Gender Imbalance in the Marriage Market and Housing Demand: Evidence from China

Abstract

Gender imbalance and high housing costs are some of the important issues currently facing China, and they are also not in line with the UN’s SDGs, particularly SDG 5, Gender Equality, and SDG 11, Sustainable Cities and Communities. This research examines the influence of gender disparities within the matrimonial arena on housing demand. Data from the 2015 and 2017 editions of the China Household Finance Survey were utilized in this work. The cultural preference for male offspring, coupled with the one-child policy introduced by the Chinese government in 1978, has contributed to the escalating gender ratio in the country. In light of this gender imbalance, it is posited that Chinese families with unmarried male children may endeavor to bolster their sons’ desirability in the marriage market by investing in real estate. The study findings reveal that households with at least one son are more inclined to purchase additional or more spacious residences. This finding substantiates the notion that gender imbalance could be a contributing factor in the escalation of housing prices.

1. Introduction

The issues of gender imbalance and high housing prices not only reflect the deep-seated connections between social structure and market behavior but are also directly related to the United Nations’ Sustainable Development Goals (SDGs), specifically Gender Equality (SDG 5) and Sustainable Cities and Communities (SDG 11). In societies with gender inequality, males may face greater social pressure to purchase property in order to enhance their competitiveness in the marriage market, a phenomenon particularly evident in China. The aim of this study is to integrate these SDGs in policy and action calls for a concerted effort to address the relationship between gender imbalance in the marriage market and the housing demand to create urban environments that are sustainable and equitable for all.

China’s accession to the WTO in 2001 marked a period of robust economic growth, accompanied by a notable rise in housing prices, notably in major cities like Beijing, Shanghai, and Shenzhen. The housing market’s privatization in 1998 facilitated free trade in real estate, propelling its swift evolution. As shown in Figure 1, from 2002 to 2022, the average housing price in China surged from CNY 2092 to CNY 10,357 per square meter, a 395% leap. This escalation has spotlighted the challenge of housing affordability. The National Bureau of Statistics reports an average annual disposable income of CNY 49,283 for urban dwellers in 2022, suggesting that a 100 square meter home would cost two decades of their income, excluding other expenses. Financial surveys indicate that home acquisition is both the predominant expense and savings goal for many Chinese people, highlighting the financial strain placed on them, particularly the less affluent youth [1]. While some studies propose the presence of rational bubbles in the housing market to explain the price hikes [2,3], others argue against this rationale [4]. They suggest that the complexity of China’s real estate system is reflected in the unpredictable nature of housing prices.

To tackle soaring housing prices, the government has enacted key policies including the enforcement of home buying limits and the levy of property taxes. Initiated in Beijing in 2010, purchase restrictions have since expanded nationwide, necessitating that prospective buyers fulfill specific conditions. In Beijing, for instance, local registered residents are capped at two home purchases, while those with a five-year tax or social insurance payment history but no local registration in the city are entitled to one. The implementation of property taxes, a topic of ongoing discussion, has been trialed in select cities like Shanghai and Chongqing since 2011. Given the interim nature of purchase caps in curbing housing prices, there is an ongoing scholarly debate on the potential shift towards a property tax regime as an alternative.

The effect of the two key policies—home buying restrictions and property taxes—remains ambiguous. In Beijing, the restriction policy has the potential to cut the housing price inflation by 7.69%, whereas in Chongqing, the property tax has a more modest impact, trimming the growth rate by only 2.52%. However, in Shanghai, the property tax’s impact on housing prices has been negligible [5]. Despite these measures, the efficacy of the restriction policy in Beijing appears limited. Figure 2, which depicts Beijing’s housing prices, reveals that the pace of price escalation has remained, particularly between 2002 and 2022. In 2002, the price was CNY 4467, and by 2022, it had risen to CNY 47,784.

This implies that multiple factors are likely contributing to the collective impact on housing prices. It is evident that the majority of implemented policies have concentrated on curbing home purchasing activities rather than directly addressing the drivers of housing price increases. In light of this, a multitude of academic bodies have recently undertaken extensive research to identify the factors propelling housing prices upward, with a particular emphasis on land supply costs, investment demand, and demographic growth. A novel perspective is proposed in this study, examining the correlation between the gender balance within cohorts of individuals prior to marriage and the demand for housing as a potential explanation for escalating property values.

