. The private fund investment sector is positioned for growth of total

investments in 2014, according to their news release. (International business times)

The Dutch economy has been preforming below the European average in recent years and this is

expected to stay this way in the near future, although the gap is becoming increasingly smaller. On the

other hand, the US economy for example, has been growing (Syntrus Achmea, 2014).

Recent Trends Next to the total increase of investments, a few relevant trends can be identified for the indirect

investment sector. Changes in the way in which managers and investors allocate capital:

Institutional investors tend to prefer core funds over value-added or opportunistic funds post crisis due

to their stabilized properties and relatively risk adverse strategies compared to value added and

opportunistic funds.

Recent rise of international opportunistic investments: Private equity firms have bought some low-cost

foreclosed properties in recent years. A good example of such a private equity firm is Blackstone

Group L.P. Blackstone has invested more than 5 Billion USD in low-cost foreclosed properties which

they have then rented out. Another example of this is their acquisition of Multi Corporation adding 56

Graph 2– Balance total of indirectly / directly owned real estate of Dutch pension funds

Source: Vastgoedmarkt Research Paper 2012


14

shopping centers to its portfolio. Other investors have recently also done opportunistic acquisitions in

the Spanish market (International business times)

Changes in occupier demands: A recent report written by PWC shows that occupier demands for the

different commercial sectors this research focuses on have changed and will keep changing in terms

of: location demands and building demands. This is further elaborated on in chapter 2.4 (PWC report;

Emerging trends in real estate 2014)

Advances in the real estate sector: more platforms for documentation of real estate indices are

constantly developing to document and benchmark real estate performance and related aspects.

(GRESB, LEED, Costar, BOMA, INREV, MSCI, NCREIF) This results in more transparency, giving all

stakeholders, mainly investors, more insight. This in turn could lead to increased confidence and trust

in the real estate sector as a valuable asset class. This is particularly interesting for international

investors who are dependent on databases to analyze potential investments.

1.3 Problem analysis

When an investment in a private real estate fund underperforms or outperforms a certain benchmark

it‘s challenging to identify which factors were responsible for it and to what extent. It could amongst

other things be due to;

Macro level aspects - Less or more investments are done by investors based on spending power and sectorial employment rates, limiting or increasing the amount of space being added to the market causing an abundance or shortage of space influencing vacancy, rental prices

and values

Meso level aspects - The demand for space in the region is unexpectedly increasing or

decreasing due to regional developments influencing vacancy, rental prices and values

Micro level aspects - Occupiers having certain asset specific real estate demands and the

portfolio on asset level being able or not being able to cater to those demands influencing vacancy, rental prices and values

These are only a few examples of things that influence a portfolios performance, but are meant to illustrate that a fund‘s performance is also dependent on the real estate demands of the tenants on an

asset based micro level alongside the quantitative demand for space.

Dutch Pension funds think they can invest billions in Real Estate without knowledge of bricks. -Property NL -Maart 2014-

The management of a real estate fund or an investor is expected to do research into these 3 different aspects amongst other policy related aspects when considering a certain investment. However in light of recent events we have seen that many investors or fund management professionals haven‘t

performed according to given benchmarks or prognoses, and in several cases made losses investing in the less desirable spaces with unfortunate timing.

For an institutional investor wanting to spread risk and/or obtain returns by investing in international real estate funds, doing qualitative research into a funds underlying assets isn‘t always conducted or done thoroughly enough according to a methodology so that decision making criteria can be

formulated on the basis of the risk and return aspects on all three scale levels.


15

Lacking research or knowledge of the influence and quality of the underlying asset specific criteria can influence funds returns or allow investors to mitigate risks concerning these aspects. This in turn can

lead to an undesirable deal for an investor and account for losses on indirect fund investments. The main problem is the shortcoming of investment methodology used by international investors when

investing in international real estate funds. Only 20% of professional investors include property specific criteria, the micro scale, into investment methodology. A lack of transparency and proper data regarding the three scale levels are also evident. Because of this problem, fund strategy and

investment analysis becomes less reliable and more difficult to fully conduct.

1.4 Study questions and research questions

In regards to the aforementioned problem analysis, the following problems which have to be solved by

the research have been formulated. In order to solve these problems a main research question is formulated.

Main problems to be solved

Improvement of the investment methodology used by institutional investors when making their international indirect real estate investment strategy decisions by adding underlying asset

analysis.

Determining which asset specific criteria are influential in regards to indirect real estate

performances based on historical performances of indirect funds.

Translating these asset specific criteria of existing international real estate portfolios into asset

specific investment criteria for future investments. Main research question

“How can asset specific analysis improve International Real Estate fund investment analysis?

Sub research questions

The detailed research questions will be segmented into different topics intended to build an answer to

the main research question, provide the required knowledge to conduct the statistical research methods, interpret findings and build the thesis conclusion upon.


2. How do the relationships between stakeholders affect investor criteria?

3. To which extent do macro and meso economic aspects influence commercial real estate

performance?

4. Which asset specific criteria can be used for underlying asset analysis and what is their relative

influence on the financial performance of commercial real estate?

There is a paragraph dedicated to each research question with the same corresponding number in the

theoretical framework in chapter 2. (2.1-2.4) the information needed to understand and answer each

research question (how and which) is provided in these paragraphs. The impact (how much, to which

extend, relative influence) of each aspect discussed in the research questions is determined and

estimated by the statistical research in chapter 4. The conclusions in chapter 5 give the final answers

to the research questions. Chapter 6 translates the gained information into an investment tool. This

tool is the embodiment of the recommendation on how to utilize the research outcome.


16

1.5 Objective, intended end result and Scope

Objectives The general objective is: Examining underlying assets of existing private real estate funds in order to

determine relevant asset specific criteria based on their historical performances. This will then determine their influence on the assets performance.

End result This research‘s intended end result is to give institutional investors and fund management professionals‘ insights into the influence of asset specific criteria and how to incorporate these into

their investment methodology. This research is also intended to be a tool for making investment decisions concerning their investments in non-listed international real estate funds.

Scope The research primarily focuses on commercial real estate but is limited to Retail, Office and Industrial assets according to the NCREIF and INREV type classification. Assets such as hotels, residential

property, personal storage etc. are not part of the scope. The research is intended for international investors aiming at funds in foreign markets. However, the

research scope is done with data solely from the US. This limits the scope to international investors investing in the US market.

The performance is measured according to Net Operating Incomes, Estimated Values and return figures. Figures such as total rents are not included into the scope due to their lack of explanatory power.

The main focus of the research is on the Micro level of assets as a determinant for performance, however to increase reliability and decrease the missing variable bias in the hedonic pricing study, the

meso, macro and fund manager influence are needed to single out their relative effects. These are explained in the theoretical framework and included in the statistical analysis as control variables.

1.6 The research design

The research design comprises and has been conducted in these consequential six steps:

Step 1- Problem Analysis (Chapter 1) The main problem is the shortcoming of investment methodology used by international investors when investing in international real estate funds. Investors generally control for risks concerning; Juridical,

financial, macro and meso influences but generally fail to identify the asset specific quality and risks of the underlying real estate. This problem has come forth due to the apparent inability of investors to pinpoint the exact reasons for the losses made on international real estate funds in the past. These

losses have affected the coverage ratios of pension funds and jeopardize the pension holders. Step 2- Literature research (Chapter 2)

This step includes gaining general knowledge on the subject as well as reading into what has been published on the topic by accredited scholars and researchers. The existing theories about concepts and the determinants of net operating income, estimated values, returns and other real estate and

investment related topics will be reviewed thoroughly in relation to the problem analysis and goals of the research. This way the conceptual model and hypothesis can be formulated and the added value of the research is determined.

Step 3- Conceptual model and hypotheses (Chapter 1) In this section the research method, research type and the research concept have been developed.

The conceptual model will be based on the hypotheses derived from the literature research and data provided by the graduation company.

The hypothesis is: Analyzing indirect real estate investments with added underlying asset specific criteria will give better insight into profits of a proposed investment.


17

In the image of the conceptual model we see how this research plans to improve existing fund

investment methodology by adding the blue asset specific analysis squares alongside the existing indicators and criteria found in step 2.

Figure 1 – Conceptual model (Ow n Image)

Step 4- Analysis Method (Chapter 3)

This phase will encompass research into the conceptual model and underlying hypotheses by statistically analyzing data. The research will be using quantitative and qualitative data from scientific and company resources.

All financial, juridical, fund and property level data for the analysis is provided by Syntrus Achmea based on data received from 9 funds that are part of the North American AREA Fund of Funds or were

part of the short list as a potential investment for the Fund. Step 5- Results (Chapter 4) The data will be explored and the results obtained during the quantitative research will be displayed

and discussed. The results are given per sector per variable.

Step 6- Conclusion and Recommendation (chapters 5 and 6)

In the last step the results will be interpreted and the research question, as well as the sub research questions, will be answered.

On the basis of the conclusions a recommendation will be made. The recommendation is embodied by an investment tool which can be used to analyze international real estate funds.


18

Research design

1.7 Scientific and societal relevance.

In general there is a lot of scientific information available on international real estate investments and

international real estate investors. This is especially the case for macro related aspects; however there

is no specific scientific information available on international investment methodology for institutional

investors available concerning asset specific criteria.

“When it comes to analyzing the performance of non-listed real estate funds the available literature remains limited”. - (Acosta, 2012)-

To spread risks many pension funds diversify their investment portfolio in national/international stocks,

bonds, mortgages and real estate. For real estate these investments can be done directly into a

project/object or into a fund spreading the equity over several objects.

Pre-crisis times where the funded ratios of our pension funds averaged around about 150% seem like

a distant memory. This does not necessarily mean that the pension holders have a problem as of

today, but when the funded ratios drop below the required 105% they might. This was the case in the

second quarter of 2013 when the average funded ratios of pension funds dropped to a shocking

101,8%. This of course isn‘t the case for all Dutch pension funds seeing not all pension funds have

invested equal amounts of money in the same types of investments. In the figure below we can see

the largest Dutch pension funds funded ratio‘s as of the last quarter of 2013.

Q

ua

ntita

tive

Re

se

arc

h

1.Problem Analysis

2.Theoretical

Framework

3.Research Methods

4.Results

5.Conclusions

Understanding the influence of asset specific variables on the financial performance of the

underlying Real Estate in funds

1. International RE investments

2. Stakeholders

3. The Macro and Meso levels

4. Asset specific criteria

HP Models

Statistical Analysis

Relate to theory and answer main

research question.

Data collection

Lin

k to

th

eo

ry

6.Recommendation

Outcomes

Figure 2 - Research design (Ow n Image)


19

The largest fund in the middle of the graph below is ABP with approximately 3 mill ion pensionholders

(250 billion Euros) which has a funded ratio of 106,3%. This is barely above the required minimum of

government regulations endangering the pensions of our society. (NOS, 2013)

From a market perspective profitable projects are not necessarily based in the country the pension

fund resides in. In response to this many institutional investors, in particularly pension funds, divest in

local real estate and invest in foreign real estate when local real estate markets provide increased

risk, low returns or losses.

Seeing that for many pension funds investing into foreign real estate projects contains certain control

and knowledge risks; research has to be done into how pension funds can make these international

markets more transparent and identify profitable real estate projects in certain areas of interest for

them to invest in.

Stating that real estate is almost always dependent on local market aspects makes it more difficult for

an international party to do these types of investments due to a lack of local knowledge and control.

Ultimately the best investment decision has to be made for the inst itutional investor to provide solid

returns for the people depending on these institutions increasing their performance. Considering

almost every working person saves money with banks, pension funds or investment companies it

affects us all if the methodology used by these institutional investors is improved.

Doing research into asset specific measurement aspects into a decision making framework for

institutional investors to increase the efficiency of international real estate investments could help

pension funds increase their total return on real estate investments and eventually their funded ratios.

1.8 The utilization potential and economic valorization

The research and final products provided through the research can be utilized by any type of

institutional investor investing in international real estate. The research contributes to the process of

investment fund or investment object identification, documentation and selection.

The outcomes of the research and the intended methodological framework are directly applicable for

analysts, researchers and decision makers in the international real estate investment industry.

This research can be used as a tool or guideline for institutional investors doing international project

and property investments. The research hopes to take relevant aspects into account of international

indirect real estate investment and ultimately increase the efficiency of the identification and selection

process.

Graph 3 – Coverage rations of Dutch pension funds

Source: Own illustration based on NOS, Dutch pension coverage ration graph Q4 2013

ABP


20

The idea of how to use to the methodology is to do a ‗quick scan‘ of an international real estate

portfolio or asset to translate the different asset specifics of the real estate investment into a risk ,

return profile. On the basis of the outcome of the tool, decisions can be made.

By comparing one‘s own company strategy and that of the proposed international real estate

investment, one can determine if such an investment is a proper fit and profitable endeavor for their

investment company.

1.9 Personal motivation

The built environment has always been of interest to me since I was young. By the age of 18 I had

lived in three different countries of which all have different types of built environments and different

ways of how these environments were created.

Being a European citizen and also having been in touch with the US real estate market made me

realize the amount of possibilities there could be if we were to be able to envision projects and invest

across international borders.

By writing this thesis and doing research into current and future investments of institutional real estate

investment companies I hope to increase and combine my technical and financial knowledge on all the

processes of international real estate investment. By doing this research I intend to improve profits and

the process of identifying profitable investments.


21

2. Theoretical framework

A theoretical framework consists of concepts and existing theory that is used in this research paper.

Together with concept definitions and reference to relevant scholarly literature it is intended to clarify

the research question. Several sub questions are (partially) answered by means of available research

and other scientific resources. The theory is separated in 4 main paragraphs concerning the thesis

topic:

1. International real estate investments

In this paragraph the focus lies on how the different forms of international private fund investments

affect investor criteria. The focus criteria are risks and returns. First the different types of returns and

how they are measured are discussed. The differences and relations between returns are discussed.

Secondly the different types of risks are elaborated on. One of the focus points of this research is

reducing the risks of international private fund investments, so it is important to understand risk. Lastly

the different investment types and the implications these hold are discussed.

2. Stakeholders in international real estate investments

In this paragraph the relationship between stakeholders and the way this affects investor criteria is

examined. This paragraph will elaborate on how the investors and fund managers manage indirect

real estate fund investments and how these stakeholders have an influence on each other. It will

become clear how these differences between management, stakeholder relationship and fee structure

are accounted for in the regression model.

3. The Macro and Meso level aspects as decision making criteria

This paragraph describes the macro and meso economic aspects in relations to the investment

objectives of international real estate investors. First the macro economic variables and their effect on

real estate values are described. Then the meso economic variables and their influence on real estate

values are explained. Finally this all comes together in a conclusion which discusses how to control for

these effects in the regression model.

4. Asset specific criteria analysis of real estate as decision making criteria

In this paragraph the focus lies on which asset specific criteria can be used for underlying asset

analysis of international private real estate portfolios. First the definition of asset specific criteria is

explained. Secondly the criteria based on location are further described. Additionally building criteria

will be elaborated on. Finally it will become clear which variables are taken on to chapter 3, where

availability of data for these variables is checked and afterwards a transformation of the variables is

performed.


22

2.1 International Real Estate Investments

2.1.1 Introduction

In this paragraph it is determined how the different forms of international private fund investments affect investor criteria. Real estate as an investment asset class has been growing in the past couple of decades and has become the third largest investment category after stocks and bonds. A large part

of invested capital is allocated to international real estate.

Geltner et al state several rationales and obstacles for ‗going international‘. The primary obstacle

discussed and dealt with in this thesis are the information risks and performance of international investments. Private real estate markets e.g. non-listed funds often have low levels of transparency and are seldom efficient at incorporating new information into the asset prices.

Investments in non-listed international real estate funds place the physical assets a few scale levels away from the investors. Firstly, the physical assets and the surrounding markets reside in a different

country with their own unique property and investment markets. Secondly, a fund has its own management which possibly and quite often uses different types of investment strategies and decision making criteria. This exposure of investors to information risk in international real estate markets can

possibly lead them to buy properties above the market value for overpriced investment values or selling properties below market value due to a lack of knowledge concerning present market conditions and value determining aspects of their investment.

Because real estate property markets usually entail infrequent and private deals in which unique assets are traded, knowing the precise market value of a real estate asset at any given time is difficult.

This lack of information adds an extra risk to real estate investments compared to an investment in stocks or bonds. This is defined in terms of the Net Present Value (NPV). While the expected NPV is approximately zero when measured on a market value basis, this is not guaranteed due to possible

fluctuating rent levels, market and exit values and unforeseen costs. (Parker, 2011). In real estate investments in the private property market, it is possible to do a deal with substantially

positive or negative NPV‘s, measured on the basis of market value. This is partially because parties conducting a transaction sometimes make mistakes often discovered and evaluated in retrospect. They may fail to discover or consider information relevant to the market value (MV) or net operating

income (NOI) of the property at hand. In some cases, one party will have better information than the other. They may have investigated the deal differently or more diligently than the other side, leading to overpriced or underpriced deals which in turn cause a loss or increase of investment performance.

This research is focused on the objectives of institutional investors allocating capital towards private international real estate funds. This category contains investors such as pension funds, insurance

companies and other investment institutions. The common objective for institutional investors is to manage assets for participants such as pension funds.

In order to enforce the investment objectives investment criteria are made (Van Gool, 2013):

The desired return

The acceptable risks and risk levels of the investment

The period in which the investor wishes to invest (investment horizon)

The desired liquidity of the investment

Use of debt in the investment (leverage)

The matching of the investment performance with the obligations of the investor

The goal of this research is to improve the investment methodology of institutional investors for indirect funds by gaining more insights into the underlying assets. This is done by focusing on the first two investment criteria and improving the methodology on the basis of the first two investment criteria. This

is done by means of an asset by asset base analysis. The main focus criteria considering this research are therefore; the desired return, the acceptable risks and risk levels of the investments. This chapter will discuss how these criteria are measured and how the different types of private fund


23

investment influence these criteria. The other discussed criteria are predetermined and regarded as knowledge needed to understand the research.

2.1.2 Returns

Investing in real estate is becoming a less isolated activity and is increasingly placing itself into the financial and rational totality of the economy. Investors are increasingly using financial concepts that

originate from scientific theories. This research is also based on such theories and further builds upon them by developing its own.

In order to understand and prove such theories, insights into the performance measurement techniques of real estate are needed. Investors always prefer a high as poss ible performance from their investments and use various analytical tools and methods to predict and secure profits. These

profits are generally measured on an annual basis by the total returns. Total returns are made up out of two other types of returns; the direct returns and the indirect returns. Each type of return is explained for both the fund scale and the asset scale. The relations between fund return and asset

return are explained. The building blocks of returns, NOI and EV are also discussed.

Fund returns

Total returns The total returns made on fund investments comprises out of both the direct and indirect returns. For the calculation of the total return and the explanation of the separate forms of return we will use the

INREV‘s method for calculating fund returns based on a certain amount of capital invested called the Dietz method. This method measures the historical performance of an investment portfolio in the presence of external flows. The modified Dietz return is calculated by dividing the gain or loss in value,

which is partially based on the value of the underlying real estate, net of external flows, by the average capital over the period of measurement. This gives the rate of return as a percentage for time period. The average capital weights individual cash flows, which are partially based on the net operating

incomes of the underlying real estate, by the amount of time from when those cash flows occur until the end of the period. In short we can say that the total returns of funds are therefore partially dependent on the value and net operating incomes. Improving these would result in an improved fund

returns. The exact formulas can be found in appendix x. The formula for calculating a fund‘s total return is a

combination of both the formula for direct return also known as income return, and the formula for indirect return also known as capital growth.

Direct returns / Income return Direct return or income return on real estate funds can be considered as the returns on incoming cash flows of the real estate during the specified holding period minus all expenses. The income return for

a fund is based on the total investment values (NAV) and Net Operating Incomes which are part of the distributions generated by the properties in the fund. In short; the bundled incomes and values of all properties for a given year form the basis of a funds income return (INREV, 2014)

Indirect returns / Capital growth Indirect returns on real estate can be considered as the return on value growth of the assets in the

fund during the holding period. The estimated values of underlying assets comprising the GAV, which is the sum of value of company owns, this then results in the NAV by subtracting the funds liabilities are appraised by registered real estate firms like JLL or CBRE. Due to international fund regulations

only certain registered firms are allowed to appraise the estimated values. This guarantees a certain quality standard of appraisals. In short; the capital growth is based on the estimated values of underlying properties in the fund. (INREV, 2014)

http://en.wikipedia.org/wiki/Investment_portfolio


24

Asset returns

Total returns The total return of a real estate asset is also the sum of the direct return and the income return. However the difference is that these returns solely reflect that of the asset and not a combination of

assets, cash, land etc. and other performance influencing factors used to calculate the return of a fund. The exact formulas used for calculation are shown in appendix x

Direct returns/ income returns The direct returns of underlying assets are determined by dividing the NOI by the sum of the EV and capital expenditure. In this research we did not have access to the capital expenditures that were

made during the examined time period and this can therefore be seen as a form of noise.

Net operating income:

The net operating income (NOI) of an asset equals all revenue from the property minus all necessary operating expenses. Operating expenses are those required to run and maintain the building and its grounds, such as insurance, property management fees, utilities, property

taxes, repairs etc. The NOI provides a clear, comparable financial performance figure of properties on asset level. The NOI’s of the underlying properties in the examined funds are used as a dependent variable in the statistical analysis of this research

Estimates values: The EV is the estimated market value of a property appraised by an external valuer.The valuation methods can include, among others:

Market approach - based on market comparables; Income approach - based on realistic market income capitalization; Other valuation models based on earnings multiples or discounted cash flow methodology;

Replacement cost less depreciation (cost approach) should only be used in specific and rare circumstances when other valuation methods cannot be applied. The EV provides a clear representation of a comparable performance indicator for value

The EV‘s of the underlying properties in the examined funds are used as a dependent variable in the statistical analysis of this research

In order to comply with fund regulations by the MSCI, the manager should ensure that the external valuer provides sufficient market evidence and comparables to support all key assumptions used in the estimation of the market value and the asset. This is important so that the estimated value reflects

an actual fair market value based on the attractiveness of the asset and not a biased value.

Indirect returns/ Capital growth

The direct returns of underlying assets are determined by dividing the sum of EV gain, capital expenditures and capital receipts by the previous estimated value. As stated before this research did not have access to the capital expenditures nor capital receipts that were made during the examined

time period and this can therefore be seen as a form of noise. Differences and relations

The manners in which returns for funds are calculated differ from that of asset returns is the fact that the fund returns are based on the amount of invested capital spread over the different contents of that fund and fund specific liabilities such as fees for a specific period. Asset returns are based on the net

operating incomes and asset values for a specific period. The s imilarities are that both returns are based on the NOI‘s and EV‘s produced by the underlying properties. The distributions in the fund equations are comprised out the NOI‘s of the properties and the NAV, which is based on the GAV,

which is the sum of estimated values of all assets in the fund. It is important to know that not all assets are stabilized properties. Indirect property funds can also contain land, developments, cash and mortgages. These are not part of the scope of this research but are also an influence on fund return

and therefore important to mention. The main difference between investing in an individual asset compared to investing in a fund is that in the long run, investing in a fund will have the advantage of diversification and thereby decreasing risk.


25

―For end investors, fund level return is the key performance indicator. Asset

performance is only one of the components of return, and can depart significantly from the return achieved

once debt, cash, non-direct investments and expenses have been accounted for‖. -(IPD, 2014)-

Causes for increased or decreased NOI’s and EV’s

Rental and sale prices for real estate can be influenced by a number of external factors. For example;

GDP, local community size or even the economic vitality of the city can play a part in the price forming process of real estate. In addition to the external factors, the expected return of an investment based on NOI‘s and EV‘s depend on factors specific to the investment itself, often referred to as the asset

specific factors. For investment properties these factors include the location, the quality of the tenant or the physical qualities of the building. To make things easier these influencing factors will be divided into three different scale levels.

The three scale levels of influence:

The Macro level influences are primarily on a national or large city scale. Factors like GDP, Population growth, E-commerce and labor prices can be considered as macro level performance indicators. This

will be explained more in depth in paragraph 2.3 and 2.4. In regards to the real estate pricing process an investor might take e-commerce growth or a decrease in consumer spending as an indicator to forecast a future decline in the square meter prices for a piece of retail space (Kaklauskas, A et

al.,2011).

The Meso level influences are mainly focused on a district or relevant business area, depending on its

size. The local competition, population growth and infrastructure can be good examples of influencing factors on a meso level scale. These are also common factors that play a part in rising or falling return in real estate. The meso level scale is also elaborated on in chapter two (Kaklauskas, A et al.,2011).

The Micro level influences are mainly focused on an individual asset basis. This scale level is the prime focus of the thesis, discussed in chapter three. The micro level encompasses everything on the

scale of location and building. Locational and building aspects e.g. distance to CBD and other amenities will play a vital role in value determination and rental growth potential. Due to the primary focus and goal of this thesis to analyze the micro level aspects of real estate assets in international

funds, an in depth explanation of the influential factors is given in chapter three (Kaklauskas, A et al.,2011).

To give a better understanding of the three scale levels one can picture the macro level as an entire forest, the meso level as the different areas inside the forest, and the micro level as each individual tree in the forest. Ultimately, investors can attempt to apply analysis techniques to ensure profits and

avoid the risks regarding each scale level explained in the next paragraph.

However, it is often in retrospect hard to say what caused a deviation in a portfolios performance. This

could be from an influence of either one or a combination of these three scale levels. The ultimate cause is always either tenants not paying for the building and/or the asset not selling for the expected book value. Ultimately the aspects that influence tenants and buyers willingness to pay are reflected in

the macro, meso and micro scale levels.

Figure 3 - Fund return composition (IPD, 2014)


26

2.1.3 Risks

As stated in the introduction one of the focus points of this research is reducing the risks of

international private fund investments. The risk which is most evident is a knowledge risk where the

risk of not knowing enough about the underlying assets in the fund leads to unfavorable transaction.

The increased knowledge could lead to a decreased asset specific risk of the investment due to the

fact that the investor could mitigate funds which have more exposure to unfavorable asset specific

criteria. To elaborate on the risks relevant for international real estate investors the term risk is first

explained from a financial perspective. Vlek et al (2013) state that in regards to real estate

investments;

“Risk is the possible deviation from an expected financ ial performance as a result of exposure to

uncertainties”

In regards to this research the possible deviation from an expected financial performance can either

be the fund returns, asset returns, NOI‘s or EV‘s. When describing the type of risk, the focus lies on

the downside risk. Risk measurement for real estate investments can be done quantitatively by means

of the standard deviation or qualitatively by means of separate risk identification and analysis. This

thesis focuses on the qualitative asset specific risks regarding each individual asset in an indirect

international fund. In addition to this the risks regarding fund investments are also briefly added to the

risk segment. The exposure to uncertainties is described below.

Qualitative risk analysis Risks also differ for indirect fund investments and for direct investments on an asset basis. Seeing the

vast amount of possible risks, the most relevant types of risk for the thesis subject associated with international real estate investments are explained per scale level:

National market risk Macro Scale

The real estate market has historically been considered relatively risky due to a lack in transparency

and reacting heavily and unpredictably to shocks in the economy. Today, supply and demand have become more liquid and transparent, thereby stabilizing the real estate market. (Ross, S. A., & Zisler, R. C.,1991). It generally reacts more quickly and more sensitively to changes in the economy. In their

paper ―Investing in International Real Estate Stocks: A Review of the Literature‖, Elaine Worzala and C.F. Sirmans (2002) found national market risk is very important to take into consideration when investing internationally. The national market risks for investments include high current account

deficits, overvalued exchange rates, high inflation, large short-term dollar denominated debt, low capital formation, poor regulation of the banking system and corrupt governments.

Regional market risk Meso Scale Surrounding buildings and construction projects, building zone planning, are amongst other aspects

important aspects. It is essential for any investment to know these regional type variables, and should be included as part of the due diligence process when purchasing an object.

Asset quality risk (Asset specific) Micro Scale

The quality of the location is something that depends on a number of factors: connections to highways

and public transportation, distance to CBD. If these aspects vary from what tenants want, returns can more easily deviate resulting in an increased risk. Building level aspects are also part of the asset quality risk. Higher returns can be achieved through savings in maintenance work, property

adjustments aiming at satisfying tenant needs, and/or avoidance of expenses related to finding new tenants. This research aims to improve the analysis methodology of the asset specifics in indirect


27

funds.

Tenant risk (Asset specific) Micro Scale

Tenant risk carries the risk of not having a high level of sustainable returns. This risk entails the risk of vacancies due to tenants not being able to pay the rent. The risk of this happening differs per type of tenant and some tenants such as government institutions have higher credibility . This is not the same

as market risk, which entails how easy it is to find new tenants. This risk can be avoided in several ways. Signing long-term tenancy agreements, checking each tenant‘s credit standing prior to investment. , and/or supporting tenant loyalty through an attractive mix of tenants and a good property

location or building quality can decrease this risk substantially. Financing risk

There are two parameters that can be adjusted. This makes financial risks easier to control compared to market and object risks. Additionally financial risk can be adjusted in order to meet the risk profile of

investors. The first parameter is the type of investment. Contained risks vary depending on if the investor grants a loan, grants a participative loan, supplies mezzanine financing or purchases regular equity. Senior debt has for instance a lower risk than venture capital. The second parameter is the

extent of financing. The more equity is used to finance the project, the lower the risk of the investment project. The debt service coverage ratio (DSCR) gives the ratio of cash available to pay off debt and all expenses related to servicing this debt, such as interest, principal and lease payments. The DSCR is

seen as a measurement of one‘s ability to maintain its debt. If the NOI is higher, this ratio is higher so the financing risk of the investment is lower. The DSCR can be calculated for an individual asset as well as of a fund.

International fund risks

Two types of risk become relevant when looking at an investment in international real estate funds. Currency risk

Currency risk is a form of risk that originates from changes in the relative valuation of currencies. If you have an investment in a foreign currency and the corresponding exchange rate decreases, profit and sometimes even the investment capital decrease. This risk is not an integral component of a real

estate investment. The real estate market is hardly related to the causes of exchange rate fluctuations and thus should not be attributed to the performance of your real estate investment. The extent to which risks should be hedged is dependent on the investor‘s portfolio strategy. Elaine Worzala and C.

F. Sirmans wrote a paper that summarizes the various findings of studies completed on the benefits of international diversification using real estate stocks. Almost all of the studies reach the same conclusion: diversification gains are possible but are often reduced if currency risk is included in the

analysis. Unsystematic risk

By investing in in different assets that are not fully correlated, losses in one investment are usually fully or partially compensated by the other investment. This limits the volatility of the investment portfolio. The investment risks that can be eliminated by diversification are called systematic risks. Risk that

cannot be eliminated by diversification is unsystematic risk. This risk is industry-specific hazard that is inherent in each investment. Because the real estate business does not correlate strongly with other investor markets, the diversification into and within real estate is a must in order to stabilize and

optimize an investment portfolio. The benefits of Integrating and improving property/asset specific risks

A recent article called ―Integrating property risks‖ from IP Real Estate magazine about international portfolio risks shows us the current trends in risk management improvement. This research is based

on a recent survey of the MSCI interviewing 138 real estate investors. The survey is conducted to see


28

how well and in which manners investors conduct their risk management. Several shortcomings are found including the lack of asset specific knowledge and risk analysis among many investors.

―The survey identified two related dimensions on which steps towards stronger risk management are taking place within the real estate department. Firstly, the benchmarking of real estate, which helps

monitor exposure and can ensure alignment with investment objectives. Secondly, the monitoring and reporting of asset and portfolio-specific risks.‖

―Beyond the use of benchmarks as risk management tools, the survey reveals considerable effort to focus on more asset and portfolio-specific risks of real estate. Monitoring these risks is seen as a way of avoiding the style drift that plagued many asset owners through the global financial crisis. This risk

monitoring can be related to the use of benchmarks, but requires additional data and monitoring.‖

―The MSCI/IPD survey provides a series of insights into the risk management of real estate within

multi-asset-class portfolios, including guidelines for best practice. One of the central conclusions is the potential for misalignment to occur between the strategic role for real es tate and the actual exposure of the real estate portfolio. This potential for strategic misalignment is often created by more tactical

mismatches, such as the use of inappropriate benchmarks or limited strategic monitoring of portfolio and asset specific risks. Both these mismatches can lead to style drift in the actual real estate exposure.‖ (IP Real Estate 2014)

Figure 4 - Allocation of capital and influence of risk (IP Real Estate, 2014)

2.1.4 Investment types

Real estate encompasses three main forms of investment classes which are the same for national and

international investments; Direct, indirect public and indirect private. Each investment class corresponds differently to investment criteria and is therefore more or less suited for each different type of real estate investor. Hereafter, the differences between direct versus indirect and public versus

private are briefly explained from an institutional viewpoint. The different pros and cons of indirect international real estate types are distinguished and elaborated on considering the research goal and institutional investment criteria.

From direct to indirect

Direct real estate investments are investments where the investor is directly the owner or majority shareholder of the real estate or financial titles that give him the right to profits made by and management of that real estate in particular.

And Indirect real estate investments are investments where the investor is not directly the owner of the real estate but owner of financial titles that do not make him the majority shareholder nor give him

control over management of that real estate. This implies that with direct international investments an


29

institutional investor either does internal management of the asset or outsources it to an external company whilst obtaining full ownership. This is different for indirect international investments.

When an institutional investor invests indirectly, the management of the asset is conducted by the investment fund that is the judicial entity owning the asset. The fund generally spreads the

participating shares over several investors making each an indirect investor in the fund which invests directly into the underlying assets.

This makes an investor in a fund thereafter dependent on the investment strategy and performance of the fund and its management. The performance of a fund is in turn dependent on the performance of the underlying assets; the properties in the investment fund. Some investment funds solely invest in

certain areas or sectors in which they are specialized to minimize the knowledge and information risks of investments regarding areas, sectors etc. in which they are not specialized. It is important that the fund itself is examined alongside the real estate in the fund.

The information regarding the performance influencing factors of the underlying assets are generally obtained, analyzed and possibly anticipated by the investment funds management depending on their

policies and capacities. Due to the fact that these performance influencing factors can cause changes in risks and profits of a fund, these factors are often translated into investment criteria for indirect investors and fund managers. These criteria are elaborated on in paragraphs three and four.

Recent years show that from 2002 and onward the total balance value of the direct real estate investments done by pension funds was decreasing untill 2007, slightly increased in 2008 and then

decreased untill 2011. For the indirect real estate investments we see that the total balance value of decreases for stock listed real estate and increased for private real estate untill the crisis. The reverse effect can be seen in the years after.

In conclusion we see that institutional investments have shifted in the last decades towards a more indirect and international approach. In 2008 investments took a halt due to economic crisis but started

increasing from 2009 onward untill today as stated by the FGH update in chapter 1. This gives reason to extensively research the influences funds and underlying assets have on the investment performances of institutional investors like pension funds.

Public versus private

There are two important subdivisions of indirect investments, the public and private investment classes. The most accessibly way of indirect investment in real estate is through the stock market.

Since the end of the nineteenth century there have been real-estate-funds listed on the stock market. Investors now have choice between over 500 publicly listed real estate funds worldwide to invest in. Thanks to the stock market listing these real estate stocks can be easily traded and one can start

investing for relatively small amounts of money.

Graph 4 – Investment ratios of Dutch Real Estate investors

Source: CBS, edited by Vastgoedmarkt (2013).


30

A large difference between public and privately traded real estate, also known as non-listed real estate funds, is that share prices of publicly traded real estate shares are vulnerable and react very quickly to

exogenous shocks to the economy and stock market. One might say that publicly traded real estate such as REITs react more heavily to macro-economic shocks and less to meso and micro economic shocks. In essence publicly traded real estate can be regarded to be more of a stock than a bundled

form of tangible assets. An investor can also indirectly invest in real estate through the use of the non-listed real estate funds.

These often distinguish themselves in terms of risks and prognosed profits mainly through the use of the Core, Value-Added, and Opportunistic INREV classification guidelines. Buying in to such a fund commonly starts at large amounts. Therefore International indirect funds can be particularly interesting

for institutional investors focusing on diversification. The private real estate market reacts more slowly to exogenous macroeconomic shocks than for

instance the public real estate market where shares are traded on a daily basis. The price forming process will therefore respond slower for private investment funds. It is important to understand that the influence of macro and meso-economic factors is very different for indirect investments. This also

means that an investor can still profit from the formal release of new information regarding the real estate he or she wishes to invest in.

The key difference between the private and listed real estate sectors is the basis of valuation. The private market together with non-listed private funds, are valued according to valuation estimates done by appraisers based on performance measurement of the individual real estate assets. In contrast,

listed real estate such as public REITS are valued based on ongoing transaction based indices. These basis of pricing are fundamentally different and can lead to substantial differences in the pricing, return performance and risk of private and listed assets, even if for instance two neighboring buildings which

are exactly the same are placed in the different funds. Listing real estate funds on the stock market adds stock characteristics to real estate. This takes away

some of the advantages of direct real estate investment compared to other investments like being able to research and identify price differences amongst the asset specific criteria. The added value adding of real estate to the portfolio next to stocks decreases. Additionally publicly listed real estate firms

increase the portfolios leverage. Because of these two effects the positive characteristics of direct unleveraged real estate are largely lost (Syntrus achmea, 2014).

Implications of the types of international private real estate funds

Institutional real estate investments used to be based on investments in core real estate. Since the early 1990‘s investments in opportunistic funds arose. Market conditions led to these investments, of falling property prices to acquire assets at significant discounts. In the 21

st century private equity real

estate emerged as an independent asset class. Since then it has experienced huge growth. Funds are available in many forms. Each form has different implications for the risks and return of the

fund. Therefore an investor in an international real estate fund must first decide in which type of fund it has to invest in order to reach its desired goals. Van Aert (2006) states different aspects in which

funds distinguish themselves. For each type different implications arise for the criteria of the institutional investor.

Singly country or multi country This differentiation is about whether the fund invests in a single country or several countries. This distinction is important considering the tax implications of the buildings. For diversification,

economical or other reasons investors might have specific preferences towards certain or multiple countries. The macro, meso and micro economic aspects of such funds are different for each country and their intercountry differences should therefore be examined. This research focuses on the US real

estate market. So the different scale levels are dependent on US economies and submarkets. Specialist or diversified


31

A fund may focus on one sector or several sectors. For example; a fund that solely invests in offices relative to a fund that can invest in any industry (office, retail, industrial, residential or other niche).

This is particularly interesting to this research due to the fact that specialist and diversified funds are examined by means of sector specific hedonic pricing studies. This is due to the fact that according to scientific literature provided in 2.3 and 2.4 different macro, meso and micro economical influences and

impacts apply to each sector. Closed or open ended

A closed-end fund has a fixed, finite lifetime. After setting up the fund, no new money can be picked up and it is difficult for shareholders to sell their shares before the end of the investment term. In contrast, an open-ended fund has no finite life and can issue and sell shares unless market activity

stagnates. There was an abundance of closed funds in the past, while nowadays mainly Core funds with an open end are formed. This is quite logical, since Core funds have a longer term focus compared to other styles. This is interesting to investors in terms of liquidity risks. And if after

analyzing the funds contents they might have the ability to perform an exit strategy Blind pool or seeded

A "blind pool" fund is a fund in which no property is present at the start of the fund. A 'seeded' fund has properties in the fund from the beginning. The advantage of this is that there is evidence that the manager is capable, or may be incapable, of acquiring high-quality real estate for the right prices. The

statistical research performed is based on seeded funds. An investor who is considering a blind pool investment has no underlying assets to analyze. Only the investment methodologies of both investors and fund management could be compared.

High versus low level of investor involvement The amount, size, and expertise of investors alongside the fund style mainly determine the degree of

investor involvement. For example, if only a few investors who invest the majority percentage in a core fund, then the chances are that these investors are closely involved in the funds management. But if many investors participate with small investment in a large Opportunistic Fund, the degree of

involvement is usually low. Depending on the nature of a fund there can be general or limited partners in funds representing a voting seat on the board. If an investor has influence they might be able to influence investment decisions regarding underlying assets.

Individual fund or fund of funds With a fund to fund, an investor invests in a fund which in turn selects its own real estate funds to

invest in. This can be interesting for investors with little expertise in selection of real estate funds and who want to obtain a diversified portfolio in a short period of time. This however causes for higher managerial fees considering there is an extra level of external management added to the investment

procedure. This also places the underlying asset another scale level away from investors.

