Data Analytics - Real Estate Financial Modelling - Hong Kong | Abu Dhabi

Data Analytics - Real Estate Financial Modelling - Hong Kong | Abu Dhabi | London | Sydney | Dubai | Cape Town | New York | Milan | Paris | Frankfurt | Oslo | Madrid | Tokyo

Transform Your Real Estate Financial Modelling Skills in Just Six Weeks!

Unlock the power of Python and AI to supercharge your real estate financial models in Excel with our comprehensive online course. Perfect for real estate analysts, professional modellers and data scientists, this course is designed to give you a competitive advantage in the industry.

This comprehensive course equips real estate finance professionals with advanced data analytics techniques to enhance Excel-based discounted cash flow (DCF) models for commercial real estate. Participants will learn to leverage Python and generative AI to generate robust inputs and deliver deeper insights, ultimately enhancing real estate investment decision-making.

Don’t miss this opportunity to elevate your investment decision-making and become a leader in real estate finance. Sign up now and transform your career with advanced data analytics skills!

This comprehensive course is designed to equip real estate finance professionals with advanced data analytics techniques to enhance Excel-based discounted cash flow (DCF) models for commercial real estate. Through a structured, modular approach, participants will gain a smooth learning experience with engaging video lessons, step-by-step guides, quizzes, and exercises. They will learn how to seamlessly integrate Python and generative AI to generate more accurate inputs, uncover deeper insights, and ultimately improve real estate investment decision-making.

This course is designed to provide practical expertise and a competitive edge in real estate financial modelling, making it ideal for real estate analysts, professionals, and data scientists looking to transition into the industry.

Course Highlights:

• In-Depth Analytics: Gain a thorough understanding of the role of data analytics in commercial real estate and how it improves financial decision-making.
• Data Mastery: Identify and work with various data types to build more accurate and reliable financial models.
• Python & Excel Integration: Learn to integrate Python and Excel for enhanced sensitivity analysis and dynamic scenario modelling, including Monte Carlo Simulation.
• AI Tools: Leverage AI tools to generate and refine financial assumptions, conduct market research, perform sentiment analysis, and automate report generation.
• Hands-On Experience: Complete a hands-on capstone project that incorporates Python and AI into a real estate financial model, demonstrating practical expertise and application of the skills learned.

Key Learning Outcomes:

• Understand the role of data analytics in commercial real estate.
• Identify and work with various data types to build accurate financial models.
• Integrate Python and Excel for enhanced sensitivity analysis and dynamic scenario modelling.
• Leverage AI tools for generating and refining financial assumptions.
• Apply AI-powered tools for market research, sentiment analysis, and automated report generation.
• Complete a hands-on capstone project incorporating Python and AI into a real estate financial model.

Who Will Benefit:

• Real Estate Analysts looking to enhance their analytical capabilities.
• Mid to senior-level Real Estate Professionals, Analysts, and Investors aiming to refine financial modelling techniques and leverage advanced analytics.
• Data Scientists interested in transitioning into the real estate industry.
• Students beginning or aspiring to enter a career in real estate.

Module Overview

• Modules 1-5: Introducing Real Estate Data Analytics and Practical Applications.
• Module 6: Capstone Project

Module 1

Introduction to Real Estate Data Analytics

At the end of this module, participants will be able to:

• Understand the role and importance of data analytics in commercial real estate.
• Identify different types of real estate data and their applications in financial modelling.
• Recognise key analytical tools used in real estate financial decision-making.

1. Introduction
2. Defining Real Estate Analytics
3. Real Estate Data & Applications
4. The Real Estate Analytics Spectrum
5. The Value Add of Real Estate Analytics to Financial Modelling and Decision-Making
6. Analytics in Practice: AI Tools & Software

Module 2

Introduction to Python – Practical Applications for Real Estate

At the end of this module, participants will be able to:

• Set up a Python environment and work with real estate datasets.
• Use key Python libraries to manipulate, visualise, and analyse real estate data.
• Understand how Python can complement Excel in financial modelling.

1. Introduction
2. Why Python?
3. Environment Setup: Choosing Between Anaconda & Google Colab
4. Essential Python Libraries for Real Estate Analytics:

• Pandas
• NumPy
• SciPy
• Scikit-Learn
• Statsmodels
• Seaborn & Matplotlib
• GeoPandas

1. Comparison of analysis for each library in Python and Excel

Module 3

Enhancing Real Estate Financial Modelling with Python

At the end of this module, participants will be able to:

• Integrate Python with Excel using xlwings
• Understand how the Python-Excel integration enhances various analyses and applications

1. Introduction
2. Integrate Python with Excel using xlwings
3. Sensitivity Analysis in Excel Using Python
4. Dynamic Scenario Analysis in Excel using Python
5. Building a Python-Based Monte Carlo Simulation in Excel

Module 4

AI Powered Analytics & Prompt Engineering for Real Estate

At the end of this module, participants will be able to:

• Craft effective AI prompts to extract meaningful insights to assist analysis
• Leverage AI-generated reports to improve financial decision-making

1. Introduction
2. Recap: AI in Real Estate
3. Fine-Tuning and Best Practice for AI-Assisted Assumption Generation
4. AI-Powered Market Research
5. AI-Driven Sentiment Analysis
6. AI-Assisted Report Generation & Report Summaries
7. How can we use these insights to Enhance Excel Model Valuation Accuracy?

Module 5

Module 5: AI-Enhanced Assumption Generation and Integration for Real Estate Financial Modelling (Hands-on)

At the end of this module, participants will be able to:

• Use AI to generate useful and realistic model-specific assumptions

• Enhance sensitivity analysis with AI-generated inputs

• Apply AI to summarise and report model findings

1. Introduction
2. Understanding Key Real Estate Model Inputs and Assumptions with AI
3. Using AI to Generate Robust Financial Assumptions for Excel Model Inputs
4. Using AI & Python for Enhanced Analysis
5. AI-Powered Financial Model Summary and Reporting

Module 6

Capstone Real Estate Financial Modelling Project –
Excel, Python & AI

In this project, participants will apply all they have learned in Modules 1-5:

• Apply Python & AI to enhance an advanced Excel-based real estate financial model and efficiently produce AI-Assisted real estate analyst reports.