Top 30 Airbnb Interview Questions in 2023

Top 30 Airbnb Interview Questions in 2023Top 30 Airbnb Interview Questions in 2023

Overview

Known for its innovative platform that connects travelers with unique accommodations worldwide, Airbnb has established itself as a leader in the sharing economy. The company continues to expand its horizons by focusing on making hosting mainstream, perfecting its core service of short-term stays, and exploring exciting new ventures beyond its traditional offerings.

If you’re aspiring to join this dynamic company, you’re in the right place. This Airbnb interview guide is designed to assist applicants in successfully navigating their interviews.

By the end of this guide, you will have the necessary knowledge and strategies to succeed confidently in your interview at Airbnb.

Why Work at Airbnb?

Health Insurance

Airbnb provides comprehensive health insurance, covering 100% of individual premiums with options for family coverage. Employees can choose between HMO and PPO plans, supplemented by generous Health Savings Account contributions. The package includes dental and vision, ensuring a robust healthcare solution.

Maternity & Paternity Leave

Airbnb offers an impressive 20-week maternity leave and a significant 10-week paternity leave. Their policy extends to adoption leave and fertility treatment support. The company also facilitates a smooth return to work, highlighting its commitment to supporting diverse family needs.

Salary

$175K
$261K
Product Manager
Median: $210K
Mean (Average): $213K
Data points: 19
$130K
$254K
Software Engineer
Median: $197K
Mean (Average): $194K
Data points: 291
$146K
$220K
Data Engineer
Median: $190K
Mean (Average): $186K
Data points: 16
$130K
$224K
Data Scientist
Median: $175K
Mean (Average): $174K
Data points: 182
$125K
$220K
Machine Learning Engineer
Median: $170K
Mean (Average): $171K
Data points: 8
Business Intelligence*
$150K
Business Intelligence
Median: $150K
Mean (Average): $150K
Data points: 1
$86K
$177K
Business Analyst
Median: $133K
Mean (Average): $130K
Data points: 6
$104K
$164K
Data Analyst
Median: $120K
Mean (Average): $128K
Data points: 5
Growth Marketing Analyst*
$125K
Growth Marketing Analyst
Median: $125K
Mean (Average): $125K
Data points: 1

Most data science positions fall under different position titles depending on the actual role.

From the graph we can see that on average the Product Manager role pays the most with a $212,684 base salary while the Growth Marketing Analyst role on average pays the least with a $125,000 base salary.

Across the board, the compensation packages—including base, median, and mean salaries—highlight Airbnb’s commitment to providing attractive remuneration for its employees.

Airbnb offers competitive salaries, with Product Managers and Software Engineers earning up to $261K and $254K in total compensation. Data-related roles also see strong salaries, with figures over $220K.

What Does an Interview Process at Airbnb Look Like?

Airbnb’s interview process is designed to be thorough and reflective of its high standards. If you’re considering a career at Airbnb, here’s what you can anticipate during their hiring process:

Step 1: Initial Contact and Phone Screening

Your journey with Airbnb begins when their recruitment team contacts you, typically for a phone screening. This initial conversation, lasting around 30 minutes, is an opportunity for both parties to explore the fit. You’ll discuss your professional history and answer basic behavioral questions. Expect queries like, “Can you describe a challenge you faced at work?” or “What’s your approach to building a team?” This call also includes a rundown of the role and team dynamics.

Step 2: Technical or Peer Evaluation

Pass the first screen, and you’re on to the next stage, which, for technical candidates, means a coding test. Here, you’ll tackle coding problems akin to those found on platforms like LeetCode, under a tight timeframe. For those in non-technical roles, expect to have insightful conversations with a hiring manager and a potential peer, focusing on your expertise and how well you mesh with the team and Airbnb’s culture.

Step 3: In-Depth Onsite Interviews

Making it to the onsite interviews indicates serious interest from Airbnb. For technical aspirants, prepare for an intense series of discussions centered on technical skills, system design, and behavioral aspects. Non-technical candidates will similarly face a series of interviews focusing on case studies, problem-solving, and cultural fit. During this stage, you also get the chance to meet team members and catch a glimpse of Airbnb’s work environment.

