Makani Search API v.2 Documentation
for Dubai Real Estate Data & Geoanalytics
Published on
Overview of Dubai Property & Geoanalytics API
Makani Search API v.2 provides web developers with powerful tools to build web applications, AI agents, and other digital products. The API delivers a wide range of geodata and analytical insights for researching the Dubai property market, combining risk indexes with geospatial data on local infrastructure.
Search by main geodata & analytics by request Makani Number
The Search Buildings by Makani Index endpoint provides access to the search function in the Makani Index dataset using a Makani Number, Parcel ID, Property ID, Project ID, Project Name (en), Makani Address (string), or point coordinates.
The search grants access to registered buildings within the administrative borders, centroid and entrance point on map of the Emirate of Dubai, UAE.
It is useful for working with address data from real estate catalogs (proptech listings for rent and/or sale of properties).
The Search Off-Plan Properties endpoint provides access to Off-Plan development projects using Project Name (en), Community Name (en), Community Number, or point coordinates.
The search returns the latest real estate developments with Off-Plan status, located within a specified geospatial point in the Emirate of Dubai, UAE.
This endpoint is useful for working with real estate broker catalogs, enabling location-based discovery and analysis of new Off-Plan projects.
Returns the nearest Off-Plan project based on the provided geographic coordinates.
Total data entries: 143
Source data: Dubai's real estate catalogs
Priority: Main priority
Updated:
The Search Land & Building Ownership endpoint provides access to land plots and building ownership data using a Makani Number, Parcel ID, address (string), Project Name (en), or point coordinates.
The search returns registered land and building records within the administrative boundaries of the Emirate of Dubai, UAE.
This endpoint is designed for working with cadastral and ownership data, enabling integration with real estate systems, address validation, and property-level analytics.
Source data: Dubai Municipality & Dubai Land Department
Priority: Main priority
Updated:
Ownership distribution:
Freehold: 36 012 (19.35%)
Commonhold: 18 505 (9.95%)
Usufruct: 27 709 (14.89%)
Musataha: 5 608 (3.01%)
Registered: 106 752 (57.37%)
Subdivided: 4 623 (2.48%)
The Search Flood Risk by Building endpoint provides access to flood risk indicators for buildings and land plots using a Makani Number, Parcel ID, or point coordinates.
The endpoint returns a building-level flood risk assessment, enabling evaluation of environmental exposure for a specific property or location in the Emirate of Dubai, UAE.
This API is designed for risk management, real estate analytics, and geospatial applications, allowing integration of flood risk data into property listings, valuation models, and urban analytics platforms.
Source data: Dubai Municipality & Dubai Land Department
Flood risk analytics based on satellite imagery (Google Earth)
Cartography: OpenStreetMap (OSM)
Priority: Main priority
Updated:
Data format: GeoJSON FeatureCollection (Polygon geometry)
The Search Transport Accessibility by Building endpoint provides access to transport accessibility indicators for buildings and land plots using a Makani Number, Parcel ID, or point coordinates.
The endpoint returns a building-level transport accessibility score, enabling evaluation of connectivity to public transport infrastructure, including metro stations, bus routes, and road networks within the Emirate of Dubai, UAE.
This API is designed for real estate analytics, urban planning, and location intelligence applications, allowing integration of transport accessibility data into property listings, valuation models, and investment decision systems.
A color-coded accessibility indicator is included, supporting visualization in maps, dashboards, and proptech platforms.
Source data: Dubai Municipality & Dubai Land Department
Analytics based on Google Earth satellite views
Cartography: OSM
Priority: Main priority
Updated:
Data format: GeoJSON FeatureCollection (GeometryCollection)
The Search Traffic Accidents endpoint provides access to a building-level analytical view derived from the Road & Traffic Accident Data dataset, using Makani Number or filtering by traffic risk level.
The endpoint returns a geoanalytical summary of traffic accident intensity for a specific building or a group of buildings in Dubai. Each result is represented as a GeoJSON Feature with Point geometry (Makani location) and a simplified analytical property set.
This endpoint is designed for evaluating road safety conditions and accident exposure at a specific location, and can be used in logistics planning, route optimization, and risk-aware navigation systems.
Supported query parameters:
makani_number — returns a single object (exact building match)
traffic_accident_rate_level — filters buildings by accident risk level (min / low / medium / high / max) page — pagination for filtered results (limit = 100, fixed)
Typical use cases include identifying high-risk locations, optimizing delivery routes, and integrating safety indicators into map-based or proptech platforms.
Risk classification: min → max (normalized distribution)
Pagination: 100 results per page (fixed)
Source dataset: Road & Traffic Accident Data
Source data: Dubai Land Department, OSM, Smart Indexes
Updated:
The Search Building Entrances endpoint provides access to the search function in the Building Entrances dataset using a Makani Number, Parcel ID, address (string), Project Name (en), or point coordinates.
Common API rule: 1 request = 2 records (the building’s administrative borders as a Polygon and the building’s entrance as a Point).
