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The purpose of this study is to quantify the marginal willingness to pay (MWTP) for clean air in China. We provide the first estimate of MWTP for clean air by implementing a hedonic method using housing price and air quality data from Shanghai. Our estimates imply that air pollution has a significant and negative impact on housing price. We also fi...
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... focus on air quality surrounding residential properties. Figure 1 shows the annual mean of daily concentrations of SO 2 , NO 2 , and PM 10 from 2001 to 2011. The daily concentration of SO 2 for different monitoring sites in Shanghai in 2010 ranges between 22.21 and 42.80 mg/m 3 (average 30 mg/m 3 ), that of NO 2 ranges between 43.57 and 67.69 mg/m 3 (average 52 mg/m 3 ), and that of PM 10 ranges between 62.77 and 95.78 mg/m 3 (average 80 mg/m 3 ). However, the mean concentrations of SO 2 , NO 2 , and PM 10 in winter are 44.71 mg/m 3 , 70 mg/m 3 , and 93.23 mg/m 3 , respectively, which are higher than the annual mean concentrations (Figure 2). The highest mean concentrations of SO 2 and PM 10 even reach 65.56 mg/m 3 and 104.93 mg/m 3 , respectively, which are beyond the national air quality level 2 standard. 4 Figure 3 shows the average rain pH values and the frequency of acid rain 5 . in Shanghai. The average rain pH value is even lower than 5 and the frequency of acid rain has been higher than 70% since 2007. All evidence indicates severe air pollution in Shanghai, especially during the ...
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... Air pollution has become an important factor for households to choose their residential locations, and cities with lower air pollution are associated with higher housing prices (Zheng et al., 2010;Grainger, 2012;Bento et al., 2015;Feng et al., 2024). For example, Chen et al. (2018) estimate the housing premium (the marginal willingness to pay (MWTP)) to be RMB 159/m 2 and RMB 238/m 2 respectively for a 1 µg/m 3 reduction in average SO 2 and PM 10 in Shanghai. Freeman et al. (2019) find that Chinese households are willing to pay approximately $21.70 ...
This paper studies the value of clean air by analyzing the effect of China's clean air policy. By exploiting the cross-city variation in the timing of policy implementation and using a panel dataset of 280 cities from 2003 to 2018, we find that the clean air policy boosts housing prices by 4.4%. The finding is robust to a series of potential issues, functional misspecifications, and falsification tests. In addition, we further examine whether the effect varies across different price-tier cities and changes over time, and we find evidence of such heterogeneous and dynamic effects.
... This result is consistent with the findings of Conroy and Milosch (2011), who discovered that the sale price decreases by $8680 each mile farther from the shore (Conroy & Milosch, 2011). Similarly, global research revealed that river view is a key house price factor (Chen et al., 2018). However, some researchers are interested in studying how this can hurt home prices. ...
Traditional linear models often struggle to capture regional housing markets' complex, non-linear dynamics. This study addresses this gap by developing and applying advanced machine learning algorithms to unlock unique insights into South Australian housing price behavior. Leveraging a comprehensive dataset of over 10,000 regional house sales from 2010 to 2021, we explore the non-linear relationships between housing prices and microeconomic factors (e.g., house size, land area, building quality) and socioeconomic characteristics (e.g., proximity to amenities and income levels). Our analysis employs a multi-step approach, including feature engineering , spatial data integration, correlation tests, multilevel modeling, and non-linear machine learning algorithms including Decision Tree, Random Forest, Gradient-Boosted Tree, and Artificial Neural Network. The key finding is that machine learning models outperform traditional econometric models in predicting regional housing prices, with higher accuracy and greater goodness of fit. Furthermore, we identify specific non-linear relationships, such as the increasing marginal impact of proximity to the sea on house prices as distance decreases. These findings offer valuable insights for policymakers, real estate professionals, and stakeholders, informing regional planning, infrastructure provision, and economic development strategies. This study sheds light on the complex dynamics of South Australian housing markets and lays the foundation for further research.
... Second, our research design allows us to explore heterogeneous responses to air pollution for households across regions and income groups. Considering the possible heterogeneous effects (Chen et al., 2018;Le Boennec & Salladarré, 2017), the analysis refines the effects of air pollution, and mitigates the risk of estimation bias as well. Lastly, we capture the dynamic change in air quality possibly affecting housing prices over time. ...
... The standard hedonic price model, when applied during the housing market valuation process, may also suffer from estimation bias. In cross-sectional data, the issue of endogeneity is often overlooked (Chen et al., 2018). In panel data, endogeneity arises from the fact that local economic activities are associated with air quality and housing prices, which may lead to a reduction in the accuracy of estimates (Chen & Jin, 2019). ...