Step 4: The Decision-Making Process

The final hiring decision at Airbnb is taken very seriously. While team feedback is crucial, the hiring manager has the autonomy to make the ultimate call, ensuring the chosen candidate aligns well with the team and company needs. This decision is also informed by a leveling committee that determines the appropriate level and position for the candidate within the company.

Throughout this process, Airbnb looks for candidates who not only have the technical know-how or the professional experience but also those who embody the values and culture of Airbnb. Preparing for this journey involves understanding not just your role but also the larger picture of Airbnb’s mission and values.

What Type of Questions Are Asked in an Airbnb Interview?

1. Tell me about a time you went above and beyond in a project

For those aiming to join Airbnb, showing your initiative is crucial. This question seeks to understand how you handle tasks that require going the extra mile.

How to Answer

Discuss a specific instance using the STAR method: Situation, Task, Action, and Result, emphasizing the extra mile you went and the added value it brought.

Example

“In my previous role, I was tasked with developing a client proposal. Recognizing the competitive market, I not only completed the proposal but also conducted a preemptive analysis of potential counter-offers. This allowed us to adjust our offer in advance, resulting in a successful bid and a new client win for the company.”

2. What strengths would your current manager highlight about you, and what areas for improvement might they point out?

Assessing your strengths and weaknesses shows self-awareness and the desire for self-improvement — qualities Airbnb values in candidates.

How to Answer

Identify strengths that are relevant to Airbnb’s work culture and a weakness that you are actively working to improve. Be honest and constructive in your assessment.

Example

“My manager would likely highlight my collaborative approach and my knack for problem-solving as key strengths. For improvement, she might suggest that I continue to develop my technical skills in data analysis. I’m currently taking an advanced course on data analytics to address this.”

3. Tell me about a time when your colleagues did not agree with your approach. What did you do to bring them into the conversation and address their concerns?

Airbnb values a collaborative culture where diverse opinions are respected and considered. This question helps interviewers understand how you handle disagreements, your ability to listen and empathize with others, and your skill in navigating through conflicts to achieve a common goal.

How to Answer

When answering this question, focus on demonstrating your ability to listen, understand different perspectives, and find a mutually beneficial solution. Emphasize your communication skills, openness to feedback, and how you ensure all voices are heard. It’s important to convey that you value team harmony and collective success over individual ego. Structure your answer to briefly describe the situation, explain the disagreement, detail your response, and conclude with the outcome.

Example Answer

“At my previous job, my team was tasked with developing a new marketing strategy. I suggested a digital-first approach, but two colleagues disagreed, favoring traditional methods. Recognizing the impasse, I proposed a meeting to discuss the merits of each approach. During the meeting, I actively listened to their concerns about digital media’s reach and effectiveness. I acknowledged the validity of their points and shared data supporting the digital approach’s potential. To address their concerns, I suggested a blended strategy incorporating both approaches, allowing us to test and compare results. This compromise was well-received, and we implemented a successful campaign that outperformed our previous efforts in both traditional and digital channels. This experience taught me the value of listening, respecting differing viewpoints, and finding a balanced approach for the team’s success.”

4. Design a dynamic pricing algorithm for Airbnb based on listing demand and availability

This question is similar to designing a dynamic pricing algorithm, focusing on how Airbnb can adapt prices based on market conditions.

How to Answer

Discuss the factors influencing demand and availability, such as seasonality, local events, and historical booking trends, and how they could be incorporated into a pricing model.

Example

“I would design a model that adjusts prices based on predicted demand, using historical booking data, time of year, and local event schedules. The model would increase prices as demand peaks and decrease them in low-demand periods to optimize occupancy rates.”

5. Develop an ETL pipeline for integrating Stripe payment data into Airbnb’s database

This question assesses your knowledge of ETL (Extract, Transform, Load) processes, which are essential for managing payment data at Airbnb.

How to Answer

Outline the steps for extracting data from Stripe, transforming it to fit Airbnb’s data schema, and loading it into the internal database for analysis.

Example

“The pipeline would start with extracting transaction data from Stripe, followed by cleaning and transforming this data to match Airbnb’s database schema. Finally, I’d automate the loading of this data into our warehouse, enabling real-time payment analytics.”

