Dear Weimer School Faculty and Fellows:
Weimer School governance is in transition as noted in the associated newsletter article titled "Weimer School Development Plan." Two noteworthy items in the Development Plan are the program for expanding the number of faculty, and the increased faculty participation in governance.
Dean Racster is to be commended for his vision and leadership in the development of the plan, which is now in its implementation. My purpose for this open letter is to share some thoughts about faculty responsibility. It also provides an opportunity to discuss real estate as a discipline.
These thoughts are also applicable to those fellows with teaching responsibilities at their respective universities, as well as industry faculty who, in some measure, are carrying forward the dissemination of knowledge garnered from the discipline of real estate.
Discipline of Real Estate
To many readers of this letter, real estate is a subset of finance or a specialty in economics. Indeed, real estate may be properly viewed in such perspectives. The contention here is that real estate may also be viewed as an interdisciplinary area of study, in contradistinction to a multidisciplinary area of study. Furthermore, from our perspective, this interdisciplinary study is, in fact, a discipline.
One purpose of this article is to present a perspective of real estate as a discipline, i.e. a field of study on its own rather than as a branch of some other field of study. Another purpose is to utilize that context to present a vision of real estate analytical systems which may significantly improve the quality of real estate decision making.
This is an ambitious task for an article. The line of reasoning may be difficult to follow, in part, because of the complexity. The biggest difficulty, however, is likely to be that each reader already views real estate from his or her own perspective, and it is difficult to set one's own paradigm aside and view the issues from a different paradigm.
One of my favorite sayings is that it is not the things we don't know that get us in trouble, it is the things we know that are not so. The reader is respectfully invited to consider a variety of ideas which are contrary to what he or she already "knows." The article will mean different things to different people - and this my intention. It may help to read it several times, looking for different dimensions.
This article may be viewed from at least three possible dimensions. The first such dimension is as a companion piece to the open letter to Weimer School Faculty and Fellows which is contained in this newsletter. The second dimension is as a follow-up piece to the Information Revolution article which appeared in Real Estate Issues earlier this year. The third dimension is as an overview to the REIT research being conducted in the Hoyt Group. There are other dimensions - but it is time to start the substance of what is intended to be presented as sketches and not detailed drawings.
If this is a biased view, I admit it. My baccalaureate and master degrees in business from UCLA had majors in Real Estate and Urban Land Economics. My interpretation is that "Economics" is modified by "Urban Land" and not "Real Estate." Additionally, the major field for my doctorate in business administration from Indiana University was Real Estate Administration. Obviously, this is a specialization in management, not economics or finance. Indeed, my other fields were finance, money and banking, business government relations, and applied economic analyses.
The relevance of this is that I personally and professionally have a close relationship with the Weimer School mission which is to improve real estate decision making. And on a personal, as well as professional note, I want to share some views with my colleagues, including not only faculty and fellows, but also those related to the Hoyt Group, in general, and the Weimer School, in particular, who receive this ASI Newsletter.
An Educational Philosophy
Each of us has his or her own philosophy of education. Some of us reflect on it more than others. My current reflection may be related to the fifth anniversary of my achieving chair professor emeritus status from The American University. But, more likely it is, at least in part, related to the change in governance of the Weimer School and the expansion of the faculty of that venerable educational institution, the Weimer School. This change is a real shift in my role, possibly on the order of magnitude of that which occurred when we lost Arthur Weimer. As such, this event is an appropriate catalyst for each of us involved to take time for a reflection on his or her philosophy. Over time, an increasing number of you will be gaining responsibility within the Weimer School. The fulfillment of that responsibility relates to your philosophy. That is what this letter is about.
It will come as no surprise to most of you that my philosophy is predicated on understanding the system. That is why my reference to "paradigm" is so frequent. Different views of the system provide different analyses and different results.
The goal of providing students an education is to give them an understanding of how different parts of the world work. The educational objectives vary, depending on the discipline and the subject.
This is not simply a matter of transmission of a body of knowledge or expanding horizons to enhance an individual perspective. It is, for those of us who are professional faculty, a matter of making a contribution to the body of knowledge and enhancing our own understanding. As educators, we have a continuing responsibility to further our own education.
The Weimer School Fellows are, by design, the nation's leading contributors to the expansion of the body of knowledge. There is no comparable group of real estate academics which even comes close in a per person achievement for an organization anywhere near this scale. This is a recognized fact, not an issue.
The issues are likely to be about perspective and focus. One area of focus of the Weimer School has been on the organization of the body of knowledge. More is to be said on this later in this letter. Another area, and this is the prime focus, is in educating the educators, which is what happens at the Weimer School sessions.
The salient element in the philosophy is that the educational process is an ongoing process. While teaching at The American University, I used to say that perhaps as much as a third of my time was devoted to getting an education of one sort or another.
For me, the focus was on the development of real estate as a discipline, which meant learning about the related disciplines or subsystems of the city as a system. This is the paradigm I use. Furthermore, the focus sharpened when the relationships among subsystems came into play.