Figure 3 shows the concerning trend in the gender ratio for unmarried individuals over the age of 15 between 2002 and 2022. Over this period, the ratio has seen variations, with a minimum of 1.35, indicating 135 males for every 100 females. This suggests that an excess of males may have difficulty finding partners. A motivation for this study is the intense competition in China’s marriage market due to gender disparities, prompting parents to improve their sons’ marital prospects through the display of affluence. As housing prices have surged, real estate has become a vital and precious asset for many families. Dwellings are not only seen as consumer and investment items but also as status symbols that enhance utility through comparison with others [6]. Thus, home acquisition has emerged as a strategic move to increase male competitiveness in the marriage market. Given the sustained housing demand, the housing price will remain at a high level.

This paper examines the potential link between the presence of marriageable sons and the tendency to acquire multiple or more spacious residences. The study’s findings point to a higher likelihood of home ownership among families with eligible bachelor sons when compared to those with daughters. Additionally, there is a positive correlation between having sons and the size of the homes they own. The research thus corroborates the theory that gender disparities in the marriage market may be a driving factor behind the surge in the housing market.

2. Literature Review

2.1. Housing Demand

Housing demand significantly impacts housing prices, with investment intentions being a key driver. In China, where financial markets remain underdeveloped, the majority of households opt for bank deposits over stocks and other financial instruments, largely due to the limited number of stock market investors who see gains. Despite the preference to save in banks, the real yield on these savings is effectively negative, given the nominal interest rates and concurrent inflation [7]. The real estate sector offered an average annual return of about 20 percent from 1998 to 2012, a stark contrast to the banking options [2]. This attractive return has steered investors away from traditional savings and towards the housing market, resulting in a thriving real estate economy. This influx of investors, through the acquisition of more properties, influences housing investment [8]. Real estate comprises about 22 percent of non-current asset investments across the economy [9].

An escalating urban population is also a key driver of housing demand, stemming from the urbanization trend that sees individuals relocating from rural to urban settings. Urban expansion often accompanies a significant migration of population, which in turn has an impact on the housing market. In the 1960s, Lee [10] analyzed the “suction” and “resistance” encountered during population migration as well as the reactions of various groups of people, summarized the various factors that affect population migration, and formed the theory of population migration. Subsequently, on this basis, scholars systematically analyzed the impact of population migration on the housing market. Ley and Tutchener [11] found a significant correlation between immigration and urban housing demand through their study of Canadian immigration data and the housing market. Sa [12] found through their study of the UK immigration population and housing prices that immigration exacerbates labor market competition, leading to a decrease in wages for workers, forcing a local population outflow, and resulting in a decrease in housing demand. Additionally, for every 1% increase in immigration population, housing prices decrease by 1.6%.

In China, this movement is motivated by the desire to access superior urban facilities, such as entertainment, education, and healthcare, often found in urban areas [13]. Data from the National Bureau of Statistics indicate that from 2000 to 2018, the urban-to-rural population ratio significantly increased from 0.36 to 0.6. This population surge has upset the equilibrium within city housing markets, with the increased demand for housing leading to higher prices [14,15,16] and congestion [17,18]. Ding [19] observed that governmental controls on urban population size could potentially result in a 0.49% drop in the housing price growth rate in most cities. Conversely, Yu [20] found a negative correlation between urban population growth and housing prices in their model, indicating that a growing population might actually lead to lower housing prices. These contrasting outcomes may stem from varying interpretations of what constitutes the urban population, including the distinction between registered (hukou) and unregistered urban dwellers.

2.2. Gender Imbalance

The majority of cited studies on population dynamics concentrate on overall numbers, often overlooking structural aspects, particularly the balance of sexes. It is clear that China experiences a skewed gender distribution. Normally, the natural sex ratio for newborns is approximately 105 to 107 males per 100 females [21]. In contrast, China saw a sharp escalation from 107.6 in 1982 to 111.3 by 1990 and further to 118 in 2001. This trend is notable when compared with the United States, which has consistently maintained a sex ratio at birth of 105, the norm for that era. Even when compared to culturally similar East Asian nations like South Korea, China’s birth sex ratio remains notably elevated [22].