6. Implement a priority queue using a linked list with specific operations

This technical question tests your coding skills, which are relevant for developing efficient data structures within Airbnb’s platform.

How to Answer

Explain the logic behind using a linked list to implement a priority queue, focusing on the operations of insert, delete, and peek.

Example

“In Python, I’d create a class for the linked list with nodes holding the data and their priority. The insert operation would place higher-priority items closer to the head. The delete operation would remove items based on priority, and peek would return the highest priority item.”

7. Investigate if a redesigned email campaign led to a true increase in conversion rates

Understanding the impact of marketing strategies, like email campaigns, is crucial for Airbnb’s customer acquisition efforts.

How to Answer

Discuss a data-driven approach to compare the conversion rates before, during, and after the campaign, controlling for external factors.

Example

“I would analyze conversion trends over time, correlating them with the timing of the email campaign while accounting for other factors like seasonal trends or changes in the market that might have influenced Airbnb’s conversion rates.”

8. Determine the frequency of incorrect pickup locations in Uber’s map system

Solving operational challenges using data analytics is akin to tasks you might face at Airbnb, ensuring an accurate and user-friendly experience.

How to Answer

Explain how you would use data analysis to quantify the issue of incorrect pickup locations, potentially using location data and user feedback.

Example

“By cross-referencing user-reported incorrect pickups with GPS data, we can identify discrepancies and frequency of these occurrences. This method could be applied to Airbnb’s location services to ensure accuracy in property listings.”

9. Explain the discrepancy in overall approval rates despite flat or increasing individual product rates

This question tests your ability to analyze data and identify underlying trends, which is important for managing Airbnb’s diverse offerings.

How to Answer

Propose possible explanations, such as changes in the mix of products or variations in the volume of approvals for individual products.

Example

“One explanation could be a change in the mix of products. If high-approval products saw reduced volume while lower-approval products gained volume, it could lead to an overall decline in approval rates. This can relate to Airbnb’s varying property types and their respective demand.”

10. How would you analyze and improve retention rates across different services?

This analytics-focused question tests your ability to use data to uncover insights and drive improvements.

How to Answer

Explain your analytical process, how you would identify and investigate retention disparities, and the steps you would take to address them.

Example

“At my last job, I identified a retention issue by segmenting user data by demographics and usage patterns. I proposed targeted communication strategies for each segment, which resulted in a 10% improvement in user retention over six months.”

11. In a machine learning model like a random forest, does increasing the number of trees always equate to better accuracy?

This technical question gauges your understanding of machine learning concepts and their application.

How to Answer

Explain the balance between model complexity and overfitting, and how increasing the number of trees in a random forest model impacts accuracy.

Example

“In my experience, adding more trees to a random forest model increases accuracy to a point. For a project, I found that after about 100 trees, the improvement plateaued. So, I focused on tuning other parameters, which optimized the model without unnecessary computation.”

12. How would you evaluate the validity of an AB test result with a .04 p-value?

Airbnb often utilizes AB testing to optimize its platform. This question tests your understanding of experiment validity in a practical scenario.

How to Answer

Discuss the importance of the p-value in determining statistical significance and how it applies to decision-making at Airbnb, considering other factors like test duration, sample size, and consistency across segments.

Example

“In evaluating a .04 p-value from an Airbnb landing page AB test, I’d confirm the statistical significance against the standard alpha level of 0.05. However, I’d also assess the result’s practical significance by considering the effect size and the test’s impact on user experience and business metrics.”

13. How would you structure an A/B test for a button color and placement change?

This question explores your approach to A/B testing for UI/UX changes, which can directly impact guest interactions on Airbnb’s platform.

How to Answer

Explain the setup of a multivariate A/B test, ensuring clear variant definition and the measurement of each change’s impact on user behavior at Airbnb.

Example

“For Airbnb’s sign-up funnel, I’d run a factorial A/B test, creating different versions for both button color and placement. I’d track click-through rates for each variant, ensuring sufficient sample size and test duration to reach conclusive results.”

14. What strategy would you use to allocate a marketing budget across multiple channels?

Determining the most efficient use of a marketing budget is crucial for Airbnb’s growth. This question assesses your strategic planning skills for budget allocation.

How to Answer

Describe a methodical approach to testing marketing channels, such as incremental cost per acquisition, and its implications for Airbnb’s marketing strategy.

