Buying a house is a huge financial undertaking, but it is even more of an emotional and social undertaking. In today’s digital era however, investors can take out some of the stress of this by leveraging data and machine learning. Real Estate has been slow to embrace change, with agents quoting their 24 years of experience to provide recommendations. Things however are changing for the better in this space now. And while the comfort providing ability of an agent cannot be replaced, the decision making can be made more predictable, and reliable, by leveraging complete information available.
Technology is in fact simplifying and empowering all phases of real estate investing process from Origination, Analysis & Due Diligence, Financing & closing, post-closing rental/ongoing maintenance and finally to exit through sale. Let’s see how it has changed each of these phases.
Origination: Nearly 95% of home buyers use the listing websites sites during the home search process with nearly half starting there. Although, buyer continue to find agents useful in leveraging their search (79% reported finding it useful) almost 88% find online websites the most powerful tool in their search, and nearly half closed on home they found in their online search. (Source: BuyProperly proprietary survey, Property Online, realestatesites.com). Artificial Intelligence approach allows websites to better match customers to their properties and investment units that are more likely to convert into sale.
In addition, websites often leverage chatbots to answer any questions users may have while on the website. Virtual tour softwares such as Pano2VR, RoundME, My360 allow customers to visit these houses virtually.
Analysis & Due Diligence: This is the stage where most consumers depend on their agents, investment advisors to provide guidance on the right value of the property. However, with technology, customers can now estimate, potential rental earnings, net cash flow, expected mortgage monthly payments and make decisions on whether the financial aspects make sense given the details of the property. A lot of listing websites now provide these tools and widgets for free to their users. Sites like Zillow ( Zestimate driven by ML model), and HouseSigma indicate the potential cash flow/ positive negative from investments in a given property based on the past sales, potential rental value and interest rates. A lot of these models work with the assumption that house prices are a function of both the features of the house and the suitability of the neighborhood and hence focused on intrinsic value (rather than market sentiment).
BuyProperly uses its proprietary ML driven model to identify investment opportunities that have high value potential.
This allows users to make decisions based on the intrinsic value of a property, rather than follow a greater fool’s theory (This finance theory says that a rational person may pay a price that seems “foolishly” high because one may have the expectation that the item can be resold to a “greater fool” later on)
Financing & closing: Automated platforms allow raising bridge loans (for home flippers), mortgage application and approval online to facilitate receiving financing approval in much shorter times. Companies like Blend, LendingHome,
Post-closing rental/ongoing maintenance: Online rental management platforms, allow for tenant verification, KYC done online through their database access, and allow tenants to raise tickets or talk to chat-bots for any issues. This significantly reduces hassles for landlords and improves satisfaction for tenants. Softwares such as Appfolio, ResMan etc. that allow property management in a seemless manner. Some softwares allow you to reach out to fixer crew (plumbers, electricians etc.) for a quick assignment.
AI models are now able to better predict tenant churn, maintenance issues, building energy requirements, elevator usage in buildings as well as space utilization, giving a better idea of potential upcoming costs and issues as well ways to engage with users.
Exit & Sale: This brings us back to the ML models driving estimates (origination models) that allow startups such as Opendoor, OfferPad and now Zillow homes and other iBuyers to decide how much to buy from a seller for, but making the process simple, and automated for the seller.