A Mechanistic Study of the Coexistence of High House Prices, Low Income, and High Homeownership Rates in China

Abstract

An important feature of China’s housing market is the coexistence of a high house-price- to-income ratio and high homeownership rates. The purpose of our study is to reveal the root causes of this paradox from a new perspective and theoretical foundation. Based on questionnaire data from Hangzhou and logistic regression models, our research finds that the most important factors driving middle and lower-income groups to buy homes are its unique household registration and school district housing system, underdeveloped housing rental market and inadequate regulatory system, and the wealth appreciation effect caused by the continued rise in housing prices. Furthermore, intergenerational wealth transfers, private lending, and China’s generous home mortgage policies have made homeownership possible for this groups. However, the high house-price-to-income ratio leads to heavy financial pressure on the middle- and low-income groups and is not conducive to sustainable and healthy economic development. To this end, we suggest that the government accelerate the equalization of public services, improve the regulatory system governing the rental housing market, and control the unreasonable rise in housing prices and diversify investment channels for residents.

1. Introduction

It is widely recognized that higher real house prices may reduce the housing affordability of marginal homebuyers and thus reduce the overall homeownership rate (Engelhardt, 1997 [1]; Engelhardt and Mayer, 1998 [2]). However, this is not the case in China. The Report on Urban Residents’ Assets Survey, released by the Department of Survey and Statistics of the People’s Bank of China at the end of April 2020, shows that household assets in China are dominated by physical assets, of which housing accounts for nearly 70 percent. On average, each family owns 1.5 suites, and the housing ownership rate has reached 96%. The average household owns 1.5 suites, and the homeownership rate is 96 percent. Even for the lowest-income 20% of households, the homeownership rate is as high as 89.1%. In stark contrast to the high homeownership rate, China has high house prices and low incomes. NUMBEO released its mid-2021 Real Estate Price Index by Country, which shows that China’s house price-to-income ratio is 27.89, ranking 9th out of 111 major countries. The four first-tier cities in China (i.e., Beijing, Shenzhen, Shanghai, and Guangzhou) have the highest housing price-to-income ratios, reaching 52.46, 43.35, 42.76, and 35.58, respectively (See Figure 1 for the housing price-to-income ratios of other major cities).

Many scholars have explained the high rate of homeownership in China from a cultural perspective (Wang, Yin, and Wu, 2017 [3]; Yang et al., 2012 [4]; Lin, Zhao, and Chen, 2019 [5]). These scholars believe that Chinese people like to live and work in peace and contentment and are therefore particularly keen to buy houses. However, this is not the case in reality. As we all know, since the founding of New China in 1949, China has been practicing a welfare housing system. The government provided public rental housing (From the 1950s to the 1980s, China’s housing system adopted a public rental housing allocation system. The state, state-owned enterprises or institutions invested in and constructed public rental housing, characterized by no property rights, no lease period, low rent, and even inheritance. At that time, a unified rent standard was implemented in cities and towns across the country. The average monthly rent per square meter was CNY 0.30, and the rent expenditure accounted for about 2–3% of the residents’ salary income) at very low rents for the majority of the population, with only a small number of people owning property rights. By the 1980s, homeownership in China was less than 30 percent, while public rental housing accounted for more than 70 percent of the housing stock. In 1988, China’s central government attempted to push through a housing reform that promoted privatization of the housing stock, primarily through the sale of public rental housing. Local governments introduced a number of incentives, such as preferential sales, tax breaks and financial subsidies, to promote the sale of public rental housing (Wang, 1990 [6]). However, in most large and medium-sized cities, the sale of public rental housing was difficult and fell far short of expectations (Niu, 1999 [7]). Although the incomes of Chinese residents were not high at that time, the income ratio of housing prices was much lower than that of today, and many families were reluctant to buy public rental housing even though they could afford to do so.

