available-date-ranges
ChatGPTCatalog of foot-traffic periods available from the REI dataset. Each record contains a date_type (month, quarter, year, ytd, ttm, or week) and its exact start_date and end_date. These values correspond to the date_range_type, start_date, and end_date fields accepted by date-based tools. Matching date types from an earlier year can serve as comparison periods for year-over-year metrics. Useful for understanding coverage, selecting a supported reporting window, and aligning current and comparison periods.
demography
ChatGPTReturns demographic characteristics associated with a property's selected geography, including household income, age, sex, race and ethnicity, education, household composition, and other available variables. Results separate chart distributions from summary values such as medians. demography_area supports the property's data-derived trade area and standardized geographies including state, ZIP code, MSA, county, radius, and drive time. Trade-area analysis reflects the observed visitor catchment; fixed radius or drive-time areas support consistent spatial comparisons. category, subcategory, and variable names are drawn from demography-categories. exclude_charts selects summary-oriented output, while distribution requests return bucket values, counts, and percentages. Date fields correspond to periods listed by available-date-ranges.
demography-categories
ChatGPTReturns the demographic taxonomy available for a property: category names, nested subcategory names, and variable names, with distribution flags and display ordering. Examples include income, age and sex, race and ethnicity, education, household composition, and related census-derived characteristics. The returned name strings are accepted by the category, subcategory, and variables fields in demography; internal IDs are metadata rather than demography inputs. is_distribution distinguishes bucketed charts from single-value summaries such as median household income or median age. skip_defaults controls whether default variables are included. Availability can vary by property and dataset coverage.
dwell-time
ChatGPTDescribes visit duration at a property for a selected period. average is mean dwell time in minutes, while chart contains visit counts by duration bucket from under 30 minutes through 120 minutes or more. Dwell estimates are directional and are especially informative in comparisons across periods or equivalent peer sets. Longer average stays combined with substantial traffic can be consistent with destination-oriented, entertainment, specialty, or sit-down dining activity. Shorter stays can be consistent with convenience, quick-service, pharmacy, pickup, or pass-through activity. ranking with rank_type=dwell-time provides peer context, and visit-frequency adds repeat-visit behavior. Property ALIs come from search; supported dates come from available-date-ranges.
favorite-chains
ChatGPTLists chains cross-shopped by a property's visitors, with shared-customer overlap, traffic averages, frequency, nearest distance, store size, location count, and industry classifications. Results can be ordered by overlap, distance, visits, visitors, or frequency. The data supports co-tenancy research, customer-affinity analysis, competitive context, merchandising studies, and comparison of nearby versus broader chain relationships. avg_shared_customers is the percentage of subject-property visitors who also visit a chain; total_shared_customers is the observed count. nearest_distance_max limits chains by proximity, and industry metadata supports category-specific analysis. Chain IDs in the result are compatible with benchmark_id in shared-customers-retailer.
performers-list
ChatGPTReturns a paginated, filterable list of properties with visits, visitors, frequency, rate of change, size, transaction fields, location details, and geographic ranks. It is designed for market-level discovery and side-by-side screening rather than a single-property summary. Properties can be filtered by state, ZIP code, MSA, coordinate radius, visit volume, growth rate, frequency, portfolio, and ranking settings. sort_by supports rate-change, visits, visitors, frequency, square-feets, and mtn-value. Comparison dates populate rate_change as percent change from the comparison period. Common analyses include market momentum, high-volume locations, audience reach, repeat visitation, property-size cohorts, and geographic candidate sets. Returned ALIs are compatible with property-specific tools.
performers-tenants
ChatGPTReturns top and bottom tenant-chain groups for a selected metric, with average visits, visitors, frequency, comparison-period change, store counts, traffic coverage, square footage, and rank distributions across geographic scopes. The tool supports tenant-category benchmarking, submarket and national chain performance studies, anchor and tenant-mix context, brand momentum analysis, and candidate-list enrichment. Bounding-box coordinates define a geographic market; company and industry filters define chain cohorts; minimum and maximum fields constrain traffic, frequency, change, coverage, or location counts. rate_change is a percentage comparison when comparison dates are supplied. locations and locations_with_traffic distinguish footprint size from measured coverage.
ranking
ChatGPTRanks one or more properties within a defined peer set by visits, visits per square foot, frequency, dwell time, or unique visitors. Each ALI maps to position, total peer count, and data coverage. Position 1 is the highest result for the selected metric; total identifies the comparison-set size and coverage indicates how much of that set has traffic data. rank_geography defines local or broad comparison areas such as MSA, state, county, ZIP code, radius, or national. rank_scope defines a general, industry, or company peer set, and tenant-mix or store-size filters can refine comparability. Common analyses include volume standing, space productivity, repeat visitation, engagement duration, and audience reach. ALIs come from search and supported periods come from available-date-ranges.
