Trulia Property Scraper

Slug: fatihtahta/trulia-property-scraper

Overview

Trulia Property Scraper collects public property listings with prices, addresses, coordinates, property characteristics, listing status, media, availability, attribution, and available contact details. Trulia is a public real estate search service whose listing pages can support property discovery, market monitoring, comparable-listing research, and operational review. The actor turns a Trulia market URL or location-based buy, rent, or sold search into repeatable structured collection. Optional enrichment adds fuller descriptions, galleries, features, history, and available agent or agency information to the same listing records. Results are delivered as structured dataset records suitable for review, export, ETL pipelines, BI dashboards, AI-agent workflows, and downstream processing. Stable identifiers, grouped objects, and run-level artifacts make recurring runs easier to compare and audit. The actor is designed for dependable recurring acquisition of data that Trulia exposes publicly at run time, without implying complete market coverage or continued listing availability.

What Makes This Actor Different

• Pipeline-ready property records: Each row uses a documented property_listing envelope with stable identity, provenance, listing, pricing, location, property, media, availability, relationship, contact, and source-specific groups.
• Deduplication-friendly identity: record_id is the recommended upsert key, while url, listing.listing_id, property.property_id, and source identifiers provide additional audit context when available.
• Standard and enriched collection: Standard runs retain search-result listing data. Enriched runs can add descriptions, larger image galleries, structured features, price or tax history, and available public listing contacts without changing the base record family.
• Coverage-aware collection: maximize_coverage can collect deeper within the selected location, listing mode, and criteria when a broad search exposes more matching listings than are normally visible at once. It does not relax the selected scope and continues to respect limit.
• Operational run receipts: RUN-SUMMARY and RUN-SUMMARY.html report saved counts, duration, coverage state, enrichment outcomes, location and pricing breakdowns, warnings, representative records, and artifact keys.
• Map-ready review: Valid location.latitude and location.longitude values feed a stable interactive results-map artifact. The map reports mapped, skipped, and deduplicated marker counts, including a clear zero-location state.
• Agentic usability: Input recipes, stable field names, bounded summaries, output links, and a detailed field reference make the actor suitable for Claude, Codex, internal agents, and workflow automations without private operational context.
• Optional MCP handoff: User-authorized Apify MCP connectors can receive a compact run summary and available dataset, report, and map links after the primary outputs are ready. Full listing rows are not sent through this default connector path.

Who Should Use This Actor

• Real estate investors and analysts: Build scoped listing datasets for public comparable research, supply analysis, price segmentation, and location review.
• Brokerages and property operations teams: Monitor public inventory, listing status, advertised prices, open-house availability, and public attribution across recurring runs.
• Market research and analytics teams: Create repeatable snapshots for neighborhood, property-type, bedroom, bathroom, area, and pricing analysis.
• Proptech and data engineering teams: Ingest nested, typed JSON into warehouses, search indexes, internal applications, CRM systems, and enrichment pipelines.
• BI and reporting teams: Feed dashboards with listing counts, property mix, asking prices, geography, media coverage, and point-in-time status signals.
• AI agents and workflow automations: Run a defined property search, validate the run receipt, summarize findings, and route records into a downstream review or alert workflow.
• Lead and enrichment teams: Add public property, agent, agency, media, and contact context to an existing listing or opportunity record when enrichment exposes it.

Common Use Cases

• Market intelligence: Capture public listing supply, asking prices, status, property mix, and geographic distribution for a selected market.
• Comparable-listing research: Collect structured property attributes for a city, ZIP code, neighborhood, sale market, rental market, or recently sold segment.
• Recurring inventory monitoring: Repeat the same input on a schedule and compare stable records for new, missing, repriced, or updated listings.
• Listing enrichment: Add public descriptions, property features, image galleries, history, attribution, and available contacts to lightweight listing records.
• Portfolio and location research: Review addresses, coordinates, neighborhoods, dimensions, amenities, and nearby-school context when available.
• CRM and directory population: Upsert public property listings and related agent or agency context into operational systems using record_id.
• Dashboard refreshes: Produce JSON-first snapshots for pricing, property characteristics, status, enrichment, and map coverage reporting.
• Agentic research workflows: Let an automated agent define a supported search, inspect run artifacts, analyze records, and prepare a human review queue.