From the above analysis, it can be seen that Chinese people are not always keen on homeownership, which proves that the traditional view is questionable. Yu and Zhou (2024) [8] argued that the financial incentive to generate wealth from housing has kept housing prices high in China’s cities, leading to a considerably high house-price-to-income ratio, prompting population outflows from urban centers. Many other scholars have analyzed Chinese residents’ homeownership choices from the perspectives of economic and income status (Huang and Zhang, 2018 [9]; Wang and Zhang, 2010 [10]), family and demographic characteristics (Dong and Liu, 2021 [11]; Li and Ni, 2015 [12]), housing system and financial policies (Guo and Xia, 2020 [13]; Li, 2022 [14]), and social psychological factors (Zou and Deng, 2019 [15]; Chen, Yu and Yu, 2011 [16]). However, these studies are not sufficient to explain why China’s homeownership rate is so high against a backdrop of high house prices and low incomes. The negative effects of China’s high homeownership rate and high financial leverage are becoming increasingly evident, such as the continued decline in fertility and the downturn in the consumer market. The main purpose of this paper is to explore the root causes of the formation of high homeownership rate in the context of high house prices and low income and to explain what makes a large number of middle- and low-income groups willing and able to buy a house so as to provide a reference for the governmental departments to formulate rational housing support policies for low- and middle-income families. We plan to study this issue from a new perspective based on the functional attributes of housing in China and behavioral motivation theory.

The rest of the paper is divided into seven sections. The next section discusses the relevant literature; Section 3 presents the institutional background and theoretical framework; Section 4 describes the design concept of the empirical model, the data sources, and the descriptive statistical analysis of the data; Section 5 reports the results of the empirical model and analyses its validity; and Section 6 summarizes and discusses the conclusions of the study. Finally, Section 7 presents targeted policy recommendations.

2. Literature on the Determinants of Homeownership

There is a well-established body of literature developed over the past two decades about the determinants of homeownership in China and elsewhere. In these studies, most economists and sociologists have long argued that homeownership is associated with positive externalities and socioeconomic effects, such as wealth accumulation, better educational outcomes, more active and informed citizenry and social capital (DiPasquale and Glaeser, 1999 [17]; Dietz and Haurin, 2003 [18]; Haurin et al. 2002 [19]; Turner and Luea, 2009 [20]). Hence, boosting homeownership has been a public policy goal in most countries. Public policies favoring homeownership mainly include tax and subsidy policies, financial instruments and other programs that directly or indirectly provide substantial encouragement for households to become homeowners. Since several scholars (Herbert and Belsky, 2008 [21]; Sanchez-Moyano, 2021 [22]) have reviewed the determinants of homeownership in detail, we will provide a relatively brief literature review in this paper.

Tax and subsidy policies strongly influence a household’s decision to own or rent its home (Bourassa et al., 2012 [23]). The U.S. federal government heavily subsidizes homeownership through various tax expenditures, including tax exemptions for implied rental income on owner-occupied homes and the deduction of mortgage interest payments from income (Coulson and Li, 2013 [24]). According to Gyourko and Sinai (2003) [25], both policies were estimated to amount to nearly USD 200 billion in tax expenditures in the 1990s. Based on Swiss household survey data, Bourassa and Hoesli (2010) [26] investigated the reasons for the very low rate of homeownership in Switzerland, finding that high housing prices and taxes on implied rents are the most important factors.

In the economics literature, financial factors are central to determining homeownership preferences. Financial instruments mainly include interest rate adjustment and borrowing constraints (down payment constraints and the limit on the ratio of the mortgage payment to income). Although there have been many quantitative and empirical studies on the impact of financial instruments on homeownership, there is yet no consensus on the matter. For instance, Linneman and Wachter (1989) [27] showed that borrowing constraints adversely affect homeownership propensities. Haurin et al. (1997) [28] found ownership tendencies to be quite sensitive to borrowing constraints and that constraints reduce the probability of ownership by 10 to 20 percentage points. Furthermore, Iacoviello and Pavan (2013) [29] confirmed that borrowing constraints are important in explaining the low ownership rates among young households. However, Fisher and Gervais (2011) [30] argued that the relaxation of down payment requirements was quantitatively small. Painter and Redfearn (2002) [31] found that while interest rates may affect the timing of changes in tenure status from renter to owner, the long-run ownership rate appears independent of interest rates. In addition to government public policy factors, demographic and socioeconomic characteristics of households, like age, gender, race, education, religion and family type (Adu-Gyamfi and Antoh, 2020 [32]); household income and wealth (Robst, Deitz, and McGoldrick,1999 [33]); intergenerational transfers (Helderman and Mulder,2007 [34]); and psychological factors—such as social class, lifestyle, and personality (Bohnenkamp and Kammann,2024 [35])—are also important factors influencing tenure choice.

