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Business
Review
ISSN 0 0 0 7 -7 0 1 1

March • April 1995

Entry and Exit of Firms and the
Turnover of Jobs in U.S. Manufacturing
Rafael Rob




Making Money in the Housing Market
Is There a Sure-Fire System?
Theodore M. Crone

E F
CO 0

BBB 0
___i___

Business
Review
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2


MARCH/APRIL 1995

ENTRY AND EXIT OF FIRMS AND
THE TURNOVER OF JOBS IN U.S.
MANUFACTURING
Rafael Rob
Employment in U.S. manufacturing has
declined over the past 20 years. Behind
this trend lie intricate industry dynamics:
firms enter and exit; firms shrink or grow.
In this context, interesting questions arise
about the turnover process and the role of
different types of manufacturing firms in
this process. Rafael Rob surveys the evi­
dence and examines the theories related
to job turnover in the manufacturing sec­
tor.
MAKING MONEY IN THE HOUS­
ING MARKET: IS THERE A SURE­
FIRE SYSTEM?
Theodore M. Crone
Because houses provide both a place to
live and an investment, buyers shop for
houses both as consumers and as inves­
tors. But is the housing market a place
where the astute buyer can consistently
reap abnormally high returns? Although
economists have identified several indi­
cators of future appreciation, the costs of
buying and selling houses make it diffi­
cult to earn abnormally high returns from
this information.

FEDERAL RESERVE BANK OF PHILADELPHIA

Entry and Exit of Firms and the
Turnover of Jobs in U.S. Manufacturing
Rafael Rob*
he popular press and business and labor
leaders have for quite some time been pro­
nouncing the decline of U.S. manufacturing
and the loss of jobs to overseas competitors.
Aggregate data reveal that employment in
U.S. manufacturing has, in fact, declined at an
annual rate of 2 percent over the past 20 years.
Behind this trend, however, lies a more intri­
cate and interesting drama of industry dynam­
ics. New firms constantly enter while others
exit; some firms decline in size while others

T

*Rafael Rob is a professor of economics, Department of
Economics, University of Pennsylvania. When he wrote
this article he was a visiting scholar in the Research De­
partment of the Philadelphia Fed. The author acknowl­
edges editorial comments from various members of the
Research staff. He gives special thanks to Paul Calem for
extensive suggestions. Any remaining errors are the sole
responsibility of the author.




remain stable or grow; new jobs are created to
replace some that are lost.
In this context, interesting questions arise as
to the extent of the turnover process and the
role of different types of manufacturing firms
in this process. For example, what is the mag­
nitude of entry into and exit from various
industries? What are the characteristics of en­
tering firms? Are they as large as incumbent
firms? Are they as likely to survive? If they
survive, do they grow as fast? In this article we
survey the evidence that can be brought to bear
on these issues and the theories that can ratio­
nalize the evidence.
A related issue that may have public policy
implications is whether small firms are more
efficient at creating employment opportuni­
ties than large firms. Supporters of this view
consider small firms more dynamic and more
3

BUSINESS REVIEW

innovative, a claim which (to some extent) can
be substantiated by the data: new firms, which
tend to be smaller, are responsible for the
creation of many new jobs, and many of these
firms introduce new products that can poten­
tially "take off" and generate yet more jobs.
Opponents of this view, on the other hand,
consider large and established firms as having
proven themselves in the way they are man­
aged or through the products and services
they sell. Even though large firms are less
likely to be innovative and to grow, they are
also less likely to fail. Comparison of these
views suggests that the relevant concept is the
durability of jobs, not their mere creation.
Another objective of this article, then, is to use
the data reported here to construct a quantita­
tive measure of the durability of jobs created
by firms of different size.
THE EVIDENCE
The evolution of firms and industries is of
interest to both business practitioners and
economists. A business practitioner focuses on
what accounts for the success or failure of
individual firms and how firms can be profit­
ably restructured, given the lessons learned
from the experience of other firms. An econo­
mist focuses on systematic patterns that char­
acterize the whole set of firms, for example,
what the average lifetime of firms in the per­
sonal computer industry is and how it com­
pares with that of the restaurant industry.
Although these questions arose in earlier
literatures, interest in them has revived re­
cently with the availability of more compre­
hensive data sets and improved methods of
analyzing them, using fast digital computers.1

1Two early references are P.E. Hart and S.J. Prais in the
United Kingdom and H. Simon and C. Bonini in the United
States. Motivated by the striking similarity of the size
distribution of firms (across time, industries, and coun­
tries), these researchers sought to explain the source of this
similarity, to estimate the distribution in different indus-


4


MARCH/APRIL 1995

The evidence in this section comes from two
sources. The first is a series of papers by Timo­
thy D unne, M ark R o berts, and Larry
Samuelson.2 In these papers Dunne and his
associates analyzed more than 300,000 manu­
facturing plants and the more than 200,000
firms operating them in the United States.
These plants produce more than 99 percent of
the output of 387 industries. Dunne, Roberts,
and Samuelson followed these plants over a
20-year period and documented patterns of
entry and exit, growth and decline, degree of
diversification, and size distribution of firms,
as well as how these variables are interrelated.
The second source is a series of papers by Steve
Davis and John Haltiwanger.3 These papers
address similar questions (with somewhat
greater emphasis on macroeconomic issues),
but they use a larger set of firms and more
frequent observations.
Industry Turnover vs. Worker Turnover.
Before proceeding it's important to stress both
the connection and the distinction between

tries, and to show the effect of public policies on it. Later
contributions tried to relate the characteristics of various
industries, such as the size of efficient plant, the advertis­
ing intensity, the degree of product differentiation, or
industry growth, and their net rate of entry or exit. Their
main goal was to understand how the nature of the indus­
try or the product affects the turnover process and, in turn,
industry structure and pricing behavior.
2Timothy Dunne, Mark Roberts, and Larry Samuelson,
"Patterns of Firm Entry and Exit in U.S. Manufacturing
Industries," Rand Journal of Economics 19 (1988), pp. 495515; "Plant Turnover and Gross Employment Flows in the
U.S. Manufacturing Sector," Journal o f Labor Economics 7
(1989a), pp. 48-71; "Firm Entry and Postentry Performance
in the U.S. Chemical Industries,"Journal o f Law and Econom­
ics, 32(2), Part 2 , 1989(b), pp. S233-71.
3Steve Davis and John Haltiwanger, "Gross Job Cre­
ation and Destruction: Microeconomic Evidence and
Macroeconomic Implications," NBER Macroeconomics An­
nual, 5 (1990), pp. 123-68; "Gross Job Creation, Gross Job
Destruction and Employment Reallocation /'Quarterly Jour­
nal o f Economics 107 (1992), pp. 819-63.

FEDERAL RESERVE BANK OF PHILADELPHIA

Entry and Exit of Firms and the Turnover of Jobs in U.S. Manufacturing

industry turnover and worker turnover. The
connection arises because when one plant shuts
down and another plant opens, some jobs are
lost and others are created, so this generates
reallocation of the labor force. In this way, firm
turnover gives rise to worker turnover.
But there are other reasons workers look for
new jobs. By looking for a new job a worker can
create a better match between his or her quali­
fications and the job's requirements. Further­
more, because it takes time to determine the
quality of a match and because new workers
constantly arrive over time, workers are con­
tinually in the process of sorting and resorting
themselves into more suitable jobs.
Another reason behind worker turnover is
that workers accumulate knowledge and ex­
perience at their present jobs, qualifying them
for more advanced positions. Yet, such posi­
tions are not always available with their present
employer, either because of the nature of their
present employment or because others are
already occupying the more advanced posi­
tions. Thus, a certain fraction of job switches
occurs for career or advancement reasons.
Finally, some switches are induced by tem­
porary changes in the conditions that firms
face: workers are laid off from jobs during bad
times, with the possibility of recall at a later
date when business conditions improve. By
that time, however, some of these workers
have found different jobs, and consequently,
their positions are filled by others. Given these
causes, workers reshuffle across jobs, although
the jobs themselves remain intact. Thus, worker
turnover is not perfectly matched by firm turn­
over.4 One estimate states that the fraction of
job switches induced by firm turnover is be­
tween 35 and 56 percent.5
Volume of Turnover. Let's turn to the fea­
tures of the data. The most striking finding is
the magnitude of firm turnover: every year an
average of 8 percent of all incumbent firms in
manufacturing exit and an average of 9 per­
cent enter, resulting in 17 percent turnover



Rafael Rob

with net entry of 1 percent.6Likewise, the rate
of job destruction is 11 percent a year, and the
rate of job creation is 9 percent, resulting in 20
percent turnover with a net loss of 2 percent.7
The entrants represent either de novo firms (a
new firm with a new production facility) or
diversification by a firm already operating in
another industry but now changing the mix of
outputs in its plant or adding a new plant. The
breakdown between these two categories (av­
eraged over all manufacturing industries) is 55
de novo firms to 45 non-de novo.8
Variation Across Industries. While these
numbers represent averages across all firms in
the manufacturing sector of the U.S. economy,
there's a great deal of variation between indus­
tries. Some broad industry categories— for
example, lumber and apparel—exhibit high
firm and job turnover; others—for example,
tobacco and primary metals—exhibit low turn­
over.9Usually, industries that show high entry
*
rates (rates refer to the gross rate unless other­

4To distinguish between the two I will refer to changes
that emanate from firms as job creation and destruction (or
job turnover) and to changes that emanate from workers as
employment search; the fact that a worker changed her job
(for whatever reason) is referred to as a job switch.
5Davis and Haltiwanger, 1992, pp. 833-84.
6See Dunne, Roberts, and Samuelson, 1988, p. 503.
7Davis and Haltiwanger, 1992, p. 820.
8See Dunne, Roberts, and Samuelson, 1988, p.504.
in d u stries are often classified as two-digit and four­
digit, referring to the Standard Industrial Classification
(SIC) of sectors in the U.S. economy. The two-digit classi­
fication is a coarse breakdown of sectors into 20 categories
numbered from 20 through 39, the tobacco sector corre­
sponding to 21, for example. The four-digit classification is
a refinement into subsectors, the cigarette subsector corre­
sponding to 2117, for example. Further details on this
classification system can be found in F.M. Scherer, Indus­
trial Market Structure and Economic Performance (Houghton
Mifflin Company, 1980).

