Real Estate Data, AI & Geospatial Workflows

Real Estate Data Workflows

Collect, clean, and analyse property data responsibly.

Advanced
7 modules 34 lessons 17 exercises 4 case studies
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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 ends in a professional deliverable and stacks toward a named certificate.

Curriculum

01

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.

Mapping Your Data SourcesReading
Scraping Listings ResponsiblyReading
Asking vs Transacted PricesExercise
Exercise: Map Your Data Sources and Rate ConfidenceExercise
02

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.

Standardising Units, Currencies and AreasReading
De-duplication and Data HygieneExercise
Handling Outliers DeliberatelyCase study
Exercise: Clean a Messy Listings ExtractExercise
Case Study: From Scraped Cairo Listings to a Defensible BenchmarkCase study
Cleaning and Outliers QuizQuiz · pass 70%
03

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.

Geocoding AddressesReading
Catchments, Isochrones and Points of InterestReading
Heat Maps for Pricing and DemandCase study
Exercise: Geocode and Build a CatchmentExercise
Case Study: Reading a Catchment and Heat Map for a Retail SiteCase study
04

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.

Hedonic Pricing and AVMs at a GlanceReading
Using AI Responsibly in ResearchReading
Building a Defensible Pricing BenchmarkExercise
Exercise: Build a Defensible Pricing Benchmark, Using AI ResponsiblyExercise
AI-Assisted Research and Modelling QuizQuiz · pass 70%
05

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.

The Analyst's Data PlatformsReading
Choosing the Right Tool for the QuestionReading
Getting Started with QGISExercise
ArcGIS and Professional GIS WorkflowsReading
Exercise: Choose the Right Tool for the QuestionExercise
Software stack mastery checkQuiz · pass 70%
06

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.

Skeleton First: Structuring the DeckReading
Slide Craft: Formatting That ReadsReading
Build the Evidence DatabaseExercise
State Your AssumptionsReading
Exercise: Structure a Deck with Action TitlesExercise
Presentation discipline checkQuiz · pass 70%
07

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.

Template: Data Source MapExercise
Template: Data-Cleaning ChecklistExercise
Template: Comparable Evidence TrackerExercise
Template: GIS Mapping WorkflowExercise
Template: Assumptions and Confidence LogExercise
Template: Deck SkeletonExercise

Course assessment

AI-Assisted Research and Modelling QuizPass 70%
Cleaning and Outliers QuizPass 70%
Real Estate Data Workflows — AssessmentPass 70%
Presentation discipline checkPass 70%
Software stack mastery checkPass 70%

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The capstone project

This course ends in a real professional deliverable. Complete the course — including the capstone — and your certificate is issued automatically.

Your final deliverable

Capstone: From Messy Listings to a Market-Intelligence Dashboard

Submit a single package containing: (1) a cleaning log with row counts in, dropped, and reason at each step, on an untouched raw snapshot; (2) a one-paragraph method note explaining how the benchmark was derived; (3) the benchmark itself stated as a range with an explicit confidence rating, driven by the asking-versus-transacted nature of the sources; (4) an assumptions and confidence log, with the highest-sensitivity assumptions surfaced; and (5) a catchment read with sample-size caveats. Every figure must trace to an observed comparable or a flagged assumption. Where a real number is needed but unavailable, it must be marked requires local market data rather than invented.

Graded on five criteria

  • Method & rigourThe right framework, applied correctly, with the working shown — not just an answer.
  • Data honestyEvery figure is sourced, triangulated, or explicitly flagged “requires local market data”. Nothing is invented.
  • Analysis & judgmentAssumptions are explicit, at least one alternative is weighed, and the key risks are quantified.
  • RecommendationA clear, decision-useful conclusion a professional could act on — with the conditions that qualify it.
  • CommunicationStructured, concise, and client-ready — the argument lands.

Pass criteria

A submission passes when it meets the bar on all five criteria — the work is something a competent professional could put in front of a client or committee. The Verified Professional credential is issued on a pass.

Resubmission

Work that isn't there yet comes back marked “needs work” with specific feedback. You revise and resubmit — there is no penalty and no limit on attempts.