The Impact of Knowledge-Intensive Employment Centers on the Rent Structure

of RotterdamBy Lukas Braunschweig, 381788

Department of Applied Economics,

Erasmus School of Economics,

Supervisor:

Frank van Oort

June, 2016

AbstractThe proclamation of clusters is a popular way to attract investment into knowledge-intensive activities.

However, little research has been undertaken on their effects on the rent structure of a given city. This

thesis examines how rents behave with differing distance from knowledge-intensive employment

centers in the city of Rotterdam using a linear regression model while controlling for other relevant

factors. The results show that distance from clusters matters for rents albeit not always in the theorized

ways probably due to upwards-sloping bid-rent gradients. While moderately knowledge-intensive

clusters do exhibit negative rent gradients, very knowledge-intensive one either have insignificant or

even positive distance effects.

Table of Contents

Table of Contents.........................................................................................................................................2

I. Introduction..........................................................................................................................................3

II. A History of Bid-Rent Theory................................................................................................................5

III. Literature Review...........................................................................................................................11

IV. Hypotheses.....................................................................................................................................18

V. Methodology......................................................................................................................................19

VI. Data................................................................................................................................................20

VII. Results............................................................................................................................................25

VIII. Discussion.......................................................................................................................................33

IX. References......................................................................................................................................36

X. Appendix............................................................................................................................................39

2

I. IntroductionArguably the most successful and famous cluster in the world is the agglomeration of technology firms

just South-East of San Francisco: Silicon Valley. Its achievements in fostering innovation have helped

America to claim a leading role in the digital revolution and brought significant wealth to the region.

Hence it is not surprising that clusters play an increasingly important role in economic policies on all

government-levels in the European Union, as well. The European Commission plans to foster SME’s with

the help of clusters in its Europe 2020-strategy (European Comission, 2010) and has set up the European

Cluster Observatory to aid bureaucrats in implementing cluster strategies. National governments in the

Netherlands and Denmark also have a long history of applying clusters in their economic policies (Holtz-

Eakin, 2000). And even regional and city governments have recognized the importance of agglomeration

economies for economic growth and knowledge creation (see for example Free and Hanseatic City of

Hamburg (2011) or Paris Region Enterprises (2016)) – an ever more important feature in today’s

globalized and highly competitive knowledge economy.

Alfred Marshall (1890, 1920) described three sources of agglomeration economies that lead to increased

productivity, more innovation and higher business formation rates. Knowledge spillovers, in the form of

an exchange of tacit (that is incomplete) information between employees, enable people in a cluster to

make better decisions and foster innovation through exchange of ideas between knowledge actors. The

second advantage of clusters are non-traded local inputs. In essence, this concept means that because of

the great market for inputs, supplier of inputs can specialize and deliver inputs more economically than

for a smaller market. Finally, the existence of a local skilled labor pool reduces the search and training

costs of specialized personnel.

Being host to a cluster consisting of a few big and many small firms from the same industry certainly

spells advantages for a city’s community. Various researchers have shown that large enterprises do not

only pay higher wages to their staff ( (Oi & Idson, 1999) and (Brown & Medoff, 1989)) but also attract

more productive (i.e. better educated) workers (Oi & Idson, 1999) which increases the human capital

stock of a region. Jobs at large firms are also safer and tend to fluctuate less with overall economic

conditions (Neumark, Wall, & Zhang, 2011). Finally, as McWilliams and Siegel theorized (2001) and

Udayasankar (2008) confirmed empirically, larger firms are more willing to spend on Corporate Social

Responsibility, part of which usually is spent in local projects. The community therefore profits from

amenities that have been financed by the local employer. An illustration of this behavior can be seen in

3

Wolfsburg where Volkswagen finances museums, galleries and theatres among others and in Eindhoven

where Philips finances the museum and sport clubs (The Economist, 2016).

The presence of one or a few dominant industries also can have a negative impact on a city. Often cities’

growth depends on a small number of industries which does not only give them an out-sized influence

on municipal politics but also binds the city’s fate to the industry’s. This can be two-sided blade as the

demise of Detroit or spending cuts by Wolfsburg’s mayor, following Volkswagen’s involvement in an

emissions-scandal which negatively impacted share prices (The Economist, 2016), show.

But hosting a highly-productive cluster can also harm a city’s residents in other ways. The fact that

clusters concentrate many jobs in a small area means that roads around it will be congested and not

always are companies willing to contribute to the necessary infrastructure investments (Bowles, 2016).

And finally, if one follows a bid-rent framework, big firm campuses should also drive up rents in the

surrounding areas, as employees want to avoid the costs of commuting, driving up demand in a housing

market with slowly adapting supply. Intuitively this issue of clusters seems obvious. Research has shown

that rents in towns with a university campus, median rents are significantly higher than in towns without

academic institutions (Ogur, 1973). In a staff paper of the South Dakota State University, Langelett and

Chang even showed that the bid-rent gradient of a university campus can “overwrite” the Central

Business District’s gradient and be the determining factor of rents in college towns (Langelett & Chang,

2013).

However, besides McMillen (1996) who found that Chicago’s O’Hara airport, an important employment

center exhibits a bid-rent gradient, scientific literature has mainly focused on the effect of university

campuses on the local housing market and their bid-rent gradients and largely ignored the bid-rent

gradients of employment centers other than city centers. Therefore, this thesis aims to explore the

impact of these localized hotspots of qualitative employment on the local rental housing market in the

municipality of Rotterdam, Netherlands.

In the subsequent section a short history of bid-rent gradient theory will be provided, which is followed

by an investigation into the existing literature on bid-rent gradients. Using the insights from the history

and literature review this paper’s hypotheses will be developed. Section IV and Section V will introduce

the data and the methodology for the empirical part of this thesis, respectively. Then the result will be

presented and discussed. The paper will conclude with a discussion of the findings and their implications

for the applicability of bid-rent theory in modern cities.

4

II. A History of Bid-Rent TheoryThe Classical Beginnings of Rent Theory

Modern economic models usually assume away distance and stipulate an economy that takes place on

the famous “pinhead” (Isard, 1956). This of course, is a major oversimplification of the real world. The

ignorance towards distance (bar the extreme case of international trade) has a tradition that goes back

to the very roots of economic science. Although Adam Smith conceptualized rent as the difference

between the price of agricultural output and its production costs and recognized that the cost of

production can vary with the quality of land and therefore will influence rent, he did not consider

transport costs as a factor impacting rent (Smith, 1904).

It was David Ricardo who made Rent Theory his research focus and further advanced the discipline after

Smith’s death. In his magnus opus “On the Principles of Political Economy and Taxation”, Ricardo saw

rent as “that portion of the produce of the earth, which is paid to the landlord for the use of the original

and indestructible powers of the soil”, combined with capital and interest considerations (Ricardo, 1817).

The English-Portuguese economist made two important contributions to Rent Theory. First, he posited

that the most productive land is used first and less valuable areas are only cultivated later, as good land

becomes scarce. Secondly, he claimed that land is used in a way that maximizes economic output. This

latter argument suggests that a given plot of land will go to the highest bidder who can get most value

out of the land and therefore is able to pay the highest rent. This assumption is integral to bid-rent

theory. However, while Ricardo acknowledged the possible effect of certain cost or productive

advantages of a plot of land on rent, such as hedges, buildings or location, over an equally fertile one, he

did not explicitly include distance from a market and the associated transport costs in his considerations.

Ponsard attributes the engagement of the majority of classical economists in abstract analysis to

Ricardo’s reduction of differences in land to differences of fertility and his neglect of spatial

considerations (Ponsard, 1983, p. 11).

Rent and the Use of Land in the Von Thünen-model

This ignorance of space in economic analysis afflicted mainly Anglo-Saxon thinkers. Meanwhile, German

authors thought intensively about the spatial structure of cities and their hinterlands. It is therefore not

surprising that the next advancement of the thinking about rent (which thanks to its spatial

considerations is actually a new branch of Rent Theory) came from a German. In his 1826 book “The

5

Isolated State”, Von Thünen, a rural land-owner from Mecklenburg, was the first to connect economic

geography and traditional Rent Theory laying the foundation for the bid-rent model. Specifically, he

developed a model in which rent paid by a leaseholder is determined by the market price for the

produced crops and the costs of production (McCann, 2001)1. Assuming that all land is equally fertile and

featureless, farmers sell the same produce and have the same production function, as well as absentee

landlords and free entry into the agricultural market, production costs only differ with respect to

transport costs that in turn differ linearly with distance to the central market in the nearest city. In this

world, the closer a farmer is to the market, the smaller his transport cost will be and therefore his profits

will be higher than for farmers further away from the town. As the other farmers observe this and entry

into the agricultural market is free, the rent for these better plots will be bid up, so that all farmers will

just earn enough to cover their costs and survive while the landlords extract a higher rent (as Smith

predicted). Therefore, while all farmers earn the same, the rents will decrease with increasing distance

from the market, forming a linearly declining land-rent gradient (see Figure 1).

To illustrate let us assume a market in which corn is

sold at $100, transporting corn costs $1 per

kilometer and non-land input costs are independent

of location and fixed at $50. A farmer living directly

adjacent to the market does not incur transport

costs and therefore reaps a profit of $50, while

farmers at the 20 kilometer and 50 kilometer mark,

will earn profits of $30 and 0$, respectively, after

paying for transport ($20 and $50). Since rent is

assumed to be the residual income of land (Smith,

1904) (thanks to the competitiveness of the

agricultural market and a perfectly inelastic supply

of land) the landlords will skim these extra profits

in the form of rent, meaning that all farmers earn

the same. Increasing transportation costs will

make the gradient steeper and increasing market prices will shift the gradient outwards.

1 In the logical order and conceptualization of following explanation of Thünen’s model I will rely mainly on Chapter 4 of McCann (2001)

Figure 1: Thünen land-rent gradient Source: Urban and Regional Economics (McCann, 2001)

6

Of the many assumptions the one that is most readily relaxed is the premise that all farmers grow the

same crop. One can easily allow for more than one crop as long as the other assumptions still hold for all

crop varieties (i.e. all variables are the same for all farmers growing the same crop while they can vary

between crops). In this case competition is not only between farmers but also crops which means that

the crop with the more expensive transport costs will be located closer to the town and farmers of that

crop will have to pay a higher rent (see Figure 2).