The above literature review suggests that the factors influencing homeownership rates are complex and varied. Sociologists and economists have been trying to uncover the mystery of homeownership rates using various theoretical and empirical analytical frameworks. However, these rates vary widely across countries, even those countries with similar incentives and political, economic, cultural and other backgrounds. As a result, scholars have never reached a consensus. In this paper, we plan to look beyond these complex influences and explore the mechanisms behind China’s high homeownership rate from the perspective of housing functions and human needs and in the context of China’s unique institutional and humanistic background.

3. Institutional Background and Theoretical Framework

3.1. School District System Based on Housing Property Rights

The key secondary and elementary school system is a basic education system that was introduced after the founding of New China, with the aim of concentrating outstanding students into a small number of key schools. China’s key secondary school system can be traced back to 1953, when Chairman Mao Zedong put forward the idea of “building key secondary schools”, and by 1963, 487 key secondary schools had been built across the country (Fu, 1994 [36]). In 1977, Deng Xiaoping put forward the idea of “running key elementary school and key secondary schools, key universities” to concentrate outstanding talents in key secondary schools and key universities. The reason for running key schools at that time was that the country’s educational resources were insufficient, so it could only concentrate its limited resources on running a group of key schools to produce top talents as soon as possible. However, this system led to a great disparity in educational resources and quality between different regions and schools.

Before the implementation of China’s housing system reform in 1998, a welfare housing system was commonly implemented, where housing was allocated by the government, and residents did not have the right to make their own choices. As a result, the inequality of educational resources did not lead to the phenomenon of “school district fever” (The phenomenon of “school district fever” means that parents are scrambling to buy houses near key schools, leading to a sharp rise in property prices). The subsequent commercialization of housing allowed residents to choose their housing according to their economic power and preferences. Chinese people believe that ‘knowledge changes fate’ and parents pay extra attention to their children’s education. To avoid their children losing at the starting line, they always try every possible way to enroll their children in key schools. In China, the rights and interests behind renting and purchasing houses differ, and the only way to qualify for admission is to purchase a house and settle down, which has led parents who wish to enroll their children in key schools to rush to buy houses near these schools, thus giving rise to the phenomenon of “school district fever”.

3.2. The Housing Rental Market and Its Regulatory System

For a long time, the main supply of China’s rental housing market has been scattered between individual landlords, while specialized and institutionalized housing rental enterprises, which account for less than 2% of the share of the housing rental market, are mainly concentrated in first-tier and key second-tier cities. The number of centralized branded long-term rental apartments in the four first-tier cities of North, Shanghai, Guangzhou, and Shenzhen together reached 335, accounting for 70% of the country (Liang, 2018 [37]). The structure of the rental market, with individual landlords as the mainstay, has increased the cost and difficulty of supervision. In cities where the supply of rental housing exceeds the demand, the phenomenon of individual landlords arbitrarily increasing rents and evicting tenants is a frequent occurrence, which seriously affects tenants’ sense of security of residence (Wang, 2017 [38]). Compared to Western countries, the laws, regulations, and systems related to China’s rental housing market are imperfect, and the government lacks effective supervision over the quality, rent, and contract performance of rental housing. These shortcomings result in a lack of security and comfort for tenants (Li, 2013 [39]; Ye and Li, 2015 [40]). According to the ‘China Housing Rental Market Summary Report 2020’ (China’s large housing agencies, 58 Tongcheng and Anjuke, jointly released in December 2020), nearly 50% of the renters who participated in the study said they had encountered problems such as “landlord eviction”, “price increase during the contract period” and “housing equipment damage that cannot be repaired in time”.

3.3. The Conceptual Framework

The literature review above reveals that many scholars have analyzed the factors influencing the homeownership rate from such perspectives as public policy, the housing system, traditional culture, economic factors, and demographic characteristics. However, we intend to look beyond these complex factors to explore the motivations behind a large number of low- and middle-income groups in China to purchase housing, and the reasons why they can afford high-priced housing from the perspectives of housing functions, the Chinese kinship culture and consumer behavior.

