Economic Slowdown and Housing Dynamics in China: A Tale of Two Investments by Firms

• Published date: 01 September 2022
• Author: FENG DONG,YUMEI GUO,YUCHAO PENG,ZHIWEI XU
• Date: 01 September 2022
• DOI: http://doi.org/10.1111/jmcb.12882

DOI: 10.1111/jmcb.12882

FENG DONG

YUMEI GUO

YUCHAO PENG

ZHIWEI XU

Economic Slowdown and Housing Dynamics in

China: A Tale of Two Investments by Firms

Westudy the housing boom and economic slowdown in China in a dynamic

New Keynesian model. The model features a novel channel of rms’ dy-

namic portfolio choice between physical and housing investment. Housing

assets earn a positive return and can be used as collateral for the rm’s ex-

ternal nances. A negative productivity shock decreases the relative return

of production capital, which translates into a housing boom by increasing

the rm’s housing demand. A rise in house prices then generates competing

effects on real investment: it raises the rm’sleverage due to the collateral

effect and depresses the rm’s demand for physical capital because of the

crowding-out effect. After calibrating the model for the Chinese economy,

our quantitative exercise suggests the former effectis dominated by the lat-

ter, resulting in countercyclical housing prices. The policy analysis shows

that the capital subsidization policy targeting house prices performs better

than other macro-economic policies.

JEL codes: E32, E44, E50

Keywords: Chinese housing boom, collateral effect, crowding-outeffect,

stabilization

We thank the editor Pok-sang Lam, two anonymous referees, and Randy Wright, along with semi-

nar participants at Nanjing University, Nankai University, Southwestern University of Finance and Eco-

nomics, Sun Yat-SenUniversity, WuhanUniversity, Xiamen University,Zhejiang University,and The Fifth

HenU/INFER Workshopon Applied Macroeconomics for helpful discussions and comments. The authors

acknowledge the nancial support from the National Natural Science Foundation of China (72022011,

71903208, 72122011) and Program for Innovation Research in Central University of Finance and

Economics. Please send all correspondence to YuchaoPeng, who is the corresponding author.

F D is with School of Economics and Management, Tsinghua University (E-mail:

dongfeng@sem.tsinghua.edu.cn). Y G and Y P are with School of Finance, Central

University of Finance and Economics (E-mails: guoyumei@cufe.edu.cn and yuchao.peng@cufe.edu.cn).

Z X is with Peking University HSBC Business School(E-mail: xuzhiwei09@gmail.com).

Received April 15, 2019; and accepted in revised form August 25, 2021.

Journal of Money, Credit and Banking, Vol. 54, No. 6 (September 2022)

© 2021 The Ohio State University.

1840 :MONEY,CREDIT AND BANKING

T C  , especially the shadow

banking system, has experienced substantial expansion in the past decade. In partic-

ular, China’s “Four Trillion” scal stimulus plan in 2009 leads to an unprecedented

expansion of the shadow banking sector (Bai, Hsieh, and Song 2016), which raises

the supply of nancial assets and lowers the transaction cost for these assets. In the pe-

riods of 2010–13, the monetary policy tightening in China further boosts the shadow

banking sector and encourages small and medium banks to engage in issuing more

nancial products instead of commercial loans to exploit regulatory arbitrage (Chen,

Ren, and Zha 2018). As a result, nancial assets emerge as an important investment

instrument in Chinese rms’ portfolio decisions.1

The shadow banking system plays an important role in intermediating the mar-

ket liquidity of the housing market (Allen et al. 2018, Chen, Ren, and Zha 2018). 2

Thereby, nonnancial rms’ nancial investmentbehavior may provide an important

mechanism to understand China’s housing boom during the recent economic slow-

down. To this end, we incorporate individual rm’s portfolio choice regarding nan-

cial assets (in particular, real estate assets) and production capital into an otherwise

standard dynamic general equilibrium framework. We then use the model to quan-

titatively evaluate the aggregate implications of the housing boom for the Chinese

macroeconomy through the lens of rm-level investment decisions.

The existing empirical evidence suggests that Chinese rms’ real estate investment

crowds out the real investment in the production sector and thus presents strongly

countercyclical dynamics (e.g., Chen and Wen 2017, Chen et al. 2017). When the

economy is slowing, rms tend to substitute nancial assets for production capital

in the real sector.3A large part of a rm’s nancial assets is property investment.

