Real Estate Data Integrations: MLS, IDX, RESO Web API for PropTech

Most MLS integrations end up using one of two patterns: query-through or replicate-and-serve.

With query-through, your app calls the upstream API (or an IDX provider) on demand. This reduces storage needs, but it pushes latency, rate limits, and uptime risk into your user experience. It also makes it harder to build fast map search and “similar listings” features at scale.

With replicate-and-serve, you ingest MLS data into your own storage, normalize it, index it for search, and serve users from your system. This is the more common choice for serious PropTech products because it improves speed, supports advanced search, and enables analytics. It also creates more responsibility: data freshness monitoring, deduplication, and a correct public display layer.

A typical replicate-and-serve pipeline looks like this:

• ingestion jobs that pull incremental updates (not full reloads)
• a transformation layer that maps fields into a canonical model (often RESO-aligned)
• separate storage for structured listing data and media metadata
• a search index for fast filtering and geo queries
• audit logs and monitoring for compliance and support

Data modeling: why RESO standards save time, not just effort

The hardest part of multi-MLS products is not “calling an API.” It is keeping meaning consistent across sources.

One MLS might represent bathrooms as a decimal, another as an integer plus partial-bath flags. One might use different enumerations for property subtypes. Without a disciplined mapping layer, your UI ends up with confusing filters and inconsistent results.

RESO’s Data Dictionary helps because it defines common field names, types, and lookup values. Even with RESO, teams still need to validate each MLS implementation, because “certified” does not guarantee every optional field is populated in a way your product expects.

A strong approach is to maintain:

• a canonical schema used across your product
• per-MLS mapping and lookup tables
• automated validations (type checks, required field checks, anomaly detection)
• a “safe defaults” policy for missing data, so the UI degrades gracefully

Media handling: photos can break your platform if you treat them like text

Listing photos and virtual tour links create a different class of load than standard listing fields. Photo downloads can dominate bandwidth, slow ingestion, and increase storage costs quickly.

Treat media as its own subsystem:

• Store photo URLs, checksums, and timestamps as metadata
• Download and process media asynchronously
• Use image resizing and CDN delivery for web and mobile performance
• Implement cache invalidation when listings update photos

This also supports compliance requirements, because you can enforce rules on what is publicly displayed while keeping private data out of consumer-facing systems.

Freshness is a product requirement, not an ETL detail

Agents and consumers expect listing changes to appear quickly. Industry guidance often frames this in minutes, not hours. That expectation should be translated into measurable SLOs tied to your ingestion pipeline.

Track at least three timestamps:

• source modified time (when the MLS says the listing changed)
• ingested time (when your system ingested the change)
• served time (when your API or app returned the updated listing)

Once you track these, you can manage freshness like any other product metric. You can also set alerting when lag exceeds agreed thresholds, or when ingestion volume drops unexpectedly.

Performance – avoid making the MLS your runtime dependency

Slow search is a conversion killer. A listing search experience should feel instant, even with complex filters, map bounds, and high traffic spikes from campaigns.

Performance planning usually focuses on:

• search indexing: push filterable fields into a search engine or optimized database indexes
• geo queries: precompute geohashes or use spatial indices for map search
• pagination strategy: never load thousands of listings into a single response
• rate limits: protect upstream sources and your own API with throttling and backoff
• caching: cache metadata and common queries, while honoring data freshness rules

A common mistake is storing large blobs of JSON and filtering them at runtime. Structured columns and well-designed indices routinely outperform blob-based querying when you need predictable response times.