Available via license: CC BY 4.0
Content may be subject to copyright.
Contents lists available at ScienceDirect
Environmental Science and Policy
journal homepage: www.elsevier.com/locate/envsci
Property price effects of green interventions in cities: A meta-analysis and
implications for gentrification
⋆
M. Bockarjova
a,
*, W.J.W. Botzen
a,b
, M.H. van Schie
c
, M.J. Koetse
b
a
Utrecht University School of Economics (U.S.E), Utrecht University, Utrecht, the Netherlands
b
Institute for Environmental Studies, Vrije Universiteit, Amsterdam, the Netherlands
c
PBL, Netherlands Environmental Assessment Agency, The Hague, the Netherlands
ARTICLE INFO
Keywords:
Cities
Ecosystem services
Hedonic pricing
Nature-based solutions
Benefit transfer
Property value
ABSTRACT
Although green interventions, like nature-based solutions, contribute to more sustainable urban environments
and provide ecosystem services to urban populations, some impacts are not well understood. This particularly
applies to social impacts in the domain of environmental justice, including (green) gentrification. Gentrification
refers to a process in which green urban renewal raises property prices, which results in an influx of affluent
people, displacing poorer residents. Our study conducts a meta-analysis based on 37 primary hedonic pricing
studies, to estimate value transfer functions that can assess the effects of nature types on property prices in
various urban settings. Urban nature has positive impacts on house value in the areas surrounding it, which
depend on population density, distance to, and the type of, urban nature. We illustrate how the estimated benefit
transfer function can be applied to natural interventions in a Dutch city, and visualize the obtained effects using
mapping. These maps show the distance decay of the cumulative effects of urban nature interventions on the
house value at the city and the neighbourhood levels. Our application estimated increases in local property
values up to a maximum of 20 % compared with properties not affected by the interventions, with value
equivalent of 62,650 USD, at average prevailing price level in a particular area in Utrecht. When new nature is
being planned in urban areas our mapping approach can be used for guiding assessments of potential undesirable
effects on property values that may lead to green gentrification, and for identifying where additional policies
may be needed to contribute to environmental justice.
1. Introduction
Population projections indicate that trends of increased urbaniza-
tion will continue (UN, 2018), which will increase pressure on the
urban environment. This highlights the importance of creating sus-
tainable urban living environments which are healthy, attractive and
resilient to climate change (Estrada et al., 2017;Gill et al., 2007), but
also to take an environmental justice and sustainable development
perspective to urban development. This calls for moving towards gen-
erating, improving and maintaining social, economic and environ-
mental justice by both scientists and practitioners.
A whole array of approaches is used to address urban sustainability,
which for example focus on ecosystem services, ecosystem-based
adaptation and mitigation, green and blue infrastructure, as well as
nature-based solutions
1
. An advantage of urban green interventions
such as nature-based solutions is that they often provide multiple co-
benefits (Raymond et al., 2017). For example, a park cools the city,
captures precipitation, limits air pollution, and contributes to biodi-
versity and recreation. Moreover, introducing nature to a city can make
the city aesthetically more attractive and increase social cohesion.
However, there is insufficient knowledge on the way that different
types of nature may affect other social domains such as gentrification.
The latter refers to a process in which green urban renewal through the
provision of ecosystem services creates added value on the property
https://doi.org/10.1016/j.envsci.2020.06.024
Received 6 September 2019; Received in revised form 26 June 2020; Accepted 29 June 2020
⋆
Paper prepared for the special issue “Advancing urban ecosystem service implementation and assessment considering different dimensions of environmental
justice”in the journal Environmental Science and Policy edited by: Francesc Baró, Nadja Kabisch, Johannes Langemeyer, Edyta Łaszkiewicz.
⁎
Corresponding author.
E-mail address: m.bockarjova@uu.nl (M. Bockarjova).
1
Nature-based solutions are interventions aimed as positive responses to societal challenges that involve the innovative application of knowledge about nature,
inspired and supported by nature. These interventions can have the potential to simultaneously meet environmental, social and economic objectives (European
Commission, 2015). Examples are urban parks, forests, rivers, and lakes and combinations of so-called grey and green infrastructure, like sustainable drainage
systems, green roofs and green walls.
Environmental Science and Policy 112 (2020) 293–304
1462-9011/ © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
T
market as the effects of being located close to nature are capitalized in
house value, which raises property and rental prices, and results in an
influx of affluent people, displacing original poorer residents
(Anguelovski et al., 2018). Hence, social injustices may be produced
through ecosystem services-based urban policies and planning (Lange-
meyer, this issue). This possibility of green gentrification needs to be
considered in urban planning since the most needy can be deprived of
ecosystem services and their benefits that urban nature offers
(Hochstenbach, 2017;Hochstenbach and Musterd, 2018). Gentrifica-
tion is often associated with the United States, but green gentrification
in particular is not a foreign concept in Europe or elsewhere, with many
prominent cities seeing the departure of low-income residents in areas
with improved green conditions (Harris, 2008;Gould and Lewis, 2016;
Cole et al., 2019;Hochstenbach, 2017;Anguelovski et al., 2018).
A disturbing development for urban planners and dwellers is that
gentrification is enhanced the past decades, with an increased percen-
tage of lower income neighborhoods being displaced in 2000s com-
pared to the 1990s (Maciag, 2015). Being caused by a variety of un-
derlying processes, gentrification has recently been associated with the
increased suburbanization of low-to-middle-income earners in Europe,
and increased concentration of high-income earners in urban areas
(Hochstenbach and Musterd, 2018). As an unintended consequence of
green urban interventions, green gentrification signals latent social
processes that urban planners need to acknowledge, monitor and
manage to ensure that benefits of urban nature and ecosystem services
provided by it can be universally promoted. Therefore, potential gen-
trification consequences of green renewal or other kinds of green in-
terventions in residential areas need to be carefully considered before,
during and after their introduction. Consideration should be given as to
how environmental managers might focus activity and investment to
balance environmental opportunities with the ongoing priorities of
delivering socially inclusive, ecologically rich and climate change-re-
silient green spaces (Bell et al., 2017).
The hedonic pricing method is often used to measure the effect of
environmental amenities on house prices. This method estimates the
direct use value of nature to property owners as is embedded in house
prices (for method beckground see Champ et al., 2003; for some recent
applications see Sohn et al., 2020;Czembrowski and Kronenberg, 2016;
Schläpfer et al., 2015). This analysis provides insights into the value
local property owners attach to ecosystem services provided by nature
in cities as is reflected in a willingness-to-pay as a mark-up for prop-
erties that are located close to urban nature sites. Insights into the
benefits of nature-based solutions as well as the potential gentrification
consequences of introducing nature to an area need to be considered by
policy makers and city planners (Lafortezza et al., 2018), for which a
better understanding of how house prices relate with different types of
urban nature is useful.
In this study we perform a meta-analysis with the following objec-
tives: to estimate relationships between property prices and different
types of urban nature; and to illustrate the applicability of the derived
value transfer function. Our study can provide first steps towards a
better understanding and future modelling of potential implications of
gentrification through house market dynamics, to limit adverse social
impacts from the environmental justice and sustainable development
perspectives on urban development. Our meta-analysis is based on 37
previously published hedonic pricing studies which estimated re-
lationships between urban nature and house prices for specific areas.
Even though conducting a primary hedonic pricing study in a particular
area can give more reliable estimates for this particular area, such a
study is data intensive and time consuming. In case a detailed site-
specific valuation study is not feasible, applying benefit transfer method
and value transfer functions in particular can serve as a useful alter-
native. Value transfer may also be useful for scaling up previous value
estimates from local/regional to national or even continental levels, for
example to estimate the impact of certain interventions at a European
scale. We claim that such an application of value transfer may have the
distinct advantage that transfer errors that occur at the local scales may
average out at larger scales, potentially improving the accuracy and
credibility of value transfer results. Further research on the validity of
this claim is warranted. Moreover, a meta-analysis can give insights
into overall patterns of results found in the literature of primary he-
donic pricing studies (Bateman and Jones, 2003), and enhance our
understanding of how different nature types, or green interventions,
influence property price developments.
