Urban Structure Planners and urban structure

Chapter Four
The Economics of Urban Structure
Planners and urban structure
Ownership of land is a kind of entitlement (see Chapter Nine), and indeed ownership is established in
most advanced market economies through some form of publicly registered land title. Typically,
the owner of a parcel of land is entitled, within the parameters of land use regulations, to determine
what kind of land use the site will be devoted to: including residential, commercial, industrial or
other uses. The cumulative result of all such land use decisions by all property owners is the
resultant pattern of land uses that defines urban structure. In an urban area with a well
developed real estate market, interested parties will bid for parcels of land based on the net benefits
they anticipate receiving through the perogatives of ownership.
Urban planners affect real estate market outcomes in two ways. First, planners enact zoning bylaws and
other land use regulations that define the limits placed on the entitlements of landowners. This is a very
direct effect that planners have on the use and therefore on the value of land. Second, planners intervene
in various ways to shape the overall urban context. It is with reference to this urban context that
individuals base their own valuations of the entitlements attached to the ownership of land. While less
direct, this can be equally important in determining the value of a parcel. This set of relationships is
illustrated in figure 1, where bundles of entitlements for a specific property are evaluated with reference
to a specific urban context, which is itself embedded within a larger context of extemal factors ranging
from population growth to demographics to interest rates.
The net result of a property owner’s valuation of these bundles of entitlements determines how the owner
will utilize that particular parcel of land. The myriad decisions made by similar property owners feed
back over time to an evolving urban context, which in turn affects the evaluation of future entitlements.
What we find is a complex spatial interplay between the value of land and how land is used, with each
aspect influencing the other. The net result of this interplay is manifest in many ways in urban areas
throughout the world. While each city is unique, there are also broad similarities that apply, and in this
chapter we shall explore the economic foundations of those fundamental similarities and their
implications for planners.
Valuing access in a monocentric urban area
“Location, location, location.” Who does not know this to be the answer to the question, “What are the
three most important determinants of real estate values? ” . But location is clearly relative. Knowing the
longitude and latitude coordinates of a parcel of land is not aufficient for us to determine the value of that
parcel. Its value is derived from its location relative to other locations, which are themselves similarly
valued. More precisely, the value of any one parcel is based on its accessibility to the activities or
amenities found at many other sites. Location is contextual, and hence so are land values.
Early economic models of urban areas are termed monocentric because they focus on accessibility to one
central location, usually termed the central business district. In such models distance to the CBD is a key
determinant of the value of land, with land values dropping off steadily in all directions as one moves
further from the CBD towards the outer limits of the urban area. While it is clear that urban areas are
more complex than what is implied by a simple monocentric formulation, it is nonetheless useful to
analyse the sources of land values in a simpler model before adding complications attributable to multiple
centers. In all cases the fundamental basis for land value is access. Location is important insofar as it
affects accessibility.
“Time is money” is another truism, one that in the urban context is rooted in a recognition of the
opportunity costs associated with time spent commuting or otherwise overcoming the “friction”
associated with distance. Consider the simplest case of an urban area with one central business district
and with a population of identical households, each of whom would like to reduce its commuting distance
by living in identical neighborhoods closer to work. Clearly, not everyone can live in those preferred
locations nearer to the CBD, and so others must be persuaded somehow to opt instead for more remote
locations. For residential real estate markets to be in equilibrium, land prices must adjust to the point that
all households are equally content at all locations throughout the urban area, with the lower prices in
more remote locations just compensating for the burden of greater distance. Thus, the implicit tradeoff
between time and money is reflected in the land rent profile. Locations closer to the CBD will command a
higher rental than those farther away, and land values will reflect those land rent differentials.1
If this
condition did not hold, then people would rush to acquire land that yielded larger benefits than what is
reflected by market prices, and so market prices would adjust upwards for those sites experiencing excess
demand until equilibrium was restored.
We can quickly add variations to this basic model of land price equilibrium in a monocentric urban area
with identical households. In figure 2 we repeat the analysis for several categories of land use. The curve
labelled “residential” shows the equilibrium price of land that households would be willing to pay at
varying locations. As just discussed, the market-clearing2
equilibrium price must fall as we move further
away from the CBD as otherwise no one would be willing to purchase land at more remote locations.
This curve is often refered to as the households’ bid-rent curve, because it shows the maximum amount of
rent (or, prices in the context of our diagram) that households would be willing to bid in order to live
there. As shown in figure 2 there are similar bid-rent curves corresponding to commercial, industrial or
other types of land uses. The same basic logic applies. In the initial analysis all households were identical,
and so they each shared the same perception regarding the tradeoff between accessibility and value.
However, it stands to reason that the value-accessibility tradeoff will differ for different types of land use.
