Please wait a minute...
金融研究  2025, Vol. 544 Issue (10): 40-57    
  本期目录 | 过刊浏览 | 高级检索 |
资产价格与银行信贷配置
姜可心, 谭小芬, 朱菲菲
北京第二外国语学院经济学院,北京 100024;
北京航空航天大学经济管理学院,北京 100191;
中央财经大学金融学院,北京 100081
Asset Prices and Bank Credit Allocation
JIANG Kexin, TAN Xiaofen, ZHU Feifei
School of Economics, Beijing International Studies University;
School of Economics and Management, Beihang University;
School of Finance, Central University of Finance and Economics
下载:  PDF (562KB) 
输出:  BibTeX | EndNote (RIS)      
摘要 分析房地产价格变动对银行信贷资源配置的影响,对于金融服务实体经济以及防范系统性风险均具有重要意义。本文基于中国某股份制商业银行2010—2021年的逐笔贷款数据研究发现:房价上升会显著提升银行对企业的信贷供给,且抵押品渠道在其中发挥了重要作用;但房价上升并未对非房地产企业贷款产生明显的挤出效应。进一步分析表明,企业资金实力、地区经济制度和市场环境差异以及信贷政策松紧等因素均会显著影响房价变动对银行信贷配置的效应。本研究不仅拓展了资产价格与信贷配置的关系研究,更为当前房地产价格回调背景下银行信贷配置调整、金融结构转型以及监管政策优化提供了丰富的政策启示。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
姜可心
谭小芬
朱菲菲
关键词:  资产价格  信贷配置  抵押品效应  挤出效应    
Summary:  China has a bank-dominated financial system. As the primary force in financial resource allocation, the banking sector must promote high-quality development of the real economy through its own advancement while simultaneously balancing development and security to firmly guard against systemic financial risks. As one of the most significant asset prices, to what extent do fluctuations in real estate prices affect bank credit allocation? In the context of China's pursuit of high-quality economic development, investigating whether and how asset price fluctuations influence bank credit allocation holds significant importance for enhancing the effectiveness of financial services to the real economy and preventing systemic risks.
However, research on whether and how asset price fluctuations affect bank credit allocation remains limited in China. First, given China's bank-dominated financial system, localized empirical evidence is urgently needed to understand how banks' excessive reliance on real estate collateral, admist housing price volatility, impacts credit allocation. Second, existing literature primarily examines the indirect effects of housing price changes on non-financial enterprise investment through collateral and crowding-out channels from the firm perspective, lacking direct evidence from the banking perspective. Third, China is currently in a critical phase of transitioning toward high-quality development, where the stable operation of the banking sector is essential for coordinating development and security. Against this backdrop, a systematic analysis of housing price fluctuations' impact on bank credit allocation and their transmission mechanisms carries substantial value for preemptively predicting, analyzing, and addressing financial risk exposures arising from downward pressure on housing prices.
Based on loan-level data from a joint-stock commercial bank in China covering 2010-2021, this study finds that rising housing prices significantly increase bank lending, with the collateral channel playing a key role. However, unlike empirical evidence from advanced economies, no direct evidence was found at the data level showing that loans to real estate enterprises significantly crowd out lending to non-real estate enterprises during periods of rising house prices. The reasons are twofold: (1) Within China's shadow banking system, non-real estate enterprises often act as intermediaries for real estate enterprises to access credit. (2) During periods of real estate market boom, non-real estate enterprises actively engage in the real estate market by investing in land and properties, accumulating collateral that enhances their ability to secure bank loans. Heterogeneity analysis reveals that the effect of housing price changes on bank credit allocation varies significantly with enterprises' financial strength, regional economic institutions, market environments, and credit policy cycles.
This paper's main contributions are threefold. First, distinct from research based on Western data, it identifies no significant crowding-out effect of rising house prices within China's institutional settings. Second, differing from firm-level studies, this paper utilizes bank loan data to provide the first direct evidence from the bank's perspective, identifying that housing price fluctuations affect credit allocation via the collateral channel. Third, this research holds substantial practical relevance. Against the backdrop of recent housing price corrections, banks are becoming more cautious when lending to firms dependent on collateral, forming a “falling asset prices-credit contraction” feedback mechanism. The credit allocation mechanisms and collateral dependency paths revealed in this study provide valuable insights into understanding related credit allocation dynamics, financial risk exposures, and policy design.
