Advanced Computing in the Age of AI|Wednesday, October 21, 2020
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AI in Real Estate: Streamlined Apartment Leasing 

Real estate has long been an industry slower to adopt new technologies or be on the cutting edge of innovation. But that’s changing – this is especially true with the introduction of advanced automation, machine learning and AI into real estate and property management tools, leading to significant efficiencies to some very manual, old-school processes.

Leasing, for example, has always been challenging because it involves so much communications between leasing agents and prospects, back-and-forth scheduling, time crunches and applicant screening. Everything is seemingly part of a multi-step process that drains time and resources from everyone involved.

Advancements in artificial intelligence are revolutionizing leasing by eliminating tedious tasks for leasing agents, giving them more time to focus on business strategy and creating a more pleasant prospect experience.

Value-Add for Leasing Agents

The job of a leasing agent has trying moments: they need to constantly evolve their business and secure new tenants. Responding (or, more likely, inability to respond) to the high volume of prospect queries, vetting prospects for different buildings, tedious, back-and-forth scheduling of apartment tours all contribute to the high volume of tasks that can hamper business success.

Conversational AI is changing the leasing game, taking on more of the busy work from leasing agents. Utilizing natural language processing (NLP), conversational AI handles the initial stages of the leasing process, responding in real time via text to tenant queries. AI learns from experiences, so it can build upon the exchanges it has with prospects to determine the right questions to ask them. Based on prospects’ answers, AI can determine appropriate responses regarding details about the property, screening criteria, pet policies and even cross-selling other properties in the portfolio. Additionally, advancements in technology can go as far as to schedule unit showings for prospects, checking leasing agents’ calendars and comparing with prospect availability.

Here is an example of what that initial leasing conversation might look like:

AI: Hi, sorry I missed your call. Which type of unit at XX Apartments are you interested in? We have a one bedroom and a two bedroom available.

Prospect: I’m interested in the two bedroom.

AI: That works, can I get your name so I can schedule you?

Prospect: Jane Doe

AI: Do you want to come for a showing today at 3:00 pm? We also have tomorrow at 9:00 am. I’ll shoot you a text with our agent contact a couple hours prior to showing as a reminder

Prospect: Hmm – neither of those times work for me.

AI: Ok, we could also do Thursday at 3:30pm? Does that work?

Prospect: OK, good.

AI: Also, we use a third party to screen our applicants. Their system considers credit, income, and criminal history when making a decision for approval. Security deposit is $150 on approved credit. Please also bring your photo ID to the showing. Does all this work for you?

Prospect: Yes, sounds good. Also, just curious, do you allow any pets?

AI: Yes, we allow up to two pets, no breed or weight restrictions. There’s a $250 deposit and $25 per month pet rent (covers both pets).

Prospect: Awesome – thanks!

This upfront AI communication allows for businesses to interact with a large volume of prospective renters in a seamless way, while freeing up leasing professionals time to drive better in-person showings and customer service. The AI system ensures no leasing inquiry goes unanswered, creating a better experience for the prospective renters while ultimately increasing occupancy rates.

Regarding implementation, this type of technology doesn’t require any in-house AI expertise, it fits into operations seamlessly, without incurring downtime. This is largely due to the conversational nature of the technology. It uses NLP to determine how to best respond to prospects’ questions – the real estate company simply configures its settings (i.e. pet policies or agent showing hours) and the AI takes it from there.

AI can also capture lease data and insights, which gives property managers more visibility into overall leasing and property performance (i.e. why some properties won’t lease) and enables them to adjust their leasing workflow. The data is also void of human error – AI captures critical lead information, including their unit preferences. This also means the leasing team doesn’t have to work around the clock trying to track down this information on leads.

Value-Add for Prospects

On the prospect’s side, modern consumer expectations of an on-demand experience need to be factored. When prospects find units they like, they don’t want to wait a few business days to hear back. They want a seamless apartment hunting process, especially in crowded metro markets like a San Francisco where apartments get snapped up quickly. Having an AI respond in real-time to a request is not a bonus anymore – it’s an expectation. In fact, according to Zillow, 71 percent of prospects expect a response to their query within 24 hours. Couple that with another Zillow stat that nearly half of prospect queries go unanswered, and it’s clear the industry has a long way to go in terms of meeting consumer expectations.

The inherent value of AI early in the leasing process benefits both consumers and real estate professionals, making life easier for everyone. As technology continues to evolve, AI will likely play a larger role in other parts of the real estate ecosystem.

Nat Kunes is senior vice president of investment management at AppFolio.

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