Improving the search for affordable housing — MVP Search App
Building an affordable housing application starts with data.
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The problem
Rent and housing prices have risen in Austin 42 percent over the past five years, while the median income has only increased 17 percent higher during the same period. Affordable housing is an important city resource to ensure Austin residents with lesser means have access to the opportunities Austin provides.
Moreover, searching for affordable housing is a very difficult process for everyone involved. There is a low supply of housing and residents usually have a limited amount of time to find it. Residents are generally faced with working from printed lists with limited or out-of-date information. The other option is to go through a housing councilor who helps to place them from their own list.
Finding units that are considered affordable is just the first step in this long process. Availability is limited and residents must call each property to find out if any of the units are currently vacant. Not only that, but residents searching for affordable housing tend to have more barriers to acceptance such as broken leases, criminal history, or low credit scores. Having applications rejected due to those barriers can cause burdensome costs for the resident.
Desired outcome
The purpose of this project is to provide a unified database of affordable housing inventory to deliver affordable housing resources for those in need in Austin in one easy-to-access online portal.
MVP Data Search Tool
Our Solution
After shadowing a Section-8 voucher orientation and interviewing residents and housing experts, we found out that there were three main pieces of information that residents wanted most when searching for housing:
- Availability
- Acceptance Criteria (do they accept broken leases, low credit, etc)
- Location
We also learned that the majority of residents access the internet via a smartphone instead of a desktop so whatever solution we built needed to work well on mobile.
Armed with this knowledge, we took a look at the feasibility of getting reliable data from the data hub for the top three criteria. The location data was already there for the most part and wasn’t likely to change, we would just make it easier to interpret by plotting properties on a map. We had fields for acceptance criteria but they were not consistently filled out. Although a field was there for availability, we did not track that in the data hub because it is incredibly difficult to keep up to date.
So, we decided to focus on meeting 2 of the 3 top criteria for residents for the first iteration — location and acceptance criteria. We narrowed down the acceptance criteria category to three main items — broken leases, low credit scores, and criminal history. Since the data in the hub was incomplete for acceptance criteria, a housing councilor was tasked with calling properties to fill the holes.
While the data was getting filled in, we began designing and building the search tool.
The search starts with a series of questions about preferences. The answers to these questions are not only used to help filter properties, but they are also stored within the data hub to give the city an idea about what residents are looking for.
A sample of the filters and preferences a resident can set when searching using the tool.
Once the user has set all their preferences, they are taken to a map view of all the affordable housing properties. Different colored pins represent how closely the property matches the filters selected.
Clicking on a pin opens up a view with more details about the property to help the resident make the best decision possible.
An example of the map view and property detail list
