iProperty Scraper | Malaysia Real Estate

Slug: fatihtahta/iproperty-scraper

Overview

iProperty Scraper collects structured Malaysia real estate listing records, including property details, prices, locations, layout attributes, media, agent details, agency information, project metadata, nearby places, and available transaction history. iProperty Malaysia is a major public real estate marketplace for Malaysian sale and rental inventory, making its listing data valuable for market research, monitoring, lead qualification, and operational reporting. The actor turns matching public listings into repeatable JSON outputs that can be used by analytics systems, enrichment workflows, CRM processes, and data pipelines. It is designed for recurring data acquisition with consistent input controls, predictable output records, and practical limits for validation or larger collection runs.

Why Use This Actor

• Market research and analytics teams: build structured extraction workflows for pricing, availability, location coverage, floor area, property category, and transaction-history analysis.
• Product and content teams: normalize public listing data for company catalogs, market pages, content audits, and property-comparison experiences.
• Developers and data engineering teams: feed downstream systems with repeatable JSON records suitable for ETL, warehouses, enrichment pipelines, and monitoring jobs.
• Lead generation and enrichment teams: collect public agent, agency, project, and contact fields when available for outreach-ready real estate intelligence.
• Monitoring and competitive tracking teams: run scheduled collection for market intelligence, category movement, recently posted listings, and operational reporting.

Common Use Cases

• Market intelligence: monitor Malaysian sale and rental supply by location, price range, property type, floor area, and listing freshness.
• Lead generation: build targeted prospect lists from public property listings, agents, agencies, and available contact fields.
• Competitive monitoring: track changes in pricing, inventory, verified-agent listings, media richness, and category coverage over time.
• Catalog and directory building: populate private property, project, agent, or agency databases with structured public records.
• Data enrichment: add current public property attributes, media, location, nearby-place, and transaction-history fields to CRM, BI, or analytics datasets.
• Recurring reporting: schedule periodic runs for dashboards, alerts, valuation workflows, and historical trend analysis.

Quick Start

1. Choose the listing mode with deal_type: buy for sale listings or rent for rental listings.
2. Enter one Malaysian location, such as a city, state, township, neighborhood, or market name.
3. Select a property_type and add optional filters such as price, area, bedroom count, amenities, listing freshness, or verified-agent status.
4. Set a small limit for the first validation run, such as 10 or 25 records.
5. Run the actor in Apify Console and inspect the first dataset records.
6. Increase the limit, adjust filters, or schedule recurring runs once the output matches your workflow.

Input Parameters

The actor accepts one required Malaysian location plus optional listing mode, category, filter, enrichment, contact, transaction-history, and limit controls.

Parameter	Type	Description	Default
deal_type	string	Listing segment to collect. Allowed values: buy for sale listings, rent for rental listings.	buy
location	string	Required Malaysian location, such as a city, state, township, neighborhood, or market name.	–
property_type	string	Property category filter. Allowed values: all_residential, all_commercial, residential_bungalow_villa, residential_apartment_condo, residential_semi_detached_house, residential_terrace_link_house, residential_residential_land, commercial_commercial_property, commercial_industrial_property, commercial_agricultural_land, commercial_other.	all_residential
min_price	integer	Minimum asking price in Malaysian ringgit (MYR).	–
max_price	integer	Maximum asking price in Malaysian ringgit (MYR).	–
min_price_psf	integer	Minimum price per square foot in MYR per sqft.	–
max_price_psf	integer	Maximum price per square foot in MYR per sqft.	–
min_floor_area	integer	Minimum built-up floor area in square feet.	–
max_floor_area	integer	Maximum built-up floor area in square feet.	–
min_land_area	integer	Minimum land area in square feet.	–
max_land_area	integer	Maximum land area in square feet.	–
min_building_year	integer	Earliest building completion year. Allowed range: 1977 to 2032.	–
max_building_year	integer	Latest building completion year. Allowed range: 1977 to 2032.	–
bedroom	array of strings	Bedroom counts to include. Allowed values: studio, 1, 2, 3, 4, 5+.	–
bathroom	array of strings	Bathroom counts to include. Allowed values: 1, 2, 3, 4, 5+.	–
parking	array of strings	Parking-space counts to include. Allowed values: 1, 2, 3, 4, 5+.	–
unit_type	string	Unit layout or lot-position filter. Allowed values: duplex, triplex, studio, corner_lot, intermediate, end_lot, loft, dual_key, soho, penthouse.	–
floor_level	array of strings	Floor-level categories to include. Allowed values: ground, low, mid, high, penthouse.	–
furnishing	array of strings	Residential furnishing statuses to include. Allowed values: unfurnished, partially_furnished, fully_furnished.	–
unit_amenities	array of strings	Residential in-unit amenities to include. Allowed values: aircon, balcony, bath_tub, corner_unit, helpers_room, private_pool, renovated, terrace.	–
building_amenities	array of strings	Residential building amenities to include. Allowed values: gym, parking, swimming_pool, tennis_court.	–
keyword	string	Short keyword for a property, area, feature, or project term.	–
publication_date	string	Listing freshness window. Allowed values: 3_days, 7_days, 14_days, 30_days.	–
with_video	boolean	When enabled, collect only listings with video or virtual-tour media.	false
tenure	array of strings	Sale-listing tenure filter. Allowed values: freehold, malay_reserved_land, private_lease_scheme, leasehold, bumi_lot.	–
verified_agent	boolean	When enabled, collect only listings marked as coming from verified agents.	false
enrich_data	boolean	Adds richer listing details when available, including descriptions, listing dates, furnishing, facilities, nearby places, and extra media.	true
get_contact	boolean	Adds available agent phone and contact fields to saved records.	true
get_transaction_history	boolean	Adds available sale and rental transaction-history fields to saved records.	true
limit	integer	Maximum number of listings to save. Minimum: 1. Leave empty to collect available matching listings until the run naturally finishes.	–

