Article Citation: Xuanfei Zhang. (2021). AN ANALYSIS OF COMMON APPLICATIONS
ON HEDONIC PRICING MODEL IN VALUING ECOSYSTEM SERVICES: SIMILARITIES AND
DIFFERENCE BETWEEN EUROPE, US, AND CHINA. International Journal of Engineering
Technologies and Management Research, 8(1), 1-11. https://doi.org/10.29121/ijetmr.v8.i1.2021.849 Published Date: 23 January 2021 Keywords: Hedonic Pricing
Model Ecosystem
Services The study made a comparison with the common applications on the hedonic pricing model that valuing ecosystem services between Europe, the United States, and China. By analyzing various reasons impacting housing prices, cultural and historical backgrounds played roles in the real-world applications.
1. INTRODUCTIONThe methodology of valuation on ecosystem
services had long been an issue in the field of environmental economics. The
Hedonic pricing model, under the Revealed preference approaches, is used to
estimate the ecosystem value based on physical characteristics. For instance,
it could be used to estimate the value of houses as a function of variables
including proximity to open space. Once estimated, a certain value of
preserving open space could be calculated based on total higher property values
permitted (David R. Lee, 2020). The housing prices or the price of certain
properties could be estimated by the marginal contribution of different
attributes to property value, including, aesthetic view, water quality,
available recreational activities, parking lot, the structure of houses, around
zoning design, road design, and city planning. All these factors are related to
determining housing prices. Species habitat regulating services are included as
well (David R. Lee, 2020). In analyzing the factors impacting housing prices,
especially how the surrounding green spaces, and environmental amenities
playing roles, vary between countries. In this paper, I am going to take a
closer look at various cases in Europe, the United States, and China, by comparing
some similarities and differences. Additionally, I would like to draw some
interesting discussion regarding the future tendency of how the green space
impacting the housing prices in China might be changed after the current
epidemic of COVID-19. One of the essential steps that people made
in their life is buying a house. Not only representing the feeling of
belongingness, but people will be able to stabilize after owning a house in the
regions they are living in. Especially for those who live and work in big
cities, it always remains an internal topic over the selection of houses. The
reasons for people to decide the places and characteristics of houses vary.
Some of the reasons are socio-economic related, and some are culture related. 2.
LITERATURE REVIEW
2.1. EUROPE
Hedonic pricing analysis helps to measure the
preferences towards different attributes in impacting the housing prices, based
on a large amount of real estate data. It includes some variables that are not
easy to capture and evaluate, for instance like a nice and quiet environment,
distances to the city center, or the recreational available activities of the
nearby green space (Czembrowski and Kronenberg, 2016). Compared to studies on the hedonic
pricing model application done in some western countries, not many pieces of
research were conducted in Central and Eastern Europe areas (Brander & Koetse, 2011). Aiming to find the influence of different
types of green space on housing prices in Lodz, the largest city in Poland, Czembrowski and Kronenberg did they’re by dividing groups of green spaces into nine
categories, including small parks and forests (smaller than 18000 square
meters), medium parks and forests (1800 to 200000 square meters), large parks
and forests (larger than 2000000 square meters), the single largest forest
(over 13000000 square meters), cemeteries, and allotment gardens. The proxy of
the more general ambient condition was to set a percentage of greenery in a 500
meters radius. The area of green land in Lodz was designed not the same in
terms of size and distribution, making the city a good case study example (Czembrowski and Kronenberg,
2016). The result of the study revealed the
relationship between housing prices and surrounding environmental attributes.
As for the proximity to cemeteries, people who were thinking about the certain
property would regard it poorly with shorter distances. However, when it came
to forest and park, alongside some previous study, the larger exposure of the
open green space resulted in better property value. After applying the
percentage of greenery and different types and sizes of the green space, Czembrowski and Kronenberg found
that the accessibility in Lodz improved in property value from open green
spaces, as well as the general environmental design of the city. To be more
specific, they suggested that the existence of forests played a crucial role in
explaining the surrounding housing price, like a small forest. Though some
areas of green land might be difficult to capture with size and types, the
small forest could still be estimated as one of the strongest impacting factors
to the value of the property in Lodz (Czembrowski and
Kronenberg, 2016). (Parks
and forests in Lodz. Source: Czembrowski, P., & Kronenberg, J. (2016). Hedonic pricing and different urban
green space types and sizes: Insights into the discussion on valuing ecosystem
services. Landscape and Urban Planning, 146, 11–19. https://doi-org.proxy.library.cornell.edu/10.1016/j.landurbplan.2015.10.005) Nevertheless,
cemeteries had been shown in the results that, not welcomed by many buyers in
Lodz. In other words, having cemeteries near housing would be viewed as not as
good impacting housing prices. The same finding occurred in some other studies
as well. The existence of the cemetery would negatively be impacting the price
of a property in Hong Kong (Tse and Love, 2000).
