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There are a large number of interdependent risk factors in complex project. Risk response strategy without considering risk correlation cannot achieve good risk response. Therefore, the purpose of this paper is to propose a risk response strategy selection model considering risk correlation based on the grey K-shell algorithm.

This paper mainly focuses on the measurement of two aspects of risk factors. One is the ability of the risk factors to influence other risk factors, another is the degree affected by other risk factors. Both of the above are measured by the grey K-shell algorithm improved in this paper, and the weights of these are used for the constructing of risk response strategy selection model.

The risk response strategy considering risk relevance is more effective than the risk response strategy without considering risk relevance. Also, results indicate that as the risk response budget increases the risk response effect also increases, and the increasing trend is weakens. The relative gap between the effect of response strategies considering risk relevance and the strategy without considering risk relevance increases first and, then, decreases with the increase of budget.

The results of this paper demonstrate that the risk response strategy considering risk relevance is more effective than not considering risk relevance. The approach presented in this paper can guide project managers’ risk decisions and may also help to find the best risk response budget.

Shanghai is currently faced with a rapid increase in the ageing population and demand for elderly homes. Continuing care retirement community (CCRC) has been emerging as a high-end alternative to offer specialised accommodation to the elderly in major cities. Since the first development in 2008, the industry is now still at the infancy stage. This study aims to examine the investment barriers hindering the supply and demand of CCRCs with an aim to recommend practical and senior housing policy measures to facilitate CCRC developments.

Multiple-case study method was used to confirm whether the literature findings on investment barriers apply to the context of Shanghai. Four representative CCRC development cases in Shanghai were examined, in which qualitative data were collected from interviews with experienced CCRC development managers and quantitative data from a questionnaire survey of the CCRC residents.

Operation management experience, financial risks and government support policy were found to be the main supply barriers. Chinese traditional family-oriented culture and affordability were not the main demand barriers of CCRCs in Shanghai. Poor quality of services and living environment were identified as the main barriers suppressing the demand for CCRC.

Although common trends and views can be drawn from the representative cases in Shanghai to provide valid results, further research should be conducted on other major cities in China so that the results can be widely applied.

Successful CCRC investment strategy should focus on partnering with experienced professional eldercare management companies, provisions of high-quality medical professionals and trained care personnel and delivery of flexible care service, along with intensive capital flows for land, construction and operating costs.

Additional senior housing policy support should be established to promote the CCRC supply to address the ageing needs, particularly granting lands for CCRC developments at Tiers 1 and 2 major cities where the land cost is high.

This research’s practical and policy measures can be applied to enable and promote CCRC developments in Shanghai, thus benefitting both housing investors and the government. The findings also form a baseline for CCRC developments in other major cities.