https://doi-002.org/1018/17593890669319
Rongrong Jiangᵃ*, Wentao Wangᵇ*, Mingguang Yeᶜ, Jie Zhangᵈ#
ᵃSchool of Accounting, Tianjin University of Commerce, Tianjin, China, 300134, 2197915939@qq.com
ᵇSchool of Management and Economics , Beijing Institute of Technology, Beijing, China, 100081, 1101955875@qq.com
ᶜ School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, China, 710129, 1137627961@qq.com
ᵈ School of Accounting, Nanfang College of Sun Yat-sen University, Guangzhou, Guangdong, China, 510900
Corresponding author: zhangjie@nfu.edu.cn (J. Zhang)
Abstract
This study examines the influence of public charging pile deployment on the real estate market using panel data from 31 Chinese provinces over the period from 2016 to 2022. We employ fixed effects models, instrumental variable techniques, and mediation analysis to explore how charging infrastructure affects real estate market transactions and to investigate regional differences in these effects. Our findings indicate that an increase in the number of public charging piles is associated with a decline in the sales area of commercial and residential properties, suggesting that the expansion of charging infrastructure may moderate real estate market sales. This effect is more pronounced in eastern regions and first-tier cities. Our research provides valuable insights into the role of public charging infrastructure in shaping the real estate market and suggests that policymakers should consider regional disparities and urban scale differences when promoting public charging facilities to ensure the stability and healthy development of the real estate market.
Keywords: New Energy Charging Piles; Real Estate Market; Commercial Housing Sales Area; Residential Sales Area; New Energy Vehicle Sales
1 Introduction
In the context of global climate change and the increasing scarcity of energy resources, the rapid advancement of new energy technologies has become a global priority. As a vital component of new energy applications, the deployment of charging infrastructure is crucial for the widespread adoption of electric vehicles and a necessary step toward achieving low-carbon, environmentally sustainable goals. By the end of 2023, China’s electric vehicle fleet had exceeded 12 million units, supported by over 3.2 million installed charging piles, including approximately 900,000 public ones. This extensive infrastructure has laid a solid foundation for the large-scale application of electric vehicles and propelled the development of low-carbon transportation. Concurrently, the ongoing urbanization and persistent growth in housing demand have positioned the real estate market as a significant driver of economic development. The interplay between the rollout of charging infrastructure and the real estate market has increasingly drawn the attention of both academia and policymakers. Empirical observations suggest that the installation of charging piles can influence real estate market trends. For example, in some regions, real estate developers have integrated charging facilities into new projects to cater to evolving market needs. Therefore, the planning and construction of charging infrastructure are particularly critical in harmonizing new energy advancements with urban development, and their impact on the real estate market has become a focal area of research.
China’s rapid economic growth has achieved remarkable milestones, yet environmental challenges remain a pressing concern. The “Guiding Opinions on Further Building a High-Quality Charging Infrastructure System,” issued by the General Office of the State Council on June 19, 2023, underscores the state’s emphasis on enhancing the quality of charging infrastructure to support electric vehicle development and advance environmental goals. This guidance highlights that high-quality charging infrastructure is essential for the popularization of electric vehicles and a key strategy for addressing environmental challenges and achieving sustainable development. While the expansion of charging infrastructure has positively impacted the electric vehicle market and environmental objectives, it is crucial to ensure that infrastructure development is both extensive and high-quality to balance environmental protection with economic growth. This implies that the construction of charging infrastructure must focus not only on quantity but also on quality improvement to realize sustainable development goals.
Moreover, theoretically, the development of charging infrastructure should optimize the allocation of energy resources, thereby mitigating the misallocation of energy in the real estate market. However, in practice, factors such as lagging infrastructure construction, insufficient policy support, or fluctuating market demand may prevent the full resolution of energy resource misallocation. This misallocation presents unique opportunities for real estate developers. Given the limited availability and development potential of charging infrastructure, developers must explore innovative and sustainable ways to integrate these facilities. This has led developers to focus on the synergistic potential between charging infrastructure and real estate projects to achieve a more environmentally friendly and efficient development model.
As an emerging form of infrastructure investment, the construction of charging infrastructure can enhance the value and attractiveness of real estate projects. By effectively integrating charging infrastructure into real estate development, developers can boost their projects’ market competitiveness while meeting the growing demand for electric vehicles. In summary, the development of charging infrastructure presents both opportunities and challenges for the real estate market. Under the dual pressures of expanding charging infrastructure and the market demand for real estate, developers must seek new pathways to achieve a win-win situation for economic and environmental sustainability. This requires innovation from developers as well as collaborative support from governments, businesses, and society at large. This study aims to explore the impact of charging infrastructure on the real estate market, its underlying mechanisms, and its role in promoting real estate market development, thereby contributing to informed policy-making and market strategies.The marginal contributions of this paper are as follows: First, this paper provides an in-depth analysis of the relationship between charging infrastructure and the real estate market, constructing a theoretical framework that offers a novel perspective for related research. Second, it elucidates the mechanisms through which charging infrastructure affects the real estate market, particularly by influencing project attractiveness and market demand, thereby shaping real estate market dynamics. Finally, this paper exa mines the impact of charging infrastructure on the real estate market through the lenses of urban heterogeneity and the development level of charging infrastructure, revealing differentiated impacts under varying urban and infrastructure contexts. The findings uncover both the positive and limiting effects of charging infrastructure on the real estate market, providing valuable insights for policymakers and real estate developers regarding charging infrastructure planning, market development, and green building strategies.
