Xem mẫu
- 55
The ‘‘Rules of Thumb’’
and net income in turn is a function of vacancy and expenses,
Net Operating Income ¼ Gross Potential Income À Vacancy À Expenses
ð3-15Þ
leads us to simplify the difference between gross and net income as
Net Operating Income ¼ Gross Potential Income (1 À evrÞ ð3-16Þ
where evr ¼ expense and vacancy rate (0 < evr < 1).
Val
Rearranging the value equation, recalling that GPI ¼ GRM, and substituting
our simplifying assumption produces
Val=GRMð1 À evrÞ
Capitalization Rate ¼ ð3-17Þ
Val
Given that value and gross income are accurately reported, this makes
the honest cap rate we yearn for a function of the expense and vacancy
rate chosen.
Further rearrangement gives us an equation for evr that is dependent on
only two variables, both commonly found in reported sales of investment
property: the GRM and the cap rate, of which one, GRM, is more reliable than
the other. Using these two rules of thumb together in Equation (3-18), we can
gain some additional insight.
À Á
evr ¼ 1 À cr grm ð3-18Þ
One should be cautioned that, mathematically, it is possible for evr to be
negative. However, in real estate it can never be less than zero. Should a
negative evr be calculated by Equation (3-18) from observations in a dataset,
it virtually must result from misreporting of either or both the capitalization
rate or GRM. A moment’s thought about what it would mean for an apartment
building to sell for ten times its gross income AND at a 13% cap rate will
convince you that such things do not occur in nature. In Equation (3-19) we
solve for GRM in terms of the other variables.
1 À evr
GRM ¼ ð3-19Þ
cr
GRM is always greater than 1 (all buildings in first world countries sell
for more than their annual gross income), and cr, the reciprocal of a positive
- 56 Private Real Estate Investment
real number, must always be greater than 0 and less than 1. Thus, the only
way for both sides of the above equation to be greater than 1 is for the
numerator of the ratio on the right to be greater than its denominator. Since
evr is a rate that, by definition, is a positive number between 0 and 1, 1 À evr
must be a number between 0 and 1. For the whole right side of Equation (3-19)
to be greater than 1, 1 À evr must be larger than the caprate.
THE NORMAL APPROACH DATA
TO
Let’s look at a dataset of 1,000 actual apartment sales that took place in the
San Francisco area between October 1996 and September 2001. Each
observation shows the area, price, date sold, age, building size in square feet,
number of units, GRM, and capitalization rate. The first five observations are
displayed in Table 3-4.
Using Equation (3-18), we can combine the cap rate and GRM to create a
list of expense and vacancy ratios. It is useful to look at the range of the evr
observations in Table 3-5 and plot them in Figure 3-6.
Most practitioners in sunny California would agree that expenses of 59.18%
of income for an apartment building are at least unusual, if not unlikely.
Likewise at the other extreme, expenses of 10.43% are probably understated.
We need to adopt a healthy suspicion about the extreme observations. The
plot of an ordered list in Figure 3-6 shows, as always, a few extreme
observations, but the majority of the observations is between 25 and 45%.
TABLE 3-4 First Five Observations in San Francisco Data
Area Price ($) Date Age SF Units GRM CR
5 880,000 09/21/01 94 2,100 6 11.24 0.0697
5 1,075,000 09/21/01 48 5,302 9 8.07 0.0918
1 920,000 09/19/01 0 5,502 6 10.6 0.0603
5 1,000,000 09/14/01 42 5,368 8 14.56 0.0546
5 1,150,000 09/07/01 50 5,200 8 8.87 0.0835
TABLE 3-5 San Francisco EVR
Extreme of EVR for San Francisco data
Minimum 0.1043
Maximum 0.5918
- 57
The ‘‘Rules of Thumb’’
0.6
0.45
EVR
0.25
0.1
0 200 400 600 800 1000
Observations #
FIGURE 3-6 Plot of ordered list of EVRs.
Expense and Vacancy Ratios EVRs
140
120
100
80
60
40
20
0.2 0.3 0.4 0.5 0.6
FIGURE 3-7 San Francisco EVRs.
The histogram in Figure 3-7 provides a visual way to see the discrete
distribution of grouped evr data.
