Urban
Sprawl,
Density
Values
Land
of
and
the
Development
John R.Ottensmann
The character of the residential development occurring at the periphery of
a metropolitan area has extensive and
diverse economic and social implications.
The kinds and prices of housing produced, the population groups served, and
the cost and problems of providing
public services are all determined by the
workings of the development process.
An understanding of this process requires an examination of the relationships between land prices and the location and intensity of development.
Urban sprawl-the scattering of new
development on isolated tracts, separated from other areas by vacant land-is
frequently cited as one negative consequence of the development process. The
separation of residential areas by vacant
land leads to increased costs in providing
utilities and other public services. Residents are forced to travel farther to engage in most activities, using more
energy resources and producing more air
pollution. The scattered subdivisions
bring the negative impacts of urbanization to far larger areas of formerly agricultural or wilderness land. And finally,
although this effect is impossible to
quantify, many have strenuously objected to sprawl on aesthetic grounds,
arguing against the formless character of
typical urban development.
Urban sprawl is not without its defenders. Lessinger [1962] argued that
the scatter of new development might
prevent the development of large, homogeneous residential areas that would be
socially segregated and ultimately become large, homogeneous slums. Boyce
[1963] suggested that sprawl retains an
element of flexibility for future urban
development appropriate under conditions of uncertainty and imperfect
knowledge. The reservation of land for
later development in more intensive uses,
either residential or commercial, could
produce an urban pattern that may be
more efficient in the long run, based on
recent theoretical work by Ohls and
Pines [1975].
A basic discussion of the nature of
sprawl has been provided by Harvey and
Clark [1965]. Clawson [1962] and Sargent [1976] have suggested the importance of landowner speculation in the
process. Bahl [1968] considered the role
of the property tax, while Archer
[1973] attributed sprawl to the failure
of the residents of the new areas to be
confronted by the full costs of development.
Land values play a critical role in the
allocation of land, thereby shaping the
The author is Assistant Professor, Program in
Social Ecology, University of California,Irvine. He
wishes to thank Professors J. Bonham, F. Stuart
Chapin Jr., A. Allan Schmid and an anonymous
refereefor theircommentsand suggestions.
'For a summary of the costs of sprawl, see
Clawson [1971, pp. 140, 152-59, 320]. The most
comprehensivereviewis the report by the Real Estate
ResearchCorporation[1974].
Land Economics ? 53 * 4 * November 1977
390
Land Economics
pattern of development. Maisel [1963],
Neutze [1970] and Muth [1971] have
provided theoretical statements about
the determination of land values that are
particularly applicable to the issues of
concern here. Empirical studies of differences in land values between urban
areas include those of Maisel [1964],
Mittelbach and Cunningham [1964],
Schmid [1968] and Witte [1975].2
The density at which land is developed for residential purposes is the
final important characteristic of development at the urban periphery. Harrison
and Kain [1974] have examined the
densities of fringe development over
time and the cumulation of this incremental development to form the urban
pattern. Two studies, by Neutze [1968]
and Schafer [1964], have focused on the
development of apartments at the urban
fringe.3
This paper addresses this triad of
characteristics of peripheral urban residential development: urban sprawl, land
values and density of development. First,
an account of the development process is
given. Hypotheses are developed involving the variations of these three
factors across urban areas. Finally, the
predicted relationships between the rate
of urban growth, land values and density
of development are tested using data
from the past two decades for metropolitan areas in the United States.
THENATUREOF
PERIPHERALGROWTH
The desire for accessibility to the
urban center might be expected to result
in continuous development extending
out from that center, since people seek
more accessible locations and are willing
to pay more for them (see, for example,
Alonso [1964] ). This must be the case,
however, only in static situations or if
landowners are shortsighted and consider
just the returns from development in the
current p.eriod. More reasonably, owners
compare the returns from immediate development with their expectations of
the returns from development in the
future, deducting the costs of holding
the land and discounting the returns
to their present values.4 As a result,
some owners may withhold their land
and forgo current development while
development occurs on less accessible
land farther from the center. For example, current demand might support
only the development of single-family
housing beyond a certain distance.