My profound thought of the week, month, or year, is that it is the interrelationship among the subsystems which confounds decision makers. Or that it ought to because that is where the big errors have been made. Consider the difficulties faced by the real estate decision makers in the private and public sectors in the last decade. Where were they blindsided? Consider the public policy real estate-related decisions. What was overlooked in the change in national policy decisions regarding taxation and regulation? The contention here is that the decision makers did not understand the system, and in particular, the relationship among its subsystems. This is where an interdisciplinary approach comes in. It differs significantly from a multidisciplinary approach.
Weimer School Programs
In the late 1980's, Dr. Henry Pollakowski, then of Harvard, mentioned to me the idea of a special session of the Weimer School in order to get a report on the state-of-the-art office building research and analyses. This was the beginning of the May Sessions. The special edition of the Journal of the American Real Estate and Urban Economics Association which focussed on office buildings was an outgrowth of that special session of the Weimer School.
Since that Weimer School session on office markets, the May session has included retail and residential markets. The School then switched from types of real estate to perspectives starting with corporate asset management, and then to institutional investment. The public sector was a little difficult to fit in as such, so we went to international real estate and economic development as surrogates. The accompanying chart below shows the progression of May session topics over the years. See Exhibit 1.
Now, the focus is on institutional arrangements, particularly real estate investment trusts. This has been very practical because of the Institute's institutional research and investment program.
The REIT model is moving along very well. The Institute's cumulative rate of return since the beginning of its investment in REITs is twice the cumulative rate of return of the NAREIT equity REIT index and that is with a selection of REITs which is intended to be less risky than those included in the index. It is heartwarming to see the integration of the risk analyses model with the REIT valuation model. Indeed, the commercial application may enable the Institute to resume the grant program at the level of the late eighties.
Exhibit 1 May Session Topics Since Inception
||Focus of Session
|1990||Follow-up on Office Markets|
|Residential Real Estate Markets|
|1991||Follow-up on Office Markets|
|Follow-up on Residential Markets|
|1992||Interrelationships Among Office, Residential, and Retail Market Analyses|
|Corporate Asset Management|
|City as a System|
|1993||Institutional Real Estate Investment|
|1994||International Real Estate|
|1995||Area Economic Development Efforts|
Dean Art Weimer, our founding dean, was fond of the quote, "the future is uncertain." The combination of the March and May sessions into a single expanded session may develop with a different pattern. That future is uncertain.
The expanded faculty will, in particular, have an increasing role in determining the non-January sessions. My message is: Consider the mission of the Weimer School in your new governance role, and consider the philosophy of the school in your own teaching, at your own institutions. As Fellows, you have the opportunity to draw from an outstanding buffet of presentations from industry and academia. You may put on your plates those items which look appetizing to you. Try different foods. You can put together an authentic real estate discipline meal as you pick up on the paradigm.
Academia seems to be abandoning real estate as a discipline, opting to include it as a subset of some area or field of study. In so doing, the perspective of real estate is narrowed. The contention here is that from whichever discipline the approach emanates, the complex situations calling for a real estate decision are likely to require an interdisciplinary approach.
We, at the Hoyt Group, are working on that in several ways. Ron Racster, through Hoyt Advisory Services, has been leading the development of a new degree program, "Bachelor of Science in Real Estate & Metropolitan Development," at Baruch College, The City University of New York. Interestingly enough, it is in the College of Public Affairs.
The capstone course in that program is to be a city as a system course and will likely to use a simulation model. More about the city as a system is discussed in the companion piece, "A Vision of an Analytical System: A Portfolio Investor's Perspective for Real Estate Investment Analysis," contained in this newsletter.
The simulation model of the city as a system will probably be a variation of the one developed for the program at The American University, known as the Northern Virginia Decision Simulation (NOVADS). That version moved closer to a real estate focus than the earlier generation of its game, The Ann Arbor Growth Game, which was developed for a broader constituency at the University of Michigan.
The next version may well be developed for the CCIM (Certified Commercial Investment Member) market analysis course. As noted elsewhere in the newsletter, Hoyt Advisory Services has reviewed that course and is in the process of developing a program to bring state-of-the-art modeling to this industry instructional program, which is leading the way in the application of market analyses models and education for industry.
It is a sign of the times that industry is picking up the ball in treating real estate as a discipline. It should not come as a surprise. After all, many Fortune 500 type companies have designed their own educational programs after having become disenchanted with the programs at the universities.
For three decades, my standing included being an academic with full-time faculty status. But, I learned early on that the discipline is the vehicle for the advancement of the state-of-the-art, while the institution is a facilitator and delivery system.
Disciplines transcend institutions. The Weimer School not only accepts but welcomes that approach and purposefully seeks out a mix of disciplines to be represented among the Fellows. The result is a fruitful cross fertilization. The hope is that each Fellow will enhance his or her breadth of understanding with an interdisciplinary view of the body of knowledge which is useful for real estate decision makers.