The research yields the following policy implications: First, commercial banks must accelerate their transformation by reducing reliance on traditional collateral and expanding the scope of new types of pledged assets such as intellectual property rights, data assets, and carbon emission rights. They should also establish dynamic collateral stress-testing mechanisms with differentiated loan-to-value (LTV) thresholds to mitigate cascading risks caused by declining valuations. Second, enhanced penetrative regulation is needed to prevent the spread of financial risks. Given the hidden and contagious nature of financial risks in highly leveraged sectors, regulators must adopt targeted and penetrative supervisory measures. Third, legal and institutional reforms should be advanced to strengthen property rights protection and credit risk prevention mechanisms. This includes improving property rights protection and default recovery mechanisms by refining systems for property registration, bankruptcy liquidation, and information disclosure to enhance assets' legal protection. Additionally, corporate credit assessment systems should be refined, and early warning mechanisms for credit risks should be enhanced to reduce adverse selection problems stemming from inadequate legal protection.
Keywords:  Asset Prices    Credit Allocation    Collateral Effect    Crowding-out Effect
JEL分类号:  E32   F44   G21  
基金资助: *本文感谢国家社科基金重大项目(20&ZD101)、国家自然科学基金(72102247;72573197)及中央财经大学中央高校基本科研业务费专项资金的资助。感谢匿名审稿人的宝贵意见,文责自负。
通讯作者:  朱菲菲,经济学博士,副教授,中央财经大学金融学院,E-mail:feifeizhu.zhu@cufe.edu.cn.   
作者简介:  姜可心,经济学博士,讲师,北京第二外国语学院经济学院,E-mail:jiangkexin@bisu.edu.cn.
谭小芬,经济学博士,教授,北京航空航天大学经济管理学院,E-mail:xiaofent@163.com.
引用本文:    
姜可心, 谭小芬, 朱菲菲. 资产价格与银行信贷配置[J]. 金融研究, 2025, 544(10): 40-57.
JIANG Kexin, TAN Xiaofen, ZHU Feifei. Asset Prices and Bank Credit Allocation. Journal of Financial Research, 2025, 544(10): 40-57.
链接本文:  
http://www.jryj.org.cn/CN/  或          http://www.jryj.org.cn/CN/Y2025/V544/I10/40
[1]陈斌开、金箫和欧阳涤非, 2015,《住房价格、资源错配与中国工业企业生产率》,《世界经济》第4期, 第77~98 页。
[2]陈勇兵、刘佳祺和徐丽鹤, 2021,《房价与出口:不可贸易部门对可贸易部门的挤出效应》,《经济研究》第3期, 第186~203页。
[3]黄贤环、吴秋生和王瑶, 2021,《影子银行发展与企业投资行为选择:实业投资还是金融投资?》,《会计研究》第1期, 第100~111页。
[4]李仲飞、于守金和曹夏平, 2019,《产业信贷政策对于房地产企业债务的影响——基于银行业359号“限贷”文件的准自然实验分析》,《经济学(季刊)》第4期, 第1373~1396 页。
[5]林琳、曹勇和肖寒, 2016,《中国式影子银行下的金融系统脆弱性》,《经济学(季刊)》第3期, 第1113~1136 页。
[6]刘行、建蕾和梁娟, 2016,《房价波动、抵押资产价值与企业风险承担》,《金融研究》第3期, 第107~123 页。
[7]梅冬州、崔小勇和吴娱, 2018,《房价变动、土地财政与中国经济波动》,《经济研究》第1期, 第35~49 页。
[8]彭俞超和何山, 2020,《资管新规、影子银行与经济高质量发展》,《世界经济》第1期,第47~69页。
[9]钱雪松、徐建利和杜立, 2018,《中国委托贷款弥补了正规信贷不足吗?》,《金融研究》第5期, 第82~100 页。
[10]申洋和陈钊, 2023,《勒紧裤带还是放手花钱:住房限购政策与中国城镇居民消费》,《世界经济》第9期, 第157~180 页。
[11]王永钦和薛笑阳, 2022,《法治建设与金融高质量发展——来自中国债券市场的证据》,《经济研究》第10期, 第173~190 页。
[12]曾海舰, 2012,《房产价值与公司投融资变动——抵押担保渠道效应的中国经验证据》,《管理世界》第5期, 第125~136 页。
[13]张勋、寇晶涵和张欣, 2021,《学区房溢价的影响因素:教育质量的视角》,《金融研究》第11期, 第97~116 页。
[14]Acharya, V., T. Philippon, M. Richardson and N. Roubini, 2009, “The Financial Crisis of 2007-2009: Causes and Remedies”, Restoring Financial Stability: How to Repair a Failed System, 56(1), pp. 1~56.
