REIT Selection and Portfolio Construction - American Real Estate ...

REIT Selection and Portfolio Construction: Using Operating Efficiency as an Indicator of Performance Executive Summary. A Real Estate Investment Trust (REIT) selection and portfolio construction criteria is developed based on REIT operating efficiency and pricing multiples (price-to-net asset value). Portfolios of REITs are constructed of REITs that have relatively high operating efficiency but are trading at a relatively low price. The results show that these portfolios, constructed with the use of filtering criteria, have superior first year performance in all cases, with an average excess return of 600 basis points compared to the NAREIT Equity Index. In most cases, the REITs also had superior second and third year performance, suggesting performance persistence. Further research is needed to examine if the filter works better with more frequent portfolio rebalancing and if the criteria can be used to effectively execute a short sale strategy. This article is the winner of the REITs category (sponsored by the National Association of Real Estate Investment Trusts) presented at the 2002 American Real Estate Society Annual Meeting.

*The City University of New York–Baruch College, New York, NY 10010 or [email protected]. **Florida Atlantic [email protected].

University,

Jupiter,

FL

33458

or

by Randy I. Anderson* Thomas M. Springer**

Introduction Recently, there have been numerous academic studies examining the operational efficiency of Real Estate Investment Trusts (REITs) (see Anderson, Lewis and Springer, 2000, for a review). These studies test whether or not REITs minimize their input utilization, given their size as measured either by total assets or market capitalization and what REIT attributes influence these efficiency metrics. REITs that minimize these inputs for a given level of output operate on their efficient frontier and are said to be X-efficient. REITs that deviate from the efficient frontier are deemed Xinefficient. This type of analysis is useful on both a micro and a macro level. On the macro level, the average level of X-efficiency is theoretically related to the overall competitive structure of the industry. If an industry is characterized by perfect competition, then players in that market must operate near or on the efficient frontier in order to be able to compete and survive. On the other hand, if markets have inefficiencies such as barriers to entry, it may be possible for firms to operate off of their efficient frontier and survive. As such, efficient markets are characterized by high degrees of X-efficiency and inefficient markets are characterized by a highdegree of X-inefficiency. On a micro level, individual REITs are able to determine what they need to do in order to operate on their efficient frontier and to get an understanding of what firm characteristics enhance or detract from firm efficiency (Leibenstein, 1966). Journal of Real Estate Portfolio Management

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Randy I. Anderson, Thomas M. Springer

Theoretically, REIT analysts could use this information in conjunction with their standard bottomup stock selection techniques, such as ratio analysis and multiple analyses, as stock selection tools. However, to our knowledge, REIT security analysts do not incorporate this specific type of efficiency information because it is not readily available, it is computationally complex and it is a very new metric to the real estate sector. Additionally, none of the prior academic works have taken the idea of operational efficiency and attempted to use the information to select securities and construct portfolios. In other words, there is no empirical evidence that examines whether X-efficiency estimates are useful in understanding REIT pricing and ultimately REIT return performance. In this study, REIT efficiency is estimated using a frontier-based Data Envelopment Analysis (DEA) technique similar to that of Anderson, Fok, Zumpano and Elder (1998). The estimated measures of relative efficiency are estimated and used in combination with corresponding Price-to-Net Asset Value (PNAV) ratios to establish a selection criterion to identify REITs that may be targeted for superior financial performance. Finally, equallyweighted portfolios are constructed of the selected REITs to assess the validity of the selection criteria. The results show that the sample of selected REITs outperforms the NAREIT Equity Index in all years studied.1 For the 1995–1999 period, the first year total return of the selected REITs averaged 600 basis points in excess of the total return for the NAREIT Index. The best performance exceeded the NAREIT Equity Index total return by 1620 basis points. Second year returns showed the selected REITs outperforming the NAREIT Index in three of four cases. Third year returns show outperformance of the NAREIT Equity Index in two of three cases. The results demonstrate the usefulness of the relative efficiency measure as an additional criterion in assessing potential REIT performance. The results also show a degree of persistence over time. The article is organized as follows. The next section provides some background. The following sections 18

Vol. 9, No. 1, 2003

discuss the methodology, the sample data and the results. The final section is the conclusion.

Background The efficiency of REITs has been measured mostly from the perspective of a REIT being a portfolio of real estate assets (Anderson, Lewis and Springer, 2000). This is demonstrated by the use of total assets as the proxy for the size of the REIT. Lewis, Springer and Anderson (2000) uses both total assets and market capitalizations for output measures and find consistent results using either choice. Academic studies have shown REITs to be relatively inefficient with the degree of inefficiency depending on the methodology and the data. Several new questions arise from these findings. First, does the information on REIT efficiency translate to better valuations of REITs in the marketplace? Next, can efficiency information be used as an input to the process of selecting REITs to be included in an investment portfolio? The lack of information and missing information is always a concern to analysts and investors. Downs and Guner (1999) address the question, ‘‘Is the information deficiency in real estate evident in public market trading?’’ Although they are specifically looking at information deficiencies resulting from localized property markets and the dynamics of leases, their logic can be extended to information inefficiencies from internal operations. With respect to information on REIT operating efficiency, the issue of the effects of the introduction of new information that is unavailable to most market participants is now considered. If new information is brought to the marketplace, one issue is the level of credibility of the information and the value that the information will add to the market. If the information is not well received, it will not result in price effects or effective pricing strategies. A secondary issue is the time it will take for the information to be assimilated into the market functions. If the information is readily available, then the market will quickly absorb it and the impacts will be short-lived. If the information is difficult to obtain, then the opportunities