The effective land-rent gradient will then be the

envelope of both crops’ gradients.

In an example barley sells for $100 while wheat

only pays $50 per bushel. However, with transport

costs of $2 it is twice as expensive to move than

wheat. Both crops have fixed non-land input costs

of $50. We see that barley will be planted as far as

33 kilometers from the market while wheat only is

viable to grow beyond that mark. Nothing is grown

further than 50 kilometers from the town. We can

see that Von Thünen took up Ricardo’s

assumption that land will be allocated to the use

that pays the highest rents is reflected in this

model.

Despite its many assumptions that largely do not

hold up in the real world and its focus on only

agricultural rents, the Thünen-model is exceptional because it is the first account of an effort to explain

the spatial configuration of land use of cities and their hinterland in economies dominated by agriculture.

Nobel Prize-winning economist Paul Krugman notes that Thüenen’s model anticipates many concepts

that should become part of mainstream economics (Krugman, 1995). These include the idea of an

equilibrium (the pattern of concentric land-use rings around the town), a market value instead of an

inherent value of goods and factors (land gets its value through bidders competing for it), the

simultaneous pricing of goods and factor inputs (the maximum possible rent depends on the market

price of agricultural output), markets that achieve efficient outcomes (the highest bidder can use the

best land) and finally Von Thünen recognized the role prices play in incentivizing efficient behavior (the

more efficient a bidder is in his production, the higher he can bid).

Figure 2: Land Competition in the Thünen-model Source: Urban and Regional Economics (McCann, 2001)

7

The emergence of Modern Bid-Rent Theory

Maybe this foresight of ideas that later would become the heart of mainstream economics, explains why

it took almost 150 years until major contributions were made to Von Thünen’s theory. Although Alfred

Marshall anticipated one important prediction of modern Rent Theory, namely that higher prices will

lead to higher density (Marshall, 1890), he did not produce a spatial theory of rents. Other economists,

while interested in land-use patterns, did not build on Von Thünen’s work until William Alonso’s book

“Location and Land Use: Towards a General Theory of Land Rent” (Alonso, 1964). In this book Alonso

largely adopts Von Thüen’s assumptions but includes a small but important difference: in Alonso’s bid-

rent model the land to non-land input relationships required for production are not fixed anymore but

can differ. This means that land and non-land factors can be substituted for each other.

8

The important implication of this new

assumption is that rent is not a

residual anymore, as Smith, Ricardo

and Von Thünen assumed, but rather

one of many factors that are included

in the cost function of a firm. The cost

(dis-)advantages of a production

location therefore do not accrue to

the landlord anymore but to the

landholder. This landholder therefore

has an incentive to optimize his cost

function. To achieve efficiency, the

marginal costs (which in a competitive

economy, are equal to prices) of land

and non-land factors must be equal to each other. If this is not the case, a firm will substitute the more

expensive factors for more of the cheaper factors until balance is restored. For bid-rent theory this

means that, assuming non-land factor prices are independent of distance from the city center and the

price of land decreases with increasing distance, the costs of land fall relative to non-land costs.

Therefore, a profit maximizing firm will substitute non-land factors for more land. Conversely this

indicates that the closer a firm is to the city center the less land and the more non-land factors it will use

in its production (McCann, 2001). It is easy to see that this is exactly what Alfred Marshall predicted. The

result of this observation is a shape of the bid-rent-gradient in Alonso’s model that differs from Von

Thünen’s straight line: the gradient will be curved (see Figure 3). To see why, recall that while transport

costs t per kilometer are assumed to be fixed, the absolute land area used, increases with d from the city

center. The t per land area unit therefore decreases, which in turn decreases the rate with rent has to fall

in order to compensate for the higher t.

But what about land competition in bid-rent theory? After all it is aimed at explaining the spatial

structure of cities. Most generally we can say that the highest bidder will be closest to the city center.

Who that highest bidder is depends on the bidder’s production functions and the substitutability of land

with non-land factors and the importance of easy market access (i.e. distance to the center) in their

business models. Therefore, producers whose production is relatively land-intensive will be pushed to

the city’s outskirts, as other producers can just substitute non-land factors for less land and therefore

Figure 3: Bid-Rent Curve for an individual firm Source: Urban and Regional Economics (McCann, 2001)

9

Figure 4: Firm land competition in the Bid-Rent Curve Model Source: Urban and Regional Economics (McCann, 2001)

move closer to the center while this is option is not available to the land-intensive producers. This is one

of the reason why logistics centers or factories are seldom located in the middle of a city and why service

industries and specialized retail stores can often be found in the very center of cities. An example for

how activities could be distributed in a city can be found in Figure 4. The actual rent gradient for a city is

(similarly to Von Thünen’s model) not determined by any single firm’s but rather is composed of the

highest bidder’s at any given point. Thus, it is the envelope of all individual gradients. Finally, we can

determine the edge of the city as the point at which agricultural use is the highest bidder for land.

The reader might have noticed that all the

models discussed thus far have focused on the

location behavior of producers while this paper

is looking to investigate the effect of clusters on

rents for households and not firms. However,

extending the bid-rent framework to describe

consumer behavior seems like a natural

extension. Instead of optimizing profits,

households try to maximize utility given their

constraints. Instead of a market at the city

center, it is assumed that all employment

opportunities are located in the city center.

Commuting is increasingly costly in both

monetary and non-monetary terms with

increasing distance to the center. The exact estimation of these cost vary from $0.17 (Wilson & Frew,

2007) to $4.12 (Langelett & Chang, 2013) (in the author’s opinion the high estimate is an

overestimation). The conclusions of this household model are heavily dependent on the assumptions

one makes about the preferences of different groups of people. In most models these groups are formed

around household income. If we for example assume that transport costs are the same for each group

we will arrive at a bid-rent gradient for low income households that is very steep, as travel costs make up

a higher relative share of their income, they are less willing to move far away from the center, meaning

that the city center will be densely populated by low income individuals, while the income rises when

moving towards the city’s edge (see Figure 5). However, if we assume that because they can earn more

money per hour, the opportunity costs of commuting for high-income individuals are higher, the

conclusion turns around completely. Now the rich will have a steeper bid-rent gradient and therefore

10

occupy central locations while the poor are pushed away from employment opportunities towards the

periphery of the city (see Figure 6). Other variations of the underlying assumptions are also possible but

will not be further discussed here.

Now it is time to reflect on one implicit, underlying assumption of the household bid-rent model that has

not yet been discussed, although it is crucial for the correct empirical evaluation of the theory. Namely,

housing is seen as a homogenous commodity so that all units can be compared and utility derived from

them only varies with distance to the city center. It is not hard to see why this simplifying assumption is

highly problematic. It clearly ignores the important aspects of the real-world decision-making process

therefore distorting reality, as well as delivering faulty predictions. Maybe the most important of these

aspects is a housing unit’s environment. While an exact, generally agreed-upon definition of this term is

not yet established, the environment includes locational aspects of a housing unit that usually are

specific to that location. Locational aspects can either increase or decrease the utility derived from a

housing unit. Positive examples are amenities like good schools, parks or a variety of cafés while negative

ones can be pollution, a high crime rate or low social status of an area. One approach widely utilized to

account for environmental differences and circumvent the mentioned problems, was developed by

Sherwin Rosen (Rosen, 1974). He argues that housing is a composite commodity. A composite

commodity is a collection of goods whose relative prices do not change. Therefore, these goods can be

bundled and treated like one commodity (Oxford Reference, 2016). According to Rosen, households

choose a bundle of the individual attributes that maximizes their utility. In this point of view, one can

then isolate these single attributes and assess their individual impact on the valuation of a given housing

unit. Therefore, one can arrive at an estimation of the bid-rent gradient that is not confounded by

varying locational characteristics of housing bundles (Rosen, 1974).

Before the hypotheses for the empirical part of this thesis are developed in Section IV, the next passage

will present an overview over the literature regarding the bid-rent model.

III. Literature ReviewMost economists using the bid-rent framework follow a methodology developed by Sherwin Rosen in a

highly influential paper about hedonic prices and implicit markets (Rosen, 1974). Noting that “structural

interpretations of the hedonic method [were] not available”, Sherwin Rosen developed a methodology

that allows researchers to estimate a household’s willingness-to-pay for a given attribute of a composite

commodity and find the equilibrium in markets for composite goods. In a market for a composite

commodity and with utility-maximizing consumers, implicit prices for attributes can be estimated by

11

regressing the good’s price on its characteristics. In the next step the found prices are used to construct

an inverted demand curve. By integrating the demand curve from a lower to a higher value of the

attribute of interest one can determine the willingness-to-pay for said attribute. Although Rosen uses

this methodology to analyze the welfare implications of quality standard legislation, this approach can be

and in fact has been, widely applied to the housing market to obtain implicit prices of attributes by

academic articles several of which are also review hereafter.

In Rosen’s approach willingness-to-pay values are derived from the utility of an individual. Contrary to

that, Ellickson (1981) develops an alternative to this hedonic price modeling approach that allows a

direct and clear interpretation of results and does not need utility functions as connector of demand and

the price for an attribute of housing. Instead of inspecting the impact of housing attributes one at a time,

Ellickson proposes a multinomial logit model that can treat housing characteristics simultaneously. It also

does not focus only on hedonic price estimation but on how households actually react to these prices.

His model gives the probability of a house with certain characteristics being occupied by a consumer of a

given type. He uses data from the San Francisco Bay Area to test his model. The results clearly are in line

with bid-rent theory. All theorized factors that impact housing demand had the predicted effect. It is

interesting to note that the factors that showed the biggest differences in valuation by low- and high-

income households were the number of rooms, the median tract income and the hedonic residual. This

model is supplemented by Lerman and Kern (1983). They add a methodology that allows them to

determine the highest bidder’s willingness-to-pay for individual housing attributes that is missing in

Ellickson’s model (and exists in Rosen). However, they do not apply their model to empirical data. This in

turn was undertaken by David Gross (1988) using data from Bogota, Colombia. He furthermore compares

the findings of Ellickson’s model with the traditional hedonic price model by Rosen. Gross builds a model

with the help of seven housing attributes. These are the number of rooms, floor quality, total dwelling

area, roof quality, the mean neighborhood income, a measure of accessibility of employment centers

and the quality of sanitary facilities. Generally, the adapted Ellickson-model confirms the theory and

gives higher willingness-to-pay estimates than other models. These differences seem to be valid when

contrasting them with intuitive expectations of household preferences. The Ellickson-model’s predictive

power, however, does not instill confidence in the model with a low accurateness in predicting actual

rents. Gross attributes this to specification issues in the variables used and suggest further testing with

better data.

