Simplifying Property Performance Tracking: The Role of Analytics Platforms

As a property manager, you need to keep track of how your properties are performing and how the market is changing. You want to optimize your occupancy, rent, expenses, and returns, as well as identify new opportunities and challenges. But how do you monitor property performance and market trends?

The technological advancements in the current evolving world have made things easier for everything. With the evolution of AI and machine learning integrated with building analytics. Property performance tracking is made easier and more precise.

The real estate landscape is witnessing a major shift – from intuition-driven decisions to data-backed strategies. Advanced analytics platforms are simplifying property performance tracking like never before, arming stakeholders with actionable insights. But what is catalyzing this change, and why are analytics solutions becoming indispensable? Let’s explore how these technologies are transforming real estate.

The Evolution of Real Estate Decision Making

Real estate has historically been an industry reliant on human intuition, expertise, and retrospective data analysis. Investors and developers would leverage past performance metrics, qualitative insights from experienced professionals, and some level of gut instinct when underwriting deals, targeting sites, and allocating capital across locations.

However, while this approach has its merits, it also has inherent limitations. Human decision-making tends to anchor heavily on recent events, follow established heuristics, and be impacted by an array of cognitive biases. Moreover, in today’s rapidly evolving world, past data may no longer paint an accurate picture of future risks and returns.

As Sean Tompkins, CEO of Newmark notes, real estate is now a business driven by technology. Tenant needs and expectations are changing as generations used to on-demand services and personalized experiences enter the workforce. There is also growing pressure around sustainability and smart infrastructure as climate change concerns intensify.

Most importantly, the variety and granularity of data available today on every aspect of real estate is transforming at an exponential pace. Location analytics tapping previously inaccessible datasets can provide unmatched visibility into hyperlocal socioeconomic trends, changing consumer habits, and hidden correlations that impact property prices.

In this environment, stakeholders can no longer rely predominantly on human judgment and historical performance. The solution lies in supplementing intuition with strategic utilization of technology, statistical modeling, and machine learning algorithms to systematically analyze data.

As Tompkins succinctly puts it, “Real estate decision-making today must be data-driven.” The firms that embrace advanced analytics will be best positioned to capitalize on emerging opportunities and mitigate risks.

Building Analytics in Property Maintenance

Building analytics is a tool used in intelligent buildings for their maintenance. Buildings have a large amount of data in their systems. They are only resourceful if they are used appropriately, building analyzers analyze these data combined with smart technologies in the building such as censors, the Internet of Things (IoT), building management systems (BMS), and integrated workplace management systems (IWMSS).

From these analyzed data the building analytics software notifies the building owners and managers about the current performance of the building infrastructure and also it notifies them about the future concerns that they should take care of.

The Power of Nontraditional Data in Property Performance

While data has always informed real estate decisions, its scope today is infinitely wider. Firms are tapping unconventional data sources like foot traffic, cellular signals, micro-location activities, and social media to understand hyperlocal consumer behavior.

Some examples of how nontraditional data provides unique insights:

  • Tracking mobile phone data and geospatial analytics reveals hidden pockets of demand and undervalued neighborhoods. It can influence site selection for new developments.
  • Analyzing Yelp and other reviews uncovers Which amenities tenants value, guiding landlords on minimal viable investments to improve satisfaction and retention.
  • Monitoring peak occupancy times through WiFi and foot traffic patterns optimizes coworking space planning. It ensures flexible capacity for users.
  • Social media data performs better than surveys in predicting lifestyle choices of target demographics. It helps tailor property offerings and messages.
  • Web traffic and search data detect early signs of gentrification and value shifts even before they translate to pricing signals. This allows investors to enter emerging markets.

For instance, homes within 0.25 miles of a Starbucks in Boston saw 45% higher price appreciation between 1997 and 2014 than the city overall. By tracking nontraditional metrics like brand proximity, real estate players can identify undervalued assets and hidden opportunities.

