房地產(chǎn)業(yè)去庫存外文文獻翻譯中英文2020譯文

1、房地產(chǎn)業(yè)去庫存外文文獻翻譯中英文文獻出處：Socio-Economic Planning Sciences J Volume 69, March 2020譯文字?jǐn)?shù)：5000多字原文Measuring destocking performance of the Chinese real estate industry: A DEA-Malmquist approachKun Chen， Yao-yao SongAbstractThis paper aims to measure the evolution of destocking performance of the Chinese Real

2、 Estate Industry based on a DEA (Data Envelopment Analysis)-Malmquist approach, which is seldom used in this industry in existing literature. In 2016, the Chinese government introduced a unified national destocking policy to reduce real estate inventory to save the downturn in thereal estate market,

3、 but the effect was less than expected and led to soaringhouse pricesin first-tier cities. By analysing the destocking performance over the period from 2005 to 2015, we find the following: (1) It is impossible to use a unified policy to effectively address the national destocking issue because of th

4、e difference of DMUs destocking efficiency, input redundancy and total factor productivity score changes. (2) With the current destocking performance and investment status, the government is still ignoring the existingcommercial real estateproblems. (3) The redundancy of firm assets and staff indica

5、tes that zombie firms may exist and risk future unemployment in the real estate industry. (4) Despite the recently repeated government interventions in this industry, destocking performance remains falling since 2008, and problems in other regions is more severe than in central cities. (5) The finan

6、cial crisis triggered by the USsubprime mortgagecrisis has had a great impact on Chinas real estate industry. The destocking performance dropped sharply in 2008, forcing the Chinese government to introduce policies to stimulate the real estate market. Policy recommendations are also put forward base

7、d on the findings.Keywords：Productivity，Malmquist index，Destocking performance， Chinese real estate industryIntroductionOver the past two decades, Chinas economy has grown rapidly and ChinasGross Domestic Products(GDP) has become the second largest in the world. Large-scale population migration from

8、 the countryside to the city has occurred, and theurbanizationrate increased from 27.99% in 1993 to 56.1% in 2015.1Rapid urbanization has brought a strong real estate demand, which leads to the prosperity of the real estate industry and promotes the development of industrial clusters. Thus, the real

9、 estate industry has become one of the pillars of Chinas economy in terms of promoting the growth of GDP.As the only land supplier,2the Chinese government received a huge amount of land transfer payments, which were even more than the public budget revenue, and resulted in a so-called “l(fā)and finance”

10、 phenomenon in many Chinese cities 1. However, the rapid urbanization and “l(fā)and finance” brought about an increase inhouse prices. In Beijing, Shanghai and some other cities, the growth is even much faster than the economic growth. High housing prices have become a main concern in Chinese society, c

11、ausing not only major pressure in lives of people in China but also concerns that Chinesereal estate markethas emerged a real estate price bubble like the one in Japan during 19861991 or there will be a financial crisis like USsubprime mortgagecrisis in 2008. Accordingly, the real estate industry in

12、 China has become an important issue in maintaining the governments operation and affecting the vital interests of the Chinese people.Therefore, the real estate industry has become the focus of nationalmacroeconomicregulations and control in China. Since 2005, the Chinese central government has intr

13、oduced a series of control policies, which were aimed at saving the housing market during downturns and suppressing the market when it is in high demand, to ensure the sustainable development of the real estate industry and to avoid the emergence of real estate price bubble and financial crisis. For

14、 example, in 2014, the real estate market began to decline, and the government began to relax regulations on the housing market. Since 2010, the destocking cycle of the real estate inventory has continuously risen and the central economic work conference3held at the end of 2015 placed “reducing real

15、 estate inventory ”(RREI)4as one of the top five tasks in 2016. In early 2016, the government introduced a series of measures or regulations (e.g., reducing the proportion of down payment and reducing loan interest rates, etc.). Subsequently, all provinces and cities have also issued the correspondi

16、ng inventory reducing policies. Some financial institutions have even introduced down payment loans to help reduce the down payment ratio to a very low level as before the US subprime mortgage crisis. Unexpectedly, in 2016, the housing prices of the first-tier cities, led by Beijing and Shenzhen, sk

17、yrocketed like Japan during 19861991, and the inventory reduction significantly accelerated. In contrast, the real estate industry of many third-tier or fourth-tier cities (e.g., prefecture-level cities and counties) was stagnant and the corresponding inventory remained high.In such a case, the gove

18、rnment learned from the situation and tried to regain control of the real estate market policy. The government stressed the position that, “the house is used to live instead of to speculate”. In March 2017, The Ministry of Housing and Urban-Rural Development (MoHURD) and the Ministry of Land Resourc

