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The affordability of owner occupied housing in Beijing

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Abstract

Facilitating home ownership and providing more affordable housing has received considerable attention in urban China. However, it remains a challenge to develop better measurements of affordability due to the income disparity and housing inequality in Beijing. In this study, a new measure of affordability is defined by residual income. Therefore, a minimum required budget for a family to purchase a “standard” unit is deduced by accounting for the basic necessities and financial loans. This paper also discusses the deficiencies of the implemented “Economic Housing Plan” on bridging the housing affordability gap.

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Notes

  1. More discussion on the motivation for the housing reform can be found in Wang 2000 and in Rosen and Ross 2000.

  2. According to Beijing Statistics Bureau, the ownership rate was 67.4 and 73.6%, respectively, in year 2003 and 2004.

  3. The cheap rental housing project was started earlier in August 2001 in Beijing, but by 2004 there were only a few families that had been allocated their residences by this project.

  4. The discussion on the deficiencies of a ratio indicator as a measure of affordability can be found elsewhere, e.g. in Hancock 1993; Hulchanski 1995 and Thalmann 2003.

  5. There are in total 11 urban districts classified by authority in Beijing. In this paper only two districts, Changpin and Daxing, which are in suburban areas, are excluded in the data.

  6. We are grateful to Xin He at Tsinghua University in Beijing for helping with the data collection.

  7. The result from the cluster method indicates that the average value for 95 square meters is 45,000 RMB.

  8. Interest rate for housing accumulation found is 4.41%.

  9. A loan for housing is 70% of 418,000, which is 292,600. For a loan with an interest rate of 5.51% and 20 years maturity, the annual repayment will be \( \frac{292600}{AF}, \) where AF is defined as annual factor in finance, and \( AF = \frac{1}{0.0551} - \frac{1}{{0.0551 \times (1.0551)^{20} }}. \)

  10. Average of 2.95 persons for each family.

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Acknowledgements

The research is supported by a grant from the Swedish International Development Cooperation (SIDA). We are grateful to the seminar in the institute for housing and urban research, Uppsala University of Sweden. We also thank to the reviews of this journal for their comments on earlier versions.

Author information

Authors and Affiliations

  1. Institute for Housing and Urban Research, Uppsala University, Uppsala, Sweden

    Zan Yang

  2. Institute for Real Estate Studies, Tsighua University, Beijing, China

    Yue Shen

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  1. Zan Yang
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Correspondence to Zan Yang.

Appendices

Appendix 1: New construction projects in Beijing in 2003 and number of projects selected for the study

District

Number of new projects (%)a

Number of selected projects in database (%)

Number of selected new apartments in database (%)

Tongzhou

53 (20.2)

21 (22.58)

232 (24.04)

Caoyang

50 (19.0)

26 (27.96)

228 (23.63)

Haidian

38 (14.4)

22 (23.66)

237 (24.56)

Daxin

29 (11.0)

0

0

Fengtai

25 (9.5)

10 (10.75)

120 (12.44)

Changpin

15 (5.7)

0

0

Shijinshan

10 (3.8)

5 (5.38)

53 (5.49)

Xuanwu

8 (3.0)

3 (3.23)

32 (3.32)

Chongwen

6 (2.3)

3 (3.23)

34 (3.52)

Xicheng

3 (1.1)

2 (2.15)

23 (2.38)

Dongcheng

2 (0.8)

1 (1.08)

6 (0.62)

  1. a Source: “Research report of Beijing residential market in 2003” by the Institute for Real Estate Studies, Tsighua University, Beijing

Appendix 2: Cluster analysis

In principle, the cluster’s purpose is to identify the homogeneous subgroups to minimize within-group variation and maximize between-group variation on the housing market. There are a large number of methods that can be used for clustering procedures, and there is no consistent method found to determine the number of clusters. Nevertheless, in this study, the number of clusters is not our concern, as we are interested only in the clusters whose average area is close to 90. In this study, we achieved the cluster using K-means cluster analysis with the Euclidean distance method. As a result, three clusters were determined. Our target group is the one whose mean area of the unit is 95.

In examining the descriptive statistics for this cluster (see table), we notice that more than 70% of the units in the cluster are provided with good transportation conditions. Fewer units are located close to public facilities including school, grocery, hospital and supermarket. This evidence is consistent with our assumption of the “standard” housing unit. However, only 19% of the units in the group are located within the fifth ring and more than 40% are located outside the fifth ring. This is in fact due to the distribution of the new construction projects in 2003, as shown in Table 2. Concerning the fact that the sixth ring is newly constructed and is still under development, we still set our “standard” location in the fifth ring. The average value of this cluster is 453,461, which is a bit higher than the value we estimated for our assumed “standard” housing; this is due to the larger area and higher number of public facilities involved in the cluster. This suggests, however, that our estimated value is reasonable for the study.

Descriptive statistics for Cluster 2

 

Minimum

Maximum

Mean

SD

Area

77.4

111.06

95.4388

8.8259

Price

203,046

820,398

453,461

147688.773

Ring3

0

1

0.1089

0.395

Ring4

0

1

0.14

0.398

Ring5

0

1

0.19

0.458

Transport

0

1

0.7060

0.490

School

0

1

0.14

0.500

Supermarket

0

1

0.37

0.476

Grocery

0

1

0.09

0.294

Hospital

0

1

0.24

0.447

Observations: 316

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Yang, Z., Shen, Y. The affordability of owner occupied housing in Beijing. J Hous and the Built Environ 23, 317–335 (2008). https://doi.org/10.1007/s10901-008-9120-2

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