The birth sex ratio’s imbalance is largely due to the long-standing cultural preference for sons in China, exacerbated by the one-child policy [23,24,25]. Historically, men’s greater physical strength made them more effective in critical areas such as warfare, hunting, and farming, which were the mainstays of the imperial economy. This led to a societal hierarchy where men were dominant, occupying all official positions, while women were largely confined to domestic roles and crafts. Parents traditionally favored sons, viewing them as future leaders or entrepreneurs capable of enhancing the family’s wealth. Even in today’s society, where gender equality has made significant strides, some parents maintain a son preference. This outdated view can cause an unequal allocation of resources, such as education and finances, between sons and daughters. Sons who receive these additional resources may go on to have higher earnings and more promising prospects, which in turn can further entrench the preference for sons, perpetuating a cyclical issue. The Chinese government, aiming to control population growth since the 1960s, implemented a series of measures that culminated in the one-child policy in 1978. Under this policy, the majority of citizens were limited to a single offspring, with exceptions made for those meeting particular criteria. Breaches of this regulation often resulted in penalties [24].

Influenced by the cultural bias for sons and the one-child policy, certain parents resorted to ultrasound technology to determine the sex of their unborn child, leading to selective abortions [6,22,26]. Couples favoring a male child might choose to terminate a pregnancy upon discovering a female fetus. Furthermore, those with a daughter already would attempt various methods to conceive a son, regardless of the potential financial penalties or risk of unemployment.

2.3. Housing Demand Due to the Gender Imbalance

Research predicted an impending surplus of 23.5 million males by 2021 [22], but actual figures from the National Bureau of Statistics reveal an excess of 31 million males as of 2018. Projections by Tucker and Hook [25] indicate that the disparity between marriageable men and women will grow by 2060, potentially leaving about 24 million single men without female partners. This gender imbalance intensifies competition in the marriage market for men, bolstering the negotiating power of women [27,28]. Men may resort to showcasing their financial status to enhance their standing in the marriage market.

Real estate has become the principal asset for families [29]. Survey data reveal societal expectations that men should provide the home for their marital life, with 68% of surveyed women considering home ownership a prerequisite for marriage [30]. This perception significantly influences men’s marital prospects. Just as in the U.S., where a spacious home denotes affluence [1], Chinese society equates home ownership with economic success. Consequently, unmarried men who own more substantial and valuable real estate assets are often viewed as more desirable candidates in the marriage market, meeting the criteria for financial stability, social status, and family provision.

2.4. Conclusions

A multitude of elements contribute to the surge in the housing market, notably the various demands for housing. These demands arise from both the increase in urban population and speculative investment. The majority of scholarly works examining population growth have concentrated on overall numbers rather than the demographic composition. The combination of a cultural bias towards male offspring and the one-child policy has led to a pronounced gender disparity in China, especially noticeable among the younger population seeking to marry. In the marriage market, properties, serving as indicators of status, have a profound influence on competition, essentially becoming an essential component of matrimonial arrangements and thus fueling the demand for housing. However, there is a contradiction in the scholarly discourse regarding the correlation between the sex ratio and housing prices. The aim of this study is to offer a fresh perspective on the interplay between gender and housing demand, shedding light on the factors propelling the housing market’s growth.

3. Materials and Methods

3.1. Data

Data from the China Household Finance Survey were drawn upon for this study. The survey was conducted by the Southwestern University of Finance and Economics in 2011, 2013, 2015, and 2017, with participant totals of 8438, 28,141, 37,289, and 40,011 households, respectively. The analysis focuses on data from the 2015 and 2017 waves. The 2017 survey data include a random selection of 40,011 households from 29 provinces in the Chinese mainland region (excluding Xinjiang and Tibet), spanning 355 counties and 1428 communities, with an equal mix of rural and urban residences. The 2015 survey data comprise 37,289 samples across 29 provinces, 351 counties, and 1396 communities. The survey’s extensive data collection provides a robust representation of China’s household financial landscape. It encompasses a wide array of household-level details, such as location, income, assets, spending, and size, as well as individual member information, including sex, education, employment, earnings, age, and nationality. By amalgamating these multifaceted statistics, the survey offers a comprehensive view of each family’s financial profile.