The remainder of this paper is organized as follows: Section 2 presents a literature review, focusing on charging infrastructure, the real estate market, and their interrelationship. Section 3 outlines the theoretical framework, discussing the connection between charging infrastructure and the real estate market. Section 4 describes the research design and empirical strategy, detailing the methodology and data analysis process. Section 5 presents the empirical results, including the main findings and robustness tests to ensure the reliability and validity of the results. Section 6 concludes with a summary of the research findings and specific recommendations for policymakers, industry practitioners, and other stakeholders.
2 Literature Review
In recent years, the electric vehicle (EV) industry has experienced rapid growth, driven by heightened environmental awareness and advancements in new energy technologies. Charging infrastructure, as a critical enabler for EV adoption, has garnered significant attention (Hardman et al., 2018). Characterized by convenience, efficiency, and environmental sustainability, new energy charging piles have had far-reaching impacts across multiple domains, including urban transportation and energy utilization. From a macroeconomic perspective, the development of charging infrastructure can spur the growth of the new energy sector, catalyze technological innovations, optimize energy structures, and stimulate regional economic development (Liu et al., 2018; Bai et al., 2019; Guo & Zhao, 2015). At the microeconomic level, charging piles influence consumer purchasing decisions, improve urban traffic conditions, enhance energy allocation efficiency, and boost the utilization rates of electric vehicles. However, the expansion of charging infrastructure also faces challenges such as optimal site selection, grid capacity constraints, and charging service pricing. Notably, studies on the spatial layout of charging piles and urban planning have shown that rational deployment can address key barriers to EV adoption, such as range anxiety and charging inconvenience, thereby facilitating the widespread adoption of electric vehicles.
Research on housing sales area has a long history, dating back to the mid-20th century when urbanization accelerated. Studies have primarily focused on the measurement of housing sales area, its influencing factors, and its impact on housing prices. Scholars have employed direct measurement methods, indirect estimation techniques, and composite approaches to quantify housing sales area (Feng et al., 2011; Gibbons et al., 2014; Schläpfer et al., 2015). In terms of influencing factors, both macroeconomic conditions (e.g., urbanization, population structure changes, and government housing policies) and micro-level household demands (e.g., family size, income level, and lifestyle preferences) are key drivers (Wu et al., 2012; Tan et al., 2019). Housing sales revenue is influenced by a variety of factors, including economic cycles, government policies, regional development, and market supply and demand dynamics (Sommer & Sullivan, 2018).
While there is limited micro-level research on the impact of new energy charging piles on housing sales area, a potential connection can be inferred. New energy charging piles may influence housing sales area and revenue by enhancing regional livability, attracting specific homebuyer segments, and affecting property valuation (Charles, 2019). Specifically, the proliferation of charging piles may increase the attractiveness of certain areas, thereby influencing housing demand, prices, and sales area. Developers may leverage new energy facilities as a competitive advantage in their projects, affecting housing supply strategies and positioning, and ultimately influencing sales area and revenue. Moreover, the impact of charging piles may vary by region, being more pronounced in cities with higher EV adoption rates (Oliviero et al., 2019). Future research could further explore the direct and indirect impacts of charging pile deployment on housing sales area, as well as the differences across housing types and cities. Such studies will help better understand and predict future changes in the real estate market, providing valuable insights for policy-making and market participants.
However, the development of new energy charging piles has not been without challenges. Urban space, a fundamental resource for charging pile deployment, has recently experienced some degree of misallocation. This misallocation can be partly attributed to the rapid growth of the EV market. Factors such as lagging infrastructure construction and inaccurate user demand forecasts may lead to discrepancies between the layout of charging piles and actual demand. This misallocation presents an opportunity for real estate developers: in the context of limited urban space and strict planning, developers must consider the integration of new energy facilities to seek more attractive and efficient residential project development approaches. This raises a key research question: How does the number of new energy charging piles influence housing sales area through the intermediary variable of EV sales? This paper attempts to explore this question.
3 Mechanism Analysis and Research Hypotheses
When examining the impact of new energy vehicle (NEV) charging pile construction on urban real estate markets, it is essential to explore the potential mechanisms through which charging piles may influence these markets. As a crucial component of NEV infrastructure, the construction of charging piles not only enhances the convenience of using NEVs but also potentially affects real estate market dynamics through various pathways.
3.1 Mechanism Analysis
Firstly, the construction of charging piles increases the convenience of using NEVs. This enhanced convenience may make areas equipped with charging piles more attractive to potential homebuyers, especially in cities with higher NEV adoption rates, where the availability of charging infrastructure could be a key factor in attracting buyers. As the number of charging piles increases, the demand for real estate in areas with charging facilities may rise, thereby driving the development of the local real estate market.
Secondly, the construction of charging piles is often accompanied by improvements in urban infrastructure, which may enhance the overall quality of life and environmental conditions in a given area. These improvements in quality of life and environmental conditions can further strengthen the attractiveness of these areas to homebuyers, potentially fueling the prosperity of the real estate market, particularly in regions with dense charging pile construction.