Measures of central tendency, shape, and variance, known as descriptive
statistics, are shown in Table 3-6. The values in the shape statistics measure
something known as ‘‘tail behavior.’’ One of these is skewness, the measure of
how much the distribution is not symmetrical. The other is excess kurtosis,
- 58 Private Real Estate Investment
TABLE 3-6 Descriptive Statistics for San Francisco evr Data
Location statistics
Mean 0.333921
Harmonic mean 0.322032
Median 0.334293
Shape statistics
Skewness 0.221308
Excess kurtosis 1.07313
Dispersion statistics
Variance 0.00370466
Standard deviation 0.0608659
Range 0.487524
Mean deviation 0.046557
a measure of the thickness of the tails. These are important features of the
distribution that are ‘‘non-normal.’’ We will return to these later.
Our data is a sample. We know its shape, but we do not know the shape of
the distribution of the population from which the sample was drawn.
Assuming (naively) for the moment that the sample of expense ratio
observations are from a population of expense ratios that are distributed
normally, we can create a probability distribution function (pdf) for such a
distribution from its first two moments, the mean and the variance. We then
plot these in Figure 3-8 over a range equal to three standard deviations away
from the mean.
normally distributed EVRs
0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
FIGURE 3-8 Normally distributed San Francisco EVRs.
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The ‘‘Rules of Thumb’’
x
0.25
FIGURE 3-9 Portion of EVR observations at or below 25%.
With the information we have developed thus far, we can assess the
probability that any specific reported expense ratio will occur. To do this we
use the cumulative distribution function (CDF) and ask, simply, how much of
the probability mass resides below some specific point. For instance, suppose
we are interested in knowing the probability that the building we are
investigating has an expense ratio of 25% or less. One indication of this is that
portion of the data having expenses ratios at or below that figure. The answer
the CDF produces is plotted on the pdf in Figure 3-9. We must keep in mind
that the illustrations and computations shown below are dependent on our
assumption that the evrs are normally distributed.
Earlier we plotted the data as an ordered list and concluded that most of
the expense ratios were in the range of 25–45%. This is confirmed by the pdf
in Figure 3-10 showing most of the probability mass in that range.
Often, we want to put a number to our conclusions about probability. The
common phrase ‘‘What are the chances of that?’’ is vernacular for the more
formal ‘‘How much of the data is below that [certain point]?’’ Table 3-7 shows
several levels at which the evr on the left is matched with its respective
probability on the right.
Table 3-7 shows that 99.7% of our evr observations are at or below 50%,
while only 8.4% of them are at or below 25%. Thus, about 91% of them are
between 25 and 50%. If we are given a capitalization rate that is based on an
evr of 30%, we see from Table 3-7 that only 28.9% of the buildings have an evr
that low or lower. From this we can make an assessment of the reliability of
the capitalization rate. But how reliable is the model we employed to make
this claim?
- 60 Private Real Estate Investment
0.25 0.45
FIGURE 3-10 Majority of San Francisco EVR observations.
TABLE 3-7 Probability of Certain EVR Observations
evr P (evr)
0.25 0.0839818
0.3 0.288662
0.35 0.604179
0.4 0.861185
0.45 0.971748
0.5 0.99682
QUESTIONING ASSUMPTION NORMALITY
THE OF
Before continuing, it is useful to take a step back and ask a broad question:
Why do we use data and statistics? Used properly, they help us peer into the
unknown. The unknown may be the way a market works, something we
cannot see because it is not directly observable. The unknown may be the
future, something we cannot see because we have not arrived there yet. Either
way, we hope that samples are representative of the population from which
they are drawn, thus approximating the unknown we hope to see or predict.
A frustrating reality of using data that affects practitioners in the field is
that data never predict a specific outcome for a specific act like buying a
particular property. If helpful at all, it enables us to more accurately predict
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The ‘‘Rules of Thumb’’
the mean of a large number of repeated trials of the same act. Recall that
earlier in this chapter we counseled a ‘‘healthy suspicion about the extreme
observations.’’ This advice applies both to specifics, where we believe there is
something wrong with a reported data point, and, more generally, to the way
extreme observations affect our ability to predict the mean. Observations are
considered ‘‘extreme’’ if they lie at a point that is far[away] from the mean.
Such extreme values have an affect on the mean that exceeds their relative
importance to the entire distribution.
