TheUnderstatedCostsofOvervaluedEquity:TheCaseof
OvervaluedREITAcquisitions
Abstract
I examine the effects of acquisitions made by overvalued Real Estate Investment
Trusts (REITs) on shareholder wealth using managerial insider trading to directly
measureovervaluation.IfindthatovervaluedREITacquirersaremorelikelytopay
for part or all of the deal using stock, get negative and lower announcement
abnormal returns and have deteriorating operating performance during the three
yearsfollowingthecompletionoftheacquisition.Iconcludethatovervaluedequity
haseconomicallysignificantnegativevalueconsequencesforREITinvestorsinboth
shortandlongrun.
1.Introduction
Agrowingliteratureinfinancearguesthatovervaluedfirmsaremorelikely
to make value‐destroying acquisitions. Jensen (2005) famously argues that once
equity is overvalued, managers will try to meet market’s unrealistic growth
expectations by using overvalued equity and access to cheap debt to make
acquisitions,excessivelyincreaseinternalspendingandinvestinriskynegativenet
present value projects. Moeller, Schlingemann and Stulz (2005) document the
massive loss of shareholder value in the stock‐acquisitions of late 1990s. Other
paperslikeDong,Hirshleifer,RichardsonandTeoh(2005,henceforthDHRT),Fun,
LiandOfficer(2010),andAkbulut(2012)showfirmswithovervaluedequitymake
acquisitionsthatdestroyvaluebothinthelongandshortrun.
Surprisingly little work has been done on the value effects of overvalued
equity on Real Estate Investment Trusts’ (REIT) merger and acquisition activity.
Existing acquisition studies on REITs mostly focus on short and long‐term value
effects (Campbell, Ghosh and Sirmans 1998, 2001; Sahin 2005; Kok and Eicholtz
2008 and Campbell, Giambona and Sirmans, 2009), operating performance and
synergies (Anderson, Medla, Rottke, Schiereck 2011a), and potential explanations
for merger activity like eliminating managerial inefficiencies and improving
synergies(Anderson,Medla,Rottke,Schiereck2011b).Noneoftheexistingstudies
attempt to measure overvaluation directly; instead they infer it from the negative
acquirer announcement returns or the method of payment (see Campbell, Ghosh,
Sirmans 2001). Moreover, the existing studies focus entirely on mergers, ignoring
completely theacquisitionsofassets,whichisbyfarthemostcommonmethod of
2
acquisitions, and represent an economically significant amount of external
investment.Asaresult,theexistingliteraturedoesnotaccountfororquantifythe
fullextentofcostsimposedbyovervaluedequityonREITshareholders.Thispaper
isintendedtofillthesegaps.
Mygoalinthispaperistoexaminetheshortandlong‐termvalue‐effectsof
overvalued equity on REIT mergers and acquisitions. I measure overvaluation of
REITs directly using managers’ insider trading. REITs where managers are selling
more and purchasing less stock than usual will be labeled as overvalued. This
approach is similar tothat used in Akbulut (2012) to measure overvaluation for a
sampleof11,796mergersandacquisitionsbynon‐REITacquirers.
IfindthatovervaluedREITsgetnegativeandlowerannouncementreturns,
are more likely to include stock as part or all of the method of payment and face
worsening post‐acquisition operating performance. More specifically, overvalued
REIT acquirers get a four‐day abnormal announcement return of ‐0.75% which is
0.64%lowerthanthatfor“not‐overvalued”acquirers,aftercontrollingfordealand
firm characteristics. While cash is the overwhelming method of payment in most
acquisitions,overvaluedacquirersare92%morelikelytousestocktopayforpart
or all of the deal value. Finally operating performance for overvalued acquirers as
measured by funds from operations declines by 0.59 percentage points in the 3
yearsfollowingthecompletionofthedeal(significantat1%levelandrepresentsa
10% drop from the base level), which is 0.54 percentage points lower than the
changeinoperatingperformanceforthenot‐overvaluedacquirers,whichhaveflat
or increasing operating performance after the acquisition. My results show that
3
overvalued equity has economically significant negative value consequences for
REITinvestorsinboththeshortandthelongrun.
2.DataandMethod
A.SampleConstruction
My acquisition data comes from the Securities Data Corporation (SDC)
PlatinumMergers&Acquisitionsdatabase.Isearchforallacquisitionbidsbypublic
acquirers for public, private, subsidiary and other targets1from January 1993 to
December2011where:
1. DealFormis“Merger”or“AcquisitionofAssets”.
2. Thedealvalueisatleast1%oftheacquirer’spre‐bidmarketvalue.
3. Theacquirerownslessthan50percentofthetargetpriortotheacquisition
andbuystherestwiththeacquisition.
4. Insider trading data for the acquirer is available in the Thomson Insiders
Database.
5. Dataondealvalueandmethodofpaymentisavailable.
6. There is price and return data for both acquirer and the target in the
University of Chicago’s Center for Research in Security Prices (CRSP)
database.
1
Since I am mainly interested in the consequences of acquirer overvaluation, I do not restrict my sample to
acquisition attempts involving only public targets. By including private and public targets and subsidiaries,
I end up with a bigger sample, which increases the power of my tests.
4
Theserequirementsresultinaninitialsamplesizeof12,110deals,ofwhich7,410
areacquisitionsofassetsand4,700aremergers.FinallyIidentifythedealsmadeby
equity REIT acquirers using the list of equity REITs provided by Feng, Price and
Sirmans (2011).2This determines my final sample size as 993 deals of which 100
are mergers and 893 are acquisitions of assets. Table 1 provides a break‐down of
thesample.
[TABLE1ABOUTHERE]
Cash seems to be the most common method of payment; 84% of the 993
dealsarefinancedwithcashwhereas15%ofthedealsinvolveatleastsomeamount
ofstockasthepaymentmethod.Mostofthetargetsareeitherprivatelyheld(54%)
orsubsidiaries(37%)ofotherfirms,whereasonly6%arepublictargets.Mergeris
the preferred deal form to acquire public targets; all 63 of them are acquired in
mergers. On the other hand private and subsidiary targets are overwhelmingly
acquiredthroughacquisitionsofassets.
Looking the distribution of deals across years, two acquisition waves are
clearly visible; 1998‐1999 and 2005‐2006. 55% of all the deals in the 18‐year
sample occur during these four years, which also correspond to periods of high
valuationsintherealestatesector.
2
I use the updated version of this list which includes changes through the end of 2011 provided on
Professor McKay Price’s website at http://www.mckayprice.com/research.html. It contains all equity
REITs that are included in the calculation of the FTSE NAREIT U.S. Equity REIT Index as identified by
NAREIT.