Therefore, a rm’s portfolio choice may provide a newchannel to understand China’s

recent housing boom.

The standard macro-economic theory of housing dynamics, for example, Iacoviello

(2005) and Liu, Wang,and Zha (2013), predicts a procyclical housing market because

of the use of housing as collateral. However, the Chinese housing market presents a

strong countercyclical pattern in the past decade, which suggests that an alternative

mechanism is required to explain the uctuations in the housing market. We propose

1. The average ratio of the nancial assets to the sum of the nancial assets and xed assets across

nonnancial listed rms from 2011 to 2016 is about 20%. Online Appendix S.2 provides more data details.

2. The real estate investment accounts for a signicant portion of rms’ nancial investment.Accord-

ing to the China Stock Market & Accounting Research Database (CSMAR), the averageshare of real estate

investment in the total nancial assets over 2011Q1 to 2016Q4 is approximately 33%. If we further con-

sider the nancial investment indirectly allocated to the real estate sector,the average share would be even

larger. According to Allen et al. (2018), the portion of funds raised through trust products owed to the

real estate sector is about 30%. Since the trust products account for about 44% of nancial assets, the total

share of both direct and indirect real estate investments in the total nancial investmentsis approximately

50% (0.33 +0.3×0.44).

3. The share of nancial assets of the rm’s total assets presents a signicant upward trend over the

periods of 2011 to 2016, which is associated with a housing boom. See Online Appendix S.2 for more

details.

FENG DONG ET AL. :1841

a dynamic general equilibrium theory with heterogeneous rms’ portfolio decision

between housing and production assets. Individual rms are assumed to receive id-

iosyncratic investment efciency shocks to production capital. Intuitively, the invest-

ment decision follows a trigger strategy, where the threshold is the ratio between the

return on housing and the return on production capital. If the investment efciency is

low, the rm opts to invest in housing assets; otherwise, the rm invests in physical

capital. Moreover, rms can nance their investment from the banking sector. We

introduce nancial frictions by following Boissay, Collard, and Smets (2016) to as-

sume that rms can divert bank loans to storage technology.Since a rm’s investment

efciency cannot be observed and veried by a bank, the bank imposes an incentive

compatibility (IC) constraint on leverage to prevent rms from diverting bank loans.

The endogenous upper limit of a rm’s leverage is positively related to the return

of housing investment. We can further decompose rms’ aggregate housing into two

components: the extensive margin, that is, the measure of rms investingin housing,

and the intensive margin, that is, the amount of housing assets that rms can purchase.

When the economy is hit by a negative shock, for instance, a negative Total Factor

Productivity (TFP) shock, the relative return on housing increases. Then, more rms

invest in housing, that is, the extensive margin increases. This process would further

boost house prices and the return on housing assets. The relatively high return on

housing implies that rms have less incentive to divertbank loans to storage technol-

ogy. Therefore, the nancial constraint (IC constraint) is relaxed, and the intensive

margin increases. Consequently, a negative shock can raise housing prices.

Similarly, we can decompose the aggregate demand for production capitalinto the

extensive and intensive margin.With a negative shock, the extensive margin declines

because fewer rms invest in physical capital. Meanwhile, the intensive margin in-

creases because higher house prices boost the amount of bank loans that rms can

obtain due to a loosened borrowing constraint. The intensive margin captures the im-

pact of housing prices via the collateral effect on real investment. After calibrating

the model for the Chinese macroeconomy, we show that, for physical investment, the

collateral effect is dominated by the crowding-out effect (the extensive margin). In

turn, a negative TFP shock may dampen the real sector while stimulating the housing

sector. Therefore, our model can account for the countercyclical housing market in

China. Moreover, the model-implied rm-levelportfolio choice between housing and

physical capital is consistent with the empirical pattern from disaggregate data (e.g.,

Chen et al. 2017).

Since a housing boom may crowd out investment in the real sector,a natural ques-

tion is what type of policies can we use to mitigate the adverse impact. To address

this question, we quantitatively evaluate variousmacro-economic policies that target

house prices. Our quantitative exercise suggests that the capital subsidization policy

outperforms the other policies because the capital subsidization policy more effec-

tively stabilizes the housing markets and mitigates the crowding-out effectcaused by

the housing boom.

Literature Review. Both the collapse of the housing market in Japan in the early

1990s and the recent Great Recession have shed light on the impact of the uctua-

tion of housing markets on rms and households. In this sense, our paper falls into