Timing and the Holding Periods of Institutional Real Estate

2003 V31 2: pp. 205–222

REAL ESTATE

ECONOMICS

Timing and the Holding Periodsof Institutional Real EstateDavid Collett,∗ Colin Lizieri,∗∗ and Charles Ward∗∗∗

Literature on investors’ holding periods for securities suggests that high trans-action costs are associated with longer holding periods. Return volatility, bycontrast, is associated with shorter holding periods. In real estate, high trans-action costs and illiquidity imply longer holding periods. Research on deprecia-tion and obsolescence suggests that there might be an optimal holding period.Sales rates and holding periods for U.K. institutional real estate are analyzed,using a proportional hazards model, over an 18-year period. The results showlonger holding periods than those claimed by investors, with marked differ-ences by type of property and over time. The results shed light on investorbehavior.

In deciding whether to buy an investment asset, such as real estate, investorscarry out some appraisal of the expected profit from their decision. Minimally,this decision might involve comparing the cost of purchase with the potentialgains from holding and subsequently selling the asset at a later date. In somemarkets (e.g., currencies or financial futures), the transaction costs are verysmall and “round trips” can be effected easily at any time subsequent to initialpurchase. However, in other markets, such as commercial real estate, there aresubstantial costs involved in buying and selling the asset, and therefore theappraisal of the profitability of purchase has to take into account how longthe asset is likely to be held before the price has moved far enough for thetransaction costs to be covered. Given round-trip transaction costs of 7–8%, itwould be surprising if investors expected to hold real estate investments onlyfor short periods of time. Figure 1 illustrates the impact of transaction costson holding period returns for hypothetical investment in equities and U.K. real

∗Department of Applied Statistics, The University of Reading, Reading, RG66AW, UKor [email protected].

∗∗Department of Real Estate and Planning, School of Business, The University ofReading, RG66AW, UK or [email protected].

∗∗∗Department of Real Estate and Planning, School of Business, The University ofReading, RG66AW, UK or [email protected].

206 Collett, Lizieri and Ward

Figure 1 � Round-trip transaction costs and holding periods. Due to the effect oftransaction costs, the IRR of property lies well below that of a comparable equityinvestment until the holding period becomes long. (In effect, the transaction costs mustbe amortized over a long period.)

0.00%

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0 5 10 15 20 25

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estate.1 As can be seen, the equity investment produces an internal rate of returnwhich very quickly converges towards 10%, whereas the impact of transactioncosts in real estate investment is such that after 2 years the investment wouldonly achieve an internal rate of return (IRR) of 5.5%. An IRR of 9% is achievedonly after 10 years.

There is another reason why the consideration of holding periods is important toinstitutional and professional investors holding property in a mixed-asset port-folio. Optimal portfolio allocation will depend on the variances and covariancesof asset returns. But the parameter estimates, which should be measured overthe interval defining the investment decision, will change as the interval itselfchanges. Using unrealistically short investment decision intervals, estimates ofthe standard deviations and correlations with other asset returns will be severelybiased and the implied allocation of real estate within a mixed-asset portfo-lio will be misestimated. For example, for U.K. data, the ratio of the standard

1 For the real estate investment, it is assumed that the discount rate = 10%, initialcapitalization = 7%, acquisition costs = 5.5% and sale costs = 2.5% with a rent review(standard U.K. practice) at five-year intervals with implied rental growth rate = 3.4%and depreciation (no tax shields) = 2%. For the equity investment, the discount rate =10%, initial dividend yield = 7% and acquisition and sale costs = 0.5%.

Timing and the Holding Periods of Institutional Real Estate 207

deviation of returns from public to private real estate2 ranges from 7.3 (monthlyreturns) to 2.8 (one-year returns) to 2.0 (two-year returns). Even allowing forappraisal smoothing, private real estate looks less attractive once more realisticand longer time horizons are considered.

There must be a close association between the expected and the realized holdingperiod. Studying realized holding periods should provide useful insight into theexpected decision. Realized, holding periods will vary and we try to explainhow variations in holding periods are conditioned by property type and marketconditions.

