SPATIAL ARBITRAGE IN BELGIAN BORDER REGIONS

Roel Helgers

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SPATIAL ARBITRAGE IN BELGIAN BORDER REGIONS

Roel Helgers

Promotor: Prof. Dr. Erik Buyst

Leuven, September 2015

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Spatial Arbitrage in Belgian Border Regions

Roel Helgers∗

Center for Economic Studies†,

Faculty of Economics and Business

KU Leuven

September, 2015

∗B: roel.helgers@kuleuven.be†Address: Naamsestraat 69, B-3000 Leuven, Belgium.

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1 Introduction

It has been widely recognized that housing markets exhibit a variety of unique features, such as durability,

heterogeneity, significant transaction costs, and location fixity (Quigley, 2002). In a strand of literature

that builds upon the (theoretical foundations of the) Hedonic Pricing Method (Rosen, 1974 JPE ) it has

been shown that there are large differences in house prices that are the result of differences in structural

characteristics, (neighborhood) amenities, and other factors. Despite the aforementioned features and the

fact that the possibilities for arbitrage are hampered by, for example, substantial costs of moving it is

nonetheless to be expected that differences in house prices between locations that are close to each other

are limited (Glaeser & Gyourko, 2007). Border effects, however, which have mostly been studied in the

context of trade patterns, can have a significant impact upon economic outcomes. In a seminal paper that

was published in the American Economic Review in 1995 John McCallum shows that the border between

Canada and the US continues to have a decisive impact on trade patterns between both countries, despite

that the two countries are very similar in terms of culture, language, and institutions. In a suitably titled

paper “How wide is the border?” that was also published in the AER, Engel & Rogers (1996) estimate

that crossing the border is equivalent to 1,780 miles of distance between cities located in the same coun-

try. Even in the modern day Europe, where the 26 member states of the Schengen Area1 have abolished

passport and any other type of border controls at their common borders, Cheshire & Magrini (2009) and

Jacobs-Crisoni & Koomen (2014) show that cities still form national urban systems rather than a single

European-wide system. In the context of housing markets and housing prices Micheli et al. (2014) show

that the (initial) listing prices of comparable houses drop by about 16% when crossing the Dutch-German

border. The authors argue that these house price differences between neighboring locations on opposite

sides of a common border might be substantial when people have a strong preference to live in “their”

country .

In this chapter we investigate the determinants of housing prices along the Belgian-Dutch border and

are specifically interested in the presence and extent of spatial arbitrage. Both Belgium and the Nether-

lands are among the founding fathers of the European Union and the Benelux and share a long common

history. Despite that both countries have executed, and been subjected to, similar monetary and macro-

economic policies and shocks, the evolution of housing prices in both countries has differed substantially

1The Schengen Area comprises twenty-two of the twenty-eight European Union (EU) member states (Bulgaria, Croatia,Cyprus, Romania, Ireland, and the United Kingdom are not part of the Schengen Area for different reasons) plus theEuropean Free Trade Association (EFTA) member states Iceland, Liechtenstein, Norway and Switzerland.

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in recent decades. Where the initially higher Dutch house prices boomed from the mid 1990s onwards

as, among other things, the result of an increase in the share of interest-only mortgages (Rouwendal,

2007), Belgian house prices only started to increase strongly from the mid 2000s onwards as the result

of a more generous fiscal treatment of owner-occupiers (Damen et al., 2014). While the Dutch housing

market collapsed after the global financial crisis of 2007-2008, Belgian house prices increased further and

recently caught up to Dutch levels. Given that the distance between Belgian’s capital Brussels and the

Dutch capital Amsterdam is only 200 kilometers and there is obviously regional variation prices across

local markets, it is intuitive to study house prices in the border regions. Unlike Micheli et al. (2014) we

unfortunately do not have individual transaction data for regions on both sides of the common border, but

we are able to investigate whether higher housing prices in Dutch border regions spill over into Belgium

using a large database of individual Belgian transactions that was provided by a large Belgian franchise

system of real estate agents. In comparison to Micheli et al. (2014), who study the magnitude of the

border effect (px̄,NL −px̄,BE in figure 1) between Germany and the Netherlands, we study the proximity to

the border effect, that is ∆ln(p)/∆x for x ≥ x̄. Since the presence of a national border frequently suggests

that there are (large) differences in amenities, such as the common language spoken by people and the

educational and/or law system, which might limit the scope for arbitrage, we try to control for as many

of these factors as possible in the econometric analysis.

Figure 1: Graphical representation border effects

px̄,NL

px̄,BE

x̄x

ln p(x) Border

While the existence of border effects, by now, has been accepted in the literature, its causes have remained

relatively understudied. In the context of international trade, Chen (2004) shows that trade barriers do

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provide an explanation. In a recent study Micheli et al. (2014) argue that “the differences between

countries in the European Union that manifest themselves in border effects can probably be interpreted

to a large extent as cultural factors”, the most obvious being language. The emergence of nation-states

in the 19th century has contributed to sharp discontinuities in the languages spoken on both sides of

national borders. In the context of international trade Melitz (2008) shows that sharing a common lan-

guage increases the volume of traded goods between countries. In a recent study Helgers & Buyst (2014)

showed that linguistic differences can affect (the evolution of) housing prices within a single country. Since

Dutch is both the language spoken in the Flemish Region (our region of study) and the Netherlands, we

examine a particularly interesting case-study. As we also observe transactions in the French-speaking

Walloon Region we also have the possibility to investigate whether the arbitrage effect is stronger for

the Dutch-speaking Flemish Region than the French-speaking Walloon Region. Other factors that are

potentially able to explain the observed border effects are, for example, spatial planning and other in-

stitutional factors. Tennekes & Harbers (2012) show that there are large differences in spatial planning

between Belgium and the Netherlands. Although this potentially contributes to the observed price differ-

ences, we are not able to take this into account in our analysis. Keep in mind, however, that this might

help explaining the observed border effects, but has no obvious effect on the proximity to the border-effect.

In the last part of this chapter we also investigate whether out-of-state buyers pay a premium. It has been

well-established in the literature that different buyers pay different prices for (nearly) identical homes.