Generally speaking, the basic functions of housing include residency and investment. On the one hand, it provides a place for people to live. On the other hand, rising house prices can bring wealth appreciation and resist inflation. Although both renting and buying can meet people’s needs for housing, in China, renting makes people feel insecure because of deficiencies in the laws, regulations and systems related to renting, and landlords can evict tenants at any time, who may face the risk of random rent increases. In addition, the quality of rental housing is generally poor, and the supporting facilities are inadequate, making the comfort of renting a room relatively poor. Similar to some Western countries, in China, the availability and rank of housing are a reflection of personal status, and even some marriages require homeownership as a prerequisite. In China, housing also has another special attached functional attribute: public services (Li, et al., 2024 [41]). In this regard, public services, such as education, health care, and pensions, are tied to homeownership. As mentioned above, eligibility for admission to primary and secondary schools in China is linked to homeownership, leading parents desirous of their children attending good schools to buy high-priced key school district houses.

However, the question remains: How can a large number of low-income groups afford to buy high- priced housing? An important factor in this regard is the intergenerational transfer of wealth. Chinese scholars have found that it is common for fathers to give financial support to their offspring for home purchases, and the proportion of such support is high (Chen, 2017 [42]; Fan, 2020 [43]). In Chinese family culture, parents would exhaust all means to scrimp and save to accumulate wealth for their children and even grandchildren, always hoping to provide a safe and secure umbrella for their children, which has led to the formation of a group of “gnawers” in China (Deng, 2015 [44]). The second important factor is private lending, especially interest-free lending, among relatives and friends, which is very common in China and is an important aspect of Chinese humane culture (Gao, 2002 [45]; Liu and Zhang, 2013 [46]; Zhao, 2014 [47]; Wang and Zhang, 2021 [48]). The down payment ratio for mortgages in China is relatively high, typically around 30% for the first suite and even higher for the second or others. The intergenerational transfer of wealth and private lending have solved the down payment problem for most low- and middle-income groups, making homeownership an option for families who can repay their mortgages. Chinese banks approve mortgage loans mainly considering the ability to pay the down payment, the ability to repay the mortgage and the default record of the banking system. China’s credit system is not yet complete, the sources of personal income are not transparent, and the ability to repay mortgages is mainly guaranteed by income certificates issued by work units, which often provide proof based on mortgage loan needs, making it easier for homebuyers to obtain mortgages (Zhao, 2005 [49]).

Based on the above analysis, we constructed the conceptual framework model for this study (see Figure 2). Whether or not low- and middle-income groups buy a home is mainly governed by two major factors: the demand factor and the ability factor. Compared to renting, owning a home can provide necessary public services and a safe and comfortable living environment, enhance an individual’s social status and realize their goal of wealth appreciation. These five functions of housing provide a strong incentive for low- and middle-income groups to buy homes and encourage them to find ways to raise funds, including seeking financial support from friends and family for down payments and applying for bank loans for the balance.

4. Research Design

4.1. Empirical Model Specification

Based on the above theoretical analysis framework, we hypothesize the probability of homeownership as a function of motivation and affordability. Therefore, our model could be formally written as follows:

HO = f (INC, MOT, AFF)

(1)

where HO refers to the status of the homeowner (the value of HO is “0” (tenant) or “1” (owner)), INC is a vector of individual characteristic, including gender, age, education, marital status, household registration (rural or urban), MOT is a vector of motivational factors, including public service needs (PSN), safety needs (SN), comfort needs (CN), esteem needs (EN) and wealth appreciation needs (WAN). AFF is a vector of affordability variables, including down payment assistance (DPA), generous credit support (GCA) and revenue growth expectations (RGE). Definitions of the variables composing the model and the expected signs of the relative parameters are presented in

Table 1. Expected impact of core explanatory variables.

