Comparing Returns of Real Estate Assets in Gateway US Markets
By
Nason Khomassi
BA Spanish Language and Literature, University of Virginia, 2008
And
Swapn Shah
BT Civil Engineering, SV National Institute of Technology
MS Construction Management, Stanford
Submitted to the Department of Urban Studies and Planning in Partial Fulfillment of the
Requirements for the Degree of Master of Science in Real Estate Development
At the
Massachusetts Institute of Technology
September, 2014
© 2014 Nason Khomassi and Swapn Shah
All rights reserved
The authors hereby grant to MIT permission to reproduce and to distribute publicly paper and
electronic copies of this thesis document in whole or in part in any medium no known or
hereafter created.
Signature of Author __________________________________________________
Nason Khomassi
Center for Real Estate
July 30th, 2014
Signature of Author __________________________________________________
Swapn Shah
Center for Real Estate
July 30th, 2014
Certified by
__________________________________________________
Walter Torous
Senior Lecturer, Center for Real Estate
Thesis Supervisor
Accepted by __________________________________________________
Albert Saiz
Chairman, Interdepartmental Degree Program in Real Estate Development
1
Comparing Returns of Real Estate Assets in Gateway US Markets
By
Nason Khomassi
BA Spanish Language and Literature, University of Virginia, 2008
And
Swapn Shah
BT Civil Engineering, SV National Institute of Technology
MS Construction Management, Stanford
Submitted to the Program in Real Estate Development in Conjunction with the Center of Real
Estate on July 30th, 2014 in Partial Fulfillment of the Requirements for the Degree of Master of
Science in Real Estate Development.
ABSTRACT
The main objective of this study is to understand and analyze the risk adjusted returns of
office building and portfolios and determine whether institutional real estate investors are
allocating capital efficiently. NCREIF data from years 1999 to 2014 years will be analyzed. The
data will be split into three proportional classes, upper (Class A), middle (Class B), and tertiary
(Class C) classes based on asset price per square foot and then their risk adjusted returns will be
analyzed with the Sharpe Ratio. Further, based on these findings, the thesis will determine
whether a quantitative measure of building classification can be established. Currently, real
estate assets, office or otherwise, are only classified qualitatively.
Thesis Supervisor: Walter Torous
Title: Senior Lecturer, Center for Real Estate
2
ACKNOWLEDGEMENT
We would like to thank our families for their unconditional love and support. We would not be
here for them. We would also like to thank our classmates and friends for making this such an
unforgettable year. A special thanks to Professor Torous whose guidance made this thesis
possible. We also would like to thank the good people at NCREIF and Yiqun Wang at Real
Capital Analytics for providing the data and expertise on the subject matter.
3
Table of Contents
Abstract……………………………………………………………………………………2
Acknowledgements…………………………………………………………………....…..3
Table of Contents……………………………………………………………………...…..4
Introduction………………………………………………………………………….…....5
Literary Review …………………..………………………………………………………8
Classifying Real Estate Assets…………………………………………………...12
NCRIEF Data…………………………………………………………………….15
Methodology……………………………………………………………………………..17
Data………………………………………………………………………………17
Calculations and Analysis……….……………………………………………….21
Results……………………………………………………………………………………25
Conclusion………………………………………………………..……………………...58
Limitations and Scope for More Research……………………………………….………61
Bibliography…………………………………………………………………………..…62
Appendix…………………………………………………………………………………64
4
INTRODUCTION
The past two decades have seen strong worldwide demand for quality US real estate
assets. In particular, the trend has been marked by the continued institutionalization of real
estate products, from the iconic downtown office building, to single family homes, cell phone
towers, data centers, timberland, and other assets not typically viewed as core real estate assets.
Real estate assets are owned by institutional investors such as private equity firms, Real Estate
Investment Trusts (REITs), sovereign wealth funds, insurance companies, and other private
investment vehicles. These investors allocate capital in real estate for portfolio diversification,
strong cash flows, absolute returns, and inflation protection.
Despite the recent global financial crisis and collapse of the real estate market, the asset
class continues to be perceived as an exceptionally safe investment and an excellent store of
value. Flight to quality, low interest rates, global liquidity, and a recovering US economy have
fueled an increase in asset prices, cap rate compression, and general interest in real estate. Real
Estate asset prices and cap rates in many major US markets have equaled or exceeded quarter 4
2007 peak prices. For example, New York City Class A offices are trading at 4-4.5% cap rates
and at 126% of peak prices. Property prices have increased by 17%-23% on a per square foot
basis in New York, San Francisco, Boston, and LA County. Total yearly transactions of core
assets have also increased in major markets, led by New York with $47.2 billion in sales, a 20%
year of year increase. Los Angeles, Washington DC, San Francisco all enjoyed double digit
increases in transaction. Strikingly, secondary and tertiary markets such as Houston, Atlanta,
and Orlando saw transaction volume increases of 42%, 53%, and 137% respectively. The
5
Trophy Asset market has also seen significant activity with three New York City properties, the
GM Building, 650 Madison Avenue, and Sony Plaza, all trading at over $1 billion (RCA Global
Capital Trends 2014).
Fundamentally, the risk and reward in real estate is in its total returns, which comprises
of both the cash flows and asset appreciation. The cash flows, likely in the form of monthly
rents, are derived from landlords leasing space to tenants. These cash flows tend to be stable and
are exogenous to capital markets. On the other hand, asset appreciation and asset valuation are
more affected by the capital markets, interest rates, and investor sentiment. Asset prices, even
with the exact same rents, can fluctuate greatly from one year to the next based on prevailing cap
rates in a market. Just as important as the total return, is the total risk involved with an
investment. Similar to other assets, investors demand to be compensated for riskier assets with
higher returns.
The main objective of this thesis is to understand and analyze the risk adjusted returns of
office building and portfolios and determine whether institutional real estate investors are
allocating capital efficiently. NCREIF data from years 1999 to 2014 years will be analyzed. The
data will be split into three proportional classes, upper (Class A), middle (Class B), and tertiary
(Class C) classes based on asset price per square foot and then their risk adjusted returns will be
analyzed with the Sharpe Ratio. Further, based on these findings, the thesis will determine
whether a quantitative measure of building classification can be established. Currently, real
estate assets, office or otherwise, are only classified qualitatively.
6
The core concept of this thesis does have some precedent but has not been researched
extensively by the methods proposed. In 1999, Ziering and McIntosh completed a study
analyzing real estate on size and measuring their corresponding risk and returns. The study
found that as real estate assets grow larger, they tend to produce a higher return and have more
risk. While the study showed that markets and investors were efficient, it goes against the
popular belief that larger, more iconic assets are safer. Further, it shows that real estate may not
behave like stocks, where large cap stocks have lower returns and lower volatility, as opposed to
small cap, or growth stocks, which have higher returns and higher risk. By contrast, this study
approaches the idea of best in class asset not by total price, but price per square foot. While
buildings such as the Empire State Building or Chrysler Building may be large, expensive, and
iconic, they do not demand the highest rents in Manhattan. Therefore, for the purposes of this
study, sheer magnitude does not constitute “best in class.”
The present thesis is organized in the following manner. The next section is an overview
of relevant literature and data that is essential to understand both the context and methodologies
used in this thesis. Sources include both academia and industry and spans beyond the real estate
industry and into capital markets. The next section discusses the methodologies and data used in
the analysis. The Results section will then analyze the results of the data analysis, both at a city
by city level and holistic market level. Next, the thesis summarizes its findings in the
Conclusion with a final analysis of the subject matter. Finally, the last portion of the thesis
includes the Limitations and Scope for Further Research, and the Appendix and Bibliography.
7
LITERARY REVIEW
The purpose of our thesis is twofold. The first is to determine whether there is a relation
between price per square foot, risk, and returns. Second, is to determine whether it is possible to
create a quantifiable methodology to differentiate between office asset classes and second then to
determine their respective returns. Currently, real estate classification systems are qualitative
and subjective. Further, little academic literature has been written on the subject matter. On the
other hand, both industry and academic circles have written, studied, and researched investment
returns and volatilities. The literature review intends to investigate these matters further.
QUANTIFYING REAL ESTATE RETURNS
The thesis will determine and compare office class returns by using asset pricing models.
Assets are valued by discounting cash flows at a discount rate that is reflective of the risks
associated with an assets cash flow. Investors use asset pricing models to find the discount rate
for real estate, stocks, bonds, and other assets. The discount rate can be split into two portions,
the risk free rate and the risk premium. The risk free rate is simply the rate of return of an asset
with no risk, typical a government bond. The risk premium is the return in excess of the risk free
rate that an asset is expected to yield.
Investors seek a rate of return that compensates them for taking on risk. The Capital
Asset Pricing Model (CAPM) helps investors calculate the return they should expect. The
CAPM was independently developed by Jack Treynor (1961, 1962), William Sharpe (1964),
John Litner (1965), and Jan Mossin (1966). The theory builds off of the earlier work of Harry
8
Markowitz on portfolio theory and diversification.
