Learning Objectives

By the end of Day 1, participants will:

• Understand the purpose and structure of financial statements.
• Apply accrual accounting concepts to real-world company accounts.
• Link together income statement, balance sheet, and cash flow.
• Conduct basic ratio analysis to evaluate financial health.

Uses of Accounts & Principal–Agent Problem

• Why accounts matter to shareholders, lenders, regulators.
• Information asymmetry & governance.
• Case: WeWork IPO prospectus – revenue projections vs investor risks.

The Income Statement

• Revenues, expenses, EBIT, net income.
• Distinction between recurring vs exceptional items.
• Example: breakdown of a listed property company.
• Exercise: Adjust income statement for one-off revaluation gains.

The Balance Sheet

• Assets, liabilities, equity.
• NAV, gearing, leverage.
• Case: Compare Unite Students (PBSA) vs Landsec (offices/retail).
• Exercise: Calculate loan-to-value (LTV).

The Cash Flow Statement

• Operating, investing, and financing flows.
• Example: REIT acquisition and disposal activity.
• Exercise: Trace net income to operating cash flow.

Accrual Accounting & Key Concepts

• Accrual vs cash.
• Revenue recognition, matching principle, prudence.
• Real estate example: straight-lining leases, rent-free periods.

Linking the Statements Together

• How P&L affects balance sheet and cash flow.
• Example: £10m revaluation gain → NAV and cash.
• Group activity: build a mini 3-statement flow.

Introduction to Ratio Analysis

• Profitability, liquidity, leverage, valuation ratios.
• Industry-specific ratios: EPRA EPS, NAV/share, DSCR.
• Case: Compare SEGRO (logistics) vs Hammerson (retail).
• Exercise: Compute NAV premium/discount.

Learning Objectives

By the end of Day 2, participants will:

• Apply ratio analysis across sectors.
• Understand equity investment philosophies (Buffett, Graham, Bolton).
• Differentiate investment styles (value, growth, active, passive).
• Use frameworks to analyse businesses.
• Prepare and present an investment case.

Ratio Analysis in Practice

• Sector differences: logistics, retail, PBSA, offices.
• Exercise: Compare ratios of 3 listed companies.

Sector Analysis Deep Dive

• How sector fundamentals affect ratios and valuations.
• Case: Unite Students (PBSA) vs Shaftesbury Capital (retail).

Lessons from Buffett, Graham & Bolton

• Graham’s margin of safety → NAV discounts.
• Buffett’s moat → prime assets with strong covenants.
• Bolton’s management quality → strong REIT managers.
• Discussion: Which philosophy best fits today’s markets?

Investment Styles

• Value vs growth; Core vs Core+ vs Value-add vs Opportunistic.
• Case study: SEGRO vs retail repositioning.
• Exercise: classify investments by style.

Active vs Passive & Smart Beta

• Active management in equity vs listed REITs.
• Passive index exposure.
• Smart beta factor strategies.

Business Analysis Frameworks

• Investment checklist (management, balance sheet, ESG, valuation).
• Introduce BAlTIM framework.
• Exercise: Apply framework to Great Portland Estates.

Group Investment Case Analysis

• Groups analyse listed companies (Landsec, SEGRO, Hammerson).
• Build Buy/Hold/Sell recommendation.
• Prepare investment pitch.

Learning Objectives

By the end of Day 3, participants will:

• Distinguish between price and value.
• Apply intrinsic valuation methods (DCF, DDM, EVA, CFROI).
• Apply relative valuation methods (P/FFO, Price/NAV, EV/EBITDA).
• Build and test valuation models.
• Identify anomalies and inefficiencies.
• Present an equity investment case with valuation backing.

Price vs Value

• Distinction in equity markets.
• Case: NAV discounts and premiums in REITs.

Overview of Valuation Approaches

• Intrinsic, relative, asset-based, option-pricing.
• Which suits different sectors and companies.

Intrinsic Valuation Methods

• DCF: forecast FFO, discount at WACC, terminal value.
• Dividend Discount Model: stable REIT example.
• EVA and CFROI for developers.
• Worked example: 10-year DCF for logistics REIT.

Relative Valuation Methods

• Multiples: Price/NAV, P/FFO, EV/EBITDA.
• Case: SEGRO vs Hammerson vs Unite Students.
• Group exercise: calculate and compare multiples.