Investment style

Fund Style Classification was created to provide the industry with a robust classification and a consistent basis to understand and compare risks and returns in funds. The fund style classification obliges all INREV or NCREIF registered funds to be classified in one of either three types of

investment fund styles. The main implications fund style can have is that opportunistic and value added funds have less stabilized assets more leverage and more development oriented assets which increase the risk of such funds. Therefore less underlying assets in such funds are eligible for

research and the predictive power of such research for NOI is therefore less relevant for those funds. The return focus in value added and opportunistic funds are placed on capital gain. Research into the estimated values of their investments are therefore of greater importance.

For example if a fund is a multi-country fund specialized in industrial assets, the fund‘s performance is less dependent of the macro economical influences of one single country and more dependent on the

building and location qualities concerning industrial real estate than for instance a diversified single country fund. Due to the fact that this research uses different types of funds, each fund will be affected differently by each type of macro-economic influence corresponding with the different types.


32

The 9 funds used in the analysis are all single country, specialized or diversified, closed or open ended, seeded, core funds with low levels of investor involvement in fund management. All 9 funds are

individual funds part of the North American fund of funds of SAREF.

2.1.5 Conclusion

The paragraph about returns has shown the differences and relations between fund returns, asset returns, NOI‘s and EV‘s. It became clear that NOI and EV are underlying asset based performance figures which are of great influence on the fund and asset returns. We can conclude that both these

financial figures are comparable on asset level and do not contain as much distorting noise. The NOI and EV will therefore be used as financial performance measure. Their averages per square footage will be used as the dependent variables for the statistical analysis in chapter 4.

One of the main goals of this research is decreasing the risk of international private fund investments. In the risks segment the most important risk of real estate transactions in unfamiliar markets is

addressed. The aim of the research is to decrease this risk by adding a new form of asset based analysis to fund investment methodology. In order to decrease this risk, several other forms of risk have to be examined and decreased. Describing these types of risks has shown that most of the

relevant risks for international private real estate fund investments are micro level based risks. Country selection is clearly a key determinant of performance. All of the risks national market selection brings are important factors for minimizing the political and economic risks associated with international

investing. A second major risk is lack of transparency and includes the lack of data on performance, lack of data on investments and lack of property rights. Each of these is an important area for future research (Elaine Worzala and C.F. Sirmans,2002).

The aim of the investment type‘s paragraph was to shed light on the different types of indirect international real estate investments. As described in this paragraph, each type of investment has different implications for the risks and return of the fund. These risks and returns must be evaluated

and weighted carefully in order to make the right investment decision. As a result, an investor in an international real estate fund must first decide in which type of fund it has to invest in order to reach its desired goals.

The importance of such research has grown each year seeing that there is an ongoing increase in the amount of capital being invested in international indirect non-listed real estate. Institutional investors seemingly rely more on an indirect approach for their real estate investments and thereby fuel the

need for predicting capabilities of future performances of these types of investments.


33

2.2 Stakeholders in international real estate investments.

2.2.1 Introduction

In this paragraph the relationship between stakeholders and the way this affects investor criteria is

examined. The main stakeholders in international real estate are investors, asset managers and users. The investors concerning the international real estate investment process are institutional real estate investors and fund managers of indirect real estate funds.

Next to these main stakeholder groups we have other players in the field which influence the investment process e.g. consultants, brokerage firms and banks which play vital roles in the

structuring of investment funds. All stakeholders are bound to one another by the demand and supply of the real estate markets. If there are no users there would be no base for investors to purchase real estate. Ultimately the users‘ willingness to pay a certain amount (now and in the future) is the notion

on which smart investors do investments. In this paragraph we describe the stakeholders and their purposes in the international real estate

investment field. Ultimately the paragraph puts into perspective how the different stakeholders form a real estate pricing, leasing, selling and buying process and how this difference should be accounted for in this papers regression analysis.

Figure 5 - Relations betw een stakeholders, funds and assets (Own image based on Gijselaars, 2011)


34

2.2.2 Institutional investors

Institutional investors are institutions that because of their activities get access to assets that they need to invest and manage. These activities exist mainly out of securing pensions and offering

individual investors the possibility of the desired risk and returns of their investments. Pension funds, insurance companies and investment institutions are considered to be institutional investors. (Van Gool, 2013). The main reasons for pension funds to invest in real estate are:

1) Diversification and reduction of the overall risk of the portfolio 2) Hedging against inflation

3) Delivering steady cash flows to the portfolio Given all the obstacles and risks involved in international investment, a successful strategy for

international real estate investors will take advantage of the benefits, while avoiding as far as possible the obstacles and risks: this is effectively the definition of strategy. The way to do this is to determine the key obstacles and risks that are more cumbersome for international than for domestic players, and

draw up a strategy to deal with them. Hoesli (1997) states that there are significant practical difficulties in assembling a diversified

international portfolio. Most studies use national index data. However, with relatively small numbers of properties in each country, there is a risk of a higher tracking error due to fiscal and managerial disadvantages this was also one of the conclusions from the MSCI/IPD survey elaborated on in IP real

estate. There are also high information and monitoring costs associated with international real estate

investment and a risk of information asymmetry and lack of awareness of local market practice and circumstances. This has driven the growth of international collective investment vehicles that provide economies of scale in acquisition and management decreasing this tracking error (Hoesli, 1997).

Hoesli (1997) also states that there is some evidence that there is considerable building-level variation within sectors and regions, casting some doubt on their effectiveness in structuring the optimal

portfolio. Tenancy structures, yields and size are other possible dimensions structuring the risk-return profile.

The aim of this thesis is to reduce the tracking error mentioned above and increase an investor‘s ability to obtain better insights into the international portfolio

International funds, REITs and other listed property companies are mostly local specialists, as a steady 90% invest only in their own country. It‘s interesting to examine to what extent they overcome the obstacles and risks that have been described and what role investing in these funds can play in an

international strategy of such an institutional investor. Pension funds are important stakeholders when it comes to international real estate investments.

Aside from the general benefits of real estate investments such as relatively high returns, low risks and the stable character of real estate returns, pension funds have an added interest in investing in real estate. Pension funds aim to index the assigned pensions and they do this by indexing the underlying

real estate portfolios with actual inflation. With a substantial amount of real estate in the portfolio, annual indexation of their portfolio is conducted more easily and securely. (Syntrus achmea, 2014).


35

2.2.3 Fund Managers

The real estate fund management function is made up of property level specialists who add value to funds by executing property-level strategies. The specialists are experts in either regional market

fundamentals or in specific property types, and are responsible for optimizing the value of the assets in their custody. In addition to providing portfolio management information on local prospects and facilitating transactions, the asset manager is responsible for property -level performance.

Asset managers are responsible for both the asset care and asset exploitation. In a real estate investment fund these managers must help define and exploit on property-level opportunities. The main difference between a real estate manager and a traditional asset manager is that a real estate

manager has to be an experienced person who can optimize the value of these real estate assets. This is done through exceptional transaction and management execution. Differences in the quality of the fund and asset management can therefore lead to differences in the NOI and value of the assets

in the fund (Woodhouse, J., 1997).

Leasing

Leasing is one of the ways managers increase value, by managing the leasing at their properties. Aside from leasing, managers also approve budgets and perform expense management. Managing the leasing may seem like a simple job, but it is actually a complex function that inevitably

decides property and portfolio value. In order to obtain a successful lease, managers need to focus on finding and executing the highest possible leasing opportunities at each property, conducting a leasing program that aligns with portfolio strategy and maintaining the best relations with existing tenants

When negotiating a lease difficult decisions which may be at odds with tenants in order to optimize the value of the property. During periods of increasing rental rates, long-term leases may be locked in for

income-oriented properties, while for strategies focused on capital appreciation, lease terms may be shortened to take advantage of higher expected rates in the future. The fund manager‘s ability to manage the portfolio can significantly affect overall portfolio value. A real estate investment manager

does this by trying to increase the value of the properties in the portfolio as much as possible. This is done by choosing the right properties to invest in and managing the properties well.

Acquisition and disposition Real estate portfolio managers are experts in the fundamentals of real estate property. They know everything about local regions and about the different uses of commercial space. In order to beat the

market, make sure the portfolios are sufficiently diversified and receive a proper risk -adjusted return, managers must ensure diversification and produce adequate risk-adjusted returns, real estate portfolio managers must make bets on regional or local property markets and, in the case of multiple-asset

portfolios, the correct property mix. Thus many fund managers end up focusing on a particular region or type of property. This way market opportunity can be identified and this leads to a better asset mix and selection of property.

Similarly to traders in the stock market, real estate funds use acquisition specialist to develop and review acquisition strategies and arrange the procurement process. However the manner in which

properties are acquired, real estate funds differ substantially from more traditional investments.

Some asset managers consider that, by carrying out real estate trading activities and using real estate

cycles as an indicator, they can increase performance above that of the underlying assets, contradictory to capital market theory. However, Geltner (2007) has shown that expert forecasts regarding the development of real estate markets are systematically no better than random forecasts,

a finding that confirms the random-walk hypothesis of efficient capital markets from the neoclassical theory of finance.

This research proposes to incorporate a new dimension into the asset and fund selection process to see if it is possible to do better acquisitions based on historical performances by properties that provide certain asset qualities alongside macro and meso-economic indicators.


36

Fund manager fees and the influence on fund returns

There are many different types of fees a fund manager can have. Most of these fees are discussed in the INREV Fee glossary. In regards to fees we only discuss the percentages which fund managers charge investors in regards to their investments. The pension real estate association conducted a

detailed research on the fee structures and fee levels of 164 private real estate investment vehicles targeted at institutional investors.

The main findings of the pension real estate association (PREA) research that looks at the differences in fee structures per fund were that:

All the different fund vehicles report a changing annual management fee. The most common type of management fee was invested equity which was mostly used by value-added and opportunistic fund managers. Core funds most often used a fee structure based on the net asset value. The average

management fee was 1.28% of invested equity. The study also shows that commingled closed-end funds mostly based their fees on invested equity and charged an average fee of 1.36%. The open-end funds reported a fee of averaged 0.99 % of net asset value. So the fee structure does matter.

Andonov et al. (2013) have found that US funds have significantly higher costs than any of their global peers in any size group. This is mainly due to their greater reliance on external managers. Andonov et

al. (2013) shows that these external managers are the most costly in the US, especially in direct real estate. Cost-cutting and tougher negotiations with external managers should be a priority for foreign investors wanting to invest in US pension funds is satisfactory performance on their real estate

investments is to be attained. Fees varied between target countries. Management fees were lower for single country funds than for

multi country funds. The fee was also related to the amount of investors in a fund. The size of a fund did not appear to be of influence to the management fee. 91% of the funds reported charging incentive fees. The use of incentive fees differed by the vehicle‘s investment style, being mostly used in

opportunistic and value added vehicles.

The interesting part of the study which covers and compares the reduction in investor returns by the differential between the gross and net IRRs found that commingled closed-end funds had larger return reductions as the other types. Out of all the investment styles, opportunistic funds had the highest

reductions. This could possibly be due to the more active management style discussed in chapter 2.1 (PREA Management Fees & Terms Study 2014)

2.2.4 Users The tenants paying leases for the buildings to the fund can be seen as the customers of a fund. They pay a certain amount for each square meter they occupy in a building in that particular fund. A user‘s

willingness to pay a certain amount of money is influenced by a number of factors.

Tenants/users are the furthest scale level of stakeholders away from the investors. However they still

have a substantial impact. Users indirectly determine which properties are profitable for investors due to their willingness to pay.

For example; Öven & Pekdemir have made a meta-analysis about numerous different researches on different factors influencing office rents since the 1980‘s. They came up with 64 predictors that are ought to influence office building rents. These variables were divided into four different groups:

econometric, location, contract and building features. The last three variables are based on the preferences of the users. The statistical analysis as conducted in their research is based upon both a standard regression form as well as a simplified regression form.


37

2.2.5 Conclusion

In regards to the three main stakeholder groups: investors, funds and users. It has been shown that

each stakeholder groups earns a profit in a different manner. This can sometimes lead to a conflict of

interest between stakeholders. Thus it can be concluded that it is important to pay attention to the

interest of investors versus the interests of funds versus the interest of users.

The purpose of a pension fund is to invest pension holder capital into stocks, bonds and real estate

and ensure that ultimately the funded ratios of their pension funds are as high as possible for the

lowest amount of risk. In return for this they charge a management fee. The purpose of a real estate

investment fund in the eyes of a pension fund is to invest investor capital into real estate assets which

produce income and provide a hedge against inflation. Here the first conflict can be recognized

through the manner in which fund managers obtain their income differs from that of pension funds.

Fund managers apparently charge management fees dependent on the type and specialization of

their fund. Alongside the management fees they might charge acquisition fees for acquiring new

assets and charge performance fees for achieving a higher performance than a predetermined hurdle

rate or benchmark.

Asset managers have to make sure the portfolio preforms optimally. And because each fund has

different asset managers, different asset management techniques and commits different amount of

recourses to asset management, the performance of each fund is dependent on this.

A conflict of interest can also be present for the tenants. The users of each building in the fund are

benefited by the amount of money that is invested by the fund managers in maintenance and

improvement of their buildings. Fund managers might not be so keen on doing so due to the fact these

costs decrease the performance of an asset in terms of financial performance. Tenancy structures,

yields and size are dimensions structuring the risk-return profile. The way this stakeholder relationship

is maintained has an effect on the long run value of the real estate portfolio.

The main conclusions are that the investors should be aware of the needs of the users and how well

these needs are being satisfied by fund management and the assets in the fund, when selecting a

fund. Secondly the fee structure influences the returns. It is important to make sure the management

fees are not higher than the added value of a better portfolio. Finally it is clear that the quality and

structure of management is of influence on the NOI and estimated values. This should therefore be

controlled for in the regression models. This is done by adding a fund category variable for each asset,

next to the micro macro and meso variables in the regression model. This will in turn measure if there

is a fund specific difference on NOI‘s and EV‘s of commercial real estate assets.


38

2.3 Macro / Meso level investment decision making criteria

2.3.1 Introduction

This paragraph describes how macro and meso economic aspects influence commercial real estate

performance. These investment features influence the rent levels and exit values of real estate.

Concerning the fact that this research focuses on commercial real estate we won‘t be focusing on

residential or alternative types of real estate. For the criteria we focus on aspects as to which,

according to present data and literature, have influence on rent levels and exit values. The macro and

meso economical features concerning the international real estate investments are explained and

elaborated on so that the asset specific level can be understood in relation to these scale levels.

2.3.2 Macro scale performance indicators

To better understand how macro scale performance indicators influence real estate prices and values

we use the four quadrant model by DiPasquale and Wheaton (1996).

In the article ―The markets for Real Estate Assets and Space: A Conceptual Framework‖ the four

quadrant model is introduced and explained.

In their article Dipasquale and Wheaton (1996) created the four quadrant model. This model explains

the cyclical movements in the property market and shows how the rent of commercial real estate

should be calculated. The model shows the interrelationships between the asset market, the

development market, the space market and the construction market. Ideally all four markets should be

in equilibrium. A change in either one of the four markets has influence on all markets. Because the

asset market quadrant can be seen as a description of the capital market it is clear that property

market fluctuations can be attributed to fluctuations in the general economy and the financial market.

This model shows that real estate values have a time related nature. It can later be transformed into a

time related variable (Gijselaar, 2010).

Graph 5 – The four quadrant model and an illustrated equilibrium shift.

Source: DiPasquale and Wheaton, Philip Koppels


39

Rental value growth and yield shift

If there is a scarcity of space present in a particular market e.g. the New York office market, the real effective rent prices for a square meter of office in New York will grow due to the nature of supply and

demand. This will cause yields to shift, the capital value of buildings to increase and construction and development to spur. This would be a good time to invest in such a market. Ultimately the office stock adjusts to enforce a new equilibrium. If there would be over development or demand would drop as

seen in recent years before the crisis, rent prices will drop, yields will shift and capital value will decrease. This equilibrium is not always achieved in reality, government restrictions, limitations in building area and other factors may keep scarcity in the market. So real estate funds containing

properties with these beneficial macroeconomic characteristics at the time of investment have a bigger chance of being profitable.

Economic indicators

The growth of the economy and the demographic traits of a city influence the demand for real estate substantially. A growing economy increases the demand for real estate usage. However the supply of real estate is not dependent of these demographic and economic characteristics. A growing economy

does not necessarily lead to good real estate investment performance.

In addition to the variables that are found in real estate literature the study of Finders looks at other

factors that can explain future performance of real estate. Commonly known, real estate markets react slowly to changes in the business cycle, causing the real estate cycle to lag behind.

Since economists have been researching ways to forecast changes in business cycles by using economic indicators, this source can also be useful to predict changes in the lagging real estate markets.

Economic indicators are statistics about the economy that have proven to be useful tools for analysing economic performance and predictions of future performance. By making use of the movement of

business cycles, economic indicators can give insight into the current and future economic phases. Economic indicators are categorized in three ways: leading, lagging and coincident, based on the

timing of their movements. Leading indicators are indicators that tend to shift direction in advance of the business cycle and are

therefore useful as short-term predictors of the economy. Coincident indicators define the business cycle and provide information about the current state of the economy. Lagging indicators tend to change direction after the coincident cycles and help to confirm recent movement in the leading and

coincident indicators (Finders 2013).

Finders (2013) states that economic leading indicators give information about future business cycle behaviour but can also be used to forecast capital growth. These indicators can be a good alternative to traditional forecasting models, and the idea is that by combining them with the other real estate

performance indicators, a better view of the future performance of real estate can be created. Indicators can sometimes be leading as well as coincident. For this research the most important indicators have been explained:

Building Output

Öven and Pekdemir (2006) define office building output as the annual volume of office construction investment in the region. This econometric variable can act as a proxy for the office market, but seems to be justified as significant only for growing cities according to Sivitanides (1997) and as confirmed by

Öven and Pekdemir (2006). Tsolacos et al. (1998) found the supply side office building output variable to be less significant than demand side indicators like gross domestic product and sector employment.

This finding is related to the four quadrant model by Dipasquale and Wheaton (1996), discussed

earlier.


40

Gross Domestic Product (GDP)

GDP is defined by the Organization for Economies Co-operation and Development as ―an aggregate measure of production equal to the sum of the gross values added of all resident institutional units

engaged in production (plus any taxes, and minus any subsidies, on products not included in the value of their outputs)." GDP is measured at a certain point in time. Case, B. et al (2000) have found through time series regressions that real estate is significantly correlated to changes in GDP. This is taken into

account in this research‘s regression model by the time variable.

Employment growth

Office market models show that the driver for demand is employment. Over time, employment fluctuates with recession and boom, but the underlying trend is still determined by the number of

people of working age. Thus the higher the employment rate, the higher the demand for real estate property. Okun‘s law (Knotek, E. S. ,2007) is an empirically observed relationship between unemployment and a decrease in GDP. Due to this relationship the employment rate is also taken into

consideration by using the variable time in the regression.

Macro-economic cycles and the investment clock

All these different economic indicators allow analysis of economic performance and predictions of future performance. They tell us something about the state the economy is in at a certain point in time.

It can be taken as a sign that the market is a buyer or tenant market this may not be the best time to invest. A buyers/tenant market means that tenants and buyers can obtain higher quality buildings for lower lease and purchase prices. One must firstly define in which state the macro-economic

environment is in. After the needed research is conducted it will be possible to benefit from the qualities on an asset level. Similarly a lot more can be learned from these macro-economic indicators. The most important characteristic of these indicators and the effect they have on real estate values is

that they are all time related somehow and thus can be accounted for in the regression by a variable for time.

2.3.3 Meso scale performance indicators

The regional economic trends provide important information that describes the health and vitality of the surrounding community and region. This information can help business operators and investors make

informed decisions regarding development or investments in the community. The Center for community and economic development and the State University Ohio have developed a local community real estate analysis tool. This

To describe how regional market dynamics and aspects influence real estate, this document provides valuable information about on which aspects contribute to a successful real estate project in all

different sectors The meso scale performance indicators will be discussed in three different real estate categories.

These categories are based on the use of the property. The categories discussed in this paragraph are the markets for retail, office and industrial space.

Economic indicators for the different sectors There are many more examples of macro economic demand indicators as stated per sector. The most

significant ones per sector have been mentioned below. Regional Office market

Unemployment is a regional indicator. When structural unemployment affects local areas of an economy, it is called ‗regional‘ unemployment. This regional unemployment affects the office

properties in that region. If the unemployment rate is high, occupational fields such as finance,

http://en.wikipedia.org/wiki/Empirical_research

http://en.wikipedia.org/wiki/Prediction


41

insurance or investment companies will need less office space. Thus the demands for office spaces in that region will be lower (Treyz, G. I., et al 1980). The infrastructure of a region is also of influence on

the value of the offices in that region. The more accessible the office space the higher the predicted value. Eichholtz, P. M., et al (1995) have found strong correlations between the industrial and office property types. This is partially due to the fact that both the industrial and office markets are driven by

profits in the economy, thus shocks to profits will affect the office market. Another interest ing find is that of the Dutch Office market Wiegerick (2013), who state that exit values are nearly twice as high for centrally located offices in large gateway cities as non-centrally located offices in small cities.

According to CBRE (2012), many office investors continue to pay premium prices in the traditional gateway markets of Boston, Los Angeles, New York City, San Francisco and Washington, DC. They view these markets much like stock investors view blue chip stocks stable and highly liquid.

Regional retail market

Eichholtz, P. M., et al (1995) have shown that the retail market is driven by wages, thus shocks to wages will affect the retail market. If the wages in two regions differ substantially, this will be noticeable in the demand for retail properties. Aggregate disposable income also has an effect on the

retail market. Regions with higher disposable incomes have higher spending power and thus a higher demand for retail spaces. Perdikaki, O., Kesavan, S., & Swaminathan, J. M. (2012) have found that sales volume exhibits diminishing returns to scale with respect to traffic. This means that as traffic

increases and more costumers visit the region, the sales and thereby demand for similar properties increase. Of course the increase in traffic has to stop at a certain level, considering traffic congestion is not attractive to customers, which is why the returns are diminishing.

Regional industrial market

As stated for offices, Eichholtz, P. M., et al (1995) have found that the industrial markets is driven by profits in the economy, thus shocks to profits will affect the industrial market. Cohen, J., Brown, M. (2013) find that proximity to the airport, higher airport connectivity, and greater airport infrastructure

investments increases commercial property values. Regions with larger and more airports usually have properties with higher values. Railway stations function as nodes in transport networks and places in an urban environment. Debrezion, G., Pels, E., & Rietveld, P. (2007) find that rail way

stations positively impact industrial property value. The effect of railway stations on commercial property value mainly takes place at short distances. Commercial properties within 1/4 mile rang are 12.2% more expensive than residential properties. If a region has railway stations the price gap

between the railway station zone and the rest it is about 16.4% for the average commercial property. The more railway stations a region has, the higher the chances of industrial properties being able to benefit from having a railway station nearby and thus the higher the expected value of the sum of

properties in this region. Another characteristic that increases the values of properties in a region is the existence of a port.

The general manager of the Port of Antwerp (Vleugels, 1969) states: ―port regions seem always to have been at an advantage when compared to those regions which not situated by the sea or on rivers‖.

The presence of a port or an airport can also be established per city instead of per region. A city with an airport or a port of a minimum size is called a gateway city (see appendix I). Typically, intermodal

facilities are made up of several buildings hosting distribution, processing and manufacturing, as well as a multimodal terminal. They are located in areas with large population, deep local labor base liable to perform logistics operations, and excellent connections to Interstate Highways (Lecomte, 2008).

This makes proximity to Gateway Cities an important factor.


42

2.3.4 Conclusion

After discussing macro and meso economic indicators we can conclude that the most important

indicators for each economic scale can be captured by one or more variables in the regression model.

As macroeconomic indicators building output, GDP and employment growth have been chosen as the

most important indicators and therefore they were discussed. Each of these variables, as well as many

other macro-economic variables, tell us something about the state the economy is in at a certain point

in time. We can therefore conclude that macro-economic aspects influence commercial real estate

performance trough time related variables.

Meso economic variables describe the differences between regions and how these differences affect

the regional economy. The most important differences in regional characteristics for real estate values

are dependent on the type of real estate in question. Real estate properties were divided into three

categories, namely an office property market, a retail property market and an industrial property

market. For each of these categories the most important meso economic indicators were given and

described. From this we can conclude that these variables can be bundled up into two variables to be

used in the regression analysis. The first is ‗region‘ and the second is ‗Gateway City‘. The region a

property is in influences the value due to the differences in wages, unemployment, infrastructure etc.

The second variable takes a smaller area in consideration. It indicates whether the property is located

in a gateway city or not.

The macro and meso indicators and the impact they have on property value is important to investors.

Many institutional real estate investors devote considerable resources to these types of research.

Syntrus Achmea, where this research paper is conducted, can be seen as a specialized unit within a

real estate investment company exploring the macro and meso economic scale levels of information.

However the micro level asset specific analysis is not done by many investors seeking to diversify

internationally. The micro level indicators are elaborated on in chapter 4 of this research paper.

In Syntrus Achmea most asset specific criteria analysis is done by the business unit direct

investments. These two business units work closely together with the business unit International

investment, which coordinates the entire investment process for investing in international real estate.

Ultimately this research tries to identify if and determine how a three scale level approach could

possibly benefit the organization.


43

2.4 Asset specific criteria analysis, the Micro level

decision making criteria

2.4.1 Introduction

Geltner (2012) states that underlying assets refer to the directly productive physical capital, such as an office building or a store. In practice investment products or vehicles like non-listed funds are typically one or more levels removed from underlying assets, but are based on the underlying assets.

The market value of a property in such a fund at any given point in time consists of the structure (building) value plus the land value. So the funds value is determined by the cumulative total value of all structure and land values of each property in the fund.

“The structure value decline over time as the building depreciates. This is due to Physical

obsolescence, Functional obsolescence and Economic obsolescence” (Geltner,2012)

This is partially due to the fact that the criteria that clients put on their real estate are constantly changing; existing real estate deteriorates, and after a certain t ime period is no longer fit for purpose.

Such specific criteria may be considered to comprise the traditional real estate market concept of the investment quality of an asset while, in capital market terms, are also the principal contributors of

specific risk for that asset. Parker (2011) states that; specific criteria are considered to comprise, currently or in the foreseeable

future, either contributors to potential growth or contributors to potential risk. The challenge at hand is to include assets whose asset specific criteria exhibit positive influences on incomes and value.

Hedonic price modelling which will be used in this research can be applied to explain the value of heterogeneous goods (Dunse and Jones 1998). In this research the heterogeneity is reflected in the different characteristics of office assets or industrial assets. Based on a study of existing scientific

literature, the asset specific criteria have been divided into two main subgroups for the three commercial real estate classes:

Figure 6 - ASC subgroups per sector (Own Image)

Each subgroup is broken down into aspects that, according to literature have influence on aspects of

the net operating income and/or on the asset value. These aspects will be discussed in the paragraphs 2.4.2 and 2.4.3.

Next to offering protection from the elements, the primary function of a commercial building is to support the user in performing its business. Therefore the building should enhance this process and by doing so increases the tenant‘s willingness to pay. The building characteristics that showed to be the

most relevant indicators for the financial performance of a building are all related to its physical nature. Several studies that have tried to determine which physical building characteristics are of influence to the building‘s financial performance. Physical aspects of a building contain a lot of information that

provide different rent levels. The different characteristics that provide an insight on the physical aspect include the age of the building, the gross floor area, number of floors etc. Aesthetics building characteristics include materials, shapes, volume or type. All these variables contribute in different

levels to the overall rental price level of the building.

Retail

• Location level

•Building level

Office

• Location level •Building level

Industria

• Location level •Building level


44

2.4.2 Locational features

Because of the immobility of Real Estate, the location plays an important role for the willingness to pay

for the asset in terms of both NOI‘s and EV‘s. Important location factors vary significantly between office, retail and industrial real estate assets. Location variables like distance to a central business district (CBD) and distance to transportation nodes were examined and found to be significant by a

number of studies (Brennan et al., 1984, Clapp, 1980, Sivitanidou, 1996, Frew and Jud, 1988, Sivitanidou, 1995). Other location features from the literature are accessibility, walkability, and surrounding amenities Based on previous research done the following locational criteria have been

selected, which have proven to be of significance to net rental incomes or values of assets of the three sectors. Location criteria Measurement technique

Distance to CBD Distance in miles Distance to transportation nodes Google Walk/Transit Score/ Airport

Accessibility Google w alk / Transit score/ Parking

Walkability Google Walk Score Surounding Amenities Google Walk Score

Distance to Central Business District

The CBD provides agglomeration economies by giving access to a larger pool of services and labour (Gijselaar, 2011). Central place theory, Bid rent curve in the CBD, HBU, Polynuclear cities, MACs

NBDs CBD are all theories discussed by Geltner (2007) explaining that proximity to a central business district increases rents and values for all property sectors.

Offices According to Gijselaar (2011), within a central business district (CBD), key relationships determining rental value could be distance from the most prestigious office addresses, proximity to the intercity

train station, links with commuter train and bus networks, or closeness to the main shopping centre. Retail

Parker (2011) states that; the existence of competitors and proximity to other major retailers is important for retailers due to agglomeration advantages. Because CBD‘s contain agglomerations of retailers, proximity of a CBD might be important. Retail can also focus on car-customers, which prefer

suburban orientation due to traffic. This could be an influential variable for retail types such as neighbourhood and community centres and malls. (Kooiman, 1999).

Industrial In the research done by Lockwood and Rutterford (1996) and Beekmans et al. (2014) the location variable of distance to CBD was not significant. In addition to different studies, Colwell and Munneke

(1997) found a negative concave relationship with distance from the CBD. According to research done by Thompson (2005), the rent gradient from the CBD for a large city is downward sloping albeit very shallow. More interesting is the significance of proximity to motorway conjunctions distance to a CBD

for a large city has influence on the rent price but is sloping. It can be expected that the distance to a CBD has a positive influence on the net rental income and

value of Offices and Retail properties, but no clear influence on the net rental income and value of industrial property.

Distance to transportation nodes / Google Transit Score Offices

Distance to a freeway or highway is a very significant determinant with a negative sign (Clapp, 1980, Wheaton, 1984). Opposite claims of a negative relation with proximity to a freeway are known as well. Frew and Jud (1988) and Bollinger et al. (1998) claiming noise and congestion effects associated with

highway locations override access time consideration. However Kok (2012) and van Gooi (2013) state that proximity to train stations and other public transport services increases asset values and rent


45

prices. Data about the location of offices in Kok‘s (2012) sample shows that buildings with less efficient labels are generally located further away from railway stations.

Retail According to the research done by Ellison and Sayce (2006) availability of at least one mass public

transport node is important for the continued success (economic sustainability) of retail centres. Industrial

From the research done by Zhao (2003), it can be remarked that the location of an industrial building in relation to nearby transportation nodes may have more effect on value than other physical factors. However, the building size may have a collinear relationship with location in that industrial buildings

close to major transportation nodes tend to be smaller. Proximity to an airport also seems to have influence on the rent price but only within 3 miles distance according Colwell and Munneke (1997). Access to rail is particularly important as trains have captured a large share of domestic container

traffic at the expense of trucks. Because of congested ocean port areas and expensive land costs in coastal regions, logistics operations are being pushed inland, as far away as possible from ports. Containers often go directly from ships onto trains to be processed in an inland facility with ―inter

modal ramps‖, (transfer of containers between train and truck) (CSX Real Property, 2006). In order to measure the distance to transportation nodes, Google transit score can be used; Transit

Score is a patent pending measure of how well a location is served by public transit on a scale from 0 to 100.

It can be expected that the distance to transportation nodes has a positive influence on the net rental income and value of Offices, Retail and Industrial properties.

Accessibility The term ―accessibility‖ is often misused and confused with other terms such as mobility. According to

El-Geneidy et al (2006) ―Accessibility measures the ease of reaching valued destinations. Several cities use congestion levels and annual mobility reports to evaluate the performance of the transportation system, yet this misleads by looking only at the costs of travel while ignoring the

benefits‖. The ease factor is also implemented into the Google transit score by means of frequency and mode weight. A part of the accessibility is reflected in the Google walk score which is subsequently used for walkability and amenities as well. Due to the rising mobility of western

populations, the car can be seen as the most important (individual) means of transportation for consumers (Baker, 2002). However, the location must facilitate certain ways of transportation and amenities to make Real Estate accessible for people and goods.

Offices According to Parker (2011) because threshold against downtown car use proximity to public transport

and accessibility by other commuting options (e.g. car, bicycle etc.) is a part of the select ion criteria for the location. Selection criteria for suburban and office park locations are accessibility by car (including car parking) as well as public transport.

Retail Retail locations change with changes in mobility (Kooiman, 1999). Because of an increase of car-use,

different and more retail agglomerations moved from the city centre to periphery (Jones et.al., 1990 and Kooiman, 1999). To attract customers by car, distance to highways and intersections is an important factor. Parking nearby stores or agglomerations can be regarded as a major factor

enhancing shopping convenience. Adequate parking provision and car accessibility for shoppers are fundamental according to Ellison and Sayce (2006).

Industrial Accessibility by road and proximity of a motorway junction are important explanatory variable for industrial property value (Sivitanidou and Sivitanides, 1995; Dunse et al., 2004: in Beekmans 2014;

and Dunse and Jones, 2005). Contrary to this, Ryan‘s (2005) empirical work shows that a location close to a freeway can be a negative influential amenity for industrial properties.

http://scholar.google.nl/citations?user=xyV9lRMAAAAJ&hl=en&oi=sra


46

Accessibility by rail on industrial property prices is considered in many studies, although most analyses show the influence is limited (Ambrose, 1990; Lockwood and Rutherford, 1996; Black et al.,

1997; Ryan, 2005). Accessibility via airport is also important, but only within 3 miles distance according (Colwell and Munneke, 1997). For industrial sites the presence of water may be of importance since it creates an extra transport opportunity for especially bulky or heavy goods

(Beekmans 2013). It can be expected that accessibility has a positive influence on the net rental income and value of

Offices and Retail properties, while for industrial property the influence on the net rental income and value depends on the type of accessibility for it to be of positive or negative influence.

Walkability and Google Walk score Offices, Retail & Industrial

Pivo and Fisher (2011) describe the effects of walkability on property values and investment returns. They refer to walkability as the degree to which an area within walking distance of a property encourages walking for recreational or functional purposes. They used data from the National Council

of Real Estate Investment Fiduciaries (NCREIF) and Walk Score to examine the effects of walkability on the market value and investment returns of more than 4,200 office, apartment, retail and industrial properties from 2001-2008 in the USA.

They found that, all else being equal, the benefits of greater walkability were capitalised into higher office, retail and industrial values. On a 100 point scale, a 10 point increase in walkability increased

values by 1 to 9 per cent, depending on property type. They also found that walkability was associated with lower cap rates and higher incomes, suggesting it has been favoured in both the capital asset and building space markets. Walkability had no significant effect on historical total investment returns. Pivo

and Fisher (2011) conclude that ‖all walkable property types have the potential to generate returns as good as or better than less walkable properties, as long as they are priced correctly. Developers should be willing to develop more walkable properties as long as any additional cost for more walkable

locations and related development expenses do not exhaust the walkability premium‖. It can be expected that walkability has a positive influence on the net rental income and value of

Offices Retail and industrial properties. Amenities

Google Walk Score measures the walkability of any address using a patent -pending system. For each address, Walk Score analyses hundreds of walking routes to nearby amenities. Points are awarded

based on the distance to amenities in each category. Amenities within a 5 minute walk (0.25 miles) are given maximum points. A decay function is used to give points to more distant amenities, with no points given after a 30 minute walk. Seeing the Google walk score also incorporates the amount of

amenities in relation to distance it makes a useful metric to measure both the amount and distance of amenities in relation to real estate values. (Kok, 2012). So in this perspective the Walk Score gives a quantifiable means to value the amenities in a surrounding area relevant to all objects.

Offices Important nearby amenities for offices are catering Industry and child day cares or other activities (e.g.

sports, recreational activities etc.) that are done between work and home. Life around office buildings becomes more and more important. Kok‘s (2012) results show that tenants currently pay higher rents for space in offices with more and a large variance of amenities in the direct neighbourhood, as

compared offices at mono-functional locations. If this trend continues, the discount for more traditional office locations, without facilities in the direct neighbourhood, may further increase. According to Parker (2011), these locations need to provide facilities such as child care or a gym or

fitness centre and restaurants, theatres and shops according to PBL (2009a). Retail

For retail estates important amenities are other retailers. As already stated by Parker (2011), the existence of competitors and proximity to other major retailers is important. Another centre or retailer


47

outlet or a minimum trade area population should be nearby. According to Teller and Reutterer (2008), the ‗retail tenant mix‘ is the most influencing variable on the ‗overall attractiveness‘, which influences

the rent price. From a consumers‘ point of view such ‗‗bundling or agglomeration effects‘‘ deliver additional utilitarian and hedonic shopping values to customers (Oppewal and Holyoake, 2004; Babin et al., 1994: in Teller, C., Reutterer, T. (2008). Such an enrichment of shopping experiences compared

to those in single stores is caused by the provision of easy accessibility, parking facilities, orientation systems, a broad variety of shops, atmospheric stimuli (Kim, 2002). Other enrichment of agglomerations are gastronomy or entertainment facilities, i.e. ‗non-retail tenant mix‘ (Teller and

Reutterer 2008). Furthermore, the results emphasize the particular relevance of anchor stores within the tenant mix (Teller and Reutterer, 2008).

Industrial According to Glaeser (2010) when firms and people are located near each other in cities and in industrial clusters, they benefit in various ways, including by reducing the costs of exchanging goods

and ideas. According to Benjamin, Zietz and Sirmans (2003) and Ellison Glaeser, and Kerr (2010), agglomeration saves transport costs by proximity to input suppliers or final consumers, allows for labour market pooling, and facilitates intellectual spill-overs. Amenities might also hinder industry.

Whether an industrial site is hindered by functions surrounding it, is determined by determining the land uses surrounding the industrial site (Beekamsn, 2014).

It can be expected that amenities have a positive influence on the net rental income and value of Offices, Retail and Industrial properties while in some cases some amenit ies might have a negative influence on industrial properties.

2.4.3 Building level aspects

There is generally a lot of scientific literature which proves the significance of physical property

characteristics on net operating incomes and estimated values of assets. Relevant building criteria may vary considerably between office, retail and industrial real estate For example, door and ceiling heights may be important selection criteria for industrial real estate whereas high rise or architectural

quality may be important selection criteria for office real estate. For most office, retail and industrial real estate assets, criteria may include type, age, physical

condition, requirement for maintenance, flexibility of design and prospects of functional or technical obsolescence, in the form of type and renovation, these may be key selection criteria impact ing on potential growth or risk for office, retail and industrial assets. The literature study provided the following

building level aspects as influential criteria for either reduces of operating costs, increasing of rents and/ or value: Building criteria Measurement technique

Architectural Quality Not measured

Construction year (Age) Year

Last Renovation Year

Climate systems Type

Building flexibility Not measured

Ceiling height Feet

Number of stories Amount

Size / Total Floor Area Amount

Building amenities Not measured

Type Classification NCREIF / IPD System

Architectural quality

The added value of ‗architectural quality‘ on Real Estate has always been a point of discussion. Studies done by Geltner et al. (2007), Lusht (2012) and Lim et al. (2013) have attempted to demonstrate tangible benefits of buildings with a high level of architectural quality. Some have

carefully suggested that an extra architectural point might lead to a certain percentage of rent increase. However, major criticism regarding such studies has always been the extent to which architectural quality or building aesthetics are measurable.


48

Some suggest that a percentage of rent increases with higher architectural quality. While measuring

architectural quality or building aesthetics is always (partly) done by subjective judgment, some studies attempted to make a connection. Gat (1998) stated that one additional architectural point added over 5% rent increase. This was also highlighted by Yusof (2000) who attributed the

obsolescence of a building to the depreciation rate, as did Ozus (2009) who mentioned buildings aesthetics as an important factor of rent levels.

