Proc. IAHS, 382, 703–707, 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Tenth International Symposium on Land Subsidence (TISOLS)
The effect of land subsidence on real estate values
Wouter Willemsen1, Sien Kok1, and Onno Kuik2
1Deltares, 2629 HV Delft, the Netherlands
2Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
Correspondence: Wouter Willemsen (email@example.com)
Published: 22 April 2020
Abstract. Land subsidence in the Netherlands, mainly occurring in its western and northern peat and clay soils,
causes signiﬁcant damage to houses and infrastructure, estimated at EUR 17 billion until 2050, through differ-
ential settlement of shallow foundations, negative skin friction and fungal decay of timber piles. Various studies
and reports both in The Netherlands and abroad have addressed the potential economic impacts of subsidence
on houses: yet, these studies lack spatially detailed data and instead rely on generic assumptions on expected
damage restoration costs. By using a hedonic pricing model, this study examines the impact of subsidence on
housing prices in the Dutch cities of Rotterdam and Gouda. In contrast to earlier studies, subsidence and its
impact on property values are examined at house level. We test for the effect of subsidence with data related
to (i) general (uniform) subsidence (mm yr−1), (ii) differential subsidence of a building and (iii) subsidence of
the surrounding area in relation to the house. Results show that uniform subsidence has the largest impact on
property values with approximately −6 %, while “differential” and “surrounding” subsidence show respectively
−2 % and no effect. These results could prove useful to policymakers, homeowners and housing corporations
by generating a better understanding of the impact of subsidence on property values and subsequently to create
awareness and spur investments in measures to mitigate damage. It should be noted that these results are speciﬁc
to the research area are therefore not immediately scalable to other cities as local conditions differ.
Land subsidence is a substantial problem affecting many
cities worldwide. It potentially causes damage to houses,
businesses, agriculture and public infrastructure (Reddish
and Whittaker, 2012). These consequences are observed in
the Netherlands, which is prone to land subsidence due to ar-
tiﬁcial drainage of clay and peat soils, which leads to clay
settlement and peat oxidation (Nieuwenhuis and Schokking,
1997; Schothorst, 1977). Especially in urban areas subsi-
dence rates can vary over space due to a heterogeneous sub-
surface and loading by man-made structures such as build-
ings and infrastructure (Zeitoun and Wakshal, 2013). Sub-
sidence in the Netherlands is estimated to cause around
EUR 20 billion in damage up until 2050, of which at least
EUR 17 billion in expected repair costs of structural dam-
age – e.g. restoring foundations, cracked walls, sloping ﬂoors
– to buildings (Van den Born et al., 2016). This restoration
costs approach does not fully represent socio-economic im-
pact, as individual preferences such as risk aversion are not
1.1 Valuing subsidence risk in housing market
An approach that better reﬂects such individual preferences
is the revealed preference approach “hedonic pricing”, as
e.g. used by Yoo and Perring (2017) to assess the effect
of subsidence on house transactions in Phoenix, Arizona,
and to assess the impact of earthquake risks in Groningen
(Koster and Van Ommeren, 2015). This method takes all as-
pects which inﬂuence a buyer’s decision into account by us-
ing the actual transaction prices. Yoo and Perring (2017) ﬁnd
that houses located in a subsiding area were valued 9.9 %
less on average. This study evaluates subsidence on a large
scale – local subsidence patterns and differences in founda-
tion are not included. In the Dutch housing market, aware-
ness of real estate damage due to subsidence has recently
increased, among other things due to the spike in damage re-
Published by Copernicus Publications on behalf of the International Association of Hydrological Sciences.
704 W. Willemsen et al.: The effect of land subsidence on real estate values
ports in the dry summer of 2018 (Vereniging voor het Bouw-
en Woningtoezicht, 2019).
