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When $230M is Not Enough

How Rezi burnt all of their money leasing New York apartments.

Hey — It’s Nico.

I’m back from three weeks in SF. If things go well, I’ll be back in the Bay in a few weeks or months.

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Here’s what I got today:

  • The failure story of Rezi, a rental platform that raised $30M 🔥

  • An analysis of Hume’s new empathetic AI 📈

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Renting Made Easy

Earlier this week, the AI-powered rental platform Rezi announced it was shutting down.

Rezi was founded in 2016 with the goal of streamlining the rental process for landlords and tenants. In 2017, they joined the Winter batch of Y-Combinator, and between the end of 2021 and the beginning of 2022, they secured two rounds of debt, totaling $200M.

Unfortunately, Rezi could not survive the downturn in the prop-tech sector and failed to secure new funding, which forced them to shut down.

What was Rezi?

Rezi was different from other rental platforms. Instead of letting landlords list their apartments, Rezi paid the landlords upfront, then listed and rented the apartments themselves.

Imagine you own an empty apartment in NY. Instead of dealing with the hassle of finding the right tenant, handling contracts, and ensuring monthly payments, you could simply receive a $30,000 upfront payment and allow Rezi to lease your apartment for a year.

After that, you could forget about your apartment until the following year. Rezi would handle everything, from screening tenants to showing the apartment and collecting monthly rent. 

It's important to note that Rezi wasn't just simplifying the process; they were also taking on the risk from the landlord to themselves. 

Landlords wouldn't need to worry anymore about the cost of having the apartment empty or the possibility of tenants not paying their rent. Rezi absorbed all these risks.

On the other hand, this also meant that landlords had no say in choosing their tenants. Instead, Rezi's algorithm decided based on the individual's rental history, credit, income, and employment status to determine if they would make a good tenant.

This simplified process was also convenient from the tenant's perspective. Anyone seeking to rent could simply register in Rezi's app, browse through all available apartments, and schedule a showing at their convenience. 

Sean Mitchell, co-founder of Rezi, emphasized the importance of enhancing the tenant user experience. That's why they had a dedicated concierge team available to show their apartments at any time, allowing tenants to schedule showings whenever it suited them best.

Once Rezi's algorithm determined that you were a suitable tenant, you could view as many apartments as you liked and select your preferred one. The entire process could be completed in as little as a couple of days.

A Big Need for Funding

Many startups, including Rezi, depend on venture capital funding or debt to sustain themselves during their initial years until they achieve profitability. However, unlike typical startups that utilize this funding for various purposes such as employee salaries, product development, and marketing, Rezi faced an additional financial burden: upfront payments to landlords.

To succeed, Rezi required a large number of apartments so that tenants could have options to select from. However, to get these apartments, they had to pay large upfront payments to landlords, amplifying their reliance on continued VC funding.

This forced Rezi to create a financial instrument that allowed third-party investors to pay the upfront payments to the landlords and receive a return on their investment.

This strategy was ingenious as it enabled Rezi to allocate a larger portion of the VC funding to paying salaries and expanding their team. However, despite eight years of operation, Rezi failed to achieve profitability. Consequently, when funding ceased, they were forced to exit the market.

Reasons for Failure

  • The Need for Capital: After the shutdown, Mitchell stated that “Rezi was working to try to grow and achieve some scale, but the capital required to do that was very significant.” As I mentioned, in order to make their platform more attractive for tenants, Rezi needed a large inventory of apartments, but to get them, they required a lot of funding. 

  • An Unscalable Business Model: Rezi's inability to sustain its operations despite securing over $200M in debt speaks volumes about its business model. While offering upfront payments to landowners set Rezi apart from competitors, it also contributed to its downfall. Perhaps a more viable approach would have been to concentrate on assisting landowners in finding tenants without assuming full management of their apartments.

  • Proptech Downturn: Like many other sectors, the proptech industry experienced a surge in VC investment during 2021 and early 2022, followed by a sharp decline in 2023. This downturn explains why Rezi successfully secured $200M in 2022 but struggled to get additional funding last year.

Go Deeper

Trend Radar

Empathic Robots

Two weeks ago, GPT got a body. This week, AI is getting feelings. 

Hume.ai has released a demo showcasing EVI (Empathic Voice Interface), the first AI capable of understanding the users' emotions from their speech.

EVI can detect whether you are anxious, surprised, or frustrated and respond accordingly. 

It is by far the closest AI has come to sounding human, but there is clearly a long way to go before AI can fool us and pass the Turing Test.

There is a demo in which you can talk to EVI, and you should absolutely try it out.

I have spent several hours talking to the AI, and although the answers resemble GPTs, engaging in real-time voice conversation is a profoundly different experience. 

It is very far from perfect. The answers come off as robotic, sounding like GPT reading aloud. The voice often shifts tone and intonation abruptly, creating a disjointed experience. Many times, it feels like the answer is a patchwork of sentences from different sources rather than a cohesive paragraph.

And yet, I've never had this much fun chatting with an AI before. Pretending to be angry and seeing it try to calm me down is hilarious. If this is the future of customer support, then at least it will give us great memes.

EVI at Work

EVI API is already open to the public, so we will soon see new apps that use it in different ways. 

So, in what industries will EVI get a job?

Nothing is sure yet, but a couple of sectors come to mind:

  • Customer Support: This is probably the first thing most people think when they see the EVI video. An AI capable of understanding user emotions and adjusting its responses and tone accordingly holds great potential for customer support roles. Nevertheless, I do not think using AI to handle angry customers is a great idea. It will probably be more useful as an AI that can answer questions and provide technical support.

  • AI Assistants: I wouldn't be surprised to see the technology showcased by EVI incorporated into other AI Assistants. This integration could enhance Siri and Alexa, making them feel more like true virtual assistants rather than just voice-controlled interfaces for Google searches.

  • Recruiting: To the dismay of many, companies have increasingly turned to automating the hiring process. Many are now using AI for screenings and interviews. It wouldn't be surprising if new AI interview services emerged utilizing Hume's technology.

In my opinion, this technology is still in its infancy. If it undergoes the same exponential improvements that LLMs are experiencing, I can't imagine how advanced AI voices will be a year from now.

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