5
B.MeasurementofOvervaluation
Ofcriticalimportanceformyanalysisistoproperlyidentifytheovervalued
acquirers. Various methods have been used in the literature to measure
overvaluation; for example DHRT (2005) and Rhodes‐Kropf and Viswanathan
(2004) use market‐to‐book ratio based measures whereas Loughran and Vijh
(1997) measure overvaluation ex‐post by looking at long‐run stock returns and
Akbulut (2012) uses insider trading by acquirer managers to measure
overvaluation.
I follow Akbulut’s (2012) approach to measure overvaluation. This method
has certain advantages worth noting. First, it does not rely on accounting based
valuationratios,whichareopentomanipulationandmightproxyforotherthings
likeinformationasymmetry,riskandgrowthopportunities.Second,ithasbeenwell
established that insider trading is informed trading: managers have better
informationaboutthetruevalueofthefirmandopportunisticallyusethisintheir
trading. 3 Hence, looking at insider trading patterns before an acquisition
announcement will be informative about whether the manager thinks her firm is
overvaluedornot.Finally,thisapproachcontrolsfornon‐informationalmotivesfor
insider trading like firm and managerial characteristics, liquidity, and portfolio
rebalancing and diversification. More specifically, acquirers where managers are
sellingmoreandpurchasinglessthannormallevelsarelabeledasovervalued(OV),
and the rest are considered to be “not‐overvalued” (NOV). Controlling for insider
3
See Seyhun (1986, 1988), Rozeff and Zaman (1988), Jeng, Metrick and Zeckhauser (1999) among others.
6
trading motives unrelated to overvaluation results in a cleaner, more dependable
signalaboutovervaluation.
I start by obtaining the amount, type and date of each insider trade as well as
thetitleoftheinsiderfromtheThomsonFinancialInsidersDatabase,fromJanuary
1993 to December 2011.4Following Akbulut (2012), I analyze direct open market
sales,openmarketpurchases,andpurchasesthroughtheexerciseofoptions5,and
delete inconsistent6and amended filings. I identify the managers by using the
positiondescriptionsandexcludeinstitutionalshareholders,trusts,largeindividual
shareholders and directors who are not also managers.7I then match the firms to
CRSPdatabasebasedontheCUSIPcode,endingupwith11,365firms.
Next, for each firm j in each quarter t, I measure abnormal insider trading
usingthefirm’sabnormalnetpurchaseratio(ANPR)definedas:
ANPR ,
Buy ,
Optbuy ,
Sell , whereBuy , (Optbuy , is the abnormal number of shares acquired through open
market purchases (exercise of stock options) during quarter t, andSell , is the
abnormal number of shares sold through open market sales, all expressed as a
fraction of the number of shares outstanding of the firm at the beginning of the
quarter. The abnormal levels of these variables are calculated every quarter by
runningthefollowingmanager‐levelcross‐sectionalregressionseparatelyforopen
4
Pursuant to Sections 16(a) and 23(a) of the Securities Exchange Act of 1934, and Sections 30(h) and 38 of
the Investment Company Act of 1940, the corporate insiders must report changes in ownership to the
Securities Exchange Commission (SEC). The mean (median) time between the transaction date and when it
becomes public (the date when it is reported to the SEC) is 29 (11) days for the entire sample period.
5
The transaction codes are ‘S’ for open market sales, ‘P’ for open market purchases and ‘M’ for purchases
through the exercise of options.
6
I delete observations with cleanse codes ‘A’ and ‘S’.
7
The following position codes are used to identify managers: AV, C, CB, CEO, CFO, CI, CO, COO, CT,
EVP, GC, GM, GP, H, O, OB, OD, OE, OT, OX, P, SVP, TR, VC, and VP.
7
market purchases, purchases through the exercise of options and open market
sales8:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
The residuals from these regressions then represent abnormal insider
tradingofmanageriinfirmjinquartert.Ithenaggregatetheseresidualsbyfirmto
computeBuy , ,Optbuy , ,Sell , andconsequentlyANPR , forfirmj.Intheteststhat
follow, I always use two quarters worth of abnormal trading data: to see how
managers were trading before a given event, I add up the ANPRs for the two
quarters preceding the event quarter. More specifically, for each quarter t, I
calculatethepriorabnormalinsidertrading(PAIT)as9:
,
,
,
[TABLE2abouthere]
Depending on the model, the dependent variable Trading , and the independent variables PeerTrading ,
and SelfTrading , represent number of shares acquired through open market purchases, number of
shares acquired through option exercises or number of shares sold through open market sales, all expressed
as a fraction of the shares outstanding of the firm. The measurement of independent variables is detailed in
the caption to Table 2.
9
Akbulut (2012) notes several advantages of this method: running quarterly regressions allows the
coefficients to change over time whereas including the contemporaneous trading of a peer insider with
similar individual (age, tenure, ownership) and firm (size, past return) characteristics, as well as the past
trading of the same insider helps control for normal trading levels across both cross-section and time.
Finally, this abnormal trading measure helps identify “passive insider trading” where managers buy or sell
less than normal. For example Agrawal and Nasser (2011) find that, insiders in takeover targets engage in
profitable passive insider trading by increasing their net purchases by reducing sales more than they reduce
purchases.
8
8
Table2showsthetime‐seriesaveragesofthecoefficientsfrom76quarterly
cross‐sectionalregressions.AsinAkbulut(2012),amajorityofthecoefficientsare
significant at 1% level, and most have expected signs. Insider trading is strongly
positivelyrelatedtopeertradingandself‐tradinginthepast.Managerswithhigher
stockownershiparelesslikelytopurchasethroughtheexerciseofoptionsbutmore
likelytosellontheopenmarket.10Managersbuylessandsellmoresharesonthe
openmarketandbuymorethroughtheexerciseofstockoptionswhenpastreturns
are high. Managers with liquid shares (as measured by share turnover) buy more
sharesthroughtheexerciseofoptionsandsellmoreontheopenmarket.Ageand
firmtenurearepositivelyrelatedtostockpurchasesthroughoptionexercisesand
sales. Corporate governance related variables like analyst coverage, fraction of
institutional investors and share turnover are negatively related to open market
purchases suggesting higher levels of corporate governance results in decreased
openmarketpurchases.
Fortheremainderofthepaper,IlabeltheequityREITacquirerswhosePAIT
fallsintothebottom33%ofall12,120REITandnon‐REITacquirersasovervalued
(OV)andtherestasnot‐overvalued(NOV).Outof993equityREITacquirers,169
are identified as being overvalued at the time of the acquisition and 824 are
identifiedasbeingnot‐overvalued.
