住房空置率对房价的影响——基于35个重点城市的面板数据

doi:10.18402/resci.2020.06.11

Abstract

Abstract:

Housing price has been a closely concerned issue of the government and the people. High vacancy rate under the high housing price is a common phenomenon, causing many concerns. However, whether the high vacancy rate can restrain the rise of housing price remains to be studied. For this reason, the authors took the average selling price of commercial housing in 35 key cities of China from 2007 to 2016 as the research object. By constructing a variable coefficient model with fixed influence, we analyzed the influence of housing vacancy rate and control variables on the fluctuation of housing price. The results are as follows. Vacancy rate is the main factor that caused the fluctuation of housing price in China, and the rise of vacancy rate restrained the rise of housing price to a certain extent. At the same time, housing price was also affected by other factors. The increase of land selling price was an important factor that induced the rise of housing price. Per capita income had a certain impact on housing price. The impact of population density on housing price in built-up areas was different. The fluctuation of housing price in different classes of cities was very different, which was mainly affected by vacancy rate. The vacancy rate of the first tier cities, second tier developed cities, and third tier cities had a greater negative effect on housing price. The land transfer price in the third tier cities had no significant effect on housing price. The population density of the first tier cities caused an upward thrust on the housing price. In conclusion, high vacancy rate was the main factor to restrain the rise of house price, and it was also the main reason for the difference of housing price fluctuation in different class of cities.

Key words: housing vacancy rate, housing price, land transfer price, panel data model, China

WANG Jie, ZHANG Jiliang. The influence of vacancy rate on housing price： Based on panel data of 35 key cities[J].Resources Science, 2020, 42(6): 1135-1147.

Figures/Tables 6

Table 1

Figure 1

Figure 2

Table 2

Estimation results of coefficient of explanatory variables in each city"