After this brief overview of the two most commonly used methodologies to test the bid-rent theory we

now turn to empirical studies of several cities, mainly in the United States but also in Europe and Asia.

12

Wilson and Frew (2007) examine not only whether rent gradients exist in Portland, Oregon but also how

they evolved between 1992 and 2002. Their choice of city is due to the very restrictive urban growth

legislation during that time which means that the supply of land in Portland did not markedly increase,

while demand for land did. They therefore hypothesize “[an] increase [in] rents overall with the highest

increases occurring in the city center [as a] result of increased congestion and higher

commuting costs”. Bid-rent theory predicts an increase in rents and a pushing out of the low bidders.

Next to three variables measuring the distance to the city center, to the next freeway ramp and to the

next freeway intersection, hedonic attributes were also included in their model. They find that rents

adjusted for inflation did in fact increase over the ten-year period with the increases being highest close

to the city center and going towards zero at the outskirts of the city. Apparently the Bid-Rent model does

hold in Portland, Oregon.

Instead of looking for a bid-rent gradient, Muto (2006) examines Tokyo to see whether the land-use

predictions of the bid-rent model are correct. In doing so he utilizes Full Information Maximum

Likelihood with one land usage and two land valuation functions. The study uses publicly announced,

yearly valuations of fixed points in the city generated by the Land Evaluation Committee. These

observations are combined with information about distance to the next public transport stops, land

usage and several other factors of interest. Since his research included both residential and commercial

land usage, instead of using housing attributes, Muto uses the distances to the next park, school, coast

and road as hedonic features of a location. He finds that bid-rent gradients are steeper for commercial

land uses than for residential one. Generally, the land-use predictions of his model confirm bid-rent

theory but close to the second CBD of Tokyo one can find deviations from the model. Muto explains this

with the relatively shorter time since the commercial development in this smaller center in combination

with residents that are slow at adapting to the highest-bidding land use. As mentioned before Muto’s

paper does not directly support this paper’s hypotheses but it does establish confidence in the bid-rent

model, even in complex land markets.

A similarly mixed result is found for Stockholm in Sweden (Söderberg & Janssen, 2001). In their study of

Stockholm from 2001 Söderberg and Janssen examine the effect of distance from the city center on the

transaction price, assessment price and rental income of over 300 houses traded between 1992-1994.

They find that a small number of variables, namely age, distance from the city center, size of the

property and received subsidies can explain these three value indicators very well. Interestingly, while a

price gradient can be established for Stockholm, this is not the case for the rental income regressions,

meaning that there is no rent gradient. Söderberg and Janssen attribute this finding to Gross

13

Capitalization Rates with which rent is discounted, that differ between properties due to differences in

hedonic factors (e.g. maintenance costs) and expectations of market participants with regards to the

rent controls in the city. Of course one cannot say with certainty that without these factors there would

be a bid-rent gradient, but the existence of a land price gradient does indicate at least the possibility of

such a gradient.

These findings instill confidence in the bid-rent model. However, in their paper “The Structure of Urban

Land Prices” Colwell and Munneke (1997) challenge these since most of them assume a linear

relationship between land price and area. They argue that land urban prices decrease, concavely instead

of linearly. This would lead to an overestimation of the bid-rent gradient slopes and therefore the impact

of employment or amenity centers on rents. To test their hypothesis, they use a dataset from the Real

Estate Data Inc. for the city of Chicago in the years 1986-1992. By comparing two models, one that does

not allow for non-linear land prices and one that does, they find that the former significantly

overestimates the bid-rent gradient’s slope. While their finding challenges commonly used

methodologies, it does not reject Alonso’s Bid-Rent theory altogether since they still find significant and

negative distance coefficients.

Colwell and Munneke are supported by other researchers who do not find significant rent gradients. One

city for which this is the case is San Francisco, California (Wheaton, 1977) which is also one of the first

empirical studies on this topic. Similar to Söderberg and his colleague, Wheaton finds that a small

number of housing variables and neighborhood income (as a proxy for local externalities) already have a

high prediction power of rents. He furthermore computes a value of time between $0.05 and $1.40 per

hour. However, his Accessibility-index that estimates closeness to commercial centers exhibit a negative

sign and therefore reduces rents. This clearly contradicts the predictions of bid-rent theory. The author

theorizes that negative externalities of being close to commercial centers like noise and congestion could

outweigh the benefits of closeness. Whatever the reason for the negative sign of accessibility, this raises

first doubts about the correctness of the Alonso-model.

These are further amplified by the findings of Dubin and Sung (1987). They start their paper with the

simple observations that many cities are not simply mono-centric as assumed by bid-rent theory which

they suggest could lead to another rent gradient than the strictly negatively sloped one predicted by

Alonso. They believe that the gradient will vary by direction depending on differing characteristics that

influence rents positively or negatively. The two researchers construct four 20°-rays emancipating

outwards from the city center of Baltimore, Maryland. They then analyze a subsample of observations

within each of these rays to develop rent gradients for each ray. Their findings call into question the

14

theory behind the bid-rent model. While amenity centers (such as the CBD but also sub-centers) do

exhibit negative rent gradients, their impact is locally very limited (to around 1.5 miles). The expected

dominance by the CBD on the rent gradient could not be established, since none of the rays have an

overall negative slope. Dubin and Sung conclude that the existence of non-CBD peaks may distort the

influence of CBD’s on rents in other studies. Finally, they make the model’s assumption of a homogenous

plain responsible for the empirical failure of bid-rent theory.

The previous three paragraphs call into question the validity of the Alonso-model. But this is not really

surprising after a short consideration of how much the world has changed since the early 1960’s when

the theory was developed. Transport and information technology have leapt forward and reduced the

importance of proximity for economic activity. Indeed, this reasoning is confirmed by a long-term study

of Chicago’s bid-rent gradients (McMillen, 1996). In his often quoted study McMillen uses nonparametric

estimators to investigate how the land gradients of Chicago, Illinois have changed in the more than 150

years between 1836 and 1990. Between 1836 and 1928 one can observe a distinct, negative gradient

that flattens out over time (1836: 60% rent reduction per mile, 1928: 20%). This is in line with a mono-

centric model that predicts flatter gradients for lower transport costs. In the Chicago case this was due to

the introduction of the light-rail network. The second observed period (1960-1990) is marked by a

decline of explanatory power of the strict mono-centric model. While the CBD still has the greatest

influence on rents, these 30 years also see the rise of O’Hara airport, a major employment center, as a

second peak in Chicago’s land-value landscape. The dilution of the mono-centric model is not surprising

considering the spatial expansion and sub-urbanization of Chicago in that time. Three conclusions of this

study matter for this thesis’s purposes. First, it calls into question the validity of the mono-centric model

in modern cities. Second, the study seems to supplement our theory that not the spatial center to a city

is what matters for bid-rent theory but the concentration of employment, as exemplified by O’Hara

airport. Finally, rents still can be explained by a small number of variables in modern times.

But while many empirical studies (especially historical ones) of bid-rent gradients have focused on

Chicago, McMillen’s findings are not unique to the Windy City. Atack and Margo (1998) were the first

ones providing a historical long-term study of the land prices in New York City. By using data obtained

from newspaper announcing location and sales price, as well as other important variables, of plots

located on the Manhattan-peninsula, they largely confirm findings from Chicago. At the start of the

observed period in 1835 a negative land rent gradient with City Hall as center can be clearly

distinguished. This gradient slowly flattens out over time and disappears after the American Civil War.

Similarly, the regression’s explanatory power using distance as the only explanatory variable decreases

15

dramatically from 60% in 1835 to almost zero in 1900 due to increasing land development and land

heterogeneity. These findings clearly show the impact of technology on travelling costs, as well as that

they are the determining variable for the spatial distribution of land values. The researchers intend to

enrich the currently available plot information and repeat the analysis.

From the studies examined thus far, emerges the impression that bid-rent theory is largely failing to

explain rents in modern, non-monocentric cities. Does that mean that we have to discard the Alonso-

model altogether? Not necessarily. While it is true that it is hard to justify an application of the model to

modern cities, due to overly strict assumptions that rarely are met in the real world, smaller sub-urban

clusters might exhibit rent gradients.

And indeed, empirical findings do suggest that this suspicion has some merit. One of the more famous

accounts of rent gradients of campuses was delivered by Coulson (1991). He tried to dispel doubts over

the monocentric model by arguing that previous studies trying to find the predicted negative bid-rent

gradients failed because of their use of data from housing markets that do not conform to the theory’s

assumptions. He uses a dataset from State College, Pennsylvania, a small, monocentric college town that

is home to Penn State University. The campus forms the center of the city and is immediately adjacent to

the Central Business District. Contrary to other studies, Coulson’s observation points encompass an

entire housing market (about 10- to 15-mile radius from the center) and all have a similar level of taxes

and service provision. The model used by Coulson accounts for several housing attributes and direction

relative to the CBD of the city. He finds that a negative bid-rent gradient exists in State College and even

more importantly this gradient falls with a similar rate (slightly more than $0.5 per mile) as the

transportation costs increase. Accumulating this per mile transport costs yields a decrease in

accumulated value of $2633.41 per mile from the city center. This study does not only confirm bid-rent

theory in an ideal setting but also indicates that campuses and therefore clusters of knowledge jobs, do

have rent gradients.

The proof that a campus can replace the CBD as the center of a city was delivered by Langelett and

Chang (2013). The two researchers examined Brookings, South Dakota, a city with several employers, the

second biggest of which is the South Dakota State University. However, more than 50% of the town’s

population are students. Their linear-regression model did not only contain two distance variables (one

for the campus, one for the CBD) but also controlled for housing attributes. Surprisingly the CBD-distance

variable is insignificant at the 10%-level while the campus variable is significant. On average, being one

mile further away from campus reduces monthly rent by about $4.12. These findings suggest that the

center of a mono-centric bid-rent model must not necessarily lie in the CBD but what matters for the

16

location of the center is the share of the local population commuting towards a given area. This is highly

relevant for this thesis since it strengthens the suspicion that campuses (and local clusters) can raise

rents in the surrounding neighborhoods. However, it remains to be seen whether this relationship also

holds for clusters that are the destination for a smaller share of a city’s population’s commuters.

