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date: 15 January 2021

China’s Housing Policy and Housing Boom and Their Macroeconomic Impactsfree

  • Kaiji ChenKaiji ChenDepartment of Economics, Emory University

Summary

The house price boom that has been present in most Chinese cities since the early 2000s has triggered substantial interest in the role that China’s housing policy plays in its housing market and macroeconomy, with an extensive literature employing both empirical and theoretical perspectives developed over the past decade. This research finds that the privatization of China’s housing market, which encouraged households living in state-owned housing to purchase their homes at prices far below their market value, contributed to a rapid increase in homeownership beginning in the mid-1990s. Housing market privatization also has led to a significant increase in both housing and nonhousing consumption, but these benefits are unevenly distributed across households. With the policy goal of making homeownership affordable for the average household, the Housing Provident Fund contributes positively to homeownership rates. By contrast, the effectiveness of housing policies to make housing affordable for low-income households has been weaker in recent years. Moreover, a large body of empirical research shows that the unintended consequences of housing market privatization have been a persistent increase in housing prices since the early 2000s, which has been accompanied by soaring land prices, high vacancy rates, and high price-to-income and price-to-rent ratios. The literature has differing views regarding the sustainability of China’s housing boom. On a theoretical front, economists find that rising housing demand, due to both consumption and investment purposes, is important to understanding China’s prolonged housing boom, and that land-use policy, which influences the supply side of the housing market, lies at the center of China’s housing boom. However, regulatory policies, such as housing purchase restrictions and property taxes, have had mixed effects on the housing market in different cities. In addition to China’s housing policy and its direct effects on the nation’s housing market, research finds that China’s housing policy impacts its macroeconomy via the transmission of house price dynamics into the household and corporate sectors. High housing prices have a heterogenous impact on the consumption and savings of different types of households but tend to discourage household labor supply. Meanwhile, rising house prices encourage housing investment by non–real-estate firms, which crowds out nonhousing investment, lowers the availability of noncollateralized business loans, and reduces productive efficiency via the misallocation of capital and managerial talent.

The Role of Housing and Housing Policy in China’s Macroeconomy

Between 2003 and 2016, China experienced an unprecedented housing boom amid a long period of spectacular economic growth. The corresponding sharp rise of house prices and soaring housing-related debt raised widespread concerns about the possibility of real estate bubbles forming and the potential macroeconomic consequences of a housing bust. Such concerns are warranted not only because of the dominant roles that housing wealth and mortgage loans play in household wealth and balance sheets but also because of the sheer size of corporate real estate investment financed by bank loans collateralized by housing, which make the Chinese macroeconomy vulnerable to the risks associated with collapsing house prices.

Much of the traditional literature on the Chinese economy, however, treats housing as one component of capital, consumption, and household wealth, despite the great attention that this literature pays to the interaction of China’s high household saving rates with its macroeconomy. Less studied is the role of China’s housing policy in these macroeconomic outcomes, as much of the discussion has been limited to how this policy functions in promoting the welfare state or urban development. As China became the world’s second-largest economy, research on China began to focus on its macroeconomy. Over the past decade, a large body of research has quickly emerged that explores the linkages between China’s housing market and its macroeconomy, along with the role that the nation’s housing policy has played in this interaction. Since the 2010s, this literature has become the frontier of research on the Chinese economy.

The development of this literature has benefited from two factors: First, the increasing availability of micro data sets has made it possible to use a quasi-experimental approach to identify the causal impacts of China’s housing policy in empirical research and to use cross-sectional moments on households and firms to discipline the structural models. Second, the development of a modern macroeconomic theoretical framework containing financial frictions and allowing for household heterogeneity has permitted researchers to incorporate various aspects specific to China’s institutional features into such frameworks in order to quantify how China’s housing policy and other factors contribute to housing prices.

This article reviews the recent literature on China’s housing policy and its impact on housing markets and the macroeconomy through the lens of both empirical evidence and structural models. The discussion centers on three main topics: How China’s housing policy reform has influenced its homeownership rates and household consumption, the factors that have driven China’s housing boom since the early 2000s and the role of China’s housing policy in this boom, and how China’s housing policy impacts its macroeconomy via changes in house prices. This article focuses on the role of housing policy, but readers should bear in mind that the development of Chinese housing markets also depends on several other policies, such as fiscal constraints placed on local governments that provide strong incentives for them to tap into land values (Ambrose, Deng, & Wu, 2015), and on China’s strong growth performance since the 1990s. Accordingly, it is difficult to isolate the impacts of housing policies from those of other policies and developments.

The first section of this article, “China’s Housing Policy Since 1978: Historical Review,” provides a historical review of China’s housing policies. The next section “The Impact of Policy Reforms on China’s Housing Market” traces the effects of China’s housing policy on its homeownership rates and household consumption and analyzes the factors underlying the housing market boom. A discussion on “Housing Policy and Its Impact on the Chinese Macroeconomy” is followed by a conclusion and recommendations for further research.

China’s Housing Policy Since 1978: Historical Review

Broadly speaking, China’s housing policy can be classified into three categories according to its various goals. The first two goals, common to many economies, are to promote homeownership and to improve housing affordability for middle-income and low-income households. The third goal, which is unique to China thanks to the crucial roles housing plays in its macroeconomy, aims to strike a balance between promoting macroeconomic growth and stemming the rapid increase in housing prices. This section reviews the evolution of these three policy goals, with a focus on reforms made since the late 1990s when China fully established a market-oriented system to provide housing.

Policy for Promoting Homeownership

Since 1978, China’s housing policy began to gradually transition from a workplace-based in-kind allocation system to a market-oriented system. The policy reform took place in two stages: (a) between 1978 and 1998, a period that featured gradual reform and a dual system of housing provision, and (b) 1999 to the present, a market-dominated period.

The Dual System of Housing Provision, 1978–1998

Between 1978 and 1987, China conducted limited housing reform experiments in selected cities. These trial reforms included encouraging sales of newly built housing priced according to the construction costs, subsidizing the sales of existing public housing units, and increasing the rents charged for public housing (Garriga, Hedlund, Tang, & Wang, 2017). However, during this period, most housing market participants were employers, not individual households. Employers (the so-called work units) purchased housing and then provided these units to their employees at rents that were substantially below market-based rates.

In 1988, the State Council’s issuance of the Implementation Plan for a Gradual Housing System Reform in Cities and Towns marked the start of China’s nationwide housing reform policy. In July 1994, the State Council issued a Decision on Deepening the Urban Housing Reform, which allowed employees in the state-owned sector to purchase the full or partial property rights to their current apartment units at subsidized prices, the so-called reformed housing. The latter document also required employers to gradually terminate the in-kind housing provision system for their workers.