Besides regions in Asia, some places in the United States indicated similar
results. Anderson and West founded in the year of 2006 with a negative and
lower influence on real estate pricing from the proximity to cemeteries in the
Minneapolis-St. Paul metropolitan area (Anderson and West, 2006). Besides, the
housing prices were found negatively related to the distances to the closest
cemetery in Portland, Oregon (Lutzenhiser and Netusil, 2001). When looking at the places for cemeteries,
some are located near school and public construction areas. Comparatively
speaking, these places are more open regarding the sizes and places in Asian
countries. It would be not easy to witness cemeteries in open areas, but
rather, in the faraway mountains or countryside. People would choose to visit
past relatives during a specific period of time. In
other words, though the influences from cemeteries were found negative to the
housing prices; some cultural aspects still played roles in causing the
differences. (Cemeteries
and allotment gardens in Lodz. Source: Czembrowski,
P., & Kronenberg, J. (2016). Hedonic pricing and
different urban green space types and sizes: Insights into the discussion on
valuing ecosystem services. Landscape and Urban Planning, 146, 11–19.
https://doiorg.proxy.library.cornell.edu/10.1016/j.landurbplan.2015.10.005) As for
the hedonic pricing model application, Czembrowski
and Kronenberg argued that it might not be strong
enough to determine the impact of individual ecosystem services. That might be
due to the difficulties of measurement, and separation of the context of the
hedonic pricing model and the concept of ecosystem services (Czembrowski and Kronenberg,
2016). What’s
more, a hedonic pricing simulation approach was applied in analyzing the
socio-economic impacts of green space, urban residential, and road infrastructure
projects in Lyon, cities in France. Roebeling et al
study the additional value of the green space with the application of a hedonic
pricing simulation model. It argued that developing urban green space brought
some aspects of risk, including potentially impacting profitable residential
areas, or generating waste-disposal sites. In some cases, green spaces are
regarded as secondary development on the urban city planning agendas since the
maintenance and habitation would not be viewed as urgent in the short-run (Roebeling et al, 2017).
Nevertheless, the benefits of getting from the open-access
of green space are huge. In terms of improving the aesthetic design of the
city, the proportion of green space coverage obtains both physical and
psychological advantages. Some past studies indicated that there has been a
growing number of arguments stating that green space helps to provide important
ecosystem services, which stimulate surrounding housing prices and avoid
flooding issues in the long term. The Sustainable
Urbanizing Landscape Development (SULD), the scenario simulation decision
support tool was applied in this study (Roebeling et
al, 2007). This tool helped with future planning and rehabilitation of urban
spaces by constructing additional analysis over the values of environmental
amenities that do not yet exist, or not available data sets over the sailing
prices of certain properties, based on the original hedonic pricing model. In
the city of Lyon, water management and flood control became a problem in terms
of difficulties in maintenance and infrastructures. The Lyon Confluence Project
was designed to deal with the issue of poor conditions with pollution during
the storm event, overflow devices, and the presence of rats. (Land use
in and around the Lyon Confluence project area (based on EVA 2009. Source: Roebeling, P., Saraiva, M., Palla,
A., Gnecco, I., Teotonio,
C., & Fidelis, T. (2017). Assessing the Socio-economic Impacts of
Green/Blue Space, Urban Residential and Road Infrastructure Projects in the
Confluence (Lyon): A Hedonic Pricing Simulation Approach. Journal of
Environmental Planning and Management, 60(3–4), 482–499. https://doi-org.proxy.library.cornell.edu/http://www.tandfonline.com/loi/cjep20) The
results of the study pointed out that the areas under the Lyon Confluence
project had experienced huge increases in population, the value of the retail
housing, and the overall real estate value. People with middle and high incomes
were found more willing to reside in this area, though with smaller living
spaces, and relatively higher housing value with closer distances to urban
parks. As for people with lower incomes, they were less willing to accept a
higher value of real estate with proximity to a new urban park. Therefore, the
value of urban green spaces would bring more compact regions of households
living; population density increased as more households got attracted to
concentrated regions. Besides, the value and prices of housing would
appreciate, and the wealth of residents would increase in attractive areas. However,
the extent of the value-added depended on the quality and size of the
intervention made by the project, the places relative to existing residential
areas, urban centers, road infrastructure, and environmental amenities, as well
as the social classes attracted to the intervention area (Roebeling
et al, 2017). Similar findings were conducted by Plantinga (2003) and Wu
(2006), suggesting the most attractive population fell into high-income
households, especially with the large income gap between the rich and the poor.