Future urban growth, however, could
generate a demand for multifamily
housing yielding far higher returns,
causing the most accessible land to be
withheld during the current period.5
An explanation of urban sprawl requires more than expectations of future
growth. Given the conventional assumptions, similarly situated landowners
should face a common future and should
reach the same decisions with respect to
development. But landowners vary
widely with respect to their situations,
knowledge and attitudes, which affects
both future expectations and real and
2Many more studies have dealt with intraurban
variations in land values. A good introduction to this
work is Mills [1969]. For more recent references, see
Witte [1975].
3As was the case with land values, many studies
have dealt with intraurban variations in population
density. See, for example, Muth [1969] and Mills
[1972].
4Bahl [1968] has specified the conditions under
which a landowner should either withhold his land,
develop it or sell it.
SOhls and Pines [1975] have demonstrated that
such skipping of close-in land in favor of higher-density development at a later time may prove to be more
efficient for the society in the long run.
Ottensmann: UrbanSprawl
perceived holding costs. Some important
differences include landowner incomes,
income tax positions, alternative investment opportunities, the possible use of
the land in agricultural production and
eligibility for preferential property tax
treatment.6 These differences will
produce variations in landowner decisions to develop or withhold their land,
resulting in the fine-grained pattern of
urban sprawl that is observed as development occurs at the periphery of urban
areas.7
Differences in the levels of future expectations may be important in accounting for variations in the patterns of
residential growth between cities. Hypotheses are derived involving the relationships between expectations, urban
sprawl, land values and densities of current development. Consider two identical urban areas with the same patterns of
demand for residential development in
the current time period. Considering
only this current demand, landowners in
comparable locations would obtain
similar returns from current development in the two cities. The cities differ
only in the levels of expectations of the
landowners: In one city, only slow
growth and low levels of future residential demand are anticipated after this
current time period, while rapid growth
and high demand are expected in the
other. Thus, landowners in the first city
will tend to have lower expected present
values of returns from future development than their more optimistic counterparts in the second city. There will, of
course, still be variations in landowner
expectations within each of the cities.
When the landowners compare their
initial expectations regarding returns
from current development (the same in
both cities) with the anticipated returns
from future development, the patterns
391
of decisions will vary between cities.
Given the lower levels of expected future
returns, current development will be
more attractive to a higher proportion of
landowners in the first city. The higher
future demand in the second city will be
more appealing, causing more of these
owners to withhold their land in favor of
future development. (This decrease in
the supply of land for current development will produce an increase in land
prices and returns from current development, enticing some of the reluctant
owners back into the development of
their land before an equilibrium is
reached.) This forms the basis for the
first hypothesis: The quantity of land
withheld from current development-the
amount of urban sprawl-should vary directly with the levels of expectation concerning future residential demand. Put
another way, landowners in rapidly
growing cities will reserve more land for
future development. The more growth
they expect, the greater their tendency
will be to sit tight and wait for higher
returns to their land.
The withholding of land decreases the
supply available for current development
at any distance from the center. This will
force the price for land up (also causing
some additional land to be released for
6Clawson [1962] and Kaiser et al. [1968] provide
good accounts of the factors which influence landowner behavior.
7The argument assumed the existence of large
numbers of landowners at any given location in order
to discuss the proportions with higher or lower expectations who would or would not withhold their
land. With small numbers of landowners at any location,
the argument can still be used if their expectations are
assumed to be subject to a probability distribution
comparable to the distribution of expectations among
the larger number of landowners. Then the large landowners' decisions concerning future expectations and
the withholding of land would be probabilistically
determined, still producing random variation and
sprawl.
392
current development). Thus the second
hypothesis: Land values should vary
directly with the levels of expectation
concerning future residential demand. In
those cities that are growing more
rapidly, higher future expectations will
force current land values up.
A higher price for land will cause developers to use less land in the production of housing, substituting other inputs
for land. The third hypothesis is, then, as
follows: Density of residential development on land that is developed (and not
withheld) should vary directly with land
values and with levels of expectation
concerning future residential demand.