1.2 Study scope
Where previous hedonic pricing studies have assessed sub-
sidence effects on a larger spatial scale, this study investi-
gates the economic effects of land subsidence on the housing
market at the building level. We apply the hedonic pricing
method to assess the impact of subsidence on the property
market in two Dutch cities – both known to be affected by
subsidence and with a building-level settlement data avail-
able: Rotterdam and Gouda. We expect results could prove
useful to policymakers, homeowners and housing corpora-
tions by generating a better understanding of the impact of
subsidence on property values and subsequently to create
awareness and spur investments in measures to mitigate dam-
This study uses the hedonic pricing method. The hedonic
pricing model regards real estate property as a heterogeneous
good comprised of many characteristics: the total value of
a house is considered a function of the values of individ-
ual characteristics, such as physical building characteristics,
neighborhood characteristics, and environmental character-
istics (Freeman, 1979). In our study, the variable “subsis-
tence rate” is one of the environmental characteristics. He-
donic pricing theory assumes that home buyers are perfectly
informed on all variables, and thus take subsidence into con-
sideration when purchasing a house by adjusting their bid
price accordingly. On subsidence risk there is often limited
information available, so buyers are unlikely to be perfectly
informed. The results will tell if buyers take subsidence into
consideration, based on data available to them. To assess the
effect of subsidence on property values, we distinguish three
different types of subsidence: (i) uniform subsidence of the
house – based on data on “absolute” subsidence rate of the
building, (ii) differential subsidence – based on data on dif-
ferential (uneven) settlement of the building, and (iii) sub-
sidence of the buildings’ surroundings. The latter includes
subsidence of e.g. sidewalks, yards and streets, which is ex-
pected to lead to problems with e.g. damaged utility pipes
especially with ‘ﬁxed’ adjacent houses on (concrete) foun-
dation piles. We expect that all types have a negative ef-
fect on the property value, with differential subsidence be-
ing the most detrimental. In the end we are interested in the
marginal value buyers place on the presence of subsidence
in their prospective house. The marginal value entails how
much home buyers are willing to pay less for an additional
unit of subsidence (e.g. 1 mm extra per year).
2.1 Statistical model
According to Cropper et al. (1988), ordinary least
squares (OLS) statistical models are best used in hedo-
nic price analyses in the presence of omitted variable
bias (OVB), meaning that an unincluded variable affects the
outcomes. Even though control variables are included in the
regression, it cannot be ruled out that some OVB remains.
Furthermore, a log-linear form of the OLS model is chosen,
as this allows for easy interpretation of marginal effects and
it performs best in recovering marginal effects under model
misspeciﬁcation (Cropper et al., 1988), an issue that often
affects hedonic pricing models (Kagie and Wezel, 2007).
2.2 Site selection
Rotterdam and Gouda were selected as they both are known
subsidence-affected areas and sufﬁciently detailed subsi-
dence data was available. The city of Arnhem is used as a
control area to test the model prior to including the subsi-
dence variables. This city was chosen because of its good
comparability to Rotterdam and Gouda in terms of average
house values and it being much less affected by subsidence.
2.3 Model set-up
First, the model using data on Arnhem is used to test the
model’s performance without subsidence variables, so only
the control variables are included. This models looks like
The outcome of this model tells us if in an area without (or
with very little) subsidence the variables have the effects that
would be expected. i=1, . . . , ndenotes each housing trans-
action, Piis the selling price. The vector of control variables
is denoted by Xiand εirepresents the error term. The models
that we are interested in, for Gouda and Rotterdam, includes
the subsidence variables1:
The subsidence variables uniform (Sui), differential (Sdi)
and surrounding (Ssi) are included with annual thresholds
of 3, 1, 3 mm respectively. The thresholds are set at the lev-
els at which subsidence is expected to have a damaging ef-
fect. These levels are established based on expert knowledge
(Don Zandbergen2, personal communication, 16 May 2019).
Subsequently, neighborhood spatial ﬁxed effects are added.
This means that only houses within the same neighborhood
are compared. This means that relatively similar houses are
1This is the Gouda speciﬁcation, Sdiand Ssiare omitted for
2Geotechnical advisor at the municipality of Rotterdam.
