Has China’s Housing Security Policy Affected the Housing Market?—Analysis Based on Housing Market Data from 35 Monitored Cities

Abstract

This study investigates how China’s affordable housing policies have shaped the real estate market, using data from 35 major cities between 2010 and 2023. By analyzing housing prices, sales, and investment trends with advanced statistical methods, we found that increasing the supply of affordable housing significantly slows down rising home prices, especially in cities with high housing costs. During the COVID-19 pandemic, these policies also helped stabilize the market by boosting housing sales and reducing price volatility. Our research highlights regional differences: affordable housing works best in economically developed eastern cities to curb prices, while in less-developed central and western areas, it may temporarily increase prices due to land competition. We also show that affordable housing absorbs demand from low- and middle-income buyers, easing pressure on commercial housing markets over time. This study provides practical insights for policymakers to design targeted housing strategies, optimize land use, and enhance urban resilience during crises, like pandemics. By combining real-world data with robust analysis, we offer a clearer picture of how housing security policies can balance market stability and affordability in rapidly urbanizing economies.

1. Introduction

China’s urbanization process is approaching completion. However, since the government officially discontinued the in-kind allocation of housing in 1998 and implemented housing monetarization reforms to introduce market mechanisms, the housing market has experienced rapid expansion. Nevertheless, this has also led to a surge in housing prices and an increased housing burden on residents. Given the financial and residential characteristics of existing commercial housing, the uncontrollable rise in housing prices and the emergence of a real estate bubble have had significant implications for people’s livelihoods and have augmented the living costs of residents, especially those of the floating population. Although China has implemented policies to curb the rapid increase in housing prices, prices have still exhibited a tendency to rebound sharply. Evidently, relying solely on economic policies to stabilize housing prices proves to be a challenging task. It is essential to provide affordable housing to absorb a substantial amount of demand and relieve pressure on the commercial housing market.

China’s affordable housing system has witnessed substantial transformations since the late 20th century. From 1998 to 2002, policies primarily focused on facilitating the construction of affordable housing for low- and middle-income families, with the completion of affordable housing accounting for over 20% of the total housing volume. From 2003 to 2006, as policies shifted towards encouraging the purchase of commercial housing, the construction of affordable housing temporarily stalled, and the completion ratio of affordable housing declined to 8%. From 2007 to 2010, the security system began to diversify, extending its scope to migrant workers and incorporating new forms of affordable housing, such as public rental housing. From 2011 to 2019, public rental housing and shantytown renovation emerged as policy priorities, accompanied by a significant increase in the proportion of construction land. Particularly after the introduction of the “housing is for living in, not for speculation” policy in 2016, the government emphasized stabilizing housing prices and the housing market through long-term mechanisms, such as the standardization of the housing rental market and the enhancement of the affordable housing system. Commencing from 2020, the emphasis of affordable rental housing construction expanded to encompass young people and new citizens, with a plan to construct 8.7 million affordable rental housing units during the “14th Five-Year Plan” period, catering to approximately 26 million individuals. In the government work report on 5 March 2024, Chinese Premier Li Qiang underscored the strategy of addressing both the symptoms and root causes of real estate risks, with plans to augment the construction and supply of affordable housing and improve the commercial housing system to fulfill residents’ basic and diverse housing requirements. The report proposed stabilizing the market through government credit support and enhanced supervision, such as promoting the construction of protective teaching buildings and establishing corporate whitelists. Additionally, increasing the supply of affordable housing is regarded as a core strategy to tackle the oversupply of commercial housing.

To investigate the role of affordable housing construction within the housing market, this study selected 35 monitoring cities from the “Guiding Opinions” as the research subjects. Based on panel data of 35 cities in China spanning from 2010 to 2023, a fixed-effects model was employed to conduct an in-depth analysis of the impact of affordable housing policies on the real estate market. This paper adopts a fixed-effects model because it can effectively control the heterogeneous characteristics of cities that do not change over time, which is consistent with the idea of capturing the long-term impact of policies through cumulative supply variables in existing studies [1], and is more in line with the regionally differentiated characteristics of China’s housing policies. The empirical analysis demonstrates that the construction of affordable housing significantly curbs the escalation of housing prices and exerts a positive effect on the volume of the housing market. The GMM estimation and instrumental variable estimation further verify the robustness of our results. By analyzing historical data on housing prices and transaction volumes, this paper uncovers the regulatory impacts of affordable housing policies under different economic and market conditions. Moreover, this paper also discloses the regional heterogeneity of the effects of affordable housing policies, indicating that the construction effects of affordable housing are more prominent in areas under greater housing price pressure. And we further find that the affordable housing has a positive effect on the residential housing transaction area but has no effect on the housing investment. The newly added supply of affordable housing plays an active role in curbing the increase in housing prices and enhancing market transaction volumes during the pandemic.

The contributions of this study are primarily manifested in the following aspects: Firstly, this paper collects and collates the land transfer data earmarked for affordable housing in cities and utilizes panel data to conduct a comprehensive assessment of the short-term impact of affordable housing construction on the real estate market, thereby enriching the research body concerning the impact of affordable housing. Secondly, the endogeneity between the supply of affordable housing and real estate prices has long been a thorny issue. This study endeavors to surmount this problem through multiple methods, such as the Generalized Method of Moments (GMM) and the instrumental variable method, and arrives at relatively robust conclusions. Thirdly, the conclusions of this article further expound on the role of affordable housing supply in market stabilization in the face of external shocks, validating whether affordable housing functions as a stabilizer.

The structure of our paper is as follows: Section 1 serves as the introduction, in which the research background and objectives are expounded. Section 2 is dedicated to the literature review, where the cutting-edge research and theoretical underpinnings in related fields are deliberated. Section 3 focuses on the research design. Section 4 pertains to the empirical analysis. Section 5 is centered around the heterogeneity analysis. Section 6 involves further analysis. The final section presents the conclusions.

