AppFolio’s AI Leasing Assistant, Lisa
/Let’s delve into the past and think about the last time you tried to rent an apartment. Hopefully, this doesn’t trigger any painful memories…
First, you went to one of the familiar listing sites — Apartments.com, Zillow, or maybe even Craigslist if you’re the type to live dangerously.
You sent a message or made a phone call to the places that piqued your interest.
You heard back from some of the inquiries, but from others - silence. You might have even ended up on the phone with a grumpy landlord who acted as if you were wasting their time, or with an intern who couldn’t provide you with an ounce of concrete information.
Once you made a connection, you scheduled a showing to take a look at the place. Most likely, this was coordinated over text, email, a phone call, or social media.
You eventually found a place, but you were still left with a lingering sense of doubt. You may have missed out on another fantastic place to rent just because of a communication breakdown.
On paper, the process of finding housing sounds simple and relatively straightforward. However, in practice, it’s often a convoluted process – one that includes many hardships and unpredictability. In fact, only 5% of all rental inquiries end with the filing of an application. Put shortly, the leasing process often results in a large amount of wasted effort, both for prospective residents and property managers.
Traditionally, the point of contact for prospective renters has been a leasing agent who engages in time-consuming and repetitive conversations with potential renters. Because so many of these conversations follow a predictable script, AppFolio identified a unique opportunity to streamline the process through automation.
AppFolio AI Leasing Assistant, Lisa, is AppFolio’s response to this problem. Lisa outsources the repetitive and time-consuming parts of leasing conversations while retaining human operators to cover the long tail and provide training data for future automation. Below is a diagram showcasing the processes Lisa is capable of carrying out, and a mock scenario of Lisa at work.
Typical flow of a leasing conversation, Blue boxes indicate steps that are automated by Lisa.
Initial Onboarding of a Prospective Resident
We’ll start with the customer. George is looking for a new place and browses familiar listing sites. Eventually, he sends out a few inquiries. One of these inquiries makes it to Lisa, who automatically parses out George’s profile information, and stores George’s interest in the property’s sales database. Lisa will also use this information to initiate a text conversation with George in less than one or two minutes.
Answer Questions, Prompt for Sales Information
George has come prepared and wants to ask a series of questions most prospective renters ask when in search of a new home:
“Are pets allowed in the apartment? What is the estimated price of utilities per month? What is the floor plan of the available units?”
Lisa can detect and answer these common questions automatically by drawing on a standardized set of policies the property sets. Lisa will also nudge the conversation forward. She’ll request additional contact information and suggest times when George might come by the apartment for a tour.
LISA CONVERTS THE NATURAL LANGUAGE INTO A STRUCTURED RESPONSE THAT ALLOWS THE LOGIC LAYER TO COMPUTE THE RESPONSE.
Find a Showing Time and Handle Scheduling Conflicts
George would like a showing, but he works long hours and can only attend a showing during the evening or on weekends. This could be a problem, not only for George but also for the property managers, since agents have limited availability on the weekends, and agents are sometimes booked weeks in advance. George tells Lisa his availability and Lisa immediately cross-checks both parties’ schedules to find a time that works.
LISA PARSES THE SHOWING TIME PREFERENCE AND CAN RESPOND TO COUNTEROFFERS.
Lisa’s value proposition should be clear: Lisa can maintain any number of parallel conversations with prospective renters, provide excellent customer service, and free up time for property managers to focus less on the minutiae and more on the big picture.
Overview: How Does Lisa Work?
AppFolio retains a staff of operators, who get a chance to review conversations as they unfold, rapidly re-label messages on the fly, and use tools to handle edge cases or language that our current models fail to understand.
Lisa uses a collection of concepts and models to achieve these outcomes including:
A parser for inquiries from Internet Listing Services (ILS)
A dialog system that combines
Natural Language Understanding (NLU)
Dialogue State Tracking
Policy (a logic layer that combines conversation state and external knowledge to decide the next step)
Template-based Natural Language Generation (NLG)
A recommendation system for cross-sells
A forecasting model to determine hiring goals and set staffing levels for each shift
Stay tuned as we will discuss each of these components in greater detail as part of a series of blog posts surrounding the nuances and technologies!
Lisa integrates with several external systems via APIs and Natural language.
Authors and contributors: Christfried Focke, Shyr-Shea Chang, Tony Froccaro, Ian Murray