A similar conclusion can be drawn from the student populations at Brigham Young and Utah State

university (Lewis & Kapp, 1994). The existence of a bid-rent gradient with the university campuses at the

respective center is confirmed by the two researchers. They believe that the Alonso-model’s

assumptions hold for this student sample better than for other populations thanks to the homogeneity

of the individuals and the importance of the campuses in the students’ everyday lives. In particular Lewis

and Kapp found a negative, non-linear rent gradient at both universities. Curiously, BYU students who

were perceived as richer, exhibited a less steep gradients and therefore lower opportunity costs of time.

Although the authors do not explain why this is the case, one could speculate that the flatter gradient

results from the availability of a campus shuttle bus at the BYU. It seems therefore that accessibility and

not distance is the important factor influencing rents.

Of course this seems obvious. Distance from the CBD or a cluster of knowledge-intensive jobs is not the

actual variable that one is interested in when looking for bid-rent gradients but rather it is a proxy for the

true variable of interest: transportation costs. The first one to fully conceptualize a framework with

transportation costs instead of distance was Youngsun Kwon. In his paper “Rent-Commuting Cost

Function versus Rent-Distance Function” (2002), Kwon develops an alternative to the distance rent

function: the Rent-Commuting Cost Function (RCC). As Yinger (1993) argued this is the more appropriate

explanatory variable for urban rent gradients since what matters for the utility derived from a housing

unit is not so much the distance from the CBD but rather the costs of transportation. According to Kwon

these two variables diverged in modern cities without dense hub-and-spoke commuting network thanks

to modern transport and information technologies. The advantage of the RCC is that it is independent of

the specification of the commuting cost function, contrary to the Rent-Distance function. This allows for

better insights into suburban settings. The RCC is an interesting concept as it is more closely orientated

towards the true explanatory variable of commuting costs (rather than distance which only serves as a

proxy). This makes the fact that Kwon did not suggest a way to feasibly implement his concept in

empirical studies even more regrettable.

Although Kwon was the first to develop a satisfying Rent-Commuting Cost Function, the importance of

transport costs was recognized long before him. In 1987 Coulson and Engle tried to estimate these

monetary and non-monetary commuting costs using a dataset of six major American cities in the years

17

1974-1979 (Coulson & Engle, Transportation Costs and the Rent Gradient, 1987). Common to these cities

was that they did not have an extensive rail system which could be used for commuting. The demand for

automobile commuting therefore was seen as largely inelastic in these cities. By observing house values

(seen as capitalized rents) they find that non-gasoline costs have correctly been capitalized while time

costs and gasoline costs have been overcapitalized. Despite their somewhat crude methodology this

study suggests that using transport costs instead of distance as determinants for the bid-rent gradient

confirms the theory better. However, the authors recommend further research with better data and a

more sophisticated methodology.

The existing empirical evidence paints a cautioning but not disheartening picture of the merits of bid-

rent theory. In the years before and shortly after World War II the Alonso-model did well in explaining

land use and rent gradients in cities like Chicago and New York. Over time, however, these rent gradients

flattened out (in New York earlier than in Chicago) or disappeared completely with new sub-centers

emerging as peaks of rents in the urban spatial structure. Albeit an untested suspicion, it seems like the

driving force behind this development is the advancement of transportation and information

technologies starting with the introduction of public transport services, continuing with the wide-spread

adoption of cars and telephones and coming to a temporary peak with the use of the internet in virtually

every aspect of life. However, this does not mean that we have to discard bid-rent theory as not useful

altogether. Various researchers found that employment and educational centers do exhibit significantly

negatively sloping rent gradients in many cities. This encourages a shift of focus from the urban

perspective to the intra-urban level of the city . Apparently, city centers have lost their overarching

economic importance and gave way to a more fractured distribution of economic activities within the

city. This arguably is a better depiction of today’s modern metropolises than the strictly mono-centric

model. Therefore, a fruitful field for research could lay in the application of bid-rent theory to small intra-

urban employment centers. Another effect of new ways of transporting goods and people, as well as

exchanging information is the erosion of the correlation of cost of commuting and its proxy distance,

prompting the need to augment the theory in order to capture this development but also to develop a

methodology that allows for a better estimation of actual commuting costs in empirical studies. Despite,

the overall mixed evidence regarding the Alonso-model, the existing literature instills confidence in this

thesis’s pursuit of finding rent gradients for knowledge-intensive employment clusters.

In the next section the hypotheses that this thesis is looking to test will be developed using the insights

gained from bid-rent theory and the literature review.

18

IV. HypothesesAfter looking at the bid-rent theory and examining empirical evidence regarding its validity, it is time to

formulate the hypotheses that will be used to assess the impact of employment centers in Rotterdam.

The clusters of knowledge-intensive jobs we examine replace the city center in the theoretical Alonso-

framework, laid out in the second section of this thesis. Accordingly, I expect to observe strictly negative

rent gradients and therefore the first, most fundamental hypothesis is:

H1: The observed clusters will exhibit negative rent gradients with increasing distance

However, assuming that every cluster has the same effect on its surrounding neighborhoods, would be a

dangerous oversimplification of the real world. Therefore, one further hypothesis that builds on the first

one, will be developed in the following. As mentioned earlier, the expected steepness of rent gradients

depends on the assumptions one makes about the target population. The example of high- and low-

income households was used to illustrate this phenomenon. Assuming that knowledge workers earn

more than less educated peers, this means a higher opportunity cost of commuting to them, as they

could earn more during the time spent travelling. Of course, commuting costs make up a smaller share of

their monthly budget and therefore less sensitive to commuting costs. However, here it is assumed that

due to their higher income and decreasing marginal returns, knowledge workers have a higher marginal

utility of time than of money and therefore will choose to locate close to their jobs. This leads to the

second hypothesis:

H2: The rent gradient of clusters with more knowledge-intensive jobs will exhibit a steeper slope than less

knowledge-intensive clusters

The next section outlines the methods applied to test these two hypotheses before moving on the data

being analyzed.

V. MethodologyTo test the hypotheses postulated in the preceding section, this paper will use an OLS-regression of an

appropriate specification which is to be determined in the following chapter. The dependent variable will

be the rent a given property yields to its owner. The measure of rents will be regressed on the distance

to the nearest cluster. This procedure allows the analysis of the effect of distance from a cluster on rents.

However, clearly distance from an employment center is not the only factor affecting the rent of a piece

of land. Following Rosen (Rosen, 1974) this thesis therefore will also develop a hedonic pricing model

(1)19

that decomposes the heterogeneous good housing into several attributes to isolate their effects and to

control for Omitted Variable Bias that exists when regressing rent only on distance. Equation (1) presents

the complete model that will be used for the analysis of the data.

Rent=β0Distance+β1 Ageof Unit+β2Typeof Owner+β3 Amenities of Unit+β4 Amenities∈Neighborhood+β5Riverside+β6¿Unit

All of these factors are assumed to play a role in the determination of a housing unit’s rent and therefore

are included in this paper’s model. The following paragraphs will outline the presumed effects of each of

these factors.

The age of a housing unit is thought to affect rent in two (at least partially) contradictory ways. First and

more obvious, a given house depreciates with increasing age, as its technology becomes outdated or

even obsolete and the substance deteriorates, necessitating renovations. Therefore, the first effect of

age is assumed to have a negative influence on rent. However, depending on the market and the wealth

of agents in it, units from different architectural periods command significant price premiums or discount

(Rehm, Filipova, & Stone , 2006). These are so-called “vintage effects”. Hence, no overall prediction for

the sign of age can be made.

Next, I suspect that the type of owner of a given dwelling does influence the rent it can command. This

happens through a differing willingness. An institutional real estate investor might be more willing to

spend on the upkeep of its real estate than an occupant owner in order to maintain its asset base. The

same could hold in reverse, since occupant owners are directly affected by the effects of depreciating

technology. Therefore, I do not make a prediction about the sign of the effect of ownership on a housing

unit’s rent.

Another factor influencing a unit’s rent are the private amenities that are attached to a given dwelling

and not necessarily shared with the neighborhood. These could include a garden for single family homes

or an elevator for multi-family homes. The more private amenities a dwelling possess, the higher its rent

is expected to be. The same holds for public amenities that are shared with the neighborhood such as

public parks, connections to the public transport system or good schools.

Riverside, is a factor that is included in the model to take into account a geographic characteristic of the

observed city. Rotterdam is divided into North and South by the river Maas. Due to the South’s history as

the main center of the port and the resulting amassing of low-skilled workers close to their jobs and

relative remoteness from the city center which is found in the North, the South is still seen as a less

20

desirable place to live. Therefore, dwellings located South of the river Maas are expected to command

smaller rents, holding all else constant.

Finally, the size of a unit is expected to have a positive effect on the rent a dwelling can command.

Sign of expected Effect Factors

+ Amenities of Unit, Amenities of Neighborhood, Size of Unit

- Riverside

+/- Age of Dwelling, Type of Owner

After introducing the model that will be utilized in this paper’s analysis, the attention turns to the

variables and data that will be used to populate the model.

VI. DataIn this chapter I will first discuss the definition of a knowledge-intensive clusters for the purpose of my

analysis and then introduce the housing data and explain which variables were used to asses which

component of the theoretical model outlines above.

So far this thesis has not given an unambiguous definition of what it means when it talks about

knowledge clusters. The answer to this question lies in a feature that arguably all high-technology

clusters share: a high concentration of knowledge-intensive jobs in a given area. Therefore, I will use this

criterion to determine clusters in the city of Rotterdam. This is done with the help of LISA-database

(establishment level data from 1996-2012, available for research at Erasmus University Rotterdam).

Among other variables, LISA collects information on the number of jobs in the different postcodes of the

Netherlands and assigns them to sectors based on the SBI, a standard industrial classification based on

the “International Standard Industrial Classification of all Economic Activities”.

By using the definition of knowledge-intensive sectors of the Dutch PBL Netherlands Environmental

Assessment Agency (Weterings, Raspe, & van den Berge, 2011) (see Appendix I), the share of knowledge-

intensive jobs relative to the total number of jobs for all four digit postcodes in Rotterdam for 2012 can

be obtained. Next, the Location Quotients (LQs) for knowledge-intensive jobs can be calculated for every

postcode in Rotterdam, by dividing the obtained shares by the Dutch average share of knowledge-

intensive jobs relative to total jobs (see Equation 2).