Choosing Inputs

Use location as the primary scope control, then choose deal_type and property_type to define the market segment. Narrow filters such as price, price per square foot, area, bedroom count, amenities, tenure, listing freshness, and verified-agent status produce more targeted datasets; broader filters improve discovery and help identify available supply. For validation, start with a small limit, review the dataset shape, then increase the limit for recurring or broader collection. Use enrich_data, get_contact, and get_transaction_history when your workflow needs richer records; disable them when standard listing fields are sufficient and faster runs are preferred.

Example Inputs

Example: Recently Posted Rental Monitoring

{
  "deal_type": "rent",
  "location": "Bukit Bintang",
  "property_type": "residential_apartment_condo",
  "publication_date": "7_days",
  "verified_agent": true,
  "limit": 25
}

Example: Sale Market Price Band

{
  "deal_type": "buy",
  "location": "Mont Kiara",
  "property_type": "all_residential",
  "min_price": 800000,
  "max_price": 1800000,
  "bedroom": ["2", "3"],
  "limit": 50
}

Example: Commercial Discovery With Enrichment

{
  "deal_type": "buy",
  "location": "Johor Bahru",
  "property_type": "all_commercial",
  "min_floor_area": 1000,
  "enrich_data": true,
  "get_contact": true,
  "get_transaction_history": false,
  "limit": 30
}

Output

Output destination

The actor writes normalized JSON records to the default Apify dataset. Each normal dataset item is a property_listing record for an iProperty.com.my listing.

Record envelope and stable identifiers

Use record_id as the primary idempotency key. It is a string and is based on the iProperty listing ID when the source provides one. The same value is also preserved as listing.listing_id, entity.id, entity.external_ids.listing_id, and source_context.external_ids.listing_id for source traceability.

Primary display and audit fields are easy to find:

• entity.title, entity.url, entity.headline, and entity.description describe the listing.
• source_context.source_url, source_context.listing_url, source_context.seed_id, source_context.seed_type, source_context.seed_value, and source_context.page_index describe where the record came from.
• listing, pricing, location, property, availability, media, contact_details, relationships, and attributes hold the normalized real estate data.