For example, business offices are likely to place higher value on ready access to the downtown because
of the advantages of face-to-face communications for transacting business, and this is shown in figure 2
where the bid rent curve for commercial activities is higher than is the residential bid rent curve at
locations nearer the downtown. At the other extreme, industrial uses are shown in figure 2 to place the
least value on accessibility.

1
“Land rent” refers to the value per unit of time (for example, annual rental per square foot of land) of occupying a
particular site. The value of land reflects the capitalized value of those land rents, as discussed in this chapter, and in
Chapters Two and Three, in the context of present value and housing, respectively.
2
The market-clearing price is that price at which demand just equals supply.
The net result of the bid-rent curves depicted in figure 2 is a land-use pattern that features successive
“rings” of land use, with commercial land uses in the inner ring, and with residential and industrial rings
further out. There are several aspects of this model that deserved comment. First, the reference to
commercial, residential and industrial uses is for purposes of illustration. The exact ordering of the true
bid-rent curves by land use category will vary depending on technologies, preferences and other factors
contributing to the tradeoff between distance and value. Second, we can accomodate different types of
households in this model by creating a land use category corresponding to each each household type, so it
is easy to relax the assumption of identical households. Third, the spatial extent of the urban area occurs
at the point where the maximum bid rent value for urban land uses no longer exceeds the value of land in
non-urban uses, where that default alternative is often refered to in urban economic models as agricultural
use.
Finally, note that the steepest bid-rent curves are also the ones that have the highest values nearest to the
CBD. The steepness of the slope, or the “gradient” as it is usually referred to, tells us how much more
valuable it is to be a bit closer to the CBD. A steeper bid rent curve implies a sharper or more dramatic
loss in value as one moves away one more mile (or kilometer, or whatever unit of distance one chooses to
work with). In an urban area with multiple land uses the steeper curves must be the ones whose bid rents
are highest nearer to the CBD. Figure 3 illustrates this point, using two categories of land use, called
commercial and residential. For commercial land uses three alternative bid rent curves are depicted, each
with the same slope, and hence each with the same tradeoff between changes in accessibility and changes
in value. For the inner bid rent curve for commercial land uses in figure 3 the rent bid is nowhere
sufficient to displace residential uses, and so this would result in no commercial uses at all. The outer bid
rent curve for commercial land uses results in the opposite extreme, with no residential uses anywhere. It
is possible for either situation to arise, but the implication is that one or the other land use is not
represented at all in that urban area. Only in the middle case are both land uses manifest, with the more
steeply sloped bid rent curve for commercial dominant near the CBD, and then declining quickly to the
point of intersection with the residential bid rent curve, which in turn is dominant beyond that point of
intersection.
It is also possible for the gradients (slopes) of the bid-rent curves to vary significantly over distances,
depending on technologies and preferences, with multiple rings associated with a single type of land use
as represented in figure 4. For example, businesses may prefer proximity to the CBD in order to facilitate
face-to-face communications. Alternatively, they may also choose to invest in electronic mail, fax,
voice-mail and other telecommunications that enable businesses to function without intensive face-to-face
interactions. In the latter case, businesses may be willing to locate in peripheral locations and enjoy the
lower land rents as compensation for the loss of face-to-face communications and for the
telecommunications investment costs. Industrial land uses may have a choice between rail and road
networks for transporting their goods, and these choices may point to a preference for locations either
near a central rail depot or on the periphery with good freeway access. Likewise, households may enjoy
either the stimulation of a central urban location or the tranquility of a semi-rural setting more than a poor
compromise in the middle. In each of these examples it is the nature of household preferences or the
nature of a firm’s production technologies that determines how much value is placed on an extra bit of
accessibility, and how that value varies with distance.
Economic analysis suggests that there will be a secondary effect resulting from these land rent
differentials. With land closer to the CBD being more expensive there will be a tendency to use less of it
relative to other labor or capital inputs. For example, where land prices are high it may make sense to
build a parking garage to conserve the amount of land devoted to automobile storage, but that would not
be economical in more remote locations where land rents are much lower. Similarly, it would be lavishly
extravagant to build a ranch-style home with a large back yard in a central-city location with high land
costs. These examples illustrate a fundamental principle of economic analysis known as the substitution
principle. In this context, the substitution principle simply states that households and firms alike will tend
to substitute away from those inputs whose relative prices are higher in favor of those whose relative
prices are lower. The implication for urban land use is that more expensive land near the center (in a
monocentric model) will be used more intensively, so that each unit of land will have more capital (such
as buildings) and more labor used with it. That is why we find high-rise buildings teeming with people in
many central cities and lower densities towards the periphery.