19、es (MoLR) issued a notice on “Strengthening the recent work on the management and regulation of the housing and land supply”, which asked the local governments to formulate the land supply policies according to the length of the residential destocking cycle.5However, managing the complex situation i

20、n Chinas real estate industry according to a single indicator, the residential destocking cycle, is unreasonable. There is a need to construct a systematic approach that can analyse the real estate inventory correctly and provide reasonable advice to the government and some recommendations for other

21、 countries.This paper aims to clarify the following questions: First, based on the Communist Party of China (CPC) Central Committee and the State Councils RREI task in 2015, which provinces or cities need to reduce their real estate inventory and how much are their inventories? Considering not only

22、the real estate itself, but that the employees and corporate assets are inputs in the process of selling real estate, the redundancy of these inputs also needs to be analysed. Second, how did office buildings, shops and othercommercial real estateperform? This type of real estates is ignored by the

23、government but have an important impact on Chinas economy. Finally, how did the Chinese destocking performance from 2005 to 2015, how did the regulations and controlling policies affect the destocking performance, and what impact did the 2008 financial crisis have on Chinese destocking performance?

24、The clarification of these issues could be used as a reference for policy makers in Chinas real estate industry and have implications for other countries.Literature reviewMany scholars have studied the efficiency of real estate-related industries, which can be divided intoReal Estate InvestmentTrust

25、s (REITs) related research and real estate companies related research. This research can be used to help guide people to invest in real estate companies or funds and improve the efficiency of companies, but the focus is not on governmentpublic policy issues.Within the study of REITs, Bers and Spring

26、er 3 investigated the economies of scale of REITs in the United States (US) from 1992 to 1994, and the results showed that economies of scale exist in all years and productivity improvements can be achieved through scale expansion. Anderson et al. 2 used the DEA method to estimate the technical effi

27、ciency and economies of scale of REITs for a time series from 1992 to 1996. The results show that the technology efficiency of REITs is ineffective, mainly due to low input utilization and an inability to operate at constant returns to scale (RTS). Topuz et al. 5 studied the operational efficiency o

28、f US REITs and found that the average efficiency of REITs was low in 19891999, and most REITs were even lower in scale efficiency. Miller and Springer 4 used the stochastic frontier model and panel data to estimate the operational efficiency of REITs. In contrast to previous research conclusions, th

29、ey failed to find evidence of economies of scale and diseconomies of scale.Within the research of the construction industry and real estate companies, Wang and Chau 11 applied DEA to assess the technical efficiency of the construction industry in Hong Kong from 1981 to 1996. The results show an incr

30、easing trend in average technical efficiency. In addition, they found that a higher technical efficiency ratio came from large-scale, capital-intensive construction companies with a low degree of subcontracting and a low proportion ofintermediate inputconsumption. Hui et al. 8 studied the performanc

31、e of Hong Kong property companies and compared them to property companies in Singapore. Their results show that the property companies with diversified business outperformed the companies that focused on real estate. In addition, Hong Kong companies outperformed their Singapore counterparts, but the

32、 overall performance was still poor. You and Zi 13 analysed the performance of the Korean construction industry over the period from 1996 to 2000 using the DEA method. The results showed that the efficiency was significantly reduced over the examined period and that the difference was significant be

33、fore and after the financial crisis in 1997. They also found that the low-cost efficiency was mainly due to the allocative inefficiency. Elmashaleh et al. 6 proposed a benchmark model to analyse both the performance evaluation indicators traditionally used in the construction industry and how to all

34、ocate resources to improve the performance of the industry. Pan and Yang 9 conducted a survey on the scale economy of Chinas real estate industry from 2004 to 2008. The results found that scale economies existed in Chinas real estate industry over the examined period, but the impact of scale economi

35、es on small companies and non-state economy is greater. Horta et al. 7 used the DEA approach to assess the financial performance and competitive environment of the Portuguese construction industry between 1996 and 2007. They found that the performance of the Portuguese construction industry increase

36、d, of which large companies and small professional companies performed better. Tsolas 10 evaluated the profitability and performance of 16 Greek listed construction companies using a new framework that integrated DEA and ratio analysis, and the results show a positive correlation between profitabili

37、ty efficiency and effectiveness. Wong et al. 12 used DEA to evaluate the efficiency of Irans construction and real estate companies, and the results showed that most of the real estate companies in Iran were technical, scale and mix efficient, but real estate and construction companies were experien

38、cing diseconomies of scale.In addition, regarding the research on Chinas real estate industry, Wang 15 constructed a knowledge-baseddecision support system, which uses the DEA model to measure the performance of government real estate investment. His investment assessment framework includes a databa

39、se, a model base and a knowledge base, and creates a tool that gives government decision-making suggestions through the Internet. Li 16 used the cross-efficiency DEA method to measure the operational efficiency of the real estate industry in major Chinese cities, and obtained the cross-efficiency fl

