Data Science: A Missed Opportunity for Real Estate
07.12.22 | Operations Chat
Many real estate firms, owners, and investors are missing out on the opportunity of leveraging the advantages of data science. Based on a 2019 KPMG PropTech Survey, only 25% of respondents have a well-established data management strategy that enables insight and enhanced decision-making. 75% have no data strategy at all! It seems that the old method of executing a real estate transaction, based on intuition and traditional and retrospective data, is challenging to overcome for many leaders. The lack of a digital transformation leader, with the appropriate technology and business acumen, is another hindrance for many real estate firms.
Data Without Structure Leads To Frustration
Unlike many other business sectors, where big data is king, the built environment uses a wide variety of datasets, which include traditional and nontraditional data that can add real value to real estate transactions. These include natural light, foot traffic, access to transportation, safety, demographics, Yelp reviews, the number of coffee shops in the area, and many other factors. Big data is defined as data observations with high volume, velocity, veracity, and variety (Kelleher and Tierney 2018), while wide data refers to the preparation of data sets by joining data from a wide range of disparate (dissimilar) sources, typically data stored in different silos. Wide data approaches require substantial effort in terms of accessing, manually analyzing, and joining relevant data (Csiki 2015). As a result, a real estate organization without an established data management infrastructure will find it frustrating and painful to find useful data nuggets in their data-verse, because their data is scattered in silos and without a consistent structure.
Clean Up The Mess with Digital Transformation
To change the narrative, real estate firms, owners, and investors need to support and commit to digital transformation, especially in data science and data management. The journey should begin with assessing the organization’s culture to identify and address the barriers that will prevent the organization from achieving success on its digital transformation journey.
The data landscape for the built environment is messy and noisy, so it is critical to have an experienced and knowledgeable leader who can unearth the golden nuggets for the organization. To really grasp the field of data science, there are various concepts that must be better understood; among them are:
- Data analysis
- Data analytics
- Data science
- Machine learning
To evaluate the results of data science and eventually be rewarded from the outputs, it is important to have a thorough understanding of the underlining processes, which follow a logical path:
- framing the question,
- collecting the data,
- cleaning the data,
- exploring the data,
- modeling the data, and
- interpreting the data (Lau 2019).
Data Science Can Show You The Bigger Picture
The built environment is more than physical buildings and supporting infrastructure. It is comprised of all the physical, social, spiritual, and economic characteristics that people need to live, play, and work. Therefore, real estate firms must aggressively explore data science and related technologies to capture the common aspects and uniqueness of each space, along with its use and potential.
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Clifford Forrester brings more than 20 years of professional experience to his role as the firm’s Chief Information Officer and Leader of Berdon’s Technology Services (BTS) Practice. Previously, he was the head of IT for a global law firm and the global IT and Security manager for an Am Law 100 firm. As both an operational leader and strategist, Clifford is skilled at transforming IT into a business-oriented function that addresses the strategic needs of the organization and clients. In his career, he has successfully implemented initiatives in applications development, networking, infrastructure, cybersecurity, business continuity, outsourcing, business expansion, cloud computing, and operations management.