File: {Elsevier}Brown/Revises-II/3d/Brown-ch007.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 23.12.2004/4:07pm Page:185/188 to justify the cost of an exchange. An interesting empirical study would examine a number of exchanges to determine if perhaps one should not exchange unless one acquires a property at least 1.5 or 2 times the size of the disposed property. Suppose our investor merely retained his original property for the same total time of 12 years. Because he saves mid-holding period sales costs, he has a higher return in both IRR (14.32%) and NPV ($36,314) terms. Note in Figure 7-5 that while the total sales price in the two strategies is greater for the exchange strategy on the left, the owner’s share is greater on the right when no exchange takes place. We see that under these assumptions the primary beneficiary of the exchange is the broker. Several conclusions may be drawn from this. Once again we confirm the fact that pursuing tax objectives for their own sake is counterproductive. Another is that the primary beneficiaries of some exchanges are brokers. Commissions make up the majority of real estate transaction costs. In order to offset these transaction costs, the investor must be able to achieve significant economic gains. There is a limit to the benefits of releveraging via an exchange, and these benefits may not be sufficient to offset transaction costs. DATA ISSUES Data providers sometimes included a binary (Y/N) field to answer the question: Was an exchange involved? This is important when studying Allocation of Sales Proceeds $1,760,814 (b) Loan Balance $760,139 Sale Costs $132,061 CG Tax $153,372 Equity Reversion $715,242 Allocation of Sales Proceeds $2,173,505 (a) Loan Balance $1,228,426 Sale Costs $163,012 CG Tax $98,033 Equity Reversion $684,032 FIGURE 7-5 Allocations with (a) and without (b) exchanging. The Tax Deferred Exchange 185 File: {Elsevier}Brown/Revises-II/3d/Brown-ch007.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 23.12.2004/4:07pm Page: 186/188 markets to determine how tax policy affects investor behavior. However, a refinement is necessary. For this data field to have maximum value, it is important to identify how the exchange fits into the transaction. If the sale involved an exchange in which the seller was the last in a series and did not further exchange his property, there is a different effect than if the seller became an acquiring party an a subsequent exchange. Theoretically, there should be a cumulative effect. The last seller who does not require an exchange may reap benefits from each party lower down in the chain, especially if the 45-day deadline is shorter with each successive transaction. There is no reason this must happen, but it should increase pressure in the system as the number of exchanges in a series grows if the deadline does grow shorter. We leave this interesting study to the game theorists. What we suggest is not a trivial task for data collectors. The result of any such effort would be to track transactions after the closing and tie multiple transactions together. This is not an appetizing assignment, and we do not expect it to be completed soon. Until that is done, we will have to rely on theory to study investor behavior in a tax environment that rewards exchanging over sale and repurchase. CONCLUSION After wading through a blizzard of numbers, sorting out complex sub- calculations dependent on other variables, and running a variety of hypothetical situations, there is one conclusion that is neither a surprise nor in doubt: The investor who adds entrepreneurial labor to increase his rate of return and delays his income tax for a long time is able to build terminal wealth faster. For investors where entrepreneurial issues do not apply and annual returns are moderate, the conclusions are less certain. Given the costs, explicit and implicit, the investor who merely plods along with the rest of the economy must be very careful when undertaking an exchange. Scale factors come into play. The size of the acquired property relative to the disposed property strongly influences whether the cost of an exchange can be justified. In our continuing quest to understand real estate risk, exchanging plays a minor role. There is an analogy to the debate over double taxation of corporate dividends. A double taxation policy encourages borrowing, leading to the additional risks in the securities market. For real estate, a sequential taxation policy incrementally taxes each property in a series as it is sold. This encourages more borrowing either for non-taxable refinance and repurchase strategies that reduce investor efficiency by adding multiple locations or for borrowing to keep ownership levels where they would have been if a tax 186 Private Real Estate Investment File: {Elsevier}Brown/Revises-II/3d/Brown-ch007.