Example: property listing

{
  "record_type": "property_listing",
  "record_id": "108113501",
  "entity": {
    "id": "108113501",
    "url": "https://www.iproperty.com.my/property/kl-city-centre/megan-avenue/sale-108113501/",
    "title": "Megan Avenue, KLCC, KL City Centre",
    "external_ids": {
      "listing_id": "108113501"
    }
  },
  "listing": {
    "listing_id": "108113501",
    "listing_type": {
      "label": "For Sale",
      "deal_type": "buy"
    },
    "deal_type": "buy",
    "status": {
      "code": "ACT"
    }
  },
  "pricing": {
    "price": 929760,
    "price_text": "RM 929,760",
    "currency": "MYR",
    "price_per_area": "RM 520.00 psf"
  },
  "location": {
    "address": "Jalan Mayang Sari, KLCC, KL City Centre, Kuala Lumpur"
  },
  "property": {
    "property_type": "Office",
    "bathrooms": {
      "label": "2"
    },
    "floor_area": {
      "formatted": "1,788 sqft"
    }
  },
  "media": {
    "images": [
      {
        "url": "https://img.iproperty.com.my/example.jpeg"
      }
    ],
    "image_count": 1
  },
  "contact_details": {
    "name": "Jocelyn Shu"
  },
  "relationships": {
    "agent": {
      "id": 1024341,
      "name": "Jocelyn Shu",
      "license": "REN 06297",
      "profile_url": "https://www.iproperty.com.my/property-agent/jocelyn-shu-1024341/"
    },
    "agency": {
      "id": 50602,
      "name": "The Roof Realty - Kuchai Lama"
    }
  },
  "source_context": {
    "source_name": "iProperty Malaysia",
    "source_domain": "www.iproperty.com.my",
    "source_url": "https://www.iproperty.com.my/_next/data/build/property-for-sale.json?page=1",
    "listing_url": "https://www.iproperty.com.my/property/kl-city-centre/megan-avenue/sale-108113501/",
    "seed_id": "51c163c5f04353dcccce",
    "seed_type": "search",
    "seed_value": "klcc",
    "page_index": 1,
    "external_ids": {
      "listing_id": "108113501"
    }
  }
}

Property listing record

record_type (string, required): Stable row discriminator. Normal listing rows use property_listing. record_id (string, required): Stable listing idempotency key.

entity.id (string, optional): Source listing ID. entity.url (string, optional): Public iProperty listing URL. entity.title (string, optional): Listing, project, or location title. entity.headline (string, optional): Short listing headline. entity.description (string, optional): Public listing description when detail enrichment exposes one. entity.external_ids.listing_id (string, optional): Source listing ID preserved as a source-specific external identifier.

listing.listing_id (string, optional): Source listing ID. listing.deal_type (string, optional): Normalized deal type, such as buy or rent. listing.listing_type.label / listing.listing_type.code (string, optional): Human-readable listing segment and source segment code. listing.status.code (string, optional): Source listing status code. listing.status.source_status_code (string, optional): Secondary source status code when it differs from the main listing status code. listing.status.is_verified (boolean, optional): Whether the listing is marked verified. listing.status.is_official_listing (boolean, optional): Whether the listing is marked as an official listing. listing.status.verification (object, optional): Additional source verification details. listing.dates (object, optional): Posted, recency, available, first-posted, last-posted, updated, content-updated, expiry, and source date details.

pricing.price (number/string, optional): Listing price amount when safely available. pricing.price_text (string, optional): Human-readable source price text. pricing.currency (string, optional): Currency code, typically MYR. pricing.price_per_area (string/object, optional): Price per unit area as source text or a structured range. pricing.price_min / pricing.price_max (number, optional): Minimum and maximum price values when represented as a range. pricing.price_type (string/object, optional): Price-type label or structured source value. pricing.maintenance_fee / pricing.maintenance_fee_per_unit (number, optional): Maintenance fee details when available.

location.address (string, optional): Full public address text. location.postal_code (string, optional): Postal code as a string. location.street (string, optional): Street name. location.coordinates.latitude / location.coordinates.longitude (number, optional): Listing coordinates when available.

property.property_type (string, optional): Property type label. property.bedrooms / property.bathrooms (object, optional): Bedroom and bathroom labels plus numeric counts when available. property.floor_area / property.land_area (object, optional): Area value, display text, and unit. property.furnishing (object, optional): Furnishing label and source code. property.unit_type / property.unit_type_details (string, optional): Unit type and lot-position detail. property.floor_level (string, optional): Floor level label. property.rental_type (string, optional): Rental scope, such as entire unit. property.tenancy (object, optional): Tenancy status details. property.is_bumi_lot (boolean, optional): Whether the listing is marked as Bumi lot. property.project_id (string/integer, optional): Source project identifier. property.developer (string, optional): Developer or associated name when available. property.is_new_project / property.is_premium_project_listing (boolean, optional): Project-related listing flags.