According to Gijselaar (2010), tenants are not significantly willing to pay more for aesthetics like a special façade or a different shape of the building. A different façade type does not particularly improve the functioning of the building. Façade aesthetics do not influence the flexibil ity or the user-

friendliness for example. However, façade measurements and repetitions might have influence on partitioning and size options for internal spaces. In terms of internal climate regulation and considering the heat level, brick façades might be preferred above all-around glazing. Perhaps current technology

might prevent unpleasant factors. It can be expected that architectural quality might have a positive influence on the net rental income

and value of Offices, Retail and Industrial properties. In regards to the international aspect of this research and international investment funds not

researching, communicating or obtaining information on the architectural quality, this aspect is disregarded and not researchable.

Construction year/ age The age of a building can reveal a lot about its quality. It is commonly perceived that older buildings

tend to have lower rents as a matter of depreciation. This variable contains data that relates to the implemented technologies, probable maintenance costs and in some cases trends in typical construction types and materials of a certain period of time. It is usually considered a proxy for

technologies of infrastructure or internal layout. Offices

As a variable in empirical studies it has shown up as significant in office market studies, as important rent determinants (Slade, 2010 and Dunse & Jones, 1988), usually with a negative effect. According to Gijselaars (2011) A number of authors also report age as highly significant (Sivitanidou, 1995,

Bollinger et al., 1998, Clapp, 1980, Sivitanidou, 1996, Slade, 2000, Wheaton, 1984, Ozus, 2009), while others claim it has either a negligible or no significance (Brennan et al., 1984, Frew and Jud, 1988, Gat, 1998, Mills, 1992). Gijselaars (2011) then concluded that the influence of these variables

might be subject to different perceptions in different office markets to allow for the general conclusion that it has an influence.

Retail The age of shopping centres reduces rents. This stresses the importance of renovation and image enhancing strategies for shopping centre investors (Sirmans and Guidry 1993; Gatzlaff, Sirmans , and

Diskin 1994: in Baker and Chinloy, 2014). Industrial

Research done by Ambrose (1991), and other studies do not show significant results when it comes to the age of industrial real estate (Ambrose 1990; Fehribach et al. 1993; Sivitanidou and Sivitanides 1995; Black et al. 1997; Buttimer et al. 1997; Dunse, Jones, Brown and Fraser 2004; Ryan 2005;

Dunse and Jones, 2005) in Beekmans (2013). It can be expected that construction year/ age might have a negative influence on the net rental

income and value of Offices, has a negative influence on Retail and could possibly not have an influence on Industrial properties.


49

Last renovation year Offices

Buildings deteriorate over time due to the influence of natural forces. Existing buildings also might mismatch the user demand that changed over time. Geltner (2007) states that; due to decay of buildings age and renovations cause differences in rent prices and values across assets. If a building

has had a recent renovation the technical and economic life span might have been prolonged to the needs of future tenants.

Retail As already stated, the age of retail buildings which lower rents, stresses the importance of renovation and image enhancing strategies for shopping centre investors (Sirmans and Guidry 1993; Gatzlaff,

Sirmans , and Diskin 1994: in Baker and Chinloy, 2014). Industrial

Improvements become less suitable with age, as a result of decline in their attractiveness and due to increased maintenance costs. Technological innovation or variation in style and tastes may render existing physical structures less desirable than before, even without the factor of physical deterioration

(Greer and Kolbe, 2003). Also according to Greer and Kolbe (2003) as physical deterioration of a building can lower net rent prices and/or the competitive position compared to other buildings, renovation can bring deteriorated or functionally obsolete (inappropriately designed) buildings to an

improved physical condition or brings it up to modern design standards. It can be expected that increasing period until the last renovation year might have a negative influence

on the net rental income and value of Offices, Retail and Industrial properties. Climate systems

According to Joosstens & Itard (2012), important has been the intensification of energy consumption in HVAC systems which have now become almost essential in parallel to the spread in the demand for

thermal comfort. However, according to Lombard, et al. (2008), few sources offer data by typology and there is no consensus on a universal classification, which makes the analysis difficult.

Offices According to Joosstens & Itard (2012), Lombard, et al. (2008) and Korolija et al., (2011), energy consumption of these systems and its costs can influence the value a building and rent values. They

also state that investing in new systems with user friendly specification and monitoring system pays off in the return of investment due to the satisfaction of the tenants. Cooperation between the Facility Manager and Technical Department in the building management system is of great importance to

optimise the indoor performance to the standards of building users. Korolija et al., (2011) state that, the choice of HVAC systems impacts the life-cycle cost of the building; a building with an ineffective HVAC system or high running cost is also unlikely to be leased or sold easily. It is not surprising that

the design of comfortable, energy efficient buildings is receiving a lot of attention. Retail

According to Ellison and Sarah Sayce (2006), retailing, and in particular food retail, has substantially higher operational energy estimates per square metre than other sectors. The score in energy efficiency has significant influence on the NPV of retail property.

According to Kooiman (1999), temperature is a very important aspect to provide comfort to shoppers. This comfort contributes to keeping consumers inside the building for a longer period of time.

Industrial For industrial properties the availability of natural ventilation is an important factor according to the research done by Merrill Lynch Australia (2005).


50

It can be expected that climate systems have an influence on the net rental income and value of Offices, Retail and Industrial properties.

Building flexibility/adaptability

Offices In the current Real Estate Market with high levels of vacancy, development of buildings emphasis has been placed on the recycling of older buildings to new and unintended uses as compared to their

original function. According to Graaskamp (1981), these adaptive use efforts have been most successful where floor load capacity in the old structures was generous, ceiling heights were adequate, and column spacing was modular and flexible. Long-term investors now recognize the

probability that many buildings will change uses during the time of ownership so that investment safety is linked to project designs which anticipate convertibility of space-time units from one function to another. Re-usability is linked to higher exit values.

Retail Grenadier (1994) states that flexible and adaptable retail buildings can attract different kind of retailers

to obtain a good tenant mix. This allows dynamic optimization and tenant diversification and creates a higher portfolio value. According to Ellison and Sayce, 2006, the retail sector is characterised by frequent, regular refits of shop interiors, to support a relatively stable operational activity. The basic

functional requirements of retail units remain relatively unchanging. The exception to this could be the development of show room stores, particularly for large goods and kitchens for example, where nothing is actually taken from the store itself, purchases being delivered from a warehouse. This

changes the functional requirements of the store and, to some extent the activity of the shopper, but not to the extent that the physical arrangement of the property will have to change significantly.

Industrial Factors important in industrial property design include flexibility in technology and capacity expansion. Cleminshaw (1997) argues that a building‘s design can create functional obsolescence by creating

undesirable excess. According to Greer and Kolbe (2003), warehouses lose competitive position because of floors with inadequate load-bearing capacity, and load bearing columns with bay widths that hamper the movement of materials-handling machinery.

It can be expected that increased flexibility might have a positive influence on the net rental income and value of Offices, Retail and Industrial properties.

In regards to the international aspect of this research and international investment funds not researching, communicating or obtaining information on the flexibility of their assets, this aspect is

disregarded and not researchable. Ceiling height

Offices Gijselaar (2010) states that ceiling height is an interesting aspect considering the different value

adding capabilities it has for office buildings. Retail

Baker (2004) states at the ground floor, ceiling heights are a critical part of what makes a retai l space inviting and what makes a building feel comfortable for pedestrians on the sidewalk next to it that the squashed ceiling heights, found at both ground floors and upper floors of newer buildings, make it very

hard to achieve the feelings of space and grace appreciated so much in traditional buildings. Whether people are consciously aware of this fact or not, it has a profound impact on the comfort one feels in them.

Industrial Landlords‘ belief that tenants prefer buildings with higher ceilings may be inaccurate (Christensen,

Wisener and Campos, 1997: in Benjamin, Zietz and Sirmans, 2003). According to Ambrose (1991), ceiling height does not appear to have an impact on the property's value, unless this is lower than


51

stated by Real Estate Information Standards (elaborated in ‗Building type‘). According to Greer and Kolbe (2003), warehouses lose competitive position because of ceilings with insufficient clearance.

It can be expected that ceiling height might have a positive influence on the net rental income and value of Offices, Retail and Industrial properties.

Number of Stories

Offices According to Fuerts, (2007), the number of floors represents several characteristics. One could expect sophisticated elevator systems in tall buildings, availability of panoramic views and potential landmark

status of the building. Gutierrez (2013) states that the benefits agglomeration economies have in taller buildings are higher than in smaller buildings. Ozus (2009) found this variable as the most significant. While Clapp (1980) reported that number is floors is significant however the influence of this variable

might be subject to different perceptions in different office markets. Gutierrez (2013) also states that companies pay a premium for obtain the highest floor of a building. De Jong et al. (2007) state that feasibility of high rise is a matter of controlling the efficiency of the building. Not only the building

process itself but also the high rise building in use may be compared to the making of a ship model in a bottle. Every piece of material has to pass the bottleneck, making the logistics, the vertical transport, exceptionally important. Langdon (2009 in de Jong et al. 2007) shows that where the costs are

increasing with the height, the earning capacity of the building is decreasing, in which the vertical transport with the 5 % of the GFA for the elevators takes its substantial contribution.

Retail For retail, the ground floor is very important. The articles on the ground floor are sold with the greatest frequency (Hondelink , 1992 , p . 178). On higher floors more bulky items are sold for which customers

do not come every day, such as home furnishings, furniture and carpets). The restaurant or cafeteria is mostly situated on the top floor. The number of floors is not as important, but vertical transportation of customers must be done with ease to attract them to higher levels (Kooijman, 1999).

Industrial

Modern industrial techniques that favour single-story manufacturing plants render older, multi-storeyed facilities obsolete (Greer and Kolbe, 2003).

It can be expected that the number of stories might have a negative influence on the net rental income and value of Retail and Industrial properties. The literature does not give a one sided answer to the rent or value premium of more or less floors in Office buildings

Size

Offices According to Remøy (2010), there is a correlation between the object blueprint, flexibility and the wishes of tenants. Apparently the size, which has a logarithmic relationship with rent prices, shows

that with smaller office buildings have higher average rents as larger office buildings. Retail

The size of retail buildings may influence the tenant-mix and the presence of anchor stores. The factors influence the attractiveness of the agglomeration for customers (Kooiman, 1999). According to Sirmans et al (1993), the building size should also be positively correlated with size. However Srimans

does not control for type, as he says that larger shopping centres have higher agglomerating power due to their increased visitor attractions.

Industrial Size of the property is found to be an important variable (Ambrose 1990; Fehribach et al. 1993; Lockwood and Rutherford 1996; Buttimer et al. 1997: in Beekmans, 2013). A study done by Hartman

(1991) finds that prices consistently decline as building size increases. Hartman also finds that demand is greatest for buildings of 25,000 square feet or less regardless of market conditions.).


52

It can be expected that size might have a positive influence on the net rental income and value of Retail and influence Industrial and Office properties negatively.

Building amenities

Offices In the study of the Istanbul market (Öven and Pekdemir, 2006 in Gijselaar, 2011) building amenities such as the presence of a restaurant in the building, or a bank, a health club, a day care facility, a

conference room or a shop, were found to be important for the rent level. Although it sounds viable, no other studies, besides Ozus (2009) who mentions social facilities within the building, refer to such amenities and the question is whether these variables indeed influence the return for an investor.

Retail The most important amenities on building level are similar to amenities on a larger scale level.

Namely, other retailers, spatial facilities that stimulate customers to get and stay inside the retail building to spend more time shopping. Also gastronomy or entertainment facilities, i.e. ‗non-retail tenant mix‘ are as already stated important (Teller and Reutterer 2008).

Industrial Industrial properties generally do not have amenities in the form described in offices and retail.

However they lose their competitive position because of ceilings with insufficient clearance, floors with inadequate load-bearing capacity, and load bearing columns with bay widths that hamper the movement of materials-handling machinery. The presence of a rail track attached to the property might

positively influence pricing of industrial real estate. This can be seen as an added amenity to this property type (Greer and Kolbe, 2003).

It can be expected that building amenities might have a positive influence on the net rental income and value of Offices, Retail and Industrial properties.

Type classification

Figure 7 - Differences in returns by asset type (CBRE, 2014)

Offices, Retail & Industrials Hoesli & Lizieri (2007) set out that there is a general consensus that sector diversification, i.e.

diversification across the various property types, is more useful than regional diversification based on administratively defined areas. Another approach which has been used is one of decomposing property type and regional influences on property returns. Lee (2001) finds that for the U.K., over the

period 1981 to 1995, the performance of real estate is largely property type-driven (a similar conclusion prevails in the U.S. (Fisher and Liang, 2000). This implies that the property type composition of the real estate fund should be the first level of analysis in constructing and managing

the real estate portfolio. Hoesli et al. (1997) use cluster analysis and also find that property type is the most important dimension in determining different market behaviour.

Offices Gijselaar (2010) categorises office buildings in High rise, Complex, Pavilion, Urban and Basic Office building types. Gijselaar states that Hhigh-rise buildings or complexes have a negative influence on

net rental income, pavilions and urban offices positively affect the net rental income.


53

Retail Svets (2010) states six major categories of retail properties: standard shops, shopping centres, retail warehouse, department stores, supermarkets and other retail found in appendix I. It is difficult to

establish a single model of retail rent determination because rents vary greatly between retail properties. According to Svets (2010) there are some external factors which influence real estate assets‘ performance. Property markets are volatile and sometimes can experience rapid rises and

dramatic falls. Shopping Centers and Retail Warehouses seem to be two retail property types that are the most sensitive to external factors. supermarkets classically more stable property assets and standard Stores, Departments Stores and Other Retail was rather average.

It was also argued that rents in prime locations do not seem to react to changes in economic conditions in the same way as they do in secondary locations. The investment value of a specific retail center may be less dependent on national trends and more dependent on competition for retail sales

from other local shopping centers. Factors affecting shopping center sales and profitability include: low retail vacancy, constraints on future development (e.g., zoning, few available sites, and master plan restrictions), population growth and aggregate disposable income of the local population.

Industrial According to Yajie Zhao (2003) and the 1998 Real Estate Information Standards industrial Real Estate

can be divided in the several types of buildings found in appendix I. According to Yajie Zhao (2003) R&D has the best performance but higher volatility of performance than manufacturing and warehouse.

It can be expected that for offices pavilions and urban office buildings might have a positive influence on the net rental income and value, and that hhigh-rise buildings or complexes have a negative

influence. For retail properties it depends on the type and location to determine the influence. For industrial properties, manufacturing and warehouses might have an positive influence. Sustainability

In many markets, rental premiums are emerging in green buildings as many of today‘s best tenants are increasingly willing to pay a premium for green spaces. For these tenants, leasing green space is an opportunity to demonstrate a commitment to sustainability, attract the best employees, and improve

productivity (Institute for Building Efficiency, 2011) As sustainability being part of the building aspects, it is regarded as an entity on its own alongside the

other building aspects. Sustainability criteria Measurement technique

Sustainability labels and certif ications LEED, Energy Star

Energy Usage kWh per sqf

Water Usage Litre per sqf

Waste production CO2 per sqf

Labels & certifications

In the US, where energy assessment methodologies such as the Energy Star program and LEED certification are used, different results were published. The diversity of results can be explained by the use of different databases from various real estate markets and different methods of analysis. The way

in which control variables such as location, size and age are used in the hedonic regression model is important.

Offices According to Kuyper (2014), recent studies failed to prove any conclusions about the impact of energy performance of office buildings. Bonde & Song (2013) reported that there was no link between the

building‘s energy performance and the value of the property. However, Popescu, Bienert, Schützenhofer, & Boazu (2012) concluded that the energy efficiency and energy certification of office buildings does provide tangible benefits for the office building‘s value. Nevertheless, it could still have


54

an impact. According to Popescu et al. (2012), it might for example be that although no direct financial gains are obtained it does have a positive impact on the office building‘s image.

The most recent study into the effects of sustainability on offices values is done by Baas (2013). He concluded that values and rent prices for offices go up and energy prices go down in relation to their

energy labels.

Figure 8 - Energy performance index: Rent level versus Energy costs (source: Baas, 2013)

Retail The finding of this study that the sustainability level has no significant influence on the return is in line

with other studies on the returns of sustainable funds, as shown by Eichholtz, Kok & Yonder (2012). Larger studies on the relation between sustainability or corporate social responsibility (CSR) and the returns of investment funds indicate mixed results. Many review studies also find no significant relation

and Orlitzky (2003) finds in his large meta-analysis only a small positive correlation between sustainability and financial performance. Furthermore, the rent (and value) of a retail unit is mainly determined on the potential turnover that a retailer can realize in a specific retail unit and at a specific

location. Since the rent is only approximately 10% of the turnover and the energy costs only 1% of the turnover, the effect of lower energy costs on the total profit is limited. A retailer is probably more eager to invest in better lighting (which might use even more energy), so that the products are lit better, look

more attractive and sell better.

Industrial There seems to be lack of research into the industrial sector, despite significant innovations into sustainable distribution warehouses. This is most likely to be attributable to the initially low rental value

and lack of information for such properties. If the industry is to fully understand the effect of sustainability on property value, further research is required to develop an understanding of the impacts of various sustainability criteria in other sectors, retail in particular (Sayce, et al, 2005).

For industrial property natural ventilation, light panels and location close to road links are important. It concludes that the LPT sector is happy to be green or sustainable if it adds to the bottom line, this is thought to happen through improved reputation, enhanced income, lower costs, lower risks and an

increased investor base. It can be expected that labels & certification might have a positive influence on the net rental income

and value of Offices, Retail and Industrial properties.

Energy, water and waste

Geiger et al (2013) state that; the consideration of sustainable real estate investment (SRE) also reveals the opportunity to investors of achieving portfolios with a higher achievable expected return. Diversification advantages, the replacement of lower-generating assets and higher achievable returns

increase the attractiveness of SRE to institutional investors and determine the performance benefits of institutional investment portfolios. Both risk-averse as well as risk-friendly investors receive added value by integrating SRE. The majority of commercial property enjoys a relatively low risk of creating a

pollution incident.


55

Offices The results in Kok‘s (2012) study provide a clear market indication that sustainability matters for real

estate users, which is in line with recent evidence for the US office market. Rental growth in efficient and less efficient buildings differs markedly. Both components of sustainability have a direct impact on the valuation of ―non-green,‖ and inefficient buildings (Eicholtz, 1995, Kok, 2012 & Gijselaar, 2010).

Retail What is perhaps unanticipated is the lack of literature relating to retail and leisure sectors given that,

for these properties, energy consumption can often be extremely high and that for retail centers matters such as waste management are very important, with many new centers now seeking to design in on‐site compaction plants (Sayce, et al, 2005).

Industrial

According to Ellison and Sayce (2006), Industrial property is clearly most at risk particularly that occupied by chemicals, metals or waste management businesses.

General As a result, all real estate property sectors are making changes. Office users are demanding less space per worker as they reconfigure for more collaboration. Retailers are looking for urban formats

that will be able to serve city dwellers more efficiently. Industrial space is being designed and located where it can meet the needs of online retailers with ever faster delivery times. And multifamily space is adapting to provide less space per unit, but increased common areas (Morrison & Scott (2014).

It can be expected that energy, water and waste reductions might have a positive influence on the net rental income and value of Offices, Retail and Industrial properties.

2.4.4 Conclusion

The different asset specific criteria have been discussed and divided in location features, building features and sustainability features as part of the building features.

Most research investigated physical building characteristics in relation to the building‘s potential rent level, excluding operating and management costs. However some literature focussed on willingness to

pay of tenants, building values or additional costs associated with a change in the variable. As sustainability being part of the building aspects, it is regarded as an entity on its own alongside the other building aspects. Below the hypothesized influences of the variables on NOI‘s and EV‘s have

been summarized as influences on ‗pricing‘. Having documented the relevant micro level influences alongside the macro, meso and fund aspects.

We can now continue to the methodological part of the thesis will discussed the manner in which the criteria will be operationalized and used in the statistical research.

Location criteria Office Retail Industrial

Distance to CBD ++ + +/-

Distance to transportation Nodes

+ + ++

Accessibility + + +

Walkability + ++ +/-

Amenities + + +/-

Building criteria Office Retail Industrial

Architectural Quality + + +/-

Construction year/ Age +/- - --

Last Renovation - - -

Climate systems + + +/-

Building flexibility + + +

Ceiling height + + ++

Number of stories +/- - -

Size / Total Floor Area - + -

Building amenities + + +

Type Classification +/- +/- +/-

Labels & certification ++ + +/-

Energy, water and waste ++ + +

Indication of hypothesized influence on pricing

++ Increased influence on pricing + Moderate influence on pricing +/- Conditional influence on pricing - Moderate influence on pricing -- Increased influence on pricing 0 No influence on pricing


56

Literature guide per variable:

Criteria Office Retail Industrial

Location criteria

Distance to CBD Gijselaar 2011 Parker 2011

Kooiman 1999

Rutterford (1996) Beekmans et al. (2014)

Colwell and Munneke (1997) Thompson (2005),

Distance to transportation

Nodes

Clapp, 1980 Wheaton, 1984

Trainstations: Kok (2012) and van Gooi (2013) Negative relation freeway:

Frew and Jud (1988) Bollinger et al. (1998)

Ellison and Sayce 2006 Zhao (2003) CSX Real Property, 2006)

Airport within 3 miles: Colwell and Munneke (1997)

Accessibility Parker 2011 Kooiman, 1999

Jones et.al., 1990 Ellison and Sayce 2006

Sivitanidou and Sivitanides, 1995

Dunse et al., 2004: in Beekmans 2014 Dunse and Jones, 2005 Ambrose, 1990; Lockwood and Rutherford, 1996; Black et al., 1997; Ryan, 2005 Colwell and Munneke, 1997

(Beekmans 2013 Close to freeway is a negative amenity: Ryan‘s (2005)

W alkability Pivo and Fisher (2011) Pivo and Fisher (2011) Garreau, 1991 Beddington, 1982

Pivo and Fisher (2011)

Amenities Kok (2012)

Parker (2011) PBL (2009a)

Parker (2011)

Teller and Reutterer (2008) Oppewal and Holyoake, 2004; Babin et al., 1994: in Teller, C., Reutterer, T. (2008). Kim, 2002

Teller and Reutterer 2008

Colwell and Munneke (1997)

Beekamsn, 2014 Glaeser (2010 Benjamin, Zietz and Sirmans (2003) and Ellison Glaeser, and Kerr 2010

Gateway City Wiegerick (2013) CBRE (2012)

Eppli and Shilling 1996 Thompson, 1967; in Reimers &

Clulow, 2004

Lecomte, 2008

Building criteria

Architectural Quality

Gat (1998) Yusof (2000) Ozus (2009) Duffy (1986) Gijselaar, 2011

Merrill Lynch Australia (2005) Kooijman (1999) Teller and Reutterer (2008)

Myers (1994) Cleminshaw (1997)

Construction year/ Age

Slade, 2010 Dunse & Jones, 1988

Sirmans and Guidry 1993; Gatzlaff, Sirmans , and Diskin 1994: in Baker and Chinloy, 2014)

Greer and Kolbe (2003) Not significant: Ambrose (1991) (Ambrose 1990; Fehribach et al. 1993; Sivitanidou and Sivitanides 1995; Black et al. 1997; Buttimer et al. 1997; Dunse, Jones, Brown and Fraser

2004; Ryan 2005; Dunse and Jones, 2005) in Beekmans (2013 Greer and Kolbe, 2003

Last Renovation Geltner (2007) Sirmans and Guidry 1993; Gatzlaff, Sirmans , and Diskin 1994: in Baker and Chinloy, 2014

Greer and Kolbe, 2003

Climate systems Joosstens & Itard (2012) Lombard, et al. 2008 Korolija et al., (2011)

Joosstens & Itard (2012) Lombard, et al. 2008 Korolija et al., (2011) Kooiman (1999) Sarah Sayce (2006)

Joosstens & Itard (2012) Lombard, et al. 2008 Korolija et al., (2011) Merrill Lynch Australia (2005).

Building flexibility Graaskamp (1981) Grenadier (1994) Ellison and Sayce, 2006

Cleminshaw (1997) Greer and Kolbe (2003)

Ceiling height Fehriback et al (1993) Gijselaar (2010)

Baker (2004) Christensen, Wisener and Campos, 1997: in Benjamin, Zietz and Sirmans, 2003 Greer and Kolbe (2003) Not significant Ambrose (1991)

Number of stories Fuerts, (2007) Gutierrez (2013) Ozus (2009)

Clapp (1980) De Jong et al. (2007)

Hondelink , 1992 Kooijman, 1999

Greer and Kolbe, 2003

LFA/GFA Ratio Fuerts, et al., 2011

Clapp (1980).

lack of research lack of research

Size / Total Floor Area

Gat (1998) Gijselaar, 2011

Kooiman, 1999 Beekmans, 2013 Black et al. 1997 Hartman (1991)

Benjamin, Zietz and Sirmans, 2003

Building amenities Gijselaar, 2011

Ozus (2009)

Teller and Reutterer 2008 Greer and Kolbe, 2003

Type Classification Gijselaar (2010) Svets (2010) Yajie Zhao (2003)

Sustainability

Labels & certification

Popescu, Bienert, Schützenhofer, & Boazu

(2012) Baas (2013)

Not significant. lack of research

Energy, water and

waste

Kok‘s (2012)

Eicholtz, 1995, Kok, 2012 & Gijselaar, 2010

Sayce, et al, 2005 Ellison and Sayce (2006)


57

3. Methods

In order to answer the sub questions, linear mixed models were chosen as the statistical method to

statistically asses to which extent asset specific criteria were of influence on the financial performance

in relation to the macroeconomic (time), meso economic (region/submarket) and fund aspects. This

statistical method also allows to determine the relative influences of each of the influential factors.

This section of the report focuses on the methods used to conduct the statistical analysis needed to

ultimately determine which significant variables are part of the hedonic pricing equation and can be

used to analyse private funds.

In this chapter explanations and justifications are provided for the choices that have been made in

relation to the methods and data that were used. Since this research uses regression analysis as a

quantitative research methodology, a short introduction is also provided on the chosen regression

techniques.

3.1 Introduction

In finance, quantitative research is often used to develop hedonic models to price complex

investments such as real estate, and develop theories to exploit investment hypotheses. In this

research, this is also the case, as we are trying to improve the private fund investment methodology. in

the future would be a profitable investment based on statistical evidence produced by hedonic pricing

analysis on the influence of asset specific criteria of the underlying assets in the 9 funds contained in

the dataset. This technique requires a combination of both technical and financial aspects.

This statistical analysis will be performed with panel data out of the funds that are part of the North

America Fund of SAREF. The commercial investments studied in this fund consist out of office assets,

retail assets and industrial assets, all geographically dispersed throughout different regions of the

United States. The financial performance of each building is based upon the different independent

variables delivered to us by the each funds management. The outcome/dependent variables are

measured over a time-frame of 4 consecutive years. The first measurements are those of Q4-2010

and go on until Q4-2013. All dependent variables are reported in real terms according to the Q4 price

level.

This research uses panel data and is based upon empirical analysis using linear mixed models (LMM).

Based upon a predetermined set of variables the financial performance of the funds is analyzed by

analyzing the separate underlying assets in the fund on the basis of 5 types of dependent/outcome

variables. The set of independent/predictor variables that is used as input for the hedonic pricing

models are categorized in three different groups: location features, building features and sustainability

features. An overview of all variables that have been analyzed is provided in paragraph 3.2. During the

several modeling phases, a final model is constructed that reflects the effects of individual variables on

the dependent variables for each of the 5 dependent variables for each commercial property sector.


58

3.2 Chosen methods

Hedonic pricing models

A hedonic model identifies price factors according to the notion that a price Is determined by both internal characteristics of the good being sold and external factors affecting it.

In this particular research the internal characteristics are the location and building specific criteria of commercial real estate assets elaborated on in paragraph 2.4. The external factors affecting the price

in this case are the macro and meso economic circumstances in which the assets are situated.

The general assumption of hedonic pricing models is that the economic value of a real estate object is

affected by a particular combination of characteristics that it possesses given that properties with better qualities demand higher prices as compared to properties with lower qualities. This is referred to as the Hedonic Pricing Function. For example, the price of a real estate asset will depend on the

location quality, the building quality etc. This method helps us determine the value of an asset based on users, in this case tenants and investors, willingness to pay for assets as and when their characteristics change.

The goal of this research is to determine if and which asset specific criteria have price enhancing or decreasing effects of the different financial performance measures of assets in private international

real estate funds. The hedonic pricing method as described above is fit to do so.

On the basis of the theory examined the following hedonic pricing model has been developed for the pricing of commercial real estate assets:

∑

∑

∑

∑

P= Pricing of a commercial real estate asset, this can either entail the NOI or EV in terms of $ per

square foot.

N/i are the different assets with different asset specific criteria

T/t is the time variable which controls for the macroeconomic influence over 4 years‘ time R/r is the region variable which controls for the meso economic influence in the 8 different regions F/f is the fund variable which controls for the 9 different investment funds

The process of building the final hedonic pricing models is divided into three main steps. 1). Descriptive statistics

2). Exploratory analysis and stepwise model building 3). Mixed linear model regression and outcomes

These steps are conducted in chapter 4. Linear mixed models

A regression technique is needed to use in the hedonic model building process. In this research the linear mixed regression model is chosen. This has the following reasons:

The basic assumptions of Ordinary Least Squares (OLS) are that a model must be linear in the parameters, the data is a random sample of the population, the independent variables do not have too

much multicollinearity, the independent variables are measured so that the measurement error is negligible, the expected value of the residuals is always zero, the residuals have constant variance (homogeneous variance) and the residuals are normally distributed (Burke & Term, 2010).


59

This research however makes use of panel data (hierarchical & longitudinal data) which violates these

OLS principles. Instead, a repeated measurement design is chosen which uses linear mixed models (LMM).

Linear mixed models have the possibility to fit linear mixed effects models to data obtained from normally distributed samples. The unique capabilities linear mixed models have is that they can handle correlated data and unequal variances.

Correlated data is almost always present in situations where repeated measurements are done. A linear mixed model transforms repeated measurements into general linear models (GLM). The linear

mixed models are also capable of dealing with measurements that are placed in a hierarchy. The mixed procedure can e.g. process data from a sample of building features selected from a sample of buildings in several geographical districts (SPSS Inc., 2008). This of course is an important feature for

this research which aims to distinguish the effects of such features, making this the preferred technique

In addition to this, the linear mixed model technique solves the OLS issues by providing the possibility to incorporate estimated fixed and random effects into one model.

Ultimately, the LMM uses random intercepts. This enables the models for each study group to be located in different locations during different points in time. The random intercept is used on asset level. This causes each asset to have an own starting point and geometric space in the model. All

these LMM features benefit the overall validity and reliability of the research. Transformation of data.

Another assumption of linear regression is that the variance be the same for each possible expected value (this is known as homoscedasticity). Univariate normality is not needed for least

squares estimates of the regression parameters to be meaningful. However confidence intervals and hypothesis tests will have better statistical properties if the variables exhibit multivariate normality.

This can be dealt with by empirically examining the plotted fitted values against the residuals. In this research this is done in the exploratory analysis steps of chapter 4.

It is sometimes necessary to transform data to better fit the regression model. If the distributions are very skewed, the logarithm of the data can be used to make the distribution less skewed. Which logarithm you use is generally not very important. The independent variable can be logged, while the

dependent variable is not. In this case a change of 1% in the independent variable is associated with 1/100 times the coefficient change in the dependent variable. The dependent variable can be logged, while the independent variable is not. In this case a one-unit change in the independent variable is

associated with a 100 times the coefficient percent change in the dependent variable. Both the independent as well as the dependent variable can be logged. In this case we are looking at elasticity: percentage change in X results in percentage change in Y.

Interpretation table of log transformed variables

Dependent variable Independent variable Interpretation

LN Linear # Change in Ind is % change in dep

LN Category Change in ind cat is % change in dep

LN LN 1% change of Ind is % change in dep

Stepwise building of the models The first model that was built for retail and was based upon the net operating income (NOI). When the

model was completed the dependent variable was replaced the other performance measures (EV, Returns). The exploratory analysis shows the stepwise adding and analysis of the independent variables for the NOI‘s but are similar for the EV‘s and Returns .

http://en.wikipedia.org/wiki/Variance

http://en.wikipedia.org/wiki/Homoskedasticity

http://en.wikipedia.org/wiki/Least_squares

http://en.wikipedia.org/wiki/Least_squares

http://en.wikipedia.org/wiki/Hypothesis_test

http://en.wikipedia.org/wiki/Errors_and_residuals_in_statistics


60

For the use of the first dependent variable NOI we firstly had to determine how we would add NOI into the model. Firstly the NOI was transformed into an NOI per square foot leasable floor area (LFA)

measure to make this a comparable average financial performance indicator for each asset. Afterwards, the logarithmic transformation of the NOI proved more significant based on the price forming curve of real estate rents and improved normal distribution according to the KS and SW tests.

Therefore the logarithmically transformed NOI provided a better variable to incorporate and build the model upon. This is done for all NOI‘s and EV‘s in each commercial real estate sectors.

After determining how the NOI is operationalized, variables will be entered according to a stepwise method. This adds them one by one into the model until a final model is created. Each variable that is entered will be subject to a number of statistical checks, for example; R 2 tests, correlation analyses

and model comparison on the basis of the information criteria and significance of the outcomes. After each step the residuals of the previous model are saved so that the scatterplots for new variables that are examined are related to the other variables already inserted into the model.

3.3 Variables

The optimal dataset would consist out of the historical performances and variables of all indirect funds and their underlying assets. But reality remains that a large database in such detail is impossible to acquire and many investment funds do not disclose or track all of the incorporated variables.

For the data acquirement process, 9 large and prominent US based funds were contacted. All of these funds are either part of the Syntrus Achmea North American Fund of funds or were part of their

shortlist for investments under consideration. The funds in table x participated in the research.

Each participating fund filled in their individual templates containing the requested financial

performance figures (dependent variables), macro, meso and asset specific criteria (independent variables) .

The quality differences in terms of completeness and format of the supplied data were already noticeable between the provided datasets from each fund, confirming the notion that not all fund managers or investors track, communicate or use the requested data on the assets in their funds.

Figure 9 - variables from assets in the 9 funds (Ow n image)

Dependent variables

The financial performance measures used as the dependent variables are the net operating incomes and estimated values of the assets in the funds. These are the two main building blocks for real estate returns on asset level and on fund level as described in paragraph 2.1.


61

For this research two dependent variables were used due to the fact that the financial performance of real estate can be measured in multiple ways and returns are mainly influenced by these two financial

measures. Each dependent variable is measured for four consecutive years. (2010,11‘,12‘ and 13‘)

Net Operating Income:

This is seen as a raw or pure performance measurement variable because it corrects for vacancy, operating expenses and other costs associated with the specific property documenting more of its performance characteristics.

Estimated Value: The values of the properties are dependent on several factors but are commonly done be appraisal techniques incorporating the NOI‘s and other price influencing factors such as

timing. Due to the fact that the price forming process of NOI‘s and EV‘s are different and therefore possibly

dependent on different internal and external factors, it‘s interesting to compare the outcomes of the two dependent variables. This also increases the scope and usefulness of the research.

Independent variables

Each variable adopted from chapter 2 that will be used in this study is based upon existing scientific

literature. However, even though a variable might seem theoretically relevant, its practical use is

largely dependent upon the availability of data from the funds and the extent to which this could be

used for regression analysis. A synthesis of both theoretical relevance and practical limitations has led

to the following set of variables that were studied:

Level Variable Explanation § Operationalization

Fund level aspect

Fund Each fund manages assets differently and can have an increasing or decreasing effect on NOI‘s and EV‘s

2.2 Funds 1-9

Macro level

aspect

Timing All macroeconomic influences differ each transaction year.

2.3 Transaction Year

Meso level

aspect

Submarkets Submarkets differ in external influence, are geographically spread and should be controlled for by a

specif ied area. Gatew ay economies perform better than non-gateway economies. This is controlled for by a binary category

2.3 NCREIF 8 Regions

Gatew ay City

Micro level aspect

(ASC)

Location: Distance to CBD

Distance to Transportation Nodes Amenities

Building: Type Building age

Last renovation Building f lexibility

Tenant Density Number of f loors

Size Ceiling height Accessibility Building amenities

Building Type Sustainability labels Energy Usage Water Usage

Waste production

Distance in miles to CBD. And latent variable Google w alk measure this.

Distance in miles to public transport or Airport, Rail, Port categories or transit/w alk score Quality, distance and amount of surrounding amenities

NCREIF type and quality distinction A/B/C The technical age can be measured by

combining Last renovation and construction date. No universal quantif iable method used by funds. Impossible to determine.

Agglomeration effect of multiple tenants in a single asset. Amount of f loors per asset Size in sqf Leasable Floor Area (LFA)

Average ceiling height in feet Transformed to parking ratio per sqf Amenities of the building. Type of a building

Certif ications also incorporate energy, w ater usage and w aste production in their rating systems. Exact numbers w ere not available.

2.4 DistanceCBD Walk Score

Transit/Walk Score AltTransport Walk Score

Type / class LastUpdateAge

N/A*

Tenants/sqfLFA NumberFloors

LFA CeilingHeight ParkingLFA N/A*

BuildingType LEED/EnergyStar N/A* N/A*

N/A*

*N/A Not available or lacking information from fund management.


62

3.4 Correlation analyses

Multicollinearity is a statistical phenomenon where two or more variables in a multiple regression

model are (highly) correlated. In this case the coefficient estimates of the regression could change in response to small changes in the model or the data.

Multicollinearity has no reducing effect on the predictive power or reliability of the model in total. It only

affects calculations with individual predictor variables. This means that a model with correlated variables can display how well the entirety of variables predict the outcome/dependent variable, but possibly does not give correct results about the individual variables or which are significant in relation

to others. Since this research tries to distinguish variables and research their independent influence, high correlation between variables must be prevented by removing certain variables from the model.

The spearman and Kendall‘s Tau have been used for correlation analysis because this measurement technique also corrects for non-normality of variables. The correlation coefficient lies between −1 and

+1. A coefficient of +1 indicates a perfect positive relationship, a coefficient of −1 indicates a perfect negative relationship, and a coefficient of 0 indicates no relationship at all. The correlation coefficient is the measure used to determine the size of an effect: values of ±0.1 represent a small effect, ±0.3 is a

medium effect and ±0.5 is a large effect. Effects of 0.5 and higher are avoided. Effects of 0.8 are statistically not allowed. (Francis et al. 1990)

If variables correlate too much they could diminish the isolation capability of a single effect. For each

model a correlation study has been done between the continuous variables. Several steps were taken to increase the explanatory value of the model as much as possible. The correlations between t he variables were all tested and shown in appendix IV. The variables that were highly correlated were not

taken into the analysis simultaneously. This decreased multicollinearity in the model. Based on theoretical preferences, higher significance and AIC a choice was made between the variables to include in each model

http://en.wikipedia.org/wiki/Variable_(mathematics)

http://en.wikipedia.org/wiki/Multiple_regression

http://en.wikipedia.org/wiki/Correlation_and_dependence

http://en.wikipedia.org/wiki/Regression_coefficient


63

4. Statistical analysis In this chapter the statistical analysis is performed according to the methodology proposed and

discussed in chapter 3.The statistical analysis chapter comprises out of 4 paragraphs, one paragraph

for each of the 3 commercial real estate sectors, and one paragraph to conclude the final outcomes

and hedonic pricing models.

Each sector paragraph is divided into 3 subparagraphs:

The first is the descriptive statistics, giving insights into the sample used for statistical

analysis. These are important to gain insights into the sample and compare to the findings

The second is an exploratory analysis intended to explore the relations for each independent

variable with the corresponding dependent variable and relate these to the theories discussed

in paragraph 2.4. This is done for the dependent variable Net Operating Income to build a

model upon and then reused for the value and return variables.

The third paragraph shows the outcomes for all models and discusses the significant

outcomes for the Net Operating Income and Estimated Value models

In each sector paragraph the independent variables which remained for statistical analysis will be

filtered for significance and reliability in regards to the outcomes. The variables that remain afterwards

have passed three stages; academic research, data availability and finally the statistical analysis.

These final significant variables are then suited for conducting a portfolio analysis of a private fund.

Some variables were transformed to obtain increased reliability, increase significance or better fit the

knowledge we wish to obtain in regards to the scope of the research. Variables that did not contain

enough observations or caused unreliable results due to multicollinearity were omitted from the model.