The model states that an investment as two
types of risks, systemic risks, and unsystematic risk. Systemic risk is risk that cannot be
diversified away. Conversely, idiosyncratic risk is risk that is specific to an asset that can be
diversified away in a portfolio. Further, the CAPM explains that the return of an asset is a linear
function of risk, meaning the riskier an asset the proportionately more it should return to its
investors. The CAPM formula is as follows:
E(ri) = rf + ßi[E(rm) - rf)
Where:
E(ri)= Expected return of asset I
rf = Return on risk free asset
E(rm) = Return on the market
ßi = Beta of asset i with respect to market return
The beta measures the relative risk, or volatility, of an asset in comparison with the
overall market. For example, if the beta of an asset is 1.5, and the market rises by 20%, then the
asset’s return would increase by 30%. The formula for Beta is the following:
ßi = cov (ri/rm) / σm2
Where:
cov (ri/rm) = the covariance between the asset return and the market return
σm2 = the variance of the market returns
9
For the CAPM to hold true, it assumes investors aim to maximize their economic utility,
are rational and risk-averse, are broadly diversified across a range of investments, are price
takers and cannot manipulate pricing, can lend and borrow unlimited amounts at risk free rate,
trade without transaction or taxation costs, deal with securities that are highly divisible into small
parcels, have homogenous expectations, and assume all information is available at the same time
to all investors. While the CAPM is qualitatively accurate, it studies have shown it is not
precisely accurate quantitatively. However, it is important to note that despite its shortcomings,
the CAPM is a theoretical building block used to understand risk and return in industry and
academia (Bodie, Kane and Marcus, 2011).
In 1992, Eugene Fama and Kenneth French updated the CAPM with what is called the
Fama-French Three-Factor Model. While the CAPM uses one variable to describe asset returns,
the Fama-French model uses three. The components are a market variable, size variable, and
book to market variable. The formula for the Fama-French Three-Factor model is as follows:
E(ri) = rf + ßi[E(rm) - rf)
E(ri) = rf +ßmarket (rmarket factor) + ßsize (rsize factor) + ßbook to market (rbook to market)
Where:
Market factor = return on market index minus risk-free interest rate
Size factor = return on small-firm stocks less return on large firm stocks
Book to market factor = return on high book to market ratio stocks less return on low
book to market ratio stocks [Breely and Myrers (2001), p. 209]
10
Ling and Naranjo (1990, 1991) created a multi-factor model and studied the correlations
between the stock and bond market to macroeconomic events such as changes in interest rates
and industrial production. Their studies found that there was a correlation between real estate
assets and other asset prices based on these systemic risk factors. Ling and Naranjo determined
that changes in real estate per capital consumption, the term structure of interest rates, the real TBill rate, and unexpected inflation are systemic risks to real estate returns.
Zeiring and McIntosh (1999) wrote a paper discussing investment risks and returns of
real estate based on the property size. The study, using NCREIF data from 1981-1998,
investigated the risk-returns of 4 property classes: under $20 million, $20 million - $40 million,
$40 million-$100 million, and over $100 million. Interestingly, the study found that size is a
factor in terms of risk and reward. The largest property segment, over $100 million, exhibited
the best average returns but also the greatest volatility.
Pai and Geltner (2007) researched the NCREIF dataset to find see if they could identify
factors leading to long run investment returns in real estate. They found that real estate
characteristics such as asset size, property type, and market tier could be used to explain returns.
Further, they found large properties and the primary markets had a higher return than smaller
properties in secondary and tertiary markets.
Boudry, Coulson, Kallberg, and Lui (2012) analyzed a 10,454 repeat sale transaction
database to determine the similarity of returns of an individual property to an entire real estate
index. The study found that individual properties returns did not emulate the returns of the index
11
and that index level returns tend to be lower. The difference in returns can be explained by
property level variables such as property size, holding period, building size, land leverage,
market level liquidity, year of sale, and location. The study also concluded that real estate
returns tend to be random and cannot be attributed to set variables, or replicable characteristics.
Thus, they concluded real estate has a significant amount of idiosyncratic risk.
CLASSIFYING REAL ESTATE ASSETS
The designation of a Class A, Class B, and Class C buildings is subjective, however, the
Building Owners and Managers Association International has created a qualitative guideline for
classifying buildings. The rating system is as follows:
Class A: Most prestigious buildings competing for premier office users with rents above
average for the area. Buildings have high quality standard finishes, state of the art
systems, exceptional accessibility and a definite market presence.
Class B: Buildings competing for a wide range of users with rents in the average range
for the area. Building finishes are fair to good for the area. Building finishes are fair to
good for the area and systems are adequate, but the building does not compete with Class
A at the same price.
Class C: Buildings competing for tenants requiring functional space at rents below the
average for the area.
This thesis will strive to complement BOMA’s qualitative framework with a quantitative
approach.
12
Credit rating agencies such as Moody’s and Standard and Poor’s have a similar
qualitative system to describe the creditworthiness of a bond issuer. While each credit rating
agency uses its own methodology, the goal is the same, to express the agencies opinion about the
ability and willingness of a bond issuer to meet its obligations in full and on time. Standard and
Poor’s describes its letter ratings qualitatively in the following manner:
AAA: Extremely strong capacity to meet financial commitments. Highest rating.
AA: Very strong capacity to meet financial commitments.
A: Strong capacity to meet financial commitments, but somewhat susceptible to adverse
economic conditions and changes in circumstances.
BBB: Adequate capacity to meet financial commitments, but more subject to adverse
economic conditions.
BBB-: Considered lowest investment grade by market participants
BB+: Considered highest speculative grade by market participants
BB: Less vulnerable in the near-term but faces major ongoing uncertainties to adverse
business, financial, and economic conditions.
B: More vulnerable to adverse business, financial and economic conditions but currently
has the capacity to meet financial commitments.
CCC: Currently vulnerable and dependent on favorable business, financial and economic
conditions to meet financial commitments.
CC: Currently highly vulnerable.
C: Currently highly vulnerable obligations and other defined circumstances.
D: Payment default on financial commitments.
13
The Credit Agencies qualitative ratings have been extensively studied by the industry and
academia producing extensive quantitative research. The following is a chart of cumulative
historical default rates of municipal and corporate bonds in relation to their credit rating:
CUMULATIVE HISTORIC DEFAULT RATES
[In percent]
-----------------------------------------------------------------------Moody's
S&P
Rating categories
--------------------------------------Muni
Corp
Muni
Corp
-----------------------------------------------------------------------Aaa/AAA.........................
0.00
0.52
0.00
0.60
Aa/AA...........................
0.06
0.52
0.00
1.50
A/A.............................
0.03
1.29
0.23
2.91
Baa/BBB.........................
0.13
4.64
0.32
10.29
Ba/BB...........................
2.65
19.12
1.74
29.93
B/B.............................
11.86
43.34
8.48
53.72
Caa-C/CCC-C.....................
16.58
69.18
44.81
69.19
Investment Grade................
0.07
2.09
0.20
4.14
Non-Invest Grade................
4.29
31.37
7.37
42.35
All.............................
0.10
9.70
0.29
12.98
------------------------------------------------------------------------
As seen above, the credit rating agencies letter qualitative letter rating has a direct and intuitive
relation to the probability of default. This thesis hopes to begin to lay a similar rating framework
that should relate to risk adjusted returns of the underlying real estate asset.
There is little literature in industry or academia differentiating between primary,
secondary, and tertiary markets. Much of the difference comes down to historical preferences
and investor sentiment. Most industry professionals refer to the “big six” as primary market real
estate markets in the US. These markets include: New York City, Boston, Washington DC,
Chicago, San Francisco, and Los Angeles. Recently, cities like Houston and Seattle have gained
much investor appeal and may be considered primary. For the purposes of this thesis, we have
14
compiled the average transaction volume of the top 20 US MSA’s from 2001-2013 and used this
as a measure to gauge which are primary markets. The data is as follows (RCA see appendix):
New York City Metro
Los Angeles Metro
DC Metro
San Francisco Metro
Chicago
Boston
Houston
Seattle
Dallas
Atlanta
South Florida
San Diego
Denver
Phoenix
Philadelphia Metro
Austin
Minneapolis
Sacramento
Portland
Charlotte
$ 14,955,467,685.07
$ 6,474,103,247.79
$ 6,467,318,198.29
$ 5,895,857,476.50
$ 3,654,886,538.21
$ 3,396,762,766.07
$ 2,253,315,818.50
$ 2,230,539,296.64
$ 2,102,648,548.86
$ 1,974,039,861.29
$ 1,621,278,278.50
$ 1,510,001,104.29
$ 1,343,033,269.43
$ 1,264,513,918.86
$ 1,021,140,234.64
$
809,867,709.64
$
708,645,136.71
$
551,979,712.36
$
493,548,990.36
$
444,542,190.43
While the cutoff is arbitrary and subjective, for the purposes of this thesis we have concluded
that the top six markets by transaction volume, which agrees with investor sentiments, are indeed
the primary real estate markets in the United States.
NCRIEF DATA
NCREIF was created to help institutional real estate investors as an unbiased collector,
processor, validator, and disseminator of real estate performance data. It is a non-for-profit trade
association that serves its members, both in industry and academia. The NCREIF dataset goes
15
back to the 4th quarter of 1977 and consists of 30,000 properties historically. Currently, there are
approximately 10,000 properties that include office, industrial, retail, residential, and timberland.