Our study extends a previous meta-analysis of hedonic house price
relations with urban open space by Brander and Koetse (2011) in three
ways. First, our study extends the types of urban nature (forest, park,
green space, undeveloped land, and agricultural land) to also include
blue nature, such as lakes, rivers and canals for a more detailed ex-
amination of how house prices relate with different urban nature types.
Second, including more (recent) studies increases the statistical power
of the analyses. Moreover, this update increases the number of included
countries and regions, which allows estimating regional value transfer
functions. Third, we illustrate the application of the derived value
transfer function to actual nature-based solution projects from a re-
cently developed database called the Urban Nature Atlas (www.
naturvation.eu/atlas), and derive implications for gentrification.
Quantitative assessment tools are thus complemented in our approach
by visualised analytical tools, such as mapping, to plot price effects of
proximity to new green projects on the housing markets in cities. By
gaining a better understanding of the drivers of increased house prices,
the issues associated with gentrification can be acknowledged, better
monitored and addressed. In this way, this paper presents the quanti-
fication and assessment of ecosystem services and their impacts on
urban populations by urban nature through interdisciplinary methods
applied to understand distributive justice (Baró et al., 2020), with a
particular focus on property markets as signal of potential green gen-
trification.
The remainder of this paper is structured as follows. Section 2de-
scribes the database and statistical methods. Section 3presents the
results of the value functions and discusses how they compare with
previous studies. Section 4illustrates the applications of the derived
value transfer functions to nature-based solution projects from the
Urban Nature Atlas. Section 5concludes.
2. Data and methods
The data used for the meta-model estimation contains observations
that are value points obtained from primary hedonic pricing studies,
evaluated at various distances from urban nature, which results in
multiple value point observations per study. The meta-analysis pre-
sented in this paper builds on the earlier meta-analysis of primary he-
donic price studies conducted by Brander and Koetse (2011) that ana-
lyzed the effects of urban green space on property prices. For reasons of
consistency and comparability we have followed the same procedure
for literature search as Brander and Koetse (2007) and Brander and
Koetse (2011). In searching for studies specific key words were used
which included three main components: valuation method, location,
and the type of nature or ecosystem service. The resulting literature
search yielded a collection of papers that included 37 new hedonic
pricing studies that analyze the effect of proximity to both green and
blue open spaces on house prices in urban areas. These articles were
published between 2000 and 2017 and were not included in the ori-
ginal meta-analysis. Detailed description of the database is found in the
Supplementary Material S1.
The meta-analysis presented in this study uses a multi-level model,
in which value observations are at the first level and the primary study
is the second level (see Bateman and Jones, 2003;Schmidt and Hunter,
2004;Brander and Koetse, 2011). The idea behind this approach is that
there are characteristics in the context or in the methodological ap-
proach that determine whether observations are clustered at some le-
vels. This means that clustered observations reveal systematic patterns
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
294
between each other according to a specific characteristic, and not with
other observations in the sample. Observations included in the model
are weighted with the inverse square of sample size used in the primary
study to account for the quality of primary estimates.
The estimated model is:
=+ + + + + +XX XXyαβ β β β με
ij Sij
SMij
MTij
TDij
Djij
(1)
where
i
stands for the first level (observation) and takes values from 1
to 803, and
j
stands for the second level (primary study) and takes
values from 1 to 37. The dependent variable
y
i
j
is based on the esti-
mated coefficients extracted from the primary valuation studies and
transformed to our marginal effect size, i.e. the percentage change in
house price due to a decrease in distance to nature of 100 m. The error
term is split into two components,
μ
j
and
ε
ij
: the error term at the study
level and the error term at the observation level, respectively. Both
error terms are assumed to have zero mean and to be uncorrelated,
meaning that they have constant variances σ
μ
and σ
ε
. Model specifi-
cation with regions at the second level has been estimated, but per-
formed worse in terms of model fit, and is therefore not presented here.
Regional dummies for Asia, Europe and North America were also tested
in order to control for the level of regional effect differences, but proved
to be statistically insignificant, and were thus not included in the final
model. In the case of the primary study at the second level, it can be
expected that value observations that come from the same study might
be closer to each other than values from other studies due to some in-
trinsic unobserved determinants which cannot be captured by the in-
dependent variables included in the model, such as for example similar
data sets or preference for a particular research method, specific to each
primary study.
The independent variables are separated into four groups. The
vectors
βS
,
βM
,
βT
and
β
D
, contain the estimated model coefficients for
the variables included in
X
ij
S
,Xij
M
,Xij
Tand
X
ij
D
, respectively.
2
The vector
X
ij
S
includes study and location variables, such as estimation point, as
well as GDP per capita and urban population density. We have used
GDP per capita instead of for example purchasing power per capita as it
is consistent with the common practice and the previously conducted
meta-analysis on the effect of urban green space on house prices
(Brander and Koetse, 2011). It is also a common metric to correct for
price level in the estimated meta-model, where average effect values
from individual primary studies are analyzed. The vector Xij
M
includes
methodological variables, such a distance over which the effect was
obtained in a primary study, and model specification. The vector Xij
T
contains variables that identify the type of urban nature or biome. The
vector
X
ij
D
contains the dummy representing landscape diversity of
urban nature. This is a new variable that is added to this meta-model,
which measures whether an urban nature site contains two or more
natural landscapes, such as green parkscape and waterscape. This
variable will capture the additional green premium, or price mark-up,
that is attributed to the landscape diversity of urban nature
(Łaszkiewicz et al., 2019).
Note, that in the way multiscape is defined in this study, it does not
reflect the quality level (including maintenance, aesthetic quality, etc.)
of urban green and blue areas, their quantity or abundance of other
green and blue areas in the vicinity of valued nature.
For continuous independent variables such as evaluation point,
distance, income and population density, the centered logarithm is
introduced. The reason is that it allows us to interpret the constant as
the capitalized value of urban nature in property prices for the re-
ference urban nature type category and at the average values of in-
dependent variables, i.e. the effect at the average evaluation point and
over the average distance, for a location with average income and
density levels. Table 1 gives the coding of the variables and their means
for the overall sample, as well as for subsamples for Europe and North
America. We note here that the difference between the variables
“evaluation point”and “distance”lies in the difference between spatial
and methodological variables. In particular, “evaluation point”reflects
the average distance at which the valuation was performed in a primary
study, and is thus a spatial variable. At the same time, “distance”re-
flects the average distance over which the valuation was performed in a
primary study, is connected to the scale of effect measurement, and is
thus a methodological variable.
We estimate Eq. (1) for subsamples for Europe and North America in
order to identify different patterns that are specific to each of the value
functions of how urban nature affects property prices in these two re-
gions. Such effect differences may arise due to various factors governing
local preference for urban nature, for example its socio-cultural aspects
such as favoring specific types of urban nature, general abundance of
nature in and surrounding the cities, and income level. These aspects
may influence both the level of willingness to pay for urban nature via
property prices, and the relative significance of various factors con-
tributing to the value of urban property.
3. Results of the hedonic pricing meta-analysis
In this section we describe the results of the hedonic pricing meta-
analyses (Table 2). Model 1 is estimated for the whole sample and in-
cludes primary hedonic pricing studies from all over the globe, Model 2
is based on European studies, and Model 3 is based on North American
(essentially, U.S.) studies. Our dependent variable is a relative in-
dicator, meaning that in absolute monetary terms the value of nature or
its characteristics may differ dependent on prevailing house prices.