Real-World Questions This Data Can Answer

• Which public Trulia listings are visible for a specific city, ZIP code, neighborhood, or market and listing mode?
• What asking-price, property-type, bedroom, bathroom, and area mix appears in the collected segment?
• Which records appear new, removed, repriced, or updated when compared with a previous internal snapshot?
• Which listings expose coordinates, photo galleries, 3D-home signals, open houses, tours, or richer property features?
• Which public agent, agency, developer, attribution, or contact values are available for enriched records?
• How many listings were saved, enriched, mapped, skipped, or deduplicated in a particular run?
• Did a broad run use deeper matching coverage, reach its requested limit, or finish with a coverage warning?

Quick Start

1. Choose either one supported Trulia market-search URL or a location such as Austin, TX.
2. Select deal_type: buy, rent, or sold.
3. Set a small limit, such as 5 or 20, for the first validation run and decide whether enrich_data is needed.
4. Start the actor in Apify Console and inspect the first dataset records plus the run summary and map.
5. After validating the record shape, raise the limit, enable maximize_coverage when appropriate, export the dataset, or create a schedule.

Input Parameters

The public input supports a Trulia market URL or location-based search, listing mode, enrichment, deeper matching coverage, optional MCP summary delivery, and a result limit.

Parameter	Type	Description	Default
url	array of strings	One supported Trulia city or market search URL. URL mode is authoritative when supplied; individual property-detail URLs are not supported as search seeds. Provide one URL per run.	–
location	string	City, state, ZIP code, neighborhood, or Trulia-supported market used to build a search when url is absent.	–
deal_type	string	Listing mode. Allowed values: buy, rent, sold. For URL mode, choose the value that matches the supplied Trulia page.	buy
enrich_data	boolean	Adds available descriptions, galleries, property features, history, attribution, and public contact context. Disable for a lighter validation or monitoring run.	true
maximize_coverage	boolean	Collects deeper within the same selected criteria for broad searches. Intended for larger runs; the configured limit still applies.	true
mcpConnectors	array of connector resources	Optional user-authorized Apify connectors for compact post-run summary delivery. The primary dataset and run artifacts remain authoritative.	[]
limit	integer	Maximum property records to save. Minimum: 1. Leave empty to use the actor's standard bounded run behavior.	–

Choosing Inputs

Use url when you already have a supported Trulia city or market search page and want a repeatable target. Supply one URL and select the matching deal_type; the URL takes precedence over location. Use location when you want the actor to build a buy, rent, or sold search from a city, state, ZIP code, neighborhood, or market name.

Start with a small limit and inspect several records before increasing volume. Keep enrich_data enabled when descriptions, feature details, photo galleries, history, or public listing contacts matter; disable it when lightweight search-result fields are enough. Enable maximize_coverage for broad markets, high-volume segments, recurring inventory collection, or cases where Trulia reports more matches than are normally visible. Leave it disabled for faster exploratory runs when the first result set is sufficient. For cleaner comparisons, segment recurring work into separate runs by geography and deal_type rather than mixing unrelated markets.

Input Recipes

• Validation run: Use one focused location, select deal_type, keep maximize_coverage off, and set limit to 5. Enable or disable enrichment based on the fields you plan to inspect.
• Repeatable market URL: Supply one known Trulia market URL, choose its matching deal_type, and set a conservative limit. Reuse the same input for consistent snapshots.
• Broad market discovery: Use a city or metro-level location, minimal configuration, and a sensible limit. Review the run summary before expanding volume.
• Maximum matching coverage: Use a broad location, set a limit of at least 950 or leave it empty, and enable maximize_coverage to collect deeper within the same criteria.
• Enriched research run: Use a focused URL or location, enable enrich_data, and set a bounded limit to collect fuller property, media, history, attribution, and contact context.
• Recurring monitoring: Save one stable URL or location-and-mode configuration, run it on a schedule, and compare records by record_id plus price, status, and update fields.