Using this endpoint, a user can obtain the geo point of a selected destination when reviewing properties of interest in Dubai.
The Search Property Analytics endpoint provides access to the Property Analytics dataset using Makani Number, Parcel ID, Property ID, Community Number, or Master Project Name.
The endpoint returns a consolidated geoanalytical summary for a selected property, combining spatial geometry with ownership structure and core building attributes. Each result is represented as a GeoJSON Feature with GeometryCollection (Polygon + Point).
This endpoint is designed for property-level analysis, allowing users to evaluate ownership composition, building characteristics, and spatial context in a single response.
Supported query parameters:
makani — search by Makani Number (primary or entrance)
parcel_id — search by land plot identifier
property_id — search by unique property identifier
community_number — filter by community
master_project_en — filter by master project name
is_free_hold, is_leasehold, is_commonhold, is_usufruct, is_musataha — filter by ownership type
page — pagination (limit = 100, fixed)
Typical use cases include property comparison, ownership structure analysis, urban analytics, and integration into proptech platforms and GIS systems.
By Makani Number (primary_makani / makani_numbers)
Data format: GeoJSON FeatureCollection (GeometryCollection: Polygon + Point)
Endpoints of geodata and analytics
Main geodata & analytics by request Makani Number
The Makani Index endpoint is a reference dataset that links internal Dubai Land Department (DLD) identifiers with Makani Numbers, enabling interoperability between the cadastral system and all DLD datasets related to registered real estate in the Emirate of Dubai.
The Makani Index serves as the core reference layer that integrates Dubai Land Department (DLD) property data with parcel (plot) identifiers, master and development projects, building geometries, and Makani Numbers.
For proptech projects, the Makani Index provides a unified linkage to:
Dubai Land Department (DLD) property records
Parcel (plot) identifiers
Master and development project hierarchies
Building geometries
Makani Numbers
By Makani Number
Method: GET
Endpoint:/api/v2/data/makani-index
Total data entries: 186 070
Source data: Dubai Land Department
Priority: Main priority
Updated:
Data format: GeoJSON FeatureCollection
The Lands & Buildings Ownership endpoint is a comprehensive dataset that includes the Makani Number, Parcel ID, property address, and technical characteristics of land plots and buildings, along with ownership type and foreign ownership eligibility in special designated areas (freehold).
The HEX color scheme is derived from the ownership type or a combination of ownership types recorded for each property based on registered real estate transactions.
By Makani Number
Method: GET
Endpoint:/api/v2/lands-and-buildings-ownership
Total data entries: 186 070
Source data: Dubai Land Department
Priority: Main priority
Updated:
Data format: GeoJSON FeatureCollection (Polygon / MultiPolygon)
The Flood Risk by Building endpoint provides a flood risk index for each registered building in the Emirate of Dubai, allowing the assessment of meteorological threat levels in the event of an extreme rainfall scenario.
The quantitative (%) and qualitative risk levels (min, low, medium, high, and max) were derived from geoanalytical processing of the April 2024 extreme rainfall event, using satellite imagery and computer analysis of post-event conditions.
By Makani Number
Method: GET
Endpoint:/api/v2/flood-risk-by-building
Total data entries: 186 070
Source data: Dubai Municipality & Dubai Land Department
Analytics based on Google Earth satellite views
Cartography: OSM
Priority: Main priority
Updated:
Data format: GeoJSON FeatureCollection (Polygon geometry)
The Transport Accessibility Score by Building endpoint provides a calculated index for all registered buildings in the Emirate of Dubai, measuring their level of transport connectivity and logistical convenience.
The score is relevant for combining property price indexes with transport accessibility and other risk factors, helping investors evaluate the suitability of specific buildings for commercial, residential, or mixed-use purposes.
Data format: GeoJSON FeatureCollection (GeometryCollection: Polygon + Point)
The Traffic Accident Rate by Building endpoint is based on geo-analysis of officially registered traffic incidents occurring within a defined radius of each building’s entry point, using the coordinates of its Makani Number.
The dataset is relevant for evaluating traffic-related risk factors when comparing property price indexes, helping investors assess the safety and transport environment around a specific building in Dubai.
Data format: GeoJSON FeatureCollection (Point geometry)
The Building Entrances endpoint provides the exact geo-coordinates of officially registered building entry points in Dubai, linked to their Makani Number.
The Building Entrances endpoint is based on official geodata of Dubai Land Department to geolocation of each entry point of building in Dubai based on its coordinates by Makani Number GET request in Makani Search API.
Its actual endpoint to delivery in GIS or other application the geo point of building's entrance by Makani Number.
By Makani Number
Method: GET
Endpoint:/api/v2/data/building-entrances
Total data entries: 104 383
Source data: Dubai Land Department
Priority: Main priority
Updated:
The Property Analytics endpoint provides a structured geoanalytical profile for each real-estate object based on Makani Number, Parcel ID, or Property ID.
The dataset includes core property characteristics, ownership model, and spatial geometry in GeoJSON format, enabling standardized integration into mapping, analytics, and proptech platforms.