Let us now address the non-normal issue. The assumption of normality is
often convenient, but also often troublesome. We already suspect that the
distribution of evrs is not normal because normal distributions are
symmetrical (skewness ¼ 0) and have ‘‘skinny’’ tails (kurtosis ¼ 3). Neither
is the case for the evr data as the reports in Table 3-6 show. Skewness and
excess kurtosis are indications of non-normality which may be the result of
extreme values. Our concern is that extreme values are common in this
process (market) and that this sample is drawn from a population that has a
similarly skewed and heavy-tailed shape.
There are many tests for normality. We shall use one known as the Jarque-
Bera ( JB) test. The statistic produced by his test has a chi-squared limiting
distribution with two degrees of freedom. Provided the evr sample is large
enough to emulate the asymptotic properties of its limiting distribution, one
rejects the hypothesis of normality if the JB statistic exceeds 5.99.
!
Excess Kurtosis2
n
Skewness2 þ
JB ¼
6 4
! ð3-20Þ
1:073132
1000
0:2213082 þ ¼ 56:1464
¼
6 4
The result in Equation (3-20) clearly rejects the null, suggesting that the
data is not normally distributed.
Another important question is whether the variance is finite. That is, does
the second moment exist in the limit, a requirement for normality? There is a
large and growing literature on infinite variance models that goes beyond the
scope of this book. Reference to some of these are at the end of this chapter.
The assumption of normality imposes a set of strong conditions. Notably,
because the distribution is constructed from only its first two moments
(meaning that it is a two-parameter model dependent only on its mean and
variance), the assumption of normality requires one to ignore skewness and
heavy tails. Thus, to believe in the probability estimates in Table 3-7, one
must believe that the world from which the data is drawn is symmetrical, has
thin tails, and has a finite variance. In essence, the assumption of normality is
- 62 Private Real Estate Investment
like a set of blinders. It blocks the presence of outliers from view and
prohibits disproportional outcomes on only one side of the distribution.
Relaxing the normality assumption permits a larger view of the unknown
world we wish to see.
THE STABLE APPROACH DATA
TO
It happens that the normal distribution is a special case of a family of Stable-
Paretian (SP) distributions. The wider view of Stable distributions offers a way
to estimate probabilities when extreme values are allowed.6 The assumption
of normality distorts our view of the data away from its actual shape (see
Figure 3-11).
Comparing the expense ratio probabilities first given in Table 3-7 with
those estimated under the Stable hypothesis, in Table 3-8 we get a different
view of how likely a certain building expense ratio is. In this case the normal
assumption about the population produced probability estimates fairly close
to those if one makes a non-normal Stable assumption. This is fine when the
distribution is close to normal, but many distributions are not, leaving the
opportunity for considerable estimation error.7
LINEAR RELATIONSHIPS
In the general caution at the beginning of this chapter we cast doubt on rules of
thumb based on their linear nature. This admonition carries over to the analysis
of data. There is a very practical reason for a natural dependence on linear
models—they offer tools that are considered ‘‘tractable,’’ a term that
mathematicians use to mean manageable. However, manageable does not
necessarily mean realistic or even correct. There are a number of opportunities,
using linear models common to most spreadsheet programs, to either dazzle a
client with numbers that argue for a spurious relationship or data mine to the
point of disproving a claim that is in fact true. Time and space do not allow a full
elaboration of these traps, such being left to econometric texts. However, since
we have a dataset at hand, let’s use it to provide several examples of suspected
relationships and look a bit closer at their true nature.
6
This methodology has been developed by Prof. John P. Nolan of the Mathematics and Statistics
Department of American University in Washington, DC. As of the Fall of 2004, Dr. Nolan could
be reached via his Web site http://academic2.american.edu/$jpnolan/stable/stable.html.
7
The reader can explore this subject more at www.mathestate.com which includes a page allowing
the user to upload data and estimate Stable parameters.
- 63
The ‘‘Rules of Thumb’’
Expense and Vacancy Ratios (EVRs)
140
120
100
80
60
40
20
(a) 0.2 0.3 0.4 0.5 0.6
normally distributed EVRs
(b) 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
FIGURE 3-11 EVR histogram of actual data (Figure 3-11a) and normal pdf from data mean and
variance (Figure 3-11b).
