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Revealed preferences for outdoor recreation in natural areas - Czech and European perspective (Doctoral Dissertation)

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The dissertation thesis focuses on the investigation and synthesis of recreation welfare benefits associated with natural areas in the Czech Republic and in Europe. The dissertation thesis consists of five case studies. These represent various geographic levels of analysis: the level of one single recreation locality, the national level that takes into account large natural recreation sites in the Czech Republic (including protected areas), and a synthesis of results of studies on the European level. The methodological approach is based on the theory of environmental economics and employs non-market valuation techniques based on methods of revealed preferences, namely the hedonic pricing method and two types of travel cost modelling. In Study I, we examine how the presence and characteristics of urban greenery affect property prices in Prague. The results confirm that proximity to greenery and its area are important determinants of housing prices in Prague, which means that residents realize the positive values provided by urban greenery, including recreational ecosystem service. Benefits to residents differ with the type of greenery. Urban forests have the largest effect on property prices. Specially protected areas also affect property price positively, but the effect is smaller. The study also suggests that Prague residents prefer smaller units of urban greenery to large parks. In the subsequent two studies, I focus on the recreational values of the Šumava National Park. In a single site travel cost model, I estimate the recreation use value of this natural area and discuss how the result may be further employed. I also find that the methodological approach (the definition of the shadow price of recreation and endogenous sampling) significantly affects both the modelling results and the estimates of recreation use value. In Study IV, we apply a travel cost model based on random utility framework to disentangle the determinants of demand for Czech large-scale natural recreation areas. The outcomes propose that visitors prefer larger recreation areas for their trips, and have a significant preference for natural areas where the dominant forest is broadleaved or coniferous rather than sites with mixed forest stands. Among Czech large-scale natural recreational areas, national parks are more probably chosen for a trip than protected landscape areas, and unprotected sites have the lowest probability of visitation. Study V is based on a meta-analysis of the previous travel cost studies in Europe. The scope of this study is twofold: i) to disentangle the effect of environmental and methodological variables on the recreation value, and ii) to derive a model appropriate for a benefit transfer of forest recreation values in Europe, including Central and Eastern European natural sites. The key results from our meta-analysis of European forest recreation values are that higher recreation values are associated with remote forests in sparsely populated areas consisting of broadleaved forest stands and that protected sites are not associated with significantly different recreation values. All studies covered in the thesis prove that natural areas are associated with positive recreation values. Based on discussion of the evidence from the Studies I to V and other recent scientific evidence, the thesis brings a set of recommendations for benefit transfers of recreation values to natural areas in the Czech Republic.
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CHARLES UNIVERSITY
FACULTY OF HUMANITIES
Environment Centre
Study programme: Environmental Studies
Revealed preferences for outdoor recreation in natural areas
- Czech and European perspective
Doctoral Dissertation
Author: Ing. Kateřina Kaprová
Supervisor: Ing. Jan Melichar, Ph. D.
Year of the defence: 2019
Reference
KAPROVÁ, Kateřina. Revealed preferences for outdoor recreation in natural areas - Czech
and European perspective. Dissertation thesis (Ph. D.). Prague: Charles University, Faculty of
Humanities, Environment Centre, 2019. 201 pages. Supervisor: Ing. Jan Melichar, Ph. D.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Summary
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Summary
4
The dissertation thesis focuses on the investigation and synthesis of recreation welfare benefits
associated with natural areas in the Czech Republic and in Europe. The dissertation thesis
consists of five case studies. These represent various geographic levels of analysis: the level of
one single recreation locality, the national level that takes into account large natural recreation
sites in the Czech Republic (including protected areas), and a synthesis of results of studies on
the European level. The methodological approach is based on the theory of environmental
economics and employs non-market valuation techniques based on methods of revealed
preferences, namely the hedonic pricing method and two types of travel cost modelling.
In Study I, we examine how the presence and characteristics of urban greenery affect property
prices in Prague. The results confirm that proximity to greenery and its area are important
determinants of housing prices in Prague, which means that residents realize the positive values
provided by urban greenery, including recreational ecosystem service. Benefits to residents
differ with the type of greenery. Urban forests have the largest effect on property prices.
Specially protected areas also affect property price positively, but the effect is smaller. The
study also suggests that Prague residents prefer smaller units of urban greenery to large parks.
In the subsequent two studies, I focus on the recreational values of the Šumava National Park.
In a single site travel cost model, I estimate the recreation use value of this natural area and
discuss how the result may be further employed. I also find that the methodological approach
(the definition of the shadow price of recreation and endogenous sampling) significantly affect
both the modelling results and the estimates of recreation use value.
In Study IV, we apply a travel cost model based on random utility framework to disentangle
the determinants of demand for Czech large-scale natural recreation areas. The outcomes
propose that visitors prefer larger recreation areas for their trips, and have a significant
preference for natural areas where the dominant forest is broadleaved or coniferous rather than
sites with mixed forest stands. Among Czech large-scale natural recreational areas, national
parks are more probably chosen for a trip than protected landscape areas, and unprotected sites
have the lowest probability of visitation.
Study V is based on a meta-analysis of the previous travel cost studies in Europe. The scope of
this study is twofold: i) to disentangle the effect of environmental and methodological variables
on the recreation value, and ii) to derive a model appropriate for a benefit transfer of forest
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Summary
5
recreation values in Europe, including Central and Eastern European natural sites. The key
results from our meta-analysis of European forest recreation values are that higher recreation
values are associated with remote forests in sparsely populated areas consisting of broadleaved
forest stands and that protected sites are not associated with significantly different recreation
values.
All studies covered in the thesis prove that natural areas are associated with positive recreation
values. Based on discussion of the evidence from the Studies I to V and other recent scientific
evidence, the thesis brings a set of recommendations for benefit transfers of recreation values
to natural areas in the Czech Republic.
Key words
Recreation demand, recreation value, recreation welfare, protected areas, natural areas, urban
greenery, forests, travel cost method, single-site model, random utility model, hedonic pricing
method, benefit transfer, meta-analysis, non-market valuation, revealed preferences
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Summary
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Shrnutí
Disertační práce se zabývá analýzou a syntézou rekreačních hodnot spojených s přírodními
územími v České republice a v Evropě. Práce sestává z pěti případových studií s různou
geografickou úrovní analýzy: od analýzy jedné rekreační lokality přes analýzu velkých
přírodních rekreačních území v České republice (včetně chráněných území), po syntézu
výsledků evropských studií poptávky po rekreaci. Metodologie práce vychází z teorie
environmentální ekonomie a netržního oceňování environmentálních statků a služeb, konkrétně
je aplikována metoda hedonické ceny a metoda cestovních nákladů.
Studie I se zabývá vlivem městské zeleně (městských lesů a maloplošných zvláště chráněných
území) na cenu nemovitostí v Praze. Výsledky studie ukazují, že blízkost k zeleni a plocha
zeleně jsou významnými faktory ovlivňujícími ceny nemovitostí, což naznačuje, že obyvatelé
Prahy pozitivně vnímají přítomnost městské zeleně včetně jejích rekreačních přínosů. Velikost
přínosů poskytovaných rezidentům se liší podle typu městské zeleně. Městské lesy ovlivňují
ceny nemovitostí nejvíce. Maloplošná zvláště chráněná území mají rovněž pozitivní vliv na
cenu nemovitostí, ale efekt je nižší než efekt lesů. Preferovány jsou spíše menší plochy obou
typů zeleně.
Následující dvě studie (II a III) se zaměřují na odhad rekreační hodnoty Národního parku
Šumava. Odhad je proveden na základě aplikace metody cestovních nákladů, konkrétně modelu
jednoho místa. Ve studiích jsou zkoumány dopady metodologického přístupu k definici stínové
ceny rekreace a dopady administrace výběru respondentů s ohledem na jeho reprezentativnost
na výsledky modelu a rovněž na výši odhadu rekreační hodnoty. Studie dále diskutují využití
výsledků modelování.
Studie IV aplikuje model náhodného užitku, který je další technikou v rámci modelování
cestovních nákladů, s cílem určit determinanty poptávky po velkých přírodních rekreačních
územích v České republice. Závěry studie ukazují, že návštěvníci upřednostňují větší rekreační
území s dominantním pokryvem buď jehličnatými, nebo listnatými lesy (tedy nikoli lesy
smíšenými). Pravděpodobnost návštěvy je vyšší pro národní parky než pro chráněné krajinné
oblasti, a je nejnižší pro přírodní oblasti mimo velkoplošná zvláště chráněná území.
Studie V je založena na meta-analýze evropských aplikací metody cestovních nákladů. Účelem
studie je: i) určit, které environmentální a metodologické proměnné ovlivňují výši odhadu
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Summary
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rekreační hodnoty spojené s návštěvou přírodního území, a ii) odhadnout meta-analytický
model vhodný pro přenos rekreačních hodnot (benefit transfer) lesů v rámci Evropy, včetně
středoevropských a východoevropských zemí. Podle výsledků studie jsou vyšší rekreační
hodnoty spojeny s listnatými lesy a přírodními územími nacházejícími se v málo zalidněných
oblastech. Rekreační hodnoty chráněných území nejsou významně odlišné od rekreačních
hodnot ostatních území.
Všechny studie v disertační práci ukazují, že přírodní území poskytují společnosti značné
rekreační přínosy. Závěr práce je věnován doporučením pro přenos rekreačních hodnot na
přírodní území v České republice; tato doporučení jsou zpracována na základě výsledků Studií
I až V a zohledňují také výsledky nedávných evropských výzkumů.
Klíčová slova
Poptávka po rekreaci, rekreační hodnota, rekreační užitek, rekreační území, chráněná území,
přírodní území, městská zeleň, rekreace v přírodě, lesy, metoda cestovních nákladů, model
jednoho místa, model náhodného užitku, metoda hedonické ceny, přenos hodnot, meta-analýza,
netržní oceňování, metody odhalených preferencí
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Table of Contents
Summary ...................................................................................................................... 3
Shrnutí .......................................................................................................................... 6
Declarations .................................................................................................................. 9
Acknowledgements ...................................................................................................... 10
List of abbreviations ..................................................................................................... 12
Introduction .................................................................................................................. 14
Research aims and questions ........................................................................................ 19
Theoretical framework ................................................................................................. 21
Manuscripts - collection of works
I. Revealing preferences of Prague's homebuyers towards
greenery amenities: the empirical evidence of distance-size effect. ..... 37
Melichar, J. & K. Kaprová (2013).
Landscape and Urban Planning 109(1): 56-66.