Moreover, the number and layout of charging piles may be influenced by factors such as regional economic development, social consumption levels, and human capital availability. On one hand, regions with higher levels of economic development, consumption, and human capital may be more likely to attract investment in charging piles, thereby stimulating the activity of the real estate market. On the other hand, the construction of charging piles may, in turn, improve regional infrastructure and living conditions, further enhancing the economic vitality and development prospects of the area, creating a virtuous cycle that continuously drives the prosperity of the real estate market.
Lastly, the stock of public charging piles may indirectly affect the real estate market by increasing NEV sales. On one hand, the widespread availability of charging piles enhances the practicality of NEVs, stimulating NEV consumption and boosting vehicle sales. On the other hand, the increase in NEV sales reflects a shift in consumer attitudes towards environmental protection and lifestyle changes, which may translate into higher demands for living environments and housing conditions, thereby influencing homebuyers’ decisions and driving the development of the real estate market.
3.2 Research Hypotheses
Based on the above mechanism analysis, this study proposes the following hypotheses:
As the number of charging piles increases, the demand for real estate in areas equipped with charging piles may rise. This increase in demand stems from both the attractiveness of charging piles to NEV owners and the improvements in infrastructure and living conditions brought about by the construction of charging piles. Under the pull of this increased demand, the sales area of commercial housing and residential properties may also increase. Therefore, this study proposes the following hypothesis:
H1: The stock of public charging piles has a positive impact on the sales area of commercial housing and residential properties.
The proliferation of charging piles may stimulate NEV consumption and boost vehicle sales. The increase in NEV sales reflects changes in consumer environmental awareness and lifestyle, which may translate into higher demands for living environments. Additionally, NEV owners may prefer properties equipped with charging facilities. Thus, the growth in NEV sales may act as a link between charging pile construction and the real estate market, playing a mediating role in the impact of charging pile stock on the real estate market. Based on the above analysis, this study proposes the following hypothesis:
H2: The stock of public charging piles affects the real estate market by increasing NEV sales.
By empirically testing these hypotheses, this study aims to reveal how the construction of charging piles influences the real estate market and provide decision-making references for the development of new energy infrastructure and real estate market management, thereby promoting the sustainable development of urban economies.
4 Research Design
4.1 Data Sources
This study employs highly professional and authoritative data sources. Data on public charging piles (PCP) were obtained from the China Charging Alliance, covering the period from 2016 to 2022 and encompassing 31 provinces in China. This dataset provides a comprehensive and representative basis for the study.
In terms of variable control, this study has conducted rigorous and comprehensive considerations. Social consumption level, industrialization level, industrial structure, and demand structure were included as control variables to effectively isolate the potential interference of these socioeconomic factors on the core research relationship. Drawing on the research findings of （Lin et al. 2021) and （Tamosaitiene et al. 2021), this study further introduced human resource level and GDP growth rate as control variables to build a robust research framework from multiple dimensions. This framework ensures the accuracy and reliability of the research results by thoroughly analyzing the impact mechanisms of social and economic indicators on the real estate market. The goal is to clarify the intrinsic links between the real estate market and various external factors within the complex socio-economic system, providing a solid theoretical basis and data support for the formulation of real estate market policies and the planning of industry development strategies. Data processing and analysis were primarily conducted using Stata software, which, with its powerful data processing capabilities and flexibility, has become the preferred tool in economic research.
Through these methods, this study aims to provide a solid and reliable data foundation for subsequent statistical analysis and academic discussion.
Table 1 Descriptive Statistics of Key Variables
| Variable | N | Mean | SD | p50 | Min | Max |
| year | 217 | 2019 | 2.005 | 2019 | 2016 | 2022 |
| Commercial Housing Sales Area (10,000 m²) | 217 | 5324 | 4359 | 4193 | 53.25 | 16552 |
| Residential Sales Area (10,000 m²) | 217 | 4615 | 3802 | 3555 | 44.96 | 14362 |
| Public Charging Pile Stock (units) | 217 | 22295 | 37969 | 8258 | 9 | 382960 |
| Social Consumption Level | 403 | 0.376 | 0.0680 | 0.381 | 0.183 | 0.538 |
| Human Resource Level | 217 | 2.232 | 0.582 | 2.200 | 1.003 | 4.362 |
| Industrialization Level | 217 | 29.66 | 8.048 | 30.53 | 7.757 | 49.76 |
| Industrial Structure | 217 | 111.0 | 63.92 | 90.49 | 49.44 | 529.7 |
| GDP Growth Rate | 403 | 1.067 | 0.0530 | 1.078 | 0.704 | 1.172 |
| Structure | 403 | 0.384 | 0.0620 | 0.388 | 0.134 | 0.576 |
Table 2 Variable Definitions
Figure 1 Scatter Plots of Key Variables
The scatter plots illustrate that from 2016 to 2022, the three critical variables—public charging pile stock, commercial housing sales area, and residential sales area—each followed an upward trajectory, albeit with varying patterns and rates of growth. The most notable increase was observed in the number of public charging piles, which surged significantly, particularly after 2020. This surge reflects the accelerated development of the new energy vehicle (NEV) sector. In contrast, the real estate indicators, including the sales areas of commercial and residential properties, exhibited a more erratic upward trend. The growth in sales areas was relatively subdued, especially post-2020, potentially indicating rising housing prices.
Moreover, all variables displayed considerable regional disparities, highlighting the uneven distribution of economic development, urbanization, and policy implementation across different regions in China. The fluctuations in these variables, particularly around 2020, are likely attributable to the impact of the COVID-19 pandemic.