How do we expect this land rent gradient to evolve over time in response to population growth,
technological innovations or other significant changes that might affect urban form? Imagine first the
case of population growth, where we need not assume that there is any change in people’s locational
preferences – there are now just more people about who share those same preferences. This means that
the tradeoff between price and accessibility is unchanged from before, and so the slope of the land rent
curve will not be altered. However, land within a given distance of the center has now become more
scarce relative to demand. Of course the supply of land has not diminished, but now there are more
people bidding for the land. If land prices remain the same as they were the market will not clear because
there will be people left over who still have unmet demand. The only way for the market to clear is for
land prices to increase. If land prices increase while the slope of the bid-rent curve remains the same, then
we know that the height of the bid-rent curve is raised uniformly at all locations, as in figure 5.
Notice that this will increase the areal extent of the urban area because urban land rents at the old
urban-rural boundary will now exceed agricultural rents, so there is an incentive to convert agricultural
land to urban uses. This conversion will continue outwards until urban land rents at the margin are once
again equal to agricultural land rents. This “pure” upward shift in land rents (ie, no change in slope) may
be thought of as an upward shift in demand. The bid-rent curve is not itself a demand curve — the latter
shows how much land is demanded at a given rental price — but its upward shift is a direct response to an
upward or outward shift in the demand for land, in this case due to population change. We might expect a
similar response to increased per capita income even if population remains constant. As household
incomes rise we may expect that people will purchase more of most goods as their budgets expand.
Indeed, economists’ definition of a normal good is precisely that; it is a good for which demand increases
with income3
. There is little reason to doubt that land is a normal good by this same criterion, and so if
incomes rise there will be unmet demand for land at the old prices. By a similar reasoning as before, we
can deduce that land rents will rise as in the previous example. In monetary terms, aggregate income is
the product of population and per capita income. Many cities in developing countries receive a “doublewhammy” effect as both population and per capita incomes rise dramatically in response to rapid

3
By the same definition, an inferior good — potato gruel, perhaps — is one for which demand falls as incomes rise.
economic development in those cities, and so land rents rise and the urban perimeter expands in dramatic
fashion.
Technological change has a different effect on the urban land rent profile, with its primary impact being
on the gradient (slope) rather than on the height of the bid-rent curve. Consider the advent of the
automobile, or more recently, telecommunications advances such as fax, email and the like. These
technological innovations have the effect of reducing the “friction” associated with distance. Before the
innovation is introduced the original gradient reflects the old trade-off between distance and value. Closer
locations are more valuable because they save us time. That same principle holds after the technological
innovation, but the tradeoff is different now. It is now possible to bridge further distances in less time
and so a location further away is no longer discounted as heavily (“Only fifteen minutes by car!”). This
translates into flatter bid-rent curves as shown by the heavier lines in figure 6.
Now, let us suppose that rents in the center remain unchanged. Together with flatter bid-rent curves, this
implies that land rents rise everywhere else as depicted by the dashed lines in figure 6. The total area
beneath these “uplifted wings” is in direct proportion to the total land rent for the urban area, and we can
see that this increases dramatically if rents at the center do not drop. That will only happen if the
population as a whole decides that land has become more valuable and so they decide to spend a larger
proportion of aggregate income on land. While not inconceivable, it is more likely that as a first
approximation total land rents will remain unchanged. The heavier lines correspond to this case, where
the gradients (slopes) are smaller, but the total area beneath the bid rent curve remains unchanged. Notice
that here too the spatial extent of the urban area increases once more. This time the increase in demand
for urban land is brought about by a change in relative prices. In effect, urban land has become less
expensive because it is now easier to access (acquire), and people naturally respond to that change by
buying more of it, and so the city grows. Combining all three examples, we can readily understand why
cities with rapidly rising incomes, growing populations and experiencing technological changes undergo
dramatic changes manifested in rising land prices and expanding urban areas.
Polycentric models of urban space
A more generalized notion of accessibility makes reference to many potential destinations rather than to a
single destination (such as the CBD) alone. It is important to make this generalization if we want models
that can explain adequately the structure of modern urban areas where the central business district
typically contains less than ten percent of all metropolitan employment and where there are often several
urban centers within one metropolitan area that act as central business districts.
Figure 7 provides a cross-sectional representation of how the aggregate land rent surface may respond to
the presence of two subcenters, located at x1 and x2 respectively. Each subcenter exerts an influence on
land rents similar to what one finds in a monocentric urban area. The aggregate land rent at any location x
is then taken to be the sum of all such influences at x. Of course what we observe when we look at land
rents in an urban area is only the aggregate surface; we cannot see the individual components directly.
However, numerous empirical studies have used regression analysis to disentangle and identify the
effects of multiple centers. These studies strongly confirm that the aggregate rent surfaces for cities in the
United States and elsewhere are best understood as the outcome of an underlying polycentric urban
structure. The latter is often described in terms of one older historic central business district together with
newer subcenters.