Example Inputs

Example: location-based sale listings

{
  "location": "Austin, TX",
  "deal_type": "buy",
  "enrich_data": true,
  "maximize_coverage": false,
  "limit": 20
}

Example: repeatable rental-market URL

{
  "url": [
    "https://www.trulia.com/for_rent/Los_Angeles,CA/"
  ],
  "deal_type": "rent",
  "enrich_data": false,
  "maximize_coverage": false,
  "limit": 50
}

Example: broad sold-market research

{
  "location": "Phoenix, AZ",
  "deal_type": "sold",
  "enrich_data": true,
  "maximize_coverage": true,
  "limit": 950
}

Output

Output destination

The actor writes results to an Apify dataset as JSON records. The dataset is designed for direct consumption by analytics tools, ETL pipelines, AI agents, and downstream APIs with minimal post-processing.

The current public dataset contains one documented record shape: property_listing. Run summaries, reports, maps, and diagnostics are separate key-value-store artifacts, not dataset records.

Record envelope and stable identifiers

Every row requires record_type, record_id, url, source_context, and entity. Use record_id as the recommended idempotency key for deduplication and upserts. The typed identifier is stronger than title or address matching and makes records easier to merge, synchronize, and compare across repeated runs. url and source_context.source_url provide public source traceability, while listing.listing_id, property.property_id, and nested external IDs provide additional matching context when present.

When loading repeated runs, upsert by record_id and compare point-in-time business fields such as pricing.price, listing.listing_status, listing.updated_at, and availability values. Keep run date and input segment alongside the record in your destination system.

Example: property listing record

The following example is synthetic but preserves the supported public structure and value types. Optional groups appear only when the source exposes them and enrichment succeeds.

{
  "record_type": "property_listing",
  "record_id": "987654321_ZPID",
  "url": "https://www.trulia.com/home/1842-sample-ave-austin-tx-78704-987654321",
  "source_context": {
    "source_id": "trulia_property_scraper",
    "source_domain": "trulia.com",
    "source_url": "https://www.trulia.com/home/1842-sample-ave-austin-tx-78704-987654321",
    "seed_type": "location",
    "seed_value": "Austin, TX",
    "page_number": 1,
    "position": 3,
    "language": "en",
    "country": "US",
    "enrichment_status": "enriched",
    "enrichment": {
      "requested": true,
      "status": "enriched",
      "enriched": true
    }
  },
  "entity": {
    "title": "1842 Sample Ave",
    "description": "Updated sample residence with an open living area and a fenced yard.",
    "external_ids": {
      "typed_home_id": "987654321_ZPID",
      "zpid": "987654321"
    }
  },
  "listing": {
    "listing_id": "987654321_ZPID",
    "listing_type": "RESALE",
    "listing_status": "active_for_sale",
    "status_text": "For Sale",
    "deal_type": "sale",
    "posted_at": "2026-07-08T14:30:00+00:00",
    "updated_at": "July 10, 2026",
    "status_flags": {
      "is_active_for_sale": true,
      "is_active_for_rent": false,
      "is_recently_sold": false,
      "is_foreclosure": false
    },
    "external_ids": {
      "mls_id": "SAMPLE-1842"
    },
    "source_attribution": "Sample regional listing source",
    "history": {
      "price_history": [
        {
          "formattedDate": "07/08/2026",
          "event": "Listed For Sale",
          "price": {
            "price": 725000,
            "formattedPrice": "$725,000"
          }
        }
      ]
    }
  },
  "pricing": {
    "price": 725000,
    "price_text": "$725,000",
    "currency": "USD",
    "price_per_area_text": "$402/sqft"
  },
  "location": {
    "address": "1842 Sample Ave, Austin, TX 78704",
    "street_address": "1842 Sample Ave",
    "city": "Austin",
    "region": "TX",
    "postal_code": "78704",
    "country": "US",
    "neighborhood": "South Austin",
    "latitude": 30.2451,
    "longitude": -97.7684
  },
  "property": {
    "property_id": "987654321",
    "property_type": "SINGLE_FAMILY_HOME",
    "bedrooms": 3,
    "bathrooms": 2.5,
    "floor_area": 1804,
    "area_unit": "sqft",
    "land_area": 0.16,
    "land_area_unit": "acre",
    "amenities": [
      "Interior: Fireplace",
      "Parking: Garage"
    ],
    "features": [
      {
        "group": "Interior Features",
        "name": "Fireplace",
        "value": "Yes"
      }
    ],
    "measurement_sources": {
      "floor_area": {
        "raw_text": "1,804 sqft"
      }
    }
  },
  "media": {
    "main_image_url": "https://static.trulia-cdn.com/pictures/sample-property.jpg",
    "image_urls": [
      "https://static.trulia-cdn.com/pictures/sample-property.jpg"
    ],
    "has_3d_home": true,
    "has_video": false,
    "total_photo_count": 24
  },
  "availability": {
    "tour_available": true,
    "open_houses": [
      {
        "date_label": "Saturday",
        "start_time": "11:00 AM",
        "end_time": "2:00 PM"
      }
    ]
  },
  "contact_details": {
    "phones": [
      "(512) 555-0142"
    ],
    "contacts": [
      {
        "role": "agent",
        "name": "Sample Listing Agent",
        "phone": "(512) 555-0142"
      }
    ]
  },
  "relationships": {
    "agent": {
      "name": "Sample Listing Agent",
      "phone": "(512) 555-0142"
    },
    "agency": {
      "name": "Sample Property Group"
    },
    "listing_source": {
      "attribution": "Sample regional listing source"
    }
  },
  "attributes": {
    "tags": [
      {
        "name": "New Listing",
        "level": "HIGHLIGHT"
      }
    ],
    "trulia": {
      "provider_summary": [
        "Sample Listing Agent, Sample Property Group"
      ]
    }
  }
}