A number of studies, such as Turnbull & Sirmans (1993), Watkins (1998), Lambson et al. (2004) and

Ihlanfeldt & Mayock (2012), study whether out-of-state buyers pay a premium relative to their in-state

counterparts. The results reported in this literature, however, have been mixed. While Turnbull & Sir-

mans (1993) and Watkins (1998) find no evidence, Lambson et al (2004) and Ihlanfeldt & Mayock (2012)

find that out-of-state buyers pay significant premia. Potential explanations for these effects that have

been theorized (Lambson et al., 2004) are (1) search costs, and (2) anchoring bias, and (3) short time

horizons to purchase. Note that the chronological ordering of the results founds in the literature is, in

some sense, counterintuitive as it can be argued technological developments, such as the internet, should

have lowered search costs substantially in recent decades. Lambson et al. (2004) and Ihlanfeldt & May-

ock (2011), however, argue that the insignificant coefficients reported by Turnbull & Sirmans (1993) and

Watkins (1998) are likely to be the result of low statistical power due to a limited number of observations.2

2The # of observations used in their baseline estimation is equal to: 151 (Turnbull & Sirmans, 1993), 543 (Watkins,1998), 2,854 (Lambson et al., 2004), and 6,666 (Ihlanfeldt & Mayock, 2012).

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In this chapter we use information concerning the buyer’s previous address to quantify whether Dutch

buyers pay a premium relative to their Belgian counterparts and find evidence that Dutch buyers pay an

economically (3%) and statistically significant premium after controlling for a large variety of dwelling-

and neighborhood characteristics.

The remainder of this paper is organized as follows: in section 2 we present a highly stylized theoretical

model of housing prices where agents from two countries (A and B choose an optimal bundle consisting of

three goods (C, HA and HB) subject to their respective budget constraints. The main result of this model

is that shocks in one country spill-over into housing prices in the other country, In section 3 we provide

a general overview of the developments in the housing markets in recent decades for both countries. In

section 4 we present our main sources of data and elaborate on the Belgian and Dutch housing markets

in recent decades and present an overview of the (evolution of) house prices both countries. In section 5

we present the methodology and in section 6 we present the results of the analysis. Finally, in section 7,

we conclude.

2 A Theoretical Model of House Prices

As an introduction to the empirical work that is discussed in the next sections we present a simple stylized

model of housing prices. Assume that there are 2 countries, A and B, and that the (representative) agent

in each country can choose to consume a bundle of 3 different goods, subject to their respective budget

constraints. Agents in country A consume the consumption good CA at price PA, the housing good in

country A, HAA, at price ρA, and/or the housing good in country B, HAB, at price ρB. Similarly, agents

in country B consume CB at price PB, HBA at price ρA, and/or HBB at price ρB. Using a (nested) Cobb-

Douglas utility specification and linear budget constraints yields the following maximization problem(s):

maxCj ,HjA,HjBUj(Cj , HjA, HjB) = Cαjj (H

βjjAH

(1−βj)jB )

1−αj

s.t. CjPj +HjAρA +HjBρB ≤ wj

Where j = A,B

(1)

Deriving the 8 first order conditions and performing the appropriate substitutions yields the following

(typical) demand functions for Cj , HjA and HjB:

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Cj =αjwjPj

, HjA =(1 − αj)βjwj

ρA, and HjB =

(1 − αj)(1 − βj)wjρB

(2)

which implies that an increase in wj is simply translated into a higher consumption of all three goods

when prices (CA, CB, ρA, and ρB) are taken as given. Now assume that the price for the housing good

in both country A and B are the result of the following market-clearing conditions:

HAA +HBA = H̄A and HAB +HBB = H̄B (3)

Note, here, that we assume that the supply of housing (H̄j) in both markets is perfectly inelastic. The

demand equations presented in (2) and the market-clearing conditions presented in (3) constitute a system

of 8 equations in 8 unknowns (CA, CB, HAA, HBA, HAB, HBB, ρA and ρB) with a unique solution:

C∗j =αjwjPj

, H∗jA =(1 − αj)βjwjH̄A

ΣA,B(1 − αj)βjwj, H∗jB =

(1 − αj)(1 − βj)wjH̄BΣA,B(1 − αj)(1 − βj)wj

and ρ∗j =ΣA,B(1 − αj)βjwj

H̄j

(4)

Despite that the outcomes presented in (4) are the result of a highly stylized model there are nonetheless

some interesting implications that are also empirically tested in the next sections. Assume, for simplicity,

that αA = αB = α and consider the effects of an income shock in country A on HAA and HAB. It is

straightforward to show that ∂HAA/∂wA and ∂HAB/∂HAB are strictly larger than 0 whenever households

derive utility from HAA and HAB, that is 0 < βA < 1. This implies that whenever the housing stock in

country B, H̄B, is given agents from country A consume a larger share of the total housing stock in coun-

try B. While our model is in some sense flawed as most buyers only consume the housing good in a single

country, there are nonetheless some alternative interpretations for this effect. One possible (extreme)

interpretation is that a larger share of the households in county A migrate to country B and (continue

to) consume the same amount of housing. Another (extreme) interpretation is that while the number

of households who migrate from country A to country B remains the same, each individual household

consumes more of the housing good in country B. In the data we indeed observe that there has been both

an increase in the number of migrations from the Netherlands to Belgium and Dutch buyers, on average,

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buy more expensive dwellings.

Observe furthermore that when βj > 0 for j = A,B prices in country A are dependent upon income

developments in country B, and vice versa. Also note that the effect of an income shock in country A,

relative to an income shock in country B, has a larger effect on ρA whenever (1 − αA)βA > (1 − αB)βB.

This implies, under the assumption that agents in both countries have a similar preference αj for the

consumption good, that agents from country A derive more utility from the housing good in country A

than agents from country B, that is, βA > βB. When Dutch households have a higher preference to buy a

property in the Netherlands, relative to Belgian households, an income shock in the Netherlands will have

a stronger impact on Dutch housing prices, relative to an income shock in Belgium. Note, however, that

an income shock in the Netherlands will also affect Belgian housing prices, whenever Dutch households

derive utility from living in Belgium. So far, our model thus allows for spatial spill-over effects in housing

prices. We empirically test this hypothesis in section 6.1. In section 6.2 we test whether the effect on

housing prices is stronger in certain border regions. Despite that there is only a single price per unit

of housing in each country in our theoretical model, we test in section 6.3 whether foreign buyers pay a

premium relative to their domestic counterparts using a subsample of our transaction database where we

also know the (exact) previous address of each respective buyer.

