PurposeThe paper reflects upon the tendency of participants in the commercial real estate markets to give excess status to individuals in decision-making and to hold beliefs that are at best weakly supported empirically.Design/methodology/approachThe paper is reflexive in nature, using experiential reflection to consider institutional processes in the real estate market. It is important to use “methodology” correctly and not as a synonym of “method”.FindingsUsing reflexive mode, the paper does not have “findings” as such: if the views expressed are accepted, then a research agenda to understand decision-making processes is implied.Research limitations/implicationsThe nature of reflection is that it follows from the writer's experiential processes and interpretations. The reader may come from a different stance. Broadly accepting the propositions points to the need for a contextual analysis of decision-making processes in private real estate and consideration of the implications of privileging individual idiosyncratic decisions over analytic procedures.Practical implicationsPrior research has demonstrated the lack of a back-testing culture in real estate that would allow empirical analysis of the consequences of “gut feel” decisions overriding modelled analysis; further, entrenched beliefs about the drivers of market performance might result in misallocation of resources.Social implicationsResource allocation in commercial real estate has important consequences for land use and urban and regional development.Originality/valueThe paper reflects the views and experience of the author based on over 30 years of research into commercial real estate.Non

Lizieri, C

English

Apollo (Cambridge)

Maestros and Mythologies: Some Lockdown Reflections  Colin Lizieri, Grosvenor Professor of Real Estate Finance, University of Cambridge  The Taskbar signals another email arriving, probably the latest “impact of CoViD-19” thoughts of one of the research departments in real estate or financial service firms around the world. At least they have partly stemmed the tide of prop-tech and Brexit impact emails. They predict the end of the conventional office or the resurgence of demand for offices; the decline of large cities or a further concentration of business; V-shaped, U-shaped, W-shaped and L-shaped economic recovery; flat or rising interest rates, flat or rising inflation rates; student housing is resilient to the new market environment or highly vulnerable to travel restrictions; residential markets will recover quickly or remain in stasis, with rises (or falls) focused in suburban areas (or city centres or small towns) …  Some have a cautious, nuanced tone, others are more confident, seem more sure of their prognosis. Given the disparity in the predictions, what information, in aggregate, do they convey?   Major events such as the pandemic or the global financial crisis (and the long asset market boom/bubble that preceded it) are characterised by high degrees of uncertainty. We cannot be sure if the models and relationships that held before the event will hold afterwards, whether or not they represent a structural break. To an extent, the outcomes, observed from within the event, are definitionally “unknowable” and, while path dependent, the actual course may be determined by random events. Realistically, all we can do is to set out scenarios, perhaps assign rough probabilities to their occurrence. How, then, can all these documents be so certain? What insights do their authors have? Some may come from advanced market information, from early knowledge of the decisions of clients (who, themselves, face the same uncertainty): but more it will come from the need to be seen to make confident predictions.   From a business perspective, this is probably a rational strategy. To give clients and customers that sense of confidence, that you understand the situation and can provide expert advice is critical. More significantly, does it matter if you are right or wrong? If you are wrong, few will recall this (who other than Neil Crosby and I can remember the 1988 Richard Ellis competition to guess the timing of the first £100/foot City of London office rent?); if you are right, you become a maestro or seer – and will take every opportunity to remind the world of your presience and the fallibility of your competitors.  That was undoubtedly the case post-GFC where a number of those guessing right have created careers as authors and pundits (ironically, one of the best known perhaps renowned from a book – more accurately, an oft-repeated title of a book – which had as its main argument that forecasting finanical markets was largely impossible). In practice many researchers foresaw the market turn and asset price reversal: I wasn’t alone in the mid-2000s in arguing that commercial real estate prices were too high and leverage was too great (and I have the slides to prove it) but at issue was that no-one wanted to listen at that time. I was asked “what could possibly cause the market to turn?” and responded “a shock”. “What will that shock be?” came the response. But if I knew what the shock was going to be, then it wouldn’t be a shock, would it?    In early real estate classes, I like to put up a graph of commercial real estate investment performance and, set against it, the performance of individual investors (see Figure 1). One investment fund stands above the others, consistently outperforming. The students are asked to say why this fund has done so well. Most of the responses relate to manager skillsets – superior stock selection, superior asset allocation, market timing, specialist knowledge of sub-markets, good capital structure decisions. Others relate to fund characteristics such as size bringing economies of scale or reduced cost of capital. The fund manager is given agency: she determines the outcome; she too will attain hero status. It is very rare that the students will offer an alternative explanation: that it is just luck. But, in this case, that really is all that it is. The lines on the graph, investors A to E, are simply the market performance plus or minus a random amount. Recalculate the spreadsheet and the order changes, maybe another fund will seem to outperform, maybe they will all cluster around the market line.  