Model 1 is estimated based on 803 value observations from 37
primary studies, Model 2 based on 159 value observations from 8 pri-
mary studies, and Model 3 on 562 value observations from 25 primary
studies. Variances at both levels are statistically significantly greater
than zero for all three models, which means that clustering of errors at
the primary study level contributes significantly to the explanation of
total variance. Due to its hierarchical structure, a multi-level model
does not have a straightforward absolute fit indicator except for the log-
likelihood statistic. In this case the models cannot be compared in terms
of model fit because they are estimated on different sets of data.
The constant in the three models measures the percentage change in
house price due to moving 100 m closer to the peri-urban nature (re-
ference group of urban nature type), when the effect is measured at
average or reference values of independent variables. That is, in Model
1 the constant measures the effect of a house price change of 0.220 %
due to moving 100 m closer to peri-urban nature without a multiscape
feature, at a distance of 162 m (average evaluation point), measuring
the change over a distance of 154 m (average distance), at an average
GDP per capita (USD 43,011), for an average population density (810
inhabitants per km2), using a non-linear functional form. Respective
average values of independent variables for the interpretation of models
2 and 3 can be found in Table 1.
Among the location independent variables, evaluation point is ne-
gative and statistically significant in all models 1–3, meaning that
houses at locations further from urban nature collect lower green pre-
mium compared to houses located closer to nature, and indicating de-
creasing returns to distance as the distance from urban nature increases.
Coefficients of income per capita are both negative (model 1) and po-
sitive (models 2 and 3) but in all cases statistically insignificant against
expectations and earlier findings on the positive and statistically sig-
nificant relation between GDP per capita and WTP for urban nature
2
Property prices are likely to be influenced by other variables, like the
availability of public transport, which could not be included in our analysis
because they were lacking in the primary valuation studies. Omitted variables
would only bias our main coefficients of interest, which are the nature type
variables, in case they significantly influence the relation between nature and
property prices. For instance, this would occur if the availability of public
transport would change the relationship between property prices and the pre-
sence of nature. There is no reason to expect that this is the case systematically,
so we argue that our estimates are robust to these omitted variables.
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
295
(Brander and Koetse, 2011). The coefficient of the population density is
positive, which is in line with expectations from previous studies, but is
not statistically different from zero in either of the three estimated
models. Current findings thus indicate independence of the relative
weight of the green premium relative to distance in the composition of
property price throughout various levels of income and urban density.
A linear model specification in a primary hedonic pricing study
(methodological variable) significantly adds to the average level of the
effect of urban nature on property prices in models 1 and 3, and acts as
a methodological control variable. Distance over which the effect is
estimated is not statistically different from zero at conventional sig-
nificance levels in either of the models. This variable acts as a metho-
dological control variable of decreasing returns to distance as the
measurement range increases in primary studies.
All models in Table 2 feature a specification that includes 4 dum-
mies for urban biomes, or types of urban nature, such as urban forest,
park, other green urban spaces, blue urban nature, in addition to peri-
urban nature (reference category). These variables signify the different
ways that types of urban green space are capitalized in house prices.
Globally (model 1), urban parks (0.78 %) and blue nature (0.57 %) add
statistically significantly to the value of urban property compared to
peri-urban nature, resulting in total in 1.00 % higher property values
for a park and 0.79 % for blue nature, ceteris paribus. The multiscape
dummy in model 1 is a proxy for the diversity of urban nature, which
increases house prices by an additional 0.5 %.
The European model reveals statistically significant positive coeffi-
cients for urban forest, urban park and blue nature, resulting in the
expected increase in the house price due to moving 100 m closer to an
urban forest or park in a European city of 1.82 %, 2.06 % and 0.47 %,
respectively. The multiscape variable was excluded from the model for
the European sub-sample to avoid multicollinearity. The North
American model reveals statistically significant and positive coefficients
only for urban blue nature, which compared to peri-urban nature re-
sults in the effect size of 0.59 %. The multiscape dummy in model 3 has
a positive coefficient (p-value < 0.063), again suggesting positive re-
turns to diversity of urban nature.
We observe differences in the effects of different types of urban
nature on house prices in North America and Europe; urban blue nature
is appreciated on both continents, while urban forest and parks are
particularly appreciated in Europe. We note that the American and the
European sub-samples differ; the population density and urban income
per capita have higher average levels in American cities compared to
European ones (Table 1), which would be expected to result in stronger
effects of the presence of nature on house prices through scarcity and
income mechanisms. However, our models show no statistically sig-
nificant association between urban density respectively income, and the
effect of proximity to urban nature on house prices. This suggests that
observed differences in estimated effects could be attributed to differ-
ences in preferences for nature between American and European urban
residents.
4. Value function application for the case of Utrecht: European
Urban Nature Atlas and implications for gentrification
In this section we illustrate the application of our value transfer
functions. These functions can be used to estimate the influence of
planned nature in cities on property prices. This way city planners can
obtain insights into impacts of nature development projects on the
prices and affordability of properties. Although our analysis does not
capture rental prices directly, it can be expected that increased property
Table 1
Variable description and sample statistics (st.dev. in parentheses).
Variable Description Overal sample
mean
Europe-only
mean
North America-only
mean
N observations 803 159 562
Dependent variable
% change % change in property price due to 100 m decrease in distance to urban nature 0.924
(1.857)
1.497
(2.317)
0.823
(1.74)
minimum ; maximum −7.251; 13.550 −7.251; 13.118 −5.960; 13.55
Study and location variables:
Asia 1= primary study conducted in Asia, 0=otherwise 0.022 0 0
Europe 1= primary study conducted in Europe, 0=otherwise 0.198 1 0
North America 1= primary study conducted in North America, 0=otherwise 0.700 0 1
Evaluation point* Distance at which primary measurement took place 162 178 157
minimum ; maximum 1; 7116 1; 7116 1; 2912
GDP * GDP per capita in 2016 US dollars 43,012 42,321 45,958
minimum ; maximum 4,235; 55,747 13,145; 55,747 25,837; 52,983
Population density * Population density in number of people per km
2
810 480 1006
minimum ; maximum 23; 6014 23; 4371 138; 6014
Methodological variables
Distance* Distance over which the effect was valued in primary study 154 143 168
minimum ; maximum 0.30; 2400 1; 1000 0.30; 2400
Linear 1= linear functional form used in primary study, 0=otherwise 0.087 0.321 0.034
Double-log 1= double-log functional form used in primary study, 0= otherwise 0.621 0.302 0.696
Semi-log 1= semi-log functional form used in primary study, 0= otherwise 0.267 0.377 0.235
Box-Cox 1= box-cox functional form used in primary study, 0= otherwise 0.025 0 0.036
Type of urban nature / biome:
Forest 1=effect of an urban forest is captured in primary study, 0=otherwise 0.136 0.252 0.101
Park 1= effect of an urban park is captured in primary study, 0=otherwise 0.328 0.289 0.329
Other urban green space 1= effect of other open green space is captured in primary study (such as
neighbourhood green spaces, pocket parks, green corridors), 0=otherwise
0.146 0.195 0.125
Blue 1= effect of urban blue nature is captured in primary study (such as lake, ponds,
rivers, streams, canals, urban sea coasts, wetland), 0=otherwise
0.255 0.075 0.326
Peri-urban 1= effect of peri-urban nature bordering on urban areas is captured in primary study
(such as undeveloped land, agricultural land, golf course), 0=otherwise
0.136 0.189 0.119
Lansdcape diversity
Multiscape 1=studied nature in the primary study resembles multiple landscape types (such as
green and blue), 0=otherwise
0.412 0.572 0.331
Note: * We do not report the standard deviation as these are average values at estimation, i.e. average ln(x).