3 House Prices and Housing Markets in Belgium and the Netherlands

3.1 A General Overview

Vansteenkiste & Hiebert (2011) recently argued that despite that housing is a non-traded good that

cannot easily be substituted across geographic areas, co-movement in international housing prices could

nevertheless be expected to arise from three different channels, notably (1) common developments in

housing market fundamentals, (2) the parallel introduction of capital and mortgage innovations, and (3)

“[...]housing-specific factors, notable related to some convergence of housing risk premia associated with

returns on housing as an asset” (p. 299). Given that both Belgium and the Netherlands are both members

of the EMU, with a common monetary policy, housing market fundamentals such as interest rates are

also similar. Both economies are furthermore close trading partners which suggests that the risk premia

should also converge. In figure 2 we plot the evolution of average real house prices and the number of

transactions in Belgium and the Netherlands using data from Statistics Belgium, Statistics Netherlands,

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and the NVM 3.

Figure 2: Real house prices, # of transactions, and the % of people with the other nationality for Belgiumand the Netherlands (1985Q1-2014Q3)

1992.04 2006.01

00.5

11.5

·105

Rea

lp

rice

(ine

)

BENL

1992.04 2006.01

02

4

·104

#tr

an

sact

ion

s

BENL

1992.04 2006.01

00.5

11.5

2

·10−

2

%of

resi

den

tsw

ith

oth

ern

ati

on

ality

BENL

Note: Real house prices were calculated using data concerning the CPI for Belgium that was retrieved from the website of StatisticsBelgium. Similarly, we gathered data concerning the CPI in the Netherlands from the website of Statistics Netherlands. For bothcountries the CPI was normalized to 1 for the first quarter of 1985. The data were not seasonally adjusted. The percentage of peoplewith the Dutch nationality living in Belgium and vice versa were provided by Statistics Belgium and Statistics Netherlands, respectively.

Figure 2 shows that both the evolution of real house prices and the number of transactions differed con-

siderably between both countries in recent decades. In a recent paper Damen et al. (2014) analyze the

evolution of (nominal) house prices across different European countries and provide convincing evidence

that these exhibit a long-run relationship with the ability to pay of households, which they define as

(p. 3) “[...] a constant fraction of income that goes to housing payments, which results in an amount

that people are able to pay based on the possibility to deduct mortgage interest payments and innovative

mortgage products.” The results from their paper suggest that the higher house prices in the Netherlands

relative to Belgium are the result of a more generous fiscal regime for owner-occupiers in the former.

Dutch households could (and still can) deduct all interest payments from their mortgage loans from their

taxable income. The articles by Rouwendal (2007) and Damen et al. (2014) also help explaining the

observed run-ups observed in the Netherlands since the mid-1990s and in Belgium since 2005. Both of

these are likely to be the result of changes in underlying fundamental values. While in the Netherlands

the share of interest-only mortgages increased significantly from the 1990s onwards (Rouwendal, 2007),

in Belgium the implementation of a more generous fiscal regime in 2005 combined with a (consequent)

lengthening in the mortgage term (Damen et al., 2014) increased the ability to pay, which subsequently

translated into housing prices. It is obvious that these demand shocks, however, are only capitalized into

3Nederlandse Vereniging van Makelaars, Dutch Association of Real Estate Brokers

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house prices whenever housing supply is relatively inelastic with respect to the former. Vermeulen &

Rouwendal (2007) for the Netherlands and Helgers & Buyst (2014) for Belgium, however, show that this

is the case for both countries.

After the run-ups in both countries, though, Dutch housing prices and transaction volumes declined

after 2008, like in many other (European) countries, as a result of the financial crisis. The average (real)

house price and the number of transactions in the Netherlands decreased with respectively 41% and 12%

between the first quarter of 2008 and 2009. While the Dutch housing market experienced a bust, the effect

on Belgian housing prices and transaction volumes was only limited. Over the same period (real) house

prices and transaction volumes in Belgium decreased “only” with respectively 1.5% and 14%. Moreover,

transaction volumes in Belgium quickly recovered and even peaked in 2011, while transaction volumes in

the Netherlands remain relatively low until the present day. In a recent paper Struyven (2015) attributes

the low transaction volumes in the Netherlands to the “housing lock hypothesis”. He observes that house-

holds who bought their house at the peak have higher Loan-To-Value (LTV) ratios than earlier buyers,

and also have much lower mobility rates in every year after purchase.

3.2 Developments in border regions

Although, so far, we provided a general overview of the developments in both countries in the previous

section, it is intuitive that house prices and transaction volumes in the border regions of both countries

might have displayed substantially different patterns.4

The Belgian-Dutch border spans over a length 458 kilometers from the North Sea in the west to the

tripoint between Belgium, the Netherlands and Germany in the (south) east. The border region in the

west separates the Belgian provinces East- and West Flanders (Ghent and Bruges), from the sparsely

populated Dutch region Zeelandic Flanders, that is separated from the remainder of the province Zeeland

(and the rest of the Netherlands) by the Western Scheldt which connects the port of Antwerp to the North

Sea. The central part of the border splits the Belgian province Antwerp from the Dutch province North

Brabant (Breda, Tilburg and Eindhoven). The Eastern part of the common border, which for a large part

coincides the river Meuse, splits the Belgian provinces Limburg (Hasselt and Genk) and Liège (Liège and

Verviers) from the Dutch provinces North Brabant (Eindhoven) and Limburg (Weert and Maastricht).

4In figure A.1 in appendix A 4 maps are presented that provide some insights.

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Although house prices in the Netherlands were considerably higher throughout the 90s and 2000s, there

was (and still is) considerable regional variation that can and will be used in the econometric analysis.

Furthermore note that, although 80% of the border splits Dutch-speaking areas where Dutch is spoken in

the Netherlands and Flemish (the Dutch language as spoken in Flanders) in Flanders, the Netherlands

borders the French-speaking province Liège in the south. As language obviously is one of the largest

cultural discrepancies between countries, this variation across regions can and will be exploited in the

empirical analysis.

While house prices might differ because of differences in housing attributes, amenities, etc. spill-overs

in prices between both countries might also be observed. Since the 2000s Dutch households living in

neighboring countries can opt for the Dutch income tax system, which implies that they can benefit from

the mortgage interest deductability in the Netherlands. In our theoretical model presented in section 2

this can be interpreted as a shock in wNL in the Netherlands, which translates into higher house prices,

ρBE , in Belgium. In figure 3 we plot the evolution of real house prices, the number of transactions, and

the percentage of people who possess the nationality of the neighboring country for the different Belgian

and Dutch provinces that are located along the common border.

