While the real estate industry has rapidly shifted toward data-driven real estate over the last decade – reshaping asset managers and tenants to make business decisions – there is a critical issue: real estate data adoption is not evenly distributed across the industry.
In this context, the data revolution refers to the shift from intuition-driven and consultant-led decision-making to the systematic use of alternative data, analytics, and data science models across underwriting, site selection, investment, and operations. It is not just about having more data, but about integrating raw datasets into repeatable, decision-ready workflows that allow firms to move faster and with greater confidence.
As you can see in the graph above, some areas of real estate, like logistics companies and big chain retailers, are very sophisticated in their use of alternative data. These organizations have invested heavily in data science teams that build advanced models using raw datasets to improve decision-making.
Regional brokers, investors, and retailers, on the other hand, are far behind.
On top of that, while some areas of real estate are forward-thinking and advancing in their data adoption, as a category, real estate still has a long way to go. Compared to other industries, it remains near the bottom of the data maturity curve.
The good news is that the number of data scientists in the real estate industry has grown tenfold in the last four years. The bad news is that this growth is coming from a very small base.
The days of deal teams spending months on diligence and working with a long list of consultants are long gone. Leading retailers and large investment managers recognized this shift years ago and adjusted their processes accordingly.
The real estate world is changing fast, and there are three major real estate analytics trends to watch:
The real estate industry is fairly broad, with many different types of organizations optimizing for various use cases. Investment firms, developers, operators, and brokers often rely on the same core datasets while applying them to very different decisions.
There are many use cases across many verticals. What differentiates outcomes is not access to data, but how effectively it is applied through real estate analytics.
Underwriting is probably the most important aspect of investing. There are two dimensions of underwriting: depth and speed.
You want to be able to go deep on an asset while moving quickly.
Going deep means doing significant research and truly understanding the quality of an asset. It requires going beyond surface-level materials and widely available market studies.
It means finding sources of information that create information asymmetry between you and your competitors. That advantage often comes from data-driven real estate insights.
For example, a large commercial real estate investment firm that invests in retail properties uses a variety of datasets to power its underwriting models.
They use Point of Interest (POI) data to understand the surrounding landscape and competitive context. They use lease data to stay ahead of shifts in leasing comps while underwriting.
They also leverage consumer transaction data to quickly evaluate trends at competitive and adjacent locations to estimate stabilized top-line performance.
The second component of underwriting is speed.
When underwriting new assets, you generally have three options:
Most companies rely on some combination of the first two options. The third requires upfront investment. Building internal data capabilities depends on trusted data sources, specialized talent, and refined models.
While these require time and effort upfront, they save many hours over the long term. In competitive markets, the ability to move faster often determines who wins the deal.
Picking the right location is one of the most critical decisions in data-driven real estate.
For retail businesses, it can be a matter of survival.
Consider chain coffee shop operators. When entering a new market, they need to deeply understand neighbourhood quality, the competitive landscape within each area, and local demographics.
Most competitors rely on reports from research companies and consultants, but far fewer work directly with location intelligence.
Here’s how retail businesses like coffee shop operators can use data to get a faster view of a new market:
The larger the investment size, the greater the risk. As deal sizes increase, the cost of being wrong compounds quickly.
Hotel operators and multifamily developers are good examples of organizations that combine multiple alternative data sources when acquiring or developing high-capital assets.
One of the most powerful analyses they use is neighbourhood scoring, which helps assess sub-market quality. By using POI data, they can quickly evaluate proximity to transportation hubs, restaurants, bars, and event venues.
Hotel operators need to understand access to airports and convention centers. Multifamily investors often look for proximity to grocery stores, gyms, and high-quality schools. POI data helps address all of these considerations.
Neighbourhood scoring alone is not enough. Investors also rely on property-level data to contextualize construction quality, asset history, and comparable buildings.
There are many other data types that support smarter investment decisions. The takeaway is simple: data becomes more valuable as the stakes rise.
The value of data extends beyond commercial real estate. There is significant opportunity to generate alpha in residential real estate by using better data.
The rise of iBuying has reshaped the residential market. Companies like Opendoor and Zillow brought speed and scale to home buying. iBuyers operate as both investors and market makers. Their advantage lies in deploying capital quickly while compressing traditional buying timelines.
However, diligence should not be an afterthought.
iBuyers can use POI data to understand retail open and close trends in surrounding areas and strengthen forecasts for home price growth. They can also analyse nearby consumer spending patterns, which often serve as strong demand signals.
Speed and diligence do not need to be trade-offs. You don’t have to compromise one for the other.
Data does more than improve investment strategy. It can also enhance marketing effectiveness and operational efficiency.
Developers and brokers invest significant effort in marketing assets coming to market. Building properties is only the first step. Leasing them can be just as challenging.
When opening a new multifamily residential building, operators and brokers can run a radius analysis of nearby POIs. Using APIs to identify bars, restaurants, grocery stores, and gyms near a property enables clearer amenity positioning and more efficient leasing.
On the operations side, consider big box retailers.
Large retailers can combine alternative data with first-party data to optimize hours of operation. By analysing POI data, they can review operating hours of nearby retailers and complementary venues. This insight helps retailers adjust staffing, improve efficiency, and maintain operational discipline.
Transaction data adds another layer. By evaluating hourly transaction patterns, retailers can quickly identify peak demand periods and adjust accordingly.
One of the most successful real estate investors is Blackstone. The firm moves quickly, conducts deep diligence, and has generated strong returns across real estate cycles.
Tyler Henritze, Head of Strategic Investments at Blackstone Real Estate, discussed how Blackstone uses alternative data on the World of DaaS Podcast.
In the 2000s, Blackstone acquired homes across the southeastern United States after identifying migration trends toward states like Florida. The signal came from U-Haul data.
If you follow the U-Hauls, you often find the opportunity.
Blackstone also looks beyond location-based datasets, including
These datasets reflect broader real estate analytics strategies beyond location alone.
Choosing the right data provider can be difficult. Many data companies do not publish their schemas or pricing.
Evaluating datasets often requires multiple reviews over several months, and even then, quality differences can be hard to assess.
A few heuristics can help:
These heuristics are simple, but they are powerful in practice.
Real estate has made meaningful progress in real estate data adoption, but it still lags behind many industries on the data maturity curve. The shift to data-driven real estate is no longer optional. Reliable data reduces uncertainty, accelerates decisions, and creates durable competitive advantages. Firms that invest in data capabilities will move faster, operate more efficiently, and win better deals. Those that do not risk being left behind.
Real estate is not behind in awareness or intent, but it remains behind structurally compared to industries like fintech and advertising technology..
It is the shift toward widespread use of data, analytics, and automation in real estate decision-making.
As firms adopt AI and automation, poor data leads to amplified errors, unreliable outputs, and reduced trust. AI systems are only as good as the data they are trained on.
Larger data teams, faster deal cycles, and increased use of alternative data.
It provides insight into competition, amenities, neighbourhood dynamics, and property valuation.
Brand attribution measures how specific brands or tenants influence foot traffic, demand, and asset performance.