[15]Adrian, T. and H. S. Shin, 2010, “Liquidity and Leverage”, Journal of Financial Intermediation, 19(3), pp. 418~437.
[16]Antoniades, A., 2024, “Commercial Bank Failures during the Great Recession: The Real (Estate) Story”, Available at SSRN 2325261.
[17]Bahaj, S., A. Foulis and G. Pinter, 2020, “Home Values and Firm Behavior”, American Economic Review, 110(7), pp. 2225~2270.
[18]Bernanke, B., 1999, “The Financial Accelerator in a Quantitative Business Cycle Framework”, Handbook of Macroeconomics.
[19]Brunnermeier, M. K. and Y. Sannikov, 2014, “A Macroeconomic Model with a Financial Sector”, American Economic Review, 104(2), pp. 379~421.
[20]Campbell, J. Y. and J. F. Cocco, 2007, “How Do House Prices Affect Consumption? Evidence from Micro Data”, Journal of Monetary Economics, 54(3), pp. 591~621.
[21]Chakraborty, I., I. Goldstein and A. MacKinlay, 2018, “Housing Price Booms and Crowding-Out Effects in Bank Lending”, The Review of Financial Studies, 31(7), pp. 2806~2853.
[22]Chaney, T., D. Sraer and D. Thesmar, 2012, “The Collateral Channel: How Real Estate Shocks Affect Corporate Investment”, American Economic Review, 102(6), pp. 2381~2409.
[23]Chen, T., L. Liu, W. Xiong and L. A. Zhou., 2017, “Real Estate Boom and Misallocation of Capital in China”, Working Paper, Princeton University.
[24]Chodorow-Reich, G., O. Darmouni, S. Luck and M. Plosser, 2022, “Bank Liquidity Provision across the Firm Size Distribution”, Journal of Financial Economics, 144(3), pp. 908~932.
[25]Deng, Y., L. Liao, J. Yu and Y. Zhang, 2022, “Capital Spillover, House Prices, and Consumer Spending: Quasi-Experimental Evidence from House Purchase Restrictions”, The Review of Financial Studies, 35(6), pp. 3060~3099.
[26]Dong, F., Y. Guo, Y. Peng and Z. Xu, 2022, “Economic Slowdown and Housing Dynamics in China: A Tale of Two Investments by Firms”, Journal of Money, Credit and Banking, 54(6), pp. 1839~1874.
[27]James, C., J. Lu and Y. Sun, 2021, “Time is Money: Real Effects of Relationship Lending in a Crisis”, Journal of Banking and Finance, 133, p. 106283.
[28]Kiyotaki, N.and J. Moore, 1997, “Credit Cycles”, Journal of Political Economy, 105(2), pp. 211~248.
[29]Li, W., S. Tian and Y. Wang, 2022, “Collateral Constraint and China's Credit Boom in the Global Financial Crisis: Loan-Level Anatomy”, Available at SSRN 4191975.
[30]Lian, C. and Y. Ma, 2021, “Anatomy of Corporate Borrowing Constraints”, The Quarterly Journal of Economics, 136(1), pp. 229~291.
[31]Loutskina, E. and P. E. Strahan, 2015, “Financial Integration, Housing, and Economic Volatility”, Journal of Financial Economics, 115(1), pp. 25~41.
[32]Martín, A., E. Moral-Benito and T. Schmitz, 2021, “The Financial Transmission of Housing Booms: Evidence from Spain”, American Economic Review, 111(3), pp. 1013~1053.
[33]Mian, A. and A. Sufi, 2009, “The Consequences of Mortgage Credit Expansion: Evidence from the US Mortgage Default Crisis”, The Quarterly Journal of Economics, 124(4), pp. 1449~1496.
[34]Mian, A. and A. Sufi, 2011, “House Prices, Home Equity-Based Borrowing, and the US Household Leverage Crisis”,American Economic Review, 101(5), pp. 2132~2156.
[35]Pan, H. and C. Wang, 2013, “House Prices, Bank Instability, and Economic Growth: Evidence from the Threshold Model”, Journal of Banking and Finance, 37(5), pp. 1720~1732.
