Airbnb — designing a trustworthy reputation for a p2p economy 

As a facilitator in the peer-to-peer sharing economy, AirBnB’s business model is built on reputation and trust.

In the history of the company's scandals, they have made numerous changes to improve the lack of trust between Travelers and Hosts, yet never seem to resolve the prevailing criticisms that cause people's lack of trust in Airbnb.

This case study identifies ways we can improve AirBnB's reputation and provides design solutions to users' pains.

UX Research
User Interviews
Journey Mapping
Rapid Prototyping
Adobe XD
Adobe Ps
Google Slides
Pen + Paper
Me :-)
Class feedback
3 weeks

the challenge

How do you get complete strangers to trust each other? At the root of any P2P economy is this ongoing challenge of perpetuating a trustworthy reputation with limited time and interaction constraints. 

Airbnb continues to be abused by exploiters of the platform, how can we protect our users and reputation?

How might we increase people’s trust in Airbnb as a fair mediator of peer-to-peer transactions?

the solution

Implement product features to empower user feedback, create a community of accountability, and improve the quality and accuracy of information on Airbnb.​​


Add Helpful vs. Unhelpful ratings to allow peer reviews of the user content.


Regulate the check-in / check-out process with required in-app photo uploads and optional instant reviews.

In alignment with these designs, changes to the operation of Airbnb's standards and incentives will support our efforts to cultivate trust on a larger scale.


Enforce a Strike System to demonstrate actionable consequences.


Reward quality reviews and trustworthy reviewers.


analyzing the trust economy

With P2P sharing becoming a norm, how are other companies creating trust between strangers?

Airbnb - Competitive Analysis.png

Amazon is highly trusted by consumers due to their image of dedication to providing users with the best service: their extensive review system uses crowd feedback to give users a lot of accurate information.

  • Validation leads to accuracy, which leads to trust.

Lyft garners trust by boasting their 4.7+ star criterion for drivers, and sets the expectation that the company is taking action to protect riders. It hands the rider a feeling of control over their driver, despite the actual situation.

  • People are more willing to trust each other in the presence of a trusted mediator.


our users

With an understanding of the trust economy, we also need to empathize with the needs of the people who use Airbnb: Travelers and Hosts.

I interviewed 3 Travelers who had used Airbnb in the last 6 months and asked them about their experiences and motivations for using the app.

Some questions I asked include:

Why do you use Airbnb (compared to hotels)?

How do you decide whether or not to reserve a space?

What was your best/worst experience with Airbnb?

“I feel cheated when the place is not as expected, but all I do is complain and note it in the review, because the reviews actually matter. People won’t book places with shit reviews.” 


For Hosts' insights, I went digging on a host community forum to uncover the pains and considerations these users go through when sharing their spaces on Airbnb.

“Still don’t see how it could be 4.64 instead of 4.67 … which is the frustrating part… can’t even trust [Airbnb] to do basic math.” 


Traveler's User Journey

The biggest pain point for Travelers happens at the moment of arriving at an Airbnb. If the place is not as expected, it can cause a ripple effect of back and forth negotiations and stress during their trip. 

On the flip side, Travelers who don't encounter any notable issues may also neglect to leave any reviews or feedback for the Host after their trip ends.

User Journey - Host.png

A critical pain point for Hosts is the pressure to accept a reservation even if they feel uncomfortable. If they reject too many booking requests, their acceptance % rate will drop and make it harder for them to continue hosting. Airbnb "punishes" Hosts with an algorithm that causes their listing to appear less frequently in searches, reducing their chances of being booked.


key insights

Traveler insights:

Travelers are motivated to use Airbnb because they believe it's cheaper than hotels.

  • They find that Airbnb tends to be more affordable, with more flexibility in location, and freedom.

  • Hotels photos can be misleading because they are curated, you see the best possible room of that type. If the Airbnb listing is honest, it would show photos of the actual space.

Travelers care about the quality of a listing's reviews.

  • They will consider booking places with fewer reviews if there are a lot of details in the reviews and descriptions.

  • Listings with more and better photos are considered more trustworthy.

Repeated positive experiences are critical for a Traveler to continue using Airbnb.

  • Despite the bad press and the dangers of P2P sharing, people will continue to use Airbnb if they have had good personal experiences. 

  • One negative experience might only serve as a learning point if a Traveler has had positive experiences before.

“Generally, I trust Airbnb. I've yet to have an experience so terrible that I wouldn’t use them again... but I've heard the horror stories.” 


Host insights:

Hosts are hyperaware of their % of accepted reservations.

  • They would rather appeal to Airbnb or wait for the Traveler withdraw their request than decline it.

Hosts are interested in the details of how ratings work.

  • Airbnb could be more transparent about how their system calculates 5 star ratings.