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 23.12.2004/4:07pm Page: 187/188 deferred exchange strategy were available. Either of these, while good news for banks, is not good news for society in general if borrowing is seen as adding unnecessary risk to the system. It has been observed by many that taxes are necessary to operate a civil society. In the debate over which taxes provide the most revenue and do the least harm to the market, it is generally agreed that the best tax is the one that changes behavior the least. Income taxes have a poor record in this regard. Capital gain taxes fare no better. The study of real estate tax deferred exchanges is fertile ground for watching the contortions of investors bent on reducing their tax obligation. A final note for policymakers may be in order. Sections 1034 (applying to single family residences) and 1033 (applying to property subject to invol- untary conversion such as condemnation or casualty loss) provide different sets of rules for the sale and reacquisition of property without the payment of taxes. There may be merit in the simplification of these various sections into a single set of rules that acknowledges the benefits to society that accrue from allowing land to remain untaxed in entrepreneurial and productive hands for as long as possible. For those countries in the beginning stages of formulating tax policy, the clean slate they start with might first recognize the perverse incentives in the U.S. tax code as written and avoid expensive pitfalls. If there is one conclusion that remains it is the idea that entrepreneurial effort adds value not only to the investor’s terminal wealth, but to society’s built environment. The preservation of the nation’s housing stock and the optimization of its commercial facilities depend on the wide dispersal of ownership among the most qualified investors. Keeping those assets in capable hands for as long as possible would seem to benefit society the most. REFERENCES 1. Allen, M. (1990). Creative Real Estate Exchange: a Guide to Win-Win Strategies. Chicago, IL: National Association of REALTORS. 2. Internal Revenue Service. IRS Revenue Ruling 72-456. 26 CFR 1.1031(d)-1. 3. Sherrod, J. R. and Diggs, J. B. Merchantile Trust Company of Baltimore and Alexander C. Nelson, Trustees of the Estate of Charles D. Fisher v. Commissioner of Internal Revenue. Merchantile Trust Company v. IRS, Docket # 68338. 4. Tappan Jr., W. T. (1989). Real Estate Exchange and Acquisition Techniques (2nd ed.). Englewood Cliffs, NJ: Prentice Hall. The Tax Deferred Exchange 187 File: {Elsevier}Brown/Revises-II/3d/Brown-ch007.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 23.12.2004/4:07pm Page: 188/188 File: {Elsevier}Brown/Revises-II/3d/Brown-ch008.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 22.12.2004/3:24pm Page:189/208 CHAPTER 8 The Management Problem all that can be required of a trustee is that he conduct himself faithfully and exercise sound discretion and observe how men of prudence, discretion and intelligence manage their own affairs—not in regard to speculation, but in regard to the permanent disposition of their funds, considering the probable income as well as the probable safety of the capital to be invested. The Supreme Court of Massachusetts in Harvard College versus Amory (1830) articulating the Prudent Man Rule INTRODUCTION This chapter addresses what is known as ‘‘the agency problem,’’ recognizing that when an agent holds someone else’s capital the agent’s objectives are often different from the owner’s. In this chapter we will:  Describe the optimization problem that faces any manager of properties in multiple locations.  Describe the owner’s problem, showing how his objectives differ from the manager’s.  Illustrate the misalignment of incentives and compensation arrange- ments common to the business of managing small investment properties. THE UNAVOIDABLE MANAGEMENT ISSUE There is little debate that the ownership of real estate involves management. The debate is about (1) who shall do the management, (2) what manage- ment costs, (3) how one accounts for that cost, and (4) what management arrangement is the most efficient. We observe that some owner’s do their own 189 File: {Elsevier}Brown/Revises-II/3d/Brown-ch008.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 22.12.2004/3:24pm Page:190/208 management. Those who do add to their investment with each hour of labor applied, hours that could have been applied to other activities, profitable or otherwise. At a minimum, this has the effect of complicating the return calculation. Alternatively, to preserve both the integrity of the return calculation and the owner’s peace of mind, one may contract for management services with a third party whose fee becomes a part of the expense schedule. In this way management is charged against the property’s income before the owner’s return is calculated. Retaining a management firm sounds like a simple solution. But property size and location complicate the matter. There is a suspicion that so-called ‘‘professional’’ property management really isn’t. Especially for small proper- ties, the quality of property management can vary widely. A significant literature exists on the subject of agency, studying the separation of ownership and control. This chapter is about what often forces the combination of ownership and control. THE PROPERTY MANAGER’S DILEMMA A company offering property management services, like any firm, wishes to maximize net profit by increasing revenue and lowering costs. The rule adopted to accomplish this is called the firm’s ‘‘production function.’’ To create this function we assume that the firm generates revenue as management fees and incurs two broad classes of expenses. The first are in-house costs consisting primarily of accounting services rendered to owners. For simplicity these are assumed to be fixed, The second involves dispatching an employee to visit and inspect those properties under management. These latter costs are variable and will be referred to generally as ‘‘transaction costs’’ because each visit to a property involves a transaction that incurs, at a minimum, some travel expense (hence these may also be considered ‘‘transportation costs’’). Important factors in these variable costs are the number of properties the manager chooses to manage, the size of those properties, and the distance between them. Initially, we will assume that management fees realized are calculated as a rate per unit. In actual practice the fee is charged as a percentage of income, something we shall address later. For now, realized fees are computed by multiplying a rate, g, by the number of units, u. Therefore, the net profit function, np, is np ¼ guÀ ac Àtc ð8-1Þ where np ¼ net profits 190 Private Real Estate Investment File: {Elsevier}Brown/Revises-II/3d/Brown-ch008.