Run Summary, Map, And Artifacts

The actor exposes the default dataset as the primary output and writes these stable run artifacts to the run's key-value store:

Artifact	Format	Operational use
RUN-SUMMARY	JSON	Machine-readable receipt with input scope, saved and duplicate counts, duration, coverage state, enrichment results, price and property breakdowns, coordinate/map counts, warnings, representative records, and artifact keys.
RUN-SUMMARY.html	HTML	Human-readable presentation of the same core counts and breakdowns for operators and stakeholders.
results-map	HTML	Clustered interactive map of listings with valid coordinates, plus mapped, skipped, and deduplicated marker counts.
RUN-SUMMARY-ERROR	JSON	Best-effort public diagnostic written only when a summary or map cannot be prepared after listing records are already saved.

Use the summary as a run receipt before importing records: confirm saved totals, review warnings, see whether enrichment or deeper coverage was used, and determine whether a requested limit or incomplete-coverage condition affected the result. Property teams can review geographic distribution on the map, data teams can compare counts between recurring runs, and AI agents can use the bounded summary to decide whether to continue, alert, or request human review. These artifacts are not replacement listing rows.

Field Reference

Record envelope

• record_type (string, required): Stable family label; currently property_listing.
• record_id (string, required): Preferred deduplication and upsert key.
• url (string, required): Public Trulia property URL.

Source context

• source_context (object, required): Provenance, seed, page position, language, country, and enrichment context.
• source_context.source_id (string, required): Source identifier.
• source_context.source_domain (string, required): Public source domain.
• source_context.source_url (string, required): Public source URL associated with the record.
• source_context.seed_type / seed_value / search_query (string, optional): Input origin and resolved search text when available.
• source_context.page_number / position (integer, optional): Source page and row position.
• source_context.language / country (string, optional): Source language and country context.
• source_context.enrichment_status (string, required): Final row classification such as enriched or lightweight.
• source_context.enrichment (object, optional): Requested, status, and completion markers for enrichment.