2. Literature Review

In the domain of housing security policy, scholars have formulated a relatively comprehensive theoretical framework, dissecting the impact mechanisms and pathways of such policies on the housing market from both the supply and demand perspectives. From the supply aspect, Murray [2] validated the impact route of housing construction programs on housing supply in light of crowding-out effects. Additionally, it was discovered that the stability of the U.S. housing supply from the 1960s to the 1980s was attributable to the crowding-out of private housing investment by housing security policies. Lee [3], leveraging panel data of the South Korean real estate industry, established a VAR model and determined that as the homeownership rate ascended, the variation in housing stock diminished and the crowding-out effect became more prominent. These investigations collectively suggest that the construction of affordable housing vies with commercial housing for land, curtailing the supply of commercial housing and precipitating an elevation in housing prices. Zhang and Lian [4] employed a multiple regression model to scrutinize the factors influencing commercial housing prices, contending that if the government’s construction of affordable housing fails to rigorously segregate the affordable housing market from the commercial housing market, the policy outcomes will be arduous to realize as anticipated. Ren and Zhang [5] constructed an error correction model of panel data, and the empirical findings demonstrated that land supply exerts a long-term and stable influence on housing supply and housing prices. Constraining land supply would result in a reduction in housing supply and trigger an increase in housing prices.

From the demand perspective, a multitude of studies corroborate the suppressive effect of affordable housing on commercial housing prices. Wang and Zhao [6] formulated a real estate supply and demand model, and the empirical results indicated an inverse correlation between the construction volume of affordable housing and market housing prices, signifying that an increase in affordable housing can depress commercial housing prices. Gao [7] further dissected the internal mechanism by which affordable housing construction impacts commercial housing prices and posited that affordable housing diverts the demand for commercial housing. Wang and Gao [8] constructed an SVAR model, and upon empirical testing, concluded that the construction of affordable housing has a significant dampening effect on the escalation of commercial housing prices. Ma and Tian [9] discovered that the construction of affordable housing assimilates a portion of the latent demand in the commercial housing market, thereby exerting a role in reducing the overall level of housing prices. Jin [10] empirically analyzed the influence of affordable housing on the commercial housing market and ascertained that affordable housing, to a certain extent, curbs the rise of housing prices while fulfilling the housing requirements of low-income families. Existing research concerning the impact of affordable housing on commercial housing predominantly employs vector autoregression (VAR) models and other time series methodologies to dissect the influence of affordable housing on the commercial housing market. These studies, by establishing dynamic relationships among housing market variables, deeply probe into the long-term impact of affordable housing pricing on commercial housing prices. The merit of utilizing the VAR model resides in its capacity to disclose the intricate interrelationships between variables, rendering it particularly well-suited for capturing the dynamic traits of time series.

With the continuous progression of research, the analysis of the impact of affordable housing construction on the housing market necessitates the consideration of not only the effects on the supply and demand fronts but also certain external shocks and regional disparities to ensure the stability of the housing market and address the real estate crisis. Qian et al. [11] demonstrated in their research within the Chinese context that the epidemic led to a substantial decline in housing prices in severely affected regions. As time elapsed and effective epidemic prevention and control measures were implemented, housing prices gradually rebounded and stabilized. The study accentuated the immediate adverse impact of the epidemic on housing prices in high-infection areas, yet the long-term influence was relatively feeble. Akbari et al. [12] investigated the correlation between housing and mental health during the epidemic and noted that affordable housing played a significant cushioning role during periods of economic instability. Evidently, in the face of external shocks, such a policy can, to a certain extent, uphold market stability.

In order to quantify the impact of secure housing on residents, this is usually done by calculating various types of housing affordability indicators. Brooks’ study [13] found that different measurement methods (e.g., residual income method vs. expenditure ratio method) significantly affect the results of housing affordability assessment, especially on racial disparity assessment. Based on panel data analysis, Arnerić et al. [14] revealed that rising housing costs and inadequate supply are the main causes of the housing affordability crisis in EU countries and called for more comprehensive affordability indicators. A study [15] applied Engel’s law to analyze the housing affordability of the urban poor and found that housing policies in India still do not meet the actual income structure. Affordable housing reflects the importance of the ability to pay for housing costs and is essentially a market-based concept. The fundamental dilemma faced by the urban poor in India is that some households cannot afford adequate housing at any stage. Low-income households need to spend a larger share of their income on basic needs and do not have the disposable income to pay for affordable housing. Additionally, the affordable housing provided by the Government of India through its prestigious flagship program, the Prime Minister’s Housing Yojana (PMAY), falls far short of market realities.

The issue of supply of affordable housing has become a major concern for policy makers and urban planners, and it is critical to identify suitable locations for affordable housing in order to minimize competition for land. Suitable locations for affordable housing will increase housing satisfaction among low-income residents by increasing their social and economic well-being, providing better access to opportunities and services, and reducing the concentration of poverty. Afshan et al. [16] identify factors affecting the spatial location of affordable housing through an exploratory study of relevant literature covering different geographic contexts, socioeconomic conditions, and levels of technological and economic advances of affordable housing spaces. A total of 61 parameters involving six categories of neighborhood characteristics, urban characteristics, social factors, economic parameters, demographic factors, and housing quality were identified and, the choice of location for government development of affordable housing is spatially heterogeneous in cities. In developing countries, parameters, such as socioeconomic criteria, accessibility to various facilities, and employment opportunities, are of greater significance. In some developed countries, however, factors, such as security, environmental considerations, and type of housing, are emphasized over accessibility. Governments in different regions need to take into account local circumstances in their decision-making. In terms of regional differences, Ren and Zhang [5] discovered through panel data analysis of 35 large and medium-sized cities in China that land supply significantly impacts the newly-built commercial housing market, with policy effects varying across regions. In economically developed regions, the supply of affordable housing was inversely related to housing prices, while different outcomes might emerge in other areas. This implies that policies need to account for the distinctions among cities during formulation and be optimized in accordance with the actual circumstances to enhance policy effectiveness. Aggregating global examples (e.g., Singapore, Vienna) for case studies, it is found that affordable housing can promote inclusive growth and improve employment and community participation. The article also analyzes the economic impacts of affordable housing, such as labor stability, economic mobility, and challenges, such as finance, policy barriers, and community resistance. The centrality of affordable housing in the global socioeconomic landscape is emphasized [17].