Var.	Definition	Question Design	Options	Expected Influences
Motivation Variables
Public Service Needs (PSN)	Public service needs refer to the need for public facilities, such as education, medical care and transportation.	Does homeownership provide better access to public services, such as education and health care?	1 = Strongly disagree 2 = Disagree 3 = Neutral 4 = Agree 5 = Strongly agree Same as above Same as above Same as above Same as above	Positive influence
Safety Needs (SN)	Safety needs refer to the need for good community security, reliable housing quality and freedom from eviction.	Does homeownership provide a safer living environment?		Positive influence
Comfort Needs (CN)	Comfort needs refer to the need for a clean and tidy community environment and high-quality housing software and hardware facilities.	Does homeownership lead to more comfortable living conditions?		Positive influence
Esteem Needs (EN)	Esteem needs refer to the need to be recognized by others and society.	Does homeownership lead to higher social status?		Positive influence
Wealth Appreciation Needs (WAN)	Wealth appreciation needs refer to the need to increase wealth by investing in real estate	Investing in real estate can achieve the purpose of asset appreciation?		Positive influence
Affordability Variables
Down Payment Assistance (DPA)	Down payment assistance means being able to get down payment help from relatives, friends or other individuals	Were you able to receive financial support from relatives, friends, or others to buy a house?	1 = Less than 10% 2 = 10–30% 3 = 30–50% 4 = 50–70% 5 = More than 70%	Positive influence
Generous Credit Support (GCS)	Generous credit support means that homebuyers can easily obtain home mortgage loans	Is it easy to receive a home mortgage loan?	1 = Very difficult 2 = Difficult 3 = Normal 4 = Easy 5 = Very easy	Positive influence

.

The dependent variable in our model is a discrete categorical variable, and the analysis of this type of data usually uses logistic regression models, especially in the analysis of housing tenure choices. Specifically, the logit is defined as the natural logarithmic value of the odds in favor of a given event, that is:

$$logit\left(p_1\right)=ln\left[{\displaystyle \frac{p(y=1/x_i)}{p(y=o/x_i)}}\right]={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\cdots +{\beta }_n{x}_n$$

(2)

The model is based on the cumulative logistic distribution function p₁:

$$p_1=E\left(Y=1/x_i\right)={\displaystyle \frac{1}{1+e^{-{({\beta }_0+{\beta }_{1}x}_1+\cdots +{\beta }_n{x}_n)}}}$$

(3)

where $\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{i}\mathrm{t}\left(p_1\right)$ is a logit function of the dependent variable y that belongs to Class 1 given the observation $x_i$, $p_1$ is the probability that the output variable be equal to one (y = 1), $x_i$ are the input (explicative) variables, ${\beta }_0$ is the intercept, and ${\beta }_i$ is the regression coefficient of Class 1. The ratio ${\displaystyle \frac{p(y=1/x_i)}{p(y=0/x_i)}}$ represents the odds ratio, i.e., the odds that an event occurs (owner) to the odds it does not occur (tenant) and e is the base of the natural logarithm.

4.2. Data Source and Description

This study takes Hangzhou as the study area. Hangzhou is the capital city of Zhejiang Province, China and one of the central cities in the Yangtze River Delta. Hangzhou is a mega city with a total area of 16,850 square kilometers and an urban population of 10,203 million (with an urbanization rate of 83.6%) (data source: 2021 Hangzhou City Population Key Data Bulletin issued by Hangzhou Bureau of Statistics). It has a well-developed economy, with a regional GDP of CNY 1810.9 billion, a per-capita GDP of CNY 149,857, and a per-capita disposable income of CNY 74,700 in 2021 (data source: Statistical Yearbook published by Hangzhou Bureau of Statistics in 2022). Hangzhou is also known as a “paradise on earth”, with three major world cultural heritage sites, including the West Lake, the Beijing-Hangzhou Grand Canal and the Liangzhu Ancient City. It is also a hotspot for housing prices in China, with an average residential price of CNY 29,781 in 2021, ranking it 6th in China behind Shenzhen, Beijing, Shanghai, Xiamen, and Guangzhou. However, the general level of wages in Hangzhou is not high. More importantly, we were commissioned by the Hangzhou Housing Security and Real Estate Administration to make a wide-ranging questionnaire survey and collect enough data. We therefore chose Hangzhou as a case study.