城市	lnHVR的系数（P值）	lnLTF的系数（P值）	lnPCDI的系数（P值）	lnPD的系数（P值）
北京市	-0.3864(0.1897)	0.0164(0.8647)	0.9141(0.0009)	0.8733(0.0739)
天津市	-0.1159(0.0031)	0.1849(0.0003)	0.8118(0.0000)	0.0135(0.9560)
石家庄市	-0.2135(0.1525)	0.2509(0.3419)	1.7443(0.0008)	-1.2583(0.2397)
太原市	-0.0071(0.9716)	0.2839(0.2202)	1.7505(0.0470)	1.7536(0.4432)
呼和浩特市	-0.1539(0.3088)	0.0456(0.7013)	1.0118(0.0000)	0.3786(0.3338)
沈阳市	-0.3135(0.0000)	0.1080(0.0398)	0.9821(0.0000)	0.0374(0.8425)
大连市	-0.0015(0.9774)	-0.0051(0.9269)	0.5029(0.0005)	-0.2624(0.1381)
长春市	-0.1818(0.0156)	0.1469(0.1111)	0.6445(0.0000)	-0.0535(0.8894)
哈尔滨市	-0.0954(0.7215)	0.3370(0.0100)	0.2206(0.3100)	-1.1876(0.1075)
上海市	-0.1628(0.6177)	0.4383(0.0170)	-0.5668(0.4412)	4.4425(0.1476)
南京市	-0.3762(0.0165)	0.3046(0.0089)	1.1192(0.0270)	5.9428(0.0095)
杭州市	-0.4349(0.0433)	0.0324(0.8158)	2.0592(0.0040)	-1.1670(0.0043)
宁波市	-0.322(0.0000)	0.1343(0.0038)	1.0449(0.0000)	-0.5854(0.0000)
合肥市	-0.048(0.9272)	0.0819(0.6602)	0.8419(0.0811)	-0.8534(0.2803)
福州市	-0.0268(0.8640)	0.0641(0.5489)	0.5811(0.0548)	-1.1301(0.0565)
厦门市	-0.2167(0.6229)	0.1804(0.1011)	1.6614(0.1510)	0.2725(0.8523)
南昌市	-0.0720(0.6462)	0.0251(0.8736)	1.2362(0.0491)	0.4931(0.7261)
济南市	-0.2925(0.0418)	0.0034(0.9844)	0.7805(0.0061)	-0.4884(0.2737)
青岛市	-0.2688(0.0088)	0.0129(0.8729)	0.995(0.0000)	-0.1096(0.5886)
郑州市	-0.0799(0.4976)	0.0372(0.4513)	1.0665(0.0000)	0.0606(0.5927)
武汉市	-0.0667(0.5236)	0.0364(0.7771)	0.8149(0.0098)	0.0569(0.7172)
长沙市	0.2616(0.4283)	0.1630(0.3495)	0.0170(0.9781)	0.3642(0.5147)
广州市	-0.0830(0.6867)	0.0874(0.2039)	1.1682(0.0000)	0.0881(0.7614)
深圳市	-0.1729(0.1282)	0.1661(0.0047)	0.7648(0.0822)	3.1268(0.0093)
南宁市	-0.1021(0.0028)	0.0288(0.2145)	0.9388(0.0000)	-0.7519(0.0000)
海口市	-0.0403(0.7836)	0.1382(0.2661)	0.7169(0.1688)	0.7477(0.2273)
重庆市	-0.0242(0.9105)	0.3832(0.0542)	0.3603(0.4163)	-0.3382(0.5387)
成都市	-0.3718(0.0000)	0.2990(0.0001)	1.4348(0.0000)	0.0114(0.9770)
贵阳市	-0.0930(0.5891)	0.0146(0.9263)	0.9267(0.0006)	-0.0006(0.9986)
昆明市	0.1253(0.1600)	0.0202(0.7152)	0.5847(0.0395)	0.1880(0.5646)
西安市	0.0150(0.8985)	0.0510(0.8388)	0.6167(0.1307)	-0.3067(0.7809)
兰州市	-0.1730(0.1414)	0.0518(0.3612)	0.9475(0.0000)	-0.2173(0.6663)
西宁市	-0.0557(0.6712)	0.0220(0.7013)	1.0324(0.0000)	0.0832(0.8841)
银川市	-0.3215(0.0175)	0.0650(0.4731)	0.8558(0.0000)	0.6748(0.2695)
乌鲁木齐市	-0.6973(0.0000)	0.2689(0.0011)	0.5608(0.0051)	-0.5309(0.5567)

Table 2

Table 3

Table 4

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	lnHVR系数	lnLTF系数	lnPCDI系数	lnPD系数	模型拟合优度
模型1	0.3336				0.7329
模型2	0.1574	0.2971			0.8528
模型3	-0.0709	0.0783	0.7951		0.9125
模型4	-0.0699	0.0769	0.8015	0.0269	0.9180

城市划分	一线城市	二线发达城市	二线中等发达城市	二线发展较弱城市	三线城市
lnHVR系数均值	-0.2013	-0.2441	-0.0794	-0.0529	-0.2192
lnLTF系数均值	0.1771	0.1742	0.1006	0.1151	0.0866
lnPCDI系数均值	0.5701	1.1446	0.6013	1.1827	0.8645
lnPD系数均值	2.1326	0.4552	-0.3296	-0.0715	0.1622

The influence of vacancy rate on housing price： Based on panel data of 35 key cities

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一线城市	二线发达城市	二线中等发达城市	二线发展较弱城市	三线城市
北京	天津	济南	合肥	乌鲁木齐
上海	杭州	大连	南昌	贵阳
广州	南京	哈尔滨	南宁	海口
深圳	重庆	沈阳	昆明	兰州
	青岛	西安	石家庄	银川
	宁波	长春	太原	西宁
	厦门	长沙		呼和浩特
	成都	福州
	武汉	郑州

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