spatial arbitrage in belgian border regions

SPATIAL ARBITRAGE IN BELGIAN
BORDER REGIONS
Roel Helgers
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.
1
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 country. Even in the modern day Europe, where the 26 member states of the Schengen Area 1 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 Netherlands 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 macroeconomic policies and shocks, the evolution of housing prices in both countries has differed substantially
1
The 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 the
European Free Trade Association (EFTA) member states Iceland, Liechtenstein, Norway and Switzerland.
2
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
ln p(x)
Border
px̄,NL
px̄,BE
x̄
x
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
3
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 language 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 institutional 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 differences, 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 & Sirmans (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 & Mayock (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
2
The # 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).
4
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 dwellingand 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) CobbDouglas utility specification and linear budget constraints yields the following maximization problem(s):
α
β
(1−βj ) 1−αj
maxCj ,HjA ,HjB Uj (Cj , HjA , HjB ) = Cj j (HjAj HjB
)
s.t. Cj Pj + HjA ρA + HjB ρB ≤ wj
(1)
Where j = A, B
Deriving the 8 first order conditions and performing the appropriate substitutions yields the following
(typical) demand functions for Cj , HjA and HjB :
5
Cj =
αj wj
,
Pj
HjA =
(1 − αj )βj wj
(1 − αj )(1 − βj )wj
, and HjB =
ρA
ρB
(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:
(3)
HAA + HBA = H̄A and HAB + HBB = H̄B
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:
Cj∗ =
αj wj
,
Pj
∗
HjA
=
(1 − αj )βj wj H̄A
,
ΣA,B (1 − αj )βj wj
∗
HjB
=
(1 − αj )(1 − βj )wj H̄B
and
ΣA,B (1 − αj )(1 − βj )wj
ρ∗j =
ΣA,B (1 − αj )βj wj
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 country 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,
6
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
3.1
House Prices and Housing Markets in Belgium and the Netherlands
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,
7
and the NVM 3 .
1992.04
2006.01
1992.04
2006.01
2
·10−2
1.5
1
0.5
BE
NL
0
% of residents with other nationality
4
2
BE
NL
0
# transactions
·104
·105
1.5
1
0.5
BE
NL
0
Real price (in e)
Figure 2: Real house prices, # of transactions, and the % of people with the other nationality for Belgium
and the Netherlands (1985Q1-2014Q3)
1992.04
2006.01
Note: Real house prices were calculated using data concerning the CPI for Belgium that was retrieved from the website of Statistics
Belgium. Similarly, we gathered data concerning the CPI in the Netherlands from the website of Statistics Netherlands. For both
countries the CPI was normalized to 1 for the first quarter of 1985. The data were not seasonally adjusted. The percentage of people
with 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 considerably 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
3
Nederlandse Vereniging van Makelaars, Dutch Association of Real Estate Brokers
8
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 households 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).
4
In figure A.1 in appendix A 4 maps are presented that provide some insights.
9
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.
10
Figure 3: Real house prices, # of transactions, and the % of people with the other nationality for (parts
of ) the provinces located along the common border (1985Q1-2014Q3)
0
0.5
0
2006.01
1992.04
2006.01
1992.04
2006.01
2006.01
1992.04
2006.01
500
1,000 1,500
1992.04
0
0
Zealand
EW Flanders
0
5 · 10−2
0.1
5 · 10−2
North-Brabant
Antwerp
0
Limb. (NL)
Limb. (BE)
Liège
0.1
2006.01
1992.04
1,0002,0003,0004,000
1992.04
200 400 600 8001,000
0.1
5 · 10−2
0.5
1
1
·105
1.5
·105
2
1.5
1.5
·105
2006.01
0
% dif. nat.
Antwerp vs. North Brabant Flanders (East & West) vs. Zealand
1
0.5
1992.04
0
# transactions
0
Real price (in e)
Limburg (BE) vs. Liège vs. Limburg (NL)
1992.04
2006.01
1992.04
2006.01
Note: in order to construct average (real) house prices and transaction volumes for the Netherlands we used aggregated data provided
by the NVM at the level of 76 designated NVM regions. We then aggregated the data at the level of the provinces. For the province
Limburg (NL) we used data for the regions Zuid-Limburg, Roermond eo and Weert eo. The data for the province North Brabant were
constructed using data for the regions Eindhoven eo, Zuid Oost Brabant, Tilburg Oirschot, Breda, West Brabant, and Bergen op Zoom
eo. For Zealand we simply took the region Zealandic Flanders. The % of people with the Belgian nationality for every municipality
was provided by Statistics Netherlands. We assigned the appropriate NVM region to every municipality using a spatial join procedure
in Quantum GIS. For Belgium, we used data concerning average house prices, the number of transactions and the % of people with the
Dutch nationality at the municipal level and only withheld those municipalities for which the centroid is located within a 15 kilometer
radius of the Belgian-Dutch border. The data were then simply aggregated at the provincial level, where we considered the provinces
East 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
11
(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 priceand 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
5
6
Micheli et al. (2014) also use a 15 kilometer cut-off point.
