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How to Define the Optimal Level of Public-sector Infrastructure Development?
A Conceptual Model for Decision-making in Infrastructure Projects
Erwin Van Der Krabben; Karel Martens; Thomas De graaff; Piet Rietveld
Online Publication Date: 01 August 2008
To cite this Article Van Der Krabben, Erwin, Martens, Karel, De graaff, Thomas and Rietveld, Piet(2008)'How to Define the Optimal
Level of Public-sector Infrastructure Development? A Conceptual Model for Decision-making in Infrastructure Projects',Planning
Practice and Research,23:3,363 — 381
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Planning, Practice & Research, Vol. 23, No. 3,
pp. 363–381, August 2008
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ARTICLE
How to Define the Optimal Level
of Public-sector Infrastructure
Development? A Conceptual Model
for Decision-making in Infrastructure
Projects
ERWIN VAN DER KRABBEN, KAREL MARTENS,
THOMAS DE GRAAFF & PIET RIETVELD
Abstract
Debates with respect to financial problems of public-sector infrastructure development
increasingly focus on ways to improve value capturing. Two issues play a crucial role in this
debate: how much value can be captured and how can we maximize the value to be captured? In
this paper a conceptual model is presented that enables defining the optimal level of public sector
infrastructure development—combining a social and financial perspective. Using the model, it is
possible, in principle, to define the maximum level of value capturing. Additionally, the paper
provides empirical evidence of the potentials of value capturing in three Dutch case studies. The
case studies show that the potential value to be captured as a result of investments in rail
infrastructure is substantial, but also that it is unknown whether value capturing would be higher
in case of alternative investment levels. It will be argued that the conceptual model might be useful
to define the optimal level of investment in accessibility in each case study.
Introduction
The Dutch national government aims to reduce mobility problems by, among other
things, large public investments in new road infrastructure and public transport,
but struggles to find the resources to finance these plans. As a result, planned
developments are and will be seriously delayed in time. To avoid these delays,
innovative financial arrangements with respect to both road infrastructure and
public transport development are being investigated. The Raad voor Verkeer en
Waterstaat (RVW), the advisory board to the Minister of Transport, has suggested
instruments for value capturing in projects that combine large infrastructure
development with location development (RVW, 2004). This proposal is supported
Erwin van der Krabben, Nijmegen School of Management, Radboud University, P.O. Box 9108,
6500 HK Nijmegen, The Netherlands. Email: [email protected]
ISSN 0269-7459 print/1360-0583 online/08/030363–19 Ó 2008 Taylor & Francis
DOI: 10.1080/02697450802423609
363
Erwin van der Krabben et al.
by another influential advisory board, the VROM Raad, which advises the
Minister of Spatial Planning (VROM Raad, 2004). The RVW defines value
capturing as:
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a group of instruments that enable capturing, directly or indirectly, the
increased value of land and property as a result of public investments in
transport infrastructure, and to use it for financing the activities that are
responsible for the increased values. (RVW, 2004, p. 47; translation by
the authors)
According to the RVW and the VROM Raad, value capturing may be useful both
in cases of road infrastructure development and public transport development (see
also Offermans & Van Der Velde, 2004). An example of the first could be the
development of a new motorway, combined with business park development; an
example of the latter could be the introduction of high-speed train services and the
upgrading of a rail terminal, combined with station area (re)development. In both
cases, it is assumed that the returns on real-estate development will be higher as a
direct result of the increased accessibility and that (part of the) extra returns can be
‘captured’ (depending on the available instruments) to finance part of the
infrastructure costs.
The suggestion of both advisory boards to make better use of value capturing
raises two issues.1 First, how much value can be captured? And, second, how can we
maximize the value to be captured by varying the level of investment in publicsector infrastructure development? With respect to the first issue, the paper provides
evidence of the effects of public-sector infrastructure development on land and
property values, using a hedonistic price model. This information is valuable,
because this extra value is equal to the potential contribution of the development
industry to infrastructure development. With respect to the second issue, we are
particularly interested to find the optimal level of public infrastructure development,
in order to benefit most from value capturing. To be able to define the optimum, we
must consider not only the increased land and property values of public-sector
infrastructure development (that potentially can be captured), but the costs as well.
The paper discusses, conceptually, project effects versus society effects. We will
argue that an optimum investment level can be identified when combining a
financial, project, perspective and a societal perspective.
The structure of the paper is as follows. The next section presents a conceptual
model about the relation between the benefits of public-sector infrastructure
development and the costs of those developments. The model makes it possible to
identify the optimum level of public-sector investment in accessibility by
combining a project and a societal perspective. In the third section, a hedonistic
price model for estimating office rents in the Netherlands is applied to forecast the
increase in office rents as a result of improved rail accessibility for three station
area redevelopment projects. In the subsequent section, the possible application of
the conceptual model will be illustrated for the three case-study areas. The paper
ends with some concluding remarks regarding the usefulness of the conceptual
model that is presented in this paper, in addition to regular cost–benefit analysis,
and the possibilities to make use of this model in practice.