The goal of this study was to investigate the link between owning property and having sons. Households with at least one house were first identified from the full dataset. The focus was then narrowed to those where the respondent is the household head, as these individuals are assumed to have the most accurate knowledge of the family’s financial status. Households without children in the 15–40 age bracket were excluded in this study, given that in China, the legal marriageable age is 22 for men and 20 for women, although some marriages occur earlier. It was also considered that parents may start saving for a son’s future marital home at age 15 to enhance his appeal in the marriage market. The analysis excludes data points with missing or aberrant values, such as homes under 20 square meters, incomes below CNY 5000, or household heads outside the 18 to 90 years age range. The final dataset consisted of 7611 observations from 2015 and 6252 from 2017. Since the regressions are based on distinct annual surveys, they are cross-sectional in nature.

3.2. Hypotheses

The existing gender imbalance, as detailed in the prior literature, stems from the son preference tradition and the one-child policy. This traditional bias has led many parents to view sons as more likely to achieve success and productivity. During the 1980s, the use of ultrasound for sex-selective abortions to conceive boys was widespread, causing a notable sex ratio imbalance [24]. This view has faded among the younger population, and with the government’s legal promotion of gender equality, the birth sex ratio has been slowly correcting. However, the legacy of this imbalance is an excess of men in the marriage market, giving women greater negotiation power. To boost their competitiveness in this market, men are inclined to showcase their financial means. For many families, property serves as a significant asset, and parents may invest in additional or more spacious homes to enhance their sons’ appeal. Given that home ownership is often a prerequisite for marriage in Chinese culture, with men typically expected to provide the residence, households with sons tend to have a higher demand for housing compared to those with daughters. Thus, the following hypothesis was formulated:

H1: 

When parents have a son who is of marriageable age, they are often inclined to own multiple properties.

Parents seeking to enhance their sons’ marital prospects through wealth may not need to acquire multiple homes, especially in cities with government restrictions on additional property purchases. They might instead choose to invest in a more expansive residence which still offers significant value and fulfills requirements for living standards, investment, and competitive advantage in the marriage market. Consequently, the second hypothesis examines the potential link between the presence of a son and the size of the home owned:

H2: 

When parents have a son who is of marriageable age, they are often inclined to own larger properties.

3.3. Research Methodology

Quantitative approaches were utilized in this study to construct two distinct regression models—specifically, a logistic regression model and an ordinary least squares (OLS) model.

The logistic regression model, a form of generalized linear model, is designed to analyze nonlinear relationships through a binary outcome variable, which contrasts with the continuous dependent variable found in the general linear model. This binary variable is used to represent categories such as gender, survival status, or test results—qualitative rather than quantitative measures. The logistic model uses maximum likelihood estimation to ascertain the parameters that indicate the relationship between independent variables and the dependent variable in probabilistic terms. In the context of this study, the aim of which was to evaluate home ownership prevalence among households, applying a logistic regression model is appropriate. The model’s key statistic, the odds ratio, is compared against the benchmark of 1. An odds ratio greater than 1 for an independent variable indicates a positive influence on the dependent variable, while an odds ratio between 0 and 1 suggests a diminishing effect on the likelihood of the dependent variable’s occurrence.

The ordinary least squares (OLS) regression model, known for its BLUEs (Best Linear Unbiased Estimators), is a prevalent tool in linear modeling. It is particularly suited for analyzing variables with continuous numerical values, like housing space. The model’s utility extends to estimating the correlation between the presence of a son in a family and the size of their home. A key element within the OLS model is the coefficient assigned to each independent variable; a positive coefficient suggests a direct increase in the dependent variable with respect to the independent variable, while a negative coefficient denotes an inverse relationship.

3.4. Models

To test Hypothesis 1, a logistic regression model was utilized to establish the subsequent equation:

$${multiplehouse}_i={\beta }_0+{\beta }_{1 }{son}_i+{\beta }_2{X}_i+{\epsilon }_i$$

(1)

Within this equation, ${multiplehouse}_i$ represents the binary dependent variable, assuming the value of 1 for households with more than one property and 0 for those with one property or no properties. ${son}_i$ is the primary independent binary variable, which is 1 if there is a son of a marriageable age within the household and 0 in the absence of such. Variable $X_i$ accounts for various household features, including the logarithm of income, which is crucial for gauging a family’s purchasing power, and details about the household head, such as educational attainment, age, and gender. The head’s characteristics often dictate the family’s financial decisions, with education level being a key factor and potentially impacting the likelihood of securing a high-income job. A college degree or higher is denoted by 1 and a lower level by 0. As households age, they typically amass more wealth, increasing home affordability. In China, male heads of households are commonly perceived to earn more, with the household head defined as the individual managing financial resources or household affairs. Consequently, male-led households may be more inclined to own multiple properties. The regression model predicts a significant odds ratio of over 1 for the ${son}_i$ variable, indicating that families with sons are more prone to own multiple homes compared to those with daughters. It also suggests that households led by older, wealthy, college-educated men are likely to possess additional properties.