The subcentering phenomenon is best understood as the result of a “struggle” between two opposing
forces: agglomeration and congestion. The top section of figure 8 shows total benefits and total costs as
measured against the number of firms (N) co-locating in a central business district. The bottom section
shows the corresponding marginal benefit and cost curves which, as explained in Chapter Two,
correspond to the slopes of the total benefit and total cost curves, respectively. As the CBD begins to
grow in size total benefits in figure 8 rise quickly at first, as indicated by the relatively steep slope of the
total benefit curve. This steep rise in benefits reflects the positive agglomeration effects that firms derive
from close proximity to one another. When firms are clustered together there is a higher density of
potential clients and suppliers within easy distance, and vital information flows are greatly facilitated by
face-to-face communications. As the number of firms in the CBD increases total benefits continue to
increase but at a decreasing rate, as seen by the gradually diminishing slope of the total benefit curve and,
hence, by the declining marginal benefit curve. As N increases there is more redundancy in the potential
benefits provided by the agglomeration of more and more firms. So long as firms’ basic supply needs are
being met competitively, and so long as they are receiving essential information flows in a timely fashion,
the benefits derived from the co-location of additional firms are less valuable on the margin. The benefits
that firms provide to each other become increasingly redundant and so marginal benefits decline even as
total benefits continue to increase (albeit at a decreasing rate).
The total cost curve in the top section of figure 8 also increases with the number of firms but at an
increasing rate due to congestion effects. The bottom section shows that marginal costs are increasing
steadily. As discussed in Chapter Five, there is less rivalry at lower densities in the consumption (use) of
roads, public space and other goods, but as the number of firms increases so too does the extent of the
rivalry in consumption. This phenomenon is also described in detail in Chapter Six in the context of
traffic congestion. Both agglomeration and congestion effects are externalities because the benefits or
costs engendered by other firms are not captured by market prices. There is no direct market feedback
that encourages firms to locate in the CBD because of the agglomeration benefits they confer on others.
Likewise, firms receive no price signals from the market that indicate to them the congestion costs they
are imposing on others. Of course these congestion and agglomeration effects do show up in land prices
as firms are willing to bid more to locate near agglomeration effects. Note, however, that a higher land
price will discourage more firms from locating in the CBD, and so land prices in this example do not send
out signals that specifically encouage firms to bring their agglomeration benefits to town. Each firm
considers the benefits or costs it experiences due to the presence of other firms but does not consider its
effect on others when making its decision to locate or not in the CBD.
The principles of marginal analysis that recur throughout this book apply equally well to the situation
depicted in figure 8. In particular, we can see that total benefits net of costs are maximized when N=N0
firms are located in the CBD. At this point the marginal benefit of more firms is just sufficient to
compensate for the marginal cost. Below this point, marginal benefits are higher than marginal costs so
total net benefits can be increased by adding more firms to the CBD. Beyond N0 marginal benefits no
longer compensate for marginal costs and so more firms will bring about a reduction in net benefits.
Based on this reasoning it would be more (net) beneficial to have two centers with No firms located in
each of them rather than a single center with 2*N0 firms. Likewise, more net benefits are derived from
three centers with No located in each of them rather than a single center with 3*N0 centers, and so on.
This is a basic motivation for the emergence of subcenters within a metropolitan area.
Our analysis thus far of the optimal size of a central business district has been from a planner’s
perspective that considers total benefits and costs for all firms. What does the situation look like from the
perspective of individual firms, each one of which shares equally in the agglomeration benefits and
congestion costs associated with a central business district address? Because they share equally in these
costs and benefits, it is the average cost and benefit that is relevent for the individual firm’s decision
about where to locate. At any point N (ie, for any subcenter with N firms) the total benefit at N is
experienced equally by N firms, yielding an average benefit of
AB = TB / N
This average benefit is represented graphically by the slope of a line drawn from the origin to the total
benefit curve at the designated point. 4
Average cost is calculated in a similar fashion but with reference
to the total cost curve, so that
AC = TC / N
Careful scrutiny of figure 8 reveals that average benefit declines steadily with larger values of N while
average cost rises steadily. This means that firms will be attracted to smaller subcenters in order to
enjoy the higher average benefits and lower average costs there. Of course, as more firms flock to the
smaller subcenters they will become larger. From this we can conclude that for the situation depicted in
figure 8 there is a kind of ‘levelling action’ whereby firms relocate from larger centers to smaller ones
and where this process continues until all subcenters are of the same size. Furthermore, this outcome will
coincide with the optimum subcenter size N0 (where marginal benefits equal marginal costs) only if the
number of subcenters is just right. Too many subcenters will result in subcenters that are too small while
too few will yield subcenters that are larger than desired.
