Based on the panel data of 29 typical large and medium-sized cities in China from 2010 to 2017, this paper constructs a time-varying translog stochastic frontier model to explore the impact of housing prices and other factors on the efficiency of urban scientific and technological innovation (USTIE). The results show that: the internal R & D expenditure is the main driving force for the development of scientific and technological innovation activities, and the accumulation of R & D personnel will reduce the output level of urban scientific and technological innovation. Government support is the key factor to improve the USTIE, and there is a negative correlation between the level of economic development and the USTIE. Housing price itself can improve the USTIE, and weaken the negative correlation between the level of economic development and the development of scientific and technological innovation activities, but at the same time, it will also reduce the positive effect of government support on the USTIE.

Lingfeng Wang. Will Housing Prices Inhibit the Urban Scientific and Technological Innovation Efficiency? ——Analysis based on SFA model. Academic Journal of Business & Management (2020) Vol. 2, Issue 7: 91-107. https://doi.org/10.25236/AJBM.2020.020711.

[1] Xinhua Net. A number of Chinese cities have entered the ranks of global innovation policy makers [EB/OL]. http://www.xinhuanet.com/politics/2019-05/25/c_1124541848. htm. 2019/5/25.
[2] ZHAI Yupeng, CAO Junwen. Efficiency Analysis of Science and Technology Innovation in Jiangsu Based on Technical Efficiency and Scale Efficiency [J]. Science & Technology and Economy. 2016, 29 (4): 37-40.
[3] Zhu Pengyi, Liu Donghua etc. An evaluation of science and technology innovation efficiency of cities from the dynamic perspective by taking 9 prefecture-level cities in Fujian Province as an example [J]. Science Research Management. 2017, 38 (6): 43-50.
[4] Yu Chengxue. Evaluation and demonstration of science and technology innovation efficiency of China's sub provincial cities [J]. Science and Technology Management Research. 2010 (1): 61-63.
[5] Xu Nan, Wang Liyan. Operation mechanism and efficiency measurement of science and technology innovation system in innovative city [J]. Statistics & Decision. 2012 (13): 62-64.
[6] Fan Fei, Du Debin etc. The measure and characteristics of spatial-temporal evolution of China's science and technology resource allocation efficiency [J]. JOURNAL OF GEOGRAPHICAL SCIENCES.2014, 24 (3): 492-508.
[7] Qi Huang, Marshall S.Jiang etc. EFFECT OF GOVERNMENT SUBSIDIZATION ON CHINESE INDUSTRIAL FIRMS’ TECHNOLOGICAL INNOVATION EFFICIENCY: A STOCHASTIC FRONTIER ANALYSIS [J]. Journal of Business Economics and Management, 2016, 17 (2): 187-200.
[8] HAO Jin-lei, DONG Yuan. Study on Enterprise Sci-Tech Innovation Efficiency in West of China [J]. Journal of University of Finance and Economics. 2017, 30 (2): 57-61.
[9] Liu Fan, Deng Mingliang. The Technology Innovation Efficiency Analysis of Yangtze River Economic Belt Using PCA and SE-DEA Joint Model [J]. Science & Technology Progress and Policy. 2017, 34 (23): 48-53.
[10] LI Wei, HONG Tao etc. Does the Rise of House Prices have a Restraining Effect on the City Innovation in China? An Empirical Analysis based on Panel Data of 35 Large and Medium-sized Cities [J]. Commercial Research. 2017 (11): 61-66.
[11] CUI Ying-ying. Innovation inhibition effect and transmission mechanism brought by the growing housing price [J]. Urban Problems. 2018 (10): 4-11.
[12] SHAO Chuan-lin. Dose Housing Price Impede Regional Innovation?——A Spatial Econometric Study of 285 Prefecture-level Cities in China [J]. Modern Finance and Economics-Journal of Tianjin University of Finance and Economics. 2018 (8): 81-95.
[13] KUANG Wei-da, YU Jia-wei. Homeownership and Innovation: Evidence from China 's 69 Large-and Middle-sized Cities [J]. Journal of East China Normal University(Humanities and Social Sciences). 2019 (5): 60-66+237.
[14] Kevin Cullinane, Teng-Fei Wang etc. The technical efficiency of container ports : Comparing data envelopment analysis and stochastic frontier analysis [J]. Transportation Research Part A. 2006, (40): 354-374.
[15] Xu Nan. An empirical study on the efficiency of scientific and technological innovation in innovative cities based on SFA and DEA models [J]. Mathematics in Practice and Theory. 2011 (18): 112-117.
[16] Miao J, Wang P. Sectoral Bubbles, misallocation, and endogenous growth [J]. Journal of Mathematical Economics. 2014, 53 (8): 153-163.
[17] Yan Se, Zhu Guozhong. "House slave effect" or "wealth effect"——A theoretical analysis of the impact of rising house prices on national consumption [J]. Management World. 2013, 03: 34-47.
[18] Chaney T, Sraer D etc. The Collateral Channel: How Real Estate Shocks Affect Corporate Investment [J]. American Economic Review. 2012, 102 (6): 2381-2409.
[19] NIU Xiongying, LI Chunhao etc. The Impact of International Talent Inflow and Human Capital on Innovation Efficiency: Research on Stochastic Froniter Model [J]. Population & Economics. 2018 (6): 12-22.