10
For open market sales, a greater negative value denotes higher selling. Therefore a negative coefficient
for any independent variable should be interpreted as an increase in sales in response to the increase in the
level of the independent variable.
9
3.Results
A.UnivariateResults
IstartbyexaminingequityREITacquisitioncharacteristicsinTable3.Panel
Apresentsresultsforalldeals,PanelBfordealswherepart orallofthepayment
was made in stock, and Panel C where method of payment was 100% cash.
Overvaluedacquirersaremorelikelytopaypartorallofthedealwithstock;19.5%
ofthedealsmadebyOVacquirersinvolvedstockcomparedtojust14.2%forNOV
acquirers and the difference is significant at 10% level. Conversely they are 5.2%
lesslikelytopaywithcash.Overvaluedacquirersgetasignificantlynegativemarket
reactiontotheirdealannouncements;theirfourday11cumulativeabnormalreturn
(CAR)is‐0.75%andsignificantat1%level.Thisisalso‐0.7%lowerthantheCARof
not‐overvalued acquirers, which is ‐0.03% and statistically indistinguishable from
zero.Thissuggeststhatdealsannouncedbyovervaluedacquirersregardlessofthe
method of payment, are viewed as unfavorable by the investors. Overvalued
acquirerstendtomakebiggerdeals;averagedealsizeis$321millioncomparedto
$195millionforacquirersthatarenotovervalued.Comparedtotheacquirersthat
arenotovervalued,theyarealsomorelikelytobuypublictargets(11.8%vs.5.2%),
and structure the deal as a merger rather than acquisition of assets (16.6% vs.
8.7%)andarelesslikelytobuysubsidiarytargets(30.2%vs.38.7%).
[TABLE3abouthere]
11
Results are similar if a 3-day [-1, +1] or a 5-day [-2, +2] event window is used instead.
10
Although the sample size and the power of tests are reduced substantially
whenlookingatdealswherethemethodofpaymentinvolvesstock,theresultsare
qualitatively similar: overvalued acquirers have negative and lower average CAR
than acquirers that are not overvalued, they are more likely to buy public targets
and structure the deal as a merger. Finally looking at cash deals, overvalued
acquirersgetanaverageCARof‐0.69%uponannouncement,whichis0.74%lower
thanthatforacquirerswhicharenotovervalued,andthedifferenceissignificantat
5% level. Acquirers that are not overvalued get CARs indistinguishable from zero.
Other studies of REIT mergers like Campbell et al (1998), Campbell et al (2001),
Sahin (2005), and Campbell et al (2009b) typically find acquirer CARs between ‐
0.6%and‐1.2%.MyresultssuggestthatsuchnegativeCARsarecomingexclusively
from overvalued acquirers; indeed acquirers that are not overvalued have CARs
indistinguishablefromzero.Thissuggeststhatovervaluationisthesourceofvalue
destruction in REIT acquisitions, and that the negative acquirer CAR findings of
existingpapersonREITmergersisareflectionofthat.
Results from Table 3 suggest that investors view acquisitions made by
overvalued acquirers as value destroying, regardless of the method of payment
used. This is particularly interesting; other studies on overvalued acquisitions by
non‐REIT acquirers typically find negative announcement returns primarily for
stockdealsbutnotforcashdeals12.Thissuggeststhatmethodofpaymentmaynot
12For
example Fu, Lin and Officer (2011) find overvalued acquirers get -3.81% CAR in stock deals but
0.60% in cash deals, both significant at 1 % level. Similarly, Akbulut (2012) finds CARs of -1.12% for
stock deals and 0.74% for cash deals for overvalued acquirers.
11
be as informative for equity REIT acquisitions to signal overvaluation as it is for
non‐REITacquisitions.
[TABLE4abouthere]
Table 4 shows acquirer firm characteristics for overvalued and not‐
overvalued firms. Overvalued firms are typically larger, with lower asset growth
ratebuthighercashflowasmeasuredbyfundsfromoperations(FFO).Managersin
overvalued acquirers are abnormal net sellers of stock; the PAIT for overvalued
acquirers is ‐0.0063% of their outstanding shares, compared to 0.0029% for not‐
overvalued acquirers, whose managers are net abnormal purchasers. Overvalued
acquirers and not‐overvalued acquirers have similar market‐to‐book and price‐to‐
earnings ratios and similar past stock returns, suggesting that our overvaluation
measure captures a component of overvaluation not accounted for by these
alternative overvaluation measures. Once again, findings are similar if we look at
dealsinvolvingstockanddealspaidforbycashseparately;overvaluedacquirersare
larger, have lower asset growth rates and higher FFOs; whereas they have similar
valuationratiosandpastreturns.
12
B.MultivariateResults
B.1.MethodofPayment
Howdoesovervaluationaffectthemethodofpaymentusedinacquisitions?
According to misvaluation theory advanced by Shleifer and Vishny (2003) and
Rhodes‐KropfandViswanathan(2004),acquirerswillbemorelikelytousestockas
a method of payment when they are overvalued. Is this the case for REIT
acquisitions as well? Campbell, Ghosh and Sirmans (2001) examine the method of
payment choice in REIT mergers and conclude that information‐signaling
hypothesis, which says that the choice of stock as method of payment signals
overvaluation, explains the method of payment choice better than blockholder
monitoring hypothesis. If this is the case, we should expect to see greater use of
stock when acquirer is overvalued. I explore this using a logistic regression
frameworkinTable5.
[TABLE5abouthere]
InPanelA(B),thedependentvariableisadummy,whichissettooneifthe
methodofpaymentinvolvesanyamountofstock(onlycash).Inallregressions,the
independentvariableofinterestisOVDUM,adummyvariablewhichissettooneif
theacquirerisovervalued(PAITisinthebottom33%ofallacquirers)andzeroif
13
otherwise. I also include standard control variables, which have been shown to
explainthemethodofpaymentdecisioninacquisitions.13
Acquirersaremorelikelytousestockaspartorallofthemethodofpayment
whentheyareovervalued.ThecoefficientofOVDUMinPanelAofTable5is0.739
andissignificantat1%level.Themarginaleffectof0.0784impliesthatovervalued
acquirersare92%morelikelytousestockasamethodofpayment.14Othercontrol
variableshavetheexpectedsignsforthemostpart;acquirersarelesslikelytouse
stockiftheyhavemorecash,orwhenacquiringprivatetargets,butaremorelikely
tousestockwhentherelativedealvalueishigh.