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A Conceptual Model to Define Optimum Level of Public-sector
Infrastructure Development
This paper starts from the (political) desire to increase the potential for value
capturing from public-sector infrastructure development, in order to improve the
funding of major infrastructure projects. In the Netherlands, municipalities possess
legal instruments for cost recovery (which is commonly used in property
development processes), but a legal basis for value capturing is absent.
Nevertheless, municipalities and property developers are free to negotiate about
a certain degree of value capturing and, in a number of cases, they indeed reached
agreement on the contribution of the developer to investments in transport
infrastructure (Van Bendegem & Van der Krabben, 2006). The reason for this is
most probably that private developers believe the value of their developments will
benefit from increased accessibility. Moreover, when the public-sector infrastructure development is a sine qua non for redevelopment, a municipality may be
able to negotiate that it will not implement the infrastructure, unless the private
developers involved contribute to the funding of the infrastructure.
In an international context, a substantial body of literature exists discussing value
capturing for financing public-sector infrastructure development. Smith (2001), for
instance, refers to Nobel laureate William Vickrey, who claims that mass transit
systems should keep fares at marginal cost and fund system costs from site rent.
Smith provides an interesting overview of studies that have investigated possibilities
for value capturing. Studies that have found that increasing real-estate values may be
able to finance the infrastructure development completely include Allen (1987), Batt
(2001) and Riley (2001). Many other studies, mainly based on US-based
experiences, have demonstrated that rising office rents and apartment prices are at
least sufficient to finance part of the infrastructure costs (Cervero & Duncan, 2001;
Gihring, 2001; Rybeck, 2004; Smith & Gihring, 2006). These findings have been
confirmed for the European context by, among others, Church (1990), Nash et al.
(2001), Enoch (2002) and Jonathan (2002). Those studies have in common that they
provide empirical evidence of the value capturing potentials, but do not answer the
question whether the found potentials for value capturing are the maximum.
In this section, we present a conceptual model of the relationships between the
benefits of public-sector infrastructure development (potentially to be captured)
and the costs of those developments, aiming to define the optimal level of publicsector infrastructure development.
The analysis focuses on the impact of investments in transport infrastructure, and
especially rail infrastructure, on land and property values. We assume a positive
effect: as discussed in the literature, investments in rail facilities can result in an
increase in land and property values. However, this link is not direct. Rather, users
of real estate, like offices, value the increase in accessibility that results from an
improved transport system, and it is that valuation that results in an increase in rent
prices. In the next section of this paper we will make use of hedonic price analysis
to show the positive relation between both variables. Public-sector investment in
rail infrastructure thus results in improved accessibility, and improved accessibility
translates into higher land and property values. The question that now arises is
which level of public-sector investment might be optimal?
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Erwin van der Krabben et al.
In exploring the possible existence of an optimum, we start with a number of
assumptions with respect to the way this optimum should be defined. We
distinguish, in line with the literature, between a project optimum and a social
optimum. The project optimum refers to the best ratio between project-related
financial benefits and project-related costs. The financial benefits refer to the
additional value of the real estate in the direct vicinity of the station area as a result
of the increased accessibility, while the financial costs refer to the costs of
constructing the additional infrastructure that is necessary to achieve this increased
accessibility. Usually, the real-estate development will be a private-sector
development while the infrastructure construction will be a public-sector
development. We assume that the real-estate programme, independent of the
accessibility level, will be a profitable development. Increased accessibility levels
will result then in additional profits. Capturing those profits for financing part of the
infrastructure costs will not obstruct the development.2 The benefits can be internal
(directly reaped by the investing party—for instance, when the infrastructure
investor (the public sector) owns the land in the vicinity, he can sell this land against
a higher price) or external (reaped by other parties; in case the land is owned by
others). The latter project-related benefits represent the sum that can potentially be
captured by the investing party. The public sector aims to capture those benefits,
because the infrastructure development that is needed to achieve the increased
accessibility level shows, almost by definition, a negative financial result.3 For most
rail projects, only external benefits may exist, and the size of project-related benefits
is thus identical to the extent to which external benefits can be captured by the
government. In line with much of the literature, we limit the analysis to projectrelated benefits occurring in the land and property market.
The social optimum refers to the best ratio between social benefits and social
costs (which include non-pecuniary costs and benefits) (for example, Campbell &
Brown, 2003; Martens, 2006). We have not defined them in detail here. Social
benefits may include for instance traffic time savings for individual travellers,
positive environmental effects from modal shifts (from individual transport modes
to public transport modes), and so on. Social costs include, for instance, negative
environmental effects of the construction works, negative effects on economic
activities in other areas, and so forth.
In much of the literature, the project optimum and social optimum are treated
separately. Public projects are usually evaluated from a social perspective, while in
private projects only project-related, financial costs and benefits are included.