As of 2014, there are around 7,000 properties in the NCREIF database. Data includes detailed
appraised values, property level cash flows, net operating incomes, and capital expenditures.
For the purposes of this thesis, only office data used because retail, industrial, hotels, and
residential assets have characteristics that may produce biased results. In the case of retail,
certain product types in major markets are not owned by institutional investors or are difficult to
analyze. For example, assets on Rodeo Drive in Beverly Hills, or storefronts on 5th Avenue in
New York City would need to be part of any complete analysis, but the data is difficult to
aggregate. Further, many of the top retail institutional assets in primary markets lie outside of
typical market borders such as the top mall serving the Washington DC market, the Tysons
Corner Mall in Northern Virginia. In the case of shopping malls, the two top producing malls in
the United States are not located in the six primary markets, but rather in Miami (Bal Harbor
Shops) and Las Vegas (Forum Shops at Caesars Palace). Further, retail is operation intensive
and developers who can strike deals with anchor tenants will often have the most success.
Industrial assets, much like mall assets are often outside of the city core, such as the Inland
Empire in California, or located in strong port cities such as Houston, Miami, San Diego, or
Seattle. Also, industrial assets in each city are often quite different. In Boston for example,
Industrial may include research & development space whereas in San Francisco it may be tech
space. Hotels are an operation intensive business and are less dependent on the actual real estate.
Food and Beverage are often important components of the hotel business, thus the asset class
was omitted from the study. Multifamily assets are also difficult to analyze because of local rent
16
regulations. Cities such as New York and San Francisco have complex rent control regulations
that would alter returns in those markets as opposed to other cities with fewer regulations. Thus,
to avoid the aforementioned biases only office assets are analyzed in this thesis. Office assets
are also the most commoditized of the property types. For example, an office building in Boston
will be very similar to an office in Chicago. Unlike residential real estate, office uses are not
subject to rent escalation restrictions and in general, are unregulated.
17
METHODOLOGY
The purpose of this thesis is to determine the risk adjusted returns of office buildings in
primary US markets. Further, it aims to determine whether the price per square foot paid has an
impact on the overall volatility of the asset and the total returns. Additionally, it seeks to
determine whether it is possible create a quantifiable methodology to differentiate between office
asset classes. The thesis uses the NCREIF database and analyzed data from 1999-2014. The
analysis consisted of two parts, the first is the filtering of the data and the second is the analysis
and calculation of portfolio level internal rates of returns and Sharpe Ratios.
DATA
NCREIF DATA
The NCREIF database was used to construct portfolios of real estate assets. NCREIF has
complied over 550,000 quarters of data that needed to be sorted, filtered, and analyzed for this
thesis. The data included information regarding office, industrial, retail, and residential
institutional properties from 1999-2014. Ideally, each property would include appraised values,
operating income, operating expenses, and capital expenditures for each quarter, but this was not
the case. Much of the data is partial or incomplete, or only exists for a few quarters as the
property traded hands.
INSTITUTIONAL OFFICE PROPERTIES IN PRIMARY MARKETS
Since NCREIF only collects data from large real estate funds and pension funds, it is
assumed that all of the properties found in the database are of institutional grade and quality.
18
Thus, the first stage of data processing is to remove all data points that do not correspond to
office properties. Next, we removed all properties that were not located in primary markets. We
determined that primary markets include New York City, Boston, Washington DC, Chicago, Los
Angeles, and San Francisco. These cities were chosen because for a number of quantitative and
qualitative reasons. Quantitatively, these six cities, or MSA’s, had the highest total transaction
volume from 2001-2013. In fact, Boston, whose transaction volume was the lowest of the six,
was still 50% higher than the 7th placing city, Houston. These six cities, often referred to as the
“big six” or “sexy six,” are generally considered to be primary markets by real estate
professionals. The data is then filtered into six portfolios with each corresponding with office
buildings in their respective cities.
BUILDING CLASSIFICATION TIERS
This thesis does not attempt to classify office buildings by their traditional and qualitative
measure of Class A, Class B, and Class C. Rather, it sorts office buildings into three quantifiable
classes with the same names. Each Class represents the following:
Class A: The top 33% of office buildings in a particular market based on their appraised
price per square foot in quarter 1 of 2000 (plus or minus one quarter)
Class B: The middle 33%-66% of office buildings in a particular market based on their
appraised price per square foot in quarter 1 of 2000 (plus or minus one quarter)
Class C: The lowest 33% of office buildings in a particular market based on their
appraised price per square foot in quarter 1 of 2000 (plus or minus one quarter)
19
It is important to recognize that each of the Tiers is determined with respect to the market in
which a building is located. For example, the PSF for a Tier 1 building in Chicago will be
different New York City. Thus, the methodology sorts buildings into three groups relative to
their respective market.
WHY PRICE PER SQUARE FOOT?
The methodology employed to classify buildings does not follow the traditional method
of office building classification. The thesis does not attempt to classify each building by quality,
amenities, locations, etc. It is assumed that since all of these buildings are owned by large
institutional landlords, they are therefore institutional assets. Further, nearly all institutional
assets will be Class A buildings and Class B buildings. Typically, the method for discriminating
between a Class A and Class B buildings, although qualitative, would include some mix of
building quality, building size, building amenities, and building location. The methodology in
this thesis merely separates institutional assets into Classes by appraised PSF. It is assumed that
all of the qualities inherent to a property’s value, or class, are priced into this value. While in
theory, one could build the world’s most beautiful building with the best amenities in a rural
county, it would be silly to deem it Class A if it has no locational value. Alternately, the thesis
could have employed a rent per square foot as its basis for classification system. The danger in
this method is that an exceptional property may have a long term lease with below market rents,
which may not be indicative of the property’s true market value. Thus, we feel the appraised
PSF is the best method to classify overall building quality.
20
SAMPLE PERIOD
The range of data used in this thesis is from 1999-2014, more precisely, 1st quarter of
2000 to the 4th quarter of 2013, plus or minus one quarter. All data points that do not start and
end on these dates are not included in the sample. The start date captures real estate prices and
values at the peak of tech bubble of the late 90’s and 2000. Further, the data continues through
the subsequent tech bubble crash, recovery and boom in the post 9/11 economy, the 2008 global
financial crisis, and subsequent recovery. Thus, the data covers the entire real estate cycle and a
recovery. The analysis of a complete real estate cycle is critical in the analysis of this thesis
because different asset types may perform differently in different economic conditions. It is
assumed that real estate investors are long term asset holders due to transaction costs and thus, a
long sample range is required. Upon sorting and filtering the data, a sufficient sample size of
properties met our criteria. The data however, was far from perfect. Many quarters of data were
missing or incomplete. The quarters without a full data set of appraisal price, operating income,
operating expenses, and capital expenditures were removed. Also, properties without at least 40
quarters of complete data were disregarded.
INCOME AND TOTAL RETURN
To calculate the total returns of each specific property both property level cash flow and
appreciation needs to be accounted for. NCREIF data provides quarterly rental income that from
which we then subtract operating expenses and capital expenditures. This is the value used to
calculate property level cash flow. Currently in the real estate industry, Net Operating Income is
used to refer to the cash flow of a property. While this number is widely used in practice, it does
not include capital expenditures and is therefore not the most accurate representation of true
21
property level cash flows. Finally, asset appreciation is included to calculate the total returns of
an asset. Gains or losses from appreciation or depreciation are included in the total returns by
subtracting the final appraisal price by the first appraisal price.
CALCULATIONS AND ANALYSIS
INTERNAL RATE OF RETURN
The first step in data analysis is calculating the Internal Rate of Return for each of the
properties in our data set. The internal rate of return, or IRR, is simply the discount rate that
makes the net present values of all cash flows from a particular investment equal to zero. The
IRR in this study included cash flows from operation as well as the difference in appraisal price
in the first and final quarters. The formula used to calculate IRR is the following:
0 = CFo + CF1/(1+IRR) + CF2/(1+IRR)2 + CF3/(1+IRR)3 + ….. + CFn(1+IRR)n
Where:
CF = Cash flow
It is important to recognize that while IRR provides a number used to compare investment
returns between assets, it does not provide insights into the risks associated with an investment
nor does it regard the timing of those cash flows.
22
SHARPE RATIO
The final step in the analysis is to calculate a Sharpe ratio, every quarter, for each
property. The Sharpe ratio is used to measure risk-adjusted performance of an asset. The three
variables in the Sharpe ratio were calculated as follows:
Asset Return: The internal rate of return as discussed above
Risk Free Rate: The 3 month Treasury bill
Asset Standard Deviation: To calculate the standard deviation for a particular class of
property first we calculate the baseline rent for each individual property in a particular
quarter. We do this by taking the first quarter rent as a base and then we grow it at
quarterly rate of 1.25% every quarter. This baseline rent, calculated quarterly, will be the
basis for our calculations. We then compare the actual rent received by the property to
the expected baseline rent and calculate the percentage difference between the two rental
incomes. Then, we use percentage difference between the two rental incomes to calculate
the quarterly standard deviation of the rent. We then convert the quarterly standard
deviation to an annual standard deviation by multiplying it by (square root n). In this case
n is 4 and hence to calculate quarterly deviation we multiplied by 2 to come up with
annualized number.