In the following section, we review the relevant research on holding periods andtransactions, and we infer some expectations about the implied holding periodfor real estate before deriving four hypotheses about the realized holding periodsof real estate investors. Next, we discuss the measurement of the holding periodand the statistical method used in our paper. In the fourth section we analyze thedata, and in the fifth section we discuss the implications for expected decisionsabout investment holding periods in real estate. The final section concludes.

Towards an Analysis of Holding Periods

There is a body of evidence relating to stocks (and, to a lesser extent, bonds)that suggests that transaction costs influence holding periods. Specifically,high transaction costs (generally proxied by bid–ask spreads) are associatedwith longer periods. Theoretical and empirical backing for this is provided byDemsetz (1968), Tinic (1972), Amihud and Mendelson (1986), Bhide (1993),Umlauf (1993) and Atkins and Dyl (1997). Amihud and Mendelson proposethat assets with higher bid–ask spreads would, in equilibrium, be held in port-folios by investors who expect to hold securities for a long time. Constantinides(1986) concludes that investors “accommodate transaction costs by drasticallyreducing the frequency and volume of trade” (p. 859). Hess (1991) suggeststhat households, faced with retail transaction costs, trade less than they wouldwith wholesale costs. Accordingly, they hold portfolios that are suboptimal andhave large amounts of uncompensated, diversifiable risk. This happens becauserelatively small changes in price (return) will be insufficient to induce portfoliorebalancing.

Atkins and Dyl (1997) extend the empirical research by considering the ef-fects of firm size, bid–ask spread and volatility of returns on holding period of

2 Based on the IPD Monthly Index for directly held private real estate and the FT-RealEstate Index for publicly listed property company shares.


properties for a sample of over 2,000 Nasdaq firms and 500–1,100 NYSE firmsover the period 1981–1993. They show highly significant positive relationshipsbetween holding period and transaction costs (proxied by spread) and firm sizeand a negative relationship between price variability and holding period.

There is limited research into real estate holding periods. In prior U.S. studies,it has been argued that holding periods are driven by tax laws and, in particular,tax-related depreciation factors are important (see, e.g., Hendershott and Ling1984, Gau and Wang 1994 or, for a review, Fisher and Young 2000). Since thereare no tax breaks for depreciation on buildings in the U.K. market, this cannotprovide an explanation of the observed pattern of holding periods. Nor havethere been substantial changes to capital gains tax rules.

Given the high transactions costs associated with commercial real estate rela-tive to other capital market assets and the perceived illiquidity of the market,in part due to entry barriers, the expectation would be that holding periods forcommercial property would be much greater than for stocks and bonds. How-ever, the private nature of most transactions, the heterogeneity of property andlower levels of institutional investment might be taken to imply informationasymmetry and, hence, bring an expectation of more frequent trading. Infor-mation asymmetries and divergence of expectations are most likely to occurin downturns in the market—when, anecdotally, there are few transactions toprovide evidence. The relationship between returns and holding period in realestate markets is, thus, likely to prove complex.

Rowley, Gibson and Ward (1996) interviewed investors about their perceptionsof the effects of urban design on the performance of property investment. Theyreported that investors buying or developing new property tended to have aholding period in mind from the start. Indeed, in specific cases, their invest-ment decision included the horizon at which the property would be sold. Insome cases, for example, offices, this decision appeared to be related to thedepreciation or obsolescence factor identified by Baum (1991). However, inthe case of retail property, the decision was more complicated and the expectedholding period would depend on active management as well as the state of themarket.

We therefore state formally the questions we are addressing in this study, to-gether with the results that might plausibly be expected.

a. What is the best estimate of the realized holding period for real estate?(The holding period of real estate may be significantly longer than thatfor equities or bonds because of the less liquid market and the highertransaction costs.)


b. Are there any significant differences between the realized holding pe-riod of property of different type or size? (The holding period of largeshopping centers will be longer than that for offices because of the insti-tutional characteristics of the marketing potential for shopping centersand because shopping centers are traded in a thinner market than otherproperty types.)

c. Are the estimates reasonably stable over time or are they subject tosudden and inexplicable changes from one year to another? (The yearof purchase might influence the selling decision as if property werebought in boom periods; investors might be reluctant to sell it in periodsin which book losses might be recorded.)

d. Is the holding period (or the propensity to sell a property) influencedby the state of the market? (Holding periods would be expected to beassociated with returns, declining during periods of greater liquidityand rising during periods of less liquidity; volatility and holding periodshould exhibit a negative correlation.)