As faculty, you have a leadership role. We understand that each of you pursues his or her own vision. We see the strength of the Weimer School in being as a woven fabric, with representation of the various disciplines forming a mosaic. It includes those who accept real estate as a discipline and those who do not.
But, even those who do not, should explore the relationship among the subsystems of the city as a system. This is noted in the companion piece in this newsletter. The two pieces are intended to be read together.
Differing views of Faculty, Fellows, and others concerned with this issue are most welcome. ASI would be pleased to present some publishable views in the next issue of ASI News.
Congratulations on your new role. My personal best wishes to each of you, especially as the occasion arises for you to handle your new responsibilities.
President and Chairman
Maury Seldin Advanced Studies Institute
for Real Estate and Land Economics
The Foundation of Real Estate Analyses
Structure of Analyses
Traditional. The traditional structure of analyses for a real estate portfolio investor builds on property level analyses. Within that structure, the first component is the real estate and its leases. The second component is an analysis of the submarkets. Then comes the analysis of the local economy and its impact on the future income producing ability of the subject property.
Portfolio diversification follows. The primary focus of diversification is to avoid putting all one's eggs in a single basket. The degree of sophistication of analyses varies widely.
This is a "bottom up" approach in contradistinction to a "top down" approach. The top down approach begins with a strategic determination as to portfolio parameters, and then goes on to determine if individual real estate parcels meet those parameters.
Contemporary. A contemporary structure of analyses is to focus on two different, yet interrelated real estate markets. First, there is the space market. Second, there is the capital market.
The analysis of the space market starts with a focus on local real estate markets. Analysts generally start the analysis at least one level above the individual properties, i.e., at a submarket level, and possibly at two levels above the individual properties, i.e., the metropolitan area market level.
Regardless of the level of analysis, market evaluations focus on economic growth and stability. Once the market analysis is completed with satisfactory results, individual properties are selected using portfolio strategy so that individual properties that meet value and risk thresholds are selected. The sophistication of portfolio strategies and individual property acquisition strategies varies widely.
The current trend is to view the capital market as encompassing a variety of assets divided into quadrants based on ownership characteristics. The two sets of ownership characteristics used for the division are type of market and type of investment. Markets are either public or private, and an investment is either equity or debt.
|PUBLIC||Publicly Traded Equity e.g. Equity REITs||Publicly Traded Debt e.g. Mortgage Backed Securities|
|PRIVATE||Privately Traded Equity e.g. Direct Investments in Real Estate||Privately Traded Debt e.g. Mortgage Loans|
Each of these quadrants contains some part of the total investable real estate in the economy or real estate interests. In conjunction with the modern portfolio theory approach, some four quadrant advocates have suggested dividing the real estate portfolio allocation to the quadrants in proportion to their presence in the economy as a whole. This might be considered a first step in a "top down" approach to diversifying the portfolio to avoid unsystematic risk, i.e. risk that is unique to individual properties that can be eliminated in a well-diversified portfolio.
Diversification by ownership form deals with sets of risk attributes and a reasonable starting point for strategy, but it is not a fine cutting tool. The next section discusses different dimensions of risk not adequately dealt with when focussing on form.
The modern portfolio theory approach is predicated on an efficient frontier of risk. The purpose of the efficient frontier is to figure out the portfolio distribution that gives the best risk-return relationship at specified levels of risk. The state-of-the-art would be advanced by applying the different dimensions of risk to an efficient frontier.
The current prevailing practice is to use volatility of returns as the measure of risk. Unfortunately, it is used as a composite measure of all risks. There are problems with using volatility as a composite. First, the time period chosen heavily influences the level of volatility. More importantly, under conditions of structural change, the use of volatility as an indicator of risk provides distortion.
While diversification is theoretically based upon expected future performance, it is, as a practical matter generally based on past performance. Structural changes in the economy alter the structure of risk in a way that is not, by definition, reflected in past performance. The problems associated with structural changes as they affect different kinds of risk are serious and not very well understood. This is a dangerous combination. There has been very little work in the real estate literature dealing with a broader conception of risk, beyond volatility of return. Researchers need to do additional work on different types of risk and their importance in the context of modern portfolio theory.
Avant-garde´. We need an avant-garde´ structure of analyses, i.e. one that goes beyond the contemporary use of the quadrant approach for portfolio construction. This structure of analyses focuses on the risks associated with the property level performance, which is only a first step in understanding risk, rather than risk associated with form of ownership.
The new approach builds on the traditional with the property by property focus, although the property analysis techniques are changing as the information revolution progresses. Under this approach, the portfolio construction process begins with a design for diversification that focusses on risk diversification in contradistinction to asset type diversification.
The level of analyses in the traditional approach starts with an individual property in a metro area. If the analysis starts at the metro area level, it is generally used only as a screen to get to a set of individual properties. That set of properties is analyzed for fit within the parameters dictated in the portfolio strategy. In the new approach, the portfolio building occurs at a more elemental level, which looks at the risk characteristics of the individual assets.