These three values are then are used to calculate the Sharpe ratio. Each property’s quarterly
Sharpe ratio is the averaged over the life of the investment and then averaged within its
respective Tier. Once property level Sharpe ratios are calculated, the Sharpe Ratios for each of
23
the three investment Tiers within a specific market. Finally, all of the assets within Tier 1, 2, and
3 are averaged together to produce a three final Sharpe Ratios.
24
RESULTS
The data was compiled and analyzed for all six cities and finally, aggregated into final
portfolio results as seen in the last section here. The results are as follows:
BOSTON
Internal Rate of Return
The IRR of 19 properties in Boston were calculated over the life of 14 year investment
cycle. The data does not show a strong correlation between IRR and price per square foot. 16 of
the 19 properties returned 5-12%, with the two outliers of a -4.4% and 2.2% coming from the
lower end of the PSF, and a return of 13.9% coming from the highest end of the PSF spectrum.
25
VOLATILITY
The volatilities, taken as the standard deviation of a flat line growth rate as compared to
actual cash flow, show a clear linear pattern. On a portfolio level, as price per square foot
increases, volatility decreases. As seen, Class A volatilities are lower than Class B volatilities,
and Class B volatilities are lower than Class C volatilities.
26
SHARPRE RATIO
The following graph is the Sharpe Ratio of each property in the data set. The Sharpe
Ratio was calculated for each property quarterly, which was then averaged over the entirety of
the investment. The graph, much like the IRR data, does not show a strong correlation between
the Sharpe Ratio and price per square foot. You will recognize the same three outliers as the
IRR data.
27
CAP RATES
The cap rate of each property was calculated as the net operating income of the first
quarter over the appraised value of the first quarter. There does not appear to be a correlation
between cap rates and price per square foot. The cap rates under 5% do stand out, at least given
where cap rates stood at the turn of the century, but can be explained as properties that were
either at below market rents or with significant vacancies.
28
AVERAGE NOI/PRICE PER SQUARE FOOT
The following graph is the average NOI of all of the properties in the data set over the
average appraised price per square foot. This data was recalculated quarterly. The decrease
could come from either lower rents, increased operating expenses, or perhaps both.
29
CHICAGO
INTERNAL RATE OF RETURN
Chicago had a larger data set than Boston, totaling 36 properties. There did not appear to
be a correlation between price per square foot and returns. As you can see, the IRR of the
individual properties tend to fluctuate within a range of about 2-13%. There are two outliers at
the lower end of the spectrum, with one returning 27.4% and the other losing -2.3% for their
respective purchasers. As with any data set, outliers are expect overall however, it is clear no
distinct trend or correlation exists.
30
VOLATILITY
The relative volatilities of Class A, Class B, and Class C office assets do not follow a
distinct trend or patter. The volatility of Class A office space is higher than Class B and Class C
asset, which is a different outcome than Boston. Interestingly, there does not appear to be a
considerable difference in risk for Class B and Class C office buildings. Theoretically, this
would lead investors to invest in assets at a lower PSF to limit exposure and for diversification
purposes.
31
SHARPE RATIO
The Sharpe Ratio of real estate office assets in Chicago does not appear to have a
correlation and pattern to price per square foot. While the data set is limited, the highest end
assets in Chicago appear to perform worse than less expensive assets. Thus, it appears that assets
at a the lower end of the price per square have more value for institutional investors than assets at
the highest price points.
32
CAP RATES
There does not appear to be a correlation between price per square foot and cap rates.
This means that investors do not believe that best or worst in class assets will outperform, or
have higher growth rates.
33
AVERAGE NOI / SQUARE FOOTAGE
The overall average NOI over square footage trend flucated between two and three. The
rate, overall, remained flat over the investment horizon.
34
LOS ANGELES
INTERNAL RATE OF RETURN
The data set for Los Angeles consists of 55 assets. Properties above 200 PSF in
particular, do not perform as well as office buildings less than 200 PSF. Also, you will notice
that in comparison to Boston and Chicago, real estate assets performed better in Los Angeles.
35
VOLATILITY
The Los Angeles real estate market exhibits greater volatility than other markets. The
volatilities of Class A and Class C assets are nearly identical. Class B assets stand out as the
most volatile in this market.
36
SHARPE RATIO
There does not appear to be a correlation between price per square foot and Sharpe Ratio
for assets in Los Angeles.
37
CAP RATE / PRICE PER SQUARE FOOT
There is no correlation between price per square foot and cap rates. There are some
unusual figures, such as cap rates above 20%, as well as negative cap rates. Negative cap rates
occur when a buildings operating and capital expenditures exceed income from rents. This can
occur if in the first quarter the building had a high vacancy rate, a large percentage of tenants
with rent abatements, or was undergoing a major capital improvement.
38
AVERAGE NOI /SQUARE FOOTAGE
The graph shows that rents in Los Angeles have grown over the investment horizon of
this study. It is understandable then, that on average Los Angeles had higher IRR’s than Boston
and Chicago respectively.
39
NEW YORK
INTERNAL RATE OF RETURN
New York had a data set of 20 properties and they did not exhibit a linear trend.
However, from the data it is clear that the most expensive assets on a square foot basis all
performed very well. All but one property over 200 PSF produced a double digit IRR over the
investment time horizon. On the lower end of the investment spectrum, only one asset returned
less than 5%.
40
VOLATILITY
Office buildings in New York City are relatively volatile. They do not exhibit a linear
trend based on price paid per square foot. Class C assets are the least volatile in New York.
Class A and Class B assets are significantly more volatile, with Class B being slightly more
volatile than class A.
41
SHARPR RATIO
The Sharpe Ratio of office buildings in New York do not have a correlation with price
per square foot. It appears that assets on the lower end of the spectrum have a wider range of
Sharpe Ratios, as opposed to the Sharpe Ratio of buildings on the higher end of the price range,
which tend to be more consistent.
42
CAP RATE / PRICE PER SQUARE FOOT
New York office buildings historically have low cap rates relative to other US markets.
Here, there does not appear to be a correlation of cap rate and price per square foot.
43
AVERAGE NOI / SQUARE FOOTAGE
New York office buildings experienced strong growth over the time horizon of the study.
A sharp incline of rents is seen at the height of the real estate boom, with a decline just a sharp
after its collapse.
44
SAN FRANCISCO
INTERNAL RATE OF RETURN
There is no clear trend or relationship between IRR and price per square foot in San
Francisco. Only one of the 43 assets produced a negative return over the study time period.
Further, almost all of the buildings returned between 7-17%, a fairly strong investment by most
standards.
45
VOLATILITY
San Francisco office assets do not exhibit a major difference in volatility based on square
footage, especially in comparison to other markets. While the graph below shows a linear
relationship between volatility and class of building, relative to other markets one could assume
that this is essentially a flat result.
46
SHARPE RATIO
Office assets in San Francisco do not exhibit a relationship between price per square foot
and Sharpe Ratio.
47
CAP RATE / PRICE PER SQUARE FOOT
There is no relationship between cap rate and price per square and cap rate. This is a
trend that is consistent throughout all of the markets.
48
AVERAGE NOI / SQUARE FOOTAGE
Cash flows from real estate in San Francisco have been trending upwards in the study
period. Interestingly, they have been fairly consistent despite the 2001 dot com bubble and 2008
real estate bubble. You will also notice a strong uptick in the final quarters of the graph, likely
from recent growth of tech, social media, and startups.
49
WASHINGTON DC
INTERNAL RATE OF RETURN
This thesis analyzed 29 properties in the Washington DC market, with almost all of the
assets returning above 10% IRRs. The data does show a slight downward trend as price per
square foot increases. There is also one asset which produced an exceptional, 36.7% IRR. A
staggering figure over such a long time period.
50
VOLATILITY
Like Boston and to some extent, San Francisco, Washington DC office buildings show a
correlation between price and volatility. The volatility of Class A buildings are less than Class B
buildings, which is less than Class C buildings.
51
SHARPE RATIO
The Sharpe Ratio of assets in Washington DC may have a slight correlation, trending
upwards as price per square foot increases. The graph shows some similarities to the New York
market, where Sharpe Ratios at the high end of the spectrum appear more consistent and slightly
higher than less expensive assets.
52
CAP RATE / PRICE PER SQUARE FOOT
Cap rate data in Washington DC does not appear to have a relationship to price per
square foot.
53
AVERAGE NOI / SQUARE FOOTAGE
NOI rates in Washington DC show a clear upward trend throughout the data period.
Interestingly, there is a sharp increase around the time of the real estate crash. Presumably,
before the time of the collapse rents surged and government spending helped buoy rents through
the crisis and into the recovery.
54
NATIONAL PORTFOLIO
SHARE RATIOS
The following is a graph of all of market level and national level Sharpe Ratios of Class
A, Class B, and Class C assets. The national portfolio was constructing by taking the weighted
average of all of the Sharpe Ratios by Class. Interestingly, the data shows at a national level, the
Sharpe Ratio for Class A and Class B assets are identical at .15, and Class C is at .16. At the
local level the different classes of real estate had varying Sharpe Ratios yet at the national level
they were consistent.
