“The only way an investor can achieve sustained outperformance relative to the market and their peers is if they have the unique ability to discover important facts that are barely known by anyone else.”
โ Mark J. Higgins, CFA, CFP, CFA Institute
Great investors have an uncanny ability to spot undervalued stocks – the hidden gems. They spot market-beating stocks that most investors completely overlook. The housing market is not the stock market, but some investors jump on the best deals others miss, and they use data solutions to do so.
In this article, we explore why solutions powered by data, machine learning, and artificial intelligence are becoming essential at every stage of real estate investing. From finding properties and negotiating deals to managing projects and portfolios, real estate and real estate AI solutions help investors make data-driven decisions and increase profitability.
How Data, Artificial Intelligence, and Machine Learning are Contributing to Real Estate Investing
To outperform the market, you need to identify undervalued assets, which means evaluating their future potential and understanding all the variables that may affect your investment over time.
In the case of real estate, variables include how much cash flow the asset can generate from future rentals, whether the unit needs upgrades or renovations, market demand for the property, economic variables such as employment, crime rates and interest rates, risks to the property due to weather or disasters, etc.
In the past, this data was time-consuming to find, sometimes impossible to find, and much of it was overlooked in the rush to close a deal. Today, investors have access to all this information through data platforms and APIs. They can customize their analysis to focus on the criteria that matter to them, enabling them to make faster investment decisions.
The growth of Proptech
In the past, real estate investors relied on local networks to find potential projects. The geographic scope of the property search was limited. Real estate API data platforms remove boundary limitations by providing real estate and property data from the country level down to the granular street level. The world opens up for investors, and the only boundaries they have to worry about are neighborhood boundaries of school districts, demographics, and local home prices.
The tremendous growth of the Proptech sector, or real estate technology, has quickly created saturation. Proptech is a set of digital solutions and startups that provide tools to real estate professionals, asset managers, and property owners to make it easier to research, buy, sell, and manage properties. According to Globe Newswire, “The global Proptech market is valued at billions of dollars and is growing rapidly.” The market size is estimated to be around $19.5 billion in 2022 and is projected to grow to around $32.2 billion by 2030.
Examples of these cutting edge technologies include real estate data provider ATTOM, another data provider Zillow, 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 utilisation. Home365 is a property management solution that offers vacancy insurance rental listings, tenant management and maintenance.
Machine learning and investment decisions
Before the rise of PropTech and APIs, traditional analytics methods required investors and analysts to sift through millions of records and data points to identify patterns. By the time an investor made a potentially risky decision, the best opportunities were gone.
Let's say a developer is looking for a suitable zoning zone for development. Using advanced analytics based on artificial intelligence (AI) and machine learning, the developer can gather hyperlocal community data, projected land use, government planning data, and local economic data to assess the potential ROI of the parcel.
An investor might be considering investing in commercial real estate. Combining Yelp data with real estate price data might show a correlation between having two fine dining restaurants within a quarter mile and higher real estate prices, and having four or more restaurants within a quarter mile and lower prices. This type of information is one example of how investors can use data to identify investment targets before their competitors.
AI and machine learning solutions analyze unlimited amounts of information with the right mix of community, price and location data to deliver results.
Real estate data providers such as ATTOM offer extensive data on properties, market trends, and past sales. They also provide neighborhood data, climate data, and other valuable data that can be used for predictive modeling for risk management.
Investment decisions are just one area where data is transforming real estate investing. Property owners are also turning to technology for project management.
Artificial Intelligence, Machine Learning, and Property Management
Just as identifying potential real estate investments has become a data- and solutions-driven process, property management is also becoming digitalized. Solutions such as Appfolio and Doorloop track property performance metrics such as occupancy rates, maintenance costs, and rental income for investors.
Many of these solutions, including AppFolio and Buildium, automate rent collection and maintenance tracking, and handle communication between managers and tenants using chatbots and automated emails.
Artificial Intelligence, Machine Learning and Real Estate Portfolio Management
It used to be that sifting through Excel spreadsheets and risk ratios and following due diligence was the path to a robust portfolio with mitigated risk. But digital solutions like BiggerPockets and DealCheck analyze deals, evaluate ROI and assess risk. Plus, you can team up with an agent or broker who can educate you about investing and serve your niche.
DealCheck's software analyzes transactions such as rental property acquisitions, resales, multifamily properties, etc. Estimate profits and set deal parameters.
Admittedly, these solutions have a limitation in that they cannot build an investment strategy, which requires investors to determine their niche, direction and find projects that align with their business model, after which data analytics can support the long-term role and goals of projects and investments as strategic directions.
For example, if an investor wants to build a portfolio of multifamily properties, machine learning algorithms can identify areas with potential based on macro data and hyper-regional forecasts such as multifamily demand and government subsidies. This allows asset managers to identify undervalued properties โ hidden gems.
Realizing a fair competitive environment for real estate investors
It's true that institutional investors have the resources to hire teams of experts to build models and create architectures, as well as translators to apply findings to action, but just as online investment platforms democratized stock investing, data APIs are leveling the playing field in real estate.
Before the digital transformation, only investors who teamed up with connected and informed real estate brokers could lead real estate investments. Today, data and solution providers have opened up a world where national real estate data is at hand and informed analytical reports can help de-risk your portfolio.
Data, AI and machine learning solutions have opened doors for real estate investors, helping them narrow a competitive field that has grown globally.
Learn more about how ATTOM's data can enhance your portfolio and uncover hidden assets.