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 22.12.2004/3:24pm Page: 191/208 g ¼ rate per unit at which fee income is realized u ¼ number of units managed ac ¼ accounting costs tc ¼ transaction (transportation) costs, a function Transaction costs are modeled as an increasing function of location count and distance: tc ¼ hue dloc 2 ð8-2) where tc ¼ transaction costs h ¼ a rate at which transaction costs are incurred d ¼ a remoteness factor to indicate the average distance of each property from each other and the office loc ¼ number of locations e ¼ the base of the natural log Illustrating a model with many variables requires reducing their number by fixing some of the variables at specific values. We use several datasets to facilitate this. Table 8-1 provides the datasets we will use in this chapter. The first two of these differ only in the value we give to the distance factor, d. As one might expect, at different remoteness factors, d, the steepness of the tc function varies over different numbers of locations in a portfolio containing a fixed number of units. As a consequence, of course, net profit is a decreasing TABLE 8-1 Seven Datasets d1 d2 d3 d4 d5 d6 d7 ac 10 10 10 10 10 10 fc 50 g 500 500 500 500 h 0.05 0.05 0.05 0.05 0.05 0.05 u 50 50 50 50 50 d 0.6 0.5 0.5 0.5 0.5 0.5 loc 315 a 1.25 1.25 1.25 1.25 b 0.05 0.05 0.05 0.05 mgt 0.1 0.1 0.1 The Management Problem 191 File: {Elsevier}Brown/Revises-II/3d/Brown-ch008.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 22.12.2004/3:24pm Page: 192/208 function of the number of locations and the distance factor. Using the d1 and d2 datasets, the plot in Figure 8-1 shows how the distance factor affects net profits for the two specific values of d over a range of locations. It may appear that what really matters is average building size. But does it? Suppose that ac is a fixed resource that places an upper bound on the total number of units that may be managed. The optimization problem becomes one of finding an appropriate size building, given the fixed number of units. This involves finding the optimal building size expressed as the ratio u loc . The optimal will always be the lowest number of locations. The perfect job may be managing one large building, but the market does not always accommodate that perfect outcome. Management usually involves multiple locations. Incorporating the transaction cost function directly into the manager’s net profit, by substituting Equation (8-2) into Equation (8-1), we have np ¼ gu Àac À e dloc hu 2 ð8-3Þ Providing the fixed values of d3 for some of the variables in Equation (8-3), this time omitting a value for u which we wish to vary, in Equation (8-4) we show Equation (8-3) with real numbers and only the two arguments of interest, units and location: np ¼ 500u À 10 À 0:05e 0:5loc u 2 ð8-4Þ 246810 # Locations Net Profits d = .5 d = .6 FIGURE 8-1 Net profits at two distance factors as the number of locations change. 192 Private Real Estate Investment File: {Elsevier}Brown/Revises-II/3d/Brown-ch008.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 22.12.2004/3:24pm Page:193/208 Equation (8-4) is plotted in Figure 8-2 to show how net profit varies based solely on the number of units and locations, given fixed values of data d3 for accounting costs, the fee per unit, distance, and transaction rates. IS BUILDING SIZE REALLY IMPORTANT? We can define net profit a second way, np2, this time in terms of a new variable to represent building size, size ¼ u loc . Rearranging to express units in terms of this new size variable, u ¼ loc size, in Figure 8-3 we plot Equation (8-5), a different representation of the manager’s net profit that uses d3 data and a function for u. np2 ¼ 500 loc size À10 À 0:025e 0:5loc loc size ð8-5Þ The derivative with respect to location of this last net profit function demonstrates that the largest obtainable net profit, np2 max , is independent of building size. At first glance it appears in Equation (8-6) that np2 is dependent on size because size is in its derivative. dnp2 dloc ¼À0:0125size e 0:5loc ð2 þlocÞÀ40000 ÀÁ ¼ 0 ð8-6Þ 5 10 15 20 locations 60 80 100 120 140 units 0 20000 40000 60000 net profit FIGURE 8-2 Manager’s net profit with changes in the number of units and locations. The Management Problem 193 File: {Elsevier}Brown/Revises-II/3d/Brown-ch008.3d Creator: iruchan/cipl-un1-3b2-1.unit1.cepha.net Date/Time: 22.12.2004/3:24pm Page: 194/208 While this is true, it is not true that np2 max is dependent on size. Setting the derivative in Equation (8-6) equal to zero and solving the implicit function, the term – 0.025size ‘‘divides out,’’ leaving a function that has location as its only variable. Optimal locations under these conditions are 15.4721, rounded to 15 locations. e 0:5loc ð2 þlocÞÀ40000 ÀÁ ¼ 0 loc ¼ 15:4721 % 15 We reach the surprising conclusion that under these conditions building size does not matter. 