... Studies at the intra-urban level were mainly based on cross-sectional data. For instance, using data from Shanghai in 2010, Chen et al. (2018) found adverse effects of air pollution on urban housing prices: reducing concentrations of sulfur dioxide (SO 2 ) and PM 10 by 1 mg/m 3 increased Shanghai's housing prices, on average, by 0.6% and 0.9% respectively (or 159 yuan/m 2 and 238 yuan/m 2 ). Based on the installation and operation of an air-purifying tower in the city of Xi'an, Lan et al. (2020) adopted a quasi-experimental design, namely a difference-in-difference approach, to measure the tower's ability to mitigate haze, and found that it increased housing prices by, on average, 4% across the affected area. ...
Air pollution is a major environmental urban issue, particularly in fast-growing cities in developing countries. Reducing air pollution is thus a challenge while evaluating the economic value of air quality is crucial for environmental policies made. However, few studies accurately estimate this value as they neglect the possible endogeneity issues, as well as the dynamic and heterogeneous effects of air pollution. Under the hedonic framework, we therefore assess the economic effect of fine particulate matter (PM2.5) on housing prices in Beijing, China. We construct a panel based on resale apartment transactions matched with average quarterly PM2.5 data between 2013 and 2019. To reduce the risk of an estimation bias, we apply an instrumental variable (IV) approach. Our results show that PM2.5 is negatively associated with housing prices. Households were willing to pay an extra 0.0852% per housing unit price for an average quarterly reduction in PM2.5 of 1 µg/m³. Furthermore, we argue that high-income dwellers tend to pay more for clean air. The negative effects of PM2.5 across regions are significant and different. Compared with that in the basic year 2013, the negative effect increases in the first 3 years and then decreases in the last 3 years. Our findings enhance our comprehension of the economic impact of air quality and make a valuable contribution to the nuanced understanding of willingness to pay for air quality, which is beneficial in assessing and optimizing environmental regulations.
... Air pollution control Traffic plan The increased energy demand and growing vehicular fleet cause a reduction in air quality where this consequence directly influences housing prices in Shanghai (Chen et al. 2018 north to 33 days in the south, and the annual precipitation rate, which is 422 mm in the north and 145 mm in the south (Nasehi et al. 2022). Tehran is also one of the largest cities in West Asia, with 2.5 million apartment residential units and 360,000 nonapartment residential units (Naddafi et al. 2012). ...
... Air pollution control Traffic plan The increased energy demand and growing vehicular fleet cause a reduction in air quality where this consequence directly influences housing prices in Shanghai (Chen et al. 2018 north to 33 days in the south, and the annual precipitation rate, which is 422 mm in the north and 145 mm in the south (Nasehi et al. 2022). Tehran is also one of the largest cities in West Asia, with 2.5 million apartment residential units and 360,000 nonapartment residential units (Naddafi et al. 2012). ...
This study uses a multilevel hedonic model to examine information from official sources including the population census, the land use map, and the field survey to draw conclusions about the 32,108 Tehran residences that make up the statistical sample. These results show that other factors besides proximity to urban facilities are important in determining the value of a home in Tehran, too. The results show that proximity to education, culture, entertainment, tourism, administration, and social service centres has a positive effect on housing prices, while proximity to some urban amenities, such as medical centers, commercial strips, and parks, has a negative effect on housing prices. This gap can be explained in part by the externalities of these facilities, including the creation of through traffic, congestion and reduced security levels, which discourage home-buyers from getting too close to these amenities. Furthermore, the study found that the effect of proximity differs from that of service provision at the neighborhood level. For example, while the presence of a medical/health center at neighbourhood level was found to be a positive determinant of house prices, its proximity had a negative effect on house prices. In fact, home-buyers prefer to purchase properties in neighbourhoods that offer such amenities but are less likely to purchase properties in areas adjacent to such amenities. The higher the ratio of non-residential to residential uses, the higher the price of housing in those areas. In fact, this shows the advantage of neighborhoods with a variety of uses more than single-use neighborhoods. The data also demonstrate that the variances across neighbourhoods in Tehran account for a significant amount of the housing price variation, with 11% and 48% of the total variance attributed to local and regional levels of analysis, respectively. This highlights the nested and hierarchical nature of housing price data and the use of multilevel modelling for estimating home prices in Tehran. The results presented in this work may have theoretical and practical uses for scholars, insurance firms, banks, real estate developers, and others in the field of property economics.