II. Definition of the recreation shadow price and its implications
on recreation welfare estimation. ............................................................ 66
Kaprová, K. (2015).
Journal of Landscape Management 6/2: 38-48.
III. Recreation values and the value of recreation demand
modelling: The case of the Šumava NP. .................................................. 82
Kaprová, K. (2015).
Journal of Landscape Management 6/1: 7-16.
IV. Recreation demand for large natural areas in the Czech Republic. .... 98
Kaprová, K. & J. Melichar.
[manuscript draft for Leisure Sciences: An Interdisciplinary Journal]
V. Meta-analysis of recreation values attributed to forested areas
in Europe and the validity of meta-analytic value transfer in
Central and Eastern Europe. .................................................................... 124
Kaprová, K. & J. Melichar.
[manuscript draft for Ecosystem Services/Journal of Forest Economics]
Discussion .................................................................................................................... 168
Conclusion .................................................................................................................... 198
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Declaration of the author
1. I hereby declare that I have compiled this thesis by myself, using the listed literature and
resources only.
2. I declare to be the author of the following steps of design and accomplishment of the particular
studies that constitute the thesis (numbered to match the respective chapters of the thesis):
I. Data analysis, overall design of the article (with co-operation of the main author), first
version of the manuscript (all texts but Introduction and Conclusion), revision of the
analysis and Discussion section after Major revisions.
II. Complete design and accomplishment.
III. Complete design and accomplishment.
IV. Overall design of the article, GIS analysis, data analysis, first version of the
manuscript.
V. Design of the study, secondary data collection, marginal assistence with GIS analysis
to the co-author, design of the article, first version of the manuscript.
3. I hereby declare that my thesis has not been used to gain any other academic title.
4. I give approval to make this thesis accessible by Charles University libraries and the
electronic Thesis Repository of Charles University, and to be utilized for study and scientific
purposes in accordance with the copyrights.
In Prague on 18 February 2019 Ing. Kateřina Kaprová
Declaration of the co-author of the scientific articles that constitute the thesis
1. As a co-author, I hereby declare that I agree with inclusion of the published articles that are
found in Chapters I - III and the manuscripts of articles in Chapters IV - V into the text of this
dissertation thesis by Kateřina Kaprová.
2. As a co-author, I hereby declare that I agree with the statement of contribution of Kateřina
Kaprová that she declared above in the Declaration of the author (part 2), to the design and
accomplishment of the articles and manuscripts found in Chapters I - V of this dissertation thesis.
In Prague on 18 February 2019 Ing. Jan Melichar, Ph. D.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Acknowledgements
First of all, I would like to express my gratitude to my advisor Jan Melichar. I am also very grateful
to my colleagues and friends at the Charles University Environment Center: Markéta Braun
Kohlová, Vojtěch Máca, Milan Ščasný, Jan Urban, Markéta Sychrovská, Lukáš Rečka, Martin
Kryl, Hana Škopková and all the collective they have always been a great source of inspiration
for my work and I have learned a lot from them both on professional and personal level.
I have also very much appreciated the suggestions and comments made by the thesis opponents,
Ing. Jan Brůha, Ph. D. and Prof. Ing. Petr Šauer, CSc., dr.h.c. that we discussed during the mock
defense of this thesis, which allowed me to significantly improve the text.
The data collection and research on particular case studies was supported by the following
projects and grants:
GAUK 1544314: Specification of distance, time and cost in recreation demand models
(2014-2015)
TAČR TD020049: The use of pricing mechanism for tourism directing and financing
the management of specially protected areas in the Czech Republic (2014-2015)
7th FP EC: Global-IQ: Impacts Quantification of global changes (2011-2014)
NPV I 1R56014 (Ministry of Agriculture): Monetary valuation of recreational and
aesthetical functions of forest in the Czech Republic (20052007)
The support is gratefully acknowledged.
Preliminary results and follow-up research topics have been presented and discussed at national
and international conferences and seminars:
Conference of the European Association of Environmental and Resource Economists
(2012, Prague, Czech Republic)
Mathematical methods in economics (2012, Karviná, Czech Republic)
International days of statistics and economics (2013, Prague, Czech Republic)
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Environmental Economics and Policy: Young Researchers Perspective (2013, Prague,
Czech Republic)
EAERE/FEEM Belpasso Summer School on Environmental Economics: Spatial Context
and Valuing Natural Capital for Conservation Planning (2014, Belpasso, Italy)
Ulvön Conference on Environmental Economics (2015, Ulvön, Sweden)
Morrison School of Agribusiness series of seminars (2015, Phoenix, USA)
Aktuality šumavského výzkumu VI. (2015, Ludwigsthal, Germany)
Public recreation and landscape protection - with nature hand in hand... (2015-2018,
Křtiny / Brno, Czech Republic)
International conference on monitoring and management of visitors in recreational and
protected areas (2014 Tallinn, Estonia; 2016 Novi Sad, Serbia; 2018 Bordeaux,
France)
The 20th Annual Conference „Environmental Economics, Policy and International
Relations“ (2018, Prague, Czech Republic)
I would like to thank the parcipants of these events for their valuable insights and suggestions
on the research topic.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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List of abbreviations
CBA .............. cost-benefit analysis
CE ................. choice experiment
CEE .............. Central and Eastern European countries (in Study V)
CM ................ choice modelling
CPI ................ consumer price index
CS ................. consumer surplus
CV, CVM ..... contingent valuation method
CZK .............. Czech koruna (Czech crown)
EU ................. European Union
FE ................. fixed effects
GDP .............. gross domestic product
GIS ............... geographic information system
HICP .............. Harmonised Index of Consumer Prices
HP, HPM ...... hedonic pricing method
IIA ................ independence of irrelevant alternatives
IID ................ independent and identically distributed
IP .................. information point (in Study III)
LC ................. land cover
LM ................ Lagrange Multiplier test
LU ................. land use
MA ............... meta-analysis
MNL ............. multinomial logit
MP ................ market prices
MXL ............. mixed logit
N/A ............... not available
NFI ............... net factor income
NL ................. nested logit
NP ................. National Park
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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NUTS ........... Nomenclature of Units for Territorial Statistics
OLS .............. ordinary least squares
PF ................. production function
PLA .............. protected landscape area
PPP ............... purchasing power parity
RC ................. replacement cost
RE ................. random effects
RP ................. revealed preference
RUM ............. random utility model
SP ................. stated preference
SPA ............... specially protected area
SSM .............. single-site (travel cost) model
TC ................. travel cost
TCM ............. travel cost method
TE ................. transfer error
UES .............. urban ecosystem services
UK ................ United Kingdom
US, USA ....... United States of America
VIF ............... variance inflation factor
WNS ............. Western, Northern and Southern European countries (in Study V)
WSUT ........... weak structural ulility theoretic approach
WTP ............. willingness to pay
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Introduction
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Introduction
15
In the Czech Republic, the information on recreation values associated with recreational areas
and open spaces, as well as information on factors affecting the demand for recreational areas has
remained relatively scarce until the development of studies involved in dissertation thesis. The
existing research on recreation demand that would prove that Czech natural areas bring substantial
recreation values to their visitors encompassed only two natural sites: Jizerské mountains
(Melichar, 2007; later published as Melichar, 2014) and Bohemian Paradise UNESCO Geopark
(Špaček et Antoušková, 2013). As both the studies focused on analysis of a single site, they could
not discuss which environmental attributes are perceived as important for recreational use by the
visitors (e. g. are broadleaved forests more preferred than coniferous sites? Does the type of
management of the area play a role for recreation use? Do the recreation preferences differ among
types of visitors, length of stay or recreation uses?). Similarly, it has not been analysed yet
whether Czech recreationists prefer to visit natural sites that promote enhanced biodiversity or
the presence of rare types of landscape or biotopes rather than other types of landscape. Even
European evidence on the determinants of recreation values has not been uniform (Schägner et
al., 2018, Zandersen and Tol, 2009, de Salvo and Signorello, 2015).
The deficiency of environmental valuation studies that would shed light on the recreation values
of natural areas is not only related to the Czech context, but to the whole of Central and Eastern
Europe (with the exemption of a few existing studies, e. g. Czajkowski et al., 2014). In terms
of time and resource constraints, transfer of values from published studies to different natural
sites (e. g. Daněk et al., 2018; Melichar et al., 2016; Frélichová et al., 2014) remain the most
feasible way to express the value of a natural site for which the primary valuation study is not
available; which actually encompasses most natural sites in the post-transition countries. The
magnitude of the perceived recreation value of a specific natural area is affected by the
characteristics of the population of recreationists and their preferences regarding leisure and
landscape (as the values are “co-created by visitors - Mayer and Woltering, 2018) and also
depends on the context of the landscape involved (the character of the area, the abundance of
different types of recreation sites, their accessibility). With insufficient evidence, it has been
difficult to discuss the validity of benefit transfer of values in the post-transition countries, and
of international transfers of values in particular. Even though several quantitative (meta-
analytic) reviews on European recreation values have been conducted (Zandersen and Tol,
2009; Kaprová and Melichar, 2014; Schägner et al., 2018), the predominate body of meta-
analytic research has focused on natural areas in Western, Northern or Southern Europe, so
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Introduction
16
uncertainty regarding the transferability of values to Central and Eastern European context
prevails.
It has been proven that values associated with recreational use may vary substantially according
to the methodology applied. Research on recreation values has been based on many different
methodological approaches - valuation techniques, data collection techniques, estimation
techniques and on various geographic scales. This contributes to the fact that up to the present,
findings on recreation values associated with particular recreation sites in Europe are of high
range, due to which it is not at all straightforward to draw any generalized conclusions based
on the empirical results. Existing meta-analytic reviews on European recreation values (e. g.
Schägner et al., 2018; de Salvo and Signorello, 2015) employ only binary variables on a general
methodological level, e. g. to distinguish between stated and revealed preference studies. The
only meta-analytic study focusing on European revealed preferences (more precisely travel cost
models) by Zandersen and Tol (2009) shows slightly more detailed results, but does not focus
on the latest methodological advancements in recreation demand modelling. None of these
previous syntheses has incorporated evidence from Central and Eastern Europe (CEE).
This thesis brings new insights into the revealed preferences for recreation services associated
with urban and natural areas in the Czech Republic. The results of the thesis substantially widen
the set of primary results of revealed preferences for natural areas in the Czech Republic. I hope
to contribute to the overall knowledge on the drivers of recreation values of natural areas by
establishing several valuation models at different geographical levels.