Entity

• entity (object, required): Human-facing property identity.
• entity.title (string, required): Listing title or display address.
• entity.description (string, optional): Public property description, usually available after enrichment.
• entity.external_ids (object, optional): Typed home, property, or related source identifiers.

Listing

• listing (object, optional): Listing identity, mode, status, dates, attribution, flags, and history.
• listing.listing_id (string, optional): Source listing identifier.
• listing.listing_type / listing.listing_status / listing.status_text (string, optional): Source classification and normalized/display status.
• listing.deal_type (string, optional): Transaction mode such as sale, rent, or sold context.
• listing.posted_at / listing.updated_at (string, optional): Source-provided listing and update timestamps or display dates.
• listing.status_flags (object, optional): Boolean status signals such as active sale, active rent, recently sold, foreclosure, or off-market.
• listing.source_attribution (string, optional): Public attribution text.
• listing.external_ids (object, optional): Listing-related identifiers such as an available MLS ID. This does not imply official MLS access.
• listing.history (object, optional): Available price, status, last-sold, or related source history.

Pricing

• pricing (object, optional): Numeric, display, range, fee, and change values.
• pricing.price (number, optional): Primary asking, rental, or sold price value.
• pricing.price_min / pricing.price_max (number, optional): Source price range for range listings.
• pricing.price_text (string, optional): Display-ready price.
• pricing.currency (string, optional): Currency code, such as USD.
• pricing.price_type (string, optional): Source price classification when available.
• pricing.price_details (object, optional): Additional structured price context.
• pricing.callout_price_text / pricing.price_per_area_text (string, optional): Source display labels.
• pricing.price_change (object, optional): Available structured price-change context.
• pricing.fees (object, optional): Available rental, HOA, or other public listing fee details.

Location

• location (object, optional): Address, geography, coordinates, neighborhood, and nearby-school context.
• location.address / location.street_address (string, optional): Full and street-level public address.
• location.city / location.region / location.postal_code / location.country (string, optional): Geographic components.
• location.neighborhood (string, optional): Source neighborhood label.
• location.latitude / location.longitude (number, optional): Coordinate pair used by the map when both values are valid.
• location.nearby_schools (array or object, optional): Source-provided school context; shape can vary by listing.

Property

• property (object, optional): Classification, rooms, measurements, features, amenities, and tax history.
• property.property_id (string, optional): Source property identifier.
• property.property_type (string, optional): Source category such as SINGLE_FAMILY_HOME or MULTI_FAMILY.
• property.bedrooms / property.bedrooms_max (number, optional): Bedroom value or range.
• property.bathrooms / property.bathrooms_max (number, optional): Bathroom value or range; decimals can represent partial baths.
• property.floor_area / property.area_unit (number and string, optional): Interior area and unit.
• property.land_area / property.land_area_unit (number and string, optional): Lot or land area and unit.
• property.amenities (array of strings, optional): Deduplicated, scan-friendly amenity labels.
• property.features (array of objects, optional): Structured source feature records with available group, category, name, and value.
• property.measurement_sources (object, optional): Original measurement display text retained alongside normalized values.
• property.tax_history (object, optional): Available public tax or assessment history.

Media and availability

• media (object, optional): Image URLs and 3D/video signals.
• media.main_image_url (string, optional): Best available preview image URL.
• media.image_urls (array of strings, optional): Deduplicated image gallery URLs.
• media.photos (array of objects, optional): Structured photo records and available size variants.
• media.has_3d_home / media.has_video (boolean, optional): Source media-availability signals.
• media.total_photo_count (integer, optional): Source-reported photo total.
• availability (object, optional): Tour and open-house context.
• availability.tour_available (boolean, optional): Source tour-action signal.
• availability.open_houses (array of objects, optional): Available date and time windows.