TABLE 3-8 San Francisco EVR Probabilities under Different Assumptions about the
Distribution
evr Normal probability Stable probability Difference
0.25 0.0839818 0.0731308 0.010851
0.3 0.288662 0.281732 0.00692941
0.35 0.604179 0.620898 À 0.0167185
0.4 0.861185 0.875039 À 0.0138543
0.45 0.971748 0.969291 0.00245784
0.5 0.99682 0.990765 0.00605496
- 64 Private Real Estate Investment
LINEAR TRANSFORMATIONS
Linear transformations involve either adding a constant to or subtracting a
constant from each observation or multiplying each observation by a constant
(which, if in the form of a reciprocal, can turn into division). In general these
manipulations are harmless and permissible. Here is an example.
Suppose that we know that vacancy in the market from which our data was
drawn was 4% during the time in question. We can remove this from evr to
leave us with just the expense ratio. Note that this does not change the shape
of the distribution; it only moves the range down 4%. This is a ‘‘shift’’ of the
distribution along the x-axis. One of the important and useful properties of
the normal distribution is that it retains its shape under linear transforma-
tion.8 Subtracting a constant from each observation, as we have done for the
data’s maximum and minimum values, in Table 3-9, is a form of linear
transformation. As long as the value of the constant is known, such an
adjustment can be made and all the conclusions reached earlier still hold (see
Figures 3-12a and b).
SPURIOUS RELATIONSHIPS
One might claim that property characteristics influence the expense ratio. A
reasonable example of this would be a claim that older buildings have higher
expenses. This suggests that expense ratios increase as property age increases.
Since building age is in our dataset, we can plot these for an initial indication
of a relationship (see Figure 3-13).
It doesn’t look like there is much in the way of a relationship in Figure 3-13.
Do owners of older buildings charge higher rents to offset higher
expenses? Likewise there seems to be little relationship between our two size
measures, building square footage or number of units, and expense ratios (see
Figures 3-14 and 3-15).
TABLE 3-9 Extreme Observations
for evr Data and Expense Ratio Data
evr exp ratio
Maximum 0.591824 0.551824
Minimum 0.1043 0.0643
8
This is also true of the larger class of Stable-Paretian distributions of which the normal is just a
special case.
- 65
The ‘‘Rules of Thumb’’
Expense and Vacancy Ratios (EVRs)
140
120
100
80
60
40
20
(a) 0.2 0.3 0.4 0.5 0.6
Expense Ratios
140
120
100
80
60
40
20
(b) 0.1 0.2 0.3 0.4 0.5
FIGURE 3-12 Histograms for evr data (Figure 3-12a) and expense ratio data (Figure 3-12b).
Age vs. Expense Ratio
0.5
0.4
Expense Ratio
0.3
0.2
0.1
0
0 20 40 60 80 100
Age
FIGURE 3-13 Plot of expense ratio against building age.
- 66 Private Real Estate Investment
Building Size vs. Expense Ratio
0.5
0.4
Expense Ratio
0.3
0.2
0.1
0
0 5000 10000 15000 20000 25000
Building SF
FIGURE 3-14 Plot of expense ratio against building size.
Number of Units vs. Expense Ratio
0.5
0.4
Expense Ratio
0.3
0.2
0.1
0
0 5 10 15 20 25 30 35
Number of Units
FIGURE 3-15 Plot of expense ratio against number of units.
There might be a weak inverse relationship between GRM and expense
ratios (see Figure 3-16). However, intuition and the weakness of the
relationship in the plot does not recommend pursuing this.
Each of the foregoing have tested and discarded a relationship based on a
graph of the data. Later, while considering the price per unit (PPU) rule of
thumb, we will extend the investigation of a suspected relationship to
regression analysis.
- 67
The ‘‘Rules of Thumb’’
GRM vs. Expense Ratio
0.5
0.4
Expense Ratio
0.3
0.2
0.1
0
6 8 10 12 14
GRM
FIGURE 3-16 Plot of expense ratio against GRM.
CASH-ON-CASH RETURN (C/C)
Arguably, cash-on-cash return (C/C) is the best of the first year measures
because, if we are talking about after-tax cash-on-cash return, both debt
payments and taxes are considered. In all types and sizes of properties, market
participants value the cash-on-cash measure. Some even prefer it over multi-
period models because it requires no estimation of future income.