Overall, in the extant research concerning the impact of affordable housing on commercial housing, the prices and sales of affordable housing are typically employed as explanatory variables, and time series analyses are carried out using models, such as vector autoregression. Nevertheless, this approach frequently presupposes the homogeneity between affordable housing and commercial housing, thereby overlooking the heterogeneity that exists between the two. Existing studies mostly use time series models, but the regional heterogeneity and policy dynamics of China’s housing market require a more accurate panel data approach. This paper breaks through the traditional model’s assumption of market homogeneity by combining cumulative supply variables with fixed effects [1].

To dissect the short-term impact of affordable housing supply on the real estate market, this paper opts to employ a fixed-effects model to scrutinize the influence of affordable housing policies on the commercial housing market. The fixed-effects model is capable of effectively nullifying the impact of inherent differences in city characteristics that remain invariant over time by controlling for city-specific attributes, thereby guaranteeing the precision and robustness of the research outcomes. In the robustness test, this paper also resorted to the VAR model for estimation, which did not modify the fundamental conclusions of the paper. Furthermore, this paper designates the cumulative supply of affordable housing in cities as the core variable, analyzing the impact of affordable housing on the commercial housing market from the vantage point of policy influence rather than market homogeneity, with the aim of furnishing more accurate and representative empirical evidence for future policy formulation.

3. Research Design

3.1. Data Sources

This study opts for 35 major cities under the monitoring of the “Guidance Opinions” as the sample cities. The real estate-related data, such as the average transaction price, transaction area, transaction amount, and residential investment within real estate investment, are all sourced from the CRIC database. CRIC database grabs data in real time through the real estate transaction filing systems of 200+ cities nationwide, covering more than 90% of commodity housing transaction records. Its data quality has been cross-validated by the National Bureau of Statistics (error rate < 1.5%), but there is a limitation of insufficient coverage in some third and fourth-tier cities. In the case of Shijiazhuang, one of the 35 monitored cities with missing data in the CRIC database, the relevant real estate data are retrieved from the CREI database. Owing to the unavailability of detailed supply data of affordable housing in each city, this paper undertakes the collection and organization of the land transfer data earmarked for affordable housing purposes from the China Land Market Network via manual inquiries and Python 3.9.13 code programming [18,19]. Based on the time lag between the public announcement and the actual project completion [1], the construction area is lagged on an annual basis, and the total amount of public announcements three years prior is employed as a proxy variable for the affordable housing supply of the city in the corresponding year. The macroeconomic indicator data of each city are derived from the “China City Statistical Yearbook” and the official websites of the respective cities. The economic policy uncertainty index data are sourced from the Economic Policy Uncertainty website, which releases monthly data for different countries. This study utilizes the monthly data of China, which are obtained through the text analysis of the “South China Morning Post” by Baker et al. [20], and calculates the arithmetic mean as the annual uncertainty index. The Chinese “DMSP-OLS” nighttime light remote sensing dataset is collated and procured through the content published on the Harvard Dataverse platform.

3.2. Variable Selection

The dependent variable in this study is the annual average transaction price of residential properties within cities. In instances where no property transactions took place in specific cities during particular years, the transaction price from the preceding year is adopted as a substitute. From the vantage point of the real estate market and considering the mechanisms governing housing prices, housing prices encapsulate both the supply and demand forces within the housing market and mirror the influence of policy factors. Consequently, they serve as a suitable metric to represent the overall state of the housing market and the level of real estate risk.

The explanatory variable is the cumulative supply of affordable housing in cities. Given the difficulty in precisely ascertaining the exact quantity of all varieties of affordable housing in each city, this paper employs the land transfer area designated for affordable housing purposes, as disclosed on the China Land Market Network, as a proxy indicator to gauge the supply of affordable housing. Specifically, based on the actual commencement and completion times of the affordable housing land transfer data disclosed, the actual construction period of the project is computed to be approximately 2.8 years. Hence, the new supply of affordable housing in city t in year t is estimated from the land area disclosed three years prior (t-3).

Furthermore, considering that some cities did not disclose specific affordable housing supply area prior to 2007, this paper incorporates city fixed effects into the model to control for the potential bias that this might introduce to the results. City fixed effects can capture the immutable characteristics of each city over time, including the undisclosed affordable housing supply before 2007. Through this approach, this paper effectively mitigates the interference of the cumulative effect of early affordable housing supply on subsequent analyses, thereby ensuring the precision and robustness of the estimation.

The control variables encompass real estate-related transaction volumes and transaction values, which are utilized to measure the activity and industry scale of the real estate sector in cities on an annual basis. Specifically, this includes the total amount of actual residential real estate transactions in the city, the total area and number of units sold, and the amount of investment in the residential construction portion of the city’s real estate investment in the current year. In real estate market research, the joint use of transaction value and transaction area avoids the ‘price-volume’ paradox (e.g., transaction value rises but area falls when high-priced housing dominates) [21]. Drawing on existing research concerning the impact of affordable housing on the housing market, urban residents’ disposable income and regional gross domestic product are incorporated as control variables. Due to the time span of the dataset used and in order to reflect the impact of relevant economic policy changes during this period, the China Economic Uncertainty Index is used as a policy control variable, and this variable is used to measure changes in the policy and economic environment. It reflects fluctuations in urban economic activity by quantifying policy uncertainty, which in turn may have an impact on supply, demand, and prices in the real estate market.

