Hans Koster
The Impact of
Mixed Land-Use
2 | The impact of mixed land-use
3 | The impact of mixed land-use
The impact of mixed land-use
A hedonic analysis of the effects of mixed land-use on housing values
Hans R.A. Koster, VU University Amsterdam
4 | The impact of mixed land-use
Acknowledgements
A
fter I obtained my bachelor degree in Economics at the Erasmus
University Rotterdam I had to decide for which master course I would
apply. After some considerations I decided to apply for the master Spatial,
Transport & Environmental Economics at the VU University in
Amsterdam. By finishing this thesis I hope to obtain the masters degree in
Economics.
I want to acknowledge my supervisor dr. Jan Rouwendal for his helpful
comments. I also want to thank Jasper Dekkers for making the data
suitable for use and the enthusiastic teaching in the GIS-course which
makes me decide to use GIS in my master thesis. Furthermore, I am
especially grateful to Chris Jacobs who was very helpful in teaching me
some Visual Basic-stuff which was needed to do some crucial analyses. I
would also like to thank Henri de Groot, who was very supportive in early
stages in the realisation of this thesis. Furthermore, I thank Koos Koster,
Gert-Jan Linders and Marije van Huis for comments. Finally, I also thank
the teachers of the master course Spatial, Transport & Environmental
Economics. They provide me some methods, techniques and ideas that
where very useful in preparing this master thesis.
The realisation of this thesis was impossible without data. I acknowledge
the Dutch Association of Real Estate Agents (NVM) for making the data
available. Furthermore, I would like to thank Wouter Jacobs and the
Chamber of Commerce for making the data, which contain information
about companies in the city region of Rotterdam, available. I want to
express my thanks also to the Kadaster for geocoding the NVM-data.
5 | The impact of mixed land-use
6 | The impact of mixed land-use
Table of contents
Acknowledgements.......................................................................................... page 4
Table of contents................................................................................................page 6
Figures and tables............................................................................................. page 8
Section 1: Introduction................................................................................... page 10
Section 2: Literature review........................................................................ page 14
2.1 A definition of mixed land-use......................................................... page 14
2.1.1 History and overview...................................................................... page 14
2.1.2 A definition..........................................................................................page 16
2.2 The benefits and costs of mixed land-use................................... page 18
2.2.1 The benefits........................................................................................ page 18
2.2.2 The costs.............................................................................................. page 20
2.2.3 An efficient urban spatial structure?........................................ page 22
2.3 Measurement of benefits and costs................................................page 26
2.3.1 Methods................................................................................................ page 26
2.3.2 Revealed preference studies......................................................... page 27
2.3.3 Stated preference studies.............................................................. page 29
2.3.4 Synthesis.............................................................................................. page 30
2.4 A framework to measure the effects of mixed land-use........page 33
2.4.1 Welfare economics and land-use planning:
the hedonic framework.................................................................. page 33
2.4.2 A framework to measure the impact of mixed land-use....page 35
Section 3: Datasets and measurements................................................ page 38
3.1 Datasets...................................................................................................... page 38
3.2 Measures of mixed land-uses............................................................page 39
3.3 Regional context: the city region Rotterdam............................. page 42
7 | The impact of mixed land-use
Section 4: Model estimation and results.............................................. page 48
4.1 The econometric model.......................................................................page 48
4.2 Empirical results of log-linear models using OLS.................... page 51
4.3 Changing the buffer size......................................................................page 59
4.4 Other specification................................................................................ page 64
4.4.1 Linear and quadratic specifications..........................................page 64
4.4.2 Endogeneity of the regressors..................................................... page 64
4.4.3 Spatial autocorrelation..................................................................page 69
4.4.4 Box-Cox transformations...............................................................page 70
4.5 Discussion of the results..................................................................... page 76
4.6 Caveats........................................................................................................ page 81
Section 5: Conclusions.................................................................................... page 84
5.1 Summary and conclusions................................................................. page 84
5.2 Policy implications and recommendations for
further research...................................................................................... page 86
References............................................................................................................. page 88
Executive summary.......................................................................................... page 96
Samenvatting (in Dutch)............................................................................... page 100
Appendices............................................................................................................ page 104
A Buffering techniques................................................................................ page 104
B Descriptives................................................................................................. page 106
8 | The impact of mixed land-use
Figures and tables
Figures
Figure 1 - Outline of the thesis....................................................................... page 12
Figure 2 - An equilibrium if the internal structure of a circular
city......................................................................................................... page 24
Figure 3 - A conceptual framework for measuring the effects of
mixed land-use.................................................................................. page 36
Figure 4 - The city region of Rotterdam..................................................... page 43
Figure 5 - Population density at neighbourhood level.........................page 43
Figure 6 - Employment density at neighbourhood level.....................page 44
Figure 7 - Variety at neighbourhood level................................................. page 45
Figure 8 - Diversity at neighbourhood level............................................. page 47
Figure 9 - Job population ratio at neighbourhood level...................... page 47
Figure 10 - Relationship between willingness to accept and
employment in the industrial sector.................................... page 57
Figure 11 - The willingness to accept one unit of diversity............... page 63
Figure 12 - Comparison of the log-linear and Box-Cox
specification................................................................................... page 76
Figure A-1 - Buffering example...................................................................... page 105
9 | The impact of mixed land-use
Tables
Table 1 - Overview of different planning concepts................................page 17
Table 2 - Overview of the costs and benefits of mixed land-use......page 31
Table 3 - Results of OLS-models.....................................................................page 52
Table 4 - Marginal willingness to pay for a one unit increase in
employment......................................................................................... page 57
Table 5 - Concentration per sector............................................................... page 59
Table 6 - Results log-linear OLS with different buffer sizes...............page 61
Table 7 - The willingness to pay for different characteristics...........page 62
Table 8 - IV-estimates........................................................................................ page 66
Table 9 - Marginal willingness to pay for a one unit increase in
employment........................................................................................ page 69
Table 10 - Results of Box-Cox specification compared to a log-linear
Specification...................................................................................... page 72
Table 11 - Marginal willingness to pay for a one unit increase in
employment...................................................................................... page 74
Table B-1 – Descriptives.................................................................................... page 106
10 | The impact of mixed land-use
Section 1: Introduction
M
any contemporary regional planning policies aim to enhance
sustainability and liveability of neighbourhoods. In order to do so, it
is presumed that mixing land-uses, amongst other things, will promote
this sustainability. These mixed land-use policies are the backbone of
holistic planning programs such as Smart Growth and New Urbanism.
Since Jane Jacobs in 1961 suggested that mixed land-use is preferable over
monofunctional land use, this mixing of land-uses is a subject of debate in
the United States. The debate is still indecisive, mainly because it is not
clear how we have to control the local urban constitution but moreover, it
is not clear whether we have to control the local urban form (KOOMEN ET AL.
2008). Also in Europe as well as in the Netherlands there is a debate about
how to constitute the local economy. Both in United States and continental
Europe there are many voices which claim that mixing land uses yields
socio-economic benefits and will stimulate sustainable urban growth.
Although it is somewhat unclear what really the impact of mixed land-use
on the local economy is, several municipalities adopt the so-called ‘Smart
Growth’ concept. Examples of cities that adopt such strategies are
Arlington, Minneapolis-St. Paul and Denver. Also in the Netherlands, the
compact city concept, which aims to stimulate densities and mixed landuse, is a widely executed planning policy (KOOMEN ET AL. 2008).
However, according to HOPPENBROUWER AND LOUW (2005) mixed land-use is
an ambiguous concept, both in theory and in practice. In this thesis I first
want to overcome some of that ambiguity by carefully defining the concept
of mixed land-use. Moreover, it is examined which benefits and costs are
considered as relevant when one implements mixed land-use.
Furthermore, I perform a hedonic price analyses to investigate the impact
of mixed land-use on house prices.
11 | The impact of mixed land-use
Thus, the aim of this thesis is twofold. First, this thesis aims to integrate
different views on mixed and multifunctional land use and make an
assessment of the benefits and costs. The second objective of this study is
to provide some evidence which confirms or negates the claim of planners
that mixed land-use yields socio-economic benefits. In this study I will not
discuss the institutional dimension of this topic. Clearly, the government
has an important role in setting up mixed land-use projects and
implementing zoning policies.1 In the context of measuring socioeconomic benefits and costs the institutional dimension is however of less
importance.
This thesis is structured as follows. In section 2 the literature concerning
mixed land-use is briefly discussed. In this section I carefully define mixed
land-use, present a typology and discuss the main effects. In section 3 the
data is introduced, some measurements of mixed land-use are proposed
and there is some attention paid to the regional context. Section 4 presents
a number of hedonic price models in which I investigate the impact of
mixed land-use on house prices. Section 5 concludes and discusses some
recommendations for further research. The structure of this thesis is
summarised in figure 1.
1 A government use multiple instruments to organise the local economy. For
example, by providing subsidies to influence firms locational incentives.
12 | The impact of mixed land-use
Figure 1: Outline of the thesis
Section 1:
Introduction
EMPIRICAL APPLICATION
THEORY
Section 3:
Introduction of data and
measurements
Section 2:
Definition, typology and
conceptual framework
Section 4:
Hedonic Price Models
CONCLUSION
Section 5:
Section 5:
Conclusions
13 | The impact of mixed land-use
14 | The impact of mixed land-use
Section 2: Literature review
I
n this section I will introduce the concept of mixed land-use. An
overview of planning concepts is given wherein mixed land-use is an
important component. A definition is proposed. Using this definition, the
potential costs and benefits of mixed land-use are investigated. There is
also attention paid to the most efficient spatial structure. This section
concludes with a discussion of some methods and a framework to
systematically measure the impact of mixed land-use.
2.1 A definition of mixed land-use
2.1.1 History and overview
In the old Greek and Roman cities, but also in the cities of the middle ages,
living, working and shopping were all located within the city walls
(WRIGHT 1967, COUPLAND 1997). People often lived very close to their work
and to shops. In the early 20th century this ‘natural’ constitution of mixed
land-uses comes to an end. Due to technological progress, especially in the
transport sector, and changes in cultural behaviour, land uses were often
separated (GRANT 2004). Moreover, many policies in sixties were
promoting monofunctional areas and neighbourhoods. In 1961 the
geographer Jane Jacobs was the first who argued that mixed land-use can
be beneficial for the local economy. She hypothesised that a balanced mix
of living and working will lead to liveable, safe and viable neighbourhoods
(JACOBS 1961). After the publication of Jacobs’ The Death and Life of Great
American Cities, an increasing number of urban planners embraced the
idea of mixed land-use. It became widely accepted that mixed land-use
was a remedy for the problems that urban sprawl cause.
Although it is not clear to what extent sprawl and inefficient use of land
lead to an inefficient outcome of the local economy, a number of planning
concepts incorporate mixed land-use as a planning principle that
overcome these potential inefficiencies (GRANT 2004). Planning concepts
like New Urbanism, Smart Growth, the Compact City Concept and
15 | The impact of mixed land-use
multifunctional land use (MLU)2 all aim for a more efficient use of space.
Briefly, these concepts are characterised as follows:
- The New Urbanism concept focuses on liveable and sustainable
communities. Initiators of New Urbanism want to create such
communities, among other things, by improving the quality of life,
enhancing connectivity, promoting sustainability, increasing density
and developing neighbourhoods which are diverse in terms of land
uses, people and range of housing types. Mixed-use is promoted within
neighbourhoods, blocks and within buildings (NEWURBANISM.ORG 2009).
By implementing this concept, planners fight against the negative
effects of urban sprawl.
- The Smart Growth planning concept is much like the New Urbanism
approach although there are some differences. There is more attention
paid to the protection of open space (VREEKER ET AL. 2004) and more a
focus on sustainable growth. Mixed land-use is one of the ten key
principles and is defined as “putting uses in close proximity to one
another” (SMART GROWTH NETWORK 2009).
- The Compact City Concept is a European planning concept which aims
to improve the environmental and economic performance of cities
(VREEKER ET AL. 2004). The idea is that by increasing the urban density
and by mixing uses, the distance between different activities reduces
and therefore the demand for car travel declines (MAAT 2002).
- The concept of multifunctional land use (MLU) is to a large extent a
Dutch concept which focuses on the sustainable use of land. Thereby
there is a clear focus on the creation of synergy-effects of mixing uses
(VREEKER ET AL. 2004). Multifunctional land use is characterised by four
components (RODENBURG 2005):
1. Intensification of land use;
2. Integration of different land use functions;
2 To this concept is sometimes referred as Multiple Land Use, or Multiple and
Intensive Land Use. In this paper we will use the term Multifunctional Land Use
(MLU)
16 | The impact of mixed land-use
3. Multi-stories buildings (i.e. use of third dimension);
4. Use of the same area by several functions within a certain timeframe.
RODENBURG (2005, pp. 12) comes to a definition of multifunctional land use
where the second, third and fourth components are included:3
“A land use pattern is said to become more multifunctional when, in the
area considered, the number of functions, the degree of interweaving, or
the spatial heterogeneity increases. […]”
LOUW AND BRUINSMA (2006) and VREEKER (2004) argue that multifunctional
land use is a successor of mixed land-use. This is not really the case since
multifunctional land use is a planning concept while mixing land-uses is
only part of planning concepts. Thus, mixing land-uses is an important
component of MLU, but MLU is not the successor of mixed land-use.4
It is important to note that these concepts assume that by applying mixed
land-use policies, an economically most preferable solution is reached. In
subsection 2.3 I will look if this is really the case.
2.1.2 A definition
In the preceding paragraph I discussed some planning concepts. In table 1
the main findings are summarised. We can observe that these planning
concepts do have much overlap. They all focus on a sustainable use of land,
a diverse neighbourhood in terms of uses and they suppose that this new
configuration of the local environment is socially and economically
RODENBURG (2005) argues that intensification is not part of the definition of MLU.
According to RODENBURG ET AL. (2003) the main difference between mixed and
multifunctional land use is that the latter focuses on the synergetic effects of
different uses located nearby each other. This difference can only be important in
measuring the benefits of mixed land-use. But the ‘idea’ of mixing uses remains the
same: combining different socio-economic functions within an area. Thus, mixed
land use is part of the MLU-concept like mixed land use is part of the planning
concepts like New Urbanism and Smart Growth. That these planning concepts
differ is very clear.
3
4
17 | The impact of mixed land-use
preferable. It is important to note that mixed land-use is part of all the
discussed planning concepts and not the planning concept itself (LOUW AND
BRUINSMA 2006). A definition of mixed land-use that is compatible with all
these concepts is the definition that PRIEMUS ET AL. (2000) gave:
“The combination of different socio-economic functions in the same
area.”
Table 1: Overview different planning concepts
NEW
URBANISM
SMART
GROWTH
COMPACT
CITY
CONCEPT
MULTIUNCTIONAL
LAND USE
Characterisation
Creating
sustainable
communities
where people
enjoy living and
seeking
solutions to
environmental
problems.
A collection of
land use and
development
principles that
aim to enhance
our quality of
life, preserve the
natural
environment,
and save money
over time.
Improve
environmental
and economic
performance of
cities.
Sustainable use
of land and
creation of
synergy effects
between
different uses.
Definition
of mixed
land-use
A mix of shops,
offices,
apartments, and
homes on site.
Putting different
land uses in
close proximity
to one another.
A balanced
proportion of
the existence of
the functions:
living, working,
shopping and
education in one
single urban
district.
Implementing
multiple
functions within
a given area.
Geographical scale
Neighbourhoods
and buildings.
Neighbourhood
City, Urban
district,
Neighbourhood
Neighbourhood
18 | The impact of mixed land-use
I will use this definition in the remainder of the thesis. Socio-economic
functions can be housing, recreation, working, shopping etc. The nice
feature of this definition is that it does not prescribe which uses are good
to combine or how much the proximity to uses must be. In our study I will
find out which uses are positively affecting house prices and if there are
external effects of mixing land-uses on house prices.
2.2 The benefits and costs of mixed land-use
It is hypothesised that mixed land-use assures neighbourhood
sustainability and reduces urban sprawl but in the end, as an economist, I
am particularly interested if welfare is increased when such policies are
implemented. In the next subsection I will discuss some potential benefits
and costs that could arise when mixed land-use policies are implemented.
Finally, some attention is paid to which internal structure of a city is
economically efficient in theory.
2.2.1 The benefits
In section 2.1 we found out that a number of planning concepts consider
mixed land-use, amongst other things, as a solution to problems that
urban sprawl causes: long commuting times, congestion, air pollution and
segregation (SONG AND KNAAP 2004). The AMERICAN PLANNING ASSOCIATION
(1998) argues that mixing of different socio-economic functions can lead
to a better preservation of open space, stimulate walking, bicycling and
intensified use of public transport, encourage street activity, and support
local businesses. This is in line with the planning concepts like New
Urbanism, Smart Growth and the Compact City Concept. The
multifunctional land-use concept focuses more on the synergetic benefits
that might arise when mixed land-use is implemented.
VREEKER (2004) makes a distinction between environmental, social and
economic benefits. He argues that environmental benefits are (1) a better
preservation of open space, (2) less air pollution, since there is a reduction
in mobility: due to living and working in each other’s vicinity, the
19 | The impact of mixed land-use
commuting pattern can be more efficient, and (3) a reduction in building
energy consumption. According to VREEKER there are also potential
benefits to society. Namely, (1) an increase in the overall accessibility, (2)
reduction in crime rates and (3) an increased quality of life. Eventually
there are potential economic benefits in the form of (1) increased diversity
leading to more economic vitality of the neighbourhood, (2) more efficient
provision of infrastructure and, (3) increased productivity due to
economies of scale, density and diversity. We can see VREEKER (2004)
mixes up some benefits of increased density with the benefits of mixed
land-use. An increased intensification of land does not necessarily imply
mixed land-use. In other words, economies of density are not benefits of
mixed land-use, but benefits of the intensification of land.5 Also VAN WEE
(2003) discusses some benefits of mixed land-use. Mixed land-use may
have impact on travel safety and overall safety, because mixed land-use
probably stimulates walking and the use of the bicycle. It may also
intensify street life. Furthermore, VAN WEE (2003) expects that people
value the proximity to shops and urban amenities positively. RODENBURG ET
AL. (2003) focuses more on the economic benefits of mixed land-use.
Amongst other things, agglomeration externalities or synergetic spillovers,
an increase in shopping variety and economies of density are considered
as economic benefits of mixed land-use.
The MLU-concept focuses on synergy effects and externalities between
different uses. To analyse these externalities RODENBURG ET AL. (2003),
VREEKER (2004), VREEKER ET AL. (2004) and RODENBURG (2005) links the
economic theory of agglomeration externalities to mixing land-uses. A
distinction is made between localisation economies and urbanisation
economies (HOOVER 1936, 1948). Localisation economies occur when the
average costs of production of an individual firm decreases when the total
output in a sector increases. Urbanisation economies are experienced
when the average costs of an individual firm decreases when the total
For example, the better protection of open space is only relevant when the
building intensity is higher, so that there is more space available.
5
20 | The impact of mixed land-use
output in an urban area or city increases (VREEKER 2004). However, the
emphasis of the theories of Marshall and Hoover is on the clustering of
firms (MCCANN 2001, O’SULLIVAN 2003). Often, mixing land uses imply that
housing and firms are combined in each other’s vicinity. The
aforementioned authors do not pay attention to the fact that this
localisation economies and urbanisation economies are more difficult to
apply to a mixed urban area with both housing and employment. Are
there, for example, externalities present when housing and firms are
located in each other’s proximity? What are the sources of these
externalities? Are there existing examples of these externalities? Thus, the
theory about agglomeration economies is only applicable to the spatial
setting of firms and not to the co-location of housing and firms and is
therefore of limited relevance in the analysis of the benefits of mixed landuse.
It is almost neglected in literature that there are also external effects of
individual land-uses on other land-uses in vicinity. In our case, we take
also the effects of different land-uses on property values into account.
When in a given area more mixing is implemented, there will be for
example more commercial land use in vicinity of a house. This commercial
land can have positive effects: for example, people value shopping variety
and the short travel time positively. For each different land-use there are
different positive effects imaginable.
2.2.2 The costs
In order to present a nice picture of the ideal neighbourhood, the
aforementioned planning concepts neglect to a large extent the potential
costs of mixed land-use. Also in the literature, less attention is paid to the
costs of mixed land-use. But certainly there are also potential costs when
one implements mixed land-use policies.
VREEKER (2004) acknowledges that there can be costs involved with
mixed-use. He again makes a distinction between environmental, social
21 | The impact of mixed land-use
and economic effects. Environmental costs can be incurred due to the lack
of access to open space or concentration of environmental problems in
one location. Social costs can be imposed when there is a conflict between
activities (for example, parking vs. living) or when there is a loss of privacy
due to the intensification of land. CHESHIRE AND SHEPPARD (2002) argue that
costs of land use policies are in general reduced availability of private
land. For example, because of a Smart-Growth policy that stimulates
preservation of open space, there will be less land available for private
gardens, something people can dislike. Also VREEKER argues that the
quality of life might also reduce in areas where mixed land-use is
implemented. Economic effects comprise that there can be higher
congestion costs due to a suboptimal city size, higher wages have to be
paid or there is a lack of potential economic growth for rural areas. Which
uses are mixed and the degree of mixing can also cause social costs: GRANT
(2004) concedes that too much mixing and mixing the wrong elements
may lead to an ‘unbalanced area’. We could think of more costs that are
involved when mixed land-use is implemented that are not mentioned in
literature. Again the external effects of individual land-uses are almost
neglected: for example, when residential land is mixed with heavy
industry, people who live in that mixed area will probably not appreciate
that because of horizon pollution, air pollution and noise pollution. Again,
each different land-use can have different adverse effects.
To summarise the most important costs, there can be costs incurred
because people or firms can value the new situation of mixed land-use
worse than the old situation when (1) certain uses are combined that are
incompatible: there are adverse effects of individual land uses, (2) the
accessibility and amount of open space declines (3) there is less privacy
because of the different uses that are located nearby each other or (4) the
reduce in mobility results in increased travel time. Generally, we can
observe that mixed land-use can both incur costs as benefits on the same
issue. For example: mixed land-use can stimulate efficient commuting but
perhaps mixed land-use can also cause longer commuting times because of
22 | The impact of mixed land-use
an improper urban spatial structure. We have to look at some empirical
evidence to find out which effect dominates for different issues.
Furthermore, the forces which can lead to different spatial patterns are
not yet identified.
2.2.3 An efficient urban spatial structure?
A number of economists studied which constitution of a city would is an
economic equilibrium and if this equilibrium is optimal. Already in 1967
MILLS studied an efficient resource allocation in a city. He found that an
efficient allocation causes clustering of firms in the centre of the city, while
residential land was located outside the inner ring of the city. Residential
land which was located nearby business land had a higher value, because
of less commuting costs. But MILLS (1967), STULL (1974) and HELPMAN AND
PINES (1977) only look at the margin between residential and industrial
land use, and mixed land-use could therefore not arise. Also external
effects of business land on property values of houses are neglected. FUJITA
AND OGAWA (1982) set up a spatial model of a closed economy in a linear
city wherein there are two land-uses: residential use and business use.
Production of goods or services takes place in the city because of
production externalities. These externalities are benefits of one producer
to another business nearby. Two types of these production externalities
are localisation and urbanisation economies (MCCANN 2001). These
externalities decreases in distance which make it beneficial for a business
firm to locate close to another business firm. Workers want to live close to
their work because of commuting costs. The reason why not everybody is
locating in the city centre is the scarce availability of land. FUJITA AND