Figure 5: Summary of Expected Effects

21

Knowledge LocationQuotient=

Knowledge jobs∈givenZIPTotal jobs∈givenZIPKnowledge jobs∈NL

Total jobs∈NL¿

¿

A value of 1 or higher is considered more knowledge-intensive in jobs than the Dutch average and

therefore a potentially interesting cluster. In the analysis differing degrees of knowledge-intensity will be

considered.

Overall this procedure yielded 65 postcodes for which the LQ was not equal to zero (some postcodes are

reserved for post boxes and therefore do not contain any jobs, leading to numerator of zero in (2)). The

LQs had a mean of 1.085515, indicating that Rotterdam has slightly higher share of knowledge-intensive

jobs than the Netherlands overall. The values for the individual LQs ranged from a minimum of

0.3648455 to a maximum of 2.38057. When mapping the LQs one can observe that the most knowledge-

intensive postcodes are located centrally at Dijkzigt (LQ of 2.38) where the medical campus of Erasmus

University is located and on the north-western edge of Rotterdam (2.19) next to the Rotterdam-The

Hague Airport. The least knowledge intensive areas can be found in the west of Rotterdam in residential

areas with below-average incomes and in the very South of the city (see Appendix III).

This data on the LQs of the different postcodes was then combined with a dataset about the tax values

of homes and several important attributes of the housing units, collected by the city government of

Rotterdam in 2012 (available for research at the Erasmus University of Rotterdam). I am aware that tax

values are not the same as rents, however, data on rents in Rotterdam are currently not available.

Furthermore, it is a common practice in economics to see land values as the sum of all discounted cash

flows that would result from renting out a dwelling (see for example Muto, 2005 or Söderberg and

Janssen, 2000). The authorities estimated the tax values by taking the average of the transaction value of

three homes with similar attributes surrounding the object of interest and adjusting this average for

special characteristics of the observed home. A wide array of housing types was examined by the

authorities, ranging from one-room city center apartments to sub-urban villas with more than 9 rooms.

This procedure resulted in a dataset with 294586 observations. However, since for some of these

observations data on some of the control variables were missing, these were dropped so that 137002

observations for which the data was complete were considered. The mean tax value of observed

dwellings was 166203€ with a minimum of 16000€ and a maximum of 9270000€. A histogram of tax

values shows a strong right skew in the observations due to outliers in the form of expensive villas.

(2)

22

Figure 6: Histogram tax_value

01.

0e-0

62.

0e-0

63.

0e-0

64.

0e-0

6D

ensi

ty

0 2000000 4000000 6000000 8000000 10000000tax_value

Figure 7: Histogram lntax_value

0.2

.4.6

.81

Den

sity

10 12 14 16tax value

Therefore, the natural logarithm of the variable tax_values was obtained. This resulted in a much better

fit with the normal distribution (see Figures 6 and 7).

With the help of QGIS-software, the observations’ linear distances from their nearest postcode-centroid

were calculated. The resulting average distance from the nearest postcode-centroid for the Rotterdam

sample was approximately 517 meters but individual observations were located as close as less than a

meter or as far as more than 1800 meters from the next centroid.

Besides the tax value of dwellings, data on several other housing attributes was collected by the City of

Rotterdam. With the help of this information we can approximate the factors theorized to affect rent in

(1).

Age, the first component of (1) is approximated with two variables: y_constr and y_constr_cat. y_constr

is a continuous variable indicating the year of construction. With increasing age, a building is expected to

depreciate and therefore y_constr is expected to have a negative sign. However, due to vintage effects

houses from a certain period in time can be sought after for example for their appealing architecture.

Therefore, another time variable is included as a dummy: y_constr_cat, a categorical variable that takes

on the value 1 for houses built before 1945 and 8 for houses built after 2010.

The effect of ownership in (1) is captured by the categorical variable ownership which indicates whether

the owner is the Muncipality(=1), a Corporation(=2), an institutional investor(=3), a non-corporate

landlord with two to ten apartments(=4), a corporate owner with two to ten apartments(=5), a non-

corporate owner with a maximum of 100 dwellings(=6), a non-corporate owner with more than 100

units(=7), other non-corporate owner(=8) or an owner occupier(=9).

The variable apartment_type that categorizes dwellings Single Family Home(=1), Multiple Family Home

with elevator(=2), Multiple Family Home without elevator(=3) and other Multiple Family Home(=4) is

23

used to estimate the private amenities that a dwelling offers since these should be approximately the

same per type of housing. For example, many Single Family Homes have a garden or more than one

bathroom, while this is not the case Multiple Family Homes. The public amenities that accrue to all

homes in a given neighborhood can be estimated by environment, also a categorical variable. Its

categories include Center (=1), Center Edge(=2), Urban(=3), City(=4), Suburban(=5), Village(=6), Villas(=7)

and Industrial(=8). Again, neighborhoods in the same categories are expected to have similar amenities

while differing between types of environment.

The variable riverside describes on which side of the river Maas a given dwelling is located. This dummy

variable takes on the value 1 if a dwelling is located South of the river Maas.

Finally, the size of a housing unit is estimated by no less than four variables: plot_size, ground_m2,

sum_ground_m2 and rooms. plot_size measures the size of the plot a dwelling is built upon in square

meters, ground_m2 measures the Lettable Floor Area, a measure for the inhabitable floor space of a

dwelling, in accordance with the NEN2580-guideline, while sum_ground_m2 indicates the total

apartment ground space available for living and storage. The number of rooms of a dwelling is captured

by the categorical variable room.

Before moving to the analysis of the results, the relationships between the variables will be examined.

The correlation table (see Figure 8) several surprising findings can be worked out. The distance to the

nearest postcode centroid has a rather weak correlation with the tax value of a dwelling and even more

important it does have the (from this thesis’s theoretical point of view) unexpected sign. The scatterplot

of tax value and distance (see Figure 9) shows this low correlation and also suggest a linear functional

form for the linear regression in the next section. Another noteworthy fact is the low and negative

correlation between tax value and plot size. The rest of the correlations between the variables are either

low or of the expected high magnitude (e.g. room and ground_m2) and of the expected sign.

lntax_value dist_near_pc4 y_constr sum_ground_m2 plot_size ground_m2 riverside roomlntax_value 1dist_near_pc4 0.2290 1y_constr 0.2481 0.3296 1sum_ground_m2 0.8104 0.1539 0.0656 1plot_size -0.1763 -0.0145 0.1077 -0.1940 1ground_m2 0.7543 0.1132 0.1163 0.8483 -0.1679 1riverside -0.2883 0.0311 -0.0025 -0.1860 0.1070 -0.1568 1room 0.6688 0.1137 0.0923 0.7224 -0.1687 0.8061 -0.0595

Figure 8: Correlation Table

24

VII. ResultsWe now want to empirically assess the effect of distance from the nearest knowledge intensive cluster

on the rent a dwelling can command. First, no restrictions on the degree of knowledge-intensity of

clusters are applied (i.e. even clusters with a LQ below 1 are included in the regression) to find the most

appropriate model. After this step the degree of knowledge-intensity will be incremented in 0.25 steps

until a LQ of higher than 2 to see whether this affects the findings of the analysis.

Figure 9: Scatterplot Tax Value and Distance

25

First, the natural logarithm of tax values is regressed only on distance from the nearest centroid of a

postcode. This yields a very small, positive effect of distance which is nonetheless significant at the 1%-

level. The positive sign of distance is surprising but not yet worrying as the effect of distance probably is

entangled with effects of positive sign that grow with distance. For example, the number of rooms and

size of a dwelling tend to increase with distance from the densely populated urban centers. Because this

regression produced an adjusted R2 of slightly more than five percent and the mentioned suspicion of a

considerable Omitted Variable Bias, the control variables were added one at a time (see Figure 9).

When adding all (Regression 8 in Figure 10) controls the adjusted R2 does grow to 84.62%, suggesting a

good fit of the model with the sample. Furthermore, the influence of distance on tax values does

become negative while still being significant. On first glance the effect of distance from the nearest

postcode-centroid has an almost negligible coefficient (-0.0000316). However, one needs to keep in

mind that the depend variable is expressed as a natural logarithm and the unit of distance is one meter.

Therefore, if a house is one meter further away from the nearest postcode-centroid, its value decreases

by 0.00316%. Hence, for the average house price in Rotterdam of 166203€ this is an absolute decrease in

value of approximately 5,25€ with every meter further from the centroid, holding all else constant. The

variable y_constr has, as expected a significantly positive albeit small coefficient. The categorical variable

for age y_constr_cat interestingly changes sign between the categories for the 1970s (negative) and

1980s (positive). This indicates that while houses built after 1979 enjoy a premium, those built between

1945 and 1979 trade at a discount compared to the reference category of houses built before 1945. This

means that pre-war houses, which are a rarity in Rotterdam due to severe damages to the city, are

sought after and command a vintage premium. Houses from the reconstruction era on the other hand

are seen as less desirable. This “Reconstruction”-effect is compensated after 1979. This is presumably

due to better building standards and technology and less depreciation of these modern homes.

Looking at Model 8 also confirms the idea that ownership matters for tax values. Dwellings owned by

their owners have significantly higher tax values than for other kind of owners, safe for “other non-

corporate owners”. This difference is especially pronounced for housing units owned by the municipality,

corporations or institutional investors. As explained above, this might be due differing propensities to

maintain owned property but also self-selection of owners. For example, municipality owned social

housing commonly is found in areas in which housing prices are low.

