Investor Data Reveals Overlooked Opportunities Beyond Single-Family Homes and Land

Written by:

Rudi Davis

Date:

06 Feb 2026

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The deal analysis activity tracked on residential investment platforms reveals clear patterns in where investors direct their attention — and where they do not, according to Abhi Bharatham, co-founder of the comping platform Bricked AI. Most analyses focus on single-family homes and land, with a heavy emphasis on distressed properties priced under $300,000. But Bharatham argues this pattern reflects investor habit and comfort, not necessarily the best opportunities for returns.

“Single-family homes and land are the two biggest ones on our platform,” Bharatham says. “Single-family is definitely the largest asset class compared to others.” He describes a typical user profile: “We see mostly single-family homes, distressed properties. A lot of flippers and wholesalers target those, usually under the $300,000 price point.”

Geographic Clustering Among Investors

Investor analysis is not limited to property type but also considers location. Bharatham notes that Texas stands out for user activity, a pattern that began after Bricked AI gained traction at a wholesaling convention in San Antonio. “Georgia, Texas, and a couple of other states have very high buyer activity as well as investor activity. Offers tend to be higher, properties move faster, and days on market are much lower in some zip codes compared to others,” he says.

This clustering is driven partly by investor preference for markets they know. Many users still favor their own regions, even as virtual investing has become more common. “People prefer the area they live in,” Bharatham explains. Still, the tendency to stick with familiar or popular zip codes raises the risk of crowding, where too many investors chase the same deals, driving up prices and lowering returns.

Overlooking Multifamily and Other Asset Classes

Perhaps more telling than where investors focus is where they do not. Multifamily properties, townhomes, and mobile homes see much less analysis activity on the platform, even though they may offer better returns for investors with adequate capital. Bharatham, who considers these patterns firsthand, says, “If I were to get back into investing, I’d probably do multifamily, just because I think the problem is the same as with single-family, but the return on investment is higher.” This gap between investor attention and potential returns suggests that many are missing opportunities outside the single-family segment.

Analysis And Action

Bharatham’s data also highlights a gap between the number of times a property gets analyzed and the rate at which offers are actually made. Some property types attract rigorous screening but yield fewer actual offers. This pattern suggests that while investors use rapid analysis tools to filter deals, they hesitate to commit to less familiar asset classes or markets. It is unclear whether this hesitation stems from genuine risk concerns or simply a lack of experience with specific property types.

The use of deal analysis tools is growing quickly. “We’ve had around 1,500 weekly active users on the platform, up from about 200 last November,” Bharatham reports. This surge reflects broader adoption of systematic deal screening among residential investors. He predicts that lower interest rates would accelerate this trend further, saying, “Lower interest rates would probably cause a 50 percent spike in platform usage. As rates drop, the real estate market will pick up, with more investor activity and more money overall.”

The Challenge of Market Selection

For investors, the key challenge is distinguishing between markets that offer genuine opportunities and those that attract more analysis because they are familiar or already crowded. “There are obviously better markets than others when you’re considering nationwide,” Bharatham says. The platform factors market differences into its valuation process, but high analysis activity in a particular zip code does not guarantee strong returns. When many investors target the same areas, competition increases, and the likelihood of finding undervalued deals drops.

Risk of Herding Behavior

These concentration patterns point to a classic herding effect: investors follow one another into the same markets and property types because they are easy to analyze and widely discussed, not necessarily because the underlying fundamentals are strongest. Bharatham believes the residential investment market is showing clear signs of this dynamic. The convenience of analyzing single-family homes under $300,000 in familiar markets like Texas and Georgia may be pushing investors into crowded trades, leaving better risk-adjusted returns on the table elsewhere.

Potential Solutions and Platform Capabilities

Bricked AI is designed to help investors broaden their search beyond the usual suspects. The platform supports analysis across all residential asset classes, including single-family, townhomes, mobile homes, land, and multifamily. Enabling rapid screening of diverse property types could help investors overcome the tendency to focus only on what they know. However, it remains to be seen whether users will fully leverage these capabilities to diversify their analysis and investment activity.

Implications for Investors

As more capital flows into residential real estate, patterns emerging from deal analysis platforms are becoming essential indicators of where investors believe opportunities exist — and where they may be missing out. These tools reveal not just where capital is moving, but also where systematic mistakes may be occurring through over-concentration on familiar asset classes and markets.

In the future, investors who recognize and adjust for these blind spots may achieve higher, risk-adjusted returns. The data suggest that expanding analysis beyond single-family homes and popular markets, and closely examining under-analyzed asset classes such as multifamily, could unlock overlooked opportunities. In an increasingly competitive landscape, the ability to identify and act on these patterns will likely separate the most successful operators from those simply following the crowd.