Pro90 ProblemsSQL + Python
Real Estate SQL & Python Interview Questions
Property platforms analyze listing data, pricing trends, and buyer and seller behavior across geographic markets. These SQL and Python challenges are modeled after work at Zillow, Redfin, Opendoor, Compass, CBRE, Realtor.com, CoStar, CoreLogic, JLL, RealPage, and more. Build skills in days-on-market analysis, price per square foot trends, agent performance metrics, buyer funnel analytics, and automated valuation models.
Companies working in Real Estate
These practice problems are modeled after the kind of data and analytics challenges teams in this industry typically face.
Company names and logos are trademarks of their respective owners, used here only to describe the kind of data these companies work with. Let's Data Science is not affiliated with, endorsed by, or sponsored by any company shown. Practice problems are original works and are not real interview questions from these companies. Rights & takedowns.
Difficulty Distribution
Easy
16
18% of problems
Medium
32
36% of problems
Hard
36
40% of problems
Expert
6
7% of problems
What You'll Practice
Listing performance analysis
Days on market metrics
Price trend analysis
Agent performance scoring
Buyer funnel analytics
Market share tracking
Geographic pricing patterns
Lead conversion metrics
Topics Covered
SQL· 9
aggregationbasic queries filteringcleaning transformdate timejoinsscenario sqlset operationssubqueries cteswindow functions
Python· 12
eda statisticsfeature engineeringpandas aggregationpandas applypandas basicspandas cleaningpandas datetimepandas filteringpandas mergingpandas reshapingpandas scenariopandas window
All Problems90 total
01
Active Sale ListingsPro
SQLEasy02Luxury Properties Above $2MPro
SQLEasy03Condos With HOA FeesPro
SQLEasy04Properties Built Before 1980Pro
SQLEasy05Preapproved Buyers in CaliforniaPro
SQLMedium06Listings With Property DetailsPro
SQLEasy07Tours With Agent and Listing InfoPro
SQLMedium08Inquiries With User and Listing DetailsPro
SQLMedium09Offers With Property ContextPro
SQLMedium10Listings Without Any OffersPro
SQLMedium11Users Who Toured But Never Made an OfferPro
SQLHard12Listing Count by StatusPro
SQLEasy13Average List Price by CityPro
SQLMedium14Average Agent Response Time by ChannelPro
SQLMedium15Tour Completion Rate by AgentPro
SQLMedium16Top 5 Agents by Listing CountPro
SQLHard17Brokerages With High Avg Days on MarketPro
SQLHard18Rank Agents by Tour CountPro
SQLMedium19User Inquiry Sequence NumberPro
SQLMedium20Cumulative Offer Count by DatePro
SQLHard21Most Expensive Listing per CityPro
SQLHard22Price Change 3-Event Moving AveragePro
SQLHard23Weekly Inquiry Count ChangePro
SQLHard24Listing Price Quartile AnalysisPro
SQLHard25Properties Priced Above AveragePro
SQLMedium26Users With Both Inquiries and ToursPro
SQLHard27Latest Inquiry per UserPro
SQLHard28Agent Performance With Rank via CTEPro
SQLHard29Cities With Above-Average Listing PricesPro
SQLHard30Recent Listings (Last 30 Days)Pro
SQLEasy31Monthly Inquiry Volume TrendPro
SQLMedium32Average Escrow Duration by Financing TypePro
SQLMedium33Price Changes by Month and DirectionPro
SQLHard34Listings With Price Tier LabelPro
SQLEasy35Properties With Age CategoryPro
SQLMedium36Agent Tier With Performance SummaryPro
SQLHard37Cities With Listings or Registered UsersPro
SQLMedium38Users Who Toured But Never Made OffersPro
SQLMedium39Agent Performance ScorecardPro
SQLHard40Listing Funnel AnalysisPro
SQLHard41Buyer Journey SummaryPro
SQLHard42Price Reduction Impact AnalysisPro
SQLHard43Brokerage Market Share ReportPro
SQLHard44Property Investment ScorecardPro
SQLExpert45Sale vs Rent Market AnalysisPro
SQLExpert46Active Property ListingsPro
PYTHONEasy47Listing Status CountsPro
PYTHONEasy48Agent Brokerage SummaryPro
PYTHONMedium49Property Type Area BreakdownPro
PYTHONMedium50Large Single-Family HomesPro
PYTHONEasy51High-Value Sale ListingsPro
PYTHONEasy52Preapproved Users in Target StatesPro
PYTHONMedium53Inquiries With Fast Agent ResponsePro
PYTHONMedium54Tours Per ListingPro
PYTHONEasy55Average List Price by Property TypePro
PYTHONMedium56Inquiry Stats by ChannelPro
PYTHONMedium57Agent Tour PerformancePro
PYTHONHard58City Market SummaryPro
PYTHONHard59Listings With Property DetailsPro
PYTHONEasy60Tours With Agent NamesPro
PYTHONMedium61Properties Without Active ListingsPro
PYTHONMedium62Inquiry-to-Tour PipelinePro
PYTHONHard63Full Transaction DetailsPro
PYTHONHard64Rank Listings by Price Within CityPro
PYTHONMedium65Running Total Inquiries Per ListingPro
PYTHONMedium66Price Change From Previous EventPro
PYTHONHard673-Listing Moving Average Days on MarketPro
PYTHONHard68Extract Listing Month and YearPro
PYTHONEasy69Days Since ListingPro
PYTHONMedium70Monthly Inquiry Volume by ChannelPro
PYTHONHard71Fill Missing HOA FeesPro
PYTHONEasy72Normalize Property AreasPro
PYTHONMedium73Standardize Heating and Cooling LabelsPro
PYTHONHard74Pivot Inquiry Counts by Channel and StatusPro
PYTHONMedium75Listing Metrics Pivot by Property TypePro
PYTHONHard76Classify Listings by Price TierPro
PYTHONMedium77Listing Competitiveness ScorePro
PYTHONHard78Compute Price Per Square FootPro
PYTHONMedium79Listing Price Quartile BucketingPro
PYTHONHard80Property Age and Era FeaturesPro
PYTHONHard81Listing Feature MatrixPro
PYTHONExpert82Offer Competitiveness FeaturesPro
PYTHONHard83List Price Descriptive StatisticsPro
PYTHONMedium84Area vs Price CorrelationPro
PYTHONHard85Anomalous List Price Detection (IQR)Pro
PYTHONHard86Agent Performance ScorecardPro
PYTHONHard87Listing Demand ReportPro
PYTHONHard88Buyer Journey AnalysisPro
PYTHONExpert89Market Health Dashboard by CityPro
PYTHONExpert90End-to-End Listing ReportPro
PYTHONExpertReady to practice Real Estate?
90 SQL and Python challenges built from real real estate data. Graded instantly in your browser — no setup required.