5

BUSINESS REVIEW

wise specified) simultaneously show high exit
rates, implying high turnover. Industries that
exhibit a higher than average entry rate in a
given year tend to exhibit a higher than aver­
age entry rate in the following year. The extent
of turnover is a characteristic of the industry
that tends to persist over time.
The degree to which industrial turnover
varies across industries can be measured in
gross rates of entry and job creation. Firm
entry rates range between 5 and 13 percent
across industry categories, the average being 9
percent. Gross job creation rates range be­
tween 5.9 percent in the tobacco industry and
12.9 percent in the lumber industry (Table l) .1
0
Furthermore, even within a given industrial
category, there's a great deal of variation across
subcategories. For instance, while the average
entry rate for the food-processing industry is
8.9 percent, 10 percent of its subindustries
exhibit an entry rate not exceeding 1.4 percent,
while 10 percent exhibit an entry rate above 15
percent.
Net vs. Gross Entry and Job Creation. Since
industries with high entry rates also usually
have high exit rates, net entry of firms bears
little relationship to gross entry and turnover.
Take, for example, the transportation indus­
try, in which the number of firms declined
only 0.2 percent per year over the sample
period. Yet, it experienced gross rates of entry
and exit of 9.1 and 9.3 percent, respectively,
each of which is almost 50 times larger than the
net change!1 Similarly, the gross job creation
1
rate in the transportation industry averaged
9.4 percent per year, and job destruction aver­
aged 9.9 percent, both much larger than net job
creation (Table 1). Thus, we cannot view turn­
over in manufacturing as if it were stemming
from sectoral shifts alone, that is, firms and
jobs moving from declining industries into
10Dunne, Roberts, and Samuelson, 1988, p.505.
1 Dunne, Roberts, and Samuelson, 1988, p. 506.
1


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6
Federal Reserve Bank of St. Louis

MARCH/APRIL 1995

growing industries. On the contrary, the data
show that even within the same industry, large
flows of entry and exit occur, which shows that
turnover is based as much on the characteris­
tics of the firm as on the environment in which
it operates.
Another piece of evidence supports this
fact: the magnitude of turnover persists across
all phases of the business cycle. Even 1975, the
worst downturn year between 1973 and 1986,
saw an entry rate of 6.7 percent, while 1973, the
best upturn year, saw an exit rate of 6.1 per­
cent.1213 Similarly, even in average years there
is substantial entry and exit. Hence, it's un­
likely that macroeconomic changes alone drive
the turnover process, that is, firms exit when
conditions are bad and enter when they im­
prove. Instead, the characteristics of individual
firms (or changes in these characteristics) are
important in explaining how macroeconomic
changes will affect firms.
Persistence and Concentration. The turn­
over process demonstrates both persistence
and concentration. When a firm enters, it's
more likely to stay subsequently than to exit,
and the likelihood of continued survival rises
over time.1 Similarly, when a diversified firm
3
1
2
4
exits an industry, it's more likely to remain
inactive thereafter than to resume activity.
Also, when an incumbent firm grows, it's more
likely to retain its size than to decline again.
Therefore, these events reflect a persistent
change in a characteristic of a firm.
Second, changes in employment affect cer­
tain firms much more than others. For in­
stance, firms that fire more than 50 percent of
their workers account for 34 percent of job

12Davis and Haltiwanger, 1992, p. 830.
13One curious feature of the data is that entry is
countercyclical, i.e., it tends to be relatively high in down­
turn years.
14Dunne, Roberts, and Samuelson, 1988, p. 510.

FEDERAL RESERVE BANK OF PHILADELPHIA

Rafael Rob

Entry and Exit of Firms and the Turnover of Jobs in U.S. Manufacturing

TABLE 1

Gross and Net Job Creation Rates by Industry
(size-weighted annual average3 percent change)
Industry
(SIC code)
Food (20)
Tobacco (21)
Textile (22)
Apparel (23)
Lumber (24)
Furniture (25)
Paper (26)
Printing (27)
Chemicals (28)
Petroleum (29)
Rubber (30)
Leather (31)
Stone, clay
and glass (32)
Primary metals (33)
Fabricated metals (34)
Nonelectric
machinery (35)
Electric machinery (36)
Transportation (37)
Instruments (38)
Miscellaneous (39)
Total manufacturing
Size-weighted
cross-industry
standard deviation

Job
Creation

Job
Destruction

Net Job
Creation

Job
Turnover

MAX

8.9
5.8
7.4
11.6
12.9
10.1
6.3
9.1
6.8
6.6
10.7
9.1
9.3

10.4
8.2
11.0
15.6
16.0
12.1
7.8
8.7
8.0
9.1
11.8
14.4
12.3

-15.0
- 2.4
- 3.6
-4 .0
- 3.1
- 1.9
- 1.5
+ 0.4
- 1.3
- 2.5
- 1.1
-5 .3
-3 .1

19.3
14.0
18.5
27.2
28.8
22.2
14.1
17.8
14.8
15.7
22.5
23.5
21.6

10.8
9.0
12.4
16.8
18.8
14.3
8.9
9.9
8.9
10.0
14.3
15.2
13.6

5.9
9.5
9.6

11.4
12.0
12.1

-5 .4
- 2.5
- 2.5

17.3
21.5
21.7

12.6
13.7
14.1

9.7
9.4
9.3
10.8
9.2

10.9
9.9
9.3
14.5
11.3

- 1.1
-0 .6
- 0.2
- 3.7
-2 .1

20.6
19.3
18.6
25.3
20.5

13.0
12.3
11.2
15.6
12.9

1.6

21.0

1.5

3.4

2.3

3 Size-weighted average based on annual values for 1973-86 (but not 1974,1979,1984)
Table based on data from Table II in Steven Davis and John Haltiwanger, "Gross Job Creation, Gross Job Destruction, and
Employment Reallocation," Quarterly Journal of Economics, 107 (1992), p. 831, excerpted with permission from The MIT
Press, Cambridge, MA.

destruction.1 Therefore, job destruction is con­
5
centrated in some firms instead of being spread
evenly across firms, showing again the effect
of firm-specific attributes.
These findings confirm that heterogeneity

l5Davis and Haltiwanger, 1992, p. 836.




across firms is a crucial feature of the turnover
process. Individual firms may still be affected
by outside factors—for example, sectoral shifts
and business fluctuations—but whether a par­
ticular firm can weather these fluctuations or
even prosper from them depends on its innate
characteristics: how well it is managed, what
labor relations within it are like, whether it is
7

BUSINESS REVIEW

MARCH/APRIL 1995

industry. On the other hand, the share of firms
innovative, and so forth.
The Size of Entering and Exiting Firms. surviving decreases with age from 38.5 per­
The next issue is, what observable attributes cent to 19.9 percent to 14.0 percent. Therefore,
characterize firms that enter or exit? The most while young firms increase in size if they sur­
apparent attribute is size: although 8 to 9 per­ vive, their chances of survival are smaller. On
cent of firms turn over in any given year, balance, the latter effect dominates the former,
entering firms account for only 3 percent of the resulting in a decreasing market share as a
output in the industry they belong to. There­ cohort ages. A second prominent feature is
fore, the size of entering firms is significantly that the standard deviation of survival rates
smaller than the size of an average incumbent and market shares declines as a cohort ages.
firm. Dunne and associates estimate the aver­ That is, over time the members of a cohort
age output of an entrant to be 35.2 percent of become more homogeneous, and uncertainty
the average output of an incumbent firm.1 A about their future prospects is diminished.
6
similar pattern is detected for exiting firms:
Hence, small firms partake more actively in
they are smaller than the average surviving the entry and growth process, but they are less
likely to succeed and stay around for a long
firm.1
7
Patterns That Arise Over Time. Next, let's time.
consider how newly established firms change
over time, particularly their chances of surviv­ JOB CREATION: SMALL VS.
ing and growing. On average, across industry LARGE PLANTS
The turnover processes we have been exam­
categories, the market share of entrants during
their first five years is 16.2 percent; in their next ining—entry and exit of firms and survival,
five years it's 10.4 percent; then 8.3 percent.1 growth, or demise of entrants—are important
8
Therefore, if we follow a set of firms that enter components of job turnover overall, but they
in the same year (also known as a cohort), we are not the only components. Further contrib­
see that their market share steadily declines uting to the creation and destruction of manu­
over the years. This represents two opposing facturing jobs are growth and decline among
forces. First, during the three consecutive five- well-established firms. This section combines
year periods, the size of the average surviving information on entry and exit with informa­
firm rises from 35.2 percent to 54.3 percent to tion on growth or decline of existing firms to
127 percent of the average firm size in its determine the net impact of all of these dy­
namic influences. In particular, we compare
firms in different size categories and look at
their contributions to job stability, taking into
16Dunne, Roberts, and Samuelson, 1988, p. 503.
account the durability of jobs as well as their
17These numbers pertain to the entire population of
creation.
entrants. De novo entrants account for 55.4 percent of the
To that end, consider Table 2, which is
number of new firms, but for only 50 percent of the output
based on annual observations of manufactur­
of new firms. On the other hand, diversifying firms with
ing plants during the period 1973-88.1 The
9
new plants account for 8.5 percent of the number and 14.4
percent of the output of all new entrants. The size of a de
novo entrant is only 28.4 percent of the average firm size in
its industry, while the corresponding size of a diversifying
firm is 87.1 percent. Thus, diversifying entrants tend to be
larger than de novo entrants.
18Dunne, Roberts, and Samuelson, 1988, p. 510.


