Artificial intelligence (AI) tools, predictive analytics, and other technologies are being hailed as effective methods for processing data, gaining insights, and streamlining repetitive tasks. However, the success of AI is currently hindered by the quality of its data.
According to a recent article in Urban Land Magazine, commercial real estate (CRE) data lacks standardization and consistency. Data consultant Rick Haughey stated that the industry struggles with inconsistent data and has difficulty cleaning it up.
In addition to lacking standardization, CRE data is often fragmented due to companies using multiple software platforms that do not communicate with each other. Lisa Stanly from OSCRE International noted that one organization collects property information on 40 different software platforms without any integration between them.
Before AI can be effectively utilized in CRE investing or brokerage activities, investors must have confidence in the quality of their collected data. This requires pulling information from various systems which may yield inaccurate results if garbage-in-garbage-out situations occur.
Furthermore,the private nature of most real estate transactions makes it difficult to access all necessary sources for standardized information. Investment professionals tend to keep valuable information confidential as a competitive advantage according AJS Advisory’s Andrea Jang .
While standardizing CREdata seems like an obvious solution,it proves challengingin practice.Faropoint launched its own efforts four years ago by hiring a research team dedicated solelyto developing accurate AI models.The process involved accumulating large amounts of proprietarydata while determining key parameters such as property ageand proximityto public transportationthat impact rent prices.However,Faropoint’s Adir Levitas emphasizedthat creating an accurate modelis only30%ofthe mission.Additional timeand resourcesare neededfor consistent updatesand integratingAI into business processesfor practical useaccordingto Levitas’ interviewwithUrban Land Magazine.
The main takeaway from this article is that improved standards for collecting and organizing CREdata will greatly enhance the effectivenessof AI usage among more companies.OSCRE’s Industry Data Model,an open access database,and the performance standards recommended by LEED certification are examples of standardization efforts that can help improve AI usage in CRE.