It is used to retrieve a clean and unified representation of any building or land plot in Dubai via a single GET request.
By Makani Number
Method: GET
Endpoint:/api/v2/data/property-analytics
Total data entries: 186 070
Source data: Dubai Land Department
Priority: Main priority
Updated:
Data format: GeoJSON FeatureCollection (GeometryCollection: Polygon + Point)
The Warehouse Complexes endpoint contains detailed characteristics of registered warehouse facilities in Dubai, including infrastructure class (ABCD model), warehouse subtype, and other technical attributes.
The dataset also includes a HEX-based color scheme used for geoanalytical cartography and visualisation of warehouse segmentation.
This information is useful for analysing and selecting warehouses for rent or purchase, helping companies choose logistics-ready locations that align with operational needs and business expansion strategy in Dubai.
By Makani Number
Method: GET
Endpoint:/api/v2/data/warehouse-complexes
Total data entries: 21 554
Source data: Dubai Land Department
Priority: Main priority
Updated:
The Office Buildings endpoint contains registered buildings in Dubai that include office units, together with their assigned building class based on the ABCD model. The classification reflects both internal infrastructure and the availability of nearby services essential for employee comfort and workplace efficiency.
Using a GET request by Makani Number, the API provides an instant infrastructure profile of any commercial office building, supporting data-driven decisions in leasing, investment, and corporate relocation strategies.
By Makani Number
Method: GET
Endpoint:/api/v2/data/office-buildings
Total data entries: 4 072
Source data: Dubai Land Department
Priority: Main priority
Updated:
The Retail endpoint provides a structured dataset of retail geo-objects in Dubai, including shopping malls, community shopping centres, and commercial retail facilities.
The dataset includes classification by retail category, subtype, and child subtype, enabling detailed segmentation of retail formats for location analysis and business planning.
Each object is linked to Makani Numbers, parcel identifiers, and property records, allowing integration with other Makani-based datasets and geoanalytical workflows.
By Makani Number
Method: GET
Endpoint:/api/v2/data/retail
Total data entries: 42
Source data: Dubai Land Department
Priority: Main priority
Updated:
Data format: GeoJSON FeatureCollection
The EV Charging Stations endpoint provides a GeoJSON dataset of electric vehicle charging infrastructure across Dubai, including station locations, connector types, and capacity indicators.
Each charging station is spatially linked to nearby buildings through the Makani addressing system, enabling precise distance calculations and integration with building-level analytics.
The dataset follows a lightweight GeoJSON structure (FeatureCollection) with simplified properties and point geometry, making it suitable for real-time applications, GIS platforms, and mobility services.
It is designed for integration into navigation systems, EV routing, smart mobility platforms, and real estate analytics, allowing users to evaluate charging accessibility for any building or location in Dubai.
Notice:
Each object represents a charging station with Point geometry.
The makani_addressing field links stations to nearby buildings.
The dataset includes technical attributes such as connector types and total number of connectors.
By Makani Number
Method: GET
Endpoint:/api/v2/data/ev-charging-stations
Total data entries: 338
Source data: Dubai Land Department
Priority: Main priority
Updated:
The Freezones of Dubai endpoint provides detailed information on all officially registered free zones in the Emirate, including their business setup conditions, geographic boundaries, and transport accessibility.
The dataset enables companies to compare free zones based on regulatory model, location, and infrastructure, supporting informed decision-making when choosing a jurisdiction for launching or relocating a business in Dubai.
Each free zone is linked to a Makani Number, allowing precise geo-mapping and route planning (e.g., from airport to destination) to optimise logistics for business founders, consultants, and corporate teams.
By Makani Number
Method: GET
Endpoint:/api/v2/data/freezones-of-dubai
Total data entries: 36
Source data: Ministry of Economy & Tourism, UAE
Priority: Main priority
Updated:
The Airports endpoint provides detailed information on all public airports in Dubai, including Makani Number, registered parking zones, nearby EV charging stations, and other essential facilities.
The dataset supports navigation, mobility planning, tourism services, aviation analytics, and business logistics by enabling direct data lookup via a GET request for a selected airport.
It is useful for both corporate travel management and consumer applications that require accurate geospatial data about airport infrastructure in Dubai.
By Makani Number
Method: GET
Endpoint:/api/v2/data/airports
Total data entries: 4
Source data: Dubai Land Department
Priority: Main priority
Updated:
Geodata & analytics by request of Dubai's geo objects
The Emirates of UAE endpoint provides an enriched GeoJSON dataset for all seven Emirates of the United Arab Emirates, including administrative boundaries, population statistics, and extended spatial analytics.
In addition to basic reference information, the dataset introduces a comparison between official emirate areas and actual polygon-based areas derived from Dubai Land Department (DLD) geometry. This allows users to identify discrepancies between declared and computed territorial sizes.
New analytical fields such as water_square_sq_km, diff_square_sq_km, and diff_percent highlight the difference between land-only geometries and official total areas (including water bodies).