Measuring the Holding Period

Given the frequency of trading in equity markets, transaction volume providesa useful indicator of holding period. In the stock market literature, the holdingperiod in any period is typically defined as shares outstanding in that perioddivided by the trading volume (Atkins and Dyl 1978). Other studies such asHess (1991) simply use the trading volume as a proxy. There are a number ofdrawbacks to such an approach. First, such implied transactions figures maymask the fact that a number of assets may trade rarely and have long hold-ing periods, whereas others trade frequently. Second, transaction volumes mayfluctuate greatly from year to year, reflecting market conditions and other ex-planatory factors. These confusing variations are likely to be more problematicin real estate markets than in equity markets where, as seen above, the holdingperiods are much lower.

Such issues become more acute when one has actual sales data. In the data setemployed in this study, we have information on actual sales of property from theInvestment Property Databank (whose database is reported to account for some70% of the U.K. institutional property market). Since we have both the year ofpurchase and the year of sale, we can work out an individual holding period foreach such observation. However, the average holding period (whether mean ormedian) would be misleading, since we have no information on properties thatwere unsold over the period. Statistically, such data are described as censoredand analysis requires care and rigor.


A set of statistical models have been developed that deal with the analysisof survival data (Collett 1994). These models have their origins in industrialengineering (where they are used to estimate the useful lives of machines orcomponents) and in biomedical sciences (used, e.g., to describe survival timesfrom particular types of medical procedures). They have been used in a numberof economic problems concerning duration data, notably in the field of un-employment (for an early, but useful, review, see Kiefer 1988). An importantapplication comes from the modeling of sources of “risk”—for example, ofmortgage default—using a proportional hazards framework and the regressiontechnique developed by Cox.

Real estate applications of the proportional hazards technique include Lane,Looney and Wansley (1986), who use the Cox model to analyze bank failure,Quigley (1987), who examines housing mobility and mortgage prepayment,Kluger and Miller (1990), who model time on the market for residential sales,Vandell et al. (1993), who estimate hazard functions for home loan defaultsbased on loan terms and price movements over time and Simons (1994), whomodels industrial real estate mortgage default. As can be seen, the majorityof such studies use housing or residential mortgage data. The development ofproportional hazards techniques for assessing mortgage default and prepaymenthas been important in the evolution of pricing models for the mortgage-backedsecurities market.

In summary, the proportional hazards framework models the rate of some event(e.g., sale of an asset) t periods after a base date (e.g., date of purchase), λ(t).This quantity, which is widely referred to as the hazard function, is, in thiscontext, the instantaneous rate of the event at time t, given that the event hasnot occurred prior to t. The function is an approximation to the probability ofthe event in the tth period, given that the event has not occurred at the start ofthe period (e.g., sale of an asset that was unsold at the start of a year).

Our analysis is based on the Cox regression model for the rate at time t subjectto the value of two (categorical) variables si and pj written λij(t). According tothis model,

λij(t) = e(si +p j )λ0(t), (1)

where si is the effect of the ith category of variable s, i = 1, 2, . . . , and pj isthe effect of the jth category of variable p, j = 1, 2, . . . . λ0(t) is called thebaseline hazard function. Both the baseline hazard function and the coefficientsof the effect variables are estimated using maximum likelihood methods. Sinceno particular form of probability distribution is assumed for the survival times,the Cox model is semi-parametric.


In our model, λ0(t) is the risk of sale t years after purchase for a standard re-tail property purchased in 1981—our reference or baseline observation. Thens is the real estate sector and p is the calendar year of purchase, with s = 0for a standard retail property and p = 0 for a 1981 purchase. The exponen-tial term therefore quantifies how the sale rate is affected when a propertyof a different sector is purchased, or when the year of purchase is other than1981.