Additionally, since the annual average population density data for some cities are discontinuous, this paper utilizes nighttime light data as a substitute indicator. Specifically, the average brightness of lights (average value) within the city area is employed as a proxy variable to measure urban economic activity and population distribution. Nighttime light data are widely employed to evaluate the economic level and population distribution of cities, and its intensity can effectively reflect the overall development status of cities, thus serving as a rational substitute variable for urban population density.

Following Wooldridge [22] and Angrist and Pischke [23], this study retains the original units of the different control variables to maintain economic interpretability. Standardization was intentionally avoided as these variables reflect different but complementary dimensions of the real estate market. The summary of variables is presented in

Table 1. The summary of variables.

Variable Name	Unit	Mean	Standard Deviation	Minimum	Maximum	Observations
Average transaction price of residential buildings	CNY/square meter	15,432.64	11,424.74	4425	67,494	486
Supply of affordable housing	Square meter	181,580.6	324,046.2	0	2,897,709	490
Cumulative supply of affordable housing	Square meter	1,381,456	1,436,790	0	7,728,697	490
Transaction amount	Billions CNY	1234.22	1076.8	0	6881.73	486
Transaction area of commercial housing	Square meter	853.48	551.58	0	2937.12	486
Number of commercial housing transactions	Set	78,380.14	52,320.7	0	298,547	486
Investment in real estate development	Billions CNY	198,844.4	1,139,913	116.16	8,676,700	461
Per capita disposable income of urban residents	CNY	42,619.08	15,709.24	14,402	89,477	473
GDP	Billions CNY	10,363.85	7833.7	1121.82	47,218.66	487
China Economic Uncertainty Index	Dimensionless index	394.8172	242.8282	98.88818	791.8738	490
Night light brightness	Dimensionless index	21.5	15.01	1.96	62.68	490

.

3.3. Model Design

The fixed-effects model is theoretically grounded in the dual mechanisms of affordable housing policy, short-term land competition captured by THE Affordable Housing Supply Area, and long-term demand diversion reflected in cumulative supply. This aligns with China’s regionally differentiated policy implementation, where local governments adjust supply based on market conditions. When studying the cumulative supply of affordable housing and the price of commercial housing, the following regression model is adopted:

Y_it = β₀ + β₁X_it + β₂∑Controls_it + α_i + γ_t + ϵ_it

(1)

where Y_it is the dependent variable of individual i at time t. X_it is the explanatory variable of individual i at time t. Controls are the control variables mentioned above. α_i is the individual-fixed effect of individual i, which is used to capture the differences between different units. γ_t is the time-fixed effect of time t, which is used to capture time-related differences, such as time trends or seasonality. β_n is the dependent variable parameter in the model, and ϵ_it is the error term.

To systematically evaluate the impact of affordable housing policies on the housing market, this study organizes the empirical analysis into four interconnected components. First, baseline regressions establish the core relationship between cumulative affordable housing supply and housing prices using a fixed-effects model. Second, robustness checks address potential biases through sample trimming, variable substitution (e.g., Affordable Housing Supply Area), and alternative model specifications (e.g., PVAR). Third, endogeneity mitigation employs system GMM and Bartik-type instrumental variables to ensure causal inference. Finally, heterogeneity analysis and pandemic interaction tests (Section 6.3) dissect regional disparities and external shock effects. This framework aligns with the theoretical mechanisms of “demand diversion” [7] and “land competition” [3], while adhering to methodological standards for panel data [20,23].

4. Empirical Results

4.1. Baseline Results

Table 2. The effects of the affordable housing on the housing prices.

	(1)	(2)	(3)	(4)	(5)
	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price
Cumulative Affordable Housing Supply	−0.0003	−0.0019 ***	−0.0013 ***	−0.0013 ***	−0.0014 ***
	(0.0003)	(0.0002)	(0.0003)	(0.0003)	(0.0003)
Macroeconomic Control Variables		√	√	√	√
Real Estate Industry Control Variables			√	√	√
Policy Variable				√	√
Population Variable					√
City Fixed Effects	√	√	√	√	√
Time Fixed Effects	√	√	√	√	√
N	486	472	452	452	452
Adj. R-sq	0.554	0.796	0.828	0.828	0.829

Note: *** indicates the 1% significance level. The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

presents the regression outcomes of the fixed-effects model employed in this study. To commence, the cumulative supply of affordable housing was directly regressed against the average price of residential properties, and the results are exhibited in column (1) of Table 2. It is worth noting that the coefficient demonstrates significance at the 1% level in all columns except the first one, with the coefficient remaining consistently negative throughout. Subsequently, when incorporating other control variables, the resultant findings are displayed in columns (2) through (5) of Table 2. In general, the cumulative supply of affordable housing exhibits a significantly negative coefficient in all columns. The coefficients imply that for every 1% increment in the cumulative supply of affordable housing, the average housing price within the area experiences a decline of approximately 0.2%. In practical terms, this translates to an average decrease in housing price amounting to around CNY 30. These results suggest that the supply of affordable housing is capable of stabilizing housing prices to a certain extent and curbing the upward trend of housing prices.

4.2. Robustness Test

4.2.1. Sample Issues

To address the sample selection concerns, a series of robustness checks were carried out. Firstly, cities with substantial amounts of missing data in the sample were omitted, and the regression was re-performed. The corresponding results are presented in column (1) of

Table 3. The effects of the affordable housing on the housing prices—robustness.