Housing Market Privatization: 1998 to the Present

In 1998, the State Council issued the Notice on Furthering the Urban Housing System Reform and Accelerating Housing Construction. According to this decree, employers were no longer allowed to develop or purchase new housing units for their employees. Instead, employers had to include the provision of all implicit and in-kind housing benefits in the salaries of their workers. The same document also defined the concepts of economically affordable housing (EAH) and commercial housing, with EAH established as the major source of housing provision. EAH is priced at highly subsidized rates and commercial housing is priced at market rates. The purpose of the notice was to establish a market-based housing sector priced according to what high-, medium-, and low-income households could afford to pay. High-income households were expected to buy commercial housing, whereas those households that could not afford commercial housing could either buy EAH or rent cheap housing via the public housing system. To promote commercial housing, in August 2003 the State Council issued the Notice on Promoting a Sustainable and Healthy Development of Real Estate Market, in which the commercial housing units were established as a primary form of housing provision.

As part of the nationwide housing policy reform, China’s land-use policy has also undergone dramatic change since 1988. Before then, land allocation was administered by the state and the market for land leasehold was nonexistent. In 1988, a constitutional amendment established a legal foundation to transfer the leasehold of land, whereas the state still retains ownership of all land. Public land auctions were institutionalized in 2002 and became widespread across all cities. By August 31, 2004, all urban land leasing sales were required to be made through public auctions. In China, the land available for home construction, however, is strictly controlled by the government. The National Land Use Plan (2006–2020), passed by the State Council of China in August 2008, required that the amount of cultivated land in 2010 and 2020 be maintained at 1.818 billion acres and 1.805 billion acres, respectively, referred to as the red-line lower limit for the total amount of arable land. According to Chen and Wen (2017), the amount of arable land has stabilized since 2003, implying a de facto fixed supply of land available for residential and commercial real estate development.

Policy for Improving Housing Affordability

China’s policy for improving housing affordability has two main pillars: affordability housing policy and the housing provident fund. This section describes how these two types of policies were implemented in China along with its housing market privatization.

Affordable Housing Policy

In 1997, the Chinese government started promoting the development of affordable housing to boost the supply of homes available to middle- and low-income households. The 2003 State Council decree changed the nature of ECH from being the primary form of housing provision in urban areas, for which about 85% of urban households were eligible, to indemnificatory housing, for which only very low-income households were eligible. Since then, the importance of promoting the availability of EAH has declined. To give a sense of the scale of this policy change, Yang and Chen (2014) report that between 1999 and 2000, 413 million square meters (sqms) of EAH were sold, accounting for more than 20% of all housing units sold in urban China during this two-year period. In 2010, EAH only accounted for 2.94% of the total urban housing stock in terms of sqms and 3% of the number of housing units sold.

The public housing sector targeting low-income households also changed during the process of implementing housing market reforms. Low-income households could now obtain subsidies to rent public rental units. The government continued to provide cheap-rental housing for the lowest-income households, but the average floor space per person could not exceed 60% of the local average-sized housing unit. Similar to the curtailing of EAH, public housing construction has been very limited for most years since 2003, due to the heavy reliance of local governments on land sales revenues as their funding sources.

The Housing Provident Fund

The Housing Provident Fund (HPF) was initiated in 1994 by the State Council as part of China’s broader housing policy reform. The HPF was set up as a nationwide mandatory savings program for the purpose of funding home purchases, mortgage down payments, home improvement projects, and monthly mortgage payments. Starting in 1998, the HPF loans were made available to individual participants for home purchases. Employees in state-owned enterprises and joint ventures were the first workers eligible to participate in the HPF program. Employees in collectively owned and private firms joined the program in 1999. But self-employed, temporary, and home-based workers are not covered by the program. By the end of 2008, 77.5 million Chinese workers participated in the HPF program and the total deposits exceeded 2 trillion yuan.

To ensure that households use the HPF for purchasing owner-occupied housing, the government imposes various regulations on how the funds are used for making down payments and on the maximum amount of HPF loans. For example, the minimum down payment for housing units less than 90 sqms is 20%, while the down payment for housing units above that size threshold is typically 30% (Tang & Coulson, 2017).

Policy for Housing Market (De)Regulation

Since 2005, China’s housing policy has aimed to strike a balance between boosting GDP growth and controlling house price growth via regulating or deregulating the housing market. The policy instruments, which intervene in both the demand and supply sides of China’s housing market, include a mortgage interest rate policy, a credit policy (e.g., loan-to-value limits and a payment-to-income ratio), a tax policy (transaction taxes, capital gains taxes, sales tax exemption, property taxes), a land-use policy, and other various administrative tools (e.g., housing purchase and resale restrictions).

Policies were tightened in 2005–2008, 2010–2011, 2013, and late 2016–2018. During these periods, the government used a mix of tools to cool the overheated housing market and to curb housing speculation. For example, to discourage speculative demand for large-sized housing units, in 2006 the State Council and its seven ministries issued a document mandating a 20% down payment ratio for housing units with floor space less than or equal to 90 sqms - but requiring a 30% down payment for units above this threshold. In September 2007, the central government raised the minimum down payment ratio for second home purchases from 30 to 40%, and mandated that the interest rate on second-home mortgages be 10% higher than the benchmark rate. Meanwhile, the government introduced the limits on the ratio of mortgage payment to borrowers’ income, under which the monthly mortgage payment-to-income ratio is capped at 50% for all mortgages held by a household. In April 2008, China imposed a capital gains tax on housing sales.

From May 2010 until the end of 2011, 46 cities in China adopted a property “purchase restriction policy.” In general, purchase restrictions prohibit resident households (i.e., with registered permanent residence) from buying more than two residential units and nonresident households, with proof of local tax receipts or social security records for at least one year, are prohibited from buying more than one residential housing unit.

In 2013, the four first-tier Chinese cities (Beijing, Shanghai, Guangzhou, and Shenzhen) and the second-tier cities with heated housing markets imposed the most stringent policy restrictions on residential land supply.1 As a result, the total residential land supply for first- and second-tier cities shrunk considerably in 2014 and 2015, decreasing by 28.1 and 20.9%, respectively. On the fiscal side, taxes on residential real estate transactions have been raised and the tax exemptions for various closing fees have been eliminated. In addition, local governments have imposed tighter restrictions on real estate development and on property transactions and have raised lending standards for real estate-related loans. Besides raising down payment ratios and resuming the purchase restrictions, since late 2016 many cities have introduced policies that prohibit residential properties from being resold within two or three years from the date of purchase to limit speculative housing investment.

