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Systematic Determination of Idiosyncratic Risk in Commercial Real Estate Investments

Liang Peng Leeds School of Business

University of Colorado at Boulder 419 UCB, Boulder, CO 80309-0419

Email: liang.peng@colorado.edu Phone: (303) 4928215

December 2011

Abstract

Commercial real estate investors are likely exposed to a large amount of idiosyncratic risk, because individual properties often constitute a non-trivial share of their portfolios. The idiosyncratic risk is characterized with deviation of investment returns of individual properties from average market returns, which is a cross-sectional instead of a time series measurement and differs from the temporal variation in index returns that the existing literature traditionally focuses on. Using detailed cash flow information of 3,240 commercial properties that were held between 1977 and 2009 by institutional investors in the NCREIF database, this paper measures the property level idiosyncratic risk and finds strong evidence that it increases with the length of the investment holding period, and decreases with the average real estate investment returns in the local market but not the national market. There is also some evidence that the risk is lower if properties are acquired in periods when market valuation is low (higher cap rates) and are sold in periods when market liquidity is high. JEL classification: C51, G11, G12

Key words: commercial real estate, idiosyncratic risk

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I. Introduction

Direct commercial real estate investments constitute a large portion of the total wealth in the

United States, with the estimated value being about $2 trillion.1 Commercial real estate investors

are likely exposed to a large amount of idiosyncratic risk, because individual properties often

constitute a non-trivial share of their portfolios. However, the existing literature on the risk of

commercial real estate investments traditionally focuses on large real estate funds or indices (see,

e.g. Geltner (1989); Geltner and Goetzmann (2000), Goetzmann and Ibbotson (1990), Ling and

Naranjo (1997), Pai and Geltner (2007); Plazzi, Torous and Valkanov (2008), Peyton (2009),

among others), and measures investment risk with temporal variation in index or fund returns.

However, the idiosyncratic risk that investors are exposed to is characterized with deviation of

individual asset returns from average returns of similar properties, which is a cross-sectional

instead of a time series measurement (see, e.g. Fisher and Goetzmann (2005) for evidence of

significant differences between the two different risk measurements). Therefore, while the index-

based or fund-based research makes important contributions to the literature, the literature

provides little empirical evidence or theoretical insights regarding the determinants and asset

pricing implications of the idiosyncratic risk of commercial real estate investments.

This paper aims to conduct some very basic but fundamentally important empirical analyses

regarding the determinants of the idiosyncratic risk. In this paper, the idiosyncratic risk of a

property investment is defined as the volatility of the component of the gross total return of the

property over its holding period that is not explained by the average gross total return over the

same period of an index that track similar (the same property type) properties in the same location

(Core Based Statistical Area, or CBSA, specifically in this paper), which we call the risk

Measurement 1, or similar properties in the entire economy, which we call the risk Measurement

2. This paper analyzes the relationship between the two risk measurements with three sets of 1 2005 value, data source is Make Room for Real Estate, New York, Freeman and Company, LLC.

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variables: the length of the holding period and the local (CBSA) or national market performance

during the holding period, the average market valuation of properties (measured with median

market transaction cap rate) when the property is acquired and disposed, and the market liquidity

(turnover) when the property is acquired and disposed. The selection of these variables is indeed

relatively ad hoc, which is due to the lack of theoretical insights regarding the determinants of the

idiosyncratic risk. Nonetheless, the empirical analyses in this paper have the potential to help

establish stylized facts and stimulate future research. We hope this paper would generate more

interest and more future work that help economists and investors improve the understanding of

the large amount of idiosyncratic risk commercial real estate investors take.

We construct both risk measurements and the three sets of explanatory variables using the

National Council of Real Estate Investment Fiduciaries (NCREIF) database, which seems the

most comprehensive and accurate database that provides detailed operational and financing

information of large “institutional-quality” commercial real estate in the U.S. since 1977. We

find very strong evidence that the idiosyncratic risk increases with the length of the holding

period, and is reduced by the performance of the CBSA index but not the performance of the U.S.

market index. We find weak evidence that the risk is lower if properties are acquired when

market valuation is low (cap rate is high), and disposed when market liquidity is high. This paper

is the first, to our knowledge, that analyzes the determinants of the idiosyncratic risk of

commercial real estate investments.