Ironically, the faster growing cities,
while having more sprawl, will actually
be denser in those areas that are actually
developed.
The traditional assumption of employment being concentrated in a single
center has become less tenable with the
decentralization of commercial and industrial activity in most large urban
areas. The development of multiple centers of employment near the edge of the
fully developed portion of the city
would not alter the desire of new residents to locate close to their places of
work. However, the generally shorter
commuting distances would lessen the
resistance to locating at even greater distances from the center, increasing demand farther out and further encouraging dispersed development. In
addition, the emergence of the peripheral centers might increase the potential for future higher-density development in their vicinities, creating a greater
incentive to withhold land. Thus, decentralization of employment would be
expected to lead to even more urban
sprawl.
In summary, when expectations about
future development potential are high,
Land Economics
more land will be withheld from development, land values will be higher,
and the densities in developed areas will
be higher. More will be done on less
land, at higher prices, as the owners wait
for still higher expected returns from
future development.
THEHYPOTHESES
TESTING
The second and third predictions outlined above, involving variations in land
prices and density of development across
urban areas, have been tested empirically. The procedures followed and the
variables used in these tests are described
in this section. The first of the hypotheses involved the withholding of land
from development to produce urban
sprawl; unfortunately no data could be
found that were appropriate to examine
the prediction in this case.
A model incorporating the two hypotheses in the form of linear regressions
is tested using data from Standard Metropolitan Statistical Areas (SMSAs) in
the United States. For the first set of
regressions, measures of land value serve
as the dependent variable. Expectations
of future development measured by rates
of population growth are expected to be
positively related to land values. Two
additional independent variables are also
included in these regressions-population
and income. Larger cities have greater
aggregate demand for residential space,
and higher incomes allow individuals to
offer more for such space. Thus, both
variableswould be expected to be significant factors affecting land values and
should be positively related to these values. For the second set of regressions,
the density of current development is
the dependent variable. Land values and
population growth should both, as hy-
Ottensmann:UrbanSprawl
pothesized, be positively related to the
density of development. Population
should also be positively related, since
the greater demand for space in large
cities forces more intensive use. The role
of income is less clear in this case: Higher incomes could increase land values
and hence densities, but could also enable households to purchase more space.
A variety of land price data for metropolitan areas in the United States has
been assembled by Schmid [1968]. The
National Association of Home Builders
(NAHB) has gathered information from
its members regarding the average price
of raw suburban land purchased by them
for their own use in residential development in a large number of urban areas
for 1960 and 1964. These data on land
values per acre should be indicative of
the overall level of prices for land for
new residential development in each
SMSA, even though they cover only the
activities of the NAHB members. Like all
attempts to collect land value information, these NAHB data are undoubtedly
affected by significant inaccuracies and
problems in coverage. Thus, it was felt to
be appropriate to consider alternative
sources of information. Data on the
prices of lots sold for new residential
development are an alternative. Such information is somewhat easier to come
by, but suffers two shortcomings: lot
sizes vary and other development costs
affect the final price of a lot. Therefore,
lot prices can be only imperfect measures of the variation in land values
across urban areas. NAHB data on lot
prices in 1960 and 1964 (covering the
prices of developed lots for single-family
home building reported by NAHB members in surveys) and Federal Housing Administration (FHA) data on lot prices in
1950 and 1964 (covering the prices for
developed lots for single-family homes
393
with FHA-insured mortgages in each
SMSA) are included in the tests of the
models. These data also cover only a
portion of the land market, but provide
alternative tests and extend the temporal
range of the tests.8
Following the example of Harrison
and Kain [1974], the percentage of new
housing units constructed during a decade as single-family dwellings is taken as
the surrogate measure for density of development. Of course, percent singlefamily development is inversely related
to the density of development, so the
direction of the hypothesized relationships must be reversed. While variations
do occur in the densities at which both
single-family and multifamily development take place, the choice between
these types undoubtedly accounts for
most of the variation in development
densities across SMSAs. The data available refer to the SMSAs as a whole and
are therefore affected by central city redevelopment. However, data for the
SMSA fringes would have missed significant peripheral development occurring
within central cities.9
As mentioned earlier, rates of population growth are taken as measures of
landowner expectations regarding future
development. The current rate of growth
in the metropolitan area was considered
to be the major factor which landowners
could observe and, hence, the major factor affecting expectations. Landowners
in a rapidly growing city will be more
8
All of the land price variables were obtained from
the appendix tables in Schmid's study [1968, pp.