Contacts and relationships

• contact_details (object, optional): Deduplicated public contact values available for the listing.
• contact_details.phones (array of strings, optional): Public phone strings.
• contact_details.websites (array of strings, optional): Public agent or agency websites.
• contact_details.contacts (array of objects, optional): Role-aware public contacts.
• relationships (object, optional): Related public real estate parties and attribution.
• relationships.agent / relationships.agency / relationships.developer (object, optional): Available identity and public contact values for related parties.
• relationships.listing_source (object, optional): Listing-source attribution and summary context.

Source-specific attributes

• attributes (object, optional): Meaningful Trulia-specific values that do not belong in a stronger canonical group.
• attributes.tags (array of objects, optional): Deduplicated source tags with available name and level.
• attributes.trulia (object, optional): Compact provider and attribution-visibility context.

Data Model Notes

• Identity: Use record_id for primary matching, deduplication, and upserts. Treat title or address matching as a secondary review method.
• Provenance: Use url, source_context.source_url, and seed fields to trace a row to its public source and input scope.
• Grouped values: Main user-facing values live in entity, listing, pricing, location, property, media, availability, relationships, and contact_details.
• Point-in-time values: Prices, listing status, availability, descriptions, contacts, and media reflect what was publicly visible at collection time.
• Optionality: Null-check or presence-check nested groups; availability varies by property, listing mode, region, source visibility, and enrichment result.
• Repeated runs: Compare rows by record_id and retain Apify run metadata or your own observation timestamp alongside each snapshot.

Data Quality, Guarantees, And Handling

• Structured records: Results are normalized into predictable JSON objects for downstream use.
• Field preservation: Meaningful schema-supported listing and property values are retained in stable public fields or grouped objects when exposed; optional source values may still be absent for a specific record.
• Best-effort extraction: Fields may vary by region, availability, listing type, source visibility, public-page variation, or source-side changes.
• Optional fields: Null-check optional properties in applications, imports, transformations, and dashboards.
• Deduplication: Use record_id as the strongest documented stable key, with url and nested identifiers as supporting context.
• Freshness: Results reflect publicly available data at run time; verify material property facts before operational decisions.
• Repeated runs: Use record_id when syncing into warehouses, CRMs, search indexes, vector stores, or monitoring systems.
• Schema awareness: Depend on documented fields and handle temporarily missing optional fields gracefully.
• Run receipts: Use summary and map artifacts to audit counts, coverage, skipped outcomes, enrichment, and map readiness without treating them as dataset records.

Tips For Best Results

• Start with a limit of 5 or 20 to validate the output before scaling.
• Use one geography and one deal_type per run for cleaner recurring comparisons.
• Match deal_type to the supplied Trulia market URL.
• Enable enrich_data only when richer details justify the additional run work.
• Enable maximize_coverage for broad, high-volume matching searches where deeper collection matters more than shorter exploratory runtime.
• Schedule identical inputs for monitoring and upsert by record_id.
• Save the exact input configuration and run ID used for each recurring workflow.
• Review RUN-SUMMARY, the HTML report, the map, and a small record sample before production imports.

How to Run on Apify

1. Open Trulia Property Scraper in Apify Console.
2. Add one supported Trulia market URL or enter a location, then choose buy, rent, or sold.
3. Configure enrichment, coverage, optional connector delivery, and the maximum result count.
4. Click Start and wait for the run to finish.
5. Open the dataset, summary, report, and map to inspect the result.
6. Download records in JSON, CSV, Excel, or another Apify-supported dataset format.

Agentic And API-First Usage

Trulia Property Scraper can serve as a structured public property-data acquisition step inside a larger automated workflow. Its schema, stable identifier, run receipt, and grouped JSON make it suitable for agents that must define a scope, collect records, verify completion, and hand results to another system.

1. Generate or select an input using only the documented schema.
2. Run the actor manually, on a schedule, or through Apify platform automation.
3. Wait for completion and read the dataset records.
4. Validate required and optional fields against the Field Reference.
5. Read the run summary and map to verify counts, coverage state, limit behavior, skipped outcomes, coordinates, and export readiness.
6. Upsert records into the downstream system using record_id.
7. Trigger market analysis, alerts, BI refreshes, search or vector indexing, enrichment, or human verification.