Unfortunately, for all its value it is the least observable. Because terms of
debt rarely appear and income taxes never appear in datasets, we have only
theory on which to rely. The data to confirm any theory we may construct is
sparse at best.
PRICE PER UNIT (PPU)
Perhaps the simplest question asked is: ‘‘How much per foot?’’ or ‘‘How much
per acre?’’ Many people buy certain property types based on whether its price
is appropriately scaled to its size. Some people spend considerable time
reviewing sales and offerings to determine what price per unit the market
supports. Table 3-10 shows the average price per unit for our two cities.
We have suggested that purchasers of smaller properties tend to make
decisions based on rough measures of investment value. One could also argue
that as one buys larger properties there are economies of scale involved, more
dwelling units per land unit, and more sophisticated parties. Thus, one might
- 68 Private Real Estate Investment
TABLE 3-10 Price Per Unit for Los Angeles and San Francisco
Mean price per unit in Los Angeles $71,936
Mean price per unit in San Francisco $118,847
expect the price per unit to decline as property size increases. These are
known as ‘‘testable hypotheses.’’ That is, if we collect data associated with
such assertions we can test their validity. In Tables 3-11 and 3-12 we
compute the average price per unit for each of two size classes from our two
datasets, noting that prices in San Francisco are higher than those in Los
Angeles. (Caution is advised as the San Francisco data was gathered over a
five-year period and the Los Angeles data was gathered over approximately
the last six months of that same period. Here we faced a difficult choice.
Because the comparison is between two markets of different size, there are
fewer sale observations over the same period of time in the smaller market.
Nonetheless, it is desirable to compare datasets of approximately the same
size. As the data is included in the electronic files that accompany this
chapter, it is left as an exercise to determine what, if any, bias the time
difference introduces.)
In Table 3-11 we divide the San Francisco data into approximately half
using > 10 units as ‘‘large’’ to learn that people apparently do pay more per
unit for smaller buildings.
The same appears to hold for the Los Angeles data (see Table 3-12).
On the surface, it appears that we are correct in our theory about small
properties and price, but this merely suggests a relationship between two
groups of data. Does this relationship hold generally? The plot of the San
TABLE 3-11 San Francisco Price Per Unit by Size Category
# of sales Average price
Large SF 424 $103,901
Small SF 576 $129,849
TABLE 3-12 Los Angeles Price Per Unit by Size Category
# of sales Average price
Large LA 343 $63,550
Small LA 357 $79,992
- 69
The ‘‘Rules of Thumb’’
250000
200000
PPU
150000
100000
50000
0
0 5 10 15 20 25 30 35
Building Size
FIGURE 3-17 San Francisco price per unit vs. building size.
Francisco data in Figure 3-17 indicates a weak negative relationship between
unit price and building size. We see fewer large buildings in the higher price
per unit range. But how strong is the relationship in general? As we shall see,
it is not very strong at all.
Regression analysis is a process that attempts to specify a rule, known as a
functional relationship, which governs the connection between two variables.
Univariate analysis, which we perform here, involves a single variable,
building size. Ordinary Least Squares (OLS) Regression finds the ‘‘least
squares’’ line that purports to describe how price per unit ‘‘functions’’ as
building size grows. Table 3-13 shows the regression of value on number of
units in San Francisco. The weakness of the relationship as seen in the plot,
the low T-statistic and R-squared values in the regression analysis, suggests
that there is no relationship in this case. The low F-statistic suggests that the
model does not apply. Another interpretation of this is that most of the model
behavior is captured in the intercept, meaning that we are little better off
using the conditional probability suggested by OLS than we would be using
the unconditional mean for prediction. All of this is to warn the reader that
when using the sort of easy data analysis provided in many popular
spreadsheet packages, one must be careful to interpret the results correctly.9
If higher prices for small properties are persistent across time and other
markets, we can say it is probably not because of the number of units in the
9
One should know that simple OLS Regression produces incorrect coefficient estimates in the
presence of non-normal Stable data. An excellent source for this, including a real estate dataset as
an example, is McCulloch (1998).
