The Spatial Correlation Effect of Real-Estate Financial Risk in China: A Social Network Analysis
Abstract
:1. Introduction
2. Literature Review
2.1. Research on the Spatial Correlation of Real-Estate Financial Risks
2.2. Spatial Correlation and the Application of the Social Network Analysis Method
2.3. Research Gaps
3. Real-Estate Financial Risk: Spatial Correlation Mechanism
3.1. Influence of Population Movement
3.2. Influence of the Cross-Regional Development of Real-Estate Enterprises
4. Construction of the Spatial Correlation Network Model
4.1. Indicator of Real-Estate Financial Risk
4.2. Spatial Correlation Network: Matrix Construction
4.3. Network Topology: Characteristics
5. Analysis of the Spatial Correlation Network Characteristics of Real-Estate Financial Risks
5.1. Calculation of the Comprehensive Index of Real-Estate Financial Risk
5.2. Spatial Correlation Network: Diagram
5.3. Analysis of Network Topology Characteristics
5.3.1. Network Density
5.3.2. Network Centrality
5.4. Block Model Analysis
5.5. Analysis of Network Structure: The ERGM Model
6. Conclusions and Implications
6.1. Conclusions
6.2. Implications
6.3. Future Research Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Content |
---|---|
It is a comprehensive indicator representing the financial risk of real estate, weighted by three indicators: the debt ratio of real-estate enterprises, the proportion of annual real-estate investment in the GDP of the year, and the proportion of domestic loans of real-estate enterprises to new loans of financial institutions in the year. | |
. | |
It refers to the total population of each province. | |
The real gross domestic product in a region. | |
Real GDP per capita in a region. | |
It represents the distance between the capital cities of province i and province j. |
Definition | Features | |
---|---|---|
Degree centrality | The number of other nodes directly connected to node i. | It focuses on the ability of a node to communicate on its own does not involve controlling other nodes. In the network of real-estate financial risk, the node may spill risk to others or take spillover from others. Either way, it deserves attention. |
Closeness centrality | The inverse of the sum of the distances between node i and other nodes in the network, with larger values indicating a more central position. | It focuses on the value of a node in the network, indicating the extent to which a point is not controlled by other nodes. In this paper, the distance between nodes includes both geographical distance and the difference in the level of economic development. Closeness centrality refers to the similarity between two regions. Intuitively, it is riskier for a real-estate developer to exploit the market in an unfamiliar region than in a region with high similarity. Therefore, it is easier to form spillover relationships between two similar regions. |
Betweenness centrality | It represents whether the shortest distance between nodes passes through the node or not, if they all pass through it means the point is important in the network. | It focuses on the ability of nodes to regulate and control other points. The node with the ability has the intermediary moderation effect, which can be recognized as the external influence of a core region. The regions around the core region will experience a real-estate market boom because of the flourishing real-estate market and active policies in the core region. As a result, the risks may be accumulated and spill over to other peripheral regions. |