When examining the categories for the apartment type one can observe that different types of housing

command differing premiums and discounts relative to the reference group of “other Multiple Family

Home”. Not surprisingly single family homes command a premium of almost 30 percent, presumably due

26

to their higher amenities like a garden, more privacy etc. and their location closer to the public amities

like parks or in the countryside. Multiple family homes with elevators trade at a price approximately five

percent above the reference category, while the ones without an elevator are offered with a four

percent discount. The variables capturing public amenities riverside and environment were also

significant. Dwellings South of the river are evaluated to have tax values that are almost 18 percent

lower than the same dwelling could catch if it were located in the North of Rotterdam. The environment

of a housing unit also matters for the price of a dwelling as the significance of the coefficients for the

different categories of the variable environment shows. All but one of categories fetch lower prices than

the reference category (City Center). Apparently, living in the city center still is valued highly, despite

growing evidence that bid-rent gradients do not apply to city centers anymore (see Literature Review).

lntax_value 1. 2. 3. 4.

dist_near_pc4 0.0003685*** 0.0003195*** 0.0000847*** -0.0000136***

y_constr 0.0031763*** 0.0008464*** -0.0016416***

dyconstr2 -0.2892519*** -0.1267003*** -0.0480948***

dyconstr3 -0.1923389*** -0.0464912*** 0.0499795***

dyconstr4 -0.1933317*** -0.0339419*** 0.1180881***

dyconstr5 -0.1996341*** 0.0078125 0.201817***

dyconstr6 0.0653171*** 0.1935215*** 0.3621011***

dyconstr7 0.388351*** 0.2872492*** 0.467399***

dyconstr8 0.12008958*** 0.2404492*** 0.4500744***

duownership1 -0.7799984*** -0.4227296***

duownership2 -0.7539254*** -0.4490501***

duownership3 -0.2217548** -0.1497487

duownership4 -0.546338*** -0.2283928***

duownership5 -0.6429055*** -0.300033***

duownership6 -0.6759013*** -0.3083084***

duownership7 -0.522231*** -0.282901***

duownership8 0.0087365 0.0418429***

dapartment1 0.4299973***

dapartment2 0.0311101***

dapartment3 -0.0196627***

sum_ground_m2

plot_size

ground_m2

riverside

denvironment2

27

denvironment3

denvironment4

denvironment5

denvironment6

denvironment7

denvironment8

droom1

droom2

droom3

droom4

droom5

droom6

droom7

_cons 11.66466*** 5.553396*** 10.688589*** 15.11657***adjusted R^2

0.0524 0.1787 0.5223 0.6010

lntax_value 5. 6. 7. 8.

dist_near_pc4 -0.0000087*** 0.0000143*** -0.0000319*** -0.0000316***

y_constr 0.0001878*** 0.0010388*** 0.0011412*** 0.0007868***

dyconstr2 -0.0376283*** -0.0542932 -0.0929008*** -0.0880623***

dyconstr3 -0.0058451 -0.0357572*** -0.0598174*** -0.0492377***

dyconstr4 0.005254 -0.0622717*** -0.0690143*** -0.0392366***

dyconstr5 0.0968842*** 0.0221005*** -0.0007518 0.0206707***

dyconstr6 0.1914574*** 0.1250314*** 0.1018385*** 0.1254819***

dyconstr7 0.2294296*** 0.1415406*** 0.1147561*** 0.1542823***

dyconstr8 0.2340732*** 0.1541298*** 0.1621016*** 0.2058816***

duownership1 -0.2311193*** -0.1750904*** -0.1697595*** -0.175518***

duownership2 -0.1940236*** -0.1530427*** -0.1503186*** -0.1573265***

duownership3 -0.1171759* -.1808374*** -0.1649379*** -0.1503938**

duownership4 -0.602424*** -0.0355651*** -0.0352114*** -0.0335152***

duownership5 -0.1146295*** -0.0858741*** -0.0866015*** -0.0796636***

duownership6 -0.086373*** -0.0719515*** -0.0734888*** -0.0688149***

duownership7 -0.0880013*** -0.0946747*** -0.1104321*** -0.1121625***

duownership8 0.0259143*** 0.021526*** 0.0165713*** 0.0157875***

dapartment1 0.2763413*** 0.3248878*** 0.297996*** 0.2944313***

dapartment2 0.072774*** 0.0680328*** 0.0470676*** 0.0484409***

dapartment3 -0.0364535*** -0.0302123*** -0.0335233*** -0.0419588***

sum_ground_m2 0.005069*** 0.0046697*** 0.0045412*** 0.0044176***

plot_size 0.0000032*** 0.0000040*** 0.0000030*** 0.0000028***

Figure 10: Regression Tables without Restrictions on Knowledge-intensity

***= significant at the 1%-level

** = significant at the 5%-level

* = significant at the 10%-level

28

ground_m2 0.0028934*** 0.0028352*** 0.0028024*** 0.0023927***

riverside -0.1864484*** -0.1771982*** -0.1799992***

denvironment2 -0.1739058*** -0.1782211***

denvironment3 -0.203413*** -0.2065619***

denvironment4 -0.1401866*** -0.1393492***

denvironment5 -0.1017802*** -0.1026468***

denvironment6 -0.1195294*** -0.1249651***

denvironment7 0.0724187*** 0.0817554***

denvironment8 -0.1010124*** -0.0816854***

droom1 -0.2414717***

droom2 -0.0961367***

droom3 -0.0392677***

droom4 -0.0367461***

droom5 -0.0565204***

droom6 0.0237725***

droom7 0.038894***

_cons 10.75367*** 9.180192*** 9.189156*** 9.976766***

adjusted R^2 0.8100 0.8375 0.8462 0.8499

The three variables sum_ground_m2, plot_size and ground_m2 measuring dwelling size all have the

expected positive sign and are significant. However, especially the coefficient of plot_size is almost

negligible. The last size variable room is a little more interesting and deserves further attention. While

apartments with one to five rooms fetched a lower price than the reference category of eight or more

rooms (coefficients between -0.2415 and -0.0368), dwellings with six or seven rooms were more

expensive, holding all else constant. This calls into question the economic notion of more is always

better. One can only speculate about the reason for this finding. I suspect that the marginal utility of an

additional room is outweighed by the marginal costs of maintaining another room which makes these big

homes less attractive.

So far the analysis of the results has focused on the full sample of 137002 homes and 65 centroids and

the effect of all units to the nearest centroid of a postcode. This was done to find the regression with the

best fit and minimal Omitted Variable Bias. As can be seen in Figure 10 this regression is Model 8.

However, the aim of this paper is to evaluate the effect of distance from knowledge-intensive clusters on

a dwelling’s value. Therefore, the next paragraphs will repeat Model 8 with increasingly restrictive

definitions of knowledge-intensity to see whether the effect of distance depends on the degree of

knowledge-intensity (see Figure 11, page 31).

At first the sample was restricted to homes whose nearest centroid had a LQ of at least one. This

regression used 34 centroids and explains 84.96% of the total variation of the natural logarithm of tax

29

value. The distance from the nearest centroid is significantly negative (at the 1%-level), although not very

high. Holding all else equal, tax values decrease by 0.00606% with every additional meter from the

centroid. The other variables largely stay the same, safe for a few categories of ownership, environment

and room changing signs. An interesting finding that should be explicitly noted is that the third category

of ownership is omitted automatically due to collinearity. However, this should not be a problem since

ownership is just a control variable and dist_nearest_pc4 does not have an extraordinarily high VIF (see

Appendix IV). According to Paul Allison one can safely ignore collinearity if those two conditions are met

(Allison, 2016).

The next regression further limited the sample used to homes whose nearest centroid had a LQ of 1.25

or higher (i.e. 19 centroids). This regression explained the variation in tax values slightly better (R 2 =

88.05%). The effect of distance was only about half as big as for the k>=1 regression but still significantly

negative at the 1%-level. The third category of ownership again was omitted but the VIF of distance again

was not worryingly large.

With an adjusted R2 of 88.33% the regression limiting the sample to the ten centroids with LQs higher

than 1.5 has the best explanatory power of all models examined in this thesis. The negative effect of

distance on tax value is slightly less than in the second regression (-0.0000244) but nonetheless

significant at all levels. The omission of the third ownership category due collinearity does not pose a

problem in this regression either.

The fourth regression (LQ higher or equal to 1.75) showed a lower, but still high adjusted R2 than the

previous one (86.40%). Curiously, distance now has an insignificant effect on tax values which is directly

contradictory to the hypotheses developed in Section IV. Furthermore, next to the third category of

ownership, the fifth and the seventh category of environment were omitted due to collinearity. But the

two conditions for ignoring this, are met again.

Finally, the regression restricting the sample to two centroids with LQs higher than 2 and 343 dwellings

shows a very curious finding. Distance has a significantly positive effect on the tax values of homes. This

is against bid-rent theory and questions the hypotheses of this thesis. However, caution is necessary

when interpreting these findings. Firstly, the sample size of two centroids is very limited, meaning that

the external validity is questionable at best. Secondly, the VIFs of this regression show a high collinearity

of distance, leading to potentially biased estimators. A potential explanation for the sign of the effect of

distance could be that one of the two centroids (postcode: 3046) is located directly next to the

Rotterdam-The Hague Airport and a major highway linking Rotterdam with Delft. This hardly is a pleasant

30

area to live in. Therefore, people should be willing to pay more for living further away from this centroid.

But when running the same regressions for each of the two centroids separately one can see that the

3046-centroid (0.0002253) actually has a less steep (and insignificant) gradient than the seemingly more

attractive 3015-centroid (0.0006061) which is in the center of Rotterdam next to amenities like museums

and good public transport accessibility. A last note on the many omitted categorical variables shall be

made here. The primary reason for omission here, was the fact that there were simply no observations

relating to the omitted categories.