Additionally, past studies pointed out that the construction of green space
would result in not only the enhanced real estate prices, also the
gentrification and the displacement of lower-income households (Wolch, Byrne, and Newell, 2014). Such an argument is
reasonable when thinking in terms of the concentration of wealth. People care
about housing value, housing prices, in addition to the surrounding class of
neighbors. This is one of the important factors in housing selection, which
impacted the social quality in residential areas. Once high-income people get
attracted to certain regions, which later followed by the same social classes,
the value of the real estate would appreciate naturally. The benefits of
getting from the proximity to open green space were in the largest in the first
place. As time went by, other factors came into play and affected the housing prices
and value beyond the wealth distribution of the residents. Furthermore,
in valuing the individual characteristics and the multifunctionality of urban
green spaces, Czembrowski, et al used the integrated
method of sociotope mapping and the hedonic pricing
model to estimate the impact of the urban green spaces given to the housing
prices in Stockholm in Sweden. With more informed knowledge of the green spaces
and ecosystem services valuation, the general public
and the stakeholder paid more attention to the development, maintenance, and
preservation of green land usage and infrastructure. The multifunctionality of
the urban green land usage included the various aspects of benefits, different
levels of service could provide, and the many methods urban green space could
be used by inhabitants and policymakers. Valuing the marketable and economic
benefits of urban green space, Czembrowski, et al
raised a method that was built on the original hedonic pricing model to study
the different characteristics of environmental amenities using the non-monetary
valuation method of Geographic Information System (GIS) (Czembrowski,
et al, 2019). Czembrowski pointed out that since the hedonic pricing model was used to analyze
the specific characteristics of urban green spaces, not all the characteristics
received the same effect. For instance, the existence of cemeteries would bring
a negative impact on the real estate price. Similarly, not all the
characteristics of green spaces would be preferred for stockholders in Stockholm.
Based on the results of the study, the most desirable features of green spaces
were those designed for aesthetic purposes. The weakest impact on the housing
price was the attribute of nature, meaning the real estate values in Stockholm
would not be influenced a lot for those green spaces designed with a natural
purpose. A similar finding was obtained in Jinan City in China, with the
scenery forests becoming the more favored attributes of the green space that
people cared about when purchasing a home (Kong, Yin, and Nakagoshi,
2007). 2.2. UNITED
STATES
The
ecosystem services and environmental amenities were often neglected in the city
planning and policymaking in the United States due to the difficulties in
evaluating the economic and sustainable benefits. The application of hedonic
pricing models was conducted to capture the value of some cultural ecosystem
services, including local aesthetic quality, accessibility to outdoor
recreational areas, and associated services in Dakota County, in the Twin Cities
metropolitan area (TCMA) of Minnesota. The property values were found to be
correlated with open space by generating outdoor recreation and aesthetic view
values (Sander and Haight, 2012). According to the studies done by Crompton in
the year 2001, there are about 30 studies done on the impact of public parks on
residential property values, resulting in a positive relationship between parks
and property values (Crompton, 2001). In terms of scenic quality, many previous
studies indicated that the quality of the views was positively associated with
the surrounding residential home values (Bourassa et al, 2004). As for
the model application, the hedonic pricing model was used to estimate the
economic value of the ecosystem services based on the prices of properties.