The rest of this paper is organized as follows. Section II discusses the measurements of the

idiosyncratic risk and the empirical model. Section III discusses the data set and the construction

of explanatory variables. Section IV presents the empirical results. Our conclusions are presented

in the last section.

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II. Definitions and Models

The main measurement in this paper of the idiosyncratic risk of a property investment over its

holding period, which we call Measurement 1, is the volatility of the component of the gross total

return of the property over its holding period that is not explained by the average gross total

return of an index that track similar properties in the same location over the same period. While

properties can be similar in many ways, the type of the property – being apartment, industrial,

office, and retail – has been documented as an important determinant of real estate investment

risk and returns (see, e.g. Peng (2010), among others). Therefore, this paper considers the same

type of properties as “similar” properties.

There is a tradeoff in the definition of location. The smaller is the size of the location, the more

homogeneous are the risk and return characteristics of properties in the same location. However,

the smaller size also leads to fewer similar properties in the same location, which makes it more

difficult to measure the average investment performance of similar properties. This paper treats

each CBSA as a distinct “location”. There are roughly 40 CBSAs in our dataset where roughly

80% of the properties in the sample are located. Therefore, there are sufficient property

observations to estimate indices for these CBSAs. Further, note that it is plausible that “similar”

properties in the same CBSA are subject to similar market conditions, and thus have relatively

homogeneous investment performance.

This paper also analyzes an alternative measurement of the idiosyncratic risk, which we call

Measurement 2. Measurement 2 is the component of the gross total return of the property that is

not explained by the gross total return of an index that track similar properties in the U.S. market

over the same period. This index is estimated from the sample used in this paper. It is apparent

that the main difference between Measurements 1 and 2 is that Measurement 2 treats systematic

deviation of CBSA property returns from national average also as idiosyncratic risk, while

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Measurement 1 does not. This paper uses the alternative measurement to investigate to what

extent the use of CBSA index instead of the national index as the benchmark group affects the

results, and whether our results are robust to the choice of the benchmark groups.

Below we first provide precise definition for the gross total return of a property over its holding

period, and then presents the econometric model we use to calculate the two measurements of the

idiosyncratic risk. Note that the gross total return of investing in a commercial property is

determined by not only the value appreciation over the holding period, but also cash flows during

the period, including the net sale proceeds from a possible partial sale of the property (Partial),

the Net Operating Income (NOI), and the Capital Expenditure (Capex). Since this paper uses a

discrete time model, it assumes that NOI is received, Capex is spent, and the possible partial sale

takes place at the end of each quarter.

We define the gross total return of property i from period t to t +1 , Ri,t+1 , as

Ri,t+1 =

NOIi,t+1 −Capexi,t+1 + Partiali,t+1 +Valuei,t+1

Valuei,t

, (1)

where Valuei,t+1 is the net sale proceeds the owner would have received if she had sold the

property at the end of period t +1 , with the only exception being that it equals the acquisition

price in the acquisition quarter. The gross total return from the acquisition period, tbuyi , to the

disposition period, tselli , is defined as

Ri = Ri,tt=tbuy

i +1

tselli

∏ . (2)

While the gross total return for each interim period is unknown due to the lack of transactions and

thus the lack of market value observations before the final disposition, the gross total return over

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the entire holding period equals the internal rate of return (IRR) with the power raised to the

length of the holding period. Since all cash flows over the holding period, including the

acquisition cost, NOI, Capex, Partial, and the net sale proceeds, are known, we can calculate the

IRR from the cash flows, and then calculate the gross total return. Note that when calculating the

IRRs, there are sometimes multiple solutions. To select the most sensible IRR for each property,

we first calculate the geometric average value appreciation per period using the acquisition cost

and the final net sale proceeds only, and use it as a benchmark. While this benchmark does not

take into account interim cash flows, it captures the value appreciation component of the total

return, and thus provides a good guide regarding the sign of the actual IRR. After calculating this

benchmark for each property, we obtain all the IRR solutions for the property, and then select the

number that is closest to the benchmark as the actual IRR.

