Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique

Wang, Yang; Wang, Shaojian; Li, Guangdong; Zhang, Hongou; Jin, Lixia; Su, Yongxian; Wu, Kegong

doi:10.1016/j.apgeog.2016.12.003

Cited by 128 publications

(85 citation statements)

References 54 publications

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“…The proportion of renters is widely recognized for its significant positive effect on housing price [10]. In this study, PR has a positive effect on housing price for almost all significant cities (Figure 5c).…”

Section: Spatial Patternmentioning

confidence: 81%

“…From a cost-driven perspective, housing price consists of land cost, construction cost, related taxes, and developer profits. The ratio of land cost to housing price increased from 9% in 1998 to 24.3% in 2011 [10]. As a major component, land price is supposed to affect housing price.…”

Section: Proportion Of Renters (Pr)mentioning

confidence: 99%

“…They found that the effect of inflation rate on the housing prices was positive initially and then became negative, and the more significant negative effect was found in first-tier cities. In terms of the floating population, Wang et al [10] identified it as a significant positive factor for housing price. Chen et al [4] divided the selected cities into coastal and inland ones, and found that there is significant negative correlation for inland cities while insignificant for coastal cities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first type of cities with a lower regression coefficient and higher proportion of renters (e.g., Yulin City, Shanxi Province, coefficient = −0.033 and PR = 0.015) are mainly concentrated in Shanxi Province, where the natural resources is abundant, especially in coal and natural gas. According to Wang et al [10], although a large proportion of short-term migrant workers is present in resource-based cities, they do not exert pressure on the local housing market because they have houses in their rural hometowns and do not intend to settle in the cities. Cities of the second type, which have a lower regression coefficient and proportion of renters (e.g., Baoshan City, Yunnan Province, coefficient = 0.011 and PR = 0.003) are concentrated in the northern and southeastern part of China.…”

Section: Spatial Patternmentioning

confidence: 99%

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Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Mou

Zhou

2017

Sustainability

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Abstract:Given the rapidly developing processes in the housing market of China, the significant regional difference in housing prices has become a serious issue that requires a further understanding of the underlying mechanisms. Most of the extant regression models are standard global modeling techniques that do not take spatial non-stationarity into consideration, thereby making them unable to reflect the spatial nature of the data and introducing significant bias into the prediction results. In this study, the geographically weighted regression model (GWR) was applied to examine the local association between housing price and its potential determinants, which were selected in view of the housing supply and demand in 338 cities across mainland China. Non-stationary relationships were obtained, and such observation could be summarized as follows: (1) the associations between land price and housing price are all significant and positive yet having different magnitudes; (2) the relationship between supplied amount of residential land and housing price is not statistically significant for 272 of the 338 cities, thereby indicating that the adjustment of supplied land has a slight effect on housing price for most cities; and (3) the significance, direction, and magnitude of the relationships between the other three factors (i.e., urbanization rate, average wage of urban employees, proportion of renters) and housing price vary across the 338 cities. Based on these findings, this paper discusses some key issues relating to the spatial variations, combined with local economic conditions and suggests housing regulation policies that could facilitate the sustainable development of the Chinese housing market.

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Section: Spatial Patternmentioning

confidence: 81%

Section: Proportion Of Renters (Pr)mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Spatial Patternmentioning

confidence: 99%

See 2 more Smart Citations

Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Mou

Zhou

2017

Sustainability

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“…For example, Wang [52] first used it to explore the relationship between risk factors and certain diseases. Subsequently, it was applied to measure the effects of potential factors on geographical phenomena, such as the relationship between human activity and urban forest landscape connectivity [54], the spatial correlations among socio-economic factors and the distribution of rural settlements [55] and the effects of potential factors on housing prices [56]. In this study, we attempt to use the geographical detector method to investigate the relative importance of external factors on residential land prices.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

Yang

et al. 2017

Sustainability

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Abstract:With the rapid development of land marketization in China, the spatial patterns of residential land prices in different regions have become increasingly complicated. The very high and continuously rising residential land prices in many cities are causing significant challenges to economic development and social stability. Yet, there has only been a limited amount of attempts made to model and analyze the regional dynamic changes of residential land price systematically, especially in term of the spatially varying effects of key demographic and economic factors. In this study we provided a perspective analysis of the changes of residential land prices in 2008, 2011 and 2014 based on the land price monitoring records of 105 cities and then conducted a geographically weighted regression (GWR) analysis on the relationships between residential land price and three major impact factors (i.e., immigrant population, gross domestic product (GDP) and investment in residential buildings). Results show that the areas in which GDP had relatively strong positive impacts on residential land price expanded with time. The negative effects of immigrant population on residential land price were mainly concentrated in the cities around the Bohai Rim and the area with negative effects gradually shrank in the three studied years. Conversely, the areas with negative correlation between investment in residential buildings and residential land price gradually expanded in size over time. A geographical detector was used to examine the relative importance of factors to residential land price. It was found that the GDP had more significant influence on residential land price than other factors and the influence of the three factors to overall variation in residential land price increased over the three studied years. These results underscore the importance of taking spatially varying effects of major driving factors into account in policy-making on regional land market.

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The Impact of Housing Market Policy on House Prices in China

Liu,

2024

Applied Economic Research and Trends

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Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique

Cited by 128 publications

References 54 publications

Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

The Impact of Housing Market Policy on House Prices in China

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