The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China
Abstract
:1. Introduction
- (1)
- Unlike earlier studies that analysed investment data, this paper introduces niche theory into the field of urban housing investment research, constructs a comprehensive index of housing investment niches, and provides theoretical guidance for the efficient allocation of housing investment resources.
- (2)
- Due to the directionality of the spatial correlations within the housing investment network, the impacts of population flows, capital flows, traveling distance, and economic distance on the urban housing investment niche network are fully addressed.
- (3)
- Furthermore, regarding research methods, a TERGM (based on a static ERGM) containing endogenous structural effects, attribute effects, exogenous spatial effects, and temporal effects was constructed and dynamic network analysis was used to conduct an in-depth study of the mechanisms influencing the urban housing investment niche network.
2. Theoretical Foundation and Research Model
2.1. Complex Urban Housing Investment Niche System
Type of Urban Housing Investment Niche | Primary Index | Secondary Index (Unit) | Description of Secondary Indicators | Indicator Source |
---|---|---|---|---|
Urban resources niche | Public resources | Per capita area of urban green space (m2/person) | Urban green space/total population at year end | China Urban Statistical Yearbook |
Per capita area of urban roads (m2/person) | Urban road area/population at year end | China Urban Statistical Yearbook | ||
Number of hospital beds per 10,000 people (bed/104 persons) | (Total number of hospital beds/total population) × 10,000 | China Urban Statistical Yearbook | ||
Human resources | Number of college students per 10,000 people (students/104 persons) | (Total number of college students/population) × 10,000 | China Urban Statistical Yearbook | |
Number of employed persons in urban areas (103 persons) | Total number of employees | China Urban Statistical Yearbook | ||
Land resources | Built area (km2) | Urban built area | China Urban Statistical Yearbook | |
Residential land supply area (hectare) | Includes land for indemnificatory and commercial housing | Bureau Of Urban Planning and Natural Resources | ||
Housing market niche | Market demand | Housing sales area (103 m2) | Commercial housing sales area | China Urban Statistical Yearbook |
Natural population growth rate (‰) | Birth rate-death rate | China Urban Statistical Yearbook | ||
Urbanization rate (%) | Urban population/permanent population | China Urban Statistical Yearbook | ||
Market supply | Expected rate of return from real estate (%) | The average housing price growth rate over the previous three years | CEIC Macroeconomic Database | |
Land area purchased by real estate enterprises (104 m2) | Land area purchased by real estate development firms | CEIC Macroeconomic Database | ||
Number of real estate development firms (firms) | Number of real estate development firms | China Urban Statistical Yearbook | ||
Social economic niche | Economic environment | Per capita GDP (yuan/person) | GDP/Total population at year end | CEIC Macroeconomic Database |
Per capita disposable income of the urban population (yuan/person) | Per capita disposable income of the urban population | CEIC Macroeconomic Database | ||
Balance of loans from financial institutions (104 yuan) | Balance of loans from financial institutions | China Urban Statistical Yearbook | ||
Social environment | Infrastructure construction (108 yuan) | Fixed asset investment-real estate investment | China Urban Statistical Yearbook | |