- 70
TABLE 3-13 Regression of San Francisco Value on Number of Units
REGRESSION SUMMARY OUTPUT
Regression statistics
Multiple R 0.040353769
R square 0.001628427
Adjusted R square 0.000628054
Standard error 55,995.41781
Observations 1,000
ANOVA
df SS MS F Significance F
Regression 1 5104009925 5,104,009,925 1.627820567 0.202301623
Residual 998 3.12922E þ 12 3,135,486,815
Total 999 3.13432E þ 12
Private Real Estate Investment
Coefficients Standard error t Stat P-value Lower 95% Upper 95%
Intercept 119,205.3808 1,792.870938 66.48854544 0 115,687.1514 122,723.6103
Units À 20.25644089 15.87668672 À 1.275860716 0.202301623 À 51.41196116 10.89907938
- 71
The ‘‘Rules of Thumb’’
building. This raises more questions. Do higher prices result from a larger
land component? One can test for the significance of the price per unit
difference, given lot size. Separate from the small building value question, this
data can be used to investigate other matters. Are there economies of scale?
One can relate expense ratio to size via a closer examination of price per unit.
Is there a time component? Price per unit as a function of the date sold can
give some insight into pricing trends.
OTHER DATA ISSUES
Many excellent texts cover statistics in depth. We close this chapter with some
red flags that require further study.
One should exercise care when comparing a continuous variable such as
expense ratios to a discrete variable such as age. There are also problems
when one variable is constrained as expense ratios are to [0,1] differently than
the other such as age [R þ ]. These issues also cause problems in a regression
context, something discussed in the Regression Primer included in the
electronic files for Chapter 6.
Sample size is a factor in data analysis. We arbitrarily chose a random
sample of data from a larger dataset for the above illustrations. Taking the
entire dataset or just a larger sample could improve our conclusions. This
dataset is drawn from a large area for which we have several subsets based on
location. A reductionist view would have one analyzing the data from each
subset. What would your expectation be about data drawn from an area in Los
Angeles near the ocean versus an area in Central Los Angeles? An
expansionist view would collect data from other cities and compare them.
What would your expectation be about expense ratios in Los Angeles versus
Minneapolis or San Francisco versus Madrid, Spain?
The San Francisco dataset covers a long period of time. There is little
reason to believe that expense ratios are time dependent. On the other hand,
local building code and zoning laws together with market demands can affect
the level of services landlords offer tenants. The price of these services—in a
free market—is impounded into the rent. Expenses as a portion of rent will
change with these factors. Price-restricted markets, those with rent control
such as San Francisco, may show a decline in expense ratios as the cost of
services are transferred to tenants because landlords cannot recover those
costs in the rent.
One intuitively promising idea is that expense ratios are related to clientele
occupying the unit. To the extent that area serves as a proxy for this, we could
compare the ‘‘Area’’ field to the expense ratio data to see if there is a
relationship. One should remain neutral about the outcome of any such study.
- 72 Private Real Estate Investment
Recall that we postulated earlier that expense ratios should increase with age
and that was not supported by the data!
Current active research in the area of heavy tails deserves watching
carefully. Small comfort that it is, at present the only thing we can say for
certain is that the assumption of normality may very often produce
measurement errors. Therefore, the investigation of other aspects of
the distribution, however flawed, has a probative value and should not
be ignored.
One last time we must be reminded that, regardless of how good the data
and the models become in time, there is no magic bullet here. We still have to
rely on a careful inspection of the property itself to be confident about a
certain expense ratio or capitalization rate for that particular property.
Remember that data does not supplant good fieldwork at the site, it only
provides context about the market in which the property competes for tenants
and investors. It is the general understanding of the market that comes from
data analysis. Such understanding contributes to successful ‘‘buy’’ and ‘‘sell’’
decisions relating to individual properties.
REFERENCES
1. Adler, R. J., Feldman, R. E., and Taqqu, M. S. (1998). A Practical Guide to Heavy Tails:
Statistical Techniques and Applications. Boston, MA: Birkhauser.
2. Corgel, J. B., Ling, D. C., and Smith, H. C. (2001). Real Estate Perspectives: An Introduction to
Real Estate (4th ed.). New York: McGraw-Hill.