OGAWA show that when the commuting costs are considerably high, the
equilibrium comprise a mixed local economy where workers live next to
their job, in order to minimise commuter costs. When commuting costs are
lower, and the intensity of production externalities decrease not too fast in
distance, firms will cluster. With moderate commuting costs and a
moderate decay rate of production externalities multiple equilibria exists
with multiple business centres. ANAS AND KIM (1996) define a general
23 | The impact of mixed land-use
equilibrium model where retail firms and people interact. Shopping trips
and commuter trips are examples of this interaction. Transportation is
characterised by congestion. The city centre is the most preferable place to
locate because it has the best accessibility. In absence of production
externalities between firms, the urban spatial structure will be that houses
and firms are mixed. However, when there are substantial production
externalities while the congestion costs are not too large, firms will locate
in the city centre. In contradiction with FUJITA AND OGAWA (1982), LUCAS
AND ROSSI-HANSBERG (2002) and ROSSI-HANSBERG (2004) look at the internal
structure of a circular city instead of a linear city. Substitution between
land and labour is allowed. In figure 2 we can see a schematic map of an
equilibrium which could arise when the model of LUCAS AND ROSSIHANSBERG is implemented in a circular city with two land-uses. When the
commuting costs are high enough, people like to live next to their jobs: the
city experiences mixed use. When commuting costs decrease,
monofunctional areas will arise, sometimes next to a mixed area. When the
commuting costs are very low, the benefits of production externalities will
dominate the commuting costs: a so-called Mills city will arise with all the
employment in the geometric centre of the city and residential use in a
ring around the centre. ROSSI-HANSBERG (2004) proves that the equilibrium
outcomes of LUCAS AND ROSSI-HANSBERG are oftentimes not efficient.
Moreover, in his model, mixed areas are never an efficient outcome,
although it can be an equilibrium outcome.6 The reason why an
equilibrium outcome is not necessarily an efficient outcome is the fact that
firms do not take the positive externalities into account which they
imposes on other firms when they make their profit maximizing locational
6 To be more specific: he argues that there are two conditions needed in order to
have mixed areas. First, the stock of workers that are homeless must be zero and
the value of adding an extra worker must exactly equal the value of housing an
extra resident. ROSSI-HANSBERG finds that there are no intervals on which this is the
case, only at specific locational points. To use the language of ROSSI-HANSBERG: “The
two values may cross but their slope with respect to distance to the city centre differs.
This is called a transversal crossing.”
24 | The impact of mixed land-use
decision. ROSSI-HANSBERG argues that the mixed areas we observe in real
cities are evidence of the existence of production externalities.
When we look at the land rents of the different spatial structures, we see
in general that the land-rents are higher in business areas. These higher
land-rents in business areas are explained by the fact that production
externalities arise when firms locate next to each other. Furthermore,
LUCAS AND ROSSI-HANSBERG show that the land rents for residential land not
need to be substantially higher close to business land. However, the
difference in property values nearby and further away from business
areas depends largely on the height of the commuting costs.
Figure 2: An equilibrium of the internal structure of a circular city
SOURCE: LUCAS AND ROSSI-HANSBERG (2002)
25 | The impact of mixed land-use
In the models of FUJITA AND OGAWA, ANAS AND KIM, LUCAS AND ROSSI-HANSBERG
and ROSSI-HANSBERG it is assumed that firms experience perfect
competition. This is not a very realistic assumption: firms produce in
reality a wide variety of goods which compete with each other to some
extent. When the market is characterised by firms which are competing
imperfectly, FUJITA (1988) has shown that introducing a land market in a
model which assumes monopolistic competition (DIXIT AND STIGLITZ 1977)
will lead to multiple equilibria with either a mixed urban structure
wherein firms and residential use are mixed or segregation of uses.
Again, in all these models the external effects of land-uses (in these cases
business land) on property values of houses are neglected. In our study we
especially pay attention to these more sophisticated effects.
To summarise, economic theory does not provide decisive answers about
what is the most efficient urban spatial configuration.7 However, theory
offers insight in which forces can cause a mixed or monofunctional urban
structure. The two main forces are interaction between firms and
residents, i.e. commuting costs, and production externalities between
firms which will arise when these firms are located next to each other. The
outcomes of these models are in general characterised by multiple
equilibria, path dependence, and ‘catastrophic transitions’ (ANAS ET AL.
1998). But there is still less evidence on what is the height of commuting
costs when congestion is taken into account, and the intensity and scale of
production externalities. Moreover, the stylised nature of the models and
the simplification of the urban economy (only two land-uses, a
circular/linear city, perfect competition etc.) may reduce the applicability
of such models to reality. Hence, it is still unknown what the most efficient
urban spatial structure is: a mix of living and working or segregation of
uses.
For a more thorough review of the literature concerning the efficient urban
spatial structure, see ANAS ET AL. (1998).
7
26 | The impact of mixed land-use
2.3 Measurement of benefits and costs
In the preceding subsection I discussed the potential benefits and costs
when different uses are mixed within an area. In the subsequent
subsection I discuss some methods to measure this benefits and costs. I
also discuss a number of empirical studies which I divide into results of
revealed preference studies and stated preference studies. In the end the
main empirical results are summarised.
2.3.1 Methods
RODENBURG ET AL. (2003) and VAN WEE (2003) discuss two methods to
assess the benefits and costs of mixed land-use: cost-benefit analysis
(CBA) and multicriteria analysis (MCA). Cost-benefit analysis takes the
consumer preferences as natural starting point (VAN WEE 2003).
Furthermore, CBA tries to evaluate a project or alternative in monetary
terms. MCA does not express all units in monetary values but depends
more on the weights the units are given. The latter method has as main
disadvantage that it mixes research and politics since the weights of each
variable is subjectively defined. Therefore CBA-methods are preferred. In
the coming paragraphs I will discuss evidence for some effects of mixed
land-use, but I will not be able to present a complete CBA. 8
There are two methods to value the consumer preferences. Revealed
preference (RP) means that the preferences of the consumers can be
revealed by their economic behaviour. An assumption is that consumers
maximise their utility. When using stated preference methods (SP) one
confronts consumers with hypothetical choice sets.
With respect to mixed land-use, there is clearly a so-called NIMBY- (not-in-mybackyard) or LULU-effect (locally-unwanted land-uses). A number of activities are
impossible to mix with residential land use since people will not live near a
number of activities (e.g. heavy industry, dance clubs, cafés etc).
8
27 | The impact of mixed land-use
2.3.2 Revealed preference studies
SONG AND KNAAP (2004) analyse the effects of mixed land uses on the value
of housing. They use regional data and data with transaction prices of
houses in Washington County (OR). They define different measures of
mixed land use. Their main findings are that mixing certain types of
activities with the residential use of land will affect the property values
positively, especially when these houses are located nearby parks.
Furthermore the findings show that property values are higher in
neighbourhoods where single-family units dominate. The authors come to
the conclusion that it is very important how the land is mixed: there must
be a careful selection of activities that will be mixed. According to the
authors, more research into the configuration of the neighbourhood is
needed. In their older study which is also performed in Washington
County they investigate the effects of urban form, namely a new urbanistic
configuration of neighbourhoods. SONG AND KNAAP (2003) found that
density, commercial use, public land use and busy transportation
corridors negatively affect housing prices.
In the study of DE VOR AND DE GROOT (2009), the effects of industrial sites
(and thus industrial land use) on housing prices are measured to check
whether industrial activities cause negative effects for houses in vicinity.
They found that the negative effects of industrial sites are largely localised
and that the housing prices increase with distance to the nearest industrial
site. However, they do not study if different compositions of industrial
sites influence these results, which is probably the case. In the much older
study of CAO AND CORY (1981), a hedonic price analysis is done in order to
measure the impact of land-use externalities on property values. In the
model it is assumed that economic activity is spread over the urban area.
The study is performed in the city of Tucson, which is located in Arizona.
They find that when the amount of industrial, commercial or public land
increases, the property values of residential land tend also to increase,
ceteris paribus. Furthermore, property-owners are more concerned with
the actual land use than possible future land-use policies. Eventually, the
more there are non-single family homes in the neighbourhood, the higher
28 | The impact of mixed land-use
the value of property. They conclude, like SONG AND KNAAP (2004), that an
optimal mix of land-uses must be sought. Monofunctional land-use or
separation of the different activities must be discouraged. BURNELL (1985)
concentrates on the effects of industrial land use on residential property
values in Cook County, Illinois. BURNELL makes a distinction between
localised and non-localised externalities. An example of a localised
externality can be land use while an example of a non-localised externality
is air pollution. He found that residents value the presence of industrial
activity positively although property values are lower in municipalities
that face severe pollution. An increase in commercial use will also lead to
an increase in the property values, holding the other variables constant.
Eventually he concludes that not only the presence of industrial activity is
important but also the type of industrial activity. In an old study of
LAFFERTY AND FRECH (1978) it is found that industrial land and institutional
land affect property values of single family-dwellings positively, while
commercial land have a negative impact on property values.
It is hypothesised that mixed land-use will lead to more efficient
commuting patterns and less transport costs since people live closer to
their work and urban amenities. CERVERO (1995) investigates whether
mixed land-use will optimise commuting. He uses data from the American
Housing Survey (AHS). Two land-use variables from the AHS to identify
the degree of mixed land-use are incorporated in the model. CERVERO
found that a higher density in combination with mixed land-uses
discourages car-ownership and induces shorter commutes. Increasing
density does influence transport mode choices stronger than mixed landuses, except for the choice of walking or cycling. Furthermore, when
activities are located within a very close distance (300 ft.), this induces
people not to travel by car, while beyond this distance a mixed urban
environment stimulates car commuting.
To summarise, we see in general that other land-uses positively affect
property values. Thus, people value other land uses in their vicinity
29 | The impact of mixed land-use
positively. Furthermore, there is some evidence that mixed land-use
influence commuting patterns and mode choice although the effect is not
very strong. Density appears to influence this stronger. However there are
a number of problems that appear in a number of studies: arbitrarily
defined geographical scale, lack of robustness due to analysing only one
region or city and a rough definition of mixed land-use.
2.3.3 Stated preference studies
RODENBURG (2005) notices that stated preference techniques are not yet
much applied in the field of urban planning and urban economics. She
examines what is the willingness to pay for employees and employers for a
multifunctional design of the Amsterdam South-axis (Zuidas).
Furthermore, she investigates what is the willingness to accept a
multifunctional urban design instead of the current living environment of
the residents. It is important to note that RODENBURG compares the current
urban design with a multifunctional one, i.e. she does not compare
monofunctional land use with mixed land use. Contrary to other studies,
RODENBURG (2005) surveyed different actors (not only residents) that are
influenced by a mixed land-use project. She found that employees prefer a
multifunctional design of the south-axis: they are willing to pay € 6.88 for
non-shopping facilities.9 Also employers prefer a mixed land-use design.
They expect that additional services, open space and shopping facilities
will raise their long term profits. However only 20% of the firms located at
the south-axis is willing to pay something for a mixed design. Contrary,
residents do not prefer a mixed land-use design. Therefore the willingness
to accept a mixed urban design is computed. The results show that when
someone lives in a more expensive house, the dweller asks for more
compensation. In general, we see that actors consider a mixed urban
design as a positive side-effect of the living environment, but something
what is not worth to pay for.
Examples of non-shopping facilities are catering facilities, transport services and
‘other’ facilities (RODENBURG 2005).
9
30 | The impact of mixed land-use
The disadvantages of SP are known: cognitive incongruity with actual
behaviour, context effects that influence the results and systematic
misrepresentations of preferences. An example of the latter is that
employers and employees attach more value to the mixed land-use design
than they are willing to pay. Therefore, revealed preference methods, such
as hedonic pricing methods, are preferable (CHESHIRE AND VERMEULEN
2008).
2.3.4 Synthesis
In this section I investigated some of the costs and benefits of mixed landuse. We also looked at some empirical evidence. We saw that the idea of
mixing land-use arose in the sixties since urban sprawl and
monofunctional land uses were causing problems. A number of planning
concepts incorporated mixing land-uses as an important component to
overcome these problems. I came to an integral definition of mixed landuse, “the combination of different socio-economic functions in the same
area” (PRIEMUS ET AL. 2000), which is compatible with all important
planning concepts like New Urbanism, Smart Growth and Multifunctional
Land Use. I looked into literature to investigate what the potential benefits
and costs of mixed land-use are. These potential benefits and costs are
listed in table 2. It is also mentioned if these benefits or costs are
compatible with the definition of mixed land-use which is used in the
different planning concepts and if there is empirical evidence for these
benefits or costs.10 VREEKER (2004) made a distinction between
environmental, social and economic benefits costs. Amongst other things,
less commuting time, environmental benefits due to less emissions and
synergy-effects are potential benefits. That mixed land-use also can yield
costs is almost neglected by urban planners.
Although the evidence for the individual benefits and costs is difficult to identify
we make an attempt to link the potential benefits and costs to the empirical
literature.
10
COSTS
PLANNING
CONCEPT
EVIDENCE
Mobility
Reduction in (car)
mobility resulting
in fewer
emissions;
Shorter
commuting time
and less
congestion.
Long travel time
due to decreased
mobility and
inefficient
provision of
infrastructure.
New Urbanism,
Smart Growth
CERVERO (1995): Mixed land-use
influence transport mode choice
and stimulate walking and bicycling
within 300ft.
Proximity of
uses
Synergy effects
between uses
and land use
externalities,
less transport
costs.
Conflicts of uses.
For example the
NIMBY and LULU
effects; Loss of
production
externalities
(LUCAS AND ROSSIHANSBERG 2002)
Multifunctional
Land Use, New
Urbanism, Smart
Growth,
Compact City
Concept.
CAO AND CORY (1981), BURNELL
(1985), SONG AND KNAAP
(2003,2004), DE VOR AND DE GROOT
(2009): some land uses are valued
positively by residents when
located in proximity, others
negatively; RODENBURG (2005):
Employees and employers value
(non)-shopping variety positively.
Quality of life
Increased safety
due to
intensified
street life.
Loss in privacy.
New Urbanism,
Smart Growth,
Multifunctional
Land Use
HOPPENBROUWER AND LOUW (2005),
RODENBURG (2005): employers and
employees like the mixed urbandesign while residents of the SouthAxis value a mixed land-use design
negatively.
31 | The impact of mixed land-use
BENEFITS
Table 2: Overview of the costs and benefits of mixed land-use.
ISSUE
32 | The impact of mixed land-use
However, VREEKER (2004) and CHESHIRE AND SHEPPARD (2002) argue that
costs can be incurred when uses are mixed wrongly or when the new
situation is experienced negatively by the residents. According to
RODENBURG ET AL. (2003), the effects of mixed land-use are not only
economic but also do have a socio-environmental component. This makes
the measurement of benefits and costs of mixed land-use more difficult.
GRANT (2002) argues that
“Mixed land-use has become a mantra in contemporary planning, its
benefits taken for granted.”
Indeed we saw there was less empirical evidence supporting the idea that
mixed land-use yield social, environmental and economic benefits and
reduce sprawl. KOOMEN ET AL. (2008) admit that empirical evidence still not
lead to decisive answers:
“The sprawl-versus-containment debate relates to many different
aspects [...] and as such the answers found by research are rarely
decisive.”
However, there is some evidence showing us that some land use leads to
higher property values although the answers on which uses affect
property values positively are not decisive. CERVERO (1995) found that
mixed land-use influences transport mode choice and promote bicycling
and walking within 300ft., although the building density does have more
effect on commuter behaviour. To what extent compact city concepts like
Smart Growth and New Urbanism reduce commuter travel is still unclear
(GORDON AND RICHARDSON 1990; SCHWANEN ET AL. 2004; HANDY 2005).
In the end, the main difficulty is that the debate on organising the local
economy is highly normative, while there is still less positive evidence for
these normative assertions.
33 | The impact of mixed land-use
2.4: A framework to measure the effects of mixed land-use
In the preceding subsection we saw that there is less empirical evidence
showing that mixed land-use is beneficial for the local economy. In this
subsection we discuss some methods to measure the economic impact of
mixed land-use on housing values and we present a framework wherein
we apply these methods.
2.4.1 Welfare economics and land-use planning: the hedonic framework
Urban planners are not used to evaluate planning concepts in economic
terms and to verify if a planning concept leads to an efficient outcome.
Economists often analyse the current situation in a rational way, where
planners are often judging plans to their own believes (CHESHIRE AND
VERMEULEN 2008). The economic approach can therefore offer good new
insights in the debate about the configuration of the local economy,
because economists take the preferences of individual actors as starting
point; namely, everyone is in judge of its own welfare, which is one of the
basic postulates of welfare economics. It is important to note that although
economics can offer important contributions to the valuation of
distributional outcomes of planning policies, economists have generally
little to say on how this outcomes should be; which outcome is the most
desirable one (CHESHIRE AND VERMEULEN 2009).
Because the hedonic price model uses the individual preferences as
natural starting point, the application of hedonic price models is perfectly
suitable. It is also preferable to use in this situation since this method is
helpful to identify the impact of a number of externalities on property
values (ROSEN 1974; DE VOR AND DE GROOT 2009). In a hedonic price
analysis the price of a heterogeneous product is regressed on a vector of
characteristics of this product. In our case: transaction prices are
regressed on a number of characteristics of the house. In applying this
method, the implicit prices of the attributes can be traced, in other words,
the marginal willingness to pay for different characteristics can be
computed. The stock of literature in which one applies hedonic methods is
34 | The impact of mixed land-use
huge and still growing. Specific attention is paid to the effects of
environmental amenities and disamenities on real estate values (c.f.
KOHLHASE 1991; HITE ET AL. 2001; BONTEMPS ET AL. 2008; DEKKERS AND VAN
DER STRAATEN 2008). But there are also studies done to other aspects which
influence housing prices, such as the proximity to industrial sites (DE VOR
AND DE GROOT 2009), the amount of open space in vicinity (KAUFMAN AND
CLOUTIER 2006; ROUWENDAL AND VAN DER STRAATEN 2008), and the effects of
railway stations and networks on property values (DEBREZION ET AL. 2004,
2006). Also the studies of SONG AND KNAAP (2003, 2004) use hedonic
methods to measure the effects of land-use planning. I have discussed the
results of the latter studies in detail in section 2.3.2.
Hedonic pricing methods have also some drawbacks (FARBER 1998; KIEL
ZABEL 2001). First, the strong assumption of a housing market in
equilibrium is almost never fulfilled. Since a lot of hedonic analyses are
done in only one year, the outcomes can differ a lot if the housing market
is far from equilibrium. Therefore it is preferable to analyse housing prices
over a longer period of time (DE VOR AND DE GROOT 2009). Second, it is hard
to identify the real willingness to pay for housing characteristics. In reality
people have a limited number of options where they can live, i.e. there is a
selection problem. People can for example accept to live nearby a
disamenity and property values therefore not reflect the true willingness
to accept to live nearby a disamenity. Third, applying these hedonic
methods to our case, we are only able to measure the net-effects of mixed
land-uses on property values. Positive effects such as the appreciation of
urban amenities in vicinity can be outbalanced by adverse effects such as
noise pollution, but this we cannot identify in the results. In spite of these
limitations I adopt this method to measure the impact of mixed land-use
on real estate values.
AND
35 | The impact of mixed land-use
2.4.2 A framework to measure the impact of mixed land-use
The planning concepts like New Urbanism, Smart Growth and MLU state
that mixed land-use yield socio-economic benefits. In our opinion it is too
early to make such statements. In this research I want to measure the
impact of mixed land-use on house prices to be able to verify if one of the
keystones of these concepts, namely mixed-land use, contributes to a more
efficient local economy.
Assume that a government decides to adopt the planning principle of
mixing land-uses. This decision will affect the local environment (for
example, a neighbourhood). A mixed urban environment will arise with
both houses and different land uses (e.g. shops, light industry, advanced
producer services). These land uses will probably affect the willingness to
pay for ‘house X’, land uses that are located in proximity more than land
uses that are located further away. For example, when ‘land use B’ is heavy
industry, the price of ‘house X’ is probably lower than when ‘land use B’
are shops. But I also expect that the local environment as such will
influence the price of ‘house X’. Namely, the Smart Growth Concept and the
supporters of New Urbanism assume that people will like (and prefer) a
mixed urban environment (SMART GROWTH NETWORK (2009),
NEWURBANISM.ORG (2009)).
Thus, I expect mixed land-use will influence property values in two ways
(figure 3):
1) Via the effect of individual land-uses on housing prices
2) Via the effect of the local environment as a whole on the housing
prices.
I will apply a hedonic framework to examine the effects of mixed-land use
on property values. The different land-uses are represented by the
number of employees which are working in the different sectors, i.e.
employment is a proxy for the presence of different land-uses. Taking the
employment into account and not only the type of use is preferable since it
corrects for the intensity of presence of a land-use.
36 | The impact of mixed land-use
As already mentioned we are only able to measure the net-effects and not
identify the specific positive and negative effects as discussed in section
2.3 of both individual uses and the environment as a whole on property
values.
Furthermore, it is important to note that all the different land-uses are
taken into account, except the amount of open space and infrastructural
land-use in vicinity of a house since the land-uses are not represented by
employment. This is clearly a drawback of this analysis since I neglect
effects of open space and infrastructural quality, which most likely
influence property values.
Figure 3: A framework for measuring the effects of mixed land-use
Land use A
Land use B
House X
Land use C
Local (mixed) environment
A government implements mixed
land-use policies
Land use D
37 | The impact of mixed land-use
38 | The impact of mixed land-use
Section 3: Datasets and measurements
I
n this section I will introduce our data and discuss how different aspects
of mixed land-use are measured. This section is concluded by
considering the regional context. I will also compute some measurements
on neighbourhood level to get an idea of the local constellation of the
region.
3.1 Datasets
My analysis is based upon three datasets.
The first dataset is from the NVM (Dutch Association of Brokers). It
contains data from 10152 housing transactions in 2006 in the Rotterdam
city region. These data describe the transaction price of a house and a
number of structural characteristics of the house, such as size, number of
rooms and type of house. This database contains very rich and precise
information about structural characteristics of houses. In the NVMdatabase I delete the unrealistic values. For example I delete all the
transaction prices of houses which are below € 10,000 and above €
5,000,000. After deletions the database consists of 10082 values.
Furthermore, a database of the KvK (Chamber of Commerce) is used,
which comprises data from all companies that are located in the
Rotterdam city region in 2006. From these 43911 companies I have
information about the location of each establishment (also subsidiaries)
on a six digit postal code, the number of employees and the sector on SBI
5-digit level.
At last, I use a database from the CBS (Central Bureau of Statistics) which
contain information on neighbourhood level on the population density and
the cultural composition of the neighbourhood.
39 | The impact of mixed land-use
Derived from these three datasets one database has been constructed.
This database contains for each house, the transaction price, a number of
structural characteristics, a number of variables that indicate the presence
of different land uses expressed in number of employees in each sector, a
number of accessibility measures and a number of variables that describe
some neighbourhood characteristics such as population density and
percentage ethnic minorities. One could consider the table in appendix B
to view the descriptives of each used variable..
3.2 Measures of mixed land-use
SONG AND KNAAP (2004) present five different measures of mixed land-use.
Their unit of analysis are Traffic Analysis Zones (TAZs). A TAZ is a special
area which is used for local transportation analysis. They argue that a TAZ
is a convenient unit of analysis because the scale of a buffer is subject of
debate. However, I think that the buffer is more convenient because the
size of each ‘neighbourhood’ for each house is the same and is not subject
to arbitrarily defined neighbourhood boundaries. In our analysis I will
vary the size of the buffer to find out what the real ‘neighbourhood of
interest’ is for each house. Note that I use neighbourhood-data for some
mixed-use and control variables, because the data is not available on
address-level. For more information about our specific buffer-technique to
obtain our final database I refer to appendix A.
In the paper of SONG AND KNAAP (2004) mixed land-use is measured at four
different ways. First, some measures of accessibility are offered. Since one
can question measures of accessibility because it does not really describe
aspects of mixed land-use I only add some accessibility measures as
control variables.
Second, according to GEOGHEGAN ET AL. (1997), the scale of non-residential
use can have a significant impact on housing transaction prices. SONG AND
KNAAP use the percentage of 5 different uses to measure this scale effect. I
think this is not the right way to measure the scale of non-residential use
40 | The impact of mixed land-use
because especially when one uses buffers, it does not make clear how
much of these uses are present in absolute terms in the surroundings of the
house. Therefore I measure for each house the amount of employment in
terms of employment in each sector within a certain distance (for example
0.5 kilometres). Our sectoral composition is far more detailed than any
other study that aims to measure the benefits and costs of mixed land-use.
Third, as presented in the framework in section 2.4 consumers may value
the pattern of land-uses, in other words the constellation of the local
environment. SONG AND KNAAP use some entropy measure this variation in
land uses. An entropy measure is used which measures variety in
employment. For example, FRENKEN ET AL. (2007) apply this entropy
measure to measure the variety at a two-digit SBI-sector level. Formally,
since I have information on five-digit sectors I have to sum up the fivedigit shares, pf, to derive the broad sector shares, Pg:
Pg 
p
f S g
f
(3.1)
Where f is five-digit sector who fall under a ‘one-digit’-sector Sg, where
g=1,...,G. The entropy at the ‘one-digit’ level is then given by:
G

