WHY REAL ESTATE ENTERPRISES FAIL AT THEIR DATA STRATEGY

A trend that has emerged with probably greater clarity than any other during the pandemic is the speed at which real estate companies around the world are embracing data. With digitization giving the industry access to more data than ever, there is a growing appetite for data strategy and data-driven decision making.

 

 

It is worth noting that when surveyed, only 31% among C-suite and senior tech executives admit that the data in their company is unstructured, disparate, fragmented, in silos, incomplete, and inaccurate; and yet, over 50% do not use a centralised data repository, while over 70% of real estate professionals use Excel as their primary tool – thus demonstrating an excessive focus on proprietary data that acts as a disincentive to any collaborative effort within the enterprise.

 

 

When it comes to the processes involved, the situation is hardly any better: manual, time-intensive, largely inefficient, prone to human error, and reactive. Real estate transactions are also traditionally complex, opaque, and burdened by misaligned incentives – a recent study on the labour components among commercial real estate professionals shows that more than 85% of their time is spent on highly automatable tasks.

 

 

At the same time, the intelligence captured by real estate professionals on the field gets lost: even a simple listing description (e.g., a broker’s commentary on a property in her own words) never gets exploited for data insight purposes. This discarded value-added data however further enriches analytics and insights, while facilitating enhanced decision-making and prediction by single professionals as well as enterprises.

 

 

In addition to the above, the implementation of a data journey across the whole organisation requires specific technology resources and the scientific expertise to build the tools, all of which come rarely cheap, are in high demand, sought after by tech companies, and therefore not easily available to real estate firms.

 

 

The issue affects both commercial and residential real estate organisations and is global. Even in a critical real estate market such as Manhattan, the fragmentation, patchiness, and inconsistency of data often translates into different reads on the market, also when top companies are involved. No wonder real estate companies struggle in their efforts to move their data strategies forward.

 

 

At iReal we have developed a Real-Estate-Data-Platform-as-a-Service equipped with an AI and NLP (Natural Language Processing) engine that can extract and enrich data – all kinds of data. The system even reads descriptions and commentaries about each property, turning them to data points for both human users and enterprise applications.

 

 

iReal is a serverless, decentralised, interactive, fast predictive, multi-stage, and multi-instance real estate data- and machine learning FaaS (Function-as-a-Service) platform. We also provide a made-to-measure AI toolbox that captures and implements each organisation’s “secret sauce”. Additionally, while the overall machine learning occurs on the cloud, a local AI instance runs on the device, making it hyper-responsive and accurate, while letting users contribute local intelligence on the fly and fine-tune the outcome.

 

 

The future real estate landscape will be occupied by those companies that have gained their data edge now. iReal Real-Estate-Data-Platform-as-a-Service provides the governance, intelligence, security, and tools to deliver a cohesive view of cleansed, high-quality, pervasive, and enriched multi-source data that real estate companies can trust and fully grasp, so they can truly harness its power and value.

 

 

 

By Rosaria Giorgi, CEO of iReal

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