	(1)	(2)	(3)	(4)
	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price
Cumulative Affordable Housing Supply	−0.0014 ***	−0.0013 ***	−0.0019 ***	−0.0016 ***
	(0.0003)	(0.0003)	(0.0003)	(0.0003)
Macroeconomic Control Variables	√	√	√	√
Real Estate Industry Control Variables	√	√	√	√
Policy Variable	√	√	√	√
Population Variable	√	√	√	√
City Fixed Effects	√	√	√	√
Time Fixed Effects	√	√	√	√
N	442	400	452	443
Adj. R-sq	0.830	0.804	0.832	0.823

Note: *** indicates the 1% significance level. The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

. Subsequently, the data related to the four municipalities directly under the Central Government within the sample were excluded, and a regression analysis was conducted on the remaining cities. The obtained findings are shown in column (2) of Table 3. Moreover, to account for the influence of outliers, a 1% trimming and capping procedure was applied to the original dataset. The outcomes are depicted in columns (3) and (4) of Table 3. The regression results indicate that the coefficient is significantly negative at the 1% significance level, thereby validating the robustness of the main regression results.

4.2.2. Replacing the Explanatory Variable

To capture short-term effects, we replace the core explanatory variable with the Affordable Housing Supply Area (i.e., land transfer area disclosed in the current year, lagged by 0 years), as shown in

Table 4. The effects of the affordable housing on the housing prices—alternative variable.

	(1)	(2)	(3)	(4)	(5)
	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price
Affordable Housing Supply Area	0.0014	0.0012	0.0013 *	0.0013 *	0.0013 *
	(0.0008)	(0.0006)	(0.0005)	(0.0005)	(0.0005)
Macroeconomic Control Variables		√	√	√	√
Real Estate Industry Control Variables			√	√	√
Policy Variable				√	√
Population Variable					√
City Fixed Effects	√	√	√	√	√
Time Fixed Effects	√	√	√	√	√
N	486	472	452	452	452
Adj. R-sq	0.554	0.796	0.828	0.828	0.829

Note: The unit of ‘Annual New Supply of Affordable Housing’ is square meters, calculated based on the land transfer area disclosed in the current year (t) without lagging. This contrasts with the ‘Cumulative Supply’ variable in Table 2, which uses land transfers from t-3 to reflect project completion delays. * indicates the significance at 10%. The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

. Table 4 reports the regression results. The coefficients of the new supply variable are positive and reach 10% significance in columns (4) and (5), contrasting sharply with the negative coefficients of the cumulative supply variable in Table 2. This divergence suggests two distinct mechanisms:

A type of land competition that dominates in the short term. The new supply of affordable housing intensifies land use competition between affordable and commercial housing projects. Developers may reduce commercial housing investment in response to land allocation shifts [3], temporarily driving up prices. This aligns with the observed 0.13% price increase per 1% new supply in column 5 of Table 4, reflecting the dominance of supply-side crowding-out in the immediate policy response.

The other is a demand diversion that dominates in the long term. The cumulative supply variable (lagged by 3 years) captures the delayed but sustained demand [7], where affordable housing absorbs low- and middle-income buyers, reducing commercial housing demand over time.

This substitution highlights the dynamic duality of affordable housing policies: in the short term, the newly added supply exacerbates land competition between affordable and commercial housing projects, resulting in temporary price rebounds due to constrained land allocation; in the long term, the cumulative supply effectively diverts demand from the commercial market, stabilizing housing prices through sustained absorption of low- and middle-income buyers. The contrast between these mechanisms underscores the policy’s time-varying impacts and the necessity of distinguishing temporal horizons in housing market analysis.

4.2.3. Modifying the Model

As mentioned above, Our study also utilizes a Panel Vector Autoregression (PVAR) model for the analytical purposes. The impulse response analysis employs a Panel Vector Autoregression (PVAR) model with Cholesky decomposition to orthogonalize shocks. The 95% confidence interval is derived through Monte Carlo simulations (200 replications), accounting for heteroskedasticity and serial correlation in the panel data. Figure 1 presents the impulse response results of the cumulative supply of affordable housing on housing prices. It can be noted that a positive perturbation to the cumulative supply of affordable housing instigates a continuous and pronounced decline in housing prices, with a particularly notable impact in the initial phase, which subsequently tapers off gradually. This implies that the negative impact of the cumulative supply of affordable housing on housing prices is both long term and significant.

4.3. Endogeneity

4.3.1. System GMM Estimation

To address the endogeneity concern, a dynamic panel regression approach, namely the system Generalized Method of Moments (GMM), Elimination of endogeneity by coupling difference equations with level equations [24]. The regression results are presented in

Table 5. The effects of the affordable housing on the housing prices—system GMM estimation.

	(1)	(2)	(3)	(4)	(5)
	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price
Cumulative Affordable Housing Supply	−0.0002	−0.0011 *	−0.0007 *	−0.0007 *	−0.0004
	(0.0003)	(0.0002)	(0.0003)	(0.0003)	(0.0003)
Macroeconomic Control Variables		√	√	√	√
Real Estate Industry Control Variables			√	√	√
Policy Variable				√	√
Population Variable					√
City Fixed Effects	√	√	√	√	√
Time Fixed Effects	√	√	√	√	√
N	451	437	417	417	417

Note: * indicates the significance at 10%. The t value is in parentheses, and unless otherwise stated, the significance is expressed in the same way in subsequent tables. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

. Overall, the coefficient remains negative and exhibits consistency under diverse conditions, thereby validating the robustness and dependability of the main regression results.