[1] 潘红波, 周颖, 石宇欣. 上市公司壳资源、挤出效应与中小企业银行贷款成本——基于“不允许在创业板借壳上市”的准自然实验[J]. 金融研究, 2025, 538(4): 114-130.
[2] 董青马, 张皓越, 马剑文, 尚玉皇. 央行沟通与资产价格——识别“潜在”未预期货币政策信息[J]. 金融研究, 2024, 528(6): 40-59.
[3] 方颖, 汪怀, 郭晔. 贷款市场化定价、企业融资成本与信贷配置效率[J]. 金融研究, 2024, 526(4): 38-55.
[4] 庄子罐, 韩恺明, 刘鼎铭, 王熙. 资产价格、预期冲击与中国宏观经济波动[J]. 金融研究, 2023, 518(8): 1-18.
[5] 郭杰, 饶含. 土地资产价格波动与经济中的流动性供给——基于以地融资视角的研究[J]. 金融研究, 2022, 505(7): 76-93.
[6] 吴立元, 赵扶扬, 王忏, 龚六堂. 美国货币政策溢出效应、中国资产价格波动与资本账户管理[J]. 金融研究, 2021, 493(7): 77-94.
[7] 冀云阳, 毛捷, 文雪婷. 地方公共债务与资本回报率——来自新口径债务数据和三重机制检验的经验证据[J]. 金融研究, 2021, 492(6): 1-20.
[8] 刘冲, 杜通, 刘莉亚, 李明辉. 资本计量方法改革、商业银行风险偏好与信贷配置[J]. 金融研究, 2019, 469(7): 38-56.
[9] 姜富伟, 郭鹏, 郭豫媚. 美联储货币政策对我国资产价格的影响[J]. 金融研究, 2019, 467(5): 37-55.
[10] 鲁元平, 张克中, 欧阳洁. 土地财政阻碍了区域技术创新吗?——基于267个地级市面板数据的实证检验[J]. 金融研究, 2018, 455(5): 101-119.
[11] 刘晓星, 石广平. 杠杆对资产价格泡沫的非对称效应研究[J]. 金融研究, 2018, 453(3): 53-70.
[12] 王曦, 朱立挺, 王凯立. 我国货币政策是否关注资产价格?——基于马尔科夫区制转换BEKK多元GARCH模型[J]. 金融研究, 2017, 449(11): 1-17.
[13] 谢红军, 蒋殿春. 竞争优势、资产价格与中国海外并购[J]. 金融研究, 2017, 439(1): 83-98.
[14] 陆前进. 最优货币政策规则参数的估计和中国货币状况指数的测度[J]. 金融研究, 2016, 431(5): 35-50.
[1] 严成樑. 延迟退休、财政支出结构调整与养老金替代率[J]. 金融研究, 2017, 447(9): 51 -66 .
[2] 白俊红, 吕晓红. FDI质量与中国经济发展方式转变[J]. 金融研究, 2017, 443(5): 47 -62 .
[3] 胡聪慧, 刘学良. 大宗商品与股票市场联动性研究:基于融资流动性的视角[J]. 金融研究, 2017, 445(7): 123 -139 .
[4] 闫先东, 高文博. 中央银行信息披露与通货膨胀预期管理——我国央行信息披露指数的构建与实证检验[J]. 金融研究, 2017, 446(8): 35 -49 .
[5] 李志冰, 杨光艺, 冯永昌, 景亮. Fama-French五因子模型在中国股票市场的实证检验[J]. 金融研究, 2017, 444(6): 191 -206 .
[6] 闫妍, 顾亚露, 朱晓武. 高速公路收益权的资产证券化问题研究[J]. 金融研究, 2016, 431(5): 111 -123 .
[7] 黄宪, 黄彤彤. 论中国的“金融超发展”[J]. 金融研究, 2017, 440(2): 26 -41 .
[8] 全怡, 梁上坤, 付宇翔. 货币政策、融资约束与现金股利[J]. 金融研究, 2016, 437(11): 63 -79 .
[9] 马文杰, 徐晓萍. 信贷抑制类型识别及政策影响:千村调查证据[J]. 金融研究, 2018, 459(9): 19 -36 .
[10] 贾璐熙, 朱叶, 陈达飞. 公司名称、投资者认知与公司价值——基于公司名称评价指标体系的行为金融学研究[J]. 金融研究, 2016, 431(5): 173 -190 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《金融研究》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn
京ICP备11029882号-1