It is possible to use modern portfolio theory under this approach. However, the paradigm expands the idea of risk so as to encompass fundamental real estate risks. Volatility of returns as a single risk surrogate is no longer adequate. The risk classification system is as follows: characteristics of income, return of capital, and liquidity.
Characteristics of income refer to income stability and growth (or decline). Long term leases to top-credit tenants in high-grade buildings have great stability of income. Month to month tenancies and highly leveraged REITs using floating debt with excessive payout ratios have low stability of income. This component of the risk analysis process focusses on the likely future volatility of returns as showed by the various risks to income generation capacity.
Return of capital refers to the form and timing of the return of the initial investment to the investor. Unleveraged equity real estate investors typically get most of their investment back at the time of sale. An exception occurs when the holding period of the property is exceptionally long. If the proceeds of the sale are less than the initial investment, then the cash flow while owned includes some return of capital. Obviously, the length of time the property is owned and the sale price are determinants of recovery of the initial investment. Earnings are a residual after return of capital.
By way of contrast, investors in amortizing mortgages with level payments get increasing amounts of return of the initial investment over time. If there are no balloon payments or sale of the mortgage before maturity, there is a return of all the invested capital during the term of the loan.
Liquidity refers to the ability to convert the asset to cash quickly, without loss. Real estate is illiquid because the conversion requires a sale and the vicissitudes of the market bring fluctuating prices. Short term mortgage-backed securities, while not as liquid as treasuries, have near term due dates. As a result, they exhibit less fluctuation in value attributable to interest rate fluctuation than assets with longer durations. However, the market for mortgage-backed securities is not as well organized as the market for U.S. Treasuries. As a result, the liquidity risk is significantly greater in mortgage-backed securities than in Treasuries. REIT securities are a hybrid. These are marketable securities with security price fluctuation over the short term, so that is an impairment of liquidity. However, the liquidity of holdings, assuming moderate number of shares, is greater than the liquidity of the underlying real estate because the market is much better organized.
The key to understanding real estate risk is in looking to the underlying risks of a specific real estate investment irrespective of form (e.g. direct ownership, syndicate unit, or REIT share). This process begins with an analysis of the characteristic of income, including variables such as existing leases, lease structure, and space market conditions, to estimate return on capital. The second step is looking to the mechanisms and schedule for the return of capital. The third step is looking to the ability to obtain liquidity.
In this avant-garde´ approach, portfolio construction for direct real estate investment or combinations of different types of assets focusses on these risk characteristics rather than the form of investment. When the portfolio includes ownership forms other than direct investment, the risk assessment model should include adjustments for the differential risks inherent in the form. For example, REIT investment analysis focusses on the underlying real estate risks, and the security market risks. In the case of REITs, characteristics of income as to growth and stability depend on the real estate leverage and payout policies, as well as the income characteristics of the underlying real estate.
A Managerial Approach
An Existing Portfolio. Most real estate decision makers find themselves in a situation in which there is an existing portfolio. That condition exerts constraints to varying degrees.
In a managerial approach to real estate decision making, the process starts with developing goals. In this context, goals are generalized statements of desired conditions. The next step in the process is specifying objectives. Objectives are measurable variables used to gauge progress toward desired conditions, or goals.
One way of applying the process is to use performance measures to compare portfolio performance to specified rates of returns or to industry benchmarks. The decision maker uses benchmarks to compare portfolio performance to the performance of the rest of the field, or some portion thereof.
Real estate investment results are generally not amenable to short term measures of performance because they are, by their nature, thinly-traded long-term assets. As such, with trading being generally infrequent, there is a lack of adequate transaction data on which to base accurate value estimates essential to rate of return calculations. But since real estate investments are generally long term investments, the relevance of short term variations in current income levels and market cap rates to long term value is related to the real time horizon for the investment. If no transaction is likely, it is irrelevant what the transaction price would be and hence, the internal rate of return. Thus, if there is no contemplation of a sale in the short term, variations in current income are relevant only in terms of future expectations and time value of money. Short term variations in cap rates are also of relevance as harbingers of trends and in contemplation of future sales. In a real estate paradigm, a much longer time horizon provides the basis for expectations about the rate of return. Periodic measures of components are relevant to income changes and cap rates of income as received. Analysts often look at real estate in a finance paradigm which prefers to estimate returns as though a sale of property occurred. Therefore, the realities of real estate investment characteristics pose a problem to the analyst. Unless the time horizon used in the finance paradigm reflects reality, the information output is misleading and can lessen the quality of decisions.
Portfolio Growth. Portfolio growth, even when starting from a zero base, is designed to enhance diversification (and to increase scale) up to the point where additional diversification provides little marginal benefit.
In the four quadrant approach, the question of appropriate investment form is an important diversification issue. Diversification is important not only in allocation among quadrants, but also in allocation within quadrants, e.g. it is more practical for a small scale investor to diversify real estate by investing in real estate indirectly as in REITs rather than through direct ownership. The avant-garde´ approach also calls for diversification by ownership form as part of the investment process not as the means of diversification of risk. The difference is that the fundamental underlying risks of the real estate are the measure of diversification, and ownership form is a vehicle for obtaining diversification rather than being diversification.