60-93]. He obtained the information from a variety
of FHA and NAHB documents for which he provides
citations. No corrections were made for inflation; this
should be considered when interpreting the results.
9The percentages of new construction in singlefamily units in each SMSA were obtained from the
U.S. Bureau of the Census [1963, Table 6; 1973a,
Table A-6].
394
likely to anticipate rapid growth in the
future. Such a measure may not be ideal,
but it is difficult to imagine that expectations will not be related at all to experience.
Finally, the last two independent variables are the SMSA populations and
median family incomes at or prior to the
time of development being considered.
Thus, 1950 values are used with 1950
land prices and percent single-family development during the 1950-1960 decade, and 1960 values are used with 1960
and 1964 land prices and 1960-1970
development. The SMSA boundaries in
effect at each census were employed, as
it was felt that these areas and figures
were most relevant for landowner decisions at those times.10
Due to differences in the data sources,
various data were available for different
numbers and sets of SMSAs. Depending
upon the variables employed, regressions
could include anywhere from just over
fifty to nearly two hundred SMSAs. A
full set of data was available for fifty-one
SMSAs. Comparisons were made of the
results of regressions run with both this
limited sample and with all of the cases
available for the particularvariables. Differences were relatively minor and did
not affect the substantive interpretations
of the results. Therefore, for simplicity
and comparability, all of the results reported here refer to the same sample of
fifty-one SMSAs. l
Different functional forms were used
in regressions for some of the variables.
For example, a logarithmic transformation of the population variable was
tested in all of the regressions. None of
the tests was conclusive. Examinations
of residuals favored neither form. All of
the variables in the regressions reported
here are in arithmetic form.
The final issue involved the selection
Land Economics
of the appropriate time period of population growth to use in representing the
level of landowner expectations. The initial supposition was that the level of a
metropolitan area's population growth
just prior to or during the period of
development considered would most directly influence landowner expectations.
However, should landowners exhibit
rather more prescience than is expected,
the rate of population growth in a future
period would be a more appropriate
measure of those expectations. Tests
with alternative population growth measures failed to support a claim for any
special powers of prediction by land' SMSA population changes, populations, and
median family incomes were obtained from the U.S.
Bureau of the Census [1953, Table 2; 1962, Table 3;
1973b, Table 3].
Schmid [1968, p. 51] conducted a multiple regression analysis using the same 1960 NAHB price data
(with a larger number of cases). Schmid's dependent
variable was percent appreciation of the land prices
over farm land values. This is highly correlated with
the land prices themselves, since farm land prices are
much smaller and vary less. (However, any error in the
farm land price data is magnified by this procedure,
producing large variations in the percent appreciation
variable.) The larger set of independent variables used
by Schmid varied from the current independent variables in several ways. First, Schmid used data for both
the cities and the urbanized areas, while the present
tests use data for the SMSAs. The latter units are more
commonly used and make possible the measurement
of population change within fixed boundaries. The
changes in urbanized area boundaries from census to
census, combined with changes in the procedures used
for their delineation from 1950 to 1960 (see Clawson
[1971, pp. 23-25]), complicate their use. Second,
Schmid uses multiple measures of population and land
area for urban size and its change. Land area is meaningless for SMSAs, and single measures reduce problems of multicollinearity. Finally, Schmid includes
measures of population density, fringe population and
commutation to the central city. These also depend
upon the locations of urbanized areas of central city
boundaries, and they were not felt to be important to
the formulation of the problem given here.
