Housing Prices In Shanghai Research Articles

Unveiling investors' substitution behavior: Stock trading decisions in response to housing market dynamics

Utilizing positive quasi-random shocks to local housing prices in Shanghai, we show that stock investors who experienced significant returns from the real estate market traded less actively, took less risk, and spent less effort trading. We confirm the effect of housing price changes on investors' trading behavior in a national sample. Our findings suggest a substitution effect between the real estate and stock market and highlight the importance of understanding investors' trading behavior in light of intertemporal variations in other asset market.

JUE Insight: The impact of the school admission restriction policy on the housing market in Shanghai

This study explores the influence of a regulation of informal school choice on the housing market in Shanghai to estimate the significance of loopholes in the school district system. The policy limited the priority for enrollment in a public elementary schools given to house owners in the school's district so that each housing property is given only one priority every five years; hence, informal acquisition of priorities through frequent resale of houses is limited. From the dataset of individual housing prices in Shanghai, we show that house price within the school districts of good schools increases after the policy. In contrast, house price decreases in areas that are neighboring the school but outside its school district because they are called “babysitting communities” and provide houses to live in for households who resell their houses earlier than graduation of their children. We also implement event study analysis to estimate time-varying influences of the policy, to show that the significant price increase occurs immediately after the policy implementation and shrinks a little in the short run.

Pursuing educational equality and divergence in the housing market: How do educational equality policies affect housing prices in Shanghai?

Property prices being linked to education creates and perpetuates issues of inequality. This study explores the effect of education equality policies on housing prices using data on existing housing transactions in Shanghai. Based on the propensity score matching (PSM) and difference in difference (DID) models, we find a significant phenomenon of educational capitalization, with residential housing prices in high-quality school districts being significantly higher than those in neighboring districts by 5–19 %. The policy shock of simultaneous admission to private and public schools further exacerbates residential price divergence. The results prove that the policy aims to promote educational equity may increase educational capital premiums and lead to another form of inequality in the housing market, which requires further attention.

Hedonic Pricing of Houses in Megacities Pre- and Post-COVID-19: A Case Study of Shanghai, China

Housing price is one of the most concerning issues to the public worldwide. Studying the spatial characteristics of Shanghai’s housing prices and their explanatory factors is of great practical significance, for Shanghai is the largest city in China and serves as the national economic center and a global financial hub. By crawling the point of interest (POI) data from the Lianjia Real Estate and Gaode Map in the past decade and applying the multiscale geographically-weighted regression (MGWR) model, this study deeply explores the spatial characteristics of housing prices and their main influencing variables in Shanghai before and after the COVID-19 pandemic. Results show that housing prices in Shanghai kept rising even under the shock of the pandemic, especially in high-priced housing. After the pandemic, Shanghai’s housing price market polarization intensified. In addition, housing prices are very sensitive to location and have strong spatial heterogeneity. The influencing effects of different explanatory factors vary perceptibly in spatial heterogeneity as well as pre- and post- COVID-19.

Exploring the nonlinear impact of air pollution on housing prices: A machine learning approach

Air pollution has profoundly impacted residents’ lifestyles as well as their willingness to pay for real estate. Exploring the relationship between air pollution and housing prices has become increasingly prominent. Current research on housing prices mainly uses the hedonic pricing model and the spatial econometric model, which are both linear methods. However, it is difficult to use these methods to model the nonlinear relationship between housing price and its determinants. In addition, most of the existing studies neglect the effects of multiple pollutants on housing prices. To fill these gaps, this study uses a machine learning approach, the gradient boosting decision tree (GBDT) model to analyze the nonlinear impacts of air pollution and the built environment on housing prices in Shanghai. The experimental results show that the GBDT can better fit the nonlinear relationship between housing prices and various explanatory variables compared with traditional linear models. Furthermore, the relative importance rankings of the built environment and air pollution variables are analyzed based on the GBDT model. It indicates that built environment variables contribute 97.21% of the influences on housing prices, whereas the contribution of air pollution variables is 2.79%. Although the impact of air pollution is relatively small, the marginal willingness of residents to pay for clean air is significant. With an improvement of 1 μg/m3 in the average concentrations of PM2.5 and NO2, the average housing price increases by 155.93 Yuan/m2 and 278.03 Yuan/m2, respectively. Therefore, this study can improve our understanding of the nonlinear impact of air pollution on housing prices and provide a basis for formulating and revising policies related to housing prices.

