Identification and correction of ratchet effect of residential land price: empirical study on urban agglomeration in China

Figures & data

Figure 1. The analytical framework of land finance, adaptive expectations, and residential land price.

Figure 2. Schematic Diagram of the Spatial Distribution of the YRDUA.

Table 1. Subordination of 41 cities in the Yangtze River Delta.

Name of Province	Name of City^a
Jiangsu	Nanjing	Wuxi	Xuzhou	Huaian	Suzhou (Jiangsu)	Zhenjiang	Lianyungang
	Yancheng	Yanzhou	Nantong	Taizhou (Jiangsu)	Suqian	Changzhou
Zhejiang	Hangzhou	Ningbo	Wenzhou	Jianxing	Huzhou	Shaoxing	Jinhua
	Zhoushan	Taizhou (Zhejiang)	Lishui	Quzhou
Anhui	Hefei	Wuhu	Bengbu	Huainan	Maanshan	Huaibei	Tongling
	Huangshan	Chuzhou	Fuyang	Suzhou (Anhui)	Luan	Bozhou	Chizhou
	Anqing	Xuancheng
Municipalities	Shanghai

^aSince the English names of Taizhou city in Zhejiang province and Taizhou city in Jiangsu province, Suzhou city in Jiangsu province and Suzhou city in Anhui province are the same, so the provinces they belong to are listed behind in this paper to distinguish them.

Figure 3. Spatial heterogeneity diagram of Residential Land Prices in the YRDUA.

Table 2. Descriptive statistics of the variables.

Variables	Mean	Std.Dev	Min	Max	Nobs	Unit
$AP{R}_{i,t}$,	5027.797	5177.018	418.55	33831.35	410	yuan/m^2
$L{F}_{i,t}$	119.7887	97.44055	5.768767	567.9804	410	10^4 yuan
$L{B}_{i,t}$	3812.142	8360.342	96.7787	67567.94	410	10^4 yuan
$GD{P}_{i,t}$	2215.038	4036.011	112.1841	32679.87	410	10^4 yuan
$PS{I}_{i,t}$	45.4788	8.539878	23.34	69.9	410	%
$PCD{I}_{i,t}$	30226.05	11275.22	11149.34	68034	410	yuan
$P{D}_{i,t}$	697.1587	535.7219	145.29	3826	410	%
$F{D}_{i,t}$	43.93537	35.81817	5.259211	231.0481	410	%
$H{M}_{i,t}$	308.6267	151.7911	32	835.55	410	km
$ED{I}_{i,t}$	18.18319	7.183149	3.375751	48.75709	410	%
$U{L}_{i,t}$	58.7658	12.29332	29.1	89.6	410	%
$LH{R}_{i,t}$	67.80712	143.4676	9.67	1085.53	410	Km^2
$L{D}_{i,t}$	90.55856	6.977971	59.14397	98.84128	410	Km^2

Table 3. Results of classical linear panel data model regression.

Variables	Coefficient	t-stat	p-value
$L{F}_{i,t}$	9.955792***	5.89	0.000
$L{B}_{i,t}$	0.927825***	7.79	0.000
$GD{P}_{i,t}$	−0.87322***	−3.06	0.002
$PS{I}_{i,t}$	133.4985***	5.58	0.000
$PCD{I}_{i,t}$	0.05066**	2.56	0.011
$P{D}_{i,t}$	−2.05079**	−2.55	0.011
$F{D}_{i,t}$	−10.1335	−1.12	0.261
$H{M}_{i,t}$	−3.8253**	−2.06	0.040
$ED{I}_{i,t}$	7.456689	0.33	0.744
$U{L}_{i,t}$	104.5149***	4.53	0.000
$LH{R}_{i,t}$	−6.25112**	−2.18	0.030
$L{D}_{i,t}$	−22.8554	−0.91	0.361
${\mathit{R}}^{\mathbf{2}}$		0.7131
$\mathit{A}d\mathrm{\_}{R}^2$		0.7044

***, **, and * are significance at 1%, 5% and 10% level, and z-value is in brackets.

Table 4. Results of Lagrange multiplier statistic test.

Test Statistic	t-Statistic	Probability
LM test no spatial lag	48.4850	0.000
robust LM test no spatial lag	10.4003	0.001
LM test no spatial error	42.5993	0.000
robust LM test no spatial error	4.51460	0.034
R^2		0.8784

Table 5. Results of Hausman test of the spatial lag model.

Statistic	Degrees of freedom	Probability
25.8500	10	0.0039

Table 6. Results of LR test.

Test Statistic	Statistic	Degrees of freedom	Probability
LR-test joint significance spatial fixed effects	308.8020	41	0.0000
LR-test joint significance time-period fixed effects	55.4701	10	0.0000

Table 7. Results of dynamic spatial panel data model of individual double fixed effects.