Example

“At Airbnb, I’d allocate the budget based on an initial small-scale test on each channel to gauge cost-effectiveness. I’d scale up spending gradually on high-performing channels, measuring not just immediate conversions but also long-term guest value.”

15. How would you go about designing an A/B test to evaluate a price increase for different subscription levels?

Price optimization is a key factor for Airbnb’s profitability. This question examines your ability to test pricing strategies effectively.

How to Answer

Explain the steps of setting up an A/B test for price sensitivity, including segmentation, control for seasonality, and measuring long-term user engagement, which could translate to Airbnb’s subscription services.

Example

“At Airbnb, I would create segments based on user behavior and demographics to test different price points. The goal would be to find the optimal balance between increased revenue per user and the potential decrease in new or recurring subscriptions.”

16. How would you interpret the result of a financial reward experiment where the treatment group had a lower response rate than the control group?

This question examines your analytical abilities to deduce why an expected outcome of an experiment—like the one involving financial rewards—was contrary to the hypotheses.

How to Answer

Evaluate the experiment’s design and suggest improvements, such as examining the reward’s attractiveness or potential biases in the test setup that could be applied to Airbnb’s customer engagement strategies.

Example

“In this scenario, I would first consider whether the $10 incentive was substantial enough for the treatment group. It’s also possible that the reward did not align with the interests or motivations of Airbnb’s user base. I would suggest a follow-up experiment with varied reward types and levels to determine the most effective incentive.”

17. What approach would you take to measure the success of a new banner ad strategy on a media website?

For a role at Airbnb, where user experience is paramount, you might be tasked with assessing the impact of new features, like banner ads, on user engagement.

How to Answer

Discuss the importance of balancing monetization with user experience, measuring not only click-through rates but also the potential impact on content consumption behavior.

Example

“To assess a banner ad’s success, I would not only track click-through rates but also monitor changes in user behavior on Airbnb’s platform, such as average session duration and bounce rates, to ensure the ads are not detracting from the overall user experience.”

18. After testing a new UI with a 5% increase in conversions, what would you expect to see when it’s rolled out to all users?

A question like this tests your understanding of how changes in UI can affect Airbnb’s conversion rates on a larger scale.

How to Answer

Explain how to account for variables such as user adaptability and the possibility of a novelty effect when scaling UI changes from a test group to the entire Airbnb user base.

Example

“If the new UI led to a 5% conversion increase in the test group, I would anticipate a similar but possibly slightly lower increase when applied to all Airbnb users, as the controlled test environment often yields slightly higher results than uncontrolled, real-world application.”

19. For predicting booking prices on Airbnb, would linear regression or random forest regression be more effective, and why?

Predictive modeling is essential for Airbnb to estimate booking prices accurately. This question tests your knowledge of which model would yield the most reliable results.

How to Answer

Compare the features of linear and random forest regression, considering Airbnb’s diverse and non-linear price determinants, and justify the choice of model.

Example

“Given Airbnb’s data likely has non-linear relationships, such as the interaction between location and amenities on price, a random forest regression would typically perform better than linear regression due to its ability to capture these complex patterns.”

20. How would you write a query to find neighborhoods with zero users in a database?

This technical question evaluates your SQL skills, which are crucial for data roles at Airbnb for analyzing user demographics and market distribution.

How to Answer

Outline the SQL query structure that would allow you to identify neighborhoods without users, using JOINs and WHERE clauses effectively.

Example

“To find empty neighborhoods, I would write a SQL query that LEFT JOINs the users table to the neighborhoods table on the neighborhood field and looks for instances where the user ID is NULL, indicating no users reside in those neighborhoods.”

21. How would you design a model to predict occupancy rates for properties on Airbnb?

This question tests your ability to apply machine learning to real-world problems like predicting occupancy rates, which is vital for Airbnb’s inventory management.

How to Answer

Explain the model design process, including selecting features, training data, and evaluation metrics that would be relevant to Airbnb’s diverse property listings.

Example

“To predict occupancy rates, I’d use historical booking data, seasonal trends, local events, and property features as inputs. I would evaluate the model’s performance using metrics like RMSE (Root Mean Square Error) to gauge its predictive accuracy.”