For example, suppose λ0(12) = 0.06, that is, there is a 6% probability of thebaseline property being sold in the 12th year after purchase. Now, if an officeproperty has s0 = 0.27 and a property acquired in 1993 has p1993 = 0.82, thenλ0,1993(12) = e(s0+p1993)λ0(12) = e(1.09)0.06 = 0.178, that is a 17.8% probabilityof sale in the 12th year after purchase. In this case, the “risk ratio” is 2.97,that is, the probability of sale for an office building purchased in 1993 is 2.97times the probability of sale of the baseline property in the 12th year afterpurchase.

The Cox regression model can be further extended to handle time dependentexplanatory variables. Generalizing from Equation (1), the proportional hazardsmodel can be written

λi (t) = e[∑p

j=1 β j x ji (t)]λ0(t), (2)

where β j measures the effect of the value factor xj for the ith individual at timet. In the model described below, we examine whether the sale rate is dependentupon the rate of return on commercial real estate and on volatility in the propertymarket.3

Data and Preliminary Analysis

In this study, we used published and unpublished data from the InvestmentProperty Databank, which currently holds records of some 12,000 commer-cial and industrial properties owned, principally, by institutional investors. TheProperty Investors Digest (IPD, various) records the average number of prop-erties held on the database (categorized, e.g., by sector) and also the numberof properties sold in any one year. As widely discussed, the Databank is notstatic—the historic numbers change from one Digest to another as funds join(and, in a few instances, leave) the Databank. Nonetheless, these data allowanalysis of sales volumes and, hence, estimates of transaction-based holdingperiods similar to those found in the equity literature.

3 For further information on the estimation of proportional hazards models, see Collett(1994).


The unpublished data used in this study was provided by Investment PropertyDatabank. It consists of records of properties acquired by funds between 1981(the formal start of IPD’s records) and 1998, together with a date sold (shoulda transaction have occurred) and information on sector. Confidentiality con-straints meant that other classificatory data were not available. In total, recordsof 13,405 acquisitions were available. Comparisons are made difficult by thedynamic nature of the Databank, but the data represents around 81% of theacquisitions made over the period.4

Table 1 shows the proportion of properties in the sample that had been soldby the end of the period, arranged by property sector and by year of purchase.Over 80% of properties purchased in the early 1980s had been sold by the endof the period; for properties bought in 1990, just over half had been sold by theend of 1998.

In total, in the period 1981–1998, 5,736 of the sample properties acquired werealso sold (43% of the sample). This represents around 28% of sales recorded byIPD over this period. Since we know the time between acquisition and sale forthose properties sold, but have a substantial proportion of our sample unsold atthe end of the period, the data are (right) censored and hence appropriate foranalysis using the survival/hazards framework.

Estimating the Holding Period: Empirical Results

Holding Periods and the Hazard Function

To provide an initial benchmark analysis of transactions-based estimated hold-ing periods, we use published data from Investment Property Databank. Table 2shows the implied holding period by sector based on this data for the period1986–1998. Following the equity market literature discussed above, the impliedholding periods are calculated as

HPt = (Prt + Prt−1)/2

Trant, (3)

where Prt is the number of properties in the Databank at the end of year t andTrant is the number of sales within year t. The numerator is a proxy for theaverage number of properties held over the year as a whole.

The average holding period between 1986 and 1998 is around 11 years. Thereare striking variations, however, over time. Thus, the implied holding periods

4 The missing acquisitions are made up predominantly of sector/property types notincluded in our data and acquisitions by funds no longer included in the IPD databank.

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Table 1 � Properties sold by the end of analysis period, by sector and year of purchase.