Given this framework, strategies for portfolio growth and diversification vary. Important issues in portfolio construction include the use of elements such as market timing, pure plays, value investing, and income investing. One analyzes individual real estate for fit into a mosaic of strategies. The focus here is to find an efficient and effective way to analyze real estate risk and return in a portfolio context, without dangerous oversimplifications.
The point of the introductory section has been to set the groundwork for the discussion of real estate in the context of an analytical paradigm which I like to call the city as a system. It is the point of departure for understanding real estate risks and returns. Much of what has been said is related to work in progress on REIT investment analyses. But, the focus is to set out a paradigm of real estate in which real estate is not treated as a subset of finance or economics. Real estate is an interdisciplinary discipline, and has its own paradigm.
City as a System
The real estate paradigm is real estate decision making in the context of the city as a system. The system has many subsystems, including economic, social, political, legal, financial, geographic, and others such as environmental.
The traditional models for real estate analysis focus on economic performance -- obtaining a return on an investment. That return is possible because real estate performs a function in society. Investors in the real estate market operate within a regulated environment designed to provide investors with incentives to take investment risks to get the returns.
The traditional focus of real estate analyses has been on the micro question of risks and return at the individual property level. However, it is useful to start with a macro analysis of land use requirements in the metro area for the economic and other functions of the city.
At the macro level, factors such as population, employment, and income influence demand requirements for various types of land use. While the temptation is strong to start with demand, the fact is that, except for a new town, there is generally an existing inventory of space in place, used and unused, that is intended to meet the land use requirements of an existing community.
Accordingly, this analytical system starts with analyzing the existing inventory of space and its use. One way of representing the existing inventory is as a matrix of property types and location by submarket. Visually, however, one may grasp the spatial relationships among the buildings in the inventory better with a geographic information system (GIS). A GIS provides real estate analysts with a tool for viewing land use and other characteristics in a spatial dimension. It is also possible to use three-dimensional representations to better understand the spatial dimension.
The advantage of using the GIS is that besides type of land use, occupancy and other such characteristics as rents, the GIS can simultaneously deal with infrastructure, (e.g. roads and utilities including access), social problems, (e.g. crime), environmental quality (e.g. air and water), politics and fiscal problems (e.g. tax burden and regulation), and numerous other dimensions. It is useful to classify and map one or more dimensions preparatory to the application of an analytical model.
Irrespective of the analytic tools used, it is critical that the analyst understands the spatial dimension and the other subsystems that comprise the system in the macro perspective. The relationship among these subsystems influences how desirable the existing supply is, what the potential supply is, and how the aggregate demand in the larger area is likely to be distributed among and within submarkets at the micro level.
Economic Base Models. Traditionally, analysts have used economic base models to assess the growth and stability of local economies. My bias toward it as a form of analysis is based not only my education but the work of Homer Hoyt himself. However, the literature on the limitations and problems of economic base analysis goes back to when I was a young lad. Furthermore, while it is conceptually useful, there are more powerful techniques of analyses.
Among the alternate techniques, input-output analysis may head the list of sophistication, but at the current time, it is not practical for most situations due to data limitations. There are less sophisticated techniques that may be more applicable to a broad range of cases because they require less data. These include variations of economic base analyses such as location quotient and shift-share analysis. The efficacy of these techniques is highly dependent on the quality of data used as an input.
Understanding the quality of data input is critical because the bottom line for most real estate analysts is that they are going to use someone else's forecasts of the growth and stability of the local economy. The first step is examining the assumptions used in producing the data. Then, the analyst can use multi-year forecasts and the strength of the economic base to provide a basis for answers to the major questions. This vision of an analytic system is predicated on using one or more third party forecasts of metro area growth.
The first question in applying such analyses relates to the diversification of a local economy. The diversification of the local economy is important because of the impact of local economic performance on real estate risk and returns. (Some of the literature will dispute the relevance, but we can argue that at another time.) Location quotients and shift-share analysis are useful in dealing with these issues.
The second question relates to the strength of demand for a specified type of space use and leads us into models for property types. We use different models for different property types and sometimes for different situations.
Office/Industry Models. In the city as a system paradigm, office/industry is a surrogate for basic employment generating demand for space. In a sense, the role of office/industrial space uses as an economic driver in the city as a system paradigm is analogous to the role of industrial employment as a generator of basic employment in economic base analysis. While retail and residential can generate basic employment in some cases (e.g. retirement communities may be viewed as having retirees as basic employees), the analytic system primarily focusses on office/industrial generation of employment as a basis for macro level projections of space demand.
The most practical model for office demand is a judgmental model. Its primary function is to quantify incremental demand and reach conclusions on space absorption. It does not do as well with reallocations of existing demand among submarkets. An econometric model does better on that score. However, the econometric model is so data hungry that it is difficult to use. Hoyt Advisory Services has used a combination of the two in its consulting work with good results.