g 1

V   Pg log  1
P
g



(3.2)
Another way to measure variety or diversity is to use the inverse of the
Hirschmann-Herfindahl index or diversity index (DURANTON AND PUGA
2000). Formally:
D
1
G
(3.3)
P
g 1
2
g
Note that I do not use relative indices such as the relative diversity index
(DURANTON AND PUGA 2000) and the Krugman specialisation index
(KRUGMAN 1991). The reason for this is that I am interested in absolute
41 | The impact of mixed land-use
diversity in land uses and not in diversity compared to the average
regional or national structure.
Fourth, SONG AND KNAAP use a set of variables to describe the balance
between living and working. This is computed by dividing the total
number of employees by the population of a neighbourhood. I can
measure the number of workers in a buffer. Unfortunately, I do not know
how much people are living in a buffer around the house. Therefore, it is
assumed that the population density that holds for the neighbourhood, as
defined by the CBS, wherein a house is located also holds for the buffer
around a certain house.11 Then I can calculate the job-population ratio W
for house h as follows:
Wh 
Eh
r 2 Rh
(3.4)
Where h = 1,....,H, E is the total employment in the buffer around house h,
r is the radius of the buffer measured in kilometres and R is the population
density per km2 of house h in the neighbourhood where the house is
located.
To summarise, I measure the impact of mixed land-use by looking at the
scale of land uses that are in vicinity of a house, the structure of the
available land uses that are close by and a ratio that describes the balance
between jobs and population. Using these measures I am able to trace to a
large extent the impact of mixed land-uses on property values of houses.
Especially when the buffer is not too large, this assumption is reasonable
because population density oftentimes changes gradually between
neighbourhoods that share borders.
11
42 | The impact of mixed land-use
3.3 Regional context: the city region Rotterdam
As already mentioned I use data from the city region Rotterdam. This
administrative region is located in the west of the Netherlands and hosts
the largest and most diversified port in terms of traffic in Europe (DUCRUET
ET AL. 2009).
Rotterdam is by far the largest city in this city region with 584,060
inhabitants in 2006. The total population of the city region was 1,175,630
and the total number of jobs was 518,251. In figure 4 we see that the city
region consists of 16 municipalities. In 2007 the municipalities of
Bleiswijk, Bergschenhoek and Berkel en Rodenrijs were aggregated into
one municipality Lansingerland. In our analysis I also aggregate these
three municipalities into one. Looking at figure 5, we see that the highest
population density is found in the city centre of Rotterdam and in the
urban areas of Schiedam, Spijkenisse, Vlaardingen and some parts of
Capelle a/d IJssel. Although the city region is generally urbanised a large
part of the land in the city region is used by agricultural activities. For
example, municipalities of Westvoorne, Brielle, Hellevoetsluis and
Lansingerland experience a relatively low population density. In the port
areas Maasvlakte, Europoort, Botlek and Waalhaven the population
density is also low, but the land is quite intensively used by capital
intensive (port-related) industrial activities. This is confirmed by figure 6.
We see employment is concentrated on places where the population is
concentrated: therefore figure 5 and 6 resemble an almost identical spatial
pattern. It is somewhat surprising that the employment density in the port
areas is also quite low. As already pointed out, there is much capitalintensive industry located in these areas, which explains the low
employment density in these areas.
Planning policies in the Rotterdam city region are largely in accordance
with the compact city-concept which is widely applied in the Netherlands.
This concept focuses on the concentration of urban growth in regional
43 | The impact of mixed land-use
Figure 4: The city region of Rotterdam
Figure 5: Population density at neighbourhood level
44 | The impact of mixed land-use
Figure 6: Employment density at neighbourhood level
centres, preservation of open space and determining so-called greenbuffers between cities. Indeed, although the city region faces a high
population density the region still consists of many towns and open spaces
between them (KOOMEN ET AL. 2008). Besides, the development of port
industries and other industrial areas is bound to strict planning rules.
With respect to the configuration of the local economy I ascertain that
there are several examples of what we can characterize as an aim to
stimulate mixed land-use. Especially the municipality of Rotterdam is
developing special areas where residential and commercial activities are
co-located. In the white paper STADSVISIE ROTTERDAM 2030 (2007) we can
read the Lloyd quarter and the Wilhelminapier are examples of that
mixing of uses in one area:
“A particular area in Delfshaven is the Lloyd quarter, a former port area,
where a mix of living and working is constituted.”
And:
45 | The impact of mixed land-use
“The Wilhelminapier [is] a location with a rich history, a skyline and an
attractive mix of living, working, culture, hotels, restaurant and other
urban amenities […]. This will be the trendy, most ‘glossy’ mixed urban
area of Rotterdam.”
Although there are numerous examples of actual mixed land-use, there are
also a number of areas which are largely monofunctional. For example, the
port area and large agricultural areas in Westvoorne, Brielle, Bernisse and
Hellevoetsluis are to a large extent monofunctional. Because of the
presence of mixed land-use as well as monofunctional land-use areas, the
city region of Rotterdam is a suitable region to study the effects of mixed
land-use.12
Figure 7: Variety at neighbourhood level
Actually, we can compare the impact of having a mixed urban environment with
the effects of a more monofunctional urban economy.
12
46 | The impact of mixed land-use
In section 3.2 I introduced a number of measures to mix land-use. To get
an idea of the structure of the local economy I compute these measures at
the CBS-neighbourhood level. In figure 7 the variety within
neighbourhoods is computed. We can observe that the variety is the
highest in a number of neighbourhoods in the centre and the south of
Rotterdam. Also the port areas of Botlek, Rozenburg and Hoogvliet-Noord
experience a high variety.
A little bit surprising is the high variety of neighbourhoods in highly
agricultural areas like Zuidland (municipality of Bernisse), Bergschenhoek
and Bleiswijk. When I verify the division of labour in these areas I
encounter there is a combination of agricultural activities, activities in the
construction sector, wholesale, business services and other services
(sector O). Note that this measure does not correct for the size of the
region, therefore it is obvious that larger regions are on average more
diversified than small regions. Figure 8 resembles pretty much the same
pattern as figure 7. We see again a high diversity in Zuidland, Botlek,
Overschie and Charlois.
Figure 9 offers us the job-population ratio. We see in a large number of
areas the job-population ratio is quite high. These areas are in most cases
industrial sites, i.e. there are living no people in such areas. For example, in
the port areas of Botlek, Europoort and the Maasvlakte there is no
residential land use. In the urban neighbourhoods in Rotterdam, Schiedam
and Vlaardingen the job-population ratio is relatively low. However, we
know that there is both a high job density and a high population density;
hence we can expect that the intensity of mixed land-use is relatively high
in these neighbourhoods.
47 | The impact of mixed land-use
Figure 8: Diversity at neighbourhood level
N
A
Legend
IlO data
0.00· UlO
.
2.00-400
_4.00 - 6.00
_
&.00 - 8.00
_ ~ 8.00
Figure 9: Job population ratio at neighbourhood level
N
A
Job-population ratio
,'~
_
6\ . 70'11.
_71 -1m11
61 · ~
_
9\_100'1/,
48 | The impact of mixed land-use
Section 4: Model estimation and results
T
his section focuses on empirical results which verify the impact of
mixed land-use. In section 4.1 an econometric model is presented,
which I will make operational in the subsequent subsections. In section 4.2
a number of log-linear models is applied in which it is assumed that the
effects of mixed land-use are taking place within a buffer of 500m of the
house. In section 4.3 I relax this assumption and the size of the buffer is
changed. Section 4.4 compares the results of the log-linear models with a
more sophisticated functional form of the hedonic price function.
Moreover, some alternatives to OLS are addressed. Section 4.5
summarises and discusses the main empirical results. In section 4.6 some
shortcomings of the empirical analyses are mentioned.
4.1 The econometric model
As discussed in section 2.4, in the hedonic framework of ROSEN (1974) a
housing market is assumed which is in equilibrium. This implies that
perfectly rational house owners make their utility maximizing decisions
given the prices of the houses. The utility of an individual p living in a
house can be defined as:
u p  u ( x, q )
(4.1)
Where u is the utility, x are all other goods consumed and q is a vector K of
characteristics k of a house (for example, the size of a house is a
characteristic: we can expect people with larger houses gain utility from
that). Individuals maximise their utility subject to the following budget
constraint. Consumers spend their income either on housing or the other
goods:
y p  Ph (qk )  x
(4.2)
From this we know that the price of a house is a function of different
characteristics of a house. To be more precise, when people spend more
money on housing, one can expect that the housing quality which is a
49 | The impact of mixed land-use
function of the characteristics is higher than when one spends less money
on housing. Therefore:
Ph  f (qk )
(4.3)
Note that the marginal willingness to pay for a characteristic can be
computed from these equations, namely:
yh Ph (qk )