1981 1982 1983 1984 1985 1986 1987 1988 1989

Standard Retail 81.0% 78.2% 74.6% 73.4% 71.2% 68.2% 66.5% 59.6% 61.6%Shopping Centers 100.0% 75.0% 100.0% 33.3% 25.0% 88.9% 75.0% 62.5% 50.0%Retail Warehouses 100.0% 60.0% 80.0% 83.3% 82.4% 78.6% 77.8% 46.5% 50.0%Small Offices 77.3% 85.7% 81.8% 81.5% 77.1% 82.1% 83.3% 79.3% 70.2%Large Offices 87.0% 85.5% 78.7% 68.3% 71.0% 69.4% 66.7% 62.8% 53.6%Industrial Property 81.0% 77.6% 61.5% 75.0% 82.6% 83.6% 77.2% 58.1% 53.8%All properties 83.4% 80.7% 75.3% 73.7% 72.7% 72.0% 69.0% 60.8% 57.4%

1990 1991 1992 1993 1994 1995 1996 1997 1998

Standard Retail 45.5% 45.7% 33.5% 45.4% 38.2% 20.8% 23.1% 7.8% 10.6%Shopping Centers 66.7% 25.0% 42.1% 35.7% 13.3% 0.0% 0.0% 4.0% 0.0%Retail Warehouses 53.8% 36.8% 47.3% 42.9% 29.4% 23.9% 13.9% 6.6% 1.2%Small Offices 70.6% 69.0% 69.6% 64.6% 57.9% 44.0% 21.6% 21.7% 5.3%Large Offices 48.4% 36.7% 41.7% 36.9% 34.3% 19.8% 10.6% 7.4% 1.4%Industrial Property 42.5% 44.1% 29.0% 40.5% 27.9% 21.0% 13.0% 2.9% 2.4%All properties 50.4% 44.7% 37.5% 43.4% 36.1% 18.6% 15.8% 6.8% 4.8%

The table shows the proportion of properties in the sample sold by the end of the analysis period, broken down by date of purchase and by typeof property. For example, 81% of standard retail units purchased in 1981 had been sold by the end of 1998.

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Table 2 � Implied transaction-based holding periods, IPD 1986–1998.

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 Mean

Standard Shops 15.0 8.8 8.5 11.4 20.9 11.9 15.7 9.4 9.2 10.1 9.3 7.1 3.9 10.9Shopping Centers 26.5 21.1 13.3 14.8 37.6 33.9 20.4 12.5 20.0 11.7 14.3 11.3 5.5 18.7Retail Warehouses 36.0 12.5 24.2 19.9 35.8 27.2 25.3 11.7 13.9 13.8 12.9 9.9 6.3 19.2Offices (All) 17.6 8.7 8.6 11.2 20.2 20.2 17.9 11.9 10.3 9.9 8.4 5.8 3.5 11.9Industrial (All) 9.0 4.8 5.6 11.0 17.7 20.9 20.1 12.7 9.6 9.7 8.8 7.4 5.3 11.0Other 16.1 13.1 10.9 18.4 22.7 18.4 13.9 9.3 10.2 8.3 13.6 10.1 5.5 13.1All properties 14.5 8.2 8.3 11.9 20.7 16.1 17.0 10.6 10.0 9.9 9.5 7.2 4.2 11.4

The table shows proxy holding periods based on transaction volumes, in the manner used in prior equity market literature.


are relatively high in the early 1990s real estate slump, but they fall rapidly asthe market improves through the decade.

These notional figures imply holding periods that are about twice as largeas those implied by portfolio turnover figures derived from survey evidence inBaum et al. (2001) that suggest perceived average holding periods of around sixyears or less. Even considering just the last five years of data, median holdingperiods well exceed the investors’ own estimates. How might this anomalyarise? One reason might be that funds have large numbers of older properties intheir portfolio that are not subject to turnover. Properties acquired more recentlymight be subject to more regular investment appraisal, be more liquid and hencebe more likely to be traded. As a result, such aggregate holding period estimateswould overstate the holding period of properties. Given this potential bias, andsince information on holding period is important for establishing investmenthorizons, we turn to the proportional hazards method to analyze the trading ofcommercial real estate.

