Regional efficiency of the real estate industry in 35 large and medium-sized cities in China: a meta-frontier SBM approach

ABSTRACT

As the inputs of investment and employees in real estate sectors have continued to increase in recent years, it is crucial to study the evaluation of real estate performance. However, these rarely take the differentiation of the research objects into account, which easily causes deviations of empirical results. In order to obtain more authentic efficiency values, this study systematically analyses the regional real estate efficiency of 35 large and medium-sized cities in China from 2013 to 2017 based on the meta-frontier data envelopment analysis (DEA) with a slacks-based measure (SBM). Specifically, this study firstly simulates the production process by designing a three-stage dynamic DEA model and subsequently conducts a grouping analysis of the evaluated cities by introducing the meta-frontier framework. Through empirical analysis, we find that: (1) grouping regional efficiency evaluation is a modification of ungrouping efficiency; (2) the simulation of the real estate production process contributes to identifying the key problem which influences real estate efficiency; (3) government policy has a certain influence on the regional real estate sector in China. Finally, policy recommendations are proposed from three dimensions: the dimension of national macro policy; the dimension of policy implementation in local governments; and the dimension of management in real estate companies.

KEYWORDS:

• China’s real estate industry
• data envelopment analysis
• regional real estate efficiency
• a meta-frontier framework
• a slacks-based measure

Disclosure statement

No potential conflict of interest was reported by the authors.

Data Availability Statement

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

Notes

1. Note: China Statistical Yearbook 2018. http://www.stats.gov.cn/tjsj/ndsj/2018/indexch.htm.

2. Note: China Statistical Yearbook 2018. http://www.stats.gov.cn/tjsj/ndsj/2018/indexch.htm.

3. Note: World Urbanisation Prospects 2018. https://population.un.org/wup/Download/.

4. Note: The working meeting of MoHURD. http://www.gov.cn/xinwen/2018-12/25/content_5351829.htm.

5. Note: OECD database: https://data.oecd.org/price/inflation-cpi.htm#indicator-chart.

6. Note: Financial indicators of the real estate enterprises mainly include solvency indicators, profitability indicators and other first-level indicators. Among them, completed investment (CI) refers to the main second-level indicator in the solvency indicators, while value of commercialised housing sold (VCHS) can be regarded as the second-level indicator in the profitability indicator.

7. Note: The Central Economic Working Conference in 2015. http://www.gov.cn/xinwen/2015-12/22/content_5026370.htm.

8. Note: More Chinese cities further restrict housing purchases to cool market. http://english.www.gov.cn/news/top_news/2017/03/17/content_281475598407436.htm.

9. Note: Several Measures to Promote the Stable and Healthy Development of the Real Estate Market. http://zjw.beijing.gov.cn/bjjs/xxgk/gsgg/391380/index.shtml.

10. Note: The Business Glamour Ranking of Chinese Cities. https://www.yicai.com/news/100200192.html.

11. Note: Urbanisation rate in Shenzhen. https://baike.baidu.com/item/%E6%B7%B1%E5%9C%B3/140588?fr=aladdin.

12. Note: The analysis is based on the China Statistical Yearbook.

Additional information

Funding

We would like to acknowledge the support of the National Natural Science Foundation of China [No. 71671181].