Year | Links | Average Links | Density | Degree of Correlation |
---|---|---|---|---|
2006 | 184 | 5.935 | 0.1978 | 1.000 |
2007 | 186 | 6.000 | 0.2000 | 1.000 |
2008 | 197 | 6.355 | 0.2118 | 1.000 |
2009 | 206 | 6.645 | 0.2215 | 1.000 |
2010 | 214 | 6.903 | 0.2301 | 1.000 |
2011 | 218 | 7.032 | 0.2344 | 1.000 |
2012 | 228 | 7.355 | 0.2452 | 1.000 |
2013 | 226 | 7.290 | 0.2430 | 1.000 |
2014 | 221 | 7.129 | 0.2376 | 1.000 |
2015 | 218 | 7.032 | 0.2344 | 1.000 |
2016 | 214 | 6.903 | 0.2301 | 1.000 |
2017 | 211 | 6.806 | 0.2269 | 1.000 |
2018 | 212 | 6.839 | 0.2280 | 1.000 |
Code | Province | Indegree | Outdegree | Degree | Code | Province | Indegree | Outdegree | Degree |
---|---|---|---|---|---|---|---|---|---|
1 | Shanghai | 5 | 23 | 28 | 17 | Sichuan | 3 | 5 | 8 |
2 | Jiangsu | 4 | 23 | 27 | 18 | Xizang | 2 | 6 | 8 |
3 | Beijing | 0 | 24 | 24 | 19 | Shaanxi | 4 | 4 | 8 |
4 | Zhejiang | 3 | 17 | 20 | 20 | Qinghai | 3 | 5 | 8 |
5 | Tianjin | 18 | 1 | 19 | 21 | Xinjiang | 2 | 6 | 8 |
6 | Shandong | 11 | 4 | 15 | 22 | Shanxi | 4 | 3 | 7 |
7 | Guangdong | 2 | 12 | 14 | 23 | Anhui | 5 | 2 | 7 |
8 | Fujian | 3 | 9 | 12 | 24 | Jiangxi | 1 | 6 | 7 |
9 | Henan | 4 | 6 | 10 | 25 | Hainan | 1 | 6 | 7 |
10 | Hubei | 4 | 6 | 10 | 26 | Ningxia | 3 | 4 | 7 |
11 | Gansu | 5 | 5 | 10 | 27 | Jilin | 2 | 4 | 6 |
12 | Yunnan | 3 | 6 | 9 | 28 | Guizhou | 1 | 5 | 6 |
13 | Heilongjiang | 4 | 4 | 8 | 29 | Hebei | 2 | 3 | 5 |
14 | Hunan | 2 | 6 | 8 | 30 | Inner Mongolia | 2 | 3 | 5 |
15 | Guangxi | 2 | 6 | 8 | 31 | Liaoning | 2 | 3 | 5 |
16 | Chongqing | 5 | 3 | 8 | Total | 112 | 220 | 332 |
Code | Province | Betweenness | nBetweenness | Code | Province | Betweenness | nBetweenness |
---|---|---|---|---|---|---|---|
1 | Guangdong | 152.247 | 17.500 | 17 | Hebei | 5.741 | 0.660 |
2 | Shanghai | 139.512 | 16.036 | 18 | Guangxi | 5.509 | 0.633 |
3 | Jiangxi | 100.098 | 11.506 | 19 | Shanxi | 4.982 | 0.573 |
4 | Beijing | 76.644 | 8.810 | 20 | Liaoning | 4.565 | 0.525 |
5 | Jiangsu | 45.473 | 5.227 | 21 | Hubei | 4.186 | 0.481 |
6 | Hunan | 41.858 | 4.811 | 22 | Inner Mongolia | 2.084 | 0.240 |
7 | Chongqing | 34.379 | 3.952 | 23 | Anhui | 1.675 | 0.192 |
8 | Yunnan | 27.245 | 3.132 | 24 | Sichuan | 1.283 | 0.148 |
9 | Tianjin | 25.366 | 2.916 | 25 | Hainan | 1.269 | 0.146 |
10 | Guizhou | 24.385 | 2.803 | 26 | Heilongjiang | 0.583 | 0.067 |
11 | Henan | 23.621 | 2.715 | 27 | Shaanxi | 0.375 | 0.043 |
12 | Zhejiang | 20.886 | 2.401 | 28 | Xizang | 0 | 0 |
13 | Shandong | 19.824 | 2.279 | 29 | Jilin | 0 | 0 |
14 | Gansu | 18.417 | 2.117 | 30 | Ningxia | 0 | 0 |
15 | Fujian | 17.793 | 2.045 | 31 | Xinjiang | 0 | 0 |
16 | Qinghai | 6.000 | 0.690 |
Code | Province | inFarness | outFarness | inCloseness | outCloseness |
---|---|---|---|---|---|
1 | Beijing | 37 | 313 | 81.081 | 9.585 |
2 | Tianjin | 42 | 314 | 71.429 | 9.554 |
3 | Hebei | 57 | 304 | 52.632 | 9.868 |
4 | Shanxi | 60 | 304 | 50.000 | 9.868 |
5 | Inner Mongolia | 66 | 304 | 45.455 | 9.868 |
6 | Liaoning | 64 | 305 | 46.875 | 9.836 |
7 | Jilin | 900 | 245 | 3.333 | 12.245 |
