Real Estate Data, AI & Geospatial Workflows
Real Estate Data Workflows
Collect, clean, and analyse property data responsibly.
The promise
Build a repeatable market-intelligence pipeline from raw property data to dashboards.
By the end you can
- Collect and clean property data responsibly
- Add geospatial layers to the analysis
- Build a repeatable data pipeline
- Present findings in a dashboard
Where this course takes you
This course is part of 2 role tracks, each ending in a professional deliverable.
You'll produce: A city market snapshot — supply, demand, pricing, pipeline, risks, and a recommendation — written to a client-ready standard.
You'll produce: A market intelligence dashboard — a data pipeline, geospatial layers, indicators, and a decision-ready view.
Curriculum
Responsible Data Collection
Where real estate data comes from, how to gather it responsibly, and why provenance and collection dates are the foundation of any defensible benchmark.
Cleaning and Outliers
Turning raw, messy collections into a reproducible analytical dataset: standardising units and currencies, de-duplicating, and handling outliers with documented, defensible decisions.
GIS and Location Analysis
Turning addresses into geography: geocoding, catchments and isochrones, points of interest, and heat maps that expose the spatial drivers of price and demand.
AI-Assisted Research and Modelling
Bringing it together: hedonic pricing and AVMs, using AI responsibly without hallucinated data, and assembling everything into a defensible asking-to-transacted pricing benchmark.
The Analyst's Software Stack
The platforms professional analysts actually use — macro forecasters, consumer data, specialist intelligence, and GIS — what each does, when to reach for it, and how to get started.
Presenting the Analysis
Turning analysis into a deliverable: storyline and skeleton, slide craft, the evidence database behind every exhibit, and the discipline of stated assumptions.
Applied Toolkit & Templates
Copy-ready working templates for the real-estate data analyst: source maps, cleaning checklists, comparable trackers, GIS workflows, assumption logs, and deck skeletons. Each template includes how-to guidance and common mistakes to avoid.
Course assessment
Sign in to open lessons and track your progress.
The capstone project
This course ends in a real professional deliverable. Use the brief and the five criteria below to check your own work against the standard a competent professional would be held to.
Your final deliverable
Capstone: From Messy Listings to a Market-Intelligence Dashboard
Check your work against five criteria
- Method & rigour: The right framework, applied correctly, with the working shown — not just an answer.
- Data honesty: Every figure is sourced, triangulated, or explicitly flagged “requires local market data”. Nothing is invented.
- Analysis & judgment: Assumptions are explicit, at least one alternative is weighed, and the key risks are quantified.
- Recommendation: A clear, decision-useful conclusion a professional could act on — with the conditions that qualify it.
- Communication: Structured, concise, and client-ready — the argument lands.
When it's ready
Your deliverable is ready when it meets the bar on all five criteria — work a competent professional could put in front of a client or committee.
Revising your work
The capstone is self-directed: you check your own work against the rubric. Where a criterion isn't met yet, revise that part and check it again — as many times as you need.