In contrast to policy tightening, a loosening of housing policy has occurred during periods of a slowdown in GDP growth or when housing markets are sluggish. Episodes of policy loosening took place in late 2008–2009, 2012, and 2014–2016. For example, in October 2008 the central government abruptly reversed the policy tightening enacted during 2007 and early 2008 and instituted a series of measures to support the housing market’s recovery. The minimum mortgage interest rate was reduced to 70% of the benchmark interest rate and the down payment ratio for a second home was reduced to 30% of the purchase price. Beginning in November 2008, the transaction taxes for purchasing housing units with floor space less than or equal to 90 sqms were reduced from 3 to 1% of the property’s sale price. In early 2014, housing purchase restrictions were removed for all cities except in the four first-tier cities and in one second-tier city, Haikou. Between the 4th quarter (Q4) of 2014 and 2016 Q3, the minimum down payment ratio for purchasing a first house was reduced from 30 to 20%, and for the second home was reduced from 70 to 30%. Moreover, except for the five cities that still impose housing purchase restrictions, the policy on imposing credit limits on mortgages for the third house was eliminated.

The Impact of Policy Reforms on China’s Housing Market

This section reviews research on the impact of various housing policy reforms on homeownership rates and housing consumption. The discussion then turns to the forces that have been driving China’s housing boom since the early 2000s and the role that the country’s housing policy has played in this boom. The section concludes with a summary of the research conducted on the effectiveness of housing policies intended to moderate house price inflation.

Effects on Homeownership Rates and Household Consumption

Two major housing policy reforms affect homeownership rates and household consumption in China: policy for promoting homeownership and policy for improving housing affordability. A growing literature finds that policies for promoting homeownership had both intended and unintended consequence, while the effectiveness of policies for improving housing affordability varied between EAH and housing provident fund.

The Intended and Unintended Outcomes of Promoting Homeownership

Research indicates that the nationwide housing market privatization that began in 1994 has had a profound impact on China’s economy along several dimensions. First, this policy shift has resulted in China having one of the world’s highest homeownership rates, surging from 42% in 1995 to 78% in 2002 and 88.7% by 2007; since then, China’s homeownership rate has remained above 85%. Second, as part of the impetus for housing policy reforms, this privatization has led to an increase in both housing consumption and nonhousing consumption, which has improved the standard of living for many citizens. Third, the benefits of China’s housing privatization were unevenly distributed across households. The first and second outcomes are well-intended by the policy design, while the third is an unintended consequence of housing market reforms.

A key question in earlier studies was how enacting housing market privatization and other policies, beginning in the 1990s, contributed to the rapid increase in homeownership in China. Using 2000 census data, Logan, Fang, and Zhang (2010) find that after implementing these reforms, among China’s eight most-populous cities, the average discounted price of housing relative to the market price was as high as 38%. Hiroshi, Sicular, and Yue (2013), using data from the Chinese Household Income Project (CHIP), report that the fraction of households owning privatized public housing increased from 27% in 1995 to 61% (out of a total national homeownership rate of 78%) in 2002, whereas during this same seven-year period, the fraction of households owning commercial housing increased only modestly, from 1.3 to 7.4%. Therefore, based on the findings in these two papers, it appears that after China implemented its nationwide housing market privatization, most households that had been living in state-owned housing chose to purchase the property rights to their residences at prices discounted far below the market value.

Privatizing China’s housing market also led to an increase in the average consumption of housing services. By developing a theoretical framework with both quantity and price controls in the pre-reform state-allocated housing system, Wang (2011) shows that the privatization of state-owned housing contributed to an increase in housing demand and equilibrium housing prices. Using panel data from the China Health and Nutrition Survey (CHNS), Wang (2011) finds that households in state-owned housing units prior to the reform consumed approximately 15% less housing services than they would have chosen in the private market. This finding implies that during the pre-reform period there was a 25% welfare loss due to space misallocation. Building on these results, Wang (2012) shows empirically that China’s housing policy reform doubled the probability that former residents of state-owned housing who subsequently purchased their units would become self-employed, as owning their own homes allows them to use housing as collateral for obtaining a business loan necessary for pursuing entrepreneurship. The evidence also shows that the large gap between the market price of housing and the government-subsidized prices generated a positive wealth effect. Using the CHNS data, Yin and Gan (2009) find that after the reforms, households that had purchased public housing had a significantly higher rate of durable goods consumption a few years later.

Despite the positive aggregate impact of China’s housing market privatization on consumption, living standards, and welfare, some studies find that these benefits have been unevenly distributed across households. According to Logan et al. (2010) and Walde and He (2014), the biggest winners from China’s privatized housing market were households employed in occupations that were favored in the pre-reform housing system (e.g., work unit heads and administrative staffers). These households had substantially greater wealth in the form of home equity shortly after privatization, due primarily to their having been allocated newer and higher quality apartments as part of their employer-provided benefits prior to the reform, which they then bought at highly discounted prices after the reform. Furthermore, Yang and Wang (2011) find that in Beijing, the initially unequal allocation of housing in terms of the quality and size of the units led to subsequent inequality in housing affordability after the public housing units were privatized.

The Impacts from Improving Housing Affordability

Apart from investigating how China’s housing privatization has affected the nation’s homeownership rate, a few papers assess the effectiveness of policies designed to improve affordability for medium- and low-income households. Taking the price-to-income ratio as an indicator of housing affordability, Yang and Chen (2014) find that between 1997 and 2010, economically affordable housing (EAH) was attainable for median-income households in Beijing that were eligible for it, but not for those with incomes in the bottom four deciles. However, they also find that in 2008, only 9% of the households in Beijing that were eligible for the EAH program could afford to buy a home at the current subsidized price level, suggesting shortcomings in the effectiveness of this policy.

In contrast to the EAH program, more recent evidence indicates that the Housing Provident Fund (HPF) made significant contributions to China’s rising homeownership rate. For example, using CHNS data, Xu (2016) reports that after 1998, households in which both of the adults (or one adult) were enrolled in the HPF program had a homeownership rate that was 38 (or 14) percentage points higher than those households not enrolled in the HPF program. Using data from 2011 China Household Finance Survey ([CHFS], 2014), Tang and Coulson (2017) find corroborating evidence regarding the role that the HPF played in making homeownership more attainable. However, their evidence also indicates that these households purchased smaller properties than they otherwise might have, due to the down payment restrictions on mortgage loans that vary according to the size of a housing unit. On the theoretical front, Zhou (2020), using a heterogeneous-agent life-cycle model, finds that introducing the HPF increased the long-run values of homeownership rate and the average home size by 8.7 and 20%, respectively, through a positive wealth effect created by the program’s subsidized mortgages rates.

