Commercial real estate, or CRE, has been notoriously slow at adopting technology. After all, it’s an industry that has grown into a $24 trillion economic powerhouse driven by entrepreneurship, intuition, and asymmetric information, so why change a successful formula?
Today, CRE has matured into a mainstream asset class which has led to decreasing profitability and increasing competition. Despite this, its slow embrace of big data analytics has left a lot on the table. However, the industry is seeing something of a tech renaissance as of late, and CRE tech has received record levels of interest and investment in the past 12 months. As a result, CRE firms are experimenting with a range of new technologies that will create a massive shift within one of the world’s most economically impactful industries.
Trusting more than spreadsheets, domain expertise and gut instincts
As the CCIM Institute has pointed out, even large CRE firms continue to rely on spreadsheets and manual analysis. Many analysts pride themselves on their ability to determine value and risk primarily with market specific know-how and their ability to build a spreadsheet. After all, the value of commercial real estate drives the entire asset class in all respects.
Technology may never be able to directly assess the subjective value humans place on commercial property investments, but there is a significant amount of information now being collected that analysts can indirectly attribute in that assessment of value. Often, this information is underutilized due to this data not being captured by traditional valuation processes and just not being available.
When considering a property, qualitative assessments and data can be captured by walking through a neighborhood and assessing its qualities. Analysts, lenders, investors, and municipalities are looking to capture and understand as much useful data that’s practical to collect and determine what influences real-world value and risk. The newest generation of analytical tools, which deliver valuation and analysis, are bringing more capabilities within grasp to everyone in the decision-making process surrounding a commercial real estate investment at unprecedented speed.
Capitalizing on the expanding lake of data
Increasing the variety, richness and freshness of data available for creating financial models can provide an added dimension to the underwriting and valuation process. Advanced data analytics affords analysts the potential ability to factor many more variables into their calculations. Real-time transaction prices in similar markets, proximity to different retail vendor segments, bank deposit concentrations, population trends, etc. are available for CRE professionals to provide a much higher level of granularity to determine insights about an investment’s risk. Consider just a small sampling of what can be learned.
Hyper-local trends. How much foot traffic does an area get? How are consumers spending their income at the registers? These are all alternative data sets that help enrich an analyst’s process when valuing a property, yet most of these types of analysis has not been traditionally factored into the equation because the data is out of reach. But this is changing with the innovations happening in smart city design. Motionloft, for example, uses sensor technology to provide real-time pedestrian and vehicle traffic statistics and analysis.
Factoring into a model how traffic is changing over time could give an investor unprecedented insight into the potential for leasing floor space. The innovations in the payment and card processing world have enabled block by block analysis of consumer spending. This is now a reality with card swipe data being aggregated, anonymized, and analyzed at the major payment processing companies that have developed spending personas that project how much is being spent by their customers in stores.
IoT. The much-touted Internet of Things will undoubtedly play heavily into CRE analytics. The ability to integrate streaming data from networks of sensors in cities, outside of buildings, connected with indoor sensor systems such as InnerSpace, can allow analysts to ask questions such as: Does one part of the building experience more foot traffic in the afternoon and what is the potential impact and desirability for retailers at the property? Are there radical changes in temperature, humidity or otherwise that might contribute to mold, discomfort or other things that might shorten the duration of a tenancy or discourage high-value renters?
Unstructured data. We’re also starting to see the use of unstructured data in CRE, such as emails, social posts, images and videos which can inform and enrich a qualitative investment decision. While social media may not seem like such an obvious source of information for a property valuation model, there are trends that can be spotted on Facebook, Twitter and LinkedIn before they’re apparent anywhere else–just think about shared offices, open office layouts, and the like.
In fact, a large portion of the data that most CRE firms collect and hold as proprietary is actually in unstructured form. Think about all the information collected by CRE veterans with their decades of experience, which is locked up on individual spreadsheets and not exposed across the firm’s network when determining valuation and underwriting analysis. Currently, that information is difficult to share and integrate into modelling workflows. However, integrating such information into analyses could allow analysts, appraisers, commercial brokers, lenders, and investors to spot great opportunities before competitors catch wind of them.
Combining real-time and historical data. The ability to combine streaming and batch data has long been a holy grail of advanced data analytics. Companies are still trying to iron out the technical details with models such as the Lambda architecture, but for CRE, the payoff may be even greater than in most industries. Throughout the investment process, analysts often must factor in elements that have real-time components, such as financing rates, capitalization rates, market leases, with elements that involve privately held data that may have been collected over the course of decades, such as historical vacancy rates, lease renewal probabilities, and lease terms which aren’t readily available. On top of that, many of these and other data sources, such as investment parameters and lease terms, have both historical and real-time components.
There are of course natural limits to how these data sources can be integrated by a single analyst working within underwriting and valuation models using spreadsheet or on-premise software that is local to their laptop or desktop. Most models are currently, at best, moderately informed by this information collected manually utilizing the Mark 1 spreadsheet. But as analysts gain the ability to enrich such data sources into their models, the industry will enter a new era of greatly enriched and informed investment.
The wider impact: awakening a sleeping giant
As CRE fully embraces big data analytics, we’ll see more strategic and targeted investment, with the ability to factor in macroeconomic trends, microeconomic trends, real-time spending data, people analytics, transportation variables, major events, hyper-local data, office space trends and other information into sophisticated models that enable firms to locate, purchase, and adapt properties that provide high financial ROI and use. We’ll also likely see the development of new credit risk algorithms that analyze larger and more diverse data sources, taking into account variables that simply can’t be factored in and analyzed manually with a spreadsheet. As this happens, you’ll see a profound change, of which the economic impact will be felt well beyond the offices of commercial real estate firms.
As decisions are made faster, and are informed by a much wider range of highly accurate, timely data, we’ll see the benefits of a more robust and responsive CRE industry spread to just about every other business sector in the world. Despite the industry’s stubbornness, CRE is finally ready to utilize the power of big data analytics.
About the Author
Min Suh is CEO and Founder of Assess+RE, a cloud-based financial computation and underwriting analysis SaaS platform. A former CRE investment professional with Louis Dreyfus, Suh spent 7 years as a finance professor at Columbia University’s Masters in Real Estate Finance Program. Suh’s previous experience in CRE includes having worked for a $2B private family office in Manhattan underwriting, valuation and investment oversight.
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