4.1. Results for the 35 Large and Medium-Sized Cities in China
The results of the SLM with spatial fixed effects for real estate prices in the 35 large and medium-sized cities in China are shown by
Table 4. Logtp, logep, logtf and loglb have significantly positive coefficients, indicating that individual and household demand, real estate development enterprises’ investment, local governments’ land revenue, and bank credit have a positive impact on real estate prices in the 35 large and medium-sized cities in China. Logtp has the largest influence (0.22), followed by logep (0.19), logtf (0.18) and loglb (0.14). These are consistent with the analysis of
Section 2 that the four sectors jointly drive up the real estate prices. It is interesting that, contrary to the supply and demand theory, real estate development enterprises’ investment expansion has positive rather than negative influence on real estate prices. Relying on their strong market power in the context of urbanization and soaring real estate price, real estate development enterprises employ various strategies to promote real estate prices, such as price discrimination and property hoarding. Meanwhile, real estate development enterprises raise funds in various ways and expand their investment to seek more profit. As a result, there appears a cycle of “the higher real estate price, the more investment, the higher real estate price…”. These findings are consistent with
Lai (
2008) and
Liu et al. (
2017)’s viewpoints of investment driving growth.
The coefficient of W*logp is 0.30, indicating that the rise of real estate prices in neighbor large and medium-sized cities promotes the rise of real estate prices in this city (
Hao et al. 2016). Therefore, real estate prices have strong spatial diffusion effects among the 35 large and medium-sized cities in China. In other words, the rise of real estate price in a city will bring the increase in real estate prices in other cities.
Logtp, logep, logtf and loglb have significantly positive direct effects, at 0.22, 0.19, 0.18, 0.13, respectively, suggesting that the promotion of the four sectors’ participation in the real estate market drives up the local real estate prices. The indirect effects of the logtp, logep, logtf and loglb also have significantly positive indirect effects, at 0.09, 0.08, 0.08 and 0.06, respectively, indicating that market demand, investment expansion of real estate development enterprises, land revenue, and bank credit in one city elevate the real estate prices of other cities as well. The total effects of logtp, logep, logtf and loglb are 0.31, 0.27, 0.26, and 0.19, respectively, showing that the four sectors drive up the real estate prices in the 35 large and medium-sized cities in China.
The results of the SDM with spatial fixed effects for financial real estate risks in the 35 large and medium-sized cities in China are described in
Table 5. The coefficients of logpr/gdpr, logtf, logsa, and logbl are significantly positive, suggesting that over-growth in real estate prices, local governments’ land revenue, market demand and bank loans to the real estate industry promote the local financial real estate risks in the 35 large and medium-sized cities in China. The positive impact of over-growth in real estate prices on the local financial real estate risk is strongest (0.28), followed by local governments’ land revenue (0.16), market demand (0.15) and bank loans (0.06). However, logrdi has a negative coefficient of −0.30, indicating that the increase in real estate development enterprises’ investment brings about a decrease in the local financial real estate risks; it is the opposite, on the contrary. Real estate developers usually increase their development investment when market demand is rising, thus providing more houses to meet the increasing demand, while reducing their development investment to decrease market supply and prevent the further collapse of real estate prices in the market downturn. Therefore, real estate developers’ development investment has a negative impact on the local financial real estate risks.
W*logrdi has a positive coefficient of 0.31, suggesting that the investment expansion of real estate development enterprises in other cities raises the financial real estate risks in the local cities. Real estate development investment usually accompanies soaring real estate prices, and the expansion of real estate development enterprises’ investment in other cities means that the real estate price is rising rapidly, which will diffuse to the local city, and thus raise the financial real estate risks in the local city. W*logsa, W*logbl and W*logtf have negative coefficients of −0.19, −0.11 and −0.10, respectively, indicating that the increases in market demand, bank loans to the real estate industry and land revenue in other cities lower real estate prices in the local city. With limited resources, the rising demand, expansion of bank credit to the real estate industry and the increasing land transfer fees in other cities attract funds from the local city to other cities, and thus reduce the heat of the real estate market and financial real estate risks in the local city. The coefficient of W*fr is 0.32, indicating the strong spatial conduction effects of financial real estate risks among the 35 large and medium-sized cities in China. That is, the financial real estate risks in a city will diffuse to other cities, which is risky.
logpr/gdpr, has a strong direct effect of 0.28, indicating that the over-growth of real estate prices in a city will raise the local real estate prices and thus financial real estate risks. Similar to their coefficients, logtf, logsa, and logbl have strong positive direct effects, at 0.16, 0.14, 0.05, respectively; however, logrdi has a negative direct effect of −0.30, suggesting that the rising land revenue, market demand and bank credit to the real estate industry drive up the local financial real estate risks; however, increase in real estate development enterprises’ investment has the opposite influence. logsa, logbl and logtf have negative indirect effects of −0.20, −0.13, −0.07, respectively; however, logrdi has a positive indirect effect of 0.30. That is, the increasing market demand, bank credit to the real estate industry and land revenue in a city decreases the financial real estate risks in other cities, contrary to the impact of real estate development enterprises’ investment expansion. The total effects of logtf and logbl are 0.09 and −0.08, respectively, suggesting a positive impact of local governments’ land revenue and a negative impact of bank credit to the real estate industry on financial real estate risks across the 35 large and medium-sized cities.