Variables	Coefficient	t-stat	p-value
$AP{R}_{t-1}$（${\theta }_1$）	0.267498***	4.865234	0.000001
$L{F}_{i,t}$	0.265580***	9.898656	0.000000
$L{B}_{i,t}$	−0.046416	−0.632307	0.527187
$GD{P}_{i,t}$	0.331754***	3.161609	0.001569
$PS{I}_{i,t}$	0.103432	0.450182	0.652580
$PCD{I}_{i,t}$	0.757024***	2.922129	0.003476
$P{D}_{i,t}$	0.235220	0.972410	0.330847
$H{M}_{i,t}$	−0.093107	−1.314488	0.188682
$U{L}_{i,t}$	−0.415770*	−1.822952	0.068311
$LH{R}_{i,t}$	−0.257278**	−2.508311	0.012131
$\rho$	0.274815**	2.248046	0.024573
${\theta }_2$	0.217028	1.522625	0.127852
$Nobs$		410
$R^2$		0.9019

***, **, and * are significance at 1%, 5% and 10% level.

Table 8. Results of decomposition of dynamic spatial panel data model effects.

Variables	Direct Effects	t-stat	Indirect Effects	t-stat	Total Effects	t-stat
$AP{R}_{t-1}$（${\theta }_1$）	0.1909	4.6971	0.0668	1.7603	0.2577	4.0132
${\theta }_2$	0.2123	1.5237	0.0691	1.1419	0.2814	1.5077
$L{F}_{i,t}$	0.2707	9.5281	0.0957	1.8306	0.3664	5.629
$L{B}_{i,t}$	−0.0475	−0.5601	−0.0168	−0.469	−0.0643	−0.5486
$GD{P}_{i,t}$	0.3611	3.0895	0.1276	1.5444	0.4887	2.809
$PS{I}_{i,t}$	0.0836	0.3695	0.0308	0.3354	0.1144	0.3686
$PCD{I}_{i,t}$	0.8635	2.9068	0.3021	1.5652	1.1656	2.7211
$P{D}_{i,t}$	0.2343	0.9086	0.0819	0.7503	0.3162	0.8977
$H{M}_{i,t}$	−0.1066	−1.3344	−0.0386	−0.9924	−0.1451	−1.2923
$U{L}_{i,t}$	−0.5056	−1.8144	−0.1786	−1.2386	−0.6842	−1.7405
$LH{R}_{i,t}$	−0.283	−2.4915	−0.1002	−1.407	−0.3832	−2.311

Table 9. Results of robustness test.

Method of replace geographical weights		Method of using instrumental variables
Variables	Coefficient	Variables	Coefficient
$AP{R}_{t-1}$（${\theta }_1$）	0.269916*** (4.919095)	$AP{R}_{t-1}$（${\theta }_1$）	0.451764*** (2.947512)
$L{F}_{i,t}$	0.266756*** (9.882509)	$LU{T}_{i,t}$	0.132761* (1.712113)
$L{B}_{i,t}$	−0.051972 (−0.688164)	$L{B}_{i,t}$	0.076346 (0.899416)
$GD{P}_{i,t}$	0.346898*** (3.274491)	$GD{P}_{i,t}$	0.914513*** (5.394756)
$PS{I}_{i,t}$	0.054071 (0.226022)	$PS{I}_{i,t}$	0.479461 (0.814362)
$PCD{I}_{i,t}$	0.814546*** (3.145147)	$PCD{I}_{i,t}$	0.541214** (2.329416)
$P{D}_{i,t}$	0.147327 (0.62257)	$P{D}_{i,t}$	0.912464 (1.715365)
$H{M}_{i,t}$	−0.062528 (−0.931413)	$H{M}_{i,t}$	0.062528* (−0.931413)
$U{L}_{i,t}$	−0.362614 (−1.636406)	$U{L}_{i,t}$	0.432615 (1.448941)
$LH{R}_{i,t}$	−0.264717*** (−2.587936)	$LH{R}_{i,t}$	0.541295*** (3.597815)
$\rho$	0.126331* (1.939971)	$\rho$	0.354121** (1.979987)
${\theta }_2$	0.159745* (1.688078)	${\theta }_2$	0.238715* (1.751213)
$R^2=0.8566$		R^2 = 0.7294

***, **, and * are significance at 1%, 5% and 10% level.

Table 10. Results of GTWR model.

Variables	Average Value	Minimum Value	Lower Quartile	Upper quartile	Maximum value
$AP{R}_{t-1}$	0.117038	−0.524208	0.255834	−0.031114	0.763177
$L{F}_{i,t}$	0.280155	0.012317	0.349743	0.220070	0.568177
$L{B}_{i,t}$	0.333026	−1.338350	0.668745	0.067394	1.563209
$GD{P}_{i,t}$	0.101143	−1.251689	0.324876	−0.234598	2.127016
$PS{I}_{i,t}$	0.590891	−2.363336	1.080714	−0.116585	5.795098
$PCD{I}_{i,t}$	0.158800	−1.670214	0.473560	−0.200453	2.798831
$P{D}_{i,t}$	0.068020	−0.906631	0.194489	−0.154953	2.043258
$H{M}_{i,t}$	−0.120761	−0.641234	−0.000113	−0.291888	0.783607
$U{L}_{i,t}$	0.205078	−2.468351	0.700724	−0.431253	5.267148
$LH{R}_{i,t}$	−0.137607	−3.487084	0.147456	−0.281512	0.935964
Bandwidth: 0.114996		AICc: 286.473		${\mathbf{R}}^2$: 0.955547

Figure 4. Spatio-temporal variation pattern of the correction coefficients of Residential land prices.

Data availability statement

The data and code used in this study are available on request from the first author.