For agentic use, start with small validation runs and keep prompts grounded in supported inputs. Provide downstream AI steps with the Field Reference, record_id guidance, one representative record, and the bounded run summary. Treat optional fields as nullable instead of asking an agent to infer unavailable values. Store the Apify run ID, input configuration, and export metadata outside the listing record when building an audit trail. When context is limited, systems such as Claude, Codex, internal copilots, and property workflow agents usually need the input schema, field reference, idempotency key, and one example rather than the entire page.

Scheduling & Automation

Scheduling

Automated Data Collection

Schedule recurring runs to maintain point-in-time listing snapshots for dashboards, alerts, and change detection.

• Navigate to Schedules in Apify Console.
• Create a daily, weekly, or custom-cron schedule.
• Configure and save the input parameters.
• Enable notifications for run completion.
• Add webhooks when another system should process completed runs.

Integration Options

• BI dashboards: Monitor asking prices, status, property mix, enrichment, and geographic coverage over time.
• Warehouses and ETL: Load grouped JSON into historical listing tables and transformation pipelines.
• CRM enrichment: Sync public property, listing, agent, agency, and available contact context into operational records.
• Webhooks and alerts: Trigger validation, ingestion, Slack, email, or review workflows after completion.
• Google Sheets or Airtable: Review smaller exports, annotate listing segments, and coordinate lightweight operations.
• Search and vector indexes: Build structured or semantic property discovery for internal applications and agent retrieval.
• MCP connectors: Authorize a supported connector in Apify and select it in mcpConnectors to receive the compact listing-run summary and available dataset, report, and map links.

Export Formats And Downstream Use

Apify datasets can be downloaded or consumed by downstream systems without changing the actor input.

• JSON: Best for APIs, applications, AI agents, nested records, and data pipelines.
• CSV or Excel: Useful for stakeholder review and lightweight analysis; nested groups may require flattening.
• API access: Supports automated ingestion into internal systems and recurring workflows.
• BI and warehouses: Supports reporting, historical analysis, market snapshots, and monitoring.
• Search or vector indexes: Supports structured discovery, semantic search, retrieval workflows, and agent context.

Downstream Pipeline Guide

• Idempotency: Upsert by record_id; do not use title or position as the primary key.
• Null handling: Treat every field outside the required envelope as nullable or potentially absent.
• Type handling: Preserve numeric prices and areas, booleans, arrays, and nested objects in JSON-first systems.
• Flattening: Flatten selected nested paths deliberately for CSV or Excel, and retain the original JSON export for fidelity.
• Partitioning: Store observation date, run ID, input geography, deal_type, and workflow name alongside records.
• Change detection: Compare repeated rows by record_id and selected fields such as pricing.price, listing.listing_status, listing.updated_at, media counts, or availability.
• Quality checks: Monitor saved count, duplicate count, required identifiers, price/status fill rates, enrichment status, and coordinate coverage.
• Human review: Route missing critical values, unusual prices, changed status, or important segments into a review queue.
• Retention: Choose separate retention periods for raw JSON snapshots and normalized warehouse tables based on operational needs.

Performance And Coverage Expectations

Example metrics from a saved validation artifact are provided as evidence, not as a runtime guarantee.

Run type	Example scope	Listings	Duration	Coverage notes
Enriched URL validation	Los Angeles buy-market URL, limit: 5, enrichment enabled	5	3 seconds	5 enriched, 5 mapped, 0 duplicates, 0 warnings; deeper coverage disabled

Execution time varies with result volume, target availability, response size, enrichment depth, coordinate and map preparation, and how much information each listing exposes. Highly focused runs can finish faster, while broad discovery, maximize_coverage, enrichment, or detail-rich records may take longer. Coverage mode prioritizes deeper retrieval within the selected criteria and can trade speed for more matching records. The saved example should not be extrapolated to larger markets or treated as a completeness promise.