In the final paragraph 4, we discuss the findings for each of the sectors dependent variables EV and

NOI. The different dependent variables are compared amongst each other to gain additional

knowledge. Outcomes are examined to draw conclusions useful for chapter 5 in which the research

questions are answered.

4.1

R

eta

il

Descriptive Statistics

Exploratory Analysis

Final Model

4.2

O

ffic

e



Final Model

4.3

In

du

str

ial



Final Model

4.4 Conclusions of statistical analyses


64

4.1 Retail Assets

This paragraph entails the statistical analysis for the retail assets. This sample is based

on 412 transactions of 120 retail objects spread over 8 regions in the US. Each assets

financial performance in terms of NOI‘s and EV‘s of 4 consecutive years are used as

input for the dependent variables of the retail models.

4.1.1 Descriptive Statistics for Retail models

The assets financial performance is monitored for the years 2010, 2011, 2012 and 2013. The 4

consecutive years are incorporated as a category control variable to measure the differences in NOI

and EV over time. All NOI‘s and EV‘s are based on nominal measurements.

This variable (year) is also used as a repeated measure in the mixed linear model so that each asset

has its own starting point in the model at different moments in time, this takes into consideration that

each consecutive year is related and corrects for that effect.

Graph 6 -Descriptives NOI and EV

Source: SPSS Own illustration

By putting the mean NOI and EV of each year in a bar graph we can clearly see that they seem to

increase every year. This enforces the notion that rents and values started increasing post crisis.

The tables below shows us the mean NOI and EV per square foot LFA for each of the 8 different

regions that will be used in the regressions. This is to give an idea about the possible pricing

differences in each region, supporting the notion that meso economic influences can be controlled for

by incorporating a regional category variable. It‘s interesting to see that NOI‘s and EV‘s have the same

hierarchy from high to low. This enforces the notion that submarkets with higher NOI‘s have higher

EV‘s and that EV‘s are dependent on the NOI. Numbers 1-4 seem to outperform the sample means.

EV per Sqf LFA mean per Region

# RegionA Mean N Std. Deviation

8 East North Central 141,570 27 37,2193

5 Mideast 273,988 56 242,7236

3 Mountain 527,567 12 190,0945

1 Northeast 881,330 30 1245,2351

4 Pacif ic 457,209 46 279,0108

7 Southeast 151,495 190 105,5548

6 Southw est 222,736 11 33,2324

2 West North Central 553,525 4 20,6611

Total 283,022 376 435,9804

NOI per Sqf LFA mean per Region



5 Mideast 18,946 56 20,5851

3 Mountain 27,333 12 11,7074

1 Northeast 31,493 28 29,5232

4 Pacif ic 26,711 44 17,9066

7 Southeast 10,371 189 10,1023

6 Southw est 15,920 10 2,1714

2 West North Central 30,475 4 1,0404

Total 16,128 370 16,7052


65

Next to the regions we control for meso economic influences

by categorizing the assets on their positioning in either a

gateway or non-gateway city. The table shows us that EV‘s

and NOI‘s per square foot are far higher in gateway cities as

non-gateway cities.

Asset specific criteria descriptives

The asset specific location criteria for retail that is incorporated into the model is the latent variable

Google walk score which measures the length of walking routes to important destinations such as

CBD‘s, grocery stores, schools, parks, restaurants, and other retail. Below we can see that the

different funds have different walk score means. This gives a rough indication of the average locational

quality of each fund. The histogram shows us that most assets have a walk score of around 70.

There is a large variety in type of the retail assets in the data provided by the funds. It is however

interesting to examine the differences in NOI per LFA. It is clear by the amount of observations per

category that some types have to be omitted or categorized to use them as a variable in the model.

NOI_LFA per Retail Types

RetailType Mean N Std. Deviation

Grocery 9,228 196 3,2444

Pow er Center 9,276 46 3,5376

Lifestyle Retail Center 11,050 4 6,7283

Community Center 16,500 16 6,6144

Neighborhood Center 23,091 34 18,6523

Regional Mall 34,188 16 5,6566

Super Regional Mall 69,808 12 8,3154

Total 16,128 324 16,7052

The descriptives for the continuous variables incorporated in the model are shown below. Here we can

see the number of observations(N), minimum and maximum value of the variable, mean of all

observations and the standard deviation per variable.

Descriptive Statistics for continuous variables

Cont. Variable N Minimum Maximum Mean Std. Deviation

NumberFloors 388 1 7 1,31 ,879

GoogleWalk 412 18 100 58,27 18,372

Parkingspots_LFA 352 ,00 0,014 ,0053 ,00184

LFA 412 3482,0 876424,0 178297,350 172537,6514

TenantDensity 372 0,000018 0,001436 ,0002 ,00015

LastUpdateAge 412 ,00 33,00 10,7864 6,98542

Valid N (listw ise) 352

GatewayCity EVsqfLFA NOI_LFA

No Mean 223,272 13,770

N 326 322

Std. Deviation 196,6515 11,8566

Yes Mean 672,590 31,948

N 50 48

Std. Deviation 1009,7409 30,6127

Walk Score means per fund

Fund Minimum Maximum Mean N Std. Dev.

H 18 78 51,50 32 22,038

C 26 77 52,54 236 14,098

F 37 74 57,70 40 12,730

G 55 100 74,20 20 15,237

A 46 100 74,78 36 19,419

B 37 98 69,86 28 22,060

E 55 98 76,00 20 17,375

Total 18 100 58,27 412 18,372

EVsqfLFA per Retail Type

RetailType Mean N Std. Deviation

Grocery 134,617 196 43,5149

Pow er Center 136,493 46 50,9034

Lifestyle Retail Center 288,825 4 140,3685

Community Center 274,231 16 130,5154

Neighborhood Center 404,768 34 233,7037

Regional Mall 598,019 16 134,7778

Super Regional Mall 646,917 12 100,8287

Total 283,022 324 435,9804

Figure 10 - GoogleWalk histogram


66

4.1.2 Exploratory Analysis for Retail models

Year

The interesting thing to see is that for each year the NOIs have grown with substantial percentages, the difference between 2012 and 2013 alone is 10%. This is much greater than the growth of

approximately 2% when indexing for inflation. This could indicate that timing is of influence on the NOI‘s of retail assets. This increase in rents is of course interesting since the goal of the research is trying to find out if this is possibly due to the asset specific criteria or due to the macro, meso and

managerial influences described in chapter 2.

Graph 7- Year


Google Walk Score

The variable Google walk score is perhaps one of the most interesting due to the fact that this is a multifunctional measure combining several qualities of a location related to its walkability into a

numerical measurement form. When trying to determine in which form we had to insert this variable based on the interpretation of the

scatter plot we saw that a Cubic relationship for the standard Google Walk ( x 3) seemed to give the best R 2. For the logarithmic transformation of the Google Walk, which was unlikely in the first place due to the slope of the graph, the significance and R 2 also did not improve.

When transforming and implementing the variable cubically the overall information criteria of the model seemed to worsen. The significance of the variable also dropped. Ultimately the Linear Google Walk

Scores were incorporated into the model due to the consideration of explanatory power and overall significance. At the end of each step the residuals are saved for the model so that each graph is related to previously incorporated variables.

Graph 9- Number of floors and floor type


Graph 8- Google Walk Score



67

Floors

The scatter plot for the number of floors in relation to the residuals of the previous model shows that rents rise when going from 1 to 2 floors. However when the amount of floors exceeds 2 the NOI s per square foot seem to decrease.

In each category from 3 till 7 there are a limited number of observations causing a decrease in reliability and possibly in the significance of the variable. In order to cope with this effect the

observations were recoded into three different categories: One floor, two floors and three or more floors. This categorical variable proved more suited in terms of fitness of the model based on the information criteria and significance of the variable in the model.

Parking Ratio

The amount of parking available at a retail asset is considered to be important and beneficial to the performance figures of retail type real estate. The funds provided the number of parking spots per asset. This however, is not a useful measure due to the fact that it does not incorporate the size of

each assets in relation to the amount of available parking. Its only relevant to see if an asset has more or less parking available for the effective floor space leased by tenants or used as shopping area. This increases its desirability and accessibility.

In order to create an appropriate measure we divided the amount of parking spots by the amount of leasable floor space per asset. This gave us the variable parking spots per square foot LFA. This

gave us the scatter plot below.

The overall problem with the parking ratio variable is that with 0 parking spot assets, rents do not

necessarily have to be lower (But in general NOI increases as parking spots per sqft LFA increases). This is due to the face that assets with few parking are generally located in high value areas such as CBDs in e.g. New York or Miami. In such areas parking garages and public transportation might

replace the benefits of onsite parking due to the high land prices. In this analysis only assets with parking spots are measured to see if there is an added value of extra parking space amongst the other variables.

Graph 10 - Size


Size

This variable is a measure for the size of each asset in the funds. According to the theory increasing size of an object seems to affect the NOIs per sqf LFA in a negative way.

The previous residuals and GFA give the following scatterplot. It‘s interesting to see that rental values for smaller retail properties seem to be higher with a tipping point at approximately 250.000 GFA where the net operating incomes per square foot rise again if the size of a building increases.

Graph 11 - Parking



68

After that properties get a lot larger increasing in size and NOI per LFA up until the largest super regional malls like Dadeland Mall and Fashion Valley Mall. Super regional malls have large

agglomeration power and function as shopping hubs attracting masses of customers increasing rental values and thus NOIs.

The LN GFA possibly shows a better depiction of the relations between size and NOI. However since all other dependent measures are made for LFA‘s and LFA and GFA in the model would highly correlate. The Ln of the LFA will be chosen as the incorporated variable due its higher significance and

higher information criteria when comparing the models for LFA and GFA.

Initially the thesis also intended to include the variable GFA/LFA Ratio to research the effect of how much leasable space a property has in relation to its overall size on its rent price. Due to a lack of

data, not enough observations were collected. The provided GFA sizes that were given were highly debatable if these were truthful.

Graph 12 - Retail Type


Retail Type

The retail types present in the funds are all categorized according to the

NCREIF styles. However due to a lack of observations in each category and some funds not providing us or

providing us with the wrong type classification we had to transform the categories.

The original form caused immediate insignificance of all variables worsening of the explanatory power of the model. We chose to recode into the following A, B, C, (D).E structure. The types Neighborhood

and Community were combined due to their same catchment area, range in rental prices and overall sizes. We see a clear difference of the NOI s for each type

Ultimately grocery was removed due to the fact that some funds had unjustly recorded large

multifunctional assets or plain single tenant assets as grocery based assets and caused insignificance in the model. The categories lifestyle centers, other and single tenants were also removed due to their irrelevance when analyzing type and limited number of observations. Categories A was added to B

due to the 5% rule of thumb needed per category and the fact that both types are malls and NOI‘s are both higher as the other groups and their physical characteristics are the same.

RetailTypeD

Frequency Percent Valid Percent Cumulative Percent

Valid 264 64,1 64,1 64,1

A 12 2,9 2,9 67,0

B 16 3,9 3,9 70,9

C 68 16,5 16,5 87,4

E 52 12,6 12,6 100,0

Total 412 100,0 100,0

Recoded Type Variable

A = Super regional malls B = Regional malls C = Neighborhood, community

D = Grocery E = Power Centers Other and single tenants removed


69

Tenant density

The density of the tenants present in a certain asset is based on the amount of tenants per square foot LFA. This is measured by dividing the number of tenants by the amount of LFA. A higher ratio would result in a higher tenant density

This measure is important due to the agglomerating effect malls can for instance have on rental values meaning that higher densities might have higher NOI s The graph shows a linear relationship but also

displays 2 outliers in the far right corners of the graph. The graph shows that higher densities indeed have higher NOI s, which is in line with the theory from 2.4 about agglomerating effects.

Gateway city

Theory stated that a gateway city is a city which is used as a gateway and economical hub to other

parts of the state and surrounding areas. A list is provided in appendix I of all US based gateway cities. A great example of a gateway city is Miami, being the main seaport and airport for its region attracting millions of people attributing to its economic value as a region and thus to NOI‘s and EV‘s of

real estate in that region. This variable measures if real estate in gateway cities provide higher NOI s by separating them in a

binary manner. The graph clearly shows an increased effect on NOI s if properties reside in such a city.

Regions

The information provided to us in the dataset started out with the State in which the assets were located. This of course gave a limited amount of observations per state and provided us with no clear relationship or evidence. The incorporation of regions/ submarkets was justified in paragraph 2.3

The state variable was then transformed into the NCREIF geographical regions variable for both the 4-dimensional and 8-dimensional versions respectively Region B and Region A. The different regions

can be found in appendix I. The significance and overall model were best suited with use of the 8-dimensional region variable A. The graph also shows us clear indications that the different regions have strongly differentiating NOI s. The NE and Pacific regions show to have the highest NOI‘s this is

logically due to the fact that large cities such as NY, LA and SF are located in these two regions.

Graph 14 - Gateway City


Graph 13 - Tenants per LFA



70

Graph 15 - Regions


Age

The age of an asset can be determined by a few aspects. In order to measure the asset´s quality in terms of structural and technical depreciation, two measures were discussed in the theory; the building age and the last renovation year.

For this research we combined both measures to form the last update of age. This is displayed in the left scatter plot. This variable seemed to have no clear estimation of NOI s or significance.

This variable was then recoded into categories taking into consideration that a relatively new asset is different when renovated than when newly constructed. On the basis of this notion the categories for

Renovated or New were made. The right scatter plot shows us that the NOI s for old and new assets differ. The significance of the variable and explanatory power of the model increased with this transformation.

Transformed and omitted variables and final variables for final models The motivation behind a transformation or omittment is the improvement of AIC‘s of final models,

overall significance of variables, rules of thumb and correlation analyses . The correlation analysis can be found in APPENDIX IV. The final motivations and variables are displayed in the table below. The model input variables will be used in the final regression model for NOI‘s and EV‘s.

Graph 16 - Age



71

Variable Motivation for transforming or omitting of variables Model input variable

Year In final model Year

Fund Omitted, not enough observations in each fund

Walkscore In final model Walkscore

GoogleTransit Omitted, MC with GoogleWalk -

Floor Transformed into category better AIC and Sig. FloorType

Size Transformed as ratio between size and tenants LN_LFA

Parking Transformed as ratio between spots and size Parking_LFA

Retail Type Transformed into RetailTypeE better AIC and Sig. RetailTypeE

Distance CBD Omitted, MC 0,8 with Trans and high with Walk -

Tenants Transformed into ratio between tenants and size Tenants_LFA

Gatewaycity In final model as binary category Gatewaycity

Region In final model as RegionA RegionA

Age Used in final model for NOI as continuous variable Transformed into category for better AIC and sig in EV

LastUpdate Age and Renovated or New

4.1.3 Final models for Retail Assets.

Net Operating Income model outcomes:

In the final model for the NOl, Year, Region, Google walk, Retail Type and Size proved to have a significant influence based on 95% confidence interval, Gateway City proved to be significant within a

90% confidence interval. The table of fixed effects can be found below the model outcomes. Macro variables:

Year The NOI‘s seem to decrease from 2010 till 2011 and increase from 2011 until 2013 . This indicates

that the macroeconomic environment has had an increasing influence on the NOI‘s since 2011. This is an important conclusion for research question 3. For the retail models, the timing, which controls for the macro economic situation, is a significant influence on NOI‘s alongside meso and micro criteria.

Meso variables:

Region In the NOI model, 4 of the 8 regions proved significant. They were compared to the Southwest region which performed best. We see that Mideast (-64%) is second place, East North Central (-68%) is third

and Southeast (-98%) is fourth. According to the descriptive statistics which provided a hierarchical list of highest to lowest, the order as stated before was 6,5,8,7. This does not strongly deviate but shows that the other variables cause this to change. The main conclusion is that region and thus meso

economy does have an effect alongside the macro and micro variables. Gateway City

We see that for this binary variable the difference between assets in gateway cities and non-gateway cities is -27%. NOIs in gateway cities outperform other cities by more than a quarter. This illustrates the effect a city which functions as a hub can have on the price forming of retail assets. This adds

evidence to the fact that meso scale is influential amongst the macro and meso variables. Micro / Asset specific variables:

Google Walk Score With every score point an asset increases its Google Walk score the NOI per square foot LFA

increases by 0,7%. If an asset were to have an NOI per square foot of 10$ and a Google walk score of 50, ceteres paribus the NOI increases by 7% if the Google walk score would be 60. This asset specific locational variable is significant amongst the macro and meso variables and gives the first statistical


72

evidence that real estate performance of international funds are dependent on micro variables and can therefore be used as an added investment criteria for analyzing funds.

Retail Type The table of fixed effects shows significant results for type B (malls) and type E(power centers). We

see that in comparison to power centers NOI‘s for malls (Type B) increase by 113%. Type C was found not to be significant but has an estimate of 20% increase in NOIs for Community and neighborhood centers in comparison to Power centers. Power centers seem to provide relatively low

rents as a type. The difference is confirmed by the descriptive statistics and academic literature. Size

The log transformed variable of the leasable floor area of an asset was the most significant variable, with an increase of 1% for the Ln LFA the NOI would decrease by 0,30%. This is supported by literature that states that with an increase in size of a retail property average rents decrease due to

economies of scale. This is therefore also an influential micro variable which determines the performance of retail NOI‘s

Estimated Value model outcomes: In the final EV model the Year, Google walk, Retail Type, Size, Tenant density, Last Update Age,

Gateway City and Region variables proved to be of significant influence on the EV‘s of US based Retail Assets. The table of fixed effects can be found below.

Macro variables: Year

The differences in year for the estimated values differ from those of NOI. The leaps between each year in percentages are higher for the first years and grow each year. This means that the estimated values grew each consecutive year while the NOI‘s only grew from 2011 onward. This shows us that

EV‘s do not necessarily fluctuate per year in the same manner as NOI‘s. The timing, which controls for the differences in macro-economic situation, is also a significant influence on estimated values of retail.

Meso variables:

Region The regions for estimated value differ from those of the NOI model. In the EV model, 3 proved significant, the East North Central region and the southeast region were proven to decrease the

estimated value of an asset by approximately 50% and 61% ceteres paribus. Gateway City

This binary variable gives a decrease of 24% for non-gateway cities. The impact of this variable was somewhat the same for NOI‘s(-27%). This variable is therefore an influential criterion for both financial performance measures.

Micro / Asset specific variables:

Google Walk The Google walk score also seems to be of influence on EV‘s with an increase of 0,6% per gained walk score point. This measure of location quality is therefore a valid tool for asset value

determination. Retail Type

In regards to the NOI model, retail type C is now also significant. The difference between Type E and C is a 38% increase in Estimated Value. The difference between E and B is now 98%. This is a very influential but logical estimate seeing the many type related performance enhancing differences

described in the literature between for example a wood shop in type E and a mall in type B.


73

Size

The decrease in EV, per 1% LFA increase, is 0,16%. The estimate seems to be lower for EV‘s in regards to the NOI‘s. This indicates that values drop relatively less than NOI‘s if asset size increases.

Tenant Density This variable was not significant in the model for NOI but is in the model for EV. We see that for each 0,01 increase for a gaining in Tenants per LFA the EV´s increase by 16%. We however need to state

that the average ratios are very small so an effect of 0,01 is considered very large. Last Update Age

This variable for building age seems to be significant and useful in the EV model and not in the NOI model. For each year gained on the Last update age (Either construction or renovation age) EV´s decrease by 1,2%. The older the asset the lower the value.

In conclusion, the outcomes of all significant macro and meso variables will be used for answering sub question 3, the outcomes for all significant asset specific variables for sub question 4. A comparison

between the NOI‘s and EV‘s is made in conclusion paragraph 4.4


74

Estimates of Fixed Effects for retail NOI model

Parameter Estimate Std. Error df t Sig. 95% Confidence Interval Lower Bound Upper Bound

Intercept 6,336570 ,909268 66,473 6,969 ,000 4,521399 8,151741

[Year=2010] -,108108 ,033979 86,685 -3,182 ,002 -,175648 -,040568

[Year=2011] -,113093 ,030724 152,182 -3,681 ,000 -,173792 -,052393

[Year=2012] -,071719 ,025025 199,308 -2,866 ,005 -,121067 -,022372

[Year=2013] 0b 0 . . . . .

GoogleWalk ,006548 ,002883 65,104 2,271 ,026 ,000791 ,012306

[RetailTypeE=B] 1,129283 ,383392 65,253 2,946 ,004 ,363653 1,894914

[RetailTypeE=C] ,209646 ,156273 65,867 1,342 ,184 -,102375 ,521666

[RetailTypeE=E] 0b 0 . . . . .

Parkingspots_LFA 37,668179 24,591715 65,501 1,532 ,130 -11,437743 86,774102

LnLFA -,300299 ,070559 63,949 -4,256 ,000 -,441260 -,159338

TenantDensity 864,683121 707,125565 64,960 1,223 ,226 -547,560810 2276,927052

[Gatew ayCity=No] -,271748 ,150955 64,460 -1,800 ,077 -,573274 ,029777

[Gatew ayCity=Yes] 0b 0 . . . . .

[RegionA=East North Central] -,680617 ,309233 73,736 -2,201 ,031 -1,296814 -,064420

[RegionA=Mideast] -,644115 ,292464 75,856 -2,202 ,031 -1,226626 -,061604

[RegionA=Mountain] -,442610 ,365080 73,301 -1,212 ,229 -1,170162 ,284942

[RegionA=Northeast] -,408863 ,322292 75,043 -1,269 ,209 -1,050895 ,233170

[RegionA=Pacif ic] -,444156 ,300763 74,934 -1,477 ,144 -1,043315 ,155004

[RegionA=Southeast] -,987952 ,283244 77,800 -3,488 ,001 -1,551871 -,424033

[RegionA=Southw est] 0b 0 . . . . .

[FloorType=Double] ,042281 ,178067 71,315 ,237 ,813 -,312749 ,397310

[FloorType=Multiple 2+] -,169173 ,194224 68,288 -,871 ,387 -,556711 ,218365

[FloorType=Single] 0b 0 . . . . .

[RenovatedOrNew =Average Age] ,009329 ,076274 255,053 ,122 ,903 -,140879 ,159537

[RenovatedOrNew =New] -,241528 ,197223 256,041 -1,225 ,222 -,629913 ,146858

[RenovatedOrNew =New R] ,031385 ,132802 212,336 ,236 ,813 -,230395 ,293164

[RenovatedOrNew =Old] -,019713 ,065517 213,639 -,301 ,764 -,148857 ,109430

[RenovatedOrNew =Very Old] 0b 0 . . . . .

a. Dependent Variable: LN_NOI_LFA.

b. This parameter is set to zero because it is redundant.

Estimates of Fixed Effects for retail EV model

Parameter Estimate Std. Error df t Sig. 95% Confidence Interval Lower Bound Upper Bound

Intercept 7,349143 ,895397 68,939 8,208 ,000 5,562847 9,135439

[Year=2010] -,159978 ,026544 216,371 -6,027 ,000 -,212296 -,107660

[Year=2011] -,107965 ,020040 267,023 -5,388 ,000 -,147421 -,068509

[Year=2012] -,064775 ,012897 305,998 -5,022 ,000 -,090153 -,039396

[Year=2013] 0b 0 . . . . .

GoogleWalk ,005792 ,002830 68,459 2,047 ,044 ,000147 ,011438

[RetailTypeE=B] ,983040 ,375278 68,497 2,619 ,011 ,234282 1,731798

[RetailTypeE=C] ,388257 ,157662 68,790 2,463 ,016 ,073712 ,702801


Parkingspots_LFA -14,236445 24,181426 68,196 -,589 ,558 -62,487212 34,014322

LnLFA -,161760 ,069850 68,628 -2,316 ,024 -,301120 -,022400

TenantDensity 1599,193964 703,232759 68,106 2,274 ,026 195,954570 3002,433357

LastUpdateAge -,012287 ,006194 69,738 -1,984 ,051 -,024641 6,794745E-005

[Gatew ayCity=No] -,248064 ,148453 68,200 -1,671 ,099 -,544282 ,048154


[RegionA=East North Central] -,508245 ,292987 68,861 -1,735 ,087 -1,092760 ,076270

[RegionA=Mideast] -,338543 ,275309 68,951 -1,230 ,223 -,887776 ,210691

[RegionA=Mountain] ,099619 ,345277 68,716 ,289 ,774 -,589240 ,788478

[RegionA=Northeast] ,244515 ,304100 69,215 ,804 ,424 -,362115 ,851145

[RegionA=Pacif ic] ,098005 ,283113 69,233 ,346 ,730 -,466756 ,662766




[FloorType=Multiple 2+] ,293199 ,191836 69,622 1,528 ,131 -,089443 ,675840


a. Dependent Variable: LN_EVsqfLFA.



75

4.1.4 Statistical conclusions for Retail Assets.

In the table below the estimates of all significant predictor variables and their corresponding

changes have been reported for both the NOI and EV outcomes. These will go on to

chapters 5 and 6 for answering the research questions and the recommendation.

Variable Y-10 Y-11 Y-12 R-ENC R-ME R-SE GC GW Age Type B Type C Size TD

Change No +1 +1 +1% +.0001

NOI -11% -11% -7% -68% -64% -99% -27% 0,7% - 113% - -0,3% -

EV -16% -11% -6% -51% - -61% -25% 0,6% -1,2% 98% 39% -0,2% 1,6%

We can now conclude that both retail NOI and EV pricing is influenced by macro, meso and micro

level aspects. The final hedonic pricing equations have been displayed below. These will be used in

the retail fund analysis paragraph of the investment tool in the recommendation (Ch.6) to calculate

differences for average fund NOI‘s and EV‘s on the basis of the significant variables weighted

averages.

∑

∑

∑

Final hedonic pricing models for NOI and EV

0+ macro+ micro+ meso+ (N=Micro T= Macro R = Meso)

macro= year2010+ year2011+ year2012

micro= Googlewalk+ type+ size

meso= eastnc+ mideast+ southeast+ Gateway

0+ macro+ micro+ meso+ (N=Micro T= Macro R = Meso)


micro= Googlewalk+ type+ size+ tenantdens

meso= eastnc+ southeast+ Gateway

The relative influences of the variables have been calculated through use of the standardized Z-values

of the variables in the model. Their influences on the outcome variable in high to low form are

displayed in the table below. The macro variable time is displayed to show the influence of the macro

time category over 4 years but can‘t be used as a selection criterion for fund analysis. The point was to

control for its influence

The following variables can be seen as the

influential asset specific criteria found on the basis of the hedonic pricing studies.

Macro: Year

Meso: Region and Gateway City,

Micro / Asset specific: Google Walk Score, Retail Type, Size, Tenant Density and Age

Walk score effect on NOI per sqf LFA

# NOI Variables Scale EV Variables Scale

1 Type Mall ASC Type mall ASC

2 Region SE Meso Region SE Meso

3 Region ENC Meso Region ENC Meso

4 Region ME Meso Type NCcenter ASC

5 Gatew ay No Meso ZTenantDensity ASC

6 ZLnLFA ASC Year 2010 Macro

7 ZGoogleWalk ASC ZLnLFA ASC

8 Year 2011 Macro Year 2011 Macro

9 Year 2010 Macro ZGoogleWalk ASC

10 Year 2012 Macro ZLastUpdateAge ASC

11 - - Year 2012 Macro

0

5

10

15

20

25

10 20 30 40 50 60 70 80 90 100

NO

I pe

r sq

f LF

A


76

4.2 Office Assets

This paragraph entails the statistical analysis of the office assets. The manner in which the analyses have been performed are similar to that of the retail paragraph. The model is likewise built up according to a stepwise manner for the NOI and then used for the other

dependent variables. The model contains 240 observations for 80 office buildings.

4.2.1 Descriptive statistics for office models

By putting the mean NOI and EV of each consecutive year in a bar graph we can clearly see that they

both seem to increase until 2012 and decrease in 2013. The assets have the highest NOI‘s and EV‘s

on average in 2012.

Graph 17 - Descriptives NOI and EV for Office Assets


The tables below shows the mean NOI and EV per square foot LFA for 7 of the 8 different regions of

the US. The sample did not have any offices in the West North Central region. These tables give an

idea about the possible pricing differences of offices in each region, supporting the notion that meso

economic influences can be controlled for by incorporating a regional category variable.

It‘s interesting to see that NOI‘s and EV‘s do not exactly have the same hierarchy but are generally

spread the same from high to low. This enforces the notion that submarkets with higher NOI‘s have

higher EV‘s but also that EV‘s are not entirely dependent on NOI‘s. Numbers 1 and 2 are the only

regions to outperform the sample means in both NOI‘s and EV‘s this is also due to the fact that most

observations are in these two categories. This could indicate that the sample is predominantly spread

over better performing submarkets. This observation is verified if the regression proves these

categories to be significant with positive estimates. Then the effect is singled out and proven to be of

influence alongside timing, fund management and asset specific criteria.

EVsqfLFA

RegionA Mean N Std. Dev


2 Mideast 522,972129 57 190,2642345

6 Mountain 184,088647 13 79,1091918

1 Northeast 527,096646 34 219,3687941

3 Pacif ic 375,431248 93 174,9435082

7 Southeast 151,789046 2 1,8397390

5 Southw est 254,280149 7 37,4863024

Total 418,884548 219 201,7396262

NOIsqfLFA

RegionA Mean N Std. Dev


1 Mideast 28,919070 54 11,4298400

5 Mountain 13,206950 8 2,7532617

2 Northeast 28,440214 30 14,0220202

4 Pacif ic 18,877453 79 10,3350945

7 Southeast 11,199884 1 .

6 Southw est 12,152619 6 6,4818203

Total 22,796329 188 12,0653847


77

Next to the regions we control for meso economic

influences by categorizing the assets on their positioning in

either a gateway or non-gateway city. The table shows us

that EV‘s and NOI‘s per square foot are far higher in

gateway cities as non-gateway cities. This singles out the

effects we noticed in the regions above.


The asset specific location criteria for offices that is incorporated into the model is the latent variable

Google walk score which measures the length of walking routes to important destinations such as

CBD‘s, grocery stores, cafes, parks, restaurants, and other retail . Below we can see that the different

funds have different walk score means. This gives a rough indication of the average locational quality

of each fund. The histogram shows us that most assets have a walk score of around 90 with a mean

of 74. This shows us that the walk scores of the office asset are generally higher as retail. This means

that the locational qualities of offices are better than those of the retail sample.

There are more categorical variables analyzed for the office assets as for the retail assets. This is due to the fact that the office sample had enough observations for the LEED and Energy Star categories.

The office sample also had observations for 2 class categories. For each of these variables the

frequencies are reported to show the distribution of the sample over the different categories

incorporated into the statistical analysis.




GatewayCity NOIsqfLFA EVsqfLFA

No Mean 19,506 345,452

N 99 122

Std. Deviation 10,982 165,259

Yes Mean 26,456 511,241

N 89 97


Report

GoogleWalk

Fund Mean Minimum Maximum N Std. Deviation

H 67,11 23 98 36 21,271

F 55,32 22 100 88 24,016

G 75,83 48 100 24 21,849

A 92,32 65 100 88 8,525

B 75,92 35 98 48 23,849

E 79,88 51 97 32 14,018

Total 74,14 22 100 316 23,868

OfficeClass

Freq V. %

Valid Class A 160 81,6

Class B 36 18,4

Total 196 100,0

OfficeType

Freq V. %

Valid CBD 92 51,1

Suburban 88 48,9

Total 180 100,0

Energy star

Freq V.%

Valid No 128 45,1

Yes 156 54,9

Total 284 100,0


N Minimum Maximum Mean Std. Dev.

GoogleWalk 316 22 100 74,14 23,868

LFA 312 13400,0 1152953,0 241568,93 219725,22

Tenants_LFA 276 0,000002 0,000522 ,0001 ,00009

LastUpdateAge 312 ,00 39,00 13,2628 10,22487


LEEDBinary

Freq V.%

Valid No 88 27,8

Yes 104 32,9

Total 192 60,8

Figure 11 - Google Walk histogram


78

4.2.2 Exploratory analysis for office models

Graph 18- Year


Year Where the retail shows a steady increase in NOI‘s, the offices show a decrease in 2013. The amount of observations also increase per year. The final model will show us if the timing is significant or other

variables cause this decrease in NOI‘s Fund

For the differences in the obtained rents we clearly see that the NOI‘s from fund A on average outperform the others. When we study the separate office assets present in fund A we initially see these are abundantly situated in expensive rental markets. This could be one of the many reasons

why fund A has higher NOIs on average as the other funds. By controlling for many of these aspects this analysis will try to single out the effect a different fund management has on fund performance.

Graph 20 - Region A and Region B categories


Region The scatterplot for the 8 and 4 dimensional NCREIF regions show some similarities with retail and

some differences. The Northeast region seems to also have high NOI‘s compared to other regions.

However the Mideast has high NOI‘s in comparison to other office regions and the Mideast retail

region has lower NOI‘s than other retail regions. For the region B variable we clearly see the East

based assets have higher NOI‘s than the other 3 regions. An interesting thing to note is the

correlation between the location related variables such as Google Walk, Transit, Region and Distance

to CBD. By checking the significance levels, correlations, and AIC one must be careful in adding too

Graph 19 - Fund



79

much or too strongly correlation location variables. The final variables and motivations are provided in

the final table of this paragraph

Graph 21 - Size


Size

In the scatterplot there is no clear relationship between the size of an office asset and its NOI. The log transformation of the variable improved model significance during the stepwise adding of the variable.

Stories For the amount of stories an office has the NOI‘s seem to be lower in the 1-5 story regions and the 20+ regions than the 5-20 regions. The dataset for retail mostly contained assets with one or two

floors showing us that office assets are generally higher than retail assets. Theory suggests that extra floors should increase NOI‘s per sqf but increased size should decrease NOI‘s per sqf. The evidence for these tipping points is hard to prove since the number of floors strongly correlates with size.

Google Walk Score and Google Transit Score The Google walk and Google transit score show an increase in NOI‘s when their scores rise. The amount of observations seemed to be larger for walk scores. Since there is a too high

multicollinearity to include both variables we chose walk scores due to their higher R 2 and improved AIC of the model during the stepwise entry of both variables and model comparison process.

Graph 22- Stories


Graph 23 - GoogleTransit


Graph 24 - Google Walk



80

Parking ratio

The amount of parking spots per LFA shows no apparent relationship with the NOI‘s. This could be due to the inclusion of assets with no or few parking spots with other price enhancing aspects. This

could be the reason as to why the scatterplot show no higher NOI‘s for properties with higher parking ratios.

Number of tenants The amount of tenants a single office asset has seems to have an influence on the NOI‘s with lower amounts of tenants giving higher NOI‘s. After 5+ tenants the NOI‘s seem to drop. This could be due to

the operational expense increases multi-tenant buildings have.

Distance to CBD This variable looks purely at the geographical distance of the asset from the CBD. The theory has

shown us that the closer by an office is to the central business district the higher the NOI‘s should be. The observations show a decrease in the NOI‘s the farther an asset is from the CBD. However there is only a limited amount of observations and this variable causes too much multicolinearity.

Age This variable is the same as that for retail; a combination between the building age and the last

renovation year. We see no clear relationship between the last update age of the assets and the NOI‘s. According to paragraph 2.4, NOI‘s should lower with higher ages.

Graph 25 - Parking


Graph 26 - Number of tenants


Graph 27 - Distance to CBD


Graph 28 - Last Update Age



81

Office Class and Type

The office style classification done by the INREV and NCREIF are done according to A, B and C‘s ranging from highest to lowest. They also state that an office is either a CBD or Suburban type office. Since there weren‘t enough observations in the combined measure, these were split into two

variables. We see that CBD type assets have higher NOI‘s than suburban type assets. For Office class we see that class A has on average a higher NOI than Class B/C

LEED

The literature states that LEED certified properties should have higher NOI‘s. Instead we see the opposite for the scatter plots. The properties with no LEED certification seem to have the highest NOI‘s, followed by platinum, gold, silver, pending and certified. The observations for the individual

types of LEED certification were below the required 5% per category. Therefore the variable is ultimately transformed into a binary variable distinguishing LEED buildings from non-LEED.

Energy Star The energy star rating is a US based energy rating system which assets can participate in. The ratings of each asset differs but a distinction has been made between the ones that did and did not have a

rating. For the properties that did have a rating we see higher NOI‘s on average as the ones without. This is in line with the theory which states that sustainability has a price premium.

Graph 29 - Office Class


Graph 30 - Office Type


Graph 31 - LEED


Graph 32 - Energy Star



82

Gateway City As explained in retail we see for offices that being

located in a gateway city has an increasing effect on the NOI‘s of office properties

This variable however strongly correlates with region A, B, Google Walk and Transit and increases the AIC significantly. The variable is

therefore omitted from the final models.

Transformed or Omitted variables and final variables for final models

The motivation behind a transformation or omittment is the improvement of AIC‘s of final models, overall significance of variables, rules of thumb and correlation analyses. The correlation analysis can be found in APPENDIX IV. The final motivations and variables are displayed in the table below. The

model input variables will be used in the final regression model for NOI‘s and EV‘s. When omitting variables from the office models we looked at how as much as possible variables with

the most observations and explanatory aspects could be maintained.



Fund In final model Fund

Region In final model, as region A RegionA

Size In final model, as LN_LFA due to Normal Distribution LN_LFA

Stories Omitted, correlates highly with size. -

Google Transit Omitted, MC (0,8) with Gatewaycity and GoogleWalk -

Google Walk In final model, most observations vs. MC variables Google Walk

Parking Omitted, MC (0,8) with Gatewaycity and GoogleWalk -

NumberTenants Transformed as ratio between size and tenants Tenants_LFA

DistanceCBD Omitted, high MC with Parking, Walk and transit. -

Age In final model as latent variable with renovation LastUpdateAge

Office class Transformed, B and C combined. Binary variable Office class

Office Type In final model Office Type

LEED Transformed, not enough freq so transformed yes/no LEEDBinary

Energy Star In final model EnergyStar

GatewayCity Omitted, high MC with GoogleTrans and GoogleWalk -




83

4.2.3 Final models for Office assets.

Net Operating income model outcomes:

In the final model for the NOl, Type and Size proved to have a significant influence based on 95% confidence interval, Region, Age and Fund proved to be significant within a 90% confidence interval.

The table of fixed effects can be found below the model outcomes. For the office NOI‘s, the 4 macro timing variable categories were not significant and all have very small

estimates of around 2% which is close to annual inflation. The NOI model for this sample over these 4 years then shows that ASC, meso and fund variables are of greater influence on the price forming of NOI‘s than timing.

Meso variables:

Region The categorical regional control variable was significant for 3 of the 8 regions which shows the effects a certain region can have on the NOI‘s is significant for the Office model. The NorthEast and

EastNorthCentral regions are significant and outperform the reference group SouthW est by 103% and 83% higher NOI‘s. This would indicate that the region NorthEast containing cities such as Boston and New York would have NOI‘s twice as high as the SouthWest region containing states such as

Arkansas and Louisiana. This is a logical finding based on the pricing premium of these cities and is in line with the NOI‘s in the cities for the contained cities within the region.

Micro / Asset specific variables: Size

For the NOI‘s of the office assets we see that the LN_LFA variable (Size) is significant. For each 1% increase in LFA size the rents drop by ,29%. This is possible according to paragraph 2.4 which states that larger spaces can result in a lower sqf price due to economies of scale.

Age The age of the properties is determined by the combined variable lastupdateage. The results show

that this measure is significant and has a positive effect on the NOI‘s. This goes against the depreciating effect of age stated in paragraph 2.4. This could be due to the effect that buildings with older age are more appealing to certain tenants and/or the fact that many of these older buildings are

located in cities with extreme price premiums such as NYC and Washington which cannot be completely controlled for by region and walk scores.

OfficeType This variable contains two categories; the reference group Suburban Offices and CBD offices. We see that the suburban offices perform about -16% worse than the CBD‘s. This effect was also described in

paragraph 2.4 through the bid rent curve of CBD‘s which result in higher NOI‘s the closer an asset is to a CBD.