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8
Federal Reserve Bank of St. Louis

19Data in Table 2 are taken from Quarterly Journal of
Economics, 107, 1992, Davis and Haltiwanger, "Gross Job
Creation, Gross Job Destruction, and Employment Reallo­
cation, p. 841, and excerpted with permission of The MIT
Press, Cambridge, Massachusetts.

FEDERAL RESERVE BANK OF PHILADELPHIA

Rafael Rob

Entry and Exit of Firms and the Turnover of Jobs in U.S. Manufacturing

TABLE 2

Gross Rates of Job Growth and Decline by Plant Size
(annual percent change)
No. of employees

1-99

100-249

250-499

500-999

1000+

POSITIVE
NEGATIVE
SHARE (%)

14.0
16.4
24.6

9.9
12.0
18.5

8.6
10.5
16.2

7.0
9.3
13.4

6.0
7.8
27.3

columns of this table correspond to the size of
a plant at the date of observation; for example,
100-249 corresponds to a plant employing be­
tween 100 and 249 workers. The row labeled
POSITIVE shows, for each size category, the
average rate of job growth across all observa­
tions of plants that happen to be growing
(which includes new entrants or growing es­
tablishments). NEGATIVE shows, for each size
category, the average rate of job loss across all
observations of plants that are declining in
size. For example, plants that employ between
100 and 249 people and were declining in size
experienced an annual rate of job loss of 12
percent, on average. The last row in this table
(SHARE) represents the share of the industry
employment accounted for by different-sized
plants. For example, plants with more than
1000 employees accounted for 27.3 percent of
employment in the manufacturing sector. This
table shows that the rates of growth and de­
cline are both smaller for larger plants, which
shows the greater stability of these plants, that
is, large plants create jobs less rapidly, but they
also lose jobs less rapidly.
These numbers represent averages over all
plants. Individual plants, however, may experi­
ence different rates of growth or decline—
even if they belong to the same size category.
For example, while the average growth rate in
the positive category for the 100-249 size group
was 9.9 percent, some plants may have in­
creased by only 5 percent, while others in­
creased by 15 percent. Likewise, if we take a
long-term perspective, different plants may



undergo dramatically different employment
histories—even when they start out with the
same number of employees. To give a simple
numerical example, a certain plant may have
employed 45 workers in its first year in busi­
ness, then 49 in the second year, then 32, 47,
and 15 in the third, fourth, and fifth years, and
then may have gone out of business by the end
of its fifth year. Altogether, over the time it was
operating, such a plant provided 188 annual
jobs. Another plant may have undergone a
different employment history: 45, 22, and 0
(going out of business by the end of the second
year), resulting in a total employment of 67
annual jobs. Therefore, although these plants
started out with the same number of employ­
ees, they stayed in business a different number
of years and provided a different number of
jobs every year they operated. For ease of
reference we shall call the total number of
annual jobs generated over the time a plant
was operating "job years."
In the example above, we simply added up
jobs in different years without considering the
timing of job creation. But a firm that starts
with 100 employees and contracts to 80 em­
ployees in its second year can be viewed as
generating jobs of greater total value during
those two years than a firm that starts with 80
people and a year later expands to 100, because
the first firm is generating jobs sooner. In this
view, which we adopt, the sooner jobs are
created the better, holding total annual jobs
constant. Thus, employing a discount factor of
4 percent, we shall discount jobs in later years
9

MARCH/APRIL 1995

BUSINESS REVIEW

in counting the jobs generated over a particu­
lar firm's employment history.2 If we apply
0
this rate to the two examples in the previous
paragraph, we obtain 176 and 66 discounted
job years (instead of 188 and 67).2
1
Contributions of Firms in Various Catego­
ries to Job Stability. The next task is to deter­
mine the number of jobs that the typical firm in
a given size category can be expected to gener­
ate over future years. This will yield compari­
sons between firms in terms of how effectively
they create durable job opportunities. This
task is accomplished by using information
about the growth and decline of firms' em­
ployment by size category (as shown in Table
2) to generate a numerical assessment of the
various possible employment histories and the

20I chose this rate because it represents the rate by
which investors might discount riskless future earnings.
The results reported below have the same qualitative fea­
ture for interest rates between 0 and 20 percent.
21The discounted total jobs in the first example equal 45
+ 49/(1.04) + 32/(1.04)2 + 47/(1.04)3 + 15/(1.04)4 = 176.31.
Those in the second example equal 45 + 22/(1.04) = 66.15.

resulting average number of discounted job
years. The appendix spells out the technical
details of this estimation procedure.
The first row in Table 3 (national average)
shows the results of this estimation (ignore, for
now, the remaining rows). As the table shows,
the average number of future jobs generated
by a representative firm steadily increases with
its current size, that is, a firm that is large today
is expected to provide more jobs over its life­
time than a firm that is small today. This
finding should come as no surprise. First, a
large firm is providing more jobs at present.
Second, large firms' persistence rate is higher,
that is, a large firm is more likely to retain its
size than to decline (and a small firm is more
likely to exit than to stay in business). Both
factors favor large firms as generators of du­
rable employment opportunities. The problem
with the argument in favor of small firms is
that it puts too much weight on the increase in
their size when they happen to succeed, over­
looking the large number of small firms that
fail. As the data show, a small firm is more
likely to fail (on average) than a large one, a fact
mirrored by the smaller number of discounted

TABLE 3

Discounted Job Years Generated by Different Sized Firms
Nationwide and in Different Regions
No. of employees

1-99

100-249

250-499

500-999

1000+

REGION
National Average
New England
Middle Atlantic
South Atlantic
E. South Central
W. South Central
E. North Central
W. North Central
Mountain
Pacific

2402
2310
1969
1982
2390
2536
2360
2591
3060
2662

3661
3552
2947
3023
3681
3954
3636
3921
4608
3929

5881
5783
4772
4974
5979
6443
5915
6220
7161
6089

9541
9501
7962
8397
9768
10,483
9688
9899
11,042
9497

15,626
15,817
13,784
14,657
16,113
17,098
16,041
15,829
16,920
14,904


10


FEDERAL RESERVE BANK OF PHILADELPHIA

Rafael Rob

Entry and Exit of Firms and the Turnover of Jobs in U.S. Manufacturing

job years a small firm can be expected to
generate. A recent paper by Steve Davis, John
Haltiwanger, and Scott Schuh confirms the
result that small firms generate less durable
jobs.22
Put another way, the results in Table 3 show
that while small firms tend to grow, on aver­
age, relative to their initial size, enough of
them fail so that the representative small firm
will not create as many durable jobs as the
representative large firm that exists today.
A similar procedure can be used to assess
the number of discounted job years generated
by firms of different sizes in different regions
of the country. The results of this estimation
are shown in the last nine rows of Table 3. The
pattern revealed in these rows is similar to the
national pattern: a large firm can be expected
to generate more lifetime jobs than a small one.
However, the differences between large and
small firms are starker for some regions. For
22See Steve Davis, John Haltiwanger, and Scott Schuh,
"Sm all Business and Job Creation: Dissecting the Myth and
Reassessing Facts," National Bureau of Economic Research
Working Paper 4492, October 1993.

example, in the South Atlantic and the West
South Central regions, the "handicap" of small
firms compared with large ones was bigger
than that in the Pacific and Mountain regions.
Therefore, the long-term prospects of small
firms were better in the western regions.
Next we consider the effect of firms' age: is
a young firm more likely to generate more
discounted job years than an older firm, or the
other way around? Using a procedure similar
to the one used to generate Table 3, we esti­
mated the number of discounted job years
likely to be created' by firms of different ages
(Table 4). This table reveals an interesting pat­
tern: the age effect for small plants is negative,
that is, the older a small firm is, the fewer
discounted job years it is likely to generate. For
successively larger firms, the age effect is still
negative, but it's weaker. Finally, for the larg­
est firms the age effect is positive. Therefore,
we have a positive interaction between size
and age: the number of discounted job years
increases with firm size, but it increases even
faster if we allow a simultaneous increase in
firm age. One possible interpretation is that if
a firm is small and old, chances are it is "on its

TABLE 4

Discounted Job Years Generated by Firms
in Different Age Groups
1-99

100-249

250-499

500-999

1000+

1

3473

4764

6776

9674

13,813

2

2363

3439

5331

8418

13,544

3

2741

4305

7000

11,277

18,079

4-5

2261

3457

5617

9236

15,437

6-10

2235

3496

5798

9696

16,452

11-14

2058

3278

5574

9598

16,908

15+

1642

2642

4673

8599

16,958

No. of employees
AGE IN
YEARS




ii

MARCH/APRIL 1995

BUSINESS REVIEW

way out." On the other hand, if a firm is large
and old, its method of operation or its product
enables it to generate large sales and to survive
for a long time. Thus, age provides supple­
mental evidence concerning the success of
firms, although the implications are asymmet­
ric across small and large firms.
Finally, we can analyze the effect of firm
structure, particularly the employment pros­
pects of single- vs. multiple-plant firms. The
results of the same estimation procedure for
this case are shown in Table 5, which demon­
strates that a multiple-plant firm can be ex­
pected to generate a larger number of jobs than
a single-plant one. However, the advantage of
being part of a multiple-plant firm declines as
a plant increases in size. The ratio of dis­
counted job years generated by multiple-plant
vs. single-plant firms declines as plant size
rises. Thus, the feedback between size and
multi-plant status is negative. A possible inter­
pretation is that a plant that's part of a multi­
plant firm can freely "borrow" the expertise of
its parent company, giving it an advantage
over a firm that has no access to such expertise.