55
MARKET BASED NOI / SF
The following comparative graph reiterates that Los Angeles, New York, San Francisco,
and Washington DC saw strong income growth over the sample period. Alternatively, Chicago
saw flat rents and rents in Boston declined. Also, there is no relationship between market price
to growth.
56
PRICE VOLATILY
The price volatility of office buildings does not appear to have a correlation between
price per square foot, or class. Two cities, Los Angeles and New York, stand out as more volatile
than the rest.
57
CONCLUSION
The results obtained from analyzing the NCREIF data for six key markets in US clearly
show that the Sharpe Ratios for all the classes of office buildings are almost the same on
aggregate levels. This tells us that there is no disproportionate risk adjusted return in investing in
one class of offices as compared to other. Ex-Ante an investor should expect his risk adjusted
returns to be the same for all his office investments. Assuming that returns on all types of office
buildings have the same co-relation with other assets, this finding is in line with the portfolio
theory and proves that the market for office assets is very efficient at least in the major markets
of US.
However, a significant difference in Sharpe Ratios between various classes of buildings
have been observed within individual cities. Especially the Class C buildings in both Boston and
Chicago have a much lower Sharpe ratios then Class A and B assets. On the other hand Class C
offices in the other four markets of New York, Los Angeles, Washington DC and San Francisco
have Sharpe ratios which are either higher or at least equal to the other classes of buildings.
Upon analysis of rental growth trends in these markets we found that the Net Operating Income
per square feet have either remained stable or declined in case of first two cities whereas they
have increased in the case of the remaining four markets. This growth in NOI is highly co-related
to the GDP growth in these MSAs over the study period. As a case in point, Washington DC area
has been among the top ten growing MSAs of USA. Even the other three MSAs having a
positive NOI growth have grown faster than Boston and Chicago for past decade. So one of the
explanation of the varying Sharpe Ratios of Class C assets relative to Class A assets could be
58
that Class C offices would require higher maintenance and capital expenditure which can only be
offset in an environment of general rental growth.
There does appear to be some distinction between classes of real estate within certain
markets. However, the data does not show that there is a simple method to determine the class of
a building quantifiably instead of qualitatively. Perhaps a data set with greater scope and range
in asset quality would prove to be more better in creating a quantifiable method to differentiate
between asset classes.
So what does this all mean for an investor? An investor deciding to invest in office
buildings would not know about the future GDP growth rates of various cities. Ex-ante, it would
be very difficult to identify cities that would grow faster than the rest of the economy. An
investor should choose an office class that most suits his investment mandate or specific
capability that he might have and diversify into few different un-correlated markets. He is
unlikely to receive an extraordinary return by investing in one class of buildings as compared to
the other as long as the investments are diversified across various markets. The popular
sentiment that value add investments or investments in Class B and Class C buildings provide
extraordinary risk adjusted returns is at least not proven in the data we have analyzed.
Is there no role for an investment manager? What we have analyzed is an aggregate
market level data. What we are trying to prove here is that there is no hedging opportunity that
can be created by investing in one class of office as compared to the other. However, there is
significant variation in Sharpe Ratios of individual buildings as well as that between building
classes in a particular market. If the manager has some special skill by which he can identify
markets which are likely to grow faster in future, there is a good amount of risk adjusted return
to be made. Also if the investment manager can identify mispriced buildings in any market there
59
is obviously extraordinary money to be made by pursuing such opportunities. However, the
efficiency of the markets in general, as proven by our analysis gives very little scope to expect ex
ante extraordinary returns.
60
LIMITATIONS AND SCOPE FOR MORE RESEARCH
The thesis is limited by the short time period used in the study, only from 1999-2014.
Further analysis should be completed on multiple real estate cycles. In comparison to stocks,
which data runs from 1925, real estate data is limited. The study is also limited only to primary
markets. Institutional investors do have assets in secondary and tertiary markets and those
properties should be studied as well. Further, the scope of this thesis was limited to only office
properties. This study can be expanded to other asset classes, such as retail, multifamily, and
industrial assets.
The study was also limited by the sheer number of assets it was able to analyze. A larger
data set would have allowed the research to create more “tiers” to see if the relationship held
consistently at all price points. The study also analyzed appraised values, which are not as
accurate as transaction level data. While the thesis did could have used RCA data, which tracks
transaction pricing, it would not have been able to calculate the total return because RCA does
not provide monthly cash flow, operating expense, and capital expenditure data.
61
BIBLIOGRAPHY
Bodie, Ziv., Kane, Alex and Marcus, Alan. Investments, 9th ed. New York: MCGraw-Hill/Irwin.
2011
Boudry, W., E. Coulson, J. Kallberg and C. Lui. “What Do Commercial Real Estate Price
Indices Really Measure?” Unpublished (February, 2008)
Building Owners and Managers Association < www.boma.org/research/pages/building-classdefinitions.aspx >
Fama, E. and K. French. “The Cross-Section of Expected Stock Returns.” Journal of Finance 47,
no.2 (June 1992): 427-465
Geltner, David and N. Miller. Commercial Real Estate Analysis and Investments. Mason, OH:
Thompson South-Western, 2007.
Ling, D. and A. Naranjo, “The Fundamental Determinants of Commercial Real Estate Returns.”
Real Estate Finance 14, no. 4 (Winter 1998): 483-516
Municipal Bond Fairness Act < http://www.gpo.gov/fdsys/pkg/CRPT-110hrpt835/html/CRPT110hrpt835.htm >
Sharpe, W. “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk.”
Journal of Finance 19 (September 1964): 425-442
Ling, D. and A. Naranjo, “The Fundamental Determinants of Commercial Real Estate Returns.”
Real Estate Finance 14, no. 4 (Winter 1998): 483-516
Pai, Arvind and D. Geltner. “Stocks are from Mars, Real Estate is from Venus.” The Journal of
Portfolio Management 33, no. 5 (2007): 133-144