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Figure 3: Real house prices, # of transactions, and the % of people with the other nationality for (partsof) the provinces located along the common border (1985Q1-2014Q3)

1992.04 2006.01

00.5

11.5 ·1

05

Rea

lp

rice

(ine

)Limburg (BE) vs. Liège vs. Limburg (NL)

1992.04 2006.01

00.5

11.5

2 ·105

Antwerp vs. North Brabant

1992.04 2006.01

00.5

11.5

·105

Flanders (East & West) vs. Zealand

1992.04 2006.01

0200

400

600

8001,000

#tr

an

sact

ion

s

1992.04 2006.01

01,0002,0003,0004,000

1992.04 2006.01

0500

1,000

1,500

1992.04 2006.01

05·1

0−

20.1

%d

if.

nat.

Limb. (NL)

Limb. (BE)Liège

1992.04 2006.01

05·1

0−

20.1 North-Brabant

Antwerp

1992.04 2006.01

05·1

0−

20.1 Zealand

EW Flanders

Note: in order to construct average (real) house prices and transaction volumes for the Netherlands we used aggregated data providedby the NVM at the level of 76 designated NVM regions. We then aggregated the data at the level of the provinces. For the provinceLimburg (NL) we used data for the regions Zuid-Limburg, Roermond eo and Weert eo. The data for the province North Brabant wereconstructed using data for the regions Eindhoven eo, Zuid Oost Brabant, Tilburg Oirschot, Breda, West Brabant, and Bergen op Zoomeo. For Zealand we simply took the region Zealandic Flanders. The % of people with the Belgian nationality for every municipalitywas provided by Statistics Netherlands. We assigned the appropriate NVM region to every municipality using a spatial join procedurein Quantum GIS. For Belgium, we used data concerning average house prices, the number of transactions and the % of people with theDutch nationality at the municipal level and only withheld those municipalities for which the centroid is located within a 15 kilometerradius of the Belgian-Dutch border. The data were then simply aggregated at the provincial level, where we considered the provincesEast Flanders and West Flanders to be a single province. Real house prices were calculated using the procedure described in figure 2.

From figure 3 we immediately notice that the evolution of (real) housing prices for the different provinces

follow their respective national averages. Note, however, that there are large differences between the

different provinces. While house prices in North Brabant were approximately equal to e180,000 in 2006,

those in Zealandic Flanders were only as high as e120,000. Similarly for Belgium, while house prices in

Antwerp were approximately equal to e150,000 in 2006, those in Limburg were only as high as e100,000.

Observe, furthermore, that while house prices are generally higher in the Dutch provinces, house prices

in East-and West Flanders are higher than in neighboring Zealandic Flanders. This can partially be

explained by the fact that Zealandic Flanders is a highly peripheral and sparsely populated region of the

Netherlands that lies south of the Western Scheldt which separates it from the rest of Zealand and the

country. The (north of the) Belgian provinces East- and West Flanders on the other hand are home to

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(major) cities such as Ghent and Bruges and are densily populated. Where the higher house prices in

the Dutch provinces Limburg and North Brabant have led to an increase in the percentage of people who

possess the Dutch nationality living in Belgium, the reverse pattern in the western part of both countries

has led to a migration flow from Belgium to the Netherlands. Observe that many people who migrate

between both countries remain close to the border of their country of origin, since the percentages of

people who possess the nationality of the neighboring country reported in 3 are high with respect to

their national averages reported in figure 2. The preference of Dutch households to live close to the

Netherlands might have had non-negligible effects on the demand for and prices of Belgian houses close

to the border, which is empirically tested in section 6.1. Also observe that the percentage of people who

possess the Dutch nationality is much higher in the Belgian Dutch-speaking province Limburg than in

French-speaking province Liège, which suggests that linguistic and/or cultural factors may also play a

role. This is also empirically tested in section 6.2. The higher house prices in the Netherlands finally

might lead to an anchoring bias, as for example suggested by Lambson et al. (2004), and may cause

Dutch buyers to pay more, ceteris paribus, than their Belgian counterparts. This is empirically tested in

section 6.3.

4 Data

Before we discuss the methodology used in the empirical analysis we provide an overview of the main

sources of data used in the remainder of the empirical analysis. Our main source of data concerns a large

database, kindly provided by a large Belgian franchise system of real estate agents, that contains records

on the transaction prices and property characteristics of more than 41,000 transactions that were carried

out with the help of the one of the 115 affiliated real estate agencies across Belgium between 2003 and

2014. Approximately 8,200 (≈ 20%) of these transactions were carried out within 15 kilometers of the

Belgian-Dutch border and are thus of special interest.5 We are fairly humble in stating that we have an

extensive list of property characteristics for each dwelling/apartment at our disposal. Besides the price-

and date of sale for every property i, we have detailed information concerning its characteristics (interior

space, lot size, year-of-construction, # bedrooms/bathrooms, type of heating, ...) and its location.6 For

every property i, we know its exact address, which allows us to add the corresponding x- and y-coordinates

5Micheli et al. (2014) also use a 15 kilometer cut-off point.6An overview of the data is provided in appendix B.

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to each property.7

Figure 4: Coordinates dwellings & apartments located within 15 kilometers of the Dutch border

Source maps: Belgian HISGIS & NVM

The resulting geocoordinates allow us, using detailed GIS-layers provided by the Flemish Geographical

Information Agency (FGIA)8, to calculate (minimum) distances to different amenities, such as bus stops,

train stations, highway entries/exits, grocery stores, city centres, and so on. Table 1 provides some

descriptive statistics.

7For a substantial subsample of all transactions, the geocoordinates were already added by the real estate agencies formarketing purposes. For the remaining transactions we used the geocode3 module, that is available in Stata 12 (and higher),to convert addresses into geocoordinates.

8Dutch: Agentschap Geografische Informatie Vlaanderen (AGIV)

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Table 1: Summary statistics transaction data

Distance to the Netherlands≤ 15km > 15km

Variable # Avg. St. Dev. # Avg. St. Dev.

Apartment 7,900 0.306 0.460 33,264 0.271 0.444Sales price (in e) 7,900 204,796 84,591 33,264 196,221 90,029Living surf. (in m2) 7,571 155.27 68.72 30,991 154.84 74.49Lot size (in m2) 6,110 726.40 936.59 26,339 718.01 1,067# Bedrooms 7,900 2.78 0.960 33,264 2.76 1.09# garages 7,868 0.689 0.691 33,125 0.669 0.723Year of construction 7,771 1972 30.79 32,389 1962 32.91Year of sale 7,896 2009 3.07 33,209 2009 3.14Dist. to Brussels 7,900 68.95 18.15 33,264 58.50 37.19Wallonia 7,900 0.023 0.150 33,264 0.144 0.352

Note: the distance to the Dutch border was calculated using the spatial join procedure in ArcGIS 10.2.