However, once a plant reaches a large enough
size, it is successful enough on its own, and the
ownership effect is less relevant.
While these results provide support in fa­
vor of large firms as generators of employ­
ment, one should be careful in using them for
policy analysis. The numbers in the tables
reflect only the discounted number of job years
generated by representative firms of different
sizes, not the differential costs of operating
these firms or the subsidies that might be
needed to sustain them at their present size or
to cause them to grow (which is much harder
data to come by). It's quite conceivable that the
subsidy needed to create a new job at a large
firm is higher than the corresponding subsidy
for a small one. Whether a given subsidy or tax
break can stimulate more new jobs at a large
firm will depend on the firm's effectiveness in
creating an extra job, which is a separate issue
from the durability of a job once it is created.
Hence, further empirical analysis is needed to
determine the effectiveness of subsidies in the
hands of large vs. small firms (and to balance
that against the differential durability of jobs).

TABLE 5

Discounted Job Years Generated
by Single- vs. Multiple-Plant Firms
No. of employees

1-99

100-249

250-499

500-999

1000+

OWNERSHIP
TYPE
Single-plant firm

1927

2966

4935

8435

14,950

Multiple-plant firm

2978

4449

6888

10,619

16,314

Difference
(multi - single)

1051

1483

1953

2,184

1,364

Ratio
(multi/single)

1.545

1.50

1.395

1.258

1.091


12


FEDERAL RESERVE BANK OF PHILADELPHIA

Entry and Exit of Firms and the Turnover o f Jobs in U.S. Manufacturing

Rafael Rob

positive correlation between size and profits.
THEORIES THAT EXPLAIN
FIRM TURNOVER
That is, efficient firms produce large quantities
The sections above surveyed the facts about and generate large profits at the same time.
industry dynamics and provided a method for Likewise, as the industry matures, it becomes
estimating the number of jobs generated by more concentrated as more efficient firms gain
representative firms of different sizes, but they market share at the expense of less efficient
did not elaborate on the basic forces that drive firms. Accordingly, this process produces a
the turnover process. The purpose of this sec­ positive correlation between concentration and
tion is to survey such theories and indicate average profitability. Also, this process in­
how they relate to the systematic patterns duces a positive correlation between concen­
shown by the data.
tration and variability of profits. Again, this
Broadly speaking, theories of firm turnover occurs because a firm's size and profits are
fall into three categories: passive learning, ac­ directly related to the firm's efficiency, and
tive learning, and adjustments to outside dis­ efficiencies diverge over time as firms are sorted
out.
turbances.
Passive Learning. This theory is based on
The main shortcoming of this theory is that
the premise that firms find out about their firms in the real world continually enter and
suitability to an industry (that is, their relative exit even in mature industries, but this theory
efficiency) only by operating in it.2 According predicts such a process should eventually sub­
3
to this theory, whether a firm belongs in an side (unless industry demand keeps growing).
industry is an inherent characteristic (a type) Nonetheless, this theory has retained its popu­
that remains unchanged over time but that can larity because it can explain a broad range of
be discovered only through experience. There­ systematic patterns. It seems especially rel­
fore, the process of entry and exit can be thought evant to industries in which the success of
of as natural selection or survival of the fittest. firms depends on a difficult-to-alter special­
This idea may explain the fact that young firms ized asset (a manager or a particular location
have a comparatively low survival rate but or raw material).
Active Learning. The basic premise that
also a comparatively high growth rate when
they survive. This follows from the fact that distinguishes this theory from passive learn­
young firms are largely uncertain about their ing is that a firm's type, that is, its suitability to
type. Once they have operated, they learn a given industry, changes during its tenure in
about their type and the uncertainty is re­ the industry. This change may be the result of
duced. As a consequence, either such firms any number of things: successfully completing
become dismayed and leave or they receive a research and development project, develop­
favorable information and are able to grow ing a new product and successfully marketing
rapidly. This also explains why the variability it, hiring a particularly successful manager, or
of growth rates is highest among young firms raising morale among its employees. Alterna­
and why it declines with age, as shown by the tively, all or some of these endeavors may fail,
evidence cited earlier.
leading to an unfavorable change in the firm's
This model explains other historical pat­ type. In some formulations of the theory the
terns not previously discussed, including the process by which a firm's type changes is
explicitly incorporated, while in others only
the net outcome of such a process is speci­
fied.24,25Either way, an important consequence
23See Jovanovic, 1982, for a comprehensive treatment of
of the active learning premise is that firms
the theory.



13

MARCH/APRIL 1995

BUSINESS REVIEW

continually enter and exit even though the
industry remains stable over time.
The main findings of this theory are as
follows. First, if the process of learning to be
successful exhibits persistence—that is, if cur­
rently efficient firms are expected (on average)
to remain efficient in the future— then, for a
given cohort of firms, the range of firms' sizes,
profits and stock-m arket valuations, and the

likelihood of firm survival all increase over
time.
Second, increased cost of entry lowers the
rate of both entry and exit, the rate of turnover,
and the number of operating firms. Therefore,
the industry becomes more sluggish, and firms
collect returns on their cost of entry over a
longer period. This lowers the average profit­
ability in the industry but raises the profitabil­
ity and market share of larger and more effi­
cient firms. It also produces a positive correla­
tion between the two, which we historically
observe.2
5
2
4
6
Third, in creasin g the dem and for an
industry's output raises the entry rate as well
as the number of firms in the industry but
leaves all life-cycle properties— such as lon­
gevity and probability of survival—intact.
Furthermore, the effect that increasing demand
has on output, prices, and wages depends on
how inputs are priced: if inputs command the
same price no matter how many are purchased,
the output increases but without an increase in
the product price; otherwise, both the product
price and real wages increase, the degree to
which they increase depending on conditions
in the labor market.
Fourth, a higher fixed cost makes it more

24For example, Ericson and Pakes, 1990.

costly for a firm to stick around during "bad
times" (for example, when the variable cost of
production is high), which leads to more firm
attrition and an increase in the efficiency of
operating firms and in their profits. This fea­
ture is the theoretical counterpart of how the
presence of economies of scale leads to larger
firms.27 The main shortcoming of this theory is
that it does not adequately explain what causes
changes in firms' efficiencies and how differ­
ent sources of change affect firms differently.
Adjustments to Outside Disturbances.The
premise behind this theory is that firms enter
or exit, or grow or decline, in response to
outside changes (called shocks) that affect their
industry.28 For instance, firms respond to
changes in the demand for their final product
(because of changes in consumer tastes, for
instance), the costs of entry and exit, or the
prices they have to pay for inputs. Conse­
quently, the entry and exit process can be
understood as mirroring these changes and
how firms react to them. One feature of this
theory is the influence of history: a firm that
enters in response to a favorable demand dis­
turbance will not later exit if the disturbance
merely reverses itself; to induce exit, a larger
negative disturbance is needed. Therefore, how
many firms operate in an industry at a given
point depends on the history of demand dis­
turbances, not merely on the present state of
the industry.
This theory also establishes that a higher
cost of entry will reduce the rate of turnover
and increase the variability of the value of
firms. This is consistent with what is observed
in sectors such as agriculture or housing (where
there is a large sunk investment): the value of
firms fluctuates over a much wider range than
in retail trade or restaurants (where there is a
small sunk investment). This occurs because

25Hopenhayn, 1992.
26Similar features arise if the cost of entry is the same,
but the productive efficiency is worse upon initial entry
into the industry.


14


27See Orr, 1970.
28See Lambson, 1992.

FEDERAL RESERVE BANK OF PHILADELPHIA

Entry and Exit of Firms and the Turnover of Jobs in U.S. Manufacturing

the turnover process in industries with high
sunk cost is sluggish, and it's more economical
for firms to weather changes than to exit and
re-enter.
Furthermore, the theory attributes the coex­
istence of entry and exit to infrequent changes
in market conditions. For instance, the oil shock
of the 1970s induced exit of energy-inefficient
firms, development of more energy-efficient
techniques, and entry of new firms that use
these techniques. The limitation of this theory
is that it relies on such infrequent shocks to
explain simultaneous entry and exit, while the
data show that entry and exit occur on a regu­
lar basis without the occurrence of any un­
usual disturbances.
CONCLUSION
This article argues that the entry and exit of
firms is not solely driven by factors external to
them— macroeconomic changes or sectoral
shifts that drive firms out of declining indus­
tries and into growth industries. Instead, the
data show that entry of new firms occurs even
in the worst downturn years and exit occurs in
the best upturn years. Similarly, even when the
number of firms in an industry declines, new
firms still enter and grow. Therefore, to ac­
count for entry and exit one should look at
characteristics of individual firms—size, age,
whether they operate in a single industry or
several, where they are located, and so on.
Considering these characteristics allows us
to evaluate the number of jobs different types

Rafael Rob

of firms create. New firms tend to be smaller,
and they create the bulk of new jobs. On the
other hand, small firms tend to be short-lived,
and hence, so are the jobs they create. There­
fore, to compare small and large firms, this
article introduced a unit of measurement that
combines the flow of new jobs and their persis­
tence. We compared small and large firms in
terms of the number of durable job opportuni­
ties they generate.
Using a numerical exercise, we showed that
a representative large firm can be expected to
generate a larger number of durable job oppor­
tunities because of the greater stability of these
firms. In other words, a large firm has a smaller
probability of decline, so once it has created a
job, it tends to persist. This finding holds for
firms in different regions of the country, al­
though the effect is somewhat weaker in the
higher turnover regions—Mountain and Pa­
cific. The effect of age is negative for small
firms (the older the firm, the fewer durable
jobs it generates), but it's positive for large
firms. Finally, being part of a multi-plant firm
enhances the number of durable jobs a firm
creates, although the effect tends to be weaker
for larger firms.
In summary, this article presents evidence
on the turnover of firms in U.S. manufacturing
and estimates of the durability of jobs gener­
ated by different firms. A primary conclusion
arising from the estimates is that a job gener­
ated at a large firm tends to last longer than a
job generated at a small one.