search
ChatGPTSearches companies and individual properties by center name, retailer, landlord, address, city, or related text. Company results contain IDs representing retailer or landlord footprints; property results contain opaque ALI identifiers for shopping centers, tenants, and standalone stores, along with address, traffic availability, coordinates, and basic period metrics. Typical use cases include resolving a named center or address to a property ALI, distinguishing a tenant location from its host center, locating stores within a market, and resolving a chain to the company ID consumed by retailer-footprint analyses. location_limit controls result volume, while the southwest and northeast coordinates constrain ambiguous searches geographically. Date fields accept supported period values listed by available-date-ranges.
shared-customers
ChatGPTMeasures visitor overlap between a primary property and one or more specific comparison properties. shared_customers is the overlap percentage, total_shared_customers is the corresponding observed count, and total_population is the population used as the denominator. The results support direct cross-shopping analysis, competitive-audience comparison, co-tenancy context, sibling-location overlap, and customer-base similarity across candidate or existing locations. Higher overlap indicates more shared visitation, but interpretation depends on property type, distance, market density, and the selected trade-area settings. Both primary and comparison ALIs are property identifiers returned by search. trade_area_contribution, contribution_type, and breakdown_type define the visitor populations being compared.
shared-customers-retailer
ChatGPTMeasures overlap between a subject property's visitors and a retailer's broader company footprint. The output provides an overlap percentage, observed shared-customer count, and denominator population. Unlike shared-customers, which compares named properties, this analysis represents a retailer across included benchmark locations. The metric supports brand-location fit analysis, existing customer presence, market and relocation research, replacement-tenant context, and comparison of candidate properties against the same retailer audience. Higher overlap indicates that more of the relevant visitor population also visits the benchmark retailer; it does not by itself establish causation or future performance. benchmark_id is a company ID returned by search, and benchmark filters can include or exclude locations, companies, or landlords.
trade-area
ChatGPTReturns the census blockgroups that contribute visitors to one or more properties, including population, FIPS code, property-level visits and visitors, contribution percentage, and GeoJSON polygon boundaries. The result represents a data-derived catchment rather than a fixed administrative market. trade_area_contribution_max controls the cumulative share of traffic represented by returned blockgroups. contribution_type determines whether blockgroups are selected by proximity, radius, or contribution, while breakdown_type distinguishes combined, primary, or secondary visitor components. Concentration, geographic spread, county and state prefixes, and polygon shape help characterize neighborhood, community, regional, directional, or multi-market draw. The same trade-area concepts are available in demography and overlap tools.
traffic-summary
ChatGPTSummarizes property traffic for a selected period with total visits and unique visitors. Visits represent trips, so repeated trips by one visitor count separately; visitors represent distinct observed devices and approximate audience size. Optional comparison dates populate yoy_visits and yoy_visitors, expressed as percentage change against the comparison period (for example, 12.99 means +12.99%). The metrics support traffic trend analysis, current-versus-prior-period comparisons, property performance summaries, and separation of trip growth from audience growth. A rise in visits without similar visitor growth can indicate higher repeat frequency, while visitor growth reflects a broader observed audience. Property ALIs come from search, and available-date-ranges provides supported current and comparison periods.
visit-frequency
ChatGPTDescribes repeat visitation to a property during a selected period. average is visits per visitor, and chart distributes visitors across visit-count buckets from one visit through more than twenty visits. Values are directional and depend on period length, making like-for-like date windows and peer groups useful context. Higher frequency can be associated with routine, convenience, grocery, fitness, service, or other habitual trips. Lower frequency can be associated with occasional, destination, discretionary, or event-driven visits. Dwell-time adds visit-duration context, while ranking with rank_type=frequency places the property within a peer set. Property ALIs come from search; available-date-ranges lists supported periods.
void-analysis-api
ChatGPTIdentifies chain concepts with visitor-profile overlap at a property and limited or absent presence in the defined trade area. Results include customer overlap, demographic match, nearest existing location, average spacing, store size, location counts, industry, and available transaction metrics. The dataset supports market-whitespace research, vacancy and new-store prospecting, tenant-mix exploration, and comparison of a concept's customer affinity with its physical spacing. overlap measures the share of subject-property visitors observed at the chain elsewhere; overlap_average provides the chain's broader norm; match is a 0–100 demographic similarity score. nearest and average_spacing add geographic context. shared-customers-retailer provides a focused footprint comparison when a specific company ID is already known.