Sander and Haight concluded that the pricing of certain properties was
positively associated with the increased view areas and views of water and
lawn, higher accessibility of outdoor recreational areas, and higher coverage
of neighborhoods’ trees. Decreasing the distance between comes and lakes,
however, would increase the home sale prices. The possible reason might be due
to limited accessibility for city residents to large lakes and parks from their
homes. Increased walkability would be generated instead, resulting in stronger
desirability to the need for open areas. One of the benefits of the study is to
provide good insights to the policymakers over the questions of land-use
planning and economics valuation of ecosystem services. By applying the values
calculated by the hedonic pricing model, measuring adequate distances between
homes and the proximity to open space areas seems important. Such consideration
and planning would help to achieve maximizing property values while keeping
sustainable land-use practices and designs in the process of urbanization
(Sander and Haight, 2012). At the
same time, Sander and Haight argued about the limitations of the hedonic
pricing model and some cautions that should be considered when applying the
model into the study. There might be the possibility of underestimating some
parts of the ecosystem services values. For instance, the benefits of getting
from tourism, and the profits from the production of marketable goods would
also be considered. Some aspects of the values might be double-counted
into the model (Sander and Haight, 2012). After
drawing samples from 1789 metropolitan area census tracts from the state of
Michigan in the United States, the results of the study suggested that there
existed constraints from the supply side of the analysis that bidding up the
prices of the real estate and reducing the growth of population located in
residential areas. To be more specific, public lakes and other state-opened
space, including areas other than public parks, wildlife reserves, forests, and
recreational purpose lands, could result in bidding up prices, which in the
end, have fewer people willing to migrate to nearby regions. In other words,
the existence of a lake had impacts over the raising property value and reduction
in the quantity and quality of neighboring tracts (Sander and Haight, 2012). Meanwhile,
another crucial term that is related to the valuation of ecosystem services is
urban open space, other than green space. The benefits getting from urban open
space were huge, by providing services to the urban populations over the
recreational, aesthetic, and agricultural opportunities. Besides, not only
people living nearby the urban open space would receive benefits in the short
term, future generations could seize the values from
developing and preserving these spaces. There was lacking policy intervention
in the United States, reflected by the number of votes casted that dealt over
the issues of open space conservation at the unit of state, country, and district
level. The problem turned out to be the lack of informed knowledge on the value
of services provided by urban open spaces as well as their opportunity costs of
preservation. Without sufficient information on the trade-off between the value
of the services gained from the urban open space, either that’s marketable and
non-marketable valuation, against the cost of maintenance and preservation.
Since a lot of the time, identifying the transferrable value in the market was
not easy, policymakers would not draw enough attention to this issue (Brander
and Koetse, 2011). A
meta-analysis was conducted by Brander and Koetse in
the year of 2011, focusing on the additional consumer preferences for urban and
peri-urban open space, where the peri-urban open space referred to the areas
that were immediately adjoining an urban area. After collecting 38 contingent
valuation studies and the hedonic pricing studies on urban and peri-urban open
space, Brander and Koetse argued that, in terms of
the hedonic pricing analysis, as the dependent variable in the meta-analysis,
the changing unit of the property value was based on 10 meters decrease in the
proximity to the closest open space using the money value in 2003. The results
were an increase in housing prices for about 0.1 percent, indicated from the
sample of the primary studies. To be more specific, when the distance to the
closest open space increased, the impact on the housing price reduced (Brander
and Koetse, 2011). One thing worth noting is that,
among the characteristics of aesthetics, preservation, and recreational
opportunities, the economic value of the green space would only generate a
noticeable increase with the latter two, except for the aesthetics
characteristic of the green space design. Such findings did not align with the
argument made by Czembrowski, et al (Czembrowski, et al, 2019). In a more
general discussion, the city design in the United States is quite different
from those in some Asian countries. In terms of many cities, open space, green
space, or environmental amenities would be found surrounded in some city
centers. For instance, many parks in New York City fell into the middle of the
regions where people clustered. Other than that, people would be able to get
access to public parks from their home, with convenient transportation methods.
Therefore, the benefits and the services obtained in large cities were more
interrelated with people and other open resources. While in some country-side
regions, those open spaces might be surrounded by multi-directional highways.