REIT Selection and Portfolio Construction

to capitalize on the asymmetries will be both better and longer lasting. This study attempts to link new information, relative to the operating efficiency of REITs, to existing portfolio selection tools used by analysts. The analysis is restricted to a single analysts’ criterion, the Price-to-Net Asset Value (PNAV) ratio, in combination with the new information not readily available to analysts. A filter-based trading strategy is set up, such as that of Cooper, Downs and Patterson (1999), who assess the usefulness of price reversals in the selection of stocks. In the current study, specific criteria are used to delineate a group of REITs and then they are tested to see if their performance was superior to a benchmark. If superior performance is determined, then there is evidence that the new information is valuable to the marketplace and that the use of this information can lead to better decisions in the future. The PNAV ratio has been widely applied as a gauge of potential REIT performance. The PNAV ratio compares the market price of a REIT to the estimated value of its assets and is calculated as Price/NAV ⫺ 1. As noted in Koch (1998), the PNAV ratio is similar in concept to comparing the implicit capitalization rate to a REIT’s acquisition rate. She further notes that the PNAV ratio approach attempts to determine whether a REIT’s shares are trading at a premium or a discount to the value of the underlying real estate. A positive PNAV ratio indicates that a REIT is trading at a premium to the value of its assets. To an analyst, this could indicate either an overpriced REIT or a REIT with high growth potential. A negative PNAV ratio indicates that a REIT is trading at a discount to its asset value. To an analyst, this could indicate either an underpriced REIT or a distressed REIT. Koch further points out some of the flaws of using this ratio.

Methodology DEA Overview and Graphical Depiction of Efficiency The first step of the analysis is the generation of REIT relative inefficiency estimates using Data

Envelopment Analysis (DEA), a non-parametric method of measuring relative efficiency. DEA uses a linear programming technique to construct an efficient cost frontier from which the deviations of individual REITs are measured. These deviations represent the inefficiency of the individual REIT. Besides using a cost frontier as the basis for inefficiency measures, the primary advantage of the DEA methodology is that it allows efficiency estimations without specifying a functional form while handling a multiple-input, multiple-output production process. Efficiency studies, such as this, compare the level of inputs required for a level of output. REIT inputs are proxied as REIT total costs, which include operating costs, general and administrative costs, management fees and interest expense. REIT output is measured by REIT total assets.2 A REIT with a given level of total assets with minimum input usage (costs) is said to operate on the efficient cost frontier and is identified as X-efficient. Any input usage in excess of the optimal input level results in the REIT being characterized as Xinefficient. This study measures the REIT pure technical efficiency. A REIT is purely technically efficient when input usage (costs) cannot be decreased without decreasing output measured from a variable returns to scale frontier. While computationally rigorous, a simple graphical depiction will show how the efficiency measures are computed. In Exhibit 1, the Y-axis represents REIT output and the X-axis represents REIT inputs assuming that inputs are increased at a proportional rate moving along the axis. There are two frontiers represented in the figure—a variable returns to scale frontier denoted as gbah and a constant returns to scale frontier denoted as obe. To understand how the efficiency measures are computed, consider a hypothetical REIT c, which has an input level of g and an output level of f. First, the measure of pure technical efficiency is examined, which represents inefficiencies due to failure to operate on the variable returns to scale frontier. If REIT c was efficient in the pure technical efficiency sense, it could reduce its input usage to j. As such, the pure technical inefficiency measure can be computed as jc/fc. Notice that Journal of Real Estate Portfolio Management

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Exhibit 1

For the VRS surface base model or non-oriented model, a mathematical programming model is solved for each of the REITs in order to define an efficient production frontier. In the programming model, a matrix of s⫻n outputs is defined as Y and the matrix of m⫻n inputs as X. Assuming a nonoriented model and a VRS efficient frontier, the mathematical model is as follows:

Graphical Depiction of DEA Output

e

a

h

b

VRS (YI ,XI, uⴕ,vⴕ): min-(uⴕs ⴙ vⴕe)兩 Y␭ ⴚ s ⫽ YI; ⫺X␭

f

k

j

⫺ e ⫽ ⫺XI; 1␭ ⫽ 1; ␭

c

ⱖ 0; s ⱖ 0; e ⱖ 0, o

g Inputs

REIT c could reduce its costs even further by operating at constant returns to scale. The scale inefficiency measure for REIT c is denoted as kj/fj. Combining these two effects, overall technical efficiency is computed for REIT c, which is computed as kc/fc. In this study, the focus is on the pure technical efficiency as efficiency is computed from the variable returns to scale frontier.