qk
qk
(4.4)
We can subdivide the characteristics in structural characteristics of a
house (e.g. size, number of rooms), characteristics of the neighbourhood
(e.g. percentage of ethnic minorities, population density), accessibility
characteristics (e.g. distance to highway entrance) and the measures of
mixed land-use I defined in section 3 (e.g. employment in different sectors,
variety in uses).
Unfortunately, there is no prescription which functional form of the
hedonic price function is appropriate. Oftentimes, a linear or log-linear
specification is used because it is easy to interpret the coefficients
(FREEMAN 1993; BONTEMPS ET AL. 2008; DE VOR AND DE GROOT 2009): in a
linear specification the coefficients are equal to the (constant) marginal
prices while in a log-linear specification the coefficients denote (constant)
elasticities. However, some studies use a less restrictive functional form
because it is argued that the relation between housing values and the
related attributes is complex and non-linear (EKELAND ET AL. 2004). For
example CHESHIRE AND SHEPPARD (2002) apply Box-Cox transformations to
the hedonic price functions in their analysis of the costs and benefits of
land use planning. Furthermore, PARMETER ET AL. (2007) proposes a nonparametric estimation of a hedonic price function. Also BONTEMPS ET AL.
(2008) use other specifications of the hedonic price function, where the
structural characteristics are linearly related to the price while the
variables of interest are non-linearly related to the property value. They
compare four different specifications, respectively a non-parametric
50 | The impact of mixed land-use
model, an additive specification, a single-index specification and a fully
parametric model. They found that the variables of interest have a strong
non-linear impact on housing values. In my case, I cannot apply the nonparametric or semi-parametric methods of BONTEMPS ET AL., because I have
a large number of variables of interest (think of all the sector-variables).
Then the so-called ‘curse of multidimensionality’ will arise: the quality of
non-parametric estimates will deteriorate quickly when the number of
characteristics increases (BONTEMPS ET AL. 2008). Therefore I will start with
a well-known log-linear specification of the hedonic price function:
ln Ph      i qi    j ln q j  
iD
(4.5)
jN
Where α and the β’s are the coefficients to be estimated and ε is the error
term. qi, qj are the characteristics. D is the set of indices and dichotomous
variables i.13 N is the set of continuous variables j. Equation (4.5) can easily
be estimated using Ordinary Least Squares. Later on I will relax this
functional form by applying Box-Cox transformations.
In the first models it is assumed that the impact of mixed land-use is
restricted to a buffer of 500 meter around each house. I expect that the
effects of mixed land-use are largely localised. In a number of studies the
results indicate that effects of the environment of a house on the value of a
house have oftentimes a localised nature. For example, DE VOR AND DE
GROOT (2009) found that the negative effects of industrial sites on house
prices were largely localised. Also ROUWENDAL AND VAN DER STRAATEN
(2008) found that the effects of open space are localised (within 500m of
each house). In addition, PALMQUIST (1992) and KIEL AND ZABEL (2001) find
that a number of environmental effects are have a localised impact on
housing values. In section 4.3 I will change the size of the buffer to check
whether this assumption is appropriate.
13
To check which variables are transformed logarithmically, I refer to appendix B.
51 | The impact of mixed land-use
4.2 Empirical results of log-linear models using OLS
Four models are carried out in this subsection. In all models robust
standard errors are used, which allow for heteroskedasticity and/or
autocorrelated error terms. Furthermore, I check if multicollinearity
exists. Higher correlation between the explanatory variables influences
the standard errors of the coefficients. Moreover, multicollinearity cause
difficulties in assessing the individual importance of a predictor, it limits
the size of the R2 and it causes unstable predictor equations (FIELD 2005).
An example of correlation between predictors can be that population
density is causing variety of uses because when the density of people is
higher it can be profitable for companies to locate nearby concentrations
of people which automatically imply a more diverse regional economy. A
way to check for this multicollinearity is to check the variation inflation
factors. It is argued that when these values exceed 10 there is reason for
concern (MYERS 1990). But also lower values are proposed (BOWERMAN AND
O’CONNELL 1990; MENARD 1995; KUTNER ET AL. (2004); FIELD 2005). In the
models I executed the VIF values never exceed 5, therefore I expect that
there is no severe multicollinearity in my models.
The results of the four models which I executed are presented in table 3.
The first model contains the variety measure (equation 3.2), the second
the diversity measure (equation 3.3) and in the third model I add the
variable which contains the job-population ratio (equation 3.4).14 In the
fourth model I split up the wholesale and retail sector for reasons I will
discuss later. We see that in all models the coefficients of the control
variables are almost the same. The structural variables have in general
plausible signs. When a house is larger in terms of lot size or volume the
Obviously we cannot include all three measures in one model, since the variables
all measure the impact of a mixed environment (cf. section 2.4). Including these
variables in one model would lead to multicollinearity.
14
MODEL (3) - OLS
9.12044 (0.3045)
MODEL (4) - OLS
9.1808 (0.3094)
0.0097 (0.0014)
0.7449 (0.0557)
-0.0068 (0.0149)
0.0818 (0.0182)
0.3751 (0.0354)
0.0157 (0.0081)
0.0498 (0.0148)
0.0181 (0.0067)
-0.2503 (0.0435)
0.0706 (0.0080)
-0.1066 (0.0103)
0.1155 (0.0408)
-0.0041 (0.0074)
0.0096 (0.0087)
0.0381 (0.0074)
0.1850 (0.0096)
0.2419 (0.0140)
0.0096 (0.0014)
0.7454 (0.0558)
-0.0061 (0.0150)
0.0828 (0.0182)
0.3767 (0.0354)
0.0157 (0.0081)
0.0498 (0.0148)
0.0181 (0.0068)
-0.2494 (0.0436)
0.0701 (0.0080)
-0.1070 (0.0103)
0.1127 (0.0406)
-0.0020 (0.0074)
0.0117 (0.0088)
0.0411 (0.0075)
0.1874 (0.0097)
0.2436 (0.0140)
0.0098 (0.0014)
0.7447 (0.0558)
-0.0072 (0.0149)
0.0812 (0.0182)
0.3755 (0.0354)
0.0157 (0.0081)
0.0502 (0.0148)
0.0183 (0.0068)
-0.3205 (0.0441)
-0.0711 (0.0080)
-0.1065 (0.0103)
0.1147 (0.0409)
-0.0047 (0.0075)
0.0096 (0.0087)
0.0379 (0.0074)
0.1850 (0.0096)
0.2414 (0.0140)
-0.0766 (0.0048)
0.0043 (0.0036)
-0.0167 (0.0030)
-0.0768 (0.0049)
0.0044 (0.0036)
-0.0181 (0.0030)
-0.0769 (0.0048)
0.0055 (0.0036)
-0.0178 (0.0030)
-0.0503 (0.0037)
-0.0049 (0.0002)
-0.0517 (0.0042)
-0.0049 (0.0002)
-0.0494 (0.0037)
-0.0049 (0.0002)
52 | The impact of mixed land-use
MODEL (2) - OLS
9.1232 (0.3011)
Table 3: Results of OLS-models
VARIABLE
MODEL (1) - OLS
Constant
9.1092 (0.3010)
SRUCTURAL CHARACTERISTICS
Lot size
0.0096 (0.0014)
Volume
0.7452 (0.0558)
Terraced
-0.0065 (0.0149)
0.0823 (0.0182)
Semi-detached
0.3761 (0.0354)
Detached
0.0158 (0.0082)
Rooms
0.0498 (0.0148)
Garage
0.0181 (0.0068)
Garden
-0.2502 (0.0435)
Maintenance good
0.0701 (0.0080)
Maintenance average
-0.1069 (0.0104)
No central heating
0.1127 (0.0406)
Monument
-0.0027 (0.0074)
Construction year 1961-1970
0.0107 (0.0087)
Construction year 1971-1980
0.0398 (0.0074)
Construction year 1981-1990
0.1867 (0.0096)
Construction year 1991-2000
0.2431 (0.0140)
Construction year > 2001
ACCESSIBILITY CHARACTERISTICS
-0.0761 (0.0048)
Distance to centre Rotterdam
0.0043 (0.0036)
Distance to highway ramp
-0.0173 (0.0030)
Distance to railway station
NEIGHBOURHOOD CHARACTERISTICS
-0.0505 (0.0037)
Population density
-0.0049 (0.0002)
Percentage ethnic minorities
-0.0067 (0.0017)
0.0020 (0.0017)
-0.0063 (0.0018)
-0.0039 (0.0026)
-0.0173 (0.0024)
-0.0140 (0.0027)
0.0022 (0.0024)
0.0001 (0.0019)
0.0140 (0.0020)
0.0154 (0.0024)
-0.0043 (0.0012)
-0.0010 (0.0017)
-0.0018 (0.0019)
0.0116 (0.0031)
-0.0023 (0.0040)
0.0016 (0.0016)
-0.0068 (0.0018)
-0.0039 (0.0027)
-0.0197 (0.0023)
-0.0132 (0.0027)
0.0025 (0.0024)
-0.0005 (0.0019)
0.0136 (0.0020)
0.0163 (0.0024)
-0.0044 (0.0012)
-0.0020 (0.0017)
0.0004 (0.0018)
0.0088 (0.0030)
-0.0068 (0.0017)
0.0019 (0.0017)
-0.0064 (0.0019)
-0.0042 (0.0026)
-0.0183 (0.0024)
-0.0024 (0.0023)
-0.0100 (0.0021)
0.0026 (0.0024)
-0.0003 (0.0019)
0.0142 (0.0020)
0.0137 (0.0024)
-0.0041 (0.0012)
-0.0007 (0.0018)
-0.0015 (0.0019)
0.0116 (0.0031)
10081
10081
10081
10081
N
0.8077
0.8080
0.8077
0.8080
R2
Root-MSE
0.2135
0.2134
0.2135
0.2134
The robust standard errors are given between parentheses; the bold coefficients are significant on at least a 5% level.
53 | The impact of mixed land-use
MIXED LAND-USE CHARACTERISTICS
Variety
-0.0203 (0.0156)
Diversity
Job-population ratio
A_B_C Agricultural activities
0.0018 (0.0017)
-0.0067 (0.0018)
D Industry
-0.0040 (0.0026)
E Production electricity, water
-0.0190 (0.0024)
F Construction
-0.0129 (0.0027)
G Wholesale and retail
G_1 Wholesale
G_2 Retail
0.0024 (0.0024)
H Hotel, restaurants and cafés
-0.0004 (0.0019)
I Transport and communication
0.0136 (0.0020)
J Financial services
0.0162 (0.0024)
K Business services
-0.0045 (0.0012)
L Public services
-0.0016 (0.0018)
M Education
-0.0003 (0.0018)
N Healthcare
0.0096 (0.0031)
O Other services
54 | The impact of mixed land-use
price is higher. Furthermore, more privacy (detached vs. apartment), a
garage, a garden, a central heating and being a monument contribute also
to higher housing values. Unfortunately there are also some implausible
signs. The sign of “maintenance good” is negative, meaning that when a
house has a good state of maintenance the price will be lower compared to
a house that is maintained badly, which is counterintuitive. Looking at the
accessibility measures, the results are in line with our expectations. An
increase of 1% in the distance to the city centre leads to a ~0.08%
decrease in the house price, ceteris paribus. Also living near a railway
station leads to a higher value of the house, probably because of positive
externalities, such as shorter commuting times. Living nearby a highway
ramp has not a significant effect, probably because the negative effects of
living nearby highways (noise and air pollution) and the positive effects
(better accessibility) are balanced. The neighbourhood variables are both
very significant. A higher population density leads to lower house prices.
Also a higher percentage of ethnic minorities leads to lower house prices
since concentrations of ethnic minorities are oftentimes associated with
higher crime rates and other negative effects. However, compared to the
study of WADDELL ET AL. (1993) this effect is quite small: 1% increase in the
share of ethnic minorities will only lead to a 0.005% decrease in housing
prices while WADDELL ET AL. found differences in house prices of 20% when
the share of Hispanics and blacks in neighbourhoods was substantial.
I now turn to the variables in which I am particularly interested. The
measure of how people value the mixed environment is only significant in
model (2), where I use the diversity index. The variety index and the jobpopulation ratio do not influence housing prices significantly. The main
similarity between these measures is the sign of the coefficients. In all
three cases this sign is negative, indicating that people do not value a
mixed local environment positively.
55 | The impact of mixed land-use
When I consider the effects of individual uses I see that not all land-uses
are having a significant effect. A higher employment in the agricultural
sector, production of electricity and water, hotel and catering sector,
transport and communication sector, educational sector and healthcare
sector will not lead to significantly higher or lower housing prices. There
can be multiple reasons for this insignificance. First, the size of the sector
can be exceptionally small. There are for example very few companies in
the sector ‘production of electricity and water’ that are located in the city
region of Rotterdam. Second, the positive externalities and the negative
externalities can be balanced. For example, living near a school yields on
the one hand positive effects (e.g. less travel time) but on the other hand it
also yields negative effects (e.g. yelling children, more crime). Third, there
are no clear effects of land-uses on the environment. One can imagine that
having some types of employment in vicinity does not yield any benefits or
costs. As expected, there are also a number of land-uses that significantly
influence property values. When the employment in the industrial sector
increases with 1% in the vicinity of a house, the housing price decreases
with 0.0065%. For the construction sector and wholesale and retail sector
the effect is more negative. The negative coefficient of the wholesale and
retail sector is somewhat surprising: I expected people like to have shops
in vicinity. However, wholesale-companies are oftentimes located in
unattractive buildings on even more unattractive industrial sites.
Therefore it might be that the positive effects of retail-companies are
exceeded by negative effects of wholesale-companies. I test this hypothesis
in model (4) by decomposing the wholesale and retail sector in two
subsectors. The results do not support the aforementioned proposition.
We see retail has the highest negative impact on housing values and not
wholesale. Furthermore, we see that public services in vicinity yield a
small negative effect on property values. Having more employment in the
business, financial and other services sector will lead to higher housing
prices.
56 | The impact of mixed land-use
To get a better idea of the magnitude of the effects of different uses on
property values we compute the marginal willingness to pay for a one unit
increase in employment for the land uses which have a significant effect on
housing values. The interpretation of this willingness to pay for an
additional employee in a sector is that an employee reflects the degree of
presence of a particular land-use in vicinity. In a very capital-intensive
sector we can expect that one employee has a larger effect than an
employee in a sector which is very labour-intensive.15 We compute the
marginal willingness to pay as follows:

Ph (qi , q j )  j e
q j

 i qi    j q j  
iD
jN
qj

 j Ph
qj
(4.7)
We can see the marginal willingness to pay or accept is dependent on the
price and type of house. Therefore we calculate the marginal WTP for the
sample averages of both the transaction price and the sectoral
employment information. It is found that the average price of a house is €
217738. I use the coefficients that are presented in table 3 and in table 4
we can check the results. We see the marginal willingness to pay is the
highest for an additional employee in the financial services sector,
followed by someone who works in de business service sector. The
willingness to pay an additional employee is the most negative in the
construction sector followed by someone who works in de industrial
sector.
15 To be more explicit, consider the industrial sector which is a very capitalintensive sector where only a few employees could represent a large industrial
firm. Therefore, this few employees are accountable for all the effects industrial
land use has on property values (for example, air pollution). On the other hand, the
retail sector is very labour-intensive. Many employees are responsible for the
probably smaller effect of retail on housing values.
57 | The impact of mixed land-use
Because of the choice for a logarithmic functional form there is a strong
log-linear relationship between the willingness to pay and the
employment in a sector. We see in figure 10 an example of the relationship
between employment in the business services sector and the willingness
to pay an additional employee in the business services sector. We see that
when there are already many services located the willingness to pay for
additional employee is much lower than when there are no employees in
Table 4: Marginal willingness to pay for a one unit increase in employment
SECTOR
MODEL (1)
D Industry
F Construction
G Wholesale and retail
J Financial services
K Business services
L Public services
O Other services
MODEL (2)
- €30.50
- €72.16
- €9.81
€28.84
€11.36
- €7.69
€23.63
- €28.54
- €65.55
- €10.64
€29.82
€10.76
- €7.32
€28.41
MODEL (3)
- €31.13
- €74.63
- €10.02
€28.96
€11.41
- €7.51
€21.55
Figure 10: Relationship between willingness to accept and employment in
the industrial sector
Marginal Willingness to Pay in
€
100000
10000
1000
100
10
1
1
10
100
1000
Employment in sector K Business Services
10000
58 | The impact of mixed land-use
this sector located in vicinity of the house. Consequently, it would be
reasonable to cluster employment which affects house prices negatively,
since the extra employee in such a sector will have a relatively small effect
on the house price. The sectors that influence housing values positively
should be mixed with residential use since the additional employee has a
larger effect when there are relatively less employees of these sectors in
vicinity. I realise that this result is partly caused by the adaption of a loglinear functional form. In section 4.4 we will verify if this choice is
appropriate.
We can check if sectors that positively affect housing values are less
concentrated. Sectors which are more concentrated are probably also
more mixed with residential use. On the other hand we can check if sectors
which cause negative externalities are clustered. We can use the Ginicoefficient to measure concentration of a sector (FOWLER 2006). Formally:
N
Gs  0.5 X s ,n  Yn
n 1
(4.8)
Where G is the Gini-coefficient of sector s, where s=1,...,13. X is the share
of employment in sector s in six-digit postal code n in the total
employment in sector s. n=1,...,N. Y is the share of employment in a postal
code in the total employment in the city region. The Gini ranges from 0 to
1, where 0 represents an equally distributed system and 1 represents a
complete concentration of a sector in one postal code (FOWLER 2006). In
table 5 we can see the results for each sector. However, there is no
correlation between the concentration of a sector and the (sign of the)
willingness to pay coefficient.16
16 When we delete the outlier “sector E production of electricity and water”, there
is absence of any significant correlation, also if we correct for the size for a sector
in terms of employment (smaller sectors are probably less concentrated than
larger sectors).
59 | The impact of mixed land-use
Table 5: Concentration per sector
SECTOR
GINICOEFFICIENT
0.910
EFFECT ON
HOUSE PRICES
+/–
D Industry
0.780
–
E Electricity & water
0.978
+/–
F Construction
0.773
–
G Wholesale & retail
0.616
–
H Hotels, restaurants, cafés
0.793
+/–
I Transport & communication
0.737
–
J Financial services
0.843
+
K Business services
0.590
+
L Public services
0.898
–
M Education
0.864
+/–
N Health care
0.764
–
O Environmental and other services
0.804
+
A_B_C Agricultural
4.3 Changing the buffer size
Until now it is assumed that the effects of mixed land-use were largely
localised. Now I will vary the size of the buffer. I choose only to apply
model (2) for these different buffer sizes and compare the coefficients and
WTP for different buffer sizes, because model (2) had the best fit (R2) and
was the only model where the mixed land-use variable (the diversity
measure) was significant. Moreover, I think diversity describes the
diversity of uses in the best way.17 I range the buffer sizes from 250m to
2500m with intervals of 250m. In table 6 the results of four of the models
with different buffer sizes, respectively 250m, 1000m, 1750m and 2500m,
I made some figures of both the indices (variety and diversity measure). The
spread of the estimations of the diversity measure was better than the variety
measure. Therefore, the diversity measure describes in a better way the
differences in diversity in the local economy between buffers than the variety
measure.
17
60 | The impact of mixed land-use
are presented. Since I am particularly interested in the variables that
measure mixed land-use, only the coefficients of these variables are
presented. Full results of all variables and all buffer sizes are available
upon request.
Looking at table 6, we do not observe very clear patterns. Also when
considering large buffers, nearly all land-uses significantly influence
housing values. Probably, because in large buffers also the employment in
vicinity is taken into account what can explain the significance of the
coefficients.
We see nearly all land-uses are not significant in all ten models. An
exception is the construction sector which has always a significant
negative impact on housing prices ranging from 0.014% to 0.033% when
the employment in this sector vicinity of a house increases with 1%.
Another exception is the financial services sector. This sector influences
property values positively, ranging from a 0.009% to 0.033% increase in
housing prices when the employment increases with 1%. Other sectors
that show significant results in at least eight out of ten models are the
electricity and water sector, the wholesale and retail sector, the hotel,
restaurants and cafés sector, the public services sector, the educational
sector and finally, the environmental and other services sector. We again
compute the willingness to pay for different characteristics to reveal the
magnitude of the different land-uses.
The willingness to pay or accept is computed for the sample averages of
the land-use variables (table 7). When the buffer size increases the sample
averages of different land-uses generally increases, since there is more
employment in vicinity of the house. Therefore, we expected that in
general the willingness to pay for an additional employee will converge to
zero. This is confirmed by the estimates which show a convergence of the
WTP-coefficients to zero, when the buffer size increases.
MODEL (6) – OLS
Buffer 1000m
MODEL (7) – OLS
Buffer 1250m
MODEL (8) – OLS
Buffer 1500m
Diversity
A_B_C Agricultural activities
D Industry
E Production electricity, water
F Construction
G Wholesale and retail
H Hotel, restaurants and cafés
I Transport and communication
J Financial services
K Business services
L Public services
M Education
N Healthcare
O Other services
-0.0061 (0.0017)
-0.0075 (0.0025)
-0.0016 (0.0021)
-0.0053 (0.0046)
-0.0162 (0.0019)
-0.0100 (0.0020)
-0.0005 (0.0028)
0.0016 (0.0021)
0.0108 (0.0023)
0.0162 (0.0019)
-0.0016 (0.0016)
-0.0026 (0.0012)
-0.0025 (0.0014)
0.0047 (0.0026)
-0.0022 (0.0020)
0.0033 (0.0018)
0.0044 (0.0021)
-0.0066 (0.0016)
-0.0311 (0.0035)
-0.0198 (0.0043)
0.0074 (0.0034)
-0.0062 (0.0024)
0.0091 (0.0022)
0.0130 (0.0036)
-0.0020 (0.0011)
0.0250 (0.0042)
0.0010 (0.0031)
-0.0026 (0.0056)
-0.0037 (0.0027)
-0.0008 (0.0023)
0.0118 (0.0034)
-0.0115 (0.0013)
-0.0149 (0.0045)
-0.0248 (0.0063)
0.0326 (0.0055)
-0.0198 (0.0039)
0.0209 (0.0028)
0.0036 (0.0052)
0.0028 (0.0017)
0.0354 (0.0057)
-0.0176 (0.0038)
-0.0279 (0.0064)
-0.0180 (0.0040)
0.0074 (0.0028)
0.0027 (0.0040)
-0.0158 (0.0012)
-0.0194 (0.0061)
-0.0110 (0.0108)
0.0321 (0.0075)
0.0033 (0.0050)
0.0334 (0.0036)
-0.0179 (0.0091)
0.0111 (0.0025)
0.0698 (0.0076)
-0.0561 (0.0055)
-0.0543 (0.0087)
N
10081
10081
10081
10081
R2
0.8072
0.8085
0.8106
0.8132
0.2138
0.2131
0.212
0.2105
Root-MSE
The robust standard errors are given between parentheses; the bold coefficients are significant on at least a 5% level.
61 | The impact of mixed land-use
MODEL (5) – OLS
Buffer 250m
Table 6: Results log-linear OLS with different buffer sizes
VARIABLE
500m
750m
1000m
1250m
1500m
1750m
2000m
2250m
2500m
A_B_C Agricultural
-€ 515
€ 19
€ 17
€8
€4
€4
-€ 1
€1
€6
€3
D Industry
€1
€2
€2
-€ 34
-€ 28
-€ 2
€4
€3
€3
€0
-€ 33
-€ 16
E Electricity & water
-€ 936 -€ 136 -€ 101
-€ 53
-€ 37
-€ 25
-€ 19
-€ 16
-€ 278
-€ 65
-€ 36
-€ 11
-€ 3
F Construction
-€ 25
-€ 6
-€ 4
-€ 5
-€ 5
-€ 30
-€ 11
-€ 1
€0
-€ 7
G Wholesale & retail
-€ 4
-€ 1
-€ 2
-€ 2
-€ 1
-€ 6
€7
€ 13
€5
€ 10
H Hotels, restaurants
€6
€ 14
€9
€7
€6
-€ 3
€0
€0
€ 12
€0
-€ 4
-€ 3
I Transport
-€ 3
-€ 3
-€ 2
€ 132
€ 30
€8
€4
€3
€5
J Financial services
€4
€4
€3
€3
€0
€0
€0
€0
€0
€ 47
€ 10
€4
-€ 1
K Business services
€2
-€ 13
-€ 1
-€ 1
€1
€1
€0
-€ 7
-€ 3
€1
L Public services
-€ 1
-€ 16
-€ 1
€4
€6
€5
€5
€5
€7
€5
M Education
€ 10
-€ 7
€0
€0
€0
-€ 1
-€ 1
-€ 1
-€ 1
-€ 2
-€ 2
N Health care
€ 29
€ 16
-€ 8
€ 46
-€ 2
-€ 6
-€ 9
-€ 5
-€ 7
-€ 7
O Other services
The robust standard errors are given between parentheses; the bold coefficients are significant on at least a 5% level.
62 | The impact of mixed land-use
250m
Table 7: The willingness to pay for different characteristics
BUFFER SIZE
63 | The impact of mixed land-use
The willingness to pay for a mixed-environment is also investigated. In
table 6 we see that the coefficient of the diversity-measure is negative in
all models. Since we do not transform the diversity measure
logarithmically, the willingness to pay is constant for all values of the
diversity variable.
Figure 11 presents the willingness to accept a one unit increase in the
diversity index. The coefficients of diversity are not significant on a 5%
level in the models where the buffer size is 1000m, 1250m, 1500m and
1750m. Until 750m people value a mixed local environment negatively,
while when the buffer is larger, the coefficients become insignificant. In a
buffer larger than 2000m the willingness to accept increases quickly. This
figure might indicate that the very local neighbourhood is approximately
750m: people do significantly dislike a mixed local environment which is
the area where people walk, live and participate in street life. The area of
buffers with a radius larger then 2000m is approximately as large as urban
districts (for example, Delfshaven, Rotterdam-Noord are urban districts).
On this geographical level people again dislike a diversified local economy.
The main message is that diversity is generally not appreciated, which is in
contradiction with results from SONG AND KNAAP (2003, 2004) who found a
positive sign for the index which captures the diversity of land-uses.
Figure 11: The willingness to pay for one unit of diversity
Willingness to Pay for
Diversity in €
1000
0
-1000
250 500 750 1000 1250 1500 1750 2000 2250 2500
-2000
-3000
-4000
-5000
Buffer size
64 | The impact of mixed land-use
4.4 Other specifications
As already discussed in section 4.1 the functional form of the hedonic price
function is arbitrarily chosen. Therefore alternatives of this specification
are discussed. In the first subsection linear and quadratic specifications
are discussed. However, for several reasons, OLS may lead to biased,
inconsistent and/or inefficient estimates. In subsections 4.4.2 and 4.4.3
some of these issues are addressed. In the last subsection, by applying
Box-Cox transformations, a less restrictive functional form is chosen.
4.4.1 Linear and quadratic specifications
Instead of the log-linear functional form one can also apply a linear
specification (VISSER AND VAN DAM 2006). However, executing a linear
specification and also a quadratic specification for our data is not
appropriate since one will face severe multicollinearity; it appears that the
VIF-values are substantially higher than 10. There is in particular
multicollinearity between the land-use variables, which makes the use of a
linear and quadratic specification inappropriate.
4.4.2 Endogeneity of the regressors
OLS-estimates which are used to estimate the linear and log-linear can be
biased and inconsistent for various reasons. SONG AND KNAAP (2004) argue
that there is an identification problem with the variables that measure the
scale of mixed land-use, in our case the employment of different sectors
within a certain proximity to each house. Ordinary Least Squares-methods
are not able to include the possibility that land-uses are endogenously
determined. In other words, the assumption of the zero conditional mean
may be violated:
E[ | qi ; q j ]  0
(4.6)
In our case, the identification problem will arise when the employment of
different sectors is (also) determined by the prices of houses and are
consequently correlated with the error term.
65 | The impact of mixed land-use
However, in the Netherlands the planning policies are very strict. For
example, with respect to hotels, restaurants and cafés-sector we can read
in a white paper the following:
“Most municipalities execute a policy wherein hotels, restaurants and
cafés are concentrated in central areas. [...] [N]ew hotels, restaurants and
cafés are not permitted outside these areas.” (KVK, 2009)
For other uses than the wholesale and retail sector such planning policies
are even stricter. Consequently, companies cannot liberally choose where
to locate, but this is to a large extent determined by the government (and
therefore not by housing values). Thus, because of strict planning policies,
this identification problem will not arise in our case and no instrumental
variables are needed to overcome this potential endogeneity. An exception
is the wholesale and retail sector: the policies are less strict and we saw in
table 5 that the concentration of this sector is almost the lowest which
confirms more liberal locational policies.
Therefore we adopt an instrumental variables approach wherein the
employment in the wholesale & retail sector is treated as endogenous. I
seek instruments which are correlated with the employment in wholesale
& retail sector but uncorrelated with the error term. In this respect, SONG
AND KNAAP (2004) propose two instruments: first, the parcel’s distance to
central general commercial use. We also calculate for each buffer the
average distance from each 6-digit postal code where employment of the
wholesale & retail sector is located to major shopping-centres.18 In the
study of SONG AND KNAAP a second instrument is used which denote the
perimeter of a parcel which is adjacent to a main road. This second
instrument is not computable for our data. Therefore the only instrument
we use is the distance to central commercial use.
18
I collect data about shopping-centres from www.allewinkelcentra.nl.
MODEL (9) – IV-REGRESSION
9.5432 (0.3329)
0.0097 (0.0014)
0.7449 (0.0557)
-0.0068 (0.0149)
0.0818 (0.0182)
0.3751 (0.0354)
0.0157 (0.0081)
0.0498 (0.0148)
0.0181 (0.0067)
-0.2503 (0.0435)
0.0706 (0.0080)
-0.1066 (0.0103)
0.1155 (0.0408)
-0.0041 (0.0074)
0.0096 (0.0087)
0.0381 (0.0074)
0.1850 (0.0096)
0.2419 (0.0140)
0.0086 (0.0017)
0.758 (0.0584)
-0.0118 (0.0166)
0.0725 (0.0202)
0.4034 (0.0395)
0.0144 (0.0087)
0.085 (0.0194)
0.0098 (0.0085)
-0.3384 (0.0436)
-0.0834 (0.0105)
-0.1107 (0.0121)
0.1992 (0.0550)
-0.0163 (0.0094)
0.0023 (0.0106)
0.0609 (0.0099)
0.1892 (0.0114)
0.2344 (0.0176)
-0.0766 (0.0048)
0.0043 (0.0036)
-0.0167 (0.0030)
-0.1537 (0.0120)
0.0197 (0.0051)
-0.0208 (0.0038)
-0.0503 (0.0037)
-0.0049 (0.0002)
-0.0502 (0.0048)
-0.0046 (0.0002)
66 | The impact of mixed land-use
MODEL (2) – OLS
9.1232 (0.3011)
Table 8: IV-estimates
VARIABLE
Constant
STRUCTURAL CHARACTERISTICS
Lot size
Volume
Terraced
Semi-detached
Detached
Rooms
Garage
Garden
Maintenance good
Maintenance average
No central heating
Monument
Construction year 1961-1970
Construction year 1971-1980
Construction year 1981-1990
Construction year 1991-2000
Construction year > 2001
ACCESSIBILITY CHARACTERISTICS
Distance to centre Rotterdam
Distance to highway ramp
Distance to railway station
NEIGBOURHOOD CHARACTERISTICS
Population density
Percentage ethnic minorities
N
R2
Wald-χ2
Root-MSE
-0.0067 (0.0017)
0.0020 (0.0017)
-0.0063 (0.0018)
-0.0039 (0.0026)
-0.0173 (0.0024)
-0.0140 (0.0027)
0.0022 (0.0024)
0.0001 (0.0019)
0.0140 (0.0020)
0.0154 (0.0024)
-0.0043 (0.0012)
-0.0010 (0.0017)
-0.0018 (0.0019)
0.0116 (0.0031)
0.0043 (0.0026)
-0.0172 (0.0033)
-0.0131 (0.0024)
-0.0044 (0.0034)
-0.0536 (0.0057)
0.2037 (0.0283)
-0.0454 (0.0066)
-0.0257 (0.0042)
0.0070 (0.0027)
-0.0257 (0.0060)
0.0020 (0.0017)
-0.0155 (0.0031)
-0.0135 (0.0030)
-0.0578 (0.010)
10081
0.8080
0.2134
10081
0.6801
20155.9
0.2749
The robust standard errors are given between parentheses; the bold coefficients are significant on at
least a 5% level.
67 | The impact of mixed land-use
MIXED LAND-USE CHARACTERISTICS
Diversity
A_B_C Agricultural activities
D Industry
E Production electricity, water
F Construction
G Wholesale and retail
H Hotel, restaurants and cafés
I Transport and communication
J Financial services
K Business services
L Public services
M Education
N Healthcare
O Other services
68 | The impact of mixed land-use
To estimate our new model which contains the endogenous regressor, we
use a two stage least squares approach. In the first stage, the employment
in the wholesale and retail sector is a function of all the characteristics and
the instrument. We test for instrument relevance and it appears that the
average distance to general commercial use is a strong instrument.19 In the
second stage the price of a house is a function of its characteristics and the
predicted value of wholesale and retail employment.
In table 8 we can see the results. A Durbin-Wu-Hausman test is done to
verify if the instrumented regressor is endogenous. The χ2-value of this
test is 101.13 which is enough evidence to assume endogeneity of the
employment in the wholesale and retail sector. Looking at the coefficients
we see that the coefficients of the structural characteristics, accessibility
measures and neighbourhood features of the OLS and IV estimates are
very similar. But when one concentrates on the variables which measure
the effects of mixed land-use, considerable differences are observable.
First, the coefficient of wholesale and retail is positive now and relatively
large compared to the other mixed land-use coefficients: a 1% increase in
employment of this sector in vicinity of the house leads to a 0.2% increase
in housing values. Second, we see diversity no longer significantly affects
property values. Furthermore, the sign of the hotels and restaurants, the
transport and communication sector, business services sector and other
services sector is changed.
In table 9 the willingness to pay is computed. Again, there are substantial
differences. One is now on average willing to pay € 154 for a one unit
increase in the employment of wholesale and retail in vicinity. The high
negative willingness to pay for hotels, restaurants and cafés is also
somewhat surprising.
A common rule of thumb is that the F-test value must be larger than 10. In our
case the F-test value is 119.59.
19
69 | The impact of mixed land-use
Table 9: Marginal willingness to pay for a one unit increase in employment
SECTOR
MODEL (9) - IV
MODEL (2) – OLS
A_B_C Agricultural
-€ 176.12
D Industry
-€ 59.46
E Electricity & water
-€ 159.59
F Construction
-€ 202.98
G Wholesale & retail
€ 154.51
H Hotels, restaurants and cafés
-€ 141.36
I Transport & communication
-€ 57.46
J Financial services
€ 14.81
K Business services
-€ 18.01
L Public services
€ 3.35
M Education
-€ 23.03
N Health care
-€ 9.34
O Environmental and other services
-€ 141.73
The bold coefficients are significant on at least a 5% level.
€ 21.01
- € 28.54
- € 142.26
- € 65.55
- € 10.64
€ 6.99
€ 0.22
€ 29.82
€ 10.76
- € 7.32
- €1.53
- € 1.23
€ 28.41
For three reasons we have to take these results and computations with
caution. First, one could question the validity of the instrument that is
chosen. It may be that this instrument is not exogenous. People might
willing to pay to live close to shopping centres, although some of this effect
is take into account in the representation of employment of shops in
vicinity. When this instrument is not exogenous, the results are biased and
inconsistent. Second, one could question if the other land-uses are strictly
exogenous. In table 5 the concentration of sectors such as business
services and hotels, restaurants and cafés is also relatively low. This may
suggest that planning policies are not that strict in reality. Third, according
to HECKMAN (1997) it is assumed that the treatment of the instrument is
the same for every house owner, which is a strong behavioural
assumption. When the effect of the treatment differs over the individuals,
the instrumental variables approach will breakdown. Therefore, more
research is needed to verify the magnitude of different land-uses on
housing values.
4.4.3 Spatial Autocorrelation
ANSELIN (1988) argues that spatial data is characterised by spatial
dependencies and spatial heterogeneity. Especially when data is spatially
70 | The impact of mixed land-use
autocorrelated, using OLS will lead to biased and inefficient estimates. To
overcome these problems one can estimate a so-called spatial lag model or
a spatial error model. In the case of the spatial lag model, the dependent
variable is amongst other things dependent on the values of the dependent
variables in vicinity. For example, the price of a house is dependent on
prices of houses in close proximity. In the spatial error model it is assumed
that the error terms are spatially correlated. However, I renounce from
estimating these models for two reasons:20 First, there is oftentimes a
qualitatively and quantitatively small difference between the estimates of
hedonic models which corrects for spatial autocorrelation and estimates
which do not correct for spatial autocorrelation (LEGGET AND BOCKSTAEL
2000; IHLANFELDT AND TAYLOR 2004; DEATON AND HOEHN 2004; ROUWENDAL
AND VAN DER STRAATEN 2008; NEUPANE AND GUSTAVSON 2008). Second,
because the large number of observations computational problems arise.
For example, to apply a spatial lag model the computation of a so-called
weight-matrix is required. This matrix has the size of n rows and n
columns, where n is the number of observations. I do not have software
that is able to do such computations (c.f. DE VOR AND DE GROOT 2009).
4.4.4 Box-Cox Transformations
In literature, Box-Cox transformations are oftentimes applied to hedonic
price functions. For example CHESHIRE AND SHEPPARD (1995, 1998, 2002)
apply Box-Cox transformations. In (log-)linear regression models it is
assumed that the observations y1, y2, ..., yn are independently normally
distributed and that the dependent variable is linearly related to the
independent variables (BOX AND COX 1964). The Box-Cox hedonic price
function allows for different marginal prices of characteristics and can
overcome the identification problem as described by BROWN AND ROSEN
(1982). According to CROPPER ET AL. (1988), Box-Cox estimates produce the
DE VOR AND DE GROOT (2009) relinquish also from estimating these spatial models
because of the same reasons.
20
71 | The impact of mixed land-use
lowest mean percentage errors compared to log-linear and linear
specification, when utility functions and observed attributes were known.
By applying some power transformations to the dependent and
independent variables the aforementioned assumptions of normality and
the linear relationship are appropriate. With help of maximum likelihood
methods the coefficients and transformation variables are estimated. I
specify the Box-Cox hedonic price function as follows:
q j  1
Ph  1
    i qi    j