Keywords: Urban Amenities; Hedonic; Multi-level Modeling; Tehran.
... Here, we rearticulate the existing literature on housing prices and the magnitude of pollution. In fact, the inverse relationship between said variables via health issues is quite common and well-researched in the existing literature [31,[50][51][52][53]. Using data from cities in the United States of America, Smith and Huang, Zabel and Kiel, and Chay and Greenstone show that the total volume of suspended particulate (mainly in terms of SO 2 , CO 2 , and CO) and property values are inversely related to each other with significant a magnitude [31,50,51]. ...
... This study finds that values of fresh air per family in Jakarta range from $28 to $85 per µg/m 3 , and hence the same adverse relationship is also traced in a city located in the developing world. In a similar way, Chen et al. [53] used Shanghai as their reference and, following the hedonic pricing methodology, illustrate that the housing value may fall by 159 and 238 Yuan/m 2 when the average concentrations of sulphur dioxide and PM 10 increase by 1 µg/m 3 . ...
With the growing environmental pollution and adverse climatic conditions, it is now a globally vibrant topic whether housing prices should be associated with the quality of the environment in a particular region. From the microeconomic approach to environmental economics, it is proposed that property prices in any region should be associated with the environmental quality-the concept of hedonic pricing. A negative association between low magnitudes of pollution and high house prices is a precondition to achieving the aim of sustainable development. The study thus starts with the objective of investigating whether there are long-term relations and short-term dynamics between the magnitudes of pollution and house price in the panel of the world’s high-polluting and low-polluting cities for the period of 2012–2021 across 30 cities. Using appropriate time-series econometric procedures such as panel cointegration, panel VECM, and the Wald Test, the study arrives at the conclusion that magnitudes of pollution and house prices in the cities are cointegrated with a stable long-term relationship in all panels. Further, there are strong causal interplays in both the long- and short-term between pollution and house prices in most of the panels of the cities. Thus, policy makers should consider making proper valuations of environmental services to control pollution at the city levels first and then at global levels to reach the proposed goal of sustainable development.
... For instance, in the group-level studies, most studies focused on the impact on the stock market. Only a small number of scholars paid attention to the influence of other markets, such as the real estate market, medical market, labor market and food market (Levy and Yagil, 2013;Chen et al., 2018;Sun et al., 2017a,b), For the individual-level studies, existing research involved with health-related consumer decisions, outdoor consumption, investment decisions, and consumer preference (Barwick et al., 2018;Sun et al., 2019;Huang et al., 2017;Li et al., 2017). ...
... Air pollution has a wide range of influences on consumers' consumption decision-making behavior, such as affecting stock returns, affecting real estate and food prices, reducing tourism decisions, and increasing insurance purchases (Levy and Yagil, 2013;Chen et al., 2018;Sun et al., 2017a,b;Keiser et al., 2018;Chang et al., 2018). We have summarized these impacts separately from the group level and the individual level. ...
... Further, Zheng et al. (2014) indicated that on average with every 10% reduction in the imported pollution of a city from nearby areas, local house prices will rise by 0.76%. Similarly, Chen et al. (2018) also showed for every 1 μg/m 3 reduction in SO2 and PM10 concentrations in air pollution, house prices in Shanghai, China rose by 0.6% and 0.9%, respectively. Chen and Jin (2019) showed that for every 10% increase in PM 2.5 concentrations in air, local housing prices decreased by 2.4%. ...
Rapid industrialization and urbanization trends have brought many challenges, one of the most acute of which is air pollution. Until recently, scholars have primarily focused on the toll that air pollution exacts through its health effects while ignoring air pollution's impact on consumer behavior. The scattered research in the recent year reveals that air pollution might influence consumers' decision-making process before, during, and after consumptions in both online and offline contexts. However, no research to our knowledge has reviewed the influence effect of air quality on consumer behaviors, which impedes the scholars and practitioners from acquiring knowledge from this emerging field. This informative review aims to collect and analyze the existing scattered research for air pollution effects on consumer decision-making behavior. This review illustrates that the impact of air pollution on consumer behavior is rather extensive, ranging from health risks to emotional changes, from changes in daily habits to individual and group consumption behaviors. We propose that future research can potentially explore the impact of air pollution on consumers' consumption experience and evaluation behavior and examine the differential impacts of air pollution on consumers' individual and group consumption decisions.
... Furthermore, the effect of urban amenities may vary across different housing submarkets and social cohorts. For instance, high-income dwellers are willing to pay more for clean air than those with relatively low incomes (Chen et al., 2018). Different homebuyer characteristics may also lead to different urban amenity preferences (Bakis et al., 2019;Hitaj et al., 2018). ...