Overall, the concurrent upward trends in public charging piles and real estate market indicators suggest a potential synergistic relationship between urban infrastructure development and real estate market growth. These findings offer valuable insights into China’s urbanization process, the effectiveness of economic policies, and the dynamics of the real estate market. They also emphasize the persistent challenge of addressing regional development imbalances.
Table 2 Variable Selection for the Study
| Category | Variable Name | Symbol | Definition |
| Dependent Variables | Commercial Housing Sales Area | CHSA | Sales area of commercial housing (in 10,000 square meters). |
| Residential Sales Area | RSA | Sales area of residential properties (in 10,000 square meters). | |
| Independent Variables | Public Charging Pile Stock | PCP | Number of public charging piles installed. |
| Control Variables | Social Consumption Level | CL | Total retail sales of consumer goods and household consumption expenditure in the region. |
| Human Capital Level | HC | Measure of education, skills, and labor quality in the region. | |
| Industrialization Level | IND | Proportion of industrial value added in GDP, reflecting the degree of industrial development. | |
| Industrial Structure | IS | Proportion of value added by primary, secondary, and tertiary industries in GDP. | |
| GDP Growth Rate | GR | Annual growth rate of Gross Domestic Product. | |
| Demand Structure | DS | Measure of the demand for different types of consumer goods or services in the region. | |
| Year Fixed Effect | Year | Year fixed effects to control for time-invariant factors. | |
| Province Fixed Effects | Province | Province fixed effects to control for unobserved heterogeneity across provinces. |
4.3 Econometric Models
It appears that you are preparing to outline the econometric models utilized in your research. Should you share the detailed formulations of these models, I would be able to assist in reviewing them, elucidating their components, or proposing enhancements. Additionally, if you require guidance on choosing the appropriate models, employing estimation methods, or interpreting the results, please do not hesitate to inquire.
- Fixed Effects Model
The fixed effects model accounts for unchanging provincial characteristics over time, thereby isolating intraprovincial variations. This approach allows for a more accurate assessment of the influence exerted by the stock of public charging piles. The fundamental structure of the model is:
(1)
Here, represents the dependent variable (Commercial Housing Sales Area or Residential Sales Area), represents the independent variable (Public Charging Pile Stock) , denotes the individual fixed effects, and， represents the error term.
2 Robustness Tests
To enhance the reliability of our findings, we implemented robust standard errors to address potential autocorrelation and heteroskedasticity. Specifically, we utilized clustered robust standard errors and Driscoll-Kraay standard errors. These methods were employed to ensure that our results remain consistent across various assumptions and specifications. The formula for the robustness test is as follows:
(2)
where， robust represents the estimated coefficient corrected for robust standard errors.
- Instrumental Variable Approach
To address potential endogeneity issues, we employed the Instrumental Variable (IV) method. Selecting appropriate instruments helps mitigate the correlation between the explanatory variables and the error term, thereby yielding more accurate estimates. The IV model is specified as follows:
(3)
where， is the instrumental variable.
- Heterogeneity Analysis
To investigate the diverse effects of public charging pile stock on real estate market indicators across various regions and characteristics, we carried out heterogeneity analyses. Specifically, we conducted subgroup regressions based on region (North, South, East, Central, West) and city classification (first-tier cities, non-first-tier cities) to determine whether the model estimates vary significantly across different groups. The fundamental equation for the heterogeneity analysis is:
(4)
Here， , represents the dummy variable distinguishing different groups.
In summary, by employing the above econometric models and testing methods, we comprehensively analyzed the impact of public charging pile stock on commercial housing and residential sales areas, ensuring the reliability and robustness of the estimation results.
Table 3 Classification and Criteria of Cities
| Region | Provinces or Municipalities |
| Eastern Economic Belt | Liaoning, Beijing, Tianjin, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Hainan, Guangxi |
| Central Economic Belt | Heilongjiang, Jilin, Liaoning, Inner Mongolia, Shanxi, Henan, Anhui, Jiangxi, Hubei, Hunan |
| Western Economic Belt | Shaanxi, Gansu, Ningxia, Qinghai, Xinjiang, Sichuan, Chongqing, Guizhou, Yunnan, Tibet |
| First-Tier Provinces | Beijing, Shanghai, Guangdong |
| Non-First-Tier Provinces | Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Jiangsu, Zhejiang, Anhui, Fujian, Shandong, Henan, Hubei, Hunan, Jiangxi, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang |
5 Empirical Results Analysis
5.1 Fixed Effects Model
The fixed effects regression analysis presented in Table 4 uncovers a significant association between the stock of public charging piles and key indicators of the real estate market. This relationship is consistent across different model specifications, whether considering the overall level of public charging piles (columns (1) and (2)) or incorporating additional control variables (columns (3) and (4)). Notably, these findings diverge from Hypothesis H1.
Specifically, the stock of public charging piles exerts a significantly negative influence on both commercial housing sales area and residential sales area. This negative impact remains robust even after accounting for control variables, although its magnitude is slightly moderated. This suggests that an increase in the number of public charging piles is associated with a decline in the sales areas of commercial and residential properties.
Columns (3) and (4) of Table 4 further elucidate the role of control variables in influencing real estate market indicators. The results indicate that the level of social consumption has a significantly positive effect on both dependent variables, a finding that warrants further investigation. In contrast, other control variables, such as human capital level and industrialization level, generally exhibit insignificant effects on the dependent variables.
