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Evaluate and compare markets at a glance by retrieving aggregated metrics for MSAs and submarkets. This endpoint returns market-level summaries including rent performance, inventory levels, demographics, and transaction data. This workflow is designed for investors, asset managers, and analysts who need to:
  • Screen markets for acquisition or expansion opportunities
  • Compare rent growth, occupancy, and supply trends across metros
  • Analyze demographic drivers like population growth and income levels
  • Benchmark sales activity and cap rates across markets
  • Build market rankings based on custom criteria

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Prerequisites

  • Your API key (Bearer token)
  • Account ID
  • Geo boundary IDs for the markets you want to compare (find these using Search Geo Boundaries)
If you need an API token, see Getting started → “Get an API token”.

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1) Find markets to compare

First, search for the markets you want to analyze. Use the geo boundaries search to find MSAs or submarkets by name. 
curl -s -X POST \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "account_id": <account_id>,
    "query": "*",
    "geo_type": ["MSA"],
    "limit": 50
  }' \
  "https://data.apartmentiq.io/apartmentiq/api/v1/geo_boundaries/search"
Save the id values from the results—these are the geo boundary IDs you’ll use in the next step.
Use query: "*" with geo_type: ["MSA"] to retrieve all available metros, or search by name (e.g., “Austin”) for specific markets.

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2) Compare market metrics

Pass the geo boundary IDs to retrieve aggregated metrics for each market. 
curl -s -X POST \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "account_id": <account_id>,
    "filters": {
      "geo_boundary_id": ["94121", "94012", "94216"],
      "geo_type": "MSA"
    },
    "sort_by": "population_total",
    "sort_order": "desc",
    "limit": 50
  }' \
  "https://data.apartmentiq.io/apartmentiq/api/v1/explore_markets"
The response includes comprehensive metrics for each market:
  • Performance: Average rent, NER, rent PSF, concessions, occupancy, exposure, days on market
  • Rent by bedroom: Rent and NER broken down by studio, 1-bed, 2-bed, and 3-bed
  • Growth: Year-over-year and 90-day rent/NER growth rates, plus forecasted growth
  • Inventory: Total units, property count, new supply, under construction, delivered units
  • Demographics: Population by age group, growth rates, median income, employment
  • Sales: Transaction count, volume, price per unit, cap rates

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3) Filter by property characteristics

Use property_filters to control which properties contribute to the market aggregations. This lets you compare markets for a specific property profile. 
curl -s -X POST \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "account_id": <account_id>,
    "filters": {
      "geo_type": "MSA",
      "geo_states": ["TX", "FL", "AZ"]
    },
    "property_filters": {
      "year_built_gte": 2015,
      "total_units_gte": 200,
      "property_class": ["A"]
    },
    "sort_by": "rent_growth_rate_yoy_avg",
    "sort_order": "desc",
    "limit": 25
  }' \
  "https://data.apartmentiq.io/apartmentiq/api/v1/explore_markets"
Property filters narrow the properties included in each market’s calculations. For example, filtering to Class A properties built since 2015 shows metrics based only on that subset.

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4) Screen markets by performance

Use market-level filters to find markets that meet specific criteria. 
curl -s -X POST \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "account_id": <account_id>,
    "filters": {
      "geo_type": "MSA",
      "rent_growth_rate_yoy_avg_gte": 0.02,
      "population_total_gte": 1000000,
      "advertised_occupancy_percent_avg_t30_gte": 0.92
    },
    "sort_by": "forecasted_rent_growth_rate_yoy",
    "sort_order": "desc",
    "limit": 20
  }' \
  "https://data.apartmentiq.io/apartmentiq/api/v1/explore_markets"
This example finds large metros with positive rent growth and strong occupancy, sorted by forecasted growth.

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Filter options

Market-level filters (filters):
  • Geography: geo_boundary_id[], geo_type, geo_states[]
  • Performance: rent_avg_t30_gte|lte, ner_avg_t30_gte|lte, advertised_occupancy_percent_avg_t30_gte|lte
  • Growth: rent_growth_rate_yoy_avg_gte|lte
  • Scale: population_total_gte|lte, property_count_total_gte|lte
Property-level filters (property_filters):
  • Physical: year_built_gte|lte, total_units_gte|lte, number_of_stories_gte|lte
  • Classification: property_class[], property_type[], construction_state[]
  • Data recency: collected_at_gte|lte

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Example analyses to try

  • Market screening: “Find Sun Belt MSAs with 2%+ rent growth, 1M+ population, and 93%+ occupancy.”
  • New supply impact: “Compare markets by new_supply_unit_count_total relative to existing_unit_count_total to assess supply pressure.”
  • Demographic targeting: “Rank markets by population_growth_last_5_years_20_29 to find metros attracting young renters.”
  • Investment benchmarking: “Sort by sale_cap_rate_avg_t365 to compare transaction yields across markets.”
  • Submarket deep-dive: “Switch geo_type to BespokeSubmarket and filter to a single MSA to compare submarkets within a metro.”

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Errors and rate limits

  • 400 for invalid or missing parameters
  • 401 if authorization is missing or invalid
  • 403 if you lack permissions
  • 429 if you exceed the rate limit
  • See Errors and Rate limits