After defining gross total returns for property investments, below we present the calculation of

the idiosyncratic risk, which is achieved with the Generalized Repeat Sales Regression (GRSR)

proposed by Peng (2011). The GRSR allows the estimation of price indices for submarkets,

which are CBSAs in this paper, that have small numbers of repeat sale observations. The key

assumption that enables this estimation is that properties in each submarket are relatively

homogeneous, and the difference in price appreciation between each submarket and the overall

market can be captured with “sensitivity” parameters. Peng (2011) proposes to use the EM

algorithm to estimate the GRSR.

Specifically, assume that the log of the gross total return of property i from period t to t +1 ,

Ri,t+1 , has a national market component (the market index MarketIndext+1 ) and a local

component (local sensitivity τCBSAi for the CBSA where property i is located).

log Ri,t+1( ) = τCBSAiMarketIndext+1 + ε i,t+1 (3)

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The product of the local sensitivity and the market index is the CBSA index. Two things are

worth noting in (3). First, the only difference between the GRSR model in (3) and the

conventional RSR is that the conventional RSR forces τCBSAi to be 1. Second, it is possible to let

τCBSAi be a function of variables such as market conditions, but this paper maintains the simplest

assumption and focuses on the determinants of the idiosyncratic risk.

The gross total return (in log) of the property over the holding period can be written as

log Ri( ) = log Ri,t( )t=tbuyi +1

tselli

∑= τCBSAi

MarketIndext+1t=tbuyi +1

tselli

∑ + ε i,t+1t=tbuyi +1

tselli

∑ . (4)

We further simply the notation and denote the error term as

ei = ε i,t+1t=tbuyi +1

tselli

∑ . (5)

Measurement 1 of the idiosyncratic risk equals the squared regression residual ei2 .

This paper uses the two-step EM algorithm proposed by Peng (2011) to estimate (4). The first

step pools properties in all CBSAs, holds constant τCBSAi for each CBSA, which was estimated

from the previous iteration, and estimates (4) to obtain the MarketIndext{ }t=1T

. The initial value

of τCBSAi is set to be 1 for all CBSAs. The second step estimates (4) for each CBSA separately.

In each CBSA level regression, the national index MarketIndext{ }t=1T

obtained from step one is

treated as known, and (4) is estimated to obtain τCBSAi for the CBSA. The two steps are iterated

until both MarketIndext{ }t=1T

and τCBSAi for all MSAs converge.

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Two issues in the estimation are worth discussions. First, some CBSAs have small numbers of

observations of property investments, so the estimated τCBSAi is not reliable due to the small

degree of freedom in the second step of the iteration. To overcome this problem, while we keep

property investments in these CBSAs to improve the estimation of the national market index, we

let τCBSAi remain 1 for CBSAs with fewer than 10 property observations, and do not update the

value of τCBSAi for such MSAs in the iteration.2 In our analyses of the determinants of the

idiosyncratic risk, we exclude these CBSAs and focus on others that have more reliable

estimation of τCBSAi . Since there are only roughly 20% of observations that are located in these

CBSAs with small numbers of observations, the results are robust when all property investment

observations are included.

Second, multicollinearity sometimes presents in the first step regression of the iteration. As a

result, some consecutive quarters cannot be distinguished from each other. That is, while the

regression provides an estimate for the aggregate index value (sum of periodic index values) over

these consecutive periods, the index value for each period cannot be determined. However, it is

important to note that this does not affect the construction of the idiosyncratic risk. Equations (4)

indicates that, when constructing the factors for each property, it is the aggregate index value (the

sum of periodic index values) over the holding period that matters. The very reason why some

consecutive periods cannot be distinguished from each other is that they are all interim periods

between acquisition and disposition for all properties.

We construct Measurement 2 of the idiosyncratic risk from a restricted version of (4).

Specifically, we force τCBSAi to be 1 for all CBSAs in (4), estimate this restricted model, which is

2 All results are robust if we slightly vary the number of observations required to estimate τCBSAi .

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essentially the conventional RSR, and then calculate the squared regression residual as

Measurement 2 of the idiosyncratic risk.

After calculating both measurements of the idiosyncratic risk for each property investment, we

use the following regression to analyze the determinants of the risk.

ei

2 =α + βk Xki

k=1

K∑ +υi (6)

In equation (6), Xki{ }k=1

K are variables that are hypothesized to relate to the idiosyncratic risk.