The result is contingent, not on skill but on chance.   Figure 1: Fund Outperformance and The Role of Chance   The property market likes its maestros and seers: in real estate, it is part of it being a “people industry”, a phrase that is supposed to contrast our friendly sector with the dry mathematical approaches of financial markets and to emphasise that each property is unique, meaning that aggregate analytic models must be tempered by market feel. This, though, is true of other parts of the built environment professions: much quoted architects who can sequentially and confidentially predict the drive towards open plan large floorplate offices, then a return to smaller cellular space, then shared flexible office space and then the disappearance of offices entirely; revered planning academics whose fame rests on the prediction of the grassing over of abandonned and hollowed out city centres, shortly followed by the lauding of the redensification of cities; urban theorists whose ideas of the drivers of city success and the role of culture seem to have no firm evidential basis; esteemed developers with a track record of driving their firms into financial distress. This is understandable: we want to believe that strong-willed, skilled individuals can influence events and performance, that they can “generate alpha” (a phrase adopted widely adopted by practitioners without taking on board many of the implications of asset pricing theories), that they can “beat the market”. And if they can then, surely, so could we?   There are consequences, though, of this emphasis on individual skill and agency. The Investment Property Forum’s Hurdle Rate research, published in 2017, revealed the extent to which individual managers and investment committees in firms and funds had the right to override modelled outcomes on buy-sell decisions. Further, it showed the extent to which many of the models used were inconsistent with standard corporate finance principles and remained relatively unsophisticated, even in larger institutional investment funds. Having the right to override and decide outcomes empowers senior managers – it also provides incentives and exemplars for junior professionals to adopt the individualistic approach and abandon more quantitative toolkits acquired in the education process. This is not to advocate slavish adoption of model-based approaches: clearly for large projects individual specific risk factors can have a significant influence on outcomes and, although some of this 0.00500.001000.001500.002000.002500.00Five Investment Funds: Performance 1986-2015Market Inv A Inv B Inv C Inv D Inv Erisk will be absorbed at portfolio level, for funds with a small number of assets, missing critical risk factors could have potentially catastrophic effects. The evidence, though, suggested that, more often than not, it was negative modelled decisions that were over-turned: “it smells right” dominated “I don’t like the look of this one”. This issue is compounded by the absence of a culture of back-testing, to find out what factors influence success (and failure) in decision making and to assess the impact of an individual or investment committee override of an analytic recommendation.   Although measurement problems abound, the available empirical evidence provides little evidence to provide confidence in the superior skills of these hero investors and seers and their ability to deliver better risk-adjusted returns. Although the phrase “delivering alpha” has become a standard part of the real estate lexicon, there’s at most weak and contradictory evidence of fund managers or firms actually achieveing this; successful funds do not consistenty lead to successful follow-up funds; value-add and opportunity funds do not, on average or in aggregate, generate returns that fully compensate investors for additional risk; leverage has negative impacts on performance once risk is accounted for; there is scant evidence of market-timing in acquisition and sales decisions or in capital structuring – if anything the market gears up at the top of the cycle (being charitable, one could characterise that as “risk-sharing” and blame the banks for pro-cyclical lending behaviour). These are all relatively consistent empirical findings and yet, if stated to the industry and profession, they are not believed: why? Is it simply vested interests or some form of cognotive filtering?   The behavioural turn in economics, now increasingly used in real estate research, has generated new insights into decision-making behaviour (but also much over-statement, with conventional economics and finance caricatured as assuming all investors are profit-maximising rational agents – which is untrue, since there is a well-developed body of research on uninformed traders, uncertainty, noise and market processes, much of which precedes any citations of Khaneman and Tversky). Much of that work, however, has focussed on individual decision-making and estimation (over-confidence, anchoring, information processing biases, loss aversion and so forth) which, thus, does not really touch on why the industry searches for and misidentifies heroes. To an extent, the literature on herding behaviour does approach the issue, particularly that which related to reputational herding. If there are key opinion formers, then there may be a quasi-rationale for following them and then a validation process that highlights success and erases failures or non-conforming outcomes.   Herding and trend following may also be more rational in a market like private real estate with strong information asymmetry, entry barriers, illiquidity and specific risk factors such that “the market can stay irrational longer than you can stay solvent” may hold more strongly than in more liquid financial assets where access to mechanisms and vehicles for shorting the market are readily available. However, it does seem to me that there is an undeveloped seam of work to be mined to examine the culture of decision-making and the role of individuals that goes beyond some of the unnuanced stereotypes from the “financialisation” literature or the lionisation in the professional press. That examination could also usefully examine how beliefs become entrenched in real estate markets often, again, in the face of empirical evidence: truths that are so self-evident that contrary evidence must simply be flawed. The belief systems and the role of key opinion-formers are intertwined.   