" The sample of 51 SMSAs had a mean population
in 1960 of 1,188,000 and a mean rate of population
change from 1960 to 1970 of 19.3 percent, while the
larger sample of 169 SMSAs had a mean population of
540,000 and a mean rate of change of 16.4 percent.
Ottensmann:UrbanSprawl
owners. The rate of population change
from 1940 to 1950 was the better predictor of 1950 land values, while the
1950 to 1960 change best accounted for
variation in the 1960 and 1964 land values. Densities of development, measured
by the percent single-family units constructed in the 1950-1960 and 19601970 decades, were predicted best by
regressions including rates of population
change during those same decades. These
are the population change measures used
in the tests reported here.
The hypotheses yielded two equations
for the prediction of land price and density of development. Land price is seen
as a function of expectations (measured
by population changes) and other variables (population and income); density
of development is seen as a function of
land prices, expectations and the other
variables. As they are stated, the equations constitute a recursive system. Land
prices are determined by demand, both
present and future, and in turn determine densities of development. The recursive nature of the system is reflected
in the temporal sequence involved in the
variables selected: 1950 lot prices are
used to predict densities of development
from 1950 to 1960, and 1960 lot and
land prices are used to predict densities
from 1960 to 1970. The ten-year time
periods of the census data on densities of
development undoubtedly extend beyond the effects of the land prices at the
beginning of each decade. Therefore, the
available land and lot price data for 1964
are also used, even though this temporally follows the 1960 to 1964 development.
The possibility exists that densities of
development could affect land prices as
well, creating a situation involving simultaneous causation. In such a case, ordinary least-squares procedures for esti-
395
mating the parameterswould be inappropriate. Alternative models were considered, with the density of development
variable (from either decade) included as
a predictor of land prices. Two-stage
least-squares procedures were used to
estimate the parameters, with population, income and the population change
variables considered as exogenous. In
each case, the parameter values associated with the original three predictors
of land prices were hardly changed from
those obtained with the recursive model,
while the parameter associated with density of development was insignificant.
For this reason, only the results from the
recursive model, obtained using ordinary
least-squares regression, are reported
here.12
EMPIRICALRESULTS
The first set of regressions involved
tests of the hypothesis that land values
would be directly affected by levels of
expectation and hence population
change. In addition, population and income levels were also expected to have
direct effects on land prices. The results
of six regressions with alternative dependent variables as measures of land value
are shown in Table 1. (All regressions
involve fifty-one SMSAs. A significance
level of 0.05 is used throughout this research.)
These simple three-variable regression
models account quite well for the variation in land values. In five of the six
cases, the coefficients of determination
(R2) were significant and ranged from
0.41 to 0.55. Only with FHA lot prices
12The results of the various alternative tests referred to in this section can be provided by the author
to those who are interested.
Land Economics
396
TABLE 1
LAND VALUE REGRESSIONS
(Standard Errors in Parentheses)
Dependent Variable
Independent Variables
Population Change
(Percent)
NAHB Land
Price, 1960
NAHB Land
Price, 1964
NAHB Lot
Price, 1960
NAHB Lot
Price, 1964
FHA Lot
Price, 1964
FHA Lot
Price, 1950
Population
(Thousands)
25.49*
(7.70)
47.71*
(12.81)
4.60
(4.45)
10.03
(7.26)
9.16*
(3.79)
1.23
(1.57)
0.67
(0.15)
1.32*
(0.24)
0.27*
(0.084)
0.52*
(0.14)
0.19*
(0.072)
0.085*
(0.038)
Constant
R2
-152
0.47*
-1426
0.55*
Median Income
(Dollars)
0.21
(0.27)
0.43
(0.45)
0.37*
(0.15)
0.86*
(0.25)
0.37*
(0.13)
-0.025
(0.093)
609
-1535
0.41*
0.53*
122
0.42*
972
0.11
*Significantlydifferentfromzero at the 0.05 level.
in 1950 did the model fail to account for
very much of the variation. Land values
were positively related to population
change, population and income, as was
predicted. The only discrepancy was a
very insignificant negative coefficient for
income, again for 1950. Quite a few of
the regression coefficients were significantly different from zero. Overall, the
results lend considerable support to the
hypotheses suggested earlier.