This paper focuses on three types of variables. The first type pertains to the holding period, and

consists of the length of the holding period and the gross total return of the CBSA index (or the

market index for Measurements 2) over the holding period. The third type is related to the

average valuation of properties in the market, which are the median market transaction cap rates

in the acquisition and the disposition periods respectively. The third type captures the market

liquidity, and consists of two variables that measure the market liquidity in the acquisition and the

disposition periods respectively.

III. Data

This paper constructs the idiosyncratic measurements and the explanatory variables using the

National Council of Real Estate Investment Fiduciaries (NCREIF) database. The NCREIF is a

not-for-profit institutional real estate industry association established in 1982. It serves the real

estate investment industry by collecting, processing, validating and then disseminating

information on financing and operation of commercial real estate. The NCREIF database

comprises institutional-quality commercial properties owned or managed by NCREIF investment

managers and plan sponsors in a fiduciary setting.

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The NCREIF database used in this paper contains information on physical attributes, cash flows,

and transactions of 23,771 properties over the 1977:1 to 2009:3 period. The physical attributes of

each property include the property type (e.g. apartment, office, industrial, retail, and hospitality

properties, etc.), year built, gross square feet, street address, and the CBSA where the property is

located, etc. The cash flow and transaction information includes quarterly net operating income

(NOI), capital expenditure (Capex), as well as the acquisition cost or the net sale proceeds if

applicable. All cash flow and transaction information is on an unlevered basis.

This paper focuses on the four main property types - apartment, industrial, office, and retail

properties – which comprise 22,313 properties in the database. For each property type, we

estimate the quarterly time series of the median transaction cap rate and market liquidity over the

1977:1 and 2009:3 period. In estimating the median transaction cap rate in a quarter, we first

calculate the cap rate for each property acquired or disposed in this quarter. Specifically, we

calculate the cap rate for each property acquisition (disposition) by dividing the median of the

quarterly NOI in the four quarters after the acquisition quarter (before the disposition quarter)

with the acquisition cost (gross sale proceeds), and then annualizing the cap rate (multiplying it

with 4). We use the median NOI to mitigate the effect of errors in NOI on the cap rate

calculation. We then calculate and record the median of all transaction cap rates in each quarter if

there are at least 5 cap rate observations. Figure 1 plots the median transaction cap rate for each

of the four property types over the 1977:1 to 2009:3 period.

For each property type, we also estimate the market liquidity in each quarter, which is measured

with the fraction of NCREIF properties traded (acquired or disposed) in that quarter. We use the

information on acquisition and disposition periods to calculate the number of properties held by

NCREIF members in each quarter, and then divide the number of transactions (both acquisitions

and dispositions) in that quarter with the number of all properties. It is apparent that this

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measurement is more accurate in later sample periods when the NCREIF database comprises

more properties. However, the empirical results presented later are robust when only the second

half of the sample period is used for the analyses.3 Figure 2 plots the market liquidity for each of

the four property types over the sample period.

To construct the dependent variable in our analyses, we calculate the idiosyncratic risk for 3,240

properties that have complete and seemingly accurate cash flow and location information. The

3,240 properties are selected from the 22,313 properties using the following rules. First, note that

most of the properties (about 67%, or 15,000) have not been disposed by 2009:3. Therefore, the

investment performance of these properties is not observed. Further note that a few properties

that have been disposed have missing information on the acquisition period/cost and disposition

period/net sale proceeds. After excluding these two kinds of properties, the sample size is 7,242.

Second, we further clean the 7,242 properties. Specifically, we exclude properties that are the top

1% and the bottom 1% of the distribution of quarterly value appreciation IRR over the holding

period (to mitigate errors in acquisition cost or net sale proceeds), the top 1% and the bottom 1%

of the distribution of the ratio of average quarterly NOI to acquisition cost (to mitigate errors in

NOI), the top 1% of the distribution of the ratio of the average quarterly Capex to acquisition cost

(to mitigate errors in Capex). We also exclude properties of which the maximum quarterly Capex

is more than 50% of the acquisition cost (to mitigate errors in Capex), and properties that have

identical NOI or Capex for more than 10 consecutive quarters. After applying these rules, the

sample size becomes 3509. We then calculate the total return IRR for each property, and exclude

133 properties with missing IRR due to the limit of the R function that calculates IRR that it does

not work for holding periods longer than 48 periods. Finally, we exclude properties that are the

top 2% and the bottom 2% of the total return IRR distribution, to mitigate errors in the IRR

3 This robustness results are not reported but are available upon request.

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calculation due to the presence of multiple solutions. This leads to the final sample of 3,240

properties.