Advanced Analytics: The Game Changer

However, generating insights from raw data still requires intensive manual analysis. This is where advanced analytics proves game-changing. According to Deloitte, over 90% of data collected by organizations is never analyzed. Machine learning algorithms fill this gap by automatically aggregating, interpreting, and uncovering patterns across datasets.

Beyond forecasting demand, these intelligent systems can even suggest prescriptive actions like repurposing assets.

Beyond forecasting demand, these intelligent systems can even suggest prescriptive actions like repurposing assets. Take WeWork’s ML-driven SpaceIQ technology that provides real-time space utilization insights and recommends workspace changes for improved efficiency. 

Companies also offer comprehensive smart building analytics to optimize operations through data-driven insights. Analysing the data manually and making changes according to that is not easy and requires a lot of effort and time. Taking assistance from a well-experienced firm that provides quality, services and products can solve your problem in a more better and convenient manner.

The benefits of data-driven forecasting

  • Landlords can optimize rent growth strategies and concessions based on hyperlocal demand shifts. Setting rents too low leaves money on the table while overpricing leads to vacancies and turnover costs. Accurate forecasting minimizes these risks.
  • Investors get better visibility into assets poised for price appreciation. Targeting the right buildings boosts yields. Data-driven insight provides an edge over-relying on instincts.
  • Developers can align project timelines, typologies, and locales with future market needs. For instance, student housing near an upcoming college expansion has better value potential than luxury condos based on demographics.

Implementing Advanced Analytics in Real Estate

However, integrating analytics into real estate operations has its challenges. The first step is defining strategic use cases rather than collecting data randomly. Legacy systems and patchwork datasets make it harder to build prediction engines without cleansing processes.

Key steps for successful analytics implementation include:

  • Performing an audit of current data infrastructure – identifying gaps, bottlenecks, and opportunities.
  • Gradually connecting disparate datasets into an enterprise data warehouse with common taxonomy and access protocols.
  • Building robust workflows for ongoing data gathering, cleaning, standardization, and enrichment.
  • Having the right talent – data scientists, engineers, analysts, and business leaders – to develop models and execute strategies.
  • Defining quantifiable KPIs and measuring incremental value delivered by analytics pilots before scaling.
  • Investing in change management and training to drive company-wide adoption.

According to JLL, $79 billion has been invested globally in prop-tech firms since 2016, indicating the push to develop advanced analytics capabilities.

The Future of Real Estate Analytics

As rapid tech advancements reshape consumer expectations, the real estate sector must stay ahead of the curve. While emerging technologies like AI and IoT offer immense potential, companies need a strategic roadmap to harness data, not just digitize processes.

With accurate forecasts and tailored recommendations, analytics platforms can simplify performance tracking at unmatched speed and scale. The firms that embrace data-backed decision-making today will lead the future. But as Chris Kelly, KPMG’s Global PropTech Lead notes, “You can’t boil the ocean.” Prioritizing use cases and iterating based on outcomes, rather than perfecting strategies, will define winners.

The real opportunity lies in using analytics to ask better questions, test hypotheses faster, and course-correct in real time – driving a cycle of continuous innovation. With the right vision and partners, data can unlock lasting value. The time for change is now.


1. How does geospatial data aid location analysis and investment decisions?

Geospatial analytics combining socioeconomic data, transport access, brand proximity, and local amenities can create accurate hyperlocal demand forecasts and risk models. This guides site selection, asset allocation, and pricing strategies.

2. What are some key challenges in building real estate analytics platforms?

Key challenges include legacy systems, data interoperability issues, lack of skilled talent, and cultural resistance. Having clear use cases, robust data infrastructure, cross-functional teams, and change management are vital.

3. How can real estate firms start their analytics journey?

Firms can start by identifying high-impact focus areas like occupancy forecasting, preventive maintenance, tenant targeting, and sustainability. Adding analytics in phases while measuring ROI is crucial. Partnering with prop-tech solution providers can accelerate capabilities.