Front. Archit. Civ. Eng. China 2007, 1(3): 371–377DOI 10.1007/s11709-007-0050-y

RESEARCH ARTICLE

TIAN Ye, BI Xiangyang, LI Dexiang

Feasibility analysis of mixed-income housing in China

© Higher Education Press and Springer-Verlag 2007

Abstract In this paper, it is demonstrated that mixed-income housing is a feasible and beneficial pattern nowadays. This pattern is propitious for good living and harmonious development of society. Based on practice, data are gathered from the field, and the social investment and social distance analysis of the social network theory are used from the perspective of cross-disciplinary fields. The hypothesis is tested and the feasible and beneficial characteristics of mixed-income housing are pointed out.

Keywords mix-income housing, social network, social investment, social distance

1 Research background

Mixed-income housing in this research refers to accommoda-tion of residents having incomes of different levels, so as to form a mutually beneficial and supplementary community. After the market economy was reformed in China, land and housing became commercialized. The income disparity increased and class differentiation gradually emerged. The phenomena of differentiation and segregation among resi-dences of various classes became more distinct. This kind of differentiation and segregation appeared in the context of differentiated rent under the market economy. Spatial and social issues emerged, such as unfair resource allocation, repetitive construction of facilities, conflicts deepening among different classes, etc. It is indicated that the differen-tiation and segregation and mixed-income housing allocation

considerably affected the peaceful life of residents and the harmonious development of society.

Nowadays, in the USA and UK, the advocacy for mixed-income housing is an important trend in the national housing policy. The discussion on this issue initially focused on the academic field. Chaskin, a professor of the University of Chicago, investigated the necessity and feasibility of mixed-income housing from four aspects, namely social network, social control, culture, behavior, and political economy. In his opinion, in the point of view of social network, mixed-income housing stimulates the social vulnerable group to acquire more social investment. In the point of view of social control, it is helpful to provide an informal social control mechanism, which is propitious to the security enhancement of residential districts and stable development of society; in the context of culture and behavior, mixed-income housing weakens the impact of “poverty culture” resulting from poverty convergence, preventing the recurrent deteriorating effect of poverty in residential areas; considering the political economy, mixed-income housing will improve the political and economic status of the residential area, so that it will have a better living environment and basic establishment, while attracting the inflow of investment. Other scholars also share similar opinions. American professor Wilson also regards segregating the poor and medium-income population as going against social stability and tends to induce a poverty culture. Wilson takes mixed-income housing pattern as positive to social stability as it gets mixed-income people together.

Influenced by the above theories and ideas, mixed-income housing has been practiced in the USA and UK since the 1990s. USA mixed-income residential areas in Grand Rapids, Harbor Point, and Emery Bay Club and Apartments, as well as British mixed-income residential areas in Greenwich Millennium Village, etc., brought rich empirical experience for mixed-income housing.

The main purpose of our paper is to probe into the feasibil-ity of mixed-income housing in the context of the develop-ment of living space in China’s current transitional period, and to bring up relevant policy suggestions on the basis of this research. As a practical definition of our research theme, mixed-income housing in this paper indicates the residential

Translated from Architecture Journal, 2006, 4: 36–39 [译自: 建筑学报]

TIAN Ye ( )City Planning and Design Institute, Dalian 116012, ChinaE-mail: tian__ye@sina.com

BI XiangyangCollege of Sociology, University of Police Science and Law, Beijing 100088, China

LI DexiangCollege of Architecture, Tsinghua University, Beijing 100084, China

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community where the percentage of low-income people1 accounts for 10%–20%2 of the total population, while the other four classes distribute homogeneously.

2 General situation of case study

In August 2004, entrusted by a developer of real estate in Chongqing, the Social School and Architecture School of Tsinghua University co-conducted an investigation and research on the Huilong community in Nanan District of Chongqing, and started an integral planning on the basis of this research.

