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Added to Market Statistics

GET 

/v4/market-statistics/added-to-market

Get market statistics based on search criteria. This endpoint returns aggregated statistics for any listings that were listed within a specified timeframe.

Required: Filters must include either a boundary-id or an MLS plus one additional area filter. Area filters include city, area-level-1, area-level-2, and postal-code.

Request

Query Parameters

    time-interval stringrequired

    This is the interval of time you want aggregated market statistics for. This is an ISO 8601 time Interval with either

    • a start and end time
    • a start time and duration
    • or a duration and end time
    Specifying a begin date retrieves all data for that day. Specifying an end date retrieves all data before that day. To get an entire month's worth of data, specify the first day of the month in question and the first day of the following month. Time values in the range are discarded.

    For example, passing a time interval of 2024-04-01/2024-05-01 or 2024-04-01/P1M will return all data for April 2024. If you pass 2024-04-01/2024-04-30 or 2024-04-01T00:00:00/2024-04-3T23:59:59 you might be expecting all of April, but you'd end up missing data from April 30 in both cases.

    More information on ISO 8601 durations can be found here.
    Example: &time-interval=P1Y/2008-05-11T15:30:00Z
    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.

    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, Warehouse, Other]

    Default value: [SingleFamilyResidence, Condominium, Townhouse]

    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-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: 2025

    Example: &outlier-year-built-high=2025
    list-price-low number

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

    Example: ?list-price-low=100000
    list-price-high number

    This filters out any listings that have a list price greater than the value specified. Unlike the outlier filter, this will remove listings from all statistics, not just the price statistics.

    Example: ?list-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 specify multiple group-by parameters that are not boundary-id, only the first will be used.

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

    Example: &group-by=month
    price-segment string[]

    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.

    You may specify many different ranges of prices.

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

    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

Responses

Schema

    success booleanrequired

    data

    object[]

  • Array [

  • period string

    Time period these statistics are based on. In the format {full year} or {full year}-Q{quarter} or {full year}-M{month} or {full year}-W{week}

    priceRange string[]

    Price range of the properties

    boundaryId string[]

    If the query is grouping by boundary id, this is the id of the boundary.

    metadata objectrequired

    The MLSes that provided data for these statistics

    Example: {"attribution":[{"name":"North MLS"},{"name":"Northeast MLS"}]}

    data

    object

    required

    count numberrequired

    Total count of listings within the period

    ListPrice

    object

    required

    Statistics about the price that the property was listed for when it sold

    count numberrequired

    Amount of non null and numeric values used to make statistics that is within the outlier range specified

    Example: 10
    median numberrequired

    Median of the non null and numeric values that is within the outlier range specified

    Example: 500000
    average numberrequired

    Mean average of the non null and numeric values that is within the outlier range specified

    Example: 532561
    minimum numberrequired

    Smallest value of the non null and numeric values that is within the outlier range specified

    Example: 100000
    maximum numberrequired

    Largest value of the non null and numeric values that is within the outlier range specified

    Example: 20000000
    sum numberrequired

    Total sum of the non null and numeric values that is within the outlier range specified

    Example: 256373500

    OriginalListPrice

    object

    required

    Statistics about the First List Price before price adjustments

    count numberrequired

    Amount of non null and numeric values used to make statistics that is within the outlier range specified

    Example: 10
    median numberrequired

    Median of the non null and numeric values that is within the outlier range specified

    Example: 500000
    average numberrequired

    Mean average of the non null and numeric values that is within the outlier range specified

    Example: 532561
    minimum numberrequired

    Smallest value of the non null and numeric values that is within the outlier range specified

    Example: 100000
    maximum numberrequired

    Largest value of the non null and numeric values that is within the outlier range specified

    Example: 20000000
    sum numberrequired

    Total sum of the non null and numeric values that is within the outlier range specified

    Example: 256373500

    YearBuilt

    object

    required

    Statistics about the year the properties were built

    count numberrequired

    Amount of non null and numeric values used to make statistics that is within the outlier range specified

    Example: 10
    median numberrequired

    Median of the non null and numeric values that is within the outlier range specified

    Example: 500000
    average numberrequired

    Mean average of the non null and numeric values that is within the outlier range specified

    Example: 532561
    minimum numberrequired

    Smallest value of the non null and numeric values that is within the outlier range specified

    Example: 100000
    maximum numberrequired

    Largest value of the non null and numeric values that is within the outlier range specified

    Example: 20000000
    sum numberrequired

    Total sum of the non null and numeric values that is within the outlier range specified

    Example: 256373500

    LivingArea

    object

    required

    Statistics about the square footage of the properties

    count numberrequired

    Amount of non null and numeric values used to make statistics that is within the outlier range specified

    Example: 10
    median numberrequired

    Median of the non null and numeric values that is within the outlier range specified

    Example: 500000
    average numberrequired

    Mean average of the non null and numeric values that is within the outlier range specified

    Example: 532561
    minimum numberrequired

    Smallest value of the non null and numeric values that is within the outlier range specified

    Example: 100000
    maximum numberrequired

    Largest value of the non null and numeric values that is within the outlier range specified

    Example: 20000000
    sum numberrequired

    Total sum of the non null and numeric values that is within the outlier range specified

    Example: 256373500

    pricePerSquareFoot

    object

    required

    Statistics about the price per square foot of the properties

    count numberrequired

    Amount of non null and numeric values used to make statistics that is within the outlier range specified

    Example: 10
    median numberrequired

    Median of the non null and numeric values that is within the outlier range specified

    Example: 500000
    average numberrequired

    Mean average of the non null and numeric values that is within the outlier range specified

    Example: 532561
    minimum numberrequired

    Smallest value of the non null and numeric values that is within the outlier range specified

    Example: 100000
    maximum numberrequired

    Largest value of the non null and numeric values that is within the outlier range specified

    Example: 20000000
    sum numberrequired

    Total sum of the non null and numeric values that is within the outlier range specified

    Example: 256373500
  • ]

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