Hosts are concerned with the accuracy of info on Airbnb.

  • Even if a Traveler has positive peer host reviews, Hosts don’t entirely trust what they read.

  • Each home and host experience is different, so what may suit one Traveler may not suit another.

“I’ll give it a bit of time to see if she withdraws the request, and if not I will decline. Even with all the other recent positive reviews, it is clear that she may be difficult if anything isn’t to her liking, and I already see two possible risk factors..” 



reinforcing the reputation 

Airbnb's standards are easy enough to find online, but the consequences of violating these standards are less than transparent. Their explanation is brief, evasive, and overall unsatisfactory:

Screen Shot 2020-01-24 at 2.35.25 PM.png

A Strike System better communicates the consequences for violating the standards & expectations: After a determined number of strikes, your account is suspended with your ID verifying your individual ban for a limited or permanent span of time. By removing abusive users, we protect our community and generate a mentality of respect and trust.


More importantly, we demonstrate the service that Airbnb offers as a p2p mediator. Our user insights show that people believe reviews matter, so taking action to regulate the community based on users' feedback will further empower them and build their trust in Airbnb.


Sketches of how the design features might be incorporated into the existing interface.


Amazon and Yelp are examples of products that utilize crowd feedback to increase the accuracy of their public reviews. Helpful / Unhelpful ratings from users can reduce misleading photos, and help promote honest listings and traveler reviews.


Users will receive a ‘minor strike’ on their public profile should their reviews or photos be given too many Unhelpful ratings. Images or reviews with too many Unhelpful ratings will be put under review by AirBnB and removed.


We can reduce friction for Travelers by organizing reviews by the quantity of Helpful ratings. From our insights, we know they are looking for quality reviews. Listing the Top Reviews first allows users to get the best info at first glance. A sort and keyword search function will also give them the ability to find what they need faster.

helping our reviews


accuracy leading to trust


Travelers that have visited a space can review a listing’s photos with Helpful / Unhelpful ratings.


Hosts that consistently post Unhelpful photos will become demerited by the Strike System, which could lead to losing the ability to host. To prevent re-uploads & discourage dishonest behavior, Hosts are given a detailed photo upload guideline when creating a listing.

On the flip side, Hosts can also rate a Traveler's reviews with Helpful / Unhelpful ratings once that Traveler has stayed in their space. 

To help relieve the pressure Hosts feel when deciding whether or not to accept a booking, we can allow Hosts to set Acceptance Preferences. Travelers that don’t meet a Host’s requirements (e.g., X number of reviews by other hosts or X number of Helpful ratings by other hosts) automatically cannot book their spaces. Instead of penalizing a Host's decision to protect themselves, we can prevent the interaction from occurring in the first place.

visualizing the check points


In the journey maps, we can see both users face different pains during the check-in and check-out process. I designed a new check-in flow that requires Travelers to take quick, in-app photos of the space (with a same day deadline). This holds both parties accountable: the Host has provided the space as expected, and the Traveler has acknowledged this.

Similarly, at the end of a stay, in-app photos of the space are required in order to properly check-out. The standard deadline to submit the photos could be 1 hr after the agreed check out time. This holds the Traveler responsible for respecting the space, and gives the Host some security.

The Strike System is also used here to reinforce responsible and respectful behavior. Emergencies and accidentally forgetting to check-in or out properly are not detrimental unless the behavior is consistent.

rewarding quality reviews


While checking in, Travelers are asked to rate the space's listing photos using the Helpful / Unhelpful feature. The rating is optional, however, targeting users at this point in their journey can give us more feedback than post-stay, when Travelers are preoccupied with return trip logistics. 

Despite this low-effort alternative, we still want Travelers to leave quality reviews after their stay.  Currently, Travelers receive a $25 discount off their next booking if they leave any kind of review, but we can improve by further incentivizing users to write detailed reviews. 


Travelers with many Helpful rated reviews will be given a “Top Reviewer” status and bonus perks, like more booking discounts and fewer limitations on booking. Overall, we want to increase the trustworthiness of users through rewards as well as consequences.


Where does this flow fit in?

This check-in guide flow is Airbnb's current interface for Travelers as they follow a Host's instructions to getting the key and entering the space.


The first screen of my photo check-in flow would follow in the place of the last screen of the instructional check-in flow.

If Travelers tap the X to leave, they will be reminded this is required and to contact their Host if special circumstances are needed. 



Airbnb will *hopefully* outdate my case study as they address their PR issues, but I think the concept of digital trust and reputation will continue to challenge businesses as we progress towards a future where the sharing economy thrives and everyone lives online.

I approached this challenge knowing only what is accessible through Airbnb applications and whatever is available to the experiences of the general public. Business constraints would no doubt limit the plausibility of my ideas, but it's a good exercise in design thinking to consider the what-ifs.