The eastern side of the Huilong community starts from the western side of the Correspondence and Telecommunication College. The western side is adjacent to the Yangtse River, spanning 4–6 km. The northern side of the community begins from the 4 km junction of Chuanqian road at the south dis-trict, and shares a common boundary with Banan district of seven kilometers to the south direction. The whole district occupies 984.41 hectares of land. Huilong community is mainly divided into two parts; taking the Chuanqian road (Xuefu highway) as a line, the western side of the road is mostly for residential and industrial use, while the eastern side is mainly for the use of education.

In the residential section, there are houses for high, medi-um, and low income levels, as well as housing (margin resi-dential areas) for people who changed from rural to non-rural residence registration and whose former houses were disman-tled. According to the above definition of mixed-income housing, as well as the social and economic conditions of residents in various residential areas of Huilong community, we chose six communities as our analysis sample. This includes mixed-income housing and homogeneous-income housing. The details can be found in Table 1.

3 Sampling methods and sample evaluation

The basic condition survey is made using the multi-stage sampling method. At the first stage, the residential area

is chosen for investigation. To make the sample distribute properly in every community and reach the scale required by statistical analysis, a certain amount of quota is set for differ-ent communities according to their current population, so that it covers three different types of residential areas.

At the second stage, limited by impersonal circumstances, the surveyed residents are not chosen from the roster provided by the residential committee. Instead, a spatial sample is cho-sen according to the serial number of the building at the unit. For those sampled areas, the distribution graphs of the build-ings are drawn, the total residential number is made certain, and the residents are surveyed in a random and equal-distance manner.

At the third stage, the surveyed families are selected. First, one family is randomly selected, and sample families are cho-sen counter-clockwise according to a certain sampling space3. After entering the house, the surveyed object is determined by a random chart. According to the predetermined sampling method, a six-day questionnaire survey is conducted in Chongqing Huilong community and its circumference areas from January 30 to February 5. In this survey, 574 of the dis-tributed 680 questionnaires (84.4% of the total) were returned effectively, which meets the requirement of the questionnaire survey.

According to the main indices (gender, age, income, level of education, etc.), the sample is distributed properly and is approximate to the collective distribution status. Therefore, the surveyed sample meets the requirement.

Table 1 Percentage of mixed income population in different communities of the sample (%)

Type of community Total

Mixed-income Low-income High-income community homogeneous homogeneous community community

Upper-level 11.0 1.9 23.4 13.5Medium upper-level 26.9 9.4 31.5 26.1Medium-level 22.0 11.3 23.4 21.1Medium lower-level 23.3 9.4 8.9 17.3Low-level 16.7 67.9 12.9 22.0

1 According to Lu xueyi’s division of the current social classes in China, the ten large classes are divided into five levels according to profession. They are respectively, upper-level: national and social managerial class and the manager group; medium upper-level: self-owned business manage-rial class, specialty technical personnel; medium-level: operational personnel, individual industrial and commercial manager class; lower medium-level: commercial service people and industrial worker class; low-level: agricultural worker and jobless, unemployed and under-unemployed class.2 We regard the low-level population as the social vulnerable group in the city housing space. Therefore, mixed-income housing mentioned in our research emphasizes the existence of low-income population, referring to the housing pattern that low-income class live together with the other classes. According to Professor Zheng Kangsheng’s “China Social Research Report 2002”, if we add different social vulnerable groups together including people below the poverty line, unemployed and laid off workers, as well as peasant workers, then deduct the overlapped part (for example, some employed and laid off workers are also part of people below the poverty line) and the non-vulnerable population (such as the reemployed laid off workers, successful peasant workers, etc.) from it, we can compute roughly that the current number of vulnerable population is roughly 14 billion to 18 billion in China, which is about 11% to 14% of the total national population. Therefore, by mixed housing, we mean the low-income population takes 10% to 20% of the total population.3 Especially, we need to point out that, limited by capital, time, and personnel in our survey, the result of our research cannot be generalized in a strict sense. However, it can better embody the relationship between housing space and social investment inside the investigated area. Meanwhile, compared with pure qualitative research, this quantitative method is more generalized and reliable.