DEA Details Specifically, all of the various versions of DEA models attempt to determine which of the n REITs in the sample determine the efficient frontier or the envelopment surface, while considering m inputs and s outputs. For REIT I, xiI ⱖ 0 is the ith input value and yrI is the rth output value with XI and YI representing the respective input and output vectors used in the analysis. Envelopment surfaces can be of several types, with the most common being the constant returns to scale frontier (CRS) and the variable returns to scale frontier (VRS). X-efficiency can be evaluated with respect to both surfaces. A CRS frontier should only be employed in expected constant marginal values. Given the scale economy studies on REITs, the VRS surface is used in this study. 20

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(1)

where optimal values of the input and output variables for REIT I are denoted by the s-vector sⴕ, the m-vector eⴕ and the n-vector ␭⬘. Additionally, an optimal dual solution is given by the s-vector uⴕ, the m-vector vⴕ and the ␻ ´ vector that defines the intercept of the frontier in the VRS model. Note that the vectors (uⴕ,vⴕ) define the specific lower bounds of the dual variables u and v referred to as the relative prices. A standard or equal bounds technique is employed such that when u⬘r ⫽ 1, r ⫽ 1,...,s and v⬘I ⫽ 1, I ⫽ 1,...,m. Each of the n sets of values given by uⴕ, vⴕ and ␻ ´ are the coefficients of the hyperplane that define the surface of the efficient frontier. The values of u⬘r v⬘i are the prices, or weights or multipliers. A REIT is deemed efficient if it lies on the facet defining the hyperplane or envelopment surface. Note that for the VRS model, the surface is defined as uⴕy ⴚ vⴕx ⫹ ␻ ´ ⫽ 0. The principle of evaluation for this type of model is then given by the choice of the vectors uⴕ and vⴕ. The vectors (YI⬘,X⬘I ) ⫽ (兺j⫽1,...n, ␭j⬘Yj , 兺j⫽1,...n ␭⬘X j j) define a point on the frontier. If the REIT is efficient, or lies on the frontier, ␭⬘I ⫽ 1. For an inefficient unit, one that does not lie on the frontier, (Y⬘,X⬘ I I ) is referred to as the projected point. This point is a convex combination of the units that lie on the envelopment surface for the VRS model. Total inefficiency is computed by the distance from the actual point to the computed point, which is


given by ⌬’s ⫽ Y⬘I ⫺ YI for the output estimate of total inefficiency and ⌬⬘e ⫽ XI ⫺ X⬘I for the input estimate of total inefficiency. The deviations from efficiency can be a result of failure to proportionally reduce inputs, failure to proportionally augment outputs, and residual reductions and augmentations that can be accomplished beyond the proportional changes. This can be expressed mathematically for outputs and inputs as follows: ⌬’s ⫽ (␸ ⫺ 1)YI ⫹ ␺⬘s and ⌬⬘e ⫽ (1 ⫺ ␶)XI ⫹ ␺⬘e , where (␸ - 1) is the maximum possible proportional increase in outputs and (1 ⫺ ␶) is the maximum proportional decrease in inputs, both with respect to the optimal projected point. Residual reductions beyond proportional reductions are denoted by ␺. In estimating efficiency, the orientation of the model must also be determined. Models can be non-oriented, input-oriented or output-oriented. The choice of the model determines the path to which an inefficient REIT moves towards the efficient frontier. In the non-oriented model, the model gives no preference to either input reduction or output augmentation. This model is commonly referred to as the base model and attempts to find the shortest path to the efficient frontier. In other words, the base model maximizes weighted aggregation of the differences between the observed and the projected points. The weights are given by the prices. This weighted aggregation is referred to as Delta, and for an efficient firm, Delta would equal 0. If the model is specified as an input model, the program will determine to what extent inputs can be reduced, given a certain output level. Here, the program seeks a projected point such that the proportional reduction in inputs is maximized. On the other hand, for output-oriented models, the program will try to determine how much output can be enhanced while maintaining the same level of input. In other words, the program will seek a projected point where output enhancement is maximized (Ali, Lerme and Seiford, 1995). In this paper, input-oriented model is used that alters slightly how the total efficiency measure is computed since it seeks to minimize (1 ⫺ ␶). To measure total inefficiency, the results are focused on the input and output efficiency measures

in terms of efficiency ratios. For the non-oriented model, the two ratios are the input efficiency measure and the output efficiency measure, which are defined as rates of change in relative prices. The input measure is denoted as i’ and the output measure is denoted by o’. For the VRS model, the measures are calculated as: i⬘ ⫽ (U⬘YI ⫹ ␻ ´ )/v⬘XI and o⬘ ⫽ (v⬘XI ⫺ ␻ ´ )/u⬘YI.

(2)

The REIT is efficient when i⬘ and o⬘ are equal to 1. Notice that i⬘ is constrained to be less than or equal to 1 and o⬘ is constrained to be greater than or equal to 1. For the input-oriented model, the input measure is given by: i⬘ ⫽ (␶ ⬘ ⫺ 兺⬘/v⬘XI),

(3)

where ⫺兺⬘ ⫽ v⬘YI ⫺ v⬘XI ⫹ ␻ ´.