As noted above, we specify a proportional hazard function using the Cox re-gression model with the baseline hazard function, λ0(t), set for a retail propertypurchased in 1981. The exponential term in Equation (1), therefore, quantifieshow the sale rate is affected when a property of a different sector is purchasedor when the year of purchase is other than 1981. The suitability of the propor-tional hazard model was tested in advance of model fitting by examining logcumulative hazard plots; these proved satisfactory. After fitting the model, CoxSnell residuals were obtained. A log cumulative hazard plot of these residualsproved to be a straight line with unit slope passing through the origin. On thebasis of these diagnostics, the fitted model is satisfactory (see Collett 1994 fordetails of testing procedures). In our analysis, both sector and purchase yearwere found to be highly significant (p < 0.001). We therefore conclude thatsale rate depends on both sector and purchase year.

The estimated baseline rate of sale, λ0(t) in the Cox regression model, indicateshow the rate of sale for a property in the retail sector, purchased in 1981, changesover the period. This function is shown in Figure 2, from which we see that thesale rate typically increases over time, other factors being constant.

This figure also shows that the sale rate does not increase much for the twoyears around holding times of 10 and 15 years, possibly related to the secondand third rent reviews.5 There also appears to be quite a marked increase in

5 This would, however, assume that properties were bought in the year they were firstlet or immediately on review. It would also imply that reversionary values might bemisestimated in the market.


Figure 2 � Baseline hazard function: instantaneous rate of sale categorized by yearssince purchase, standard shop acquired in 1981.The proportion of properties sold inany one year (the “risk” of sale) increases as years since purchase increases.

0

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this rate 14 years after purchase. However, given that we have a comparativelyshort analysis period (such that there are only nine properties sold this far afterpurchase), this sale rate is imprecisely estimated.

The Impact of Sector and Date of Purchase

Table 3 shows maximum likelihood estimates for the sector variables. The riskratio compares the sales rate for the sector to the standard shop baseline. Avalue less than one implies a lower risk of sale and, hence, a longer holding

Table 3 � Maximum likelihood estimates (standard shops as base case).

Sector No. Props Parameter Risk Ratio

Shopping Centers 209 −0.329∗∗ 0.72Retail Warehouses 899 −0.047 0.95Small Offices 872 0.271∗∗∗ 1.31Large Offices 2554 −0.061∗ 0.94Industrial Property 772 −0.082∗∗ 0.92

The table shows the estimated parameter and the “risk of sale” relative to a standardshop.

Note: ∗indicates significantly different from zero at 10%, ∗∗ at 5% and ∗∗∗ at 1% level,respectively, using the Wald χ 2 test.


Figure 3 � Maximum likelihood estimates of risk ratios (standard shops as base case)by year of purchase. The risk of sale for a standard shop increases over time, implyinga fall in the median holding period.

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1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

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period. Marked differences emerge. Small Offices (current value less than£1.5 million) have the highest sales rates; all other sectors have sales ratesbelow those of standard shops. Properties with the largest lot size (ShoppingCenters, Large Offices) have the longest holding periods. This may reflect liq-uidity (entry barriers restrict the number of potential purchasers), the trophynature of such buildings or, in the case of shopping centers, the fact that value isonly maximized after a period of management. The Retail Warehouses marketdeveloped over the analysis period. In the property slump of the early 1990s,retail warehouses continued to generate positive returns, which may explain thelower sales rate. In the downturn, Industrial Property outperformed retail andoffice markets.

Translating the results into median holding times (for a baseline 1981 acqui-sition) indicates that Small Offices have the shortest average holding period(10 years). The other sectors have median holding periods of around 12 years,with the exception of Shopping Centers, which are held on average for anadditional 2 years.

Figure 3 plots maximum likelihood estimator risk ratios against year of pur-chase. Sales rates for properties acquired between 1982 and 1984 are statisticallysimilar to the baseline rate. After 1985, there is a marked jump in the sale rate,with the risk ratio rising to around 1.4. There is a further jump in 1993 wherethe risk ratio exceeds 2. Although it is important to distinguish between yearpurchased and year sold, a similar effect can be seen in overall transactionsvolume on IPD (note the fall in implied holding periods in Table 1, above),


Table 4 � Estimated median holding period (standard shops) by year of purchase.

