REIT Trends: Innovative Data Strategies for Better Investments

By Josh Miramant, CEO and founder of Blue Orange Digital

Data transformation is this decade’s differentiator for REITs (Real Estate Investment Trusts). Predictive acquisition analytics, occupancy optimization, and rent retention are the dream, but, before that, you need to unify the disparate spreadsheets, one-off databases, and 3rd party apps that cut across the Real estate industry. Building the infrastructure to unify data enables real estate investment funds to find better investments and make better decisions.

Introduction

In the past decade, data has been at the center of the digital revolution. It has enabled new applications, created new business opportunities, and has enabled innovation across the entire CRE sector. Leading organizations engage in data-driven problem solving on a daily basis. Data has become the compass that is guiding both operational and investment decisions.

However, in 2020, data is ubiquitous. Simply having access to data is not enough for an actual competitive advantage. Making sense of the vast amounts of data available today requires computing infrastructures, flexible processing architectures, and advanced algorithms. Without these, simply having data is useless. Moreover, data could turn into an expense if the appropriate tools are not used for handling it.

Below are some technologies and data strategies for investors to leverage while capitalizing on the opportunity of distressed commercial assets. The foundation of any successful predictive analytics model is a unified data storage solution and machine learning(ML) automation.

How to take advantage of third party data

Unified Data

Traditional data storage tools are built for department-specific data, aggregated in isolated warehouses. Such storage solutions have worked well in the past but only offer access to a limited section of data. To exemplify, the marketing warehouse may contain social media metrics, while the sales warehouse contains prospect company assessments. The lack of overlap of this information prohibits a better understanding of both sets of data.

Modern data storage tools (such as data lakes) work on the idea of unified data. They make it possible to store a large variety of data sources in the same location. This makes it easy to integrate both internal and external data sources and to use ALL data available for analytics.

Storage choices should no longer limit the ability to manage and make sense of your data.

Unified Data Benefits at a Glance:

• Easier data access

Users with different access levels can access both structured and unstructured data across the entire organization

• Faster data preparation

Adequate storage solutions translate to reduced data wrangling and preparation efforts

• Enhanced agility

Query tools enable datasets to be assembled “on the fly”, resulting in datasets that can be repurposed

• Streamlined data exploration and analysis

Larger, broader data sets are easily available for a variety of use cases: from advanced analytics models to basic business reports

Benefits of Unified Data in Commercial Real Estate

Unstructured data turns into more in-depth customer and market understanding

Unified data offers REITs access to a 360-degree market view, by providing access to a variety of real estate features. Rental values, sociodemographic data, and even geographic features of a location can be analyzed simultaneously. Traffic, area walkability, social media reviews of neighborhood amenities, and even IoT sensor data can also be integrated on top. All these different data sources offer the possibility to gain insights at a property-level. Such granular insights can only be gained from unstructured data and enable REITs to minimize risks and deploy capital in the right places.

ML speeds up processes to improve internal decision making

Unified data makes it possible for Machine Learning algorithms to tackle a variety of real-estate tasks: from property management applications to tedious back-office tasks. Remote property monitoring solutions and live tracking of public and private spaces are some common examples of ML-enabled applications. At the same time, automated

property valuation models are nowadays ubiquitous and configured to integrate a variety of data sources. Once in place, such ML-tools can either work autonomously or give suggestions to human decision-makers. Such man-algorithm cooperation ensures increased performance, minimized operational costs, and more efficient, streamlined internal processes.

Predictive analytics for increased ROI

Unified data enables predictive analytics on real-time data and thus enables REITs to identify investment opportunities ahead of their competition. Predictive technologies fill in the gaps where traditional BI tools and descriptive models fail to deliver. Instead of answering questions like “what is happening now?”, predictive analytics helps figure out “what could happen in the future?”. Quantitative evaluations with regards to investment opportunities, risk assessment, and market volatility give REITs a solid base for tackling operational efficiency. When data insights are replacing subjective interpretations, it is ensured that capital is spent in the appropriate places and unnecessary costs are cut to a minimum.

The IDC reports that investments in the new applications can pay for themselves in 9 months and achieve an ROI of 415% over 5 years.

Case Study – Increased Accuracy of Property Valuation Modeling

Research conducted in 2017 tackles “the effects of walkability on property values and investment returns.”. In order to model walkability and to assess its impact on real estate property values, they took into consideration a variety of features characterizing local activity.

“Several different physical and social attributes of the area around a property can affect walkability. As such, it is a multi-dimensional construct composed of different factors which together comprise a single theoretical concept. Contributing attributes include urban density, land use mixing, street connectivity (i.e. the directness of links and the density of connections), traffic volume, distance to destinations, sidewalk width and continuity, city block size, topographic slope, perceived safety, and aesthetics.”

That is an impressive list of features and we can assure you: such attributes would rarely be found in traditional, historical sales datasets. These are features that are known as “alternative” or “orthogonal.”

They originate in third-party data sources and were originally perceived to have little connection to the problem being tackled. However, through machine learning algorithms they were found to be quite influential when taken into consideration for investment properties. While humans may not be able to see all the factors impacting an outcome variable (e.g. property value), algorithms do a great job identifying complex patterns. For this reason, integrating “seemingly unrelated” third-party data for analytics has proven to be the best way to learn from data.

Alternative data completes the image that the traditional data sets are painting. It brings in a fresh perspective, with the possibility of discovering unexpected patterns. Most importantly, orthogonal data is usually data generated by humans and their day-to-day activity. Since this is much closer to how humans make decisions, it only makes sense to look at this data in order to discover buying patterns and real estate trends.

The benefits of alternative data can be seen well beyond this example. With a variety of data sources that are emitting data, state-of-the-art CRE applications have used anything from social media feeds, to geolocation information, and IoT sensor data.

Conclusion

A unified data infrastructure plays a critical role in data transformation strategies. Not only does it bring agility and flexibility to existing data architectures, but also opens up new opportunities for data access and management.

Unified data tools are an investment in the future. As real estate data is becoming more and more diverse, investment decisions will also become more complex. REITs that invest in data processing expertise and modern tools will see their benefits for years to come.

Blue Orange Digital works closely with organizations to identify, plan, and implement data transformation strategies. Our team of Ph.D. data scientists anad engineers has vast experience setting up data-centric architectures and implementing modern analytics solutions.

Blue Orange Digital makes it possible to use the right tools and the right people for your data transformation strategy. REITs now have a chance to not let data turn into waste and actually transform it into a useful asset, that drives strategies and optimizes their investment decisions.