Effects on Housing Prices

China’s housing market privatization beginning in the late 1990s not only affected its homeownership rate and household consumption, but also led to a persistent increase in housing prices since the early 2000s. After presenting some stylized facts, this section analyses the sustainability of China’s housing boom from an empirical perspective, followed by a review of recent theoretical work on the factors that fuelled its housing boom.

Stylized Facts

How did the massive housing reforms China enacted beginning in the late 1990s affect the nation’s housing prices and housing market? The official housing price index is the Price Indices for Real Estate in 70 Large and Medium-Size Cities (aka 70 Cities Index), reported regularly by China’s National Bureau of Statistics (NBS). The 70 Cities Index covers housing prices for the 35 major cities: the four first-tier cities, 31 second-tier cities including two autonomous municipalities, the capital cities of 24 provinces, and four coastal economic centers (see Table 1). These 35 major cities represent the most important cities in China in terms of the economy and housing market. The remaining 35 cities in the index are third-tier cities, which are regional and economic centers. According to the NBS, between 2002 and 2012 the average annual growth rate of house prices for China’s 35 major cities was 12.68%. However, as Wu, Deng, and Liu (2014) point out, the official housing price indices failed to control for changes in housing quality and location due to the ongoing trends of urbanization and the increase in building density that occurred in most cities over this decade. To correct for this issue, Wu et al. (2014) apply a hedonic method to construct housing price indices based on the sales of newly built housing units in the 35 major cities between 2004 and 2010. Yet a key challenge in employing such an approach, as Fang, Gu, Xiong, and Zhou (2016) point out, is that this method may not be able to capture all the relevant housing characteristics as regressors to control for heterogeneity in housing quality. Fang et al. (2016) developed a hybrid approach to construct housing price indices for 120 Chinese cities, including the 35 major cities and 85 third-tier cities, between 2003 and 2013 using a transaction-level mortgage loan data set.

Table 1. China’s Major Cities

First-Tier Cities

Beijing, Shanghai, Guangzhou, Shenzhen

Second-Tier Cities

Tianjin, Shijiazhuang, Taiyuan, Hohhot, Shenyang, Dalian, Changchun, Harbin, Nanjing, Hangzhou, Ningbo, Hefei, Fuzhou, Xiamen, Nanchang, Jinan, Qingdao, Zhengzhou, Wuhan, Changsha, Nanning, Haikou, Chongqing, Chengdu, Guiyang, Kunming, Xi’an, Lanzhou, Xining, Yinchuan, Urumqi

Note: The 35 major cities in China are the four first-tier cities and 31 second-tier cities, including two autonomous municipalities, the capital cities of 24 provinces, and four coastal economic centers. These 35 major cities represent the most important cities in China in terms of the economy and housing market.

Despite the differences in data sources and methodology used in the two studies, both Wu et al. (2014) and Fang et al. (2016) find that China experienced an enormous and sustained house price appreciation in the decade since 2003. For example, Wu et al. (2014) show that the national real housing price indices for the 35 major cities increased 17% per year between 2006 Q1 and 2010 Q4. Fang et al. (2016) find an average annual real growth rate of 13.1% in the four first-tier cities, 10.5% in the second-tier cities, and 7.9% in the third-tier cities between 2003 and 2013. Using the data from Fang et al. (2016), Glaeser, Huang, Ma, and Shleifer (2016) show that annual house price growth in first-tier U.S. cities between 1996 and 2006 was slower than the annual house price growth that occurred in third-tier Chinese cities between 2003 and 2013, and was only 40% of the annual price house growth of first-tier Chinese cities during this period. Liu and Xiong (2020) use the housing price indices of Fang et al. (2016) for 2003–2012 and the NBS 70 cities index for 2013–2017 to display the housing price dynamics between 2003 and 2017. They show that following a temporary slowdown between late 2013 and 2014, housing prices in both the first- and second-tier cities experienced a remarkable pickup during 2015 and 2016, before prices stabilized in 2017. The phenomenon of the persistent increase in housing prices in major cities of China between 2003 and 2016 constitutes China’s housing boom.

A distinctive feature of China’s housing boom is the high vacancy rates. The CHFS (2014) found that in 2013, the average homeowner vacancy rates in the first-, second-, and third-tier cities were 21.2, 21.8, and 23.2%, respectively. Moreover, 35.1% of entrepreneurial households (those who own businesses) own vacant houses. Glaeser et al. (2016) extend the computation of vacancy rates to include both homeowner vacancy rates and developers’ inventory by using data from local housing bureaus and the Urban Household Survey. They find that in first- and second-tier cities, high vacancy rates reflect the large number of vacant units owned by residents, while the vacancy rates in third- and fourth-tier cities reflect developer-owned inventory, which will eventually need to be sold.

The Sustainability of China’s Housing Boom

The rapid growth in China’s housing prices has triggered a spirited debate regarding whether this boom represents an asset bubble or if it can be rationalized by economic fundamentals. One strand of the literature empirically examines both the demand and supply side of the housing market and argues that China is vulnerable to substantial house price risks. For example, employing the classic user-cost approach by Poterba (1984), Wu, Gyourko, and Deng (2012) find that, for eight major cities at the prevailing price-to-rental ratios, the risks of owning a house is quite high in seven of these cities. In Beijing, even a modest 2% decrease in expected annual house price appreciation would lead to large price decline of over 40%. By extending their sample cities to China’s 35 major cities, Wu, Gyourko, and Deng (2016) conclude that 13 of these cities, primarily located in the interior of the country, experienced at least a 30% oversupply of housing units, while another 11 cities had at least a 10% oversupply, and that 11 other cities remained heavily exposed to risks due to a downward shift in expectations. Measuring the supply-demand imbalance by floor space, Chivakul, Lam, Liu, Maliszewski, and Schipke (2015) confirm that, as a result of overbuilding in the boom years, China’s housing oversupply problem is nationwide, and is particularly pronounced in the third- and fourth-tier cities and those cities in the country’s northeast region.

Beyond the high price-to-income or price-to-rental ratios, corroborating evidence shows that much of the increase in housing prices is fueled by an increase in land values, rather than by higher construction costs. Using data from Beijing’s local land auction market, Wu et al. (2012) find that real constant-quality land values in Beijing increased by nearly 800% between 2003 and 2010. Accordingly, land values contributed, on average, 37% to house prices before 2008, but they then found that this ratio rose above 60% after 2010. Constructing a hedonic residential land price index across 35 major cities, Wu et al. (2016) find that real land values rose by 14.4% per annum, on average, between 2004 Q1 and 2015 Q3, with a remarkable 27.5% per year increase in Beijing during this period. Chen, Liu, Xiong, and Zhou (2019) hand collected land-auction parcel data to construct land price indexes for 284 cities, and find that residential land prices increased by an annual average of 15.22% between 2004 and 2015. On the other hand, Glaeser et al. (2016), exploiting two independent data sources for construction costs, find that building costs typically account for less than one-third of the selling prices for housing in China. They conclude that in many aspects, China’s housing market displays all the aspects of a classic asset price bubble.