To get access from home, people would have to choose by driving private
vehicles. The areas of open space seemed to be more concentrated and had higher
proximity to residential areas. Such design is quite different from those in
China; most of the parks and other urban open-spaces were consistent with the
country-side design in the United States. People would have to drive private
cars to the closest green space and urban open space. For those getting close
to the residential regions, many of them were designed and constructed many
years ago, since the value of the land and the scarcity of households limited
the regions of real estate that could be made around the open space. These days,
more and more people moved far away from the city center and recited into the
residential areas with limited services available. That might
due to the enhanced average household income in China, resulting in the percent
of household ownership of private vehicles increased, and people weighted
relatively less on the proximity to the closest public open spaces compared to
those in the past. The willingness and self-wishes in the demand for urban open
space might come into play. 2.3. CHINA
The cities in the United States, the
valuation of green space, as well as the impact brought on the pricing in the
real estate market are studied in various scopes. The transferrable values and
the environmental benefits getting from the urban green space were not easy to
be captured in the open market. Many policymakers and city planners faced
difficulties in improving the quality of urban green space due to the lack of
noticeable trade-off advantages. The hedonic pricing model, therefore, was
applied to capture and measure the values of the non-market priced
recreational, aesthetic, environmental, and agricultural resources for the sake
of the general public and society. Kong, Yin, and Nakagoshi pointed out in the year of 2006 that, there
existed a wide and complex range of factors impacting the value prices of the
property, including the housing design and structure, quality of people living
in the neighborhood and the characteristics of the neighborhood environment,
accessibility to the Central Business District (CBD), and the surrounding
environmental amenities that helped to bring values to the property (Kong, Yin,
and Nakagoshi, 2006). The
number of studies that had been done in the past over the valuation of urban
green spaces in Mainland China was lacking, which might be due to the complex
market pricing in the real estate market; the hedonic pricing model application
was limited (Wang and Huang, 2005). (The
study area and geographic distribution of the 124 sample properties. Source:
Kong, F., Yin, H., & Nakagoshi, N. (2007). Using
GIS and landscape metrics in the hedonic price modeling of the amenity value of
urban green space: A case study in Jinan City, China. Landscape &
Urban Planning, 79(3/4), 240.) After
collecting 124 housing clusters in Jinan City in China, located within the
urban areas, Kong, Yin, and Nakagoshi suggested that
the hedonic pricing model could improve by adding the analysis using the GIS
and landscape metrics. The results of the study found the housing price was
associated with a reduction in the accessibility to the scenery forest.
Meanwhile, an increase in housing prices would occur with larger size and
distance to the scenery forest, better design, and quality of green space. To
some extent, people would be in favor of closer proximity to the green housing
districts and easier accessibility to the scenery forest areas. However,
necessary attention should be drawn considering the number of people relocating
into residential areas that were close to open green space and the preservation
of the open environmental amenities; since based on the finding, the population
density would become more concentrated which further impacting the development,
maintenance, and the preservation of the green spaces. Additional, new green
space programs and the exploitation of the new residential areas could draw
some attention to such finding, that in line with previous studies on the
significant positive effects on housing prices impacting from the economic
value of the urban green space and environmental amenities (Kong, Yin, and Nakagoshi, 2007). Looking
at other cities in China, Shenzhen, for instance, in terms of evaluating the
influence of urban green space on the residential housing prices, Wu, et al
used the GIS approach and hedonic pricing model to capture the effect of public
resources on the value of the properties, especially green space in the year of
2015. (Study
area and geographic distribution of sample properties (data from SOFANG 1999)
Source: Wu, J., Wang, M., Li, W., Peng, J., & Huang, L. (n.d.). Impact of
Urban Green Space on Residential Housing Prices: Case Study in
Shenzhen. JOURNAL OF URBAN PLANNING AND DEVELOPMENT, 141(4). https://doi-org.proxy.library.cornell.edu/10.1061/(ASCE)UP.1943-5444.0000241) The
collecting transaction price of 6494 units in the Shenzhen real estate trading
center, Wu et al, pointed out that the impact of the urban public resources on
the housing prices was shown statistically significant, with the greatest
importance over the distance to the central business district, and the worst
importance over the distance to the subway station. Shenzhen, unlike some other
cities in China, is composed of small sectors of sub-central business districts
scattering out, therefore; such impact would vary over various populations
living in different residential areas. In other words, the importance of the
proximity to the city center might not be the same for all individuals located
in diverse regions in Shenzhen. Besides, the distance to public parks was
founded, having the second-largest impact on the housing price. The increase in
the distance to the parks would diminish in the extent of the effect and became
constant as the distance increased (Wu, et al, 2015). In
considering the consequences of the environmental services in China, one of the
important elements in the ecosystem services was the urban design of forests.