In the preceding discussion we took as granted some fixed number of subcenters that compete for firms.
But in fact subcenters may emerge as a result of firms’ location decisions. For the conditions described by

4
One way to grasp this point is to recall that the slope of a curve is given by “the rise over the run”. In this case, TC
is “the rise” while N is “the run”.
figure 8 we have seen that firms are attracted to smaller subcenters. Taken to its logical extreme we
would expect the situation in figure 8 to result in a highly diffused urban structure with each firm forming
its own center. Common sense tells us that there is a minimum threshold below which the agglomeration
benefits of subcenters do not take hold. In figure 9 we modify the shape of the total benefit and total cost
curves to reflect this reality. The shape of both curves beyond N2 is similar to the situation depicted in
figure 8, with declining marginal benefits and rising marginal costs. However, the total benefit curve in
figure 9 is no longer concave everywhere with steadily declining marginal benefits. Instead, for smaller
values of N the benefits of agglomeration begin to kick in only gradually at first, and then more rapidly as
the subcenter reaches critical mass. Likewise, total costs in figure 9 rise quickly at first for smaller
subcenters that are not large enough to enjoy economies of scale in the provision of infrastructure and
other basic services.
As before, total benefits net of costs are maximized at N0 where marginal benefits are just sufficient to
compensate for marginal costs. The astute reader will notice that the marginality condition MB=MC is
also satisfied at N1 , but it is clear that N1 does not correspond to the optimum city center size because
total costs exceed total costs at N1 while at N0 the reverse is true. Additionally, it is clear that firms will
be attracted only to subcenters that are larger than N2 and smaller than N3. Subcenters smaller than N2 are
not of sufficient mass to generate agglomeration benefits that compensate for the heavy infrastructure and
other fixed costs of maintaining a viable subcenter. Subcenters larger than N3 experience congestion costs
so severe that they swamp any agglomeration benefits the firms might otherwise enjoy. Only within the
interval [N2, N3] do average benefits exceed average costs, and so firms will only be attracted to these
locations. At the planner’s optimum, N0 , average benefits do exceed average costs. It is possible that
some other value of N nearby will support an even wider divergence of benefits from costs so that firms
may be more attracted to some value of N other than N0 .5
Notwithstanding that fact it is apparent from
inspection that the equilibrium level of N in figure 9 is “fairly close” to the optimum level, and that it
therefore approximates the kind of result that economists are most fond of — where incentives at the
micro level are compatible with optimal outcomes at the societal level.
It is useful at this point to recapitulate the significant points that emerge from the preceding discussion.
We have seen that the optimal size of urban subcenters can depend on the relative strength of
agglomeration benefits versus congestion costs. In both figures 8 and 9 that optimum (labelled No in both
cases) occurs where the marginal benefits from additional size just compensates for the marginal costs.
We have also seen that this optimum point may not correspond closely to the equilibrium size of
subcenters, defined as the size of subcenter that firms themselves are most attracted to. The reason for this
divergence is that firms base their location decisions on the average benefits and costs encountered at
subcenters while the planner’s optimum is derived by examining marginal benefits and costs. There is
more justification for planning intervention where the divergence between equilibrium and optimum
subcenter size is large. The extent of the divergence depends on the precise nature of the agglomeration
and congestion effects. In figure 8 the divergence is large while in figure 9 it is much less serious.
Where do subcenters come from?
Several questions remain. First, what is the mechanism by which subcenters emerge? Second, where will
these subcenters locate? And finally, why do cities vary with respect to the number and size of subcenters
they comprise? With all of these questions we also want to know what the potential role for planners
might be in bringing about more beneficial outcomes. Regarding the first question, we saw in figure 8 that
average net benefits were largest for the smallest subcenters, and so there was no incentive for firms to
cluster together to form centers. Or, if the number of centers was predetermined (for example, if land use

5
The most attractive subcenter from an individual firm’s perspective is one with the greatest positive divergence
between average benefits and average costs. Recall that the average benefit (cost) at any point is just the slope of the
line connecting the origin to the total benefit (cost) curve at that same point. Then, the greatest average net benefit
will be found at a subcenter of size N with the greatest angle between the average benefit and average cost
connecting lines.
regulations prohibited certain classes of firms from locating outside designated subcenter districts), then
firms would tend to relocate away from larger centers in favor of smaller ones in figure 8 and equilibrium
would not be obtained until all of the subcenters were of equal size. If the planners were to choose the
number of subcenter districts correctly this equilibrium size could correspond closely to the optimum size
N0.