Share of the tertiary industry in GDP (%) | Tertiary industry output/GDP | China Urban Statistical Yearbook | ||
Real estate policy niche | Housing policy | Weighted average interest rate on individual housing loans (%) | Converted to real interest rates with city-specific CPIs | CEIC Macroeconomic Database |
Monetary policy | M2 growth rate-GDP growth rate-inflation rate (%) | M2 growth rate-GDP growth rate-inflation rate | CEIC Macroeconomic Database | |
Land policy | Land price (yuan) | Average transaction price of land | CEIC Macroeconomic Database |
2.2. Comprehensive Index of Urban Housing Investment Niches: Niche Theory
2.3. Establishing Niche Networks for Urban Housing Investment: A Modified Gravity Model
2.4. Analysis of the Spatial Correlations in the Network Structure of the Urban Housing Investment Niche: A Temporal Exponential Random Graph Model (TERGM)
3. Urban Housing Investment Niche Network Construction and Impact Mechanism Design
3.1. Construction of the Urban Housing Investment Niche Network
- (1)
- Spatial characteristics of the network as a whole
- (2)
- Spatial characteristics of locally clustered networks
- (3)
- Spatial characteristics of individual networks
3.2. Description of the Mechanisms of Influence
- (1)
- Endogeneity of the network structure
- (2)
- Node attributes
- (3)
- External network effects
- (4)
- Temporal effect
4. Characteristics of the Dynamic Changes in the Network Structure
4.1. Spatial Characteristics of the Network as a Whole
4.2. Spatial Characteristics of the Local Clustering Networks and Individual Networks
5. Mechanism Analysis
5.1. Results
5.2. Robustness Check
6. Conclusions and Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
City | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
---|---|---|---|---|---|---|---|---|---|
Beijing | 0.191 | 0.193 | 0.191 | 0.199 | 0.202 | 0.200 | 0.164 | 0.194 | 0.194 |
Tianjin | 0.125 | 0.120 | 0.125 | 0.136 | 0.138 | 0.139 | 0.117 | 0.138 | 0.142 |
Shijiazhuang | 0.064 | 0.060 | 0.064 | 0.058 | 0.055 | 0.066 | 0.051 | 0.061 | 0.067 |
Taiyuan | 0.043 | 0.045 | 0.043 | 0.046 | 0.048 | 0.049 | 0.042 | 0.058 | 0.054 |
Hohhot | 0.044 | 0.048 | 0.044 | 0.043 | 0.049 | 0.047 | 0.037 | 0.048 | 0.052 |
Shenyang | 0.054 | 0.056 | 0.054 | 0.060 | 0.053 | 0.043 | 0.037 | 0.044 | 0.050 |
Dalian | 0.052 | 0.055 | 0.052 | 0.049 | 0.041 | 0.054 | 0.036 | 0.047 | 0.053 |
Changchun | 0.052 | 0.050 | 0.052 | 0.054 | 0.051 | 0.055 | 0.037 | 0.044 | 0.050 |
Harbin | 0.041 | 0.045 | 0.041 | 0.038 | 0.040 | 0.041 | 0.031 | 0.041 | 0.042 |
Shanghai | 0.086 | 0.081 | 0.086 | 0.079 | 0.082 | 0.083 | 0.067 | 0.091 | 0.095 |
Nanjing | 0.142 | 0.138 | 0.142 | 0.144 | 0.141 | 0.143 | 0.124 | 0.149 | 0.145 |
Hangzhou | 0.071 | 0.076 | 0.071 | 0.070 | 0.089 | 0.073 | 0.072 | 0.083 | 0.093 |
Ningbo | 0.083 | 0.073 | 0.083 | 0.071 | 0.069 | 0.070 | 0.062 | 0.081 | 0.082 |
Hefei | 0.056 | 0.058 | 0.056 | 0.055 | 0.054 | 0.053 | 0.048 | 0.065 | 0.065 |
Fuzhou | 0.039 | 0.041 | 0.039 | 0.038 | 0.039 | 0.040 | 0.040 | 0.042 | 0.051 |
Xiamen | 0.065 | 0.069 | 0.065 | 0.065 | 0.059 | 0.055 | 0.053 | 0.069 | 0.072 |
Nanchang | 0.061 | 0.066 | 0.061 | 0.066 | 0.067 | 0.065 | 0.059 | 0.070 | 0.064 |
Jinan | 0.046 | 0.047 | 0.046 | 0.058 | 0.053 | 0.050 | 0.040 | 0.054 | 0.053 |
Qingdao | 0.047 | 0.054 | 0.047 | 0.052 | 0.053 | 0.052 | 0.043 | 0.055 | 0.061 |
Zhengzhou | 0.049 | 0.050 | 0.049 | 0.058 | 0.070 | 0.073 | 0.054 | 0.071 | 0.080 |
