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🙂 Which borough had the highest total inventory in June 2018?

GenAI

😎 RAG from Cocoon: Putting the question in context using Cocoon's ER story...

- Description: City-wide indices reveal overall NYC real estate trends.
  Name: NYCOverallRealEstateIndices
  Type: Group
- Description: Borough-specific indices show varying real estate performance.
  Name: NYCBoroughRealEstateIndices
  Type: Group
- Description: Neighborhood indices highlight local real estate value shifts.
  Name: NYCHousingPriceIndices
  Type: Group
- Description: Detailed metrics expose nuanced market behavior (2010-2018).
  Name: NYCRealEstateMarketMetrics
  Type: Group

🤓 We've found the related relations: NYCBoroughRealEstateIndices, NYCRealEstateMarketMetrics

Story behind the relationships (oval for entity, box for relation, octagon for table group))

[NYCOverallRealEstateIndices]: City-wide indices reveal overall NYC real estate trends.
[NYCBoroughRealEstateIndices]: Borough-specific indices show varying real estate performance.
[NYCHousingPriceIndices]: Neighborhood indices highlight local real estate value shifts.
[NYCRealEstateMarketMetrics]: Detailed metrics expose nuanced market behavior (2010-2018).

Reasoning: The question is asking about the borough with the highest total inventory in June 2018. In the story, it is asking for specific, detailed real estate market information at the borough level for a particular month and year. This requires data that breaks down inventory by borough and provides monthly statistics up to 2018.

GenAI

😎 RAG from Cocoon: Checking out all the related tables Cocoon set up...