However, in case public funds are lacking to finance the level of investment
required to achieve the accessibility level that is necessary to achieve the social
optimum (see the line of argument below), we argue that the optimal level of
public-sector investments is neither identical to the project optimum nor the social
optimum. Rather, the investment level is optimal—from a societal perspective—if
the total value that can be captured (the additional real-estate value as a direct
result of the increased accessibility of the location) is equal to the total financial
costs of the project (the costs of constructing the infrastructure that is necessary to
achieve this increased accessibility). This we call the budget-neutral optimum.
Note that in the project optimum the real-estate programme may result in an extra
profit (above the ‘regular’ profitability of real-estate development): the additional
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Conceptual Model for Decision-making in Infrastructure Projects
real-estate value exceeds the costs of constructing the extra infrastructure that is
necessary to achieve the increased accessibility at the project optimum level.
When effective value capturing instruments are missing, this extra profit will go to
the real-estate owners.
Figure 1 provides an example of a typical situation. In the figure, which is based
on the assumptions mentioned above, the marginal costs and benefits of
investment in transport infrastructure are depicted. The marginal costs show a
convex trend, reflecting decreasing marginal costs at low levels of investment,
followed by increasing marginal costs at higher investment levels. The marginal
benefit curve, in turn, is shown as a log-normal distribution—reflecting, on the one
hand, that improvements in accessibility only influence real-estate values if the
improvement is substantially large, and, on the other, that the increase in realestate values will diminish after a certain improvement in accessibility has been
achieved (as a result of diminishing marginal returns).
In Figure 1, Qs reflects the optimal investment level from a welfare perspective:
marginal social benefits are equal to marginal social costs. In other words, without
budget restrictions, it would be efficient to invest in new infrastructure until a total
of Qs units of accessibility improvement has been added to the existing situation.
Qf, in the same figure, reflects the optimal investment level from a financial
perspective: marginal financial benefits are equal to marginal financial costs. To
achieve the best ratio between value capturing potentials and financial costs, it
would be efficient to invest in new infrastructure only until a total of Qf units has
been added. In Qf, the ‘extra real-estate development profit’ (which remains
FIGURE 1. Optimal levels of investment in rail accessibility, from a financial (Qf) and social
perspective (Qs).
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Erwin van der Krabben et al.
after the infrastructure costs have been financed, following our assumptions), as a
result of the increased accessibility in Qf, will be the surface between A and B
in Figure 1.
Figure 1 depicts a situation in which the financial benefits that can be captured
exceed the financial cost of an investment project for a range of quantities of units
of accessibility improvement. Thus, an extra profit from the real-estate programme
can be realized, even after all infrastructure costs have been financed. We believe
that this situation may be applicable to node infrastructure development in
combination with greenfield real-estate development (for instance, a new light rail
station at the edge of an urban area, combined with new office or retail
developments). In other situations the financial costs may always exceed the
benefits, no matter which quantity is provided, while social benefits exceed social
costs for a particular range of investments levels. This situation refers, for instance,
to a railway station area redevelopment project in or close to a town centre (new
rail terminal, improved accessibility and brownfield real-estate development). The
case studies in the next section are examples of such situations.4 We will come
back to this issue in the fourth section (‘Model Application to Case Studies’).
Now consider the social optimum in Figure 1, which will be achieved when Qs
accessibility units have been added. When the available public budgets for
infrastructure investments are sufficient for Qs, the government will be investing
above the level optimal from a project perspective. The financial cost of investing
at this social optimum level, compared with investing for the project optimum Qf,
is BCD. The value that can be captured at the social optimum is equal to the value
that can be captured at project level (AB) minus BCD. In Figure 1 this will be a
negative value, because BCD (the extra loss) exceeds AB. Nevertheless, from a
welfare perspective, the government would do best to provide the amount of Qs
units of accessibility improvement, provided sufficient government funds are
available to finance the gap between BCD and AB. Any other quantity would
result in a lower level of welfare. Contrarily, when the objective would be to
maximize the ratio between value captured and project costs, Qf units of
accessibility improvement should be provided. In that case, however, the missed
social benefits will be equal to EFG.
Referring to the results of some of the earlier studies mentioned above, it would
be interesting if we are able to define the level of accessibility improvement where
the government is able to finance the project solely on the basis of budgets
generated through value capturing (in case no public funds are available at all),
while achieving the maximum possible social benefits. In this situation, as we
mentioned above, the total value that can be captured (the additional real-estate
value as a direct result of the increased accessibility of the location) is equal to the
total financial costs of the project (the costs of constructing the infrastructure that
is necessary to achieve this increased accessibility).
This budget-neutral level of investment is depicted in Figure 2, at the point
where total financial costs equal total financial benefits (investment level Qn). At
this point, AB equals the area BCD. We can be sure that in Qn the accessibility
improvement can be financed completely by value capturing.5 Note that at level
Qn, the government exchanges the budget surplus (the extra profit from the realestate development as a result of the increased accessibility) occurring at Qf
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Conceptual Model for Decision-making in Infrastructure Projects
FIGURE 2. Budget neutral (Qn) versus financial optimal (Qf) level of investment in rail accessibility.
