“The only way for investors to achieve sustained outperformance relative to the market and their peers is if they have a unique ability to uncover material facts that are almost completely unknown to everybody else.”
— Mark J. Higgins, CFA, CFP, CFA Institute
The best investors have an uncanny ability to identify undervalued stocks — the hidden gems. They see a stock that will outperform the market where most investors see nothing at all. The housing market is not the stock market, but some investors manage to jump on the best deals that others miss, and they are tapping data solutions to do so.
In this article, we explore how data, machine learning, and artificial intelligence-powered solutions are now integral to real estate investing at every stage. From property searches and deal negotiations to project and portfolio management, real estate and property AI solutions can help investors to make data-driven decisions and be more profitable.
How Data, Artificial Intelligence, and Machine Learning Are Powering Property Investing
To outperform the market, you need to identify undervalued assets. That means assessing an asset’s future potential and understanding all the variables that might affect your investment over time.
In the case of real estate, the variables include how much cashflow an asset can produce from future rentals, whether units need upgrades or refurbishments, the market demand for properties, economic variables, such as employment, crime rates, and interest rates, any risks to the property due to climate or hazards, and more.
Finding such data used to be time-intensive, if it could be found at all, and much of it might be overlooked in the rush to seal a deal. Today, however, investors have all of this information accessible from data platforms and APIs. Investors can tailor analytics to focus on the criteria they care about and still make fast investment decisions.
It used to be that real estate investors relied on networking in their locales to find out about potential projects. The geographic areas for sourcing properties were limited. Real Estate API data platforms have removed boundary limitations by providing real estate and property data on a national level and down to the granular street level. The world has opened up for investors, and the only boundaries investors worry about now are neighborhood boundary lines for school districts, demographics, and local house prices.
The incredible growth in Proptech sector, or property technology, had created rapid saturation. Proptech are digital solutions and startups providing tools to real estate professionals, asset managers, and property owners. They facilitate the researching, buying, selling, and managing of real estate. According to Globe Newswire “the worldwide PropTech market was valued at billions of dollars and growing rapidly.” Market size was around USD 19.5 billion in 2022 and is predicted to grow to around USD 32.2 billion by 2030.
Examples of these cutting-edge technologies are ATTOM, a property and real estate data provider; Zillow, another dataset provider; Opendoor, a digital platform for buying and selling homes, and Homelight, which matches buyers and sellers. Other players include Axonize, a Smart Building “Software as a Service” (SaaS), that uses IoT to help property owners optimize energy consumption, reduce costs, and improve space utilization. Home365 is a property management solution that offers vacancy insurance rental listings, and tenant management and maintenance.
Machine-Learning and Investment Decisions
Before the rise of Proptech and APIs, conventional analytical methods required investors and analysts to wade through millions of records or data points to discern patterns. By the time an investor arrived at a decision, and probably a risky one, the best opportunities were gone.
Let’s say a developer is looking for parcel zones suitable for development. Using advanced analytics based on artificial intelligence (AI) and machine learning, the developer can collect hyperlocal community data, expected land use, government planning data, and local economic data to assess the potential ROI of a parcel.
An investor might be looking for a commercial property investment. Combining Yelp data with property price data might show that having two upscale restaurants within a quarter of a mile correlates with higher property prices, while more than four correlates with lower prices. This type of information is an example of how an investor might use data to identify investment targets quicker than their competitors.
AI and machine-learning solutions parse an unlimited amount of information that is the right mix of community, pricing, and location-based data to provide results.
Real Estate Data providers like ATTOM offer expansive data about properties, market trends, and historical sales. They offer neighborhood data, climate data, and other valuable data that can be used for predictive modeling to manage risk.
The investment decision is just one area where data has changed real estate investing. Property owners also use technology for project management.
Artificial Intelligence, Machine Learning, and Property Management
Just as identifying potential real estate investments is now a data and solution-driven process, property management is also now digitalized. Solutions like Appfolio and Doorloop track property performance metrics like occupancy rates, maintenance costs, and rental income for investors.
Many of these solutions, including AppFolio and Buildium, automate rent collection, maintenance tracking, and will take care of communications between management and tenants using chatbots and automated emails.
Artificial Intelligence, Machine Learning and Real Estate Portfolio Management
Pouring over Excel spreadsheets and risk ratios and following due diligence used to be the way to a robust, risk-mitigated portfolio. But digital solutions like BiggerPockets and DealCheck will analyze deals, assess ROI, and evaluate risk for you. They will even educate you on investing and team you up with agents and brokers that serve your niche.
DealCheck’s software analyzes deals such as rental property acquisitions, flips, and multi-family buildings. It will estimate profits and configure deal parameters for you.
Granted, these solutions are limited in that they cannot structure an investing strategy. For that, investors must decide their niche or direction and find projects that follow their business model. Then, data analytics can support that strategic direction with long-term roles and goals for projects and investments.
Let’s say an investor wants to build a portfolio of multifamily buildings, machine learning algorithms can identify neighborhoods with potential based on macro data and hyperlocal forecasts, such as the demand for multifamily housing and government subsidies. This allows the asset manager to identify the undervalued properties – the hidden gems.
Leveling the Playing Field for Real Estate Investors
It’s true that institutional investors have the resources to hire teams of experts to build models and create architecture. They can hire translators to apply findings to actions. But just like online investing platforms democratized stock investing, data APIs are leveling the playing field for real estate.
Pre-digital transformation, only investors teamed with connected and informed real estate brokers could lead real estate investing. Today, data and solutions providers have opened up a world where nationwide property data is at their fingertips and informed analytical reports are mitigating portfolio risk.
Data, AI, and machine-learning solutions have opened the gates for savvy real estate investors. They are helping to narrow down a competitive field that has reached global proportions.
Learn more about how ATTOM’s data can power your portfolio and reveal the hidden gems.