We are furthermore fortunate enough that the real estate agent/agency reported the previous address of

the buyer for a substantial fraction (18,040 observations, roughly 45%) of the transactions in our sample.

The addresses are also geocoded in Stata 12 using the geocode3 -module and used to examine whether

Dutch buyers pay a premium relative to their Belgian counterparts in section 6.3.

A second source of information used in the remainder of the empirical analysis is aggregated house

price data provided by the Dutch Association of Real Estate Agents (NVM) and Statistics Belgium . The

realtors of the NVM have a combined market share of approximately 70% in the Netherlands and were

kind enough to provide us with data concerning the average and median quarterly transaction regional

transaction prices for the Netherlands. The data are aggregated at the level of 76 designated regions,

8 of which border Belgium directly.9 From Statistics Belgium we retrieved average yearly transaction

prices for all 589 municipalities from 1973Q1 up to 2014Q3. As can already be seen in figure 3 there is

considerable variation in house prices in different Dutch (NVM) regions and Belgian districts (level of

aggregation we will will use in the remainder of the empirical analysis). In figure 5 average nominal house

prices for 2003 and 2011 are plotted.

9Zeeuws-Vlaanderen, West-Brabant, Breda, Tilburg, Zuid-Oost Brabant, Eindhoven, Midden-Limburg, and Zuid-Limburg.

14

Figure 5: Nominal house prices in different regions (2003 & 2011)

(a) 2003 (b) 2011

Note: all maps were created in QGIS. Source maps: Belgian HISGIS & Statistics Netherlands. Source data: Statistics Netherlands &Statistics Belgium.

Observe that in 2003 all Dutch NVM-regions, with the exception of Zeelandic Flanders, are more expensive

than their Belgian neighbors. Large price differences exist especially between Turnhout and Maaseik in

Belgium and their Dutch neighbors. In 2011, we observe similar patterns, but house prices in East-

and West Flanders (BE) are now higher than in Zeelandic Flanders (NL) and the price differences other

Belgian and Dutch regions decreased.

5 Methodology

Now that we have provided an overview the Belgian and Dutch housing markets and of the data used in

the remainder of the empirical analysis, we proceed by discussing the variables that will be used in the

regression analyses and the estimation methods used.

5.1 Measures of spatial arbitrage

As Dutch immigrants increase demand for housing especially close to the border our first natural starting

point is the (crowfly) distance to the border that is calculated in ArcGIS 10.2 using the x- and y-

coordinates of properties sold and a map of the Netherlands.10

10In a future version of this paper we plan to use the actual travel time/distance to the closest border crossing that canbe calculated using the traveltime3 module available in Stata 12 or higher.

15

Figure 6: Example distance to border

(yi, xi)

(yj , xj)

The Netherlands

Belgium

Border

Distance to borderi =√

(yi − yj)2 + (xi − xj)2/1000 (5)

Given that the price differences between Belgium and the Netherlands have decreased we expect that the

distance to the border has become less important. Therefore, we interact the distance to the border with

the year of sale.11 As the French-speaking Walloon region also borders the Netherlands in the north,

we also generate an interaction variable that is equal to the distance to the border when the property

is located in the Walloon Region and zero otherwise. As there are also large differences in house price

(differentials) across border regions between Belgium and the Netherlands that might affect house prices

we furthermore calculated a (spatially weighted) average house price at time t− 1, where t refers to the

year of sale, for the Netherlands:

Price NLi,t = ΣJj=1

d−1i,j

ΣJj=1d−1i,j

p̄j,t−1 (6)

where d−1i,j denotes the inverse distance between object i and NVM-region j, and p̄j,t−1 denotes the average

transaction price in region j at time t−1.12 We only use data from the 8 NVM-regions directly adjacent to

Belgium. Subsequently, we log-transform the resulting variable and allow it to interact with the distance

11In practice, we subtract the average year of sale from the year of sale for every property i.12The distances between the objects and the different NVM-regions are calculated using the spatial join module in ArcGIS

10.2.

16

to the border.

5.2 Estimation

Given the nature of our data (individual transaction prices and property characteristics) and consistent

with the existing literature we use Hedonic Pricing Method for which the theoretical foundations were

established in a seminal paper by Sherwin Rosen that was published in the Journal of Political Economy

in 1974. More specifically, we estimate the following regression equation:

ln(pi) = XiβX + LiβL +AiβA + �i (7)

where ln(pi) denotes the logarithmically transformed sales price of property i., Xi is the vector of the

property i’s characteristics as described in section 4, and βX represents these characteristics’ implicit

prices. The vector Li contains information concerning the location of the property, such as proximity to

amenities and the vector βL denotes the respective implicit prices. Finally, the vector AiβA is of special

interest, as it contains our measures of spatial arbitrage. Given that we know the x-and y-coordinates for

each property, we can furthermore allow the error term �i to be correlated across space in the following

manner:

�i = λW�i + νi (8)

This specification, which is often referred to as a Spatial Error Model or SEM, has been frequently

applied in the literature (e.g., Kim et al., 2003) and is estimated using either a Maximum Likelihood

(ML) procedure or the so-called GS2SLS -estimator proposed by Kelejian & Prucha (1998).13 In this

paper we use the GS2SLS-estimator (Kelejian & Prucha, 1998), since it allows for heteroskedasticity

in the error terms. Unobserved local attributes that affect housing prices and are not captured by the

observables in our model are thus captured by the spatially correlated error term.

6 Results

In the next few sections we discuss the results. In section 6.1 we investigate whether housing prices are

higher in regions where prices across the border are higher, after controlling for an extensive list of other

13We use the SPMAT module to construct the appropriate spatial weighting matrices, W . We use the SPREG moduleto estimate the model. Both modules are available in STATA.

17

observables. In section 6.2 we examine whether certain markets are affected more than others. Finally,

in section 6.3 we investigate whether certain types of buyers pay a premium.