availability.is_available_now (boolean, optional): Whether the listing is marked available now. availability.available_from (string, optional): Availability date when exposed by the source. availability.tenancy (object, optional): Occupancy or tenancy context when available.

media.cover[] / media.floor_plans[] / media.images[] (array, optional): Image objects with public media URLs and source media metadata such as IDs, captions, type, MIME type, cover flags, and sort order when available. media.has_floor_plans / media.has_stream (boolean, optional): Source media flags. media.thumbnail_url (string, optional): Thumbnail image URL. media.image_count (integer, optional): Number of image objects represented by the record.

contact_details.name (string, optional): Primary contact display name. contact_details.contacts[] (array, optional): Contact values collected from listing, agent, organization, or contact enrichment data.

relationships.agent (object, optional): Embedded agent details, including ID, name, license, profile URL, verification flags, awards, and agent-specific attributes. relationships.agency (object, optional): Embedded agency details. relationships.organization (object, optional): Embedded organization details. relationships.project (object, optional): Embedded project details, including IDs, type, tenure, facilities, category, property subtype, developer, floors, total units, and completion fields.

attributes.nearby_places (object, optional): Nearby MRT, school, mall, and other point-of-interest groups when detail enrichment exposes them. attributes.transaction_history.sales / attributes.transaction_history.rentals (object, optional): Sale and rental transaction-history counts, items, filter level, insight type, and source flags. attributes.source_specific (object, optional): Remaining meaningful source-specific values such as external IDs, cross-listing type, co-broke flags, offering flags, title type, land title type, and other source fields that do not fit a stronger normalized group.

source_context.source_name (string, optional): Source marketplace name. source_context.source_domain (string, optional): Source domain. source_context.source_url (string, optional): Source URL associated with the listing collection context. source_context.listing_url (string, optional): Public listing page URL. source_context.detail_url (string, optional): Detail-enrichment source URL when available. source_context.seed_id / source_context.seed_type / source_context.seed_value (string, optional): Run seed context for tracing the record back to the input scope. source_context.page_index (integer, optional): Source result page index. source_context.external_ids.listing_id (string, optional): Source listing ID. source_context.publishing_source (object, optional): Source publishing metadata. source_context.contact_url (string, optional): Contact-source URL when contact enrichment is enabled. source_context.transaction_history_urls.sales / source_context.transaction_history_urls.rentals (string, optional): Transaction-history source URLs when transaction history is enabled.

Data Quality, Guarantees, And Handling

• Structured records: results are normalized into predictable JSON objects for downstream use.
• Best-effort extraction: fields may vary by region, session, availability, listing type, or public interface changes.
• Optional fields: null-check optional fields in downstream code, especially contact details, media, nearby places, transaction history, and source-provided flags.
• Deduplication: use record_id as the primary stable key, with entity.url or source_context.listing_url as a secondary key when needed.
• Freshness: results reflect the publicly available data at run time.
• Repeated runs: use the recommended idempotency key when syncing data into warehouses, CRMs, or search indexes.

Tips For Best Results

• Start with a small limit to validate the output shape before scaling up.
• Use one location, deal_type, and property_type combination per run when you need cleaner segmentation.
• Leave optional filters empty when the goal is broad discovery.
• Add price, area, room, amenity, freshness, and verified-agent filters gradually to understand how each field changes coverage.
• Enable enrich_data, get_contact, and get_transaction_history when richer records are required for analysis or outreach.
• Schedule recurring runs for monitoring workflows instead of relying on manual one-off collection.
• Use id for deduplication when storing results over time.

How to Run on Apify

1. Open the actor in Apify Console.
2. Configure the available input fields for the target location, listing mode, category, filters, and enrichment options.
3. Set the maximum number of outputs to collect with limit.
4. Click Start and wait for the run to finish.
5. Open the dataset and download results in JSON, CSV, Excel, or other supported formats.

Scheduling & Automation

Scheduling

Automated Data Collection

You can schedule runs to keep Malaysian real estate datasets fresh for reporting, monitoring, and enrichment workflows. Recurring runs are useful when you need consistent snapshots for the same location, category, or filter set.