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
296
values are in the end passed on to renters in the form of higher renter
prices, although this depends on local conditions, such as price reg-
ulations or quotas in rental markets. Price increases as a result of the
development of nature in cities may under certain circumstances result
in gentrification, if lower-income households cannot afford the high
prices and are replaced over time by new residents with higher in-
comes. Our value transfer functions can be used to raise awareness of
this issue and serve as a first quick scan by showing whether high
property price increases can be expected in an area as a result of
planned nature development, which may trigger a gentrification pro-
cess. However, it should be realized that the occurrence of gentrifica-
tion is a complex process that, among others, depend on the socio-
economic conditions in an area, how the development process is con-
ducted, and what other policies are in place in a city that may limit
gentrification processes, like social housing. If the quick scan applica-
tion of the value transfer function shows the potential of substantial
property price increases, then additional examination should be con-
ducted to explore whether indeed gentrification will be problematic
and how such adverse effects can be limited. Moreover, apart from
gentrification, our value functions show the value of urban green and
blue through house prices, irrespective of the owner and use of the
house.
4.1. Value transfer function
To illustrate the application of our estimated value transfer func-
tion, we have made use of the Urban Nature Atlas (the NATURVATION
project: www.naturvation.eu/atlas). It includes an extensive, albeit not
exhaustive, selection of green urban initiatives and interventions across
Europe that have been completed, are being planned or are in im-
plementation in varying urban conditions, with wide differences in
socioeconomic, ecological and geographic circumstances. The Urban
Nature Atlas includes intervention-specific information, such as the
type of urban nature and landscape, ecosystem services provided by it,
the type of intervention and institutional context, urban sustainability
challenges, budget amount, etc. (Almassy et al., 2017). We illustrate the
application of our estimated value transfer functions for a Dutch city,
Utrecht. with 10 green urban initiatives (described in the Online Sup-
plementary Material S2), although the value functions are more broadly
applicable to other cities since they are based on global primary va-
luation studies. Utrecht was chosen as an illustrative example since it
experiences environmental pressures as well as ongoing population
growth due to its central position in the country and rich cultural
heritage which attracts new inhabitants. Moreover, affordability of
housing for low-income households and gentrification is a concern of
city planners in Utrecht (Utrecht Municipality, 2019).
For the application, the global value transfer function based on es-
timated meta-regression model 1 is reported below (Eq. (2), results
from Table 2). LN stands for a natural logarithm, D stands for a dummy
variable that takes a value of 1 if true, and 0 otherwise. We also recall
that all continuous explanatory variables are centered logarithms of
respective average values of independent variables as found in Table 1.
=−
−
+−
+
−+
+
++
+
% change in house price due to 100m decrease in distance to nature
(GLOBAL) 0.220 – 0.549*(LN(Evaluation point) LN (162))–
0.204*(LN(GDPpc ) LN(43.011))
0.170*(LN(Po pulation density) LN(810) )
0.683*D(Linear mo del)– 0.031*(LN( Distance)
LN(154) ) 0.526*D (Forest)
0.781*D(Urb an park)
0.504*D(Other urb an green) 0.572*D(Blue)
0.500*D(Multiscape)
lnc
(2)
Table 3 presents relevant variables for applying the value transfer
function to nature interventions in Utrecht. Inner-city variability in
income can be quite substantial. In this application we have used city
average values for population density and per capita income, conform
Table 2
Hedonic pricing meta-regression estimation results. The dependent variable is
the % change in house price due to 100 m decrease in distance to nature
(coefficients are reported with their t-statstics in parentheses).
MODEL 1 MODEL 2 MODEL 3
Whole sample
(global)
Europe only North America
only
Constant 0.220 0.127 0.241
(0.75) (0.16) (0.88)
Location variables
Evaluation point (ln) −0.549*** −0.263** −0.581***
(-4.28) (-2.03) (-3.04)
GDP per capita (ln) −0.204 0.898 0.230
(-0.42) (0.58) (0.25)
Population density (ln) 0.170 0.096 0.232
(1.03) (0.20) (1.24)
Methodological variables
Linear functional form 0.683*** 1.585 0.761***
(2.72) (1.57) (2.64)
Distance (ln) −0.031 −0.698* −0.024
(-0.47) (-1.82) (-0.39)
Type of urban nature
(reference group: peri-urban nature)
Forest 0.526 1.821** 0.373
(0.93) (2.50) (1.18)
Park 0.781*** 2.056*** 0.548
(3.11) (5.67) (1.49)
Other urban green space 0.504 −0.035 0.739*
(1.35) (-0.05) (1.66)
Blue nature 0.572** 0.471** 0.591**
(2.25) (2.48) (2.35)
Lansdcape diversity
Multiscape 0.500** 0.680*
(1.97) (1.86)
Random variables
Estimate variance
(study)
1.096** 3.719** 0.882**
(0.35) (2.84) (0.57)
Residual variance 2.046** 2.757** 1.776**
(0.46) (0.61) (0.59)
Estimation statistics
−2LL −1467 −318 −986
AIC 2960 660 1992
VPC 0.349 0.574 0.332
N observations 803 159 562
N studies 37 8 25
*. **. *** indicate statistical significance at the 10 %. 5 %. and 1 % level.
respectively.
Table 3
Key explanatory variables for the city of Utrecht.
Utrecht. Netherlands
Average GDP per capita (USD)
a
53.558
Average house price (2017, USD)
a,b
341.588
Average value of housing property (2017, USD)
a,c
260.850
Average population density (pers/km
2
) 3658
Range of the evaluation point (m) 100−4000
a
Assumed prevailing exchange rate of 1 EUR = 1.11 USD.
b
Average house price is determined on the basis of all sold housing prop-
erties in a given year, within a municipality. Source: Statistics Netherlands
(CBS, https://opendata.cbs.nl/statline/#/CBS/nl/dataset/83625NED/table?
ts=1567154700379).
c
Average value of house price (WOZ. in Dutch) is the value that is de-
termined by each municipality for all housing property within its adminis-
trative borders for the purposes of taxation. It is based primarily on the market
value of housing property (Waarderingskamer, 2010,2017). Source: Statistics
Netherlands (CBS. https://opendata.cbs.nl/statline/#/CBS/nl/dataset/37610/
table?fromstatweb).
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
297
Fig. 1. City-level application: estimated extent of spatial impacts of multiple nature-based interventions on house value in Utrecht using model 1 (global value
transfer). Panel A: maximum effect for the overlapping values; panel B: the sum of effects for the overlapping values.
*Average housing values at the neighbourhood level in Utrecht for the base level of 2017, (CBS, 2017).
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
298
the derivation of the value function in the meta-function. Information
on the value of housing property on the neighborhood level was ob-
tained from the Statistics Netherlands (Table 3).
4.2. Effect visualisation
For visualization purposes, we have mapped the data on the esti-
mated effects of the selected interventions on the housing market. To
our knowledge, this is a first use of mapping to such application where
socio-economic impacts are related to the location of urban nature.
Earlier uses of mapping in relation to urban green included a variety of
health effects of urban green and green interventions (Reid et al., 2009;
Dadvand et al., 2012;Norton et al., 2015;Flacke et al., 2016), mental
health (Gascon et al., 2015), physical activity (Lwin and Murayama,
2011;Brown et al., 2014), social need (McPhearson et al., 2013), crime
and violence (Gorham et al., 2009;Wolfe and Mennis, 2012). By
combining our classical representation of results, as shown in previous
tables, with a visual application on a map, we can make a more in-
tegrated representation of the application results, at a city level. Such
visualisation to our knowledge has not been done in previous meta-
analysis applications. The background layer of the map on Figs. 1 and 3
contains different shades of grey on building footprints to provide a
better visualization of urban fabric, and the average values of housing
property at the neighbourhood level in Utrecht for the base level of
2017, which are readily available from the Statistical Office (CBS,
2017). Cumulative effects (as explained below in this section) are dis-
played as aggregates on a a per-pixel basis, leading to a spatially con-
tinuous representation. Highways act as additional orientation land-
marks in the city.