4.3.2. Instrumental Variables

Although this study has employed a series of methods to mitigate endogeneity issues, there may still be unobservable omitted variables that simultaneously affect commercial housing prices and the supply of affordable housing in cities. Therefore, this paper uses the instrumental variables to further address potential endogeneity problems. Drawing on the idea of Bartik’s instrumental variable [25], a shift–share instrument is created, and the specific construction is as follows. The rationale for using Bartik instrumental variables in this study is based on two points: first, the long-term nature of the 2010 secure housing program, with the base period shares reflecting policy inertia; and second, the fact that the national growth rate serves as an exogenous shock to exclude local demand disturbances

IV_i = S_i0 × g_it

(2)

Among them, IV₁ represents the instrumental variable processed from the cumulative supply, and IV₂ indicates the instrumental variable processed from the Affordable Housing Supply Area. S_i0 denotes the initial share of the corresponding treated variable in the base period (2010), and g _it is the growth rate of the national total amount of this treated variable in year t. The regression results using these two instrumental variables and their combination are shown in columns (1) to (6) of

Table 6. The effects of the affordable housing on the housing prices—instrumental variable regression.

	(1)	(2)	(3)	(4)	(5)	(6)
	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price	Average Housing Price
IV 1	−212,109.5 *				−226,167.8
	(73,771.95)				(232,299)
IV 2			−437,184.8 *		37,737.19
			(153,072.1)		(500,537.8)
Cumulative Affordable Housing Supply		−0.0028 ***		−0.0024 *		−0.0013 ***
		(0.0007)		(0.0009)		(0.0003)
Macroeconomic Control Variables	√	√	√	√	√	√
Real Estate Industry Control Variables	√	√	√	√	√	√
Policy Variable	√	√	√	√	√	√
Population Variable	√	√	√	√	√	√
City Fixed Effects	√	√	√	√	√	√
N	356	356	388	388	403	403
CD Wald F	22.051		16.381		11.01
KP Wald Rk F	8.267		8.157		5.37
Stock-Yogo Critical Value (25%)	5.53		7.25		7.25

Note: * and *** indicate significance at 10% and 1% significance levels respectively. The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

. The regression results show that the coefficient remains significantly negative, further validating the robustness of the main regression results.

5. Heterogeneity Analysis

This paper categorizes the 35 sample cities into three groups based on their geographical location and level of economic development for heterogeneous analysis: Eastern (including Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin, Hangzhou, Nanjing, Qingdao, Jinan, Dalian, Xiamen, Fuzhou, Ningbo, and Suzhou, totaling 18 cities), Central (including Zhengzhou, Wuhan, Changsha, Harbin, Hefei, Changchun, Taiyuan, and Nanchang, totaling 9 cities), and Western (including Chongqing, Chengdu, Xi’an, Kunming, Guiyang, Nanning, Urumqi, and Shenyang, totaling 8 cities). This grouping method helps to delve into the differential impact of affordable housing policies across different regions.

Table 7. The effects of the affordable housing on the housing prices—regional heterogeneity.

	(1)	(2)	(3)
	Average Housing Price	Average Housing Price	Average Housing Price
Cumulative Affordable Housing Supply	−0.0014 **	0.0025 ***	0.0002
	(0.0004)	(0.0003)	(0.0002)
Macroeconomic Control Variables	√	√	√
Real Estate Industry Control Variables	√	√	√
Policy Variable	√	√	√
Population Variable	√	√	√
City Fixed Effects	√	√	√
Time Fixed Effects	√	√	√
N	236	114	101
Adj. R-sq	0.866	0.913	0.949

Note: ** and *** indicate significance at 5% and 1% significance levels respectively. The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

presents the results of the regression for the three groups.

The regression outcomes demonstrate that in the eastern region, the coefficient is significantly negative at the 5% significance level, signifying a substantial negative correlation between the supply of affordable housing and the prices of commercial housing in the eastern area. This might be attributed to the highly sophisticated real estate market in the eastern region, where affordable housing policies exert a significant restraining influence on the housing demand side. Given the relatively tight housing supply and demand situation in the eastern region, the augmentation of affordable housing supply effectively mitigates the upward pressure on housing prices.

Conversely, the regression results for the central region reveal that the coefficient is significantly positive at the 1% significance level, and although the coefficient for the western region is also positive, it is not statistically significant. This implies that in the central and western regions, the supply of affordable housing may have a stimulative effect on the prices of commercial housing. Such a disparity may arise from the intricacy of the supply and demand relationship in the affordable housing market of the central and western regions. The pressure on housing prices in the central and western regions is relatively lower compared to that in the eastern region, and the housing demand is also relatively smaller. The impact of affordable housing construction on aspects ,such as land competition, might be more pronounced than its diversion effect on the demand for commercial housing, resulting in an overall increase in the prices of commercial housing. Moreover, the development of the real estate market in the western region is restricted by infrastructure and external investment factors, rendering the short-term impact of affordable housing construction on commercial housing prices insignificant.

The regional heterogeneity analysis in Table 7 reveals a stark divergence in policy effects. In eastern China, where housing demand is most intense, the cumulative supply of affordable housing significantly suppresses commercial housing prices. In contrast, central and western regions exhibit positive coefficients, suggesting that land use competition dominates in these areas with lower market pressures. This aligns with the theoretical framework that policy impacts vary with local demand elasticity and land allocation dynamics.

To further explore the impact of affordable housing construction on housing prices in regions with different economic levels, this study divides the 35 cities into three groups based on average housing prices: high housing price group (average housing price higher than the third quartile), medium housing price group (average housing price between the first and third quartiles), and low housing price group (average housing price lower than the first quartile). This grouping explicitly captures the market pressure gradient, where high-price cities represent areas with acute demand-supply imbalances, while low-price cities reflect relatively stable markets. The regression results in

Table 8. The effects of the affordable housing on the housing prices—house price heterogeneity.

	(1)	(2)	(3)
	Average Housing Price	Average Housing Price	Average Housing Price
Cumulative Affordable Housing Supply	−0.0004	−0.0005 *	0.0002
	(0.0007)	(0.0002)	(0.0003)
Macroeconomic Control Variables	√	√	√
Real Estate Industry Control Variables	√	√	√
Policy Variable	√	√	√
Population Variable	√	√	√
City Fixed Effects	√	√	√
Time Fixed Effects	√	√	√
N	236	114	101
Adj. R-sq	0.866	0.913	0.949

Note: * indicates the 10% significance. The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

demonstrate that the price-suppressing effect of affordable housing is most pronounced in medium-price cities, whereas low-price cities exhibit a counterintuitive positive correlation, likely driven by land use competition [3] in underdeveloped markets. The regression results are shown in Table 8, corresponding to columns (1) to (3).

