Commercial brokers have always crunched data to make sure an investment makes financial sense. But high-tech analytics can take that analysis to a new level.
LONDON – On some level, institutional investors in the commercial real estate (CRE) space have always collected and assessed data to make informed decisions when buying, operating or disposing of properties. Data analytics, therefore, is not a new topic. But the granular level of data that is now being aggregated, along with the latest developments in artificial intelligence, is paving the way for a new era in CRE. That is the reason why data analytics is now a central topic in boardroom conversations, with the main question being: How can management convert this data into knowledge that can deployed to be more efficient in managing CRE investments?
Data analytics defined
The Institute for Operations Research and the Management Sciences (INFORMS) defines data analytics as the scientific process of transforming data into insight for making better decisions. In general, data analytics can be classified in three main categories of techniques: descriptive analytics, predictive analytics and prescriptive analytics.
The benefits of real estate analytics
The benefits of real estate analytics are numerous. CRE investors can leverage analytics across key steps in the asset life cycle, from deal sourcing, acquisitions, operations to asset disposals. We set out below some examples on the application of real estate analytics in CRE.
Example 1: Real estate developerA real estate developer wants to identify underused but high-value parcels zoned for development. The developer can look into the sales and other information contained in previous listings on Property Finder for example or similar websites. These sources are the traditional cornerstone of information for CRE investors. However, these databases have their limitations and may not be able to anticipate future potential.
The developer can rely on advanced analytics to quickly identify areas of interest, then assess the value of a given plot of land with a predictive lens. The developer can incorporate a number of variables in its model, some of which can be traditional (e.g. macroeconomic, demographic, median age of occupiers, etc.) with some other non-traditional (e.g. number of luxury restaurants with a 1 mile radius, building energy consumption, reviews of nearby businesses on commercial apps, etc.) in order to optimize the timing of the development, the mix of uses in the development, price segmentation and other factors to maximize value.
Example 2: Mall operatorA retail mall investor can use traditional property data around performance, combine it with alternative retail sales data that is retrieved from various sources (e.g. mobile sensors, social media, physical store sales, etc.) and use machine learning algorithms to analyze the behavior of consumers within a specific area or to profile retail tenants.
Example 3: Asset managerAn asset manager wants to expand and optimize a portfolio of office buildings. Machine learning algorithms can rapidly combine macro and hyperlocal data and forecasts (e.g. distance from the asset to metro, number of nearby coffee shops and gyms, etc.) with a view to prioritizing the areas with the highest demand for offices. This allows the asset manager to identify buildings in these areas that are undervalued but rising in popularity.
The benefits of real estate analytics are clear. This begs the question: what is preventing its scalability in the CRE space?
We believe that there are three barriers that CRE investors need to overcome.
Real estate analytics cannot serve as a crystal ball. Its primary function is to assist CRE investors and managers in validating the investment thesis. But when it comes to the typical real estate challenges, advanced analytics can provide powerful tools to assist in the decision-making process and to help in identifying what matters most. Organizations who realize the potential of advanced analytics and invest in a robust data architecture will, most certainly, gain a competitive advantage vis-à-vis their peers for years to come.
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