An overview of the data is provided in appendix B.
12
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.
7
For a substantial subsample of all transactions, the geocoordinates were already added by the real estate agencies for
marketing purposes. For the remaining transactions we used the geocode3 module, that is available in Stata 12 (and higher),
to convert addresses into geocoordinates.
8
Dutch: Agentschap Geografische Informatie Vlaanderen (AGIV)
13
Table 1: Summary statistics transaction data
Variable
Apartment
Sales price (in e)
Living surf. (in m2)
Lot size (in m2)
# Bedrooms
# garages
Year of construction
Year of sale
Dist. to Brussels
Wallonia
#
7,900
7,900
7,571
6,110
7,900
7,868
7,771
7,896
7,900
7,900
Distance to the Netherlands
≤ 15km
> 15km
Avg. St. Dev.
#
Avg. St. Dev.
0.306
204,796
155.27
726.40
2.78
0.689
1972
2009
68.95
0.023
0.460
84,591
68.72
936.59
0.960
0.691
30.79
3.07
18.15
0.150
33,264
33,264
30,991
26,339
33,264
33,125
32,389
33,209
33,264
33,264
0.271
196,221
154.84
718.01
2.76
0.669
1962
2009
58.50
0.144
0.444
90,029
74.49
1,067
1.09
0.723
32.91
3.14
37.19
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.
9
Zeeuws-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 Eastand 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 ycoordinates of properties sold and a map of the Netherlands.10
10
In a future version of this paper we plan to use the actual travel time/distance to the closest border crossing that can
be calculated using the traveltime3 module available in Stata 12 or higher.
15
Figure 6: Example distance to border
The Netherlands
(yj , xj )
Belgium
(yi , xi )
Distance to borderi =
Border
q
(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−1
i,j
ΣJj=1 d−1
i,j
p̄j,t−1
(6)
where d−1
i,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
11
12
In practice, we subtract the average year of sale from the year of sale for every property i.
The 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 ) = Xi βX + Li βL + Ai βA + 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 Ai βA 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
13
We use the SPMAT module to construct the appropriate spatial weighting matrices, W . We use the SPREG module
to 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
Distance to border (in km.)
Wallonia*Distance to border
(1)
(2)
(3)
(4)
(5)
(6)
-0.00402***
(0.00152)
0.00104
(0.0110)
-0.00775***
(0.00232)
0.000177
(0.0110)
0.000538**
(0.000245)
-0.00660***
(0.00137)
0.00249
(0.0107)
-0.00623***
(0.00137)
0.00192
(0.0106)
0.762***
(0.0697)
0.773***
(0.0691)
-0.0264***
(0.00837)
-0.0126***
(0.00216)
0.00119
(0.0106)
0.000852***
(0.000237)
0.786***
(0.0705)
-0.0112***
(0.00230)
0.00101
(0.0106)
0.000696***
(0.000256)
0.790***
(0.0698)
-0.0190**
(0.00906)
Year of sale*Distance to border
Price NL
Price NL*Distance to border
ρ
Observations
R-sq.
Year of sale
Dwelling
Location
Quality
0.394***
(0.0337)
0.400***
(0.0338)
0.324***
(0.0368)
0.320***
(0.0367)
0.331***
(0.0371)
0.325***
(0.0369)
3,995
0.812
3,995
0.791
3,993
0.809
3,993
0.811
3,993
0.755
3,993
0.770
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Note: robust standard errors are presented between parentheses. ***, ** and * denote that the coefficient is statistically significant at
respectively 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 ) = Zi βZ + Li βL + ΣJj=1 γj Di,j Distance to the borderi + βA0 A0i + 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
Antwerp
Turnhout
Bruges
Dendermonde
Eeklo
Ghent
Sint-Niklaas
Lige
Verviers
Hasselt
Maaseik
Tongeren
Distance to border * year of sale
Price NL
R-sq.
Obs.
γ̂j
Standard error
-0.006**
-0.011***
0.001
-0.003
-0.005
-0.009***
-0.008***
-0.037***
-0.026***
-0.018***
-0.017***
-0.025***
0.000***
0.801***
0.830
3,993
(0.002)
(0.003)
(0.002)
(0.003)
(0.004)
(0.002)
(0.002)
(0.005)
(0.007)
(0.003)
(0.002)
(0.005)
(0.000)
(0.066)
Note: robust standard errors are presented between parentheses. ***, ** and * denote that the coefficient is statistically significant at
respectively 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
Distance to border (in km.)
Year of sale*Distance to border
Price NL
Price NL*Distance to border
ρ
Observations
R-sq.
Year of sale
Dwelling
Location
Quality
Limburg
Turnhout
Antwerp
EW Fl.