Unfortunately, these models generally fail to deal with structural change occurring in our society. Innovations such as office hotelling and virtual offices have affected the demand for office space and will probably grow in the future. The existing models generally deal with demand allocations based on a specified relationship between supply and demand. Structural changes impact the relationship and should be incorporated in future projections.
A look at general societal and business trends can be of assistance in adjusting the models. The bottom line, however, is that there is some supply of space, and some changing characteristics of demand. The analysis is generally a process of matching up supply and demand to see how a potential parcel fits into the picture.
Beyond changes in demand, one must understand changes in supply. Although most analysts focus on additions to supply through construction, subtractions from supply can and do occur, affecting the system. In looking at subtractions from the existing inventory, analysts should consider that there will be conversion or destruction of some office inventory. Other properties will be redeveloped and upgraded as to type.
The view of property by property analyses should be in the context of changing supply and demand relationships. Finance-paradigm based spreadsheets using internal rate of return calculations are meaningless without understanding the spatial dimension, in particular, and also general market changes. When viewed in the proper context, the model outputs are useful information to the decision making process. The relevant model outputs are absorption of space and changes in occupancy and rent levels.
Residential Models. Residential models perform some of the same basic functions as office models. First, there is a forecast of total demand and then a disaggregation by market segments. The next step is analysis and disaggregation of the forecast to produce a forecast of housing demand by tenure and price. This leads to a project by project absorption after analysis in conjunction with a competitive analysis. Judgmental models of the residential sector do well for the analyses of top down quantitative changes.
The allocation of demand among submarkets is a more difficult issue. For most analysts, the focus is on a specific project and all that is necessary is to get an allocation to the specific submarket. That can be a very judgmental matter looking at the stages of development of the subarea. This may be done for all areas and calibrated as a distribution.
Essentially, these models make use of a top down perspective to make specific allocations among submarkets. They reveal turning points in activity and adjust for changes in demographics and other variables that effect tenure and other aspects of housing consumption decision making.
Alternatively, the bottom up approach takes the view of a competitive analysis. It provides better detail but does not do as well with projecting turning points. It's best used in conjunction with a top down analysis.
The key in the city as a system approach is to understand the signals of a change and then build them into the modelling process. One can identify these relationships by using sophisticated econometric models with the judgmental models.
Retail Models. The model we like best for retail is a gravity model developed by Morton O'Kelly with support from HHI. It integrates a geographic information system. Essentially, that model focuses on the distribution issues.
In the model, forecasts of demand are in the form of total spending. Then, there is an allocation by type of expenditure and type of retail structure. Recent changes in competitive position and the development of interactive video shopping may bring additional major structural changes.
Space Market Modelling. Most models disaggregate totals. The disaggregation is based on a series of judgments. All such judgmental models provide the ability to assess the impact of varying assumptions about changed conditions. Econometrics can be effective in quantifying some past relationships in order to apply these to current situations.
Taken together, the office/industrial, residential, and retail models provide some insight into the functioning of the space market. The space market is a critical part of the structural element of real estate analyses, especially in the city as a system paradigm. However, it is not the only element. Real estate is a capital intensive industry. The willingness of investors to provide capital, both as equity and debt, is a very real part of the decision making process. As such, the city as a system paradigm also incorporates models of the capital market. The capital market models examine the relationship between the space and capital markets and quantify its impact on real estate risk and returns.
Capital market factors are exogenous variables that affect city as a system space market models. In the real estate paradigm, the traditional finance analysis is adapted as a real estate application in order to deal with capital markets. Using the capital market paradigm, investors assign value to real estate assets based on risk and return characteristics of this asset class compared to other asset classes. Then, investors go beyond space markets to look at other factors that have a bearing on the value of real estate in the capital market. These factors include competitive returns on alternate investments and perceptions of relative risk and return potential.
Not only do the aforementioned factors influence the value assigned to real estate in the capital market, but change in taxation and institutional regulation policy affect real estate decision making. Expectations in real estate then influence the availability of capital for real estate. Accordingly, the city as a system paradigm considers these factors as exogenous variables.
In the capital market, the attraction of real estate is a result of expectations in the space market as well as conditions in other non-real estate segments of the capital market. Therefore, it is possible for the value of real estate on Main Street to be greater or less than the value of the same real estate on Wall Street. These conditions provide for a cap rate arbitrage.
When the value of real estate in the capital market is high, there is a strong incentive for real estate decision makers to add to supply for sale. If the capital market expectations for the space market are unrealistic and capital flows are large, space suppliers will generally respond with additional product. This can cause an oversupply that negatively affects real estate performance. It can also lead to reduced availability of capital in subsequent periods, even when space market conditions are favorable. Such imbalances have created turmoil in the real estate market.
The real estate analyst needs to understand the effect that these exogenous variables can have on real estate investment performance. In the city as a system paradigm, the analyst views the space market analysis with an understanding of the interaction of the space and capital markets focussing on cap rate expectations. The Japanese learned a lesson the hard way by accepting unrealistically low cap rates. Other investors bought real estate from the RTC at very high cap rates. Both sets of conditions are disruptive of real estate markets.