RCA Global Capital Trends 2014
Standard and Poors < http://www.standardandpoors.com/ratings/definitions-and-faqs/en/us >
62
Ziering, B and W. McIntosh. “Property Size and Risk: Why Bigger is Not Always Better.”
Journal of Real Estate Portfolio Management 5, no. 2 (1999): 105-112
63
APPENDIX
Office Building Transaction Volume (RCA)
Average Office Cap Rate per Market (RCA)
64
National Portfolio Averages
NOI / SF Excel Data
Year
Class A
Class B
Class C
Overall
Dec-99 5.608081
2.85497 1.344235 3.269095
Mar-00 5.009734 2.790087
1.29748 3.032434
Jun-00
5.29645 3.088293 1.400024 3.261589
Sep-00 5.330565 3.065992 1.538169 3.311576
Dec-00 5.474779 3.107653 1.402754 3.328395
Mar-01 5.622646 3.104361 1.469122
3.39871
Jun-01 5.423629 3.137418 1.518078 3.359708
Sep-01 5.396427 3.314835 1.572353 3.427872
Dec-01 5.150685
3.13761 1.695229 3.327841
Mar-02 5.057127 2.998212
1.69849 3.251277
Jun-02 5.374014 3.230661 1.668737 3.424471
Sep-02 5.564828 3.246245 1.616317 3.475797
Dec-02 4.991322 3.113756 1.592512
3.23253
Mar-03 4.864761 3.024823 1.634459 3.174681
Jun-03 4.990801 3.101717 1.772816 3.288445
Sep-03 5.134236 3.115113 1.605807 3.285052
Dec-03 5.110799 3.424086 1.450812 3.328566
65
19994
20001
20002
20003
20004
20011
20012
20013
20014
20021
20022
20023
20024
20031
20032
20033
20034
Mar-04
Jun-04
Sep-04
Dec-04
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
Jun-06
Sep-06
Dec-06
Mar-07
Jun-07
Sep-07
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
Mar-09
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
4.967684
4.929118
4.936209
4.933379
4.849663
5.137286
4.673126
4.939833
5.058221
5.377754
5.294645
5.714835
5.381106
5.750178
5.420259
5.952034
6.215205
6.806801
6.333151
6.447521
6.070459
6.141023
5.920603
6.09782
5.969311
6.055985
6.015076
6.234562
6.098445
6.216361
6.047784
5.98981
6.048935
6.237212
5.902281
6.141347
6.015799
6.491826
6.375143
6.324237
6.051058
3.031863
3.029506
3.099701
3.085335
3.10961
3.359978
3.105854
3.06414
3.083782
3.244306
3.132132
3.209923
3.235827
3.251378
3.339951
3.363255
3.355217
3.531593
3.372315
3.544574
3.537645
3.507772
3.400002
3.328342
3.520033
3.469619
3.459501
3.477376
3.420677
3.291995
3.297709
3.406468
3.364085
3.432531
3.340955
3.47121
3.497173
3.644694
3.761283
3.434097
3.485197
1.409873
1.446564
1.436213
1.406643
1.512642
1.615189
1.559638
1.511519
1.59465
1.581999
1.518767
1.539834
1.575269
1.545746
1.524113
1.532558
1.627453
2.113097
1.536217
1.621869
1.518402
1.493802
1.457896
1.456255
1.342895
1.443774
1.385806
1.408095
1.325416
1.359574
1.325153
1.372226
1.434578
1.388414
1.417532
1.374075
1.455988
1.474339
1.519633
1.564298
1.546846
3.136474
3.135063
3.157374
3.141786
3.157305
3.370818
3.112873
3.171831
3.245551
3.401353
3.315182
3.488197
3.397401
3.515767
3.428108
3.615949
3.732625
4.150497
3.747228
3.871321
3.708835
3.714199
3.592834
3.627472
3.610746
3.656459
3.620128
3.706678
3.614846
3.622643
3.556882
3.589501
3.615866
3.686053
3.553589
3.66221
3.65632
3.870287
3.885353
3.774211
3.694367
66
20041
20042
20043
20044
20051
20052
20053
20054
20061
20062
20063
20064
20071
20072
20073
20074
20081
20082
20083
20084
20091
20092
20093
20094
20101
20102
20103
20104
20111
20112
20113
20114
20121
20122
20123
20124
20131
20132
20133
20134
20141
City Level NOI / SF Excel Data
Year
Dec-99
Mar-00
Jun-00
Sep-00
Dec-00
Mar-01
Jun-01
Sep-01
Dec-01
Mar-02
Jun-02
Sep-02
Dec-02
Mar-03
Jun-03
Sep-03
Dec-03
Mar-04
Jun-04
Sep-04
Dec-04
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
Jun-06
Sep-06
Dec-06
Mar-07
Jun-07
Sep-07
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
Mar-09
Boston
4.286859
4.182234
3.666149
3.741686
3.90383
3.648794
3.91163
3.830057
3.811096
3.822783
4.250538
3.782616
3.456536
3.390943
3.478187
3.461434
3.438934
3.495494
3.277576
3.16256
3.340562
3.012737
3.007831
2.833555
3.13175
2.823502
2.995472
3.011205
3.118345
2.795354
2.903614
2.803542
2.932167
2.785908
3.375742
2.990856
3.187652
2.874462
Chicago
2.874307
2.381056
2.650269
2.786211
2.771134
2.753484
2.840374
2.75289
2.609195
2.119335
2.674545
2.959727
2.714865
2.50588
2.888332
2.720761
3.109294
2.195817
2.456344
2.371857
2.460489
2.224974
2.370689
2.347812
2.455735
2.129522
2.617287
2.400569
2.917533
2.083293
2.60991
2.621025
2.606615
2.261664
2.562137
2.507533
2.48531
2.121444
Los
Angeles
2.275958
2.21334
2.246696
2.327677
2.256616
2.397251
2.270848
2.413916
2.207523
2.332591
2.386398
2.425089
2.388218
2.377276
2.344494
2.316245
2.240138
2.298938
2.308958
2.348149
2.289749
2.394261
2.497419
2.488324
2.492087
2.613805
2.658926
2.560063
2.753674
2.875139
2.803819
2.921848
2.966234
3.239501
3.406644
3.325641
3.704461
3.30664
New
York
5.432152
4.41897
4.986693
4.408513
4.837742
5.084631
5.29704
5.182872
5.23175
4.682663
5.135312
5.32839
4.711713
3.858135
4.774834
4.771605
5.55224
4.689128
4.770189
4.884223
4.754253
4.627582
5.578082
4.240995
4.682635
4.895842
4.910788
5.180531
4.733828
4.925827
5.484133
4.591631
5.545842
6.210246
8.420443
5.623249
5.475925
6.101365
67
San
Washington
Francisco DC
3.386756
3.775585
3.381656
3.335801
3.654786
3.782521
4.008326
3.645583
3.701695
3.944939
4.045369
3.852516
3.919159
3.757946
4.005952
4.094244
3.89968
4.059528
4.093171
3.9594
4.052585
3.880476
3.672619
4.268085
3.911962
3.507839
4.113746
3.6984
3.932394
3.648014
4.074413
3.714064
3.720483
3.753498
3.934386
3.623602
3.73652
3.646195
3.696316
3.901246
3.619616
3.882277
3.876635
3.994597
4.115526
4.120434
3.633898
4.092553
3.487021
4.077361
3.815194
4.322667
3.941318
4.430893
3.779133
4.323687
4.021936
4.402286
4.277912
4.277445
4.031035
4.496224
3.882321
4.570852
4.387336
4.318185
4.556954
5.462716
4.449591
5.83517
4.696448
5.184554
4.555074
5.572057
4.47761
5.573393
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
2.596773
2.494547
2.814095
2.733506
2.620047
2.595857
2.638649
2.471382
2.510037
2.593342
2.580451
2.759502
2.605024
2.671398
2.71021
2.722501
2.927697
3.585928
2.83544
2.46123
2.439112
2.596118
2.479271
2.223667
2.484358
2.519165
2.452355
2.239535
2.314618
2.452402
2.602671
2.124739
2.604523
2.162766
2.561807
2.152199
2.579234
2.673518
2.715961
2.122231
3.146914
3.154046
3.029928
3.136442
3.147085
3.018993
3.241721
3.141313
3.141502
3.066421
2.926289
2.94636
2.933287
2.769567
2.989745
3.088954
3.178124
3.059925
2.865926
2.983183
5.934288
5.476187
5.645964
5.620922
6.03678
5.803536
6.332104
6.400605
6.272116
6.538128
6.722883
6.584921
7.067495
6.632174
6.720232
6.345971
6.990225
7.110213
7.135629
6.562917
4.580206
4.243459
4.092764
4.576732
4.415726
4.422287
4.420962
4.428994
4.405099
4.152618
4.136053
4.516944
4.237594
4.366421
4.168319
4.511484
4.348288
4.614489
4.298745
5.146136
5.845971
5.579721
6.138207
5.485738
5.518386
5.663921
5.548889
5.341768
5.500324
5.142297
5.348027
5.470204
5.659978
5.614871
5.623224
5.5702
5.679681
5.387892
5.419616
5.164347
Boston Excel Data
Date
Dec-99
Mar-00
Jun-00
Sep-00
Dec-00
Mar-01
Jun-01
Sep-01
Dec-01
Mar-02
Jun-02
Sep-02
Dec-02
Mar-03
Jun-03
NOI
PSF
4.286859 363.71
4.182234
268.79
3.666149
245.79
3.741686
221.72
3.90383