As an example, consider the widely-held argument that major global urban centres and gateway cities generate superior performance. To be fair, this is challenged, but tends to reassert its dominant position as received widsom. Empirically, in general they do not, there is abundant evidence of that. The commercial real estate markets and, in particular, the office markets of such cities tend to be more volatile, to have significant downside risks and to be more closely aligned, such that shocks propogate from one market to another, undermining diversification benefits. This is often denied by practitioners or counter-benefits proposed: greater transparency simply means the volatility is revealed, there is greater liquidity (ofen cited by those who do not need that liquidity and should not be paying a premium for it), that the markets recover faster from downturns (yes, partly because of the investment flows, but equally typically they fell more and that recovery does not drive risk-adjusted return). A more valid rationale might be scale of investment for the largest players, but the strongest advocates of large city plays are often smaller funds and even larger investors may be compromising diversification and hence the risk-adjusted returns of their portfolios.   A related example is the entrenched belief that economic growth is captured in real estate rents. This is a complex area, but once again the empirical evidence is against it for standing investments. Long-run data for a wide range of cities in diverse locations shows, typically, that real per square metre rental growth is close to zero over long periods of time and over periods of substantial urban growth (Figure 2 shows real office rents in the City of London market 1984-2019: over 35 years which included the restructuring of the City of London following financial deregulation: the average annual growth in rents afer inflation is -0.9%). This might, at first sight, seem counter-intutive - surely if the economy in a market becomes more productive and profitable then rents must rise to capture the improved locational advantage? However, this ignores the supply-side adjustment (even though this may be lagged and generate cyclical behaviour). Land values capture that rise in productivity, through change of use, redevelopment and densification (to an extent limited by topographical or planning constraints for a period). However, that may be of little benefit for a finite life investor buying standing investments in that market, except to the extent that the redevelopment option is embedded in the exit value of the investment (but it was also presumably embedded in the entry price): hence any outperformance will depend on the state of the cycle (and the issues of market timing noted above). Again, this is an empirical finding that is, typically, simply rejected by many practitioners when confronted with it.   Figure 2 City of London Real Office Rental Values  Source: adapted from CBRE, ONS. Rental value index deflated by RPI index.   In passing, it is worth noting that the point about supply adjustment applies to many of the currently favoured sectors of the property market. For example, it appears to be received wisdom that logistic warehouses must generate growth due to the growing role of digital technology through internet retailing and just-in-time supply chain logistics. How long, though, does it take to build a box and what 0.020.040.060.080.0100.0120.0140.0160.0180.0200.0Rent Index, 1984 = 100are the land and land-use planning constraints to prevent supply side adjustment? What might this imply for the implicit rental growth in the rapidly falling cap rates in the logisitics sub-sector? Do similar concerns apply in relation to data centres and other of the alternative real estate sectors sought out by return seeking investors?   To some extent, the reluctance to accept the evidence of low after-inflation rental growth, while puzzling, relates to behavioural traits that fail to distinguish between nominal and real growth (such as money illusion). This in turn points to another entrenched belief, in real esate’s inflation hedging properties. In part this is a semantic issue (inflation hedging is not the same as delivering long-run above inflation returns). To an extent, commercial property acts as a real asset in that rents, ceteris paribus, will tend to rise with inflation (although that cannot be guaranteed, as much of the retail sector shows, and one must allow for structural breaks, technological change and firms substituting capital, technology and labour for land and building). However, the relationship between inflation and interest rates and, in turn, the relationship between (real) interest rates and (real) cap rates makes the inflation / capital value relationship much more nuanced and complex than is often assumed, often critically dependent on the source of inflationary pressures.    It would be easy to provide many other examples of tenets and beliefs that are weakly founded or that run counter to empirical evidence – and indeed, there are older examples that have been confined to history and forgotten too but were central in their time (applying substantially higher discount rates to the bond-like over rented cashflows in the 1990s since they were obviously high risk comes to mind). Why, though, are such beliefs so sticky? Why do they persist, in the face of contrary evidence? Is it a function of the specific characteristics of private real estste markets or is it something about the processes driving education, recruitment, employment and progression of staff?  How do these two drivers interrelate? That real estate is a “people industry” might be one of its key USPs, but it has real consequences. The cult of the individual and entrenchment of core beliefs in the face of evidence might well merit more detailed study.  

Maestros and mythologies: some lockdown reflections

https://www.repository.cam.ac.uk/bitstream/1810/307193/1/Maestros%20and%20mythologies.pdf

Maestros and mythologies: some lockdown reflections

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