The multifamily housing industry has reached an inflection point. With competition heating up, construction costs rising, and revenue growth slowing, owners and operators need to find creative ways to maximize yields. The answer may lie in the vast amounts of data being generated across multifamily portfolios.Â
By harnessing big data and advanced AI analytics, multifamily firms can uncover actionable insights to optimize pricing, boost occupancy, retain residents, and ultimately drive NOI.
The Power of data-driven decisions
In today’s information age, data is playing a bigger role than ever in how multifamily companies evaluate opportunities, set strategies, and operate properties. Business decisions grounded in accurate, timely data versus gut instinct can lead to improved financial performance. Consider these examples:
- Setting rents based on robust market data rather than competitor rates down the street. This enables pricing at the exact point of market demand.
- Identifying reasons for a sudden uptick in move-outs based on exit survey data. This allows a correction of issues impacting retention.
- Forecasting leasing velocity 12 months out based on historical seasonal demand patterns. This enhances strategic planning and preparedness.
- Monitoring resident satisfaction metrics to resolve problems early before negative reviews spread. This helps maintain a community’s reputation.
- Analyzing prospect traffic sources to optimize digital marketing spend. This ensures effective acquisition of new renters.
The firms realizing success today are the ones embracing data-driven decision making across all aspects of their business.
Capturing big data for multifamily
The multifamily sector actually sits on a tremendous amount of operating, financial, and market data. Typical sources include:
- Property management systems storing leasing data, rent rolls, accounting records etc.
- Revenue management software tracking pricing adjustments, occupancy metrics, concessions etc.
- Listing sites providing analytics on unit views, tours, applicant profiles
- Website and online traffic stats measuring visits, conversions, search performance
- Applicant tracking systems recording prospect outreach, leasing interactions
- Resident portals, surveys, and IoT smart home tech capturing satisfaction, usage patterns
- Social media sites and review platforms measuring reputation and sentiment
- Local market benchmarks from data firms like CoStar, STR, Yardi Matrix
- Macroeconomic data related to demographics, employment, construction etc.
The challenge is pulling all this data together into meaningful insights. Teams often struggle analyzing multifaceted data housed across disconnected platforms. But here at Beekin, we’ve found that new opportunities arise from examining relationships and trends across multiple data sets.
Advanced analytics and algorithms
Consolidating big data is just the first step. Making sense of it requires advanced analytics and algorithms built specifically for multifamily. Sophisticated AI can detect subtle data patterns and scenarios that humans simply miss when looking at massive datasets. Analytics can enable functions like:
- Automated daily pricing calibrated to market conditions
- Granular forecasting models down to the unit type and floor plan
- Revenue management analytics like price elasticity and opportunity cost
- Demand-based “what if” scenarios to optimize yield
- Red flag reporting of unexpected performance changes
- Real-time monitoring of review sites and online reputation
- Custom benchmarking against the competition set
- Automated personalized outreach campaigns to prospects
- Predictive analytics to anticipate move outs and renewals
Platforms like LeaseMax use machine learning and AI to continuously refine rental rate recommendations and forecasting models per asset and market. This level of precision isn’t possible with basic reporting.
Making data-driven decisions
The most robust analytics are ineffective if teams don’t embrace a data-driven culture. Change management and executive buy-in are critical when introducing new data tools. Cross-departmental training helps ensure company-wide adoption. Ongoing monitoring of key metrics and reviews of reports, forecasts and recommendations should become ingrained in daily workflows. Rather than just set-and-forget software, big data systems require engagement across the business to realize ROI.
Another key is making data digestible and actionable for frontline teams. Ongoing collaboration with field staff ensures revenue management platforms like ours provide the right data points in an intuitive format. Operations teams need clear guidance on how insights should inform leasing interactions, pricing changes, concession offers, renewals and more. Bridging the gap between analytics and on-site execution is vital for process improvement.
Incremental revenue lift possible
For many multifamily firms, big data analytics remains an untapped opportunity. But the revenue lift of leveraging data can be significant. Utilizing revenue management technology has resulted in additional income growth – for one of our multifamily developer clients, new lease rents grew 1.2% and renewal prices increased by 4.4% in a single quarter after implementing LeaseMax. That translates to millions in net operating income for portfolios, especially when amplified across thousands of units.
And by splicing data in new ways, there are always incremental gains to be achieved. Savvy operators are finding value in blending traditional property metrics with novel data streams from IoT devices, prospect website behavior, social media, and macroeconomic data. The more data fused together,the more potential revenue opportunities through better forecasting, risk analysis, and strategic planning.
The future of data-driven multifamily
We are only scratching the surface of ways big data can optimize operations and investment performance in multifamily housing. As technology continues advancing, so too will the analytics unlocking hidden insights from data. Owners and operators that don’t yet have a data strategy – or are relying on basic legacy tools – have significant upside ahead.
Now is the time for multifamily firms to build a foundation harnessing the data they already possess and prepare for even more potent technologies ahead. With big data fueling smarter decisions at every level, revenue growth opportunities abound.
And by splicing data in new ways, there are always incremental gains to be achieved. Savvy operators are finding value in blending traditional property metrics with novel data streams from IoT devices, prospect website behavior, social media, and macroeconomic data. The more data fused together,the more potential revenue opportunities through better forecasting, risk analysis, and strategic planning.
Unlock your multifamily data goldmine
The application of big data and advanced analytics is now an imperative for multifamily developers, operators, and investors seeking to maximize yields. To learn how our AI-powered multifamily revenue management software LeaseMax can help you tap into new revenue opportunities, schedule a demo today.
LeaseMax combines robust data consolidation, actionable analytics, and intelligent algorithms purpose-built for multifamily.
In a personalized demo, our team will assess your current data environment, strategic goals, and pain points. We’ll then map out ways LeaseMax can help overcome challenges to start driving results optimizing your assets.
Now is the time for multifamily firms to build a foundation harnessing the data they already possess and prepare for even more potent technologies ahead. With big data fueling smarter decisions at every level, revenue growth opportunities abound.
And by splicing data in new ways, there are always incremental gains to be achieved. Savvy operators are finding value in blending traditional property metrics with novel data streams from IoT devices, prospect website behavior, social media, and macroeconomic data. The more data fused together,the more potential revenue opportunities through better forecasting, risk analysis, and strategic planning.