Year 1981 1982 1983 1984 1985 1986

Median 12 11 12 11 11 9

Year 1987 1988 1989 1990 1991 1992

Median 9 9 9 10 9 9

Year 1993 1994 1995 1996 1997 1998

Median 7 7 7 7 8 ∗

The median holding period has fallen from 11–12 years at the start of the analysisperiod to well under 10 years by the end, suggesting an increase in turnover.

∗The 1998 figure is not reported because the standard error was too large for ameaningful estimate.

which suggests some sort of market shift. There were a number of institutionalportfolio transactions in that period as funds sought to rebalance coming outof the market downturn. However, overall transaction volume falls sharply inthe period 1990–1992, reflecting the difficult market conditions. That this doesnot appear in the proportional hazard results provides interesting informationon the behavior of markets in downturns.

The impact of year of purchase may be shown more clearly by the medianholding period, the time beyond which 50% of the properties are sold. Table 4shows estimated median holding times for a property in the retail sector pur-chased in years 1981–1997. We see that these holding periods average 12 yearsfor properties purchased between 1981 and 1985, but then reduce by some2 years. In contrast, the proxy-estimated holding periods based on aggregateIPD transactions fall up to 1988 and then rise sharply over the early years of the1990s, during the real estate slump, before falling again. The estimates basedon the IPD aggregates reflect the fall in overall transactions over the recession.This difference emphasizes the value of the proportional hazards approach overthe trading volume proxies typically used in equity studies in analyzing holdingperiods in real estate.

That median holding times fall in the Cox model suggests that properties pur-chased during that period were sold on, either due to profit taking as marketsrecovered in the mid-1990s or, during the downturn, because they were moreliquid in nature (funds might be unwilling to purchase assets considered illiquidwhile markets are performing badly). Recall that theory suggests that informa-tion asymmetries (likely to be at their greatest in recessions) should lead to moresales, and hence falling average holding periods, consistent with our results.However, funds might be unable to find buyers for older, secondary property


and for larger, less liquid assets—explaining both the disparity between the pro-portional hazard results and observed sales volume and the sectoral differencesshown above.

Variation over Time: The Impact of Return and Risk

Data on the returns from the IPD sector indices in each of the years 1982–1998were also available. This enabled us to investigate the dependence of rate of saleon return. Since return changes over the holding period of a property, we cannotinclude this in our model in the same way as for variables such as sector that areconstant over this period. Instead we regard return as a time-dependent variableand include it in the model in the manner described above. We utilize capitalreturns in our calculations; there is a very high correlation between capital andtotal returns given the stability of income returns over time.

We found that the return in a given year has a highly significant impact on salerate in that year (p < 0.0001); the higher the return, the greater is the sale rate.There is evidence in the data that this effect is not linear, in that the effect ofreturn on sale rate increases as return increases.

In some years, return was negative, and so we investigated whether the effectof return on sale rate was essentially explained by whether return was positiveor negative. Although this binary categorization led to return having a clearimpact on sale rate, it could not explain as much variation in the data as theterm corresponding to the actual return values. Moreover, including return in themodel does not affect conclusions about the effect of purchase year and sectoron the rate of sale. This is consistent with the expected answer to question(d) above. The return in the year after sale is still significant but with lowerexplanatory power.

The equity literature predicts a positive relationship between volatility andtrading (and, hence, a negative relationship between volatility and holding pe-riod). Volatility cannot be measured directly in the data available. Therefore, wetested four proxy measures. The first two examine “internal” or cross-sectionalvariability in the IPD. RetVar is the standard deviation of total returns for the“market segments” in the databank.6 CapVar measures the standard deviation ofcapital value growth for market segments. The rationale for these proxies is thatif there are large differences in market performance across sectors and space,then there will be greater uncertainty in the marketplace. The third measure,

6 Market segments are properties grouped by geography and/or type, for exampleLondon Office Parks, Southern Retail Warehouses and Distribution Warehouses.