The best results came in the prediction of NAHB land prices in 1960 and
1964. These variables were the most
satisfactory measure of land values. Coefficients of determination of 0.47 and
0.55 mean that about half of the variation in these land prices across metropolitan areas was accounted for. The regression coefficients varied between the
two regressions for 1960 and 1964 but
give an idea of the strength of the relationships. A one percent increase in the
rate of population growth produced a
twenty-five to fifty dollar per acre in-
crease in land prices across the fifty-one
metropolitan areas. Each additional
thousand in the population was associated with something on the order of a
dollar increment in land values. A onedollar increase in median incomes produced a twenty- to forty-cent increase in
land values. The coefficients for income
were not significant and were subject to
considerably more error.13
Roughly comparable relationships
held with the four lot price variables.
The coefficients tended to be much
smaller for population change and population. Since the dependent variable was
price per lot, not price per acre, and lots
'3Schmid [1968, p. 51] found population to be
significantly related to the percent appreciationin
landprices(see note 10). Percentpopulationchangein
the centralcity was also significant,but changein the
urbanizedarea was not. This may have resultedfrom
the problemsinvolvedin the use of urbanizedareasto
measurepopulationchange,noted earlier.In addition,
Schmid'sresultswereaffectedby multicollinearity.
397
Ottensmann: UrbanSprawl
TABLE 2
DENSITY OF DEVELOPMENT REGRESSIONS
(Standard Errors in Parentheses)
Independent Variables
Regression
Land Value
(Thousands)
% Single-Family Development,
1960-70, with:
NAHB Land Price, 1960
NAHB Land Price, 1964
NAHB Lot Price, 1960
NAHB Lot Price, 1964
FHA Lot Price, 1964
%Single-Family Development,
1950-60, with:
FHA Lot Price, 1950
Constant
R2
95.6
0.59*
94.7
0.56*
96.2
0.57*
93.2
0.57*
96.2
0.61*
109.9
0.28*
Median
Population
Income
Change
Population
(Percent)
(Millions) (Thousands)
-1.45*
(0.64)
-0.51
(0.39)
-2.01
(1.19)
-1.21
(0.73)
-3.66*
(1.26)
-0.22*
(0.080)
-0.24*
(0.085)
-0.27*
(0.079)
-0.25*
(0.080)
0.26*
(0.075)
-1.58
(0.81)
-1.88*
(0.89)
-2.05*
(0.77)
-1.93*
(0.81)
-1.94*
(0.70)
-3.55*
(1.27)
-3.60*
(1.32)
-2.97
(1.37)
-2.73
(1.43)
-2.39
(1.31)
-8.68*
(3.23)
-0.043
(0.037)
-0.39
(0.88)
-4.05
(2.05)
*Significantlydifferentfromzero at the 0.05 level.
tend to be smaller than an acre, the
lesser values of the regression coefficients were understandable. Income
tended to have a greater impact on lot
prices. Higher median incomes may have
led to greater lot sizes in some metropolitan areas, accounting for higher lot
prices and a stronger relationship.
The second set of regressions tested
the hypothesis that the density of residential development would vary directly
with land value, population change and
population (Table 2). Since the percentage of new housing units constructed
during a decade as single-family dwellings
is being used as the surrogate for density,
the expected direction of all relationships
is negative. Six estimates are reported.
Attempts were made to predict percent
single-family development during the
1960-1970 decade using each of the
1960 and 1964 land price and lot price
variables. The 1950 FHA lot price variable was used, with the 1950-1960 development as the dependent variable.
The regression model was very effective in accounting for variations in the
percentage of single-family development
across the metropolitan areas in the sample. The coefficient of determination
was significant in all of the five predictions of development density during the
period from 1960 to 1970. The model
was slightly less successful for the preceding decade. Land values, population
change, population and income are all
inversely related to the percentage of
single-family development in each of the
equations. The first three relationships
were predicted, while the effect of income had been uncertain.
