REAL ESTATE DATA ANALYTICS
Introduction
The Real Estate Data Analytics Program is a 30-hour course spread over 10 days, covering the full spectrum of real estate data management, analysis, and advanced analytics techniques, from data collection to machine learning, geospatial intelligence, ESG, and predictive modeling.
The course is aimed at data analysts, asset managers, portfolio analysts, research teams, investment teams, and digital transformation leaders working in real estate.
The goal of the program is for participants to acquire the knowledge and hands-on skills needed to manage, analyze, and extract actionable insights from complex real estate datasets to support investment, portfolio, and asset management decisions.
Why this program?
- Professional instructors with extensive experience in real estate analytics, data science, and investment advisory.
- All sessions are recorded so you won’t miss anything and can rewatch them anytime.
- Hands-on case studies and access to real-world datasets for all topics covered, including valuation, pricing, forecasting, GIS, ESG, and risk analysis.
- Certificate awarded upon completion of the program.
Module 1
Real Estate Data Strategy & Data Architecture
Objective
Understand how to build and manage a scalable real estate data ecosystem aligned with business value.
Topics
- Data governance, quality, and master data in real estate.
- Connecting operational, valuation, financial & market data.
- Data ETL pipelines and cloud data warehouses.
- Internal vs third-party data integration (CoStar, CBRE, JLL, etc.).
- Real-time vs batch data for asset management.
Case Study
- Designing a data architecture for a Pan-European office portfolio integrating rent rolls, valuations, leases, operating expenses, and market comparables.
Module 2
Data Collection, Scraping & External Market Data
Objective
Learn to gather and process external real estate data sources to enhance asset- and portfolio-level analytics.
Topics
- Web scraping for leasing comps, rental rates & listings.
- APIs for footfall, mobility, demographic and economic indicators.
- Satellite and GIS data for asset scoring.
- IoT building sensors (occupancy, HVAC, energy consumption).
- Data cleaning and structuring for analysis.
Case study
- Collecting & cleaning logistics warehouse market data from multiple sources to build a dataset of prime rents, land prices, absorption, vacancy and yields.
Module 3
Exploratory Data Analysis (EDA) for Real Estate
Objective
Apply structured exploratory analysis to uncover patterns and drivers of value.
Topics
- Descriptive statistics and distribution analysis.
- Correlation matrices for real estate variables.
- Time-series behavior in rents, yields and NOI.
- Detecting outliers and anomalies (bad leases / abnormal vacancies).
- Segmenting assets for comparative analytics.
Case Study
- Performing EDA on a diversified office portfolio to identify underperforming assets and value-creation opportunities based on vacancy, incentives, tenant mix and OPEX levels.
Module 4
Real Estate Valuation & Automated Valuation Models (AVM)
Objective
Build data-driven valuation models using machine learning.
Topics
- Hedonic regression for rent and price prediction.
- Feature engineering for location, age, amenities, ESG, micromarkets.
- AVM accuracy metrics & overfitting control.
- Prediction intervals vs deterministic valuations.
- Model explainability for investment committees.
Case study
- Building an AVM for residential multifamily assets to predict market rents and capital values using ML (XGBoost/Random Forest).
Module 5
Rental Pricing Optimization & Revenue Management
Objective
Use analytics to optimize rent levels and incentives to maximize revenues.
Topics
- Elasticity of demand and price sensitivity.
- Machine learning for pricing optimization.
- Incentive modeling (rent-free periods, fit-out contribution).
- Predicting tenant churn and renewal probability.
- Internal + market benchmarking.
Case Study
- Optimizing the rent strategy for a retail shopping center by identifying rent levels that maximize occupancy & NOI.
Module 6
Geospatial Analytics & GIS in Real Estate
Objective
Apply geospatial intelligence to identify investment opportunities and risk.
Topics
- GIS fundamentals and spatial joins.
- Isochrones, walkability and transport analysis.
- Mapping demand drivers: demographics, income, workplaces, tourism.
- Heatmaps for risk / return / demand.
- Geospatial interpolation for location scoring.
Case Study
- Mapping mobility + demographic + income patterns to identify new locations for self-storage expansion.
Module 7
ESG, Sustainability & Energy Efficiency Analytics
Objective
Use data to quantify ESG impact and financial performance.
Topics
- Carbon footprint and energy intensity assessment.
- Green certification analytics (LEED/BREEAM).
- Predicting CapEx for energy retrofits.
- Linking ESG metrics to valuation & yields.
- Modeling regulatory risk by region.
Case Study
- Evaluating ESG-driven CapEx for office buildings and quantifying NOI uplift and valuation sensitivity with and without retrofits.