The dataset is designed for advanced geoanalytical use cases, including regional benchmarking, data normalization, and validation of spatial datasets at the emirate level.
It is commonly used in mapping systems, analytics dashboards, and national-scale applications that require a reliable base layer with extended spatial metadata for the UAE.
By Emirates ID
Method: GET
Endpoint:/api/v2/data/emirates-of-uae
Total data entries: 7
Source data: Dubai Land Department
Priority: Supported priority
Updated:
The Sectors of Dubai endpoint provides basic geodata for the administrative sectors officially defined within the governance structure of the Emirate of Dubai.
The dataset is used for high-level spatial analysis, territorial segmentation, and geo-referencing of urban planning layers, logistics, and strategic development projects.
It can be requested via the Makani Search API to retrieve sector-level metadata for mapping, analytics dashboards, and government or enterprise applications.
Method: GET
Endpoint:/api/v2/data/sectors-of-dubai
Total data entries: 9
Source data: Dubai Land Department
Priority: Supported priority
Updated:
The Communities of Dubai endpoint provides official geodata for each community in the Emirate, defined by administrative boundaries.
The dataset includes core characteristics such as land area, registered population, and population density, based on the Population Bulletin released by the Dubai Statistics Center.
It is used for demographic analysis, spatial planning, investment research, and geoanalytics applications requiring community-level territorial segmentation.
Method: GET
Endpoint:/api/v2/data/communities-of-dubai
Total data entries: 222
Source data: Dubai Land Department, Dubai Statistics Center
Priority: Supported priority
Updated:
The Flood Risk by Community endpoint provides flood risk indicators derived for every community in Dubai, based on the effects of the extreme rainfall event that occurred in April 2024.
The dataset includes both quantitative and categorical flood risk levels, enabling comparison of meteorological exposure across different communities.
It is used in risk-based geoanalytics, investment screening, insurance models, and location assessment for real-estate development in Dubai.
Method: GET
Endpoint:/api/v2/data/flood-risk-by-community
Total data entries: 222
Source data: Dubai Land Department
Priority: Supported priority
Updated:
The Traffic Accident Rate by Community endpoint provides an enriched GeoJSON dataset with traffic safety indicators aggregated at the community level across Dubai.
The dataset is based on officially registered traffic incidents, spatially processed
and assigned to community polygons. Each community is represented as a geoanalytical unit
with normalized accident metrics, percentile ranking, and categorical risk levels.
In addition to the accident rate, the dataset includes detailed analytical attributes such as
accident_count, accident_weight, and time boundaries of incidents
(first_accident, last_accident), enabling deeper temporal and risk analysis.
The registered_traffic_incidents field is now represented as a structured JSON array, providing direct access to incident periods and quantities without additional parsing.
The dataset is designed for integration into GIS systems, dashboards, and analytical models,
supporting use cases such as logistics optimization, urban planning, insurance risk scoring,
and investment due diligence.
It provides a standardized framework for benchmarking traffic safety across all Dubai communities, enabling data-driven decision-making for transport infrastructure, mobility, and real estate analytics.
The Annual Population by Community endpoint provides yearly population statistics for each community in Dubai, reflecting demographic change and residential growth across the city.
The dataset enables analysis of population growth rates, density dynamics, and long-term settlement patterns, supporting urban planning, investment assessment, and socio-economic research.
Population values are sourced from officially published demographic data for completed calendar years.
The Peak Activity Population endpoint provides official statistics on the number of active individuals present in Dubai during peak hours.
The dataset includes values for the resident population, workers commuting from outside the Emirate, temporary residents, and the total volume of active individuals recorded per year.
This information is relevant for analysing population dynamics during peak activity periods, supporting decision-making in transport planning, retail strategy, infrastructure development, and smart-city analytics.
Method: GET
Endpoint:/api/v2/data/peak-activity-population
Total data entries: 10
Source data: Dubai Statistics Center
Priority: Supported priority
Updated:
The Average Crime Rate by Community endpoint provides a calculated crime-risk index for each community in Dubai, based on officially recorded statistics and geoanalytical modelling.
The dataset is structured as a GeoJSON FeatureCollection, where each community is represented as a spatial object with analytical attributes including crime rate, population density, and percentile-based rankings.
The crime index is expressed as a normalized numerical value, accompanied by qualitative levels (min, low, medium, high, max) and HEX-based color segmentation.
In the updated version, the dataset includes a forecasting model with historical baseline values and projection coefficients, enabling dynamic estimation of crime trends over time.
This dataset is used for risk management, real estate analytics, insurance scoring, urban planning, and AI-driven location recommendations.
average_crime_rate_rank — filter by crime level (min / low / medium / high / max)
page — pagination (limit = 20)
Total data entries: 222
Source data: Dubai Police, Dubai Statistics Center
Priority: Supported priority
Updated:
The Major + Secondary Roads endpoint provides a detailed geospatial representation of Dubai’s road network, including both major highways and secondary roads, based on an OpenStreetMap (OSM) data model enriched with Makani integration.
