Disconnected Data Limits AI’s Impact in Multifamily
As AI-powered solutions continue to proliferate in the multifamily housing industry, operators are recognizing the limiting factor in their AI strategies: disconnected data. While multifamily does not lack for actionable metrics, the traditionally siloed structure of pricing, leasing, budgeting and maintenance systems effectively fragments property data and curtails impactful AI-enabled decision-making.
The situation stems from multifamily’s purchasing practices. Operators often seek out and implement technology to address a specific pain point in operations, buying a point solution to alleviate the problem. These disconnected systems make it very hard to create the cleansed and governed data structure necessary for good AI implementations. Even inside the property management system, data is siloed by function. Without access to those relational dynamics, agentic AI can’t generate impactful insights or accurately steer operations.
Essentially, the industry gets exactly what it is buying. Complaints about data fragmentation and jumbled proptech are common, but operators who keep scratching itches with point solutions naturally get these disconnected tech stacks. Without a strategic plan to account for connectivity, the result is going to be fragmented data that AI struggles to work with.
Operators committed to leveraging their point solution data from a holistic property perspective face significant data reconciliation work. Running operations reports from multiple systems, consolidating report data into an Excel file, then connecting the data through VLOOKUP and INDEX/MATCH statements, is an exhaustive process.
Multifamily analysts spend 80% to 90% of their time collating data, and only 10% or 20% of their time actually analyzing it. Operators cannot move at the speed of curiosity in that environment. They may spend hours on collation to arrive at a result, then later wonder, ‘Does that vary by bedroom count?’ If bedroom count wasn’t factored in initially, it will take another hour of collation to incorporate it. Operators need to be able to think of a question, get an answer and act upon it immediately. Taking things a step further with AI, operators need the ability to interact with AI, ask AI a question and based on its answer ask a follow-up question, ask a clarifying question. That’s what AI brings to the table, but it requires a connected data foundation to empower operators to make better decisions faster.
Multifamily is embracing the potential role AI can play in optimizing operations, but fear over quality and accuracy is still holding back widespread deployment. Horror stories of pioneering organizations taking the AI plunge and encountering a disaster haunt the industry. But if a root cause analysis is conducted in those situations, it usually isn’t the AI that was wrong, it was the underlying data. It was fragmented data. It was ‘garbage in, garbage out.’ The companies that succeed with AI go through the trouble and grunge of a data cleansing exercise and have proper data governance. For the industry to get comfortable with AI, data accuracy and confidence is essential.
Data management solutions strive to solve the clean data issue through an automated Extract, Transform, Load (ETL) process that extracts and cleans large volumes of data from various systems and prepares it for reporting and analytics. The process, which typically takes place overnight in multifamily, also enforces data governance and surfaces errors so data sources can be corrected.
Acting on clean, connected data, agentic AI is equipped to expose relationships between systems and functions that directly impact operations. For example, AI can connect the dots between slow unit make-ready times and leasing/pricing underperformance due to vacancy loss, and relay that information to the maintenance department. When agentic AI makes that connection, it can implement a PRM program that provides feedback to the maintenance supervisor so they can eventually track how the pricing team was able to leverage reduced vacancy times to drive revenue.
With fragmented data, and without AI insights derived from multiple systems, it’s unlikely that a maintenance supervisor would organically connect with the pricing team to create a cross-functional project. But AI can shine light or even initial similar collaborative opportunities when siloed system data is automatically connected.
Multifamily won’t suddenly move on from point solutions to a connected platform overnight. At this point, the technology investment is too great. But an AI-powered data platform capable of reconciling data from various systems and painting over the cracks in the technology stack allows those point solutions to sing in harmony from an analytics perspective. That’s the next verse for the industry.
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