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4 Context and hypotheses of research

Bian presented three conclusions in his research on social net-work and social space [1]: first, there is a large discrepancy between the amount of social network and capital possessed by different people; second, the disparity in social investment can be investigated from different aspects, including peoples’ class and position, the extent of social relation, and the objec-tive material environment having an influence on living space; third, the advantage of social investment can produce subjective and objective effects for its owners, bringing in the payback of income objectively, and enhancing the self-evaluation on social and economic status subjectively. Exist-ing social investment researches regard that there is a positive correlation between social economic status and the acquired social investment, which means low-income residents have lower social investment acquiring capacity compared with those of medium or high income. However, they fail to give a specific explanation to the social investment acquiring situation of the low-income residents under different housing patterns.

In our opinion, spatial effect is the most important factor that influences people’s social interaction and social investment acquisition, and affects the subjective aspects of people’s social interaction, such as spatial distance. There-fore, two primary methods are adopted in the social network theory, namely social investment analysis and social distance analysis. Combining the aim of this research, theoretical hypotheses (Hypothesis I–III) and working hypotheses (H1–H5) are proposed according to the existing theories and practices.

Hypothesis I Spatial factor is an important dimension in social interaction.

H1: Resident-social space type plays a significant role in social investment acquisition.

Hypothesis II Mixed-income housing will lessen the disparity of acquirement of social investment brought about by the different income.

H2: In the mixed-income housing pattern, the emotional interaction between low-income population and high or medium income people are higher than that in the homoge-neous housing pattern.

H3: In the mixed-income housing pattern, the instrumental interaction between low-income population and high or medium income people are higher than that of the homoge-neous housing pattern.

H4: The social investment acquired by low-income popu-lation is higher in the case of mixed-income housing pattern than that of homogeneous housing pattern.

Hypothesis III Mixed-income housing pattern aids in alleviating the feeling of segregation.

H5: The social distance between high and low income classes is smaller in mixed-income residential area compared with that in homogeneous residential area.

5 Feasibility test of the mixed-income housing pattern

5.1 Test of Hypothesis I

In this paper, the multilevel linear model is applied to test Hypothesis I, whether housing space type has a significant effect on social investment acquisition. On the measurement of social investment, usually two methods, orientation meth-od and network difference method, can be adopted. In the former method, four aspects are considered. They are social network scale, top of network, network differences, and net-work structure. In the next one, only the network difference is taken into account. According to the research of Bian [1], it is advisable to adopt network difference as the only index in social investment measurement. Network difference has roughly the same explanation power as the gross index of social investment. In our research, we measured social invest-ment with network difference method, dividing the surveyed people into five ranks4 according to ten different vocational types, and the network difference is the difference of scores given to their vocations.

To accurately verify the relationship between space type and social network acquisition, we adopt gender, age, educa-tion, social class, and marriage status as the background variables, so as to test the causal relationship between the two when these variables are controlled.

Variable pretreatment: as variables such as sex and educa-tion belong to classifying and ranking variables, they are required to be treated as dumb variables before being adopted into the model. For gender, the male is taken as reference variable; for education level, illiterate is regarded as a refer-ence variable; for social class, middle class is treated as a reference variable; for marriage status, single is the refer-ence variable; lastly, for community type, the homogeneous community is the reference variable.

The output of the multilevel linear model shows that, the effects of such background variables as gender, age, educa-tion level, and marriage status are not significant on network differences, while the type of resident-social space plays an important role on the size of the network. This result validates the assumption of Hypothesis I that the type of resident-social space has a determinant effect on the social investment acqui-sition. It is inferred that by controlling the factor of residential district type, the situation of low-income population can be improved in the acquisition of social investment, which is displayed in Table 2. The regression model passed the R2 test, significance level = 0. Table 3 shows the results after the non-significant variables are removed.

If we replace the original dependent variable with interac-tion in one’s own community, the output is basically the same as the previous data. Community type and some of the class variables play a significant role, while the other background

4 The ranking method is the same as the five-level classification methods in the definition of mixed housing.

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variables still do not. However, marital status variable becomes significant now due to the frequent interaction with the spouses.