8 | Heilongjiang | 900 | 243 | 3.333 | 12.346 |
9 | Shanghai | 33 | 294 | 90.909 | 10.204 |
10 | Jiangsu | 33 | 303 | 90.909 | 9.901 |
11 | Zhejiang | 41 | 299 | 73.171 | 10.033 |
12 | Anhui | 53 | 313 | 56.604 | 9.585 |
13 | Fujian | 72 | 295 | 41.667 | 10.169 |
14 | Jiangxi | 56 | 288 | 53.571 | 10.417 |
15 | Shandong | 47 | 301 | 63.830 | 9.967 |
16 | Henan | 50 | 303 | 60.000 | 9.901 |
17 | Hubei | 58 | 292 | 51.724 | 10.274 |
18 | Hunan | 59 | 287 | 50.847 | 10.453 |
19 | Guangdong | 62 | 287 | 48.387 | 10.453 |
20 | Guangxi | 86 | 289 | 34.884 | 10.381 |
21 | Hainan | 91 | 288 | 32.967 | 10.417 |
22 | Chongqing | 109 | 286 | 27.523 | 10.490 |
23 | Sichuan | 138 | 285 | 21.739 | 10.526 |
24 | Guizhou | 89 | 287 | 33.708 | 10.453 |
25 | Yunnan | 89 | 283 | 33.708 | 10.601 |
26 | Xizang | 930 | 258 | 3.226 | 11.628 |
27 | Shaanxi | 842 | 205 | 3.563 | 14.634 |
28 | Gansu | 840 | 198 | 3.571 | 15.152 |
29 | Qinghai | 842 | 198 | 3.563 | 15.152 |
30 | Ningxia | 930 | 177 | 3.226 | 16.949 |
31 | Xinjiang | 930 | 258 | 3.226 | 11.628 |
Centrality | Sub-Indicator | Province |
---|---|---|
Degree centrality | indegree | Tianjin, Shandong, Shanghai, Anhui, Chongqing, Gansu |
outdegree | Beijing, Jiangsu, Shanghai, Zhejiang, Guangdong, Fujian | |
Betweenness centrality | / | Guangdong, Shanghai, Jiangxi, Beijing, Jiangsu, Hunan, Chongqing |
Closeness centrality | incloseness | Shanghai, Jiangsu, Beijing, Zhejiang, Tianjin, Shandong, Henan |
outcloseness | Ningxia, Gansu, Qinghai, Shaanxi, Heilongjiang, Jilin, Xizang, Xinjiang |
Block | Province | Total Relations Received from Other Blocks | Total Relations Sent to Other Blocks | Block Role |
---|---|---|---|---|
Block 1 | Beijing, Jiangsu, Shanghai, Zhejiang, Guangdong | 14 | 99 | Strong net spillover |
Block 2 | Fujian, Jiangxi, Hunan, Guangxi, Yunnan, Guizhou, Hainan, Xizang, Xinjiang | 17 | 56 | Weak net spillover |
Block 3 | Henan, Hubei, Sichuan, Qinghai, Jilin, Ningxia, Hebei, Inner Mongolia, Liaoning, Gansu, Heilongjiang, Shaanxi | 38 | 52 | Broker |
Block 4 | Shanxi, Chongqing, Anhui, Shandong, Tianjin | 43 | 13 | Primary loss |
Variables | Coefficients (Model 1) | Coefficients (Model 2) |
---|---|---|
Edges | −2.54 *** (0.25) | −2.44 *** (0.27) |
Dland | 0.94 *** (0.17) | 1.21 *** (0.21) |
GDP_growth | 0.21 (1.06) | 0.13 (1.17) |
mutual | 1.36 *** (0.25) | |
balance | 0.05 *** (0.02) | |
AIC | −324.67 | −302.02 |
BIC | −305.33 | −282.68 |
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Xu, Y.; Li, J.; Qi, H. The Spatial Correlation Effect of Real-Estate Financial Risk in China: A Social Network Analysis. Sustainability 2022, 14, 7085. https://doi.org/10.3390/su14127085
Xu Y, Li J, Qi H. The Spatial Correlation Effect of Real-Estate Financial Risk in China: A Social Network Analysis. Sustainability. 2022; 14(12):7085. https://doi.org/10.3390/su14127085
Chicago/Turabian StyleXu, Yunsong, Jiaqi Li, and Hanying Qi. 2022. "The Spatial Correlation Effect of Real-Estate Financial Risk in China: A Social Network Analysis" Sustainability 14, no. 12: 7085. https://doi.org/10.3390/su14127085
APA StyleXu, Y., Li, J., & Qi, H. (2022). The Spatial Correlation Effect of Real-Estate Financial Risk in China: A Social Network Analysis. Sustainability, 14(12), 7085. https://doi.org/10.3390/su14127085