Overall, the expansion of public charging piles appears to result in a modest reduction in sales areas. This may reflect a relative decrease in available land resources for real estate development as more space is allocated to charging infrastructure. Such a shift could constrain real estate projects and subsequently impact sales areas. This finding offers a novel perspective on the interplay between new energy infrastructure development and real estate market dynamics. As new energy vehicles and their supporting infrastructure continue to proliferate, this relationship may evolve and warrants ongoing attention and research.
Table 4 Results of Fixed Effects Model
| (1) | (2) | (3) | (4) | |
| VARIABLES | CHSA | RSA | CHSA | RSA |
| CPC | -0.009*** | -0.009*** | -0.009*** | -0.009*** |
| (0.002) | (0.002) | (0.002) | (0.002) | |
| CL | 3,889.109** | 3,738.871** | ||
| (1,739.682) | (1,543.860) | |||
| HC | -300.670 | -211.261 | ||
| (205.505) | (182.373) | |||
| IND | 14.391 | 9.506 | ||
| (14.451) | (12.825) | |||
| IS | -1.526 | -1.549 | ||
| (1.130) | (1.003) | |||
| GR | 1,929.435* | 1,391.458 | ||
| (1,142.053) | (1,013.501) | |||
| DS | -3,490.284* | -3,148.533* | ||
| (1,956.551) | (1,736.317) | |||
| Constant | 5,517.334*** | 4,807.967*** | 3,778.893*** | 3,512.764*** |
| (65.274) | (57.858) | (1,305.447) | (1,158.503) | |
| Year | Y | Y | Y | Y |
| Province | Y | Y | Y | Y |
| Observations | 217 | 217 | 217 | 217 |
| R-squared | 0.9793 | 0.9786 | 0.9804 | 0.9797 |
| Adjusted R-squared | 0.975 | 0.974 | 0.976 | 0.975 |
| F | 18.07 | 22.94 | 3.99 | 4.63 |
5.2 Robustness Tests
To capture the potential time-lag effects of public charging pile stock on real estate market indicators and to mitigate concerns regarding reverse causality, we conducted regression analyses using the one-period lagged value of public charging pile stock. As shown in columns (1) and (2) of Table 5, the results indicate that the lagged public charging pile stock exerts a statistically significant influence on current-period real estate market indicators, with a notable negative impact on both commercial housing sales area and residential sales area. This suggests that an increase in public charging piles may lead to a subsequent decline in sales areas in the following period, revealing the complex dynamics through which public charging infrastructure influences the real estate market.
further test the robustness of our findings, we employed winsorized data in columns (3) and (4). The results consistently demonstrate significant negative effects, corroborating the conclusions drawn from columns (1) and (2).
Overall, these analyses confirm the robustness of our conclusions, underscoring that the impact of public charging pile construction on the real estate market is characterized by a significant time-lag effect.
Table 5 Results of Robustness Tests
| (1) | (2) | (3) | (4) | |
| VARIABLES | CHSA | RSA | CHSA（Win） | RSA（Win） |
| L.CPC | -0.013*** | -0.013*** | -0.010*** | -0.010*** |
| (0.004) | (0.004) | (0.003) | (0.003) | |
| CL | 3,299.382 | 3,299.382 | 3,749.696** | 3,534.760** |
| (2,042.127) | (2,042.127) | (1,767.965) | (1,600.296) | |
| HC | -745.035 | -745.035 | -260.914 | -176.779 |
| (587.852) | (587.852) | (207.102) | (187.461) | |
| IND | 33.315 | 33.315 | 12.269 | 7.862 |
| (22.896) | (22.896) | (14.951) | (13.533) | |
| IS | -1.309 | -1.309 | -1.640 | -1.604 |
| (1.173) | (1.173) | (1.441) | (1.304) | |
| GR | 1,153.400 | 1,153.400 | 1,882.902 | 1,289.154 |
| (1,279.770) | (1,279.770) | (1,158.683) | (1,048.796) | |
| DS | -3,083.635 | -3,083.635 | -4,168.489** | -3,756.569** |
| (2,151.282) | (2,151.282) | (2,079.716) | (1,882.482) | |
| Constant | 5,118.049** | 5,118.049** | 4,128.079*** | 3,913.963*** |
| (1,989.221) | (1,989.221) | (1,329.686) | (1,203.582) | |
| Year | Y | Y | Y | Y |
| Province | Y | Y | Y | Y |
| Observations | 186 | 186 | 217 | 217 |
| Adjusted R-squared | 0.978 | 0.978 | 0.975 | 0.973 |
| R-squared | 0.9797 | 0.9826 | 0.9800 | 0.9785 |
| F | 4.63 | 2.50 | 2.25 | 2.37 |
5.3 Instrumental Variable Approach.
To address potential endogeneity concerns stemming from omitted variable bias and measurement errors, we utilized the Instrumental Variable (IV) method in conjunction with Two-Stage Least Squares (2SLS) regression analysis.
In selecting appropriate instruments, we first considered that the current stock of public charging piles might be influenced by its level in the previous period. Accordingly, we employed the one-period lagged stock of public charging piles (L1.Public Charging Piles) as an instrument for the contemporaneous stock of public charging piles. Second, drawing on related research (e.g., He et al., 2023), which explored the relationship between complementary technologies and disruptive innovation in China’s electric vehicle industry, we introduced R&D intensity as an additional instrument. This instrument captures the potential linkages between charging infrastructure and real estate market indicators, specifically the sales areas of commercial housing and residential properties.
