,

Last week I promised to feature the work of a guest author, and today I am excited to share it. This platform keeps growing because of the curiosity, generosity, and engagement from all of you. Thank you for reading, sharing, and bringing new people into this community every week.

I am especially proud to introduce Elyse Lin, a talented sophomore in the Huntsman Program who brings both rigor and clarity to her work. Elyse dedicated meaningful time to shaping this piece, and I am thrilled to spotlight her voice here. It is inspiring to see emerging thinkers step forward with ideas that push the conversation forward.

Part Two, the market map, will arrive next week. And as always, if you or someone you know would like to be a guest author, simply reply to this email.

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Maintenance predictions, tenant screening, dynamic pricing—where does automation help vs. where does it hurt vulnerable renters?

Written by Elyse Lin

Before I turned ten, I had moved nearly ten times, from rental house to rental house, until my parents finally planted roots in a small suburb outside Seattle. Back then, our landlord would personally stop by if something needed fixing. We knew our landlord’s name, and they knew which grade I was in.

In my parents’ prehistoric times (the late 1990s and early 2000s)[Note from the editor, Ilona - ouch Elyse! Some of us still think of this as yesterday!], they recall finding rentals by scanning the local newspaper, calling a realtor, or relying on word of mouth. But today, that model of landlord-tenant connection seems to be growing obsolete. In its place are modernized websites listing thousands of properties tailored to your niche preferences, managed by algorithms that can predict when your washing machine is about to break—even before you notice or call for maintenance.

Today, with plummeting home sales, less-than-desirable interest rates, and Wall Street buying up single-family homes en masse, the rental market is surging. Among my fellow Gen Zers, renting has become the default. Homeownership feels increasingly out of reach—and, for many, less appealing than it was for our parents’ generation. 

Meeting this surge in demand, a new player is entering the scene: AI-driven landlords. These systems are transforming the rental market through dynamic pricing, targeted advertising, and automated facilities management designed to cut costs and boost efficiency. Touching both supply and demand, here are four key ways AI is reshaping the future of renting.

This past spring, while taking a real estate data analytics class, we had the opportunity to explore newer comparative analysis platforms beyond the traditional ARGUS. One company we focused on was CoStar, which collects vast amounts of data on commercial real estate to produce market- and property-specific analyses for investors, property managers, brokers, lenders, and more. Its data collection methods are meticulous: drawing from on-the-ground researchers, client input, and even drone and aerial imaging.

For landlords, this means being able to see what comparable rentals in their area are charging and receiving data-backed recommendations on setting competitive rates. In turn, they can negotiate rents more effectively with tenants and offer pricing strategies designed to boost retention. 

Because RealPage used client data to generate its pricing recommendations, the algorithm allegedly suggested higher rent levels across certain markets, which impacted thousands of renters. This case highlights a growing vulnerability in the rental landscape and indicates a need for greater vigilance and clearer antitrust legislation governing AI-driven pricing tools.

Last fall, while working as a realtor in Philadelphia, I had the chance to help several clients search for rentals. Traditionally, realtors have played a central role in residential transactions, from buying and selling, to leasing homes. But after a few months in the field, I noticed a clear shift: the tools most realtors rely on, such as the MLS, feel outdated in a market where more transactions are happening directly through platforms like Zillow and Redfin. In one case, my clients, despite paying my brokerage’s service fee, ultimately found their rental on Facebook Marketplace.

As self-service real estate platforms like Zillow, Redfin, and Opendoor become standard, and newer AI tools such as Ridley AI enter the space, the role of the middleman—often a realtor, but sometimes a friend or local ad—is quickly diminishing. Traditionally, landlords paid a fee to realtors for connecting them with tenants. But just as Airbnb and VRBO streamlined short-term rentals, long-term leasing is following the same trajectory. Increasingly, rental transactions are happening directly between landlords and tenants, not only on informal platforms like Facebook Marketplace but also through emerging AI-driven platforms such as Renty.ai and Sunny.com. As these technologies cut out third-party costs, the savings that benefit landlords may also begin to reach tenants, making renting more efficient for both sides.

Scraping data from across the internet, platforms such as RealPage, AppFolio, and MagicDoor now offer automated tenant screening, drawing on information like credit history, rental records, and other online data points. In some Chinese AI startups, similar technology is even being used in banking to assess creditworthiness using seemingly unrelated metrics—such as a person’s average phone battery percentage. It may only be a matter of time before rental screening algorithms begin incorporating comparable forms of extrapolated data.

Perhaps the greatest concern with AI screening tools lies in the potential biases embedded within their algorithms that can discriminate against traditionally marginalized or underrepresented populations. During my research on the use of AI in public administration this past summer, many sources cited algorithmic bias as one of the most pressing issues surrounding the technology. Because of both human manipulation and the opaque nature of many AI systems, biases can find their way into algorithms, both intentionally and unintentionally. This is yet another area where leadership and accountability will be necessary from the tech industry and policymakers alike to protect tenants.

Gone are the days of holding your washing machine together with duct tape and sending your landlord ten follow-up texts to remind them to fix it. In the era of the Internet of Things (IoT), where everything from fridges to vacuum cleaners to sous-vide machines is connected online, predictive maintenance and smart property management are becoming increasingly widespread. Companies such as EliseAI offer tools that can anticipate and schedule property maintenance before issues arise, while AppFolio provides a resident experience platform that simplifies move-ins, payments, maintenance requests, and even renters insurance in one place.

Although many AI-driven landlord tools are designed primarily to streamline workflows for property owners and investors, tenants stand to benefit as well. They can submit maintenance requests more reliably, compare rental options more easily, and gain leverage when negotiating for better pricing or living conditions. Over time, AI-managed properties may even bring greater consistency to the rental experience, making it clearer what tenants can expect, and what they shouldn’t have to.

With all technology and things AI, there are major gains as well as potential risks. Ten years ago, my family had a relationship with our landlord; now, it’s possible I might not know who my landlord is at all behind the AI interface. Yet ten years ago, my appliances couldn’t schedule their own maintenance. Efficiency, consistency, and data-driven decisions are quickly becoming the norm, driving smoother operations and improved tenant experiences alike. Still, these innovations raise critical questions about transparency, bias, and fairness in housing. Like many AI-sphere innovations, AI property management tools will require a certain level of regulation and standards to ensure both landlords and tenants win.

Next week I will be publishing Elyse’s proptech market map, highlighting where early stage investors should be focusing as this transformation unfolds. Subscribe so you do not miss it.

🖊️If you’re interested in betting scandals, crypto, and Polymarket, check out my latest Forbes piece.

👉 Read on Forbes

🎧 I released a new episode with Jaina Anne, and her story stopped me in my tracks. Her earliest Money Memory begins with a $38,000 rental property in Indiana, a purchase she made long before she felt ready. That little house became her classroom for fear, responsibility, and wealth building. Her story is a reminder that the first step might look small on paper, but it can change the entire arc of a life.
👉 Listen on NPR, Apple, Spotify, or wherever you listen to podcasts

📌We Spent the Night Shift With the Repo Man, Who Is Busier Than Ever

📌Do women’s periods actually sync up with each other?

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Till next week,
Ilona

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