The analyses presented in the dissertation thesis stem from the theory of environmental
economics. In particular, I apply several non-market valuation techniques based on revealed
preference methods: i) the hedonic pricing method and ii) two models of travel cost method
(on-site single-site model, off-site random utility model). In addition, I develop a meta-analytic
model of European forest recreation values including values derived in CEE countries,
originating in travel cost methodology.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Introduction
17
The dissertation thesis consists of five case studies, which represent various geographic levels
of analysis:
Level of one natural area (the Šumava national park) and its recreation values (Studies
II and III)
Level of one residential area (Prague) and the benefits provided by its urban greenery
units (including recreation value) (Study I)
Country scale level - estimation of recreation demand for large-scale natural recreational
areas in the Czech Republic, including specially protected areas (Study IV)
European level, where I examine the existing evidence on European forest recreation
values using the technique of meta-analysis (Study V)
In the first three Studies (two of which, Study II and III, are devoted to Šumava National Park,
one, Study I, to Prague greenery), I present the results on these study sites. Study IV is focused
on a country-level random utility model. The results of the Study V provide a synthesis of
European evidence on forest recreation values and provides measures of validity of the potential
value transfer from European sites to the Czech context.
The Discussion section provides a summary of the findings on the research questions and
hypotheses of the thesis based on the results of Studies I to V encompassed in the thesis, and
its discussion with regard to the other latest scientific evidence. This section also provides
a broader discussion on recreation valuation beyond the scope of Studies I to V and focuses on
the implications of their findings for benefit transfer exercises of recreation values in the Czech
Republic.
The main findings of the thesis are summarized in brief in the Conclusion section. The
conclusions of the thesis may be important not only for the employment of social values
of recreation in policy analyses (such as cost-benefit analysis exercises), but also as an
inspiration for benefit transfers of social values of other ecosystem services across Europe (in
particularly into areas where the results from primary research are still not very common, such
as Central and Eastern Europe), where similar questions as those on which I focus on are highly
relevant.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Introduction
18
References
Czajkowski, M., M. Giergiczny, J. Kronenberg, P. Tryjanowski (2014). The economic value of
a white stork nesting colony: a case of ‘stork village’ in Poland. Tourism Management 40, 352-360.
Daněk, J., D. Vačkářů, E. K. Lorencová (2018). Economic value of ecosystem services in
Protected Landscape Areas in the Czech Republic. Beskydy 10 (1,2), p. 99-112.
De Salvo, M., G. Signorello (2015). Non-market valuation of recreational services in Italy:
A meta-analysis. Ecosystem Services 16, p. 47-62.
Frélichová, J., D. Vačkářů, A. Pártl, B. Loučková, Z. V. Harmáčková, E. Lorencová (2014). Integrated
Assessment of Ecosystem Services in the Czech Republic. Ecosystem Services 8, p. 110-117.
Kaprová, K., J. Melichar (2014). Review and meta-analysis of monetary values for non-market
goods and services affected by climate change: land use, ecosystem, leisure. Deliverable No.
D2.1 „Analysis of key determinants of costs and benefits of mitigation policy“of GLOBAL IQ
- Impacts Quantification of global changes (Project no 266992). Chapter 1.
Mayer, M., M. Woltering (2018). Assessing and valuing the recreational ecosystem services of
Germany’s national parks using travel cost models. Ecosystem Services 31, p. 371-386.
Melichar, J. (2007). Aplikace metody cestovních nákladů v oblasti Jizerských hor. Dissertation
thesis. Prague: University of Economics, 150 p.
Melichar, J. (2014). Measuring recreation benefits of forest quality change with contingent
behavior model. Journal of Landscape Management 2, p. 10-15.
Melichar, J., K. Kaprová, M. Ščasný (2016). The biodiversity change in the Czech Republic,
driving forces and associated welfare impacts. Journal of Landscape Management 7/2, p. 37-44.
Schägner, J. P., L. Brander, J. Maes, M. L. Paraccini, V. Hartje (2016). Mapping recreational
visits and values of European National Parks by combining statistical modelling and unit value
transfer. Journal for Nature Conservation 31, p. 71-84.
Špaček, J., M. Antoušková (2013). Individual single-site travel cost model for Czech Paradise
geopark. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis LXI (7), p.
28512858.
Zandersen, M., and R. S. J. Tol (2009). A Meta-analysis of Forest Recreation Values in Europe.
Journal of Forest Economics 15, p. 109-130.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Research aim and questions
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Research aims and questions
20
The main objective of the dissertation thesis is to derive demand for recreation services
of natural areas and open spaces in the Czech Republic, to disentangle the implicit recreation
value that people associate with particular sites and to identify and discuss the factors that affect
these values (including the environmental attributes of the recreation sites).
The central hypothesis of the thesis is that surrogate markets in the Czech Republic positively
reflect recreation values of natural and urban recreation areas. With regard to the current level
of knowledge summarized in the Introduction section, the thesis specifically focuses on the
following research questions:
1. Which attributes affect the revealed preferences of people for recreation?
a) Which environmental characteristics of natural sites do people prefer for recreation use?
b) Are protected areas associated with a significantly different recreational value than other
open space areas?
2. Does the methodology of recreation studies play an important role in estimating recreation values?
3. What are the recommendations for benefit transfer of recreational values:
a) from one site in the Czech Republic to another site in the Czech Republic,
b) from an international transfer (with special regard to meta-analytic transfers)
to the Czech Republic,
to minimize the benefit transfer errors?
To answer the research questions, a series of five case studies has been accomplished. The
methodological extent of the series of studies within the dissertation thesis is sufficiently broad
to elaborate on the research questions stated. The methodological framework focuses on values
accrued to the main two groups of recreational beneficiaries: visitors and residents; and the
analysis is developed on three geographical levels: level of one single recreation locality,
national level that takes into account 27 large natural recreation sites in the Czech Republic
(including specially protected areas), and a synthesis of results on the European level,
incorporating studies from all across Europe.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
21
Theoretical framework
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
22
Outdoor recreation and underlying economic values have been studied using a variety of non-
market valuation techniques, i. e. techniques that account for the absence of existing markets
for recreational services of natural areas, where these services would be explicitly traded and
from which we could derive their market value (Haab and McConnell 2002, Freeman 2003,
National Research Council, 2005). Environmental economic theory usually distinguishes
between two main approaches to non-market valuation (Markandya et al., 2002, Haab and
McConnell 2002, Freeman 2003, Mäler and Vincent, 2005): methods of revealed preferences
and methods of stated preferences. The former employ data on real behaviour of people on an
existing market to disentangle values of a non-marketed environmental good, such as recreation
in nature. These methods include travel cost method (TCM) and hedonic pricing method
(HPM). In stated preference surveys, the researcher employs hypothetical market situations to
derive the potential behaviour of people in making trade-offs among the hypothetical situations
and to value non-market goods. Choice experiment techniques (CE) and contingent valuation
method (CVM, as a specific case of choice modelling - CM) fall within this category (Shrestha,
Loomis 2001). Combination of approaches and methods also exist, most important of which
are the contingent behavior methods (i. e. a combination of TCM and CVM as in Melichar,
2014) or joint models combining of TCM and CM (Adamowicz et al., 1994, not yet applied in
the Czech Republic) and hedonic travel cost method (i. e. a combination of HPM and TCM
employed for estimation of values associated with secondary housing; not yet applied in the
Czech Republic). Unlike the two revealed preference methods that I have just introduced, the
benefit transfer is a method of valuation where the researcher does not collect primary data on
individuals. On the contrary, the method relies on secondary data from studies that have been
published in a different geographic, time or policy context.
The empirical models in this dissertation thesis are based on the following environmental
valuation methods:
Travel cost method
Hedonic pricing method
Benefit transfer (meta-analysis)
In the following text, I introduce the theoretical framework and the state-of-the-art of the three
approaches in more detail, including the description of the specific models applied in this thesis.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
23
1. Travel cost method
Recreation demand models are also referred to as travel cost models because the costs of travel
are major part of accessing the recreation area. The methodology of recreation demand stems
from the neo-classical microeconomic theory of demand. The basis of recreation demand
models stem from the fact that each visit to a recreation area requires that the visitor must incur
travel costs to access the site. Travel cost models link the quantity demanded (visitation) with
the travel cost as a shadow price of the visit; and as in any conventional demand analysis, we
observe that with increasing travel costs, the quantity demanded decrease (Freeman, 2003;
Phaneuf and Smith, 2005). Travel costs reflect real travel expenses (e. g. on fuel) and also
opportunity cost of time that the visitor need to spend to get to the recreation site. The
opportunity cost of time is estimated using wage as a shadow value of time. Thus, all recreation
demand models are models of the allocation of time; they must meet the individual’s time
constraint and income constraint.
Recreation demand models have considerably developed since their initial use in the 1950´s.
Models of the demand for recreation currently range from the most complex corner solution
models to much simpler single site demand model. In this thesis, I focus on two commonly
applied models: the single site travel cost model, and the site choice model that is based on
random utility theory (Parsons and Massey, 2003).
1.a. Single-site travel cost model
Single site demand models represent downward sloping models of individual demand curves.
The quantity demanded is the number of trips realized by individual to particular recreation site
and the price are travel costs. The number of trips is usually measured per year and modelled
as a function of independent variables, which include travel costs, income and other socio-
economic variables. The environmental quality of the recreation site is however usually fixed
for the given period of past visitation for all visitors in the sample and therefore is not explicitly
modelled
1
.
1
These characteristics may be varied by combining the data on past behaviour with stated data in a model of contingent
behavior, as stated above.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
24
Since the data on quantity of trips are non-negative integers, count data models are preferred
over ordinary least squares regression for the analysis. The simplest form is the Poisson model,
the probability function of which may be expressed as:
!
)|Pr( y
e
yY
y
where Y denotes the number of trips, the parameter λ is the expected number of trips and is
a function of independent variables specified in the model. The expected value and the variance
of Y are equal to λ. The number of trips is a non-negative variable and therefore λ usually takes
a log-linear form:
λij = exp(xij β1 + piβ2)
where xij are j socio-economic variables of respondent i and other variables determining his/her
trip, and pi are the travel cost spent by the respondent (i = 1, 2, …, n) on the trip. β1, β2 are
unknown regression parameters.
The Poisson model imposes the restriction that the mean and variance have to be equal (i. e.
require no overdisperion in the data). Real data on trips of the individual taken to the recreation
site are often of a large range, and typically a large portion of the sample of recreationists take
just one trip. The data therefore do not comply with this assumption and the variance typically
significantly exceeds the mean. The consequence of overdispersion is the fact that the standard
errors in the case of the Poisson model would be underestimated (Haab and McConnell, 2002).