6.1 Do We Find Evidence for Spatial Arbitrage?

Using the estimation procedures described in section 5.2 and the measures of spatial arbitrage described

in section 5.1 and controlling for a large variety of control variables, such as year-dummies, dwelling-,

location-, and quality characteristics, we find the results presented in table 2:

Table 2: Arbitrage in border regions

Variable (1) (2) (3) (4) (5) (6)

Distance to border (in km.) -0.00402*** -0.00775*** -0.00660*** -0.00623*** -0.0126*** -0.0112***(0.00152) (0.00232) (0.00137) (0.00137) (0.00216) (0.00230)

Wallonia*Distance to border 0.00104 0.000177 0.00249 0.00192 0.00119 0.00101(0.0110) (0.0110) (0.0107) (0.0106) (0.0106) (0.0106)

Year of sale*Distance to border 0.000538** 0.000852*** 0.000696***(0.000245) (0.000237) (0.000256)

Price NL 0.762*** 0.773*** 0.786*** 0.790***(0.0697) (0.0691) (0.0705) (0.0698)

Price NL*Distance to border -0.0264*** -0.0190**(0.00837) (0.00906)

ρ 0.394*** 0.400*** 0.324*** 0.320*** 0.331*** 0.325***(0.0337) (0.0338) (0.0368) (0.0367) (0.0371) (0.0369)

Observations 3,995 3,995 3,993 3,993 3,993 3,993R-sq. 0.812 0.791 0.809 0.811 0.755 0.770

Year of sale Yes Yes Yes Yes Yes YesDwelling Yes Yes Yes Yes Yes YesLocation Yes Yes Yes Yes Yes YesQuality Yes Yes Yes Yes Yes Yes

Note: robust standard errors are presented between parentheses. ***, ** and * denote that the coefficient is statistically significant atrespectively the 1, 5 and/or 10% level. The full regression tables are available upon request.

The results presented in table 2 indeed suggest that houses that are nearer to the Dutch border, ceteris

paribus, are more expensive. According to the estimates presented in column 1 house prices decrease 0.4%

every kilometer that they are further from the Dutch border, which implies that there is a 6% difference

between properties located at the border and properties located 15 kilometers from the border. Also

note from the first column that border effect does not appear to be different for transactions that are

located in the Walloon, and thus French-speaking, region of Belgium, since the interaction effect is not

18

significant. In column (2) we add an interaction effect between the year of sale and the distance to the

border. The results suggest that the magnitude of the border effect has diminished over the years, which

is intuitive since the Dutch housing markets suffered from a “bust” in the second half of our period of

analysis. Since prices collapsed in the aftermath of the financial crisis, which resulted in a “housing lock”

for a substantial fraction of all households (Struyven, 2015), the sign of the interaction effect might simply

indicate a decrease in demand of Dutch households for Belgian dwellings over time. While the regressions

presented in columns (1) and (2) indicate that houses close to the border are more expensive it might

well be that house prices close to the border are more expensive as a result of, for example, proximity

to amenities located in the Netherlands. In column (3) we drop the year-distance interaction effect but

include a spatially weighted average house price in the Netherlands, Price NL, and as a regressor. In

column (4) we additionally interact Price NL with the distance to the border. The results indicate that

the house prices in Belgium and the Netherlands show a large degree of co-movement as the coefficient

is positive and approximately equal to 1. There are different explanations for this effect: (1) unobserved

, (2) “pure” arbitrage, where Belgian house prices react to house prices across the border. Note that the

distance to the border -effect does not disappear which suggests that there are spill-overs. In column (6)

we include all potential effects in a single regression. Observe that the distance to the border remains

significant, but that the effect decreases over time. Observe furthermore that house prices in Belgium

are higher in regions where neighboring Dutch house prices are also higher and that the distance to the

border -effect is also larger in these regions.

6.2 Is There Heterogeneity Across Housing Markets?

While the results in the previous section suggested that houses close to the border are, ceteris paribus,

more expensive the specification presented did not allow for any heterogeneity in the estimated border

effect. As we have seen, however, in figure 5 there are (large) differences in house price(s) (differentials)

along the Belgian-Dutch border, that might be correlated with the (estimated) spatial arbitrage effect.

Moreover, the willingness to pay of different households for certain attributes, that is captured by the β’s

in the regression analysis, might not be constant over space. Therefore, firstly, we estimate a model where

we allow for heterogeneity in the border effect whilst keeping preferences for other attributes constant.

Thereafter, we allow for coefficient heterogeneity in both the border effect and the other characteristics

by estimating separate regressions for different regions.

19

6.2.1 Interaction effects

Our first approach to allow for heterogeneity across markets is straightforward. We estimate a similar

regression model as those presented in table 2, but now allow for a district-specific border effect. We thus

estimate the following regression equation:

ln(pi) = ZiβZ + LiβL + ΣJj=1γjDi,jDistance to the borderi + βA′A

′i + �i (9)

where Di,j is equal to 1 when property i is located in district j and zero otherwise. The magnitude, and

their respective level of significance, for the different γj ’s are plotted in figure 7 and presented in table 3.

Figure 7: γ̂j: Districtj * Distance to border

Source maps: Belgian HISGIS & Statistics Netherlands

20

Table 3: γ̂j: Districtj * Distance to border

Variable γ̂j Standard error

Antwerp -0.006** (0.002)Turnhout -0.011*** (0.003)Bruges 0.001 (0.002)Dendermonde -0.003 (0.003)Eeklo -0.005 (0.004)Ghent -0.009*** (0.002)Sint-Niklaas -0.008*** (0.002)Lige -0.037*** (0.005)Verviers -0.026*** (0.007)Hasselt -0.018*** (0.003)Maaseik -0.017*** (0.002)Tongeren -0.025*** (0.005)Distance to border * year of sale 0.000*** (0.000)Price NL 0.801*** (0.066)R-sq. 0.830Obs. 3,993

Note: robust standard errors are presented between parentheses. ***, ** and * denote that the coefficient is statistically significant atrespectively the 1, 5 and/or 10% level. The full regression tables are available upon request.

The results from the regression analysis presented in figure 7 and table 3 suggest that the distance to the

border -effect varies between -0.037 (Liège) and 0.002 (Bruges). Compared to (6) of table 2 there are 6

regions (Liège, Verviers, Tongeren, Hasselt, Maaseik and Turnhout) that display a stronger effect, while

the absolute value of the coefficient is smaller for the remaining 6 regions (Ghent, Sint-Niklaas, Antwerp,

Eeklo, Dendermonde and Bruges). With the exception of Eeklo, Dendermonde and Bruges, however, all

districts show significant border effects. Note that the coefficients largely correspond with our expectations

as house prices in the Eastern part of Belgium are relatively low compared to those in neighboring regions

in the Netherlands, while the more expensive provinces of East- and West Flanders border the highly

peripheral and relatively cheap Zeelandic Flanders in the Netherlands. Note furthermore that the largest

effects are found for the districts Liège and Verviers, which are both located in the French-speaking part

of Belgium. Although this result is counterintuitive, we have to note that we have only few, namely 34,

observations (20 for Liège and 14 for Verviers). It might furthermore be the case that the willingness to

pay for certain attributes differs between regions. Therefore, we estimate separate regressions for different

subsamples of data in the next section.

