Background on the Problem

Apartment rentals have been increasing throughout America. In Chicago alone, there are over 300,000 apartments that constitute the housing stock. In 2016, approximately 40% of renters in the Chicago area decided not to resign their lease and look for new apartments. That means over 100,000 apartments that enter the rental market every year in Chicago alone. For individuals seeking a rental unit, the sheer number of available apartments can be daunting. Some experts recommend that apartment seekers should visit 5-7 apartments a day, making for a very time consuming and complex process. Current websites, such as Zillow.com, allow searches to screen and filter results, but more often than not there are still a large number of apartments that may match the search criteria. Therefore there is a need to develop a solution that can cut down on the stress and time required for a successful apartment search.

Description of the Solution

Our solution will generate a targeted set of property listings for consumers that align with their taste profile and preferences. This will solve the issue that many consumers face with being overwhelmed by the multitude of listings on sites such as Craigslist and Zillow, and instead will generate a high-potential list of properties that users will be interested in viewing and ultimately renting/buying. Users will generate a simple profile in which they indicate some basic criteria regarding what they are looking for (e.g. “suburb of Chicago,” “2 beds/2bath,” “$3k/month or less”), and then they will be prompted to “rate” ten different property listings (note: number subject to change based on test results). The algorithm will then match them with listings based on comparing them to other users who provided similar ratings.

The key difference between our solution and a matching algorithm such as Cinematch is that our solution needs to provide different suggestions based on location. Therefore, we would need more “humans in the loop” to train the algorithm on how to provide similar types of suggestions across disparate data sets. Initially, the additional humans in the loop would come in the form of hired realtors and/or interior designers. We would show them a set of listings that a particular user indicated that they liked, and they would choose additional listings to recommend to the user. Upon reaching a critical mass of active users, we would phase out the use of experts, and rely solely on an increasing number of user ratings to provide the best recommendations. The algorithm would be based both on initial user ratings and additional data on actual rental decisions. We believe having a large number of users will create a network effect in the form of high quality recommendations, so by being first to market and reaching a large enough user base we will be able to fend off competitors attempting to replicate our product.

Empirical Demonstration

For our empirical demonstration, we will pit our product against a real estate agent and see who produces the best suggestions. Users will create a profile on our platform, including rating an initial set of properties. The user will also meet and discuss their desires with a real estate agent. Our application will generate ten recommendations, and the real estate agent will provide ten recommendations. The user will be shown these 20 recommendations in random order, and select which recommendations they are interested in. We will quantify the success of our application compared to the realtor in terms of how likely individuals were to like suggestions from each source.

Pilot

For our pilot, we will launch the product in Chicago. As mentioned, we will need expert feedback from realtors to initially train our model, and there are several we have in mind to cooperate with, including Mark Allen Realty, Dream Town Realty, and Chicago Properties. To source the apartment listings, Zillow has a light and user-friendly API available for public use. We will also have access to Chicago accelerators such as Polsky Center to ensure we reach a wide audience for a successful pilot.

Team: Shallow Blue
Members: Will Thoreson-Green, Curt Ginder, Holly Tu, Tom Kozlowski, Ram Nayak
[1] http://www.nmhc.org/Content.aspx?id=4708#Large_Cities
[2] http://www.chicagotribune.com/business/ct-renters-re-signing-leases-0520-biz-20160519-story.html
[3] http://streeteasy.com/guides/renters-guide/finding-an-apartment/