(P1–P2) for a higher level of accessibility improvement at Qn, while adhering to
the condition of budget neutrality.
What are the social and project effects of investing Qn units of accessibility
improvement? This is demonstrated in Figure 3. Compared with Qf, the financial
deficit in Qn will be JKN, but the social advantage of investing at Qn—compared
with the Qf level—would be RSUV. Compared with Qs (the social optimum), we
will miss at the budget-neutral optimum Qn the amount of STU of social benefits,
but the financial advantage of investing at the Qn level—compared with the Qs
level—would be KLMN.
The question to be answered now is whether it is possible to make use of this
model to define the optimal level of accessibility in station area redevelopment
processes. The usefulness of the model completely depends on the availability of
the right data: are we able to calculate the marginal financial costs and benefits,
and the marginal social costs and benefits, related to different levels of
accessibility? We argue that hedonic price modelling may be able to produce
part of the necessary data. The next section presents the results of hedonic price
modelling of office rents in Dutch cities.
Case Studies: Forecasting the Effect of Improved Accessibility
on Property Values
This section presents the results of three case studies of (planned) station area
redevelopment projects in Dutch medium-sized cities (Breda, Arnhem and
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FIGURE 3. Consequences of an investment level at the social optimum (Qs), the financial optimum
(Qf) and the budget neutral level of investment (Qn).
Schiedam). We have selected these projects, because they encompass (major)
public investments in rail services and major real-estate developments. The three
selected projects can be seen as typical examples of station area redevelopments
that currently take place in many Dutch cities (for more detail on various aspects
of station area redevelopment in European cities, see Bertolini & Spit, 1998;
Nijkamp et al., 2002; Peek, 2002; Koppenjan, 2005; Peek & Louw, 2006; Pels &
Rietveld, 2007).
The objectives of the case studies are to calculate the effects of improved train
accessibility on real-estate values and to test some of the crucial parts of the
conceptual model presented in the earlier section ‘A conceptual model . . . ’. With
respect to the conceptual model, we will test how the real-estate values in the casestudy areas respond to improved accessibility.6
The case studies consisted of the following three steps:
1. Analysis of the planned real-estate programme for the station area and the
planned improvement of rail services (‘Case Studies’).
2. Calculation of the effect of an increased Rail Station Quality Index (RSQI)
on office rents and, consequently, the value increase of the planned
office developments as a direct result of the increased RSQI (‘Case
Studies’).
3. Analysis of the effects of different RSQI scenario’s on the real-estate
value of the planned office developments (‘Model Application to Case
Studies’).
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Planned Real-estate Programme and Planned Rail Improvements
in Case-study Cities (Step 1)
Table 1 provides information about the improvement of rail services and the realestate programme for each of the station area redevelopment projects. Both the
Breda and the Arnhem station area redevelopment projects have been appointed
by the national government as one of the so-called national key development
projects. These projects include the new high-speed train stations in the
Netherlands (respectively linked to the new HST line Amsterdam–Brussels–Paris
and the partly upgraded and partly new HST line Amsterdam–Cologne–
Frankfurt). The key development projects all receive national government money
for upgrading rail services. Rail services in Breda and Arnhem will be upgraded to
high-speed train rail services. The Breda railway station will provide HST shuttle
services, linked to the new Amsterdam–Brussels–Paris high-speed train services.
‘Arnhem’ will be a stop on the HST rail line Amsterdam–Cologne–Frankfurt. The
plans for Breda and Arnhem include, in addition to upgraded rail services, a new
HST rail terminal, extra platforms and improved feeder services. The rail services
in Schiedam remain on an ‘intercity level’, but improved.
The local authorities in Breda, Arnhem and Schiedam have initiated major
redevelopment projects in the direct vicinity of the rail stations. Those projects
include office developments, new apartments, retail developments, parking space
and open space/park development, and aim to take—in a functional way—
maximum advantage of the improved public transport services (Table 1). To
realize the planned developments, the local authorities must cooperate with private
developers that already owned land in the redevelopment area (in all cases NS
Vastgoed, the real-estate company owned by the Dutch national railway company)
or recently acquired land in the area (various developers).7 The development
processes for the station area projects are typical examples of the public
development model that is commonly used by Dutch municipalities, both in cases
of greenfield and brownfield developments. This development model involves a
public developer (the municipality) that buys all the land to be developed,
readjusts the parcels into forms suitable for the desired development, and sells
those parcels (see Needham, 2007; Van Dijk et al., 2007). The developers are
TABLE 1. Station area development plans Breda, Arnhem and Schiedam
Town
Breda
Arnhem
Schiedam
Segment
Office space
Apartments
Retail space
Parking space
Open space
Rail terminal
Platforms
Development programme
115, 000 m2
71, 000 m2
6, 000 m2
750 cars
20, 000 m2
HST rail terminal
3 HST platforms
80, 000 m2
15, 000 m2
10, 000 m2
1, 000 cars
–
HST rail terminal
4 HST platforms
225, 675 m2
85, 500 m2
14, 200 m2
6, 000–8, 000 cars
10, 000 m2
Intercity rail terminal
4 intercity platforms
Source: Van Bendegem & Van der Krabben (2006).