The development of urban greening in China could be traced back many decades
ago. Within the increasing attention and public awareness of preserving and
managing the green space in these years, Jim and Chen argued about three
factors involved in the study or urban forests, including the design and
construction of urban forest, evaluation and assessment of the ecosystem
services, and the management and planning of the forest. All three elements
interrelated to the short term and long-term sustainable growth of urban forest
planning in China (Jim and Chen, 2009). Past
literature indicated that the influence over the ecosystem services brought out
contained various components to balance the carbon emission in the air, reduce
air pollution, and noise pollution (Jim and Chen, 2009). Additionally, it also
carried out an impact on regulating the microclimate, recreational
opportunities, and environmental amenities, which further shaping the
sustainable growth of the environment and the quality of living for the
population (Jim and Chen, 2006). The results of the study pointed out the lack
of methods that could be used to measure the ecosystem services generated by
urban forests, suggesting improvements over the methods of evaluating the high
level of benefits getting from the environmental services, and improving in the
data collection process and requirement in the valuation of ecosystem services
by the region of urban forests, while associating with the urban green space,
and urban green system planning and management (Jim and Chen, 2009). As the
economic development and living conditions improved in China, people valued
additional factors when purchasing houses; urban green spaces were one of the
elements being considered. With the rapid urbanization, larger areas of the
land could be used as the potential resources for residential area
exploitation, both in the city centers and country-sides. The tendency in the
real estate market was inclined with settlement in cities that were closer to
the wide ranges of environmental and recreational resources. Though the general
size of the public green space was smaller compared to those in the country-side, people still placed convenience on commuting
in the most or second most crucial role. Many people who migrated and relocated
to big cities tended to reply to public transportation other than private
vehicles. Not only due to the consideration of the costs of transporting but
also on the avoidance of traffic jams. The recent trend in the real estate
market, however, shifted to the housing selection in country-side regions. People
value and get more attention to the benefits of the urban green spaces nearby
the residential areas. The greater proximity to the open green space for
countryside housing was associated with the improvement in economic freedom and
self-willingness to the accessibility of environmental services. However, such
impact and considerations varied across different parts of the cities in China. Guangzhou,
located in the southern part of China, had been studied on the impacts of the
urban environmental elements on the housing prices by Jim and Chen in the year
of 2005. The government’s administrative allocation and management system of
real estate could be traced back to the middle and the end of the twentieth
century. After this period of time, public housing came
into play which brought difficulties in the design and construction of
residential housing. To find out the extent of the impact of the urban green
spaces and environmental amenities on the price of the properties in Guangdong,
the hedonic pricing model was used in the study. The results suggested that the
application of the hedonic pricing model could somehow capture the changes in
the housing price that the distances to the water areas and green spaces would
generate an increasing impact on the surrounding housing price. (Map of
the central core area of Guangzhou city showing the locations of the four
sampled residential housing developments. (Source: Jim, C. Y., & Chen, W.
Y. (2006). Impacts of urban environmental elements on residential housing prices
in Guangzhou (China). Landscape and Urban Planning, 78(4), 422–434. https://doi-org.proxy.library.cornell.edu/10.1016/j.landurbplan.2005.12.003) What’s
more, the impact of getting from the proximity to the wooded region was
limited, as well as the existence and intensity of traffic noise. This means,
either people received a high level of tolerance on the noise generated by
transportation, or they were used to it when selecting residential areas,
especially in large cities (Jim and Chen, 2005). Nevertheless, given the fact
that the tendency for housing selection generally shifted to the countryside
suggested a reasonable argument that, if people faced options of living far
away from the noisy regions, they would choose to relocate. As for population
lived and worked areas that were difficult to avoid such environmental
externalities, they would not take serious importance when purchasing houses. In 2007,
Jim and Chen focused instead on the consumption preferences and environmental
externalities in Guangzhou. The local housing preferences served as complex and
changing topics in the real estate market.
The broad macroeconomic environment and the social opportunities related
to housing selection were not the same depending on the time and place in
Chinese society. In terms of trend and growth in the housing market, the
attention and focus shifted based on public policies and personal preferences.