Another important strand of the literature emphasizes changes in various economic fundamentals during this period and offers a more sanguine picture regarding the sustainability of China’s housing prices. Among the various factors, the rapid growth in disposable household income features prominently in rationalizing the strong housing demand. For example, Fang et al. (2016) point out that the enormous house price appreciation was accompanied by an equally impressive growth in disposable income (except for households in a few cities). However, their evidence also suggests that low-income home buyers often assumed an enormous financial burden, with many purchasing homes worth about 8 to 10 times their annual incomes. Such purchase decisions, as Fang et al. (2016) argue, cannot be explained by simple consumption motives, but rather are dependent on expectations of high-income growth persisting into the future. By comparing the actual price appreciation with equilibrium price appreciation rates due to changes in fundamentals from the demand and supply sides of the housing market, Wang and Zhang (2014) claim that changes in fundamentals can explain a major portion of the actual housing price appreciation during the 2002–2008 period, except for the price increases that took place in a few coastal cities. Feng and Wu (2015) echo this view by showing that the actual rent-to-price ratios for 60 large- and medium-sized cities at the end of 2013 fall into the range of the computed user costs of owner-occupied housing. Ren, Xiong, and Yuan (2012) reach a similar conclusion. They compute the time series of the hazard rates of positive housing returns and find no evidence of any housing bubbles in the 35 major cities. More recently, by studying housing expenditure-to-income ratio for a sample of 275 prefecture cities, Li, Qin, and Wu (2020) provide evidence that, except for a few “superstar cities,” overall housing affordability in Chinese cities remained stable or even improved during the 2014–2018 period.

What Factors Fueled China’s Housing Boom?

As Hurst (2015) points out, it is hard to make predictions about whether China’s house price growth is sustainable without first having a framework to evaluate why housing prices are changing. Hence, a key question is what fueled China’s housing boom? This section takes a theoretical approach to explore how various factors and features in China’s economy helped to generate the housing boom.

One of the most prominent driving forces fueling China’s housing boom is the role that housing plays as an investment good. To explain why China’s housing prices have grown faster than household incomes and to account for the high vacancy rates in most of the 35 major cities, Chen and Wen (2017) develop and calibrate a two-sector growth model in which the housing boom is seen as a rational bubble emerging naturally from China’s economic transition. During this transition, productive entrepreneurs in the private sector enjoyed a high return to capital that was sustained by a reallocation of surplus labor unleashed from traditional state-run enterprises to the emerging private sector. Rational expectations of an eventual depletion of a surplus labor and thus a significantly lower future rate of return to capital can induce the current generation of entrepreneurs to invest in housing as alternative store of value for their rapidly growing wealth. Thus, the high annual growth rate of housing prices during China’s transition to a market-based economy can be justified by the high capital returns enjoyed by entrepreneurs in the private sector, the marginal investors participating in China’s housing market. Along similar lines, Jiang, Miao, and Zhang (2019) develop a model of housing bubbles and attribute the high housing price growth in China to the stochastic features of bubbles, which demand high returns for housing investment.

Recent studies have shown that besides the demand for housing investment made by entrepreneurs, the household sector in general has also been pivotal in driving the housing boom. Unlike the options available to households in developed economies, in China, investment in housing is pretty much the only way that an average household can store and amass wealth, due to the underdevelopment of China’s financial markets and the country’s tight capital controls. Zhang (2019) uses a dynamic portfolio choice model to show that the reason house prices grew faster than incomes may have been due to a liquidity constraint, under which the growth in a household’s net worth involved a feedback loop on housing demand via equilibrium housing prices. Quantitatively, however, the model predicts a faster increase in the household wealth-to-income ratio than shown in the data from the 2003–2012 period, which might be explained by the absence of both housing indivisibility and heterogeneity in housing wealth in his model. Using loan-level data on mortgage originations, Chen, Wang, Xu, and Zha (2019) find that the relaxation of limits on loan-to-value ratios between 2014 Q4 and 2016 Q3 had a heterogeneous impact on the household sector’s demand for mortgages and housing, with highly educated, middle-aged households acting as the main driver of the increase in aggregate mortgage demand. Disciplined by this fact, in their heterogeneous-agent life-cycle housing model, wealthy middle-aged households, which generate a nontrivial fraction of China’s total housing demand, are constrained in their ability to purchase large-sized housing units purely for investment purposes. Accordingly, a cut in the minimum down payment ratio for buying non-owner-occupied houses as an investment vehicle leads to quantitatively large impacts on housing prices and mortgage demand by allowing wealthy middle-aged households to overcome the credit constraint via the capital gain realized by selling their existing homes at higher prices.

The crucial role played by households’ investment demand for housing also implies that increase in income inequality may have a substantial impact on housing prices and the effectiveness of housing policies to tame housing markets. Zhang, Jia, and Yang (2016) use data from the 2002–2009 Urban Household Survey and find that cities with higher income Gini coefficient have higher price-to-income ratios and higher vacancy rates. They estimate that about 6% of the increase in the housing price-to-income ratio and 10% of the increase in the housing vacancy rate can be attributed to inequality. Similarly, Ge and Wu (2017) find a strong positive correlation between income inequality and housing prices, and suggest that redistributive policies to address the underlying income inequality is important for the effectiveness of policy remedies to make housing affordable. Cao, Chen, and Zhang (2018), using 2010 and 2011 household survey data, find that the elasticity of housing demand to expected capital gains is higher for wealthier households. Accordingly, their results suggest that investment motivations of wealthy households are likely to play a key role in the formation of housing prices, and that the impact of housing policies (such as purchase restrictions) cannot be evaluated without taking into account the behavior of this class of households.

The increasing household demand for housing consumption represents another key dimension for understanding the housing boom. By introducing a rich set of fundamentals into an otherwise standard life-cycle model, Han, Han, and Zhu (2018) find that the fundamentals influencing housing consumption can only explain 70% of the observed growth in housing prices under reasonable parametrizations of the model. In their model, the rural-to-urban migration is taken as exogenous. By contrast, Garriga, Hedlund, Tang, and Wang (2017) use a multisector model to investigate the role that the ongoing structural transformation of China’s economy, driven by rural-to-urban migration, has played in the housing boom. By calibrating their model to match the rural-urban migration flow, their model can generate an average annual 6.4% growth rate in national housing prices between 1998 and 2012, with productivity growth contributing about 20% to the rise in housing prices.