Table 1 summarizes the 3,240 properties in the final sample, which comprise 911 apartment, 898

industrial, 1,012 office, and 419 retail properties. It is apparent that these properties are

“institutional” real estate – they tend to have large size and high values. For the four property

types, the average purchase price is about $24 million, $15 million, $37 million, and $25 million

respectively. The average net sale proceeds is about $30 million, $18 million, $46 million, and

$30 million respectively. The average annual total return IRR is respectively 8.26%, 6.61%,

7.43%, and 10.10%, but the IRRs have large standard deviations. To visualize the distribution of

the property IRRs, Figure 3 plots the histogram of the quarterly property gross total return IRR

over the holding periods for all 3,240 properties. Table 1 also shows that the average holding

period is 18 quarters for apartment, office, and retail properties, and 15 quarters for industrial

properties.

Table 2 reports the mean and the standard deviation of the two Measurements of the property

idiosyncratic risk for the four property types. A few things are worth noting. First, Measurement

1 is smaller than Measurement 2, which is not surprising since both the national market index and

the CBSA common component is excluded when calculating the risk measurement 1. Second,

there is large variation in both measurements – the standard deviation for each property type and

each measurement is roughly twice as large as the mean. Third, the magnitude of the risk seems

to vary across the four property types. Particularly, office properties have the largest average

idiosyncratic risk regardless the measurement used.

Table 3 reports the across-time mean and the standard deviation of the median transaction cap

rate and the liquidity (NCREIF turnover) in the market for each of the four property types. Note

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that the statistics of the cap rate are not comparable across property types since each type has

missing information for different time periods. The liquidity/turnover measure is more

comparable than the cap rate, as there are much fewer periods with missing information. This

table shows that industrial properties have the lowest average liquidity/turnover among the four

property types, and apartment properties have the highest average liquidity/turnover.

IV. Results

The empirical regressions of the risk idiosyncratic risk use properties located in CBSAs for which

CBSA indices are estimated, so the sample size is slightly smaller than 3,240. Table 4 reports the

regressions of idiosyncratic risk on holding-period related variables. Panel A reports regressions

of Measure 1 of the idiosyncratic risk of each property on an intercept term, the length of the

holding period, and the gross total return over the same period of the CBSA index constructed

using the GRSR in (4). All four types of properties have consistent and strong results. First, the

idiosyncratic risk is positively related to the length of the holding period. The coefficient ranges

from 0.002 for apartment to 0.04 for the other three types of properties, and are statistically

significant at the 5% for apartment and at the 1% for others. This result indicates that commercial

properties are similar with residential properties in the sense that the idiosyncratic risk increases

with the holding period (see, e.g. Case and Shiller (1987), Goetzmann (1992), among others, for

evidence of the positive relationship between the idiosyncratic risk and the length of holding

period).

Second, there is strong and consistent evidence that a better performance of similar properties in

the CBSA helps reduce the risk. The coefficient of the CBSA total return index over the holding

period is -0.097 (significant at the 5%) for apartment, -0.037 (significant at the 5% level) for

industrial, -0.049 (significant at the 10%) for office, and -0.09 (significant at the 5% level) for

retail properties. This finding is documented for the first time in the literature, and seems to

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suggest that when commercial real estate investors experience greater returns, they are also

exposed to lower idiosyncratic risk. We hope future theoretical work can help understand this

novel finding and its implications on investment.

Panel B reports regressions of Measurement 2 on the length of the holding period and the gross

total return of the U.S. index over the holding period. The length of holding period is still

statistically significant in increasing the property idiosyncratic risk for all four types of properties.