Valuation Anomalies & Inefficiencies

• Market bubbles and crashes.
• COVID mispricing in retail REITs.
• Optionality in development land.

Hands-On Modelling Workshop

• Build 5-year FFO forecast.
• Apply DCF, NAV multiple, and dividend yield valuation.
• Compare intrinsic vs relative outputs.

Group Presentations

• Present Buy/Hold/Sell case with valuation.
• Defend assumptions (exit yield, rental growth, WACC).

Introduction to AI in Real Estate

• What is AI? Demystifying AI for real estate professionals
• AI vs. traditional software: Practical real estate examples
• Overview of AI tools: ChatGPT, Claude, Gemini, CoPilot, Midjourney, DALL·E
• Live demonstration: AI-assisted vs. traditional property analysis

AI for Market Research & Property Analysis

• Leveraging AI for market research and trend analysis
• Effective prompt engineering for real estate insights
• AI-driven sentiment analysis and price predictions
• Hands-on Practice:
  • Use AI to analyse a local market
  • Generate price trend predictions
  • Create a competitor analysis report

AI-Powered Marketing & Lead Generation

• AI for property listings and content creation
• Visual marketing with AI-generated images
• AI-driven chatbots and automated email sequences
• Hands-on Practice:
  • Write AI-enhanced property listings
  • Generate social media content plans
  • Create AI-powered marketing visuals

AI for Legal Document Analysis

• AI in transactions: Risks and opportunities
• AI-assisted contract analysis and lease abstraction
• When to use AI vs. professional legal review
• Hands-on Practice:
  • Summarise lease agreements with AI
  • Extract key terms from contracts
  • Compare agreements using AI

AI for Workflow Automation & Productivity

• Identifying automation opportunities in real estate
• AI tools for task automation, CRM, and meeting summaries
• Cost-benefit analysis of AI implementation
• Hands-on Practice:
  • Map out automation opportunities in your business
  • Set up AI-driven task automation

Capstone Project: Your AI Implementation Plan

• Identify key AI implementation areas for your business
• Develop a 30/60/90-day AI adoption strategy
• Define success metrics and integration roadmaps

Automating Excel Models with VBA

Use Case: Automated Sensitivity Analysis for Property Investment

• Introduction to VBA for financial modelling
  • When to use VBA vs. Python vs. Javascripts
  • Basics of macros and custom VBA scripts
• Using ChatGPT to generate and improve VBA code
• Building a simple Excel automation tool:
  • Automate a sensitivity analysis for IRR and NPV based on different rent levels
  • Create a macro button to execute different investment scenarios
• Hands-on exercise:
  • Modify and test VBA code with AI assistance

AI-Powered Data Analysis with Python

Use Case: Automating Data Extraction & Analysis

• Why use Python in real estate financial modelling?
  • Overcoming Excel limitations
• Using ChatGPT for Python scripting
• Building a Python tool:
  • Extract and clean data from an Excel real estate model
  • Perform automated analytics (average rent, NOI trends, IRR calculations)
  • Output results back into Excel
• Hands-on exercise:
  • Adapt the Python script to their own model

Creating a Web-Based IRR Calculator with Javascript

Use Case: Interactive Investment Analysis

• Introduction to Java for financial applications
  • When to use Java over VBA/Python
• Using ChatGPT to generate and optimise Javascript code
• Building a Java-based IRR calculator:
  • User inputs: Purchase price, rent, expenses
  • Output: Monthly cash flow, IRR, ROI, cap rate
  • Simple interactive interface
• Hands-on exercise:
  • Modify the Java tool to include additional financial metrics

AI-Powered Financial Model Automation

• Objective: You will use your acquired knowledge during the day to automate a part of a real estate model using AI-generated code (VBA, Python, or Javascript)
• Project ideas:
  • Automate a financial metric calculator (IRR, NPV, DSCR)
  • Create a customised scenario generator for different rent levels
  • Build an AI-driven data extraction tool to clean and format property datasets
  • Develop a simple chatbot for real estate financial queries
• Hands-on:
  • Participants design, build, and test their projects
  • AI-assisted troubleshooting and debugging

Project Presentations & Course Wrap-Up

Q&A and next steps

Participants showcase their projects

Explain their AI-assisted automation and improvements

Feedback & discussion

Key takeaways & future AI opportunities