The dataset follows the standard GeoJSON FeatureCollection format, where each road segment is represented as an individual LineString object. This structure enables precise routing, segment-level analytics, and seamless integration into GIS and navigation systems.
Unlike the previous version, the dataset is no longer based on GIS shapefile exports. It now adopts a modern OSM-style road model, including attributes such as road class, number of lanes, speed limits, direction (oneway), and surface type.
An important enhancement is the integration with Makani addressing, allowing each road segment to be linked to nearby buildings. This enables advanced geoanalytics such as last-meter navigation, accessibility scoring, and proximity-based routing.
The dataset is designed for use in:
navigation and routing systems,
logistics and delivery optimization,
transport infrastructure analysis,
smart-city and mobility applications,
AI-driven geospatial analytics.
Method: GET
Endpoint:/api/v2/data/roads
Query Parameters
road_class — motorway / primary / secondary
road_name — road name (e.g. Sheikh Mohammed Bin Zayed Road)
road_ref — highway reference (e.g. E311)
lanes — number of lanes
maxspeed — speed limit
oneway — true / false
page — pagination (limit = 20)
Total data entries: 117,585 road features
Cartography: OpenStreetMap (OSM)
Makani integration: Dubai Land Department (DLD)
Priority: Main priority
Updated:
The Road Segments endpoint provides a granular representation of Dubai’s road network, where each road is divided into short segments for routing and graph-based analysis.
The dataset is derived from the Major + Secondary Roads layer and transformed into a graph structure, where each segment represents a directional edge between two geographic points.
Each segment is defined by a start and end coordinate, enabling precise modelling of:
routing algorithms and shortest-path calculations,
logistics optimisation and delivery planning,
traffic simulation and mobility modelling,
AI-driven geospatial analysis.
The endpoint follows the GeoJSON FeatureCollection format, where each segment is returned as a LineString geometry generated from start and end coordinates.
Method: GET
Endpoint:/api/v2/data/road-segments
Query Parameters
road_id — filter by road (OSM way id)
road_class — motorway / primary / secondary
maxspeed — speed limit (km/h)
oneway — 1 (one-way) / 0 (two-way)
page — pagination (limit = 20)
Total data entries: 957,949 road segments
Source data: OpenStreetMap (OSM)
Derived from: Major + Secondary Roads dataset
Data format: CSV → GeoJSON transformation
Priority: Main priority
Updated:
The Road & Traffic Accident Data endpoint provides a unified geospatial dataset of Dubai’s road network, enriched with traffic incident statistics, Makani-based spatial indicators, and administrative community attribution.
The dataset follows a GeoJSON FeatureCollection structure, where each road segment is represented as a LineString geometry and includes both infrastructure attributes (such as lanes, road class, and surface) and analytical metrics.
Each road contains aggregated traffic incident data for a defined time period, along with a calculated traffic risk score and normalized risk level classification (min, low, medium, high, max). This enables consistent comparison of road safety across the entire network.
The dataset is spatially linked to Dubai’s official administrative boundaries through community_number (numeric identifier) and community_name, allowing reliable filtering and aggregation without ambiguity in naming.
An additional enrichment layer integrates Makani addressing, providing proximity-based indicators such as makani_count and makani_density, which reflect building intensity and urban activity around each road segment.
This dataset is designed for:
transport safety analysis and risk modelling,
logistics and route optimisation with risk-aware routing,
urban planning and infrastructure assessment,
AI-driven geospatial analytics and decision systems.
Risk classification: min → max (normalized distribution)
Pagination: 100 results per page (fixed)
Source dataset: Road & Traffic Accident Data
Source data: Dubai Land Department, OSM, Smart Indexes
Updated:
The Metro (2025–2030) endpoint includes geodata for both operational and planned metro infrastructure in Dubai, reflecting the city’s transport development strategy through 2030.
The dataset contains metro line geometry, station locations, and line color codes, enabling transport accessibility analysis, urban planning, and mobility research.
It is relevant for geoanalytics platforms, real-estate valuation models, logistics routing, and smart-city applications that require multimodal transport data.
Method: GET
Endpoint:/api/v2/data/metro-(2025-2030)
Total data entries: 724
Source data: Dubai Land Department
Priority: Supported priority
Updated:
The Master Projects endpoint provides a geospatial dataset of Dubai’s master-planned developments, including project boundaries, developer attribution, and detailed descriptive metadata.
Each object is represented as a GeoJSON Feature with Polygon geometry, describing the spatial extent of a master project and its relationship to administrative communities.
The dataset includes enriched attributes such as developer_id, master_developer_id, development status, construction_year, and infrastructure_class, enabling structured analysis of project maturity and quality.
Additional semantic fields like segment (e.g. comfort, premium), short_description, and long_description provide contextual understanding of each project’s positioning and characteristics.
This dataset is designed for:
real estate and investment analysis,
developer and project benchmarking,
urban planning and land-use analysis,
AI-driven geoanalytics and property intelligence systems.