Once the relative efficiency measures are estimated using DEA,3 the efficiency measures and the PNAV ratios are compared for all of the REITs for which data are available. Exhibit 2 shows the decision model used to select the REITs targeted for performance testing. The matrix illustrated in Exhibit 2 shows two quadrants where the decision criteria is to ‘‘pass’’ or ‘‘hold,’’ the premise being that these REITs are more likely to be priced correctly and, thus, less likely to offer superior returns in the immediate future. The combination of a low PNAV ratio with high efficiency indicates a ‘‘buy.’’ These REITs are inexpensive with respect to the estimated value of their underlying assets, but

Exhibit 2 Decision Matrix Price-to-NAV Ratio High

No Opportunity [Expensive and efficient]

Targeted Buy [Cheap and efficient]

Targeted Sell (short-sell) [Expensive and inefficient]

No Opportunity [Cheap and Inefficient]

Efficiency Scores Low

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at the same time indicate high performance from efficiency. Stated differently, relative to their peer group, these stocks appear underpriced because they are the efficient stocks of the cheaper opportunities in the investment set. Finally, the combination of a high PNAV ratio with low estimated efficiency indicates a ‘‘sell.’’ These are the REITs that are expensive in terms of the PNAV ratio, but at the same time indicate greater inefficiency. In other words, these stocks appear overpriced relative to their peer group, because they are both expensive and inefficient. The specific criteria delineating high and low are highly subjective. For this study, the sample mean is the delineation. Thus, the target REIT portfolio to buy is comprised of REITs with above average efficiency and below average PNAV ratios.

The Data REIT cost and asset data were collected for 1995 through 1999 from the SNL REIT Data Source. Because much of the data were incomplete or otherwise unusable, the initial sample sizes used to estimate REIT efficiency are as follows: 169 (1995), 171 (1996), 179 (1997), 189 (1998) and 158 (1999). From this data, REIT relative efficiency estimates (Efficiency) were obtained using the DEA methodology. The PNAV ratio is from Solomon Smith Barney (SSB). Whereas SNL provides a range of NAV estimates, SSB offers a specific point estimate. That is, SSB uses market estimates of the appropriate cap rates to estimate NAV, whereas with SNL the choice of cap rate is left to the user. Because the data are from two different sources, they do not directly match-up. Efficiency is best calculated using as much data as possible, thus the SNL data is used. Then, when the data is paired to the smaller SSB sample, part of the sample is lost. The final sample is composed of REITs for which PNAV ratios are available and has the following sizes: 78 (1995), 86 (1996), 97 (1997), 101 (1998) and 93 (1999). Thus, this study does not cover all REITs, it only applies to the subset of REITs that are tracked by SSB. Inputs for the DEA model are defined as various costs, namely Interest Expense, Management Expense, Operating Expenses, and General and 22

Vol. 9, No. 1, 2003

Administrative Expenses. Similar to other REIT efficiency studies, the output is defined as Total Assets. The variable, Efficiency, is extracted from the DEA procedure and measures total REIT efficiency for the individual REIT relative to the efficient cost frontier. Higher values coincide with higher efficiency. As seen in Exhibit 3, which summarizes the data, average efficiency measures decreased and then increased over the study period. To gauge the performance of the selected REITs, total returns were obtained from CRSP. An equally weighted average total return was computed for the selected REITs and it was compared to a benchmark. Two benchmarks were used. First, the NAREIT Equity Total Return Index obtained from NAREIT.com was used. While a quality index, more information was needed. The sample is effectively limited to the REITs tracked by SSB. These REITs, on average, are larger and more liquid REITs. As such, simply finding that the selection criteria outperforms the NAREIT Equity Index may simply be a function of the fact that the securities tracked by SSB systematically outperform the index. As such, we compute an equally weighted total return index based on the relevant sample set of REITs.

Results For the first year (1995), eight REITs satisfied the selection criteria for above average efficiency and below average PNAV ratio. For 1996, seventeen met the criteria. For 1997 through 1999, eleven, thirteen and thirty-nine REITs, respectively, satisfied the selection criteria. Many REITs appear in consecutive years. One REIT appears in four consecutive years, three for three consecutive years and eighteen REITs appeared twice in a row. Overall, fifty-six REITs met the selection criteria over the five years studied. Of these, twenty-four had multiple appearances with eight appearing three times and two appearing four times. Exhibit 4 summarizes the results. The Appendix shows limited detail on the selected REITs. The property focus of the eight REITs chosen for 1995 range from retail properties (3) to industrial, office, residential, self-storage and hotels (1 each).


