Real Estate Analytics with GIS Training Course

Participants can select suitable spatial data structures and coordinate systems for property analysis.

• Vector geometry: points, lines, polygons and multipart property features
• Raster surfaces representing elevation, land cover, heat and hazard
• Coordinate reference systems, projections and distance measurement implications
• Attribute tables linking geometry with property and market records
• Unique identifiers for parcels, buildings, units and transactions
• Geodatabases structured for repeatable real estate analysis

Participants can specify and evaluate data sources required for a property analysis.

• Cadastre, land registry and parcel boundary data sources
• Transaction, listing, rent and vacancy datasets assessed
• Census demographics, business registers and consumer expenditure sources
• Planning zones, permits and development pipeline records
• Transport networks, points of interest and infrastructure datasets
• Data licences, permitted uses and supplier due diligence

Participants can assess property data quality and prepare records for spatial analysis.

• Completeness, accuracy, timeliness, consistency and uniqueness quality dimensions
• Missing values, duplicate properties and implausible transaction records
• Field standardisation for dates, currencies, areas and property types
• Metadata documented using source, date, scale, method and limitations
• Outlier checks using distributions, percentiles and interquartile ranges
• Audit trail requirements for investment-grade analytical outputs

Participants can geocode property records and diagnose match-quality issues.

• Address components parsed into number, street, locality and postcode
• Geocoding services matched against authoritative address reference data
• Match scores, match types and positional uncertainty interpreted
• Unmatched records resolved through standardisation and manual review
• Rooftop, parcel-centroid and street-segment locations compared
• Geocoding quality log produced for property datasets

Participants can analyse parcel and building geometry for land and asset decisions.

• Parcel boundaries checked against title, survey and planning records
• Building footprints linked with floorspace, use and construction attributes
• Topology rules detect gaps, overlaps and invalid parcel geometry
• Parcel area, frontage, depth and compactness measures calculated
• Assemblage opportunities identified through contiguous ownership patterns
• Legal boundaries distinguished from market and operational geographies

Participants can integrate transaction and market data with mapped property assets.

• Attribute joins using parcel identifiers, building codes and addresses
• One-to-many transaction histories related to property records
• Temporal alignment of sales, rents, valuations and market indicators
• Property type classifications harmonised across vendor datasets
• Derived measures calculate price per square metre and yield
• Join diagnostics identify unmatched, duplicated and conflicting records

Participants can design thematic maps that communicate property market patterns accurately.

• Choropleth classification using quantiles, natural breaks and equal intervals
• Graduated symbols display transaction values, rents and floorspace
• Density maps distinguish concentration from absolute market activity
• Colour schemes selected for sequential, diverging and categorical variables
• Map scale, labels, legends and annotations support interpretation
• Misleading visual patterns caused by aggregation and classification recognised

Participants can use spatial queries to identify comparable properties and market segments.

• Selection by location isolates properties within defined market boundaries
• Selection by attribute filters property type, price, age and tenure
• Buffer analysis identifies assets near transport, amenities and disamenities
• Spatial joins aggregate nearby transactions and demographic characteristics
• Comparable property sets defined through location and attribute criteria
• Market segments tested for distinct price, rent and supply patterns

Participants can define and assess trade areas for retail, service and mixed-use assets.

• Trade areas defined using rings, drive times and observed customer origins
• Primary, secondary and tertiary catchments distinguished by access patterns
• Population, households and expenditure aggregated within catchment boundaries
• Competitive supply mapped by brand, format and floorspace
• Cannibalisation risk assessed between existing and proposed locations
• Catchment assumptions documented for retail property recommendations

Participants can measure network-based accessibility for site and portfolio assessment.

• Road and pedestrian networks prepared for routing analysis
• Drive-time polygons model realistic access to candidate sites
• Network distance compared with straight-line distance measures
• Nearest facility analysis identifies closest hospitals, stations and competitors
• Origin-destination matrices quantify access between assets and demand
• Travel-time assumptions tested for peak, off-peak and freight movements

Participants can rank candidate sites using transparent spatial suitability criteria.

• Suitability criteria derived from strategy, planning and operating requirements
• Constraint mapping excludes prohibited, hazardous and unavailable land
• Weighted overlay combines standardised spatial criterion scores
• Analytic Hierarchy Process compares relative importance of site criteria
• Sensitivity analysis tests alternative weights and decision thresholds
• Ranked sites reviewed against qualitative deal and delivery considerations

Participants can incorporate location variables into property valuation and comparable analysis.

• Comparable selection considers distance, property characteristics and transaction timing
• Hedonic price models separate structural and locational value drivers
• Spatial autocorrelation checked using Moran's I statistics
• Amenity, view, frontage and transport variables quantified spatially
• Sales adjustment grids document comparable property differences
• Valuation maps communicate evidence without overstating model precision

Participants can map demand indicators and competing supply for development assessment.

• Demand proxies selected from population, employment, income and mobility data
• Development pipeline mapped by status, use, units and completion date
• Supply-demand gaps assessed within defined market and catchment boundaries
• Absorption rates compared with planned delivery volumes
• Scenario maps test growth, infrastructure and zoning assumptions
• Development timing risks identified through spatial supply concentration

Participants can assess portfolio patterns using mapped asset, performance and exposure data.

• Portfolio assets mapped by value, use, tenure and ownership structure
• Concentration measures identify exposure by district, market and tenant
• Occupancy, rent growth and valuation change compared geographically
• Asset clusters reveal operational, leasing and capital planning priorities
• Portfolio gaps assessed against target markets and strategic locations
• Geographic diversification evaluated alongside asset-level investment risk

Participants can identify and report spatial risks affecting property value and operations.

• Flood, wildfire, coastal and heat hazard layers interpreted
• Environmental contamination records screened around candidate properties
• Planning restrictions, heritage designations and easements mapped
• Climate scenarios linked to asset exposure and adaptation options
• Risk buffers distinguish proximity alerts from confirmed site impacts
• Location risk register records source, likelihood, impact and response

Participants can use imagery-derived evidence to identify land and property change.

• Satellite imagery selected by resolution, date, cloud cover and purpose
• Land cover classification distinguishes built, vegetated and impervious surfaces
• Change detection identifies new construction and land-use conversion
• Aerial imagery verifies site conditions and surrounding development
• Image interpretation limitations documented for investment decision use
• Remote sensing evidence cross-checked against field and registry records

Participants can present real estate GIS findings through clear maps, dashboards and briefings.

• Key performance indicators selected for property decision audiences
• Interactive dashboards combine maps, filters, charts and summary metrics
• Map layouts tailored for investment committees and planning reviews
• Narrative structure links evidence, assumptions, findings and recommendation
• Uncertainty communicated through ranges, caveats and data-quality statements
• Version control maintains consistency across maps, data and presentations

Participants can establish controls for reliable, lawful and repeatable real estate GIS work.

• Data ownership, stewardship and approval responsibilities assigned
• Reproducible workflows document inputs, transformations, parameters and outputs
• Quality assurance checks embedded before publishing maps and analyses
• Personal data risks managed in address, tenant and customer datasets
• Bias assessed in data coverage, scoring criteria and market interpretation
• Retention, access and sharing rules defined for spatial datasets

Participants can build, review and present a complete GIS-based real estate decision analysis.

• Decision brief defines asset objective, geography, criteria and stakeholders
• Property, market and contextual datasets prepared with documented metadata
• Spatial analysis combines queries, catchments, accessibility and suitability scores
• Findings tested through sensitivity checks and data-quality review
• Decision pack includes maps, dashboard outputs, assumptions and recommendations
• Peer review challenges evidence, limitations and proposed action