The 2SLS regression results presented in Table 6 confirm the relevance of our instruments. In columns (1) and (3), both L1.Public Charging Piles and R&D intensity are significantly and positively associated with the current stock of public charging piles at the 1% significance level. Columns (2) and (4) reveal that, after accounting for potential endogeneity, the stock of public charging piles remains significantly and negatively correlated with both commercial housing sales area and residential sales area at the 1% level. This suggests that an increase in public charging piles may be associated with a modest decline in sales areas.
Overall, these results further validate the robustness of our conclusions. Even when controlling for potential endogeneity, the impact of public charging pile construction on the real estate market remains significant, underscoring the importance of considering dynamic interactions between infrastructure development and market outcomes.
Table 6 Results of Instrumental Variable Approach
| (1) | (2) | (3) | (4) | |
| VARIABLES | PCP | CHSA | PCP | RSA |
| LPCP | 1.555*** | |||
| (36.23) | ||||
| R&D | 755,675.988*** | |||
| (3.46) | ||||
| PCP | -0.012*** | -0.011*** | ||
| (-4.50) | (-4.73) | |||
| CL | 22,848.823 | 5,078.067** | 19,748.278 | 5,535.060** |
| (0.99) | (2.19) | (0.35) | (2.55) | |
| HC | -2,811.816 | -593.746** | -8,564.343* | -463.230 |
| (-1.60) | (-1.96) | (-1.76) | (-1.64) | |
| IND | 184.030 | -15.051 | 634.694** | -17.814 |
| (1.51) | (-0.60) | (2.07) | (-0.75) | |
| IS | -12.538 | -1.172 | 0.481 | -1.356 |
| (-0.86) | (-0.87) | (0.01) | (-1.07) | |
| GR | 12,978.778 | 1,485.977 | 46,487.576 | 1,200.128 |
| (0.62) | (1.01) | (0.87) | (0.87) | |
| DS | -16,241.608 | -5,213.002** | -102,270.598 | -5,508.551** |
| (-0.64) | (-2.13) | (-1.63) | (-2.40) | |
| Constant | -14,795.920 | -26,757.633 | -26,757.633 | -14,795.920 |
| (-0.65) | (-0.47) | (-0.47) | (-0.65) | |
| Year | Y | Y | Y | Y |
| Province | Y | Y | Y | Y |
| Observations | 186 | 186 | 217 | 186 |
| R-squared | 0.906 | 0.268 | 0.265 | 0.268 |
| Number of id | 31 | 31 | 31 | 31 |
| F | 139.38 | 6.76 | 5.63 | 6.88 |
5.4 Mediation Effects
Yu and Chou (2022) recently explored the ratio of new energy vehicle (NEV) charging piles in China using system dynamics and grey prediction models. Their study forecasts that by 2030, the ratio of NEVs to charging piles will evolve from 1.79:1 to a more balanced 1:1. This projection highlights a strong correlation between the expansion of charging infrastructure and the growth in NEV sales, suggesting that NEV sales may mediate the relationship between public charging piles and the real estate market. To examine this potential mediating effect, we employed a mediation model for empirical analysis.
Our results uncover several important insights. First, the stock of public charging piles significantly boosts NEV sales, as evidenced in column (1). This finding indicates that enhanced charging infrastructure effectively stimulates the adoption of NEVs. Second, the impact of NEV sales on the real estate market is complex. Columns (2) and (3) reveal that one-period lagged NEV sales have a significant negative effect on both commercial housing sales area and residential sales area at the 1% significance level. This suggests that higher NEV sales are associated with a modest decline in real estate sales areas. Finally, the overall effect of public charging piles on the real estate market remains significant. While public charging piles drive NEV sales, they also appear to have a nuanced, albeit slightly negative, impact on real estate sales areas.
In summary, our findings underscore the critical role of public charging piles in both promoting NEV sales and influencing the real estate market. These results are consistent with Hypothesis H2, which posits that the construction of charging piles affects the real estate market indirectly through its positive impact on NEV sales.
Table 7 Mediation Effect Tests
| (1) | (2) | (3) | |
| VARIABLES | NEV | NEV | NEV |
| PCP | 1.908*** | ||
| (0.119) | |||
| CHSA | -0.004*** | ||
| (0.001) | |||
| RSA | -0.004*** | ||
| (0.001) | |||
| CL | 59,654.825 | 4,027.358** | 3,943.037*** |
| (100,910.905) | (1,715.566) | (1,495.311) | |
| HC | -5,636.328 | -323.542 | -236.014 |
| (11,920.369) | (202.614) | (176.601) | |
| IND | -463.094 | 13.437 | 7.987 |
| (838.255) | (14.246) | (12.417) | |
| IS | 97.273 | -1.107 | -1.109 |
| (65.542) | (1.115) | (0.972) | |
| GR | -48,206.797 | 1,702.077 | 1,162.076 |
| (66,245.221) | (1,124.800) | (980.391) | |
| DS | -14,310.020 | -3,521.461* | -3,196.402* |
| (113,490.430) | (1,928.412) | (1,680.831) | |
| Constant | 63,018.201 | 4,040.287*** | 3,792.676*** |
| (75,722.938) | (1,287.722) | (1,122.396) | |
| Year | Y | Y | Y |
| Province | Y | Y | Y |
| Observations | 217 | 217 | 217 |
| Adjusted R-squared | 0.834 | 0.976 | 0.976 |
| R-squared | 0.8668 | 0.9809 | 0.9810 |
| F | 39.24 | 4.83 | 6.60 |
5.5 Heterogeneity Analysis by City
The analysis of Table 8 uncovers significant regional variations in the influence of public charging pile stock on commercial housing sales. Notably, the Eastern Economic Belt experiences the most pronounced negative impact, driven by low land resource utilization efficiency, which exacerbates existing land supply constraints as a result of charging pile construction. As indicated in column (2) of Table 8, a one-unit increase in the stock of charging piles corresponds to a 0.026-unit decline in commercial housing sales, thereby directly substantiating the mediating mechanism.
