Real estate investment managers now have significantly more information at their fingertips. But many are under-resourced when it comes to aggregating, assimilating and sometimes even understanding the data they have. Dirk Holz, head of origination and business development for private capital services, and Jamie Stevenson, managing director and global product head for data and analytics at RBC Investor & Treasury Services, discuss how the industry should tackle the big issue of big data.
PERE: Why is data gathering and analysis increasingly important for real estate investors?
Jamie Stevenson: The use of new techniques such as machine learning and artificial intelligence support the creation of insights from vast amounts of structured and unstructured data. Institutional investors ultimately expect their investments to be managed in terms of returns and risk, and consequently asset managers are expected to ensure they respond to the rapidly changing landscape in which collecting and analyzing vast amounts of data is the norm. It is taken for granted that asset managers can drill down in increasing detail or provide more frequent and responsive insight.
Data and intelligent reporting and analytics will become one of the main, if not the main, differentiators for real estate managers in the future.
Dirk Holz: We are seeing a lot of new institutional investment money coming into real estate and private capital strategies that has traditionally been used for investing into liquid and listed assets. Those investors are accustomed to having daily valuations and a lot of details on the portfolio, which is something that real estate asset managers generally haven’t provided. We are seeing a trend toward monthly, not quarterly, reporting and investors are requiring more data, even at a property level, as well as more analytics and benchmarking data. In the future we expect a significant increase in demand from institutional investors to receive value-added information out of the data that managers hold on their portfolios.
JS: Data management will increasingly be linked to services that create value out of investors’ regulatory and performance data for reporting and analytics. Specialized service providers in the private capital space that already fulfil the role of depository and administrator have access to that data from investor to investment level and will be well-placed to exploit it. Data lakes and the use of advanced data analytics technologies are the most cost effective and viable options for gathering and analyzing data. The use of technologies such as Hadoop enable vast amounts of data to be captured and stored, which previous data warehouse solutions could not. Once data are captured there are many techniques for accessing relevant information, often supported by data engineers and data scientists. Presenting information in a dynamic and intuitive form also requires mastery of data visualization tools to highlight exceptions and trends to the less technically minded.
PERE: What is the most efficient way to tackle the data skills gap in real estate?
JS: The demand for data engineering and data science skills is high and the supply is limited. To add to the challenge, the ideal talent pool would have some background knowledge on the asset class or specifics at the property level. The difficulty of talent attraction and retention should not be underestimated, and it is not simply a matter of remuneration. The opportunity for talent to gain new experiences, “play” with new technology and put their skills to practical application in an environment that meets the candidates’ preferred lifestyle choices requires a cultural and mindset shift for potential employers.
“Data and intelligent reporting and analytics will become one of the main, if not the main, differentiators for real estate managers in the future”
DH: Deployment of resources could also be a challenge. Even if an asset manager was able to hire the right people, they would need to maintain them and keep them actively engaged all the time. But would that be the scenario if they reach a point in their business cycle when they are liquidating funds and not raising new ones? Then that becomes a challenge. Asset managers are beginning to realize the potential value in setting up systems to collect data, standardize it, and make something valuable out of it. However, the real estate and asset management business industry has been a little bit behind from an emerging technology and data perspective. Some of them are realizing that they will not be able to keep up with the constant evolution of all the technological changes globally, and so outsourcing makes much more sense.
JS: Data-as-a-service will see significant growth. Given the large investments that need to be made, it is inevitable that service providers will provide new technologies and services to clients in order for them to perform their own aggregation and analytics, or alternatively managers will outsource those functions to talent attracted to the scale that can be provided by the larger players. It’s about a partnering economy and having a focused business objective rather than simply developing data analytics expertise. In outsourcing data aggregation and analytics activities there will still be a requirement to ensure that the specialist real estate business knowledge and subject matter expertise is engaged with data analysts, who need to clean, transform and interpret data appropriately.
PERE: What uses can data analysis be put to within the real estate sector?
JS: There are really no limits to how a data scientist might attempt to provide insight, but having empathy with the client is the key. Consider the possibilities of an investor or an asset manager drilling down from the total investment and gaining deeper insights into the types of exposures within their portfolio. There are potential benefits at all levels of the investment chain – at investor, fund, asset and even property level – in areas such as risk and performance, fund distribution, management information to drive fund decisions, and efficient asset management.
Data analysis can provide the opportunity to gather news and social media sentiment then connect that into analytics about the factors impacting the demand for property. The ability to transform unstructured data, such as contracts and agreements, into key data points and then combine that information with the automation of tasks through robotics will generate opportunities to increase the efficiency of the administration and processing associated with real estate investments.
Blockchain and artificial intelligence should be seen as a positive disruptor to the industry. The evolving technologies have the potential to overhaul existing models and reduce the number of intermediaries or agencies within the value chain, speed up processes, and ultimately reduce cost. This will have the biggest impact for real estate around specialized innovations, such as how you deal with property management or title exchange. Take capturing data on the use and ownership of buildings, for example: historically that has been poor, but models are being created to make databases that are real-time and the quality of information much improved.
PERE: New technology also brings new risks. How should investors prepare for them?
JS: Investors and managers will need to be wary of ethical considerations regarding the use of data – you only have to look at the recent controversies surrounding Facebook and Google for an illustration of the dangers. Cybersecurity is also key, and with developments in quantum computing, which could create machines with the ability to crack currently unbreakable codes, it becomes even more important to stay focused on this threat. Maintaining strong data governance, ethical standards and a broad view across the global regulatory and political landscape are crucial, and not for the faint-hearted.
This article is sponsored by RBC. It appeared in the Regulation and Fund Domiciles supplement with the May 2018 issue of PERE.