The finance industry and the property industry have a lot of differences. The property industry deals with the real, physical world and all of the unpredictability that comes with. The finance industry, on the other hand, deals with the theoretical world. They are able to break companies and loans and commodities down into imaginary parts able to easily fit into neat mathematical models. But as the finance industry starts to look at its interaction with real estate it has realized that, unlike in the broader financial industry, essential property data is not always accessible in an easy-to-understand format. This is a problem that has plagued the real estate landscape for decades. But, thanks to the help of technology, the finance industry is increasingly able to better understand real assets by designing the same types of data sharing systems that it has used for stocks, bonds, and commodities for decades.
“Despite the large market size, the information flow has historically been opaque and fragmented across dispersed sources” says Luis Valdich, Managing Director of Citi Ventures, the corporate venture arm of Citi. Data analytics and machine learning are a core investment category for Citi Ventures. A focus on emerging technologies drives their investment strategy.
Citi Ventures are active in their search for PropTech investment opportunities. Citing the problems associated with data, Valdich notes, “The infrequent and typically private nature of CRE property transactions combined with the heterogeneous nature of properties makes it difficult to access accurate data.”
Even with the challenges associated with harnessing large volumes of data, financial firms are eager to build solutions where this data can be localized and transformed. In some instances, the products are informed by the financial firm’s experiences with industry clients that also own real estate.
Moody’s Analytics Accelerator partners with technologists and startups to create data analytics solutions for the commercial real estate industry. Moody’s Analytics is a subsidiary of Moody’s Corporation, and the Analytics Accelerator represents its foray into emerging PropTech. The accelerator’s competitive edge is sharpened through its customer centricity. “We always start from our customers in all the innovation we do. The core of our client base has historically been with the banks and insurance companies,” says Keith Berry, Executive Director of Moody’s Analytics Accelerator. “As we talked to them, one of the things that’s very clear is that they’re holding more and more commercial real estate on their balance sheets. It’s becoming a key asset class for them to be aware of, and that was really what started to get us interested in the space.”
As a result of engaging with their clients, Moody’s Analytics launched the Commercial Location Score (CLS). The CLS combines data from multiple sources traditional and non-traditional sources to provide a score for the commercial viability of real estate parcel. By matching data across diverse sources, real estate firms are privy to a clearer picture of the investment landscape in an area, and are empowered to make data-driven decisions based on the information gathered from the CLS. The fine-grained data analysis of the CLS and other tools like it, enable commercial real estate firms carry out a thorough assessment.
While VC investment has dominated the PropTech scene, PropTech investments from financial conglomerates prove that industry leaders are keen to develop roots within real estate. Reonomy, a commercial real estate analytics company, recently raised $60m from investors including Citi and Wells Fargo.
Speaking on the investment, Valdich says, “We believe Reonomy’s data network with billions of data points via integrations with over 3,000 data sources (e.g. local county assessors, secretary of states, census data, title companies, commercial data & geospatial providers) will enable Reonomy to connect the fragmented, disparate world of CRE data into a single source of truth, the Reonomy ID.”
Investments of this sort are likely to continue and be an avenue through which financial services will widen their reach into the commercial real estate industry.
While residential real estate innovations trend towards disintermediation that simplify the purchase of a home, the finance industry sees opportunity for mutually beneficial collaborations in commercial real estate. It’s one thing to derive meaning from large swathes of data. The next logical step is to make it context-specific.
Speaking on Moody’s Analytics’ Commercial Location Score, Berry says, “We combine all of that into workflow solutions like origination systems and portfolio systems that really help streamline the process.”
True value comes from the appropriate positioning of the data. Where homebuyers might turn to crowdsourcing for a mortgage, real estate brokers are crowdsourcing and sharing information with each other. This valuable exchange of information, is what attracted Moody’s Analytics to invest in Compstak.
‘It’s an interesting business model. They’ve gamified the whole approach,” says Berry. “They get real estate brokers to provide data into their database and in return for the data that they provide, they, the real estate brokers can find additional data on properties they might not know about.”
If the finance industry’s current interest in commercial real estate is steeped in improving access to existing data and closer collaboration, the future outlook will build upon the exploitation of new data through advanced technological capabilities. While tools like Moody’s CLS provide a score through aggregate data, the strength of those tools is dependent on the accumulation and discovery of new data that will guide decision making for real estate firms.
Comparing the finance industry to commercial real estate, Valdich says, “Data and machine learning are increasingly allowing people to make better decisions quicker—bringing real estate closer in transparency to other asset classes. PropTech has the potential to follow a similar path as fintech, fueling similar and better diligence and decision making.”
Berry agrees that the real estate industry is catching up with the speed of analysis that exists in the world of finance. “I think we’re already on the road. I don’t even know in the financial world if we’re completely there. The trends are very similar.“
Drawing comparison to the financial markets’ use of alternative data such as foot traffic from cell phone signals and credit card transactions to predict retailer earnings, Berry envisions a similar approach for Moody’s Analytics. “Foot traffic is an area we’re exploring. What does foot traffic mean for a retail location? It’s more of a 360 view of a property that you can have with more and more data.”
Grigorios Reppas is COO of Advan Research. He is hoping to take his experience in the finance industry using smart phone data to help those taking a more data centric approach to commercial property. “What the real estate industry can learn from finance is how to use alternative data,” he said. “Geolocation data, for example, can help understand not only a building but its users. What their demographics are, where they are coming from and going, this is the kind of data that the finance industry has been harvesting for years.”
The boom of FinTech and PropTech may draw parallels between both worlds, but their overlap is is specific and highly contextual, especially in relation to commercial real estate. Technological advancement and an increased interest in property data by the finance industry point towards continued convergence. Solutions and technologies that were born in the theoretical financial world will be expanded to encompass the messy physical world as well. While the finance industry can inspire some innovation, the collaboration between both industries is the only way to usher in a new generation of data analytics for commercial real estate.