Yet, all the elements were interrelated, and people valued environmental
services which helped in the short-term satisfaction of living in suitable
residential areas, and the long-term potential left to the future generations
(Jim and Chen, 2007). The
hedonic pricing model suggested in the study that green space generated a
positive impact on the development of local amenities, landscape, and
recreational opportunities. Homebuyers, therefore, would be willing to make the
selection on real estate containing such factors. Moreover, since 1998, the
open market strategies applied with the removal of the subsidized housing
allocation system, Chinese consumers faced drastic changes and challenges in
the supply and demand sides in housing market prices. In Guangzhou, the findings
of the study argued that the most desirable change the people were looking for
was the improvements over the floor area and the living conditions, with the
biggest determining factor of the housing price. Internal influencing factors
were found relatively less important compared to the external influencing
factors. The biggest rated important element was the quality of the property
management, and surprisingly, people cared a lot for the safety and security
design of the residential housing. That might be caused by the increasing
disturbance in social activities and dissatisfaction with the laws in
Guangzhou. Among areas located in major cities, distance to grocery stores,
level of convenience in commuting, and accessibility of public transportation
were found crucial. Within areas further from the major cities, people valued
more on the quality of the environmental services, and the safety design of the
residential areas. However, proximity to the metro station and schools were
founded within less importance. (Jim and
Chen, 2007). But the
importance of the proximity to schools differed within various regions in
China. As for the cultural factors in Chinese society, young people would tend
to choose to live with their parents. When they start with their own business
and jobs, they might get the chance to move into large cities in search of a
wide range of job opportunities and recreational services. In that case, they
might put the attention more over the proximity to the open green space, and
the urban open spaces while selecting houses. Therefore, real estate sellers
would then emphasize the benefits combined within the urbanized and
environmental services available in larger cities. One thing important for
people to think about is the distance to school. As for families having young
kids that were thinking about school attainment, this became the most crucial
determining factor. In the recent Chinese society, even the
quality of housing was poor in some places, people were still willing to
purchase for a high price. That’s when the idea of
Hukou came into play. Hukou means the official registration of residents in
certain regions. Only after having an official Hukou status, kids are then able
to attain school, get medical care, insurance care, as well as some other
social services. For those schools with high demand, parents have surged to
purchase houses nearby the school. In that case, if they are having the
registered Hukou, and able to live close to school, those families would then
receive higher chances of getting accepted by the school due to the excessive
demand. The determining factors in the housing selection process, environmental
services would not play as crucial roles, instead; more cultural and social
elements were those factors people really cared about. 3.
CONCLUSIONS
In
conclusion, similarities and differences existed in the application of hedonic
pricing models associated with various historical and cultural backgrounds.
Thinking back to the current global housing market, under the epidemic of
COVID-19, many countries and regions have undergone challenges on the
macroeconomics downturn, resulting in a large number of
people getting unemployed and even becoming homeless. For a short period,
people would value more on the problem of surviving. But after the end of the
crisis, there is still a high chance to value back the benefits generated from
the environmental services. In those cases, the hedonic pricing model could be
used to value the ecosystem services payments. SOURCES OF FUNDING
This
research received no specific grant from any funding agency in the public,
commercial, or not-for-profit sectors. CONFLICT OF INTEREST
The
author have declared that no competing interests exist. ACKNOWLEDGMENT
None. REFERENCES
[1] Anderson, S. T., & West, S. E.
(2006). Open space, residential property values, and spatial context. Regional
Science and Urban Economics, 36(6), 773–789.
https://doi-org.proxy.library.cornell.edu/10.1016/j.regsciurbeco.2006.03.007 [2] Bourassa, S. C. (1), Sun, J. (1),
& Hoesli, M. (2,3). (n.d.). What’s
in a view? Environment and Planning A, 36(8), 1427–1450.
https://doi-org.proxy.library.cornell.edu/10.1068/a36103 [3] Brander, L. M., & Koetse, M. J. (2011). The value of urban open space:
Meta-analyses of contingent valuation and hedonic pricing results. Journal of
Environmental Management, 92(10), 2763–2773.
https://doi-org.proxy.library.cornell.edu/10.1016/j.jenvman.2011.06.019 [4] Crompton, J. (2001). The impact of
parks on property values: A review of the empirical evidence. JOURNAL OF
LEISURE RESEARCH, 33(1), 1–31. [5] Czembrowski, P., & Kronenberg,
J. (2016). Hedonic pricing and different urban green space types and sizes:
Insights into the discussion on valuing ecosystem services. Landscape and Urban
Planning, 146, 11–19. https://doi-org.proxy.library.cornell.edu/10.1016/j.landurbplan.2015.10.005 [6] Czembrowski, P., Łaszkiewicz,
E., Kronenberg, J., Engström,
G., & Andersson, E. (2019). Valuing individual characteristics and the
multifunctionality of urban green spaces: The integration of sociotope mapping and hedonic pricing. PLoS
ONE, 14(3), 1–16.
https://doi-org.proxy.library.cornell.edu/10.1371/journal.pone.0212277 [7] David R. Lee (2020). Lecture 5.2:
Valuation of Ecosystem Services [PowerPoint slides]. Retrieved from Cornell
University AEM 6600 Natural Resources and Economic Development. [8] [8Elena G. Irwin, P. Wilner Jeanty, & Mark D.