An alternative and more sociological approach to explaining China’s house price boom examines the role that housing serves as status good in the nation’s social culture. For example, Wei, Zhang, and Liu (2017) theorize that since housing serves as a status good in the marriage market, a rise in the male-to-female population ratio under the one-child policy led to an increase in housing prices and in the price-to-rent ratio. Consistent with this hypothesis, they find that cities with a more unbalanced sex ratio experienced higher housing prices between 2003 and 2009, implying that a greater imbalance in the male-to- female sex ratio accounts for between 30 and 48% of the increase in real housing prices in the 35 major cities during this period. In a similar spirit, Minetti, Peng, and Jiang (2019) introduce the cultural tendency of Chinese households to use homeownership to compete for social status in their communities, and incorporate this factor into a dynamic general equilibrium model to show that such an external factor driving housing consumption amplifies the responses of house price dynamics to housing demand shocks.

Despite these different theoretical explanations for why housing demand in China is increasing, a consensus view holds that the nation’s land-use policy, via its influence on the supply side of the housing market, serves as a central element driving the house price boom. For example, in the structural model of Chen and Wen (2017), the limited supply of land available for construction purposes under China’s current land-use policy makes housing a natural investment option for entrepreneurs to realize future capital gains. The quantitative exercise in Garriga, Hedlund, Tang, and Wang (2017) shows that the regulated land supply in China can explain 38% of the housing price growth that occurred between 1998 and 2012. A similar finding is made by Han et al. (2018), which shows that limiting the supply of land used to construct housing caused Beijing housing prices to increase by more than 20% in 2014. These theoretical predictions are supported by the earlier empirical finding in Liang, Lu, and Zhang (2016), which shows that land supply policies have led to the rapid growth of housing prices in the cities where the land supply has been restricted (mainly in the eastern region).

The Effectiveness of Policies in Cooling China’s Housing Market

With the overheated Chinese housing market, a natural question is what kinds of regulatory housing policies are effective in curbing the speculative demand for housing and thus stemming China’s fast-growing housing prices? Among the various housing policies, researchers have examined the role of housing purchase restrictions and property taxes. This section gives a brief description of recent research on the effects of these types of policies on lower housing demand and prices.

Housing Purchase Restrictions

A stream of empirical papers finds that the house purchase restriction policy implemented in China between 2010 and 2014 was effective in controlling house prices and housing investment. For example, Du and Zhang (2015) find that the purchase restriction adopted during this period reduced the annual growth rate of housing prices in Beijing by 7.69%. Han and Zheng (2016) extend the sample cities to all 46 cities that adopted the purchase restriction policy during this period and make a similar point, finding that the same policy lowered the annual growth of property prices by 0.36 percentage points and property investment by 11.3%, respectively. Such policy restrictions, moreover, effectively lowered firms’ business default risks in the first- and second-tier cities. Li, Cheng, and Cheong (2017) further show that the housing purchase restriction policy exerted larger downward pressure on the prices of small- or large-sized housing units than on the medium-sized housing units. Regarding its impact on land prices, Chen, Liu, et al. (2019) find that the cities that restricted house purchases between 2010 and 2014 experienced both a decline in commercial and residential land prices and a reduction in land investment.

However, using the house purchase restriction adopted by various cities between late 2016 and 2017 as a quasi-policy experiment, Deng, Liao, Yu, and Zhang (2019) find that such restrictions tend to have opposite effects on housing prices in the regulated and nonregulated cities, as housing investors in the regulated cities switch to investing in nearby cities without house purchase restrictions. According to their study, in the 19 regulated cities the average monthly growth rate of house prices dropped from 4% in September 2016 to 1.8% in October 2016. By contrast, house prices in the nearby nonregulated cities increased by 6 to 10% within a few months after the house purchase restriction policies were adopted in the regulated cities, although there was no differential change in rents between regulated and unregulated cities. Similar spillover effects of housing purchase restrictions were found by Li, Qin, and Wu (2020), who show that, after 2016 when house purchase restriction were adopted, housing-expenditure-to income ratio of nearby nonregulated cities increased by 4.5–4.8%.

Property Tax

In the absence of a nationwide property tax system, Shanghai and Chongqing introduced a pilot property tax program in 2011 to curb soaring housing prices. The annual tax rates range from 0.4 to 0.6% in Shanghai and from 0.5 to 1.2% in Chongqing, with both rates varying with house values. A number of empirical papers have used the above policy experiments in Shanghai and Chongqing to evaluate the effectiveness of property taxes on controlling the rise in house prices. The evidence, however, is mixed. For example, Bai, Li, and Ouyang (2014) find that enacting property taxes lowered the annual rise in Shanghai’s average housing prices by 11–15% but increased Chongqing’s annual average housing prices by 10–12%. They argue that the positive effect of property taxes on housing prices in Chongqing is because this city mainly imposes property taxes on high-end properties, which effectively shifts purchases from the higher-end to lower-end properties. However, Du and Zhang (2015) find that Chongqing’s trial property tax reduced the annual growth rate of housing prices by 2.52%. In contrast, their results show that Shanghai’s trial property tax had no effect on the city’s house price growth. Du and Zhang (2015) claim that a possible reason for this disparity in policy impact is that Chongqing has a much higher proportion of taxable high-end homes (10%) than Shanghai (0.6%).

On the theoretical front, Wang, Wang, and Zhang (2019) examine alternative property tax changes in terms of both tax rates and the coverage across different Chinese cities in a multiregion computable general equilibrium model and find that implementing a nationwide system of property taxes would reduce housing construction but improve welfare if regions with high income levels are taxed. Consistent with this view, Jiang et al. (2019) show that imposing a permanent uniform but unexpected property tax can temporarily reduce housing prices via a negative wealth effect, without affecting the long-run growth of housing prices. Minetti et al. (2019), on the contrary, point out that enacting a nationwide property tax can reduce the long-run level of housing demand and housing prices.

While many of the policy discussions on taming housing markets have centered on housing purchase restrictions and property taxes, researchers have started to look into legal and financial infrastructure for stabilizing the housing market and limiting the potential fallouts from bursting bubbles. Koss and Shi (2018), for example, make a detailed persuasive case for the importance of shoring up mortgage underwriting and mortgage requirements to reduce housing market risks.

Housing Policy and Its Impact on the Chinese Macroeconomy

Via price dynamics, the impact of China’s housing policies naturally extends beyond housing markets to the household and corporate sectors, and thus to the whole macroeconomy. This section provides an analysis of these macroeconomic effects viewed through the lenses of empirical research and structural models.