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Erwin van der Krabben et al.
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willing to put their land into this pool so that the location can be developed more
efficiently as a whole. When the building land has been serviced, the developers
take land out of the pool proportionate to the amount they contributed. The
developers have agreed to the amount, the price and the location of building land
that they will acquire after land readjustment before they sell their land to the
municipality. Usually, private developers agree to a system of cost recovery, in
which they pay for public plan-related costs (like local roads and open space).
These costs are usually included in the price the developers pay for the building
land (which is almost always a higher price than the price the municipality paid
before, when it acquired the land from the developers).
Effect of Increased RSQI on Office Rents and Value Increase
of Planned Office Developments (Step 2)
The second step of the case studies consisted of an analysis, for each project, of the
increase of the RSQI, related to the (planned) improved rail services and its effects
on office rents. We introduce the RSQI to be able to measure the accessibility
improvement.8 To estimate the increased RSQI, we have analysed changes
with respect to: the number of train services per day, the travel time to other
railway stations, and the ratio between travel times and distances to other railway
stations.
Subsequently, for each project, it was estimated how high the office rents would
be in the vicinity of the station both without the new transport infrastructure and
with the new infrastructure. The difference (Euros per square metre per year) can
be attributed to the investment in transport infrastructure. These estimates were
made using a property value model developed by De Graaff and Rietveld (De
Graaff et al., 2007; see also Debrezion Andom, 2006). This model is based on a
hedonic price analysis of office rents, using a data-set of more than 11, 000
transactions in the Dutch office market between 1983 and 2004. The office rents
were related in a multiple regression model to four groups of characteristics (De
Graaff et al., 2007, p. 2): building type and user characteristics, locational
characteristics, regional characteristics and accessibility characteristics (both by
car and by public transport).9 The results of the hedonic price analysis show that
‘accessibility’ has a positive impact on property values.10
Using this hedonic price model, we are able to forecast the effect of both the
present and the increased RSQI on the office rents. The model forecasts office
rents in the station area with and without (improved) public transport services. The
total impact of the public transport accessibility (current accessibility plus
improvements) on office rents—respectively, 16.0%, 17.4% and 17.4% increase—
can then be estimated by comparing the difference in rent levels (Table 2). The
impact of the improved public transport accessibility (the planned developments
mentioned in Table 1) on office rents is much smaller—respectively, 1.2%, 0.7%
and 0.3% increase.
To define the value that can be capture, it is arguable whether the total impact of
the rail station quality on office rents should be the input for the calculations
(present plus extra RSQI) or only the extra RSQI (the expected impact of the
planned public-sector infrastructure investments). In this paper we assume that
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Conceptual Model for Decision-making in Infrastructure Projects
TABLE 2. Increase in office rents (based on estimations property value model)a and estimated ‘extra’
real-estate value (as a consequence of station area redevelopment)
Office rents in station areas
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Breda
Arnhem
Schiedam
e178.91
e205.14
0.957
e208.79
(þ16.7%)
0.995
e240.22
(þ17.1%)
þ0.11
e210.04
(þ17.4%)
e1.25
þ0.03
e240.83
(þ17.4%)
e0.61
102, 350
e2.17
71, 200
e1.25
200, 250
e0.61
6.2
6.3
5.6
7.8
7.6
7.0
e3, 582, 250
e2, 847, 430
e1, 412, 698
e1, 171, 053
e2, 181, 295
e1, 745, 036
Present situation (without positive impact station area)
Annual office rents per m2
e181.52
Present situation (before station area redevelopment)
RSQI
0.796
Annual office rents per m2
e208.39
(þ14.8%)
Improved situation (after station area redevelopment)
Changes in RSQI
þ0.09
Annual office rents per m2
e210.56
(þ16.0%)
Estimated total increase of
þe2.17
annual office rents per m2
‘Extra’ real-estate value office development
Input for calculations
Net size of office development (m2)
Forecasted increase of annual
office rent per m2
Gross CAP-rate, positive economic
growth scenario (%)b
Gross CAP-rate, negative economic
growth scenario (%)b
Estimated ‘extra’ real estate valuec
Positive economic growth scenario
Negative economic growth scenario
a
The rents predicted by the model under ‘present situation’ are not necessarily equal to the actual
rents in the ‘present situation’, due to the fact that the variables related to the characteristics of the
building/tenant are left aside. This means that the model predicts rents for ‘average’ office space,
specified for the locations involved.
b
Source: DTZ Zadelhoff (2007).
c
Total rental income in first year of exploitation divided by capitalization rate.
only the effect of the additional RSQI can be captured.11 It was assumed that this
value effect would be the same for all new offices to be developed in the station
area. So the estimated rental increase was applied to the whole office-building
programme for that area. Finally, with help of a standard valuation technique, the
increase in the capital value of the office developments as a direct consequence of
the public investments in rail infrastructure (including the rail terminal) was
estimated (Table 2).