Fund variable: Fund

The only fund which is significant in relation to the reference fund B is H. This fund seems to have around -53% lower NOI‘s on average as the reference fund. If we look at the scatterplot for fund we see that these were initially the funds with the lowest NOI‘s and that H was clearly lower as the other

funds. This is an interesting finding due to the fact that controlling for all other aspects still cause that the assets in that specific fund underperform in regards to the reference fund and all other funds. This might be reason to believe that the office assets in the H fund have higher and/ or more NOI

decreasing influences as other funds.


84

Estimated Value model outcomes:

It‘s interesting to see that the results from the EV model relate to different variables and give different estimates as the NOI model. This proves that there is a difference in the way these assets are valued

and price forming of NOI‘s takes place. Macro variables:

Year For the year variable we see that each year the EV‘s increase, confirming the notion that the macro-

economic influence is positively affecting values and the market is recovering from 2010 onward till 2013. The estimates are -27% (2010), -12% (2011) and -6% (2012) in relation to the reference variable 2013. The drop of EV‘s from 2012 on to 2013 witnessed in the descriptives and exploratory

paragraphs seemed to be caused by other variables in the model. Meso variables:

Region For the EV‘s the region variable also shows a significant outcome. The estimate which is significant

shows that the mountain states, which have no gateway cities nor cities with high NOI‘s such as NY or Boston show EV‘s lower by -63% than the SouthWest reference region. The same regions as NOI perform better than the reference region but are insignificant and therefore not considered valid

results. Micro / Asset specific variables:

Google Walk The latent variable walk score, measuring amenities in the vicinity and their proximity in walkable

distances shows that it is of significant influence on the Estimated Values of office properties in the final model. According to Pivo this is in line with the walkability premium he describes for offices. For each point the Walk Score increases, the Estimated Value of an office asset in the sample increases

by 0.7%. LEED Binary

Obtaining a LEED certification validates the quality of a property in terms of sustainabi lity this has an increasing effect on value according to the theory in chapter 2.4. For the Estimated Values of the offices in the sample a LEED certification increases the EV‘s by 22%.

Office Class There we too little observations for the C class offices. The C class offices were combined with the B

class offices to form a class which is lower as A and compared to class A. There is a hierarchical relation from A lowering to C in terms of object quality. The Office Class variable is considered a latent variable created by the NCREIF for the differences between tenant qualities and property maintenance

thus building quality. The reference group B/ C and A show an increase of 65% for the A class. This latent variable is therefore a strong influence on the average value per sqf of an office building.

In conclusion, the outcomes of all significant macro and meso variables will be used for answering sub question 3, the outcomes for all significant asset specific variables for sub question 4. A comparison between the NOI‘s and EV‘s is made in conclusion paragraph 4.4


85

Estimates of Fixed Effects for office EV model

Parameter Estimate Std. Error df t Sig. 95% Confidence Interv al

Lower Bound Upper

Intercept 4,558482 1,668133 45,327 2,733 ,009 1,199358 7,917607

[Year=2010] -,267974 ,035403 98,333 -7,569 ,000 -,338227 -,197720

[Year=2011] -,115601 ,025955 132,455 -4,454 ,000 -,166941 -,064261

[Year=2012] -,059297 ,016921 152,185 -3,504 ,001 -,092728 -,025865

[Year=2013] 0b 0 . . . . .

GoogleWalk ,006724 ,003933 45,117 1,710 ,094 -,001196 ,014645

LN_LFA -,051308 ,119909 45,251 -,428 ,671 -,292781 ,190164

LastUpdateAge ,000329 ,006639 47,190 ,050 ,961 -,013024 ,013683

[Fund=H] -,189298 ,261426 44,052 -,724 ,473 -,716151 ,337555

[Fund=F] ,872883 ,546491 45,865 1,597 ,117 -,227234 1,973000

[Fund=G] ,830118 ,527925 45,691 1,572 ,123 -,232733 1,892969

[Fund= A] 1,077713 ,561548 45,697 1,919 ,061 -,052827 2,208254

[Fund=B] 0b 0 . . . . .

[RegionA=East North Central] -,017891 ,357701 45,869 -,050 ,960 -,737961 ,702178

[RegionA=Mideast] ,105897 ,307522 45,862 ,344 ,732 -,513163 ,724957


[RegionA=Northeast] ,381201 ,322963 45,759 1,180 ,244 -,268982 1,031383

[RegionA=Pacific] ,011691 ,304018 45,722 ,038 ,969 -,600365 ,623748

[RegionA=Southeast] -,373786 ,603967 46,095 -,619 ,539 -1,589441 ,841868

[RegionA=Southwest] 0b 0 . . . . .

[OfficeType=CBD] ,231795 ,191111 44,363 1,213 ,232 -,153275 ,616865

[OfficeType=Suburban] 0b 0 . . . . .

[LEEDBinary=No] -,218063 ,158476 44,929 -1,376 ,002 -,537263 ,101138

[LEEDBinary=Yes] 0b 0 . . . . .

[Energystar=No] ,271010 ,207244 44,449 1,308 ,198 -,146543 ,688564

[Energystar=Yes] 0b 0 . . . . .

Tenants_LFA 317,4889 990,42281 44,195 ,321 ,750 -1678,3285 2313,3064

[OfficeClass=Class A] ,646368 ,187039 46,845 3,456 ,001 ,270061 1,022676

[OfficeClass=Class B] 0b 0 . . . . .



Estimates of Fixed Effects for office NOI model

Parameter Estimate Std. Error df t Sig. 95% Confidence Interv al

Lower Bound Upper Bound

Intercept 4,601816 2,155949 30,162 2,134 ,041 ,199772 9,003860

[Year=2010] ,028455 ,074520 134,827 ,382 ,703 -,118925 ,175835

[Year=2011] ,010840 ,058095 128,422 ,187 ,852 -,104108 ,125788

[Year=2012] ,020336 ,040903 108,444 ,497 ,620 -,060737 ,101410

[Year=2013] 0b 0 . . . . .

GoogleWalk ,000885 ,005073 29,603 ,174 ,863 -,009481 ,011252

LN_LFA -,291541 ,151541 29,690 -1,924 ,064 -,601164 ,018082

LastUpdateAge ,019066 ,008490 32,142 2,246 ,032 ,001775 ,036357

[Fund=H] -,526889 ,296540 28,059 -1,777 ,086 -1,134266 ,080489

[Fund=F] ,993536 ,634271 36,486 1,566 ,126 -,292230 2,279303

[Fund=G] ,325850 ,619425 34,837 ,526 ,602 -,931861 1,583560

[Fund=A] ,979964 ,635170 35,492 1,543 ,132 -,308860 2,268789

[Fund=B] 0b 0 . . . . .

[RegionA=East North Central] ,825733 ,485376 32,657 1,701 ,098 -,162165 1,813631

[RegionA=Mideast] ,674900 ,423411 32,885 1,594 ,121 -,186651 1,536451

[RegionA=Mountain] ,074234 ,526265 29,851 ,141 ,889 -1,000768 1,149236

[RegionA=Northeast] 1,030014 ,458792 31,806 2,245 ,032 ,095262 1,964765

[RegionA=Pacific] ,345645 ,422437 32,483 ,818 ,419 -,514328 1,205618

[RegionA=Southeast] ,476074 ,762173 34,888 ,625 ,536 -1,071396 2,023545


[OfficeType=CBD] ,155736 ,216025 28,276 ,721 ,030 -,286576 ,598049


[OfficeClass=Class A] ,466172 ,343160 28,463 1,358 ,185 -,236243 1,168588


[LEEDBinary=No] -,310461 ,197348 30,036 -1,573 ,126 -,713478 ,092556


[Energystar=No] ,163108 ,234469 29,104 ,696 ,492 -,316360 ,642576


Tenants_LFA -1242,985 1167,361 28,869 -1,065 ,296 -3630,977 1145,006

a. Dependent Variable: LN_NOIsqfLFA.



86

4.2.4 Statistical conclusions for Office Assets.

In the table below the estimates of all significant predictor variables and their corresponding

changes have been reported for the NOI and EV outcomes. These will go on to chapters 5 and

6 for answering the research questions and the recommendation.

Variable Y10 Y11 Y12 R-ENC R-Mtn R-NE GW Age Type Class Size Fund Fund LEED

Change +1 +1 CBD A +1% H A No

NOI - - - 83% - 103% - 1,9% 16% - -,29% -53% - -

EV 27% 12% 6% - -63% - 0,7% - - 65% - - 103% -22%

We can now conclude that office NOI‘s are affected by meso, micro and fund variables. The

interesting thing to note here is that the macro influence, year, was not significant and the region and

asset specific criteria were. This of course supports how important ASC are amongst macro analyses

for investment analysis. EV‘s pricing is influenced by macro, meso and micro level aspects. The final

hedonic pricing equations have been displayed below. These will be used in the office fund analysis

paragraph of the investment tool in the recommendation (Ch.6) to calculate differences for average

fund NOI‘s and EV‘s on the basis of the significant variables weighted averages.

∑

∑

∑

∑

Final hedonic pricing models for NOI and EV

0+ micro+ meso+ fund+ (N=Micro T= Macro R = Meso F=Fund)

micro= classA+ typeCBD+ size+ LastUpdateAge

meso= eastnc+ northeast f und= H

0+ macro+ micro+ meso+ (N=Micro T= Macro R = Meso F=Fund)


micro= Googlewalk+ classA+ NoLEED

meso= mountain f und= A

The relative influences of the variables have been calculated through use of the standardized Z-values

of the variables in the model. Their influence on the outcome variable in high to low form is displayed

in the table below. The macro variable time is displayed to show the influence of the macro time

category over 4 years but can‘t be used as a selection criterion for fund analysis. The point was to

control for its influence

The following variables can be seen as the influential asset specific criteria found on the basis of the

hedonic pricing studies.

Macro: Year,

Meso: Region,

Micro / Asset specific: Google Walk, Size, Office Type, Office Class, LEED , Age

Fund

Walk score effect on EV per sqf LFA


1 Region NE Meso Fund Fund

2 Region ENC Meso Class A ASC

3 Type CBD ASC Region Mtn Meso

4 Fund Fund Year 2010 Macro

5 ZLN_LFA ASC LEED ASC

6 ZLastUpdateAge ASC ZGooglew alk ASC



0

100

200

300

400

500

600

700

10 20 30 40 50 60 70 80 90 100

EV p

er

LFA


87

4.3 Industrial Assets

This paragraph entails the statistical analysis of the Industrial assets. The method in

which the analyses have been performed are similar to that of the retail and office

paragraphs. The model is built stepwise for the NOI and then used for the other

dependent variables. The model contains 892 observations for 230 industrial assets.

4.3.1 Descriptive statistics for industrial models

By putting the mean NOI and EV per sqf of each consecutive year in a bar graph we can see the

differences between the NOI‘s and the EV‘s The NOI‘s seem to fluctuate around a mean of 5 dollar

per sqf with a steady rise from 2011 till 2013. The EV‘s increase each year from 2010 till 2013. The

interesting thing to note is that the EV‘s do not reflect the change in NOI‘s In 2010. This enforces the

notion that valuations are dependent on more than just income approaches.

Graph 34 - Descriptives NOI and EV


The tables below shows us the mean NOI and EV per square foot LFA for 6 of the 8 different regions

that are used in the regressions. This is to give an idea about the possible pricing differences in each region, supporting the notion that meso economic influences can be controlled for by incorporating a regional category variable. It‘s interesting to see that NOI‘s and EV‘s have about the same hierarchy

from high to low. This enforces the notion that submarkets with higher NOI‘s have higher EV‘s and that EV‘s are dependent on the NOI. Regions 1 and 2 seem to outperform the sample means. These are the regions with cities with high price premiums such as NY, LA, SF.

Report

NOI_LFA



3 Mideast 4,774 27 1,8012

1 Northeast 6,462 68 4,5625

2 Pacif ic 6,132 312 6,5289

5 Southeast 4,065 80 2,9256

4 Southw est 4,438 60 3,0300

Total 5,232 684 5,0242

Report

EV_LFA



3 Mideast 80,764 28 29,7110

2 Northeast 102,761 69 72,3351

1 Pacif ic 105,732 315 73,2514

5 Southeast 66,893 85 26,7497

4 Southw est 76,939 61 31,3574

Total 87,838 700 59,1645


88

Next to the regions we control for meso economic influences by categorizing the assets on their positioning in either a gateway

or non-gateway city. The table shows us that average EV‘s and NOI‘s per square foot are far higher in gateway cities as non-gateway cities. This singles out the effects we noticed in the

regions above. For all three sectors the NOI‘s and EV‘s seem to be higher in gateway cities.


The asset specific location criteria for industrials that is incorporated into the model is the latent

variable Google transit score which measures how well a location is served by public transit routes.

This includes all kinds of transport except airports. Below we can see that the different funds have

different transit score means. This gives a rough indication of the average locational quality of each

fund. The histogram shows that most assets have transit scores of around 0 and 40 with a mean of 34.

The industrial sample had a lot more type categories as the retail and office sample. However this

caused for too little observations to compare general warehousing to. For the industrial warehousing a

research has been done to observe if an asset was located within a mile of an airport. This was

important since transit scores do not entail airports and there is a pricing premium if an industrial asset

is close to an airport. A nice find is that the amount of air cargo facilities only made up 3.1% of the

sample whereas the one mile Airport yes category made up 34,1%.





N Minimum Maximum Mean Std. Deviation Variance

GoogleTrans 252 0 84 30,57 18,564 344,628

LFA 892 13234,0 2153632,0 347061,404 332311,5284 110430951880,169

LastUpdateAge 888 ,00 95,00 19,5270 14,46994 209,379


Report

Gatew ayCity NOI_LFA EV_LFA

No Mean 5,123 87,551

N 609 626


Yes Mean 5,997 89,832

N 87 90


Report

GoogleTransit

Fund Mean Minimum Maximum N Std. Deviation

H 23,00 1 47 12 19,670

F 27,50 1 43 16 16,757

B 26,75 16 32 16 6,588

D 31,40 0 84 200 19,528

E 35,00 31 39 8 4,276

Total 30,57 0 84 252 18,564

IndustrialType

Frequency Valid Percent

Valid None 56 6,1

Air Cargo Facility 28 3,1

Flex / R&D 32 3,5

Land 40 4,4

Light Industrial 20 2,2

Office/Showroom (S) 8 ,9

Truck Terminal 12 1,3

Warehouse (W) 720 78,6

Total 916 100,0

Airport

Frequency Valid Percent

Valid 8 ,9

NO 596 65,1

YES 312 34,1

Total 916 100,0

Figure 12 - Google Transit histogram


89

4.3.2 Exploratory analysis for industrial models

Year In 2011 and 2013 rent was visibly lower than in 2010 and 2013. This is not supported by

macroeconomic events. A possible explanation is that the properties in the portfolio in 2011 and 2013

preformed below the benchmark.

Region A

The northeast region has the highest net operating income, while the southwest and

The east north central regions have the lowest. A possible explanation could be that most airport

related properties are locates in these regions.

Region B

Net operating income is highest in east and lowest in Midwest and south.

The different means are displayed for the 8 dimension region variable showing that the means can

differ for around 40%. This is to numerically display each variable. If there is a doubt about the means

or frequencies of the models these tables were made.

Graph 35 - Year


LN_NOI_LFA

RegionA Mean N Std. Dev.

,9827 11 ,53527

East North Central 1,2939 133 ,50178

Mideast 1,4940 27 ,38842

Northeast 1,7168 66 ,66199

Pacific 1,5893 296 ,74914

Southeast 1,3036 76 ,61559

Southwest 1,2468 59 ,84174

Total 1,4665 668 ,69654

Graph 36 - Region A


Graph 37 - Region B



90

State/City As the scale level get smaller, the difference between regions become more apparent. NY has a much

higher net operating income than the other states. NY is a very large contributor to the regions above, ―East‖ and ―Northeast‖ which is why their net operating income is higher than of the other regions. When the states are split up into cities the difference becomes even more substantial.

Parkingspots_LFA

The points are clustered as to resemble a rising straight line with a positive slope. This means that an increase in parking spots increases the net operating income. This is a logical effect in regards to the

hypothesized effect in paragraph 2.4 Gateway City

The scatterplot shows us that industrial properties in gateway cities tend to have a higher net operating income. This is logical since economic activity in gateway cities is larger than non-gateway

cities, driving demand for industrial space.

Graph 38 - State


Graph 39 - City


Graph 40 - Parkingspots_LFA





91

Google Walk and Google Transit There is no way of determining a relationship between Google Walk score and net operating income

from these points. There is no evidence of a straight line. The transit scatterplot shows a positive relationship between Google Transit score and net operating income. This is a logical effect since industrial space is more dependent on transport oriented

locations and not walkable amenities.

Alternative Transport nearby (5 miles)

From this scatterplot we can conclude that proximity to an airport or port increases net operating income more than proximity to a railway, a railway and an airport or to no transport station at all. In

their paper, Lockwood and Rutherford found that nearby transportation has a positive effect on property value. They found that the effect of a nearby airport was much larger than the effect of a nearby rail siding, which is confirmed by the scatterplot. If the property is close to both an airport and a

railway station, it is usually less close to an airport compared to the properties that have no railway station nearby. This decreases the effect on net operating income.

Fund The fund D has assets with higher net operating incomes, while E has the smallest. This may be due to the fund‘s management.

Graph 42 - Google Walk


Graph 43 - Google Transit


Graph 44 - Alternative Transport nearby


Graph 45 - Fund



92

Distance to CBD The pattern is neither rising nor falling. There is no evidence of a relationship. There was also a limited

number of observations and it seems to correlate strongly with other location variables. The variable is omitted due to the lack of observations and

Size The points are clustered as to resemble a falling straight line with a negative slope. This means that smaller spaces tend to have a higher net operating income. An increase in square feet in industrial

properties does not lead to an increase in the price per square feet.

Age There is no clear relationship visible between the age of a property and the net operating income.

Industrial Type

A truck terminal has a much higher net operating income than the rest. The other variables also seem

to have different average NOI‘s. Since the types provided by the funds differ from the scientific

literature it is hard to hypothesize about which properties provide higher NOI‘s. However airport

oriented properties should have higher NOI‘s due to their land value. This can also be seen in the

scatterplot

Graph 46 - DistanceCBD


Graph 47 - Size


Graph 48 - Age


Graph 49 - Industrial Type



93

Class Type

Class type seems to be higher for B Class type industrial properties. This is against common theory

which indicates that class B is hierarchal lower as A for NCREIF style classification. However there is no reliability to this variable due to the lack of observations in each category

Energy Star rating Properties with an energystar have a higher net operating income. This makes sense in ordinance with

paragraph 2.5, but the result is not credible due to the lack of observations. Transformed or Omitted variables and final variables for final models

The motivation behind a transformation or omittment is the improvement of AIC‘s of final models, overall significance of variables, rules of thumb and correlation analyses. The correlation analysis can

be found in APPENDIX IV. The final motivations and variables are displayed in the table below. The model input variables will be used in the final regression model for NOI‘s and EV‘s.



Region In final model as Region A due to AIC and Sig. RegionA

Parkingspots Omitted due to bad data (Combined cars and docks) -

GatewayCity In final model GatewayCity

GoogleWalk Omitted, MC with Google transit -

GoogleTransit Log transformed in final model for better Sig and AIC LNGoogleTransit

Alt. Transport Unclear effects, transformed to Airport binary Airport

Fund In final model Fund

Distance CBD Omitted due to MC with GatewayCity. One of two ^ -

Size In final model, transformed to LN_LFA due to NormDist LN_LFA

Age In final model, transformed to latent LastUpdateAge LastUpdateAge

Industrial Type Omitted, Lack of observations/ frequencies 5% per cat. -

Industrial Class Omitted, Lack of observations/ frequencies 5% per cat. -

EnergyStar Omitted, Lack of observations/ frequencies 5% per cat. -

Graph 50 - Industrial Class


Graph 51 - Energystar



94

4.3.3 Final models for Industrial assets.

Net Operating Income and Estimated Value model outcomes:

In the final model for the NOl, Year, Region and Airport proved to have a significant influence based on 95% confidence interval, Gateway city and Size proved to be significant within a 90% confidence

interval. The table of fixed effects can be found below the model outcomes. The significant variables for the EV model are the year and the Google Transit Score. Both are within

the 95% confidence interval. The airport categorical variable is significant within the 90% interval. Macro variables:

Year In 2010 the NOI was 36% lower compared to 2013. The other years also show an increase in NOI‘s

per square feet but weren‘t found significant. If we compare these results to the what the descriptives showed us we can conclude that the other price enhancing variables in the model have caused the higher NOI‘s in 2010.

For the EV model we see that with the variable year, the properties value increase each year. This is in line with the recovering trend of the US economy and is the same as the descriptives

Meso variables:

Region The categorical regional control variable was significant for 3 of the 8 regions which shows the effects a certain region can have on the NOI‘s is significant for the industrial model. NOI‘s in the northeast and

pacific regions are ceteres paribus 147% and 83% higher than in the southwest reference region. The gap between these regions is now almost twice as high as the descriptives, showing us that the sole effect is even larger as anticipated. The northeast and pacific regions apparently have the best pricing

premium for industrial properties in the US Gateway cities

If the industrial property is not located in a gateway city the effect on NOI is 48% lower compared to the same property in a gateway city. According to the theory, gateway cities function as an international and domestic gateway for products and thus have a higher demand for industrial

properties. This then is a logical and important variable to analyze Micro/ Asset specific variables:

Airport According to the model outcome, a property without an airport nearby is has a NOI that is 47% lower

than a property within a mile of an airport. Airports have a price premium on industrial properties due to the improved accessibility via airplane and higher land values in proximity to airports.

In the EV models, the estimate for assets with no airport within a mile is -22%. So both for NOI‘s and asset values, an airport close by plays an important role in the price forming process.

Size An increase in size of an industrial property is negatively related to NOI‘s. A 1% increase in size decreases the NOI‘s by 0,22%. An increase in size does therefore not lead to an increase in price for

industrial properties. This could be due to the effects Google transit score

For the EV the model shows us that if the score increase by 1%, the estimated value of a property increases by 0.15%. This is in line with what the theory suggest about increased accesibi lity causing a price premium for industrial real estate.


95

Estimates of Fixed Effects for Industrial NOI model

Parameter Estimate Std. Error df t Sig. 95% Confidence Interval

Low er Bound Upper Bound

Intercept 4,785896 1,799325 43,065 2,660 ,011 1,157369 8,414424

[Year=2010] -,362059 ,154367 56,500 -2,345 ,023 -,671232 -,052886

[Year=2011] -,149229 ,131766 101,864 -1,133 ,260 -,410590 ,112131

[Year=2012] -,056774 ,103249 113,870 -,550 ,583 -,261313 ,147764

[Year=2013] 0b 0 . . . . .

[RegionA= ] ,765314 ,754018 39,214 1,015 ,316 -,759566 2,290194


[RegionA=Pacif ic] ,832811 ,392218 48,470 2,123 ,039 ,044401 1,621220

[RegionA=Southeast] ,128849 ,377465 45,052 ,341 ,734 -,631380 ,889078


LNGoogleTrans -,046953 ,100222 46,076 -,468 ,642 -,248681 ,154774

[Airport=NO] -,471980 ,203610 43,090 -2,318 ,025 -,882575 -,061385

[Airport=YES] 0b 0 . . . . .

[Fund=H] -,297764 ,666121 48,953 -,447 ,657 -1,636416 1,040889

[Fund=F] -,399010 ,664134 57,707 -,601 ,550 -1,728561 ,930542

[Fund=B] -,133757 ,703881 41,087 -,190 ,850 -1,555182 1,287669

[Fund=D] -,097828 ,548771 44,637 -,178 ,859 -1,203358 1,007703

[Fund=E] 0b 0 . . . . .

LN_LFA -,219419 ,113513 42,769 -1,933 ,060 -,448376 ,009537

LNLastUpdateAge -,087245 ,137788 45,310 -,633 ,530 -,364713 ,190222

[Gatew ayCity=No] -,482370 ,259899 40,258 -1,856 ,071 -1,007541 ,042800




Estimates of Fixed Effects for Industrial EV model



Intercept 5,619503 1,112489 47,122 5,051 ,000 3,381616 7,857391

[Year=2010] -,210815 ,035579 175,010 -5,925 ,000 -,281034 -,140596

[Year=2011] -,158425 ,027232 165,666 -5,818 ,000 -,212191 -,104659

[Year=2012] -,071849 ,018251 147,134 -3,937 ,000 -,107916 -,035782

[Year=2013] 0b 0 . . . . .

[RegionA= ] -,103285 ,482193 45,859 -,214 ,831 -1,073970 ,867400

[RegionA=Northeast] ,367974 ,301322 48,137 1,221 ,228 -,237830 ,973778

[RegionA=Pacif ic] ,001622 ,223962 49,149 ,007 ,994 -,448412 ,451656

[RegionA=Southeast] -,192431 ,229963 48,250 -,837 ,407 -,654742 ,269879


LNGoogleTrans ,143554 ,056968 48,184 2,520 ,015 ,029023 ,258084

[Airport=NO] -,219608 ,127298 46,543 -1,725 ,091 -,475766 ,036549

[Airport=YES] 0b 0 . . . . .

[Fund=H] -,176986 ,402592 46,707 -,440 ,662 -,987030 ,633059

[Fund=F] ,077608 ,386009 47,812 ,201 ,842 -,698594 ,853809

[Fund=B] -,314366 ,448328 45,645 -,701 ,487 -1,216993 ,588261

[Fund=D] -,103614 ,338920 46,356 -,306 ,761 -,785684 ,578455

[Fund=E] 0b 0 . . . . .

LN_LFA -,089619 ,070831 46,599 -1,265 ,212 -,232145 ,052907


[Gatew ayCity=No] ,046404 ,164741 46,171 ,282 ,779 -,285169 ,377977


a. Dependent Variable: LN_EV_LFA.



96

4.3.4 Statistical conclusions for Industrial Assets.

In the table below the estimates of all significant predictor variables and their

corresponding changes have been reported for the NOI and EV outcomes. These will go

on to chapters 5 and 6 for answering the research questions and the recommendation.

Variable Y-10 Y-11 Y-12 Reg-Pac Reg-NE GT Airport Size GC

Change +1% No +1% No

NOI -36% - - 83% 146% - -47% -0,22% -47%

EV 21% 16% 7% - - 0,14% -22% - -

We can now conclude that Industrial NOI‘s are affected by macro, meso and micro and variables. The

interesting thing to note here is that the fund influence was not significant and the time, region and

asset specific criteria were. This shows that not every sector in the regressions has significant

differences per fund management on the NOI‘s and EV‘s, EV‘s pricing is influenced by macro and

micro level aspects. It is interesting to see that regions weren‘t significant, showing that values are

more subject to changes in the other variables. The final hedonic pricing equations have been

displayed below. These will be used in the industrial fund analysis paragraph of the investment tool in

the recommendation (Ch.6) to calculate differences for average fund NOI‘s and EV‘s on the basis of

the significant variables weighted averages.

∑

∑

∑

∑

Final hedonic pricing models for NOI and EV 0+ macro+ micro+ meso+ (N=Micro T= Macro R = Meso F=Fund)

macro= year2010 micro= size+ AirportNO

meso= mountain+ northeast+ gateway 0+ macro+ micro+ (N=Micro T= Macro R = Meso F=Fund)


micro= Googletrans The relative influences of the variables have been calculated through use of the standardized Z-values

of the variables in the model. Their influence on the outcome variable in high to low form is displayed in the table below. The macro variable time is displayed to show the influence of the macro time category over 4 years but can‘t be used as a selection criterion for fund analysis. The point was to

control for its influence.

The following variables can be seen as the influential asset specific criteria found on the basis of the hedonic

pricing studies:

Macro: Year,

Meso: Region and Gateway City,

Micro / Asset specific, GoogleTransit, Airport property, Size, Age

ransit score effect on EV per sqf LFA


1 Region NE Meso Airport ASC

2 Region Pacif ic Meso Year 2010 Macro

3 Gatew ay city Meso Transit score ASC

4 Airport ASC Year 2011 Macro

5 Year Macro Year 2012 Macro

6 Size ASC

0

20

40

60

80

100

120

140

EV

pe

r sq

f L

FA


97

5. Conclusion

5.1 Introduction

First of all the most important findings of the research will be presented. This will be done by

answering the sub questions. This will lead to the central research question being answered. It will be

determined if analyzing indirect real estate investments with added underlying asset specific criteria

will improve investment methodology. Finally some shortcomings are elaborated on.

5.2 Answering the research questions


To answer this question the criteria chosen to focus on are the risks and returns. These are the two

most important factors of criteria that are influenced by international private fund investments. The

types of returns, return measures, investment types and risks were discussed to come to the

conclusion. It is important to state that the focus points of this research are reducing the risks of

international private fund investments, providing knowledge and finally an investment tool which goal

is to assist in increasing returns on indirect funds.

It became clear that NOI and EV are underlying asset based performance figures which are of great

influence on the fund and asset returns. We can conclude that both these financial figures are

comparable on asset level and do not contain as much distorting noise. The NOI and EV were

therefore used as financial performance measures. The underlying assets averages per square

footage were used as the dependent variables for the statistical analysis in chapter 4.

Another conclusion taken from this paragraph is that most of the relevant risks for international private

real estate fund investments are micro level based risks. Country selection is clearly a key determinant

of performance. All of the risks national market selection brings are important factors for minimizing

the political and economic risks associated with international investing. A second major risk is lack of

transparency and includes the lack of data on performance, lack of data on investments and lack of

property rights. Each of these is an important area for future research (Elaine Worzala and C.F.

Sirmans,2002). As for the conclusion on investment type it can be stated that risks and returns of each

different type of investment must be evaluated and weighted carefully in order to make the right

investment decision. As a result, an investor in an international real estate fund must first

decide in which type of fund it has to invest in order to reach its desired goals.

2. How do the relationships between stakeholders affect investor criteria? To answer this question the way investors and fund managers manage indirect real estate fund

investments and how these stakeholders have an influence on each other has been discuss ed. There

are three main stakeholders for international private fund investments which are relevant to the

outcomes of the research and investor criteria: the investors, the fund managers and the users of the

asset. Firstly it is interesting to conclude that the different stakeholder groups each earn a profit in a

different manner and can have positive or negative influences on investor criteria. This can sometimes

lead to a conflict of interest between stakeholders.

The purpose of an investor such as a pension fund is to invest pension holder capital into the real

estate and ensure maximum profitability. In return for this they earn a salary. For private investors their

earnings are dependent on their returns. The purpose of a real estate investment fund in the eyes of a

pension fund is to invest investor capital into real estate assets which produce income and provide a


98

hedge against inflation. Here the first conflict can be recognized through the manner in which fund

managers obtain their income differs from that of pension funds.

Fund managers charge several types of fees, such as performance and management fees, for the

work they do. These fees directly influence the returns made by investors. So, if a fund manager is

determined to obtain higher profits he might invest in properties that do not necessarily benefit the

pension fund but allows the manager to charge a higher fee. It is therefore important to distinguish the

profitable relationships.

A conflict of interest can also be present for the tenants. The users of each building in the fund are

benefited by the amount of money that is invested by the fund managers in maintenance and

improvement of their buildings. Fund managers might not be so keen on doing so due to the fact these

costs decrease the performance of an asset in terms of financial performance. Experienced fund

managers know when to strategically time improvement investments when they are feasible and

needed in order to keep tenants satisfied and keen to prolong their rental agreement. However if this

isn‘t the case, fund management could ignore certain tenants wishes for improvement and run the risk

of vacancy.

Ultimately there were a few important conclusions made the investors should be aware of the needs of

the users and how well these needs are being satisfied by fund management and the assets in the

fund, when selecting a fund. Secondly the fee structure influences the returns. It is important to make

sure the management fees are not higher than the added value of a better portfolio. Finally it is clear

that the quality and structure of management is of influence on the NOI and estimated values. To

research the possible influence and control for the effect different fund management has on NOI‘s and

EV‘s the variable ‗fund‘ was added to the regression model. This was also relevant for each sector. So

‗fund‘ is neither a macro meso nor micro aspect but is definitely of influence. So this should also be

taken into consideration when performing an investment analysis. Some funds clearly outperform

others.

3. To which extent do macro and meso economic aspects influence commercial real

estate performance?

This question is answered by explaining and elaborating on the macro and meso economical features

concerning the international real estate investments so that the asset specific level can be understood

in relation to these scale levels. When conducting an investment analysis an investor looks at if the

proposed property or fund is priced attractively in relation to the current and anticipated macro and

meso-economic factors. An indication has to be made to which extent these factors influence

commercial real estate performance.

The influential macro and meso economic indicators are different per sector. Some indicators are

useful for all sectors but have different impacts. The most important conclusion for this research

question was not which aspects exactly influence macro or meso performance, but how these are

made measurable in comparison to the micro level. The macro level indicators such as GDP,

employment growth, building output etc. are all time related. This was ultimately controlled for by a

time variable. This was incorporated into the multi-level mixed linear model by means of measuring 4

consecutive years of real estate transactions.

The meso level indicators were all related to the specific region an asset is placed in. From this we can

conclude that these variables can be bundled up into two variables to be used in the regression

analysis. The first is ‗region‘ and the second is ‗Gateway City‘. The region a property is in influences

the value due to the differences in wages, unemployment, infrastructure etc. The second variable


99

takes a smaller area in consideration. It indicates whether the property is located in a gateway city or

not.

The effect macro and meso indicators have on real estate value are usually taken into consideration

when researching potential investments. The extent to which the different aspects influence the

financial performance are shown in paragraph 4.3. The most important conclusion for the research

was that; micro economic variables are not given the importance they deserve due to the difficult y of

measuring them and their compared relative influence, which is sometimes larger for the ASC.

Ultimately this research tries to identify if and determine how a three scale level approach could

possibly benefit an organization.

4. Which asset specific criteria can be used for underlying asset analysis and what is their

relative influence on the financial performance of commercial real estate?

The ability to use certain asset specific criteria (ASC) was dependent on a few factors throughout the

course of this research. The statistical analysis for the main research topic, which are the ASC, is based on the North American Portfolio of Syntrus Achmea which allowed us to analyze data for over 420 properties for the

Retail, Office and Industrial sectors divided over 9 core funds. The ASC have passed the following stages;

Scientific evidence The theoretical framework provided a wide array of asset specific influences which have proven to be

of influence according to the scientific research from paragraph 2.4.

Time related One of the goals of this research is to perform such an asset specific analysis in a relative short period

of time. Variables like building flexibility which require considerable amounts of time to obtain, report and analyze were therefore not incorporated into the research and final list of incorporated ASC.

Data limitations

Not all funds were willing to provide or collected the required data on property level. It was already interesting to see which funds management actually researched these criteria in relation to asset performance. For some ASC observations were lacking in certain categories to conduct statistical

research upon and obtain reliable results.

Statistical Analysis After the three former stages, the list of ASC was narrowed down. The results of the statistical analysis have shown us which ASC are significant for the specified dataset when analyzing underlying assets

on the basis of investment criteria through means of the NOI‘s and EV‘s. When it comes to the return figures the macro and meso criteria seem to be the only significant

variables. When we examine the NOI‘s and the EV‘s the asset specific criteria become significant. A possible explanation for this could be the added noise return figures have as explained in paragraph 2.1. Each fund can also have a different manner of calculating returns whereas NOI‘s are universal

and EV‘s have to meet certain common standards described in paragraph 2.1. The ASC which proved to be significant and influential on EV‘s and/or NOI‘s are displayed per sector:

Figure 13 - Final influential ASC on NOI and EV

Re

tail •Walk score

•Retail Type

•Size

•Tenant density

•Age

Off

ice

s •Walk score

•Office Type

•Office Class

•LEED Certification

•Age Ind

ust

rial

•Transit score

•Airport property

•Size


100

Analyzing portfolios on the basis of these criteria gives an indication of the quality of the underlying assets in regards to their financial performance considering the price for which shares were purchased

in that portfolio. For the entire overview of each statistical result and the estimates the tables in chapter 4 provide the exact estimates for each financial performance measure for each sector. This demonstrates their relative influence on financial performance.

Main research question to be answered

“How can asset specific analysis improve International Real Estate fund investment analysis?” The investment analysis of indirect funds can be improved by incorporating asset specific analysis as

part of the investment process. The improvement is made on the basis of institutional investor criteria by the following:

The asset specific analysis gives a better understanding of the determinants of financial performances of the underlying commercial assets in private real estate funds. This decreases the lack of knowledge/information risk of the investment.

Funds can be compared on the basis of the influential ASC provided by the research outcomes of chapter 4. This ultimately leads to an added selection criteria alongside the existing investment methodology. This is developed in the form of a tool in chapter 6.

The answer to the main research question was formed by a combination of the answers to the previous sub questions. Each sub question provided different outcomes to build an answer upon.

In paragraph 2.1 the different influences on investment criteria of institutional investors (risk and return) were examined. It became clear that Investment methodology is different for each type of fund. Paragraph 2.2 states that the type of fund is also an influential factor on returns regarding the fee

structure. The beneficial aspects micromanagement of the assets can have on the fund should also be examined. In regards to this fund was made a categorical variable for the statistical analysis. For the offices fund has shown to be an influential variable in both NOI and EV of offices. So forming a clear

analysis of the influences the fund has on the investment criteria should be an important aspect of the investment process. Controlling for fund was important to single out the effects ASC had. Paragraph 2.3 about the macro(time) and meso(region) level determinants showed us the different underlying

reasons on how they can affect asset performance and the importance that the identification of tactical investment opportunities in the private real estate market required the application of multiple levels of information not solely present in the macro or meso scale levels. Improvement of this tactical

investment identification process is ultimately done by proving that ASC adds to the level of knowledge. Paragraph 2.4 showed us the wide array of scientific literature already present discussing the micro or underlying asset specific influences.

The outcomes of the statistical research presented in chapter 4 showed us that the time variable; year (macro level aspects) proved to be significant for each dependent financial performance variable. The

present and future stage of the economical investment clock and four quadrant models must be carefully examined to determine in which stage of the real estate cycle a proposed investment is. Buying into funds right before 2008 would have been a bad idea in regards to post crisis discount

buying. A tactical investment opportunity from market recovery can then be obtained. The different regions have shown a different impact in each different sector for the different dependent

financial performance variables. In regards to this, geographical spread amongst submarkets needs to be examined. Determining which region is most profitable for each sector is a valuable lesson learned from the hedonic pricing models in 4.4. This concludes the supporting knowledge gained to answer

the main research question. The final step was the addition of asset specific criteria. This step is placed lastly in the sequence of

steps due to its scale level and time requirements. However even if the first three steps result in a negative view on the investment, there is still a possibility the investment should be made after all. Analyzing a proposed fund for each sector on the basis of the influential asset specific criteria found in

chapter 4, can give an indication of the quality of the underlying assets in regards to the financial


101

performance. If assets seem to satisfy the asset specific criteria this could result in a profitable investment. This is because these ASC have proven to be of positive influence for the NOI‘s and EV‘s

If assets seem to satisfy the asset specific criteria this could result in a profitable investment. This is because being aware of and evaluating ASC gives investors the ability to make better decisions and thus improve the NOI‘s and EV‘s. If the price of a share is less than it‘s estimated worth calculated by

the investment tool, the investor is able to invest with decreased information risk. So by using the investment tool investors can improve their risk-return ratio. This confirms the hypothesis: Analyzing indirect real estate investments with added underlying asset specific criteria will give better insight into

profits of a proposed investment.

6. Recommendation

6.1 Introduction

A thorough analysis of the underlying assets of a fund investment is needed to gain insights into their

performance influencing aspects. In this chapter the different influential variables from the statistical

outcomes of ―International Real Estate Investment Analysis: The use of asset specific criteria when

investing in non-listed funds‖ have been used to create an investment tool for non-listed funds. By

using the historical performances of 9 different non-listed international real estate funds of Syntrus

Achmea Real Estate & Finance (SAREF) over the past 4 years, relevant, obtainable and researchable

asset specific variables have been found which have proven to be of significant influence on the Net

Operating Incomes (NOI) and Estimated Values (EV) of commercial real estate assets.

The recommendation is divided into 6 different paragraphs. Paragraph 2 shows the methodological

framework of the tool and describes where in the investment process the tool can be used.

Paragraphs 3, 4 and 5 discuss the relevant variables for Retail, Office and Industrial assets, and

further explain how to translate their measurements into a comparable format by means of comparing

2 fictive funds. The paragraph then shows how to calculate their relative impact on NOI‘s and EV‘s.