It is possible that nature interventions in urban areas appear close to
each other, and that their effects can therefore overlap. If this occurs,
assessments of an individual impact of each intervention may not be the
best way of understanding, presenting and analysing its impacts as they
are not able to show those overlapping impacts of nature on a neigh-
bourhood or a city level. First, we shall describe and interpret in-
dividual effects (marginal and cumulative) of a specific intervention on
the house value, which will be followed by a reflection on the value of
overlapping effects of multiple interventions on the house value on the
neighbourhood level.
Marginal effects resulting from the application of the global value
transfer (model 3) should be interpreted in the same way as the results
of the estimated meta-models in Section 3. For example, for interven-
tion 6 (Maxima Park), houses that are found 300 m away from the park
would expectedly increase in value by 1.41 % if they would be found
200 m away from the park (see Table S3, column 3 in the Supplemen-
tary Material). However, marginal effect representation on a map is not
straightforward as it does not reflect the total effect of an intervention
on property value at a specificlocation, which is often used for esti-
mations of welfare changes. Alternatively, in such cases cumulative
effect is able to reflect such a total effect as it sums marginal effects
throughout the range at which a positive marginal effect of urban
nature is estimated.
The maps on Fig. 1 thus portray cumulative effects of urban nature
interventions on property values for 10 interventions in the city of
Utrecht, which included urban parks, small green patch parks and blue
nature (canal renovation and sustainable drainage system). We notice
that different interventions have expectedly different effects on the
house value, in particular in terms of the range at which this effect is
positive, as well as the size of the effect. For example, for urban parks
like interventions 6 and 8, the positive effect stretches up to 3900 m
from the intervention and reaches a maximum cumulative effect of 20
% for properties found directly at the park compared to those found at
3900 m distance. This is essentially the effect of the park on the prop-
erty value compared to the property where this effect is not positive, or
is not present anymore. For urban blue nature like interventions 1 and
2, the positive effect stretches up to 1000 m with the maximum cu-
mulative effect of 4.77 %, and for other urban green nature with a
multiscape like intervention 4, the positive effect stretches up to
2300 m with the maximum cumulative effect of 11.64 %. Fig. 2 re-
sembles the distance decay functions estimated for Utrecht interven-
tions, by type of intervention. Besides, Supplementary material contains
Table S3 where application results for the interventions 3 through 6 are
presented and include both marginal and cumulative estimated effects.
The resulting differences in house value depend on the prevailing
house value in the area, which differ throughout the city. For the case of
the Maxima Park (intervention 6), the maximum cumulative effect of
20.05 % would translate into the expected difference in value of 62,656
USD for the average house value of 312,400 USD in the area. At the
same time, for the City Island Ring Park (intervention 4), the maximum
cumulative effect of 11.64 % would translate into the expected
Fig. 2. Distance decay functions for the 10 urban nature interventions in the city of Utrecht. plotting the estimated cumulative effect of urban nature-based solutions
(nbs) on house value using model 1 (global value transfer), with the marked 1 percent threshold value.
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
299
Fig. 3. Case-specific application: estimated effects of the City Island park ring (4) and the Maxima Park (6) with their respective shapes, plotting the cumulative effect
of urban green on house value using model 1 (global value transfer). Panel A: maximum effect for the overlapping values; panel B: the sum of effects for the
overlapping values.
*Average housing values at the neighbourhood level in Utrecht for the base level of 2017, (CBS, 2017).
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
300
difference in value of 22,535 USD for the average house value of
193,600 USD in the area. For a green neighborhood square (interven-
tion 3), the maximum cumulative effect of 4.11 % would translate into
the expected difference in value of 8736 USD for the average house
value of 212,300 USD in the area.
As we pointed above, the effect ranges and effect sizes differ for
various green and blue interventions as we have applied them to the 10
cases in the city of Utrecht. Fig. 2 shows the distance decay functions
for all these cases, marking the maximum cumulative effects and the
spread of their respective ranges. We note that due to the functional
form of the global value transfer function, some depicted distance decay
functions have a long tail when approaching zero. For example, the
estimated positive cumulative effect of interventions 6 and 8 (urban
parks) stretches as far as 3900 m, which may seem unrealistic. Marking
the 1 % threshold for the cumulative effect, below which the effect on
the house value may essentially be seen as negligible, may provide a so-
called effective range of cumulative effects. In such a case, the effective
range of the cumulative effect for urban green and blue interventions
drops to 2900 m for the urban parks (nr. 6 and 8), and to 500 m for
urban blue nature (nr. 1 and 2). Such an approach to interpretation of
value transfer function application results may offer more realistic in-
dications of actual value changes at the city level.
However, as Fig. 1 shows, the cumulative effects of each particular
intervention overlap in many instances, even for the selection of 10
interventions which are taken as illustrations in this particular case,
with other interventions in a city. This means that the overall resulting
effect on the house value may be even higher than the effect of a single
intervention. Besides, some of the interventions cover a bigger area,
which means their point representation on a map is not realistic. To
explore this, we have zoomed into the intervention 4, the City Island
Ring park, where a continuous park ring is being created along the
canals surrounding the City Island of Utrecht. Fig. 3 shows the shape of
the City Island and its park (in green), and its effect sizes throughout the
surrounding area. In this particular case, the effects stretch both within
the City Island, and outside the City Island. We have also depicted in-
terventions 3 and 5 to illustrate the overlap of a major intervention 4
with minor interventions at a neighborhood scale. For example, houses
at the third ring of intervention 4 (the City Island Ring park), within the
City Island would expectedly increase in value by 8.54 %. However, the
presence of intervention 5 (Food for Good garden) may add extra value
to those houses as they overlap with the first ring of intervention 5.
Adding both overlapping effects together at this location would result in
the expected increase in property value of 12.66 %, or 25,153 USD for
the average house value in the area (Fig. 3, panel B). Another example
of overlapping effects at this local scale is the cumulative effect of the
major urban park in Utrecht, Maxima Park (intervention 6), which we
have also depicted with its actual shape. In this way, the non-trivial
effects of the Maxima Park overlap with the non-trivial effects of the
City Island Ring Park, which was not evident at the city level (Fig. 1A,
the two interventions do not seem to overlap). As Fig. 3B shows, there is
an extensive area where the cumulative effects of interventions 4 and 6
overlap in the non-trivial range. For example, in the area where the
yellow effect rings of both interventions overlap, the properties located
in this area may experience a double effect on their value, adding up to
10–11 % in total. It is important to notice here that theory does not
provide a rule of thumb concerning value transfer function applications
and overlapping effects of multiple urban green and blue areas as in the
cases discussed above. Essentially, it is natural to assume that vicinity of
multiple urban nature areas would add more to the property value in a
particular location than vicinity of a single piece of nature. However
simple addition of individual cumulative effects of multiple interven-
tions should be overestimating the true effect, due to the decreasing
returns to scale and, thus, not only due to the distance decay, but also
due to the amenity satiation effect. We further discuss this point in
Section 5. We thus suggest that the highest of the overlapping effects
would serve as a lower bound for the total effect estimation (Figs. 1 and
3, panel A), while addition of all overlapping effects would serve as its
upper bound (Figs. 1 and 3, panel B). For the same example, the effect
of the City Island Ring park (effect ring 6 of intervention 4 in Supple-
mentary material S3) in the Hoge Weide area would alone correspond
to the expected increase of house value of 5.58 %, or 14,500 USD; the
additional effect of the Maxima Park (effect ring 15 of intervention 6,
idem) would add extra 5.67 % to the expected increase of house value
and total 33,380 USD, at average prevailing value in the area of
289,300 USD per property.