It should be noted that the above calculations involve only the value increase of
new office developments. Additionally, we may assume that there will also be
positive effects on the value of the new apartments and retail space that are part of
the plan. Moreover, value increases of existing offices, apartments and shops,
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adjacent to the development area, are not included. Finally, we have only
calculated the positive effects of the increased accessibility by rail for the three
railway station areas. We have not calculated the positive effects of other publicsector investments (i.e. improved road accessibility and improved quality of public
space). Important for the argument in this paper is that the calculations show the
positive relation between public-sector investments and real-estate values.
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Model Application to Case Studies: Determining Optimal Public-sector
Investment
The conceptual model in the second section suggests that, lacking sufficient public
funds, the optimal level of public investment is neither identical to the project
optimum nor to the social optimum. Rather, we argued that, under conditions of
limited public funds, the optimal investment level is identical to the point where
the total sum of external benefits that can be captured is identical to total project
costs. This level of investment combines budget neutrality with maximum
improvement in accessibility and a maximum ratio between social benefits and
costs (investment level Qn in Figures 2 and 3).
The case studies presented in the previous section have shown that rail
accessibility has a substantial impact on rent prices of offices located in the
vicinity of railway stations; accordingly, substantial financial benefits occur.
Hence, additional investments create a potential for value capturing. The model
presented in the second section now provides a framework to explore whether an
optimal level of public-sector investment has occurred in the three case-study
cities.
A full application of the conceptual model requires four sets of data for a range
of levels of public-sector investment in infrastructure: total project costs, total
project benefits (i.e. increased land and property values), total social costs, and
total social benefits (the latter two include pecuniary and non-pecuniary
externalities). In case these data are available for a range of investment levels,
marginal costs and benefits can be derived. The hedonic price model that has been
used in the section ‘Case Studies’ enables the estimation of data on total and
marginal financial benefits for a variety of levels of public-sector investments. In
order to do so, it is only necessary to increase the RSQI for the station areas and
calculate the impact on office rents and, subsequently, on the extra value of the
planned office development. In this case, we have calculated the impact of various
RSQIs, ranging from the current RSQI level of each station to the RSQI level of
the train station of Den Bosch, which is a city comparable in terms of size and
structure of the office sector to the three case-study cities but with a much higher
RSQI level. Figure 4 presents the marginal benefits of improving the RSQI levels
in Schiedam, Arnhem and Breda from the current level to the Den Bosch level.
The figure shows that the marginal financial benefits decrease when the RSQI is
further improved. This implies that the impact of accessibility improvements on
real-estate values decreases at higher levels of RSQI. The case studies in the
previous section suggest that, in principle, value can be captured from real-estate
development near railway stations. However, Figure 4 also shows that the effects
of further improvements of the accessibility level are rather small.
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FIGURE 4. Marginal financial benefits as a result of increases in RSQI. Note: Total financial benefits
for each RSQI level have been calculated with help of the property value model that was used in the
third section. Based on the estimated rent levels for a certain RSQI level, we have used the same
standard valuation technique to estimate the capital value of the office developments. Marginal
financial benefits have been derived from the total financial benefits: they reflect the additional
financial benefits as a result of an extra unit of RSQI improvement (the extra financial benefit of 10%
RSQI improvement).
In order to be able to determine the optimal level of public-sector investment in
rail accessibility, more data are necessary, particularly with respect to the financial
costs of public-sector investments and the social costs and benefits. Within the
context of the present study, it was not possible to generate these data. However, it
is possible to explore in a theoretical sense the optimal level point of investment in
each case study using the model presented above. Note that in the explorations
below, the size of real-estate development is kept constant (see Table 1).
The first case to be discussed here is Schiedam, which has the highest RSQI of
the three case-study cities in the initial situation (Table 2). Hence, it may be
assumed that a relatively low level of investment is necessary to bring Schiedam to
the RSQI level of Den Bosch. Furthermore, Schiedam benefits from its location in
the Randstad, which implies that investments in rail come to the benefit of a
relatively large number of stations. The costs for rail improvements that can be
related to the Schiedam station area may therefore be expected to be relatively low.
Furthermore, the office development in Schiedam is large in size, resulting in a
high level of total financial benefits from RSQI improvements.12 Figure 5 depicts
this situation. Total financial benefits (TFB) of office development are relatively
high, while total financial costs (TFC) are considered relatively low in comparison
with the two other case-study cities. The result is that financial benefits exceed
financial costs (p1–p2) at the planned level of infrastructure investment (Qp).
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FIGURE 5. Application of the conceptual model to the Schiedam case.