Looking at cash deals in Panel B, we see the opposite result; overvalued
acquirers are less likely to use cash as a method of payment. The coefficient of
OVDUMis‐0.705,withamarginaleffectof‐0.0788,whichimpliesthatovervalued
acquirersare10%lesslikelytousecashastheirmethodofpayment.15
These results are consistent with studies on non‐REIT acquisitons16and
provide direct evidence for Campbell, Ghosh and Sirman’s (2001) conjecture that
the usage of stock signals overvaluation in REIT mergers. They also confirm the
predictionofmisvaluationtheoryofShleiferandVishny(2003)andRhodes‐Kropf
13
Martin (1996) finds that the likelihood of stock financing increases with higher pre-acquisition market
and acquiring firm stock returns, higher growth opportunities, and decreases with higher cash availability.
14
Holding all the other variables constant at their means, a change in OVDUM from 0 to 1 increases the
probability of using stock from 8.6% to 16.4%. This represents a 92% increase in the probability of using
stock.
15
Holding all the other variables constant at their means, a change in OVDUM from 0 to 1 decreases the
probability of using cash from 90.7% to 82.8%. This represents a 10% decrease in the probability of using
cash.
16
See DHRT (2005) find overvalued acquirers are between 14% and 24.7% more likely to use stock
whereas Akbulut (2012) finds they are 21% more likely to use stock as a method of payment.
14
and Viswanathan (2004) that acquirers will be more likely to use stock when
overvalued.
B.2.AcquirerAnnouncementReturns
Next, I look at acquirer announcement returns in Table 6. Announcement
returnsarecalculatedrelativetotheFama‐Frenchthree‐factormodelforthefour‐
day window [‐2,+1] around the announcement day. The independent variable of
interestisOVDUM,whichisadummyvariablesetto1ifacquirerisovervalued,and
zero if otherwise. I also include independent variables to control for firm
characteristics(market‐to‐bookratio,cash,returnonequity,salesgrowth,leverage,
price‐to‐earnings ratio, firm size, past stock return and volatility), deal
characteristics (relative deal size, dummy variables indicating whether target is a
privatefirm,whethertheattitudeofthedealishostile,whetherthereweremultiple
bidders, and whether acquirer owned any shares in the target before the deal) as
wellaspastacquisitionactivity(numberofbidsmadeinthepastyear).Finallyto
control for time‐fixed effects, the regression also includes year‐dummies (not
shown).
[TABLE6abouthere]
Results confirm my earlier findings; overvalued acquirers earn significantly
lower announcement returns compared to acquirers that are not overvalued. The
coefficientofOVDUMis‐0.641(significantat5%level)whichmeansanovervalued
acquirer gets 0.641% lower announcement return than an acquirer which is not
15
overvalued, even after controlling for various firm and deal characteristics, past
acquisitionactivityandtime‐fixedeffects.ThisresultissimilartoAkbulut(2012)’s
findingof0.814%lowerannouncementreturnforovervaluednon‐REITacquirers.
SoitseemsovervaluedREITacquirersmakeacquisitionsthatdestroyvalueinthe
short run. But is this true in the long run as well? Do overvalued acquirers fare
betterorworsethanacquirersthatarenotovervalued?
B.3.OperatingPerformance
To answer this question, I compare the long‐run operating performance of
overvalued acquirers to those acquirers that are not overvalued in Table 7. I use
funds from operations (FFO) as my measure of operating performance. FFO is a
commonly used measure to assess the operating performance of REITs and the
National Association of Real Estate Investment Trusts (NAREIT) advocates its use
andarguesthatitbetterreflectstheperformancecomparedtoGAAPnetincome.17
[TABLE7abouthere]
Panel A of Table 7 shows the change in FFO from the first fiscal year‐end
afterthedealcompletionyear(year0)toone,twoandthreeyearyearsafterward.
PanelAshowschangeinFFOfromyear0toyears+1,+2and+3whereasPanelB
shows the change in industry‐adjusted FFO over the same periods. Industry
17
This is because GAAP net income includes historical cost depreciation that assumes the value of real
estate assets diminish predictably over time. This is clearly not the case for real estate assets.
16
adjustedFFOiscalculatedbyadjustingeachacquirer’sFFOeachyearbysubtracting
themeanindustryFFOforthatyear.
Ifirstlookatallacquirers,regardlessoftheirovervaluation.Theresultsare
somewhatmixed;PanelAshowsaninsignificantdeclineinoperatingperformance
of0.14percentagepointswhereasindustryadjustedoperatingperformanceshows
asignificantincreaseof0.16percentagepoints.
On the other hand, there is strong evidence for worsening of operating
performance for overvalued acquirers; Panel A shows declines by 0.22, 0.37 and
0.59percentagepointsinone,twoandthreeyears,significantat10%,5%and1%
levelsrespectively.Theseareeconomicallysignificantchanges;consideringthatthe
average operating performance in year 0 for overvalued acquirers is 6.19% (not
reported), these changes translate into 4%, 6% and 10% declines from the year 0
performance level. Industry‐adjusted change in performance in Panel B tells a
similarstory;overvaluedacquirers’industry‐adjustedperformanceworsensby0.29
percentagepointsinoneyearand0.25percentagepointsintwoyears,significantat
5% and 10% levels respectively. On the contrary, acquirers which are not
overvalued do not see a worsening of operating performance; Panel A shows that
changesinperformanceforallyearsclosetozeroandinsignificant.Infact,theysee
an improvement in industry‐adjusted performance; Panel B shows that in three
years,theirindustry‐adjustedperformanceimprovesby0.23percentagepoints.
Finally I look at the differences between operating performance changes
between overvalued and not‐overvalued acquirers. Panel A shows that operating
performance of overvalued acquirers worsen considerably compared to not‐
17
overvaluedacquirers;inthreeyears,changeinovervaluedacquirer’sperformance
is 0.54 percentage points lower than the change in the performance of acquirers
that are not overvalued (significant at 5% level). Results are similar if industry‐
adjustedchangeinperformanceisused;thistimethechangeintheperformanceof
overvalued acquirersis 0.42 percentage points lower inthreeyears (significant at
5%level).
