Module 3: Market Forecasting

Learning Objectives

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

  • Understand time series characteristics in real estate markets
  • Apply decomposition methods to identify market patterns
  • Evaluate forecast accuracy using multiple metrics and backtesting techniques
  • Build ARIMA models for property market forecasting
  • Incorporate exogenous variables into ARIMAX forecasting models
  • Use Prophet for business-friendly forecasting workflows
  • Conduct scenario planning and Monte Carlo simulations

Module Overview

This module teaches time series analysis and forecasting techniques specifically applied to real estate markets. Students learn to predict market trends, identify cycles, and support strategic decision-making with quantitative forecasts.

Sections

1. Time Series Fundamentals, Decomposition & Evaluation

Master time series components, decomposition methods, and forecast evaluation. Learn to separate trend, seasonal, and cyclical patterns while building a complete evaluation framework with MAE, RMSE, MAPE, and backtesting techniques.

2. ARIMA Model Development 🔒

Build autoregressive integrated moving average models for property market forecasting. Apply stationarity tests, ACF/PACF analysis, and model selection techniques.

3. Exogenous Variables and ARIMAX 🔒

Incorporate economic indicators and external factors into forecasting models. Learn to enhance ARIMA models with mortgage rates, employment data, and other exogenous variables.

4. Prophet for Business Forecasting 🔒

Use Facebook’s Prophet for user-friendly time series forecasting workflows. Apply automated seasonality detection and trend changepoint analysis to real estate data.

5. Scenario Planning and Monte Carlo Simulation 🔒

Conduct scenario analysis and risk assessment for real estate investment decisions. Build probabilistic forecasts using Monte Carlo methods for uncertainty quantification.

Prof. Tim Frenzel | Version 1.1.0