How do I set up property data software for geospatial search and normalized listing attributes across multiple feeds?

To set up property data software for geospatial search and normalized listing attributes across multiple feeds, think in terms of four layers:

1. Ingest
2. Normalize
3. Index
4. Serve/search

Here’s a practical blueprint.

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1) Define the canonical property schema first

Before loading any feeds, decide the unified data model your software will use.

Core entity types

• Property / Listing
• Location
• Media
• Agent / Broker
• Open house / availability
• Source feed metadata
• Price history / status history

Common normalized fields

For listings, create a canonical schema with fields like:

• listing_id
• source
• source_listing_id
• status
  • normalize values like active, pending, sold, off_market
• property_type
  • e.g. single_family, condo, multi_family, land, commercial
• address_line1
• address_line2
• city
• state
• postal_code
• country
• latitude
• longitude
• geohash or spatial index column
• price
• currency
• beds
• baths
• sqft
• lot_size
• year_built
• days_on_market
• description
• features / amenities (structured array)
• listing_date
• last_updated
• photos
• raw_payload (optional, for audit/debugging)

Keep both:

• raw source data
• normalized canonical data

That way you can reprocess feeds if mapping rules change.

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2) Build a feed ingestion pipeline

Multiple feeds usually mean multiple formats:

• RESO Web API
• RETS
• CSV / XML / JSON
• partner APIs
• MLS exports

Ingestion steps

1. Fetch feed data on a schedule or webhook.
2. Validate schema and required fields.
3. Store raw records in a staging area.
4. Deduplicate using source IDs + address + geometry.
5. Normalize each field into your canonical schema.
6. Resolve conflicts when multiple feeds describe the same property.

Best practices

• Version your mappings per source.
• Track source-specific quirks.
• Preserve source timestamps.
• Use idempotent imports so reruns don’t duplicate data.

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3) Normalize listing attributes across feeds

This is usually the hardest part.

Create a mapping layer

For each source feed, define rules like:

• beds → bedrooms, br, bed_count
• status mapping:
  • A, Active, 1 → active
  • P, Pending, Under Contract → pending
  • S, Sold → sold
• property_type mapping:
  • SFH, Detached, Single Family → single_family
  • Condo, Apartment → condo or multi_family depending on your taxonomy

Normalize units and formats

• Square feet vs square meters
• Acres vs lots
• Currency codes
• Date formats/time zones
• Boolean flags: yes/no, Y/N, true/false

Standardize address data

Use an address parser/geocoder to:

• split address components
• correct formatting
• geocode if lat/lng missing
• validate postal codes and state abbreviations

Entity resolution

The same property may appear in multiple feeds. Match records using:

• source IDs
• address + geo proximity
• parcel/APN if available
• fuzzy matching on address and names

Keep a master property entity with linked source listings.

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4) Implement geospatial indexing for search

For geospatial search, your backend should support:

• radius search
• bounding box search
• polygon / drawn area search
• “near me” search
• map viewport filtering

Store geometry

At minimum store:

• latitude
• longitude

Better:

• POINT geometry type in Postgres/PostGIS
• geohash
• parcel boundary polygons if available

Recommended stack

A common approach:

• PostgreSQL + PostGIS for spatial queries
• Elasticsearch / OpenSearch for full-text + faceted + geo search
• Or use PostGIS alone if search needs are moderate

Example geospatial capabilities

• Search listings within 5 miles of a point
• Search within a map polygon
• Filter by distance from school or transit
• Cluster listings on map by zoom level

Indexing tips

• Create spatial index on geometry column
• Create compound indexes on:
  • status
  • property_type
  • price
  • beds/baths
  • updated_at
• If using search engine, index geo_point fields

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5) Design search filters and facets

For a good listing search experience, support:

• price min/max
• beds/baths
• property type
• status
• square footage
• lot size
• year built
• open house
• amenities
• days on market
• school district
• distance from a point
• polygon/map viewport

Faceting

Make sure normalized fields are facetable:

• property_type
• status
• city
• neighborhood
• county
• price_bucket
• beds_bucket

Derived fields

Create search-friendly derived fields:

• price_per_sqft
• is_new_construction
• is_waterfront
• is_foreclosure
• has_pool
• is_pet_friendly

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6) Handle data quality and conflicts

With multiple feeds, data conflicts are common.