5.2 Test of Hypothesis II

5.2.1 Instrumental interaction and emotional interaction

The contents of interaction are tested among residents in dif-ferent-type residential areas, mainly concerning the frequency of emotional and instrumental interaction that happens among the low-income class and the other social classes6.

Our research indicates that in the mixed residential area, the emotional interaction that happens between the low-income class and the other social classes accounts for 49.6% of the total options. However, the corresponding percentage in the homogeneous residential area is only 14.4% and 9.0%. With respect to the instrumental interaction that happens

among the low-income class and the other social classes, it accounts for 52.50% of the total options, while in the homogeneous residential areas they are 11.5% and 9.0%, respectively.

The result indicates that in the mixed housing, the low-income class can acquire more emotional and instrumental aid from the other income classes compared with the homo-geneous communities, thus validating our hypothesis of H2 and H3 (Table 4).

Table 2 Multivariable regression model5

Non-standardized coefficient Standardized coefficient t-test Significance level

Non-standardized Standardized Standardized regression coefficient error regression coefficient

Constant 0.100 0.342 0.292 0.770Mixed housing 0.450 0.134 0.278 3.364 0.001High-income Homogeneous 0.553 0.140 0.333 3.937 0.000Gender (female) −0.058 0.078 −0.036 −0.736 0.462Age −0.002 0.004 −0.027 −0.445 0.657High class 0.526 0.131 0.220 4.005 0.000Medium upper class 0.024 0.109 0.013 0.216 0.829Medium lower class −0.146 0.119 −0.069 −1.227 0.220Low class 0.512 0.130 0.259 3.923 0.000Primary school 0.239 0.307 0.059 0.778 0.437Secondary school 0.325 0.270 0.159 1.205 0.229High school 0.420 0.283 0.217 1.485 0.138College level 0.326 0.286 0.198 1.140 0.255Graduate and above 0.301 0.305 0.120 0.985 0.325Married −0.116 0.116 −0.056 −0.996 0.320Other marital status −0.306 0.230 −0.073 −1.331 0.184

Table 3 Multivariate regression model (after deleting the non-significant variables)

Non-standardized coefficient Standardized coefficient t-test Significance level

Non-standardized Standardized Standardized regression regression coefficient error coefficient

Constant 0.183 0.127 1.445 0.149Mixed housing High-income 0.481 0.130 0.298 3.706 0.000Homogeneous 0.610 0.133 0.367 4.585 0.000High class 0.539 0.111 0.226 4.853 0.000Low class 0.447 0.099 0.226 4.525 0.000

5 Since this case contains both data from individual level and community level, the most suitable method is multilevel linear model. However, because the involved community number in this research is relatively few, we adopt the general multivariate linear regression model.6 With respect to the content of relationship, in our questionnaire, minor aid in everyday life, settling of problems when others are in trouble, mutual help in life and business, aid in children’s education and assistance during economic plight are regarded as the instrumental aid; everyday chat, cares during illness, greetings and present exchange during holidays, suggestions in irresolute condition, trouble elimination, enlightening while upset and helping out in the family, marriage as well as love affair issues (introducing boy/girl friend, dealing with family entanglement, etc.) are emotional aid.

Table 4 Instrumental and emotional interaction between the low-income class and the other social classes in different types of housing

Mixed housing Low-income High-income homogeneous homogeneous

Emotional interaction 49.60% 14.40% 9.00%Instrumental interaction 52.50% 11.50% 9.00%Total 51.80% 12.20% 9.00%

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5.2.2 Capacity of acquisition of social investment

The same as testing Hypothesis I, we use the net difference method in measuring the acquisition of social investment, i.e. to replace social investment acquisition indices with the net difference indices.

Our research indicates that different housing patterns are remarkably different in terms of net difference. Especially, the net difference has the highest average in the high-income homogeneous residential areas, but the lowest average in the low-income homogeneous residential areas, with mixed-income in the middle. The reliability level of the variance analysis is 0.000, lower than 0.05, which is significant under statistical standards (refer to Table 5).

homogenous housing, therefore the social investment acqui-sition capacity increased by a large extent correspondingly. In this view, mixed-income pattern helps the low-income residents to acquire more social investment, thereby bringing better subjective and objective effects for them.