22. What approach would you take to develop a recommendation engine for Airbnb users?

As Airbnb strives to personalize guest experiences, this question explores your approach to building a system that recommends listings.

How to Answer

Discuss building a recommendation system using user demographic data, interests, and listing metadata, emphasizing how you’d tailor it to Airbnb’s unique accommodations and guest preferences.

Example

“I would create a collaborative filtering model complemented by content-based filtering to recommend Airbnb listings. The model would factor in user behavior, preferences, and listing characteristics like location and amenities.”

23. What factors would you consider when setting rental prices for new Airbnb listings?

This question addresses the business acumen needed to price rental units competitively on Airbnb’s platform.

How to Answer

Detail considerations for pricing, such as market demand, property location, and amenities, and how you’d use data analysis to establish optimal pricing.

Example

“When pricing new Airbnb listings, I’d analyze comparable listings, considering factors like seasonality, average local occupancy rates, and special amenities. Then, I’d apply a pricing model to determine the optimal range that maximizes earnings while remaining attractive to guests.”

24. How would you determine the optimal Airbnb host location among a group of friends?

This algorithms-based question examines your problem-solving skills, relevant to features like Airbnb’s group booking suggestions.

How to Answer

Outline the function you’d write to calculate the minimum total travel distance for the group, potentially using geometric means or centroid location principles.

Example

“I’d write a function that calculates the geometric median of all friends’ locations to find the most central point. This approach minimizes total travel distance and identifies the most convenient host for an Airbnb group booking.”

25. How can a media company measure the impact of entering the podcast space on customer lifetime value?

Expanding into new areas like podcasts can be analogous to Airbnb exploring new services. This question assesses your approach to measuring business impact.

How to Answer

Discuss how to conduct a cost-benefit analysis, considering subscription fee changes, potential market reach, and engagement metrics that could apply to Airbnb’s service expansions.

Example

“To measure impact, I’d run a pilot podcast series, tracking engagement and subscription changes. This data helps project long-term customer value changes, informing whether Airbnb should invest in similar content expansions.”

26. What considerations would you take into account when building a restaurant recommender feature for a platform like Airbnb?

This system design question is about creating a feature that enhances the user experience, similar to how Airbnb might recommend local experiences or restaurants to guests.

How to Answer

Describe the data acquisition process, the recommender system’s architecture, and potential challenges like data privacy or relevance to user preferences.

Example

“In building this feature, I’d leverage user dining preferences and past review data. The system must align with user privacy standards and provide relevant, personalized dining recommendations, enhancing the overall Airbnb experience.”

27. Determine the optimal cancellation fee for a ride-sharing service

While Airbnb doesn’t operate a ride-sharing service, this question about optimizing cancellation fees can be analogous to optimizing Airbnb’s cancellation policies.

How to Answer

Discuss analyzing user behavior data to determine the fee that minimizes cancellations while maintaining user satisfaction and revenue.

Example

“I would conduct A/B testing with the three fee levels, measuring the impact on user cancellation behavior and overall satisfaction. The goal is to find a fee that discourages unwarranted cancellations but doesn’t deter users from booking with Airbnb in the future.”

28. Build a recommendation algorithm for type-ahead search

Similar to enhancing Airbnb’s search functionality, this question explores your approach to improving user experience through predictive typing.

How to Answer

Explain how to use user interaction data and machine learning to predict and suggest search terms, enhancing the efficiency of Airbnb’s search feature.

Example

“I’d develop an algorithm that learns from past search data and user interactions. It would use natural language processing to predict what a user is likely to type next, making Airbnb’s search more intuitive and user-friendly.”

29. Set a time threshold for canceling a ride request without a penalty

This question can relate to determining a fair and strategic cancellation policy for Airbnb bookings.

How to Answer

Evaluate the average time taken for booking confirmations and propose a cancellation time frame that balances flexibility for users with operational efficiency for hosts.

Example

“For Airbnb, I’d analyze data on how long it typically takes for hosts to prepare for a guest and set a cancellation window that allows hosts enough time to adjust their plans without being too restrictive for guests.”

30. Calculate the average lifetime value for a SAAS company

Understanding customer lifetime value is crucial for Airbnb’s long-term revenue planning and customer retention strategies.