iD
jN
(4.9)
Where α, β, φ, λ are the parameters to be estimated. When φ, λ are close to
1, a linear specification is appropriate. When φ, λ are close to 0, a loglinear specification is suitable.
In table 10 the results of a Box-Cox hedonic price model for a buffer of 500
meter are presented. The size and sign of the coefficients of the
untransformed variables are largely in line with the coefficients of the loglinear specification. Also the signs of the transformed variables are the
same as in the log-linear case. Looking at the transformation coefficient,
we notice that the transformation coefficient of the dependent variable φ
is very close to zero, which makes a logarithmic transformation of the
transaction price a convenient choice. The transformation coefficient of
the continuous independent variables λ is in between 1 and 0, so both a
logarithmic transformation and a linear specification are not appropriate.
MODEL (10) – BOX-COX
12.0533 (…)
0.0097 (0.0014)
0.7449 (0.0557)
-0.0068 (0.0149)
0.0818 (0.0182)
0.3751 (0.0354)
0.0157 (0.0081)
0.0498 (0.0148)
0.0181 (0.0067)
-0.2503 (0.0435)
0.0706 (0.0080)
-0.1066 (0.0103)
0.1155 (0.0408)
-0.0041 (0.0074)
0.0096 (0.0087)
0.0381 (0.0074)
0.1850 (0.0096)
0.2419 (0.0140)
0.0038* (82.44)
0.1546* (5243.22)
-0.0125 (1.38)
0.0856 (48.78)
0.3754 (343.02)
0.0049 (2.35)
0.0324 (5.87)
0.0282 (12.82)
-0.4000 (109.95)
-0.0805 (80.91)
-0.1429 (193.20)
0.1044 (6.13)
-0.0088 (0.90)
0.0229 (5.65)
0.0589 (41.78)
0.2402 (676.56)
0.3093 (563.19)
-0.0766 (0.0048)
0.0043 (0.0036)
-0.0167 (0.0030)
-0.0045* (156.07)
0.0006* (2.19)
-0.0023* (43.24)
-0.0503 (0.0037)
-0.0049 (0.0002)
-0.0055* (324.47)
-0.0558* (487.68)
72 | The impact of mixed land-use
MODEL (2) – OLS
9.1232 (0.3011)
Table 11: Results of Box-Cox specification compared to log-linear specification
VARIABLE
Constant
STRUCTURAL CHARACTERISTICS
Lot size
Volume
Terraced
Semi-detached
Detached
Rooms
Garage
Garden
Maintenance good
Maintenance average
No central heating
Monument
Construction year 1961-1970
Construction year 1971-1980
Construction year 1981-1990
Construction year 1991-2000
Construction year > 2001
ACCESSIBILITY CHARACTERISTICS
Distance to centre Rotterdam
Distance to highway ramp
Distance to railway station
NEIGBOURHOOD CHARACTERISTICS
Population density
Percentage ethnic minorities
-0.0067 (0.0017)
0.0020 (0.0017)
-0.0063 (0.0018)
-0.0039 (0.0026)
-0.0173 (0.0024)
-0.0140 (0.0027)
0.0022 (0.0024)
0.0001 (0.0019)
0.0140 (0.0020)
0.0154 (0.0024)
-0.0043 (0.0012)
-0.0010 (0.0017)
-0.0018 (0.0019)
0.0116 (0.0031)
-0.0245* (21.30)
0.0005* (0.37)
-0.0022* (9.13)
-0.0022* (4.50)
-0.0042* (26.29)
-0.0029* (19.18)
0.0020* (3.76)
0.0003* (0.32)
0.0039* (30.04)
0.0023* (13.77)
-0.0008* (3.91)
0.0012* (3.10)
-0.0016* (11.17)
0.0044* (15.61)
φ
λ
-
0.0203 (0.0147)
0.3284 (0.0092)
N
R2
10081
-
10081
-121064
The chi2-values (coefficients Box-Cox) or standard errors (OLS, transformation
coefficients) are between parentheses; the bold coefficients are significant on at least a
5% level. The transformed variables are indicated with an asterisk.
73 | The impact of mixed land-use
MIXED LAND-USE CHARACTERISTICS
Diversity
A_B_C Agricultural activities
D Industry
E Production electricity, water
F Construction
G Wholesale and retail
H Hotel, restaurants and cafés
I Transport and communication
J Financial services
K Business services
L Public services
M Education
N Healthcare
O Other services
74 | The impact of mixed land-use
The interpretability of the Box-Cox coefficients is somewhat difficult.
Therefore we have to calculate the willingness to pay or accept for the
different variables. From (4.8) we can compute the marginal willingness to
pay. The price of a house is determined as follows:



q 1
Ph       i qi    j
     1



iD
jN




j
1

(4.10)
From this we can take the derivative to characteristic qj to obtain the
marginal willingness to pay:

q 1 
Ph 1 
     i qi    j
     1

q j  

iD
jN



j
1
1

  j q j 1 
Simplifying the equation above leads to the following formula of the
marginal price of characteristic qj:
Ph
 Ph1  j q j 1
q j
(4.12)
In table 11 we can observe that the magnitude of the willingness to pay
coefficients is in general the same for both models although there are
some noticeable differences. Especially the marginal willingness to pay an
additional employee in the production of electricity and water sector and
the construction differ a lot.
75 | The impact of mixed land-use
Table 11: Marginal willingness to pay for a one unit increase in employment
SECTOR
MODEL (10) - BOX-COX
MODEL (2) – OLS
A_B_C Agricultural
€10.11
D Industry
-€27.51
E Electricity & water
-€113.51
F Construction
-€47.26
G Wholesale & retail
-€11.00
H Hotels, restaurants and cafés
€19.86
I Transport & communication
€2.59
J Financial services
€29.70
K Business services
€8.39
L Public services
-€5.48
M Education
€6.93
N Health care
-€5.56
O Environmental and other services
€36.64
The bold coefficients are significant on at least a 5% level.
€ 21.01
- € 28.54
- € 142.26
- € 65.55
- € 10.64
€ 6.99
€ 0.22
€ 29.82
€ 10.76
- € 7.32
- €1.53
- € 1.23
€ 28.41
We again look at the relationship between the willingness to pay and the
employment in the business sector in vicinity of the house. We fit a
trendline through the observations of both the log-linear specification
model (2), and the Box-Cox specification which we applied in model (9).
The R2’s of the trendlines are respectively 0.913 and 0.838 which is
convenient. We see in figure 12 that the trendline of the Box-Cox
specification is somewhat flatter. The reason for this is that the
transformation variable of the Box-Cox specification is not zero (as in the
log-linear case) but 0.33.
From this section we can conclude that the use of a Box-Cox specification
is more appropriate than the use of a log-linear specification since it is less
restrictive and it allows for more flexibility. Although the Box-Cox is
preferable we saw that the differences in willingness to pay were not that
large. Therefore the results of the antecedent subsections are still valid.
76 | The impact of mixed land-use
Figure 12: Comparison of the log-linear and Box-Cox specification
Marginal Willingness to Pay in €
100000
(2) Log-linear specification
10000
(10) Box-Cox specification
1000
100
10
1
1
10
100
1000
10000
Employment in sector K Business Services
4.5 Discussion of the results
Mixed land-use is the backbone of a number of planning concepts like
Smart Growth and Multifunctional Land Use. In the previous subsections I
examined the impact of mixed land-use in various ways by applying a
number of models and by computing the willingness to pay for different
aspects of mixed land-use. Although mixed land-use is not always a
dominant objective of municipal planning policies, many neighbourhoods
in the city region of Rotterdam are experiencing mixed-use to some extent.
With respect to this spatial configuration of the local environment SONG
AND KNAAP (2003) argue that neither a totally mixed-use nor a
monofunctional neighbourhood is preferable: the ideal neighbourhood fall
probably on the spectrum of these two extremes.
The impact of mixed-use is measured in two different ways which
complement each other. First, I measured the effect of the local
environment as a whole. The local environment can be diversified in terms
of uses or one may experience a high job-population ratio, which also
77 | The impact of mixed land-use
describes the intensity of mixing. It is found that only the diversity
measure was significant in a large number of models, although all
measures had negative signs. Probably because of the high population
density, people in the Randstad like privacy and a gentle local
environment (JANSEN 2002, KORSTEN 2002). Therefore dynamic multi-use
neighbourhoods are generally not appreciated. This result is in line with
the study of RODENBURG (2005) who also found a negative effect of a
diverse local environment on housing owners. She found that the
willingness to accept a mixed urban environment increases in housing
values, which is also confirmed by my results. The negative willingness to
pay for a diverse neighbourhood differs with respect to buffer size, in
other words if the size of the neighbourhood considered is changed, the
willingness to pay for a diverse neighbourhood first slightly increases but
then decreases substantially. Although a diverse local environment affects
housing values negatively, some individual land-uses which are located in
vicinity contribute to higher property values. Therefore it can be beneficial
for the local economy to locate land uses, which affect property values
positively, in vicinity of houses. At least until the positive effects of these
land-uses are larger than the negative effects of diversity, mixed land-use
will at least not negatively influence housing prices. This outcome is in line
with allegations of GRANT (2004) and SONG AND KNAAP (2003), who state
that too much mixing could be harmful for the local economy, and
consequently, there must be sought to an optimal degree of mixing.
When one zooms in on which uses significantly affect property values, I
first notice that the definition of ‘a neighbourhood’ is important. Because
for each house the relevant neighbourhood is determined by establishing a
buffer around each house, the results are not biased by arbitrarily defined
neighbourhood boundaries. In general, the willingness to pay or accept
converges to zero when the buffer size is bigger. This is in line with our
expectations: because there are more employees in vicinity, the
willingness to pay will be lower (c.f. equation 4.7). The effects of individual
uses are discussed in more detail:
78 | The impact of mixed land-use
-
-
Agricultural use only affects property values in a buffer of 250m
negatively. Because of adverse effects (for example, the undesirable
smell of fertilizer), living very close to a farm is not preferred. Beyond
250m, these negative effects are compensated: the willingness to pay
to live close to agricultural use is insignificant or very close to zero in
the remaining models. Besides, the generally small positive effect of
agricultural use is in line with the result of ROUWENDAL AND VAN DER
STRAATEN (2008) who found also a positive coefficient with respect to
agricultural use.
Industrial use is negatively affecting housing prices with a buffer size
of 1000 meters or smaller. This difference probably explains why a
number of studies found different effects of industrial use in
proximity. For example, LAFFERTY AND FRECH (1978) found no
significant effects, ROUWENDAL AND VAN DER STRAATEN (2008) a
negative effect, while CAO AND CORY (1981) and BURNELL (1985) found
a positive effect. BURNELL corrects for non-localised effects, which can
be pollution. Probably, because of these non-localised effects that
affect property values in the whole city region with more or less the
same amount, the willingness to pay to live close to industrial use is
not negative beyond a buffer of 1000m. The definition of industrial
use is also very important. One could sector E, F and I (respectively
‘production of electricity and water’, ‘construction’ and ‘transport and
communication’) also identify as industrial use. We see that these
sectors, irrespective of the buffer size, are significantly negatively
influencing housing values. Negative externalities of these sectors are
easy to sum up: we could think of visual, noise and air pollution and
less access to open space. Positive effects of industrial use in vicinity
could be the availability of employment and the appreciation of an
industrial environment. Furthermore, industrial land is in general
well accessible (think of industrial sites which are oftentimes located
near highway ramps), which makes a house which is located nearby
industrial land better accessible. This could be also a benefit of having
industrial land in close proximity.
79 | The impact of mixed land-use
-
-
-
The negative effect of wholesale and retail is somewhat
counterintuitive. Although the willingness to pay an employee in the
wholesale and retail sector is very close to zero when the buffer is
larger than 1250 meters, the sign of the coefficient is still negative for
all buffers. GRANT (2004) argues that the current shopping behaviour
is changed because of the increased car mobility (GLAESER AND KAHN
2003): people travel to large shopping malls which are located on
unattractive industrial areas instead of shopping in local shops.
Therefore, having a shop in the vicinity does not yield any benefits
anymore, only costs (noise, lack of parking space etc.). However, the
estimates of the instrumental variables approach, where the
wholesale and retail variable is treated as endogenous, suggests a
positive effect on housing values. This is also in line with SONG AND
KNAAP (2004): they recommend mixing of small commercial
development with residential use.
Hotels, restaurants and cafés generally affect house prices positively.
This can be explained by the fact that at least restaurants and cafés
encourage active street life which is generally appreciated. This result
is in line with research of GLAESER ET AL. (2001) who found a positive
relation between the number of restaurants and hotels and the
growth of cities: people like to live where these urban amenities are
present.
Private services influence housing values in general positively. I
notice that the willingness to pay for an employee in the financial
services sector is more than two times as large as the WTP in the
business services sector. Possibly living close to work can be
important in valuing these uses positively. Especially young urban
professionals value reduced commuter travel time higher than other
people. This can explain to some extent the high values of apartments
in the city centre of Rotterdam, where many financial services firms
are located. This outcome is in line with one of the conclusions of
SONG AND KNAAP (2004) who state that new businesses in mixed areas
should be service-oriented. Also CAO AND CORY (1981) and ROUWENDAL
80 | The impact of mixed land-use
VAN DER STRAATEN (2008) found a positive relationship between
the amount of services in vicinity and the value of a house.
Educational employment in close vicinity has a negative impact on
property values while when the buffer increases the willingness to
pay for education becomes positive. Apparently, people dislike a
school in direct vicinity (250-500m). Reasons can be noise pollution
and higher crime rates. However, people like to have a school not too
far away: travel times of bringing and picking up children would
increase otherwise. Nevertheless, positive effects exceed the negative
effects of local externalities when the neighbourhood considered is
large enough. Thus, the effect of education on property values is more
subtle than results of other studies suggest (e.g. DUBIN AND GOODMAN
1982).
Finally, public services and healthcare negatively influence property
values. ‘Other services’ first positively affect housing values, while
when the buffer is larger than 750 meters (or in the IV-estimates,
500m), negative effects dominate. The interpretation is somewhat
difficult because of the unclear composition of this sector.
To summarise, uses which are generally compatible with residential use
are private services, education, hotels, restaurants and cafés. Contrary,
industrial use (using the broad definition of industrial use), public services
and healthcare influence house prices negatively. Again, mixed land-use
will be beneficial for the economy up to the point where the benefits of
compatible uses in vicinity are exceeded by the costs of a diverse local
economy. Moreover, it would be sensible to cluster employment in sectors
which negatively affects housing values and spread employment which
positively affect housing values, because the willingness to pay or accept
an additional employee in a sector converges to zero when employment in
this sector increases. On the one hand, this concentration and segregation
of uses which are not compatible with residential use already takes place:
a lot of hazardous manufacturing and unattractive low-grade activities are
located on industrial sites (DE VOR AND DE GROOT 2009). In case of the city
region of Rotterdam already 20% of the employment is concentrated on
AND
81 | The impact of mixed land-use
industrial sites. We can also observe spreading of uses which positively
affect housing values: we can find business services nearly everywhere.
Also education is found in nearly all neighbourhoods. On the other hand,
our Gini-estimates show no relationship between concentration and the
impact of a land-use on property values. Therefore, a more advanced
policy is needed which takes the magnitude and sign of effects of different
sectors explicitly into account.
In our empirical results it is reflected that I focus only on mixed-uses. This
is in contrast to other papers that I know of which evaluate a holistic
planning policy as a whole, which consists of different components (e.g.
SONG AND KNAAP 2003; SONG 2005). Therefore I leave, for example,
pedestrian walkability, accessibility by light rail, connectivity and street
design out of the analysis. I only add some measures of accessibility and
density as control variables to our models. We saw density had a negative
impact on values of houses, which is in line with findings of SONG AND
KNAAP (2003) and the idea that Dutch people like to have some privacy.
This demand for privacy is in line with the signs of the housing type
coefficients: nearly all housing types are preferred over an apartment. The
accessibility measures were significant and contribute to higher values of
houses. In general we can conclude that even other aspects of planning
policies, which might reimburse the potential adverse effects of mixed use,
are in some cases not contributing to higher housing values (e.g. density).
82 | The impact of mixed land-use
4.6 Caveats
It is necessary to mention some inadequacies of our study. First, the
conclusions are to some extent not very general. The analysis is executed
in one area of The Netherlands in one year. One can think of multiple
reasons why results will differ when the analysis was executed in other
regions or in other years. Second, although I evaluate every housing type
(and not only single-family units) I only focus on the impact of mixed landuse on home owners. There are many more actors involved in the local
economy which also value different aspects of mixed-use development.
Third, the effects of mixed land-uses which are represented by
employment are only taken into account. Therefore the effects of open
space and infrastructural use are neglected. However, according to other
studies (c.f. ROUWENDAL AND VAN DER STRAATEN 2008), open space positively
affects housing values. Our conclusions are applicable in this respect: it is
better to spread parks and open space since the willingness to pay per
square meter open space will probably decrease when the amount of open
space in vicinity increases. Other shortcomings are more methodological.
Although a Box-Cox specification is more general than a (log)linear
specification, it still assumes a specific version of a single index model
(BONTEMPS ET AL. 2008). Moreover, for various reasons the coefficients and
standard errors can be flawed because of spatial autocorrelation and
endogeneity of the mixed-use variables. We saw the estimates of the
instrumental variables approach substantially differ from the OLSestimates although one could question the quality of the instrument that is
used. Therefore, we have to carefully consider the magnitude of
willingness to pay outcomes. More research is needed to really capture the
effects of different uses on housing values. The size and magnitude of the
willingness to pay estimates is also very dependent on the functional form
one chooses, although the log-linear and Box-Cox estimates resemble
largely the same results. Finally, it is also important to notice that these
results may be in contradiction with an efficient urban spatial
configuration. Namely, ROSSI-HANSBERG (2004) has shown that an
equilibrium outcome is not always the most efficient one. In our results we
83 | The impact of mixed land-use
neglect the benefits of production externalities and we do not know if
consumers value the commuting costs in a proper way. Therefore, mixing
sectors with residential use can lead to a substantial loss in production
externalities, which may exceed the benefits of higher housing values.
More research is needed to the scale and intensity of these production
externalities to verify if this is the case.
84 | The impact of mixed land-use
Section 5: Conclusions
5.1 Summary and conclusions
S
ince Jane Jacobs in 1961 argued that mixed land-use could be a
solution to many urban problems, such as urban sprawl, mixed-use is
the backbone of a number of well-known planning policies. Renowned
American planning programs like Smart Growth and New Urbanism
incorporated mixed land-use as one of their key principles. Also European
and Dutch policies are generally aiming for a mixed urban environment.
Although there is some agreement among planners that mixed-use is a
solution to urban sprawl, the socio-economic benefits and especially the
costs are rarely measured in literature.
An economic approach to measure these socio-economic effects
contributes to a better understanding of the effects of planning policies
which comprehend mixed-uses. However, it appears that the concept is
seldom carefully defined. I came to a definition which Hugo Priemus
already employs, namely, the combination of different socio-economic
functions in a given area. This definition is compatible with all different
planning policies. Furthermore, it covers a large number of different
dimensions of mixed-use but not prescribes how land must be mixed and
which uses are good to combine.
There is some qualitatively oriented literature about what the effects of
mixed land-use could be. It is recognised that mixed-use can lead to a
better valuation and preservation of open space, optimised mobility, since
people might live next to their work, synergy effects between uses and an
increase in the quality of life due to intensified street life. It is also argued
that there can be adverse effects of mixing uses. Examples are a loss of
mobility due to an inefficient provision of infrastructure, conflicts of uses,
loss in production externalities between firms and a loss of privacy. We
saw economic theory does not provide decisive answers with respect to
the most efficient urban structure although the most important forces,
85 | The impact of mixed land-use
which are causing monofunctional or mixed land use, are identified: the
production externalities between firms and the costs of interaction
between firms and residents, i.e. commuter costs. It is also argued that an
equilibrium in the urban economy is not always the most efficient
outcome, since firms do not take the positive externalities into account
which they impose on other firms.
In my measurement of the impact of mixed land-use there is a focus on
home owners and what the effects are on housing values. Thereby, I
neglect the effects of open space and infrastructural land use because
there is no data available about the amount of open space and
infrastructural land use. I divide the impact of a diversified urban
environment in two main effects: the effect of the environment as a whole
and the effects of individual land-uses. A hedonic approach to evaluate
these effects is adopted. It is found that house owners generally dislike a
diversified urban environment while individual land-uses can contribute
to either higher or lower property values. Industrial activities, public
services and healthcare have generally a negative impact on property
values, while advanced producer services, education, hotels, restaurants
and cafés positively affect housing values. Agricultural activities and other
services do not have a straightforward effect. Also the effect of wholesale
and retail on housing values is a unclear, since OLS and IV resemble
different outcomes. The willingness to pay for an additional employee in a
sector ranges from minus € 936 to € 132. However, in these estimates the
average employment in a sector in vicinity of a house is taken into account.
When there is less employment in a sector in vicinity of the house, the
willingness to pay for an additional employee is substantially larger. Note
that these are the effects of one employee. In the case of firms, this amount
should approximately be multiplied by the size of a firm in terms of
employment. Until the positive effects of individual uses exceed the
negative effects of a diverse environment and the adverse effects of other
individual uses, mixed land-use will have at least not a negative effect on
86 | The impact of mixed land-use
housing values compared to the situation where there is monofunctional
use.
I found also a negative relationship between the willingness to pay an
additional employee in a particular sector and the employment of that
sector in vicinity of the house. To be more specific, when there is already
much employment in immediate surroundings of the house, the owner of
the house will value the extra employee less. As a result, sectors which
negatively affect housing values should be clustered while sectors which
positively affect prices of houses should be spread over the region.
To conclude, the impact of mixed land-use is multidimensional and
complex to comprehend. Oftentimes, negative externalities and positive
externalities of mixed land-uses are balanced which makes it difficult to
identify specific effects of both individual uses and the effects of the
environment as a whole. Although this identification of effects is difficult I
present a framework wherein the overall effects of both land-uses and the
local urban environment on housing values are evaluated. We saw mixeduse can be beneficial when compatible uses are mixed with residential
uses up to the point where the costs of diversity exceed the benefits of
having individual uses in proximity.
5.2 Policy implications and recommendations for further
research
In the previous subsection the main conclusions are formulated: mixed
land-use could be beneficial for the local economy. But very well-defined
planning policies are needed in order to gain benefits from mixed-use.
Policy makers should take into account the following contentions:
Not every land-use can be mixed with residential use. We saw
industrial activities, public services and healthcare have generally a
negative impact on property values, while advanced producer
services, education, hotels, restaurants and cafés positively affect
87 | The impact of mixed land-use
-
-
housing values. Agricultural activities, other services and wholesale
and retail do not have a straightforward effect.
Planning policies should both promote mixed and monofunctional
land use. Land uses which negatively affect housing values should be
clustered, for example on industrial sites. Land uses which have a
positive effect on property values should be spread over the city.
However, when land-uses are mixed with residential land-use, the
loss of positive production externalities must be taken into account.
Policy makers should take into consideration that people generally
dislike a diversified urban environment. The benefits of uses which
positively affect property values should exceed the adverse effects of
a diversified urban environment.
Because of a number of limitations of our empirical analysis there are
numerous possibilities for further research in this topic. First, I only take
into account home owners to validate the impact and effectiveness of
mixed land-use. Further research can also take preferences of the other
actors into account and check, for example, whether mixed land-use will
lead to better performance of individual companies. Second, I only analyse
one region, namely the city region of Rotterdam in the Netherlands. For a
variety of reasons this region is not comparable to other regions, which
might lead to different conclusions when this analysis was executed in
other regions. Therefore further research can focus on comparing
different regions instead of analysing only one region. Third, research can
put more attention to the methodological aspects of measuring the effects
mixed-use. For example, the development of detailed econometric models
is needed in order to verify causality. Also on econometrical issues, such as
endogeneity, one has to put attention to. Finally, more research is needed
to the very nature of production externalities: what is the distance decay
of these externalities and do mixed land-uses impede these externalities?
88 | The impact of mixed land-use
References
AMERICAN PLANNING ASSOCIATION (1998). The principles of smart
development. PAS report #479, APA. Chicago IL.
ANAS, A., ARNOTT, R., SMALL, K.A. (1998). Urban spatial structure. Journal of
economic literature 36, pp. 1426-1464.
ANAS, A., KIM, I. (1996). General equilibrium models of polycentric urban
land use with endogenous congestion and job agglomeration. Journal of
Urban Economics 40(2), pp. 232-256.
ANSELIN, L. (1988). Spatial econometrics: methods and models. Kluwer,
Dordrecht
BONTEMPS, C., SIMONI, M., SURRY, Y. (2008). Semiparametric hedonic price
models: assessing the effects of agricultural nonpoint source pollution.
Journal of Applied Econometrics 23, pp. 825-842.
BOWERMAN, B.L., O’CONNELL, R.T. (1990). Linear statistical models: an
applied approach. Duxbury, Belmont CA.
BOX, G.E.P., COX, D.R. (1964). An analysis of transformations. Journal of the
Royal Statistic Society B 26 (2), pp. 211-252.
BURNELL, J.D. (1985). Industrial land use, externalities, and residential
location. Urban Studies, Vol. 22, No. 5, 399-408.
BRUECKNER, J.K. (2001). Urban sprawl: lessons from urban economics.
Brookings-Wharton Papers on Urban Affairs: 2001.
BRUECKNER, J.K. (2000). Urban sprawl: diagnosis and remedies. International
Regional Science Review 23,2, pp. 160-171.
CAO, T.V., CORY, D. (1981). Mixed land uses, land-use externalities, and
residential property values: a re-evaluation. Annals of Regional Science
16, 1-24.
CERVERO, R. (1995). Mixed land-uses and commuting: evidence from the
American housing survey. Transportation Research Part A 30, 361-377.
CHESHIRE, P., SHEPPARD, S. (1995). On the price of land and the value of
amenities. Economica 62(246), pp. 247-267.
89 | The impact of mixed land-use
CHESHIRE, P., SHEPPARD, S. (1998). Estimating the demand for housing, land
and neighbourhood characteristics. Oxford Bulletin of Economics and
Statistics 60(3), pp. 357-382.
CHESHIRE, P., SHEPPARD, S. (2002). The welfare economics of land-use
planning. Journal of Urban Economics 52, pp. 242-269.
COUPLAND, A. (ed.) (1997). Reclaiming the city: mixed-use development.
London: E & FN SPON (1997).
CROPPER, M.L., DECK, L.B., MCCONNELL, K.E. (1988). On the choice of functional
form for hedonic price functions. The review of Economics and Statistics
70(4), pp.668-675.
DEATON, B.J., HOEHN, J.P. (2004). Hedonic analysis of hazardous waste sites in
the presence of urban disamenities. Environmental Science and Policy 7,
pp.499-508.
DEBREZION, G., PELS, E., RIETVELD, P. (2006). The Impact of Rail Transport on
Real Estate Prices: An Empirical Analysis of the Dutch Housing Market.
Tinbergen Institute Discussion paper: TI 2004-089/3.
DEBREZION, G., PELS, E., RIETVELD, P. (2004). The Impact of Railway Stations
on Residential and Commercial Property Value: a Meta Analysis.
Tinbergen Institute Discussion paper: TI 2004-023/3.
DEKKERS, J., STRAATEN, J.W. VAN DER (2008). Monetary valuation of aircraft
noise: a hedonic analysis around Amsterdam Airport. Tinbergen
Institute Discussion paper: TI 2008-064/3.
DIXIT, A.K., STIGLITZ, J.E. (1977). Monopolistic competition and optimum
product diversity. American Economic Review 67(3), pp. 297-308.
DUBIN, R.A., GOODMAN, A.C. (1982). Valuation of education and crime
neighborhood characteristics through hedonic housing prices.
Population & environment 5(3), pp. 166-181.
DUCRUET, C.B., KOSTER, H.R.A., BEEK, D.J. VAN DER (2009). Commodity variety
and seaport performance. To be published in Regional Studies.