Descriptions: short and long project descriptions included
Source data: Dubai Land Department, Smart Indexes
Priority: Supported priority
Updated:
The Flood Risk by Master Project endpoint provides a geospatial dataset of Dubai’s master-planned developments enriched with flood risk indicators derived from real meteorological events.
Each master project is represented as a GeoJSON Feature with Polygon geometry, allowing precise spatial analysis of flood exposure within project boundaries.
The dataset combines project-level metadata (developer, segment, infrastructure class, status) with quantitative flood metrics, enabling integrated risk assessment at the master project level.
Flood risk indicators are calculated based on the extreme rainfall event of April 2024 and include:
flood_intensity_percent — share of flooded area within the project,
flood_risk_level — classified risk level (Low / Medium / High).
Additional spatial indicators such as distance_from_coast_km and flood_square_sq_km provide deeper context for environmental and coastal risk analysis.
The Traffic Accident Rate by Master Project endpoint provides a geoanalytical dataset of traffic safety indicators calculated within the official boundaries of Dubai’s master development projects.
The dataset is structured as a GeoJSON FeatureCollection, where each master project is represented as a Polygon geometry enriched with traffic accident statistics, normalized risk scores, and temporal incident data.
Each project includes aggregated accident metrics such as traffic_accident_rate, accident_density, and traffic_risk_score, allowing consistent comparison of road safety conditions across different development zones.
The dataset also provides time-bounded incident summaries and raw accident counts, enabling both snapshot analysis and temporal risk evaluation.
This endpoint is designed for:
investment and risk assessment for real estate projects,
insurance modelling and underwriting,
urban mobility and safety analysis,
AI-driven geospatial analytics and decision systems.
The Dubai 2040 Urban Zones endpoint contains official geodata based on the administrative
boundaries of future urban development zones defined in the Dubai 2040 government strategy.
This dataset is relevant for geo-analysis of expanding urban areas in the context of long-term
development, investment planning, and strategic urban modelling.
A GET request can be performed using the English title of an urban zone, allowing the API output
to be easily integrated into analytical platforms, GIS systems, or user applications.
Method: GET
Endpoint:/api/v2/data/dubai-2040-urban-zones
Total data entries: 5
Source data: Dubai 2040 Urban Master Plan
Priority: Supported priority
Updated:
Request Rules & General API Logic
Rule
Description
Request Types
API supports two modes:
Single Makani Number lookup — returns data for one unique Makani Number.
Bulk dataset export — returns up to 100 records per request with pagination.
Authentication
HTTPS only. Two methods:
Session-based (Web UI) — login at /login with API key.
Bearer Token (Programmatic) — header Authorization: Bearer <API_KEY>. Access granted after dataset selection and payment.
JWT Tokens
Optional: exchange API key for JWT at /api/v2/auth/token. Tokens are long-lived and include user metadata (email, allowed datasets, rate limits).
Pagination
For bulk exports use query params limit (1–100, default 100) and offset (≥0, default 0).
Response includes has_next boolean.
Per-second/minute/hour limits reset after the interval. Daily resets at 00:00 UTC.
Monthly resets on the 1st day of each month.
Unused Quota
Remaining requests do not roll over to the next billing period. Each period starts with fresh quota.
Methods & Format
GET only. Responses: Content-Type: application/json; charset=utf-8.
Base URLs:
/api/makani-search-api/
/api/v2/data/{dataset_key} (bulk export)
Dataset Access Control
Users can only access datasets selected during subscription. Non-selected datasets: 403 Forbidden.
Response Structure
Standard JSON:
{"success": true, "pagination": {...}, "geometry": {...}, "data": [...]}.
Geometry is returned in GeoJSON when available.
Error Codes
400 Bad Request, 401 Unauthorized, 403 Forbidden,
404 Not Found, 422 Unprocessable Entity,
429 Too Many Requests, 500 Internal Server Error,
503 Service Unavailable.
Versioning
Via query ?v=1 or header Accept: application/vnd.smartindexes.v2+json.
Current version: v2.
Coordinates
WGS84 (EPSG:4326), order: [longitude, latitude]. Geometry via ST_AsGeoJSON.
Retry & Rate Limiting
Safe to retry idempotent GETs. Use exponential backoff on 429/503.
Respect Retry-After header when present.
CORS
Allowed origins configured per client. Controlled via
Access-Control-Allow-Origin, Access-Control-Allow-Methods,
Access-Control-Allow-Headers.
Client Integration
Store API key in environment variables (e.g., API_KEY=sk_live_...).
Include header: Authorization: Bearer <API_KEY>.
Works with Python, Node.js, Java, etc.
Use cases
The following use cases demonstrate the correct way to connect AI Agent queries to Makani Search API v.2.
Each case shows:
which key the model must extract (Makani Number - ex. makani_number, Development Project Name - ex. project_name_en, community - community_name_en, coordinates in entrance_point, centroid, (multi-)polygon, etc.),
which API endpoint to call,
what the result looks like,
how the result supports the user scenario in the AI Agent.