How to Answer

Use the given churn rate and average customer duration to calculate the total revenue a typical customer generates over their lifecycle.

Example

“The formula for average lifetime value would be the product’s monthly cost multiplied by the average duration a customer stays. With a 10% monthly churn, the average customer lifetime is 1 / 0.10 = 10 months, so the lifetime value is 100 dollars * 10 months = 1000 dollars.”

Tips on How to Ace Your Airbnb Interview

Here are tips you can take note of if you’re looking for ways to further increase your chances of passing your Airbnb interview.

Research Airbnb’s Mission and Values

Understanding Airbnb’s core mission and values is crucial. Show how your personal and professional values align with theirs, demonstrating you’re not just a fit for the role but also for the company culture.

Understand the Role and Its Impact

Dive deep into the specific role you’re applying for. Understand how it contributes to Airbnb’s goals, and be ready to discuss how your skills and experiences make you the ideal candidate for this role.

Explore the specific role at Airbnb through our Learning Paths, particularly the Data Science and Data Analytics courses, to gain a comprehensive understanding of how your skills and experiences align with the requirements of this position.

Prepare for Behavioral Questions Using the STAR Method

Airbnb often uses behavioral questions to assess candidates. Familiarize yourself with the STAR (Situation, Task, Action, Result) method to structure your responses in a compelling and organized manner.

You can familiarize yourself with behavioral questions commonly asked at Airbnb by visiting our Interview Questions section. It offers a wide range of practice questions to help you structure your responses effectively using the STAR method.

Demonstrate Your Problem-Solving Skills

Be prepared to showcase your analytical and problem-solving skills. Airbnb values candidates who can think critically and creatively to overcome challenges.

Showcasing your analytical and problem-solving skills, which are essential for Airbnb, can be done by actively participating in our Challenges. This will enable you to think critically and creatively in order to overcome interview obstacles.

Showcase Your Adaptability

Airbnb operates in a dynamic environment. Show that you’re adaptable and can thrive in changing circumstances, which is essential in a fast-paced company like Airbnb.

Highlight Your Teamwork and Communication Skills

Airbnb’s collaborative work environment demands excellent teamwork and communication skills. Share examples of your experience working in teams and how you effectively communicate and collaborate.

Participate in Mock Interviews to practice and showcase your teamwork and communication skills. Share examples from these sessions to demonstrate your experience in collaborative environments.

Be Prepared with Questions for the Interviewer

Have thoughtful questions ready for your interviewer. This demonstrates your interest in the role and the company and gives you valuable insights into what it’s like to work at Airbnb.

Understand Airbnb’s Products and Market

Gain a thorough understanding of Airbnb’s services, target market, and competitors. This knowledge will help you answer questions more effectively and show that you are well-informed and enthusiastic about the company.

Practice Technical Skills Relevant to the Role

If you’re applying for a technical role, ensure your skills are sharp. Be prepared to solve coding problems, discuss system design, or analyze data, depending on the position’s requirements.

If you’re applying for a technical role, use our Takehomes and Coaching Services to ensure your skills are finely tuned for the interview. Be prepared to solve coding problems or discuss system designs relevant to Airbnb.

Reflect on Your Travel Experiences

Given Airbnb’s industry, sharing personal travel experiences and how they shape your understanding of customer needs can be a unique way to connect with the interviewer and demonstrate your passion for the travel and hospitality sector.

Conclusion

As we wrap up our comprehensive guide on acing an Airbnb interview, we hope that the insights, strategies, and tips provided have equipped you with the knowledge and confidence needed to excel.

If you don’t feel like putting all your eggs in one basket, feel free to explore our Company Interview Guides, where we delve into other companies in ways similar to this one.

While we don’t want to oversell ourselves, here at Interview Query, we prioritize your best interests, so check out our pricing.

Not only can we guide you with every question you have, but we can also provide you with insights on related topics. These can be useful when applying not just to Airbnb but to other companies as well.

We wish you the best of luck in your Airbnb interview. Embrace the process, stay true to yourself, and, most importantly, enjoy the journey.

Here’s to hoping you’ll soon be part of the Airbnb team, contributing to the world of travel and hospitality in your own unique way.