3. McCulloch, J. H. (1998). Linear regression with stable disturbances. Adler, R. J., Feldman, R. E.,
and Taqqu, M. S., Eds. A Practical Guide to Heavy Tails: Statistical Techniques and Applications.
Boston, MA: Birkhauser, pp. 359–376.
4. Nolan, J. P. (1998). Maximum Likelihood Estimation and Diagnostics for Stable Distributions.
[Working Paper].
5. Nolan, J. P. (1997). Numerical calculation of stable densities and distribution functions.
Communications in Statistics – Stochastic Models, 13(4), 759–774.
6. Nolan, J. P. ( June 1998). Parameterizations and modes of stable distributions. Statistics and
Probability Letters, 38(2), 187–195.
7. Nolan, J. P. (1998). Univariate stable distributions: Parameterization and software. Adler, R. J.,
Feldman, R. E., and Taqqu, M. S., Eds. A Practical Guide to Heavy Tails: Statistical Techniques
and Applications. Boston, MA: Birkhauser, pp. 527–533.
- 4
CHAPTER
Fundamental Real
Estate Analysis
Compound interest is the eighth wonder of the world.
Attributed to Albert Einstein and others
INTRODUCTION
There are a number of excellent real estate analysis programs for practitioner
use in ‘‘numbers crunching.’’ Essentially, these are variant forms of spread-
sheets that perform numerical analysis. An Excel version of such a program is
included with the electronic files for this chapter to provide the reader with a
fully elaborated example in a familiar context. Programs such as these all
perform the same basic tasks, usually offering different menus of reporting
and printing options to dress up the appearance of the analysis for use with
clients.
In this chapter we will:
List all the relevant variables required to perform a complete multi-
period discounted cash flow analysis of a real estate investment.
Perform that analysis, computing various performance measures, includ-
ing net present value and internal rate of return.
Demonstrate the interdependencies between the variables.
Set up a series of scenarios based on the variables for simple risk
evaluation via sensitivity testing.
Introduce ways in which data may be used to further understand the
process.
THE ROLE COMPUTATIONAL AIDS
OF
While spreadsheet technology has removed much of the tedium involved
in illustrating and projecting investment performance, it should not be
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considered a complete tool kit. Technical computing software performs a
different function than spreadsheet programs. It can make any numerical
calculation that can be done by a spreadsheet program, but it is primarily used
to perform symbolic analysis. That is, rather than operating on the numbers of
a specific real estate project, it solves for the relationships between the
variables used in the mathematics to reach the conclusions that drive business
decisions for all projects. Such analysis provides a more general view of
the process not tied to any particular investment and permits one to look
behind spreadsheet icons at their inner workings to more fully understand the
total picture.
Understanding general rules permits one to draw on that knowledge when
encountering exceptions, when addressing more complex problems, and
when using data. Simply stated, there are a few rules that virtually always
work. The deductive reasoning behind those rules is expressed in symbols
and equations. Familiarity with those symbols and equations leaves one
with an understanding of why good business decisions are good business
decisions.
The market is competitive. Principals and brokers compete for the best
deals. Many decisions must be made very fast. Although spreadsheets are
ubiquitous, in a competition between two parties, one of whom knows only
how to click on a spreadsheet icon and the other knows what is happening
behind that icon, the latter is able to react more swiftly, avoid fundamental
errors, and perhaps consummate better transactions.
The use of data can be tricky. The old saying, ‘‘You can prove anything
with numbers,’’ is only true for a loose definition of ‘‘prove.’’ The analyst
who is well grounded in the theory of real estate and its underlying
mathematics is able to separate the proper use of data from the improper.
Understanding symbolic analysis is how one becomes well grounded in
theory.
We can observe sale prices and, to some extent, extrapolate returns
investors achieve. Risk is another matter, much harder to observe. Behind
every sale observation in our database are two parties, a buyer and a seller.
These players engaged in a negotiation, arriving at the price we see in our
data. Inherent in that negotiation was an evaluation of the risk attendant to
choices made by each party. We are curious about how that process works. It
is useful to understand risk/return tradeoffs each party must make to reach
agreement on price.
All powerful software must be used appropriately. Software is agnostic
about the appropriateness or usefulness of its output. All software is merely a
tool in the hands of the user. The beginning point of using software correctly
is defining one’s terms, a task to which we now turn.
nguon tai.lieu . vn