The following equation using OLS regression was used for Hypothesis 2:

$$lnhousespace={\beta }_0+{\beta }_1{son}_i+{\beta }_2{X}_i+{\epsilon }_1$$

(2)

Equation (2) variables align with those in Equation (1), differing only in the dependent variable $lnhousespace$, which is the natural log of total housing space in square meters, consistent with the approach by Wei et al. [6]. The notion is that having a son prompts parents to acquire homes with large space and substantial value, whether a single expansive residence or multiple properties, signifying wealth. The anticipated outcome for the ${son}_i$ variable in this regression analysis is a positive, significant coefficient, thereby supporting Hypothesis 2. For the other control variables, the model predicts the same outcomes as those of Model 1, suggesting that male household heads who are older, wealthier, and possess a higher level of education are inclined to buy more spacious houses.

4. Results

4.1. Descriptive Statistics

Table 1. Descriptive statistics of the 2017 wave.

Variables	N	Mean	Min	Max
Housespace	6265	183.1	20	5203
Multiplehouse	6265	0.265	0	1
Son	6265	0.697	0	1
Income	6265	103,569	5000	5.000 × 106
Member	6265	0.112	0	1
Education	6265	0.166	0	1
Gender	6265	0.703	0	1
Age	6265	55.336	31	90

provides descriptive statistics for all variables within the 2017 dataset. Among the 6265 surveyed households, around 26.5% possess more than one property, with an average household living space of 183.1 square meters. The smallest home size recorded is 20 square meters, contrasting with the largest total area of 5203 square meters for those with multiple residences. Nearly 69.7% of households include at least one son aged 15 to 40, while 30.3% do not have a son. This demographic reflects the impact of the one-child policy, under which most parents had a single child, with exceptions for households meeting specific exemption criteria. The average household income is CNY 103,569, surpassing the government-reported annual urban household income. This could be due to the omission of extremely low-income households that might have been misreported in surveys. Additionally, the selection of households in this study, all of which own at least one home, suggests a higher income level among participants. The income range spans from a minimum of CNY 5000 to a maximum of CNY 5,000,000, with the survey’s income cap set at CNY 5,000,000.

Information on household heads reveals that, with all families having children aged 15 to 40, 11% of the household heads in the sample are members of the Communist Party of China. The average age of these heads is 55.336 years. The age range spans from a minimum of 31 to a maximum of 90 years. In terms of education, 16.6% have achieved a college degree or higher. The majority of household heads, accounting for 70.3%, are male. This aligns with the survey’s definition of a household head as the person primarily in charge of financial matters or household management, reflecting the gender disparity where men are more likely to earn higher incomes and occupy higher-status positions than women.

4.2. Regression Results

Table 2. Estimation results of Equation (1).

Variables	(1)	(2)
	Coefficient	Odds Ratio
Son	0.237 ***	1.268 ***
Lnincome	0.529 ***	1.696 ***
Education	0.080 ***	1.084 ***
Gender	0.091 *	1.096 *
Member	−0.073	0.929
Age	−0.005 ***	0.995 ***
Constant	−7.361 ***	0.0006 ***
Observations	6265	6265

*** and * indicate statistical significance at 1% and 10%, respectively.

details the logistic regression outcomes for Equation (1) based on the 2017 data wave. Coefficients are listed in the first column, with the subsequent column depicting odds ratios that signify the likelihood of possessing multiple homes. Regarding the independent variable, the odds ratio stands at 1.268, surpassing 1 and holding statistical significance at a 99% confidence level. This suggests that families with a marriageable son are 26.8% more likely to own several houses when compared to those without sons aged between 15 to 40, thereby reinforcing Hypothesis 1, which posits that a marriageable son prompts families to own multiple homes to bolster the son’s marital prospects. The income variable’s odds ratio also exceeds 1, indicating that increased household income is associated with a higher probability of owning multiple properties. Furthermore, the odds ratios for variables reflecting the household heads’ characteristics, excluding age and member, are significantly above 1, with values of 1.08 for education and 1.096 for gender. This implies that households headed by educated males are more inclined to have multiple houses.