• Navigate to Schedules in Apify Console
• Create a new schedule: daily, weekly, or custom cron
• Configure input parameters
• Enable notifications for run completion
• Add webhooks for automated processing

Integration Options

• CRM enrichment: sync public listing, agent, agency, contact, and property attributes into account or lead records.
• BI dashboards: monitor pricing, availability, location coverage, property categories, and transaction-history trends.
• Google Sheets or Airtable: review smaller listing datasets, qualification lists, and monitoring samples with non-technical teams.
• Webhooks: trigger validation, notification, or ingestion workflows after each completed run.
• Data enrichment pipelines: merge listing records with valuation, territory, or customer datasets.
• Warehouses and ETL jobs: load normalized JSON records into historical reporting and analytics systems.

Export Formats And Downstream Use

Apify datasets can be exported or consumed by downstream systems for analytics, operations, and automation.

• JSON: for APIs, applications, and data pipelines
• CSV or Excel: for spreadsheet workflows and manual review
• API access: for automated ingestion into business systems
• BI and warehouses: for reporting, dashboards, and historical analysis

Performance

Estimated run times:

• Small runs (< 1,000 outputs): ~3-5 minutes
• Medium runs (1,000-5,000 outputs): ~5-15 minutes
• Large runs (5,000+ outputs): ~15-30 minutes

Execution time varies based on filters, result volume, and how much information is returned per record. Highly filtered runs can finish faster, while broad discovery or detail-rich records may take longer.

Limitations

• Availability depends on what https://www.iproperty.com.my publicly exposes at run time.
• Some optional fields may be missing on sparse listings, older listings, commercial records, or listings without public enrichment data.
• Very broad searches may take longer or require higher limit values to collect the desired coverage.
• Target-side changes can affect field availability, naming, or formatting.
• Regional, listing-type, account, or availability differences may change visible results.
• Contact and transaction-history fields are included only when available for the listing and enabled in the input.

Troubleshooting

• No results returned: check filters, location or category spelling, and whether iProperty has matching public records for the selected scope.
• Fewer results than expected: broaden filters, raise limit, or verify that the target contains enough matching records.
• Some fields are empty: optional fields depend on what each public listing provides.
• Run takes longer than expected: reduce scope, lower limit for validation, or split broad collection into smaller location or category segments.
• Output changed: compare the current output with the field reference and include a small sample if support is needed.

FAQ

What data does this actor collect?

It collects public iProperty Malaysia real estate listing data, including property details, prices, locations, layout fields, media, agent and agency details, project information, nearby places, and available transaction history.

Can I filter by location, category, date, price, or other criteria?

Yes. The schema supports location, deal_type, property_type, price range, price per square foot, floor area, land area, building year, room counts, parking, unit type, floor level, furnishing, amenities, keyword, publication date, tenure, video availability, and verified-agent status.

Why did I receive fewer results than my limit?

The selected filters may match fewer public listings than the requested limit. Broaden the location, category, price range, room filters, or freshness window to increase coverage.

Can I schedule recurring runs?

Yes. Use Apify schedules to run the actor daily, weekly, or on a custom cron pattern for recurring market monitoring and reporting.

How do I avoid duplicates across runs?

Use record_id as the primary idempotency key. If your destination system is URL-oriented, store entity.url or source_context.listing_url as a secondary deduplication key.

Can I export the data to CSV, Excel, or JSON?

Yes. Apify datasets support exports in JSON, CSV, Excel, and other supported formats from the dataset view.

Does this actor collect private data?

The actor is intended to collect publicly available listing information from iProperty Malaysia. Users are responsible for using any personal or contact information lawfully and responsibly.

What should I include when reporting an issue?

Include the input used, the run ID, expected versus actual behavior, and a small output sample when helpful. Redact any sensitive private notes before sharing.

Compliance & Ethics

Responsible Data Collection

This actor collects publicly available Malaysia real estate listing information from https://www.iproperty.com.my for legitimate business purposes, including:

• Real estate research and market analysis
• Lead qualification and enrichment
• Inventory monitoring and operational reporting

This section is informational and not legal advice. Users are responsible for ensuring their use of collected data complies with applicable laws, regulations, and platform terms.

Best Practices

• Use collected data in accordance with applicable laws, regulations, and the target site’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, or other harmful purposes
• Follow relevant data protection requirements where applicable, including GDPR and CCPA

Support

For help, use the actor page or Issues section. Include the input used, with sensitive values redacted if necessary, the run ID, expected versus actual behavior, and a small output sample when it helps explain the issue.