Exhibit 3 Description of Selected Variables Variable

Year

N

Price / NAV [(Price / NAV) ⫺ 1]

1995 1996 1997 1998 1999

78 86 97 101 93

0.009 0.081 0.252 0.003 ⫺0.156

0.004 0.063 0.238 ⫺0.021 ⫺0.171

0.198 0.195 0.215 0.176 0.170

Efficiency

1995 1996 1997 1998 1999

78 86 97 101 93

0.311 0.343 0.183 0.469 0.687

0.240 0.150 0.110 0.390 0.680

0.282 0.298 0.177 0.239 0.177

Total Assets (thousands)

1995 1996 1997 1998 1999

169 171 179 189 158

$440,016 599,852 973,460 1,427,986 1,697,789

$324,133 381,360 617,128 773,352 897,831

$499,702 755,276 1,354,707 1,995,685 2,241,897

Interest Expense (thousands)

1995 1996 1997 1998 1999

169 171 179 189 158

$15,342 17,880 22,397 37,275 54,485

$9,407 11,779 14,753 20,501 25,865

$24,676 27,642 31,751 54,256 80,150

Management Expense (thousands)

1995 1996 1997 1998 1999

169 171 179 189 158

$469 558 825 1,049 1,388

$0 0 0 0 0

$1,647 1,954 2,789 4,507 5,699

Operating Expense (thousands)

1995 1996 1997 1998 1999

169 171 179 189 158

$21,586 27,295 37,948 52,663 67,493

$1,948 12,113 15,371 20,625 33,291

$36,225 47,148 69,400 83,092 102,302

Thus, it is clear that the selection criteria did not isolate REITs of a specific property type. The eight REITs had an average market cap of $680.8 million with a range from $373.5 million to $1.1 billion. The 1996 average total return for the 1995 portfolio was 51.5%, exceeding the return for the NAREIT Equity Index by 1620 basis points and the sample index by 1480 basis points. Individually, the selected REITs had returns ranging from 16% to 66%. For the second year out, the 1995 portfolio had an average total return of 29.1%, an excess of 880 basis points over the NAREIT Index and 1090 over the sample index. Individual REIT returns ranged from 4.8% to 67%. Finally, the 1998 return for the 1995 portfolio was a meager 1.2%, but still exceeded the NAREIT Index, which suffered a 17.5% loss by 1870 basis points and the sample index by 1060 basis points. Six of the eight REITs showed negative returns, with a range of returns from ⫺38% to 14%.

Mean

Median

Std. Dev.

For 1996, of the sixteen REITs selected, five were carry-overs from the previous year. The property focus of these REITs was also diverse, with five REITs focusing in residential properties, five in retail and two each in office, industrial and selfstorage properties. These REITs ranged in size from a market cap of $423.2 million to $3.0 billion and averaged $1.3 billion. The 1997 average total return for this portfolio was 21.0%, a mere 70 basis points above the NAREIT Index return and 270 points above the sample REIT index. Individually, returns ranged from ⫺2% to 67%. The 1998 return for the 1996 portfolio was ⫺9%, a much smaller loss than the 17.5% loss measured by the NAREIT Equity Index but only a slightly smaller loss than the sample REIT index. Thirteen of the seventeen REITs experienced negative returns, with an overall range of returns from ⫺38% to 9%. Finally, the 1999 average total return for the 1996 portfolio was ⫺2.6%, an even 200 basis points above the Journal of Real Estate Portfolio Management

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Exhibit 4 Performance of Selected REITs: Buy Strategy Panel A1: First Year Total Return to NAREIT Year 1995 1996 1997 1998 1999

N 8 16 11 14 39

Average Return: Following Year (%) 51.5 21.0 ⫺9.5 ⫺1.6 28.5

Corresponding Return: NAREIT Equity Index (%) a

Excess Return (%)

35.3 20.3b ⫺17.5c ⫺4.6d 26.4e

16.2 0.7 8.0 3.0 2.1

Sample REIT Index (%)

Excess Return Over Sample REITs (%)

Panel A2: First Year Total Return to Sample Index Year

N

Average Return: Following Year (%)

1995 1996 1997 1998 1999

8 17 11 13 39

51.5 21.0 ⫺9.5 ⫺1.6 28.5

36.7a 18.3b ⫺9.6c ⫺2.2d 25.3e

14.8 2.7 0.1 0.6 3.2

Panel B1: Second Year Total Return to NAREIT Year

N

Average Return: Two Years Out (%)

Corresponding Return: NAREIT Equity Index (%)

Excess Return (%)

1995 1996 1997 1998

8 16 11 14

29.1 ⫺8.9 ⫺7.5 28.4

20.3b ⫺17.5c ⫺4.6d 26.4e

8.8 8.6 ⫺2.9 2.0

Corresponding Return: Sample REITs (%)


Panel B2: Second Year Total Return to Sample Index Year

N

Average Return: Two Years Out (%)

1995 1996 1997 1998

8 17 11 13

29.1 ⫺8.9 ⫺7.5 28.4

18.2b ⫺9.9c ⫺2.5d 25.4e

10.9 1.0 ⫺5.0 3.0

Panel C1: Third Year Total Return to NAREIT Year 1995 1996 1997

N 8 16 11

Average Return: Three Years Out (%)

Corresponding Return: NAREIT Equity Index (%)

Excess Return (%)

1.2 ⫺2.6 10.5

⫺17.5 ⫺4.6d 26.4e

18.7 2.0 ⫺15.9

Corresponding Return: Sample REITs (%)


c

Panel C2: Third Year Total Return to NAREIT to Sample Index Year

N

Average Return: Three Years Out (%)