To conclude, overvalued acquirers see a dramatic worsening of their
operating performance in the years following the deal, whereas not‐overvalued
acquirers actually see an improvement in performance. Once again this result is
similartostudiesofnon‐REITovervaluedacquirerslikeFu,LinandOfficer(2011)
andAkbulut(2012)whofindbetween‐0.51and‐0.65percentagepointsdeclinein
operatingperformanceforovervaluedstockacquirersinthethreeyearsfollowing
thedealcompletion.Thereissimilarevidenceofworseningoperatingperformance
followingseasonedequityofferings(SEOs)byREITs.SEOsaretypicallyassociated
withovervaluationsincemanagerswillbemorewillingtosellsharesthroughSEOs
when they believe the shares are overvalued. In this context, Ghosh, Roark and
Sirmans(2011)findasignificantdeclineinoperatingperformancefollowingSEOs,‐
0.67percentagepointsfromyear‐1to+3,and‐0.53percentagepointsfromyear0
to+1,whichtheyattributetomarket‐timingbythemanagersofovervaluedREITs.
Myresultsonceagainconfirmthis;overvaluedequitycanleadtovalue‐destroying
over‐investment,eitherintheformofacquisitionsorSEOs.
18
4.Discussion
Ibelievethispapermakesseveralimportantcontributionstotheliterature
onREITacquisitions.First,itexaminestheshort‐termandlong‐termwealtheffects
of a comprehensive set of 993 REIT mergers and acquisitions from 1993 to 2011.
ExistingstudiesalmostexclusivelyfocusonREITmergers(ignoringcompletelythe
acquisitionofassets),whicharefarfewerinnumber,limitingthestatisticalpower
and scope of tests to be conducted18. I believe including acquisitions of assets is
informative; they represent economically significant external investment made by
the managers (average size of acquisitions of assets in my sample is $139 million
andthemediandealsizeis$51million),thesuccessorfailureofwhichwouldhave
adirectimpactonshareholderwealth.
Second, to best of my knowledge, this is the first paper, which looks at the
impact of overvaluation on REITmergerand acquisitions. Although REITs operate
under strictly defined rules like paying out 90% of earnings as dividends, which
would reduce the amount of free‐cash flow available to the management and
mitigateagencycosts(Bauer,EichholtzandKok2011),Ineverthelessfindevidence
of value‐destroying external investment by REITs, driven almost entirely by
overvaluedacquirers.
Third, this is the first paper, which directly measures the overvaluation of
REIT acquirers. Existing REIT merger studies do not attempt to measure
overvaluation directly, instead they look at announcement returns and method of
18
Campbell, Ghosh, Petrova and Sirmans (2009) examine mergers during a comparable time period (19972006), but end up with a sample size of 70, which is actually the largest sample size among papers like
Campbell, Ghosh and Sirmans (2005), and Sahin (2005) looking at similar periods.
19
paymenttoinferovervaluation(seeCampbellGhosh,Sirmans2001).Beingableto
measure overvaluation for REITs directly will help answer research questions
beyondthetopicofmergersandacquisitions.
Fourth, I find that the oft‐cited ‐0.6% to ‐1.2% acquirer announcement
returnsinREITmergersisalmostexclusivelyduetoovervaluedacquirers.InfactifI
onlylookatacquirersthatarenotovervalued,theacquirerannouncementreturnis
virtually zero. This suggests that the negative announcement return results from
merger‐only samples used in studies like Campbell, Ghosh and Sirmans (1998,
2001),Sahin(2005),Campbell,Ghosh,PetrovaandSirmans(2009)arelikelytobe
driven by overvalued acquirers. To see if this is really the case, in unreported
results,Icomputedtheacquirerannouncementreturnsforthe100mergersinmy
sample for overvalued and not‐overvalued acquirers separately. 28 of these
acquirersareovervaluedandgetanannouncementreturnof‐1.54%(significantat
10%level)whereastheremaining72acquirerswhicharenotovervaluedgetonly‐
0.33% which is not statistically significant. This confirms the significant role of
overvaluationinexplainingthenegativeacquirerreturnsinREITmergers.
Fifth, I provide evidence of long‐run wealth destruction from acquisitions
made by overvalued REITs. Overvalued REIT acquirers see their operating
performance deteriorate significantly within the three years following the deal,
whereastheoperatingperformanceofacquirerswhicharenotovervaluediseither
flatorshowsanincrease.Thissuggeststhatovervaluedequitycreateslonglasting
problems for the REIT investors, and is consistent with studies like Ghosh, Roark
20
and Sirmans (2011), which present evidence on the long‐run underperformance
followingREITSEOs.
5.Conclusion
REITs are important investment vehicles and represent a combined
capitalizationof$450billionasoftheendof2011.19Despitethestrictpayoutrules
theymustadhereto,whichlimitstheirfreecashflowandreducestheseverityofthe
agency problems, it would be unrealistic to assume that REITs, which constitute
suchasignificantpartofthestockmarket,wouldbeimmunetothecostsimposed
byovervaluedequity.Ishowthattheyaren’t.
Byusingamanagers’insidertradingtodirectlymeasureREITovervaluation,
I find that overvalued REIT acquirers are more likely to use stock as a method of
payment when overvalued, get negative and lower announcement returns than
acquirerswhicharenotovervalued,andfaceworseningoperatingperformancein
the three years following the completion of the deal. Moreover, announcement
resultsandlong‐termperformanceresultsarecomparableinmagnitudetothosefor
overvalued non‐REIT acquirers and represent economically significant value
destructionforshareholders.HenceovervaluationiseverybitofaproblemforREIT
firmsasitisfornon‐REITfirms.
19
http://www.reit.com/DataAndResearch/US-REIT-Industry-MarketCap.aspx
21
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fancy? Real Estate Finance, 1998, 15, 45–54.
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payment in mergers: Evidence from real estate investment trusts (REITs). Real Estate
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in REIT Mergers. Journal of Real Estate Finance and Economics, 2005, 31, 225–239.
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22
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23
Table 1
Descriptive Statistics – Acquisition Data
This table summarizes the acquisition sample by year. Acquisition sample comes from SDC Platinum Mergers & Acquisitions Database and includes
all mergers and acquisitions of assets where acquirer was an equity REIT listed on the NYSE, AMEX, or NASDAQ during 1993-2011. “Involves
stock” means part or all of the payment was made in stock.
