How AI Adoption Can Create More M&A Opportunities for Real Estate Tech

As artificial intelligence (AI) continues to advance, it’s natural for Real Estate Tech founders to wonder how it can benefit them. After all, founders who harness AI to improve their company may attract more business and more M&A prospects. The key could be finding a proprietary data source.

The real estate data landscape

To offer a valuable AI tool, you must first feed it with data. In real estate, two of the biggest data providers are RealPage and CoreLogic, and many industry software companies build apps with their data.

However, to gain a competitive edge, real estate tech could diversify away from the big players’ datasets and offer proprietary data. That doesn’t mean real estate software companies can’t incorporate RealPage or CoreLogic data. It just means they would do well to supplement that information with unique data. After all, trying to compete directly with the industry’s established data providers is difficult.

For example, mortgage lenders rely on property value data to make underwriting decisions. Software with proprietary data insight into particular markets could give underwriters more accurate property valuations, allowing them to offer better interest rates than their competitors. Conversely, it could help protect them from extending loans where the proprietary data reveals outsized risk.

Of course, the data advantage of smaller software companies is typically regional, and therefore not scalable nationwide. Still, it could be a valuable advantage that can help position your business to be more attractive to potential clients and investors.

The real estate AI landscape

On the flip side of real estate data are AI models that process data and turn it into an output. For example, OpenAI, Anthropic, Google, Microsoft, Meta, and other firms create large language models (LLMs) for general use.

Competing against these industry behemoths by building an AI model from scratch is difficult and time-consuming, and consequently an unwise use of capital. Instead, software startups can gain a better advantage by feeding an existing AI algorithm with unique data. That way, you still control the output even if you don’t own the underlying AI.

The alternative is to feed commonly available real estate data (e.g. from RealPage or CoreLogic) to a custom AI model. However, not only would developing the AI be costly, but you’d be dependent on the big real estate data providers. If they increased their prices (as monopolies often do), your business could be forced to eat the extra cost.

The real question for most software founders is what unique data can you feed to existing AI models to improve your product, analytics, and business decisions?

For example, software that helps optimize apartment rent and occupancy rates via market data can be valuable to busy landlords. This is exactly what RealPage offers with one of its products (and what the firm recently came under fire for from San Francisco city officials opposed to software-based pricing).

Ultimately, whether it’s minimizing loss-to-lease costs due to accurate rental data or optimizing underwriting decisions due to better property valuations, AI can help end users (e.g. landlords and lenders) improve their businesses.

Where to focus AI development from an M&A perspective

At the product level, real estate tech founders could focus on leveraging proprietary data, using an existing AI model and appending it with a unique data source.

Of course, finding proprietary data can be challenging. Some options include gathering it from your customer base or crowdsourcing it via surveys. Both approaches may require offering incentives to share data and enabling an application programming interface (API) to ingest data from various sources.

From there, one of the most common ways to drive value for users is cost-cutting. For example, if your software allows a real estate firm to cut a team of four employees down to one, that could be a huge savings in labor costs, which can lead to increased profitability.

Eventually, the goal would be for the AI-enabled profitability to show up in your financials, making your business worth more than a less profitable business that’s otherwise identical, and helping you to attract potential M&A opportunities.

However, be careful not to seek investment too soon. Wait until your AI improvements impact your bottom line. That way, you should be able to sell into an improving profit margin profile and increase your chances of a favorable deal.

Must real estate tech founders adopt AI to attract investors?

There are many ways AI can help real estate tech founders cut costs or increase revenue. However, it need not be part of your core product or value proposition. Many real estate tech companies are sold without having any AI.

That said, if your company doesn’t use AI, you should have an answer for why not. Investors will likely want to know what immunizes your business from AI disruption. Ultimately, however, they care more about attractive growth and retention metrics.