DURANTON, G., PUGA, D. (2000). Diversity and specialisation in cities: why,
where and when does it matter? Urban Studies 37, pp. 533-555.
EKELAND, I., HECKMAN, J.J., NESHEIM, L., (2004). Identification and estimation of
hedonic models. Journal of Political Economy 112, pp. 60–109.
90 | The impact of mixed land-use
FARBER, S. (1998). Undesirable facilities and property values: a summary of
empirical studies. Ecological Economics 24, pp. 1-14.
FIELD, A. (2005). Discovering statistics using SPSS. Sage Publications, London.
FOWLER, C.S. (2006). Reexploring transport geography and networks: a
case study of container shipments to the West Coast of the United
States. Environment and Planning A 38, pp. 1429-1448.
FRENKEN, K., OORT, F. VAN, VERBURG, T. (2007). Related variety, unrelated
variety and regional economic growth. Regional studies 41, pp. 685-697.
FUJITA, M. (1988). A monopolistic completion model of spatial
agglomeration: differentiated products approach. Journal of Regional
Science and Urban Economics 18(1), pp. 87-124.
FUJITA, M., OGAWA, H. (1982). Multiple equilibria and structural transition of
non-monocentric urban configurations. Regional Science and Urban
Economics 12, pp. 161-196.
GAN, Q., HILL, R.J. (2008). A New Perspective on the Relationship between
House Prices and Income. SSRN Working paper series.
GLAESER, E.L., KAHN, M.E. (2003). Sprawl and urban growth. NBER Working
Paper Series, 9733.
GLAERER, E.L., KOLKO, J., SAIZ, A. (2001). Consumer city. Journal of Economic
Geography 1, pp.27-50
GORDON, P., RICHARDSON, H.W. (1990). Gasoline consumption and cities: a
reply. Journal of the American Planning Association 55, pp. 342-346.
GORDON, P., RICHARDSON, H.W. (1997). Are compact cities a desirable planning
goal? Journal of the American Planning Association 63, pp. 95-106.
GORDON, P., RICHARDSON, H.W. (2000). Critiquing sprawl’s critics. Policy
Analysis 365.
GEOGHEGAN, J., WAINGER, L.A., BOCKSTAEL, N.E. (1997). Spatial landscape indices
in a hedonic framework: an ecological economic analysis using GIS.
Ecological Economics 23, pp. 251-264.
GRANT, J. (2002). Mixed use in theory and practice: Canadian experience with
implementing a planning principle. Journal of the American Planning
Association, 68(1), pp. 71-84.
91 | The impact of mixed land-use
GRANT, J. (2004). Encouraging mixed use in practice. Paper presented at the
International Planning Symposium on Incentives, Regulations, and Plans –
The Role of States and Nation-States in Smart Growth Planning
HABIFORUM EXPERTISE NETWORK (2009). http://www.habiforum.nl.
HECKMAN, J. (1997). Instrumental variables: a study of implicit behavioural
assumptions used in making program evaluations. Journal of Human
Resources 32(3), pp. 441-462.
HELPMAN, E., PINES, D. (1977). Land and zoning in an urban economy: further
results. American Economic Review 67, pp. 982-986.
HITE D., CHERN, W., HITZHUSEN, F, RANDALL, A. (2001). Property-value impacts
of an environmental disamenity: the case of landfills. Journal of Real
Estate Finance and Economics 23, pp. 185-202.
HOOVER, E.M., (1936). Location theory and the shoe lether industry. Harvard
University Press, Cambridge.
HOOVER, E.M., (1948). The location theory of economic activity. McGraw Hill,
New York.
HOPPENBROUWER, E., LOUW, E. (2005). Mixed-use development: theory and
practice in Amsterdam’s Eastern Docklands. European Planning Studies Vol.
13, No. 7
IHLANFELDT, K.R., TAYLOR, L.O. (2004). Externality effects of small-scale
hazardous waste sites: evidence from urban commercial property
markets. Journal of Environmental Economics and Management 47, pp.
117–139.
IMBENS, G., ANGRIST, J. (1994) Identification and estimation of local average
treatment effects. Econometrica 62, pp. 467–476.
JACOBS, J. (1961). The death and life of great American cities. Random House,
New York.
JANSEN, Y. (2002). OR en Privacy. Kluwer.
KAUFMAN, D.A., CLOUTIER, N.R. (2006). The impact of small brownfields and
greenspaces on residential property values. Journal of Real Estate
Finance and Economics 33, pp. 19-30.
92 | The impact of mixed land-use
KIEL, K., ZABEL, J. (2001). Estimating the economic benefits of cleaning-up
superfund sites: the case of Woburn, Massachusetts. Journal of Real
Estate Finance and economics 22, pp. 163-184.
KOHLHASE, J.E. (1991). The impact of toxic waste sites on housing values.
Journal of Urban Economics 30, pp. 1-26.
KOOMEN, E., DEKKERS, J., DIJK, T. VAN (2008). Open-space preservation in the
Netherlands: planning, practice and prospects. Land Use Policy 25, pp.
361-377.
KORSTEN, A.F.A (2002). De staat van de natie: Nederland voor Belgen
verklaard. Lezing te Diepenbeek.
KRUGMAN, P. (1991). Geography and trade. Cambridge: MIT Press.
KUTNER, M.H., NACHTSHEIM, C.J., NETER, J., LI, W. (2005). Applied Linear
regression models. McGraw-Hill, New York.
KVK (2009). Chamber of Commerce, http://www.kvk.nl/regio/rotterdam
/wettenenregels/400_Ruimtelijke_ordening_en_milieu/bestemmingsplan
LAFFERTY, R.N., FRECH, H.E. (1978). Community environment and the market
value of single family homes: the effect of dispersion of land uses. Journal
of Law and Economics, Vol. 21, 381-394.
LAU, S.S.Y., GIRIDHARAN, R., GANESAN, S. (2005). Multiple and intensive land use:
case studies in Hong Kong. Habitat International 29, pp. 527-546.
LEGGETT, C.G., BOCKSTAEL, N.D. (2000). Evidence of the effects of water
quality on residential land prices. Journal of Environmental Economics
and Management 47, pp. 117–139.
LOUW, E., BRUINSMA, F., (2006). From mixed to multiple land use. Journal of
Housing and the Built Environment 21:1–13.
LUCAS, R.E., ROSSI-HANSBERG, E. (2002). On the internal structure of cities.
Econometrica 70(4), pp. 1445-1476.
MAAT, K. (2002). Effects of the Dutch compact city policy on travel behaviour.
In: FEITELSON, E., VERHOEF, E.T. (eds.) (2002). Transport and environment: in
search of sustainable solutions, pp. 208-230.
MENARD, S. (1995). Applied logistic regression analysis. Sage university paper
series on quantitative applications in social sciences, pp. 7-106. Sage
Publications, Thousand Oaks CA.
93 | The impact of mixed land-use
MCCANN, P. (2001) Urban and Regional Economics. Oxford University Press,
New York.
MILLS, E.S. (1967). An aggregate model of resource allocation in a
metropolitan area. American Economic Review 57, pp. 197-210.
MOONAW, R.L. (2003). Multifunctional land use: lessons from Jane Jacobs and
Shopping Malls. In: NIJKAMP, P., RODENBURG, C.A., VREEKER, R. (eds.) (2003.
The economics of multifunctional land use, pp. 49-53.
MYERS, R. (1990). Classical and modern regression with applications.
Duxbury, Boston MA.
NEWURBANISM.ORG (2009). http://www.newurbanism.org/.
NEUPANE, A., GUSTAVSON, K. (2008). Urban property values and
contaminated sites: a hedonic analysis of Sydney, Nova Scotia. Journal
of Environmental Management 4, pp.1212-1220.
NIJKAMP, P., RODENBURG, C.A., VREEKER, R. (eds.) (2003). The economics of
multifunctional land use. Vrije Universiteit Amsterdam, Expertisenetwerk
Habiforum
O’SULLIVAN, A. (2003). Urban economics, 5th edition. McGrawHill New York,
pp. 41-53.
PALMQUIST, R.B. (2006). Property value models. In: MÄLER, K.G., VINCENT, J.R.
(eds.) (2006). Handbook of Environmental Economics 2, pp. 621-669.
PARMETER, C.F., HENDERSON, D.J., KUMBHAKAR, S.C. (2007). Nonparametric
estimation of a hedonic price function. Journal of Applied Econometrics
22, pp. 695–699.
PRIEMUS, H., NIJKAMP, P., DIELEMAN, F. (2000). Meervoudig ruimtegebruik:
stimulansen en belemmeringen. Delft: Delftse Universitaire Pers.
RODENBURG, C.A., VREEKER, R., NIJKAMP, P. (2003). Multifunctional land use: an
economic perspective. In: NIJKAMP, P. RODENBURG, C.A., VREEKER, R. (eds.)
(2003). The economics of multifunctional land use, pp. 3-15.
RODENBURG, C.A. (2005). Measuring benefits of multifunctional land use:
stated preference studies on the Amsterdam Zuidas. Vrije Universiteit
Amsterdam, PhD. Thesis.
94 | The impact of mixed land-use
ROSEN, S. (1974). Hedonic prices and implicit markets: product
differentiation in pure competition. Journal of Political Economy 82, pp.
34-55.
ROSSI-HANSBERG, E. (2004). Optimal urban land use and zoning. Review of
Economic Dynamics 7, pp. 69-106.
ROUWENDAL, J., Straaten, J.W. van der (2008). The costs and benefits of
providing open space in cities. Tinbergen discussion paper: TI 2008001/3
SCHWANEN, T., DIJST, M., DIELEMAN, F. (2004). Policies for urban forms and their
impact on travel: the Netherlands experience. Urban Studies 41, pp. 579603.
SONG, Y., KNAAP, G.J. (2003). New urbanism and housing values: a
disaggregate assessment. Journal of Urban Economic, 54, pp. 218-238.
SONG, Y., KNAAP, G.J. (2004). Measuring the effects of mixed land uses on
housing values. Regional Science and urban Economics 34, pp. 663-680.
SMART GROWTH NETWORK (2009). http://www.smartgrowth.org.
STADSVISIE ROTTERDAM (2007). Rotterdam 2030. Gemeente Rotterdam.
STEAD, D., HOPPENBROUWER, E. (2004). Promoting an urban renaissance in
England and the Netherlands. Cities, Vol. 21, No. 2, pp. 119–136.
STULL, W.J. (1974). Land use and zoning in an urban economy. American
Economic Review 64, pp. 337-347.
VISSER, P., DAM, F. VAN (2006). De prijs van een plek. RPB publication.
VOR, F. DE, GROOT, H.L.F. DE (2009). The impact of industrial sites on
residential property values. Tinbergen Institute Research Paper: TI 2009035/3.
VREEKER, R. (2004). Urban Multifunctional Land Use and Externalities.
Working paper
VREEKER, R., GROOT, H.L.F. DE, VERHOEF, E.T. (2004). Urban multifunctional land
use: theoretical and empirical insights of economies of scale, scope and
diversity. Built Environment 30-4, 289-307.
WADDELL, P., BERRY, B.J.L., HOCH, I. (1993). Residential property values in a
multimodal area: new evidence on the implicit price of location. Journal
of Real Estate Finance and Economics 7, pp. 117-141.
95 | The impact of mixed land-use
WEE, B. VAN (2003). Assessment of the benefits of multifunctional land use.
In: NIJKAMP, P., RODENBURG, C.A., VREEKER, R. (eds.) (2003). The economics of
multifunctional land use, pp. 75-79.
WRIGHT, A. (1967). Chang’an. In: Toynbee, A. (ed.) (1967). Cities of destiny, pp.
138-139.
96 | The impact of mixed land-use
Executive Summary
In contemporary urban planning literature mixed land-use is an
oftentimes discussed subject. In a number of American and European
planning concepts an important component is mixing different socioeconomic functions in a given area, often a neighbourhood. This thesis first
integrates the different literature on mixed land use in order to get a
complete picture of the economic benefits and costs of mixed land-use.
Looking into economic literature, we observe there are no decisive
answers which urban spatial structure is the most efficient: a mixed
structure, a monofunctional configuration or something in between.
Furthermore, from the literature it becomes clear that there is less
evidence that support the belief of planners that mixed land-use yields
socio-economic benefits and is a solution to problems urban sprawl
causes. In the remainder of the thesis I confront these lacunae head on by
analysing the impact of mixed land-use on house prices. To structure the
effects of mixed land-use it is argued that there are two main effects on
house prices: (1) the effect of proximity to different uses on the house
prices and (2) the external effect of mixed land-use. The effects of open
space and infrastructural land which are not expressed in terms of
employment in vicinity are neglected.
The analysis is based upon three datasets. First, a database of the chamber
of commerce which consist of all the companies located in the Rotterdam
city region in 2006. Second, a database of the NvM with all housing
transaction prices in 2006 in the Rotterdam city region. Finally, a database
of the CBS with detailed information (e.g. population density, percentage
immigrants) on neighbourhood level is used. I construct a database, using
GIS-software, where for every dwelling the structural characteristics are
combined with information about the neighbourhood and, more
important, information about employment in different sectors in
proximity and variables that measure to what extent the land-uses are
mixed.
97 | The impact of mixed land-use
Using this newly constructed database, a number of hedonic price models
are applied to measure the aforementioned effects. The results show that
house owners generally dislike a diversified urban environment while
individual land-uses can contribute to either higher or lower property
values. Industrial activities, public services and healthcare have generally
a negative impact on property values, while advanced producer services,
education, hotels, restaurants and cafés positively affect housing values.
Agricultural activities and other services do not have a straightforward
effect. Also the effect of wholesale and retail on housing values is unclear,
since OLS and IV resemble different outcomes. The willingness to pay for
an additional employee in a sector ranges from minus € 936 to € 132. Note
that these are the effects of a marginal employee. In the case of firms, this
amount should approximately be multiplied by the size of a firm in terms
of employment. Until the positive effects of individual uses exceed the
negative effects of a diverse environment and the adverse effects of other
individual uses, mixed land-use will have at least not a negative effect on
housing values compared to the situation where there is monofunctional
use. I find also a negative relationship between the willingness to pay or
accept an additional employee in a particular sector and the employment
of that sector in vicinity of the house. To be more specific, when there is
already much employment in immediate surroundings of the house, the
owner of the house will value the extra employee less. As a result, sectors
which negatively affect housing values should be clustered while sectors
which positively affect prices of houses should be spread over the region.
However, when sectors are mixed one has to take into account the loss of
production externalities between firms.
Eventually, the shortcomings and recommendations for further research
are discussed. A main shortcoming of this study is that it is not very
general. The analysis is performed in one year, only in one area
(respectively 2006 and the city region Rotterdam). To be able to come to
general conclusions it would be better to apply this methodology to more
regions. Furthermore, it only focuses on house owners. There are probably
98 | The impact of mixed land-use
also important effects of mixed land-use which influence the performance
of firms, for example the loss of production externalities. There are also
some methodological deficiencies, i.e. the choice of functional form and
potential endogeneity of the regressors.
To conclude, the impact of mixed land-use is multidimensional and
complex to comprehend. Oftentimes, negative externalities and positive
externalities of mixed land-uses are balanced which makes the
identification of specific effects of both individual uses and the effects of
the environment difficult. Although this identification of effects is difficult I
present a framework wherein the overall effects of both land-uses and the
local urban environment on housing values are evaluated. We saw mixeduse can be beneficial when compatible uses are mixed with residential
uses up to the point where the costs of diversity exceed the benefits of
having individual uses in proximity.
99 | The impact of mixed land-use
100 | The impact of mixed land-use
Samenvatting (Nederlands)
In de wetenschappelijke literatuur omtrent het plannen en indelen van de
wijk en/of buurt is het mixen van diverse functies (wonen, werken,
recreatie) een onderwerp van veel debat. Zo is het mixen van diverse
socio-economische functies in een bepaald gebied (vaak een buurt of wijk)
een belangrijke component in Europese en Amerikaanse planningconcepten. Deze masterthesis gaat eerst in op de huidige literatuur
omtrent deze functiemenging. Door deze literatuuranalyse worden een
aantal mogelijke kosten en baten van het implementeren van mixed landuse geïnventariseerd. In economische literatuur blijkt er ook onderzoek
gedaan te zijn welke stedelijke structuur het meest efficient is. Het blijkt
dat het laatste woord hier nog niet over gesproken is: de resultaten van de
modellen of een gemixte stedelijke omgeving, een monofunctionele
indeling of iets er tussenin tot optimale uitkomsten leidt is door de sterk
gesimplificeerde modellen vaak moeilijk toe te passen op de realiteit. Uit
de stedelijke literatuur blijkt verder dat er nog weinig empirisch bewijs is
dat functiemenging leidt tot socio-economische baten, ondanks dat
planners hier heilig in geloven. In de rest van the thesis concentreer ik me
of het vergaren van meer empirisch bewijs óf functiemenging leidt tot
baten voor huiseigenaren. Om de effecten te structuren beargumenteer ik
dat er twee belangrijke soorten effecten zijn op huizenprijzen, namelijk (1)
het effect van de nabijheid van verschillende socio-economische functies
in nabijheid van woningen en (2) het effect van de omgeving als geheel. Ik
negeer de effecten van parken e.d. en infrastructurele effecten omdat deze
niet zijn uit te drukken in werkgelegenheid, wat bij andere effecten wel het
geval is.
Om de impact van functiemenging te meten gebruik ik drie databases. Als
eerste gebruik ik een database van de Kamer van Koophandel. Deze
database bevat gegevens over alle in de Rotterdamse stadsregio
aanwezige bedrijven op 6-digit postcode niveau. Als tweede gebruik ik een
database van de NvM die alle transacties en gedetailleerde gegevens
101 | The impact of mixed land-use
omtrent verkochte huizen bevat. Als derde gebruik ik data van de CBS met
gedetailleerde informatie op buurtniveau. Ik construeer een database, met
behulp van GIS-software, die voor elk huis, behalve de structurele
eigenschappen, ook informatie bevat over werkgelegenheid in de
omgeving en buurtdata.
Deze nieuwe database gebruik ik om een aantal hedonische prijsanalyses
uit te voeren. De resultaten laten zien dat huiseigenaren over het
algemeen een diverse lokale omgeving niet waarderen. Desondanks
kunnen individuele functies wel leiden tot hogere huizenprijzen. Er zijn
ook bepaalde sectoren die een negatieve invloed uitoefenen op de
huizenprijs. Industriële activiteiten, publieke diensten en gezondheidszorg
zijn voorbeelden van sectoren die huizenprijzen negatief beïnvloedden.
Hoogwaardige dienstverleners, educatie, en horeca-activiteiten hebben
een gunstige invloed op de waarde van een huis. Agriculturele activeiten
en overige diensten hebben geen structureel effect. Het effect van grooten detailhandel op huizenprijzen is nog onduidelijk. De uitkomsten van
diverse econometrische modellen spreken elkaar namelijk tegen. De
betalingsbereidheid (willigness to pay) voor een marginale werknemer in
een sector ligt tussen de min € 936 en de € 132. Om de
betalingsbereidheid voor een heel bedrijf te achterhalen, zal dit bedrag
ruwweg moeten worden vermenigvuldigt met het aantal werknemers die
actief zijn in dit bedrijf. Functiemenging zal positieve invloed hebben op
huizenprijzen zolang de positieve effecten van individuele functies de
kosten van een diverse omgeving overschrijden. Verder is een belangrijk
resultaat dat de betalingsbereidheid afneemt naarmate de
werkgelegenheid in een bepaalde sector in de omgeving van een huis
toeneemt. Daarom zullen sectoren die een negatieve invloed op
huizenprijzen hebben moeten worden geclusterd: immers, de extra
werknemer heeft niet zo’n groot effect meer als wanneer de extra
werknemer zou gaan werken op een plaats waar nog geen
werkgelegenheid in deze sector aanwezig is. Sectoren die een gunstige
invloed uitoefenen op de waardes van huizen zullen juist moeten worden
102 | The impact of mixed land-use
verspreidt zodat de betalingsbereidwilligheid voor dergelijke werknemers
overal zo hoog mogelijk is. Toch zal men rekening moeten houden met het
verlies van zogenaamde productie-externaliteiten tussen bedrijven
wanneer gemixt wordt met woningen. Er wordt namelijk vanuit gegaan
dat deze productie-externaliteiten hoger zijn wanneer bedrijven zich in
elkaars nabijheid bevinden.
Natuurlijk zijn er ook een aantal zwakke punten in mijn analyse te
ontdekken. Allereerst is de vraag of de conclusies ook gelden voor andere
gebieden en andere jaren: ik analyseer alleen de Rotterdamse stadsregio
in het jaar 2006. Waarschijnlijk is het verstandig om de gebruikte
methodologie ook toe te passen op andere regio’s om zo te kijken of de
gevonden conclusies ook in andere regio’s standhouden. Voorts, ik
analyseer alleen de effecten van functiemenging op huiseigenaren en
besteed daarbij geen aandacht aan andere actoren die actief zijn in de
lokale omgeving. Hierbij kan men denken aan de overheid en de
aanwezige bedrijven. Ook zijn er wat mogelijke methodologische
problemen, bijvoorbeeld door de specificatiekeuze en endogeniteit.
Tenslotte, the impact van het mixen van diverse socio-economische
functies in één gebied is multidimensionaal en moeilijk om compleet in
beeld te krijgen. We zagen dat negatieve en positieve effecten vaak tegen
elkaar worden weggestreept wat de identificatie van individuele kosten en
baten van functiemenging moeilijk maakt. Ondanks deze moeilijkheid
hebben we de totale netto effecten kunnen achterhalen van zowel
individuele functies als het effect van de omgeving an sich. We zagen dat
functiemenging voordelig kan zijn voor de huiseigenaar zolang de baten
van bepaalde sectoren de negatieve effecten overschrijden.
103 | The impact of mixed land-use
104 | The impact of mixed land-use
Appendix A: Buffering techniques
In order to obtain our final database with the structural variables, the
accessibility variables, the neighbourhood variables, and most important,
the mixed-land use variables I have to use Geographic Information
Systems (GIS) to be able to generate the dataset. I use the software of ESRI,
namely ArcInfo.
The first step is geocoding both the NVM databases with the houses and
their structural information. Also the database with the 6-digit postal code
information with for each sector the employment I have to geocode. In
figure A-1 we see an imaginary example of projected houses and 6-digit
employment information. The second step is only possible by using the
Visual Basic plug-in which is available in ArcInfo.21 I let the computer
generate a buffer of size X (measured in meters) for each house and check
which postal codes fall in these buffers. We see for example, the house in
the middle have two postal codes which are reachable within X meter
distance. The last step is to integrate the data. I sum up the employment
over different sectors in the PC-6 locations that are within the buffer of a
house. For example, the house in the middle sums up the employment of
two PC-6 locations while the other houses only can reach one PC-6
locations. I end up with a database where there are 13 columns added
with information about the employment in the 13 different sectors for
each dwelling.
The accessibility measures are easily computed by using the functionality
“near” which is available in ArcInfo. The neighbourhood information of
CBS is joined with the NVM database by using the functionality “spatial
join” which is also available in ArcInfo.
21
The programmed code is available upon request.
105 | The impact of mixed land-use
Figure A-1: Buffering example
•
•
STEP 1
.
•
•+
•
Hou "
•
•
+
•
+
•
' ''''0< 000' 0( _'
Bull.. .. "",," .""h''''''''' 0( , ,,,, L.,
•
--
.,
STEP 2
•+
•
",,-,, ><
•
+
••
.
•
, ,,,,,,,," " PC_6
" "uH. - ,,,,,,,Ki """h huo"" of , ,,,, ,. ,
El ....... ~ "' ''''· M ....... ~
,
"
,
"
""
STEP 3
.
..
~ .~
+
-
........
. ,,
,.
,.",.
'"
,
"
""
•
,.,
""
"
106 | The impact of mixed land-use
Appendix B: Descriptives
Table B-1: Descriptives
VARIABLE
DESCRIPTION
STRUCTURAL CHARACTERISTICS
Transaction price
Transaction price of a
house in euros in the year
[ln]25
2006.
Lot size [ln]
Size of the lot in m2.
Volume [ln]
Volume of the house in m3.
Apartment
Dummy variable
indicating if a house is an
apartment (=1). This is the
reference category.
Terraced
Dummy variable
indicating if a house is an
terraced house (=1).
Semi-detached
Dummy variable
indicating if a house is an
semi-detached house (=1).
Detached
Dummy variable
indicating if a house is an
detached (free-standing)
house (=1).
Rooms
Variable that describes the
number of rooms of a
house.
Garage
Dummy variable
indicating if a house has a
garage.
SRCE22
MEAN23
ST.DEV.24
NVM
217738.1
142035.53
NVM
NVM
NVM
193.74
317.33
0.49
896.05
145.96
0.50
NVM
0.31
0.46
NVM
0.17
0.37
NVM
0.04
0.19
NVM
4.05
1.32
NVM
0.05
0.21
NVM = Dutch association of brokers; KvK = Chamber of commerce; NS = Dutch
railways; CBS = Central Bureau of Statistics.
23 For the mixed land-use characteristics: a buffer of 500 meter around is house is
taken. Obviously, this value will change when the buffer size is changed.
24 For the mixed land-use characteristics: a buffer of 500 meter around is house is
taken.
22
25
[ln] indicated which variables are transformed logarithmically
107 | The impact of mixed land-use
Garden
Maintenance good
Maintenance
average
Maintenance bad
No central heating
Monument
Construction year <
1960
Construction year
1961-1970
Construction year
1971-1980
Construction year
1981-1990
Dummy variable
indicating if a house has a
garden.
Dummy variable
indicating if a house has a
good maintenance quality
(=1).
Dummy variable
indicating if a house has an
average maintenance
quality (=1).
Dummy variable
indicating if a house has a
bad maintenance quality
(=1). This is the reference
category.
Dummy variable
indicating if a house has
no central heating (=1).
Dummy variable
indicating if a house is a
monument (=1).
Dummy variable
indicating if a house has
been built before 1960
(reference category).
Dummy variable
indicating if a house has
been built between 1961
and 1970.
Dummy variable
indicating if a house has
been built between 1971
and 1980.
Dummy variable
indicating if a house has
been built between 1981
and 1990.
NVM
0.48
0.50
NVM
0.01
0.07
NVM
0.88
0.32
NVM
0.11
0.32
NVM
0.08
0.27
NVM
0.00
0.06
NVM
0.33
0.47
NVM
0.14
0.35
NVM
0.14
0.35
NVM
0.15
0.36
108 | The impact of mixed land-use
Construction year
1991-2000
Dummy variable
indicating if a house has
been built between 1991
and 2000.
Construction year > Dummy variable
2001
indicating if a house has
been built after 2001.
ACCESSIBILITY CHARACTERISTICS
Distance to city
Variable indicating the
centre [ln]
distance (in meters) to the
Rotterdam city centre. The
‘Beurstraverse’ is taken as
centre.
Distance to highway Variable indicating the
ramp [ln]
distance in meters to the
nearest highway ramp.
Distance to railway
Variable indicating the
station [ln]
distance in meters to the
nearest NS-railway
station.
NEIGHBOURHOOD CHARACTERISTICS
Population density
The population density
[ln]
measured in number of
people per km2 in the
neighbourhood where the
house is located.
Percentage ethnic
The percentage of ethnic
minorities
minorities in the
neighbourhood where the
house is located.
MIXED LAND-USE CHARACHERISTICS
Variety
Variable which describes
the variety of uses in
vicinity of the house. (Cf.
equation 3.2)
Diversity
Variable which describes
the diversity of uses in
vicinity of the house. (Cf.
equation 3.3)
NVM
0.17
0.37
NVM
0.07
0.25
KvK
8203.07
6001.92
CBS
2275.48
1643.20
NS
2880.24
2887.04
CBS
7438.39
4316.96
CBS
28.88
17.09
KvK
0.74
0.17
KvK
4.55
1.55
109 | The impact of mixed land-use
Job-population ratio
A_B_C Agricultural
activities [ln]
D Industry [ln]
E Production
electricity, water
[ln]
F Construction [ln]
G Wholesale and
retail [ln]
H Hotel, restaurants
and cafés [ln]
I Transport and
communication [ln]
J Financial services
[ln]
K Business services
[ln]
Variable which describes
the job-population ratio in
vicinity of the house. (Cf.
equation 3.4)
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
KvK
0.38
0.89
KvK
21.23
117.70
KvK
47.80
116.05
KvK
6.04
45.63
KvK
57.46
135.26
KvK
287.05
413.57
KvK
69.91
142.50
KvK
97.33
291.63
KvK
102.50
498.12
KvK
310.76
629.60
110 | The impact of mixed land-use
L Public services
[ln]
M Education [ln]
N Healthcare [ln]
O Other services [ln]
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
Describes the employment
in this sector within a
given distance of the
house.
KvK
128.69
439.61
KvK
146.24
223.50
KvK
315.62
583.12
KvK
88.83
198.14
111 | The impact of mixed land-use