In the case of figure 9 we encounter a different problem. As noted previously, no firms will be attracted to
subcenters smaller than N2 because the agglomeration benefits for such centers are too small to
compensate for the relatively large infrastructure costs. The problem is that no new subcenters can emerge
spontaneously in figure 9 because in order to generate a subcenter larger than N2 one must first pass
through prior stages with fewer firms. Stated another way, large subcenters do not appear magically, they
grow from smaller ones, but the situation in figure 9 discourages the development of smaller subcenters
that might otherwise grow into larger ones later on. This suggests a potentially positive role for planners
to intervene through incentives and other strategies designed to bring emerging centers past the critical
threshold at N2. The danger, of course, is that planners may inadvertently squander large sums of public
money attempting to foster the development of subcenters that are not truly viable. The potential gains or
losses are large either way, and so it can be a difficult call. The best strategy is to develop a sound
understanding of the particulars of any given situation and to make judgements accordingly.
The question of where subcenters will arise is a difficult one to answer a priori. In hindsight one can
usually point to particular locational advantages or historical incidents that triggered an urban
agglomeration at one location versus another, but it is much more difficult to do so in advance 6
. We can
say that accessibility remains a key consideration and that the potentiality of a location is likely to be
highly correlated with any reasonable measure of accessibility. And of course land use regulations and
other public sector interventions can be instrumental in determining whether centers emerge (or do not
emerge) in specified locations. Access to potential clients/customers or suppliers is an especially
important consideration, and this has long been recognized in models of urban structure. Central place
theory, in particular, sees urban space as being organized in terms of hierarchical market catchment areas.
Another question we raised is why cities have different numbers of subcenters of varying size and
character. The reasons are multifaceted but focus primarily on the nature of the agglomeration and

6 If that were not so I might well be speculating in real estate rather than writing textbooks.
congestion effects depicted in figures 8 and 9. The shape and position of the total cost and total benefit
curves are affected by factors as diverse as natural resources and amenities, public sector investments,
historicial antecedents, the mobility of firms, the labor market, transportation and communications
technology and geographical considerations. Moreover, firms in different sectors will respond differently
to the same set of conditions. One thing is clear, however, from studying many of the largest cities in the
world, and that is that it is increasingly common to find metropolitan areas with several active and viable
subcenters with distinctive characters. In megacities in particular – those with populations of ten million or
more – it is apparent that subcentering is a natural if not inevitable response to the opposing forces of
agglomeration and congestion. Firms that are located in any one of the subcenters of a megalopolis are
positioned to enjoy many of the agglomeration benefits that accrue to a city of that size while avoiding
the more acute congestion problems that would arise if there were only one urban center within those
metropolitan regions.
The urban sprawl debate
One aspect of urban structure that has received a great deal of attention by planners and regular citizens
alike is sprawl, a term that refers to low density suburban development that encroaches, often in
“leap-frogging” fashion, upon agricultural land on the urban periphery. Urban sprawl is roundly
denounced by its many vocal opponents on several grounds: (i) it leads to a loss of precious agricultural
land, (ii) its low density character is inefficient from the perspective of infrastructure and basic service
provision, and (iii) it results in an unimaginative, stifling suburban form that is sadly lacking in urban
amenities. Based on this reasoning, many planners view it as their duty to enact land use regulations that
limit urban sprawl. Let us examine each of these charges in tum.
There is no doubt that the outward expansion of an urban area leads to a loss of agricultural or other
non-urban land use activities. The supply of land is essentially fixed, and more land devoted to urban uses
necessarily implies that less land is available for non-urban uses. Moreover, in many cases it is prime
agricultural land that is the first to be converted, thereby compounding the overall sense of loss. Of
course it would be unbalanced in the extreme to look only at what is lost without considering what has
been gained as well. Certainly land is valuable as a factor input for agricultural uses, but it is also
valuable as an input for urban uses. The discussion pertaining to figures 6 and 7 earlier in this chapter
indicates that urban expansion occurs because of a growing demand for urban land, and we know that
demand reflects benefit. The reason someone is willing to pay more for land in one use compared to
another is because the perceived benefits are higher. Chapter Two discusses in detail the manner in which
markets allocate scarce land resources between competing uses so as to maximize the total market value
of land. The example used there is residential versus non-residential, but those same arguments apply
equally well to the urban-rural land use debate. A reduced supply of agricultural land should be reflected
in higher food prices. If the demand for food is relatively elastic 7
, then we can expect a significant shift
in consumption pattems away from agricultural produce in favor of other items competing for household
budget shares. If, as is more likely, the demand for food is relatively inelastic, we can expect that food
consumption will remain fairly stable, and higher prices will result in increased retums to agriculture, and
this will enable farmers to compete more aggressively in land markets along the urban periphery. Thus,
from a strict market perspective, we would expect that the vaunted value of agricultural land uses would
be underpinned by a strong, relatively inelastic demand for agricultural produce.