40、uctuating range for each city. Jiang et al. 17 established the real estate management efficiency evaluation model through DEA to analyse the real estate industry efficiency in Ningbo, China, and discussed its influencing factors and suggested an adjustment to the industrial structure and the directi

41、on for improving industry efficiency. Wei et al. 18 established a real estate industry investment efficiency evaluation model based on the super-efficiency DEA, and evaluated 35 large and medium cities in China. The results show that excessive investment and inefficient investment are problems in Ch

42、inas real estate industry. He also found that compared with the central region of China, the investment efficiencies of the eastern coastal areas and the Northwest region are higher, and they are or are close to DEA efficient. Zheng et al. 14 used the DEA method to establish a ranking system for eva

43、luating the listed real estate companies. Through empirical analysis of 94 Chinese real estate companies, it was found that the average total efficiency, pure technical efficiency and scale efficiency were ineffective, and 69% of the companies can increase their operational efficiency by expanding t

44、he scale. The inputs and outputs used in several previous literatures are listed in Section4.3.From the above literature, although many studies attempt to assess the efficiency of REITs, construction and real estate companies, few studies intend to assess the performance of the real estate industry

45、from the perspective of administrative divisions and the inventory reduction policy. Additionally, few studies have used the DEA-Malmquist approach to track the efficiency evolution (productivity) of the real estate industry. Since the construction land is mainly supplied by the government and there

46、 is no private land in China, research regarding administrative divisions over time is meaningful for the better understanding of the real estate industry in China.Conclusions and discussionsIn this paper, we use the DEA-Malmquist method to measure the destocking efficiency and its TFP changes of th

47、e real estate industry in China from 2005 to 2015, and the following results were obtained: (1) No central cities or other regions need to reduce their land supply, 33/35 central cities and 25/27 other regions need to increase the supply of residential land, meanwhile the accumulation ofcommercial r

48、eal estatestocks is larger than that ofresidential real estate, and the stock of other regions is larger than that of central cities. (2) The efficiency of the real estate industry in 2015 shows that there were 24 inefficiencies in 35 central cities and 16 inefficiencies in 27 other regions, and the

49、 efficiency of central cities is lower than that of other regions. The residential, commercial, asset, and staff input redundancies of DMUs are quite different. (3) In 2005 to 2015, the TFP score of most DMUs 1 or=1. From the geographical distribution, the DMUs which TFP scores 1 or=1 are mainly con

50、centrated in the eastern part of China, especially in East China, while there are no DMUs with TFP scores 1 or=1 in the northwest or southwest parts of China. From the difference between the central cities and the other regions, which both belong to one province, the formers average TFP score is lar

51、ger than that of the latter, indicating that the destocking productivity of the other regions declines faster than that of the central cities. (4) Chinas average TFP score is 0.963 from 2005 to 2015, indicating that the destocking productivity of Chinas real estate industry declined. The average TFP

52、 score of other regions is 0.944, which is smaller than that of central cities, 0.978, indicating that the regress rate of other regions destocking productivity is greater than that of the central cities. From the Malmquist index decomposition, the pure technology efficiency index and the scale effi

53、ciency index are very close to unity, indicating that the decline in the TFP score is largely affected by the decline in the technological progress index.Through the analysis and discussion of the results, we can draw the following conclusions: (1) It is impossible to use a unified policy to address

54、 the national destocking issue, for the difference of DMUs destocking efficiency, the input redundancy and change of TFP scores. Policies should be developed based on the actual situation in different regions which may be very different or even opposite. (2) From the DMUs destocking efficiency and i

55、nvestment redundancy in 2015, we predicted that the RREI policy would not work in 2016, and even now the Chinese government is still ignoring the existing commercial real estate problems. (3) The redundancies of firm assets and practitioners indicate that zombie firms may exist and that there is a r

56、isk of future unemployment in real estate industry. (4) The Chinese government frequently introduced policies to intervene in thereal estate marketbetween 2005 and 2015. However, the destocking productivity has been declining since 2008 indicating that the problem of destocking was becoming worse, a

57、nd the problem is mainly from other regions for the destocking productivity of other regions decreased by 35.2% while the destocking productivity of central cities increased by 6.5%. (5) The financial crisis triggered by the USsubprime mortgagecrisis has had a great impact on Chinas real estate indu

58、stry. The destocking productivity dropped sharply in 2008, forcing the Chinese government to introduce policies to stimulate the real estate market.Based on the above analysis, we propose the following recommendations: (1) For industries with large volume and wide coverage, policy makers should form

59、ulate appropriate policies according to different levels and types of policy objects. In the case of Chinas real estate industry, policy makers should develop different policies according to the real estate inventory situation of different prefecture-level or even county-level cities. Besides, policies for commercial real estate should also be developed, since the destocking problem