scholarly journals Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai

2021 ◽  
Vol 9 ◽  
Author(s):  
Ziliang Lai ◽  
Xinghua Liu ◽  
Wenxiang Li ◽  
Ye Li ◽  
Guojian Zou ◽  
...  
Keyword(s):  
Willingness To Pay ◽  
Spatial Heterogeneity ◽  
Air Pollutants ◽  
Negative Impact ◽  
Housing Prices ◽  
Real Estate Market ◽  
Marginal Rate ◽  
Marginal Willingness To Pay ◽  
Clean Air ◽  
Gwr Model

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.

scholarly journals How Much Are People Willing to Pay for Clean Air? Analyzing Housing Prices in Response to the Smog Free Tower in Xi’an

2021 ◽  
Vol 18 (19) ◽  
pp. 10210
Author(s):  
Haiyong Zhang ◽  
Sanqin Mao ◽  
Xinyu Wang
Keyword(s):  
Air Quality ◽  
Willingness To Pay ◽  
Environmental Governance ◽  
Housing Prices ◽  
Threshold Value ◽  
Real Estate Market ◽  
Market Regulation ◽  
Transportation Accessibility ◽  
Clean Air ◽  
The City

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.

scholarly journals Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective

Land ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 4
Author(s):  
Hang Shen ◽  
Lin Li ◽  
Haihong Zhu ◽  
Yu Liu ◽  
Zhenwei Luo
Keyword(s):  
Deep Learning ◽  
Spatial Heterogeneity ◽  
Geographically Weighted Regression ◽  
House Prices ◽  
Real Estate Market ◽  
Weighted Regression ◽  
Learning Methods ◽  
Nonlinear Complexity ◽  
Geographical Perspective ◽  
Gwr Model

Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.

scholarly journals Exploring the spatial heterogeneity and temporal homogeneity of ambient PM10 in nine core cities of China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rui Feng ◽  
Rong Zhou ◽  
Weiwei Shi ◽  
Nanjing Shi ◽  
Xuekun Fang
Keyword(s):  
Spatial Heterogeneity ◽  
Thermodynamic Equilibrium ◽  
Air Pollutants ◽  
High Performance ◽  
Yangtze River Delta ◽  
River Delta ◽  
Hebei Plain ◽  
Predominant Factor ◽  
Inorganic Aerosol ◽  
The Pearl River

AbstractWe focus on the causes of fluctuations in wintertime PM10 in nine regional core cities of China using two machine learning models, Random Forest (RF) and Recurrent Neural Network (RNN). RF and RNN both show high performance in predicting hourly PM10 using only gaseous air pollutants (SO2, NO2 and CO) as inputs, showing the predominance of the secondary inorganic aerosol and implying the existence of thermodynamic equilibrium between gaseous air pollutants and PM10. Also, we find the following results. The correlation of gaseous air pollutants and PM10 were more relevant than that of meteorological conditions and PM10. CO was the predominant factor for PM10 in the Beijing-Tianjin-Hebei Plain and the Yangtze River Delta while SO2 and NO2 were also important features for PM10 in the Pearl River Delta and Sichuan Basin. The spatial heterogeneity and temporal homogeneity of PM10 in China are revealed. The long-range transported PM10 was substantiated to be insignificant, except in the sandstorms. The severity of PM10 was attributable to the lopsided shift of thermodynamic equilibrium and the phenology of indigenous flora.

What drives housing prices, rent and new construction in China

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue
Keyword(s):  
Real Estate ◽  
Empirical Test ◽  
Housing Prices ◽  
Panel Cointegration ◽  
Real Estate Market ◽  
Estimation Methods ◽  
Content Type ◽  
New Construction ◽  
Using Data ◽  
First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

scholarly journals Spatial Heterogeneity of Willingness to Pay for Forest Management

2016 ◽  
Vol 68 (3) ◽  
pp. 705-727 ◽  
Author(s):  
Mikołaj Czajkowski ◽  
Wiktor Budziński ◽  
Danny Campbell ◽  
Marek Giergiczny ◽  
Nick Hanley
Keyword(s):  
Forest Management ◽  
Willingness To Pay ◽  
Spatial Heterogeneity

scholarly journals Teoretyczne i metodyczne aspekty wyznaczania indeksów cen na rynku mieszkaniowym

2018 ◽  
Author(s):  
Radosław Trojanek
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Estate Market ◽  
Secondary Market ◽  
Property Right ◽  
Sources Of Information ◽  
Single Family ◽  
Starting Point ◽  
Price Indexes

In the book, an attempt was made to catalogue knowledge concerning the importance of research into the dynamics of housing prices for social and economic development. The analysis of the experience of countries with well-developed real estate markets in the aspect of building price indexes was carried out. Based on original databases of asking and transaction prices, price indexes were built, which were then subjected to numerous resistance tests. The aims of these research tasks were as follows: 1) to examine the quality of offers for sale as a source of information about changes in the real estate market, 2) to find out whether the repeat sales method can be used for building price indexes and to critically assess this method in terms of the stability of the obtained results, 3) to analyze hedonic methods and indicate the preferred one in terms of the ratio of the quality of results to how time-consuming and cost-intensive it is to build such indexes, 4) to establish the importance of methods and sources of information for building price indexes in different time horizons, 5) to identify how important it is for the fluctuation of price indexes if the cooperative property right to a flat is not taken into account. In order to perform the research tasks and accomplish the goals scopes of the work were defined. The subject followed the aim of the study and refers to prices in the secondary housing market, encompassing both the property right and cooperative property right to a flat or house. The broad scope concerns the discussion in the general part, being narrowed down to the secondary market of flats located in multi-family and single-family buildings. The time scope covers the years 2000-2015, which is connected to the range of empirical studies carried out. They focused both on actual transactions and on offers of flats for sale. On this basis, we built databases which served as the starting point for further analyses. The study involved transactions and offers in the area of Poznan.

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