There are special considerations that may apply in the case of agriculture and its competition for land
along the urban periphery. In some countries, Japan being a notable example, preserving agricultural
land is seen as a means of avoiding an undue dependency on foreign countries for essential food supplies.
This is certainly an example of a market failure that may arise because markets for agricultural produce
are unlikely to factor into food prices any considerations of national security. An economist’s instinct is to

7
We say that the demand for a good is elashc (inelastic) if a percentage change in price leads to a larger (smaller)
percentage change in the quantity demanded.
try to correct any such market failure by incorporating the relevant price signals into market prices.
Tariffs on imported produce or subsidies in support of domestic produce would work in this direction and
would allow land markets to function independently of any considerations of national food security or the
like However, trade barriers of this type are frowned upon severely by the World Trade Organization and
other intemational bodies that seek to promote free trade, so many governments resort to more
roundabout measures, including land use controls, to protect agricultural land.
One method that has been employed, most notably in the Seoul metropolitan region, is the designation of
a protected green belt area around the urban periphery wherein no urban development may occur. The
consequences for land rents of a green belt policy are illustrated in figure 10, where the initial bid rent
curve for urban land is denoted by R0, and D0 marks the initial urban-rural boundary where the urban bid
rent curve just meets the value of land in agricultural use, RA. Let us suppose that a green belt policy is
imposed at this time so that no urban development can take place from D0 outwards to some point D1. As
the urban area grows, the bid rent curve eventually reaches some level R1, and this would normally cause
urbanization to encroach into the rural area, but the green belt policy disallows that. The result is that the
interval [0, D0 ] over which urban land is supplied is inadequate to meet demand at the prices implied by
R1, and so the price of urban land must rise in order for the market to clear, and so a new bid-rent curve is
established at R2. Notice that at this price level urbanization will leapfrog over the green belt and take
root on the far side.
The efficiency implications of the resultant land use pattern can be viewed from two perspectives. One is
the increased rent given by the vertical distance R2 – R1 . This increase in rent does not represent extra
benefit nor does it measure inefficiency per se; it is simply a higher scarcity premium on urban land.
Inefficiency arises from a misallocation of land; the interval [ O, D0 ] is still devoted to urban use, and so
it is not being misallocated. The misallocation, from a market perspective, occurs over the interval [ D0,
D1 ]. A more direct measure of inefficiency is given by the triangle wedged between R1 and RA within
the interval [ D0, D1 ] . This is directly comparable to the triangle of inefficiency seen in Chapter Two
arising from zoning restrictions on land. This measures the inefficiency of misallocating agricultural land
for urban uses. Countering these inefficiencies, there may be some amenity benefits (positive
externalities) generated by the green belt itself. These amenities are a public good in the technical,
non-rival, sense of the term as discussed in Chapter Five. It is conceivable but by no means assured that
the amenity benefits generated by the greenbelt may outweigh the countervailing inefficiency costs.
In many settings agricultural land is often depicted also as being a precious link to a way of life that is
portrayed in nostalgic and even moralistic terms. From this premise the argument that urban sprawl is
“immoral” is only a half-step away. Similar arguments are made regarding environmental treasures and
other non-urban uses. Of course there is nothing to prevent anyone in a market context from purchasing
land to devote to agricultural or other non-urban uses, nor is anyone compelled by reasons other than
opportunity cost to sell agricultural land to those who would use it for other purposes. Land ownership
consists of a bundle of entitlements and as explained in detail in Chapter Nine, entitlements are a form of
wealth, so the struggle to restrict land to agricultural uses can be understood as an attempt to transfer
entitlement wealth from the owners of agricultural land to those who prefer to see such land remain
non-urban.8
Arguments against urban sprawl are fortified by a view that the conversion of land from rural to urban
use is irreversible and so, once gone, agricultural land is “lost forever”. While not wholly irreversible,
there is clearly a very large transaction cost barrier that does make it impractical to convert urban land
back to non-urban use. Indeed, this is indicative of a more general problem arising from durable capital in
any form, and certainly in urban form. Massive buildings or freeways built from concrete and steel
impose themselves emphatically on the landscape, with lifespans measured in decades or even centuries.
