Artificial intelligence and other related tech like machine learning have had an incredible impact on the real estate industry as a whole. While historically slow to adopt new practices and technologies, the past 5-10 years have represented the beginning of a new era for the space. Especially in the multifamily sector, AI has a tremendous potential that’s only starting to be realized by certain enterprising companies. However, with any technology (new or old) comes a series of considerations that need to be taken into account. 

When implemented in the right ways, AI can be an incredible tool for both property management teams and renters alike. At the same time, it can also perpetuate harmful biases and compromise people’s data and security if the proper safeguards aren’t put in place. Today we’re going to take a closer look at a few of the most important factors AI companies in multifamily should consider before, during, and after developing new applications for this tech.

5 top considerations for AI companies in multifamily 

Privacy 

Data makes the (tech) world go ‘round. And as such, it’s one of the most valuable assets a company can have when developing a new AI product. Better data can improve an algorithm’s performance, and even expand its capabilities. However, the way data has historically been collected and managed has increasingly come under more scrutiny by consumers. Fortunately, developers are listening to these concerns, with 82% of developers saying that ethical concerns are now much more of a consideration than before. 

The crux of data privacy revolves around companies asking themselves what they should do, instead of what they can do with the data they have at their disposal. In the multifamily space, this could look like protecting renter information instead of selling it to third parties, and only collecting data with people’s explicit consent.  

Security

Keeping people’s information private is one thing, but being able to protect that information from cybersecurity threats is a whole other ball game. You can have the most revolutionary AI product, but if you don’t have sound security infrastructure around that product and your operations, the data you’ve worked so hard to collect ethically could easily be stolen. This is not just a matter of privacy ethics, but trust as well. Once your company has been subject to a cyberattack or data leak, it will be much harder for property management groups and consumers to trust using your product. 

Accountability

If there’s ever a question of ethics, who should a developer turn to? Creating a clear hierarchy or structure for where/how/to whom concerns should be raised is essential to building applications that ultimately adhere to compliance regulations and best practices. What’s more, establishing this culture of accountability and responsibility early on can help cement better practices as a non-negotiable aspect of your company in the long-term. Oftentimes, it’s a lack of institutional accountability – not a single developer – that’s responsible for an AI product failing to meet certain ethical standards.  

Biases

A big topic of discussion in the tech world today is centered around our own internal biases and how we can unconsciously instill those biases into AI if we’re not careful. In real estate, things like racial biases have a long, painful history of exclusion, and in order to stop perpetuating that history, companies need to intentionally craft algorithms and systems that aren’t shaped by unconscious biases. How can teams go about doing this? There are several ways to reduce bias within AI, including: 

• Understand your training data: Sometimes commercial and academic data sets include labeling systems that can bring bias into your algorithm. When choosing training data, make sure to take into account the full spectrum of your intended user-base and use-cases. 

• Diversify your teams: Including a diverse array of experiences and perspectives on your team will help you engage with your product in different ways. And with people asking questions that you might not have thought of, you might be able to catch potential issues before they arise – or expand your algorithm’s capabilities in a way you hadn’t thought of.

• Making testing and feedback routine: The easier you can make it to provide feedback, the quicker you’ll be able to take action. The goal for these products should be to evolve and get better with time and feedback, so build consistent testing and feedback sessions into your process.

Approaching AI in multifamily from a place of sensitivity and curiosity when it comes to ethics is key to building a better future for this tech in the industry. Want more up-to-date multifamily resources and news? The BetterBot blog has got you covered.