Wuhan | 0.059 | 0.058 | 0.059 | 0.068 | 0.076 | 0.072 | 0.064 | 0.076 | 0.078 |
Changsha | 0.054 | 0.058 | 0.054 | 0.061 | 0.060 | 0.060 | 0.051 | 0.065 | 0.068 |
Guangzhou | 0.084 | 0.086 | 0.084 | 0.088 | 0.090 | 0.090 | 0.074 | 0.110 | 0.114 |
Shenzhen | 0.122 | 0.129 | 0.122 | 0.139 | 0.134 | 0.127 | 0.106 | 0.135 | 0.128 |
Nanning | 0.077 | 0.082 | 0.077 | 0.083 | 0.086 | 0.090 | 0.082 | 0.092 | 0.078 |
Haikou | 0.037 | 0.038 | 0.037 | 0.043 | 0.043 | 0.041 | 0.039 | 0.053 | 0.053 |
Chongqing | 0.085 | 0.083 | 0.085 | 0.074 | 0.079 | 0.076 | 0.104 | 0.083 | 0.086 |
Chengdu | 0.087 | 0.094 | 0.087 | 0.098 | 0.093 | 0.085 | 0.074 | 0.090 | 0.093 |
Guiyang | 0.064 | 0.067 | 0.064 | 0.072 | 0.066 | 0.066 | 0.055 | 0.070 | 0.069 |
Kunming | 0.046 | 0.051 | 0.046 | 0.055 | 0.060 | 0.051 | 0.050 | 0.056 | 0.056 |
Xi’an | 0.058 | 0.061 | 0.058 | 0.063 | 0.064 | 0.060 | 0.054 | 0.065 | 0.067 |
Lanzhou | 0.053 | 0.053 | 0.053 | 0.058 | 0.058 | 0.057 | 0.047 | 0.064 | 0.062 |
Xining | 0.030 | 0.033 | 0.030 | 0.040 | 0.041 | 0.037 | 0.031 | 0.037 | 0.037 |
Yinchuan | 0.032 | 0.032 | 0.032 | 0.033 | 0.035 | 0.030 | 0.031 | 0.039 | 0.038 |
Urumqi | 0.050 | 0.050 | 0.050 | 0.050 | 0.045 | 0.045 | 0.044 | 0.062 | 0.075 |
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Classification | Variable Name | Description | Configuration | Statistic | Definition |
---|---|---|---|---|---|
Endogenous network structures | Edge | Intercept | The intercept term is the same as that in a linear regression model | ||
Mutual | Reciprocity | Examines the reciprocity in the relationship between housing investment in two cities in the network | |||
Ctriple | Cyclic triad | Examines whether there is a circular tripartite relationship in urban housing investment | |||
Ttriple | Transitive triad | Examines whether there is a transitive tripartite relationship in urban housing investment | |||
Node attributes | Homophily | Presence of the same attributes | Indicates whether cities with the same attributes tend to have investment relationships | ||
Sender | Sender effect | Indicates whether cities with certain attributes are more likely to invest in other cities | |||
Receiver | Receiver effect | Indicates whether cities with certain attributes are more likely to attract investment from other cities | |||
External network effects | Edgecov | Exogenous effect | Indicates whether spatial distance affects the tendency of cities to build housing investment relationships | ||
Time effect | Stability | Degree of stability | Indicates whether the network pattern in period t − 1 affects the network pattern in period t |
Year | Cluster | City | Density |
---|---|---|---|
2011 | Cluster 1 | Shanghai, Nanjing, Hangzhou, Ningbo, Hefei, Fuzhou, Xiamen, Nanchang, Xi’an, Urumqi | 0.533 |
Cluster 2 | Beijing, Tianjin, Shijiazhuang, Taiyuan, Hohhot, Shenyang, Dalian, Changchun, Harbin, Jinan, Qingdao, Zhengzhou, Chongqing, Yinchuan | 0.528 | |
Cluster 3 | Wuhan, Changsha, Guangzhou, Shenzhen, Chengdu, Nanning, Haikou, Guiyang, Kunming, Lanzhou, Xining | 0.582 | |
2015 | Cluster 1 | Beijing, Tianjin, Shijiazhuang, Taiyuan, Hohhot, Shenyang, Dalian, Changchun, Harbin, Jinan, Qingdao, Zhengzhou, Chongqing, Xi’an | 0.550 |
Cluster 2 | Shanghai, Nanjing, Hangzhou, Ningbo, Hefei, Fuzhou, Xiamen, Nanchang, Wuhan, Changsha, Guangzhou, Shenzhen, Chengdu, Nanning, Guiyang, Kunming, Lanzhou, Xining, Yinchuan, Urumqi, Haikou | 0.448 | |
2019 | Cluster 1 | Nanjing, Hangzhou, Ningbo, Hefei, Fuzhou, Xiamen, Nanchang, Nanning | 0.607 |