Merger
Form of Deal
Acq. of
Assets
Total
Method of Payment
Involves Stock
Cash
Other
67
32
1
83
804
6
150
836
7
Target's Public Status
Public
Private
Subsidiary
Other
63
17
14
6
524
356
13
63
541
370
17
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Total
100
893
993
Total
Form of Deal
Acq. of
Merger
Assets
7
2
11
3
11
14
60
11
153
8
186
3
57
6
15
5
10
3
32
9
37
10
37
7
82
8
92
4
32
2
14
1
6
32
4
19
100
893
Method of Payment
Involves
Stock
Cash
7
3
10
5
9
20
53
29
133
27
166
8
51
6
15
7
8
8
27
6
40
6
41
6
83
10
88
2
34
2
14
2
5
1
31
2
21
157
836
Total
7
13
14
74
164
194
60
21
15
35
46
47
89
100
36
16
7
32
23
993
Table 2
Quarterly Cross-Sectional Insider Trading regressions
This table shows the time-series averages of the coefficients from the 76 quarterly regressions of insider trading on control variables from 1993 to 2011. The dependent variables
are open market purchases during quarter t in column (1), purchases through exercise of options during quarter t in column (2), and open market sales during quarter t in column
(3), all expressed as a percentage of the shares outstanding at the beginning of the quarter, and winsorized at 1 percent level. Peer Trading in Quarter t is the trading activity (open
market sales for column 1, purchases through the exercise of options for column 2 and open market sales for column 3) of a peer insider during the current quarter. Each insider is
matched to a peer based on firm size (same asset decile), past firm return (return within 10 percent), age (within 5 years), tenure (within 5 years) and the value of shareholdings
(nearest dollar value of shareholdings, but within 50 percent) in that order. I also require that the firm of the peer insider is not an acquirer or a target in an acquisition during
quarters t-4 through t+1. Self trading in quarter t-4 is insider’s trading during the same calendar quarter one year ago. Ownership is the shares held by the insider divided by the
shares outstanding at the beginning of the quarter. Peer Trading, Self Trading, and Ownership are winsorized at 1 percent level to remove the effects of outliers. Tenure is the
number of days since the insider first appeared in the insider data file under his current firm. Age is the number of days since the insider first appeared in the insider data file under
any firm. Analyst coverage is from IBES database and shows the number of analysts following the firm. Fraction denotes the ratio of a firm’s shares held by institutional investors
relative to total shares outstanding in CRSP. Concentration is the Herfindahl Index calculated over the distribution of the fractions of company stock owned by institutional
investors. Fraction and concentration are measured at the beginning of the current quarter. Fraction is set to 100 percent if it is greater than 100 percent and Concentration is set to
10,000 if it is greater than 10,000 (the maximum for Herfindahl Index). Data on institutional investors is obtained from CDA/Spectrum, a database of quarterly 13-F filings of
money managers to the U.S. Securities and Exchange Commission. Firm size is the log of total assets (log [COMPUSTAT quarterly item ATQ]). Share turnover is the trading
volume in quarter t-1 divided by the shares outstanding at the beginning of quarter t-1 (winsorized at 1 percent). Past stock return is the stock return for the previous four quarters.
Past stock volatility is the annualized volatility of daily stock returns measured over quarters t-4 through t-3. Change in volatility is the difference between volatility measured over
quarters t-2 through t-1 and volatility measured over quarters t-4 through t-3. All independent variables except for Peer Trading and Self Trading are scaled by multiplying with 103
. The t-statistics are based on Newey-West (1987) corrected standard errors.
Table 2 (continued)
Panel A: Full Sample
Peer Trading in Quarter t
Self Trading in Quarter t-4
Ownership
Tenure
Age
Analyst Coverage
Fraction of Inst. Investors
Concentration of Inst. Investors
Share Turnover
Log Assets
Past Return
Volatility
Change in Volatility
Intercept
R-Square
Number of Observations
(1)
Open Market Purchases
Mean
T-stat.
0.0074
4.64
0.3401
18.24
0.0040
5.43
-0.0001
-8.91
0.0001
8.5
-0.0020
-5.43
-0.4570
-10.87
0.0001
6.01
-0.0260
-0.84
-0.0270
-5.77
-0.1210
-5.51
0.2956
7.99
0.1685
5.05
0.0074
7.87
3.97%
76
(2)
***
***
***
***
***
***
***
***
**
***
***
***
***
***
Purchases through
Exercise of Options
Mean
T-stat.
0.0032
2.30
0.2310
29.21
-0.0200
-1.82
0.0001
7.42
0.0000
4.00
-0.0060
-3.70
-0.0620
-0.50
-0.0835
-1.39
0.1329
5.39
-0.1720
-16.51
0.4915
8.66
-0.4160
-5.71
-0.1780
-3.19
0.0032
15.25
2.83%
76
(3)
***
***
***
***
***
***
*
***
***
***
***
***
***
Open Market Sales
Mean
T-stat.
0.0235
6.88
0.1860
15.12
-0.0003
-10.48
-0.0001
-1.97
-0.0001
-2.01
-0.0011
-4.88
-4.0090
-9.26
0.0019
6.79
-0.3245
-5.59
0.7281
10.64
-1.8020
-9.83
1.1217
3.77
0.8857
3.87
-0.0048
6.88
4.99%
76
***
***
***
*
*
***
***
***
***
***
***
***
***
Table 3
Mean Acquisition Characteristics sorted by Acquirer’s Prior Abnormal Insider Trading
An acquirer is labeled as Overvalued (OV) in quarter t if its past abnormal insider trading (PAIT) is in the bottom 33 percent of the
distribution of PAITs for all acquirers (REIT and non-REIT). A firm is labeled as Not-overvalued (NOV) in quarter t if its PAIT is in the top
67 percent. PAIT for each quarter is defined as the sum of the abnormal net purchase ratios (ANPR) of the two preceding quarters. ANPR for
a given quarter is defined as the abnormal open market purchases plus abnormal purchases through the exercise of stock options minus
abnormal open market sales (all expressed as a percentage of shares outstanding) for that quarter. Abnormal open market purchases,
purchases through the exercise of options and open market sales are measured each quarter for each insider as the residuals from the
quarterly cross-sectional insider-level regressions in Table 2. These residuals are then aggregated to calculate the firm-level abnormal trading
for each quarter. Probability of involving stock payment is the percentage of bids where part or all of the payment is made in stock.
Probability of cash payment is the percentage of deals where the method of payment is pure cash. Public target is the percentage of targets
that are publicly listed. Subsidiary target is the percentage of targets that are subsidiaries. Acquirer and target cumulative abnormal returns
(CAR) are measured for the four-day window [-2,+1] around the announcement date using the Fama-French three factor model estimated
using return data for the one year period ending at day -64 relative to the announcement date. Deal size is the total dollar value of the
consideration paid by the acquirer for the target. Relative size of the bid is the ratio of deal size to acquirer’s size which is measured as the
market value at day -64 relative to the announcement day (day 0). For each variable the difference in means between OV and NOV acquirers
are computed, and the statistical significance of the difference is assessed using a two-sample t-test. T-statistics are reported in parentheses
below the differences. The symbols ***, **, and * denote significance levels of 1%, 5% and 10%, respectively, for the two-tailed hypothesis
test that the difference equals zero.