Negative binomial models are less restrictive in this respect - they allow for differences between
mean and variance and are frequently more suitable for the data. The negative binominal
regression model addresses the failure of the Poisson model by adding a parameter, α, that
reflects unobserved heterogeneity among observation. The negative binominal distribution
assumes the following form of the probability distribution (see e.g. Haab and McConnell,
2002):
Where Γ() is the gamma function. The expected value of the negative binominal distribution is
equal to λ. However, the variance of the dependent variable is V = λ (1 + α λ). The parameter α
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
25
is the overdispersion parameter. If α = 0, no overdispersion exists. But if α > 0, then
overdispersion exists and the Poisson model is rejected in favor of the negative binominal
distribution.
Further problems with on-site collected data are associated with truncation of the sample to one
trip, and also the more frequent users occur in the sample, which leads to endogenous
stratification (Creel and Loomis, 1990; Englin and Shonkwiler, 1995). To correct the
probability function for truncation, we replace y by y-1 in the basic Poisson function (Parsons
and Massey, 2003; Haab and McConnell, 2002). Then the function assumes the following form:
)!1(
),0|Pr(
1
y
e
yyY
y
In practical applications, the influence of overdispersion, truncation and endogenous
stratification needs to be tested.
1.b. Multi-site recreation demand model (Random utility model)
Random utility models stem from McFadden´s random utility framework (1984) that was
rationalized by Hanemann (1984). Contrary to a single-site model which predicts the number
of visits for one site, the random utility models (RUM) analyse the choice of the particular
recreation site among many competing options. The choice among alternative recreation areas
is dependent on the price of a visit and the characteristics of the area (Haab and McConnell,
2002; Freeman, 2003).
In terms of modeling choice between sites, the RUM actually works in a very similar fashion
to the choice experiment approach, in that visitors´ choices over sites are modeled as being
dependent on site attributes (one of which is price), plus an error term. The crucial difference
is that the data is taken from actual choices, using information on which sites respondents have
visited over some time period, how far/long it takes them to reach these sites, and which are the
environmental attributes of the sites. As in the single-site travel cost model, distance and time
costs are combined into an overall monetary travel cost, which is then included as one
determinant of site choice in the econometric model.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
26
The random utility model in the case of travel cost method may be described as follows: Every
trip to natural site of interest i brings utility Vi. Vi is determined by a vector of m explanatory
variables, which include travel costs to the site of interest tci and various forest site
characteristics qi :


Error term εi then captures every (random) effect unmeasured by the explanatory variables
included in the equation - unobserved individual and site characteristics.
The visitor always has also a status-quo choice, which is not to choose any of the recreation
sites offered for recreation and “stay at home”. The status-quo option is associated with a utility
of V0 and may be expressed as a function of n socio-economic characteristics z of individual i:


It is assumed that individual chooses the alternative yielding the highest expected utility
(Parsons et al., 2000). Further we assume that
is independent and identically distributed (IID)
with an extreme value distribution (Haab and McConnell, 2002).
Most frequent specification of random utility models within environmental valuations are
multinomial logit (MNL), nested logit (NL) or mixed logit (MXL), out of which application of
MNL has been the most common (for example in Caulkins et al., 1985; Parsons et al., 2000).
MNL has been popular mostly for simplicity of the estimation, which is based on logit probability
for choosing any given alternative k in the choice set (i. e. a location to visit in travel cost RUM).
The probability for choosing any given alternative k from the choice set using MNL is:
  


The main drawback of MNL is assumption of independence of irrelevant alternatives (IIA),
which results from IID. IIA means that ratios of probabilities between each two alternatives in
the choice set are not affected by changes in choice set (adding or deleting an alternative). It is
likely to be violated particularly if several alternatives are similar.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
27
Nested and mixed logit do not implicitly assume that IIA holds (Morey et al. 1993, Milon 1988).
Nested logit is based on idea that the respondent sees some alternatives in the choice set as
nested under a category and measure effect of the categories. The ratios of probabilities are then
allowed to differ between subgroups, but are assumed to be the same within each group after a
change in the choice set (IIA has to hold within groups, but not among groups). Examples of
nested logit in recreation behaviour analysis are Carson et al. (2009) and Lew et al. (2009).
Mixed logit (also called Random parameters logit) is then a generalization of MNL model. It is
theoretically more robust and in addition to IIA it accounts also for preference heterogeneity
among population, which is second problem that may arise with simple MNL. Model
specification is described e. g. by Haab and McConnell (2002). Mixed logit in recreation utility
estimation was used for example by Train (1998).
At present recreational demand models represent one of the most sophisticated models of
microeconomic modelling of consumer´s behaviour.
2. Hedonic pricing method
Hedonic pricing method stems from consumer theory first mentioned by Lancaster (1966) and
expanded by Griliches (1971) and Rosen (1974). According to this theoretical approach,
housing is considered a composite good the final price of which is not determined by the good
as such, but by a particular combination of attributes that characterize housing as a market good.
Market price of housing may be then disaggregated into a set of prices which refer to particular
housing attributes. Under equilibrium, the value of each attribute perceived by consumer is then
reflected in market clearing price of the good and is interpreted as the marginal (implicit) price
of the attribute.
Many of the housing attributes may be treated as given at least in the short run, as it is practically
impossible to adjust their level according to market demand. Equilibrium market price of
housing is then formed mainly by the demand side and reflects subjective values perceived by
consumers. To reach the equilibrium, it is also assumed that market subjects are rational and
well-informed about the level of attributes of housing properties (other assumptions are
discussed by Bateman et al., 2001, Bateman et al., 2004; Garrod and Willis, 1999; McConnell
and Walls, 2005).
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
28
The hedonic pricing method consists of a statistical analysis of two phases. In the first phase,
the hedonic price function is derived. The hedonic price function may be characterized by the
following form (Markandya et al, 2002):
),...,,,...,,,...,( 111 mkj EENNSSfP
where P is the market price of the property, which is a function of explanatory attributes of
housing. The traditional hedonic variables are (Markandya et al, 2002):
Structural variables (S1, ... , Sj): number of rooms, extent of living area, type of
heating, construction material etc.,
Neighborhood variables (N1,, ... , Nk): proximity to city center, transport facilities, crime etc.,
Environmental variables (E1, ... , Em): proximity to urban open areas, air quality, noise level,
flood risks etc.
Hedonic price function reflects specific conditions of supply and demand that exist at a particular
property market, which has implications for transfers of the results to other markets (Day, 2001).
The results of the first-stage model yield so called implicit price of the environmental attribute,
which is based on the statistical relationship between the price Ph and the attribute Eh, and
represents the isolated difference in property prices brought about by differences in the levels of
the environmental attribute (e. g. number of trees in front of the house):
 
 ,
where p is the partial derivative of the hedonic price function P with respect to the particular
environmental attribute (E1 to Em), and forms the implicit price function of the particular
environmental attribute.
The implicit price function further allows to determine the welfare effect of marginal changes
in the environmental attribute levels, which is done in the second step of the analysis. Implicit
price represents one point on the demand curve of a particular household. The demand curve
may be estimated using the implicit price function and the levels of socio-economic and
demographic characteristics of the household. Only the second phase of the analysis yields
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
29
estimates of the willingness to pay (WTP) of households for changes in level of environmental
attributes that affect their living. Implicit prices for attributes that behave like normal goods
tend to be lower than the WTP.
Hedonic pricing method has the potential to include into the estimation of greenery value
direct and indirect use values practically all recreational, aesthetical and other ecosystem
services that homebuyers consider when deciding about dwelling purchase or rent. Those
involve particularly recreational and aesthetic services, but also microclimatic services
generated in surroundings of the dwelling due to presence of greenery. However, the values of
separate services are not usually differentiable from the aggregate value estimated for given
green area. Because this technique could only capture those services that are directly linked to
the property market, HPM does not enable to estimate non-use values bequest or existence
that are an important part of the total economic value of urban ecosystem services.
Most frequent definitions of greenery open space amenities in hedonic price models in the
neighborhood of a house are the following (Kaprová, 2014):
Distance to nearest the urban greenery unit
Area of the nearest urban greenery unit
Joint influence of distance and area of the nearest urban greenery unit (use of interaction
terms)
Percentage of the area of greenery in given perimeter around the house/flat (length of
perimeter varying from 50 to 500 m among studies)
View on green area (binary, or % of view covered with greenery)
The traditional hedonic pricing model does not account for the spatial dimensions of the housing
and the fact that the housing prices or some characteristics of the neighbouring buildings may
be spatially correlated. Also, other sources of spatial correlation may be common omitted
eplanatory variables and measurement errors (if the price expectations on the housing market
are formed based on neighboring values, then we will observe that selling prices of houses
affect the price of houses in their vicinity). Spatial dependence is widely observed in real estate
data and lead to spurious results of traditional hedonic regression (that is usually estimated by
ordinary least squares technique) and may affect the parameter estimates of the model. To
correct for spatial autocorrelation, approaches originating in spatial econometrics and
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
30
geographic analysis (Anselin, 1988; Anselin, 2006; LeSage and Pace, 2009) permeate to
hedonic pricing modelling. Spatial lag model and spatial autoregressive error model are the two
most widely used econometric models that allow to correct for this problem (e. g. Panduro and
Thorsen, 2013). As Kaprová (2014) shows, also in the case of Prague reality market, there is
evidence of spatial autocorrelation. An analysis that would account for its structure represents
a desirable way of development of the further hedonic modelling studies of Prague property
market (Ibid.).
3. Benefit transfer
Benefit transfer (BT) employs existing values as an approximation of the value of a new site.
Value is derived from a ‘study site’ where the original study based on primary data was
conducted, and is transferred to a ‘policy site’. This method is important in the view of the fact
that the estimates of benefits may be crucial for decision-making concerning recreational areas
(or may constitute an essential input to cost-benefit analyses), but time and budget constraints
often do not allow for performance of a primary study at the policy site.
The main techniques of benefit transfer include (Navrud and Ready, 2007):
1. Value transfer from original study to a new site with correction (e. g. correction for
income level between the two sites)
2. Transfer of benefit function from the original study to a new site (enables to correct for
characteristics the levels of which differ between the two sites)
3. Meta-analysis of previous studies (a complex method that yields a new benefit function,
the source of which are a number of studies focusing on different sites; meta-analyses
may enable to correct for a range of characteristics that differ between sites)
The validity of benefit transfer, particularly in an international setting, remains to large extent
uncertain, is discussed and needs to be tested. Concerning recreation demand for natural areas
in the Czech Republic, the dissertation thesis brings the first discussion on the validity of
domestic and international transfer exercise available. Suggested dimension of thesis will then
represent important insight if and how the Czech recreation demand fits into the context of
foreign estimates.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
31
3.a. Meta-analysis
In meta-analysis, the individual value estimates from different studies are treated as observations
in a regression analysis. Meta-analysis, as the most complex means of benefit transfer methods,
allows for synthesis of previous recreation demand research results, testing hypotheses with
respect to the effects of particular determinants of value, and facilitates validity testing or testing
for the potential publication bias.
