德勤美国最新研报:《房地产机构投资者和管理者智能决策的未来》(16页英文文字版)

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Infusing data analytics and AI
注入数据分析和人工智能
The future of smart decision-making for real estate institutional investors and managers
房地产机构投资者和管理者智能决策的未来
The commercial real estate market has lagged behind other financial sectors in fully embracing data analytics capabilities. Learn why now may be an opportune time to change that.
商业房地产市场在充分利用数据分析能力方面落后于其他金融部门。了解为什么现在可能是改变这种状况的合适时机。
Real estate institutional investors face a challenging environment
房地产机构投资者面临挑战环境
SEVERAL factors appear to be limiting growth and profitability for real estate institutional investors. Faced with higher risks and competition, commercial real estate (CRE) investors posted negative average annual returns in 2018, both globally (-5.6 percent) and in the United States (-4.1 percent).1 In comparison, average annual returns were 6.4 percent and 6.9 percent globally and in the United States, respectively, during the 2014 –2017 period.2 Deloitte’s 2018 Global Real Estate Institutional Investor survey of 500 global institutional investors seems to show respondents remain committed to CRE as an asset class: 97 percent of the respondents plan to increase their capital commitment through 2019. However, their investment decisions are likely to be challenged by geographic market, tenant, and financing/interest rate risks (see figure 1). There are also growing headwinds around the 2020 US presidential elections, the potential threat of trade tariffs, a flattening yield curve, and a global economic slowdown. To respond to these possible challenges, investors are seeking new ways to improve efficiency and effectiveness.
一些因素似乎限制了房地产机构投资者的增长和盈利能力。面对更高的风险和竞争，商业地产（CRE）投资者在2018年的平均年回报率为负，全球为负（-5.6%），美国为负（-4.1%）。1相比之下，全球和美国的平均年回报率分别为6.4%和6.9%，在2014-2017年期间。2德勤对全球500家机构投资者的2018年全球房地产机构投资者调查似乎显示，受访者仍将信用评级作为一种资产类别：97%的受访者计划在2019年之前增加资本承诺。然而，他们的投资决策可能会受到地理市场、租户和融资/利率风险的挑战（见图1）。围绕2020年美国总统选举的不利因素也在增加，贸易关税的潜在威胁，收益率曲线趋平，以及全球经济放缓。为了应对这些可能的挑战，投资者正在寻求提高效率和效力的新途径。
Investors and investment managers can use data analytics and artificial intelligence (AI) in their existing acquisition, disposition, and portfolio management processes to manage rising risks and complexities more effectively and mitigate fees and margin pressure. There is much more data available today than there was even just a few years ago. Information such as net effective rents, leasing spreads, lease comps, market demand, and tenant information have now become much more accessible and granular.3 In addition, alternative datasets from IoT sensors, social media, geospatial information, and satellite imagery are increasingly being used. And new forms of analytics solutions, backed by AI, can help investment managers harness this broader range of data to make more informed decisions faster and more accurately.
投资者和投资经理可以使用数据分析和人工智能（AI）在其现有的收购、处置和投资组合管理过程中，以更有效地管理日益上升的风险和复杂性，并减轻费用和利润压力。今天的数据比几年前多得多。诸如净有效租金、租赁价差、租赁公司、市场需求和租户信息等信息现在变得更容易获取和细化。3此外，物联网传感器、社交媒体、地理空间信息和卫星图像的替代数据集也越来越多地被使用。而由人工智能支持的新形式的分析解决方案，可以帮助投资经理利用这一范围更广的数据，更快、更准确地做出更明智的决策。

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房地产机构投资者和管理者智能决策的未来
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