Analysis of Commodity Housing Price in Shanghai Based on Multiple Linear Regression Model

As Shanghai is committed to building a vibrant, thriving home for all residents, the pursuit of a high quality of life is correspondingly strengthening. In particular, housing has probably become an important indicator to measure the quality of people's lives and the house price is the key to the housing problem. This article analyses the annual date of the commodity house prices in Shanghai from 2001 to 2020 and aims to explore the affecting factors of commodity housing price, which include the number of ordinary high schools, medical and health institutions and cultural institutions in Shanghai, urban green coverage and residents' disposable income. Therefore, a multiple linear regression model of commodity house price has been established and successfully underwent the test of multicollinearity, heteroscedasticity and sequence correlation.

Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China

Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements. However, limited by the interpretability of the machine learning model, existing studies cannot fully excavate the complex nonlinear relationships between housing prices and environmental elements, as well as the spatial variations of impacts of urban environmental elements on housing prices. This study explored the impacts of urban environmental elements on residential housing prices based on multisource data in Shanghai. A SHapley Additive exPlanations (SHAP) method was introduced to explain the impacts of urban environmental elements on housing prices. By combining the ensemble learning model and SHAP, the contributions of environmental characteristics derived from street view data and remote sensing data were computed and mapped. The experimental results show that all the urban environmental characteristics account for 16 percent of housing prices in Shanghai. The relationships between housing prices and two green characteristics (green view index from street view data and urban green coverage rate from remote sensing) are both nonlinear. Shanghai’s homebuyers are willing to pay a premium for green only when the green view index or urban green coverage rate are of higher value. However, there are significant differences between the impacts of the green view index and urban green coverage rate on housing prices. The sky view index has a negative influence on housing prices, which is probably because the high-density and high-rise residential area often has better living facilities. Residents in Shanghai are willing to pay a premium for high urban water coverage. The case of Shanghai shows that the proposed framework is practical and efficient. This framework is believed to provide a tool to inform the decisions of housing buyers, property developers and policies concerning land-selling and buying, property development and urban environment improvement.

Analyzing housing prices in Shanghai with open data: Amenity, accessibility and urban structure

Skyrocketing housing prices in China's megacities have generated broad concerns. By integrating open data from Lianjia.com, Dianping.com, Mobike.com, and Baidu Map POI, we analyze spatial patterns of apartment prices and their association with local attributes in Shanghai. We find that Shanghai's residential market still has a monocentric structure because of the centralized distribution of public transport facilities and amenities. Hedonic models further confirm that structural attributes, accessibility, as well as public and private service amenities significantly shape the real estate market. These factors also are differentiated so as to form a pattern of concentric rings. In the inner-city and expanded inner-city areas, public service amenities such as parks, schools, hospitals, and banks, as well as private service amenities such as entertainment, shopping, and residential service facilities, boost housing prices. In the suburbs, better access to bike sharing, bus stops, and metro stations are the top preferences for apartment buyers. Our study also indicates that the Chinese government needs to make public and private services more accessible, not only spatially in urban peripheries and villages, but also institutionally to lower income families who cannot afford apartments in expensive neighborhoods.

Spatial interactive effects on housing prices in Shanghai and Beijing

When connected through economic, financial, and policy shocks, cities can be considered to be neighbors in the social-economic sense. Therefore, a spatial correlation may exist in cities that are not geographically contiguous. The paper simultaneously studies the spatial correlation within each city and the spatial interactive effects among different cities on housing prices. We build a spatial autoregressive hedonic pricing model in a system of interrelated networks, which allows for multiple spatial interactive effects among housing units inside the same city and from other cities. For estimation, we propose a two-stage least squares (2SLS) method and the maximum likelihood estimation (MLE). Finite sample properties of these two methods are investigated in a Monte Carlo simulation. After applying these steps to March 2016 housing prices in Shanghai and Beijing, the empirical findings show that the spatial correlations within each city are significantly large, i.e., approximately 0.6–0.8, and spatial interactive effects between the two cities exist, although the magnitude is smaller, i.e., approximately 0.1–0.2.

The Structural Analysis of the Determinants of Shanghai Real Estate Price

What are the factors that play the most significant role in affecting Shanghai real estate price? This essay has established an econometric model and uses the perspective of structural analysis to substantiate the real factors that affect the housing price in Shanghai during the 1994— 2015. The essay first collects data from multiple authoritative sources according to potential factors. Then, the essay selects several optimal factors and conducts verification on them. The result is being analyzed. Finally, a conclusion has been reached: factors on the supply side of housing individually have small impact on the price while factors on demand side and policy demonstrate strong impact statistically. This conclusion further suggests that policy implemented by the government with respect to housing price problem in Shanghai could restrain the unreasonable growth to some extent.