Limitations

• Results depend on what Trulia publicly exposes at run time.
• Optional descriptions, history, schools, media, contacts, agents, agencies, fees, and availability may be absent on sparse records.
• URL mode supports market-search pages, not individual property-detail pages as seeds.
• Broad or coverage-aware searches can take longer and may still be constrained by the requested limit or public result availability.
• Regional, listing-status, visibility, and source-side presentation differences can change fields and visible results.
• Prices, descriptions, contacts, status, and availability are point-in-time public signals and should be independently verified before operational decisions.
• The actor provides structured public real estate information, not legal, financial, investment, valuation, appraisal, ownership, or brokerage advice.

Troubleshooting

• No results returned: Check location spelling, ensure the URL is a supported Trulia market-search page, match deal_type to the intended market, and confirm public matches exist.
• Fewer results than expected: Raise limit, consider maximize_coverage for broad markets, or verify that enough matching public listings are available.
• Some fields are empty: Optional fields depend on the listing and whether enrichment exposes additional public detail.
• Duplicate-looking records: Compare record_id; similar addresses or titles can still represent distinct source records.
• Run takes longer than expected: Lower limit, disable enrichment for validation, or split broad work into separate geographic or deal-type segments.
• Output changed: Compare the current sample with the Field Reference and include a small redacted record when requesting support.
• Downstream import failed: Check JSON validity, nullable fields, nested objects, arrays, and whether the destination expects flattened columns.

FAQ

What data does this actor collect?

Public Trulia property listings with identity, pricing, status, location, property characteristics, media, availability, attribution, and optional enriched history or contact context.

Which search inputs are supported?

Use one supported Trulia market-search URL or a location-based search, plus deal_type set to buy, rent, or sold. The public input does not expose separate price, bedroom, bathroom, property-type, or keyword filters.

Why did I receive fewer results than my limit?

limit is a maximum, not a guaranteed count. The selected public market may expose fewer matching listings, and source availability can change.

What does maximize_coverage do?

It collects deeper within the same selected geography, listing mode, and criteria when a broad search exposes more matching listings than are normally visible. It does not relax the scope and still respects limit.

What does enrichment add?

When available, enrichment adds fuller descriptions, galleries, structured features, history, attribution, and public agent, agency, or contact context. Individual rows can remain lightweight if those details are unavailable.

Where are the summary and map?

Open the run's Output or key-value-store links for RUN-SUMMARY, RUN-SUMMARY.html, and results-map.

How should I choose a first limit?

Start with 5 or 20 records, inspect the schema and optional-field fill, then increase the limit for production work.

How do I avoid duplicates across runs?

Upsert by record_id and retain a run date or observation timestamp in your downstream system.

Can I schedule and automate the actor?

Yes. Use Apify schedules, webhooks, API access, exports, or supported MCP summary delivery.

Can I export CSV, Excel, or JSON?

Yes. JSON preserves the nested contract best; CSV and Excel are useful when nested groups are flattened deliberately.

Does the actor provide private, official MLS, ownership, or appraisal data?

No. It collects publicly visible Trulia listing information and does not provide private records, verified ownership, official MLS access, appraisal-grade valuation, or professional advice.

Compliance & Ethics

Responsible Data Collection

This actor collects publicly available property listing information from Trulia for legitimate business purposes, including real estate research and market analysis, operational listing monitoring, and data enrichment or reporting workflows. Users are responsible for ensuring that their collection, storage, and use comply with applicable requirements. This section is informational and not legal advice.

Best Practices

• Use collected data in accordance with applicable laws, regulations, and Trulia's terms.
• Respect individual privacy and personal information.
• Use data responsibly and avoid disruptive or excessive collection.
• Do not use this actor for spamming, harassment, discrimination, unlawful housing practices, or other harmful purposes.
• Follow relevant data protection, fair housing, consumer protection, and sector-specific requirements.
• Review retention, access-control, and data-sharing policies before operationalizing the dataset.

Support

Use the Issues tab on the actor page when you need help. Include the redacted input, Apify run ID, expected behavior, actual behavior, and an optional small output sample. For export or pipeline problems, also include the destination system or export format and the field or nested group that failed. No service-level response commitment is implied.