Increases in land values caused de-
398
dines in the development of single-family housing. During the 1960-1970 decade, a one-thousand-dollar rise in land
values produced a one-half to three and
one-half percent decline in the percentage of development in single-family
units. Two of the five regression coefficients were significantly different from
zero. (The wide variation in the five tests
was not unexpected given the great differences in the five measures of land
values, including prices both per acre and
per lot.) The rate of population change
had a significant (five out of five) impact
on the density of new development; a
one percent increase in the growth rate
produced about a quarter of a percent
decrease in the level of single-family
development. Likewise, a million in population was associated with a two percent decline in the dependent variable.
Finally, median income had a sometimes
significant negative effect, with a thousand dollars in income associated with
about a three percent lower level of single-family housing. This suggests, perhaps, that the effect of income in raising
land values (measured imperfectly here)
may be more important than any increase in the demand for space by the
higher-income households.
The one model for the prediction of
the percentage of development in singlefamily units from 1950 to 1960 yields
generally comparable results. The effect
of FHA lot prices in 1950 was dramatic
(and significant), with a one-thousanddollar increase in lot prices associated
with nearly a nine percent decline in the
level of single-family development. Population change and population have somewhat smaller effects, while the effect of
income is slightly greater. None of these
coefficients was significant, however.
The results of these empirical tests
confirm the original hypotheses: The
Land Economics
rate of population change positively affects the levels of land values, while
these two variables in turn clearly and
positively affect the densities of residential development, as measured by the
percentage of development in singlefamily units. The models account for
approximately half of the variation in
the dependent variables in all but a few
of the tests.
CONCLUSION
The success of the land value model
helped confirm the theoretical account
of the role and importance of landowner
expectations in the residential development process. Other alternative explanations of the levels of land values have
been provided, however; the derived demand model developed and tested by
Witte [1975] is one of the best examples. She has achieved higher coefficients
of determination but only at the expense
of considering a greater number of independent variables. The simple, straightforward model tested here, with but
three independent variables, must be
considered as a valid alternative.
The success in predicting density of
development provides far more encouragement for the theoretical account of
the residential development process presented above. Both land values and landowner expectations clearly influence the
percentage of development in single-family units, as predicted. Neutze [1968, pp.
93-102] examined the influences of
land prices, city size and growth on
apartment development, but his results
were not conclusive. The Harrison and
Kain predictions of density [1974, pp.
66-67] used city size as a surrogate for
land rents. Their examination of densi-
Ottensmann: Urban Sprawl
ties over an extended period made the
use of land rents impossible in their research. The present model effectively
predicts the density of new residential
development, relating it to both land values and landowner expectations within
the context of a theoretical account of
the peripheral growth process.
The first hypothesis-that levels of
landowner expectations directly affect
the quantity of land withheld and hence
urban sprawl-was not tested. The conceptual problems involved in the measurement of sprawl are very great, and
appropriate data are not available in
comparable forms across metropolitan
areas.
The description of the nature of peripheral urban development given in this
paper highlights the role of landowner
expectations concerning future growth
in shaping the pattern of future development. Relatively little is known about
the way in which these expectations develop or the correspondence between
these expectations and reality. Schmid
[1968] has pointed out that landowner
expectations might well diverge from a
realistic appraisalof future urban growth
possibilities, and further observed that
"there is no a priori reason to expect
that a bad guess about the future will
not continue for a number of years, with
resulting higher monopoly-like prices to
many consumers" [1968, p. 42]. Such a
bad guess would also be reflected in the
entire pattern of urban development,
with more land being withheld-and
more sprawl-than would actually be
warranted. Especially at the present
time, when many urban areas seem to be
passing from periods of rapid expansion
into an era of much slower growth, possible lags in landowner expectations
could present a serious problem. This
paper has provided a point of departure
399
for the investigation of such problems by
providing an explanation of the nature
of the development process incorporating the important elements of landowner
expectations, urban sprawl, land values
and the density of development.
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