After presenting these findings, this thesis will conclude with a discussion and conclusion about the

effect of knowledge-intensive clusters on rents in Rotterdam.

lntax_valuek>=1 k>=1.25 k>=1.5 k>=1.75

dist_near_pc4 -0.0000606*** -0.0000289*** -0.0000244*** -0.0000013

y_constr 0.0011908*** 0.001491*** 0.0023255*** 0.0022152***

dyconstr2 -0.0412048*** -0.0524683*** 0.1181875*** -0.019215

dyconstr3 -0.0652561*** -0.1286271*** -0.2263896*** -0.1630647***

dyconstr4 -0.0320055*** -0.11609*** -0.1737266*** -0.2037045***

dyconstr5 0.0110428* 0.0367814*** -0.0301773*** -0.0661864***

dyconstr6 0.0861825*** 0.05833371*** -0.0264361** -0.0174679***

dyconstr7 0.1186978*** 0.0721456*** 0.0308603** 0.0309742**

dyconstr8 0.1328276*** 0.0827074*** 0.1428488*** 0.0624175**

duownership1 -0.149063*** -0.0607364*** 0.1141728*** -0.1923162***

duownership2 -0.1772638*** -0.1441764*** -0.1334286*** -0.089958***

duownership3 0 (omitted) 0 (omitted) 0 (omitted) 0 (omitted)

duownership4 -0.0498645*** -0.0070905 0.0412503*** 0.0281216**

duownership5 -0.0919179*** -0.0483708*** -0.0441373** -0.0140326

duownership6 -0.0886258*** -0.0443189*** 0.045316*** 0.0044404

duownership7 -0.1708318*** -0.1861158*** -0.1336861*** -0.0995685***

duownership8 -0.0114313*** -0.197992** 0.052848*** 0.012941

dapartment1 0.3181974*** 0.3430796*** 0.327683*** 0.3646274***

dapartment2 0.0414126*** 0.0349353*** 0.019628*** 0.0576776***

dapartment3 -0.038341*** -0.026041*** -0.303017*** -0.0757103***

sum_ground_m2 0.0042631*** 0.0033254*** 0.0028292*** 0.001595***

plot_size 0.0000017*** 0.0000002 0.0000033*** 0.0000011***

ground_m2 0.0024612*** 0.0030986*** 0.0038589*** 0.0040338***

riverside -0.1908576*** -0.191606*** -0.2766607*** -0.6045056***

denvironment2 -0.3610923*** -0.1234366*** -0.0735269*** -0.1364981***

denvironment3 -0.4001172*** -0.1198175*** -0.1489159*** 0.0607601

denvironment4 -0.2675589*** 0.0121255 0.1359702*** 0.373466***

31

denvironment5 -0.2385027*** 0.0162464 0.0182966 0 (omitted)

denvironment6 -0.256082*** -0.0235732 0.0506407* 0.2700543***

denvironment7 -0.0006791 0.3416255*** 1.09735*** 0 (omitted)

denvironment8 -0.2728962*** 0.0383364 0.0796857** 0.137553***

droom1 -0.1926041*** -0.1716144*** -0.0406069 -0.0071746

droom2 -0.0309875*** -0.00014 0.1473815*** 0.2380516***

droom3 0.0129498 0.0285744* 0.1794369*** 0.2713711***

droom4 0.0061724 0.0279441* 0.1694286*** 0.2640565***

droom5 -0.0027606 0.0163113 0.164025*** 0.2250236***

droom6 0.0756637*** 0.382606*** 0.2325924*** 0.2530362***

droom7 0.1157993*** 0.0853812*** 0.2786105*** 0.4153534***

_cons 9.352747*** 8.509706*** 6.749644*** 7.003961***

adj. R^2 0.8496 0.8805 0.8833 0.8640

Figure 11: Regression Tables with Restrictions on Knowledge-intensity

32

lntax_valuek>=2 Only 3015 Only 3046

dist_near_pc4 0.0003822*** 0.0006061*** 0.0002253

y_constr 0.003843*** 0.0053318*** 0.0124691

dyconstr2 -0.2992539** -0.4187808*** 0 (omitted)

dyconstr3 -0.1933095 0.5131276** -0.8623801

dyconstr4 0.2496645 0 (omitted) -0.520663

dyconstr5 0.0734633 0.3708953** -1.1175265

dyconstr6 -1.239897*** 0 (omitted) -1.949331

dyconstr7 -0.1372672 0.1459356 -1.216097

dyconstr8 0 (omitted) 0 (omitted) 0 (omitted)

duownership1 1.000993*** 0 (omitted) 0.7301033

duownership2 -0.4690373*** -0.542688*** 0 (omitted)

duownership3 0 (omitted) 0 (omitted) 0 (omitted)

duownership4 -0.0481216 -0.1418146** 0.1519459

duownership5 -0.1696528** -0.280471*** 0 (omitted)

duownership6 -0.0976562 -0.1740088*** 0 (omitted)

duownership7 0 (omitted) 0 (omitted) 0 (omitted)

duownership8 -0.4100074** 0 (omitted) -0.2119704

dapartment1 0.8625005*** 0.4244441*** 0 (omitted)

dapartment2 0.0110917 0.0119607 0 (omitted)

dapartment3 0.0021564 0.0299371 0 (omitted)

sum_ground_m2 0.0000643 0.0017315*** 0.0000108

plot_size 0.0000107 -0.0001866*** 0.0000247**

ground_m2 0.0038233*** 0.0027989*** 0.0010431

riverside 0 (omitted) 0 (omitted) 0 (omitted)

denvironment2 -0.2684563*** -0.2470764*** 0 (omitted)

denvironment3 0 (omitted) 0 (omitted) 0 (omitted)

denvironment4 0 (omitted) 0 (omitted) 0 (omitted)

denvironment5 0 (omitted) 0 (omitted) 0 (omitted)

denvironment6 0 (omitted) 0 (omitted) 0 (omitted)

denvironment7 0 (omitted) 0 (omitted) 0 (omitted)

denvironment8 -0.831523*** 0 (omitted) 0 (omitted)

droom1 -0.3146396*** -0.2646323** -0.4918073

droom2 0.1569341 0.2149585* -0.5570457

droom3 0.2197384** 0.2718141** -0.3417601

droom4 0.3282782*** 0.3768*** -0.2603551

droom5 0.410431*** 0.504768*** 0.1990015

droom6 0.4644084*** 0.4907834*** 0.054926

droom7 0.6919235*** 0.7534334*** 0 (omitted)

_cons 4.180003 1.212693 -11.0815

adj. R^2 0.8714 0.8556 0.7471

VIII. DiscussionFinally, the question that remains is what the above findings mean for the general applicability of the

bid-rent framework in modern, polycentric cities. Bid-rent theory is a child of the early 19 th-century when

transportation was slow and expensive and urbanization just started to set in. The rapid growth of cities

sparked the interest in their spatial structures. However, as this growth accelerated and cities became

too big to have a single center, the validity of the bid-rent theory using a single city center as a reference

point was questioned both by theorists and empirical findings. Today, the search continues for a

replacement of the city center or even a completely new framework of thinking. Literature offered

airports (e.g. in Chicago), University Campuses (e.g. in Collegetown) or multi-centric models as

alternative to the Alonso-model.

This thesis tests whether knowledge-intensive employment centers can be a viable alternative to the

(single) city center in a mono-centric model as suggested by Joel Garreau in his book “Edge City” (1991).

It tried to establish whether the distance from knowledge-intensive clusters has a significant impact on

the rent a housing unit can fetch on the market holding all other relevant factors constant. A linear

regression model with tax values as dependent variable and distance from the nearest centroid of a

postcode as variable of interest was developed Furthermore, controls for various factors were added to

eliminate Omitted Variable Bias and increase the precision of the estimators. This model was applied to

data collected by the city of Rotterdam.

***= significant at the 1%-level

** = significant at the 5%-level

* = significant at the 10%-level

33

The results are mixed. While distance seems to have the predicted significantly negative effect on house

values in four out of the six regressions run, for one the effect was insignificant and for the last one

distance had the opposite of the predicted effect. Therefore, the first hypothesis cannot be rejected,

meaning that knowledge-intensive clusters generally do exhibit negative bid-rent gradients. However,

the second hypothesis must be rejected. It is not the case that a higher knowledge-intensity always leads

to steeper gradients. In fact, as can be seen from Figure 11, the gradients become steeper with a higher k

until k reaches 1.75, after which they become flatter again. This suggests that the relationship between

the effect of distance from a cluster on rents and the degree of knowledge-intensity is not linear but

rather U-shaped. The reason for this shape is unclear. But it might be due to different commuter group

profiles. As explained above differing assumptions about a population group lead to differing bid-rent

gradients. In this case for example, the primary group of people working in the two clusters might have

upward sloping rent gradients due to some shared characteristic. For example, both employment centers

are located close to highways, making them easily accessible by car. As argued in McCann (2001). High-

income groups often have upwards sloping bid-rent gradients and prefer to live on the outskirts of a city

commuting to work by car. However, further research has to confirm the upward slope for cities other

than Rotterdam and the provided explanation.

0.997

0.9975

0.998

0.9985

0.999

0.9995

1

1.0005

1.001

Without Restrictionsk>=1k>=1.5k>=1.25k>=1.75k>=2

Distance

Val

ue

Figure 11: Rent Gradients of all Regressions

34

This leads us to the limitations of this study. Not only, was the sample restricted to one city, and as a

result some regressions had as little as two clusters to observe. The next logical step would be to use the

same definition for knowledge-intensive clusters and to collect similar housing data for the whole of the

Netherlands to test the robustness of the findings in this thesis. Additionally, a more sophisticated

regression model should be developed and different specifications of the distance-rent relationship

should be tested and a better proxy for transport costs than distance should be incorporated. Doing this

unfortunately was beyond the scope of this paper. The last limitation that should be addressed by

further research is the exclusion of distance effects other than the nearest postcode centroid. Ideally,

one would run a regression controlling for the effect of clusters located further away than the next one

but that still potentially have an impact on rent. Imagine for example a house that is situated between

two clusters but is one meter closer to one of them. This thesis’ methodology completely ignores the

effect on rent of the cluster that is further away, although there is little reason to believe that this effect

vanishes with one additional meter of distance.

Overall, one can say that this paper suggests that knowledge-intensive clusters matter for the rent

structure of modern cities. However, the results are not clear enough to present clear-cut policy

recommendations. For moderately knowledge-intensive employment clusters there seem to be negative

externalities to residents in the form of higher rents. These should be taken into consideration when

designing cluster policies. For very knowledge-intensive employment centers on the other hand, these

externalities are positive. Governments should therefore try to locate clusters in areas where the

accumulated effects of these externalities are minimized. This would mean placing them in industrial and

not in residential areas.

Hopefully, this thesis is a starting point for further research in the exact nature of the influence of high-

tech clusters on the rent structure of cities and its robustness in different settings.

35

IX. References

Allison, P. (2016, July 18). Statistical Horizons. Retrieved from When Can You Safely Ignore Multicollinearity?: http://statisticalhorizons.com/multicollinearity

Alonso, W. (1964). Location and Land Use: Towards a General Theory of Land Rent. Cambridge: Harvard University Press.

Atack, J., & Margo, R. A. (1998). "Location, Location, Location!" The Market for Vacant Urban Land: New York 1835-1900. Journal of Real Estate Finance and Economics, 151-172.

Bowles, N. (2016, May 16.). Cupertino's Mayor Urges Apple to Pay More Tax: 'Where's the fairness?'. Retrieved from The Guardian Website: https://www.theguardian.com/technology/2016/may/05/apple-taxes-cupertino-mayor-infrastructure-plan

Brown , C., & Medoff, J. (1989). The Employer Size-Wage Effect. Journal of Political Economy, 1027-1059.