This study takes the middle- and low-income groups as the research object. In China, there is no unified and clear official definition for middle- and low-income groups. In classifying income groups, the National Bureau of Statistics of China (NBS) divides the Chinese population into the following five equal parts according to per capita disposable income: 20% lowest income, 20% lower-middle income, 20% middle income, 20% upper-middle income, and 20% higher income. However, the Hangzhou Bureau of Statistics only published per-capita disposable income and did not classify the groups according to this method. In this study, the middle- and low-income groups are defined as those with disposable income lower than the per capita disposable income in Hangzhou. This definition provides guidelines for selecting our questionnaire respondents.

We estimate our model by using data collected from a questionnaire that randomly selected about 2000 households in Hangzhou. The purpose of the survey was to understand the factors influencing the phenomenon of “buying over renting” in China’s housing market. The questionnaire contains a survey of basic personal information, such as individual housing ownership status and per capita household disposable income, as well as seven other questions listed in Table 1. Survey respondents were asked about their motivation to buy a house using a five-point Likert scale comprising the following categories: strongly disagree, disagree, neutral, agree, and strongly agree. We recovered 1852 questionnaires, of which 1724 were valid. The main reason for the invalid questionnaires was that the respondents gave incomplete answers or their income exceeded the low- and middle-income criteria.

Preliminary descriptive statistical analysis found that of the 1724 respondents, 1428 were owners and 296 were renters, with a homeownership rate of 82.8%. These findings indicate relatively high homeownership rates among low- and moderate-income groups, and the ratio between owners and renters is similar to the urban residential property survey results mentioned above.

Table 2. Descriptive statistics for core explanatory variables.

Var.	Mean		Std. Deviation
	Owner	Tenant	Owner	Tenant
Public Service Needs (PSN)	3.96	2.84	0.751	1.181
Safety Needs (SN)	3.66	2.89	1.032	1.091
Comfort Needs (CN)	4.02	2.93	0.840	1.180
Esteem Needs (EN)	4.04	2.58	0.958	0.998
Wealth Appreciation Needs (WAN)	3.92	2.52	0.929	1.101
Down Payment Assistance (DPA)	4.11	2.10	0.873	0.777
Generous Credit Support (GCS)	4.41	2.25	0.815	0.699

shows some key statistics about the variables used in the model. We can observe that the mean values of all seven variables are significantly higher for homeowners than for renters, which could mean that these variables have a positive effect on homeownership, as we hypothesized in Table 1.

5. Empirical Estimation

5.1. Goodness-of-Fit

The goodness of fit refers to how well the model is constructed and how far it differs from the real or ideal situation. Linear regression analysis generally uses R² to reflect the effect of the goodness of fit. Logistic regression goodness of fit includes two categories: the quantitative and the qualitative evaluations of the goodness of fit. The quantitative evaluation of the goodness of fit refers to the −2 log Likelihood, Cox and Snell R², and Nagelkerke R² tests similar to R². In our case, the values of −2log Likelihood, Cox and Snell R², and Nagelkerke R² tests are 137.859, 0.567, and 0.945 (

Table 3. The quantitative evaluation of the goodness of fit.

Step	−2 Log Likelihood	Cox and Snell R Square	Nagelkerke R Square
1	137.859 a	0.567	0.945

^a Estimation terminated at iteration number 10 because parameter estimates changed by less than 0.001.

), which is a low value compared to the perfect regression. The qualitative evaluation of the goodness of fit refers to the Hosmer and Lemeshow Test. In our case, the p-value is 0.072, indicating that the model fit is not very good (

Table 4. The qualitative evaluation of the goodness of fit.

Step	Chi-Square	df	Sig.
1	48.311	8	0.072

). However, the quantitative and qualitative evaluations of the goodness of fit are highly debated and cannot be considered in isolation.

In addition to the methods described above, our model’s goodness of fit could be estimated more simply by comparing the number of correct previsions of the model with the number of total observations in the sample. Thus, the model’s fit is assessed by testing the accuracy of the predictions, explained in the following table.

5.2. Marginal Effects and Odds Ratios

Table 5. Estimated coefficients.