Partridge. (2014). Amenity Values versus Land Constraints: The Spatial Effects
of Natural Landscape Features on Housing Values. Land Economics, 90(1), 61. [9] Jim, C. Y., & Chen, W. Y.
(2006). Impacts of urban environmental elements on residential housing prices
in Guangzhou (China). Landscape and Urban Planning, 78(4), 422–434.
https://doi-org.proxy.library.cornell.edu/10.1016/j.landurbplan.2005.12.003 [10]
JIM,
C.., & CHEN, W. (2007). Consumption preferences and environmental
externalities: a hedonic analysis of the housing market in Guangzhou. Geoforum, 38(2), 414–431. [11]
Jim, C. Y., & Chen, W. Y. (2009).
Ecosystem services and valuation of urban forests in China. Cities, 26(4),
187–194. https://doi-org.proxy.library.cornell.edu/10.1016/j.cities.2009.03.003 [12]
Kong,
F., Yin, H., & Nakagoshi, N. (2007). Using GIS
and landscape metrics in the hedonic price modeling of the amenity value of
urban green space: A case study in Jinan City, China. Landscape & Urban
Planning, 79(3/4), 240. [13]
Lutzenhiser, M., & Netusil,
N. R. (2001). The Effect of Open Spaces on a Home’s Sale Price. Contemporary
Economic Policy, 19(3), 291.
https://doi-org.proxy.library.cornell.edu/10.1093/cep/19.3.291 [14]
Roebeling, P. C. (1), Fletcher, C. S. (1), Hilbert, D.
W. (1), & Udo, J. (2). (n.d.). Welfare gains from urbanizing landscapes in
Great Barrier Reef catchments? A spatial environmental-economic modelling
approach. WIT Transactions on Ecology and the Environment, 102, 737–749.
https://doi-org.proxy.library.cornell.edu/10.2495/SDP070712 [15]
Roebeling, P. (1), Saraiva, M. (1), Fidelis, T. (1), Palla, A. (2), Gnecco, I. (2), Teotónio, C. (3), Martins, F. (3), Alves, H. (3), &
Rocha, J. (3). (n.d.). Assessing the socio-economic impacts of green/blue
space, urban residential and road infrastructure projects in the Confluence
(Lyon): a hedonic pricing simulation approach. Journal of Environmental
Planning and Management, 60(3), 482–499. https://doi-org.proxy.library.cornell.edu/10.1080/09640568.2016.1162138 [16]
Sander,
H. A., & Haight, R. G. (2012). Estimating the economic value of cultural
ecosystem services in an urbanizing area using hedonic pricing. Journal of
Environmental Management, 113, 194–205. https://doi-org.proxy.library.cornell.edu/10.1016/j.jenvman.2012.08.031 [17]
Tse Raymond Y.C., & Love Peter E.D. (2000).
Measuring residential property values in Hong Kong. Property Management, 18(5),
366–374. https://doi-org.proxy.library.cornell.edu/10.1108/02637470010360669 [18]
Wolch, J., Byrne, J., & Newell, J. (2014).
Urban green space, public health, and environmental justice: The challenge of
making cities “just green enough.” [19]
Wang,
D., Huang,W.S., 2005. Hedonic house pricing method
and its application in urban studies. Urban Plann. 29
(3), 62–71. [20]
Wu,
J., & Plantinga, A. J. (2003). The influence of public open space on urban
spatial structure. Journal of Environmental Economics and Management, 46(2),
288–309.
https://doi-org.proxy.library.cornell.edu/10.1016/S0095-0696(03)00023-8 [21]
Wu,
J. (2006). Environmental Amenities, Urban Sprawl, and Community
Characteristics. Journal of Environmental Economics and Management, 52(2),
527–547. https://doi
org.proxy.library.cornell.edu/http://www.sciencedirect.com/science/journal/00950696 [22]
Wu,
J., Wang, M., Li, W., Peng, J., & Huang, L. (n.d.). Impact of Urban Green
Space on Residential Housing Prices: Case Study in Shenzhen. JOURNAL OF URBAN
PLANNING AND DEVELOPMENT, 141(4).
https://doi-org.proxy.library.cornell.edu/10.1061/(ASCE)UP.1943-5444.0000241 [23]
Zhang,
Y., & Dong, R. (n.d.). Impacts of Street-Visible Greenery on Housing
Prices: Evidence from a Hedonic Price Model and a Massive Street View Image
Dataset in Beijing. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 7(3). https://doi-org.proxy.library.cornell.edu/10.3390/ijgi7030104.
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