1995 1996 1997

8 17 11

1.2 ⫺2.6 10.5

Notes: a 1996 b 1997 c 1998 d 1999 e 2000

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⫺9.4c ⫺2.6d 25.1e

10.6 0.0 ⫺14.6


NAREIT Equity Index. The third year return was the same as the sample REIT index. Of the eleven REITs selected for 1997, four carried over from 1996 and one appeared in both of the previous years. Again, the property focus of the selected REITs is diverse, but, noticeably, nearly half were retail REITs. The average market capitalization of these REITs was $1.2 billion and ranged from $359.1 million to $3.3 billion. The 1998 return for the selected portfolio shows a loss of 9.5%, which is 800 basis points better than the 17.5% loss shown by the NAREIT Equity Index and a mere 10 basis points higher than the sample REIT index. For 1998, seven of the selected REITs had negative returns, two had positive returns and two did not have available return information. The longer run performance of this portfolio includes the only two selected portfolios that failed to outperform the NAREIT Equity Index. The second and third year returns underperformed the NAREIT Index and the sample REIT index. For 1998, of fourteen selected REITs, two carried through from the previous year, with one appearing in its third consecutive year. Again, there was a diversity of property focuses, but thirteen of the fourteen selected REITs specialized in either office, residential or retail properties. The average market cap of the selected REITs was $2.2 billion. They ranged in size from $705.2 million to $6.2 billion. The average 1999 return for this group was a loss of 1.6%. These REITs as a group outperformed the NAREIT Equity Index by 300 basis points and the sample REIT index by 60 basis points. Individually, six showed positive returns, six showed negative returns and two had no return information available. The second year return was 28.4%, with only one REIT having a negative return. The selected REITs outperformed the NAREIT Equity Index by 200 basis points and the sample REIT index by 300 basis points. Finally, for 1999, out of a total of thirty-nine selected REITs, nine carried over from the previous year. One of the carry-overs appeared for the third consecutive year, while another appeared for the fourth consecutive year. Again, there was a diverse selection of property types with a concentration in

residential (12) and retail (11) REITs. The size of the selected REITs ranged from a market capitalization of $78.3 million to $6.2 billion and averaged $1.5 billion. The 2000 average total return for this group of REITs was 28.5%, compared to the NAREIT Equity Index total return of 26.4% and a sample REIT index return of 25.3%. Overall, the results show (1) that using the selection strategy results in excess returns for the following year in all five years, 2) that second and third year returns were more modest, but still exceeded the index in all but two cases (three when using the sample REIT index) and (3) the overall ability to earn excess returns was very strong in the first year and has dramatically tapered off since the first period hold.4 Finally, it should be noted that the results were stronger using the NAREIT Index as a benchmark than the sample set of REITs as the sample set performed better than the universe of equity REITs. As such, it would be interesting to be able to apply the selection criteria to the universe of REITs. Overall, the results lend credence to the use of operating efficiency as a portfolio selection tool. In combination with the PNAV ratio, the efficiency measures have been demonstrated as an effective screen to identify REITs with stronger performance potential.

Conclusions and Implications This article introduces a new selection tool, relative REIT efficiency, for constructing portfolios and deciding which REITs to add to a portfolio. The results show that selection of REITs, using the relative efficiency and the PNAV ratio as filtering criteria, to have superior first year performance in all cases. In most cases, the REITs selected by this process also had superior second and third year performance. Clearly, this is an effective initial effort into reaching a better understanding of the relationship between REIT operating efficiency and REIT performance. This technique provides the basis for a REIT selection criteria based on the use of various performance measures as screens and adding the additional tool of relative operating efficiency, basically incorporating information on how well a Journal of Real Estate Portfolio Management

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REIT is actually operated in relation to other REITs. Future issues to be resolved include the specific delineation points for a REIT to meet the

criteria, and whether or not other criteria besides the PNAV ratio will work better in combination with relative efficiency.

Appendix The Selected REITs Using ‘‘Buy’’ Criteria Company Name

Ticker

Focus

Specialty

Market Cap ($)a

2000 Market Cap ($)a

Properties (2000)

CEI EQR GGP LRY RFS RSE SHU TCO

Office Residential Retail Industrial Hotel Retail Self-Storage Retail

NA Multifamily Enclosed Mall Flex / Serv. Ctr Ltd. Service Enclosed Mall NA Enclosed Mall

802.8 1,072.2 565.9 588.2 373.5 976.4 626.2 441.3

2,388.5 7,335.3 1,891.9 1,948.3 320.2 1,731.0 727.6 557.6

163 1,152 99 714 58 193 400 19

AIV ASN BRE CEI CLI DDR EQR FR GGP GRT LRY MAC PPS PSA SHU SPG

Residential Residential Residential Office Office Retail Residential Industrial Retail Retail Industrial Retail Residential Self-Storage Self-Storage Retail

Multifamily Multifamily Multifamily NA NA Shopping Center Multifamily Flex / Serv. Ctr Enclosed Mall Shopping Center Flex / Serv. Ctr Enclosed Mall Multifamily NA NA Enclosed Mall

423.2 1,727.3 813.8 1,906.7 1,121.3 805.0 2,110.1 909.4 993.0 481.6 808.6 672.5 882.4 2,956.2 764.9 3,003.3