Effects on the Household Sector

Recent studies find that China’s housing boom affects the household sector via households’ non-housing consumption, saving rates and their labor supply. In particular, high housing prices have a heterogenous impact on the consumption and savings of different types of households but tend to discourage household labor supply.

Effects on Nonhousing Consumption

According to the literature, a housing boom affects nonhousing consumption via two channels, one that reduces nonhousing consumption and one that increases non-housing consumption. First, fast-growing house prices imply high expected capital gains from housing investment. Taken together with a binding liquidity constraint, this expectation would encourage households to postpone nonhousing consumption and increase their savings for housing purchases (Campbell & Cocco, 2007). Another potential channel for higher housing prices to affect consumption is through the housing wealth effect, which tends to increase household consumption (Li & Yao, 2007). Unlike the United States and other developed economies, in China there is no mortgage refinancing or home equity line of credit, although homeowners may cash out or borrow against their housing equity. However, such wealth effects on consumption may still apply to homeowners with multiple houses, which is a common trait among many Chinese households.

Xie, Wu, Li, and Zheng (2012) investigate the effects that housing prices have on household consumption. Using data from the Urban Household Survey conducted across 12 first- and second-tier cities, they find that higher housing prices tend to be associated with lower household consumption in those cities, especially among households with marrying-age men and lower-valued housing, as these owners eventually tend to upgrade their housing. More recently, Waxman, Liang, Li, Barwick Jia, and Zhao (2020), using city-level credit and debit card data from 2011 to 2013, reached a similar conclusion: a 10% annual rate of appreciation in housing prices would result in a 7.4% reduction in automobile purchases and a 6.2% drop in spending at supermarkets and department stores. Moreover, a 10% yearly increase in housing prices is associated with a 1.9% annual increase in savings deposits, a finding that is consistent with the view that households that face tight borrowing constraints are deferring consumption to save for home purchases. However, since their analysis is at the city level, it is difficult to distinguish which group of households experienced a decrease in consumption or an increase in savings.

More recent evidence using household-level data indicates that the crowding-out effects of China’s fast-growing house prices on nonhousing consumption not only hold for households that plan to purchase houses, but also those that have bought a house—due to the mortgage debt burden that these households have had to assume. Using the CHFS data for the period 2011—2017, Chen, Wang, et al. (2019) find that following the relaxation of housing policy between 2016 Q4 and 2016 Q3, middle-aged, highly educated households reduced their nonhousing consumption at the same time that they upgraded their housing, and those households with higher mortgage debt burdens experienced lower consumption growth. The quantitative housing model in Chen, Wang, et al. (2019) suggests that the nonhousing consumption of highly educated, middle-aged households falls for two reasons: first, via making a higher investment in housing, these households choose to defer current consumption for future consumption; and second, having a higher mortgage debt burden forces them to reduce their nonhousing consumption.

In contrast to the crowding-out effects that high housing prices have on consumption, Deng et al. (2019) find that increased house prices in nearby cities that do not restrict housing purchases have a crowding-in effect on consumption: during late 2016 and 2017, household spending on new automobiles in the unregulated cities increased 11–17%. Moreover, as supportive evidence for the housing wealth effects, the spending response is strongly positive for multiproperty owners, is indistinguishable from zero for one-property owners, and is significantly negative for renters.

Effects on Household Saving Rates

An extensive body of literature has studied how the growth in housing prices influences household saving rates. Wei and Zhang (2011), using both cross-regional and household-level evidence, argue that high housing prices force Chinese parents with a son to raise their savings for a housing purchase intended to improve their son’s relative competitive advantage in the marriage market. This competitive social behavior generates an externality: even those parents without a son have to save more in order to afford housing. Consistent with this view, Chen and Yang (2013) find that between 2002 and 2007, rising house prices forced households to save more for housing purchases, with a stronger effect on would-be homebuyers and homeowners who planned to purchase either a larger house or another house for their children. More recently, using data from various waves of the CHIP, Zhang et al. (2018) show that homeowners had an annual saving rate 3 percentage points higher compared to that of renters during the first decade of the 2000s, whereas since 2013 the average annual saving rate of homeowners has been 2 percentage points lower compared to renters.

How do the observed heterogenous responses of household saving rates to the rise in housing prices translate into the aggregate saving rate? Wang and Wen (2012) use a simple consumption-saving model to show that under a severe down payment constraint, housing prices that are growing faster than average household incomes increase the share of would-be homebuyers (savers) relative to current homebuyers (dis-savers). Accordingly, rising housing prices can trigger higher aggregate saving rates. However, quantitatively, in their calibrated version of the model, rising housing prices only generate an additional aggregate saving rate of 4.3%, suggesting that the higher saving motive for a future housing purchase might not be quantitatively important for explaining the high aggregate household saving rate observed in China.

Effects on Household Labor Supply

Recent research also shows that rising housing prices have a strong negative effect on labor supply via housing wealth effects. Using survey data from the CHFS and the China Family Panel Survey, Li, Li, Lu, and Xie (2020) report that the preferential mortgage and tax policies offered on units with floor space under 90 sqms, enacted in 2006 and 2008, respectively, led to a faster increase in prices for housing units satisfying this size requirement than those exceeding this size limit. The positive wealth effect from higher housing prices seems to have incentivized individuals owning these appreciated properties to participate less, on average, in the labor market as compared with others, with a stronger effect seen on females, younger generations, and households with a strong repayment capacity.

Regarding hours worked, using data on credit card transactions, Gu, He, and Qian (2018) report that following the announcements of record-breaking prices for land in three major Chinese cities (Shanghai, Hangzhou, and Xiamen) in 2008 and 2009, employed homeowners in these cities were 1.7% more likely to engage in nonwork-related credit card transactions during work hours. Moreover, they became more likely to show up late for work and leave before lunch or earlier at the end of the workday. This effect was stronger among employees with lower incentives to work, such as older people approaching retirement age and individuals who worked at state-owned enterprises.

Effects on the Corporate Sector

In addition to influencing economic behavior in the household sector, there is growing evidence that China’s housing boom affects the corporate sector by distorting capital investment decisions, thus increasing misallocation of capital and managerial talent.

Effects on Real Estate Investment by Firms

An emerging body of empirical work indicates that China’s house price boom led firms outside of the real estate sector to engage in massive housing or land investments to capture rents or capital gains. Using transaction-level land auction data, Deng, Morck, Wu, and Yeung (2015) find that China’s monetary stimulus between 2009 and 2010 induced large numbers of centrally controlled state-owned enterprises to hold huge portfolios of potentially overvalued land holdings and real estate properties, as these enterprises bid up land prices by an average premium of 16%. This evidence is echoed by Chen and Wen (2017). Using China Stock Market and Accounting Research data, they find that between 2007 and 2013 about 45% of Chinese firms in the nonhousing sector had invested in real estate for the purposes of earning rents and capital gains, rather than conducting their own core business operations. The investment share in such property averaged about 15% of these firms’ total physical assets and was stable over time. Similarly, Chen, Liu, et al. (2019) report that between 2000 and 2015, land investment accounted for 27% of the total investments made by publicly listed firms in China, excluding those in firms in the financial, real estate, and construction sectors. Importantly, land zoned for commercial uses constitutes a major part of the land investments held by these firms, accounting for 75% of the total land value held by publicly listed firms in China.