Wet markets, recently thrust into the limelight during the coronavirus pandemic, play a necessary role in daily life in Asia. Yet, the value of wet markets has not ever been sufficiently explored. While an increasing body of work has studied the implicit value of urban public amenities through the hedonic price model and traditional measurements of accessibility and density, the subjective perceptions of amenities have been neglected, specifically for consumption amenity. This is especially the case with wet markets: traditional approaches are unable to capture their comprehensive effects. By employing data on Beijing housing transactions in 2019 and online review scores of wet markets, we reduce this knowledge gap by exploring both the amenity and dis-amenity effects of wet markets and capturing the objective and subjective perspectives. Our results indicate a nonlinear relationship between wet market accessibility and urban housing prices. Considering the perceived quality of wet markets, this paper further indicates that housing prices near high-scoring wet markets appreciate while housing prices depreciate near low-scoring markets. Interestingly, the negative influence of low-scoring markets is statistically larger than the positive influence of high-scoring wet markets. Taking the housing price as a reflection of an owner's wealth and income level, we argue that high-income dwellers tend to pay more for perceived quality than for convenience. Our findings, therefore, offer new and refined insights for scholars and urban planning decision-makers.
... In recent years, the continuous increases in energy demand, industrial expansion, and private car ownership in megacities have led to a serious deterioration of air quality (1). According to the 2019 Bulletin on the State of China's Ecology and Environment, in 2019, only 157 of 337 cities at or above the prefectural level met the air quality standard in China, while 180 cities exceeded the standard. ...
... The hedonic method is the most representative and widely used statistical method for revealing the complex relationship between the built environment and housing prices (1,4,29). Rosen first adopted the hedonic method to estimate the impact of specific site facilities and clear air on the value of real estate in 1974, and then this method began to be very popular in the research of the impact of the built environment on house prices (30). ...
... The hedonic method is generally implemented by ordinary least squares (OLS) regression (1,9,10,12,13). The basic assumption of OLS methods is that the data on housing prices in different regions are spatially independent and static. However, due to the local interaction and spatial instability between different regions, housing prices and their influencing factors show strong spatial heterogeneity, and the OLS method is not applicable because it ignores spatial changes (31). ...
Previous studies have paid little attention to the spatial heterogeneity of residents' marginal willingness to pay (MWTP) for clean air at a city level. To fill this gap, this study adopts a geographically weighted regression (GWR) model to quantify the spatial heterogeneity of residents' MWTP for clean air in Shanghai. First, Shanghai was divided into 218 census tracts and each tract was the smallest research unit. Then, the impacts of air pollutants and other built environment variables on housing prices were chosen to reflect residents' MWTP and a GWR model was used to analyze the spatial heterogeneity of the MWTP. Finally, the total losses caused by air pollutants in Shanghai were estimated from the perspective of housing market value. Empirical results show that air pollutants have a negative impact on housing prices. Using the marginal rate of transformation between housing prices and air pollutants, the results show Shanghai residents, on average, are willing to pay 50 and 99 Yuan/m² to reduce the mean concentration of PM2.5 and NO2 by 1 μg/m³, respectively. Moreover, residents' MWTP for clean air is higher in the suburbs and lower in the city center. This study can help city policymakers formulate regional air management policies and provide support for the green and sustainable development of the real estate market in China.
... Air quality has increasingly become an important factor influencing housing choices [18,19,24,45]. In confronting serious air pollution, it is common and efficient to control pollution from the source. ...
The Smog Free Tower (SFT) in the city of Xi’an, China, is the world’s first outdoor architecture that uses solar energy and filtration technology to purify polluted air. It provides a unique opportunity to explore residents’ willingness to pay for air quality and their related behaviors. Drawing on data collected after the establishment of the SFT, this paper reveals the characteristics of changes in people’s willingness to pay for clean air. We found that, prior to the release of an assessment report on the SFT, housing prices had an inverted U-shaped relationship with the distance to the SFT, which indicated people tended to purchase houses a certain distance away from the SFT. The threshold value of distance was inversely related to the greening ratio of the residential area. However, after the publication of the experimental report on the SFT, housing prices decreased as the distance to the SFT increased, indicating the closer the house was to the SFT, the more likely people were to buy it. These changes confirmed that people are willing to pay for clean air. The convenience of transportation had a significant moderating effect on the willingness to pay for clean air, however. In other words, people may buy houses with lower air quality if they have better transportation accessibility. The findings of this paper may have practical implications for environmental governance, urban planning, residential satisfaction, and real estate market regulation.