Spatial and hedonic analysis of housing prices in Shanghai

Spatial and hedonic analysis of housing prices in Shanghai

Local Public Expenditure, Public Service Accessibility, and Housing Price in Shanghai, China

The level of public service accessibility differs by residential locations. Existing literature has documented the effects of public service accessibility on housing prices. However, few studies have examined how public service provision affects housing prices under the Chinese centralized governance system. Using data from different geographic scales, this study uses a multilevel research design to examine how public service provision is associated with housing prices in Shanghai. Results show that though the total expenditure at the urban district level is not related to housing prices, government redistributive expenditure is positively associated with housing price and the developmental expenditure is on the opposite. The expenditure effects are further mediated by the accessibility to public facilities at the community level. The findings suggest that, as public service accessibility has been capitalized into housing prices, the decentralized fiscal system with relatively centralized governance structure may have reinforced urban polarization in Shanghai.

An Empirical Analysis of Factors Affecting the Housing Price in Shanghai

Being one of the most booming and prosperous cities of China, Shanghai is attracting more and more talents from all over the world. Although the development of real estate market contributes a lot to the economic take-off, the excessively high the housing price may cause big troubles. In order to find the main factors affecting the housing price in Shanghai, an econometric model is established and seven explanatory variables, per capita disposable income in Shanghai, land transaction price index, construction cost, urbanization rate, interest rate, CPI of residence and investment in real estate in Shanghai are taken into consideration based on the supply and demand theory. With the help of regression analysis, only interest rate is kicked out and the other six can all be a good explanatory variable for the housing price.

The impacts of distance to CBD on housing prices in Shanghai: a hedonic analysis

It is widely recognized that location is the primary determining factor of housing price. But to what extent the variation of housing price in Shanghai can be explained by the locational factor has not been empirically examined. In this paper, we examine the power of applying the hedonic method to the spatial-statistical analysis of housing prices in Shanghai. The data we use covers all new commercial residential housings sold in Shanghai during July 2004 and June 2006. The main focus in this paper is to examine the effect of geographical distance to city centre on the selling price of residential housings in Shanghai. We also discuss how the price gradient varies at different directions in Shanghai. Finally, we demonstrate the importance of applying quality control on the development of a housing price index. The statistical methodology and empirical results obtained in this paper carry interesting implications for other cities in China as well.

Price‐rent ratio in china's housing market: Proper interval, measurement and an empirical study

Abstract It has been a hot topic concerning about whether price bubbles exist in the housing market in several cities in China or otherwise. However, much of the debate lacks convincing evidence. This paper discusses the two factors which may lead the housing price to deviate from its fundamental value: rational bubble and economic irrationality. Also, the paper considers the price‐rent ratio as a valid indicator in reflecting the rationality of the housing price. Based on an international comparative study and the Income Capitalization Method, this paper will show that the proper interval of the price‐rent ratio in China's housing market should be around 150–200. At the end of this paper, an empirical study will be presented after an alternative measurement method for China's housing market is proposed. It will illustrate that housing prices in Shanghai and Hangzhou are overvalued while that in Beijing and Guangzhou are comparatively closer to the proper interval.

PRICE-RENT RATIO IN CHINA'S HOUSING MARKET: PROPER INTERVAL, MEASUREMENT AND AN EMPIRICAL STUDY

It has been a hot topic concerning about whether price bubbles exist in the housing market in several cities in China or otherwise. However, much of the debate lacks convincing evidence. This paper discusses the two factors which may lead the housing price to deviate from its fundamental value: rational bubble and economic irrationality. Also, the paper considers the price‐rent ratio as a valid indicator in reflecting the rationality of the housing price. Based on an international comparative study and the Income Capitalization Method, this paper will show that the proper interval of the price‐rent ratio in China's housing market should be around 150–200. At the end of this paper, an empirical study will be presented after an alternative measurement method for China's housing market is proposed. It will illustrate that housing prices in Shanghai and Hangzhou are overvalued while that in Beijing and Guangzhou are comparatively closer to the proper interval.

Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition

A class of differentiated products is completely described by a vector of objectively measured characteristics. Observed product prices and the specific amounts of characteristics associated with each good define a set of implicit or "hedonic" prices. A theory of hedonic prices is formulated as a problem in the economics of spatial equilibrium in which the entire set of implicit prices guides both consumer and producer locational decisions in characteristics space. Buyer and seller choices, as well as the meaning and nature of market equilibrium, are analyzed. Empirical implications for hedonic price regressions and index number construction are pointed out.