The concept of meta-analysis is based on underlying utility theoretic model (Boyle et al., 1994,
Smith et al., 2002). To disentangle variability of results among studies according to all
mentioned factors, weak structural utility theoretic approach (WSUT) is applied in this thesis,
as it is more frequent in most MA studies (Rosenberger and Loomis, 2000). As opposed to
strong structural utility theoretic approach (SSUT) which is based solely on core economic
variables that determine visitors´ demand for recreation, WSUT concept employs also study
design variables (Bergstrom and Taylor, 2006) which may significantly affect the magnitude
of the estimated values.
Core economic variables represent recreation site and user population characteristics and
correspond to underlying utility theoretic model. Study design variables are used to widen the
scope of the model so as to explain more heterogeneity across studies and to improve the
predictive power. The model may be formalized as follows:
    ,
where WTPij is WTP estimate i from study j, SITEij is a vector of recreation site characteristics,
USERij a vector of socio-economic variables describing particular user population and STUDYij
vector of methodologic variables characterizing study design. The STUDY vector also enables
to widen the data set by including different valuation approaches such as in Shrestha and
Loomis (2001), Zandersen and Tol (2009) or Kaprová and Melichar (2014). The random error
term then captures residual unobserved variability of WTP.
Meta-analysis may be carried using studies within one evaluation technique only, or across
methods, as in Shrestha, Loomis 2001; where Smith (2002) provides an insight into calibrating
value estimates originating in different valuation methodologies (Marshallian vs. Hicksian
surplus measures).
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
Theoretical framework
32
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Manuscripts - collection of works
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I. Revealing preferences of Prague's homebuyers towards greenery
amenities: the empirical evidence of distance-size effect
Melichar, J. & K. Kaprová (2013).
Landscape and Urban Planning 109(1): 56-66.
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Abstract
The proximity principle of urban greenery and its positive effect on the prices of residential
buildings is well documented in empirical literature. Application of hedonic price method in
many urban and environmental settings has confirmed the proximity principle indicating
residents´ positive preferences toward environmental amenities provided by green spaces. As
proved in previous applications, the proximity effect lowers with increased distance from the
dwelling, and the extent of the citizens´ demand for urban open space also differs with type and
size of open space. The empirical evidence is relatively scarce with respect to combining both
the distance to and the size of the nearest greenery. The majority of hedonic price studies on
green space services have been conducted in the US and Western Europe, but the empirical
evidence from the transforming economies of the former communist states of Central Europe
is scarce. Similarly to many other European cities, urban sprawl has threatened green areas and
agricultural land in Prague since the starting of the transformation process at the beginning of
the 1990s.
Therefore, our intent is to contribute with two aspects: (i) trace the value of urban greenery
amenities on the housing market in the newly transformed economy using a hedonic price
model and (ii) capture the distance and size joint effect of green space on the property market
by interaction effects. The study confirmed that proximity to greenery and its area are important
determinants of housing prices in Prague, and benefits to residents differ with the type of
greenery.
Keywords
Urban green spaces, Recreational services, Property market, Hedonic price method, Implicit
price, Geographic information system
1. Introduction
Green areas constitute an important concept of urban solutions in the cities. Urban areas contain
many forms of greenery from small lawnsand gardens over avenues of trees to large urban
forests, recreational parks or protected areas. It is well known that urban greenery performs
many social, spatial, technical and urban ecosystem services (UES) and as such has also great
potential in contribution to the well-being of urban residents.
According to Bollund and Hunhammar (1999), urban open spaces provide many services that
are directly used by residents. These include recreational services, where vegetation provides
a setting of relaxation and relief from urban stresses and congestion. The scope and magnitude
of recreational services may differ with greenery type. Potential to develop recreation
opportunities are generally expected to play the more significant role the larger the greenery
unit is (i. e. for example when comparing small plots of greenery among buildings with urban
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forest). Urban vegetation has also aesthetic value, due to which urban greenery forms a pleasant
environment for living when located in residential areas, or may be even also used as
architectural feature.
Greenery also provides ecosystem services that are less obvious to residents, but are of no less
importance. Those may support recreational services, but urban residents tend to percept them
rather indirectly, through overall pleasant environment for living in the city. As Costanza et al.
(1997) state, main ecosystem services include the effect of greenery on urban microclima.
Greenery absorbs carbon dioxide emissions, which are in the context of the city emitted mainly
from urban transport. Further, presence of vegetation close to road network decreases
secondary emissions of particulate matters and works as an acoustic screen between traffic and
residential areas (Morancho, 2003). Another unquestionable factor is the effect on hydrological
cycle, where green areas increase air humidity in predominantly hard surfaced area of cities,
and through that reduce the intensity of urban heat island. Green areas also enable imbibition
of water, which may have positive technical effects on sewerage system in terms of decreasing
the risk of become clogged due to rains or flash floods. To these ecosystem services, also
support of biodiversity should be added, as especially larger units of greenery play invaluable
role in interconnection of urban environment and surrounding countryside, which is important
for migration of species. Increased biodiversity may also support recreation services of urban
greenery.
The magnitude of ecosystem services provided by particular unit of greenery may also differ
with type, quality and area of urban green spaces. Microclimatic and hydrological services are
generally expected to have positive relationship with quality and area of greenery. Poudyal et
al. (2009) suggest similar relationship for biodiversity. Larger greenery units with higher
ecological stability may provide habitats for various species and therefore considerably
contribute to biodiversity enhancement of the area, while smaller units (for example avenues
of trees) serve typically as migration facility without additional habitat provision. Urban
greenery involves also protected areas, where the emphasis of management is put directly on
biodiversity conservation. As such, these types of greenery should evince greater ecological
stability and species richness than non-conserved areas.
In past decades, the evidence of above-mentioned positive effects provided by urban greenery
has led to growing tendencies to integrate urban green areas into decision-making about spatial
solution of cities as a legitimate part of urban planning. However, inclusion of urban ecosystem
services into decision-making process is not straightforward, as these services do not have
explicit prices reflected on existing market. In economic terminology, we speak about external
benefits. The obvious consequence of external character of services is that the benefits
associated with greenery are not fully taken into account in urban planning and decision-
making about publicly owned urban green areas (Tyrvainen, 1997; Luttik, 2000).
The value of non-marketed services associated with urban greenery has then to be estimated
indirectly on the basis of information from surrogate or hypothetical market. The most common
empirical approach based on surrogate markets is hedonic price method (HPM) that reveals
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households’ preferences towards UES from housing market. On the other hand, stated
preference methods based on hypothetical markets such as contingent valuation or choice
analysis use specially designed surveys that enable elicit households’ preferences for open
green spaces directly.
Vast amount of hedonic price studies on UES provided by green spaces has been managed in
US and Western Europe but the empirical evidence from the transforming economies of the
former communist states of Central Europe is relatively scarce. One example could be hedonic
price study that was only aimed at assessing of recreational opportunities in urban forests in
Prague (Melichar et al., 2009).
Similarly to many other European cities, urban sprawl has threatened green areas and arable
land in Prague since the starting transformation process at the beginning of 1990s. Therefore,
our intent is to contribute with the application of hedonic price method on the transforming
housing market in the Czech Republic in order to investigate the positive effects of different
type of urban vegetation not only large urban forests but also less ecologically valuable land
such arable land, or greenery spaces under protection regimes on this market. In the legal
setting of Czech Republic, the greenery is almost entirely provided to citizens through public
budgets. As many effects of green areas do not come through markets, the decision-making
about optimal quantity of greenery supplied comes to be heavily problematic. Concerning this,
contribution of urban vegetation to the well-being of residents may become a fundamental
guide-post for public bodies.
The central hypothesis is that residential property market in Prague positively reflects urban
ecosystem services provided by large greenery spaces, i.e. there is presence of positive
externalities. The type of positive externalities provided to residents may significantly differ
with type of urban vegetation cover, area and other characteristics of greenery (Anderson and
West, 2006; Kong et al., 2007). According to that, we compare three different types of greenery
large urban forests, arable land, and small protected areas in Prague in terms of their
proximity to the dwelling and greenery area, and we analyze also interactions between both
measures. Moreover, we investigate the joint influence of all types of green spaces expressed
as the relative coverage of greenery on cadastral area.
The second section reviews the literature focusing on the green open space valuation in an
urban context. In the third section, bases of the theoretical foundations of the hedonic approach
are described. The fourth section presents the study area, the fifths one then describe data and
explains how the variables entering into the hedonic model are measured. The empirical models
used in the hedonic regression including their results are presented in the sixths section. The
seventh section provides discussion with respect to utilization of the results. The eighth section
concludes.
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2. Literature search
Applications on valuation of urban ecosystem services are relatively considerable in number
and focus on a wide range of greenery types and associated services. As was outlined above,
the magnitude of the value tends to differ according to type, quality and area of greenery and
reflects also specific characteristics of greenery units. Most of the studies concentrate on the
effects of large areas urban parks, forests and greenbelts (Asabere and Huffmann, 2007;
Anthon et al., 2004). Other studies pay specific attention to inclusion of protected areas into
the model in contrast to ordinary urban vegetation. The evidence of differential in value of
protected and unprotected vegetation is mixed although urban parks equipped with trails may
provide good opportunities for recreation (Sander and Polasky, 2009), protected areas may
bring more environmental functions and therefore their implicit price may be higher
(Lutzenhiser and Netusil, 2001).
Most of the previous research show that greenery has overall positive effect on property prices
(Morancho, 2003; Poudyal et al., 2009; Crompton, 2001), though there exist some exemptions
(Tyrvainen, 1997; Luttik, 2000). Positive effects of ecosystem services may be driven into
negative when overweighted by some specific characteristic of the urban green area in the city,
for example when it is associated with crime (Troy and Grove, 2008) or heavy recreation use
(Tyrvainen, 1997).
Crompton (2001) reviewed 25 studies aimed at analysis of impact of parks on property prices,
from which 20 reported significant positive impact of park, ranging from 10 to 20% of property
values. The negative impacts were then caused mainly by methodological limitations of
reported studies. Crompton also finds that the impacts vary considerably with characteristics
of the park.