Use Case #1. Property Analytics by Project Name
Task: provide an analytics summary for the user's request by development project title.
Step-by-step instruction
The user asks about a specific development project (for example: "Show me analytics for Centrium Tower").
The AI Agent extracts the project name from the query and normalizes it to project_name_en (e.g. Centrium Tower).
The AI Agent calls the endpoint:
GET /api/v2/search/property-analytics?project_name_en=Centrium%20Tower
The AI Agent uses this response to generate a detailed explanation of the project’s characteristics for the user.
Use Case #2. Transport Accessibility by Project Name
Task: provide transport accessibility information for the user's request by development project title.
Step-by-step instruction
The user asks about transport accessibility for a specific project (for example: "How accessible is Jumeirah Gate by public transport?").
The AI Agent extracts the project name and normalizes it to project_name_en (e.g. Jumeirah Gate).
The AI Agent calls the endpoint:
GET /api/v2/search/transport-accessibility-score-by-building?project_name_en=Jumeirah%20Gate
Makani Search API v.2 returns:
the transport accessibility score and related indicators,
geometry of the project.
The AI Agent explains the project’s accessibility (metro, roads, overall score) to the user based on the returned values.
Use Case #3. Risk Management Summary for a Master Project
Task: provide a risk management summary for a master development project based on the user's request by master project title.
Step-by-step instruction
The user asks about the safety or risk profile for a large master project (for example: "What is the risk profile of Dubai Marina master project?").
The AI Agent extracts and normalizes the master project name.
The AI Agent first retrieves the list of master projects to confirm the exact record:
GET /api/v2/data/master-projects?limit=100&offset=0
The AI Agent identifies the correct master project in the response and obtains its identifier or exact name.
The AI Agent then calls the related risk endpoints:
GET /api/v2/data/traffic-accident-rate-by-master-project?limit=100&offset=0
GET /api/v2/data/flood-risk-by-master-project?limit=100&offset=0
GET /api/v2/data/average-crime-rate-by-community?limit=100&offset=0
The AI Agent filters all three datasets by the selected master project and its related communities.
The AI Agent aggregates flood, traffic accident and crime indicators into a single "risk management summary" and presents it to the user.
Use Case #4. Top 10 Safest Locations by Flood Risk
Task: provide a Top 10 ranking of the safest Dubai locations (master projects) by flood risk.
Step-by-step instruction
The user asks for the safest places to buy property in terms of flood risk (for example: "Show me the 10 safest master projects for flood risk").
The AI Agent calls the endpoint:
GET /api/v2/data/flood-risk-by-master-project?limit=100&offset=0
Makani Search API v.2 returns flood risk metrics for all master projects, including total_flood_risk.
The AI Agent sorts all master projects by total_flood_risk (ascending — from safest to highest risk).
The AI Agent selects and displays the top 10 master projects with the lowest risk, explaining why they are considered safer.
Use Case #5. Building Entrance by Makani Number
Task: provide the building entrance geo point based on the user's request by Makani Number.
Step-by-step instruction
The user provides or refers to a Makani Number (for example: "Show me the entrance for Makani 2658886097").
The AI Agent extracts the Makani Number and validates its format.
The AI Agent calls:
GET /api/v2/search/makani-index?makani_number=2658886097
Makani Search API v.2 returns:
entrance point geometry (Point),
building polygon geometry (Polygon),
address and technical attributes for this entrance.
The AI Agent uses this information to show the exact entrance location and related details to the user.
Use Case #6. Comparing Development Projects by Title
Task: provide an A / B / C comparison for the user's request by development project titles.
Step-by-step instruction
The user wants to compare two or more development projects (for example: "Compare Centrium Tower and Bayz").
The AI Agent extracts all mentioned project names and normalizes them to project_name_en.
For each project, the AI Agent calls:
GET /api/v2/search/property-analytics?project_name_en=Centrium%20Tower GET /api/v2/search/property-analytics?project_name_en=Bayz
Makani Search API v.2 returns, for each project:
geometry (location on the map),
registered traffic incidents,
average crime rate,
transport accessibility score,
other property analytics.
The AI Agent builds a comparison table or narrative, highlighting differences in safety, accessibility, and location between the selected projects.
Use Case #7. Land Ownership Type by Makani Number
Task: provide the land ownership type, or combination of types, for the user's request.
Step-by-step instruction
The user asks about ownership type for a specific property (for example: "Is this property freehold or leasehold? Makani 4082585948").
The AI Agent extracts and validates the Makani Number.
The AI Agent calls:
GET /api/v2/search/lands-and-buildings-ownership?makani_number=4082585948
Makani Search API v.2 returns ownership flags:
is_free_hold,
is_usufruct,
is_commonhold,
is_musataha,
is_leasehold,
is_subdivided,
and other related fields (values "0"/"1" or boolean).
The AI Agent interprets these flags and explains the ownership type in natural language to the user inside the AI Agent.
Use Case #8. Retail Malls Nearby a Building
Task: provide retail (malls) nearby the actual point of a building's entrance.