In the figure it is assumed that project costs reveal a convex trend (reflecting the
fact that it may be increasingly expensive to further increase rail accessibility), and
that the marginal social benefit (MSB) and marginal social cost (MSC) curves
follow largely the trend of the financial curves. Assuming that these curves depict
the actual situation in case of Schiedam, the figure shows that the level of publicsector investment can be increased to the social optimum QS. At this point,
marginal social benefits equal marginal social costs, while project benefits still
exceed project costs (p3–p4). Thus, the challenge for Schiedam would be to locate
the social optimal and increase public investment to this level.13
The RSQI of the Arnhem station area is largely comparable with Schiedam, but
it may be assumed that cost and benefit trend lines will differ substantially. The
Arnhem station will be upgraded to a stop on the HST line to Germany, which
implies considerable costs. Furthermore, given its relatively peripheral location in
the Netherlands, a large share of the investments in the upgrading has to be linked
to the Arnhem station area. Finally, taking into account the relatively modest
development programme, the marginal and total financial benefits of improvements in the RSQI may be expected to be limited, as was shown in Figure 4.
Figure 6 depicts the overall situation for Arnhem. The total financial benefits are
relatively limited and total financial costs are relatively high, even for the lower
range of investment levels. As a result, costs exceed the benefits that can be
captured at the planned investment level Qp (p1–p2 5 0). The same holds true at
the social optimum Qs, which would result in an even larger financial deficit
(p3–p4). The figure furthermore shows that, under the mentioned hypothetical
conditions, costs exceed benefits for all levels of infrastructure investment.14
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FIGURE 6. Application of the conceptual model to the Arnhem case.
Hence, a budget neutral investment level Qn does not exist in the Arnhem case.
This implies that no level of accessibility improvement can be financed solely
through value capturing; hence additional government funds are required to
finance the accessibility improvements.
Finally, the Breda case provides a third example to show the workings of the
conceptual model. Like Arnhem, Breda will be linked to a HST line, implying
substantial costs related to improvements in the RSQI. However, given its more
central location and the fact that Breda will be developed into a HST shuttle
station rather than a full-fledged HST station, costs for RSQI upgrading may be
expected to be lower than in Arnhem. Furthermore, the somewhat larger real-estate
programme will result in larger increases in total land and property values to be
captured. Based on these assumptions, and the data on total and marginal financial
benefits presented in Figure 4, the situation for Breda is presented in Figure 7. The
planned investment programme results in the provision of Qp units of accessibility
improvement, with a financial deficit of p1–p2. Like in the Arnhem case, the social
optimal would require an additional investment in rail infrastructure to Qs, but the
financial deficit would be substantially higher (p3–p4). Given tight budget
restrictions, the social optimal is thus infeasible to attain within the short term.
Following the argument presented above, it is therefore necessary to reduce the
planned level of public sector investment until Qn, at which point benefits in terms
of increased real-estate values equal investment costs.
The analyses presented above are obviously speculative in nature, as only data
on project benefits are available from the hedonic price model presented in the
paper. In order to apply the model in practice, additional data are necessary about
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FIGURE 7. Application of the conceptual model to the Breda case.
project costs and about social costs and benefits, for a range of levels of publicsector investment. However, when we compare the three cases—including the
assumptions for each case regarding costs and benefits—we can conclude that the
financial situation in the Schiedam case allows one to achieve the social optimum,
while no optimum is feasible in the Arnhem case without additional funding. To
achieve the project optimum or the social optimum, additional (government) funds
are needed. Finally, the Breda case shows that in this situation a budget neutral
optimum is feasible.
While traditional cost–benefit analysis does provide insight into (social) costs
and benefits, the analyses are usually limited to project alternatives that are
comparable in scope. Rarely are data available for a range of levels of investments.
The conceptual model, the empirical results, and the speculations presented in this
paper together provide a strong argument to widen the scope of cost–benefit
analysis and explore the impacts of a wide range of public-sector investment
levels. This might not only result in projects that are more worthwhile from a
societal perspective, but may also solve part of the financing problems for
infrastructure developments faced by governments throughout the western world.
Conclusions
Debates with respect to financial problems of public-sector infrastructure
development increasingly focus on ways to improve value capturing. In this
paper we have addressed two questions related to this debate: How much value
can be captured?; and How can we maximize the value to be captured? Do the
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findings of our (partly hypothetical) case studies contribute to answering these
questions?
A conceptual model has been presented that enables defining the optimal level
of public-sector infrastructure development, by linking financial and social costs
and benefits. Will this model of the optimal level of ‘accessibility improvement’
help us in decision-making processes with respect to infrastructure development?
We believe that, compared with the usual cost–benefit analysis for infrastructure
development, the surplus value of the proposed model lies in its possibility to
compare—in financial and social terms—alternative levels of public-sector
investments (resulting in different accessibility levels). Thus, in principle, it will
be possible to define the most desirable infrastructure project, as has been shown
in the previous section. In the case of a regular cost–benefit analysis we are able to
compare costs and benefits for alternatives that are comparable in scope. However,
we are unable to assess whether this is the optimal scope of public-sector
investment (either in financial or in social terms). The conceptual model presented
in this paper may be able to solve this information gap.