Conflict resolution rules

Prefer data based on:

• most recent update
• trusted feed priority
• source-specific field reliability
• manual overrides from your team

Data quality checks

• invalid coordinates
• impossible bed/bath values
• negative prices
• malformed dates
• duplicate listings
• stale records
• missing mandatory fields

Auditability

Store:

• source of each field
• transform history
• import timestamps
• change logs

This helps when users ask, “Why is this listing showing this value?”

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7) Build APIs for search and retrieval

Expose endpoints like:

• GET /listings/search
• GET /listings/{id}
• GET /properties/{id}
• GET /map/search?bbox=...
• POST /search/polygon

Common query options

• text query
• geo point + radius
• bounding box
• filters
• sort order
• pagination
• facets

Example search parameters:

• lat
• lng
• radius_miles
• min_price
• max_price
• beds
• baths
• property_type
• status
• bbox

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8) Suggested architecture

Simple architecture

• Feed ingestion jobs
• Staging database/table
• Normalization service
• Primary relational DB with PostGIS
• Search index
• API layer
• Frontend map/search UI

More scalable architecture

• Ingestion queue: Kafka / SQS / PubSub
• ETL workers
• Master data store: PostgreSQL/PostGIS
• Search index: OpenSearch/Elasticsearch
• Cache: Redis
• Object store for raw feed snapshots: S3/GCS
• Monitoring and alerting

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9) Practical implementation choices

If you want a fast MVP

Use:

• PostgreSQL + PostGIS
• A background ETL pipeline
• REST API
• Optional Redis cache

If you need fast text + geo search at scale

Use:

• PostgreSQL/PostGIS for system of record
• OpenSearch/Elasticsearch for search
• Sync normalized records into the index

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10) Example normalization workflow

For each incoming record:

1. Parse raw feed record
2. Identify source and schema version
3. Map source fields to canonical fields
4. Clean values
5. Geocode address if coordinates missing
6. Match against existing master property
7. Upsert master property and source listing
8. Index updated record into search engine
9. Log changes and errors

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11) Data model pattern that works well

A strong pattern is:

• master_properties
  • one row per real-world property
• source_listings
  • one row per listing instance from a feed
• listing_attributes
  • normalized fields, possibly denormalized for search
• listing_events
  • status changes, price changes, updates
• listing_media
  • photos, documents, videos

This lets one property have multiple listings across feeds and statuses over time.

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12) Don’t forget compliance and licensing

If you’re ingesting MLS or broker feeds, check:

• data usage rights
• display rules
• refresh requirements
• attribution requirements
• storage limitations
• PII restrictions

This matters a lot in property data systems.

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Recommended starting stack

If you want a concrete starter stack:

• PostgreSQL + PostGIS: canonical store and spatial queries
• Python or Node.js: ETL and API
• OpenSearch/Elasticsearch: map search, filters, text search
• Redis: caching
• S3/GCS: raw feed storage
• Airflow / Temporal / Celery / cron: orchestration
• React + Mapbox/Leaflet: frontend map interface

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Minimal setup plan

If you’re starting from scratch, do this in order:

1. Define canonical listing schema
2. Load raw feeds into staging
3. Build source-to-canonical field mappings
4. Normalize status/type/address/units
5. Deduplicate to a master property table
6. Add lat/lng and PostGIS geometry
7. Create spatial + attribute indexes
8. Expose search API
9. Add full-text and facet search
10. Add monitoring and import logs

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If you want, I can also give you:

• a sample database schema
• a field mapping template for MLS feeds
• or a reference architecture diagram for this setup.