AbsCha, measures the absolute percentage change in capital values, since majorupward or downward movements may indicate volatility. Finally, StockSD wasconstructed by carrying out a single index regression of the monthly DataStreamproperty company returns index on equity market returns, then calculating thestandard deviation of the residual. As in other studies, the residual is a proxyfor the “unique” real estate element of property company performance withthe overall effect of the stock market removed. The standard deviation thusmeasures public property market volatility, which may proxy private marketbehavior.

All four volatility measures have the expected positive relationship to sales rate.However, the relationships are comparatively weak. The models still require theinclusion of returns to provide an acceptable degree of explanation. However,with returns included, the extra effect of volatility is not significant. The proxiesthat produce the greatest effect are CapVar and RetVar, both of which havep-values of 0.18 (the two measures are strongly correlated). The other twomeasures have p-values in excess of 0.30. We are unable to say whether thisreflects the particular characteristics of the private real estate market or whetherit results from the need to use imperfect proxy measures of volatility.

Conclusions

Knowledge of the expected holding period of commercial real estate assetsis important for a number of reasons. Investment appraisal to determine assetworth requires specification of an analysis period; asset allocation depends uponthe variances and covariances of assets that may be affected by the interval ofanalysis. Furthermore, if holding periods vary according to the characteristicsof the asset and to market conditions, this information should be incorporatedin analysis. Real estate, characterized as an asset class with high round-triptransaction costs, thin trading and information asymmetries, might be expectedto exhibit different patterns of holding periods compared to more liquid assetstraded in public market places.

Analysis of realized holding periods provides useful information for consideringexpectations. However, conventional measures of holding periods are eitheranecdotal or rely on a measure based on volume of transactions. The lattermeasure is flawed since some assets may trade more regularly than others,while some assets remain unsold throughout the period of analysis, that is, thedata are censored. This makes use of statistical procedures developed for theanalysis of survival data highly appropriate.

The data used in the analysis presented here is of properties acquired byfunds contributing to the Investment Property Databank over an 18-year


period. Thus the properties in the sample represent the type of real estate thatinstitutional investors have been appraising in recent years. The results may,thus, be considered valid for current analysis of investment decisions. Otherproperties held by funds may have different characteristics and, hence, resultsbased on aggregate sales may be inappropriate. The absence of depreciationallowances in the United Kingdom means that the results are not driven by taxeffects.

Our analysis suggests that the median holding period has varied over time. Atthe end of our analysis period, the median is about seven years. Such a medianholding period for U.K. real estate is considerably longer than the holdingperiods reported for equities, confirming the impact of illiquidity and hightransaction costs. Sales rates vary across the holding period, possibly linked tothe rent review cycle and lease structures. Holding periods vary by property type.Large, expensive properties like shopping centers trade infrequently, requireexpenditure that must be amortized and, thus, have longer holding periods thansmaller more liquid assets such as small offices.

Holding periods have varied over the time period, generally falling from around12 years in the early 1980s to less than 8 in the late 1990s. Return has a signif-icant impact on holding periods. Consistent with our expectations, the greaterthe return, the greater the propensity to sell the property (and, by implication,the lower the holding period). Investors appear to react to contemporaneousincreases in values. There was weak evidence of the expected negative relation-ship between volatility and holding period, but the absence of a strong proxyfor volatility limited analysis.

The evidence presented here is broadly consistent with prior literature on hold-ing periods in other markets and confirms that real estate holding periods differfrom those of other asset classes. The limited time series and lack of high fre-quency transactions data limit the scope for further causal analysis. Nonetheless,we believe that the results reported here represent an advance over aggregatesales-based proxies of holding periods and contribute to a better understandingof investor behavior.

We are very grateful to Investment Property Databank and, in particular, to Tony Key,for making the acquisition, sale and return data available. The views expressed in thispaper are of the authors alone and should not be taken to represent the opinions ofIPD. A preliminary version of this paper with a smaller data set was presented at theESRC-funded seminar on property finance organized by the Property Economics andFinance Research Network. We acknowledge the many helpful comments of partici-pants at that seminar and also the suggestions of the editors and anonymous refer-ees of Real Estate Economics, which have contributed greatly to the paper presentedhere.


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Timing and the Holding Periods of Institutional Real Estate