Rate of Return. Rate of return analysis is relevant before the acquisition and when considering disposition. At the time of acquisition, it is the expected rate of return that influences asset allocation within the constraints of the asset allocation model. Projections of rate of return should reflect both space and capital market conditions to protect the investor against unanticipated volatility of return. Variations in the capital market have significant impact for real estate investors. These, however, are portfolio decisions which may utilize the real estate paradigm but are exogenous to the model.
Capital Flows. There is very little high quality data about real estate capital market flows. As a result, real estate decision makers often have little information on which to base the capital market portion of their real estate analysis. Often, anecdotal evidence is the only information available. Unfortunately, the lack of quality data does not preclude the necessity of conducting the analysis. The analysis is necessary in properly executing an asset allocation strategy.
Asset Allocation. Asset allocation decisions depend on strategy and models. In the capital market paradigm, real estate is balanced against all competing uses of capital to develop an efficient frontier. The real estate asset allocation is one asset among a larger bundle of assets. In the real estate decision making context, the decision as to the size of the overall commitment to real estate has usually already been made, and the task is one of allocation among various types of real estate assets.
A strategy usually revolves around assembling a portfolio of real estate assets that maximize returns within specific risk constraints. The modelling process is simply a system for risk assessment and efficient frontier testing. The more refined the estimates of risk and return, the greater the precision of the efficient frontier estimation. The city as a system paradigm is specifically designed to address the issue of the real estate risk. Thus, the paradigm goes beyond the simplistic model of the composite risk variable, volatility.
The relevance of this is that while the finance paradigm may take one to real estate, the analysis of the risk and return of real estate is best done in its own paradigm. The results may be used elsewhere, but the treatment of risk as though volatility were the only measure is an oversimplification.
The Vision: A Mirror World
Information System for Real Estate Analyses
A vision may be defined as an object of imagination. It may also be defined as an unusual discernment or foresight. The vision concerned here is presumably both.
Having accepted the mission of improving real estate decision making, it is useful to have a vision as to how that is to be done. As a preface to this, some of the Weimer School programs are described in the companion piece labelled as an "Open Letter." Other aspects of the Hoyt Group's work are contained elsewhere in this and previous ASI Newsletters (available upon request).
The focus of this article is the improvement of the analytical system for real estate decision making. This is done in the real estate paradigm, which is one facet of decision making. The open letter discusses that real estate paradigm.
The first element in making good real estate decisions is understanding the system. The previous section, "The Foundation of Real Estate Analyses", sketches one perspective utilizing the city as a system framework, including the variety of subsystems.
Situations which are similar to previous situations require very little analyses in order to reach good decisions. They become routine because all the decision maker needs to do is to put the situation in the right category, and the decision is readily made.
The quality of experience comes into play when the situation differs from those situations previously encountered. The more one has learned from experience, the more likely the prospect of good judgment which leads to better decisions.
The difficult decisions are those in which the situation is so different from previous experiences that it is not clear which experience is relevant. A theoretical structure then becomes critical because the decision maker may take the new situation and relate it to the theoretical framework in order to identify the relationships which would be useful in forecasting the outcome of alternate courses of action.
This is where the modelling comes into play. It simplifies reality to focus on the critical relationships. Thus, the decision maker can focus on only those variables which are relevant. Experience will help in selecting the variables. The broader the base of experience, the more likely the decision maker is to look at related subsystems to see their impact on the situation at hand.
Thus, in order to improve the quality of the decision, one needs to start with a solid theoretical foundation, or paradigm -- a way of looking at the situation. Next, one needs the way in which to process information through the models or paradigm. And finally, one needs the information to process.
The idea is to apply this conceptual approach to real estate decision making, utilizing the structure of analyses just described as avant garde´ with a managerial approach using the appropriate analytical models.
The application of the concepts requires an integrated information system. Such an information system provides a topsight view of the system.
One way to illustrate this is to use, as an example, the system the Hoyt Group is building for its REIT investment portfolio management. While the system is operative, but not complete in the sense that the vision has outpaced development, the model is more elaborate than may be justified with the Institute's modest-sized portfolio. The development of the model is justified in part as a research effort. Thus, some of the following as an application is vision in the sense of "an object of imagination." Some of it, however, is "unusual discernment or foresight."
That which follows focusses only on the real estate analyses portion of the model although some comment will be made on the REIT leverage and management analyses in the model, which is both a valuation and risk analysis model.
Modeling and Forecasting
The first part of the Hoyt Model disaggregates net operating income for each of the 50 REITs in the Hoyt data base. The income is disaggregated by property type and metro area. Thus, the focal point for analyses is the space market for clusters of real estate of the same type in the same metropolitan area.
For portfolio investors with direct investments, or others with property level detail, the net operating income may be handled on a property by property basis. In the topsight view, the institutional investor would cluster properties by type and market or submarket and deal with aggregate demand/supply analysis.