This is done by means of a sized based weighting system to make each asset more easily comparable

in relation to the research it‘s pricing per Sqf. An investor might opt to use a capital weighted system to

account for the influence each single assets unique variables have on the overall portfolio

performance.

Paragraph 6 shows how to use the outcomes for each variable in order to determine which fund has

the assets with the optimal performance enhancing aspects according to the analysis.

All separate variables are part of a hedonic pricing function for the sector of which the variable is

relevant the final percentage influence is calculated by adding all separate influences for that sector.

The relevant variables for each sector are calculated differently for each sector. For each variable a

proper method and formula is given to calculate the funds (weighted) average or percentage for that

variable. The estimates for each variable originate from paragraph 4.4

The purpose of this tool is to give investment professionals who are generally a few scale levels away

from the assets a relative quick method of examining the underlying assets of a private real estate

fund. This gives them an idea of the NOI and EV influencing qualities of the assets. This can protect

an investor from buying into a fund with bad assets or aid an investor in choosing the fund with better

asset specific criteria.


102

6.2 Methodological framework

The private fund investment process of SAREF consists out of multiple steps. In the due diligence

phase (step 4), SAREF performs an analysis of the proposed funds in the shortlist on the basis of their

investment criteria. These criteria include macro-economic criteria, meso-economic criteria, policy

criteria, liquidity criteria etc. The ultimately chosen fund is the optimal mix of these criteria. The

addition of the asset specific criteria (ASC) analysis should therefore be placed in this step so that it

can be taken into account when comparing funds. The image below shows us the different scale levels

of the short list, the funds, the properties and the ASC.

Figure 14 - Scale levels betw een investment process and criteria (Own image)

A theoretical framework of the asset specific analysis is provided below. In this image the different

steps which have to be performed in order to determine the averages of each variable and their impact

in percentages on the NOI‘s and EV‘s have been explained. Their corresponding paragraphs have

been noted for textual and mathematical explanation of the step.

Figure 15 - Investment tool and investment process (Own Image)

Asset Specific Criteria

Underlying Assets

Eligible Funds

Due dilligence Short List

Office Fund A

Retail Fund A

Property 1

ASC ASC

Property 2

ASC ASC

Indstrial Fund A

Office Fund B

Retail Fund B

Property 1

ASC ASC

Property 2

ASC ASC

Indstrial Fund B


103

6.3 Analysis of retail assets

The significant variables that were of influence on the Net Operating Incomes (NOI) and Estimated

Values (EV) of retail assets were:

Variable Sig for Scale Explanation

Year NOI / EV Macro The year in which the transaction is measured for the assets

Region NOI / EV Meso The 8 NCREIF geographical regions the assets can be located in

Gateway city NOI / EV Meso If the asset is located in a gateway city, yes or no.

Google Walk NOI / EV Micro A latent variable measuring location qualities in a score from 1-100

Type NOI / EV Micro The NCREIF type classification of a retail object

Size NOI / EV Micro The size of an asset in Leasable Floor Area.

Age EV Micro The age of an asset since its construction or last renovation

Tenant Density EV Micro The amount of tenants per square foot LFA

In order to analyse a portfolio on the basis of these aspects a tool has to be made which can compare

funds on the basis of these aspects so that relevant results can be obtained for investment decisions.

Each influential variable will be discussed and explained on how to be made operational for analysis.

The estimates used in the calculations can be found in the appendix.

Macro-economic analysis of a proposed fund.

Year

The macroeconomic influence is measured by time. The time variable is transformed into a transaction

year variable. According to the 4 consecutive years studied in this research the transaction year

variable was of influence. The Strategy and Research department of SAREF determines if the timing

of the proposed investment is adequate based on their existing research methodologies.

Meso economic analysis of a proposed fund.

Region

The macroeconomic influence is measured by looking at the Region in which an asset is located.

SAREF already conducts analyses of the geographical dispersion of their proposed fund investments

and the meso economic influences of each corresponding region. This research used the 8 NCREIF

geographical regions and found that some regions showed significant differences in NOI s and EV´s.

In order to compare the geographical dispersion of funds based on the outcomes of this research the

percentage of total LFA´s in each geographical area need to be calculated.

Region NE ME ENC WNC SE SW Mountain Pacific

Fund A 15% 10% 5% 20% 7% 13% 10% 20% Fund B 10% 12% 3% 10% 10% 20% 10% 25% Example of geographical dispersion of funds table

If it is evident that a certain fund is spread over geographic regions

with better outlooks than the other fund the results can be used as

a decision making criteria for choosing which fund to invest in.

Gateway City Another meso economic influence which has shown to be of influence is the presence of an asset in a

gateway city or non-gateway city. The positive influence of being located in a gateway city can be

seen in both NOI s and EV´s for retail assets. The percentage of the gateway city LFA´s of the total

fund LFA´s has to be calculated. The fund with the highest % of LFA´s located in gateway cities is

relatively most likely to have higher NOI s and EV´s. This is of course only the case if the other

aspects were identical for both funds.

Gateway City Yes No

Fund A 30% 70% Fund B 10% 90%


104

Micro economic / asset specific analysis of a proposed fund.

Location - Google Walk Score The latent variable walk score gives us a quantified score from 1 till a 100 as to how high the quality of

a certain location is on asset level. Thus the average score of a fund represents the quality of the fund. In order to calculate the average scores for a fund one must then relate the separate scores to each individual assets size and then calculate a weighted score based on the total amount of LFA´s in the

fund. For example, Fund A and B both have 3 assets with different walk scores and different sizes :

To calculate the weighted average walk score mean for each fund we multiply the size of the asset by

the assets walk score. Then we divide the sum of all these (532500) by the total amount of space

(7500). We then get the Average walk score for fund A (71). Fund B had an average walk score of

61.33.

∑

∑

When using the abovementioned formula we can calculate and compare weighted average walk

scores for funds. Funds with higher weighted average walk scores contain assets which have better

locations and should have higher NOI s and EV´s. According to the estimate obtained for the NOI‘s

and EV‘s they should be 9.66 x 0.65% = 6,5% and 9.66 x 0.58% = 5,6% higher for fund A.

Building - Type The different types of real estate seem to have different NOI‘s and EV‘s. It‘s therefore important to

identify how much LFA‘s of each type there are in each fund. The types which were analysed are

mentioned below with an example of how the percentages of a portfolio can be divided:

Retail Type % LFA’s in Fund A % LFA’s in Fund B

A. (Super) regional malls 12% 8% B. Neighbourhood and community centres 43% 37% C. Power centres 22% 30% D. Other types 23% 25%

It is however important to note that not all possible types are included in the recommendation due to

limited or corrupted data or insignificance of some type in the analysis. A fund can therefore contain

more types of retail than the A-C types mentioned above. To demonstrate this, category D is added.

When looking at the NOI‘s and EV‘s of types of real estate there is a significant difference between

category A, malls and category C, power centres. Malls seemed to have higher NOI‘s and EV‘s than

power centres. This indicates that funds with higher percentages of category A and lower percentages

of category C should provide higher NOI‘s and EV‘s. For the EV‘s, category B can also be used and

showed us that this category had lower EV‘s as A and higher EV‘s as C. For category D, no statistical

evidence was produced but should be analysed separately to gain complete insights into the effects of

type on the entire fund.

Fund A Size in LFA Google Walk x

Asset 1 4000 65 260000 Asset 2 1500 75 112500 Asset 3 2000 80 160000

7500 532500

Fund Average Walk score 71

Fund B Size in LFA Google Walk x


7500 460000 Fund Average Walk score 61,33


105

Building - Size The size of an asset seems to influence the NOI‘s and EV‘s negatively, when gaining 10% in size

NOI‘s and EV‘s seem to drop by 3% and 1.6%, respectively. In order to analyse the sizes of the asset in different funds in relation to each other the average asset size has to be determined. This can be done as followed:

Fund A Size in LFA Fund B Size in LFA

Asset 1 6000 Asset 1 12000 Asset 2 6500 Asset 2 4000 Asset 3 10000 Av Asset Size Asset 3 7500 Av Asset Size

7500 7833

∑

When comparing the average asset sizes of each fund one must consider that the analysis is considered only to be relevant for the asset sizes in the research. However, the range was very large

due to the inclusion of small single tenant stores and super regional malls so this should not be a problem. But extremely small booths and extremely large outlet malls might be outside of the scope of the research.

The average fund size of Fund B seems to be 4% higher than that of fund A, this means that the NOI‘s and EV‘s of fund B should be lower by approximately 1,2% and 0,7%.

Building - Age The higher the numbers of years since the construction date or last renovation referred to as building

age, the lower the EV‘s are. In order to compare the funds we must then compare the average asset ages of the funds. This must however be related to the presence they have in the fund determined by their size. This is a similar procedure as that of the Google walk scores. An example of the calculation

per fund is shown below. Fund A and fund B both contain 3 assets with different ages and different sizes:

∑

∑

We see that the average age of assets in Fund A is higher than that of Fund B, indicating that on average all LFA‘s in fund B are newer. It is estimated that the EV drops by 1,2% per added year of age. Ceteres paribus this means that the EV‘s on average should be lower in fund A by about 1%.

Building - Tenant density The density of tenants per square foot was a measure used to research the effects of having more

tenants per floor area in a retail asset. This is particularly interesting to research the agglomerating effects of shopping centres. The statistical research has shown us that with the increase of the tenant density by 0.001 the estimated value would increase by 1.6%. In order to calculate the average tenant

density of a Fund we have to divide the amount of tenants in the fund by the LFA‘s in the fund. We can also calculate this by dividing the sum of all tenant densities from all the assets by the amount of assets in the fund. The mean tenant density for all assets in the statistical analysis was 0,0002 with a

standard deviation of 0,00015 a change of such will then influence EV‘s by only 0.24%. An example of such a calculation is shown below.

Fund A Size in LFA Age x


7500 40000

Fund Average Age 5,3

Fund B Size in LFA Age x


7500 34000

Fund Average Age 4,5


106

Fund A and fund B contain 3 assets with different amount of tenants and different sizes:

∑

∑

We see that fund A‘s tenant density is 0.0008 higher; this would then indicate an approximate increase in the average EV‘s of fund A by 1.3% compared to fund B.

6.4 Analysis of office assets

The significant variables that were of influence for the Net Operating Incomes (NOI) and Estimated

Values (EV) of Office assets were:

Variable Sig for Explanation

Year EV The year in which the transaction is measured for the assets

Region NOI / EV The 8 NCREIF geographical regions the assets can be located in

Google Walk EV A latent variable measuring location qualities in a score from 1-100

Type NOI The NCREIF type classification of an office asset

Class NOI / EV The NCREIF class type classification of an Office asset

LEED EV A binary variable checking if an asset has a LEED certification yes or no

Size NOI The size of an asset in Leasable Floor Area.

Age NOI The age of an asset since its construction or last renovation

The methods used for Year, Region, Google Walk, Type, Size and Age are similar as those of retail

assets. However the estimates for each variable are different and the ranges between the variables of

the funds are undoubtedly different than those of the retail assets. This means that when analyzing

office assets, the different variables might have larger or smaller impacts. For instance; an increase in

walk score for retail assets means a 0.58% increase in EV‘s, for offices this means a 0.67% increase.

The measurement techniques for the variables Office Class and LEED will be explained below:

Building – Office Class Offices have different classes according to the NCREIF. The A

class offices are considered to be qualitatively better offices than

the B and C class types. Due to a lack in observations the C class

was added to the B class to distinguish the best from the lesser assets.

In order to check the quality of the fund we have to calculate how much percent of the total amount of

LFA‘s are divided over the different classes.

The influence this has according to the estimates of NOI and EV are quite different. The NOI‘s seem to

be 16% higher and EV‘s 64% higher for assets when all other variables remain unchanged. One can

imagine that this variable can have a large impact on an office funds‘ financial performance. If 100% of

all LFA‘s of one fund would be class A and the other fund would have 100% class B/C the differences

would be 16% and 64%. However we see that fund A has 15% more class A offices as fund B. This

could indicate a 2.4% and 9.6% increase in NOI‘s and EV‘s for fund A. Of course this statement would

only hold according to the previous research and if all other variables remained unchanged.

Fund B Size in LFA Tenants Density

Asset 1 2000 3 0,0015 Asset 2 4000 2 0,0005 Asset 3 1500 5 0,0030

75000 10 0,0050

Fund Average Density 0.0013

Fund A Size in LFA Tenants Density

Asset 1 4000 10 0,0025 Asset 2 1500 5 0,0033 Asset 3 2000 1 0,005

7500 16 0,0063

Fund Average Density 0,00213

Class A B/C

Fund A 40% 60% Fund B 25% 75%


107

Building – LEED certification A form of measuring the sustainability related qualities of office assets is often done by LEED

certification. The research has proven that the difference between LEED certified and non LEED

certified buildings can have a significant impact on the EV‘s of office buildings, a 22% decrease in the

EV‘s. The manner in which this can be calculated for funds is done in a similar matter as office class.

In order to check the quality of the fund we have to calculate how

much percent of the total amount of LFA‘s are divided over LEED

and non-LEED certified properties.

According to the example the percentage of LFA‘s in the YES category is 15% larger for Fund A.

Therefore the EV‘s should be higher on average for fund A. According to the estimates this could be

0.15 x 22% = 3.3%.

6.5 Analysis of industrial assets

The significant variables that were of influence for the Net Operating Incomes (NOI) and Estimated

Values (EV) of Industrial assets were:

Variable Sig for Explanation

Year EV The year in which the transaction is measured for the assets

Region NOI The 8 NCREIF geographical regions the assets can be located in

Google Transit EV A latent variable measuring location qualities in a score from 1-100

Airport NOI / EV The property being located within 1 mile of a freight airport.

Size NOI The size of an asset in Leasable Floor Area.

Gateway City NOI The presence of an asset in a gateway city

The methods used for Year, Region, and Size are similar to those of retail assets. However the

estimates for each variable are different and the ranges between the variables of the funds are

undoubtedly different than those of the retail assets. This means that when analyzing Industrial assets,

the different variables might have larger or smaller impacts. For instance the locations of an asset in a

non-gateway city can have a -47% impact on NOI‘s for industrial assets and -27% for retail assets,

ceteres paribus.

Location - Google transit score The Google transit score is calculated in a similar manner as the Google walk score but measures the

traffic related accessibility of a location. This is a determining factor for Industrials since traffic plays a

more vital role for EV‘s. The statistical research has shown that a 1% Google transit score increase for

industrial assets increases their EV‘s by 0.14%, ceteres paribus. In order to calculate the weighted

averages for transit score of a fund we conduct a similar method as walk score. For example:

Fund A and B both have 3 assets with different walk scores and different sizes

To calculate the weighted average transit score for each fund we multiply the size of the asset by the

assets transit score. Then we divide the sum of all these (532500) by the total amount of space

(7500). We then get the weighted average transit score for fund A (71). Fund B had a weighted

average walk score of 61.33.

LEED Yes No

Fund A 60% 40% Fund B 45% 55%

Fund A Size in LFA Google Transit x


7500 532500 Fund Average Transit score 71

Fund B Size in LFA Google Transit x


7500 460000 Fund Average Transit score 61,33


108

∑

∑

When using the abovementioned formula we can calculate and compare weighted average transit

scores for funds. Funds with higher weighted average transit scores contain assets which have better

locations and should have higher NOI s and EV´s. The transit score rose by approximately 15,7%

According to the estimate obtained for the EV‘s they should be 15,7 x 0,14% = 2,2% higher for fund A.

Location – Airport location The airport location variable is one that is unique to the industrial class in this analysis. The estimate

for this variable is 49% lower for the industrial assets which are not located within 1 mile of an airport.

This variable is a binary variable like the LEED variable for offices. So the amounts of LFA‘s in airport

locations have to be compared to the amount of LFA‘s in non-airport locations.

We once again compare 2 funds to set an example. Fund A has

20% LFA‘s in airport locations and fund B only 5%. The difference

between them is them 15%. We then calculate this 15% by the

estimate which is 0.15* 47%= 7.05% higher NOI‘s for fund A. For the EV‘s this means 0.15*22%=

3.3%

6.6 Analysis results and fund comparison

Each sector has been given 2 example funds for comparison. Each fund has different distributions of assets over the different categories of the variables or has different weighted scores for continuous variables. This was done so that an example could be made for comparing funds.

Now that for each variable a fund average and influence on either NOI or EV is obtained we can now formulate an investment decision. In the Retail, Office and Industrial tables we can see the different percentage estimates each variable has on NOI and EV.

Variables for Retail assets Fund A Effect NOI-

A Effect EV-A

Fund B

Effect NOI-B

Effect EV-B

Retail

Average Fund Walk Score 71 +6,5% +5,6% 61,33 0 0

Average Type percentages A. (Super) regional malls B. Neighbourhood and community centres C. Power centres D. Other types

12% 43% 22% 23%

+13,44% 0

+11,8% +16,7% 0

8% 37% 30% 25%

+9% 0

+7,9% +14,63% 0

Average Fund asset size 7500 0 0 7833 -1,2% -0,65%

Average Age 5,3 -1% 4,5 0

Average tenant density 0,00213 +1,28% 0.0013 0 Better ASC for retail fund +19,9% +34,38% 8,2% 21,88%

Airport Yes No

Fund A 20% 80% Fund B 5% 95%


109

Variables for Office assets Fund A Effect

NOI-A Effect EV-A

Fund B Effect NOI-B

Effect EV-B

Offices

Average Fund Walk Score 71 +6,5% 61,33 0

Average Type percentages CBD Type. Suburban Type

22% 78%

+3,25% 0

5% 95%

+0,8% 0

Average Class percentages Class A. Class B or lower

40% 60%

+26% 0

25% 75%

+16,25% 0

Average LEED percentages LEED Yes LEED No

60% 40%

+13,2% 0

45% 55%

+9,9% 0

Average Fund asset size 7500 0 7833 -1,3%

Average Age 5,3 +3,4% 4,5 0

Better ASC for Office fund 32,65% 19,7% 15,75% 9,9%

Variables for Industrial assets Fund A Effect

NOI-A Effect EV-A

Fund B Effect NOI-B

Effect EV-B

Industrial

Average Fund Transit Score 71 +2,2% 61,33 0

Average Airport percentages Airport Yes. Airport No

20% 80%

+7.05% 0

+3.3% 0

5% 9%

+2,45% 0

+1,1% 0

Average Fund asset size 7500 0 7833 -1,1%

Better ASC for Industrial fund 7,05% 5,5% 1,35% 1,1%

By comparing the sums of the calculated estimates of all the variables for both funds we can see which fund‘s asset specific criteria have the highest overall positive influence on NOI‘s or EV‘s.

NOI A NOI B EV A EV B Difference NOI Difference EV

Retail fund comparison +19,9% +8,2% +34,4% +21,9% A +11,7% A +12,5% Office fund comparison +32,7% +15,8% +19,7% +9,9% A +16,8% A +9,8% Industrial fund comparison +7.05% +1,35% +5,5% +1,1 A +6,7% A +4,4%

Figure 16 - Fund comparison outcome (Ow n image)

The outcomes indicate that fund A is the fund with higher EV and NOI increasing aspects in all three sectors. These outcomes can then be compared against the Investment Memorandums buy in prices per LFA to determine which fund on average has higher EV and NOI producing criteria in relation to

their per LFA investment.

20%

34% 33%

20% 7% 6% 8%

22% 16%

10% 1% 1% 0,00%

10,00%

20,00%

30,00%

40,00%

Retail NOI Retail EV Office NOI Office EV Industrial NOI Industrial EV

Fund A

Fund B


110

There are a few assumptions which have to be taken into account when using the results:

Check carefully if you are comparing NOI‘s or EV‘s. They both have different influential

variables and coefficients.

Results have to be put in relation with Macro and Meso level aspects. If the meso and/or macro level influences on a certain fund have a large impact this can alter the decision of which fund to choose.

Impacts of effects are dependent on the spread between funds. If the spread between the walk scores, size or ages become larger, the difference between the NOI‘s and EV‘s of the funds become larger as well.

This tool can amongst fund comparison be used for:

Identifying underpriced or overpriced funds. When the asset specific criteria of a certain fund should result in better NOI‘s and EV‘s for a fund but

the average NOI and/or EV per LFA don‘t reflect this. This could mean that the contract rents or values

for assets in that specific fund do not reflect their actual value or possible NOI‘s. This could indicate

that returns might increase due to contracts for those funds being renegotiated or assets being

revalued accordingly. This could also mean that funds are overpriced in the same manner.

Comparing sectors When an investor wants to know which invested sector is spread over better locations, variables used

for different sectors such as Google walk can be used. These coefficients differ for each sector. It has

to be kept in mind that the importance of variables, such as walk scores varies between sectors.

Comparing NOI‘s to EV‘s

Certain funds might reflect the influence of their ASC in their NOI‘s but not in their EV‘s or vice versa.

This could mean that valuations are lagging or contract rents are lagging in comparison to each other.

Since direct return is largely dependent on the NOI‘s and EV‘s and indirect return is largely dependent

on EV‘s one of both might be under or overpriced. This is due to the fact that fund NOI‘s and EV‘s are

time related and might need time to adapt to the given standards.

6.7 Limitations and recommendations for future research

Limitations of the research: The scope of this research is large and places the macro, meso, micro and fund level influences

researched into perspective to the context of US based real estate for international fund investors. There are however, a few limitations to this research in relation to the aforementioned field of real estate.

Firstly all data is provided by one Graduation Company giving a limited amount of data. Each different fund of the 9 in total gave different types and incomplete data, causing for omitted variables. The

required data is in many cases not available or funds are not willing to give them. This is normal for all types of research but might cause for different outcomes under certain circumstances.

The research is focussed on commercial real estate sectors but fails to include many different types of real estate commonly present in funds like Hotels, personal storage or residential real estate. This research can be used as a guideline for research into international residential portfolios or other types

of real estate. The research still contains gray areas due to the use of time and regions as variables instead of more

specific, detailed variables related to the investment clock and regional impact on real estate pricing. If, for example, the underlying influences on the investment clock, such as employment growth or interest rates, were used as variables the research could distinguish the different magnitudes of

impact these variables could have had over time. Instead, time remains a vague concept for one which


111

cannot control but asses the overall state of the economy. Investors commonly look at the specific demand for one real estate product such as the net absorption of industrial space, but if one could

determine through use of the same regression techniques, the underlying macro and meso economic drivers behind the net absorption, he could obtain preemptive insights into profitable investments.

One of the most important limitations but interesting opportunities of this research is its current limitation but ability to be adapted to the future qualitative demands of commercial real estate. Society is constantly changing and important inventions such as 3d printing could change the way we see,

build and use real estate in its entirety. The quantitive methods used should be adapted and incorporate the new found criteria. This can then assess their influence in relation to the already existing proven influences. The defining aspect which limits the research is the rapidness of society

developing. This development could render certain building types obsolete like we can increasingly see in office use. So called flex working and third workplace is causing an impact on an entire type of real estate. Nobody can precisely say how quick these developments take place but macro-economic

models commonly do not anticipate such changes. The research is focused on one single international country causing the model to be most useful for

US fund investments. It would be interesting for future research to look into multi country funds or other countries. This is important due to the fact that; Real Estate is not regarded as a universal homogenous product providing the same values and functions different countries and societies require

of them. It would be interesting to see if an office, store, or warehouse in the US has different price determining aspects as an office in, for example, Germany. These aspects could of course also be the same for major cities in all countries but differ in other parts of the country. However the US is rated as

the most transparent real estate market next to the EU. Doing this for Chinese investments could cause some problems with obtaining the required data.

Finally, multicollinearity limits the research. Multicollinearity occurs when two or more predictor variables in a multiple regression model are highly correlated. Multicollinearity misleadingly inflates the standard errors.

Two examples of such multicolinnearity are the LEED and EnergyStar correlations in the model, these caused for an illogical negative influence of Energystar. The gateway city and Google transit also

seem to give correlated results resulting in opposite cancelled out effects. In retrospect one of either should have been taken out. This means it makes some variables statistically insignificant while they should be otherwise significant. To reduce the multicollinearity

choices between variables had to be made. These choices were made on the basis of which variable suits the research question and sector better. An example of multicollinearity in this research paper was the correlation between Google Walk and Google Transit. Because of the high correlation

between the two, a choice had to be made to drop one. For offices and retails Google Walk was used because it is the more relevant score when it comes to these sectors. For industrials Google Transit was used, because for this sector the transit score was more important.

Interesting subjects for future research:

It would be interesting to start benchmarking between the portfolios of large investors. One can then measure the outcomes of their portfolio analysis to that of another investor. If many investors would participate a market standard benchmark could be made for which funds could aim to outperform.

Redo the research every few years with updated variables. With markets and demands changing and the world becoming more international each year, it is interesting to see if the tool changes over the years and how.

Conduct research with more assets. The more assets are used the broader and more reliable the results will be. And conduct on Residential assets. It is very interesting to extend the research to residential real estate. This way investing in that market can also improve and hopefully the tool will be

a useful as this one. Using different variables will give even more transparency into the investments. This may lead

to even lower risk and thus a more profitable portfolio. Adding future variables can also be very

interesting. How future resistant is the portfolio for the changing demand of the future. Think of 3D printing, flying cars, future of malls etc.


112

7. Reflection This chapter elaborates a personal reflection on the research process of this thesis. The reflection

describes the process from the beginning till the end. The goal of this reflection is to provide more

insight into the process for mentors and students who want to do (further) research on relevant topics.

While looking back on the thesis process, I have to say that one of the most difficult tasks was

collecting the data. Some private real estate funds did not have all the data on hand, others could not

release the data easily due to legal reasons, while others simply did not want me to use their data.

Because retrieving the right data took much longer than expected the research process was delayed.

From this situation I have learned that it is best to start working with the available data than waiting

until all of the data is collected. Thankfully, the most important data had been collected and I could

start working on the research paper.

The second challenge I faced was modeling of the data. The fact that this was never taught at the

university and thus it was all new to me made it very difficult. This meant a lot of self-study was

required, which was very time consuming. Learning to work with SPSS was challenging and

interesting at the same time. I learned a lot. I enjoyed interpreting the results because a lot of

interesting properties, facts and numbers came up. I am sure this new knowledge will be very helpful

in the future, especially considering I would like to migrate to the US and work in the real estate sector

there.

The third challenging part of the research was to translate complex theories and methods into simple

and effective ideas. Additionally combining the technical side of real estate with the financial theories

was a lot more difficult than expected.

Both my graduation mentors have helped me very well during this process. Philip Koppels‘ knowledge

on statistical research was very useful and sped up the statistical part of the research. He gave

valuable input and feedback during the entire research process. Hans de Jonge has guided me

throughout the process and has made sure the research is clear and well written. He also caused me

to critically reflect on the research limitations. His expert knowledge has helped scientifically form the

thesis and give me better insight on possible improvements to increase the quality of the thesis.

My company mentor, Victor Hagenbeek, has also given me valuable feedback on the contents of my

report but has mainly showed me how everything is conducted in practice when doing research for a

company like Syntrus Achmea. I am very grateful to have had the opportunity to conduct my research

at the company. We had the common goal of developing this investment methodology to improve their

investment strategy.

The whole process taught me a lot about international real estate investment funds and how to

perform professional research at a technical university. I would advise future researches to focus on a

smaller research scope instead of aiming for a very broad and long research. This could improve the

quality of the research due to time limitations of approximately 1 college year.

The research project was not intended for graduating easily and within the time. Instead it focused on

combining the technical knowledge of assets with acquired new knowledge obtained by venturing past

the regular curriculum of the TU. I knew from the moment I started that this could cause me to lag in

certain instances but I have never regretted my choice. I have always loved a challenge and this was

certainly one that taught me a great deal about real estate research. I have thus gained a lot of

relevant scientific knowledge, which is the purpose of becoming an MSc in my opinion.


113

Literature and other sources

Acosta, J. M. (2012). Leverage and Performance-Effects in the Non-Listed Real Estate Funds.

Univeristy of Amsterdam

van Aert D.S. (2006) Investeren in Europese private vastgoedfondsen : een onderzoek naar de

selectiecriteria voor institutionele beleggers

Ambrose (1991) An Analysis of the Factors Affecting Light Industrial Property Valuation

Andonov, A., Kok, N., & Eichholtz, P. (2013). A Global Perspective on Pension Fund Investments in

Real Estate. The Journal of Portfolio Management, Special Real Estate Issue.

Baal, R. v. (2013). Joint venture als onderdeel van de vastgoedmarktcyclus. Amsterdam: Amsterdam

School of Real Estate.

Baas, L.C. (2010) The incorporation of sustainability into the real estate investment portfolio, Real

Estate & Housing, TUDelft

Baker (2004), Why Can‘t New Buildings be as Nice as Old Buildings?

Barkham, R., & Ward, C. (1999). Investor sentiment and noise traders: Discount to net asset value in listed property companies in the UK. Journal of Real Estate Research, 18, 291–312

Beekmans, J., Beckers, P., van der Krabben, E., & Martens, K. (2014). A hedonic price analysis of the value of industrial sites. Journal of Property Research, 31(2), 108-130.

Benjamin, J. D., Zietz, E. N., & Sirmans, S. G. (2003). The environment and performance of industrial real estate. Journal of Real Estate Literature, 11(3), 279-324.

Bonde, M., & Song, H. S. (2013). Does greater energy performance have an impact on real estate revenues? (No. 13/9).

Buttimer, R., Rutherford, R. and Witten,R. (1997). ―Industrial warehouse rent determinants in the Dallas/Fort Worth area‖. Journal of Real Estate Research. Vol. 13 No. 1. Pp 47-55.

Case, B., Goetzmann, W. N., & Rouwenhorst, K. G. (2000). Global real estate markets-cycles and

fundamentals (No. w7566). National bureau of economic research.

Cohen, J., Brown, M., (2013) Impact of Vancouver Airport on Commercial Property Values

Colwell, P.F. and Munneke, H.J. (1997). ―The structure of urban land prices‖. Journal of Urban Economics, Vol. 41 No.3, pp.321-36

Cuppen, J. (2011). Private en institutionele beleggers in een privaat vastgoedfonds. Amsterdam:

Amsterdam School of Real Estate.

Damodaran, A. (2012) Investment Valuation: Tools and Techniques for Determining the Value of Any Asset, University Edition

Debrezion, G., Pels, E., & Rietveld, P. (2007). The impact of railway stations on residential and

commercial property value: a meta-analysis. The Journal of Real Estate Finance and

Economics, 35(2), 161-180.

http://repository.tudelft.nl/search/ir/?q=creator%3A%22Baas%2C%20L.C.%22

http://repository.tudelft.nl/search/ir/?q=title%3A%22The%20incorporation%20of%20sustainability%20into%20the%20real%20estate%20investment%20portfolio%22

http://repository.tudelft.nl/search/ir/?q=department%3A%22Real%20Estate%20%26%20Housing%22


http://books.google.nl/books?hl=en&lr=&id=ciJwGJfjHGkC&oi=fnd&pg=PA1&dq=Investment+Valuation&ots=rJtgh0fl8e&sig=Az9ebBgy58zZiH4uCnKt_wsTWMo

http://books.google.nl/books?hl=en&lr=&id=ciJwGJfjHGkC&oi=fnd&pg=PA1&dq=Investment+Valuation&ots=rJtgh0fl8e&sig=Az9ebBgy58zZiH4uCnKt_wsTWMo


114

DiPasquale, D., & Wheaton, W. C. (1996). Urban economics and real estate markets (Vol. 23, No. 7). Englewood Cliffs, NJ: Prentice Hall.

Downtown and business district market analysis. Center for community and economic development, State University Ohio

Dunse, N. en C. Jones (1998). A Hedonic Price Model of Office Rents. Journal of Property Valuation & Investment 16(3): 297-312.

Eichholtz, P., Hoesli, M., MacGregor, B. and Nanthakumaran, N. (1995). ―Real estate portfolio

diversification by property type and region‖, Journal of Property Finance, 6(3), 39-59.

El-Geneidy, A. M., & Levinson, D. M. (2006). Access to destinations: Development of accessibility

measures (No. MN/RC-2006-16). Minnesota Department of Transportation, Research Services Section

Ellison, G., Glaeser, E. L., & Kerr, W. (2007). What causes industry agglomeration? Evidence from

coagglomeration patterns (No. w13068). National Bureau of Economic Research.

Fehribach, F. A., Rutherford, R. C., & Eakin, M. E. (1993). An analysis of the determinants of industrial property valuation. Journal of Real Estate Research, 8(3), 365-376.

Fisher, J. D. and Liang, Y. (2000). ―Is sector diversification more important than regional

diversification?‖ Real Estate Finance, 17(3), 35-40.

Francis, D. P., Coats, A. J., & Gibson, D. G. (1999). How high can a correlation coefficient be? Effects of limited reproducibility of common cardiological measures. International journal of cardiology, 69(2), 185-189.

Franz Fuersta, G. M. (2013). Analysing the performance of nonlisted real estate funds: a panel data

analysis. Cambridge: University of Cambridge.

Geiger, P., Cajias, M., Bienert, S, (2013) "The asset allocation of sustainable real estate: a chance for

a green contribution?", Journal of Corporate Real Estate, Vol. 15 Iss: 1, pp.73 - 91 Geltner, M. (2007). Commercial Real Estate Analysis & Investment. Mason: Cengage Learning.

Gijselaar, R.S.,(2010) Decision-making Criteria for the Acquisition of Office Buildings. Do building

features decide? Real Estate & Housing, TUDelft

van Gool, P., Jager, P., & Weisz, R. M. (2013). Onroerend goed als belegging. Stenfert Kroese.

Graaskamp, J. A. (1981). Fundamentals of Real Estate. Development. Risk Management, 9, 10. Greer, G. E., & Kolbe, P. T. (2003). Investment analysis for real estate decisions (Vol. 1). Dearborn

Real Estate Gutierrez Llaguno, R.(2013) The Aura of Office Buildings; The impact of image and prestige on

financial performance of office buildings in Monterrey, Real Estate & Housing, TUDelft

Hoag, J. W. (1980). Towards indices of real estate value and return. The Journal of Finance, 35(2),

569-580.

Hoesli, M. and Hamelink, F. (1997). ―An examination of the role of Geneva and Zurich housing in

Swiss institutional portfolios‖, Journal of Property Valuation and Investment, 15(4), 354-371.

http://www.emeraldinsight.com/action/doSearch?target=emerald&logicalOpe0=AND&text1=Geiger,%20P&field1=Contrib

http://www.emeraldinsight.com/action/doSearch?target=emerald&logicalOpe0=AND&text1=Cajias,%20M&field1=Contrib

http://www.emeraldinsight.com/action/doSearch?target=emerald&logicalOpe0=AND&text1=Bienert,%20S&field1=Contrib

http://repository.tudelft.nl/search/ir/?q=creator%3A%22Gijselaar%2C%20R.S.%22

http://repository.tudelft.nl/view/ir/uuid:a34351c1-cc2c-4ccc-98f7-df2035bd10ea

http://repository.tudelft.nl/view/ir/uuid:a34351c1-cc2c-4ccc-98f7-df2035bd10ea


http://repository.tudelft.nl/search/ir/?q=creator%3A%22Gutierrez%20Llaguno%2C%20R.%22

http://repository.tudelft.nl/search/ir/?q=title%3A%22The%20Aura%20of%20Office%20Buildings%3B%20The%20impact%20of%20image%20and%20prestige%20on%20financial%20performance%20of%20office%20buildings%20in%20Monterrey%22

http://repository.tudelft.nl/search/ir/?q=title%3A%22The%20Aura%20of%20Office%20Buildings%3B%20The%20impact%20of%20image%20and%20prestige%20on%20financial%20performance%20of%20office%20buildings%20in%20Monterrey%22



115

Hoesli, M., & Lizieri, C. (2007). Real estate in the investment portfolio. A report prepared for the investment strategy council of the royal ministry of finance: Norway, May.

Ibbotson, R. G., & Siegel, L. B. (1984). Real estate returns: a comparison with other investments. Real

Estate Economics, 12(3), 219-242.

Institute for Building Efficiency, (2011) Green Building Asset Valuation: Trends and Data

IVBN. (2011). Het risk management van institutionele vastgoedbeleggers. IVBN.

Joosstens, F., & Itard, L. (2012) Building Management for a Good Indoor Climate and Low Energy Use.

Kaklauskas, A., Kelpsiene, L., Zavadskas, E. K., Bardauskiene, D., Kaklauskas, G., Urbonas, M., &

Sorakas, V. (2011). Crisis management in construction and real estate: Conceptual modeling at the

micro-, meso-and macro-levels. Land Use Policy, 28(1), 280-293.

Knotek, E. S. (2007). How useful is Okun's law?. Economic Review, (Q IV), 73-103.

Kok, N., & Jennen, M. (2012). The impact of energy labels and accessibility on office rents. Energy

Policy, 46, 489-497.

Kooijman, D (1999), Machine en theater Ontwerpconcepten van winkelgebouwen

Korolija, I., Marjanovic-Halburd, L., Zhang, Y., & Hanby, V. I. (2011). Influence of building parameters

and HVAC systems coupling on building energy performance. Energy and Buildings, 43(6), 12471253.

Krause, A., & Bitter, C. (2012). Spatial econometrics, land values and sustainability: Trends in real estate valuation research. Cities, 29, S19-S25.

Kuenen, M. B., van Gool, P. (2008) De stationsomgeving.

Kuyper, M.J.A. (2014) The impact of building features on the building's financial performance. "Do pre-crisis and after-crisis features decide?" ,Real Estate & Housing, TUDelft

Lee, S. L. (2001). ―The relative importance of property type and regional factors in real estate returns‖,

Journal of Real Estate Portfolio Management, 7(2), 159-167.

Lieser, K., & Groh, A. P. (2011). The determinants of international commercial real estate investment. The Journal of Real Estate Finance and Economics , 1-49.

Lockwood, L. J., & Rutherford, R. C. (1996). Determinants of industrial property value. Real Estate

Economics, 24(2), 257-272.

Lusht, K. M. (2012). Real Estate Valuation principles and applications. State College: KML publishing.

Sotelo, R., & McGreal, S. (2013) Real Estate Investment Trusts in Europe

Morrison, D., Scott, L. (2014) Emerging Trends in Real Estate® The global outlook for 2014

Op‗t Veld, H., & Vlasveld, M. The Effect of Sustainability on Retail Investment Performance:

International Evidence.

Öven, V. A., & Pekdemir, D. (2006). Office rent determinants utilizing factor analysis—a case study for Istanbul. The Journal of Real Estate Finance and Economics , 33(1), 51-73.

http://repository.tudelft.nl/search/ir/?q=creator%3A%22Kuyper%2C%20M.J.A.%22

http://repository.tudelft.nl/search/ir/?q=title%3A%22The%20impact%20of%20building%20features%20on%20the%20building%27s%20financial%20performance.%20%22Do%20pre-crisis%20and%20after-crisis%20features%20decide%3F%22%22

http://repository.tudelft.nl/search/ir/?q=title%3A%22The%20impact%20of%20building%20features%20on%20the%20building%27s%20financial%20performance.%20%22Do%20pre-crisis%20and%20after-crisis%20features%20decide%3F%22%22



116

Parker, D. (2012). Global Real Estate Investment Trusts: People, Process and Management . John Wiley & Sons.

PBL Netherlands Environmental Assessment Agency (2009a). De waarde van de kantooromgeving. Den Haag, Planbureau voor de Leefomgeving

Perdikaki, O., Kesavan, S., & Swaminathan, J. M. (2012). Effect of traffic on sales and conversion

rates of retail stores. Manufacturing & Service Operations Management , 14(1), 145-162.

Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption

information. Energy and buildings, 40(3), 394-398.

Pivo, G., & Fisher, J. D. (2011). The walkability premium in commercial real estate investments. Real

Estate Economics, 39(2), 185-219. Popescu, D., Bienert, S., Schützenhofer, C., & Boazu, R. (2012). Impact of energy efficiency measures on the economic value of buildings. Applied Energy, 89(1), 454–463.

Reimers, V. Clulow, V. (2004) Retail concentration: a comparison of spatial convenience in shopping strips and shopping centres

Remøy, H. (2010). Out of Office: A Study on the Cause of Office Vacancy and Transformation as a

Means to Cope and Prevent. Delft: Technische Universiteit.