3,562.4 3,163.1 1,454.3 2,388.5 1,627.5 730.6 7,335.3 1,307.3 1,891.9 297.8 1,948.3 644.9 1,459.4 3,314.8 727.6 4,126.7

1,178 209 95 163 280 238 1,152 914 99 107 714 52 88 1,330 400 256

BPP FR GLB GRT LRY NXL PPS PSA SSI TCO WEA

Retail Industrial Office Retail Industrial Retail Residential Self-Storage Hotel Retail Retail

Shopping Center Flex / Serv. Ctr NA Shopping Center Flex / Serv. Ctr Shopping Center Multifamily NA Limited Service Enclosed Mall Shopping Center

359.1 1,316.2 934.6 534.1 1,505.1 1,348.1 1,244.2 3,293.0 NA 659.9 1,246.6

149.6 1,307.3 468.8 297.8 1,948.3 1,144.5 1,459.4 3,314.8 NA 557.6 1,059.1

28 914 89 107 714 342 88 1,330 NA 19 40

ARI ASN AVB BXP CPP CRE EOP FCH HIW

Office Residential Residential Office Office Office Office Hotel Office

NA Multifamily Multifamily NA NA NA NA Full Service NA

1,447.1 2,902.1 2,188.1 1,937.6 NA 1,722.2 6,237.6 1,565.6 1,541.5

1,599.1 3,163.1 3,368.0 3,768.4 NA 2,035.9 10,014.8 1,256.9 1,445.8

145 209 125 106 NA 131 241 186 600

Panel A: Selected REITs—1995 Crescent Real Estate Equities Company Equity Residential Properties Trust General Growth Properties, Inc. Liberty Property Trust RFS Hotel Investors, Inc. Rouse Company Shurgard Storage Centers, Inc. Taubman Centers, Inc. Panel B: Selected REITs—1996 Apartment Inv. and Mgmt. Company Archstone Communities Trust BRE Properties, Inc. Crescent Real Estate Equities Company Mack-Cali Realty Corporation Developers Divers. Realty Corporation Equity Residential Properties Trust First Industrial Realty Trust, Inc. General Growth Properties, Inc. Glimcher Realty Trust Liberty Property Trust Macerich Company Post Properties, Inc. Public Storage, Inc. Shurgard Storage Centers, Inc. Simon Property Group, Inc. Panel C: Selected REITs—1997 Burnham Pacific Properties, Inc. First Industrial Realty Trust, Inc. Glenborough Realty Trust, Inc. Glimcher Realty Trust Liberty Property Trust New Plan Excel Realty Trust, Inc. Post Properties, Inc. Public Storage, Inc. Sunstone Hotel Investors, Inc. Taubman Centers, Inc. Westfield America, Inc. Panel D: Selected REITs—1998 Arden Realty Inc. Archstone Communities Trust AvalonBay Communities Inc. Boston Properties, Inc. Cornerstone Properties, Inc. CarrAmerica Realty Corporation Equity Office Properties Trust FelCor Lodging Trust, Inc. Highwoods Properties, Inc.

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Appendix (continued) The Selected REITs Using ‘‘Buy’’ Criteria Company Name

Focus

Specialty

Market Cap ($)a

JDN PPS SPG UDR WEA

Retail Residential Retail Residential Retail

Power Center Multifamily Enclosed Mall Multifamily Shopping Center

705.2 1,462.6 4,753.1 1,068.8 1,265.1

347.2 1,459.4 4,126.7 1,105.2 1,059.1

142 88 256 277 40

AIV AMB AML ARI ASN AVB BRE BXP CTR CUZ DDR DRE EOP EQR ESS FCH FR GGP IRT JAMS JDN KIM KRC LRY MAC MHC NXL PLD PNP PP PPS PSA PSB REG SHU SMT SPG SPK SRW SUS TCR VNO WEA

Residential Industrial Residential Office Residential Residential Residential Office Industrial Divers. / Other Retail Office Office Residential Residential Hotel Industrial Retail Retail Hotel Retail Retail Office Industrial Retail Residential Retail Industrial Retail Office Residential Self-Storage Divers. / Other Retail Self-Storage Residential Retail Office Residential Self-Storage Residential Divers. / Other Retail

Multifamily Warehouse Multifamily NA Multifamily Multifamily Multifamily NA Warehouse NA Shopping Center NA NA Multifamily Multifamily Full Service Flex / Service Ctr Enclosed Mall Shopping Center Budget Power Center Shopping Center NA Flex / Service Ctr Enclosed Mall Manuf. Home Shopping Center Warehouse Shopping Center NA Multifamily NA NA Shopping Center NA Multifamily Enclosed Mall NA Multifamily NA Multifamily NA Shopping Center

2,659.6 1,697.3 343.1 1,271.1 2,849.7 2,276.9 1,013.7 2,113.7 746.4 1,091.9 766.1 2,453.5 6,195.2 5,440.6 613.7 1,090.5 1,046.8 1,447.5 255.6 78.3 538.6 2,059.5 622.2 1,657.5 709.1 554.6 1,384.5 3,115.1 346.7 801.3 1,485.4 3,033.3 537.9 1,138.5 678.2 471.4 3,972.0 2,367.0 731.3 842.9 377.4 2,805.9 903.1