Of course, as we have mentioned before, the usefulness of this model in practice
strongly depends on data availability: are we able to calculate the marginal
financial costs and benefits and the marginal social costs and benefits, related to
different levels of accessibility? In the paper we have argued that hedonic price
modelling may be able to produce part of the necessary data. With help of the
hedonic price analysis of Dutch office rents, we have been able, for given
situation, to estimate the effect of improved (rail) accessibility on office rents and
(related) real-estate values in the station areas of three Dutch cities. This
information provides part of the answer to the first question. The case studies have
revealed the potential value to be captured from real-estate development (limited
to office space). For the complete answer we must also include in the analysis
information about the profitability of the real-estate developments. This
information is not publicly available, but the private developers involved of
course know the answer to this question.
Finally, to be able to define the optimal investment levels, we need to include
the total and marginal costs of different levels of infrastructure development as
well. In principle, this can be done. The case studies do not reveal those optimal
investment levels. However, they show—based on hypothetical situations(that in
practice different situations are likely to occur in which, with or without additional
government funds, the project optimum, the social optimum or the budget-neutral
optimum can be reached.
Although we are aware of the fact that many data-related difficulties have to be
overcome, we believe that the conceptual model presented here may contribute to
a more systematic approach to determining the scope of, and the level of publicsector investment in, new infrastructure development.
Acknowledgements
This paper is based on a study carried out of as part of the research project ‘Real
Estate Values and Accessibility’, which is financed by TRANSUMO, a Dutch
research programme on sustainable mobility. Participants in this study include
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Erwin van der Krabben et al.
Free University of Amsterdam, Radboud University Nijmegen, Goudappel
Coffeng, Buck Consultants International and Montefeltro.
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Notes
1. A third issue—which instruments can be used for value capturing—is left aside in this paper. However, the
RVW (2004) argues, quite rightly, that efficient instruments for value capturing in the Netherlands are
missing. In Van der Krabben and Needham (forthcoming), we suggest a new legal instrument for value
capturing.
2. Note that reality may be different from this. Depending on the profitability of the development, this extra
value may be necessary to cover the costs of the real-estate development programme itself. In Van der
Krabben and Needham (forthcoming) we have argued that scoping might be a good solution to improve the
profitability of the real-estate development, thus offering better opportunities for value capturing.
3. It is assumed here that there will be no additional financial benefits from the increased accessibility level, like
extra ticket sales for public transport.
4. Two main reasons can be held responsible for this situation. First, infrastructure costs are substantial. Second, in
many cases real-estate development in the vicinity of an infrastructure node concerns the redevelopment of a
brownfield area (particularly in the rail station projects that we use in this paper as case studies). In those projects
the redevelopment costs often exceed the revenues from real-estate development. Eventual extra profits as a
result of increased accessibility are necessary to improve the profitability of the real-estate project itself.
5. We assume that the profitability of the real-estate developments—the financial benefits in our conceptual
model are related to the increasing real-estate values in these developments—allows the increased real-estate
values to be captured. We ignore projects where the increased real-estate values are necessary to ensure the
profitability of the real-estate developments: in those projects, the government attempts for value capturing
would probably mean that the private developers would withdraw from the project and that the project would
not be implemented at all.
6. Due to the limitations of the present version of the hedonic price model, we are only able to estimate the
effects of improved public transport accessibility on office rents. Effects of improved road accessibility
cannot be estimated. Moreover, effects on other real-estate segments (apartments, retail) are left aside as well.
7. The reason for this is that, according to Dutch law, land owners that can prove to have the skills to carry out
the planned development have the legal right to do so. In this situation they can not be expropriated by the
municipality.
8. Referring to Figure 1, we assume that RSQI reflects the Q accessibility units at the x axis.
9. See also many international studies about this subject, including Damm et al. (1980), Weinburger (2001), and
Cervero and Duncan (2001).
10. See Van der Krabben and Needham (forthcoming, Appendix A) for an explanation of the hedonic price
model.
11. A legal basis for capturing the value of historical rail investments is lacking. Moreover, one can argue that the
state has already captured part of this value by the tax system.
12. The Schiedam case resembles thus the situation described in our conceptual model (Figure 1): for a range of
quantities of units of accessibility improvement, the financial benefits exceed the financial costs.
13. Note that the social optimum could also lie at a point where project costs exceed project benefits. In that case,
the analysis presented in Figure 2 applies and that discussed for Arnhem and Breda applies.
14. The Arnhem case (and the Breda case as well) thus differs from the situation in the conceptual model in
Figure 1: the financial costs always exceed the benefits, no matter which quantity is provided (as we assume
will be the case in many station area redevelopment projects).
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