5.3 Test of Hypothesis III

With respect to the social segregation, we adopt the Bogardus social distance measurement chart. Taking Bogardus chart as a reference when choosing the variables, we also adjusted the sub-item contents according to the real situation in research, and finally fixed seven questions to form the measurement chart:

(1) live in different communities from mine, and try not to visit my community;

(2) work in my community, yet live in different communi-ties from mine;

(3) live in the same community;(4) participate in the administration of the community

affairs together;(5) be a neighbor;(6) be intimate friends;(7) be relatives or intermarry.These seven items gradually decreases with respect to the

social distance, and the given value to them also decreases from 8 to 1. Among them we give the highest social distance value 7 to “live in different community from mine and try not to visit my community”, while the lowest social distance value 1 to the “be relatives and intermarry”. We added up the scores through the measurement chart, to form the distance measurement value of some social class to the other classes, then use 8 to subtract this value to get the social distance value

social distance of a certain class = 8-social distance measurement chart value of a certain class and other classes.

We measured two types of social distance; one is the aver-age value of social distance inside the community, which is the social distance among different classes, with the aim to observe the overall social distance disparities of different communities. The other type is the social distance between low-income class residents and the other residents, with the aim to observe the extent of segregation between low-income class residents and the other residents in different communities.

Statistical analysis result shows that the average level of social distance in mixed-income and homogeneous dis-tricts are similar where there is basically no difference. This indicates that the mixed-pattern community is not inferior to the homogeneous community with respect to overall social distance, which can be inferred from Table 7.

On the other side, between mixed-pattern community and homogeneous community, there is a distinct difference with regard to the social distance between low-income residents and others. The social distance between low-income residents and others is lowest in the mixed-income community

Table 5 Differentiation in network difference of low-income people in different housing patterns

Housing type Average Case involved Std. error

Low-income homogeneous 0.488 7 50 0.6077 9Mixed housing 0.792 3 226 0.7650 4High-income homogeneous 0.969 8 171 0.8705 0Total 0.826 2 447 0.8037 1

Table 6 Mean values in network difference of low-income people in different housing patterns

Housing type Average Case involved Std. error

Low-income homogeneous 0.22 147 0.628Mixed housing 1.15 130 1.342High-income homogeneous 1.56 104 1.306Total 0.90 381 1.241

This result indicates that H4 holds that low-income popu-lation has a higher social investment acquisition capacity in the mixed-income housing than in the low-income homo-geneous housing, although lower than the high-income homogeneous one. However, this outcome still shows that mixed-income housing helps improve the social investment acquisition capacity for the low-income class.

Consider that there could be two possible interaction scopes for interactions between different classes: inside and outside of the residential area. The variance of interaction scope could influence the size of network difference; there-fore we should conduct further analysis by confining the interaction scope to one community. The result indicates the above trend as well. This means that in the mixed-income housing pattern, the network difference of interaction between the low-income class and the other classes in the residential area is much higher than that in the low-income

Table 6 shows the average value of net difference of the low-income class in different residential areas. In the low-income homogeneous community, the average value of net difference is 0.22, while in mixed-income and high-income homogeneous residential areas it is 1.15 and 1.56, respectively.

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(Table 8). The variance analysis shows the result of 0.043, which is less than 0.05, indicating statistical reliability of the outcome.

6 Conclusions

Through social investment and social distance analysis we found that the pattern of mixed-income housing is advantageous and feasible to some extent.

In this paper, the validity of Hypothesis I that spatial living type plays a determining role in the acquisition of social investment is first tested. With respect to the social invest-ment acquisition, the social investment acquisition capacity of low-income residents could be improved when taking the housing type as a controllable variable. Meanwhile, in the mixed-income housing, low-income population can get rela-tively more instrumental and emotional interactions. It is indicated that there exists a positive relationship between the social investment acquisition and the other classes’ social and economical status in the community where low-income popu-lation resides, which also indicates that mixed-income living pattern is helpful for the low-income population to acquire social investment.