Table 3 (continued)
Panel A: All Deals
NotOvervalued Overvalued Diff.
(OV)
(NOV)
N=169
N=824
Prob. of involving stock payment
Prob. of cash payment
Acquirer's Ann. CAR
Deal size ($ millions)
Relative size
Public Target
Subsidiary
Form of Deal is Merger
19.5%
79.9%
-0.75%
321
14.3%
11.8%
30.2%
16.6%
14.2%
85.1%
-0.03%
195
18.0%
5.2%
38.7%
8.7%
5.3%
-5.2%
-0.7%
126
-3.7%
6.6%
8.5%
7.8%
T-Stat
1.76
-1.69
-2.79
2.28
-0.98
3.23
-2.09
3.09
*
*
***
**
***
**
***
Panel B: Deals involving Stock
NotOvervalued Overvalued Diff. T-Stat
(OV)
(NOV)
N=33
N=117
Acquirer's Ann. CAR
Deal size ($ millions)
Relative size
Public Target
Subsidiary
Form of Deal is Merger
-0.89%
840
43.9%
48.5%
27.3%
66.7%
0.12
500
40.5%
29.1%
29.1%
38.5%
-1.0%
340
-3.4%
19.4%
-1.8%
28.2%
-1.38
1.33
-0.23
2.11
-0.20
2.94
Panel C: Cash Deals
NotOvervalued Overvalued
Diff.
(OV)
(NOV)
N=135
N=701
**
***
-0.69%
194
7.1%
2.96%
31.1%
4.4%
0.05%
144
14.3%
1.28%
40.4%
3.7%
-0.6%
50
-7.2%
1.7%
-9.3%
0.7%
T-Stat
-2.33
1.24
-2.15
1.44
-2.02
0.40
**
**
**
Table 4
Mean Acquirer Firm Characteristics sorted by Acquirer’s Prior Abnormal Insider Trading
Panel A shows acquirer firm characteristics for all deals, Panel B for deals in which method of payment includes any amount of stock and Panel C for deals where
method of payment is only cash. An acquirer is labeled as Overvalued (OV) in quarter t if its past abnormal insider trading (PAIT) is in the bottom 33 percent of the
distribution of PAITs for all acquirers (REIT and non-REIT). A firm is labeled as Not-overvalued (NOV) in quarter t if its PAIT is in the top 67 percent. All accounting
variables are calculated as of the fiscal quarter-end which ends before the beginning of the current quarter and are winsorized at 1% level. Size is acquirer’s total assets
(quarterly COMPUSTAT data item ATQ). Market-to-book ratio is calculated as market value of equity (CSHOQ*PRCCQ) over book value (CEQQ). Price-to-earnings
ratio is the ratio of fiscal quarter-end stock price to earnings per share (quarterly COMPUSTAT items PRCCQ/EPSPXQ). Past stock return is the stock return for the
previous four quarters. Past stock volatility is the annualized volatility of daily stock returns during the previous four quarters. Asset growth is the proportional change in
assets (log [ATQ / ATQ (t-1)]). Sales growth is the proportional change in sales (log [SALEQ / SALEQ (t-1)]). Cash is the ratio of cash to total assets [CHEQ / ATQ].
Leverage is the ratio of long-term debt to assets (DLTTQ / ATQ). Return on equity is the ratio of earnings to average equity [NIQ / ((BEQ + BEQ (t-1))/2], where
BEQ=ATQ-LTQ-PSTKQ+TXDITCQ. FFO is funds from operations from the SNL database and is scaled by assets (ATQ). For each variable the difference in means
between OV and NOV acquirers are computed, and the statistical significance of the difference is assessed using a two-sample t-test. T-statistics are reported in
parentheses below the differences. The symbols ***, **, and * denote significance levels of 1%, 5% and 10%, respectively, for the two-tailed hypothesis test that the
difference equals zero.
Table 4 (continued)
Panel A: All Deals
Notovervalued
Diff.
(NOV)
N=824
1,515
1,345
0.0029
-0.009
2.05
0.01
94
23
0.25
0.2
0.27
-0.01
0.10
-0.05
0.09
-0.02
0.03
-0.001
0.46
0.002
0.023
-0.036
0.057
0.005
Size ($ millions)
PAIT (Acquirer)
Market-to-book ratio
Price-to-earnings ratio
Past stock return
Past stock volatility
Asset growth
Sales growth
Cash
Leverage
Return on Equity
FFO
Overvalued
(OV)
N=169
2,860
-0.0063
2.06
117
0.23
0.26
0.05
0.07
0.03
0.46
-0.013
0.062
Size ($ millions)
PAIT (Acquirer)
Market-to-book ratio
Price-to-earnings ratio
Past stock return
Past stock volatility
Asset growth
Sales growth
Cash
Leverage
Return on Equity
FFO
Panel B: Deals involving stock
NotOvervalued
overvalued
Diff.
(OV)
(NOV)
N=33
N=117
2,362
1,613
749
-0.0050
0.0028
-0.0078
1.91
1.94
-0.03
157
100
58
0.19
0.24
-0.05
0.32
0.26
0.06
0.04
0.09
-0.05
0.05
0.07
0.03
0.02
0.02
0.004
0.41
0.46
-0.04
0.022
0.023
-0.001
0.066
0.064
0.003
T-Stat
6.43
-25.02
0.04
1.39
-0.68
-0.53
-3.39
-1.20
-0.12
0.19
2.03
2.29
***
***
***
**
**
T-Stat
1.59
-9.26
-0.12
1.29
-0.83
1.76
-1.58
0.94
1.02
-1.24
-0.11
0.44
***
*
Overvalued
(OV)
N=133
2,987
-0.0067
2.09
103
0.24
0.25
0.06
0.08
0.03
0.47
-0.022
0.062
Panel C: Cash Deals
Notovervalued
Diff.