Liège
-0.00719***
(0.00261)
0.000977**
(0.000464)
-0.0132
(0.292)
-0.00136
(0.0170)
-0.0117
(0.00774)
0.00283***
(0.000814)
-0.131
(0.757)
0.0495*
(0.0299)
-0.00505
(0.00533)
0.000394
(0.000774)
0.0580
(0.796)
0.00131
(0.0322)
0.0122***
(0.00176)
7.08e-05
(0.000375)
0.00230
(0.00415)
2.10e-05
(0.000945)
0.253***
(0.0617)
0.252***
(0.0774)
0.136
(0.115)
0.142**
(0.0666)
0.236***
(0.0770)
1,467
0.812
792
0.859
442
0.902
1,258
0.855
354
0.837
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Note: robust standard errors are presented between parentheses. ***, ** and * denote that the coefficient is statistically significant at
respectively 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 origin
the Netherlands
Belgium
#
Avg. St. Dev.
#
Avg. St. Dev.
Variable
Sales price (in e)
Living surf. (in m2)
Lot size (in m2)
# Bedrooms
# garages
Year of construction
Year of sale
Dist. to border
119
118
116
119
118
119
119
119
248,650
211.61
1,174
3.30
1.05
1969
2008
4.59
71,349
59.65
849.47
0.916
0.638
25.27
1.98
3.34
654
617
642
654
652
652
654
654
220,740
187.71
965.78
3.18
0.891
1970
2010
4.97
72,658
64.30
939.76
0.778
0.583
24.40
2.55
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 ) = βB Bi + βL Li + βZ Zi + βA Ai + i ,
(10)
where = λW + ν
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
25
Yes
No
No
No
Year of sale
Dwelling
Location
Quality
Yes
Yes
No
No
694
0.784
0.378***
(0.0791)
-0.00937***
(0.00286)
0.000584
(0.00156)
0.0280*
(0.0161)
(2)
Yes
Yes
Yes
No
694
0.790
0.329***
(0.0851)
-0.0117***
(0.00363)
-0.00412
(0.00305)
0.0333**
(0.0161)
(3)
Yes
Yes
Yes
Yes
667
0.842
0.163
(0.110)
-0.00841***
(0.00304)
-0.000638
(0.00228)
0.0312**
(0.0142)
(4)
Yes
Yes
Yes
Yes
667
0.842
0.164
(0.108)
0.000309*
(0.000183)
-0.00802***
(0.00305)
-0.000353
(0.00228)
(5)
Yes
Yes
Yes
Yes
667
0.843
0.158
(0.110)
0.100***
(0.0369)
-0.00842***
(0.00300)
-0.000472
(0.00224)
(6)
Yes
Yes
Yes
Yes
667
0.843
0.158
(0.109)
0.000108
(0.000206)
0.0901**
(0.0420)
-0.00844***
(0.00301)
-0.000461
(0.00224)
(7)
Note: robust standard errors are presented between parentheses. ***, ** and * denote that the coefficient is statistically significant at respectively the 1, 5 and/or 10% level. The full
regression tables are available upon request.
768
0.036
0.418***
(0.0645)
-0.00148
(0.00610)
-0.00159
(0.00336)
0.109***
(0.0306)
(1)
Observations
R-sq.
ρ
Price dif. buyer-property (%)
Distance buyer-property (in km.)
Buyer: Netherlands
Traveltime Eindhoven (in min.)
Distance to border (in km.)
Variable
Table 6: Do Dutch buyers pay a premium?
suggest that Dutch buyers pay a premium of 10% relative to their Belgian counterparts, when only controlling 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
14
We calculate these measures for all buyers and not only for Dutch buyers.
26
References
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 the
neighboring 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
Days-on-market
Year of sale
Type of residence
Type of construction
Year of construction
# Bedrooms
# Garages
State
Heating material
Heating type
Heating elements
Glazing
Basement
Bathroom
Kitchen
Various
Location
List- and sales prices
Ready to move in, luxuriously finished, ...
Gas, wood, electricity, ...
Central heating, individual heating, ...
Radiator, underfloor heating ,...
Single, double, triple
Storage room, wine cellar, ...
Two/multiple bathrooms, bathtub, ...
Well-maintained, dishwasher, ...
Alarm, swimming pool,..
x- and y-coordinates
Dutch Assocation of Real Estate Brokers
Transaction prices
1985Q1-2014Q4, NVM-regions
Flemish Geographical Information Agency
Bus stops
Train stations
Highway entries/exits
Grocery stores
Shopping centers
x- and y-coordinates
Statistics Belgium
Transaction prices
Inhabitants (per nationality)
Statistical sector data
1973Q1-2014Q3, municipalities
1990-2013
Housing stock
Population
Statistics Netherlands
# inhabitants (per nationality)
1996-2013
Apartment vs. dwelling
(Semi-)detached vs. terraced
Note: the list of variables presented here is not exhaustive. More details are available upon request.
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