9 Anywhere Real Estate SQL Interview Questions (Updated 2024)
At Anywhere Real Estate, SQL used for analyzing property data for market trends, and managing customer information for personalized marketing campaigns. That's why Anywhere Real Estate frequently asks SQL problems in interviews for Data Science and Data Engineering positions.
So, to help you practice, here’s 9 Anywhere Real Estate SQL interview questions – able to solve them?
9 Anywhere Real Estate SQL Interview Questions
SQL Question 1: Analyze Property Sales Over Time
Your company, Anywhere Real Estate, specializes in buying, selling, and renting properties. You have been asked by your manager to analyze the number of sales made by each sales agent over a period of time.
Specifically, they want you to write a SQL query that will provide the total cumulative sales (in terms of quantity) made by each sales agent at the end of each month, over time. The data tables Include:
- table which lists each sales agent, their unique id, and their name.
- table which includes each property sale, its unique id, the property id, the sales agent responsible for the sale (referenced by their id), and the sale date.
Example Input
| agent_id | name |
|---|---|
| 101 | John Doe |
| 102 | Jane Smith |
| 103 | Emily Johnson |
Example Input
| sales_id | property_id | agent_id | sales_date |
|---|---|---|---|
| 2001 | 3001 | 101 | 01/01/2022 |
| 2002 | 3002 | 101 | 01/02/2022 |
| 2003 | 3003 | 102 | 01/03/2022 |
| 2004 | 3004 | 102 | 02/01/2022 |
| 2005 | 3005 | 103 | 02/02/2022 |
| 2006 | 3006 | 101 | 02/03/2022 |
Answer:
The above SQL block applies a window function to calculate the cumulative count of sales per agent ordered by month. It starts by truncating the to the beginning of the month with . We then join the table with the table on the field. After that, we apply the window function , which counts the number of sales per agent over time. The result is sorted by month and agent's name.
To solve a similar window function question on DataLemur's free interactive SQL code editor, solve this Google SQL Interview Question:
SQL Question 2: Department Salaries
You're given a table of Anywhere Real Estate employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Code your solution to this question and run your code right in DataLemur's online SQL environment:
The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department Salaries.
Check out the Anywhere Real Estate Careers page and see how you might fit within the company!
SQL Question 3: What are the ACID properties in a DBMS?
A DBMS (database management system), in order to ensure transactions are relaible and correct, tries to mantain the following ACID properties: Atomicity, Consistency, Isolation, and Durability
Here is what each of the ACID properties stands for:
Atomicity: ensures that a transaction is either completed in its entirety, or not completed at all. If a transaction fails halfway, the database does a rollback on the commit.
Consistency: ensures that a transaction will only be completed if adheres to the constraints defined in the database of the DB.
Isolation: ensures that concurrent transactions are isolated from each one another, so that the changes made by one transaction cannot be seen by a 2nd transaction until the 1st transaction is done.
**Durability: ** ensures that once a transaction has been committed, the database permanently stores the results in the DB.
As you can see, it's pretty important for Anywhere Real Estate's data systems to be ACID compliant, else they'll be a big problem for their customers!
SQL Question 4: Find Total Properties Listed By Each Agent in 2021
As an analyst at Anywhere Real Estate, you have been tasked with extracting information from the database about the total number of properties listed by each real estate agent in the year 2021. Write an SQL query to filter out all agents who listed one or more properties in 2021 and display their agent_id, agent_name and the count of properties they listed.
Example Input:
| agent_id | agent_name |
|---|---|
| 101 | John Doe |
| 102 | Jane Smith |
| 103 | Robert Brown |
| 104 | Emma Johnson |
| 105 | Michael Williams |
Example Input:
| property_id | listing_date | agent_id |
|---|---|---|
| 5010 | 2021-03-15 | 101 |
| 5026 | 2021-05-18 | 102 |
| 5045 | 2021-07-24 | 101 |
| 5096 | 2021-12-20 | 104 |
| 5119 | 2021-08-14 | 105 |
| 5111 | 2021-08-22 | 105 |
| 5077 | 2021-10-30 | 103 |
Answer:
This query uses the clause to combine records from both and on common . clause filters out the properties listed in the year 2021. statement groups the results by and , and clause is used to filter out the output of the clause, returning only agents who listed one or more properties. The function is used to calculate the total number of properties listed by each real estate agent.
SQL Question 5: Could you explain what a self-join is and provide examples of when it might be used?
A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
For instance, suppose you had website visitor data for Anywhere Real Estate, exported from the company's Google Analytics account. To assist the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to generate all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair."
You could use the following self-join:
This query returns the url of each page () along with the url of the page that referred to it (). The self-join is performed using the field, which specifies the id of the page that referred the visitor to the current page, and avoids any pages that referred themself (aka data anomalies).