The regression results manifest that within the high housing price group; the augmentation of affordable housing construction does not exert a significant influence on housing prices (with a coefficient of −0.0004 and a standard error of 0.0007). This suggests that in cities characterized by relatively higher housing prices, the direct impact of affordable housing construction on restraining housing price escalation is not pronounced. For cities falling within the medium housing price group, the cumulative supply of affordable housing has a significant effect on housing prices at the 10% significance level (with a coefficient of −0.0005 and a standard error of 0.0002), signifying that affordable housing construction effectively curbs the excessive growth of housing prices in such cities. Nevertheless, in the low housing price group, there exists a positive correlation between affordable housing construction and housing price growth (with a coefficient of 0.0002 and a standard error of 0.0003), implying that in cities with lower housing prices, increasing the supply of affordable housing might indirectly drive up housing prices via factors, such as intensified competition for land use. This is analogous to the regression results obtained for the western region, indicating that in areas where the housing market is less developed, affordable housing policies may need to place greater emphasis on local market demand and development circumstances.

6. Further Discussion

6.1. The Real Estate Sales Area

During the COVID-19 pandemic, a paradoxical phenomenon emerged in China’s housing market. Despite housing prices remaining stable or even experiencing an upward trend due to proactive government regulatory measures, the volume of housing transactions witnessed a significant decline. This indicates that the transaction volume serves as a direct indicator of the market’s responsiveness to shocks. In order to conduct a more comprehensive analysis of the impact of affordable housing construction on the housing market, this section substitutes the annual residential transaction area for the dependent variable to assess the policy’s influence on sales volume. Given the market volatility following the pandemic-induced shock and the reforms in real estate-related policies, this part segments the sample into three time periods: the entire period, the pre-2020 period, and the post-2020 period. The corresponding results are presented in columns (1)–(2), (3)–(4), and (5)–(6) of

Table 9. The effects of affordable housing on the real estate sales area.

	(1)	(2)	(3)	(4)	(5)	(6)
	Residential Housing Transaction Area	Residential Housing Transaction Area	Residential Housing Transaction Area	Residential Housing Transaction Area	Residential Housing Transaction Area	Residential Housing Transaction Area
Cumulative Affordable Housing Supply	0.3320 ***		0.2380 **		0.0060
	(0.0563)		(0.0896)		(0.0715)
Affordable Housing Supply Area		0.2870 **		−0.3200		0.0865
		(0.1060)		(0.2110)		(0.0798)
Macroeconomic Control Variables	√	√	√	√	√	√
Real Estate Industry Control Variables	√	√	√	√	√	√
Policy Variable	√	√	√	√	√	√
Population Variable	√	√	√	√	√	√
City Fixed Effects	√	√	√	√	√	√
Time Fixed Effects	√	√	√	√	√	√
N	417	417	309	309	73	73
Adj. R-sq	0.971	0.969	0.975	0.974	0.992	0.992

Note: ** and *** indicate significance at 5% and 1% significance levels respectively. The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

, respectively. The regression results reveal that, with the exception of the Affordable Housing Supply Area volume prior to 2020, which led to a reduction in sales volume, the policy contributed to an increase in residential sales volume in all other instances. Notably, the coefficient the housing investment was significantly positive at the 5% level during the entire period and before 2020. Owing to the adoption of a high-turnover model by real estate companies, China’s commodity housing inventory has expanded rapidly, and a substantial amount of unsold inventory and unfinished projects emerged subsequent to the real estate crisis. Affordable housing policies have enhanced the confidence of residents, particularly young individuals, in home purchases, thereby promoting residential sales, alleviating real estate risks, and facilitating the turnover and return of funds, such as debt.

6.2. The Real Estate Investment

In addition to affecting the sales of residential properties, the stability of the housing market may further attract investors. To verify this impact, the dependent variable was replaced with the residential portion of real estate investment, with all other conditions being the same as when the section was replaced with residential sales volume, and the regression results are shown in

Table 10. The effects of affordable housing on real estate investment.

	(1)	(2)	(3)	(4)	(5)	(6)
	Real Estate Investment in Housing	Real Estate Investment in Housing	Real Estate Investment in Housing	Real Estate Investment in Housing	Real Estate Investment in Housing	Real Estate Investment in Housing
Cumulative Affordable Housing Supply	−0.0085		0.0054		−0.0526
	(0.0203)		(0.0219)		(0.1040)
Affordable Housing Supply Area		−0.0296		0.0102		−0.0174
		(0.0355)		(0.0507)		(0.1220)
Macroeconomic Control Variables	√	√	√	√	√	√
Real Estate Industry Control Variables	√	√	√	√	√	√
Policy Variable	√	√	√	√	√	√
Population Variable	√	√	√	√	√	√
City Fixed Effects	√	√	√	√	√	√
Time Fixed Effects	√	√	√	√	√	√
N	417	417	309	309	73	73
Adj. R-sq	0.971	0.969	0.975	0.974	0.992	0.992

Note: The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

.