In terms of city size, the negative effect of charging pile stock is highly significant in first-tier cities (at the 1% level), whereas non-first-tier cities do not exhibit a statistically significant impact. This divergence can be attributed to the distinct land development contexts of these cities. In first-tier cities, where land use is highly saturated, the construction of charging piles directly encroaches on real estate space. In contrast, non-first-tier cities can mitigate spatial competition by utilizing additional land or reallocating inefficient land use.
Furthermore, in the Western Economic Belt, the GDP growth rate and industrialization level significantly and positively influence the real estate market at the 5% and 1% levels, respectively. This highlights the role of regional development stages in shaping the pathways through which macroeconomic variables impact the real estate market.
Table 8 Heterogeneity Analysis of Commercial Housing Sales Area by City
| (1) | (2) | (3) | (4) | (5) | |
| VARIABLES | Eastern | Central | Western | First-tier Province | Non-First-tier Provinces |
| PCP | -0.011*** | -0.026* | -0.018* | -0.021*** | -0.006 |
| (0.003) | (0.014) | (0.010) | (0.003) | (0.004) | |
| CL | 6,229.427** | 6,569.891 | -3,560.851 | -6,737.330 | 3,166.277* |
| (2,807.821) | (4,281.776) | (3,580.750) | (5,774.212) | (1,841.470) | |
| HC | -278.546 | -121.250 | -576.202** | -4,631.966** | -300.234 |
| (522.014) | (648.633) | (249.125) | (1,475.567) | (220.461) | |
| IND | -22.537 | -23.997 | 64.252*** | -179.711 | 18.233 |
| (30.085) | (42.316) | (20.827) | (103.509) | (15.359) | |
| IS | -1.555 | -5.747 | -0.500 | 2.809 | -1.577 |
| (2.380) | (5.111) | (1.161) | (2.343) | (1.199) | |
| GR | -3,162.590 | 1,328.512 | 3,603.379** | -12,557.302 | 1,885.273 |
| (2,406.844) | (2,561.296) | (1,660.565) | (7,015.188) | (1,163.568) | |
| DS | 989.178 | -4,214.843 | -6,336.243 | 47,070.246** | -2,944.256 |
| (3,871.826) | (4,160.235) | (4,891.992) | (15,874.774) | (2,026.134) | |
| Constant | 9,041.217*** | 5,701.929* | 3,395.855 | 19,649.354* | 3,618.387*** |
| (2,687.466) | (3,112.448) | (2,038.557) | (8,019.348) | (1,334.933) | |
| Year | Y | Y | Y | Y | Y |
| Province | Y | Y | Y | Y | Y |
| Observations | 84 | 63 | 70 | 21 | 196 |
| Adjusted R-squared | 0.981 | 0.962 | 0.977 | 0.997 | 0.973 |
| R-squared | 0.9862 | 0.9752 | 0.9843 | 0.9992 | 0.9783 |
| F | 3.44 | 1.04 | 4.83 | 13.43 | 1.43 |
Table 9 Heterogeneity Analysis of Residential Sales Area by City
| (1) | (2) | (3) | (4) | (5) | |
| VARIABLES | Eastern | Central | Western | First-tier Province | Non-First-tier Provinces |
| PCP | -0.010*** | -0.023* | -0.021** | -0.020*** | -0.006 |
| (0.002) | (0.012) | (0.009) | (0.003) | (0.004) | |
| CL | 4,402.000* | 7,394.236* | -2,385.051 | -4,908.426 | 3,236.788** |
| (2,588.216) | (3,723.384) | (3,135.683) | (5,441.603) | (1,628.022) | |
| HC | -167.622 | -84.893 | -382.744* | -2,838.666* | -257.751 |
| (481.186) | (564.044) | (218.160) | (1,390.571) | (194.907) | |
| IND | -31.196 | -21.312 | 45.347** | -144.643 | 16.324 |
| (27.732) | (36.797) | (18.238) | (97.547) | (13.578) | |
| IS | -1.529 | -4.994 | -0.765 | 2.011 | -1.612 |
| (2.194) | (4.444) | (1.016) | (2.208) | (1.060) | |
| GR | -3,272.235 | 1,142.286 | 3,048.599** | -8,314.604 | 1,393.149 |
| (2,218.599) | (2,227.274) | (1,454.166) | (6,611.094) | (1,028.697) | |
| DS | 894.935 | -4,394.558 | -5,076.789 | 35,653.921* | -2,845.571 |
| (3,569.002) | (3,617.694) | (4,283.946) | (14,960.345) | (1,791.281) | |
| Constant | 8,912.589*** | 4,750.634* | 2,586.658 | 13,076.022 | 3,359.641*** |
| (2,477.273) | (2,706.550) | (1,785.176) | (7,557.412) | (1,180.198) | |
| Year | Y | Y | Y | Y | Y |
| Province | Y | Y | Y | Y | Y |
| Observations | 84 | 63 | 70 | 21 | 196 |
| Adjusted R-squared | 0.978 | 0.965 | 0.973 | 0.996 | 0.972 |
| R-squared | 0.9846 | 0.9769 | 0.9816 | 0.9991 | 0.9778 |
| F | 3.15 | 1.37 | 4.23 | 14.18 | 1.60 |
Table 9 illustrates that the stock of public charging piles has a significant negative impact on commercial housing sales across China’s eastern, central, and western regions, albeit with varying intensities. The eastern region exhibits the most pronounced negative effect, significant at the 1% level, followed by the western region at the 5% level, and the central region at the 10% level. This pattern suggests that while the inhibitory influence of charging infrastructure on the real estate market is geographically widespread, its magnitude varies regionally. The particularly strong negative effect in the eastern region may stem from low land resource utilization efficiency, where the construction of charging piles further strains the already limited land supply. Data in column (2) of Table 9 indicate that a one-unit increase in charging pile stock in the eastern region reduces commercial housing sales by 0.01 units, confirming the presence of land resource competition.