The models alluded to in the earlier section of the paper may be applied to analyses of clusters of properties as well as on a property by property basis. The economies of scale in the information age augur for clustering properties and devoting greater effort and resources to analyzing the market and submarket for long run forces and submarket competitive position, rather than concentrating on the specifics of an individual property at the expense of using boilerplate local economic and market analyses.
The portfolio construction may take cognizance of different location quotients of the metro areas, rather than geographic diversification. Or, a classification of cities into clusters may be used. Whatever the approach, the economic demand drivers are analyzed for portfolio diversification.
The growth and stability of each local economy are then evaluated. Data from one or more economic forecasters may be used. Then, the appropriate disaggregation model may be applied to see the prospects for the space market's various clusters of real estate in the REITs under consideration.
Finally, the demand information is integrated with forecasted supply, and a spreadsheet for net operating income can be constructed on a property by property basis or for clusters.
In the Hoyt Model, the expected income is capitalized to get values used in the REIT valuation process. But, periodic valuation of the real estate is not necessary except if one is looking to calculate in accrued internal rate of return or to make buy and sell decisions. Or as in the case of REITs, to see premiums or discount to values of the REIT components compared to the market cap of the stock. Thus, answering the value question is only one aspect of the analyses. It may be necessary to a decision but rarely sufficient.
Obviously, there is more to property analyses than just described. That is why the foundation is laid in the earlier part of the paper. Even that is really an introduction. The point here is that the intention is to forecast the stream of income to be produced property by property, or by property cluster.
Monitoring the system obviously includes monitoring net operating income. What may not be obvious is that the key variables in the models used for forecasting need to be monitored.
Take each and every variable which is critical to assessing the forces which will affect the net operating income of the real estate and identify the key variables. Some of the variables will be easy to monitor. They are published government data that are readily accessible. Others are property data, also available at a price. Much of this information may be accessible in electronic form or even on-line.
Some data are just too difficult or costly to get on a timely basis. The desired information to be gleaned from hard-to-get data is dealt with by accepting lesser quality data and using surrogate information. Anecdotal data may become critical here because that may be all one can reasonably get. This is especially relevant when assessing the impact of changes indigenous to non-economic subsystems, e.g. changes in environmental regulations and political changes.
The comparison is not only in level of detail and quality, but also in time and periodicity. Lags for some data are long. Other data are available only infrequently. The models need to be designed to deal with reality. The point is to monitor factors which are likely to affect the forecasted course of events which will produce different results.
What the decision maker looks for is variation in events from anticipated results. When actual numbers are in an acceptable range, nothing more needs to be done until the reiterative cycle revises forecasts and calibrates models.
The key is designing the early warning system to show when some variables show tilt, especially the critical variables. The clue may come earlier in the general news about industry closedowns or expansions. That is a catalyst for a substantial revisit to the model. Not all warning signals come in numbers.
The regularly scheduled revisit checks the variables to see if the performances are within range. When any deviation is evident, the level of examination is moved to the next finer level of detail. The topsight executive should have the ability to go to as fine a cut in detail as anyone else in the organization. But, he or she should not be burdened with that data on the screen unless it is called up in response to the clue that something is out of kilter.
In the book Mirror World, by David Gelernter, this vision is for a real-time monitoring of events. That will take some more time - it is the next generation.
This generation focusses on mobilizing the data in an electronic system in which the decision maker can periodically review performance. It also enables one to put potential additions of real estate assets, in whatever investment form, into the context to see the portfolio impact.
All of the old-style real estate analyses can still be done. Some of the results, however, are obtained by new analytical systems which are much more economical because of the information revolution. By looking at the real estate in the context of this paradigm, it is not treated is as a branch of economics or a subset of finance. It is real estate as a discipline, and it has its own analytical system.
This vision of an information system is now a work in progress. The information revolution is changing the way we do our analyses, even real estate analyses. This now is a good time to review the applicability of the discipline we apply and to get the best paradigm for the new decision-making environment.
Advanced Studies Institute Board of Directors
Dr. Maury Seldin, Chairman and President
Dr. Ronald L. Racster, Vice President
Dr. Halbert C. Smith, Secretary
Dr. Jeffrey Fisher, Director
Dr. Kerry Vandell, Director
Advanced Studies Institute Faculty Members
Dr. Peter Colwell, University of Illinois-Urbana/Champaign
Dr. Robert H. Edelstein, University of California-Berkeley
Dr. Jeffrey Fisher, Indiana University
Dr. G. Donald Jud, University of North Carolina-Greensboro
Dr. Norman Miller, University of Cincinnati
Dr. Henry Pollakowski, Boston College
Dr. Ronald L. Racster, The Ohio State University
Dr. Lynne Sagalyn, Columbia University
Dr. Maury Seldin, The Hoyt Group
Dr. Halbert C. Smith, University of Florida
Dr. Kerry Vandell, University of Wisconsin at Madison
Dr. Susan Wachter, University of Pennsylvania