CBRE Data Analyst Interview Questions + Guide in 2024

CBRE is a global leader in commercial real estate services, offering a wide range of solutions to property owners, investors, and occupiers. They are deeply committed to innovation, sustainability, and delivering exceptional client service.

For those aspiring to join CBRE as a Data Analyst, you will play a critical role in driving business intelligence and data science initiatives. This position involves conducting in-depth data analysis to support strategic decisions, ensuring data accuracy, and turning insights into actionable recommendations. The remote role spans various Data Science & Analytics responsibilities, including programming, data visualization, and engaging with clients in a dynamic environment.

Through Interview Query, we provide a comprehensive guide to help you excel in their interview process, equipping you with the tools to effectively demonstrate your technical and analytical expertise, especially in dealing with their CBRE data analyst interview questions. Let’s dive in!

The interview process usually depends on the role and seniority; however, you can expect the following on a CBRE data analyst interview:

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the CBRE Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions are typically a part of the screening process. Sometimes, the CBRE hiring manager may join the screening round to address your queries about the role and the company itself. They may also engage in surface-level technical and behavioral discussions.

The entire recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the CBRE Data Analyst role is usually conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview may revolve around CBRE’s data systems, ETL pipelines, and SQL queries.

For Data Analyst roles, take-home assignments concerning product metrics, analytics, and data visualization may be included. In addition, your proficiency in hypothesis testing, probability distributions, and basic Excel questions might be assessed during this round. You may also be asked about your understanding of the commercial real estate market.

Panel Interview

If you clear the technical screening, you’ll be invited for a panel discussion. This is typically a mix of behavioral and technical questions with minimal SQL coding, conducted by a panel of 3 team members. You might be required to present your past work and demonstrate your analytical and presentation skills. This stage lasts about 1 hour and can include a demonstration of SQL proficiency, such as removing duplicates from a table or handling large databases.

Onsite Interview Rounds

For some positions, there might be additional onsite interview rounds, although this has become rare for remote roles. During these onsite interviews, your technical and presentation skills will be further assessed, potentially including a review of any take-home assignments you completed.

Typically, interviews at CBRE vary by role and team, but commonly, Data Analyst interviews follow a fairly standardized process across these question topics.

1. What considerations should be made when testing hundreds of hypotheses with many t-tests?

When conducting multiple t-tests, you need to consider the risk of Type I errors due to multiple comparisons. How would you control for this, and what statistical methods would you use to adjust for multiple testing?

2. How would you explain what a p-value is to someone who is not technical?

Explain the concept of a p-value in simple terms to someone without a technical background.

3. How should you handle a right-skewed distribution when predicting real estate home prices?

If home prices in a city are skewed to the right, should you take any action? If so, what steps should you take? Bonus: How would you handle a heavily left-skewed target distribution?

4. Write a SQL query to select the 2nd highest salary in the engineering department.

Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

5. Create a function precision_recall to calculate precision and recall metrics from a 2-D matrix.

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

6. Select the top 3 departments with at least ten employees and rank them by the percentage of employees making over 100K.

Given employees and departments tables, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

7. Write a function traverse_count to determine the number of paths in an (n\times n) grid.

Given an integer (n), write a function traverse_count to determine the number of paths from the top left corner of an (n\times n) grid to the bottom right. You may only move right or down.

8. Develop a function is_subsequence to check if one string is a subsequence of another.

Given two strings, string1 and string2, write a function is_subsequence to find out if string1 is a subsequence of string2. A subsequence can be derived by deleting some elements without changing the order of the remaining elements.

9. How does random forest generate the forest and why use it over logistic regression?

Explain how random forest generates multiple decision trees and why it might be preferred over logistic regression for certain tasks.

10. How do we handle missing square footage data in housing price prediction models?

You have 100K sold listings with 20% missing square footage data. Describe methods to handle this missing data to construct a reliable model.

11. How would you combat overfitting in tree-based classification models?

When training a classification model, particularly tree-based models, explain strategies to prevent overfitting.

12. Does increasing the number of trees in a random forest always improve accuracy?

Discuss whether sequentially increasing the number of trees in a random forest model will continuously improve its accuracy.

13. How to implement k-means clustering algorithm in Python from scratch?

Given a two-dimensional NumPy array data_points, number of clusters k, and initial centroids initial_centroids, implement the k-means clustering algorithm to return the cluster of each point.

A few tips for acing your CBRE interview include:

1. Understand the Data Environment: CBRE’s data analyst position may require knowledge of PowerBI, PowerApps, SQL, or Tableau. Brush up on your knowledge and practice using these tools.

2. Know the Industry: Familiarize yourself with the commercial real estate market, as questions regarding this field often come up.

3. Presentation Skills: Be ready to present your past projects and demonstrate your expertise in storytelling with data.

What is the average salary for a Data Analyst at Cbre?

What kind of technical skills are required for a Data Analyst at CBRE?

To succeed as a Data Analyst at CBRE, you’ll need experience in programming/development and data visualization. Familiarity with tools like Microsoft Power Platform, Dataverse, PowerBI, PowerApps, SQL, or Tableau is crucial. You should also be capable of analyzing large datasets and resolving data quality issues.

What can I expect in terms of work environment and responsibilities?

The Data Analyst role at CBRE is remote and supports mostly Pacific Time. You’ll be responsible for maintaining data integrity, asset management, and resolving data issues. Additional tasks include coordinating data aggregation, performing strategic reviews of data, and developing ad-hoc reports. The role requires a balance of technical abilities and customer-facing skills.

Why should I consider working at CBRE?

CBRE offers comprehensive benefits, including health, vision, and life insurance, 401k plans, and personal time off starting on the first of the month. The company values diversity, provides equal employment opportunities, and promotes a collaborative and inclusive work environment.

At CBRE, you can expect a supportive environment where teamwork and collaboration are highly valued. The role offers a variety of engaging responsibilities, from maintaining data integrity to developing sophisticated business intelligence insights. With robust support for professional development, CBRE is an excellent place for data analysts who are eager to apply their skills and contribute to meaningful projects.

If you want more insights about the company, check out our main CBRE Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about CBRE’s interview process for different positions.

You can also check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!