Regarding city size, the stock of public charging piles significantly impacts first-tier cities but not non-first-tier cities. Results in column (4) suggest that the highly saturated land development in first-tier cities may amplify the substitution effect of charging pile construction on real estate, leading to a contraction in sales area. In contrast, non-first-tier cities, with relatively ample land reserves, do not exhibit significant spatial competition effects.
Additionally, macroeconomic variables exhibit regional divergence. In the western region, both GDP growth rate and the industrialization process positively drive the real estate market, significant at the 5% level. This reflects the influence of regional development stage differences on real estate market dynamics.
Overall, the inhibitory effect of public charging pile construction on the real estate market follows a pattern of “strong in the east, moderate in the central and western regions,” and is particularly pronounced in first-tier cities. Non-first-tier cities may be buffered by greater land elasticity, thus remaining largely unaffected. Meanwhile, macroeconomic factors such as consumption expansion and economic growth exert differentiated impacts on the real estate market across regions and city sizes, underscoring the need for policy coordination that integrates infrastructure layout with regional economic characteristics.
Conclusions
Amid the growing importance of public charging pile construction in supporting the new energy vehicle (NEV) industry, this study examines the impact of public charging pile stock on commercial and residential housing sales using panel data from 31 Chinese provinces over the 2016–2022 period. Employing fixed effects models, instrumental variable methods, and mediation effect models, we derive the following key conclusions:
First, an increase in public charging pile stock is associated with a significant reduction in commercial and residential housing sales. This suggests that the expansion of charging infrastructure may lead to a decline in real estate sales, likely reflecting the market impact of charging piles as supportive facilities.
Second, the impact of public charging pile stock on the real estate market exhibits notable regional heterogeneity. The central region experiences a more pronounced negative effect, possibly due to the relatively underdeveloped real estate market in this area, where charging pile construction competes with real estate development for land resources. In contrast, other regions display a less evident negative impact, indicating that real estate markets in different regions respond differently to charging pile construction.
City size also influences the role of public charging pile stock on the real estate market. In first-tier cities, characterized by scarce land resources, the impact of charging pile construction on real estate development is more pronounced, leading to a decline in commercial and residential housing sales. However, this impact is not significant in non-first-tier cities, suggesting that the market environment and resource constraints in first-tier cities are more sensitive to charging pile construction.
Additionally, macroeconomic variables such as GDP growth rate, social consumption level, and industrialization level exert differentiated impacts on the real estate market across regions and city sizes. For instance, an increase in GDP growth rate in the western region stimulates housing demand, while higher social consumption levels in non-first-tier cities positively influence commercial and residential housing sales.
Based on these findings, we propose the following recommendations:
First, policymakers should consider regional development disparities and city size differences when promoting public charging pile construction and develop differentiated regulatory policies. While supporting the growth of the NEV industry, it is crucial to mitigate potential negative impacts on the real estate market. In the central region, increased support for public charging pile construction should be balanced with coordination between charging infrastructure and real estate development to avoid adverse effects. In first-tier cities, policies should focus on optimizing land resource allocation to reduce the crowding-out effect of charging pile construction and balance the relationship between NEV promotion and real estate market development. Additionally, the government should monitor macroeconomic variables such as unemployment rates and implement targeted regulatory measures to ensure the stable and healthy development of the real estate market.
Second, regional policies should be tailored to local contexts. In the central region, enhancing support for charging infrastructure is recommended to mitigate negative impacts on the real estate market. In first-tier cities, optimizing land resource allocation is essential to reduce the crowding-out effect of charging pile construction on the real estate market.
Third, society and enterprises should actively collaborate with government policies to promote the coordinated development of green technology and infrastructure. Enterprises should leverage tools such as digital finance to reduce the costs of green technology innovation and enhance its application effectiveness. At the same time, they should remain attentive to changes in market demand and adjust strategies accordingly to ensure sustainable development within resource constraints.
In summary, through the joint efforts of the government, society, and enterprises, the coordinated development of public charging pile construction and the real estate market can be effectively achieved, leading to a win-win situation for both economic growth and environmental sustainability.
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