Effects on Nonhousing Investment

The housing investments made by non-real-estate firms necessarily affects their ability to make capital investments in their original business. There is ample evidence that real estate booms in developed economies facilitate corporate investment by allowing financially constrained firms outside of the real estate sector to borrow against their real estate assets. However, evidence on how the collateral channel operates in China suggests that China’s real estate boom failed to boost or even crowded out productive business investment.

Wu, Gyourko, and Deng (2015) examined the impact of a city’s land prices during 2003–2011 on the investment activities undertaken by non-real-estate publicly listed firms located in that city, and found no evidence that increase in land prices in the 35 major Chinese cities stimulate business investment. Using housing purchase restrictions during 2011–2014 to identify real estate shocks, Chen, Liu, et al. (2019) further argue that rising commercial land prices crowd out financing to non-land-holding firms and their productive investment; their evidence shows that following the implementation of the housing purchase restriction policy, non-land-holding firms increased their investments and increased their R&D expenditures and patent applications, while land-holding (manufacturing) firms reduced their investments in commercial land and increased their R&D expenditures and patent applications. Li, Shao, and Tao (2016) find corroborating evidence for the crowding-out effects of real estate booms on nonhousing investment: between 2007 and 2013, firms outside of the real estate sector with investment property underinvested in their original core business by about 10% compared to their industry benchmark.

Beyond a crowding-out effect on manufacturing investment, Jiang et al. (2019) emphasize a crowding-in effect of real estate booms on infrastructure investment. By incorporating the interaction between housing and infrastructure investment into a general equilibrium model of the housing sector, their model generates a persistent increase in the ratios of infrastructure investment and local government debt to GDP and the shrinking contribution of nonhousing sectors of the economy (e.g., manufacturing) to China’s GDP growth. The coexistence of the crowding-out effect on productive manufacturing investment and the crowding-in effect on infrastructure investment implies severe resource misallocation caused by the real estate boom in China.

Effects on Allocative Efficiency

The speculative real estate investments made by firms outside of the real estate sector suggest that resource misallocation occurred both within and across firms. Capital misallocation is one of these channels. In their theoretical framework, Chen and Wen (2017) show that by crowding out investment by productive firms in nonhousing capital, China’s housing boom has effectively misallocated productive capital and lowered aggregate productivity growth. Empirically, Chen, Liu, et al. (2019) show that the housing purchase restrictions policy in place between 2010 and 2014 significantly improved allocative efficiency by reducing either the loss in total factor productivity or mark-up dispersions.

Another channel by which the housing boom reduces allocative efficiency is through the misallocation of managerial talent. Shi (2018) finds a positive empirical correlation between the preboom total factor productivity of individual firms and their likelihood of starting real estate businesses. She shows that such behavior results in a misallocation of managerial talent because productive managers moving to the booming real estate sector from other industries did not have a comparative advantage in managing real estate businesses.

Conclusion and Going Forward

The house price boom that began in China during the early 2000s has triggered substantial research on the role that China’s housing policy has played in its residential real estate market and macroeconomy. During the past decade, an extensive empirical and theoretical literature exploring these issues has developed. This article has provided an overview of the evolving state of this research.

A promising agenda for future research is to explore how China’s policy choices that govern the supply of housing and the land used for construction purposes affect its housing market. So far, most investigation on the implications of China’s housing policy have focused on the demand for housing. China’s housing policy impacts the supply side of the housing market via both real estate developers and local governments’ incentives for making land available for housing. For example, in 2009, as part of the stimulus package enacted to combat the global economic crisis, China’s central government began to allow local governments to create a unique funding mechanism known as Local Government Financing Vehicles, through which future revenue from land sales can be pledged as collateral. One of the central questions regarding this policy is how it has impacted real estate, infrastructure, and manufacturing in different regions of the country. A further consideration is to what extent this policy has contributed to the soaring growth in local government debt that has taken place. Answering these questions is of first-order importance for designing an effective housing policy that strikes a balance between achieving the short-run stabilization and the long-run sustainability of GDP growth.

Another interesting line of research would further explore the impact that China’s housing policy has on the financial sector and on the development of systemic financial risks. As documented by Liu and Xiong (2020), in 2016 Q3, property-related loans totaled 55 trillion RMB, accounting for about 25% of China’s banking assets. Therefore, identifying the role that housing policy plays in exposing banks to risks related to the housing sector is a central policy question that relates to China’s financial stability. For example, how did a loosening of housing policy, say during the economic stimulus enacted in 2009 and 2010 or between 2014 Q4 and 2016 Q3, affect banks’ risk-taking behavior in lending to real estate developers, approving mortgages, and granting business loans collateralized by housing investments? To what extent does a looser housing policy expose China’s banking system to more systemic financial risk? How effective was a tightening of housing policy in discouraging risk taking by small- and medium-sized banks in the context of rising shadow banking activities? An in-depth investigation of these questions calls for the use of new microdata at the bank level or transaction level and for building models that capture richer heterogeneity at the regional, bank, or firm level, to give a fuller picture of macroeconomic implications of China’s housing policy.

Acknowledgments

The views expressed herein are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Atlanta or the Federal Reserve System.

Further Reading

  • Chen, K., & Zha, T. (2020). Macroeconomic effects of China’s financial policies. In M. Amstad, G. Sun, & W. Xiong (Eds.), Handbook of China’s financial system (pp. 151–182). Princeton, NJ: Princeton University Press.
  • Gan, L., Yin, Z., Jia, N., Xu, S., Ma, S., & Zheng, L. (2013). Data you need to know about China: Research Report of China Household Finance Survey. Berlin, Germany: Springer.
  • Song, Z., & Xiong, W. (2018). Risks in China’s financial system. Annual Review of Financial Economics, 10, 261–286.
  • Yuan, Z., Li, W., & Fan, X. (2018). The expansion of balance sheets and the growth of real economy (in Chinese). Beijing, China: CITIC Press Group.

References

Notes

  • 1. The four first-tier cities are the top four Chinese cities in terms of economic volumes, which together contribute to one eighth of China’s GDP and one sixth of China’s total fiscal revenue.