4.2. Results for the Cities in Different Regions in China
4.2.1. The Eastern Region
The results of the SLM with spatial fixed effects for real estate prices in the eastern region in China are shown by
Table 6. The coefficients of logtp, loglb, logtf and logep are 0.21, 0.17, 0.15 and 0.11, respectively. That is, individuals and households’ demand has the strongest positive impact on the local real estate price, followed by bank credit expansion, local governments’ land revenue, and real estate enterprises’ investment expansion in the large and medium-sized cities in the eastern region of China.
W*logp has a significantly positive coefficient of 0.28, suggesting the strong spatial diffusion effects of real estate prices among the large and medium-sized cities in the eastern region of China.
logtp, loglb, logtf and logep have direct effects of 0.21, 0.18, 0.16 and 0.11, and indirect effects of 0.08, 0.07, 0.06 and 0.04, respectively, suggesting the positive impacts of individuals and households’ demand, bank credit, local governments’ land revenue, and real estate enterprises’ investment on the local real estate prices, as well as real estate prices in other cities. The total effects of logtp, loglb, logtf and logep are 0.28, 0.24, 0.22 and 0.16, respectively, indicating that they drive up the whole real estate prices in the large and medium-sized cities in the eastern region of China.
The results of the SDM with spatial fixed effects for financial real estate risks in the large and medium-sized cities in the eastern region of China are shown in
Table 7. logpr/gdpr, logtf and logbl have significantly positive coefficients of 0.36, 0.11 and 0.05, respectively, suggesting that over-growth in real estate prices has the strongest positive influence on the local financial real estate risks in the large and medium-sized cities in the eastern region of China, followed by local governments’ land revenue and bank loans to the real estate industry. However, the coefficient of logrdi is −0.18, indicating that real estate development enterprises’ investment has a negative impact on the local financial real estate risks.
W*logrdi has the coefficient of 0.25, indicating the positive impacts of real estate development enterprises’ investment in other cities on the financial real estate risks in the local city in the eastern region of China. W*logsa and W*logbl have negative coefficients of −0.19 and −0.13, respectively. That is, the market demand and bank credit to the real estate industry in other cities lowers the financial real estate risks in the local city, because investors and speculators are attracted from the local city to other cities with hot real estate markets. W*fr has the coefficient of 0.19, suggesting that the financial real estate risks in a city can diffuse to other cities in the eastern region of China.
The direct effects of pr/gdpr, logtf and logrdi are 0.36, 0.11 and −0.18, respectively, suggesting the positive impacts of over-growth of real estate prices and local governments’ land revenue on the local financial real estate risks, while real estate enterprises’ investment has a negative influence. logrdi, logsa and logbl have indirect effects of 0.25, −0.22 and −0.15, indicating that real estate enterprises’ investment expansion in local city elevates the financial real estate risks in other cities; however, the market demand and bank credit to the real estate industry have the opposite impacts. The total effects of logtf, logsa and logbl are 0.12, −0.17 and −0.11, respectively, showing the positive influence of local governments’ land revenue and the negative impacts of market demand and bank credit to the real estate industry on the whole financial real estate risks in the large and medium-sized cities in the eastern region of China.
4.2.2. The Middle Region
The results of the SLM with spatial fixed effects for real estate prices in the middle region of China are shown by
Table 8. loglb, logtf and logtp have the coefficients of 0.23, 0.22 and 0.16, respectively, suggesting the strongest positive influence of bank credit expansion on the local real estate prices in the large and medium-sized cities in the middle region of China. The coefficient of W*logp is 0.49, suggesting that the rise of real estate prices in other cities elevates the real estate prices in the local city.
The direct effects of loglb, logtf and logtp are 0.25, 0.24 and 0.17, while indirect effects are 0.21, 0.20 and 0.14, respectively. That is, the rises in bank credit, local governments’ land revenue and individuals and households’ demand elevate the real estate prices in both the local city and other cities. loglb, logtf and logtp have the total effects of 0.46, 0.44 and 0.31, respectively, showing the positive impacts of bank credit, local governments’ land revenue, and individuals and households’ demand on the whole real estate prices in the large and medium-sized cities in the middle region of China.