5. Discussion
5.1. Value transfer function and its limitations
In this section we address main limitations associated with the re-
sults of our estimated value transfer function and its application. In
general, a meta-analysis can only take into account variables that are
common to all primary valuation studies that are included in the ana-
lysis. As such, a first possible limitation of our study is that is that the
set of explanatory variables included in our model is restricted. For
example, relevant excluded variables could include additional con-
textual or spatial variables, such as area sizes of studied nature areas or
availability of other nature areas that may be used as substitutes for the
nature areas under study. These variables were not available for all
primary studies in our sample and can therefore not be included in the
presented meta-models. Systematic inclusion of such additional in-
formation in future primary valuation studies should not only enrich
the primary hedonic pricing studies, but will strengthen the evidence
base for meta-analyses and thus improve the benefit transfer applica-
tions by better informing practitioners and policy-makers on the ex-
pected effect of green policies in urban areas.
Another limitation of hedonic pricing studies is the lack of in-
formation from transactions data on specific ecosystem services that
homeowners are willing to pay for when they pay a higher price for a
property that is closely located to nature. This means that without ad-
ditional data from each primary study it is not possible to explicitly
pinpoint and hence control for ecosystem services in our analysis. We
added Supplementary Tables S4.1 and S4.2 which show the distribution
of ecosystem services that were mentioned in primary valuation studies,
as assumedly being related to the type of nature examined in that study.
These tables show that a broad range of ecosystem services were as-
sociated with the specified nature types including: (1) provisioning
services, like raw materials from forests, (2) regulating services, like
flood and climate regulation from blue nature, and (3) habitat services,
like biodiversity from parks. The five most frequently reported services
are: recreation, aesthetic appreciation, noise reduction, flood regulation
and water purification.
Although our case study in Utrecht is merely meant as an illustra-
tion to demonstrate general applicability of the method, it also reveals a
trade-offthat this method poses in terms of scale. While a city level
application offers a quick scan of effects of multiple nature interven-
tions, an application at a local level may offer more precise estimates
especially if the actual shape of interventions can be depicted. Here, the
practitioner has to realise that obtained effect estimates are originating
from averaged affects and studies collected all over the world, without
specific reference to urban morphology or the housing market.
Therefore, it is important to stress that our approach can be used for
applications in other EU cities, and more widely for assessing the values
of urban green and blue at national or even continental scales. We
argue that such scaling up may have the added advantage that transfer
errors that occur at local scales may average out at larger scales, which
would improve the accuracy and credibility of value transfer results. As
far as we could find, empirical work on this particular issue is lacking,
and future studies are needed to look into the validity of this claim.
Our estimates of the (dollar) value changes may be considered quite
conservative. This is due to the fact that while the indicator of average
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
301
property value is related to house sales prices, it lags behind the on-
going developments in the housing market which may bring about
greater changes in the spot sales prices. When new nature is being
planned in urban areas our mapping approach can be used for assessing
potential undesirable property price effects that may lead to green
gentrification, and for identifying where additional policies may be
needed to limit such effects, like social housing.
Simple addition of multiple overlapping effects may lead to over- or
underestimation of values. As table S1 reveals, 18 out of 37 primary
studies used in our meta-analysis included multiple nature types in the
vicinity of housing properties, all of which were assumed to simulta-
neously influence the purchasing price. This means that if primary
studies have modelled the availability of other natural areas in the
study site, they should have identified unique effects of each of those
multiple types of nature on the house price. This implies that effect sizes
of home properties obtained in the meta-analysis can be attributed to a
particular type of urban nature, independent of the presence of other
types of urban nature in the vicinity of property of interest. Because we
have not explicitly modelled substitution in our meta-analysis due to
data restrictions, value transfer should lead to value underestimation
(overestimation) when the policy site has a lower (higher) availability
of substitutes compared to the average availability of substitutes of
study sites in our meta-analysis sample. More empirical work on the
effects of substitute availability is needed, especially in the context of
meta-analysis and value transfer.
Finally, our example serves purely illustrative purposes, and appli-
cations for other locations can be made depending on valuation needs
and data availability. While value transfer based on the European and
the North American functions (models 2 and 3, respectively) can be
derived in a similar way, we note that the European function based on
model 2 should be treated with particular caution for benefit transfer
purposes as it appears to overestimate the effects of most types of
nature on house prices, judging by the fact that the value function
suggests that value effects of urban nature spread over unrealistically
long distances. In that respect the predictive validity of the global value
transfer function is substantially higher. Therefore, for all applications,
the global value transfer (model 1) is suggested as the preferred func-
tion at the moment.
5.2. Using the value transfer functions to identify potential gentrification
issues
Our application of the value transfer functions illustrates how dif-
ferent green interventions have a variety of effects on the housing
market, and under which circumstances large effects on house value
can occur. Neighbourhoods in the vicinity of multiple green interven-
tions may experience higher percentage increases in house value due to
the cumulative effects. Clearly, the absolute amount of house value
changes depends on local conditions, such as prevailing house value in
the area, which may vary substantially within a city. Our mapping
analysis can be used to visualise in which areas high percentage in-
creases in house value can be expected due to the creation of additional
nature in cities, and show whether this occurs in areas where house
value are already high. This information can be used to visualise the
quantified information on the expected effects on the housing market,
and serve as a quick scan for identifying areas where green gentrifica-
tion may occur (Hochstenbach. 2018), although gentrification is a
complex process for which additional examination would be needed to
determine its severity and possible solutions. For instance, in some
neighbourhoods the development of nature can increase value of
housing where the average house value is already high. In such a
neighbourhood, a gentrification process can already be ongoing and
improved green infrastructure may further accelerate it. As in the ex-
ample above of Hoge Weide area above, a yearly gross income of ap-
proximately 71,700 USD, which is about twice modal income in the
Netherlands, would be necessary to be able to obtain a mortgage for
financing a purchase of a 322,680 USD house. Moreover, rental prices
and house prices are connected to the value of house property. This
implies that increasing house prices are reflected in higher rental prices
that may become unaffordable for lower-income households, in parti-
cular for younger adults who often have to rely on the financial support
from their parents even after moving out of their parents’home
(Hochstenbach, 2018;Amsterdam Municipality, 2019).
It should be noted that the development of urban nature is often not
the main cause of problems with affordability of housing for low-in-
come households (Amsterdam Municipality, 2019), but the effects of
nature on property value and property prices can exacerbate such
problems. This does not mean that additional nature should not be
developed, since it brings various benefits that are clearly valued by
people as is reflected in their willingness to pay higher prices for
property close to nature. It does mean that possibly undesirable effects,
like green gentrification, should be assessed when new nature is being
developed in urban areas, and additional policies may be needed to
limit such effects. Mapping approach such as multi-layering of data can
be even more helpful for policy and practice offering an integrated
approach to the solution of urban challenges. For example, combining
city maps of nature intervention impact on the house value with ad-
ditional information, such as physical and mental health status of city
residents or prevailing environmental conditions (Flacke et al., 2016;
Gascon et al., 2015) as well as crime and violence levels (Gorham et al.,
2009;Wolfe and Mennis, 2012) may better guide city planners on the
decisions concerning the best areas for green interventions as well as
signal problematic areas for potential green gentrification. Possible
solutions could be the development of social housing in existing green
neighbourhoods in order to promote social and environmental inclusion
and keep neighbourhoods accessible for low-income families. More-
over, a more even provision of green spaces throughout the city would
ensure equal benefits for all inhabitants.
