A number of well-established professional bodies provide best practices on governance, but operations and performance remain the least developed areas of risk mitigation for commercial real estate firms.
Poorly constructed leases, missed lease obligations, insufficient due diligence on tenants, inaccurate reports, cost overruns, misinformed decisions, erroneous forecasts and incomplete historical information all contribute to costs.
The challenge is threefold:
Today, all of the critical information used by CRE investors and owners to mitigate risk, maximize returns, set proper internal and external expectations and make informed decisions exists almost exclusively in data silos.
Sources like PDF and Word documents, static spreadsheets, software point solutions, news articles, public filings, press releases and even paper files are all commonly used. To make matters worse, the level of detail and conventions in each source is inconsistent - from national and market insights to property and suite specifics – with no ability to establish contextual links among them. The data is available but unstructured – not organized in a defined manner, typically text-heavy, with irregularities and ambiguities that make it difficult to extract meaning. Simply put, the data is not in a “state” ready and enabled for business intelligence, let alone the promise of AI, IoT, machine learning, and predictive analytics.
Often, CRE firms hire Chief Risk Officers, teams of attorneys, Financial Analysts, and specialized Data Scientists to tackle the data problem head on, but few firms also invest in a digital data strategy to support these efforts.
As a result, people continue to be at the center of the cycle, reading, interpreting, and inputting data manually in order to mine business insights. These manual processes only serve to produce volumes more data about the data, forcing executives to wrangle with the meta rather than the actual. Errors from metadata propagate and redundant verification processes multiply. Business decisions don’t wait for the numbers (cue misinformed strategy), analysts burnout and turnover, teams grow increasingly frustrated, and executives are left to manage the higher overhead costs that result.
For those firms searching for new solutions, there are abundant technologies from which to choose. As of August 2018, 3000+ PropTech firms worldwide and 170+ in the US alone, including stalwarts like Yardi, VTS, and Argus, are all investing to create growth engines for data-driven CRE firms.
But there is one thing in common across many of the purported platforms available today – once again, they all rely on metadata as the way to extract meaning from unstructured information. And yes, this metadata is created by people at the center of each solution.
In order to truly advance the way CRE owners execute their business priorities and achieve higher returns on their investments, data from inside and outside the organization – and within structured and unstructured formats – needs to come together in a way that has not been available before.
Platforms that can extract data directly from its source, break the dependence of important insights on metadata, and create intelligent, integrated capabilities that minimize human intervention, are the future of commercial real estate. With this evolution, CRE Executives are freed to pursue higher order activities like future-looking strategy and competitive differentiation.
Imagine a world where you could:
Predict not just a single property’s performance but also your entire portfolio’s performance
Evaluate tenant risk profiles across your entire portfolio any time and in real time
Curate your tenant mix to mitigate risk
Analyze market vs. in-place rents or rollup/rolldown with just a few clicks
Never miss a critical business responsibility like maintaining COIs, LOCs or commencement letters
Have tenant options and encumbrances automatically built into leasing models and portfolio risk profiles
Create a dynamic audit trail from source documents directly to the data to validate its usage and context