Robijn, B. (2011). De betá van vastgoed. Master scriptie. Amsterdam School of Real Estate.

Ross, S. A., & Zisler, R. C. (1991). Risk and return in real estate. The Journal of Real Estate Finance

and Economics, 4(2), 175-190.

Sayce, S., & Ellison, L. (2006). The Sustainable Property Appraisal Project.

Sirmans, Clemon F., and Krisandra A. Guidry. "The determinants of shopping center rents." Journal of Real Estate Research 8.1 (1993): 107-115.

Sayce, S., & Ellison, L. (2006). The Sustainable Property Appraisal Project.

Syntrus Achmea, (2014), De meerwaarde van vastgoed in beleggingsportefeuille.

Syntrus Achmea, (2014), Outlook Internationaal

Svets (2010) The Determinants of Retail Real Estate Returns: Evidence from UK Property Market

Teller, C., Reutterer, T. (2008) The evolving concept of retail attractiveness: What makes retail

agglomerations attractive when customers shop at them? Thomas, J. O. (2002). Environmental contamination and industrial real estate prices. Journal of Real

Estate Research, 23(1), 179-200.

Treyz, G. I., Friedlaender, A. F., & Stevens, B. H. (1980). The employment sector of a regional policy

simulation model. The Review of Economics and Statistics , 63-73.

Vlek, P., Van Oosterhout, T., Rust, W., Van den Berg, S., & Chaulet, T. (2009). Investeren in

vastgoed, grond en gebieden. Vlaardingen, Netherlands: Management Producties . van der Werf, H. (2013). Enhanced diversification by using unbundled returns. Amsterdam School of

Real Estate. Amsterdam: Amsterdam School of Real Estate.


117

Wiegerinck, E. (2013). Restwaarde kantoren toplocatie verdubbelt. Vastgoedmarkt, 50-62.

Woodhouse, J. (1997). What is Asset Management?. Maintenance and Asset Management, 12, 26-28.

Xu, Q. (2012). Risk analysis on real estate investment decision making. Delft: TUdelft.

Yajie Zhao 2003 Industrial Property Performance and Building Functionality

Yunus, N., Hansz, J. A., & Kennedy P. J. (2010). Dynamic interactions between private and public real estate markets: Some international evidence. Journal of Real Estate Finance and Economics, 45(4),

1021–1040.

Websites: http://www.nu.nl/economie/3651290/beleggers-onttrekken-geld-vastgoedfonds.html

http://www.nu.nl/economie/2861128/vij f-vragen-dekkingsgraad-pensioenfondsen.html

http://www.nu.nl/economie/3658100/pensioenfondsen-halen-hoogste-dekkingsgraad-sinds-2011.html

http://nos.nl/dossier/142574-aow-en-pensioen/tab/353/infografiek -de-staat-van-uw-fonds/

http://www.cbs.nl/nl-NL/menu/themas/financiele-zakelijke-diensten/cijfers/extra/beleggingen-

weegschaal.htm

http://www.fghbank.nl/nl/fgh-vastgoedbericht/samenvatting/Paginas/trends-

vastgoedbeleggingsmarkt.aspx

http://www.realfuture.ch/anlagewissen_e5.html

www.inrev.org

www.ncreif.org

http://www.cbre.com/EN/research/research-investment-review/investment-thesis/Pages/Gateway-and-

Non-Gateway-Markets.aspx

www.ipd.com

http://www.institutebe.com/InstituteBE/media/Library/Resources/Green%20Buildings/Research_Snapshot_Green_Building_Asset_Value.pdf

http://www.nu.nl/economie/3651290/beleggers-onttrekken-geld-vastgoedfonds.html

http://www.nu.nl/economie/2861128/vijf-vragen-dekkingsgraad-pensioenfondsen.html

http://www.nu.nl/economie/3658100/pensioenfondsen-halen-hoogste-dekkingsgraad-sinds-2011.html

http://nos.nl/dossier/142574-aow-en-pensioen/tab/353/infografiek-de-staat-van-uw-fonds/

http://www.cbs.nl/nl-NL/menu/themas/financiele-zakelijke-diensten/cijfers/extra/beleggingen-weegschaal.htm

http://www.cbs.nl/nl-NL/menu/themas/financiele-zakelijke-diensten/cijfers/extra/beleggingen-weegschaal.htm

http://www.fghbank.nl/nl/fgh-vastgoedbericht/samenvatting/Paginas/trends-vastgoedbeleggingsmarkt.aspx

http://www.fghbank.nl/nl/fgh-vastgoedbericht/samenvatting/Paginas/trends-vastgoedbeleggingsmarkt.aspx

http://www.realfuture.ch/anlagewissen_e5.html

http://www.inrev.org/

http://www.ncreif.org/

http://www.cbre.com/EN/research/research-investment-review/investment-thesis/Pages/Gateway-and-Non-Gateway-Markets.aspx

http://www.cbre.com/EN/research/research-investment-review/investment-thesis/Pages/Gateway-and-Non-Gateway-Markets.aspx

http://www.ipd.com/




118

APPENDIX I – Explenetory information


119

Gateway cities according to NCREIF and airports

* Atlanta – Hartsfield-Jackson Atlanta International Airport

* Boston – Logan International Airport

* Chicago – O'Hare International Airport

* Detroit – Detroit Metropolitan Wayne County Airport

* Houston – George Bush Intercontinental Airport

* Los Angeles – Los Angeles International Airport

* Miami – Miami International Airport

* Newark – Newark Liberty International Airport

* New York City – John F. Kennedy International Airport

* Orlando – Orlando International Airport

* San Francisco – San Francisco International Airport

* Seattle – Seattle-Tacoma International Airport

* Washington D.C. – Washington Dulles International Airport

Google Eearth observations for Arport, rail or port.

* Rail property


120

Industrial property types:

Manufacturing Industrial buildings w ith less than 3 stories and a parking ratio less than 2.5:1 for w hich less

than 25% of the NRA is demised or planned as off ice space. (If non-office space is "high-clear", the space may be w arehouse/distribution.) (Yajie Zhao, 2003). Buildings w ith 10- to 16-foot ceilings or suff icient height to accommodate overhead cranes must provide f loor-height and dock height loading (Real Estate Information Standards, 1998).

Warehouse/Distribution Industrial buildings w ith the same criteria as Manufacturing buildings and for w hich at least 50%

of "non-off ice" space has a clear height of 18 feet or greater (Yajie Zhao, 2003). Properties w ith at least 50,000 square feet w ith up to 15% off ice space, and the balance of the structure having 18- to 30-foot ceiling height; all loading must be dock-height.

Research & Development Industrial buildings w ith one to three stories for w hich at least 25% but less than 75% of the NRA is demised or planned as off ice space or highly improved, and have a parking ratio greater

than or equal to 2.5:1. Flex space is included in this category (Yajie Zhao, 2003). Properties are one- and tw o-story, 10- to 15-foot ceiling heights w ith up to 50% off ice/dry lab space (w ith the remainder in w et lab, w orkshop, storage and other support), and w ith specif ic dock-height and f loor-height loading (Real Estate Information Standards, 1998).

Special Purpose

Manufacturing buildings for w hich there are limited manufacturing uses due to configuration,

special nature of improvements, or other criteria determined by management. This category should not be used for "non-manufacturing" purposes (Yajie Zhao, 2003).

Flex space structures single-story buildings w ith 10- to 18-foot ceilings w ith both f loor-height and dock-height loading, including w ide variation in off ice space utilization, ranging from retail and personal(Real Estate Information Standards, 1998).

Service to distribution light industrial and occasional heavy industrial use (Real Estate Information Standards, 1998).

Retail property types:

Standard Store Specialty store- is a small stores w hich is oriented on the specif ic range of merchandise and related items. General store- is a store that carries a general line of merchandise (village shop in rural areas;

corner shop in urban areas or suburbs).

Shopping Center Complex of shops representing leading merchandisers. Classif ied by size (local / regional / super regional) and type of retail (normal / premium / outlet)

Retail Warehouse Limited variety of merchandise sold in bulk at a discount to customers. Some key characteristics: 1. no frills and no service outlets 2. generally carry non-food items, but some may carry food items;

3. located in low rent area Department Store A retail establishment w hich specializes on satisfying a w ide range of the consumer's personal and

residential durable goods product needs; and at the same time offering the consumer a choice multiple merchandise lines, at variable price points, in all product categories.

Supermarket A supermarket (also a grocery store) offers a wide variety of food and household merchandise, organized into departments. It is larger in size and has a w ider selection than a traditional grocery store and it is smaller than a hypermarket or superstore

Other Retail All other types of retail real estate w hich cannot be classif ied into one of f ive categories mentioned

above.


121

APPENDIX II – Return models

Retail Return Models

Indirect / Appreciation Return Direct / Income Return

For the appreciation/indirect return of the retail sector we see that the variables Tenant density and region A are found to be significant. For the tenant density the results show that with each 0,001 increase in tenant density the indirect return grows by 30%. To give an example if there would be 2

tenants per 100.000 sqft instead of 1, IR for the property would grow by 3% In the case of the regional differences we see 3 significant differences between the 8. The southeast and mountain regions perform -9,6% and -12,2% worse than the Southwest. For the Income Return or

Direct Return (DR) we see that Year and tenant density are both significant within the 95% interval. The categorical variable renovated or new falls into the 90% interval. The only significant year was 2010 in relation to 2013 where DR´s seemed to be lower by 0,44 % in 2010. This is a logical finding

given the macro economic recovery phase, and yearly increasing EV´s from the previous hedonic pricing model concerning the same assets. The differences between the significant categories for the renovated or new variable are not in line with scientific literature. According to our literature research in

chapter 2 we researched that performance figures are supposed to be higher for newer buildings. The outcomes for the DR model show us that DR´s for the newly renovated assets and average aged assets are 1,3% and 0,6% lower as the very old assets. This could be due to other factors being

stronger determinants for DR´s and older buildings have indeed performed stronger than the newer buildings.

Total Return

For the total return we see the same as the indirect returns,

region A and tenant density are both significant. The influence on this return is however different from the previous returns. We see that for the regional influence, the

Southeast, mountain and Mideast regions perform -11,1% and -14,7% and 10,3 worse than the Southwest region. These are close to that of the appreciation returns but are

not supported by findings from the DR´s. So amongst the IR´s there are also other effects strengthening the gap between the performances of these regions.

Type III Tests of Fixed Effectsa

Source Num Den F Sig.

Intercept 1 98,833 2,193 ,142

Year 3 243,993 1,082 ,357

GoogleWalk 1 94,986 ,111 ,740

RetailTypeD 3 100,974 ,183 ,907

Parking_LFA 1 97,860 ,069 ,794

LnLFA 1 98,303 2,471 ,119

TenantDensity 1 95,391 5,617 ,020

GatewayCity 1 100,163 ,150 ,699

RegionA 6 98,376 2,438 ,031

FloorType 2 110,197 ,446 ,641

RenOrNew 4 131,638 ,853 ,494

a. Dependent Variable: AppreciationReturn.



Intercept 1 71,976 15,247 ,000

Year 3 214,952 3,002 ,031

GoogleWalk 1 70,236 1,753 ,190

RetailTypeD 3 72,662 ,429 ,733

Parking_LFA 1 73,144 2,713 ,104

LnLFA 1 71,085 2,766 ,101

TenantDensity 1 72,875 5,271 ,025

GatewayCity 1 71,091 ,225 ,636

RegionA 6 72,887 1,586 ,164

FloorType 2 76,197 ,563 ,572

RenOrNew 4 236,052 1,994 ,096

a. Dependent Variable: IncomeReturn.



Intercept 1 96,643 ,398 ,530

Year 3 205,984 1,131 ,338

GoogleWalk 1 93,059 ,003 ,959

RetailTypeD 3 98,852 ,250 ,861

Parkingspots_LFA 1 95,927 ,000 ,988

LnLFA 1 96,145 1,315 ,254

TenantDensity 1 93,583 3,668 ,059

GatewayCity 1 98,014 ,129 ,720

RegionA 6 96,358 2,278 ,042

FloorType 2 107,627 ,553 ,577

RenovatedOrNew 4 133,295 ,889 ,473

a. Dependent Variable: TotalReturn.


122

Office Return Model Outcomes



Intercept 1 11,447 ,020 ,891

Year 3 49,904 2,817 ,048

GoogleWalk 1 12,519 ,043 ,840

LN_LFA 1 11,612 ,027 ,872

LastUpdateAge 1 11,735 2,863 ,117

Fund 3 13,576 1,165 ,359

RegionA 6 12,931 ,389 ,873

OfficeType 2 13,496 ,586 ,570

LEEDBinary 2 12,471 ,351 ,711

Energystar 1 11,350 ,202 ,661

Tenants_LFA 1 11,589 ,058 ,814

OfficeClass 1 15,673 ,047 ,831




Intercept 1 12,314 ,851 ,374

Year 2 29,778 4,585 ,018

GoogleWalk 1 15,592 ,072 ,792

LN_LFA 1 12,718 1,390 ,260

LastUpdateAge 1 11,358 ,481 ,502

Fund 3 17,558 ,285 ,836

RegionA 6 15,520 1,000 ,459

OfficeType 2 16,489 ,184 ,833

LEEDBinary 2 13,403 ,459 ,641

Energystar 1 11,289 1,465 ,251

Tenants_LFA 1 11,685 ,046 ,834

OfficeClass 1 26,106 ,093 ,763


For all return models of the offices the only significant variable is the Year.

For the direct return the years 2010, 2011 were significant in relation to the reference year 2013. 2010.

DR‘s were 2,2% and 1,2% lower than 2013. This is in line with the recovering macro economic trend.

For the indirect return only 2011 was significant showing a 4,7 % increase in 2011 inr elation to 2013.

This is against the macro trend. For total return this is the same but only with 6%. It can then be

said that the time variable year shows that 2011 was a better year for Indirect and total

returns. . .



Intercept 1 12,787 1,214 ,291

Year 2 30,446 5,727 ,008

GoogleWalk 1 16,451 ,204 ,657

LN_LFA 1 13,246 1,552 ,234

LastUpdateAge 1 11,307 ,064 ,804

Fund 3 18,618 ,248 ,862

RegionA 6 16,113 1,390 ,277

OfficeType 2 17,164 ,565 ,579

LEEDBinary 2 13,633 ,376 ,694

Energystar 1 11,498 3,131 ,103

Tenants_LFA 1 11,749 ,012 ,914

OfficeClass 1 29,761 ,055 ,817



123

Industrial Return model outcomes

Direct Return Appreciation Return



Intercept 1 30,580 ,000 ,996

Year 3 69,057 ,869 ,461

RegionA 5 29,779 ,988 ,442

Airport 1 28,115 ,542 ,468

Fund 4 28,646 ,811 ,528

LN_LFA 1 29,974 ,296 ,590

GatewayCity 1 39,440 ,030 ,863


For the Direct returns we see no apparant significant factors influencing these performances. When we look at the Indirect return we see that there is an apparent difference in Funds.

[Fund=H] 6,2% [Fund=F]10,8% [Fund=G] 7,1% [Fund=E] reference

Total Return



Intercept 1 39,856 ,014 ,908

Year 3 81,676 2,547 ,062

RegionA 5 40,295 ,210 ,956

Airport 1 38,958 1,035 ,315

Fund 4 40,311 3,704 ,012

LN_LFA 1 39,410 ,213 ,647

GatewayCity 1 47,037 ,112 ,740


The difference between the return outcome variables is interesting to see. We see that for the total returns Year and Fund play a significant role in the determination of the total performance of an asset.



Intercept 1 39,370 ,031 ,860

Year 3 81,675 1,777 ,158

RegionA 6 45,455 ,369 ,895

Airport 1 38,758 1,075 ,306

Fund 4 39,102 2,450 ,062

LN_LFA 1 38,642 ,004 ,947

GatewayCity 1 45,362 ,026 ,872



124

APPENDIX III – EFE tables SPSS per Model

Retail model for NOI per LFA

Estimates of Fixed Effectsa



Intercept 6,336570 ,909268 66,473 6,969 ,000 4,521399 8,151741

[Year=2010] -,108108 ,033979 86,685 -3,182 ,002 -,175648 -,040568

[Year=2011] -,113093 ,030724 152,182 -3,681 ,000 -,173792 -,052393

[Year=2012] -,071719 ,025025 199,308 -2,866 ,005 -,121067 -,022372

[Year=2013] 0b 0 . . . . .

GoogleWalk ,006548 ,002883 65,104 2,271 ,026 ,000791 ,012306

[RetailTypeE=B] 1,129283 ,383392 65,253 2,946 ,004 ,363653 1,894914

[RetailTypeE=C] ,209646 ,156273 65,867 1,342 ,184 -,102375 ,521666



LnLFA -,300299 ,070559 63,949 -4,256 ,000 -,441260 -,159338

TenantDensity 864,683121 707,125565 64,960 1,223 ,226 -547,560810 2276,927052

[Gatew ayCity=No] -,271748 ,150955 64,460 -1,800 ,077 -,573274 ,029777


[RegionA=East North Central] -,680617 ,309233 73,736 -2,201 ,031 -1,296814 -,064420

[RegionA=Mideast] -,644115 ,292464 75,856 -2,202 ,031 -1,226626 -,061604



[RegionA=Pacif ic] -,444156 ,300763 74,934 -1,477 ,144 -1,043315 ,155004






[RenovatedOrNew =Average Age] ,009329 ,076274 255,053 ,122 ,903 -,140879 ,159537

[RenovatedOrNew =New] -,241528 ,197223 256,041 -1,225 ,222 -,629913 ,146858

[RenovatedOrNew =New R] ,031385 ,132802 212,336 ,236 ,813 -,230395 ,293164

[RenovatedOrNew =Old] -,019713 ,065517 213,639 -,301 ,764 -,148857 ,109430





125

Retail Final model for Estimated Value




Intercept 7,349143 ,895397 68,939 8,208 ,000 5,562847 9,135439

[Year=2010] -,159978 ,026544 216,371 -6,027 ,000 -,212296 -,107660

[Year=2011] -,107965 ,020040 267,023 -5,388 ,000 -,147421 -,068509

[Year=2012] -,064775 ,012897 305,998 -5,022 ,000 -,090153 -,039396

[Year=2013] 0b 0 . . . . .

GoogleWalk ,005792 ,002830 68,459 2,047 ,044 ,000147 ,011438

[RetailTypeE=B] ,983040 ,375278 68,497 2,619 ,011 ,234282 1,731798

[RetailTypeE=C] ,388257 ,157662 68,790 2,463 ,016 ,073712 ,702801



LnLFA -,161760 ,069850 68,628 -2,316 ,024 -,301120 -,022400

TenantDensity 1599,193964 703,232759 68,106 2,274 ,026 195,954570 3002,433357

LastUpdateAge -,012287 ,006194 69,738 -1,984 ,051 -,024641 6,794745E-005

[Gatew ayCity=No] -,248064 ,148453 68,200 -1,671 ,099 -,544282 ,048154



[RegionA=Mideast] -,338543 ,275309 68,951 -1,230 ,223 -,887776 ,210691

[RegionA=Mountain] ,099619 ,345277 68,716 ,289 ,774 -,589240 ,788478

[RegionA=Northeast] ,244515 ,304100 69,215 ,804 ,424 -,362115 ,851145

[RegionA=Pacif ic] ,098005 ,283113 69,233 ,346 ,730 -,466756 ,662766









126

Retail Final model for Indirect Return




Intercept -15,271646 17,177311 101,455 -,889 ,376 -49,344955 18,801664

[Year=2010] -2,193150 1,820741 284,585 -1,205 ,229 -5,776978 1,390678

[Year=2011] -1,344395 1,793512 285,600 -,750 ,454 -4,874573 2,185783

[Year=2012] -3,054539 1,758945 174,150 -1,737 ,084 -6,526133 ,417055

[Year=2013] 0b 0 . . . . .

GoogleWalk ,017392 ,052308 94,986 ,332 ,740 -,086453 ,121236

[RetailTypeE=B] 5,229427 7,074480 104,758 ,739 ,461 -8,798339 19,257192

[RetailTypeE=C] ,752076 3,017287 101,328 ,249 ,804 -5,233175 6,737327



LnLFA 2,043353 1,299767 98,303 1,572 ,119 -,535894 4,622600

TenantDensity 29738,810527 12548,028151 95,391 2,370 ,020 4829,143175 54648,477878

[Gatew ayCity=No] 1,048604 2,704001 100,163 ,388 ,699 -4,315950 6,413159


[RegionA=East North Central] -7,798972 5,901127 103,232 -1,322 ,189 -19,502154 3,904210

[RegionA=Mideast] -9,083930 5,653987 103,576 -1,607 ,111 -20,296540 2,128679

[RegionA=Mountain] -12,165905 6,978743 102,137 -1,743 ,084 -26,007986 1,676175

[RegionA=Northeast] -3,840131 6,397204 103,005 -,600 ,550 -16,527470 8,847207

[RegionA=Pacif ic] -1,500228 5,860099 107,273 -,256 ,798 -13,116853 10,116397

[RegionA=Southeast] -9,638515 5,620737 104,948 -1,715 ,089 -20,783461 1,506432


[FloorType=Double] -1,242713 3,749815 118,789 -,331 ,741 -8,667857 6,182430

[FloorType=Multiple 2+] -3,597713 4,037060 102,745 -,891 ,375 -11,604506 4,409079


[RenovatedOrNew =Average Age] -,966930 2,178113 110,072 -,444 ,658 -5,283407 3,349548

[RenovatedOrNew =New] -8,073126 5,872808 144,500 -1,375 ,171 -19,680831 3,534579

[RenovatedOrNew =New R] 1,817462 4,109002 121,526 ,442 ,659 -6,317035 9,951959

[RenovatedOrNew =Old] -2,121106 2,217761 129,369 -,956 ,341 -6,508882 2,266670





127

Retail Final model for direct Return




Intercept 13,691336 3,220695 73,366 4,251 ,000 7,273039 20,109632

[Year=2010] ,443809 ,191039 287,911 2,323 ,021 ,067799 ,819820

[Year=2011] ,158225 ,166426 279,946 ,951 ,343 -,169381 ,485830

[Year=2012] -,049322 ,124446 224,707 -,396 ,692 -,294553 ,195908

[Year=2013] 0b 0 . . . . .

GoogleWalk -,013291 ,010040 70,236 -1,324 ,190 -,033313 ,006732

[RetailTypeE=B] 1,255152 1,313547 72,735 ,956 ,342 -1,362905 3,873209

[RetailTypeE=C] ,381456 ,563278 72,953 ,677 ,500 -,741167 1,504078



LnLFA -,410765 ,247001 71,085 -1,663 ,101 -,903260 ,081730

TenantDensity -5473,507146 2384,036528 72,875 -2,296 ,025 -10225,022348 -721,991943

[Gatew ayCity=No] ,242296 ,510282 71,091 ,475 ,636 -,775154 1,259746


[RegionA=East North Central] -,209113 1,069011 82,220 -,196 ,845 -2,335631 1,917404

[RegionA=Mideast] -1,111412 1,017666 84,290 -1,092 ,278 -3,135050 ,912226

[RegionA=Mountain] -2,018920 1,269413 81,518 -1,590 ,116 -4,544410 ,506570

[RegionA=Northeast] -1,759964 1,154938 83,398 -1,524 ,131 -4,056926 ,536999

[RegionA=Pacif ic] -1,721189 1,054547 81,370 -1,632 ,107 -3,819261 ,376884

[RegionA=Southeast] -1,193643 1,003977 85,965 -1,189 ,238 -3,189496 ,802209


[FloorType=Double] ,019358 ,652522 83,222 ,030 ,976 -1,278431 1,317147

[FloorType=Multiple 2+] -,807924 ,762163 70,272 -1,060 ,293 -2,327907 ,712059


[RenovatedOrNew =Average Age] -,637069 ,333898 192,033 -1,908 ,058 -1,295647 ,021510

[RenovatedOrNew =New -1,103144 ,787107 246,817 -1,402 ,162 -2,653446 ,447159

[RenovatedOrNew =New R] -1,344244 ,590745 224,432 -2,276 ,024 -2,508362 -,180127

[RenovatedOrNew =Old] -,348870 ,304062 241,053 -1,147 ,252 -,947827 ,250087





128

Retail Final model for Total Return




Intercept ,049734 17,787245 99,107 ,003 ,998 -35,243547 35,343015

[Year=2010] -1,730782 1,820445 278,510 -,951 ,343 -5,314360 1,852796

[Year=2011] -1,229625 1,790167 286,965 -,687 ,493 -4,753150 2,293899

[Year=2012] -3,095386 1,709635 179,891 -1,811 ,072 -6,468903 ,278132

[Year=2013] 0b 0 . . . . .

GoogleWalk ,002775 ,054273 93,059 ,051 ,959 -,104999 ,110550

[RetailTypeE=B] 6,071789 7,318479 102,376 ,830 ,409 -8,443739 20,587318

[RetailTypeE=C] ,926596 3,124372 99,342 ,297 ,767 -5,272572 7,125764



LnLFA 1,545093 1,347201 96,145 1,147 ,254 -1,129028 4,219214

TenantDensity 24931,620239 13017,468482 93,583 1,915 ,059 -916,370014 50779,610492

[Gatew ayCity=No] 1,007548 2,801042 98,014 ,360 ,720 -4,551019 6,566114



[RegionA=Mideast] -10,331459 5,850448 101,879 -1,766 ,080 -21,935958 1,273041

[RegionA=Mountain] -14,742635 7,224370 100,414 -2,041 ,044 -29,074855 -,410415


[RegionA=Pacif ic] -3,543194 6,057515 105,226 -,585 ,560 -15,553827 8,467438

[RegionA=Southeast] -11,103971 5,813690 103,258 -1,910 ,059 -22,633712 ,425770


[FloorType=Double] -1,015229 3,864405 116,360 -,263 ,793 -8,668920 6,638461

[FloorType=Multiple 2+] -4,278700 4,178967 100,095 -1,024 ,308 -12,569555 4,012155


[RenovatedOrNew =Av Age] -1,330328 2,249008 110,408 -,592 ,555 -5,787151 3,126495

[RenovatedOrNew =New] -9,159458 6,015109 145,710 -1,523 ,130 -21,047589 2,728674

[RenovatedOrNew =New R] 1,499534 4,229926 122,578 ,355 ,724 -6,873632 9,872700

[RenovatedOrNew =Old] -2,246008 2,278507 131,784 -,986 ,326 -6,753190 2,261173





129

Office final model NOI Estimates of Fixed Effectsa



Intercept 4,601816 2,155949 30,162 2,134 ,041 ,199772 9,003860

[Year=2010] ,028455 ,074520 134,827 ,382 ,703 -,118925 ,175835

[Year=2011] ,010840 ,058095 128,422 ,187 ,852 -,104108 ,125788

[Year=2012] ,020336 ,040903 108,444 ,497 ,620 -,060737 ,101410

[Year=2013] 0b 0 . . . . .

GoogleWalk ,000885 ,005073 29,603 ,174 ,863 -,009481 ,011252

LN_LFA -,291541 ,151541 29,690 -1,924 ,064 -,601164 ,018082

LastUpdateAge ,019066 ,008490 32,142 2,246 ,032 ,001775 ,036357

[Fund=H] -,526889 ,296540 28,059 -1,777 ,086 -1,134266 ,080489

[Fund=F] ,993536 ,634271 36,486 1,566 ,126 -,292230 2,279303

[Fund=G] ,325850 ,619425 34,837 ,526 ,602 -,931861 1,583560

[Fund=A] ,979964 ,635170 35,492 1,543 ,132 -,308860 2,268789

[Fund=B] 0b 0 . . . . .

[RegionA=East North Central] ,825733 ,485376 32,657 1,701 ,098 -,162165 1,813631

[RegionA=Mideast] ,674900 ,423411 32,885 1,594 ,121 -,186651 1,536451



[RegionA=Pacif ic] ,345645 ,422437 32,483 ,818 ,419 -,514328 1,205618

[RegionA=Southeast] ,476074 ,762173 34,888 ,625 ,536 -1,071396 2,023545


[Off iceType=CBD] ,155736 ,216025 28,276 ,721 ,030 -,286576 ,598049


[Off iceClass=Class A] ,466172 ,343160 28,463 1,358 ,185 -,236243 1,168588


[LEEDBinary=No] -,310461 ,197348 30,036 -1,573 ,126 -,713478 ,092556


[Energystar=No] ,163108 ,234469 29,104 ,696 ,492 -,316360 ,642576


Tenants_LFA -1242,985348 1167,361281 28,869 -1,065 ,296 -3630,977273 1145,006577

a. Dependent Variable: LN_NOIsqfLFA.



130

Office final model Estimated Values



Low er Bound Upper

Intercept 4,558482 1,668133 45,327 2,733 ,009 1,199358 7,917607

[Year=2010] -,267974 ,035403 98,333 -7,569 ,000 -,338227 -,197720

[Year=2011] -,115601 ,025955 132,455 -4,454 ,000 -,166941 -,064261

[Year=2012] -,059297 ,016921 152,185 -3,504 ,001 -,092728 -,025865

[Year=2013] 0b 0 . . . . .

GoogleWalk ,006724 ,003933 45,117 1,710 ,094 -,001196 ,014645

LN_LFA -,051308 ,119909 45,251 -,428 ,671 -,292781 ,190164

LastUpdateAge ,000329 ,006639 47,190 ,050 ,961 -,013024 ,013683

[Fund=H] -,189298 ,261426 44,052 -,724 ,473 -,716151 ,337555

[Fund=F] ,872883 ,546491 45,865 1,597 ,117 -,227234 1,973000

[Fund=G] ,830118 ,527925 45,691 1,572 ,123 -,232733 1,892969

[Fund=A] 1,077713 ,561548 45,697 1,919 ,061 -,052827 2,208254

[Fund=B] 0b 0 . . . . .

[RegionA=East North Central] -,017891 ,357701 45,869 -,050 ,960 -,737961 ,702178

[RegionA=Mideast] ,105897 ,307522 45,862 ,344 ,732 -,513163 ,724957



[RegionA=Pacif ic] ,011691 ,304018 45,722 ,038 ,969 -,600365 ,623748



[Off iceType=CBD] ,231795 ,191111 44,363 1,213 ,232 -,153275 ,616865


[LEEDBinary=No] -,218063 ,158476 44,929 -1,376 ,002 -,537263 ,101138


[Energystar=No] ,271010 ,207244 44,449 1,308 ,198 -,146543 ,688564


Tenants_LFA 317,4889 990,42281 44,195 ,321 ,750 -1678,3285 2313,3064

[OfficeClass=Class A] ,646368 ,187039 46,845 3,456 ,001 ,270061 1,022676





131

Office Final model for Direct Return Estimates of Fixed Effectsa



Intercept ,498151 19,476757 11,786 ,026 ,980 -42,023866 43,020169

[Year=2010] 2,209031 ,767049 54,865 2,880 ,006 ,671745 3,746316

[Year=2011] 1,226796 ,615621 63,426 1,993 ,051 -,003262 2,456854

[Year=2012] ,500743 ,423649 59,487 1,182 ,242 -,346832 1,348319

[Year=2013] 0b 0 . . . . .

GoogleWalk -,008632 ,041835 12,519 -,206 ,840 -,099364 ,082101

LN_LFA ,234623 1,421852 11,612 ,165 ,872 -2,874836 3,344082

LastUpdateAge ,189306 ,111880 11,735 1,692 ,117 -,055072 ,433683

[Fund=H] -4,330141 2,598265 11,256 -1,667 ,123 -10,033051 1,372768

[Fund=F] ,878727 4,492670 15,424 ,196 ,847 -8,674314 10,431768

[Fund=G] -,225572 4,176005 14,246 -,054 ,958 -9,167749 8,716605

[Fund=B] 0b 0 . . . . .

[RegionA=East North Central] 2,131418 3,380333 12,971 ,631 ,539 -5,173009 9,435846

[RegionA=Mideast] ,391124 2,957621 13,262 ,132 ,897 -5,985634 6,767883

[RegionA=Mountain] -,781674 3,333915 12,230 -,234 ,819 -8,030533 6,467186

[RegionA=Northeast] 1,860724 3,137092 12,712 ,593 ,563 -4,932198 8,653646

[RegionA=Pacif ic] -,857129 3,097798 13,206 -,277 ,786 -7,538933 5,824676

[RegionA=Southeast] 1,506495 5,484685 13,977 ,275 ,788 -10,258785 13,271774


[Off iceType=CBD] -2,198321 2,574303 11,524 -,854 ,411 -7,833075 3,436433

[OfficeType=Suburban 0b 0 . . . . .

[LEEDBinary=No] 2,648806 3,401806 11,588 ,779 ,452 -4,792431 10,090042


[Energystar=No] -,979136 2,177301 11,350 -,450 ,661 -5,753375 3,795102


Tenants_LFA -1865,383858 7759,159095 11,589 -,240 ,814 -18837,910591 15107,142875

[OfficeClass= ] ,937691 4,332918 15,673 ,216 ,831 -8,263279 10,138662

[OfficeClass=Class A] 0b 0 . . . . .




132

Office Final model for Indirect Return Estimates of Fixed Effectsa



Intercept -30,575963 36,205476 14,238 -,845 ,412 -108,107578 46,955651

[Year=2010] -3,225884 2,374715 34,548 -1,358 ,183 -8,049068 1,597300

[Year=2011] 4,674553 2,252773 30,661 2,075 ,046 ,077932 9,271173

[Year=2013] 0b 0 . . . . .

GoogleWalk ,037112 ,082149 16,451 ,452 ,657 -,136650 ,210873

LN_LFA 3,254046 2,611919 13,246 1,246 ,234 -2,378007 8,886098

LastUpdateAge -,051015 ,201103 11,307 -,254 ,804 -,492175 ,390146

[Fund=H] 3,038063 4,501376 10,385 ,675 ,514 -6,941441 13,017567

[Fund=F] 4,972506 9,939069 29,517 ,500 ,621 -15,339719 25,284730

[Fund=G] ,053357 8,741945 24,610 ,006 ,995 -17,965505 18,072219

[Fund=B] 0b 0 . . . . .

[RegionA=East North Central] -13,012020 6,568692 15,841 -1,981 ,065 -26,948358 ,924317

[RegionA=Mideast] -14,622831 5,914271 17,225 -2,472 ,024 -27,088450 -2,157211

[RegionA=Mountain] -13,411751 6,214991 13,973 -2,158 ,049 -26,743971 -,079530

[RegionA=Northeast] -14,105212 5,993904 14,673 -2,353 ,033 -26,905788 -1,304637

[RegionA=Pacif ic] -13,889329 6,185669 17,619 -2,245 ,038 -26,905087 -,873572

[RegionA=Southeast] -24,721360 11,344030 22,483 -2,179 ,040 -48,218173 -1,224547


[Off iceType=CBD] 4,976455 4,713859 12,294 1,056 ,311 -5,267022 15,219933

[OfficeType=Suburb 0b 0 . . . . .

[LEEDBinary=No] -2,599298 6,050853 11,420 -,430 ,675 -15,857653 10,659057


[Energystar=No] 6,813195 3,850181 11,498 1,770 ,103 -1,616455 15,242846


Tenants_LFA -1546,816056 13981,142862 11,749 -,111 ,914 -32081,358133 28987,726021

[OfficeClass= ] 2,252276 9,631402 29,761 ,234 ,817 -17,424304 21,928856





133

Office Final model for Total Return Estimates of Fixed Effectsa



Intercept -34,810028 44,338105 13,447 -,785 ,446 -130,274010 60,653954

[Year=2010] -,885827 2,758765 36,985 -,321 ,750 -6,475690 4,704036

[Year=2011] 6,007776 2,484920 32,123 2,418 ,021 ,946922 11,068629

[Year=2013] 0b 0 . . . . .

GoogleWalk ,026797 ,099800 15,592 ,269 ,792 -,185220 ,238813

LN_LFA 3,784584 3,209995 12,718 1,179 ,260 -3,165832 10,735000

LastUpdateAge ,172696 ,249068 11,358 ,693 ,502 -,373397 ,718789

[Fund=H] -1,410187 5,603152 10,444 -,252 ,806 -13,823293 11,002919

[Fund=F] 6,544020 11,732878 25,809 ,558 ,582 -17,581927 30,669968

[Fund=G] -,048930 10,420085 21,570 -,005 ,996 -21,683893 21,586033

[Fund=B] 0b 0 . . . . .


[RegionA=Mideast] -14,276825 7,166496 16,401 -1,992 ,063 -29,439019 ,885369

[RegionA=Mountain] -13,596397 7,614714 13,446 -1,786 ,097 -29,991670 2,798875

[RegionA=Northeast] -12,121893 7,322202 14,240 -1,655 ,120 -27,801707 3,557920

[RegionA=Pacif ic] -14,788785 7,488470 16,607 -1,975 ,065 -30,616576 1,039007

[RegionA=Southeast] -23,737013 13,579087 20,071 -1,748 ,096 -52,056115 4,582089


[Off iceType=CBD] 2,766497 5,812718 12,150 ,476 ,643 -9,881005 15,413999


[LEEDBinary=No] ,606864 7,491405 11,384 ,081 ,937 -15,814038 17,027766


[Energystar=No] 5,769609 4,767206 11,289 1,210 ,251 -4,690248 16,229465


Tenants_LFA -3701,674443 17282,312402 11,685 -,214 ,834 -41469,485701 34066,136816





134

Industrial Model NOI per LFA


Source Num df Den df F Sig.

Intercept 1 44,832 8,101 ,007

Year 3 70,200 1,999 ,122

RegionA 4 40,475 2,807 ,038

LNGoogleTrans 1 46,076 ,219 ,642

Airport 1 43,090 5,373 ,025

Fund 4 49,524 ,160 ,958

LN_LFA 1 42,769 3,736 ,060

LNLastUpdateAge 1 45,310 ,401 ,530

GatewayCity 1 40,258 3,445 ,071





Intercept 4,785896 1,799325 43,065 2,660 ,011 1,157369 8,414424

[Year=2010] -,362059 ,154367 56,500 -2,345 ,023 -,671232 -,052886

[Year=2011] -,149229 ,131766 101,864 -1,133 ,260 -,410590 ,112131

[Year=2012] -,056774 ,103249 113,870 -,550 ,583 -,261313 ,147764

[Year=2013] 0b 0 . . . . .

[RegionA= ] ,765314 ,754018 39,214 1,015 ,316 -,759566 2,290194


[RegionA=Pacif ic] ,832811 ,392218 48,470 2,123 ,039 ,044401 1,621220

[RegionA=Southeast] ,128849 ,377465 45,052 ,341 ,734 -,631380 ,889078


LNGoogleTrans -,046953 ,100222 46,076 -,468 ,642 -,248681 ,154774

[Airport=NO] -,471980 ,203610 43,090 -2,318 ,025 -,882575 -,061385

[Airport=YES] 0b 0 . . . . .

[Fund=H] -,297764 ,666121 48,953 -,447 ,657 -1,636416 1,040889

[Fund=F] -,399010 ,664134 57,707 -,601 ,550 -1,728561 ,930542

[Fund=B] -,133757 ,703881 41,087 -,190 ,850 -1,555182 1,287669

[Fund=D] -,097828 ,548771 44,637 -,178 ,859 -1,203358 1,007703

[Fund=E] 0b 0 . . . . .

LN_LFA -,219419 ,113513 42,769 -1,933 ,060 -,448376 ,009537


[Gatew ayCity=No] -,482370 ,259899 40,258 -1,856 ,071 -1,007541 ,042800




Information Criteriaa

-2 Restricted Log Likelihood 400,458

Akaike's Information Criterion

(AIC)

406,458

Hurvich and Tsai's Criterion

(AICC)

406,602

Bozdogan's Criterion (CAIC) 418,865

Schw arz's Bayesian Criterion (BIC)

415,865

The IC are displayed in smaller-is-better forms.



135

Industrials Estimated Value per LFA Model.



Intercept 1 48,129 29,777 ,000

Year 3 142,424 12,700 ,000

RegionA 4 46,587 1,006 ,414

LNGoogleTrans 1 48,184 6,350 ,015

Airport 1 46,543 2,976 ,091

Fund 4 47,614 ,301 ,876

LN_LFA 1 46,599 1,601 ,212

LNLastUpdateAge 1 95,158 ,888 ,348

Gatew ayCity 1 46,171 ,079 ,779





Intercept 5,619503 1,112489 47,122 5,051 ,000 3,381616 7,857391

[Year=2010] -,210815 ,035579 175,010 -5,925 ,000 -,281034 -,140596

[Year=2011] -,158425 ,027232 165,666 -5,818 ,000 -,212191 -,104659

[Year=2012] -,071849 ,018251 147,134 -3,937 ,000 -,107916 -,035782

[Year=2013] 0b 0 . . . . .