Second, through social distance analysis, it is indicated that in a mixed-income housing pattern, the social distance between low-income class residents and other residents are smaller than the homogeneous housing pattern. Meanwhile, the average social distances in mixed-income and homoge-neous housing are similar and there is basically no difference. It is demonstrated that concerning integral social distance, mixed housing is not inferior to homogenous housing. In summary, it is indicated that the social segregation feeling in mixed-housing pattern is weaker than that in the homoge-neous housing. Meanwhile, regarding social distance symme-try, there is stronger symmetry in the mixed-housing pattern than that in the homogeneous housing. From a practical sense, mixed-income housing is conducive to lessening the social distance between low-income classes and other resi-dents, and helps to relieve the issues of self-insulation and self-segregation of the low-income population.

As to the mixed-income housing, one of the main prob-lems in the common view is the mutual repulsion between low-income residents and other classes due to the differences in social and economic status as well as in living patterns.

Table 7 Average social distance level in different communities

Mean Std. error

Mixed housing 4.675 0.609 8Homogeneous housing 4.735 0.449 9Total 4.705 0.531 2

Note: The regression model did not pass the R square test, significance level = 0.694

Table 8 Social distance between low-income class and other classes in different communities

Mean Std. error

Mixed housing 4.02 0.458 2Homogeneous housing 4.38 0.291 0Total 4.20 0.431 2

Note: The regression model passed the R squared test, significance level = 0.043

Table 9 Asymmetrical social distances between low-income class and other class in different-typed communities

Mixed housing Low-income High-income homogeneous housing homogeneous housing

Upper level 3.79 4.20 3.84Medium upper level Lower level 3.46 4.39 4.06Medium level 3.79 3.76 3.97Medium lower level 2.83 3.50 3.92

Mean value 3.47 3.96 3.95

Upper level 4.19 3.96 4.15Lower level Medium upper level 3.59 3.71 4.54 Medium level 4.19 3.44 4.24 Medium lower level 4.10 4.70 4.64

Mean value 4.02 3.95 4.38

We need to point out that asymmetry in social distance exists between low-income residents and other residents. This asymmetry is mainly reflected from two aspects:

(1) In different patterns of community, even inside the communities, social distance asymmetry exists between low-income residents and other residents.

In mixed housing and homogeneous housing patterns, the social distance between low-income class and other population is higher than the latter. This means that self-isolation of low-income population exists in different types of communities.

(2) The symmetry in social distance is stronger in mixed-income housing than its homogeneous counterpart.

Further analysis indicates that in the case of mixed-income housing, social distance between low-income residents and other residents is less than that in the homogeneous housing pattern. This means that self-isolation of the low-income population is weaker in the mixed-income housing. Detailed data can be found in Table 9.

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However, according to our analysis in this case, we conclude that it is easier for the low-income population in the mixed-income community to acquire more social investment. Besides, they have lower social distances with the other classes and have less self-insulation problems. These factors help the low-income residents to improve their social and economic level, and enhance their subjective evaluation on their social status.

For those social classes with medium or high incomes, integral social distance in the mixed-pattern housing is not remarkably different from that in the homogeneous housing, which means the income difference does not bring in signifi-cant difference in integral residential social distance between mixed-pattern housing and homogeneous-pattern housing. Generally speaking, there may exist some latent coexistence relationship between medium or high income residents and low income people. For example, the medium or high income residents can provide job opportunities for the low-income residents such as community guard, hourly work, child-care, care for the old people, etc. On one side, this means job opportunities for the low-income population; meanwhile it will possibly lessen the concerns for community security on the medium and high income population. Although we did not prove the possibility of this idea, in the future the issue of how different classes benefit from the mixed-income housing should be gradually dug into and verified.

Another point to be clarified is that our research only involves six communities. In addition to verification of feasibility and advantage of mixed-income housing pattern,

other large-scale surveys and researches are expected on the similar topic in the future.

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