However, the return of the U.S. index is no longer significant, except for apartment properties

(coefficient being -0.121 and significant at the 5% level). This difference between Panels A and

B seems to indicate that the risk-reduction effect of market performance is at the regional level. It

is the performance of the local real estate market - not the national market - that affects the

idiosyncratic risk of commercial property investments. This finding also highlights the important

asset pricing implications of the location-heterogeneity of the commercial real estate market.

Table 5 reports regressions of both measurements on the median market transaction cap rates in

the acquisition and disposition periods. Panel A shows weak evidence for a negative relationship

between the cap rate in the acquisition period and the idiosyncratic risk. Properties acquired

when the median market cap rate is higher, which means properties are “cheaper”, tend to have

lower idiosyncratic risk. However, note that while the coefficient is negative for all four types of

properties, it is only significant for apartment (at the 1% level) and retail (at the 10% level)

properties. Panel B provides some but even weaker evidence for the relationship between the

median cap rate in the acquisition period and the idiosyncratic risk. The coefficient is significant

(at the 5% level) only for apartment properties. Both Panels do not provide any evidence for the

relationship between the market cap rate at the disposition period and the risk.

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Table 6 reports the regressions of both risk measurements on market liquidity at the acquisition

and disposition periods. There is no evidence for any relationships between the market liquidity

at acquisition and the magnitude of the idiosyncratic risk; however, there is some evidence that

market liquidity at disposition helps reduces the risk. Specifically, Panel A reports negative

coefficients of market liquidity at disposition for apartment and retail properties, significant at the

5% and the 1% level respectively. Similar but weaker evidence can be found in B. The

coefficient is significantly negative (at the 5% level) for apartment properties only. Overall, it

seems that selling properties when the market is liquid can reduce the idiosyncratic risk for

apartment and retail but not the other two types of properties.

We have also conducted some robustness checks by replicating Tables 4 to 6 using properties

acquired in the later half of the sample period. This is to see if the results we find are driven by

the less accurate measurements of the explanatory variables, such as the market cap rate and

liquidity, in earlier periods. The sub-sample analyses provide consistent results with Tables 4 to

6, which are not reported but available upon request.

V. Conclusion

This paper is the first that estimates the idiosyncratic risk of commercial property investment, and

empirically analyzes its relationship with a variety of real estate market conditions. The results

indicate that the idiosyncratic risk increases with the length of holding period, decreases with the

market performance in the local market, but not the national market, during the holding period.

Further, for apartment and retail properties, the risk is reduced if properties are acquired when

market valuation is low and sold when the market is liquid. We hope this paper can generate

more interest in the research area about the determinants and the implications of the idiosyncratic

risk of commercial real estate investments, which is important given the large size of the

commercial real estate market and the significant impact of its performance on the economy.

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References Case,  Karl,  and  Robert  Shiller,  1987,  Prices  of  single  family  homes  since  1970:  New  indexes  

for  four  cities,  New  England  Economic  Review  46-­‐56.  Fisher,   Jeffrey,  and  William  Goetzmann,  2005,  The  performance  of  real  estate  portfolios:  A  

simulation  approach,  Journal  of  Portfolio  Management  32-­‐45.  Geltner,  David,  1989,  Estimating  real  estate’s  systematic  risk  from  aggregate  level  appraisal-­‐

based  returns,  AREUEA  Journal  17,  463-­‐481.  Geltner,   David,   and   William   Goetzmann,   2000,   Two   decades   of   commercial   property  

returns:  A  ncreif  index  using  independent  appraisals,  Journal  of  Real  Estate  Finance  and  Economics  21,  5-­‐22.  

Goetzmann,  William  N.,   1992,   The   accuracy   of   real   estate   indices:  Repeat   sale   estimators,  Journal  of  Real  Estate  Finance  and  Economics  5,  5-­‐53.  

Goetzmann,  William  N.,  and  Roger  G.  Ibbotson,  1990,  The  performance  of  real  estate  as  an  asset  class,  Journal  of  Applied  Corporate  Finance  3,  65-­‐76.  

Ling,   David,   and   Andy   Naranjo,   1997,   Economic   risk   factors   and   commercial   real   estate  returns,  Journal  of  Real  Estate  Finance  and  Economics  14,  283-­‐307.  

