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Chapter 1

Descriptive Statistics: The language of finance is twofold: statistics and accounting. This lecture exposes individuals to the basics covered in Statistics 101.

Discounted Cash Flow: Real estate modelling, at its core, is a quantitative representation of the cash flow(s) produced by an asset. Discount Cash Flow analysis is the quantitative valuation of these cash flows to a single value, i.e. Net Present Value/Internal Rate of Return.

Risk Return Portfolio: The mature method to analyze/value an asset, e.g. real estate, is not as a single-asset in a vacuum but rather as a contribution to an overall portfolio. True valuation is the marginal contribution of an asset when included or divested in a portfolio. Risk quantification and portfolio basic fundamentals are introduced.

Chapter 2

Capital Structure and Leverage: Real Estate returns are magnified by using debt within the capital structure, i.e. combining equity and debt. The benefits (and horrors) of changes in capital structure and the percentage of debt vs equity (leverage) are discussed.

Project Valuation (Options): Building upon Discounted Cash Flow, project specifics and financial instruments, e.g. derivative options, are introduced. The ability to financially hedge a physical real estate asset is introduced.

Chapter 4

Interest Rates: The single-largest performance enhancer/detractor in real estate is the cost of capital. The cost(s) are determined by the level of interest rates which govern the cost for funds at different risk levels and different time periods. How capital is priced is introduced.

Chapter 5

Amortization: The two largest assets on individual balance sheets are homes and cars, in general. The payments are typically constant throughout the life of the loan(s). The payment schedule is called an “Amortization Schedule”. Building and understanding this schedule is critical and is introduced.

Chapter 6

Charting: While numbers in a spreadsheet are required, the interpretation can be difficult. These slides provide a brief overview of how to visually display, through graphics, quantitative data visually.

Custom Functions: P(Gain) was first introduced in the first edition of Foundations of Real Estate Financial Modelling. However, it is not a standard function in MS Excel. These slides demonstrate how to build P(Gain), and other custom functions, into the base of MS Excel.

MS Excel: A text on Real Estate Financial Modelling using MS Excel as the backbone would not be complete without lecture notes on MS Excel. The basics of MS Excel are introduced.

Chapter 7

Residential Construction (Review): A general overview of residential construction. While residential and commercial real estate are considered different industries, an understanding of residential basics is critical.

Chapter 8

Valuation of Income Properties: Residential properties cover, generally, For Sale properties. Commercial properties cover For Lease and typically modelled as if not sold but held for a determined amount of years. Therefore, while the basic valuation equations are equivalent, the methodology of understanding For Lease cash flows is different.

Chapter 9

Financing Land Development: Land is a tricky real estate asset as it does not produce rents. Therefore, it can be difficult to underwrite, i.e. value. As a result, lenders, e.g. banks, have specific standards for providing loans on non-income producing assets.

Financing Project Development: The transformation of raw land into an income producing real estate product requires project financing. Typically, an acquisition (raw land), development (horizontal construction), and construction (vertical construction), i.e. ADC loan, is required. The underwriting is different from raw land.

Leasing (during development): The true value of real estate is the leases which the physical product attracts. The ability to understand the lease and attract tenants is critical. Often there are requirements, by lenders, to pre-lease during development.

Chapter 10

For Sale (buyer’s perspective): Providing a different perspective than that of owner, these slides provide the sales process from the buyer’s perspective. Real estate is fraught with asymmetrical information and motivations. Understanding both sides of a transaction assists significantly with negotiations.

Chapter 12

Financial Statement Analysis: Analyzing the financial statements and forecasting is critical. Ratio analysis and interpretation is often done by analysts. It is critical that the project owner/sponsor understand how lenders and investors analyze these statements.

Financial Statement Overview: The emperor has no clothes! Perhaps the case with Hans Christian Anderson but not with real estate. The “clothing” for real estate are the financial statements which provide a consistent approach to analysis. No asset can be valued without an understanding of the basic financial statements and their differences: (1) Balance Sheet, (2) Income Statement, (3) Statement of Cash Flow, and (4) Statement of Retained Earnings.

Chapter 13

Waterfall: Not every investor has the same appetite for risk. Not every investor has the same required timing of their return. As such, cash flows can be financially engineered to provide investors the desired results.

A waterfall in a spreadsheet model that bifurcates cash flows according to risk appetite.

Chapter 14

Crystal Ball: Most models view inputs as static numbers. Crystal Ball is an MS Excel add-on that allows the analyst/user to view recognize and quantify inputs as stochastic variables, i.e. having a probability distribution rather than being static. This provides the user an output with a probability distribution best quantifying and demonstrating risk. These slides are an overview of Crystal Ball, the MS Excel add-on.

Simulation: How a simulation is developed, run, and analyzed is important. These slides introduce the correct methodology(ies) when using Crystal Ball.

Chapter 15

OptQuest: Crystal Ball introduced stochastic modelling. OptQuest takes dynamic inputs to the next level through optimization. Decision variables are introduced providing discrete decisions for optimized results.