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W. Willemsen et al.: The effect of land subsidence on real estate values 705
compared, but also takes away some of the variation as sub-
sidence rates within neighborhoods might be more similar
than between neighborhoods. This makes sense when look-
ing at a phenomenon which shows so much local variability
as subsidence. The regressions analyses are run for each city
separately. This is done to account for regional market differ-
To execute the hedonic pricing analysis, we used data on
the selling price of houses, housing characteristics, neighbor-
hood characteristics and the extent of subsidence at the build-
ing level. The subsidence data is based on InSAR technology
and hasan error margin of 1 mm (SkyGeo, 2019). This data is
not open source, but was kindly provided by the municipal-
ities of Rotterdam and Gouda. For Rotterdam only uniform
subsidence data was available. Micro-data on selling prices
and housing characteristics was provided by the Dutch asso-
ciation of real estate brokers (NVM). Neighborhood control
variables were obtained from Statistics Netherlands (CBS).
Transactions between 1985–2018, 2009–2015 and 2016–
2018 are used for Arnhem (N=40 890), Rotterdam (N=
23 116) and Gouda (N=3033) respectively. These are the
periods for which data was available.
This chapter discusses the results that were obtained from the
regression analyses. It is divided into four sections: one for
the results of each city and a subsequent sensitivity analysis
of the outcomes.
In the test model for Arnhem with just the control vari-
ables, most coefﬁcients show the sign that was expected.
The size of the house, the house being a monument, having
a garage and central heating all have a signiﬁcant positive
effect on the selling price. The year in which the sale took
place also positively inﬂuences the prices. This makes sense,
as property values in the Netherlands have shown a positive
trend over the past 30 years. This test analysis shows that the
model produces credible results.
Only the results obtained from the ﬁxed effects analysis
are presented and discussed, as these are deemed to be the
most trustworthy given the local variability of land subsi-
dence (as discussed in Sect. 2).
For Rotterdam, the model shows a signiﬁcant effect on prop-
erty values of around −7 %, which gives reason to believe
that uniform subsidence has a negative impact of uniform
subsidence on property values in Rotterdam.
In Gouda, the model shows negative effects for uniform sub-
sidence and differential subsidence. The effects are respec-
tively −6 % and −2 %. Surrounding subsidence does not
have a signiﬁcant effect. This means that uniform subsidence
shows the clearest negative effect on property values, while
differential subsidence shows a smaller, but still negative, ef-
3.3 Sensitivity analysis
Although the subsidence thresholds (minimum subsidence
rate) were carefully considered based on expert judgement,
we test the results for different threshold values. Thresholds
of 1, 2, 3 and 4 mm were tested for all subsidence variables.
In Rotterdam, the uniform subsidence coefﬁcient becomes
more negative with an increasing threshold, as to be ex-
pected: the more severe the subsidence, the larger the effect
on the property value. In Gouda, the same trend is observed
for uniform subsidence. Differential subsidence also shows a
more negative effect when the threshold is increased. How-
ever, the 4 mm was not signiﬁcant anymore. No trend was
found for the variable surrounding subsidence.
Using the threshold of 3 mm yr−1, the effect of uniform sub-
sidence on property values is negative and signiﬁcant. This
is in line with the hypothesis that was posed for this effect.
Uniform subsidence is found to have an effect of −7 % and
−6 % in Rotterdam and Gouda respectively.
We expected differential subsidence to have a larger ef-
fect than uniform subsidence, as it has the potential to cause
more structural damage. This is not reﬂected in the results
for Gouda. Compared to the uniform subsidence effect in the
ﬁxed effects model, the differential subsidence threshold has
a rather small effect of −2 % and is only slightly signiﬁcant.
A possible explanation for this might be the fact that there
is a myriad of foundation problems in gouda, of which uni-
form subsidence is a better overarching proxy than differ-
ential settlement. The results of surrounding subsidence do
not present a clear negative effect. The model shows an ef-
fect of zero. This points to the fact that house buyers do not
typically consider negative effects of surrounding subsidence
when buying a house – this is consistent with the relatively
low awareness and long-term impact of this effect.