Pai,  Arvind,  and  David  Geltner,  2007,  Stocks  are   from  mars,  real  estate   is   from  venus:  The  cross-­‐section  of  long-­‐run  investment  performance,  Journal  of  Portfolio  Management  33,  134-­‐144.  

Peng,   Liang,   2010,   Risk   and   returns   of   commercial   real   estate:   A   property   level   analysis,  Available  at  SSRN:  http://ssrn.com/abstract=1658265.  

Peng,   Liang,   2011,   Repeat   sales   regression   on   heterogeneous   properties,   Journal   of   Real  Estate  Finance  and  Economics  forthcoming.  

Peyton,   Martha   S.,   2009,   Capital   markets   impact   on   commercial   real   estate   cap   rates:   A  practitioner’s  view,  Journal  of  Portfolio  Management  35,  38-­‐49.  

Plazzi,  Alberto,  Walter  Torous,  and  Rossen  Valkanov,  2008,  The  cross-­‐sectional  dispersion  of   commercial   real   estate   returns   and   rent   growth:   Time   variation   and   economic  flucuations,  Real  Estate  Economics  36,  403-­‐439.  

 

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Table 1 Summary of Sample Properties This table reports the number of properties in the sample and the mean and the standard deviation of the following variables for each property type: the gross square feet, the purchase price, the net sale proceeds, the purchase cap rate (the annual NOI after the purchase divided with the purchase price), the going-out cap rate (the annual NOI before the disposition divided with the net sale proceeds), the holding period (the number of quarters from the acquisition to the disposition). Apartment Industrial Office Retail Properties 911 898 1,012 419 Gross Square Feet Mean 288,071 333,187 240,098 223,664 Gross Square Feet Std. Dev. 160,288 414,858 266,088 230,105 Purchase Price Mean $23,823,798 $14,867,896 $36,560,020 $24,695,338 Purchase Price Std. Dev. $15,671,753 $17,515,553 $56,970,313 $27,817,381 Net Sale Proceeds Mean $30,459,085 $17,612,355 $45,716,430 $29,963,181 Net Sale Proceeds Std. Dev. $23,153,760 $22,134,178 $79,713,453 $35,025,231 Purchase Cap Rate Mean 7.14% 8.53% 8.49% 8.69% Purchase Cap Rate Std. Dev. 2.26% 2.63% 2.92% 2.56% Going-out Cap Rate Mean 6.50% 7.43% 7.40% 7.79% Going-out Cap Rate Std. Dev. 2.07% 2.74% 2.89% 2.32% Annualized IRR Mean 8.26% 6.61% 7.43% 10.10% Annualized IRR Std. Dev. 12.01% 14.53% 12.86% 12.59% Holding Period Mean 18 15 18 18 Holding Period Std. Dev. 10 10 11 11

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Table 2 Summary of Idiosyncratic Risk This table reports across-property mean and standard deviation of the two measurements of property level investment idiosyncratic risk for apartment, industrial, office, and retail properties. Measurement 1 for a property is the squared difference between the total return (in log) of the property during its holding period and the total return (in log) of an index of properties of the same type in the same CBSA during the same period. Measurement 2 is the squared difference between the total return (in log) of the property during its hold period and the total return (in log) of a national index of properties of the same type during the same period. Apartment Industrial Office Retail

Measurement 1 Mean 0.093 0.091 0.141 0.092 Std. Dev. 0.176 0.154 0.294 0.199

Measurement 2 Mean 0.099 0.098 0.155 0.099 Std. Dev. 0.183 0.179 0.326 0.216

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Table 3 Real Estate Market Conditions This table reports the temporal mean and the standard deviation of the national average cap rate and turnover calculated from NCREIF transactions for apartment, industrial, office, and retail properties respectively. The correlation between the cap rate and the turnover is also reported. Apartment Industrial Office Retail Cap Rate Mean 7.20% 8.81% 8.35% 8.34% Cap Rate Std. Dev. 1.44% 1.26% 1.40% 1.30% Market Liquidity Mean 6.39% 3.89% 5.08% 4.37% Market Liquidity Std. Dev. 4.84% 1.89% 3.17% 3.45% Cap Rate/Turnover Correlation 0.12 0.11 0.00 0.12

