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MarketStatisticsController_getMarketStatisticsOverview

GET 
/v4/market-statistics/overview

MarketStatisticsController_getMarketStatisticsOverview

Request​

Query Parameters

    mls string[]

    The MLS code you want associated boundary IDs from.

    Example: &mls=recolorado&mls=pikespeak
    boundary-id string[]

    The boundary id or ids you want aggregated market statistics for. Boundary ids are the only way to get data by legally defined geometry. All other parameters like city, search on the postal address. See Cities vs Postal Cities. By default, all boundary IDs will be grouped together. If you want to group by individual boundary IDs, you can use the 

    group-by
    query parameter. Note: at least one boundary-id, city, local-area-1, local-area-2, or postal-code is required.

    Example: &boundary-id=593979d6da3374b282bb7bbb&boundary-id=593979d6da3374b282bb7bcb
    property-type string[]

    The property type or types you want aggregated market statistics for.

    Possible values: [Residential, ResidentialLease, Commercial, CommercialLease, Land, Industrial, Other, Timeshare]

    Default value: [Residential]

    Example: &property-type=Residential&property-type=Commercial
    property-sub-type string[]

    The property sub type or sub types you want aggregated market statistics for. Sub Types are used to distinguish between the types of residential or commercial data available.

    For instance, the most common suburban home would be a SingleFamilyResidence. A Condominium is usually defined as a subsection of a building made to be a place of dwelling. The place of dwelling is usually only 1 story tall. A Townhouse is usually a multilevel building attached to other multilevel buildings with no dwelling above or below it. See the RESO Standard Lookups for more information on property sub types.

    Disclaimer: LiveBy ensures the availability of SingleFamilyResidence, Townhouse, and Condominium in each MLS. The availability of other PropertySubTypes may vary and is more limited by MLS.

    For Residential Listings, we recommend using SingleFamilyResidence, Townhouse, and Condominium.

    Possible values: [SingleFamilyResidence, Agriculture, Apartment, BoatSlip, Business, Cabin, Condominium, DeededParking, Farm, Hotel, Industrial, ManufacturedHome, MixedUse, Mobile, MultiFamily, Office, OwnYourOwn, Ranch, Retail, StockCooperative, Timeshare, Townhouse, Land, UnimprovedLand, Warehouse, Other]

    Example: &property-sub-type=SingleFamilyResidence&property-sub-type=Condominium
    city string[]

    The postal city or cities you want aggregated market statistics for. You can use this instead of a boundary id. This uses the RESO Standard field “City”.

    Example: &city=Lincoln&city=Omaha
    area-level-1 string[]

    The first political subdivision you want aggregated market statistics for. You can use this instead of a boundary id. This is often called “state”, “province” or “district” in various countries. This uses the RESO standard field “StateOrProvince”.

    Example: &area-level-1=Nebraska&area-level-1=Iowa
    area-level-2 string[]

    The second political subdivision you want aggregated market statistics for. You can use this instead of a boundary id. This uses the RESO standard field “CountyOrParish”.

    Example: &area-level-2=Lancaster&area-level-2=Seward
    postal-code string[]

    The postal code or codes you want aggregated market statistics for. You can use this instead of a boundary id. This uses the RESO standard field “PostalCode”.

    Example: &postal-code=68521&postal-code=68502
    mls-area-major string[]

    The MLS Area Major for which you want market statistics.

    Example: &mls-area-major=napa
    outlier-original-list-price-low number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 5000

    Example: &outlier-original-list-price-low=5000
    outlier-original-list-price-high number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 1000000000

    Example: &outlier-original-list-price-high=1000000000
    outlier-list-price-low number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 5000

    Example: &outlier-list-price-low=5000
    outlier-list-price-high number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 1000000000

    Example: &outlier-list-price-high=1000000000
    outlier-close-price-low number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 5000

    Example: &outlier-close-price-low=5000
    outlier-close-price-high number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 1000000000

    Example: &outlier-close-price-high=1000000000
    outlier-days-on-market-low number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Example: &outlier-days-on-market-low=0
    outlier-days-on-market-high number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 1000

    Example: &outlier-days-on-market-high=1000
    outlier-living-area-low number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Example: &outlier-living-area-low=0
    outlier-living-area-high number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 10000

    Example: &outlier-living-area-high=10000
    outlier-year-built-low number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 1700

    Example: &outlier-year-built-low=1700
    outlier-year-built-high number

    LiveBy by default cleans the data to remove outliers. LiveBy does not remove outlier data from the total counts, only the field’s counts, and statistics. You can use the count value of each field to determine if that field had outliers and was available from the MLS data sources.

    Default value: 2026

    Example: &outlier-year-built-high=2026
    close-price-low number

    This removes any listings that have a close price less than the value specified. Unlike the outlier filter, this will remove listings from all statistics, not just the price statistics.

    Example: &close-price-low=100000
    close-price-high number

    This removes any listings that have a close price more than the value specified. Unlike the outlier filter, this will remove listings from all statistics, not just the price statistics.

    Example: &close-price-high=10000000
    group-by string[]

    This lets you group data by date range using one of the options provided below.

    If this is not specified you will get an aggregate of all data. This is useful for making rolling averages of data.

    If you specify two years of data by using 

    &time-interval=2022-01-01T00:00:00Z/P2Y
    and group by year, you will get an array of 3 results.

    The first array will be for the first year, and the second array will be for the second year. You will also get an aggregate of all data combined.

    If you specify quarter, you will get 11 results in the array, 8 quarters, (4 quarters * 2 years) + 2 years + 1 total.

    If you specify month, you will get 27 results in the array, 24 months (12 months * 2 years) + 2 years + 1 total.

    For information on grouping by price, see the price-segments parameter.

    If you would like to get multiple boundaries, you can also specify 

    &group-by=boundary-id
    .

    If you would like to get data groupbed by PropertySubType, you can also specify 

    &group-by=property-sub-type
    .

    If you would like to get data groupbed by PropertyType, you can also specify 

    &group-by=property-type
    .

    If you specify multiple group-by parameters that are time ranges, only the first will be used.

    Possible values: [year, quarter, month, week, boundary-id, property-sub-type, property-type]

    Example: &group-by=month
    price-segment any

    If this parameter is provided, an aggregate is created for each price range defined by the price segments provided. It is implied that the first price range starts at 0. Also, the low price is inclusive (>=) and the high price is exclusive (<). See the example below.

    This will always provide one more range than the number of price-segment parameters provided.

    You may also specify either best-fit for an auto generated set of price segments or default to use the default price segment options.



    In this example, the price range aggregates will be:

    • 0 - 499,999
    • 500,000 - 999,999
    • 1,000,000 - 1,499,999
    • 1,500,000 +
    Example: &price-segment=500000&price-segment=1000000&price-segment=1500000
    price-segment-amount number

    This parameter specifies the number of price segments to when the price-segment option is set to best-fit or default.

    This parameter can only be 5 or 10. The default is 10.

    While this parameter specifies either 5 or 10 segments, 6 or 11 total buckets will be returned, since there is always an upper and lower bound.

    Possible values: [5, 10]

    Example: [&price-segment-amount=10, &price-segment-amount=5]

Responses​