REFERENCES
Davis, Steven, John Haltiwanger, and Scott Schuh. "Published Versus Sample Statistics from the ASM:
Implications for the LRD," Proceedings of the American Statistical Association, Business and
Economics Statistics Section (1990).
Davis, Steven, and John Haltiwanger. "Gross Job Creation and Destruction: Microeconomic Evidence and
Macroeconomic Implications," NBER Macroeconomic Annual, 5 (1990), pp. 123-68.



15

BUSINESS REVIEW

MARCH/APRIL 1995

Davis, Steven, and John Haltiwanger. "Gross Job Creation, Gross Job Destruction, and Employment
Reallocation," Quarterly Journal of Economics, 107 (1992), pp. 819-63.
Demsetz, H. "Industry Structure, Rivalry and Public Policy," Journal of Law and Economics, 16 (1973).
Dixit, A. "Investment and Hysteresis," Journal of Economic Perspectives, 6 (1992), pp. 107-32.
Dixit, A., and Rob, R. "Switching Costs and Sectoral Adjustments in General Equilibrium with Uninsured
Risk," Journal of Economic Theory (forthcoming).
Dunne, Timothy, Mark Roberts, and Larry Samuelson. "Patterns of Firm Entry and Exit in U.S.
Manufacturing Industries," Rand Journal of Economics, 19 (1988), pp. 495-515.
Dunne, Timothy, Mark Roberts, and Larry Samuelson. "Plant Turnover and Gross Employment Flows
in the U.S. Manufacturing Sector," Journal of Labor Economics, 7 (1989a), pp. 48-71.
Dunne, Timothy, Mark Roberts, and Larry Samuelson. "Firm Entry and Postentry Performance in the U.S.
Chemical Industries," Journal of Law and Economics, 32(2), Part 2, 1989(b), pp. S233-71.
Dunne, Timothy, Mark Roberts, and Larry Samuelson. "The Growth and Failure of U.S. Manufacturing
Plants," Quarterly Journal of Economics, 104 (1989c), pp. 671-98.
Ericson, Richard, and Ariel Pakes. "An Alternative Theory of Firm and Industry Dynamics," Working
Paper, Yale University (1989).
Evans, David. "Tests of Alternative Theories of Firm Growth," Journal of Political Economy, 95 (1987a), pp.
657-74.
Evans, David. "The Relationship Between Firm Growth, Size and Age: Estimates for 100 Manufacturing
Industries," Journal of Industrial Economics, 35 (1987b), pp. 567-81.
Hall, B. "The Relationship Between Firm Growth, Size and Age: Estimates for 100 Manufacturing
Industries," Journal of Industrial Economics, 35 (1987), pp. 583-606.
Hart, P.E., and S.J. Prais. "The Analysis of Business Concentration," Journal of Royal Statistical Society, 119
(part 2, 1956), pp. 150-81.
Hopenhayn, H. "Entry, Exit, and Firm Dynamics in Long-Run Equilibrium," Econometrica, 60 (1992a), pp.
1127-50.
Hopenhayn, H. "Exit, Selection and the Value of Firms," Journal of Economic Dynamics and Control, 16
(1992b), pp. 621-53.
Jovanovic, B. "Selection and the Evolution of Industry," Econometrica, 50 (1982), pp. 649-70.
Lambson, Val. "Industry Evolution with Sunk Costs and Uncertain Market Conditions," International
Journal of Industrial Organization, 9 (1991), pp. 171-96.
Lambson, Val. "Competitive Profits in the Long-Run," Review of Economic Studies, 59 (1977), pp. 125-42.

16


FEDERAL RESERVE BANK OF PHILADELPHIA

Entry and Exit of Firms and the Turnover of Jobs in U.S. Manufacturing

Rafael Rob

Leonard, J. "In the Wrong Place at the Wrong Time: The Extent of Frictional and Structural Unemploy­
ment," in D. Lang and J. Leonard, eds., Unemployment and the Structure of Labor Markets. New York:
Basil Blackwell, 1987.
Lippman, S.A., and R. P. Rummelt. "Uncertain Imitability: An Analysis of Interfirm Differences in
Efficiency Under Competition," Bell Journal of Economics, 13 (1982), pp. 418-38.
Lach, S. and R. Rob. "R&D, Investment, and Industry Dynamics," University of Pennsylvania CARESS
Working Paper 92-02.
Lucas, R., and E.C. Prescott. "Investment Under Uncertainty," Econometrica, 39 (1971), pp. 659-81.
Orr, D. "The Determinants of Entry: A Study of the Canadian Manufacturing Industries," Review of
Economic Statistics, 56 (1974), pp. 58-66.
Pakes, A., and R. Ericson. "Empirical Implications of Alternative Models of Firm Dynamics," Working
Paper, Yale University, 1990.
Pindyck, R.S. "Irreversibility, Uncertainty and Investment," Journal of Economic Literature, 26 (1991), pp.
1110-48.
Rob, R. "Learning and Capacity Expansion Under Demand Uncertainty," Review of Economic Studies, 58
(1991), pp. 655-75.
Simon, H.A., and C. Bonini. "The Size Distribution of Business Firms," American Economic Review, 48
(1958), pp. 607-17.
Troske, K. "The Dynamic Adjustment Process of Firm Entry and Exit in Manufacturing, Finance,
Insurance, and Real Estate," U.S. Census Bureau Working Paper (March 1993).
Troske, K. "The Importance of the Entry-Exit Process in Accounting for Industry Change," U.S. Census
Bureau Working Paper (February 1993).




17

MARCH/APRIL 1995

BUSINESS REVIEW

APPENDIX
Estimating the Number of Discounted Job Years Generated by
Different-Sized Firms
Given the data in Table 2, the first step is to estimate the probabilities of increase and decrease in the
size of firms as a function of their present size. It is assumed that firms can only switch to adjacent size
groups and that the probability of switches are given by:

Pi \
,i- =NEGi[l+(i-l)kl]/2,
P,,+=POS,[l+(i-l)fc2
1
]/2,
P„=l-P,>rP,,+
1
where POSTs the actual rate of growth of firms in size class i (i=l,...,5), NEGTs the actual rate of decline,
and kj and k2 are parameters to be estimated. This generates a Markov chain, and we can compute its
steady state. The numerical values that k] and k2 take are chosen so that the steady state is as close as
possible (in a chi-square sense) to the actual size distribution of firms (as given by the last row of Table
2).
Given this transition matrix, we can determine the expected number of jobs that each firm can generate
during the course of its existence. For firms in class i this number is denoted n.. Let 6= 1/1.04 represent
the discount factor used to add up jobs generated in different periods, and let z. represent the number of
(current) jobs for firms in class i. For firms in class 1, which corresponds to firms with 1 to 99 employees,
Zj=50; in general, z. is the midpoint of the class i interval (and for firms in the 1000+ size class, z. = 1500).
Then the following system of equations determines n.:

n,=z,+8(P,w +P,,n,+P,,+ni+ (=0,1,...,5, where nA
ti,,1
3 I),
=0.
The solution to this system (the numerical values obtained for n.'s) is in the first row of Table 3.
To generate the rest of Table 3 we repeat the same simulations, except that POS. and NEG. are first
adjusted to reflect the region-specific rate of growth/decline. A similar procedure is followed for age
effects (Table 4) and for single- vs. multiple-plant firms (Table 5).


18


FEDERAL RESERVE BANK OF PHILADELPHIA

Making Money in the Housing Market:
Is There a Sure-Fire System?
Theodore M. Crone*
lmost two-thirds of the nation's house­
holds own the house in which they live.
Although more than half of those houses are
mortgaged, homeowner equity constitutes
about one-third of all household wealth in the
United States. For most people the largest
single investment they will ever make is their
house. It's not surprising, then, that housing
market conditions command a great deal of
attention in the media and that economists
have devoted considerable effort to examining
how well housing markets work.
Prospective buyers shop for houses both as
consumers and as investors. As consumers,

A

*Ted Crone is assistant vice president in charge of the
Regional Economics section in the Philadelphia Fed's Re­
search Department.




they are most interested in the characteristics
of the house and the neighborhood—the num­
ber of bedrooms and baths, the presence of a
garage or central air conditioning, and the
quality of the schools to which they will send
their children. As investors, buyers are inter­
ested in the return they are likely to realize on
the house when it comes time to sell. The
investment aspect of purchasing a house may
receive little attention in discussions between
prospective buyers and real estate agents, but
it is an important concern for the buyer. In
surveys of four different housing markets,
Karl Case and Robert Shiller (1988) found that,
for 44 to 64 percent of buyers, the investment
factor was a major consideration in their deci­
sion to buy. And in only one of the markets
were more than 5 percent of the houses bought
19