They encapsulate their developers’ best guesses at the time regarding future conditions, but unlike

8
There may well be individuals who fall into both camps: owners of agricultural land who desire to see that land
remain in agricultural use. There is no contradiction between the two, but there are clear implications for which
group at large holds the relevant entitlements.
economic goods with much shorter lifespans their quantities cannot be adjusted rapidly in response to
changes in demand or other variable market conditions. Once a building or freeway is in place, it is likely
to remain so for a long time. Subsequent unanticipated changes in demand will result in windfall gains or
losses to the owners at the time. These are important aspects of durable products that any investor should
be cognisant of, but they are not necessarily arguments against urban sprawl per se. The developer, as
much as anyone (expect, perhaps, his lender) is the one who is bearing the greatest burden of risk of
developing on the urban periphery prematurely. Developing too late also has its risks, because it gives
one’s competitors an opportunity to line up their market shares early, and many a developer has gone bust
by coming in just at the end of a boom cycle.
A second major criticism of urban sprawl is that its low density leap-frog pattern of development is
inefficient from the perspective of infrastructure based service provision. The argument is that a higher
density form of development could be serviced at lower cost to taxpayers. There are two related issues
here; one is the extent to which the marginal cost of service provision is (or is not) reflected in the cost of
the final “product”, and the second issue is one of objectives. These two issues are joined in households’
decisions about what kind of development they may choose to live in. Chapter Seven on fiscal impacts
explains in detail how to evaluate the impacts of a development on service provision. Essentially, a
service-based impact is defined as the minimal cost required to maintain existing service standards. We
show that one requires a sound knowledge of the underlying production process by which services are
produced in order to calculate reasonable estimates of these impacts. Opponents of urban sprawl argue
that the fiscal impacts of sprawl are higher than what might reasonably be justified. The key, from our
perspective, is whether those impacts are reflected in the price of the housing and other developments that
result from sprawl. If they are, then the market is given an opportunity to do what it does best — facilitate
tradeoffs using price signals. Finally, it is useful to examine the premise that sprawl results in lower
density development. In the long run a leap-frogging development pattern may in fact result in higher
densities overall as infill gradually occurs.9
A related issue is what the ultimate objectives of development are. Infrastructure is a means to an end, it
is not an end in itself. The same principle applies quite generally. In constructing a building one doesn’t
necessarily configure the rooms strictly on the basis of keeping costs at a minimum, although costs are
certainly a major consideration. If it costs more to configure the rooms to suit the tastes of the end user
the relevent question is whether the benefits to the user outweigh the costs. The surest way to get an
answer to that question is to price the alternatives so that they reflect the underlying cost structure. If the
user is willing to pay a premium then we may conclude with assurance that the extra benefit does indeed
outweigh the additional cost, and so net benefits are increased by proceeding with construction. The same
argument applies to sprawl. The issue is not whether the cost of infrastructure provision rises, although
that is an important consideration. Instead, the issue is whether the entire “package” of benefits and costs
embodied in sprawl development are an improvement over the relevent alternatives. So long as the
relevent costs are properly incorporated in end prices then the end user is the best judge of whether
benefits exceed costs. Indeed, if keeping infrastructure costs at a minimum was the sole objective then we
would be best advised not to allow any development at all. That solution may not do much to help our
housing problem but it certainly keeps costs at a minimum.
The third major criticism of sprawl is that it results in boring, unimaginative forms of suburbia that stifle
creativity and constrain lifestyle choices. That may or may not be true, but in either case similar criticisms
can be levelled at television programming, fast food outlets (which by no means are confined to
suburbia), and many other aspects of consumer culture. Should these forms of consumer culture be
banned along with sprawled suburban development? There is certainly a pervasive tendency to appeal to
the “lowest common denominator” and those tendencies are strongly reinforced by economies of scale in
production — it is easier to keep the price of burgers to fifty-nine cents each if one is measuring
production volume in the billions. The housing industry is also characterized by significant economies of

9
See R. Peiser and E. Heikkila, “Urban Sprawl, Density, and Accessibility”, Papers in Regional Science: The
Journal of the Regional Science Association, vol. 71(4), 153-166, 1992.
scale and that creates an incentive to build self-contained planned unit developments or new towns on the
urban periphery. That incentive is compounded by the attractions of working with a “clean slate” where
the developer is relatively unencumbered by the unwanted legacy of earlier developments that may now
be out of fashion or otherwise unmarketable. The real issue is one of so called consumer sovereignty. The
typical developer is not seeking to impose her tastes on some unwitting consumer — her business motives
are to earn a profit by building homes for less money than people are willing to pay for them. If no one is
willing to pay a sufficient price for suburban sprawl we may be assured that she will not continue to build
them. The real estate developer is an agent of final consumer demand and it is both trite and simplistic to
assign blame for tasteless sprawl to the developer, just as it would be to blame fast food chains for the
fact that we appear to gobble up their burgers as quickly as they can produce them. If one doesn’t like fast
food one is not obliged to eat it, nor is one obliged to live in sprawled suburban development, yet there
we are. To adapt a well-known phrase: “We have met the purveyors of bad taste, and they is us.”

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