1 When the distance between properties, d, increases, the number of optimal locations decreases as one might expect (Figure 8-4). This suggests the intuitively satisfying result that dense urbanization offers more management efficiency than rural or sparsely urbanized areas, something we may have guessed from things we learned in Chapter 1. We set aside these insights for the moment to address the other party’s problem. 5 10 15 20 locations 0 10 20 30 40 5 0 size 0 100000 200000 300000 net profit FIGURE 8-3 Net profit with changes in the number of locations and building size. 1 This is not to say that larger buildings do not produce more net profit, something clearly evident from the plot in Figure 8-3. The plot shows that for all sized buildings, given that they are all the same as they are in our stylized example, the optimum number of locations is 15. The effect is more pronounced in larger buildings. 194 Private Real Estate Investment [...]... objectives, the market, and his property Ultimately, he is the manager regardless of who he hires to execute specific tasks In real property ownership the buck stops at the owner Denying this is folly REFERENCES 1 Institute of Real Estate Management (1991) Principles of Real Estate Management Chicago, IL: Institute of Real Estate Management 208 204 Private Real Estate Investment 2 Jensen, M C , and Meckling,... correlation on the data to find À0.0369 The correlation appears non-existent How can this be? There must be SOME relationship between what economists say and what happens Undaunted, we plot the data in Figure 8-11a LO AND BEHOLD!! A PATTERN!! 208 206 Private Real Estate Investment 0.5 0.4 0.3 0.2 0.1 0.2 0.4 0.6 0.8 FIGURE 8-11a Plot of economists’ predictions and outcomes We wish heartily that our data supports... techniques and how they can affect buyers and the transaction  Examine due diligence standards for various property sizes  Develop the beginnings of a Bubble Theory based on the connection between the loan-to-value ratio (ltv), debt coverage ratio (dcr), and capitalization rate  Explore how data on capitalization rate trends can guide lenders and borrowers to better decisions LENDERS AND THEIR RULES... reading for hundreds of cubic feet (HCF) consumed (converted to gallons in column D) and column E shows the number of days between meter readings The mean of the data is 451.8 gallons per day, and standard deviation is 228.9 gallons per day If the data are distributed normally, 95% of all observations should be within 1.645 standard deviations of the mean (we only care about one tail) Those above that (828... the buyer to furnish an appraisal or the lender completes an appraisal in-house The decision to grant the loan, how much loan to offer, and the terms of the loan are all dependent on this appraisal Appraisal standards have evolved over many years To provide background and context, we will focus on two of these standards and how they apply to the income approach to value THE CAPITALIZATION RATE APPROACH... change has taken place at the site Such changes should be of interest to the alert owner and may not (although one can argue that they should) come to the attention of the manager 208 202 Private Real Estate Investment Gal / Day 828 FIGURE 8-9 Water consumption history The electronic files for this chapter stores a database of actual water consumption for a small residential property over approximately... each with a constant coefficient, to create a shape that fits the data We test with 5 and then 9 constants and find that the fit of the data to our model is not too impressive, but 15, shown in Figure 8-12a, makes the fit a very close one The conclusion is that there is a 15-term Fourier sine series relationship between what economists say and what actually happens! Or, more likely, the correlation value... the choice of a and b is important to achieve this result, but the size of scheduled rent, sr, is also involved In our present model, the interplay between these variables results in either an unrealistic rent or an unrealistic vacancy factor The best model is the simplest model While it might be possible to impose inequality constraints such that 0 sr 2000 and 0 vf 0.05 to improve realism, it would... of this later 208 200 Private Real Estate Investment Collected Rent 300 ••• Mgr’s Net Profit Net Op Inc CR Mgr NP NOI 200 100 0 • • • • • •• 0 • • • 100 • • • 200 • • • • • • • • • • • • • 300 400 500 Scheduled Rent FIGURE 8-7 Optimal scheduled rent for manager’s profit, net operating income, and collected rent But there is a problem Recall that we solved Equation (8-6) using data d3 to conclude that... collected rent The successful Tier II investor is a hands-on owner If he is not, there is reason to suspect his ownership tenure will be short In addition to the perverse incentives described in the model, some brokers use their management (or lack of it) to obtain listings on properties Real estate brokerage is often more profitable than management, and brokers find that if they have control of the management . Denying this is folly. REFERENCES 1. Institute of Real Estate Management. ( 199 1). Principles of Real Estate Management. Chicago, IL: Institute of Real Estate Management. The Management Problem 203 File:. in column D) and column E shows the number of days between meter readings. The mean of the data is 451.8 gallons per day, and standard deviation is 228 .9 gallons per day. If the data are distributed. now, realized fees are computed by multiplying a rate, g, by the number of units, u. Therefore, the net profit function, np, is np ¼ guÀ ac Àtc ð8-1Þ where np ¼ net profits 190 Private Real Estate