3,562.4 2,171.8 440.7 1,599.1 3,163.1 3,368.0 1,454.3 3,768.4 779.5 1,374.8 730.6 3,150.3 10,014.8 7,335.3 1,008.3 1,256.9 1,307.3 1,891.9 246.6 64.6 347.2 2,790.2 756.2 1,948.3 644.9 610.9 1,144.5 3,677.6 715.7 985.5 1,459.4 3,314.8 640.6 1,347.8 727.6 687.2 4,126.7 3,297.4 1,030.1 857.9 368.9 3,325.7 1,059.1

1,178 227 71 145 209 125 95 106 379 66 238 920 241 1,152 87 186 914 99 91 129 142 458 109 714 52 153 342 514 111 81 88 1,330 143 232 400 63 256 374 63 450 72 204 40

Ticker

2000 Market Cap ($)a

Properties (2000)

Panel D: Selected REITs—1998 (continued) JDN Realty Corporation Post Properties, Inc. Simon Property Group, Inc. United Dominion Realty Trust, Inc. Westfield America, Inc. Panel E: Selected REITs—1999 Apartment Inv. and Mgmt Company AMB Property Corporation AMLI Residential Properties Trust Arden Realty Inc. Archstone Communities Trust AvalonBay Communities Inc. BRE Properties, Inc. Boston Properties, Inc. Cabot Industrial Trust Cousins Properties Incorporated Developers Divers. Realty Corporation Duke-Weeks Realty Corporation Equity Office Properties Trust Equity Residential Properties Trust Essex Property Trust, Inc. FelCor Lodging Trust, Inc. First Industrial Realty Trust, Inc. General Growth Properties, Inc. IRT Property Company Jameson Inns, Inc. JDN Realty Corporation Kimco Realty Corporation Kilroy Realty Corporation Liberty Property Trust Macerich Company Manufactured Home Communities, Inc. New Plan Excel Realty Trust, Inc. ProLogis Trust Pan Pacific Retail Properties, Inc. Prentiss Properties Trust Post Properties, Inc. Public Storage, Inc. PS Business Parks, Inc. Regency Centers Corporation Shurgard Storage Centers, Inc. Summit Properties, Inc. Simon Property Group, Inc. Spieker Properties, Inc. Charles E. Smith Residential Realty, Inc. Storage USA, Inc. Cornerstone Realty Income Trust Inc. Vornado Realty Trust Westfield America, Inc.

Notes: All data is from SNL REIT DataSource. a 000,000s.

Journal of Real Estate Portfolio Management

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Endnotes 1. REIT performance is also compared to an equally-weighted index of the sample REITs to control for potential measurement bias using the NAREIT Index. 2. For a discussion of input and output specifications, see Lewis, Springer and Anderson (2000). 3. As in Anderson, Fok, Springer and Webb (2002), the appropriateness of pooling REITs based on investment type (mortgage, equity or hybrid) and on investment focus (apartment, office, etc) was examined. It would be appropriate to pool if the efficiency distributions were the same. As in the prior study, pooling based on investment type was not effective but pooling REITs on differing property types was employed. 4. This result suggests that the REIT market has started to capture and price the information present in the selection criteria. This is consistent with increases in market efficiency.

References Ali, A. I., C. S. Lerme and L. M. Seiford, Components of Efficiency Evaluation in Data Envelopment Analysis Models, European Journal of Operational Research, 1995, 80, 462–73.

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Anderson, R., R. Fok, L. V. Zumpano and H.W. Elder, Measuring the Efficiency of Residential Real Estate Brokerage Firms, Journal of Real Estate Research, 16, 1998, 139–58. Anderson, R. I., D. Lewis and T. M. Springer, Operating Efficiencies in Real Estate: A Review of the Literature, Journal of Real Estate Literature, 2000, 8:1. Anderson, R. I., T. M. Springer, R. Fok and J. R. Webb, Technical Efficiency and Economies of Scale: A Non-parametric Analysis of REITs, European Journal of Operations Research, 2002, 139, 598. Cooper, M., D. H. Downs and G. A. Patterson, Real Estate Securities and a Filter-based, Short-term Trading Strategy, Journal of Real Estate Research, 1999, 18:2, 313–54. Downs, D. H. and Z. N. Guner, Is the Information Deficiency in Real Estate Evident in Public Market Trading?, Real Estate Economics, 1999, 27:3, 517–42. Koch, R. L., Analyzing REIT Stocks: Valuation and Performance Issues, Real Estate Review, 1998, Summer, 12–23. Leibenstein, H., Allocative Efficiency vs. ‘‘X-Efficiency,’’ American Economic Review, 1966, 56, 392–414. Lewis, D., T. M. Springer and R. I. Anderson, The Cost Efficiency of Real Estate Investment Trusts: A Bayesian Stochastic Frontier Approach, Journal of Real Estate Finance and Economics, 2003, 26, 65–80.