21

6.2.2 Regressions on subsamples

Although the results presented in the previous section allow for heterogeneity in the border effect across

districts, the willingness to pay for various characteristics is assumed to be homogeneous across space,

which might not necessarily correspond with reality. Given that we have sufficient transactions we can

estimate separate regressions for different regions. We split our total sample at the level of the provinces

in Belgium, with three notable exceptions. Given that we observe only few transactions in West Flanders

and both East- and West-Flanders border Zeelandic Flanders in the Netherlands, we estimate a single

model for these two provinces. A second notable exception concerns the province Antwerp. Since the

districts Antwerp, which largely coincides with the city of Antwerp and its respective commuting region

differs from the more peripheral district Turnhout we estimate separate regressions for these two regions.

As the number of transactions within 15 kilometers of the Netherlands for the province Liège, finally, is

rather limited (approximately 130 transactions), we include all transactions that are located within 25

kilometers of the Dutch border. The results are presented in table 4.

Table 4: Regressions for separate regions

Variable Limburg Turnhout Antwerp EW Fl. Liège

Distance to border (in km.) -0.00719*** -0.0117 -0.00505 0.0122*** 0.00230(0.00261) (0.00774) (0.00533) (0.00176) (0.00415)

Year of sale*Distance to border 0.000977** 0.00283*** 0.000394 7.08e-05 2.10e-05(0.000464) (0.000814) (0.000774) (0.000375) (0.000945)

Price NL -0.0132 -0.131 0.0580(0.292) (0.757) (0.796)

Price NL*Distance to border -0.00136 0.0495* 0.00131(0.0170) (0.0299) (0.0322)

ρ 0.253*** 0.252*** 0.136 0.142** 0.236***(0.0617) (0.0774) (0.115) (0.0666) (0.0770)

Observations 1,467 792 442 1,258 354R-sq. 0.812 0.859 0.902 0.855 0.837

Year of sale Yes Yes Yes Yes YesDwelling Yes Yes Yes Yes YesLocation Yes Yes Yes Yes YesQuality Yes Yes Yes Yes No

Note: robust standard errors are presented between parentheses. ***, ** and * denote that the coefficient is statistically significant atrespectively the 1, 5 and/or 10% level. The full regression tables are available upon request.

Our estimates indicate that border effects are specially present in Turnhout and Limburg and and to a

22

lesser extent in Antwerp. East- and West Flanders now show a reverse pattern, while there is no statistical

relationship in Liège. Recall that the observed price differences between Belgium and the Netherlands

as shown in figure 5 were especially large in Limburg and and Turnhout, which makes this result not

very suprising. Also observe, that consistent with our expectations, the importance of proximity to the

border has decreased over time as the interaction effect is positive and significant. Given that the price

gap between Belgium and the Netherlands has decreased as a result of a more generous fiscal treatment

of owner-occupiers in Belgium since 2005 and a housing market bust in the Netherlands since 2008, this

result is not suprising. Finally, observe that our measure of fit, the R2 has increased for almost all regions

in comparison with a single model which suggests that estimating different regressions for separate regions

is more appropriate.

6.3 Do Foreign Buyers Pay a Premium?

Data provided by Statistics Belgium suggest that the number of people in Belgium who possess the Dutch

nationality, a rough proxy for migration, has increased from 0.7% in 1995 to 1.2% in 2009. As we already

mentioned, we are fortunate enough that the real estate agent/agency reported the previous address of

the buyer for a substantial fraction (18,040 or ≈ 50%) of the transactions in our sample, which allows us

to make a (small) contribution to this strand of literature. We observe that the previous address of the

buyer was located in the Netherlands for 247 or approximately 1.4% of all transactions. Given that the

results in the previous section showed that the can be substantial variation in coefficient across regions

and the fact that almost 40% of all buyers with a previous address located in the Netherlands bought a

dwelling in the Flemish district Maaseik, where the percentage of sales of dwellings to Dutch buyers is

approximately 15% (814 transactions), we limit our regression analysis to this particular district. A first

glance at the descriptive statistics presented in table 5 suggest that Dutch buyers pay, on average, more

than their Belgian counterparts.

23

Table 5: Summary statistics: transactions within a radius of 15 kilometers of the Dutch border in the(Belgian) district Maaseik

Country of originthe Netherlands Belgium

Variable # Avg. St. Dev. # Avg. St. Dev.

Sales price (in e) 119 248,650 71,349 654 220,740 72,658Living surf. (in m2) 118 211.61 59.65 617 187.71 64.30Lot size (in m2) 116 1,174 849.47 642 965.78 939.76# Bedrooms 119 3.30 0.916 654 3.18 0.778# garages 118 1.05 0.638 652 0.891 0.583Year of construction 119 1969 25.27 652 1970 24.40Year of sale 119 2008 1.98 654 2010 2.55Dist. to border 119 4.59 3.34 654 4.97 3.39

Note: the distance to the Dutch border was calculated using the spatial join procedure in ArcGIS 10.2.

Note, however, that Dutch buyers also buy larger dwellings in terms of interior space (212m2 vs. 188m2),

lot size (1,174m2 vs. 966m2, the number of bedrooms (3.3 vs. 3.18). and the number of garages (1.05

vs. 0.891). Further note that there are no significant differences in the years of construction (1969 vs.

1970) and the distance to the border (4.59 vs. 4.97), but that the share of transactions of Dutch buyers

has decreased over the years (2008 vs. 2010). To investigate whether foreign buyers pay higher prices for

comparable dwellings we estimate the following econometric model:

ln(pi) = βBBi + βLLi + βZZi + βAAi + �i,

where � = λW�+ ν

(10)

where B is a vector of characteristics that is related to the buyer of the property. The (empirical) literature

(e.g., Lambson et al., 2004) has argued that different buyers might pay different prices because of (1)

search costs, and (2) anchoring bias. Therefore, we do not only include a dummy-variable that is equal to

1 when the previous address of the buyer is located in the Netherlands and 0 otherwise, but also estimate

regressions where we add the distance between property i and the previous address of the buyer and/or

the average price at time t − 1 in the district or NVM-region of origin (anchoring bias) as explanatory

variables. The results are presented in table 6.

The first four columns of table 6 solely include a dummy-variable that is equal to 1 whenever the previous

address of the buyer is located in the Netherlands, and 0 otherwise. The results presented in column (1)

24

Tab

le6:

Do

Du

tch

buye

rspa

ya

pre

miu

m?