Lutzenhiser and Netusil (2001) focused on two types of open spaces natural areas and urban
parks. Their findings indicate that each type of greenery brings different effects on housing
prices. While proximity to urban park decreases the price, natural parks have positive effect on
the price. For both open spaces the residential prices increase with their size. Bolitzer and
Netusil (2000) also conclude that the size of the open space has statistically significant positive
effect on sales prices of housing.
In the Czech Republic, Melichar et al. (2009) conducted a hedonic analysis aimed at recreation
functions of urban forest parks. 19 forest parks in Prague were covered in the study.
Recreational aspects of forest parks were interlinked with equipment variables number of
benches and length of trails. 1 km increase in the distance from urban forest leads according to
the results to 1.61% fall in the housing price.
Anderson and West (2006) analyzed the effect of several open spaces on property prices in
Minneapolis-St. Paul metropolitan area. Significant positive effects were found for proximity
to neighborhood parks and special parks (defined as national, state and regional parks,
arboretums, nature centers, natural areas, and wildlife refuges). Although the estimated effects
are relatively low, the analysis proved that special parks have greater effect on housing price
(1% decrease in distance leads to 0.0252% increase in price) in comparison with neighborhood
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parks (0.0035% increase in sales price). Tyrväinen and Miettinen (2000) confirmed that
increased amount of forested areas in the surroundings of apartment increases its price.
According to their results, decreasing the distance to the forest by 1 km leads to 5.7% increase
in price.
Asabere and Huffman (2007) used data set from Bexar County (Texas) to confirm the
hypothesis that not only green belt, but also golf course, which represents an open space least
rich in vegetation, has a positive effect on prices of houses in the neighborhood. Interestingly,
the golf course adds to property prices 9%, which is more than the estimated effect of
recreationally equipped greenbelt (5%).
Cho et al. (2008) analyzed benefits of green areas that differed by composition of tree layer.
While proximity to a broad-leaved forest has according to results of their study positive impact
on residential prices, proximity to coniferous or mixed forest decreases the prices. Garrod and
Willis (1992) came with analogous conclusions.
As for analysis of smaller units of greenery, the study by Morancho (2003) suggests that the
area of the park does not have any significant effect on price and the analysis is concluded with
suggestion that in urban areas it might be appropriate to include rather smaller green areas than
a few large urban parks. The distance to the nearest urban green space is nevertheless proved
to be inversely related with housing price. However, Poudyal et al. (2009) have come to
opposite results using data set from Virginia. Their study suggests that from recreational and
aesthetic point of view one large continuous green area more important that the same area
dispersed around the residential property.
Tyrvainen (1997) found that although the amount of forested area in a housing district affects
property values positively, proximity to small forest parks has negative effect on prices.
Mansfield et al. (2005) test hypothesis that trees on a parcel or in the neighborhood around it
in North Carolina may serve as substitutes for living near large urban forests. The value of
private forest is lower for houses with more vegetation on the housing plot, all else equal, so
the authors conclude that proximity to private forest may be substituted by private greenery on
the parcel. Irwin and Bockstael (2001) point out that measurement of greenery on the parcels
adjacent to observed dwellings may be imprecise due to the fact that use of these areas is
determined by homeowners, as opposed to other green areas in the city. All characteristics of
privately owned greenery are then endogenous to housing value. Almost none of the studies
conducted so far accounts for this problem, which may be the cause of underestimation or
insignificance of estimates of small green areas.
As may be seen from previous research cited above, urban greenery amenities may be
considered very site specific. It is necessary to address also the question whether different
forms of greenery bring benefits of diverse magnitude. The studies reviewed above range in
analyzed geographic areas from United States, Europe and China. The characteristics of
housing market and homebuyers are also important factors that cause the results to vary among
geographic areas. In Central Europe the empirical evidence is very scarce, which offers us a
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unique opportunity to contribute to worldwide estimates and see to which extent they differ
from tendencies in other regions of the world.
Compared to previous hedonic price study realized on Prague’s residential market (Melichar
et al., 2009) which was aimed on urban forests only, this study widens the question on urban
greenery benefits brought to residents by incorporating effects of three other types of urban
open spaces arable land, greenery under protection, and relative coverage of greenary into
the model. We also precisely address the isssues of greenery areas that directly threatened by
housing development. The variable set was enriched by several important housing
characteristics, which contribute to precision of the model. We also address methodological
issue of variable construction interaction effects capturing the joint influence of distance and
size of greenery mentioned by Kong et al. (2007). Moreover, final data set was widened by
3,500 observations, which is expected to bring more accurate results in terms of estimated
implicit prices.
Although methods of environmental valuation are used to estimate effects of various
environmental amenities and disamenities, in research practice it is common to aim separate
studies on separate effects. In accordance to this trend, many reviewed studies do not contain
other environmental characteristics of the city than presence of greenery (Anderson and West,
2006; Asabere and Huffmann, 2007; Donovan and Butry, 2010; Cho et al., 2007; Melichar et
al., 2009). We account also for air quality, which is measured by composite index of four
pollutants.
3. Analytical approach
Hedonic pricing method stems from consumer theory first mentioned by Lancaster (1966) and
expanded by Griliches (1971) and Rosen (1974). According to this theoretical approach,
housing is considered a composite good the final price of which is not determined by the good
as such, but by a particular combination of attributes that characterize housing as a market
good. Market price of housing may be then disaggregated into a set of prices which refer to
particular housing attributes. Under equilibrium, the value of each attribute perceived by
consumer is then reflected in market clearing price of the good and is interpreted as the
marginal (implicit) price of the attribute. The application of the method is usually focused on
valuation of environmental externalities experienced by households in a given area.
Many of the housing attributes may be treated as given at least in the short run, as it is
practically impossible to adjust their level according to market demand. Equilibrium market
price of housing is then formed mainly by the demand side and reflects subjective values
perceived by consumers. To reach the equilibrium, it is also assumed that market subjects are
rational and well-informed about the level of attributes of housing properties.
The model may then be characterized by the following form:
),...,,,...,,,...,( 111 hmhhkhhjhhh EENNSSfP
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where Ph is the market price of the property, which is a function of explanatory attributes of
housing. The traditional hedonic variables are:
Structural variables (Sh1, ... , Shj): number of rooms, extent of living area, type of heating,
construction material etc.,
Neighborhood variables (Nh1,, ... , Nhk): proximity to city center, transport facilities, crime etc.,
Environmental variables (Eh1, ... , Ehm): proximity to urban open areas, air quality, noise level,
flood risks etc.
Hedonic pricing method has the potential to include into the estimation of greenery value
direct and indirect use values practically all recreational, aesthetical and other ecosystem
services that homebuyers consider when deciding about dwelling purchase or rent. Those
involve particularly recreational and aesthetic services, but also microclimatic services
generated in surroundings of the dwelling due to presence of greenery. However, the values of
separate services are not usually differentiable from the aggregate value estimated for given
green area. Because this technique could only capture those services that are directly linked to
the property market, HPM does not enable to estimate non-use values bequest or existence
that are an important part of the total economic value of UES.
3.1. Methodological issues
In spite of the fact that the model has been used in wide range of studies, some methodological
issues have not yet been overcome. Among these, the choice of functional form may be
mentioned. The economic theory does not provide much guidance about which functional form
should be chosen. According to the law of diminishing marginal utility, it is possible to assume
a nonlinear (concave) relationship between amenity attributes and housing price, which leads
to frequent use of logarithmic (Poudyal et al., 2009; Sander and Polasky, 2009), quadratic (Troy
and Grove, 2008) or Box-Cox transformations of the variables (Asabere and Huffman, 2007).
However, Cropper et al. (1988) show that flexible Box-Cox models may fail when key
explanatory variables are omitted. In such case simple linear model generates more accurate
estimates of implicit prices.
Other practice is to analyze interact effects between variables, which may improve explanatory
power of the model and enable more complex estimation with respect to how effects of
greenery vary with changes in other attributes of given type of greenery (Mansfield et al., 2005)
or other characteristics of housing (Anderson and West, 2006). According to Kong et al.
(2007), many previous studies do not address the question of spatial pattern in using separate
variables for distance and area of analyzed open space. Inclusion of these two types of variables
for one type of open space leads to biased estimation, because the estimation then does not take
into account the fact that two green spaces with the same area may be perceived differently
when located in different distances from the dwelling. In other words, it is necessary to include
also a variable which relates these two measures.
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While Kong et al. (2007) constructed a size-distance index, more usual way to deal with this
issue is to test inclusion of the two variables and their multiplication interaction effect.
However, previous hedonic greenery research using interaction effects usually omits the area
category (Anderson and West, 2006) or both base measures (Kong et al., 2007), which may
bias the results as well when not tested for the significance of the variables in the model. We
therefore clearly use both base measures and their interaction effect and test the stability of
models estimated this way.
3. 2. Use of HPM in public management
Hedonic pricing method may serve for evaluation of public projects effects of which involve
increases or decreases in quantity or quality of environmental housing amenities. It may be
useful for quantification of benefits into cost-benefit analysis of projects aimed at enhancement
or regeneration of urban green areas, as well as it has the potential to serve as a guide-post in
decision-making about permissions to build up an open space in the city.
In cost-benefit analysis, output of hedonic pricing model as such can be directly used in case
of relatively small project resulting in fundamental changes in urban greenery supply only on
a local scale (Garrod and Willis, 1999).
Hidano (2002) suggests using HPM also over this conventional approach, in case of non-
marginal projects, up to projects with vast and long-term effects on utility of residents. For
small projects with local effects, partial equilibrium analysis is used and we analyze merely the
effects on the property market. When the effects of given project are expected to cause changes
in utility prevailing in the long time and to spread over more markets in the economy (for
example when amenity of more pleasant environment attracts more inhabitants, which
increases labor supply in the area), general equilibrium approach has to be adopted.. However,
on which side of the borderline between „sufficiently small“ and „large“ projects the effects of
the project lie has to be specified individually for each project.
4. Study site
Prague is the capital of the Czech Republic, which lies in Central Europe. With a population
of approximately 1 million, the city constitutes the largest urban area in the country. The city
center is located approximately in the middle. The housing market may be characterized by old
luxurious apartments in the center, mixed housing development in circumjacent districts, which
in some parts smoothly passes to newly developed family houses in the suburbs.
The city may be seen as relatively rich in urban greenery, which is dispersed in various forms,
types, sizes and species composition all around the area. There exists a vast diversity among
particular open spaces ranging from separate lawns to spacious urban parks or specially
protected areas with considerable species richness. Prague as an urban complex is unique due
to the fact that green areas and other open spaces are present from the suburbs to the city center,
while in the center there can be found also features of naturally valuable areas.