	B	S.E.	Wald	df	Sig.	Exp(B)	95% C.I. for EXP(B)
							Lower	Upper
Public Service Needs	0.861	0.249	11.978	1	0.001	2.366	1.453	3.854
Safety Needs (SN)	0.512	0.246	4.324	1	0.038	1.668	1.030	2.701
Comfort Needs (CN)	0.379	0.237	2.565	1	0.109	1.461	0.919	2.323
Esteem Needs (EN)	1.147	0.453	6.411	1	0.011	3.150	1.296	7.656
Wealth Appreciation Needs (WAN)	0.640	0.373	2.944	1	0.086	1.896	0.913	3.937
Down Payment Assistance (DPA)	2.635	0.424	38.631	1	0.000	13.942	6.074	32.003
Generous Credit Support (GCS)	3.642	0.522	48.617	1	0.000	38.154	13.708	106.194
Constant	−29.740	3.195	86.669	1	0.000	0.000

presents the coefficients of the logit model obtained by computing the data in SPSS. Wald chi-square values (Wald) and p-values (sig.) are hypothesis tests for the regression Coefficient B. Table 5 shows that five variables—public service needs, safety needs, esteem needs, down payment assistance, and generous credit support—have a significant effect on the choice of homeownership at the 5% level of significance. The other two variables—comfort needs and appreciation—have a significant effect at the 10% level of significance.

The signs of regression Coefficient B are all positive, indicating that all seven variables have a facilitative effect on housing purchase, which is consistent with our previous expectations. However, in the binary model, the information provided by regression Coefficient B is still limited to the direction of the effects of the independent variable on the dependent variable. The marginal effects can be calculated to outline the amplitude of these influences. In fact, the marginal effects represent the variation in the probability of homeownership compared to renting in the case of a marginal change in one parameter.

The marginal effects are usually analyzed based on the odds ratios (OR) values, i.e., the EXP(B) values in Table 5. Taking the public service needs as an example, the EXP(B) is 2.366, which means that the probability of buying a home increases 2.366 times for each additional level of the gap in public service needs. Similarly, we can conclude that the probability of buying a home increases by 1.668 times for each additional level of the gap in safety needs, 1.461 times for each additional level of the gap in comfort needs, 3.150 times for each additional level of the gap in esteem needs and 1.896 times for each additional level of the gap in appreciation needs. The statistical analysis also reveals that the EXP(B) values for down payment assistance and generous credit support are 13.942 and 38.154, respectively, which are much larger than the other independent variables, indicating that these two factors have a much more important role in promoting homeownership among low- and middle-income groups.

5.3. Predictive Ability of the Model

As mentioned earlier, we can judge the quality of the model fit by the predictive ability of the model.

Table 6. Predictive ability of the model.

Observed		Predicted
		Tenure		Percentage Correct
		0	1
Tenure	0	281	15	94.9
	1	8	1420	99.4
Overall Percentage				98.7

shows the prediction results of the final model. Of the 1724 predicted cases, 281 out of 296 renters were correctly predicted, with a 94.9% correct rate. Additionally, 1420 out of 1428 homeowners were correctly predicted, with a 99.4% correct rate. The correct overall rate was 98.7%. We objectively consider this result to be good.

6. Conclusions and Discussions

Since the implementation of 1998 China’s housing system reform, i.e., the abolition of welfare housing allocation and the commercialization of housing, and especially since the Chinese government positioned the real estate industry as a pillar industry of the national economy in 2003, housing prices and homeownership rates have increased rapidly. However, the growth of residents’ income has been relatively slow, causing China to have a very high price-to-income ratio. Clearly, this situation is a paradox. Why are China’s low-income residents willing and able to purchase high- priced commercial housing? There are many studies on housing choice and homeownership rates. However, the phenomenon of high homeownership rates in the context of high housing prices and low income in China is difficult to rationally explain using the usual theories and methods.

In this paper, based on survey data in Hangzhou, we explain this phenomenon in terms of both the motivation and the ability to purchase a home among the middle- and low-income groups in China. Our research confirms that the main motivations for homeownership among China’s low- and middle-income groups include: First, China’s unique household registration system and school district housing system have led the vast majority of Chinese parents who place great importance on their children’s education to purchase housing in cities with quality education. Second, China’s underdeveloped housing rental market and unsound regulatory system for the rental market have led to a lack of security and comfort for rental groups. As a result, they will try everything to buy a house once they have the opportunity and ability to do so. Finally, the continued rapid rise in housing prices has made buying a home the safest and most profitable investment in China. China has not experienced a prolonged decline in housing prices since the housing system was reformed, and Chinese residents generally believe that the government depends on land finance. Thus, they are bound to maintain the momentum of house price growth.