(NOV)
N=694
1,501
1,486
0.0029
-0.0096
2.06
0.03
93
9.6
0.25
-0.01
0.27
0.02
0.11
-0.05
0.10
-0.02
0.03
-0.001
0.46
0.02
0.023
-0.045
0.056
0.006
T-Stat
6.31
-23.30
0.09
0.53
-0.29
-1.87
-3.06
-0.89
-0.24
1.04
-2.09
2.25
***
***
*
***
**
***
Table 5
Cross Sectional Logistic Regression Estimates of the Likelihood of Using Stock and Cash as Method of Payment
In Panel A, the dependent variable is a dummy which is set to one if the method of payment involves any amount of stock and zero if otherwise. The independent
variable of interest is OVDUM which is a dummy variable set to one if the PAIT for the acquirer is in the bottom 33 percent of the distribution of the PAITs of
all acquirers. Private target dummy is equal to one if the target is private or zero if otherwise. Number of bids in the past year shows the number of acquisition
bids made by the acquirer in the last one year. The calculations of the rest of the independent variables are detailed in the captions to Tables 3 and 4. Regressions
include year dummies. In each model, coefficient is shown in the first column, followed by the z-statistic and the marginal effect. Z-statistics are corrected for
heteroskedasticity and firm-level clustering. The symbols ***, **, and * denote significance levels of 1%, 5% and 10%, respectively, for the two-tailed
hypothesis test that the coefficient equals zero.
Panel A: Prob (Method of payment involves stock)
OVDUM
Market-to-book ratio
Cash
Return on equity
Sales growth
Leverage
Price-to-earnings ratio x 10-3
Firm size (log assets)
Past stock return
Past stock volatility
Relative deal size
Private target dummy
Number of bids in the past year
Intercept
Number of observations
Pseudo R2
Coef.
0.739
-0.031
-10.741
2.720
-1.493
-0.098
0.001
0.261
-1.712
-0.835
1.642
-0.735
0.022
-13.902
976
0.17
Z-stat.
2.67
-0.51
-2.48
0.83
-1.24
-0.08
1.81
1.74
-2.74
-0.65
4.47
-3.12
0.60
-11.14
***
**
*
*
***
***
***
***
Marg. Eff.
0.0784
-0.0027
-0.9310
0.2360
-0.1290
-0.0085
0.0001
0.0226
-0.1480
-0.0724
0.1420
-0.0661
0.0019
Panel B: Prob (Method of payment is pure cash)
Coef.
-0.705
0.032
10.027
-2.646
1.405
0.028
-0.001
-0.279
1.574
0.903
-1.572
0.658
-0.005
14.077
976
0.16
Z-stat.
-2.57
0.56
2.47
-0.82
1.25
0.02
-2.03
-1.91
2.66
0.70
-4.52
2.81
-0.12
11.55
**
**
**
*
***
***
***
***
Marg. Eff.
-0.0788
0.0030
0.9300
-0.2450
0.1300
0.0026
-0.0001
-0.0259
0.1460
0.0838
-0.1460
0.0630
-0.0004
Table 6
Ordinary least squares estimates of the relation between PAIT and acquirer’s announcement cumulative abnormal return
The dependent variable is acquirer’s four-day announcement cumulative abnormal return (CAR) for the window [-2, 1] around the
announcement day which is measured relative to the Fama-French three-factor model. The independent variable of interest is
OVDUM, a dummy variable set to one if the PAIT for an acquirer is in the bottom 33 percent of the distribution of the PAITs of all
acquirers. Private target dummy is equal to one if the target is private or zero if otherwise. Multiple bidders dummy is set to one if
there are competing acquirers to buy the same target or zero if otherwise. Hostile bid dummy shows whether the bid was a hostile
offer. An acquisition is considered hostile if the “attitude” field in SDC was marked “unsolicited” or “hostile”. Toehold dummy is set
to one if the acquirer owns 5 percent or more of the target at the announcement date. Number of acquisitions in the past year shows the
number of acquisition bids made by the acquirer in the last one year. The calculations of rest of the variables are detailed in the
captions to Tables 3, 4 and 5. All accounting variables are measured at the end of the fiscal quarter immediately preceding the
beginning of the quarter in which the acquisition is announced. Past stock return and past volatility are measured over the one year
period ending before the announcement month. The regression includes year dummies. T-statistics are calculated using White’s
heteroskedasticity consistent errors and adjusting for clustering at the acquirer firm level. The symbols ***, **, and * denote
significance levels of 1%, 5%, and 10%, respectively, for the two-tailed hypothesis test that the coefficient equals zero.
OVDUM
Market-to-book ratio
Cash
Return on equity
Sales growth
Leverage
Price-to-earnings ratio
Firm size (log assets)
Past stock return
Past stock volatility
Relative deal size
Private target dummy
Multiple bidders dummy
Hostile offer dummy
Toehold dummy
Number of bids in the past year
Intercept
Number of Observations
R2
Coef.
-0.641
0.059
3.326
-0.089
1.725
-0.563
-0.001
0.015
-0.146
-0.746
0.253
0.137
0.974
-2.995
-0.747
-0.023
0.081
976
0.069
T-stat.
-2.23
2.11
1.2
-0.41
1.75
-0.76
-1.16
0.14
-0.31
-0.49
0.66
0.59
1.13
-5.48
-1.24
-0.62
0.07
**
**
*
***
Table 7
Change in post-acquisition Operating Performance
This table reports average change in post-acquisition raw and industry-adjusted operating performance over 1-year, 2-year and 3-year
horizons, starting at the first fiscal year ending after the deal completion date (year 0). Change in raw operating performance is
measured as the change in Funds from Operations (FFO) for years 1, 2 and 3 relative to the FFO for year 0. Change in industry
adjusted operating performance is measured as the change in the industry-adjusted Funds from Operations (FFO) for years 1, 2 and 3
relative to the industry-adjusted FFO for year 0. Industry adjusted FFO is calculated as FFO minus the mean FFO for the industry for
that first fiscal year. FFO is obtained from the SNL database, and is scaled by dividing by the book value of assets. One-tailed (twotailed) p-values are reported in parentheses (brackets). The symbols ***, **, and * denote significance levels of 1%, 5% and 10%,
respectively.
All Acquirers
Panel A: Change in raw performance
N
1-year
2-year
3-year
671
0.29%
-0.02%
-0.14%
(0.318)
(0.418)
(0.119)
Panel B: Change in industry-adjusted performance
N
1-year
2-year
3-year
671
-0.02%
0.04%
0.16%
(0.351)
(0.336)
(0.087)
Overvalued Acquirers
116
-0.22%
(0.082)
-0.37%
(0.022)
-0.59%
(0.001)
116
-0.29%
(0.038)
-0.25%
(0.092)
-0.19%
(0.165)
Not-Overvalued Acquirers
555
0.08%
(0.105)
0.05%
(0.333)
-0.05%
(0.354)
1,136
0.03%
(0.310)
0.10%
(0.184)
0.23%
(0.044)
-0.31%
[0.078]
-0.42%
[0.054]
-0.54%
[0.023]
-0.33%
[0.067]
-0.36%
[0.055]
-0.42%
[0.039]
Difference