199.21
3.648794
183.84
3.91163
178.50
3.830057
176.86
3.811096
175.68
3.822783
152.18
4.250538
140.59
3.782616
134.79
3.456536
133.54
3.390943
117.94
3.478187
102.54
Cap
Rate
PSF
IRR
9% 363.71
9%
268.79
7%
245.79
7%
221.72
10%
199.21
9%
183.84
9%
178.50
2%
176.86
8%
175.68
8%
152.18
8%
140.59
11%
134.79
10%
133.54
10%
117.94
9%
102.54
68
PSF
13.9% 363.71
7.8%
268.79
10.3%
245.79
11.7%
221.72
6.9%
199.21
5.0%
183.84
8.2%
178.50
10.1%
176.86
10.2%
175.68
4.8%
152.18
8.0%
140.59
9.5%
134.79
5.1%
133.54
8.0%
117.94
7.7%
102.54
Sharpe
Ratio
0.214299
0.111326
0.149532
0.176062
0.09174
0.054566
0.113756
0.148832
0.152423
0.049443
0.104071
0.134681
0.058274
0.105757
0.096737
Sep-03
Dec-03
Mar-04
Jun-04
Sep-04
Dec-04
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
Jun-06
Sep-06
Dec-06
Mar-07
Jun-07
Sep-07
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
Mar-09
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
3.461434
3.438934
3.495494
3.277576
3.16256
3.340562
3.012737
3.007831
2.833555
3.13175
2.823502
2.995472
3.011205
3.118345
2.795354
2.903614
2.803542
2.932167
2.785908
3.375742
2.990856
3.187652
2.874462
2.596773
2.494547
2.814095
2.733506
2.620047
2.595857
2.638649
2.471382
2.510037
2.593342
2.580451
2.759502
2.605024
2.671398
2.71021
2.722501
2.927697
3.585928
2.83544
66.11
64.90
64.50
60.50
11%
5%
4%
9%
66.11
64.90
64.50
60.50
69
2.2%
8.5%
-4.4%
10.9%
66.11 0.007503
64.90 0.112541
64.50 -0.10173
60.50 0.154356
Mar-14
2.46123
Chicago Excel Data
Date
Dec-99
Mar-00
Jun-00
Sep-00
Dec-00
Mar-01
Jun-01
Sep-01
Dec-01
Mar-02
Jun-02
Sep-02
Dec-02
Mar-03
Jun-03
Sep-03
Dec-03
Mar-04
Jun-04
Sep-04
Dec-04
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
Jun-06
Sep-06
Dec-06
Mar-07
Jun-07
Sep-07
Dec-07
Mar-08
NOI
PSF
2.874307
2.381056
2.650269
2.786211
2.771134
2.753484
2.840374
2.75289
2.609195
2.119335
2.674545
2.959727
2.714865
2.50588
2.888332
2.720761
3.109294
2.195817
2.456344
2.371857
2.460489
2.224974
2.370689
2.347812
2.455735
2.129522
2.617287
2.400569
2.917533
2.083293
2.60991
2.621025
2.606615
2.261664
301.74
296.44
278.80
229.88
225.95
204.68
198.34
192.00
191.03
171.67
161.41
157.92
151.99
150.38
129.58
127.46
125.82
124.81
124.03
119.3103
118.6202
114.9667
110.1738
102.1383
98.41336
97.97422
90.17153
68.60262
57.44167
55.81156
50.78313
49.40367
47.98211
41.25814
Cap
Rate
9%
1%
11%
9%
8%
6%
10%
12%
5%
9%
8%
5%
7%
2%
7%
8%
11%
6%
10%
9%
8%
10%
4%
15%
9%
9%
9%
10%
10%
10%
10%
9%
10%
11%
PSF
IRR
301.74
296.44
278.80
229.88
225.95
204.68
198.34
192.00
191.03
171.67
161.41
157.92
151.99
150.38
129.58
127.46
125.82
124.81
124.03
119.3103
118.6202
114.9667
110.1738
102.1383
98.41336
97.97422
90.17153
68.60262
57.44167
55.81156
50.78313
49.40367
47.98211
41.25814
70
1.5%
7.3%
10.8%
12.5%
14.5%
9.3%
7.9%
10.5%
10.0%
10.9%
1.8%
10.6%
11.5%
4.3%
8.8%
10.1%
6.0%
4.9%
2.9%
13.5%
8.0%
8.6%
9.6%
7.3%
11.2%
10.8%
8.5%
-2.3%
10.3%
11.6%
8.4%
8.6%
8.7%
5.0%
PSF
Sharpe
Ratio
301.74
296.44
278.80
229.88
225.95
204.68
198.34
192.00
191.03
171.67
161.41
157.92
151.99
150.38
129.58
127.46
125.82
124.81
124.03
119.3103
118.6202
114.9667
110.1738
102.1383
98.41336
97.97422
90.17153
68.60262
57.44167
55.81156
50.78313
49.40367
47.98211
41.25814
-0.00918
0.104683
0.170572
0.200228
0.249125
0.148011
0.115132
0.172437
0.159194
0.175483
-0.00622
0.174083
0.245939
0.070905
0.176911
0.221922
0.110287
0.077773
0.0203
0.296146
0.16219
0.171049
0.200102
0.138541
0.102982
0.097846
0.072241
-0.04711
0.092516
0.106454
0.071403
0.073335
0.077934
0.033317
Jun-08
Sep-08
Dec-08
Mar-09
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
2.562137 30.58435
2.507533 24.58414
2.48531
2.121444
2.439112
2.596118
2.479271
2.223667
2.484358
2.519165
2.452355
2.239535
2.314618
2.452402
2.602671
2.124739
2.604523
2.162766
2.561807
2.152199
2.579234
2.673518
2.715961
2.122231
9% 30.58435
5% 24.58414
27.4% 30.58435 0.282009
11.5% 24.58414 0.105371
Los Angeles Excel Data
Date
Dec-99
Mar-00
Jun-00
Sep-00
Dec-00
Mar-01
Jun-01
Sep-01
Dec-01
Mar-02
Jun-02
NOI
PSF
2.275958 306.38
2.21334
259.17
2.246696
259.00
2.327677
258.41
2.256616
241.50
2.397251
240.55
2.270848
220.72
2.413916
216.16
2.207523
188.12
2.332591
168.79
2.386398
166.78
Cap
Rate
PSF
IRR
6% 306.38
5%
259.17
4%
259.00
5%
258.41
6%
241.50
8%
240.55
8%
220.72
7%
216.16
10%
188.12
8%
168.79
22%
166.78
71
PSF
12.7% 306.38
6.6%
259.17
7.9%
259.00
9.4%
258.41
13.5%
241.50
16.5%
240.55
17.4%
220.72
13.8%
216.16
19.2%
188.12
9.4%
168.79
8.6%
166.78
Sharpe
Ratio
0.157715
0.067318
0.087718
0.110175
0.169366
0.214284
0.227732
0.179474
0.254409
0.109085
0.097676
Sep-02
Dec-02
Mar-03
Jun-03
Sep-03
Dec-03
Mar-04
Jun-04
Sep-04
Dec-04
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
Jun-06
Sep-06
Dec-06
Mar-07
Jun-07
Sep-07
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
Mar-09
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
2.425089
2.388218
2.377276
2.344494
2.316245
2.240138
2.298938
2.308958
2.348149
2.289749
2.394261
2.497419
2.488324
2.492087
2.613805
2.658926
2.560063
2.753674
2.875139
2.803819
2.921848
2.966234
3.239501
3.406644
3.325641
3.704461
3.30664
3.146914
3.154046
3.029928
3.136442
3.147085
3.018993
3.241721
3.141313
3.141502
3.066421
2.926289
2.94636
2.933287
2.769567
2.989745
164.94
164.58
159.47
149.58
119.97
119.84
108.34
100.78
100.5366
94.46461
85.94496
81.8174
77.48021
70.03479
69.70793
67.48735
67.01696
66.07735
61.37943
60.7752
60.75896
59.71784
57.09492
57.00107
54.76344
54.35519
51.92513
51.77717
51.75935
51.57799
51.36821
50.75758
50.75489
49.57737
49.04022
47.839
45.44048
44.82759
44.72793
43.10986
41.27735
40.72029
7%
10%
9%
4%
9%
10%
9%
9%
20%
9%
10%
9%
10%
9%
1%
9%
9%
-1%
8%
8%
9%
11%
9%
8%
8%
8%
7%
1%
9%
9%
9%
12%
9%
9%
4%
14%
13%
9%
9%
10%
-3%
6%
164.94
164.58
159.47
149.58
119.97
119.84
108.34
100.78
100.5366
94.46461
85.94496
81.8174
77.48021
70.03479
69.70793
67.48735
67.01696
66.07735
61.37943
60.7752
60.75896
59.71784
57.09492
57.00107
54.76344
54.35519
51.92513
51.77717
51.75935
51.57799
51.36821
50.75758
50.75489
49.57737
49.04022
47.839
45.44048
44.82759
44.72793
43.10986
41.27735
40.72029
72
9.9%
13.3%
15.4%
13.2%
17.2%
11.6%
15.6%
9.7%
24.2%
20.2%
14.7%
14.3%
14.7%
13.0%
23.5%
13.2%
12.2%
12.4%
12.9%
10.6%
14.4%
16.0%
15.3%
13.4%
14.2%
13.4%
12.0%
13.1%
13.8%
14.2%
12.1%
13.6%
12.3%
15.4%
11.4%
13.7%
14.8%
12.8%
13.3%
15.2%
14.1%
16.9%
164.94
164.58
159.47
149.58
119.97
119.84
108.34
100.78
100.5366
94.46461
85.94496
81.8174
77.48021
70.03479
69.70793
67.48735
67.01696
66.07735
61.37943
60.7752
60.75896
59.71784
57.09492
57.00107
54.76344
54.35519
51.92513
51.77717
51.75935
51.57799
51.36821
50.75758
50.75489
49.57737
49.04022
47.839
45.44048
44.82759
44.72793
43.10986
41.27735
40.72029
0.116681
0.167947
0.199388
0.172408
0.224095
0.14765
0.203292
0.113382
0.184196
0.152307
0.105974
0.102753
0.10552
0.091701
0.179803
0.093967
0.08583
0.087329
0.093004
0.074814
0.103169
0.118733
0.110266
0.094484
0.103866
0.094669
0.083637
0.092032
0.169433
0.176037
0.146842
0.164597
0.148459
0.194154
0.135234
0.168286
0.184595