Step-by-step instruction
The user asks about nearby retail infrastructure (for example: "What shopping malls are near this building?").
The AI Agent first determines the building location:
either by Makani Number, or
by coordinates [lon, lat] provided in the query.
If coordinates are used, the AI Agent calls:
GET /api/v2/search/makani-index?coordinate=55.443806%2C25.248494
Makani Search API v.2 returns the building entrance geometry (Point) and building properties.
The AI Agent then requests the retail layer:
GET /api/v2/data/retail?limit=100&offset=0
The AI Agent performs a spatial search:
for each retail object (Point geometry),
calculates the distance to the building entrance,
filters objects within the selected radius (e.g. 100m, 250m, 500m).
The AI Agent lists nearby malls and retail objects, using attributes such as:
retail_object_title,
retail_category, retail_subcategory,
makani_address, area_name_en,
and other fields from the retail dataset.
Use Case #9. EV Charging Stations Nearby a Building
Task: provide EV charging stations nearby the actual point of a building's entrance.
Step-by-step instruction
The user asks about EV charging infrastructure close to a building (for example: "Are there EV chargers near this building?").
The AI Agent identifies the building’s coordinates via /search/makani-index
(by Makani Number or direct coordinates), same as in Use Case #8:
GET /api/v2/search/makani-index?coordinate=55.443806%2C25.248494
Makani Search API v.2 returns building entrance geometry (Point or Polygon).
The AI Agent then calls the EV charging stations dataset:
GET /api/v2/data/ev-charging-stations?limit=100&offset=0
From the EV data, the AI Agent uses:
charger location geometry (Point),
location_name, location_address,
makani_number,
total_nb_of_connectors, connector_type, etc.
The AI Agent calculates the distance from the building to each EV charger and filters by the required radius.
The AI Agent presents a list of nearby EV charging stations with details useful for the user (distance, connector types, address).
Use Case #10. Community Summary (Population and Density)
Task: provide a community summary based on the user's request.
Step-by-step instruction
The user asks for a summary of a community (for example: "Give me population and density for Al Sabkha community").
The AI Agent extracts and normalizes the community name (or community number, if provided).
The AI Agent calls:
GET /api/v2/data/communities-of-dubai?limit=100&offset=0
The AI Agent finds the record matching the target community and obtains its identifiers (community name/number).
The AI Agent then calls:
GET /api/v2/data/annual-population-by-community?limit=100&offset=0
The AI Agent filters this dataset by the same community and retrieves:
historical and current population,
historical and current density,
total_square_sq_km and related metrics.
The AI Agent builds a "community summary": population trend, current density, area size — and presents it to the user in a human-readable form.
Frequently Asked Questionsfor users of Makani Search API
Makani Search API v2 is a core geoanalytics API for Dubai real estate, providing access to 186K buildings, ownership data, flood risk, transport accessibility, and traffic accident analytics. It is designed for proptech platforms, AI agents, and GIS applications.
The key difference is full address coverage. Makani Index coverage increased from 51% to 100% through advanced geo-matching of OSM building polygons, DLD entrance points, community boundaries, and master projects. While property_id may not always ensure perfect building-level matching, Makani Number coverage is now complete, ensuring maximum geospatial accuracy across Dubai as of 2026.
Makani Search API v2 is a core intelligence layer for proptech in Dubai. It allows startups and companies to avoid complex data aggregation and directly use real estate analytics in property listings, dashboards, maps, and AI systems. It also supports logistics optimization with road network data and traffic accident risk analysis for safer routing.
Makani Search API provides full building coverage (186K objects), official DLD-based data, building-level risk scoring (flood, transport, traffic), and AI-ready geoanalytics. It is one of the most comprehensive APIs for building proptech and real estate intelligence products in Dubai.
Makani Search API v2 combines multiple authoritative data sources including Dubai Land Department (DLD), OpenStreetMap (OSM), Dubai Municipality datasets, and Smart Indexes geoanalytical processing. This multi-layer integration ensures high accuracy of building geometries, address matching, and risk analytics.
Real-estate search, geospatial analytics, PropTech app integration (maps, AI assistants, dashboards), logistics and last-mile delivery, and urban planning.
It provides structured geo-data, risk metrics, and a full hierarchy from communities to buildings, enabling accurate training of AI agents, recommendation engines, and smart-ranking systems.
It enables risk-based ranking of assets by analyzing crime rates, transport accessibility, flood exposure, accident risks, and community development quality, making investment decisions more informed and defensible.
It links geoanalytics to each Dubai building via its unique Makani Number, providing structured data on flood risk, crime levels, transport accessibility, accident rates, nearby infrastructure, and more—all presented through color-coded signals.
Yes. As of November 13, Smart Indexes provides 28 use cases — one for each API endpoint. To view them, open the section of a specific endpoint inside the Makani Search API v1 Documentation. For example, in the “Makani Index” section, use cases are available under the tab named “Use Cases”. Follow the source link at the bottom of this answer.