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In urban planning of Prague, increasing attention is paid to preservation and enhancement of
green areas within the city. New urban plan which is being prepared includes creation of
greenbelt on the agricultural lands around the city (with area of cca 2,000 ha). This evidence is
in conflict with urban development pressures and raising problems with urban sprawl in the
city. According to Czech office for surveying, mapping and cadaster urban development, in
Prague the amount of built-up areas increases to the detriment of arable lands, permanent
grasslands, gardens and orchards. From 1990 to 2008 totally 764 ha of these urban areas were
developed, which is 1.5 % of the area of the city. The trend is expected to continue also in the
future. In spatial planning of the city there have also been tendencies to seriously consider
development of recreationally and environmentally most valuable areas, such as natural park
Prokopske udoli.
Analysis of socioeconomic value attributed to green areas in Prague in this context provides a
means to support ecological arguments in development planning of the city and may also
outline the magnitude of benefits changes associated with vegetation cover transformations in
individual areas.
5. Data
Data set used in hedonic price application consists of 8,568 apartment sales from 2005 to 2008
and was acquired from real estate catalogue operated by Czech company reality.cz (see
http://www.reality.cz). These data included sales price, date of sale, structural variables and
location of the observations. The rest of variables were generated by Geographical Information
System (GIS), in software ArcGIS, according to data layers from City Development Authority
Prague and Czech Statistical Office. Czech Statistical Office data served for completion and
validation of structural variables stated in real estate catalogue. The other two data sources
were used to generate environmental and neighborhood variables. All variables used in the
regression analysis, including their brief definition, expected influence on the dependent
variable and summary statistics, are summarized in Table 1.
GIS also served for validation of the spatial data location within residential building areas and
for detection of duplicate observations. Spatial analysis detected 2,815 observations as wrong.
Another 554 observations were removed due to incorrectly recorded data and in exploratory
analysis as extreme values and observations. The final data set used in the analysis consists of
5,199 observations. Their geographic distribution over the city is depicted in Figure 1.
Variable PRICE is the selling price of apartment recorded by reality.cz in period of 2005-2008.
This period is relatively stable regarding the fluctuations on the property market. Data have
been transferred to 2008 price level using price index for housing in Prague produced by the
Czech Statistical Office.
In regression model, we use structural variables that relate either to the apartment, or to the
building in which it is situated. BAD_STATE dummy variable includes apartments that are
described in real estate catalogue as „before reconstruction“ or „in bad state“. These apartments
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have not been maintained before sale and in most cases have to be reconstructed before
inhabitation. AREA is the built-up area of apartment and AREA_BUILD represents area of the
building in which the observation is situated. Other attribute related to the dwelling is
INHABITANTS, which denote how many persons live in the building. APART_BUILD stands
for apartments located in apartment building. BRICK_ROCK is a dummy variable indicating
that the structure of the building is brick, rock or their combination. Apart from BAD_STATE,
all structural variables were derived from GIS data layers the source of which is the Czech
Statistical Office. Further, Euclidean distance to the city center was used in the analysis, which
may serve as a proxy for accessibility possibilities. City center is represented by Saint
Wenceslas Statue standing on Saint Wenceslas square.
To test the hypothesis that different forms of greenery contribute to property prices with
different magnitude, we analyze effect of three types of open spaces: specially protected areas,
urban forests and fields.
In Prague, there can be found 89 small-scale specially protected areas, which account for 9%
of greenery area in Prague and 4% of total area of the city. They comprise 7 national nature
reserves, 15 nature reserves and 67 national monuments. Specially protected areas cover a wide
range of vegetation types: forest ecosystems, meadows, floodplain vegetation, rocks and
quarries. The access to these areas is not restricted in any way, so the recreation function may
be fully utilized by residents.
Urban forests represent greater complexes of urban greenery, with area ranging from 0.15 to
1,720 ha. Forest land accounts for 10% of the area of Prague. Geographical representation of
large urban forests is depicted in Figure 2. All forests in the city are categorized as special
purposes forests, which means that none of these forests serves for lumbering and is managed
with respect to enhancement of recreation function. Part of forested areas are forest parks, and
as such contain also maintained forest meadows and cultural lawns. Tree layer is formed mainly
by broadleaved trees, mixed forests are also common.
Fields are seen as a type of vegetation with low diversity as opposed to the other two greenery
features. They are expected to have minor contribution to urban ecosystem services, which
should be reflected in the significance or magnitude of value estimates. However, agricultural
land in the neighborhood of the dwelling may be seen as attractive by homeowners as it still
represents an open space. Agricultural land is located mainly in the suburbs and represents also
greater complexes of open space, with area of 0.15 to 1,310 ha.
In the regression models, each type of greenery is represented by distance to the nearest urban
space of given type and its area. Distance is measured as Euclidean, which is consistent with
previous research (Poudyal et al., 2009; Tyrvainen and Miettinen, 2000; Cho et al., 2008;
Mansfield et al., 2005).
Apart from greenery variables, we account also for air quality in the surroundings of the
apartment. Especially in wither time, urban air pollution is a serious problem due to damages
on human health, building materials and vegetation. Natural services of UES can effectively
mitigate and absorb air pollutants PM10, NOX, SO2 and O3. Air quality is our model
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represented by air quality index, which includes effects of PM10, SO2, NO2 and benzene. The
index is methodically based on CITEAIR (Common Information to European Air) initiative
and is derived from proportions of average yearly concentrations of partial pollutants to
respective imission limit. The overall quality index is the average of partial indexes of each
pollutant. The index ranges from 0.31 to 1.66, where the higher the number, the lower the air
quality is. All environmental variables were coded from data layers provided by City
Development Authority Prague.
6. Results
As outlined above, our intent is to find out the interconnection between the residential property
market and urban ecosystem services that could influence the property prices particularly
through recreational, aesthetical and microclimatic services. Therefore, we have constructed
and econometrically estimated several hedonic price models.
We first analyze the effect of various types of greenery, as specified by previous studies
(Lutzenhiser and Netusil, 2001; Asabere and Huffman, 2007). To test stability of estimates, we
estimated firstly separate models for each type of open space, and then covered all greenery
variables in one model. We test also significance of interaction effects between variables
describing each type of greenery. As for specification of variables in regression models,
logarithmic transformation of variables was tested, but in the end specification with linear price
and distances to greenery proved to capture the highest portion of variability in price. However,
we used logarithmic transformation on greenery area, following the assumption that intensity
of price change due to increment of open space area declines with the acreage of the greenery.
That means, residents tend to consider more increments in area of smaller greenery units than
in vast open spaces, where additional increase in area may not be noticed at all.
We also found out that greenery has significant effect on property price only if the open space
is sufficiently close to the dwelling. In the case of Prague, distance to nearest green area in the
data set exceeds 7 km for urban forests and 4.5 km for specially protected areas and fields.
With increasing distance also the complexity of other variables potentially influencing the price
increases and the effect of proximity to urban space becomes intangible, as shown by models
we estimated. Crompton (2001) found that large areas of urban greenery have substantial
impact on property price up to 500 or 2,000 feet (152 - 609 meters). Mansfield et al. (2005)
proved that overall effect of greenery is significant only to 3,200 m distance. We therefore
assume the proximity to the three types of areas analyzed in this paper to have clear effect on
property prices to 2,000 m only. This also leads to reduction of multicollinearity problem in
the model to acceptable level (as measured by VIF). As Breusch-Pagan test rejected
homoscedasticity of residuals, we estimated the models with White robust standard errors.
Table 2 shows results on three models which account for analyzed forms of greenery
separately. All structural variables proved to be significant, with expected signs. Distance to
all open spaces is inversely related to the price, although the effect of field is not significant.
With every additional meter the property price decreases by 4 thousands CZK for specially
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
I. Revealing preferences of Prague's homebuyers towards greenery amenities:
the empirical evidence of distance-size effect
49
protected areas and urban forests. The area of specially protected has negative impact on
housing prices, which means people prefer to live near smaller units of these type of greenery.
Area of urban forests affects price only through distance to the forest, as may be seen from the
significance of coefficients. With increasing distance the magnitude of positive effect of
proximity to urban forest shrinks. Even within 2,000 meters people tend to consider the area of
forest only if it is located sufficiently close to the dwelling. Distance to field does not show to
be related to property price, indicating that residents are not willing to pay any extra money for
location close to these forms of open space. The price then does not seem to reflect any
recreational or environmental functions provided by this type of open space. As expected, air
quality brings positive effects on property price the lower the index of air quality, the higher
the observed price, all else constant.
Model 4, which includes all types of greenery, brings similar results in terms of direction and
significance of coefficients (see Table 3). In accordance with previous research, most important
variables which influence property prices are structural variables. Whilst structural variables
account for 56.62% of the variability in price, environmental variables add only a few
percentage points to the explained variability. Analysis of variance inflation factors does not
indicate any major problems with multicollinearity (Table 4). After omission of mutually
related variables (interactions), which are by their definition expected to reach high values,
neither of VIF values exceeds 5.
The model confirms that presence of fields has not any significant effect on price. When
accounting for all types of greenery, the area of specially protected areas becomes insignificant
and has effect on price through distance greenery only. The average decrease in price of 1,304
CZK for every additional meter to SPA declines with its acreage. Distance to forests is
inversely related to the price, such as the area of forests. The interaction term denotes that for
larger forests, the decline in price associated with distance to forest declines. The overall
findings are in accordance to Morancho’s conclusion (Morancho, 2003) that the presence of
smaller plots of greenery is sufficient for residents. However, it is necessary to point out that
this conclusion refers only to larger greenery units, as we have not considered small dispersed
greenery areas in the model.
Having proved positive effects of proximity to two different types of ecologically valuable
urban spaces, we focus on estimation of specific effects of developing urban open spaces in
Prague. As stated before, in Prague the evidence shows that mainly ecologically less valuable
greenery units are being developed at present. Those include mainly arable land, permanent
grasslands, orchards and gardens. While the separate effect of fields was not significant in
previous models, we expect that with broadening the definition of areas threatened by
development by grasslands, orchards and gardens the results will show that the overall effect
of amenity of neighborhood with such types of open space becomes an important determinant
of housing price. That also proves that housing development of undeveloped land brings
negative effects to residents, although these types of open space do not belong to highly valued
in terms of recreation and environmental facilities.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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50
We cover all these types of open space with one variable GREEN_SEL, which is determined
as percentage of area of these types over the area of cadastral territory, as derived from data
layers of City Development Authority Prague. For this exercise, Prague is divided into 112
cadastral territories.