Module 8
Portfolio Optimization & Risk Analytics
Objective
Combine financial, operational and market data to optimize portfolio allocation.
Topics
- NOI variability models.
- Monte Carlo simulations on vacancy & rent distributions.
- Diversification and sector correlation.
- Efficient frontier for real estate portfolios.
- Capital allocation under risk constraints.
Case study
- Optimizing a mixed portfolio (office, logistics, multifamily, retail) to minimize risk and maximize risk-adjusted returns.
Module 9
Predictive Analytics for Net Operating Income (NOI) & Cash Flows
Objective
Forecast NOI under market uncertainty to support business plans and valuation.
Topics
- Time-series forecasting (ARIMA, Prophet, LSTM).
- Scenario modeling for rent, vacancy and ERV.
- OPEX forecasting and sensitivity analysis.
- Tenant default and lease break prediction models.
- Linking analytics to underwriting and business plan models.
Case Study
- Building a predictive NOI model for a logistics portfolio based on forecasted rent, vacancy, inflation, incentives and operating expenses.
Module 10
Data Visualization, Storytelling & Decision Dashboards
Objective
Transform analytics into insights for investment committees, lenders and asset managers.
Topics
- Best-practice dashboard design for real estate.
- KPI design for asset, portfolio and fund reporting.
- Integrated view: financial + market + ESG + risk.
- Power BI and Tableau interactive dashboards.
- Automating insights for leadership.
Case study
- Designing a CEO / CIO dashboard for an office portfolio including rent & occupancy KPIs, leasing pipeline, ESG performance and underwriting vs actual comparison.
Fees and Dates
ADMISSION PROCESS
- The course will remain open until all spots are filled.
- To reserve your spot, you must complete the payment through the following link.
Price: 1.550 €
Schedule: 17:30 to 20:30 (CET)
Timetable in Madrid:
If you have any questions about the course, please send us an email at academic.office@realestatefinanceinstitute.com
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13.04 | Real Estate Data Strategy & Data Architecture.
15.04 | Data Collection, Scraping & External Market Data.
20.04 | Exploratory Data Analysis (EDA) for Real Estate.
22.04 | Real Estate Valuation & Automated Valuation Models (AVM).
27.04 | Rental Pricing Optimization & Revenue Management.
29.04 | Geospatial Analytics & GIS in Real Estate.
04.05 | ESG, Sustainability & Energy Efficiency Analytics.
06.05 | Portfolio Optimization & Risk Analytics.
11.05 | Predictive Analytics for Net Operating Income (NOI) & Cash Flows.
13.05 | Data Visualization, Storytelling & Decision Dashboards.
FREQUENTLY ASKED QUESTIONS
The course is designed for data analysts, asset managers, portfolio analysts, research teams, investment teams, and digital transformation leaders in the real estate sector. It is particularly suited for those looking to enhance their skills in real estate data management, analytics, machine learning, GIS, and predictive modeling to support investment, portfolio, and asset management decisions.
In previous editions, participants had an average of 15 years of experience, and we have welcomed students from over 20 countries.
The course is completely ONLINE. The classes are live at the scheduled time and will be recorded for you to watch again whenever you want. During the sessions, you can ask questions to the instructors who will answer them live.
Yes, the course offers opportunities to network with other participants who are open to connecting. Additionally, in some courses, you will have the chance to solve case studies in study groups, collaborating with peers from different companies and countries.
In the past, we have had participants from 17 different countries, creating a rich and diverse networking environment.
You can make the payment by bank transfer or on the website itself with a debit/credit card.
Unfortunately, we do not offer payment plans at this time.
Every student in the course has the same conditions and there are no scholarships or individual discounts. Companies that register 3 or more employees have a 10% discount by employee while companies that register 6 or more employees have a 20% discount.
Yes, if you miss any session, you will be able to watch the recorded version. All recorded sessions will remain available for 2 months after the course ends, allowing you to catch up at your convenience.
To earn the course certificate, students are required to pass a test or complete a case study. This requirement can be fulfilled even if you miss a live session, as the necessary materials and guidance will be provided.
The course consists of 10 sessions, each lasting 3 hours. In total, the program spans 30 hours of interactive and engaging content.
Yes, you will have access to all course materials for 2 months after the course concludes. This allows you to review the content and reinforce your learning at your convenience during this period.
All Excel and PDF files provided during the course are downloadable.
Yes. At the end of the program, students who have successfully completed all the course modules will receive a certificate accrediting that they have completed and passed the course successfully.
Contact us through our email: academic.office@realestatefinanceinstitute.com
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academic.office@realestatefinanceinstitute.com