Real Estate Analytics

Course Overview

This course provides comprehensive training in real estate data analysis, from foundational analytics to advanced predictive modeling and market forecasting. Students learn to apply data science techniques to real estate valuation, market analysis, and investment decision-making.

Learning Outcomes

By the end of this course, students will be able to:

[Data engineering] Assemble multi-source real estate data, build an analytics pipeline, and engineer location, comps, time, and macro features.
[Valuation ML] Frame valuation for machine learning, define targets and splits, choose algorithms, and build a baseline AVM that highlights key drivers.
[Geospatial ML] Apply Geospatial machine learning to geocoded assets, build proximity and neighborhood signals, and quantify spatial effects on valuation.
[Model training] Train and compare gradient boosting and regularized models, control leakage with time or geography splits, and select appropriate metrics.
[Interpretation] Explain model outputs clearly, rank key drivers, and present results through a modern dashboard tailored to investment committees and operating partners.
[Forecasting] Produce market forecast workflows with Time series decomposition, stationarity checks, ARIMA with exogenous economic data, and rolling-origin evaluation.
[Decision making] Turn analytic findings into executive recommendations that highlight tradeoffs and specify next steps across alternative futures.

Prerequisites

Basic understanding of statistics and data analysis
Familiarity with spreadsheet software (Excel/Google Sheets)
No prior programming experience required (Python and SQL taught from basics)

Course Structure

Module 1: Analytics Foundations

Establish the foundation for successful real estate analytics projects by covering business problem framing and data preparation techniques. Learn descriptive statistics, visualization principles, and feature engineering approaches for property market analysis.

Module 2: Predictive Valuation

Begin with responsible AI use principles, then explore advanced machine learning algorithms and spatial analysis for property valuation. Learn geocoding techniques, ensemble methods, feature importance analysis, and build ML-powered dashboards for real estate investment decisions.

Module 3: Market Forecasting 🔒

Master time series analysis and forecasting techniques for real estate markets. Learn ARIMA modeling, economic indicator integration, and scenario planning for strategic market analysis.

Course Materials

All course materials are provided within each module. Students will work with real-world real estate datasets and build practical analytical solutions.