6. Conclusion
Ecosystem services provided by nature interventions, such as
nature-based solutions, present an opportunity for cities to offset pro-
blems from increased urbanization and climatic changes. By combining
natural characteristics with grey infrastructure. urban nature creates
benefits for citizens and visitors, and results in a more aesthetically
pleasing city. However, the economic and social impacts of natural
interventions remain insufficiently considered in urban ecosystem ser-
vices assessments (Marshall and Gonzalez-Meler, 2016). Nature-based
solutions often impact the housing market with rising house and rental
prices. As a result, and most often unintentionally, lower income po-
pulations become displaced by better-offinhabitants, and thus become
deprived of the advantages of urban nature that are of particular benefit
to them (Lovell et al., 2018). Such processes of green gentrification
need close monitoring that can be facilitated by an assessment of the
impact of (newly) developed green and blue areas on housing markets.
This study conducted a meta-analysis to estimate value transfer
functions that can be used for assessing the impacts of urban nature on
house prices at different distances from the nature site. Compared to a
previous study (Brander and Koetse, 2011) our updated meta-analysis
includes more types of urban nature and more observations from dif-
ferent countries, which among others allows for estimating regional
benefit transfer functions. The results show that urban nature has an
impact on house prices in the areas surrounding it, and that the mag-
nitude of this effect decreases as house distance from nature increases,
revealing conventional distance decay relationship. Furthermore, the
results show that the impact on property prices differ by type of nature
intervention. In particular, homebuyers value the presence of a park or
blue nature in the vicinity of their property more the presence of other
types of urban nature. This effect can be explained by the high direct
use value (Hein et al., 2006) created by many ecosystem services of
urban parks and blue nature such as aesthetics, recreational
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
302
opportunities and local climate regulation.
In our application of the European value transfer function to green
interventions in a Dutch city of Utrecht, we have used maps to overlay
the effects of these interventions on the housing market with the
average value of housing property in the city. This information can be
used for identifying areas where green gentrification may occur. These
maps show the distance decay of the cumulative effects of urban nature
interventions on the house value at the city and the neighbourhood
levels. We estimated increases in local property values up to a max-
imum of 20 % compared with properties not affected by the interven-
tions, with value equivalent of 62,650 USD, at average prevailing price
level in a particular area in Utrecht. Besides, overlapping effects of
nature may in specific neighbourhoods double the increases in property
values from 5 to 6% to 10–11 %. We recognize that simple addition of
individual cumulative effects of multiple interventions should be
overestimating the true effect, and thus suggest that the highest of the
overlapping effects could serve as a lower bound for the total effect
estimation, while addition of all overlapping effects would serve as its
upper bound. Further research should shed light into the dynamics of
multiple overlapping effects of urban nature.
Our analysis showed that the presence of urban nature has a distinct
positive impact on housing prices in the areas surrounding it. This in-
sight is useful for monitoring the societal dimension of green inter-
ventions, tailoring policy and helping stakeholders build understanding
of the environmental, economic, and social impacts of green urban
interventions. Future research on nature-based solutions in cities and
their impacts on house prices can focus on nature interventions and the
process of gentrification using integrated urban justice assessments,
because planning and implementation of nature interventions require
integration of multiple data and balancing stakeholder priorities and
interests. Doing this could provide more direct insights into the po-
tential societal impacts that nature-based solutions can create, in ad-
dition to assessing benefits that they bring to cities on an ecological or
aesthetic level.
CRediT authorship contribution statement
M. Bockarjova: Conceptualization, Methodology, Software,
Validation, Formal analysis, Investigation, Data curation, Writing -
original draft, Writing - review & editing. W.J.W. Botzen:
Conceptualization, Methodology, Validation, Writing - original draft,
Writing - review & editing, Supervision, Project administration,
Funding acquisition. M.H. van Schie: Software, Writing - review &
editing, Visualization. M.J. Koetse: Conceptualization, Methodology,
Validation, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
Acknowledgments
This research received funding from the Horizon 2020 Framework
Programme of the European Union, NATURVATION project (grant
number 730243). We are also indebted to Nikola Lipovac and Alberto
Oeo for their assistance in database compilation.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.envsci.2020.06.024.
References
Almassy, D., Pinter, L., Rocha, S., Naumann, S., Davis, M., 2017. Urban Nature Atlas: A
Database of Nature-Based Solutions across 100 European Cities. H2020 Naturvation
Project Report. (Accessed on 20 Aug 2019). https://naturvation.eu/result/results-
urban-nature-atlas.
Amsterdam Municipality, 2019. Programmaplan Huisvesting Kwetsbare Groepen 2019 -
2022. Gemeente Amsterdam. 14 juli 2019. www.amsterdam.nl/woonagenda (in
Dutch).
Anguelovski, I., Connolly, James J.T., Masip, Laia, Pearsall, Hamil, 2018. Assessing green
gentrification in historically disenfranchised neighborhoods: a longitudinal and
spatial analysis of Barcelona. Urban Geogr. 39 (3), 458–491. https://doi.org/10.
1080/02723638.2017.1349987.
Baró, F., Kabisch, Nadja, Langemeyer, Johannes, 2020. Edyta Łaszkiewicz (____).
Introduction to the special issue on environmental justice. Environ. Sci. Policy cur-
rent SI.
Bateman, Ian J., Jones, Andrew P., 2003. Contrasting conventional with multi-level
modeling approaches to meta-analysis: expectation consistency in UK woodland re-
creation values. Land Economics 79 (2), 235–258. https://doi.org/10.2307/
3146869.
Bell, S.L., Westley, Michael, Lovell, Rebecca, Wheeler, Benedict W., 2017. Everyday green
space and experienced well-being: the significance of wildlife encounters. Landscape
Res. 43 (1), 8–19. https://doi.org/10.1080/01426397.2016.1267721.
Brander, L.M., Koetse, M.J., 2007. The Value of Open Space: Meta-Analyses of Contingent
Valuation and Hedonic Pricing Results. IVM Working Paper 07/03. Institute for
Environmental Studies, VU University, Amsterdam.
Brander, L.M., Koetse, M.J., 2011. The value of urban open space: meta-analyses of
contingent valuation and hedonic pricing results. J. Environ. Manage. 92 (10),
2763–2773. https://doi.org/10.1016/j.jenvman.2011.06.019.
Brown, G., Schebella, M.F., Weber, D., 2014. Using participatory GIS to measure physical
activity and urban park benefits. Landscape Urban Plann. 121, 34–44. https://doi.
org/10.1016/j.landurbplan.2013.09.006.
CBS, 2017. Neighbourhood Level Statistics 2004–2018. (Accessed on 20 Aug 2019).
https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/wijk-en-buurtstatistieken.
Champ, P.A., Boyle, K.J., Brown, T.C., 2003. A Primer on Nonmarket Valuation. The
Economics of Non-Market Goods and Resources Vol. 3https://doi.org/10.1007/
97894-007-0826-6.
Cole, H.V.S., Triguero-Masa, Margarita, Connollya, J.T., Anguelovskia, Isabelle, 2019.
Determining the health benefits of green space: does gentrification matter? Health
Place 57, 1–11. https://doi.org/10.1016/j.healthplace.2019.02.001.
Czembrowski, P., Kronenberg, J., 2016. Hedonic pricing and different urban green space
types and sizes: insights into the discussion on valuing ecosystem services. Landscape
Urban Plann. 146, 11–19. https://doi.org/10.1016/j.landurbplan.2015.10.005.
Dadvand, P., de Nazelle Figueras, A.F., Basagaña, X., Su, J., Amoly, E., Jerrett, M.,
Vrijheid, M., Sunyer, J., Nieuwenhuijsen, M.J., 2012. Green space, health inequality
and pregnancy. Environ. Int. 40, 110–115. https://doi.org/10.1016/j.envint.2011.
07.004.
Estrada, F., Botzen, W.J.W., Tol, R., 2017. A global economic assessment of city policies to
reduce climate change impacts. Nat. Clim. Change 7, 403–406. https://doi.org/10.
1038/nclimate3301.
European Commission, 2015. Towards an EU Research and Innovation Policy Agenda for
Nature-Based Solutions & Re-Naturing Cities. Report of the Directorate-General for
Research and Innovation. EU, Brussels. https://doi.org/10.2777/765301.