The exact size of the effects found in this study should
not be emphasized too much, as the real impact could devi-
ate. The information which buyers have about subsidence is
limited and not everyone is likely to take subsidence into ac-
count. The negative and (mostly) signiﬁcant coefﬁcients do
seem to persist across most of the different cities and subsi-
dence types however, meaning that a negative effect of sub-
sidence as a whole is likely realistic.
proc-iahs.net/382/703/2020/ Proc. IAHS, 382, 703–707, 2020
706 W. Willemsen et al.: The effect of land subsidence on real estate values
The study results compare relatively well to the results of the
hedonic pricing analysis of Yoo and Perring (2017). The au-
thors ﬁnd a decreased value of −9.9 % for houses located in
a subsiding area. Compared to this, the (uniform subsidence)
results of this study are more conservative at −6 % to −7 %.
Other existing research is difﬁcult to compare to this study, as
both subsidence and house values are not measured at house
4.2 Study limitations and further research
Limitations of this study include the NVM sales data: it may
be the case that houses suffering from subsidence damage
are more difﬁcult to sell, and are therefore underrepresented
in the sample. Additionally, the selection of cities and study
periods potentially limits the generalizability of this study.
Future research with a larger database of settlement data and
sales data would substantiate and strengthen the results. Fur-
thermore, it would be interesting to see how effects will dif-
fer once information regarding subsidence damage (e.g. In-
SAR data) becomes more available. At present this data is of-
ten not freely accessible and home buyers not always aware
of subsidence issues. To compare: information disclosure on
wildﬁre risk in California led to a decrease in prices of those
houses at risk (Donovan et al., 2007).
This study analyzed the effect of land subsidence on prop-
erty values in Rotterdam and Gouda, the Netherlands, though
a hedonic pricing analysis. We tested for three subsidence-
related variables: uniform (proxy for overall subsidence of
building), differential (proxy for uneven subsidence of build-
ing) and surrounding subsidence (house is ﬁxed but adjacent
area subsides). All three variables were expected to nega-
tively affect property values, with the largest impact coming
from differential subsidence, as this leads to most structural
The ﬁxed effects models for both Rotterdam and Gouda
show a signiﬁcant negative effect of uniform subsidence on
property values of around −6 %. Differential subsidence is
found to have a slightly signiﬁcant negative effect of −2 %.
The results indicate that surrounding subsidence has no ef-
Based on the outcomes, the total effect of land subsidence
of property values is concluded to be negative. This study, is
the ﬁrst of its kind at this level of detail and sheds new light
on the economic impact of land subsidence on the housing
market. In a societal context, the results may be of interest
to policymakers and other actors related to the housing mar-
ket. Although difﬁcult to compare to damage estimates based
on restoration cost approach, this study provides further ev-
idence that land subsidence negatively affects the built envi-
Data availability. The datasets used for this article are unfortu-
nately not publicly available. For the dataset owned by the Dutch
Association of Real Estate Brokers (NVM), a non-disclosure agree-
ment had to be signed. The same applies to the InSAR data obtained
from the municipalities of Rotterdam and Gouda. This is sensitive
data showing settlement of individual houses, which, if made pub-
lic, could possibly inﬂuence future selling prices. Hence, the data
was only provided conditional on conﬁdentiality.
Author contributions. This article was written in collaboration
with SK and OK. Both contributors played a vital role. SK concep-
tualized the research topic, methodology, provided resources, su-
pervised the work and carried out editing and reviewing. OK con-
tributed to the formal analysis, methodology, project administration,
supervision, validation and carried out editing and reviewing.
Competing interests. The authors declare that they have no con-
ﬂict of interest.
Special issue statement. This article is part of the special is-
sue “TISOLS: the Tenth International Symposium On Land Sub-
sidence – living with subsidence”. It is a result of the Tenth Inter-
national Symposium on Land Subsidence, Delft, the Netherlands,
17–21 May 2021.
Acknowledgements. I am very grateful to Sien Kok for giving
me crucial feedback and the opportunity to write this article at
Deltares, as well as involving me in other projects from which I
have learned a lot. Furthermore, I would like to thank Onno Kuik.
Our progress meetings were very useful and contributed a great deal
to the end result. This article stands or falls on the data that is used.
Hence I am indebted to the Dutch association of real estate bro-
kers (NVM) and the municipalities of Rotterdam and Gouda for
kindly providing essential data.
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