[RegionA= ] -,103285 ,482193 45,859 -,214 ,831 -1,073970 ,867400

[RegionA=Northeast] ,367974 ,301322 48,137 1,221 ,228 -,237830 ,973778

[RegionA=Pacif ic] ,001622 ,223962 49,149 ,007 ,994 -,448412 ,451656

[RegionA=Southeast] -,192431 ,229963 48,250 -,837 ,407 -,654742 ,269879


LNGoogleTrans ,143554 ,056968 48,184 2,520 ,015 ,029023 ,258084

[Airport=NO] -,219608 ,127298 46,543 -1,725 ,091 -,475766 ,036549

[Airport=YES] 0b 0 . . . . .

[Fund=H] -,176986 ,402592 46,707 -,440 ,662 -,987030 ,633059

[Fund=F] ,077608 ,386009 47,812 ,201 ,842 -,698594 ,853809

[Fund=B] -,314366 ,448328 45,645 -,701 ,487 -1,216993 ,588261

[Fund=D] -,103614 ,338920 46,356 -,306 ,761 -,785684 ,578455

[Fund=E] 0b 0 . . . . .

LN_LFA -,089619 ,070831 46,599 -1,265 ,212 -,232145 ,052907


[Gatew ayCity=No] ,046404 ,164741 46,171 ,282 ,779 -,285169 ,377977





-2 Restricted Log Likelihood -84,930

Akaike's Information

Criterion (AIC)

-78,930

Hurvich and Tsai's Criterion

(AICC)

-78,794

Bozdogan's Criterion (CAIC) -66,351

Schw arz's Bayesian Criterion (BIC)

-69,351

The IC are in smaller-is-better forms.



136

Industrial Model for Direct Return

Very little observations obtained for return indices model is not the same as NOI‘s and EV‘s . Therefore the model contains less

variables and is less reliable.

Statistics

IncomeReturn

N Valid 117

Missing 799



Intercept 1 30,224 ,067 ,798

Year 3 69,948 1,022 ,388

RegionA 5 29,994 1,120 ,371

Airport 1 27,906 ,564 ,459

Fund 4 28,495 ,929 ,461

LN_LFA 1 29,660 ,089 ,768

Gatew ayCity 1 38,865 ,076 ,784





Intercept -,796294 9,198696 30,126 -,087 ,932 -19,579254 17,986667

[Year=2010] -1,177198 ,719349 99,918 -1,636 ,105 -2,604380 ,249984

[Year=2011] -,544788 ,576471 89,277 -,945 ,347 -1,690175 ,600598

[Year=2012] -,423002 ,395395 70,601 -1,070 ,288 -1,211475 ,365470

[Year=2013] 0b 0 . . . . .

[RegionA= ] 1,343244 2,147221 25,264 ,626 ,537 -3,076700 5,763189

[RegionA=East North Central] ,946556 2,434859 27,896 ,389 ,700 -4,041863 5,934974

[RegionA=Mideast] ,527447 2,744990 29,976 ,192 ,849 -5,078762 6,133655

[RegionA=Pacif ic] 2,903322 2,110355 25,947 1,376 ,181 -1,435002 7,241646

[RegionA=Southeast] 3,690079 2,353279 27,185 1,568 ,128 -1,136917 8,517076


[Airport=NO] ,896466 1,194049 27,906 ,751 ,459 -1,549802 3,342734

[Airport=YES] 0b 0 . . . . .

[Fund=H] 1,233922 1,554570 25,283 ,794 ,435 -1,965958 4,433802

[Fund=F] 3,158276 2,067658 30,334 1,527 ,137 -1,062496 7,379049

[Fund=B] 1,707805 1,588921 32,526 1,075 ,290 -1,526670 4,942280

[Fund=D] 2,742259 1,558794 26,226 1,759 ,090 -,460546 5,945064

[Fund=E] 0b 0 . . . . .

LN_LFA ,187883 ,631499 29,660 ,298 ,768 -1,102431 1,478197

[Gatew ayCity=No] -,614461 2,225706 38,865 -,276 ,784 -5,116877 3,887955






Akaike's Information Criterion (AIC) 496,896

Hurvich and Tsai's Criterion (AICC) 497,143


Schw arz's Bayesian Criterion (BIC) 504,741

The information criteria are displayed in smaller-is-better

forms.



137

Industrial model for Indirect Return

Very little observations obtained for return indices model is not the same as NOI‘s and EV‘s. Therefore the model contains less


Statistics

AppreciationReturn

N Valid 115

Missing 801



Intercept 1 21,540 ,028 ,868

Year 3 72,899 1,816 ,152

RegionA 6 25,312 ,236 ,961

Airport 1 18,183 ,913 ,352

Fund 4 19,251 1,669 ,198

LN_LFA 1 20,342 ,085 ,774

Gatew ayCity 1 37,713 ,522 ,474





Intercept -5,529696 21,195954 21,724 -,261 ,797 -49,519846 38,460454

[Year=2010] -3,954213 2,530029 72,056 -1,563 ,122 -8,997667 1,089241

[Year=2011] 1,594880 2,324060 69,863 ,686 ,495 -3,040470 6,230231

[Year=2012] -2,820866 2,041114 63,005 -1,382 ,172 -6,899700 1,257969

[Year=2013] 0b 0 . . . . .

[RegionA= ] -,371745 4,097553 15,163 -,091 ,929 -9,097313 8,353823

[RegionA=East North Central] -1,017965 4,884729 18,709 -,208 ,837 -11,252583 9,216653

[RegionA=Mideast] -4,648642 7,415253 31,629 -,627 ,535 -19,759975 10,462691


[RegionA=Pacif ic] -2,202146 4,121453 16,435 -,534 ,600 -10,920476 6,516185

[RegionA=Southeast] -2,302430 4,731570 17,115 -,487 ,633 -12,280083 7,675223


[Airport=NO] -2,296287 2,403754 18,183 -,955 ,352 -7,342743 2,750169

[Airport=YES] 0b 0 . . . . .

[Fund=H] 6,372383 3,069187 16,549 2,076 ,054 -,116519 12,861285

[Fund=F] 8,048911 4,553735 19,240 1,768 ,093 -1,474117 17,571939

[Fund=B] 7,783715 4,000579 24,268 1,946 ,063 -,468257 16,035687

[Fund=D] 3,433781 3,289959 15,656 1,044 ,312 -3,553098 10,420660

[Fund=E] 0b 0 . . . . .

LN_LFA ,391871 1,345449 20,342 ,291 ,774 -2,411669 3,195411

[Gatew ayCity=No] 4,781235 6,615932 37,713 ,723 ,474 -8,615372 18,177841




Industrial model for Total Return







The IC are displayed in smaller-is-better forms.



138

Very little observations obtained for return indices model is not the same as NOI‘s and EV‘s. Therefore the model contains less


Statistics

TotalReturn

N Valid 112

Missing 804



Intercept 1 42,858 ,012 ,915

Year 3 86,579 2,565 ,060

RegionA 5 42,868 ,177 ,970

Airport 1 41,186 1,242 ,271

Fund 4 41,967 3,767 ,010

LN_LFA 1 42,249 ,237 ,629

Gatew ayCity 1 50,366 ,162 ,689





Intercept -4,209036 18,249613 42,924 -,231 ,819 -41,014783 32,596712

[Year=2010] -5,091779 2,351587 95,870 -2,165 ,033 -9,759724 -,423835

[Year=2011] 1,299795 2,159361 92,813 ,602 ,549 -2,988382 5,587973

[Year=2012] -3,074688 1,941703 64,855 -1,584 ,118 -6,952701 ,803324

[Year=2013] 0b 0 . . . . .

[RegionA= ] 1,131872 3,396467 36,582 ,333 ,741 -5,752680 8,016423

[RegionA=East North Central] -,392252 4,173564 41,104 -,094 ,926 -8,820287 8,035784

[RegionA=Mideast] -1,788280 6,370152 48,034 -,281 ,780 -14,596098 11,019538

[RegionA=Pacif ic] ,729539 3,443281 37,336 ,212 ,833 -6,245093 7,704171

[RegionA=Southeast] 2,320347 3,949888 40,099 ,587 ,560 -5,662061 10,302755


[Airport=NO] -2,284795 2,049797 41,186 -1,115 ,271 -6,423876 1,854286

[Airport=YES] 0b 0 . . . . .

[Fund=H] 8,508881 2,604323 37,170 3,267 ,002 3,232833 13,784928

[Fund=F] 12,348526 3,865081 42,346 3,195 ,003 4,550366 20,146686

[Fund=B] 9,129138 3,405300 49,633 2,681 ,010 2,288137 15,970140

[Fund=D] 6,488953 2,741079 38,047 2,367 ,023 ,940154 12,037752

[Fund=E] 0b 0 . . . . .

LN_LFA ,562247 1,155284 42,249 ,487 ,629 -1,768802 2,893297

[Gatew ayCity=No] 2,332633 5,802562 50,366 ,402 ,689 -9,320059 13,985325










The information criteria are displayed in smaller-is-better forms.



139

APPENDIX IV – CORRELATION ANALYSES

Correlations between operationalized variables

INDUSTRIAL Year GoWalk GoTrans LN_NOI_LFA

LastUpdateAge

LN_LFA

Kendall's tau_b

Year Cor 1,000 ,000 ,000 -,051 ,077** ,000

Sig. . 1,000 1,000 ,080 ,002 1,000

N 916 872 252 668 888 892

GoogleWalk Cor ,000 1,000 ,503** ,107

** ,291

** -,157

**

Sig. 1,000 . ,000 ,000 ,000 ,000

N 872 872 248 649 864 860

GoogleTrans Cor ,000 ,503** 1,000 ,226

** ,329

** -,118

**

Sig. 1,000 ,000 . ,000 ,000 ,007

N 252 248 252 195 252 252

LN_NOI_LFA Cor -,051 ,107** ,226

** 1,000 ,098

** -,193

**

Sig. ,080 ,000 ,000 . ,000 ,000

N 668 649 195 668 668 668

LastUpdateAge Cor ,077** ,291

** ,329

** ,098

** 1,000 -,190

**

Sig. ,002 ,000 ,000 ,000 . ,000

N 888 864 252 668 888 884

LN_LFA Cor ,000 -,157** -,118

** -,193

** -,190

** 1,000

Sig. 1,000 ,000 ,007 ,000 ,000 .

N 892 860 252 668 884 892

Spearman's rho

Year Cor 1,000 ,000 ,000 -,069 ,100** ,000

Sig. . 1,000 1,000 ,077 ,003 1,000

N 916 872 252 668 888 892

GoogleWalk Cor ,000 1,000 ,658** ,159

** ,406

** -,230

**

Sig. 1,000 . ,000 ,000 ,000 ,000

N 872 872 248 649 864 860

GoogleTrans Cor ,000 ,658** 1,000 ,328

** ,478

** -,178

**

Sig. 1,000 ,000 . ,000 ,000 ,005

N 252 248 252 195 252 252

LN_NOI_LFA Cor -,069 ,159** ,328

** 1,000 ,137

** -,279

**

Sig. ,077 ,000 ,000 . ,000 ,000

N 668 649 195 668 668 668

LastUpdateAge Cor ,100** ,406

** ,478

** ,137

** 1,000 -,283

**

Sig. ,003 ,000 ,000 ,000 . ,000

N 888 864 252 668 888 884

LN_LFA Cor ,000 -,230** -,178

** -,279

** -,283

** 1,000

Sig. 1,000 ,000 ,005 ,000 ,000 .

N 892 860 252 668 884 892

**. Correlation is significant at the 0.01 level (2-tailed).

Google walk and google transit seem to correlate. This is logical since both analysis methods use the

same metrics and study an assets locational aspects. Combining both into one model might cause

biased effects.


140

Correlations between operationalized variables

OFFICE

LN_NOIsqfLFA

LN_LFA Parkingspots_LFA

DistanceCBD

Stories LastUpdateAge

GoogleWalk

GoogleTransit

KT LN_NOIsqfLFA

Cor 1,000 ,073 -,083 -,170 ,146* -,069 ,193** ,082

Sig. . ,146 ,271 ,125 ,011 ,176 ,000 ,222

N 181 181 87 43 146 181 181 122

LN_LFA Cor ,073 1,000 -,171** -,318** ,352** -,015 ,242** ,315**

Sig. ,146 . ,002 ,000 ,000 ,689 ,000 ,000

N 181 312 164 144 192 312 312 220

Parkingspots_LFA

Cor -,083 -,171** 1,000 ,529** -,334** -,104 -,495** -,661**

Sig. ,271 ,002 . ,000 ,000 ,057 ,000 ,000

N 87 164 164 120 104 164 164 100

DistanceCB

D

Cor -,170 -,318** ,529** 1,000 -,092 -,067 -,453** -,580**

Sig. ,125 ,000 ,000 . ,347 ,264 ,000 ,000

N 43 144 120 144 60 144 144 96

Stories Cor ,146* ,352** -,334** -,092 1,000 ,143** ,304** ,151*

Sig ,011 ,000 ,000 ,347 . ,005 ,000 ,028

N 146 192 104 60 192 192 192 128

LastUpdateAge

Cor -,069 -,015 -,104 -,067 ,143** 1,000 ,051 -,050

Sig ,176 ,689 ,057 ,264 ,005 . ,199 ,311

N 181 312 164 144 192 312 312 220

GoogleWalk Cor ,193** ,242** -,495** -,453** ,304** ,051 1,000 ,715**

Sig ,000 ,000 ,000 ,000 ,000 ,199 . ,000

N 181 312 164 144 192 312 316 224

GoogleTransit

Cor ,082 ,315** -,661** -,580** ,151* -,050 ,715** 1,000

Sig. ,222 ,000 ,000 ,000 ,028 ,311 ,000 .

N 122 220 100 96 128 220 224 224

S

R

LN_NOIsqfL

FA

Cor 1,000 ,108 -,131 -,265 ,235** -,109 ,282** ,112

Sig. . ,147 ,225 ,086 ,004 ,145 ,000 ,220

N 181 181 87 43 146 181 181 122

LN_LFA Cor ,108 1,000 -,198* -,416** ,503** -,018 ,352** ,431**

Sig. ,147 . ,011 ,000 ,000 ,749 ,000 ,000

N 181 312 164 144 192 312 312 220

Parkingspots_LFA

Cor -,131 -,198* 1,000 ,702** -,427** -,154* -,697** -,844**

Sig ,225 ,011 . ,000 ,000 ,049 ,000 ,000

N 87 164 164 120 104 164 164 100

DistanceCBD

Cor -,265 -,416** ,702** 1,000 -,145 -,065 -,591** -,730**

Sig ,086 ,000 ,000 . ,267 ,436 ,000 ,000

N 43 144 120 144 60 144 144 96

Stories Cor ,235** ,503** -,427** -,145 1,000 ,206** ,424** ,210*

Sig ,004 ,000 ,000 ,267 . ,004 ,000 ,017

N 146 192 104 60 192 192 192 128

LastUpdate

Age

Cor -,109 -,018 -,154* -,065 ,206** 1,000 ,059 -,073

Sig ,145 ,749 ,049 ,436 ,004 . ,303 ,281

N 181 312 164 144 192 312 312 220

GoogleWalk Cor ,282** ,352** -,697** -,591** ,424** ,059 1,000 ,868**

Sig ,000 ,000 ,000 ,000 ,000 ,303 . ,000

N 181 312 164 144 192 312 316 224

GoogleTransit

Cor ,112 ,431** -,844** -,730** ,210* -,073 ,868** 1,000

Sig ,220 ,000 ,000 ,000 ,017 ,281 ,000 .

N 122 220 100 96 128 220 224 224

*. Correlation is signif icant at the 0.05 level (2-tailed).

**. Correlation is signif icant at the 0.01 level (2-tailed).


141

Correlations betw een operationalized variables

RETAIL

LN_NOI_LFA

GoogleWalk

GoogleTransit

DistanceCBD

NumberFloors

Parkingspots_LF

A

LnLFA LastUpdateAge

TenantDensity

ParkingLFA

KT LN_NO

I_LFA

Cor 1,000 ,169** ,356** -,233* ,275** ,107** ,050 -,083* ,120** ,137**

Sig. . ,000 ,000 ,013 ,000 ,005 ,150 ,021 ,001 ,000

N 370 370 110 58 349 322 370 370 330 314

GoogleWalk

Cor ,169** 1,000 ,438** -,356** ,145** -,144** ,006 -,034 -,281** -,117**

Sig. ,000 . ,000 ,000 ,000 ,000 ,868 ,322 ,000 ,002

N 370 412 132 88 388 352 412 412 372 344

GoogleTransit

Cor ,356** ,438** 1,000 -,733** ,352** -,231** -,158** -,256** -,393** -,154

Sig. ,000 ,000 . ,000 ,000 ,003 ,010 ,000 ,000 ,053

N 110 132 132 24 112 84 132 132 104 80

DistanceCBD

Cor -,233* -,356** -,733** 1,000 ,042 ,278** ,242** -,015 ,198** ,278**

Sig. ,013 ,000 ,000 . ,633 ,000 ,001 ,847 ,009 ,000

N 58 88 24 88 88 88 88 88 88 88

NumberFloors

Cor ,275** ,145** ,352** ,042 1,000 ,059 ,155** -,181** -,011 ,048

Sig ,000 ,000 ,000 ,633 . ,173 ,000 ,000 ,794 ,272

N 349 388 112 88 388 352 388 388 368 344

Parkin

gspots_LFA

Cor ,107** -,144** -,231** ,278** ,059 1,000 ,128** -,163** ,053 1,000**

Sig. ,005 ,000 ,003 ,000 ,173 . ,000 ,000 ,145 .

N 322 352 84 88 352 352 352 352 352 344

LnLFA Cor ,050 ,006 -,158** ,242** ,155** ,128** 1,000 -,007 -,105** ,091*

Sig. ,150 ,868 ,010 ,001 ,000 ,000 . ,826 ,003 ,013

N 370 412 132 88 388 352 412 412 372 344

LastUpdateAge

Cor -,083* -,034 -,256** -,015 -,181** -,163** -,007 1,000 ,081* -,168**

Sig. ,021 ,322 ,000 ,847 ,000 ,000 ,826 . ,023 ,000

N 370 412 132 88 388 352 412 412 372 344

TenantDensity

Cor ,120** -,281** -,393** ,198** -,011 ,053 -,105** ,081* 1,000 ,053

Sig. ,001 ,000 ,000 ,009 ,794 ,145 ,003 ,023 . ,143

N 330 372 104 88 368 352 372 372 372 344

Parkin

gLFA

Cor ,137** -,117** -,154 ,278** ,048 1,000** ,091* -,168** ,053 1,000

Sig. ,000 ,002 ,053 ,000 ,272 . ,013 ,000 ,143 .

N 314 344 80 88 344 344 344 344 344 344

S

R

LN_NO

I_LFA

Cor 1,000 ,249** ,489** -,227 ,347** ,151** ,075 -,114* ,161** ,197**

Sig. . ,000 ,000 ,087 ,000 ,007 ,147 ,029 ,003 ,000

N 370 370 110 58 349 322 370 370 330 314

GoogleWalk

Cor ,249** 1,000 ,581** -,513** ,182** -,218** ,002 -,052 -,404** -,177**

Sig. ,000 . ,000 ,000 ,000 ,000 ,961 ,289 ,000 ,001

N 370 412 132 88 388 352 412 412 372 344

GoogleTransit

Cor ,489** ,581** 1,000 -,829** ,469** -,309** -,274** -,358** -,515** -,201

Sig. ,000 ,000 . ,000 ,000 ,004 ,001 ,000 ,000 ,074

N 110 132 132 24 112 84 132 132 104 80

DistanceCBD

Cor -,227 -,513** -,829** 1,000 ,053 ,359** ,345** ,000 ,289** ,359**

Sig. ,087 ,000 ,000 . ,627 ,001 ,001 ,997 ,006 ,001

N 58 88 24 88 88 88 88 88 88 88

Numbe

rFloors

Cor ,347** ,182** ,469** ,053 1,000 ,076 ,196** -,226** -,024 ,062

Sig. ,000 ,000 ,000 ,627 . ,157 ,000 ,000 ,642 ,250

N 349 388 112 88 388 352 388 388 368 344

Parkin

gspots_LFA

Cor ,151** -,218** -,309** ,359** ,076 1,000 ,190** -,235** ,075 1,000**

Sig. ,007 ,000 ,004 ,001 ,157 . ,000 ,000 ,162 .

N 322 352 84 88 352 352 352 352 352 344

LnLFA Cor ,075 ,002 -,274** ,345** ,196** ,190** 1,000 -,016 -,135** ,136*

Sig. ,147 ,961 ,001 ,001 ,000 ,000 . ,749 ,009 ,011

N 370 412 132 88 388 352 412 412 372 344

LastUpdateAge

Cor -,114* -,052 -,358** ,000 -,226** -,235** -,016 1,000 ,128* -,242**

Sig. ,029 ,289 ,000 ,997 ,000 ,000 ,749 . ,013 ,000

N 370 412 132 88 388 352 412 412 372 344

TenantDensity

Cor ,161** -,404** -,515** ,289** -,024 ,075 -,135** ,128* 1,000 ,074

Sig. ,003 ,000 ,000 ,006 ,642 ,162 ,009 ,013 . ,172

N 330 372 104 88 368 352 372 372 372 344

Parkin

gLFA

Cor ,197** -,177** -,201 ,359** ,062 1,000** ,136* -,242** ,074 1,000

Sig. ,000 ,001 ,074 ,001 ,250 . ,011 ,000 ,172 .

N 314 344 80 88 344 344 344 344 344 344

**. Correlation is signif icant at the 0.01 level (2-tailed).

*. Correlation is signif icant at the 0.05 level (2-tailed).


142

APPENDIX V – Z-Value Estimates tables.

Z- Estimates Retail

NOI




Intercept 1,446176 ,556713 78,051 2,598 ,011 ,337857 2,554496

[Year=2010] -,163511 ,051392 86,685 -3,182 ,002 -,265664 -,061358

[Year=2011] -,171050 ,046469 152,182 -3,681 ,000 -,262857 -,079243

[Year=2012] -,108474 ,037850 199,308 -2,866 ,005 -,183111 -,033837

[Year=2013] 0b 0 . . . . .

ZGoogleWalk ,181960 ,080113 65,104 2,271 ,026 ,021969 ,341952

[Reta ilTypeE=B] 1,708017 ,579872 65,253 2,946 ,004 ,550017 2,866017

[Reta ilTypeE=C] ,317085 ,236359 65,867 1,342 ,184 -,154840 ,789009

[Reta ilTypeE=E] 0b 0 . . . . .

ZParkingspots_LFA ,104984 ,068539 65,501 1,532 ,130 -,031878 ,241847

ZLnLFA -,375830 ,088306 63,949 -4,256 ,000 -,552245 -,199414

ZTenantDensity ,196419 ,160629 64,960 1,223 ,226 -,124383 ,517221

[GatewayCity=No] -,411014 ,228316 64,460 -1,800 ,077 -,867065 ,045037

[GatewayCity=Yes] 0b 0 . . . . .

[RegionA=East North Central] -1,029418 ,467708 73,736 -2,201 ,031 -1,961403 -,097434

[RegionA=Mideast] -,974210 ,442346 75,856 -2,202 ,031 -1,855246 -,093174



[RegionA=Pacific] -,671776 ,454898 74,934 -1,477 ,144 -1,577992 ,234440

[RegionA=Southeast] -1,494256 ,428401 77,800 -3,488 ,001 -2,347172 -,641340





[RenovatedOrNew=Average Age] ,014110 ,115364 255,053 ,122 ,903 -,213076 ,241297

[RenovatedOrNew=New] -,365306 ,298295 256,041 -1,225 ,222 -,952730 ,222119

[RenovatedOrNew=New R] ,047469 ,200860 212,336 ,236 ,813 -,348467 ,443404

[RenovatedOrNew=Old] -,029816 ,099094 213,639 -,301 ,764 -,225143 ,165510

[RenovatedOrNew=Very Old] 0b 0 . . . . .

a . Dependent Variable: Zscore(LN_NOI_LFA).


1. Type Mall Micro ASC

2. Region SE Meso

3. Region ENC Meso

4. Region ME Meso

5. Gateway No Meso

6. ZLnLFA Micro ASC

7. ZGoogleWalk Micro ASC

8. Year Macro


143

EV Estimates of Fixed Effects

a



Intercept ,768141 ,465122 71,644 1,651 ,103 -,159142 1,695423

[Year=2010] -,219072 ,036231 220,486 -6,047 ,000 -,290475 -,147670

[Year=2011] -,147852 ,027368 271,354 -5,402 ,000 -,201732 -,093971

[Year=2012] -,088705 ,017629 308,632 -5,032 ,000 -,123393 -,054018

[Year=2013] 0b 0 . . . . .

ZGoogleWalk ,145731 ,070919 71,065 2,055 ,044 ,004325 ,287137

[Reta ilTypeE=B] 1,346154 ,511966 71,102 2,629 ,010 ,325347 2,366960

[Reta ilTypeE=C] ,531796 ,215088 71,403 2,472 ,016 ,102965 ,960627

[Reta ilTypeE=E] 0b 0 . . . . .

ZParkingspots_LFA -,035879 ,060790 70,793 -,590 ,557 -,157096 ,085339

ZLnLFA -,183262 ,078850 71,237 -2,324 ,023 -,340475 -,026049

ZTenantDensity ,328913 ,144087 70,700 2,283 ,025 ,041591 ,616235

[GatewayCity=No] -,339677 ,202525 70,797 -1,677 ,098 -,743520 ,064166

[GatewayCity=Yes] 0b 0 . . . . .


[RegionA=Mideast] -,463579 ,375582 71,561 -1,234 ,221 -1,212367 ,285209

[RegionA=Mountain] ,136460 ,471035 71,321 ,290 ,773 -,802683 1,075603

[RegionA=Northeast] ,334845 ,414862 71,835 ,807 ,422 -,492199 1,161889

[RegionA=Pacific] ,134441 ,386229 71,852 ,348 ,729 -,635521 ,904402






ZLastUpdateAge -,117478 ,059029 72,376 -1,990 ,050 -,235140 ,000184

a . Dependent Variable: Zscore(LN_EVsqfLFA).


1. Type mall

2. Region SE

3. Region ENC

4. Type NCcenter

5. ZTenantDensity

6. Year 2010

7. ZLnLFA

8. Year 2011

9. ZGoogleWalk

10. ZLastUpdateAge

11. Year 2012


144

Z-Estimates Offices

NOI Estimates of Fixed Effectsa



Intercept -3,162599 1,561411 34,134 -2,025 ,051 -6,335311 ,010113

[Year=2010] ,053112 ,139093 134,827 ,382 ,703 -,221975 ,328199

[Year=2011] ,020234 ,108435 128,422 ,187 ,852 -,194318 ,234785

[Year=2012] ,037958 ,076346 108,444 ,497 ,620 -,113367 ,189282

[Year=2013] 0b 0 . . . . .

ZGoogleWalk ,039433 ,226002 29,603 ,174 ,863 -,422385 ,501251

ZLN_LFA -,521374 ,271006 29,690 -1,924 ,064 -1,075085 ,032336

ZLastUpdateAge ,363868 ,162037 32,142 2,246 ,032 ,033866 ,693870

[Fund=H] -,983443 ,553495 28,059 -1,777 ,086 -2,117120 ,150233

[Fund=F] 1,854447 1,183874 36,486 1,566 ,126 -,545450 4,254345

[Fund=G] ,608202 1,156164 34,837 ,526 ,602 -1,739329 2,955733

[Fund=S] 1,829115 1,185552 35,492 1,543 ,132 -,576492 4,234721

[Fund=B] 0b 0 . . . . .

[RegionA=East North Central] 1,541240 ,905959 32,657 1,701 ,098 -,302682 3,385163

[RegionA=Mideast] 1,259709 ,790302 32,885 1,594 ,121 -,348386 2,867804



[RegionA=Pacif ic] ,645151 ,788483 32,483 ,818 ,419 -,959999 2,250300

[RegionA=Southeast] ,888598 1,422604 34,888 ,625 ,536 -1,999773 3,776969


[Off iceType= CBD] 1,028895 ,452955 32,018 2,272 ,030 ,106275 1,951515


[Off iceClass=Class A] ,870116 ,640512 28,463 1,358 ,185 -,440951 2,181183


[LEEDBinary=No] -,579479 ,368351 30,036 -1,573 ,126 -1,331715 ,172758


[Energystar=No] ,304443 ,437638 29,104 ,696 ,492 -,590489 1,199375


ZTenants_LFA -,203461 ,191083 28,869 -1,065 ,296 -,594347 ,187424

a. Dependent Variable: Zscore(LN_NOIsqfLFA).


Region NE

Region ENC

Type CBD

Fund

ZLN_LFA

ZLastUpdateAge


145

EV Estimates of Fixed Effectsa



Intercept -2,620588 1,175321 46,075 -2,230 ,031 -4,986283 -,254894

[Year=2010] -,486427 ,064264 98,333 -7,569 ,000 -,613952 -,358903

[Year=2011] -,209840 ,047114 132,455 -4,454 ,000 -,303033 -,116647

[Year=2012] -,107636 ,030716 152,185 -3,504 ,001 -,168320 -,046951

[Year=2013] 0b 0 . . . . .

ZGoogleWalk ,291326 ,170392 45,117 1,710 ,094 -,051836 ,634487

ZLN_LFA -,089234 ,208544 45,251 -,428 ,671 -,509199 ,330730

ZLastUpdateAge ,006114 ,123215 47,190 ,050 ,961 -,241736 ,253965

[Fund=H] -,343615 ,474542 44,052 -,724 ,473 -1,299960 ,612731

[Fund=F] 1,584461 ,991993 45,865 1,597 ,117 -,412476 3,581399

[Fund=G] 1,506834 ,958291 45,691 1,572 ,123 -,422458 3,436126

[Fund=S] 1,956270 1,019325 45,697 1,919 ,061 -,095892 4,008432

[Fund=B] 0b 0 . . . . .

[RegionA=East North Central] -,032476 ,649300 45,869 -,050 ,960 -1,339549 1,274597

[RegionA=Mideast] ,192225 ,558215 45,862 ,344 ,732 -,931495 1,315945

[RegionA=Mountain] -1,134529 ,610553 45,816 -1,858 ,070 -2,363642 ,094585


[RegionA=Pacif ic] ,021222 ,551854 45,722 ,038 ,969 -1,089785 1,132229

[RegionA=Southeast] -,678498 1,096323 46,095 -,619 ,539 -2,885159 1,528163


[Off iceType=CBD] ,420756 ,346905 44,363 1,213 ,232 -,278225 1,119736


[Off iceClass=Class A] 1,173290 ,339515 46,845 3,456 ,001 ,490215 1,856365


[LEEDBinary=No] -,395828 ,287666 44,929 -1,376 ,002 -,975243 ,183586


[Energystar=No] ,491939 ,376190 44,449 1,308 ,198 -,266005 1,249884


ZTenants_LFA ,050541 ,157664 44,195 ,321 ,750 -,267170 ,368251

a. Dependent Variable: Zscore(LN_EVsqfLFA).


Fund

Class A

Region Mtn

Year 2010

LEED

ZGooglewalk

Year 2011

Year 2012


146

Industrial Z-values

NOI




Intercept ,335461 1,024039 43,966 ,328 ,745 -1,728398 2,399320

[Year=2010] -,519797 ,221619 56,500 -2,345 ,023 -,963666 -,075927

[Year=2011] -,214243 ,189172 101,864 -1,133 ,260 -,589470 ,160983

[Year=2012] -,081509 ,148232 113,870 -,550 ,583 -,375158 ,212140

[Year=2013] 0b 0 . . . . .


[RegionA=Pacif ic] 1,195639 ,563094 48,470 2,123 ,039 ,063745 2,327533

[RegionA=Southeast] ,184985 ,541914 45,052 ,341 ,734 -,906451 1,276421


ZLNGoogleTrans -,092614 ,197686 46,076 -,468 ,642 -,490517 ,305289

[Airport=NO] -,677606 ,292317 43,090 -2,318 ,025 -1,267083 -,088129

[Airport=YES] 0b 0 . . . . .

[Fund=H] -,427490 ,956328 48,953 -,447 ,657 -2,349349 1,494370

[Fund=F] -,572845 ,953475 57,707 -,601 ,550 -2,481639 1,335949

[Fund=B] -,192030 1,010539 41,087 -,190 ,850 -2,232725 1,848664

[Fund=D] -,140448 ,787853 44,637 -,178 ,859 -1,727622 1,446726

[Fund=E] 0b 0 . . . . .

ZLN_LFA -,294369 ,152287 42,769 -1,933 ,060 -,601533 ,012795

ZLNLastUpdateAge -,107069 ,169096 45,310 -,633 ,530 -,447580 ,233443

[Gatew ayCity=No] -,692523 ,373128 40,258 -1,856 ,071 -1,446493 ,061447


a. Dependent Variable: Zscore(LN_NOI_LFA).


Region NE, Region Pacific, Gateway city, Airport, Year, Size




Intercept ,943146 1,004163 46,654 ,939 ,352 -1,077365 2,963658

[Year=2010] -,480159 ,081036 175,010 -5,925 ,000 -,640093 -,320225

[Year=2011] -,360833 ,062024 165,666 -5,818 ,000 -,483292 -,238374

[Year=2012] -,163646 ,041568 147,134 -3,937 ,000 -,245793 -,081499

[Year=2013] 0b 0 . . . . .


[RegionA=Pacif ic] ,003695 ,510103 49,149 ,007 ,994 -1,021318 1,028707



ZLNGoogleTrans ,449217 ,178268 48,184 2,520 ,015 ,090820 ,807614

[Airport=NO] -,500187 ,289938 46,543 -1,725 ,091 -1,083619 ,083245

[Airport=YES] 0b 0 . . . . .

[Fund=H] -,403108 ,916956 46,707 -,440 ,662 -2,248092 1,441875

[Fund=F] ,176762 ,879186 47,812 ,201 ,842 -1,591140 1,944665

[Fund=B] -,716011 1,021127 45,645 -,701 ,487 -2,771865 1,339843

[Fund=D] -,235995 ,771936 46,356 -,306 ,761 -1,789500 1,317509

[Fund=E] 0b 0 . . . . .

ZLN_LFA -,190743 ,150755 46,599 -1,265 ,212 -,494092 ,112606

ZLNLastUpdateAge -,124642 ,132275 95,158 -,942 ,348 -,387236 ,137951

[Gatew ayCity=No] ,105691 ,375219 46,171 ,282 ,779 -,649511 ,860893


a. Dependent Variable: Zscore(LN_EV_LFA).


Airport, Year 2010, Google Transit, Year 2011, Year 2010


147

APPENDIX VI – Syntaxes for LMM

Retail syntax:

Final Model NOI MIXED LN_NOI_LFA WITH GoogleWalk Parkingspots_LFA LnLFA TenantDensity By Year RetailTypeE GatewayCity RegionA FloorType RenovatedOrNew /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Year GoogleWalk RetailTypeE Parkingspots_LFA LnLFA TenantDensity GatewayCity RegionA FloorType RenovatedOrNew | SSTYPE(3) /METHOD=REML /PRINT=SOLUTION TESTCOV /RANDOM=INTERCEPT | SUBJECT(ID) COVTYPE(ID) /REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1). Final Model EV MIXED LN_EVsqfLFA WITH GoogleWalk Parkingspots_LFA LnLFA TenantDensity LastUpdateAge By Year RetailTypeE GatewayCity RegionA FloorType /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Year GoogleWalk RetailTypeE Parkingspots_LFA LnLFA TenantDensity LastUpdateAge GatewayCity RegionA FloorType | SSTYPE(3) /METHOD=REML /PRINT=SOLUTION TESTCOV /REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1). Final Model Return MIXED IncomeReturn WITH GoogleWalk Parkingspots_LFA LnLFA TenantDensity By Year RetailTypeD GatewayCity RegionA FloorType RenovatedOrNew /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Year GoogleWalk RetailTypeD Parkingspots_LFA LnLFA TenantDensity GatewayCity RegionA FloorType RenovatedOrNew | SSTYPE(3) /METHOD=REML /PRINT=SOLUTION TESTCOV /RANDOM=INTERCEPT | SUBJECT(ID) COVTYPE(ID) /REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1). MIXED AppreciationReturn WITH GoogleWalk Parkingspots_LFA LnLFA TenantDensity By Year RetailTypeD GatewayCity RegionA FloorType RenovatedOrNew /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Year GoogleWalk RetailTypeD Parkingspots_LFA LnLFA TenantDensity GatewayCity RegionA FloorType RenovatedOrNew | SSTYPE(3) /METHOD=REML /PRINT=SOLUTION TESTCOV /REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1). MIXED TotalReturn WITH GoogleWalk Parkingspots_LFA LnLFA TenantDensity By Year RetailTypeD GatewayCity RegionA FloorType RenovatedOrNew /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Year GoogleWalk RetailTypeD Parkingspots_LFA LnLFA TenantDensity GatewayCity RegionA FloorType RenovatedOrNew | SSTYPE(3) /METHOD=REML /PRINT=SOLUTION TESTCOV /REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1).


148

Office Syntax

Model for NOI and EV

MIXED LN_NOIsqfLFA With GoogleWalk LN_LFA LastUpdateAge Tenants_LFA By Year Fund RegionA OfficeType OfficeClass LEEDBinary Energystar /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001)

HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Year GoogleWalk LN_LFA LastUpdateAge Fund RegionA OfficeType OfficeClass

LEEDBinary Energystar Tenants_LFA | SSTYPE(3) /METHOD=REML /PRINT=SOLUTION TESTCOV

/RANDOM=INTERCEPT | SUBJECT(ID) COVTYPE(ID) /REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1)

MIXED LN_EVsqfLFA With GoogleWalk LN_LFA LastUpdateAge Tenants_LFA By Year Fund RegionA OfficeType OfficeClass LEEDBinary Energystar /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001)

HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Year GoogleWalk LN_LFA LastUpdateAge Fund RegionA OfficeType LEEDBinary

Energystar Tenants_LFA OfficeClass | SSTYPE(3) /METHOD=REML /PRINT=SOLUTION TESTCOV

/RANDOM=INTERCEPT | SUBJECT(ID) COVTYPE(ID) /REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1)

Model for Returns MIXED ......Return With GoogleWalk LN_LFA LastUpdateAge Tenants_LFA By Year Fund RegionA

OfficeType OfficeClass LEEDBinary Energystar /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,

ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Year GoogleWalk LN_LFA LastUpdateAge Fund RegionA OfficeType LEEDBinary Energystar Tenants_LFA OfficeClass | SSTYPE(3)

/METHOD=REML /PRINT=SOLUTION TESTCOV /REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1)


149

Industrial Syntax:

Model for NOI and EV MIXED LN_NOI_LFA With LNGoogleTrans LN_LFA LNLastUpdateAge By Year RegionA Airport Fund

GatewayCity /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,

ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED= Year RegionA LNGoogleTrans Airport Fund LN_LFA LNLastUpdateAge GatewayCity | SSTYPE(3)

/METHOD=REML /PRINT=SOLUTION TESTCOV /RANDOM=INTERCEPT | SUBJECT(ID) COVTYPE(ID)

/REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1) MIXED LN_EV_LFA With LNGoogleTrans LN_LFA LNLastUpdateAge By Year RegionA Airport Fund

GatewayCity /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,

ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED= Year RegionA LNGoogleTrans Airport Fund LN_LFA LNLastUpdateAge GatewayCity | SSTYPE(3)


/REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1) Model for returns

MIXED TotalReturn With LN_LFA By Year RegionA Airport Fund GatewayCity /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001)

HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED= Year RegionA Airport Fund LN_LFA GatewayCity | SSTYPE(3)


/REPEATED=RepIndex | SUBJECT(ID) COVTYPE(AR1)

Documents

International Real Estate Investment Analysis