MARCH/APRIL 1995

BUSINESS REVIEW

as rental properties. Less than 10 percent of
buyers professed that the investment aspect
was not a consideration at all. Prospective
buyers, then, should value any information
that could help in predicting the return they
will realize on their investment. If there is
some way to identify houses likely to yield
unusually high returns, homeowners or inves­
tors could potentially profit by buying and
selling simply to reap the better-than-average
returns. If someone could exploit publicly avail­
able information to earn abnormal returns in
housing, the housing market would not be
efficient.
THE NORMAL RETURN TO HOUSING
Like the return to any other asset, the return
to housing equals the cash flow (actual or
imputed) from the asset plus any capital gain
or loss, i.e., any increase or decrease in the
value of the property.1In the case of stocks, the
cash flow is simply the dividends earned. In
the case of rental housing, the cash flow to the
landlord is equal to the rent received minus the
expenses incurred. On an annual basis, the
pre-tax rate o f return on rental housing equals
the yearly rent minus operating and mainte­
nance costs plus capital gain, all divided by the
value of the house at the beginning of the year.
Economists think about the return to owneroccupied housing in a similar way, except that
there are no actual cash flows for rent or for
any repairs that the homeowner performs him­
self. So, for homeowners, economists impute
cash flows for rent and maintenance equal to

what they would receive or spend for equiva­
lent rental properties.
Tax considerations further complicate the
calculation of the rate of return on rental and
owner-occupied housing. A landlord is allowed
to depreciate the property for tax purposes and
to deduct the cost of maintenance, but he must
also pay tax on any capital gains that he has
realized on the property when he sells it.2
Homeowners do not get to deduct housing
depreciation on their income taxes, but they do
not pay tax on the imputed rent they receive.
Moreover, most homeowners are exempt from
tax on the capital gains they receive from their
primary residence.3
In calculating the rate of return on housing
the capital gain is difficult to determine for
houses that are not sold. For stocks held in
one's portfolio, the prices of identical stocks
sold in the market provide a precise measure of
the capital gain. But houses are seldom identi-

2Algebraically the after-tax rate of return on rental
property equals

(1-t , (RrMr Dt) + (1— ^ ( A + D t)
)
_________________ (1+r)

Vt
where R( equals rent in period t, M equals maintenance
costs in period t, Dt equals the depreciation allowance in
period t, A equals the change in the market value in period
t, V equals the value of the house at the beginning of the
period, r. equals the income tax rate, r equals the capital
gains tax rate, S equals the period when the house is sold
and capital gains taxes are paid, and r equals the discount
rate.
3Thus, the after-tax return for homeowners is much
simpler than that for landlords. It equals

aTo simplify this discussion we will concentrate on the
return on assets rather than the return on equity. We will
assume that houses are not mortgaged and the equity of
the homeowner or landlord is equal to the value of the
asset. For those landlords or homeowners whose houses
are mortgaged, their own equity in the property is less than
the value of the house, and the mortgage interest payments
are part of the annual costs.


20


R,-Mt+A,

~V,
where R( is the imputed rent the homeowner receives in
period t, M t is the maintenance cost including the value of
any labor on maintenance by the homeowner in period t, A
is the change in the market value in period t, and V is the
value of the house at the beginning of period t.

FEDERAL RESERVE BANK OF PHILADELPHIA

Making Money in the Housing Market: Is There a Sure-Fire System?

cal, and they are sold at infrequent intervals.
Therefore, analysts regularly use some average
measure of housing price increases or decreases
in the local market to estimate the capital gain
or loss for houses not for sale.4
The problems in measuring the rate of return
on housing in general and on owner-occupied
housing in particular make it difficult to deter­
mine the long-run average rate of return to
residential real estate. But according to the best
estimates, the annual compound rate of return
on residential real estate from the late 1940s to
the early or mid-1980s was between 7.4 percent
and 8.1 percent. The rate of return on stocks
over that period was considerably higher— 11
percent or more. On the other hand, the rate of
return on Treasury bills was lower—less than 5
percent per year.5Why should the rate of return
on housing differ from the return on other
assets? One reason is that the risks for holding
different types of assets are not the same. In­
vestors demand a higher average return on
those assets that entail more risk. An asset's
"normal rate of return" includes the return on
a risk-free asset, such as a Treasury bill, plus a
risk premium, that is, an additional amount to
compensate the investor for the added risk.
A common measure of the riskiness of an
asset is how widely its return varies over time.
Consider two stocks whose returns move up
and down together, one in a range of -2 percent
to 6 percent and the other in the range of -5
percent to 9 percent. The first is less risky than
the second, and investors would demand a
higher average return on the second stock to
compensate for the additional risk or uncer-

4A sizable literature has been developed on estimating
appreciation rates for single-family houses. See Crone and
Voith and the special issue of The Journal of the American
Real Estate and Urban Economics Association, Vol. 19, No. 3
(Fall 1991).
5See Ibbotson and Fall, Ibbotson and Siegel, and
Goetzmann and Ibbotson.




Theodore M. Crone

tainty about the return they will receive. The
variation in the annual return to housing has
been considerably lower than the variation in
stock returns.6 Consequently, the average longrun rate of return on housing has been lower
than the return on stocks. The opposite has
been true of the relative return on housing and
Treasury bills. Both the variation in the rate of
return and the average return have been higher
for housing than for Treasury bills, reflecting
the fact that housing is a riskier investment
than Treasury bills.
Not only does the variation in the return to
residential real estate over time differ from the
variation in the returns to other assets, but in
any one period there is a great deal of variation
within the housing market itself. The return on
housing varies from market to market and
even among houses in the same local market.7
Of course, the rates of return on different
stocks also vary because of the differing for­
tunes of one company versus another. A stock
investor, however, can protect herself against
the unforeseen bad fortune of a particular
company by diversifying her portfolio, that is,
by buying a number of different stocks that
reflect the overall market or by buying shares
in a mutual fund that diversifies its holdings.
Unexpected bad fortune for one company rep-

6See Ibbotson and Fall, Ibbotson and Siegel, and
Goetzmann and Ibbotson. Ibbotson and Siegel estimated
that between 1947 and 1982 the standard deviation (a
common measure of variability) of annual returns to a
portfolio of stocks on the New York and American ex­
changes and in the over-the-counter market was four and
a half times as great as the standard deviation of the annual
return to residential real estate. Goetzmann and Ibbotson
estimated that the standard deviation of returns on the
stocks in the S&P 500 was more than three times the
standard deviation of the return on housing between 1947
and 1986.
7For evidence on the variation in returns between mar­
kets see Case and Shiller (1987), and for variations within
markets, see Kiel and Carson.

21

BUSINESS REVIEW

MARCH/APRIL 1995

resented in the portfolio is likely to be matched ing in terms of risk. Patric Hendershott and
by unexpected good fortune for another com­ Sheng-Cheng Hu have suggested that the clos­
pany in the portfolio. This kind of diversifica­ est alternative to an investment in owner-oc­
tion in the housing market is not available to cupied housing is a portfolio of mortgages.
the ordinary homeowner. Her entire real es­ Since some of the features of owner-occupied
tate investment is likely to be in one house and housing, such as the inability to diversify or the
subject to the fortunes of a single, local market. lack of liquidity, do not apply to the mortgage
There is no mechanism for the homeowner to market, housing and mortgages are not exact
distribute her equity over a large number of substitutes in terms of risk. Nevertheless, a
houses in different markets. Theory suggests comparison of the return to housing with mort­
that this inability of homeowners to diversify gage returns shows periods when housing has
their investment raises the rate of return buy­ earned a higher return than mortgages and
ers must expect before they are willing to periods when it has earned a lower return.
H endershott and Hu
invest in a house.8
compared the return on
Besides the inability to
housing to the after-tax
diversify their invest­
return on mortgages for
m ent
in
h ou sing ,
Can the savvy
v ariou s overlap p in g
hom eow ners face an­
investor predict when
other problem that does
eight-year periods be­
housing will earn a
tween 1956 and 1979.
not confront the holders
From 1956 to 1963 a
of stocks and bonds. It is
higher-than-normal rate
homeowner in the 30
not easy to sell a house
of return?
p ercen t tax brack et
quickly; or in the jargon
would have earned 6.53
of fin an cial m arkets,
percent less per year af­
houses are not very liq­
uid. Unlike stocks and bonds that are traded ter taxes by investing in an owner-occupied
frequently in organized markets with large home than by investing in mortgages. From
numbers of buyers and sellers, houses are sold 1960 to 1967 the homeowner would have made
in markets where bids are received rather .05 percent more per year by investing in a
infrequently and the final price is usually the house than in mortgages. From 1968 to 1975
result of some negotiation between the buyer the homeowner would have made 7.1 percent
and the seller. If the seller needs the equity in more per year by investing in a house than by
a house quickly, she may have to sell at a price investing in mortgages.
The return on housing relative to other as­
lower than one she might have negotiated
under normal circumstances. Theory suggests sets has clearly fluctuated over time. More­
this lack of liquidity for housing would tend to over, in any one period, returns in some hous­
ing markets are clearly higher than returns in
raise the normal return expected by a buyer.
Because of the special features of the hous­ others. For example, the appreciation rate for
ing market it is not possible to identify an housing in the Boston metropolitan area from
investment that corresponds exactly to hous­ 1983 through 1985 was 20 percent a year or
higher, while in Los Angeles it was less than 7
percent in each of those years. On the other
8Case, Shiller, and Weiss have suggested the creation of
hand, from 1987 through 1989 Los Angeles had
index-based futures and options markets to offer the
appreciation rates ranging from 11.5 percent
homeowner protection against the risk associated with
this inability to diversify.
to 27.9 percent while Boston's rates were 6.2

22


FEDERAL RESERVE BANK OF PHILADELPHIA

Making Money in the Housing Market: Is There a Sure-Fire System?