Flacke, J., Schüle, S.A., Köckler, H., Bolte, G., 2016. Mapping environmental inequalities
relevant for health for informing urban planning interventions—a case study in the
City of Dortmund, Germany. Int. J. Environ. Res. Public Health 13 (7), 711. https://
doi.org/10.3390/ijerph13070711.
Gascon, M., Triguero-Mas, M., Martínez, D., Dadvand, P., Forns, J., Plasència, A.,
Nieuwenhuijsen, M.J., 2015. Mental health benefits of long-term exposure to re-
sidential green and blue spaces: a systematic review. Int. J. Environ. Res. Public
Health 12 (4), 4354–4379. https://doi.org/10.3390/ijerph120404354.
Gill, S.E., Handley, J.F., Ennos, A.R., Pauleit, S., 2007. Adapting cities for climate change:
the role of the green infrastructure. Built Environ. 33 (1), 115–133. https://doi.org/
10.2148/benv.33.1.115.
Gorham, M.R., Waliczek, T.M., Snelgrove, A., Zajicek, J.M., 2009. The impact of com-
munity gardens on numbers of property crimes in urban Houston. HortTechnology 19
(2), 291–296. https://doi.org/10.21273/HORTSCI.19.2.291.
Gould, K.A., Lewis, T.L., 2016. Green Gentrification: Urban Sustainability and the
Struggle for Environmental Justice. Routledge, London. https://doi.org/10.4324/
9781315687322.
Harris, A., 2008. From London to Mumbai and back again: gentrification and public
policy in comparative perspective. Urban Stud. 45 (12), 2407–2428. https://doi.org/
10.1177/0042098008097100.
Hein, L., van Koppen, K., de Groot, R.S., van Ierland, E.C., 2006. Spatial scales, stake-
holders and the valuation of ecosystem services. Ecol. Econ. 57 (2), 209–228. https://
doi.org/10.1016/j.ecolecon.2005.04.005.
Hochstenbach, C., 2017. Inequality in the Gentrifying European City. PhD thesis.
University of Amsterdam ISBN 978-94-91602-95-5.
Hochstenbach, C., 2018. Spatializing the intergenerational transmission of inequalities:
parental wealth, residential segregation, and urban inequality. Environ. Plann. A:
Econ. Space 50 (3), 689–708. https://doi.org/10.1177/0308518X17749831.
Hochstenbach, C., Musterd, S., 2018. Gentrification and the suburbanization of poverty:
changing urban geographies through boom and bust periods. Urban Geogr. 39 (1),
26–53. https://doi.org/10.1080/02723638.2016.1276718.
Lafortezza, R., Chen, Jiquan, van den Bosch, CecilKonijnendijk, Randrup, T.B., 2018.
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
303
Nature-based solutions for resilient landscapes and cities. Environ. Res. 165,
431–441. https://doi.org/10.1016/j.envres.2017.11.038.
Łaszkiewicz, E., Czembrowski, P., Kronenberg, J., 2019. Can proximity to urban green
spaces be considered a luxury? Classifying a non-tradable good with the use of he-
donic pricing method. Ecol. Econ. 161, 237–247. https://doi.org/10.1016/j.
ecolecon.2019.03.025.
Lovell, R., Depledge, M., Maxwell, S., 2018. Health and the Natural Environment: A
Review of Evidence, Policy, Practice and Opportunities for the Future. Defra Report.
Project Code BE0109. Department for Environment, Food and Rural Affairs, UK.
http://randd.defra.gov.uk.
Lwin, K.K., Murayama, Y., 2011. Modelling of urban green space walkability: eco-friendly
walk score calculator. Comput. Environ. Urban Syst. 35 (5), 408–420. https://doi.
org/10.1016/j.compenvurbsys.2011.05.002.
Maciag, M., 2015. “Gentrification in America Report,”Governing the States and
Localities. . February http://www.governing.com/gov-data/census/gentrification-in-
cities-governing-report.html.
Marshall, K.A., Gonzalez-Meler, M.A., 2016. Can ecosystem services be part of the solu-
tion to environmental justice? Ecosyst. Serv. 22, 202–203. https://doi.org/10.1016/j.
ecoser.2016.10.008.
McPhearson, T., Kremer, P., Hamstead, Z.A., 2013. Mapping ecosystem services in New
York City: applying a social–ecological approach in urban vacant land. Ecosyst. Serv.
5, 11–26. https://doi.org/10.1016/j.ecoser.2013.06.005.
Norton, B.A., Coutts, A.M., Livesley, S.J., Harris, R.J., Hunter, A.M., Williams, N.S.G.,
2015. Planning for cooler cities: a framework to prioritise green infrastructure to
mitigate high temperatures in urban landscapes. Landscape Urban Plann. 134,
127–138. https://doi.org/10.1016/j.landurbplan.2014.10.018.
Raymond, C.M., Frantzeskaki, N., Kabisch, N., Berry, P., Breil, M., Nita, M.R., Calfapietra,
C., 2017. A framework for assessing and implementing the co-benefits of nature-
based solutions in urban areas. Environ. Sci. Policy 77, 15–24. https://doi.org/10.
1016/j.envsci.2017.07.008.
Reid, C.E., O’Neill, M.S., Gronlund, C.J., Brines, S.J., Brown, D.G., Diez-Roux, A.V.,
Schwartz, J., 2009. Mapping Community determinants of heat vulnerability. Environ.
Health Perspect. 117 (11), 1730–1736. https://doi.org/10.1289/ehp.0900683.
Schläpfer, F., Waltert, F., Segura, L., Kienast, F., 2015. Valuation of landscape amenities: a
hedonic pricing analysis of housing rents in urban, suburban and peri-urban
Switzerland. Landscape Urban Plann. 141, 24–40. https://doi.org/10.1016/j.
landurbplan.2015.04.007.
Schmidt, F.L., Hunter, J.E., 2004. Methods of Meta-Analysis: Correcting Error and Bias in
Research Findings, second edition. Sage publicationshttps://doi.org/10.4135/
9781483398105.
Sohn, W., Kim, H.W., Kim, J.H., Li, M.H., 2020. The capitalized amenity of green infra-
structure in single-family housing values: an application of the spatial hedonic pricing
method. Urban For. Urban Green., 126643. https://doi.org/10.1016/j.ufug.2020.
126643.
UN, 2018. UN World Urbanisation Prospects 2018. (Accessed on 20 Aug 2019). https://
population.un.org/wup/.
Utrecht Municipality, 2019. Woonvisie: Utrecht Beter in Balans. Gemeente Utrecht. juli
2019. https://omgevingsvisie.utrecht.nl/thematisch-beleid/wonen/ (in Dutch).
Waarderingskamer, 2010. Market Value as a Basis for Determination of Real Estate Value.
Netherlands Value Assessment Chamber, the Hague. https://www.
waarderingskamer.nl/fileadmin/publieksportaal/documents/public/klopt-mijn-woz-
waarde/Brochure_Marktwaarde_als_waarderingsgrondslag.pdf.
Waarderingskamer, 2017. Memo on Model-Based Determination of Real Estate Value. 21
August 2017. Netherlands Value Assessment Chamber, the Hague. https://www.
waarderingskamer.nl/fileadmin/publieksportaal/documents/public/klopt-mijn-woz-
waarde/Notitie_modelmatige_waardebepaling_2017-08-21.pdf.
Wolfe, M.K., Mennis, J., 2012. Does vegetation encourage or suppress urban crime?
Evidence from Philadelphia, PA. Landscape Urban Plann. 108 (2–4), 112–122.
https://doi.org/10.1016/j.landurbplan.2012.08.006.
M. Bockarjova, et al. Environmental Science and Policy 112 (2020) 293–304
304