But how is it that low- and middle-income groups can afford high housing prices? Our research confirms that down payment assistance and generous credit support are important factors. Our survey found that the vast majority of low- and middle-income homebuyers received down payment assistance from their parents, relatives, or friends. Wealth transfer across generations and mutual lending among relatives and friends are very common in China, which is an important part of Chinese kinship culture. Over the past four decades, many families and individuals have accumulated substantial wealth against the backdrop of continued rapid economic development, making down payment support possible. Banks in China usually have very strict control over personal credit loans but are partial to home mortgages. Generally, there is no strict income test for homebuyers. As long as they can make the down payment and do not have a serious default record in the Chinese banking credit system, they can successfully obtain a home mortgage.

This paper takes Hangzhou as an example to empirically analyze the formation mechanism of high owner-occupied housing rate in the context of high house price and low income. Although Hangzhou has a strong representation, overall the breadth and depth of the data is still insufficient, and more typical cities can be selected to conduct panel data analysis in the future. In addition, the findings of this paper provide research ideas for the study of rental market regulation and government intervention in the housing market.

7. Policy Recommendations

As mentioned above, homeownership has many positive effects. Therefore, increasing the rate of homeownership is an important public policy goal for many governments. However, a high percentage of homeownership among low- and middle-income groups tends to have negative economic and social effects. For example, a disproportionate share of housing expenditures can lead to reduced demand for other goods and lower fertility intentions. Therefore, we propose the following policy recommendations based on the above analysis:

First, governments at all levels should accelerate the equalization of basic public services. It is the basic right of citizens to enjoy basic public services, and it is an important duty of the government to ensure that everyone enjoys basic public services. Housing property rights are currently associated with basic public services such as education, medical care and pension. Therefore, the issue of housing is closely related to the issue of basic public services. As Professor Chen Jie of the China Institute of Urban Governance of Shanghai Jiao Tong University pointed out, “In the value composition of housing, housing construction cost and furniture decoration cost are only a small part, the most important value comes from the value of public resources and public services bundled with housing property rights, why houses are easy to be speculated, the essence is to speculate on public resources and services”. Therefore, the government can solve the housing problem only by vigorously promoting equitable access to basic public services, especially the equalization of basic education resources.

Second, governments should establish a sound regulatory system for the rental market. On the one hand, the government should establish a quality control system for rental housing. China’s housing rental market is dominated by individual landlords, and some landlords often divide large rooms into several small rooms with inadequate supporting facilities in order to maximize rental income, resulting in discomfort for renters. Moreover, the change in housing structure also brings safety hazards. The government should establish a rental housing quality standard system to clarify the standards of rental housing and improve the living comfort of rental groups. On the other hand, the government should improve housing rental laws and regulations and the contract supervision system. In China, the rental group is a vulnerable one, and due to the lack of legal protection, landlords may raise rents and terminate contracts at will, creating a serious sense of insecurity for the rental group. It should further improve the laws and regulations related to housing leasing, clarify the specific conditions for landlords to terminate the contract, strictly control the power of landlords to terminate the contract on their own initiative, as well as enhance the right of tenants to independently choose the contract term.

Finally, the government should expand investment channels for residents while controlling the unreasonable rise in housing prices. The lack of diversified investment channels for Chinese residents has resulted in a large influx of capital into the real estate market, driving up housing prices. The rapid rise in housing prices has led to a further increase in “passive” and “speculative” demand for housing. Some residents have no choice but to buy housing in advance because of their concern that housing prices will continue to rise. Conversely, some other investment speculators achieve asset preservation and appreciation through various ways and means, allowing them to buy a large number of housings, further aggravating the contradiction between supply and demand in the housing market. The continued rise in housing prices not only puts enormous pressure on the public but also sows hidden dangers for the Chinese economy. At the policy level, the government should adopt more neutral financial, fiscal, and tax policies to stimulate the joint development of the commercial housing market and the housing rental market. In addition, it should accelerate the introduction of a property tax to raise the cost of owning a home and thus reduce speculative housing demand.