percent or lower.9 If the relative risks associ­
ated with housing in general or with specific
housing markets change over time, these fluc­
tuations in returns can be explained as changes
in the risk premium investors demand for
investing in housing.1 But if, as most studies
0
assume, the relative risks do not change, these
fluctuations indicate that there have been peri­
ods of abnormal returns to housing. But can
the savvy investor predict when housing in
general or certain housing markets will earn a
higher-than-normal rate of return?
M ost d iscu ssions of m arket strategy,
whether by economists or by financial advi­
sors, have focused on the stock and bond
markets. For more than 20 years economists
have debated whether it is possible to system­
atically "beat the market," that is, to earn
profits above those earned on similarly risky
assets by consistently predicting abnormal re­
turns. Private or insider information can be used
to earn abnormally high returns on stocks.1
1
The real question is whether publicly available
information can be used in the same way. If the
current price of a stock fully reflects all pub­
licly available information, the information
cannot be used to earn a higher-than-normal
return and the market for the stock is said to be
efficient.1 Any new information relevant to
2

9See Case and Shiller (1994).
'''Unfortunately, Hendershott and Hu do not provide
any statistics on the variation in mortgage rates or in the
return to housing during the periods they examine. Such a
statistic would indicate whether the relative risks between
the two assets had changed. Nor do Case and Shiller (1994)
give any measure of relative risks in the Los Angeles and
Boston housing markets in the 1980s.
1'See the article by Jaffe.
12For an early article on the efficient markets hypoth­
esis see Fama. For a review of the literature see LeRoy.
Certainly, market analysts are rewarded for identifying
stocks that are undervalued, but any above-average return
they may receive from trading these stocks should simply




Theodore M. Crone

future earnings is immediately reflected in the
price of the stock. Housing markets are differ­
ent from stock markets, so any conclusions
about the efficiency of the stock market do not
necessarily apply to the housing market. But
the basic questions about the market's effi­
ciency remain the same. Is all publicly avail­
able information reflected in housing prices, or
can investors systematically make an abnor­
mally high return from this information?
PREDICTING THE RATE
OF RETURN ON HOUSING
Before a prospective buyer can make a profit
in the housing market from publicly available
information, that information must help him
forecast future returns. Among the available
public information, a buyer might consider
past rates of return or appreciation, or popula­
tion, job, and income growth to forecast the
return on his investment. The simplest forecast
models in terms of collecting data use only
past returns to predict future returns or past
appreciation to predict future appreciation.
Therefore, researchers draw a distinction be­
tween using only past returns or appreciation
and using all publicly available information to
forecast future returns or appreciation.1
3
What Can Past Rates of Return and Appre­
ciation Tell Us? One clue to the future return
on any asset is past returns. For example, most
estimates of the normal return to stocks, bonds,

cover the costs of obtaining the necessary information and
performing the analysis plus a normal return on the invest­
ment. As these informed analysts purchase the stocks and
bid up the prices, those who have not borne the costs of
obtaining the information and doing the analysis will not
share in any extra profits derived from this information.
See Grossman and Stiglitz.
l3If one cannot forecast abnormal returns using only
past rates of return or appreciation, the market is said to be
weak-form efficient. If one cannot forecast abnormal returns
using any publicly available information, the market is
said to be semi-strong efficient.

23

BUSINESS REVIEW

or housing are based on the long-run average
return for these assets. These normal returns
are generally expressed in real terms, that is,
after taking account of inflation. But knowing
the long-run average or normal return to hous­
ing is of little help to the home buyer. The
opportunity to make a better-than-average
profit in the housing market depends on one's
ability to forecast above-normal returns from
past returns. Depending on the historical pat­
tern of returns, an investor in the housing
market might adopt one of two strategies. If
local housing markets with abnormally high
returns in one year generally experience ab­
normally high returns in succeeding years, the
investor would purchase a house in a market
where returns had been abnormally high on
the assumption that these higher returns would
continue. On the other hand, if abnormally low
returns have historically been followed by ab­
normally high returns, the investor would buy
a house in a market that had just experienced
relatively low returns. If either strategy worked
consistently to produce abnormally high re­
turns for the investor, the housing market
could be said to be inefficient because prices do
not incorporate all the information available to
the buyer and seller.
For homeowners or investors, a higher-thannormal return could come in the form of a
higher rent (actual or implicit) or higher-thanaverage appreciation or both. Some research­
ers have estimated total returns to housing
(rent minus operating costs plus capital gain),
but more often researchers have concentrated
on the capital gain component of the return,
that is, the appreciation.
In a 1987 study, Karl Guntermann and Rich­
ard Smith used price data on FHA-financed
houses in 57 metropolitan areas to compute a
yearly appreciation rate for each market from
1968 to 1982. They also estimated what they
consider to be the "normal" relationship be­
tween each metropolitan area's housing ap­
preciation and the average for all 57 areas.

24


MARCH/APRIL 1995

Yearly appreciation was deemed abnormally
high or low depending on how far it deviated
from this estimated relationship. The authors
then looked for a pattern of abnormally high or
low appreciation that was offset after some
fixed number of years. They found no signifi­
cant correlation between abnormal apprecia­
tion in one year and abnormal appreciation in
each of the succeeding five years, but they did
find some offsetting appreciation rates in years
6, 7, 8, and 10.1
4
In contrast to Guntermann and Smith's re­
sults, more recent studies have found a posi­
tive correlation in abnormal returns over short
periods of time. Using information on houses
that were sold more than once between 1970
and 1986, Karl Case and Robert Shiller (1989
and 1990) estimated housing appreciation rates
for four different metropolitan areas (Atlanta,
Chicago, Dallas, and San Francisco). In two
(Chicago and San Francisco) of the four areas,
yearly appreciation rates were positively re­
lated to appreciation rates in the previous
year.1 Case and Shiller also used local rental
5

14While this pattern held in general, for some episodes
abnormal appreciation was not offset but rather enhanced
in years 7 and 8. Guntermann and Smith also examined
patterns of abnormal appreciation after controlling for
higher- or lower-than-average rental yields. With these
adjustments there is no significant correlation of abnormal
appreciation rates for years one through three, but there
are significant correlations for years 4 ,6 ,7 , and 8. Because
of the statistical technique used to estimate this "norm al"
relationship between appreciation in each metro area and
the national average, positive (negative) deviations from
the relationship in one year will necessarily be offset by
negative (positive) deviations in other years. There was no
necessity, however, for the intervals between the offset­
ting appreciation rates to be the same across cities as
Guntermann and Smith found.
15The authors also found a significant positive relation­
ship when the data for all four metro areas were combined.
For the San Francisco area, Case and Shiller used data only
from Alameda County. In a similar study Dogan Tirtiroglu
found that appreciation rates in selected communities in

FEDERAL RESERVE BANK OF PHILADELPHIA

Making Money in the Housing Market: Is There a Sure-Fire System?

indexes to develop a measure of excess returns
and found that in three of the markets (Chi­
cago, Dallas, and San Francisco), abnormally
high returns in one year were positively corre­
lated with abnormally high returns in the pre­
vious year.1 James Poterba confirmed these
6
results using data on 39 metropolitan areas
from the National Association of Realtors. The
findings of Richard Meese and Nancy Wallace
also support the persistence of abnormal re­
turns in the short run. Meese and Wallace
found that during the 1970s and 1980s the rate
of return to housing in 14 of 16 municipalities
in the San Francisco metropolitan area was
significantly related to rates of return in the
previous three years.
Most studies have looked for higher-thannormal returns in local housing markets over
relatively short periods of time, but Joseph
Gyourko and Richard Voith examined relative
appreciation rates over a longer span of time.
For the period between 1971 and 1989 they
identified only two markets (San Francisco
and San Jose) from among 56 metropolitan
areas that had significantly higher-than-average appreciation. Gyourko and Voith also
found no consistent pattern of abnormal ap­
preciation within individual markets. Abnor­
mally high or low appreciation rates tended to
persist for some time in five of the 56 markets.
But in three of the markets abnormally high or
low appreciation tended to be offset in the near
term.
The evidence on whether future increases in
house prices can be predicted by past increases
alone is not conclusive. Even when Case and
Shiller (1989) found evidence of a positive

the Hartford metropolitan area were positively correlated
with the previous year's appreciation in neighboring com­
munities.
16These results on total returns indicate that, at least for
these housing markets, higher appreciation rates are not
offset by reduced cash flow.




Theodore M. Crone

correlation between price increases in one year
and increases in the following year, they also
found that information on recent appreciation
in the local market was not helpful in predict­
ing the appreciation of individual houses.
Do Other Data Help? Past rates of return or
housing appreciation, of course, are not the
only information that might help in forecasting
future returns or appreciation. There are good
theoretical reasons to believe that other demo­
graphic and economic variables such as popu­
lation growth, income growth, or construction
costs could influence the appreciation rate or
rate of return on single-family housing. There­
fore, researchers have looked at the pattern of
appreciation rates in combination with other
variables.1 Some of these other factors have
7
often proven to have an independent effect on
future appreciation, at least in the short run.
Increases in population, especially in the
adult population, clearly increase the amount
of housing demanded (see the study by Mills).
But how do house prices respond to predict­
able changes in the adult population? In a
widely quoted article, Gregory Mankiw and
David Weil argued that house price apprecia­
tion in real terms is closely linked to the current
growth in the population over 20 years of age.
Since the size of this population group is pre­
dictable 20 years in advance, Mankiw and Weil
concluded that housing price increases or de­
creases should be predictable many years into
the future. But Americans who bought houses
in the 1950s and 1960s apparently did not take
the post-World War II baby boom into ac­
count; they did not bid up the price of housing
in anticipation of "predictable" higher appre­
ciation rates in the 1970s. Prices rose only as

17They have almost always found that when these
other factors are taken into account, recent past apprecia­
tion is significant in explaining current appreciation. See
Hamilton and Schwab, Case and Shiller's 1990 study,
Poterba, DiPasquale and Wheaton, and Abraham and
Hendershott.