SQL Question 6: Average Selling Price Per City
As an analyst at Anywhere Real Estate, the management team wants you to analyze the company's historical property sales data. Specifically, they are interested in the average selling price of properties for each city that the company operates in to gain insights into market trends. For this task, you have been given access to the table in the company’s database system. It includes the following columns:
| column_name | data_type | description |
|---|---|---|
| property_id | int | The unique identifier for a property |
| sale_date | datetime | The date the property was sold |
| sale_price | float | The price at which the property was sold |
| city | varchar | The city in which the property is located |
Provide a SQL query to generate a report that shows the average property selling price for each city.
Example Input:
| property_id | sale_date | sale_price | city |
|---|---|---|---|
| 1 | 01/05/2021 00:00:00 | 500000 | New York |
| 2 | 02/10/2021 00:00:00 | 750000 | San Francisco |
| 3 | 03/18/2021 00:00:00 | 300000 | New York |
| 4 | 04/26/2021 00:00:00 | 650000 | San Francisco |
| 5 | 05/05/2021 00:00:00 | 400000 | New York |
Example Output:
| city | avg_sale_price |
|---|---|
| New York | 400000 |
| San Francisco | 700000 |
Answer:
This query works by using the SQL clause to group the properties by city. Then, within each group, it uses the aggregate function to calculate the average sale price. The result is a list of cities along with the average selling price in each city.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for analyzing sales data or this Robinhood Cities With Completed Trades Question which is similar for operations in different cities.
SQL Question 7: Could you describe the function of UNION in SQL?
{#Question-7}
In SQL, the operator combines the result of two or more SELECT statements into a single result set. Note that each statement within the UNION must have the same number of columns and the columns must have similar data types. The operator removes duplicates from the final result set. If you want to include duplicates in the final result set, you can use the operator instead.
Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of Anywhere Real Estate's Facebook ads and their Google ads:
This statement would return a combined result set of both Facebook and Google ads that have a spend greater than $500.
SQL Question 8: Average Property Price by City for Anywhere Real Estate
Question: For Anywhere Real Estate, find the average property price per city. Assume all prices are listed in USD.
Example Input
| property_id | property_name | city | price |
|---|---|---|---|
| 1001 | Grand Villa | New York | 2000000 |
| 1002 | Sea Residence | Los Angeles | 1500000 |
| 1003 | Sunny Apartment | New York | 1200000 |
| 1004 | Elegant Loft | Los Angeles | 1800000 |
| 1005 | Modern Flat | San Francisco | 1400000 |
Example Output:
| city | avg_price |
|---|---|
| New York | 1600000 |
| Los Angeles | 1650000 |
| San Francisco | 1400000 |
Answer:
This SQL query works by grouping all the properties according to their cities using the GROUP BY clause. Once the data is grouped by city, it calculates the average property price for each city using the AVG(data column) function in PostgreSQL. The output of this query will be a list of cities along with the average property price in each city.
SQL Question 9: Joining Customer and Properties Tables
Write a SQL query that will join the table to the table on their and fields respectively. The query should select the , , from the table and , , from the table. The output should then filter to only show information where the customer is also a property owner.
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 101 | John | Doe |
| 102 | Jane | Smith |
| 103 | Jim | Brown |
| 104 | Jill | Johnson |
Example Input:
| property_id | owner_id | property_type | price |
|---|---|---|---|
| 201 | 101 | House | 350000 |
| 202 | 102 | Condo | 200000 |
| 203 | 105 | Townhouse | 300000 |
| 204 | 101 | Land | 80000 |
Answer:
This query joins the and tables using the and fields. The SELECT statement then returns the , , columns from the table and the , , columns from the table.
It will return the following results:
Example Output:
| customer_id | first_name | last_name | property_id | property_type | price |
|---|---|---|---|---|---|
| 101 | John | Doe | 201 | House | 350000 |
| 101 | John | Doe | 204 | Land | 80000 |
| 102 | Jane | Smith | 202 | Condo | 200000 |
Because join questions come up routinely during SQL interviews, try this Spotify JOIN SQL question:
Anywhere Real Estate SQL Interview Tips
The best way to prepare for a Anywhere Real Estate SQL interview is to practice, practice, practice.
Beyond just solving the earlier Anywhere Real Estate SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each SQL question has hints to guide you, detailed solutions and best of all, there's an online SQL code editor so you can right online code up your SQL query answer and have it executed.
To prep for the Anywhere Real Estate SQL interview it is also wise to solve SQL questions from other real estate and REIT companies like:
In case your SQL foundations are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers topics including functions like SUM()/COUNT()/AVG() and Self-Joins – both of which come up often in Anywhere Real Estate SQL assessments.
Anywhere Real Estate Data Science Interview Tips
What Do Anywhere Real Estate Data Science Interviews Cover?
In addition to SQL interview questions, the other question categories tested in the Anywhere Real Estate Data Science Interview are:
- Probability & Statistics Questions
- Python or R Programming Questions
- Product Data Science Interview Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for Anywhere Real Estate Data Science Interviews?
The best way to prepare for Anywhere Real Estate Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Google & Microsoft
- A Crash Course on SQL, Product-Sense & ML
- Amazing Reviews (1000+ 5-star reviews on Amazon)