The variable coefficients of the second regression model, designed to evaluate Hypothesis 2, are detailed in

Table 3. Estimation results of Equation (2).

Variables	(1)
	Coefficient
Son	11.089 ***
Lnincome	9.971 ***
Education	−10.939 ***
Gender	33.59 ***
Member	−0.219
Age	−1.06 ***
Constant	124.783 ***
Observations	6265

*** indicate statistical significance at 1%.

. The independent variable’s coefficient is 11.089, which is positive and significant at the 99% confidence level, and correlates with the dependent variable, the logarithm of the house space. This significant positive correlation supports the notion that households with marriageable sons are inclined to own larger homes, confirming Hypothesis 2. Consistent with Model 1’s outcome, Model 2 also suggests that affluence enables households to afford more spacious residences. However, among the household heads’ characteristics, only gender is found to have a positive significant effect on the home’s size.

4.3. Robustness Test

The robustness of the regression model results was checked using data from the 2015 wave, and the findings are presented in

Table 4. Robustness test results using the 2015 wave data.

Variables	(1)	(2)	(3)
	Logit Coefficient	Odds Ratio	OLS Coefficient
Son	0.040 *	1.040 *	5.902 **
Lnincome	0.468 ***	1.597 ***	5.529 ***
Education	0.490 ***	1.633 ***	0.550 **
Gender	0.038	1.038	0.761
Member	0.026	1.026	6.21 *
Age	0.002 **	1.039	0.181 *
Constant	−6.432 ***	0.002 ***	41.666 ***
Observations	7611	7611	7611

***, ** and * indicate statistical significance at 1%, 5% and 10%, respectively.

. Focusing on the dependent variable, in the second model, the odds ratio is 1.040, indicating that there is a 4% higher likelihood for families with marriageable sons to own multiple houses compared to households with daughters aged 15–40. Additionally, the coefficient for the dependent variable in the second model is 5.902. These findings also substantiate that marriageable sons indeed have an impact on housing demand.

Another robustness check was carried out to distinguish between rural and urban samples, as detailed in

Table 5. Robustness test results distinguishing rural and urban samples.

Variables	(1)	(2)	(3)	(4)
	Odds Ratio (Rural)	Odds Ratio (Urban)	OLS Coefficient (Rural)	OLS Coefficient (Urban)
Son	1.067	1.372 ***	−7.403 *	15.154 ***
Lnincome	1.590 ***	1.822 ***	19.383 ***	10.313 ***
Education	1.182 ***	1.073 ***	9.128 ***	−8.198 ***
Gender	0.091 *	1.096 *	3.836	27.117 ***
Member	1.060	0.916	8.778	0.115
Age	1.003 ***	0.993 ***	−0.416 ***	−1.101 ***
Constant	0.0007 ***	0.0003 ***	−0.0989	103.573 ***
Observations	1926	4339	1926	4339

*** and * indicate statistical significance at 1%, 5% and 10%, respectively.

. The first and the third column contain the results of rural samples, while the second and the fourth column show the results of urban samples. From this table, it is clear that in urban areas, families with marriageable sons could have a greater demand for houses in terms of quantity and size. By contrast, this theory is not applicable to rural residents.

5. Discussion

The findings from both regression models reinforce the hypotheses detailed in Section 3.2, indicating that households with a marriageable son are likely to have heightened housing demand. This is attributed to the fierce competition in the marriage market, driven by a pre-marital gender imbalance and the increased bargaining power of women in that market. The conclusions align with the research of Wei et al. [6] yet contrast with the outcomes of the studies by Zhang et al. [31] and Gao and He [32].

In their study preceding the examination of links between housing and gender, Wei and Zhang [1] identified that a rising sex ratio results in increased savings, a consequence of the intense competition within the marriage market. While they acknowledge saving for marriage-related housing as a motivation for these savings, they do not delve deeply into the interplay between housing and gender. Wei et al. [6] utilized a Tobit regression model to establish a correlation between increased sex ratios in 35 major cities from 2003 to 2009 and higher housing prices. Their findings also indicate that households with unmarried sons are more likely to invest in more spacious and valuable homes.

