Owners and asset managers head into this year facing the same problem they had last year. The decisions are getting faster while the data behind them stays fragmented. Experts agree the multifamily industry is poised for a transformative year. Economic uncertainty lingers, but AI and data analytics are finally emerging as the game-changers the industry needs, promising new levels of operational efficiency, financial performance and resident satisfaction. AI in multifamily 2024 comes down to five trends gaining real traction, plus the prerequisite most firms have not finished.
The first clear sign of AI in multifamily 2024 is forecasting. AI-powered forecasting models are rapidly gaining traction. They use large datasets of market trends, historical performance and economic indicators to predict future rental rates, occupancy levels and operating expenses with unparalleled accuracy.
The shift matters because of what it replaces. Gone will be the days of relying on gut instinct and guesswork for crucial business decisions. Forecasting models process volumes of data that no analyst can hold at once. That gives owners and operators a real advantage in understanding the future state of their investments and deciding accordingly.
The value of speed and accuracy rises as acquisitions activity heats up. The Mortgage Bankers Association anticipates “a 19% increase in multifamily transactions” in 2024.1 Greater trade volume means real-time data and analysis get adopted faster. Firms need to adapt to market changes and optimize strategy in a competitive environment.2
AI and data analytics are paving the way for hyper-personalized benchmarking. It compares a property’s performance against similar assets within its specific market and competitive set.3 That level of granularity is what turns a portfolio review into an action list.
With it, owners and operators can identify areas for improvement, tailor resource allocation and implement targeted strategies that lift NOI. An operator who can see that a specific amenity is underperforming has something to fix. So does one who can see that resident communication at one property is costing renewals. An operator comparing against a portfolio average does not.
The practical requirement is a comparison set that is genuinely comparable — same metric definitions, same accounting treatment, same period. That is what a multifamily benchmarking approach built on standardized data is for. It is also why benchmarking projects usually stall at the data step rather than the analysis step.
The multifamily industry has long struggled with data fragmentation and a lack of standardized metrics. This year is poised to see a surge in efforts toward data standardization. It should also bring wider adoption of key performance indicators that give a clear picture of a property’s health.
“A unified ecosystem would not only streamline workflows but also enhance data visibility and accessibility, making operations more efficient and primed for intelligent, data-driven decision making.”
Pavesi expects increased focus “on developing a unifying ecosystem to address disparate software applications.” The payoff is concrete. Enhanced data standardization enables apples-to-apples comparisons across properties. It lets investors make informed decisions, and it allows asset managers to track progress toward strategic goals with greater clarity and precision.4
Getting there means moving data out of each system of record and into a common structure. An ETL and standardization layer for multifamily data is the mechanism. Extract from every PMS and accounting system in the portfolio, transform to one data model, and load it somewhere every downstream tool can reach.
The industry’s top-of-funnel outreach has traditionally been plagued by one-way communication and generic resident experiences. AI-powered platforms are now personalizing interactions, predicting needs and proactively addressing concerns before they become complaints.
Perq.com’s article “5 Multifamily Technology Trends” highlights the rise of smart apartments that anticipate resident needs and automate tasks, from temperature control to package delivery. These technologies target an individual resident’s preferences. That might be a personalized offer for a fitness class based on activity patterns, or a maintenance notification before a potential issue arises.
Beyond engagement during occupancy, AI will play a critical role in how prospects find housing at all.
“We expect 2024 to bring a new wave of AI-powered tools specifically for renters. It will soon become commonplace for renters to use AI in their apartment searches to search, compare, and coordinate actions.”
Popov’s prediction appears in “7 Key Predictions for the 2024 Multifamily Rental Housing Market.”5 Taken together, these data-driven interactions foster a sense of community and individualize the relationship. They improve resident satisfaction and ultimately boost rental and retention rates. Operators looking at where to apply this first tend to start with AI automation for multifamily workflows that are already repetitive and high-volume.
The fifth trend in AI in multifamily 2024 is less a technology than an obligation. While AI promises immense benefits, its ethical implications must be carefully considered industrywide. Bias in datasets can lead to unfair algorithms, and concerns around data privacy remain paramount.
Multifamily leaders must prioritize ethical AI development and implementation. That means responsible data governance and clear communication with residents about how their data is used. This is not a compliance afterthought. A leasing or pricing model that inherits historical bias will reproduce it at scale and at speed. The organization deploying it owns that outcome, regardless of which vendor built the model.
Practically, governance means knowing what data you hold, where it came from, who can see it, and what each field is allowed to influence. Firms that answer those questions before deployment are the ones who can defend a decision after the fact.
This year marks a pivotal moment for the multifamily industry, where AI and data are poised to drive a fundamental shift in the landscape. From optimizing financial performance to personalizing resident experience, the impact of multifamily data analytics on overall performance is expected to grow exponentially. Algorithms will become more sophisticated, allowing more accurate predictions and more advanced analysis. Integration with other emerging technologies, such as the Internet of Things, will increase that impact further.
But the honest assessment of AI in multifamily 2024 is that we have a long way to go before the full potential is realized. The industry is still in the earliest stages of identifying — let alone implementing — the power of AI. Until property managers and asset owners unlock their data, the ability to use AI applications to make better decisions and drive performance will be incremental at best.
That is the whole prerequisite, stated plainly. Unlocked data has three properties:
Everything above depends on it. Forecasting needs the history, benchmarking needs the comparability, and resident personalization needs the coverage.
Five trends define AI in multifamily 2024: AI-powered financial forecasting of rents, occupancy and operating expenses; hyper-personalized benchmarking against a specific competitive set; data standardization and the adoption of clear KPIs; AI-driven resident and prospect outreach; and the governance work needed to keep all of it ethical. The first four depend on the third.
Unlocked data. Until property managers and asset owners can get data out of the systems that created it, standardized so a metric means the same thing at every property, AI applications will deliver incremental gains at best. The industry’s long-standing data fragmentation is the binding constraint on AI in multifamily 2024, not model sophistication.
Hyper-personalized benchmarking compares a property against similar assets in its own market and competitive set rather than against a portfolio average. That granularity lets owners identify underperforming amenities or inefficiencies in resident communication, allocate resources accordingly, and implement targeted strategies that lift NOI and retention.
Forecasting, benchmarking and resident personalization all run on the same foundation: portfolio data extracted from every system and standardized into one model.
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