Var

iab

le(1

)(2

)(3

)(4

)(5

)(6

)(7

)

Dis

tan

ceto

bor

der

(in

km

.)-0

.00148

-0.0

0937***

-0.0

117***

-0.0

0841***

-0.0

0802***

-0.0

0842***

-0.0

0844***

(0.0

0610)

(0.0

0286)

(0.0

0363)

(0.0

0304)

(0.0

0305)

(0.0

0300)

(0.0

0301)

Tra

velt

ime

Ein

dh

oven

(in

min

.)-0

.00159

0.0

00584

-0.0

0412

-0.0

00638

-0.0

00353

-0.0

00472

-0.0

00461

(0.0

0336)

(0.0

0156)

(0.0

0305)

(0.0

0228)

(0.0

0228)

(0.0

0224)

(0.0

0224)

Bu

yer:

Net

her

lan

ds

0.109***

0.0

280*

0.0

333**

0.0

312**

(0.0

306)

(0.0

161)

(0.0

161)

(0.0

142)

Dis

tan

ceb

uye

r-pro

per

ty(i

nkm

.)0.0

00309*

0.0

00108

(0.0

00183)

(0.0

00206)

Pri

ced

if.

bu

yer-

pro

per

ty(%

)0.1

00***

0.0

901**

(0.0

369)

(0.0

420)

ρ0.

418***

0.3

78***

0.3

29***

0.1

63

0.1

64

0.1

58

0.1

58

(0.0

645)

(0.0

791)

(0.0

851)

(0.1

10)

(0.1

08)

(0.1

10)

(0.1

09)

Ob

serv

atio

ns

768

694

694

667

667

667

667

R-s

q.

0.0

36

0.7

84

0.7

90

0.8

42

0.8

42

0.8

43

0.8

43

Yea

rof

sale

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Dw

elli

ng

No

Yes

Yes

Yes

Yes

Yes

Yes

Loca

tion

No

No

Yes

Yes

Yes

Yes

Yes

Qu

alit

yN

oN

oN

oY

esY

esY

esY

es

Note:

rob

ust

stan

dard

erro

rsare

pre

sente

db

etw

een

pare

nth

eses

.***,

**

an

d*

den

ote

that

the

coeffi

cien

tis

stati

stic

ally

sign

ifica

nt

at

resp

ecti

vel

yth

e1,

5an

d/or

10%

level

.T

he

full

regre

ssio

nta

ble

sare

availab

leu

pon

requ

est.

25

suggest that Dutch buyers pay a premium of 10% relative to their Belgian counterparts, when only con-

trolling for the year of sale of the properties in the regression analysis. The descriptive statistics presented

in table 5, however, already indicated that Dutch buyers buy larger dwellings. The results presented in

columns (2)-(4) indeed indicate that the premium paid by Dutch buyers decreases but does not disappear

when controlling for various dwelling, location and quality characteristics. The results in column (4), the

most general specification, suggest that Dutch buyers, ceteris paribus, pay a premium of 3.1% relative to

Belgian buyers.

As mentioned previous the literature has provided two potential explanations that might explain this

finding, namely (1) search costs and (2) anchoring bias. In columns (5)-(7) we try to control for these

effects using either the (crowly) distance between the property and the buyer as a measure of search costs

(column 5), or the price difference between the region the buyer was previously located and the region

the property is located as a measure of potential anchoring bias (column 6), or both (column 7).14 Our

results provide support for the anchoring bias hypothesis, as the coefficient is highly significant in both

column (6) and (7). Keep in mind, however, that approximately a large share (approximately 73%) of

all Belgian buyers have a previous address in the same district (Price dif. buyer-property = 0), while for

almost all Dutch buyers the price difference is positive.

7 Concluding Remarks

The results presented in this chapter suggest that dwellings, ceteris paribus, closer to the Dutch border

are more expensive as a result of spatial spill-overs in housing prices. The relatively inelastic supply of

housing combined with a more generous fiscal treatment of owner-occupiers in the Netherlands relative

to Belgium translated into higher house prices in the former. Innovations in mortgage markets in the

1990s boosted housing prices in the Netherlands even further, which caused some Dutch households to

migrate to Belgium. In this paper we show that the housing price developments in the Netherlands led to

higher house prices in Belgian border regions. Since most Dutch households prefer to live close to their

country of origin housing prices are higher, especially, close to the border. We furthermore show that this

effect decreases over time, which can potentially be explained by the more generous fiscal treatment of

owner-occupiers in Belgium since 2005, which increased house prices in Belgium, and the housing market

bust in the Netherlands since 2008. Both evolutions decreased the price gap between both countries and

14We calculate these measures for all buyers and not only for Dutch buyers.

26

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thus the scope for spatial arbitrage. Our results furthermore reveal that the border effect is especially

present in the (north of) the province of (Belgian) Limburg and the district of Turnhout. This can be

explained by the large price differentials that exist(ed), Our results furthermore suggest that housing

markets are not necessarily efficient as Dutch buyers pay a premium for similar dwellings.

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References

Appendices

A Belgian and Dutch border municipalities

Figure A.1: Border regions

(a) Borders & cities (b) Population density

(c) % of people who possess nationality of theneighboring country (d) Growth rate (1990-2014) (c)

Note: all maps were created in QGIS. Source maps: Belgian HISGIS & Statistics Netherlands.

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References

B Overview of the data

Table B.1: Variables & sources of data

Source Variable Description

ERA Belgium Transaction prices List- and sales pricesDays-on-marketYear of saleType of residence Apartment vs. dwellingType of construction (Semi-)detached vs. terracedYear of construction# Bedrooms# GaragesState Ready to move in, luxuriously finished, ...Heating material Gas, wood, electricity, ...Heating type Central heating, individual heating, ...Heating elements Radiator, underfloor heating ,...Glazing Single, double, tripleBasement Storage room, wine cellar, ...Bathroom Two/multiple bathrooms, bathtub, ...Kitchen Well-maintained, dishwasher, ...Various Alarm, swimming pool,..Location x- and y-coordinates

Dutch Assocation of Real Estate Brokers Transaction prices 1985Q1-2014Q4, NVM-regions

Flemish Geographical Information Agency Bus stops x- and y-coordinatesTrain stationsHighway entries/exitsGrocery storesShopping centers

Statistics Belgium Transaction prices 1973Q1-2014Q3, municipalitiesInhabitants (per nationality) 1990-2013Statistical sector data Housing stock

Population

Statistics Netherlands # inhabitants (per nationality) 1996-2013

Note: the list of variables presented here is not exhaustive. More details are available upon request.

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