In contrast, Zhang et al. [31] explored the link between housing price growth rates and sex ratios across various age groups, identifying a weak correlation between high sex ratios and housing prices due to marriage competition. Gao and He [32] assessed the connection between Chongqing’s housing prices and its overall sex ratio, noting that an increased sex ratio is associated with lower housing prices. This outcome may stem from their inclusion of the sex ratio for all ages, despite the fact that housing demand is not uniform across all age groups, especially considering that married individuals with stable lives may have less need for new housing. The sex ratios for middle-aged and older populations, which are sometimes below 100, could also impact the overall sex ratio figure.

This inconsistency may be attributed to the multitude of factors influencing housing prices, with the sex ratio being just one of many. The direct link between sex ratios and housing prices may not be as clear-cut as initially thought. Additionally, the one-child policy may have led some parents to forego registering their daughters, skewing the accuracy of sex ratio data. Given these inconsistencies, this paper shifts the focus to the relationship between the presence of marriageable children and household housing demand, rather than directly correlating sex ratios with housing prices.

6. Conclusions

The surge in the housing market has prompted a thorough examination of factors influencing housing prices, with some suggesting that the sex ratio may be a key determinant in the escalation. The proposed rationale is that the one-child policy, coupled with a preference for male offspring, has led to a gender imbalance since 1978, with fewer female births compared to male. This has positioned marriageable females with greater bargaining power, thus intensifying competition among marriageable males in the marriage market. Parents are motivated to enhance their sons’ prospects by displaying wealth, with home ownership serving as a tangible and compelling indicator of financial stability. In Chinese tradition, it is often expected that the fiancé will supply the marital home, a practice that has become nearly mandatory for marriage. Consequently, if the gender imbalance persists, the demand for housing among males is anticipated to continue well into the future.

Using data from the 2015 and 2017 waves of the China Household Finance Survey, logistic and OLS regression analyses were conducted in this research to examine the influence of having sons aged between 15 and 40 on a household’s propensity to buy homes. The results reveal that households with marriageable sons are more inclined to invest in additional properties compared to those without sons, and there is a positive association between the presence of a marriageable son and the aggregate housing space owned. These conclusions are more reflected in urban populations. This study bolsters the notion that gender disparities in the marriage market contribute to one of the reasons driving house prices to a higher level.

Thus, the argument of gender imbalance presents a novel approach that could help reduce housing prices. To curb the rise in housing prices, the current state of the marriage market, where there are fewer females than males, should be changed. The crucial element is to lower the sex ratio at birth. Figure 4 illustrates the sex ratio for the 0–4 age cohort from 2003 to 2022. While China’s birth sex ratio has seen a decline from 121.22 in 2003 to 109.88 in 2022, it remains above the norm. With the one-child policy repealed since 2015, parents are now free to have two children, eliminating the need for sex-selective abortions when a female embryo is detected. The state further reduced sex-selective abortion rates by outlawing non-medical gender determination and gender-selective abortion in 2016. However, the son preference persists among certain age groups, despite legal gender equality provisions.

This preference is rooted in the allocation of more resources to males, leading to higher incomes and social status. Some parents may still seek sons, even engaging in illegal activities like human trafficking. To truly shift the son preference, gender inequality must be addressed. A cultural shift away from this preference, coupled with the relaxation of the one-child policy, could normalize the birth sex ratio. As these children grow, a balanced gender ratio in the marriage market may reduce the need for parents to buy additional homes to improve their sons’ marital prospects, potentially decreasing housing demand and moderating housing price increases. However, this transformation is expected to be a lengthy one, given the timescales of human growth and social change. Therefore, addressing the impact of gender imbalance on housing demand requires a multifaceted strategy that not only promotes gender equality but also fosters responsible consumption and production. This necessitates a collaborative effort from governments, the private sector, and civil society to achieve gender equality and sustainable housing market development through education, awareness enhancement, data research, and policy formulation. Such concerted efforts will contribute to the construction of a more just, equitable, and sustainable society, aligning with the core objectives of the United Nations’ 2030 Agenda for Sustainable Development.