Estimating the effect of long-term mechanism for real estate in China: a regression discontinuity approach

Figures & data

Figure 1. Trend of housing prices and the intensity of central government regulatory policies, 1998-2016.

Note: The left axis represents housing prices, data from the CEI database. The right axis represents the number of real estate regulatory policies (only those at the central government level are counted), as determined by the text of previous year’s central government regulations.

Source: data from CEI (China Economic Information Network) database.

Table 1. Descriptive statistics for key variables.

Variables	N	Mean	St. dev	Min	Median	Max
$hp$	455	0.00	0.06	−0.16	0.00	0.30
$\mathit{invest}$		6.64	0.82	4.30	6.71	8.16
$\mathit{cons}\_\mathit{area}$		8.70	0.63	6.80	8.72	10.27
$\mathit{sales}\_\mathit{area}$		6.94	0.92	3.90	6.70	8.81
$\mathit{land}\_\mathit{area}$		5.31	0.98	1.00	5.40	7.70
$\mathit{income}$		10.21	0.32	9.30	10.00	11.00
$\mathit{loan}$		0.89	0.49	0.18	0.66	8.50
$\mathit{pop}$		6.40	0.65	5.10	6.60	8.14
$\mathit{rate}$		3.01	0.93	−0.95	3.20	6.90
$\exp$		6.45	1.00	4.20	6.43	8.79

Source: Authors’ research with original data from the National Bureau of Statistics of China, CEI and China Statistical Yearbook.

Figure 2. Regions and 35 large- and medium-sized cities in China.⁴

Source: Map data are from DataV.GeoAtlas (http://datav.aliyun.com/portal/school/atlas/area_selector), then edited by the authors.

Figure 3. hp for 35 Large- and Medium-sized Cities and Cities in the Eastern, Central and Western.

Source: Authors’ research.

Table 2. Tests for continuity of control variables.

Bandwidth	income (1)	loan (2)	rate (3)	pop (4)	exp (5)	invest (6)	cons_area (7)	land_area (8)	sales_area (9)
+/-50	0.98 (0.09)	0.39 (0.35)	0.30 (0.35)	0.98 (0.27)	0.97 (0.35)	0.92 (0.31)	0.95 (0.23)	0.81 (0.37)	0.49 (0.28)
+/-100	0.97 (0.09)	0.45 (0.37)	0.35 (0.36)	0.98 (0.28)	0.98 (0.36)	0.91 (0.29)	0.94 (0.24)	0.82 (0.38)	0.46 (0.26)
+/-150	0.94 (0.14)	0.73 (0.58)	0.39 (0.58)	0.99 (0.44)	0.97 (0.26)	0.92 (0.49)	0.95 (0.37)	0.76 (0.27)	0.33 (0.46)

Note: Robust standard errors are in parentheses and clustered to sample cities using clustering robust standard errors. *, ** and *** denote 10%, 5% and 1% significance levels respectively.

Source: Authors’ research.

Table 3. Impact of the long-term mechanism implementation on housing prices.

Variables	Dependent variable: hp
Method	RDiT nonparametric estimation			OLS linear estimation			2SLS linear estimation
Bandwidth	+/-2 (1)	+/-3 (2)	+/-4 (3)	+/-2 (4)	+/-3 (5)	+/-4 (6)	+/-2 (7)	+/-3 (8)	+/-4 (9)
Long-term mechanism	0.047*** (0.02)	0.045*** (0.02)	0.039*** (0.01)	0.040** (0.02)	0.055*** (0.02)	0.052*** (0.01)	0.052* (0.03)	0.071*** (0.01)	0.082*** (0.03)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Fixed effects	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes
High-order term	Yes	Yes	Yes	No	No	No	Yes	Yes	Yes
F-test				18.32	13.59	12.96	32.83	37.81	17.34
R^2				0.32	0.19	0.24
N	175	245	280	175	245	280	175	245	280

Note: Robust standard errors are in parentheses and clustered to sample cities using clustering robust standard errors. *, **, *** denote 10%, 5%, and 1% significance levels, respectively. 2SLS linear estimation results with F-values are the first-stage F-statistics, and the higher-order terms are quadratic results. The control variables include economic fundamentals and real estate market indicators.

Source: Authors’ research.

Table 4. heterogeneity test (grouped by first- and second-tier cities).

Variables	Dependent variable: hp
Region	First-tier cities		Second-tier cities
Bandwidth	+/-3 (1)	+/-4 (2)	+/-3 (3)	+/-4 (4)
Long-term mechanism	−0.075 (0.06)	−0.018 (0.02)	0.050*** (0.008)	0.045*** (0.006)
High-order term	Yes	Yes	Yes	Yes
N	24	32	186	248

Note: Robust standard errors are in parentheses and clustered to sample cities using clustering robust standard errors. *, **, *** denote 10%, 5%, and 1% significance levels, respectively.

Source: Authors’ research.

Table 5. Heterogeneity test (grouped by eastern, central and western cities).

Variables	Dependent variable: hp
Region	The eastern		The central		The Western
Bandwidth	+/-3 (1)	+/-4 (2)	+/-3 (3)	+/-4 (4)	+/-3 (5)	+/-4 (6)
Long-term mechanism	0.014 (0.03)	0.020 (0.01)	0.061*** (0.03)	0.059*** (0.01)	0.083*** (0.03)	0.046*** (0.01)
High-order term	Yes	Yes	Yes	Yes	Yes	Yes
N	96	128	56	64	77	88

Note: Robust standard errors are in parentheses and clustered to sample cities using clustering robust standard errors. *, **, *** denote 10%, 5%, and 1% significance levels, respectively. The higher-order terms are quadratic results. The control variables include economic fundamentals and real estate market indicators.

Source: Authors’ research.

Table 6. Placebo test: set the long-term mechanism implementation date to the end of 2014.

Variables	Dependent variable: hp
method	RD non-parametric			OLS			2SLS
Bandwidth	+/-2 (1)	+/-3 (2)	+/-4 (3)	+/-2 (4)	+/-3 (5)	+/-4 (6)	+/-2 (7)	+/-3 (8)	+/-4 (9)
Long-term mechanism	−0.04 (0.01)	−0.004 (0.01)	0.011 (0.01)	−0.03 (0.03)	−0.03 (0.03)	−0.03* (0.01)	−0.059 (0.04)	−0.036 (0.03)	−0.043* (0.02)
Control variables	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes
Fixed effects	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes
High-order terms	Yes	Yes	Yes	No	No	No	Yes	Yes	Yes
F-test				9.01	8.77	7.01	42.61	56.79	50.5
R^2				0.28	0.19	0.14
N	175	245	280	175	245	280	175	245	280

Note: Robust standard errors are in parentheses, *, **, *** denote 10%, 5%, and 1% significance levels, respectively. 2SLS linear estimation results with F-values are the first-stage F-statistics, and the higher-order terms are quadratic results. The control variables include economic fundamentals and real estate market indicators.

Source: Authors’ research.