0.155869
0.162068
0.189443
0.178698
0.214097
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
3.088954 40.51029
3.178124 35.24567
3.059925
2.865926
2.983183
13% 40.51029
11% 35.24567
18.9% 40.51029 0.243632
14.6% 35.24567 0.18202
New York Excel Data
Date
Dec-99
Mar-00
Jun-00
Sep-00
Dec-00
Mar-01
Jun-01
Sep-01
Dec-01
Mar-02
Jun-02
Sep-02
Dec-02
Mar-03
Jun-03
Sep-03
Dec-03
Mar-04
Jun-04
Sep-04
Dec-04
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
Jun-06
Sep-06
Dec-06
Mar-07
NOI
PSF
5.432152 570.11
4.41897
414.46
4.986693
360.39
4.408513
344.68
4.837742
309.35
5.084631
300.83
5.29704
277.46
5.182872
260.34
5.23175
240.16
4.682663
233.19
5.135312
191.16
5.32839
183.24
4.711713
156.18
3.858135
147.58
4.774834
127.19
4.771605
101.50
5.55224
89.15
4.689128
57.16
4.770189
55.57
4.884223 52.60159
4.754253
4.627582
5.578082
4.240995
4.682635
4.895842
4.910788
5.180531
4.733828
4.925827
Cap
Rate
PSF
IRR
11% 570.11
4%
414.46
7%
360.39
8%
344.68
5%
309.35
18%
300.83
8%
277.46
8%
260.34
4%
240.16
3%
233.19
3%
191.16
16%
183.24
8%
156.18
11%
147.58
3%
127.19
9%
101.50
8%
89.15
5%
57.16
8%
55.57
13% 52.60159
73
PSF
13% 570.11
15%
414.46
13%
360.39
13%
344.68
17%
309.35
18%
300.83
18%
277.46
16%
260.34
16%
240.16
8%
233.19
9%
191.16
4%
183.24
11%
156.18
15%
147.58
9%
127.19
11%
101.50
11%
89.15
7%
57.16
13%
55.57
13% 52.60159
Sharpe
Ratio
0.140287
0.164425
0.140832
0.139922
0.19556
0.198203
0.188014
0.161737
0.158195
0.068111
0.08313
0.023329
0.101609
0.155423
0.185481
0.225455
0.237946
0.121077
0.261011
0.284472
Jun-07
Sep-07
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
Mar-09
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
5.484133
4.591631
5.545842
6.210246
8.420443
5.623249
5.475925
6.101365
5.934288
5.476187
5.645964
5.620922
6.03678
5.803536
6.332104
6.400605
6.272116
6.538128
6.722883
6.584921
7.067495
6.632174
6.720232
6.345971
6.990225
7.110213
7.135629
6.562917
San Francisco Excel Data
Date
Dec-99
Mar-00
Jun-00
Sep-00
Dec-00
Mar-01
Jun-01
NOI
PSF
3.386756 721.99
3.381656
321.71
3.654786
299.77
4.008326
289.00
3.701695
272.94
4.045369
271.59
3.919159
271.45
Cap
Rate
PSF
IRR
6.9% 721.99
6.4%
321.71
7.1%
299.77
10.0%
289.00
3.4%
272.94
5.5%
271.59
7.0%
271.45
74
PSF
12.3% 721.99
-2.1%
321.71
16.2%
299.77
12.4%
289.00
23.5%
272.94
8.2%
271.59
11.3%
271.45
Sharpe
Ratio
0.107823
-0.03897
0.151366
0.10817
0.226157
0.068574
0.09725
Sep-01
Dec-01
Mar-02
Jun-02
Sep-02
Dec-02
Mar-03
Jun-03
Sep-03
Dec-03
Mar-04
Jun-04
Sep-04
Dec-04
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
Jun-06
Sep-06
Dec-06
Mar-07
Jun-07
Sep-07
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
Mar-09
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
4.005952
3.89968
4.093171
4.052585
4.268085
3.911962
4.113746
3.932394
4.074413
3.720483
3.934386
3.73652
3.696316
3.619616
3.876635
4.115526
3.633898
3.487021
3.815194
3.941318
3.779133
4.021936
4.277912
4.031035
3.882321
4.387336
4.556954
4.449591
4.696448
4.555074
4.47761
4.580206
4.243459
4.092764
4.576732
4.415726
4.422287
4.420962
4.428994
4.405099
4.152618
4.136053
271.44
271.38
252.74
235.41
234.49
227.47
224.78
213.58
208.79
208.31
199.31
198.11
196.5022
192.164
184.9981
179.3091
174.1722
161.789
160
141.3833
128.1761
127.3997
122.51
119.7181
115.1392
107.5342
88.6081
68.15077
65.08575
57.17098
56.47048
53.01095
46.76083
46.26501
45.8561
32.25954
4.2%
8.1%
6.9%
6.4%
6.8%
3.6%
8.8%
8.7%
10.4%
7.3%
8.7%
7.9%
9.3%
9.1%
9.7%
6.1%
7.9%
12.0%
9.1%
7.6%
7.3%
2.0%
8.7%
3.7%
10.5%
8.9%
6.7%
10.3%
8.6%
8.5%
8.5%
9.1%
9.1%
8.3%
9.1%
9.3%
271.44
271.38
252.74
235.41
234.49
227.47
224.78
213.58
208.79
208.31
199.31
198.11
196.5022
192.164
184.9981
179.3091
174.1722
161.789
160
141.3833
128.1761
127.3997
122.51
119.7181
115.1392
107.5342
88.6081
68.15077
65.08575
57.17098
56.47048
53.01095
46.76083
46.26501
45.8561
32.25954
75
12.0%
11.6%
10.9%
9.7%
11.8%
10.6%
14.2%
10.2%
16.5%
9.7%
14.8%
15.0%
12.9%
13.9%
17.2%
12.5%
15.3%
4.6%
13.0%
12.6%
5.9%
10.0%
8.7%
7.1%
14.6%
6.7%
8.8%
17.6%
6.7%
14.1%
10.3%
10.7%
12.3%
12.8%
12.7%
14.9%
271.44
271.38
252.74
235.41
234.49
227.47
224.78
213.58
208.79
208.31
199.31
198.11
196.5022
192.164
184.9981
179.3091
174.1722
161.789
160
141.3833
128.1761
127.3997
122.51
119.7181
115.1392
107.5342
88.6081
68.15077
65.08575
57.17098
56.47048
53.01095
46.76083
46.26501
45.8561
32.25954
0.109099
0.104392
0.092427
0.08077
0.102448
0.090256
0.128473
0.145017
0.261116
0.136808
0.227079
0.229985
0.196545
0.211786
0.269896
0.186201
0.23647
0.049247
0.195254
0.187841
0.069781
0.142456
0.14111
0.102428
0.258002
0.09337
0.142265
0.309566
0.101915
0.241468
0.175212
0.170238
0.208331
0.217503
0.213665
0.256672
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
4.516944
4.237594
4.366421
4.168319
4.511484
4.348288
4.614489
4.298745
5.146136
Washington DC Excel Data
Date
Dec-99
Mar-00
Jun-00
Sep-00
Dec-00
Mar-01
Jun-01
Sep-01
Dec-01
Mar-02
Jun-02
Sep-02
Dec-02
Mar-03
Jun-03
Sep-03
Dec-03
Mar-04
Jun-04
Sep-04
Dec-04
Mar-05
Jun-05
Sep-05
Dec-05
Mar-06
NOI
PSF
3.775585
3.335801
3.782521
3.645583
3.944939
3.852516
3.757946
4.094244
4.059528
3.9594
3.880476
3.672619
3.507839
3.6984
3.648014
3.714064
3.753498
3.623602
3.646195
3.901246
3.882277
3.994597
4.120434
4.092553
4.077361
4.322667
342.01
312.91
292.02
289.61
283.48
243.55
235.56
218.32
207.62
205.26
193.41
190.11
176.35
157.87
156.03
155.30
154.38
138.96
131.01
111.8572
111.7067
86.05632
82.95708
80.10232
78.40674
76.4
Cap
Rate
6.8%
10.0%
8.8%
9.2%
7.4%
10.1%
10.4%
10.0%
9.1%
16.7%
10.0%
8.1%
8.6%
6.2%
17.5%
9.2%
8.8%
7.2%
4.4%
7.3%
-1.8%
9.8%
9.6%
8.9%
11.0%
2.3%
PSF
IRR
342.01
312.91
292.02
289.61
283.48
243.55
235.56
218.32
207.62
205.26
193.41
190.11
176.35
157.87
156.03
155.30
154.38
138.96
131.01
111.8572
111.7067
86.05632
82.95708
80.10232
78.40674
76.4
76
12.7%
12.0%
14.0%
15.2%
13.1%
12.6%
12.1%
5.5%
6.3%
12.2%
12.8%
10.8%
14.6%
36.7%
14.9%
12.5%
16.8%
9.8%
14.1%
13.7%
16.4%
16.4%
17.5%
13.1%
16.8%
17.2%
PSF
Sharpe
Ratio
342.01
312.91
292.02
289.61
283.48
243.55
235.56
218.32
207.62
205.26
193.41
190.11
176.35
157.87
156.03
155.30
154.38
138.96
131.01
111.8572
111.7067
86.05632
82.95708
80.10232
78.40674
76.4
0.266696
0.244238
0.296458
0.321984
0.281447
0.263754
0.253794
0.091149
0.106786
0.249882
0.201715
0.163981
0.234638
0.655216
0.242808
0.203384
0.276825
0.151302
0.229025
0.21846
0.235524
0.228158
0.245592
0.175715
0.238411
0.244737
Jun-06
Sep-06
Dec-06
Mar-07
Jun-07
Sep-07
Dec-07
Mar-08
Jun-08
Sep-08
Dec-08
Mar-09
Jun-09
Sep-09
Dec-09
Mar-10
Jun-10
Sep-10
Dec-10
Mar-11
Jun-11
Sep-11
Dec-11
Mar-12
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
4.430893 50.6025
4.323687 49.83468
4.402286 42.59743
4.277445
4.496224
4.570852
4.318185
5.462716
5.83517
5.184554
5.572057
5.573393
5.845971
5.579721
6.138207
5.485738
5.518386
5.663921
5.548889
5.341768
5.500324
5.142297
5.348027
5.470204
5.659978
5.614871
5.623224
5.5702
5.679681
5.387892
5.419616
5.164347
9.3% 50.6025
11.1% 49.83468
7.9% 42.59743
77
13.9% 50.6025 0.189378
12.7% 49.83468 0.170577
21.2% 42.59743 0.293499