The results of the SDM with spatial fixed effects for financial real estate risks in the large and medium-sized cities in the middle region of China are shown by
Table 9. The coefficients of logtf, logtp, and logbl are significantly positive, at 0.25, 0.21 and 0.07, respectively. That is, local governments’ land revenue has the strongest positive impact on the local financial real estate risks in the large and medium-sized cities in the middle region of China, followed by individuals and households’ demand and bank loans to the real estate industry. The coefficient of logrdi is −0.45, indicating that real estate development enterprises’ investment has a negative impact on the local financial real estate risks.
W*logrdi has the coefficient of 0.32, indicating that real estate development enterprises’ investment expansion in other cities elevates the financial real estate risks in the local city in the middle region of China. W*logbl has a negative coefficient of −0.17, suggesting the negative impacts of bank credit to the real estate industry in other cities on the financial real estate risks in the local city, because the investors and speculators in the local city are attracted to other cities with more bank credit, and thus reduce the financial real estate risks in the local city. The coefficient of W*fr is 0.35, indicating the strong spatial diffusion effects of financial real estate risks among the large and medium-sized cities, which is the most influential factor for the rise in the local financial real estate risks in the middle region of China.
logtf, logsa and logrdi have the direct effects of 0.25, 0.21 and −0.44, respectively. That is, local governments’ land revenue and market demand increase the local financial real estate risks; however, real estate enterprises’ investment lowers the local financial real estate risks. Logrdi and logbl have the indirect effects of 0.24 and −0.22, indicating the positive impacts of real estate enterprises’ investment expansion in the local city on the financial real estate risks in other cities, while bank credit to the real estate industry has the opposite influence. Logtf, logrdi and logbl have the total effects of 0.33, −0.20 and −0.17, respectively. That is, the local governments’ land revenue has a strong positive effect, while the real estate enterprises’ investment and bank credit have negative influences on the whole financial real estate risks in the large and medium-sized cities in the middle region of China.
4.2.3. The Western Region
The results of the SLM with spatial fixed effects for real estate prices in the western region of China are described in
Table 10. The coefficients of logep, logtf and loglb are 0.51, 0.17 and 0.14, respectively. That is, real estate development enterprises’ investment expansion is the most influential factor for the rise of local real estate prices in the large and medium-sized cities in the western region of China, followed by local governments’ land revenue and bank credit. W*logp has the positive coefficient of 0.24, suggesting the strong spatial diffusion among real estate prices in the large and medium-sized cities in the western region of China.
logep, logtf and loglb have the direct effects of 0.52, 0.17 and 0.14, and the indirect effects of 0.16, 0.05 and 0.04, respectively. Thus, the real estate development enterprises’ investment expansion has the most positive influence on real estate prices in both the local city and other cities in the western region of China, followed by local governments’ land revenue and bank credit. The total effects of logep, logtf and loglb are 0.68, 0.23 and 0.19, respectively, suggesting the positive impacts of real estate development enterprises’ investment, local governments’ land revenue and bank credit on the real estate prices in the large and medium-sized cities in the western region of China.
The results of the SDM with spatial fixed effects for financial real estate risks in the large and medium-sized cities in the western region of China are shown in
Table 11. The pr/gdpr, logtf, and logbl have the positive coefficients of 0.32, 0.19 and 0.17, respectively, indicating the strongest influence of over-growth in real estate prices on the local financial real estate risks, followed by the local governments’ land revenue and bank credit in the large and medium-sized cities in the western region of China. logrdi has a coefficient of −0.45, suggesting that increase in real estate development enterprises’ investment lowers the local financial real estate risks.
The coefficients of W*logrdi and W*logbl are 0.52 and −0.39, respectively, indicating that real estate development enterprises’ investment expansion in other cities elevates the financial real estate risks in the local city in the middle region of China, while bank credit has the opposite effect.
Similar to their coefficients, pr/gdpr, logtf, logbl and logrdi have a direct effect of 0.33, 0.19 and 0.15 and −0.44, respectively, indicating that the over-growth in real estate prices, local governments’ land revenue and bank credit to the real estate industry elevates the local financial real estate risks, and real estate development enterprises’ investment has the opposite influence. The indirect effects of logrdi and logbl are 0.53 and −0.43, respectively. That is, real estate development enterprises’ investment in the local city increases the financial real estate risks in other cities, while the bank credit to the real estate industry is to the contrary. logtf and logbl have the total effects of 0.15 and −0.28, respectively, showing that the local governments’ land revenue increases the whole financial real estate risks in the large and medium-sized cities in the western region of China, while the bank credit to the real estate industry has the opposite influence.