Cluster 2 | Beijing, Tianjin, Shijiazhuang, Taiyuan, Hohhot, Shenyang, Dalian, Changchun, Harbin, Shanghai, Jinan, Qingdao, Yinchuan | 0.474 | |
Cluster 3 | Zhengzhou, Wuhan, Changsha, Guangzhou, Shenzhen, Chongqing, Chengdu, Xi’an, Guiyang, Kunming, Lanzhou, Xining, Urumqi, Haikou | 0.550 |
Variable | ERGM | TERGM |
---|---|---|
Edges | −0.47 *** | −1.55 *** |
(0.29) | (0.19) | |
Mutual | 1.76 *** | 1.15 *** |
(0.27) | (0.17) | |
Ctriple | −0.46 *** | −0.28 *** |
(0.12) | (0.06) | |
Ttriple | −0.09 | −0.00 |
(0.05) | (0.01) | |
Homophily (GDPHigh) | 0.06 | −0.04 |
(0.16) | (0.12) | |
Homophily (PeopleHigh) | 0.17 | 0.05 |
(0.17) | (0.12) | |
Sender (GDPLow) | −0.34 | −0.16 |
(0.25) | (0.15) | |
Sender (PeopleLow) | 0.33 | 0.12 |
(0.28) | (0.17) | |
Receiver (GDPHigh) | 0.71 *** | 0.43 *** |
(0.20) | (0.12) | |
Receiver (PeopleHigh) | 2.65 *** | 1.35 *** |
(0.30) | (0.14) | |
Stability | 2.87 *** | |
(0.06) |
Variable | Model 1 | Model 2 | Model 3 |
---|---|---|---|
2011–2019 | 2011–2015 | 2015–2019 | |
Edges | −0.79 *** | −0.58 *** | −0.74 * |
(0.06) | (0.15) | (0.36) | |
Mutual | 0.95 *** | 1.14 *** | 0.70 * |
(0.13) | (0.25) | (0.28) | |
Ctriple | −0.29 *** | −0.40 *** | −0.26 ** |
(0.06) | (0.11) | (0.10) | |
Ttriple | −0.03 | −0.02 | −0.06 * |
(0.02) | (0.03) | (0.03) | |
Homophily (GDPHigh) | −0.03 | −0.34 | 0.04 |
(0.11) | (0.26) | (0.18) | |
Homophily (PeopleHigh) | 0.11 | −0.14 | 0.15 |
(0.13) | (0.24) | (0.20) | |
Sender (GDPLow) | −0.22 | −0.14 | −0.14 |
(0.14) | (0.29) | (0.24) | |
Sender (PeopleLow) | 0.27 | −0.03 | 0.26 |
(0.17) | (0.34) | (0.27) | |
Receiver (GDPHigh) | 0.43 *** | 1.55 *** | 0.43 * |
(0.12) | (0.30) | (0.19) | |
Receiver (PeopleHigh) | 1.53 *** | 0.91 ** | 1.57 *** |
(0.15) | (0.28) | (0.23) | |
Distance | −0.15 *** | −0.17 *** | −0.13 *** |
(0.05) | (0.06) | (0.04) | |
Stability | 2.81 *** | 3.12 *** | 2.62 *** |
(0.06) | (0.12) | (0.09) |
Variable | 2011–2019 | 2011–2015 | 2015–2019 |
---|---|---|---|
Edges | −1.02 * | −0.99 * | −0.76 * |
[−1.41; −0.59] | [−1.34; −0.21] | [−1.16; −0.19] | |
Mutual | 1.27 * | 1.76 * | 0.66 * |
[0.78; 1.62] | [1.03; 2.18] | [0.56; 0.74] | |
Ctriple | −0.33 * | −0.55 * | −0.20 * |
[−0.45; −0.17] | [−0.66; −0.18] | [−0.26; −0.12] | |
Ttriple | −0.01 | 0.02 | −0.06 |
[−0.07; 0.03] | [−0.08; 0.09] | [−0.14; 0.01] | |
Homophily (GDPHigh) | −0.02 | −0.30 * | 0.04 |
[−0.21; 0.13] | [−0.54; −0.15] | [−0.12; 0.26] | |
Homophily (PeopleHigh) | 0.12 | −0.19 | 0.15 |
[−0.16; 0.29] | [−1.11; 0.33] | [−0.16; 0.35] | |
Sender (GDPLow) | −0.17 | −0.11 | −0.09 |
[−0.55; 0.31] | [−0.62; 0.68] | [−0.63; 0.44] | |
Sender (PeopleLow) | 0.31 * | 0.03 | 0.25 |
[0.04; 0.55] | [−0.85; 0.65] | [−0.12; 0.74] | |
Receiver (GDPHigh) | 0.44 * | 1.65 * | 0.42 * |
[0.18; 0.62] | [1.04; 1.93] | [0.11; 0.61] | |
Receiver (PeopleHigh) | 1.54 * | 0.89 * | 1.57 * |
[1.25; 1.79] | [0.34; 1.55] | [1.22; 1.86] | |
Distance | −0.13 * | −0.15 * | −0.12 * |
[−0.18; −0.07] | [−0.22; −0.08] | [−0.15; −0.07] | |
Stability | 2.82 * | 3.18 * | 2.63 * |
[2.61; 3.15] | [2.96; 4.27] | [2.50; 3.03] |
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Wang, L.; Hu, H.; Wang, X. The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China. Sustainability 2022, 14, 3523. https://doi.org/10.3390/su14063523
Wang L, Hu H, Wang X. The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China. Sustainability. 2022; 14(6):3523. https://doi.org/10.3390/su14063523
Chicago/Turabian StyleWang, Linyan, Haiqing Hu, and Xianzhu Wang. 2022. "The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China" Sustainability 14, no. 6: 3523. https://doi.org/10.3390/su14063523