https://orcid.org/0000-0002-3046-9964
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Abstract
This study examines the cross-city variation in the performance of China’s Housing Provident Fund (HPF) program, a collective saving scheme that provides subsidized lending to support participants’ home purchases. It finds that while the program as a whole is limited in both participation and benefit provision, the level of HPF activities has differed across localities. Panel-data analysis of HPF lending in seven cities reveals that local housing affordability was an important determinant of who benefited from the program. Rising housing price increased the demand for HPF loans. But if price rose too high relative to household income, the share of participants who used HPF loans declined. This shows that as the program currently operates, expanding HPF participation would only increase the inequality in the distribution of program benefits. Finally, we did not find evidence for the counter-cyclic effects that HPF lending was expected to have in relation to bank lending. These findings have important implications for the program’s future reform.
Acknowledgements
The authors would like to acknowledge the valuable input from the late Prof. Qianjin Hao from Fudan University in the project’s early stage of development and the help from Qingyun Shen and Shilong Li on initial data collection. The authors would also like to thank all participants at “The Role of Housing in China’s Urban Transformation” Symposium in July 2018 for their valuable feedback and Prof. Ray Forrest and Prof. Ngai Ming Yip for organizing the symposium.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Lan Deng is Associate Professor of Urban and Regional Planning and Faculty Director of the Real Estate Development Graduate Certificate Program, Taubman College of Architecture and Urban Planning, University of Michigan.
Xiang Yan is a Doctoral Candidate in the Urban and Regional Planning Program, Taubman College of Architecture and Urban Planning, University of Michigan.
Jie Chen is a Professor at the School of International and Public Affairs/China Institute for Urban Governance, Shanghai Jiao Tong University.
Notes
1 Seventeen percent is the ratio of the outstanding balance of HPF home mortgage loans divided by the total outstanding balances of all commercial-bank mortgage loans and HPF mortgage loans.
2 Before 2016, HPF savings earned interest at the rate of the three-month bank deposit. After 2016, the interest rate was increased to the one-year bank deposit rate.
3 Due to their special setup, HPF management branches have largely evaded public scrutiny and there is little public information about them. HPF centers, on the other hand, must follow a standard governance structure and are required (at least on paper) to release records of their activities.
4 Yeung & Howes (2006) studied the early years of the HPF program when the fund was used to support affordable housing development. In the early years of the HPF program, the program could use the fund to support housing construction, but starting in 2000, it was no longer allowed to do so. In 2009, however, the government started allowing a few pilot cities to offer construction loans for social housing development.
5 The 30 cities are Beijing, Changchun, Changsha, Chengdu, Chongqing, Dalian, Fuzhou, Guangzhou, Guiyang, Ha’erbin, Hangzhou, Hefei, Hohehaote, Jinan, Kunming, Lanzhou, Nanchang, Nanjing, Ningbo, Qingdao, Shanghai, Shenzhen, Shijiazhuang, Tianjin, Wuhan, Wulumuqi, Xi’an, Xiamen, Yinchuan and Zhengzhou.
6 An alternative way to calculate a loan beneficiary rate is to use the number of new loans issued in the year of reporting divided by the number of new participants in the same year. However, this calculation is problematic in two ways. First, the number of new loans can fluctuate widely from year to year, depending on the conditions of the local housing markets and the macroeconomic environment in a given year. Second, new loans being issued do not align with new participants in the same year, since most new loans are taken out by existing participants. Discussion with two HPF center directors also confirmed that our approach is a more reliable way to measure the program’s performance in HPF lending.
7 Authors’ interview (July 5, 2017).
8 It is possible for this ratio to exceed one. While the main funding source for HPF lending comes from HPF savings, HPF centers have also earned revenues through their investments over the years. In addition, as we will discuss later, some HPF centers have started to experiment with new lending products that enable them to provide more loans than their savings allow.
9 According to the National Bureau of Statistics of China, urban non-private work units include state-owned institutes, collective enterprises, joint ventures, limited liability corporations and foreign-invested enterprises, while private work units are private firms and self-employed individuals.
10 We calculated the average annual interest rate since the interest rate can fluctuate within a year.
11 To address the possible multi-collinearity issue between the two variables, we did test a model (not presented) that only included the housing price variable. The adjusted R-Square for the unpresented model is 0.64, compared to the adjusted R-Square of 0.71 in our model 1, showing that both variables are needed to capture the different aspects of demand for HPF mortgage loans.
12 Because of the limited number of observations we have in the dataset, our choices of variables also reflected the consideration that we need to limit the number of independent variables so that the degrees of freedom are adequate in our regression models.
13 The Levin, Lin and Chu unit root test also rules out the existence of nonstationary time-series, individual and common unit roots.