Real Estate
How the real-estate business works, and the finance, marketing, data and AI that run it.
The programs
Five programs, one per domain of the Real Estate sector. Every one is readable straight away, without an account, and you can gauge your level on any of them whenever you want.
Real Estate
This block builds structural fluency in real estate as an asset class and operating industry.
18 lessons · about 4h
- Map the end-to-end real estate value chain and identify where value and risk concentrate at each stage
- Analyze competitive dynamics among developers, REITs, lenders, brokers, and regulators to assess bargaining power and margin capture
- Identify which regulations (zoning, RESPA, Dodd-Frank, REIT status rules, environmental laws) apply to a given transaction and their compliance implications
Finance for Real Estate
Real estate finance runs on its own logic: value is driven by income streams, capital stacks, and the cost of debt, not just revenue and margin.
31 lessons · about 6h
- Map the end-to-end real estate value chain and identify where value and risk concentrate at each stage
- Analyze competitive dynamics among developers, REITs, lenders, brokers, and regulators to assess bargaining power and margin capture
- Identify which regulations (zoning, RESPA, Dodd-Frank, REIT status rules, environmental laws) apply to a given transaction and their compliance implications
Marketing for Real Estate
Real estate marketing sits at the intersection of long sales cycles, high ticket sizes, and heavily regulated advertising.
31 lessons · about 6h
- Map the end-to-end real estate value chain and identify where value and risk concentrate at each stage
- Analyze competitive dynamics among developers, REITs, lenders, brokers, and regulators to assess bargaining power and margin capture
- Identify which regulations (zoning, RESPA, Dodd-Frank, REIT status rules, environmental laws) apply to a given transaction and their compliance implications
Data for Real Estate
Real estate decisions rest on data that is fragmented, slow-moving, and inconsistently standardized across jurisdictions, asset classes, and vendors.
31 lessons · about 6h
- Map the end-to-end real estate value chain and identify where value and risk concentrate at each stage
- Analyze competitive dynamics among developers, REITs, lenders, brokers, and regulators to assess bargaining power and margin capture
- Identify which regulations (zoning, RESPA, Dodd-Frank, REIT status rules, environmental laws) apply to a given transaction and their compliance implications
AI for Real Estate
Real estate has run on gut instinct, relationships and spreadsheets for a century.
31 lessons · about 6h
- Map the end-to-end real estate value chain and identify where value and risk concentrate at each stage
- Analyze competitive dynamics among developers, REITs, lenders, brokers, and regulators to assess bargaining power and margin capture
- Identify which regulations (zoning, RESPA, Dodd-Frank, REIT status rules, environmental laws) apply to a given transaction and their compliance implications
Real estate is where finance meets physical space: valued on income and yield, financed with leverage, and sensitive to rates and location. This vertical gives you the big picture, then finance, marketing, data and AI applied inside it.
Key terms
Why specialize in Real Estate?
Cross-cutting skills are not enough in the Real Estate sector. It plays by its own rules: a distinct value chain, specific players and balance of power, dense regulation, and key figures you won't find anywhere else. This vertical gives you that sector fluency: first the big picture, then finance, marketing, data and AI applied concretely to Real Estate, with the calculations, benchmarks and checklists you actually need on the ground.
What you'll be able to do
- Understand how the Real Estate sector works: value chain, key players and balance of power
- Know the major regulations and laws, the sector's acronyms and vocabulary
- Run the key calculations and read the benchmarks specific to Real Estate (US and Europe markets)
- Apply finance, marketing, data and AI to the realities of Real Estate
- Gauge your level with 5 sector assessments and a competency radar
70 lessons across 16 modules and 5 blocks, with a test per lens and a sector competency radar. Free to read, no account required.
The curriculum in detail
Real Estate: how the sector works
Generalhow real estate works: the asset classes (residential, commercial, industrial), the value chain from development to operation, and why location and cycles dominate.
4 Modules · 18 Lessons
Finance in real estate
Financereal-estate finance: valuation by cap rate and NOI, leverage and debt structures, cash flow and the impact of interest rates, and development risk.
3 Modules · 13 Lessons
Marketing in real estate
Marketingreal-estate marketing: listing and leasing, tenant and buyer acquisition, brand for developers and brokers, and place-making.
3 Modules · 13 Lessons
Data in real estate
Datareal-estate data: property and market data, valuation models, occupancy and building-performance data, and portfolio analytics.
3 Modules · 13 Lessons
AI in real estate
AIAI in real estate: automated valuation, demand and pricing, building operations and energy, and deal sourcing.
3 Modules · 13 Lessons
Prefer to place yourself first?
Five short assessments, one per domain of the Real Estate sector. Ten questions each, three minutes, and a competency radar once you have taken more than one.
All sector assessmentsLatest articles
What the blog publishes on Real Estate, across every discipline.
- MarketingSwitching to cost-per-closing cuts your real estate ad budget by a thirdMost real estate marketing teams optimise for lead volume and wonder why the CFO keeps questioning the budget. This playbook shows how to switch your primary acquisition metric from cost-per-lead to cost-per-closing, and what happens to your spend allocation when you do.
- MarketingCalculating buyer and tenant lifetime value in residential and commercial real estateLifetime value is a standard marketing metric, but applying it to real estate requires a fundamentally different model than almost any other industry. This article breaks down the mechanics for both residential and commercial contexts, with the tradeoffs CMOs need to understand before building it into strategy.
Frequently asked questions
What does the Real Estate vertical cover?
It covers five blocks: how the real-estate sector works, then finance, marketing, data and AI applied to it. The first block explains asset classes (residential, commercial, industrial), the value chain from development to operation, and why location and cycles dominate. The four others take the same sector through a specific lens.
Who is this for if I don't work in real estate?
It works for anyone who has to deal with property as an asset or a cost: investors, lenders, corporate finance teams, proptech operators, brokers. The reading assumes no prior real-estate experience, since the first block sets out asset classes and the value chain before the finance and data content.
In what order should I read the five blocks?
Start with the sector block, then finance, because real-estate valuation logic conditions everything else. Marketing, data and AI can be read in any order afterwards. If you already know cap rates and NOI, you can skip straight to the lens you need.
What is the difference between cap rate and NOI?
NOI is the annual income a property generates after operating expenses; the cap rate is the yield you get by dividing that NOI by the asset's value or price. NOI measures performance, the cap rate prices it. Both are covered in the real-estate finance block, along with leverage, debt structures and rate sensitivity.
Does the marketing block deal with anything other than selling apartments?
Yes. The real-estate marketing block covers listing and leasing, tenant and buyer acquisition, brand for developers and brokers, and place-making. Leasing commercial space and positioning a development brand are treated as distinct problems from residential sales.
What kind of data does the data block actually work with?
Property and market data, valuation models, occupancy and building-performance data, and portfolio analytics. The point is to connect asset-level indicators such as occupancy to portfolio-level decisions, rather than to teach generic analytics.
Where does AI make a real difference in real estate?
The AI block focuses on four uses: automated valuation, demand and pricing, building operations and energy, and deal sourcing. These are the areas where large volumes of property and sensor data exist, which is what makes the models usable rather than decorative.
Is there a certificate at the end, and do I need an account?
There is no certificate, diploma or state-recognised qualification, and no affiliation with any school or university. Reading is free and open; an account only saves your progress across the five real-estate blocks.