The results (see Table 5) suggest that every per cent of total area covered by those types of
greenery adds 0.00213 per cent to the price. When a project leading to increase of selected
greenery types over the area of cadastral territory by 1 percentage point is implemented, the
price of apartment (computed at average price 5,018,006 CZK) will increase by 10,697 CZK.
7. Result’s utilization and discussion
Although the definition of variable GREEN_SEL is rather aggregated when compared to usual
measures of distance and area, it may be helpful for inclusion of estimates into decision-
making. Measuring effects of distance or area of open space may not lead to straightforward
estimation of aggregate benefits associated with realization of a project to enhance green open
spaces in the city (say, when a new open space is to be developed). To be able to aggregate the
effects with utilization of estimates measured by distance from a conducted study (either
directly from policy site, or by using methods of benefit transfer), the decision-makers would
have to find out for how many households and by how much the distance to nearest open space
has decreased. Further, as our results suggest, the distance and area of nearest open space have
effect on price up to certain distance only, which also complicates the aggregation.
However, simple measures as percentage of greenery on cadastral area defined within the city
(which have to be sufficiently small to ensure variability) ease the utilization of estimates in
practical decision-making. In our case, we can estimate the total increment to all apartments
within given district in case of realization of project that would increase the amount of
aggregate of selected types of greenery by 1 percentage point. For this exercise, we used
logarithm of price to enable the implicit price to vary across districts of Prague.
Resulting estimates of implicit prices are found in Table 6. Implicit prices are used to compute
increase in price of housing for all apartments in the district due to increase by 1 percentage
point in area of GREEN_SEL. The procedure relies on assumption that construction of new
urban greenery unit impacts only houses in some distance from the location of new greenery
unit, which is here represented by one district. We can see that when taken into account all
housing units in a district, increase by 1 percentage point leads to considerable increase in
aggregate of housing prices from 239 to 1,119 mill. CZK. When the magnitude of the change
in supply of urban greenery is not considered to impact all housing units in a district, similar
computation may be used for smaller cadastral units.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
I. Revealing preferences of Prague's homebuyers towards greenery amenities:
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51
8. Conclusion
The proximity principle of urban open green spaces and its positive effect on the prices of
residential buildings is well documented in empirical literature. Application of hedonic price
method in many urban and environmental settings has confirmed the proximity principle
indicating residents’ preferences toward urban greenery amenities. As proved in previous
applications, the proximity effect is lowering with increasing distance from the dwelling and
the extent of citizens’ demand for urban open space also differs with type and size of open
space. The empirical evidence is relatively scarce with respect to combining two major green
amenity effects, both the distance to and the size of the nearest greenery and either with respect
to analysis of more greenery types.
Concerning these issues, the evidence from the Czech Republic and Central Europe is very
scarce. This paper should fill in this gap, accounting for three different types of open space. In
the legal setting of Czech Republic, the greenery is almost entirely provided to citizens through
public budgets. As many effects of green areas do not come through markets, the decision-
making about optimal quantity of greenery supplied comes to be heavily problematic.
Concerning this, contribution of urban vegetation to the well-being of residents may become a
fundamental guide-post for public bodies. To contribute to this topic, we take into account
vegetation types the area of which has been for the most part subject to housing development
in recent years.
Results show that green open spaces in urban areas constitute a considerable part of housing
prices in Prague. Having accounted for separate effects of proximity, area and their interactions
to avoid the problem of bias due to unobserved spatial pattern (Kong et al., 2007), we confirm
that proximity to green open spaces and their area are important determinants of housing prices.
The study proves that benefits to residents differ with type of greenery. Proximity to specially
protected areas and urban forests brings expected significant positive effects on residential
property prices. Fields as such were proved to be significant only when taken into consideration
as a part of open spaces threatened by housing development. Although probably less rich in
magnitude of ecosystem services, open spaces threatened by housing development are still
relevant for residents. Every percentage point of these areas on cadastral territory increases the
price of apartment by 10,697 CZK.
Acknowledgement
The research on this study was supported by the grant of the Czech Ministry of Transport
CG712-111-520 Quantification of external costs of transport in the Czech Republic. Data about
apartment sales was acquired from real estate catalogue operated by Czech company reality.cz
(http://www.reality.cz). GIS data layers were provided by the City Development Authority
Prague and the Czech Statistical Office. The financial and data support is gratefully
acknowledged.
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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52
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List of tables
Table 1 Definitions and descriptive statistics of variables entering the hedonic model
Table 2 Regression results for partial hedonic models (1-3)
Table 3 Regression results for full hedonic model (4)
Table 4 Variance inflation factors
Table 5 Regression results for selected greenery model (5)
Table 6 Implicit prices and aggregate results
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Table 1 Definitions and descriptive statistics of variables entering the hedonic model, N=5,199
Variable
Description
Measure
Expected sign
Mean
Std. Dev.
Min
Max
PRICE
Sales price of apartments sold in years 2005-2008
CZK 2008
DV*
5,018,006
3,910,050
817,697
55,600,000
AREA
Built-up area of the flat
m2
+
75.8319
36.2774
13
430
BAD_STATE
Bad state of apartment (reconstruction is necessary
before living)
dummy
-
0.03808
0.19142
0
1
AREA_BUILD
Area of the building
m2
+
476.214
561.779
2.33
4,588.7
INHABITANTS
Number of inhabitants in the building
number
-
20.6349
17.1336
0
92.7
DIST_CENTER
Distance to city center (Old-town square)
m
-
5,757.11
3,991.58
75.8161
19,057.02
APART_BUILD
Apartment is located in apartment house
dummy
-
0.71052
0.45356
0
1
BRICK_STONE
Building material is brick or stone
dummy
+
0.54299
0.4982
0
1
SPA_DIST
Distance to the nearest specially protected area
m
-
1,287.57
899.523
0
4,758.414
SPA_AREA
Area of the nearest specially protected area
m2
+
226,185
370,730
1,590.56
1,448,013
FOREST_DIST
Distance to the nearest urban forest
m
-
1,504.76
1,219.75
0
7,123.17
FOREST_AREA
Area of the nearest urban forest
m2
+
1,550,270
2,147,259
1,459.17
17,200,000
FIELD_DIST
Distance to the nearest field
m
-
1,587.51
1,310.5
0
4,627.877
FIELD_AREA
Area of the nearest field
m2
+
299,429
724,020
1,475.81
13,100,000
GREEN_SEL
Percentage of selected greenery area (threatened by
development) on cadastral territory area
%
+
14.91235
20.1493
0.53
88.89
AIR
Index of air quality
index
-
0.5981
0.1689
0.31
1.66
* Dependent variable
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Table 2 Regression results for partial hedonic models (1-3)
Model 1: SPA
Model 2: Urban forest
Model 3: Field
Term
Coeff.
t-Ratio
Sig.
Coeff.
t-Ratio
Sig.
Coeff.
t-Ratio
Sig.
Intercept
2,992,128
2.16
*
-165,669.4
-0.14
-5,273,929
-4.65
**
ln(AREA)
5,113,868
34.33
**
5,402,471
32.82
**
4,259,604
27.55
**
BAD_STATE
-984,254
-6.1
**
-1,195,688
-6.87
**
-529,167
-4.25
**
ln(AREA_BUILD)
98,220.15
2.3
*
120,704.1
2.56
*
143,222.5
3.36
**
INHABITANTS
-16,744.5
-10.24
**
-17,315.45
-9.13
**
-10,724.3
-8.06
**
ln(DIST_CENTER)
-1,774,454
-16.33
**
-1,828,801
-15.3
**
-883,343
-9.62
**
APART_BUILD
-42,0062.9
-4.85
**
-443,722
-4.71
**
-520,560
-6.85
**
BRICK_STONE
411,909.3
4.62
**
462,714
4.66
**
705,511.9
8.79
**
SPA_DIST
-4,044.063
-6.94
**
ln(SPA_AREA)
-309,657.4
-6.29
**
SPA_DIST*ln(SPA_AREA)
346.4415
6.76
**
FOREST_DIST
-4,010.967
-4.34
**
ln(FOREST_AREA)
-87,488.7
-1.85
FOREST_DIST*ln(FOREST_AREA)
289.4986
4.28
**
FIELD_DIST
-319.458
-0.71
ln(FIELD_AREA)
-45,752.1
-1.49
FIELD_DIST*ln(FIELD_AREA)
22.71126
0.59
AIR
-1,962,353
-7.83
**
-2299493
-7.30
**
-1,055,432
-6.39
**
dependent variable
PRICE
PRICE
PRICE
N
4,086
3,595
3,299
R2
0.6140
0.6153
0.5651
F (18; 5,180)
171.27
170.25
135.54
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Table 2 Regression results for partial hedonic models (1-3) - cont.
Prob > F
0.000
0.000
0.000
Robust estimation (White standard errors)
* Significant at 95% confidence level
** Significant at 99 % confidence level
Revealed preferences for outdoor recreation in natural areas - Czech and European perspective
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Table 3 Regression results for full hedonic model (4)
Term
Coeff.
St. Error
t-Ratio
P-value
95% Confidence Interval
Sig.
Intercept
-3,707,255
1,130,580
-3.28
0.001
-5,924,117
-1,490,393
**
ln(AREA)
4,594,486
180,142.9
25.50
0.000
4,241,258
4,947,714
**
BAD_STATE
-650,080.5
128,307.5
-5.07
0.000
-901,668.3
-398,492.6
**
ln(AREA_BUILD)
139,536.3
48,346
2.89
0.004
44,738.55
234,334.1
**
INHABITANTS
-12,432.01
1,717.314
-7.24
0.000
-15,799.35
-9,064.668
**
ln(DIST_CENTER)
-1,009,759
100,732.8
-10.02
0.000
-1,207,278
-812,240.6
**
APART_BUILD
-561,071.4
85,475.41
-6.56
0.000
-728,673.2
-393,469.5
**
BRICK_STONE
664,486.6
90,953.43
7.31
0.000
486,143.4
842,829.9
**
SPA_DIST
-1,304.048
344.2676
-3.79
0.000
-1,979.095
-629.0019
**
ln(SPA_AREA)
-0.146225
0.103067
-1.42
0.156
-0.348320
0.055870
SPA_DIST*ln(SPA_AREA)
120.0097
31.08737
3.86
0.000
59.05295
180.9664
**
FOREST_DIST
-2,960.691
939.1046
-3.15
0.002
-4,802.105
-1,119.277
**
ln(