25

BUSINESS REVIEW

MARCH/APRIL 1995

the baby boomers reached their adult years. stronger than the evidence for a long-term
Mankiw and Weil cite this episode as evidence effect.
The demand for housing is fueled not only
that housing markets are not efficient.
But Stephen Holland has questioned the by population growth but also by income
causal connection between the coming of age growth. As incomes rise, more and more indi­
of the baby boomers and the rise in housing viduals or families are able to set up separate
prices in the 1970s. His statistical tests showed households, and people are able to spend more
that, over the long run, real house prices did money on housing. But how does this affect the
not necessarily move together with the growth appreciation of a typical house? One fairly
of housing demand. In another challenge to consistent result in the literature on housing
Mankiw and Weil, James Poterba examined prices is that greater increases in real income or
how their measure of housing demand esti­ real income per capita in one year lead to
mated from the size of the population adjusted greater increases in real house prices in the
following year. Studies
for the age distribution
by Hamilton and Schwab
affected real house prices
and Poterba directly sup­
in 39 metropolitan areas.
Just because we can
port this conclusion. Al­
When per capita income
and construction costs
though Case and Shiller
predict future price
were taken into account,
(1990) found little evi­
increases... does
dence that an increase in
no statistically significant
not necessarily imply
real income resulted in a
relationship emerged be­
near-term increase in real
tween increases in the
that the market is
house prices, they did
real price of houses and
not working well.
find evidence that, tak­
the demographically de­
term ined dem and for
ing the implicit rent into
consideration, real in­
housing. This is consis­
tent with James Follain's earlier results that, in come growth did increase excess returns to
the long run, the cost of new housing net of housing. A recent study by John Clapp and
land will be determined by the cost of supply­ Carmelo Giaccotto provided indirect evidence
ing houses, that is, by construction costs.
of the effect of income growth on house price
While the long-term relationship between appreciation. They found that a decline in the
population growth and housing appreciation unemployment rate in one year was associated
continues to be debated, two studies have with an increase in real house prices in the
provided evidence that over the short term following year. And usually a decline in the
faster population growth does lead to higher unemployment rate represents an increase in
appreciation. In several variations of their real income.1 *
8
model of the housing market, Case and Shiller
The weight of the evidence suggests that
(1990) found that faster population growth in increases in population and income can result
one year was related to higher appreciation or in higher real house prices at least over a oneexcess returns for housing in the following to three-year period. But the price of housing is
year. Bruce Hamilton and Robert Schwab
came to the same conclusion in their study of
18Clapp and Giaccotto did not control for changes in
house price appreciation in 49 metropolitan real income. Case and Shiller (1990) found that once in­
areas. Thus, the evidence for a short-term ef­ come growth was taken into account, employment growth
fect of population growth on house prices is did not affect future price increases or excess returns.

26


FEDERAL RESERVE BANK OF PHILADELPHIA

Making Money in the Housing Market: Is There a Sure-Fire System?

determined not only by factors that affect de­
mand but also by supply considerations such
as the cost of building new houses. Are con­
struction costs, then, an indicator of future
changes in house prices? Case and Shiller (1990)
addressed this question in their study of four
major metropolitan areas, and the results were
mixed. In many cases, they found that the
higher the ratio of construction costs to price,
the higher were housing price increases or
excess returns in the following year. But this
result depended on which metropolitan area
was being considered and which other factors
were being taken into account.
In trying to identify information that would
help predict future increases in house prices,
most researchers have looked at the funda­
mental factors driving housing demand (popu­
lation and income) or the cost of supplying
housing (construction costs). Peter Linneman,
however, has taken another tack. Using data
from the Annual Housing Surveys for the Phila­
delphia metropolitan area in 1975 and 1978, he
identified houses that were undervalued in
1975 based on what their characteristics (num­
ber of bedrooms, central air conditioning, etc.)
suggested the house should be worth.1 He
9
found that houses that were undervalued rela­
tive to their characteristics in 1975 appreciated
more than other houses in the following three
years. In many ways, Linneman's experiment
mirrors what happens in the housing market.
Home buyers shop for the best value based on
the features of the house. They purchase the
one that has the features they want at the

19Linneman used the common hedonic regression tech­
nique to determine the value of various housing character­
istics in 1975. Unfortunately, Linneman did not have mar­
ket prices for the houses in his sample but only the owners'
estimates. He tried to overcome this limitation by redoing
his experiment only with houses recently purchased in
1975 on the assumption that estimates by owners of these
recently purchased houses would be close to the purchase
price. He got the same results with this smaller sample.




Theodore M. Crone

lowest price. Linneman admits that the higher
appreciation he observed was not enough to
offset any transactions costs that a short-term
investor would incur if he tried to buy these
undervalued houses and sell them within a
three-year period.20 Thus, the short-term in­
vestor could not profit from such a strategy.
Various attempts over the past decade to
find indicators of future appreciation of house
prices have been at least partially successful.
While there is little evidence of any ability to
predict abnormal appreciation over the long
term, a number of studies have identified indi­
cators of abnormal appreciation over periods
ranging from one to three years.
BUT WHO CAN PROFIT
FROM THESE PREDICTIONS?
Just because we can predict future price
increases from publicly available information
does not necessarily imply that the market is
not working well. We must also be able to
systematically earn an above-normal return
from these predictions before we can conclude
that the market is not efficient. In February of
each year, for example, a gas station operator
may be able to predict that the price of gasoline
will rise by the Fourth of July. He is not likely
to make any abnormal profit from this infor­
mation, however. If he buys gas for delivery in
July, he will have to pay the higher price. If he
buys extra gas in February to sell in July, the
storage and carrying costs are likely to eat up
any extra profit he would have made.
In the housing market, those who already
own their homes will profit from any abnor­
mal appreciation whether it is predictable or
not. If they can actually predict a higher than
normal appreciation over the near term, some
may even delay selling their homes to realize

20Short-term investors who purchase a property based
solely on expected capital gain are sometimes referred to
as speculators.

27

BUSINESS REVIEW

that return. But what about those who do not
own a house in a market where higher than
normal appreciation is predicted over the short
term? For these potential buyers, certain fea­
tures of the housing market make it difficult to
earn abnormal returns.
A major difference between buying and
selling a house and buying and selling finan­
cial investments such as stocks or bonds is the
cost associated with the transaction. Discount
brokers often charge 0.7 percent or less of the
value of the stock to execute a purchase or sell
order. For residential real estate the transac­
tion costs include transfer taxes, deed-record­
ing fees, title insurance, loan origination fees,
and real estate commissions, and some of these
costs can be substantial. For example, loan
origination fees are typically 2 to 3 percent of
the value of the mortgage, title insurance is
usually 0.5 to 1.0 percent of the purchase price
of the house, and real estate commissions are
typically 6 percent or more of the selling price.
For the investor in the housing market these
transaction costs may be more of a hurdle than
for the homeowner because the investor's af­
ter-tax return is likely to be lower. U.S. tax law
favors homeowners over investors. Both get to
deduct the interest on any mortgage on the
property. But homeowners do not have to pay
income tax on the implicit rent they receive,
and in most cases the capital gain on their
primary residence is also exempt from taxes.
Therefore, a situation that may present an
abnormal after-tax return to a homeowner
may not present an abnormal return to an
investor. Even for homeowners, however, the
prospect of high short-term appreciation may
not be enough to induce them to buy a house
that is relatively far from where they work or
different from the one they prefer. They will
want to move to the house of their choice after
the period of abnormal appreciation, but the
cost of buying and selling a house over a short
span of time as well as the cost of moving is
likely to wipe out any excess profits the

28


MARCH/APRIL 1995

homeowner might have expected.
Besides the costs involved in actually buy­
ing and selling houses, the costs of gathering
information in the housing market tend to
discourage speculation. Housing markets are
very localized even within metropolitan areas,
and information about one market may not
apply to other nearby markets. Information
about what is for sale and recent sale prices is
available for local markets. However, the prices
of otherwise identical houses can differ greatly
from one locality to another, and in this sense
housing markets are local markets. The cost of
gathering information for a small housing
market may be almost as great as the cost for a
large market, but the number of opportunities
to profit from the information on a small mar­
ket is limited. The investor has to weigh the
cost of gathering the information against the
profit he can expect to reap from the informa­
tion. Prospective buyers already in the market
for a house might increase their return by
shopping around for an undervalued house.
Whether they actually increase their return
will depend on the costs of searching for the
undervalued house, and these costs may vary
from buyer to buyer. For example, a person
who does not have to travel far to the neighbor­
hood in which he intends to buy will incur
lower costs than someone who must travel
some distance to search for a house. Search
costs may also be lower for someone who can
take his time searching because he does not
have to move quickly.
CONCLUSION
Most home buyers undoubtedly hope to
make a better-than-average return on their
investment, and there are even people who
speculate in housing, buying units in neigh­
borhoods where they anticipate higher-thanaverage appreciation. Indeed, some of them
may realize abnormally high profits relative to
the risk they take on certain investments. Oth­
ers, of course, lose money on their investment.
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Making Money in the Housing Market: Is There a Sure-Fire System?

But there is no convincing evidence that over
the long term speculating in real estate pro­
duces abnormal profits relative to the risks
involved. Recent economic research has indi­
cated that there are some good indicators of
higher-than-average appreciation rates over
the short term. But the high cost of obtaining
that information and of buying and selling
houses suggests that investors may not be able

Theodore M. Crone

to systematically make abnormally high prof­
its in the housing market.
Does this mean that any prospective home
buyer wastes his time looking for the "under­
valued" house? Not necessarily. The literature
suggests he may find such a house. Whether
he earns higher-than-average profits depends
on the costs of his search.

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MARCH/APRIL 1995

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FEDERAL RESERVE BANK OF PHILADELPHIA

Making Money in the Housing Market: Is There a Sure-Fire System?

Theodore M. Crone

Meese, Richard, and Nancy Wallace. "Testing the Present Value Relation for Housing Prices: Should I
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