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Table 4 Idiosyncratic Risk and Holding Period This table reports the regressions of the two measurements of the property investment idiosyncratic risk on holding-period related variables. The explanatory variables comprise an intercept term, the length of the holding period, and the gross total return over the same period of the CBSA index (Panel A) or the national index (Panel B). Heteroskedasticity-robust standard deviations are in parentheses. ***, **, and * indicate a significant level of 1%, 5%, and 10% respectively. Apartment Industrial Office Retail

Panel A. Risk Measurement 1 Intercept 0.090***

(0.012) 0.040*** (0.008)

0.076*** (0.011)

0.039** (0.016)

Holding period duration 0.002** (0.001)

0.004*** (0.001)

0.004*** (0.001)

0.004*** (0.001)

Holding period CBSA return -0.097** (0.043)

-0.037** (0.017)

-0.049* (0.026)

-0.090** (0.037)

Sample size 717 732 820 181 Adjusted R2 0.01 0.06 0.02 0.13

Panel B. Risk Measurement 2 Intercept 0.090***

(0.012) 0.030*** (0.010)

0.062*** (0.013)

0.049*** (0.015)

Holding period duration 0.003*** (0.001)

0.005*** (0.001)

0.005*** (0.001)

0.003** (0.001)

Holding period market return -0.121** (0.055)

-0.010 (0.025

-0.025 (0.036)

-0.039 (0.040)

Sample size 717 732 820 181 Adjusted R2 0.01 0.07 0.03 0.04

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Table 5 Idiosyncratic Risk and Market Valuation This table reports the regressions of the two measurements of the property investment idiosyncratic risk on the median market transaction cap rates in the acquisition and disposition periods. Heteroskedasticity-robust standard deviations are in parentheses. ***, **, and * indicate a significant level of 1%, 5%, and 10% respectively. Apartment Industrial Office Retail

Panel A. Risk Measurement 1 Intercept 0.303***

(0.040) 0.153*** (0.058)

0.294*** (0.070)

0.172* (0.092)

Market cap rate at acquisition -2.454*** (0.885)

-0.729 (0.758)

-1.069 (0.972)

-1.947* (1.009)

Market cap rate at disposition -0.454 (0.745)

-0.007 (0.465)

-0.889 (0.643)

1.013 (0.769)

Sample size 717 732 820 181 Adjusted R2 0.01 0.00 0.00 0.02

Panel B. Risk Measurement 2 Intercept 0.316***

(0.043) 0.130** (0.062)

0.287*** (0.075)

0.019 (0.123)

Market cap rate at acquisition -2.292** (0.907)

-0.089 (0.886)

-0.686 (1.092)

0.199 (1.409)

Market cap rate at disposition -0.732 (0.763)

-0.331 (0.599)

-1.011 (0.667)

0.592 (0.711)

Sample size 717 732 820 181 Adjusted R2 0.03 0.00 0.00 0.00

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Table 6 Idiosyncratic Risk and Market Liquidity This table reports the regressions of the two measurements of the property investment idiosyncratic risk on the market liquidity in the acquisition and disposition periods. Heteroskedasticity-robust standard deviations are in parentheses. ***, **, and * indicate a significant level of 1%, 5%, and 10% respectively. Apartment Industrial Office Retail

Panel A. Risk Measurement 1 Intercept 0.144***

(0.019) 0.091*** (0.024)

0.085** (0.039)

0.081*** (0.025)

Market liquidity at acquisition -0.008 (0.205)

-0.109 (0.390)

0.435 (0.390)

0.759 (0.490)

Market liquidity at disposition -0.758** (0.303)

0.067 (0.323)

0.562 (0.794)

-0.682*** (0.192)

Sample size 717 732 820 181 Adjusted R2 0.01 0.00 0.00 0.04

Panel B. Risk Measurement 2 Intercept 0.158***

(0.019) 0.084*** (0.028)

0.126*** (0.048)

0.060** (0.025)

Market liquidity at acquisition -0.089 (0.207)

0.094 (0.432)

0.228 (0.414)

0.715 (0.466)

Market liquidity at disposition -0.789** (0.310)

0.239 (0.391)

0.329 (0.917)

-0.253 (0.304)

Sample size 717 732 820 181 Adjusted R2 0.01 0.00 0.00 0.01

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Figure 1

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Figure 2

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Figure 3