Colwell, P. F., & Munneke, H. F. (1997). The Structure of Urban Land Prices. Journal of Urban Economics, 321-336.

Coulson, E. N. (1991). Really Useful Tests of the Monocentric Model. Journal of Land Economics, 299-307.

Coulson, E. N., & Engle, R. F. (1987). Transportation Costs and the Rent Gradient. Journal of Urban Economics, 287-297.

36

Dubin, R. A., & Sung, C.-H. (1987). Spatial Variation in the Price of Housing: Rent Gradients in Non-Monocentric Cities. Journal of Urban Studies, 193-204.

Ellickson, B. (1981). An Alternative Test of the Hedonic Theory of Housing Markets. Journal of Urban Economics, 56-79.

European Comission. (2010, March 03). EUROPE 2020 - A Strategy for Smart, Sustainable and Inclusive Growth . Brussels.

Free and Hanseatic City of Hamburg. (2011). Hamburg’s Cluster Policy: Reaching The Top Together. Hamburg: Free and Hanseatic City of Hamburg.

Garreau, J. (1991). Edge City: Life at the New Frontier. New York: Doubleday.

Gross, D. J. (1988). Estimating Willingness to Pay for Housing Characteristics: An Application of the Ellickson Bid-Rent Model. Journal of Urban Economics, 95-112.

Holtz-Eakin, D. (2000). Public Policy towards Entrepreneurship. Small Business Economics, 283-291.

Isard, W. (1956). Location and Space Economy. New York: John Wiley.

Krugman, P. (1995). Development, Geography, and Economic Theory. Cambridge: MIT Press.

Kwon, Y. (2002). Rent-Commuting Cost Function versus Rent-Distance Function. Journal of Regional Science, 773-791.

Langelett, G., & Chang, K.-L. (2013, February). Rent Gradient of a College Town. South Dakota State University's Economics Staff Paper Series. Brookings: South Dakota State University.

Lerman, S. R., & Kern, C. R. (1983). Hedonic Theory, Bid Rents, and Willingness-to-Pay: Some Extensions of Ellickson’s Results. Journal of Urban Economics, 358-363.

Lewis, C. W., & Kapp, T. J. (1994). The Rent-Distance Trade-off for Student Housing: An Empirical Analysis. Regional Sciene Perspectives, 42-55.

Marshall, A. (1890). Principles of Economics.

McCann, P. (2001). Urban and Regional Economics. Oxford: Oxford University Press.

McMillen, D. P. (1996). One Hundred Fifty Years of Land Values in Chicago: A Non-parametric Approach. Journal of Urban Economics, 100-124.

McWilliams, A., & Siegel, D. (2001). Corporate Social Responsibility: A Theory of the Firm Perspective . The Academy of Management Review, 117-127.

Muto, S. (2006). Estimation of the Bid Rent Function with the Usage Decision Model. Journal of Urban Economics, 33-49.

Neumark, D., Wall, B., & Zhang, J. (2011). Do Small Business Create More Jobs? New Evidence For The United States From The National Establishment Time Series. The Review of Economics and Statistics, 16-29.

Ogur, J. D. (1973). Higher Education and Housing: The Impact of Colleges and Universities on Local Rental Housing Market. The American Journal of Economics and Sociology, 387-394.

37

Oi, W. Y., & Idson, T. L. (1999). Firm Size and Wages. In O. Ashenfelter, & D. Card, Handbook of Labor Economics (pp. 83-108). London: Routledge.

Oxford Reference. (2016, May 29). Oxford Reference: verview: Composite Commodity. Retrieved from Oxford Reference Website: http://www.oxfordreference.com/view/10.1093/oi/authority.20110803095629568

Paris Region Enterprises. (2016, May 13). Homepage Paris Region . Retrieved from Paris Region Website: http://parisregionentreprises.org/en

Ponsard, C. (1983). History of Spatial Economic Theory. Berlin: Springer Verlag.

Rehm, M., Filipova, O., & Stone , J. (2006). The Influence of Vintage on House Value. Pacific Rim Property Research Journal, 232-253.

Ricardo, D. (1817). On the Principles of Political Economy and Taxation. London: John Murray.

Rosen, S. (1974). Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. The Journal of Political Economy, 34-55.

Smith, A. (1904). An Inquiry into the Nature and Causes of the Wealth of Nations (5th Edition ed.). (E. Cannan, Ed.) London: Methuen and Co., Ltd.

Söderberg, B., & Janssen, C. (2001). Estimating Distance Gradients for Apartment Properties. Journal of Urban Studies, 61-79.

The Economist. (2016, February 27.). Town and Company. The Economist, pp. 55-56.

Udayasankar, K. (2008). Corporate Social Responsibility and Firm Size. Journal of Business Ethics, 167-175.

von Thuenen, J. (1826). Der Isolierte Staat. Hamburg: Perthes.

Weterings, A., Raspe, O., & van den Berge, M. (2011). The European Landscape of Knowledge-intensive Foreign-owned Firms and the Attractiveness of Dutch Regions. The Hague: PBL Netherlands Environmental Assessment Agency.

Wheaton, W. C. (1977). A Bid Rent Approach to Hosuing Demand. Journal of Urban Economics, 200-217.

Wilson, B., & Frew, J. (2007). Apartment Rents and Locations in Portland, Oregon: 1992-1994. Journal of Real estate Research, 201-217.

Yinger, J. (1993). Around the Block: Urban Models with a Street Grid. Journal of Urban Economics, 305-330.

38

X. Appendix

Appendix I: List of Knowledge-intensive Industries (taken from Weterings et. al. (2011), page 104)

39

40

ZIP CodeLocation Quotient ZIP Code

Location Quotient ZIP Code

Location Quotient ZIP Code

Location Quotient

3000 - 3031 0,786112 3062 1,417001 3093 -

3001 - 3032 1,567105 3063 1,133249 3094 -

3002 - 3033 1,64225 3064 0,9693574 3095 -

3003 - 3034 1,524731 3065 1,702389 3096 -

3004 - 3035 1,147953 3066 1,500552 3097 -

3005 - 3036 0,8574769 3067 0,7728564 3098 -

3006 - 3037 1,035812 3068 0,943135 3099 -

3007 - 3038 0,9503129 3069 0,5863227

3008 - 3039 1,256072 3070 -

3009 - 3040 - 3071 0,9623153 Observations 65

3010 - 3041 0,4530251 3072 1,745873 Minimum 0,7501127

3011 1,234389 3042 0,6270131 3073 0,6464555 Mean 1,16261414

3012 0,7501127 3043 0,7547053 3074 0,9256984 Std. Dev. 0,64463975

3013 0,8850687 3044 0,7040626 3075 1,778433

3014 1,194715 3045 1,331951 3076 1,3553628

3015 2,38057 3046 2,193705 3077 1,089

3016 1,000873 3047 0,6616901 3078 0,7154721

3017 - 3048 - 3079 1,942365

3018 - 3049 - 3080 -

3019 - 3050 - 3081 1,174206

3020 - 3051 0,9638782 3082 1,31047

3021 1,001869 3052 1,329921 3083 1,381862

3022 1,141769 3053 0,8398102 3084 0,8522414

3023 0,9989169 3054 0,7915879 3085 0,6418592

3024 1,289741 3055 1,212155 3086 0,6652775

3025 0,7585588 3056 1,288567 3087 0,4563027

3026 1,190727 3057 - 3088 0,3689086

3027 1,207223 3058 - 3089 0,3648455

3028 1,223328 3059 0,8792165 3090 -

3029 1,181351 3060 - 3091 -

3030 - 3061 0,9183446 3092 -Appendix II: List of all Rotterdam ZIP-Codes with corresponding Location Quotients

41

Appendix III: Map of Rotterdam with ZIPs and their Location Quotients

42

Variable k>=1 k>=1.25 k>=1.5 k>=1.75 k>=2

dist_near_pc4 7.28 1.27 1.32 1.72 7.28

y_constr 37.60 16.38 21.48 20.89 37.60

dyconstr2 1.47 1.93 1.10 1.06 1.47

dyconstr3 1.91 7.93 9.02 10.97 1.91

dyconstr4 2.44 4.63 5.18 8.92 2.44

dyconstr5 35.31 8.36 10.67 7.96 35.31

dyconstr6 3.17 13.82 20.46 12.36 3.17

dyconstr7 26.81 6.43 5.56 8.65 26.81

dyconstr8 - 1.35 1.49 1.33 -

duownership1 2.42 1.36 1.06 1.03 2.42

duownership2 11.63 3.19 3.24 2.75 11.63

duownership3 - - - - -

duownership4 3.73 1.30 1.23 1.17 3.73

duownership5 1.74 1.05 1.07 1.14 1.74

duownership6 4.88 1.54 1.33 1.34 4.88

duownership7 - 1.31 1.25 1.28 -

duownership8 1.60 1.05 1.04 1.04 1.60

dapartment1 12.45 4.73 5.41 4.82 12.45

dapartment2 5.05 2.84 3.10 3.07 5.05

dapartment3 1.53 2.26 2.16 3.03 1.53

sum_ground_m2 2.79 3.92 3.57 2.66 2.79

plot_size 2.99 1.49 1.59 1.74 2.99

ground_m2 5.84 5.73 5.55 5.31 5.84

riverside - 1.81 5.39 95.91 -

denvironment2 5.32 109.16 85.25 9.67 5.32

denvironment3 - 141.09 53.21 17.94 -

denvironment4 - 215.79 97.71 637.11 -

denvironment5 - 150.11 116.53 - -

denvironment6 - 115.43 107.39 642.57 -

denvironment7 - 14.40 1.04 - -

denvironment8 16.01 1.97 1.99 2.31 16.01

droom1 - 6.31 10.44 15.23 -

droom2 27.23 37.33 57.43 96.16 27.23

droom3 16.17 60.34 117.55 192.19 16.17

droom4 4.99 52.28 101.56 199.26 4.99

droom5 9.24 35.93 69.76 106.78 9.24

droom6 2.73 6.01 9.20 10.38 2.73

droom7 1.98 2.51 2.61 192.19 1.98Appendix IV: VIFs for Regressions with Restrictions on Knowledge-intensity

43