Based on the regression results, it is evident that both the newly added supply of affordable housing and the cumulative supply exhibited negative coefficients throughout the entire time frame and after 2020, in contrast to the positive coefficients observed prior to 2020. This alteration may be attributable not solely to the pandemic and modifications in real estate policies but also to the innovative initiatives of affordable housing policies in recent years. With the aim of diversifying the origins of affordable housing, the government has introduced novel models, such as the dual-direction allocation. Under this model, developers are mandated to convert approximately 5% to 10% of the properties within a project into affordable housing without remuneration. Such a policy may have already exerted an impact on developers’ profit projections, consequently dampening their investment enthusiasm. The two-track allocation policy suppresses developer profits through land cost pass-through, leading to investment contraction (see Table 10 for the post epidemic period coefficient of −0.0526), a result that complements Gao’s [7] theory of demand diversion. The shift in policy impacts signifies that the readjustment of affordable housing policies has not only perturbed the supply structure of the housing market but has also exerted a profound influence on investor conduct and market trends.

6.3. The Effects of Epidemic

The research period encompasses the COVID-19 pandemic spanning from 2020 to 2022. The eruption of the pandemic has introduced substantial shocks and a high degree of uncertainty into the housing market. Since the severity of the epidemic in the sample cities is highly correlated with the intensity of the policy response (e.g., purchase restrictions were implemented earlier in the eastern cities), the use of dummy variables avoids multiple covariate interference. Consequently, in order to conduct a comprehensive assessment of the efficacies of affordable housing policies, this section incorporates a pandemic dummy variable and formulates its interaction term with affordable housing policies, thereby further probing into the influence of the pandemic on housing prices and transaction volumes. Specifically, the appended dummy variable designates the years 2020–2022 as 1 and other years as 0, with the intention of appraising the performance of affordable housing policies in stabilizing housing prices and facilitating residential transactions during the pandemic, as well as the direct impact of the pandemic on market fluctuations.

The regression outcomes in

Table 11. The effects of the affordable housing on the housing prices—COVID-19 epidemic shock.

	(1)	(2)	(3)	(4)
	Average Housing Price	Average Housing Price	Residential Housing Transaction Area	Residential Housing Transaction Area
Cumulative Affordable Housing Supply	−0.0009 **		0.2990 ***
	(0.0003)		(0.0676)
Affordable Housing Supply Area		0.0026 *		−0.5230 **
		(0.0011)		(0.1980)
COVID-19 Pandemic	−69.35	1014.4	−21.66	−76.42 *
	(2177.4)	(2132.2)	(37.44)	(36.75)
COVID-19 Pandemic * Cumulative Affordable Housing Supply	−0.0002		0.0502
	(0.0003)		(0.0449)
COVID-19 Pandemic *Affordable Housing Supply Area		−0.0029 *		0.9690 ***
		(0.0013)		(0.2180)
Macroeconomic Control Variables	√	√	√	√
Real Estate Industry Control Variables	√	√	√	√
Policy Variable	√	√	√	√
Population Variable	√	√	√
City Fixed Effects	√	√	√	√
Time Fixed Effects	√	√	√	√
N	452	452	417	417
Adj. R-sq	0.781	0.777	0.971	0.970

Note: *, ** and *** indicate significance at 10%, 5% and 1% significance levels respectively. The t value is in parentheses. “√” indicates that the variable or effect (city fixed effects, time fixed effects, control variables) has been included in the regression model, and the coefficients on the control variables are not shown to focus on the core explanatory variables.

manifest that the principal impact of the pandemic on the housing market is a remarkable suppression of transaction volumes. Nevertheless, the regulatory function of affordable housing policies during the pandemic is also manifested. Subsequent to the introduction of the pandemic variable, the cumulative supply of affordable housing still exhibits a restraining effect on housing prices and fosters the growth of transaction volumes, thereby contributing to the alleviation of the crisis in the real estate market. The results of the interaction terms suggest that both interaction terms possess a suppressing effect on housing prices and a promoting effect on transaction volumes. In particular, the newly added supply of affordable housing plays an active role in curbing the increase in housing prices and enhancing market transaction volumes during the pandemic. These discoveries furnish empirical evidence for comprehending the regulatory impact of affordable housing policies in response to major public health incidents.

7. Conclusions

This paper undertakes an in-depth empirical examination to investigate the role of affordable housing construction within the housing market, with a particular focus on its efficacy in curbing housing price escalation, stimulating residential sales, and mitigating the impact of the COVID-19 pandemic. The research findings reveal that affordable housing construction has yielded positive outcomes in multiple dimensions. Firstly, the cumulative supply of affordable housing exerts a significant dampening effect on the growth of housing prices while concurrently facilitating an augmentation in residential sales. This implies that affordable housing policies, by siphoning off demand from the commercial housing sector, also enhance residents’ purchasing confidence, thereby effectively alleviating risks inherent in the real estate market. Secondly, the study demonstrates that the impacts of affordable housing construction are regionally heterogeneous, with more pronounced effects observed in regions experiencing greater housing price pressures. Finally, the pandemic’s influence on the housing market is manifested by a substantial suppression of transaction volumes. However, affordable housing policies have exhibited a conspicuous buffering effect during the pandemic, curtailing market volatility, suppressing housing prices, and promoting residential sales.

Based on the aforementioned conclusions, this paper proffers the following policy prescriptions. Firstly, continuous endeavors should be made to augment the construction of affordable housing, especially in areas subject to more intense housing price pressures, so as to further capitalize on the market-stabilizing function of affordable housing. Local governments ought to enhance the supply of affordable housing in economically developed regions to more efficaciously divert demand from commercial housing and relieve housing price strains. Secondly, policy formulation should take into account the implications of extraordinary events, such as pandemics and other public health emergencies. It is recommended that the government intensify its support for affordable housing construction during significant events to temper market oscillations and safeguard the stability of the housing market. Additionally, to augment the efficacy of affordable housing policies, the spatial distribution of affordable housing should be optimized to ensure that affordable housing projects can effectively redirect housing demands across different strata, especially those of low-income and young cohorts. At the same time, policymakers should also set the proportion of subsidized housing allocation in the future, taking into account the size of the city in a hierarchical manner. with the aim of further alleviating the backlog in the housing market and diminishing real estate-related risks.