Sqft

Redefining house hunting experience
Sqft
OVERVIEW
Sqft is a passion project I worked on to test out a product idea that aims to redefine the house hunting experience in Hong Kong.  Through a mix of system suggestions leveraging smart filters and user customization, Sqft helps with user’s searching process and better informs the decision-making. 
WHAT I DID
Competitive Analysis
User Interview
Journey Mapping
Prototyping 
User Testing
UX/UI Design
DURATION
5 Weeks
 
 
COMPETITIVE ANALYSIS

I first talked to the non-locals and new comers around to get an idea about their house-hunting approaches.

What I found - 

  • The social nature in Wechat made it easier for posts looking for roommates/houses to go viral and matched the right people quickly.

  • If Facebook page and Wechat where people couldn't filter any post were still popular approaches, did that mean existing house-hunting apps could not do a sufficient job?

  • Real estate agents reduced the searching burden, yet there were communication barriers and trust issues. 

 
USER RESEARCH

To get more in-depth insights about people's renting experiences, I interviewed 8 people including college students/graduates who moved out from student dormitories and expats who had just relocated to Hong Kong. Their decision-making process during the searching immediately drew my attention.

Existing filters could not help with user's sifting process in browsing listings - information still overwhelmed users. 

1. Criteria went from clear-cut yes/no to uncertainty

People were very clear when they first considered location, budget, room type...But when I presented them a couple of listings online and asked them to choose their top first, their attitudes became vague and they couldn't tell exactly what they did not like about certain listings.

2. Choosing from abundance led to frustration

Basic filters in existing platforms did not significantly help with the sifting process. Listings that met the ‘hard criteria’ were plenty, leaving users the burden of going through hundreds of posts to make further comparison. This overwhelmed their cognitive load and led to their frustration.

The sweet spot to tackle the question lies in the searching and filtering process. 

User Journey Map

 

This means,

REFRAMING THE PROBLEM

What did we mean by better searching and filtering process? The obvious approach was to add more filters so as to reduce the number of choices. Yet, as shown by the interviews, what was really time-consuming were the soft, subjective criteria that couldn’t be easily captured by traditional filters. 

The questions we should ask, that would help approximate to a more successful house-hunting experience are,

How can we inform user's decision-making?

How can we learn more about user’s preferences?

 
DESIGN SOLUTIONS
1. Location filters tapping into user’s social profile 

Location is an important factor to narrow down user’s searching scope at the very beginning, but a simple map is not conveying the real important information - where exactly should they live? What would be a better location choice for the user?

Initial Prototype

Asking users ‘where to live’ was a tough question for users to start with, because as the non-locals or new-comers of the city, they had no idea either!  

Iteration

Second iteration decided to show popular areas, but in the user testing I realized popular did not equal to suitable. Users’ location preference varied from where they worked/went to school.

Final Design

Show users how others who share a similar social profile with them choose their housing location (e.g. people who go to the same school). This provides a benchmark for them to see options for a suitable location.

2. Leverage visual filters to better understand user's 'soft' criteria

Natural lighting, layout of the room, levels of renovation…some of these factors are the 'game-changers' for users to decide whether they want to look into the listing. Yet, they cannot be captured by traditional text filters. Sqft uses visual filters to learn about these 'soft' criteria and preferences and update its recommendations. 

Visual filters provides a new way to measure user's soft criteria that cannot be captured by traditional filters. 

3. Soliciting in-the-moment feedbacks to improve recommendations

The bookmarked listings can tell a lot about what a user likes, as well as the listings that the user doesn't like. Soliciting users' feedback right after they like or dislike a listing can help better gauge their personal preferences and return more accurate results. 

Swiping a recommended list away triggers a just-in-time survey

to ask about user's feedbacks so as to improve future system recommendations. 

 
REFLECTIONS

Pinpointing the problem to solve was the true 'AHA' moment for me in the project. There were a lot of potential improvements that could be made in the house-hunting journey if I had more time to spend on it , for example, better communication channel for users and listers, more features helping the user compare and decide among a few options. 

 

Yet, with time and resource constraints, designers often have to decide on what would be the most impactful problem to approach, which feature would be most value-adding to have. Sometimes, priorities do need to be set and solutions do come with tradeoffs.   

© 2019 designed by ann peng.