- partitons: The data for NYCRealEstateMarketMetrics is partitioned into 43 tables
  table_names:
  - stg_A1_totalInventory_All
  - stg_A3_medianAskingPrice_All
  - stg_A5_recordedSalesVolume_All
  - stg_A6_medianSalePrice_All
  - stg_A7_saleListRatio_All
  - stg_A8_priceCutShare_All
  - stg_A9_daysOnMarket_All
  - stg_B1_totalInventory_Condo
  - stg_B3_medianAskingPrice_Condo
  - stg_B5_recordedSalesVolume_Condo
  - stg_B6_medianSalePrice_Condo
  - stg_B7_saleListRatio_Condo
  - stg_B8_priceCutShare_Condo
  - stg_B9_daysOnMarket_Condo
  - stg_C1_totalInventory_Coop
  - stg_C3_medianAskingPrice_Coop
  - stg_C5_recordedSalesVolume_Coop
  - stg_C6_medianSalePrice_Coop
  - stg_C7_saleListRatio_Coop
  - stg_C8_priceCutShare_Coop
  - stg_C9_daysOnMarket_Coop
  - stg_D1_totalInventory_Sfr
  - stg_D3_medianAskingPrice_Sfr
  - stg_D5_recordedSalesVolume_Sfr
  - stg_D6_medianSalePrice_Sfr
  - stg_D7_saleListRatio_Sfr
  - stg_D8_priceCutShare_Sfr
  - stg_D9_daysOnMarket_Sfr
  - stg_E1_rentalInventory_All
  - stg_E2_medianAskingRent_All
  - stg_E3_discountShare_All
  - stg_F1_rentalInventory_Studio
  - stg_F2_medianAskingRent_Studio
  - stg_F3_discountShare_Studio
  - stg_G1_rentalInventory_OneBd
  - stg_G2_medianAskingRent_OneBd
  - stg_G3_discountShare_OneBd
  - stg_H1_rentalInventory_TwoBd
  - stg_H2_medianAskingRent_TwoBd
  - stg_H3_discountShare_TwoBd
  - stg_I1_rentalInventory_ThreePlusBd
  - stg_I2_medianAskingRent_ThreePlusBd
  - stg_I3_discountShare_ThreePlusBd
  table_desc: The table shows real estate data for New York City. It includes metrics
    like inventory (the number of rental listings), prices cut (the exact middle price
    cut as a percentage of total asking price), Sale-to-List Price Ratio (the final
    recorded sales price of a home divided by its initial asking price), etc. Data
    is split by property type (all, condo, coop, SFR). It covers rental data too.
    Metrics are tracked monthly from 2010 to 2018. Areas are divided into Manhattan
    submarkets.
  attributes:
  - Area
  - Borough
  - AreaType
  - date_2010_01
  - date_2010_02
  - date_2010_03
  - date_2010_04
  - date_2010_05
  - date_2010_06
  - date_2010_07
  - date_2010_08
  - date_2010_09
  - date_2010_10
  - date_2010_11
  - date_2010_12
  - date_2011_01
  - date_2011_02
  - date_2011_03
  - date_2011_04
  - date_2011_05
  - date_2011_06
  - date_2011_07
  - date_2011_08
  - date_2011_09
  - date_2011_10
  - date_2011_11
  - date_2011_12
  - date_2012_01
  - date_2012_02
  - date_2012_03
  - date_2012_04
  - date_2012_05
  - date_2012_06
  - date_2012_07
  - date_2012_08
  - date_2012_09
  - date_2012_10
  - date_2012_11
  - date_2012_12
  - date_2013_01
  - date_2013_02
  - date_2013_03
  - date_2013_04
  - date_2013_05
  - date_2013_06
  - date_2013_07
  - date_2013_08
  - date_2013_09
  - date_2013_10
  - date_2013_11
  - date_2013_12
  - date_2014_01
  - date_2014_02
  - date_2014_03
  - date_2014_04
  - date_2014_05
  - date_2014_06
  - date_2014_07
  - date_2014_08
  - date_2014_09
  - date_2014_10
  - date_2014_11
  - date_2014_12
  - date_2015_01
  - date_2015_02
  - date_2015_03
  - date_2015_04
  - date_2015_05
  - date_2015_06
  - date_2015_07
  - date_2015_08
  - date_2015_09
  - date_2015_10
  - date_2015_11
  - date_2015_12
  - date_2016_01
  - date_2016_02
  - date_2016_03
  - date_2016_04
  - date_2016_05
  - date_2016_06
  - date_2016_07
  - date_2016_08
  - date_2016_09
  - date_2016_10
  - date_2016_11
  - date_2016_12
  - date_2017_01
  - date_2017_02
  - date_2017_03
  - date_2017_04
  - date_2017_05
  - date_2017_06
  - date_2017_07
  - date_2017_08
  - date_2017_09
  - date_2017_10
  - date_2017_11
  - date_2017_12
  - date_2018_01
  - date_2018_02
  - date_2018_03
  - date_2018_04
  - date_2018_05
  - date_2018_06
  - date_2018_07
  - date_2018_08
  - date_2018_09
- partitons: The data for BrooklynRealEstateIndices is partitioned into 2 tables
  table_names:
  - stg_priceIndex_condos_co_ops_homes_Brooklyn_2018_09
  - stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09
  table_desc: The table shows price indices and rent indices for Brooklyn. Each index
    uses a repeat-sales method of comparing the sales prices of the same properties
    since January 1995 in Manhattan and January 2005 in Brooklyn and Queens. It covers
    condos, co-ops, and homes. Data is split into quintiles. Monthly and yearly changes
    are included. The table starts from 1995 but has missing early data. It likely
    continues to 2018.
  attributes:
  - date_
  - brooklyn_price_index
  - brooklyn_q1_price_index
  - brooklyn_q2_price_index
  - brooklyn_q3_price_index
  - brooklyn_q4_price_index
  - brooklyn_q5_price_index
  - brooklyn_price_index_mom
  - brooklyn_q1_price_index_mom
  - brooklyn_q2_price_index_mom
  - brooklyn_q3_price_index_mom
  - brooklyn_q4_price_index_mom
  - brooklyn_q5_price_index_mom
  - brooklyn_price_index_yoy
  - brooklyn_q1_price_index_yoy
  - brooklyn_q2_price_index_yoy
  - brooklyn_q3_price_index_yoy
  - brooklyn_q4_price_index_yoy
  - brooklyn_q5_price_index_yoy
- partitons: The data for QueensRealEstateIndices is partitioned into 2 tables
  table_names:
  - stg_priceIndex_condos_co_ops_homes_Queens_2018_09
  - stg_rentIndex_condos_co_ops_homes_Queens_2018_09
  table_desc: The table shows price indices for Queens real estate. It includes data
    for all properties and five quintiles. The table has monthly and yearly changes.
    It covers condos, co-ops, and homes. The data starts from 1995. The rent index
    partition likely has a similar structure.
  attributes:
  - date_
  - queens_all_index
  - queens_q1_index
  - queens_q2_index
  - queens_q3_index
  - queens_q4_index
  - queens_q5_index
  - queens_all_mom_change
  - queens_q1_mom_change
  - queens_q2_mom_change
  - queens_q3_mom_change
  - queens_q4_mom_change
  - queens_q5_mom_change
  - queens_all_yoy_change
  - queens_q1_yoy_change
  - queens_q2_yoy_change
  - queens_q3_yoy_change
  - queens_q4_yoy_change
  - queens_q5_yoy_change
- partitons: The data for ManhattanRealEstateIndices2018 is partitioned into 2 tables
  table_names:
  - stg_priceIndex_condos_co_ops_homes_Manhattan_2018_09
  - stg_rentIndex_condos_co_ops_homes_Manhattan_2018_09
  table_desc: The table shows price indices for Manhattan properties. Each index uses
    a repeat-sales method of comparing the sales prices of the same properties since
    January 1995 in Manhattan and January 2005. Values are given for different dates.
    Monthly and yearly changes are calculated. The rent index partition likely has
    a similar structure for rental prices.
  attributes:
  - date_
  - manhattan_price_index
  - manhattan_q1_price_index
  - manhattan_q2_price_index
  - manhattan_q3_price_index
  - manhattan_q4_price_index
  - manhattan_q5_price_index
  - manhattan_price_index_mom
  - manhattan_q1_price_index_mom
  - manhattan_q2_price_index_mom
  - manhattan_q3_price_index_mom
  - manhattan_q4_price_index_mom
  - manhattan_q5_price_index_mom
  - manhattan_price_index_yoy
  - manhattan_q1_price_index_yoy
  - manhattan_q2_price_index_yoy
  - manhattan_q3_price_index_yoy
  - manhattan_q4_price_index_yoy
  - manhattan_q5_price_index_yoy

🤓 We've identified the related tables: stg_A1_totalInventory_All

🧠 Explanation: The available data is sufficient to answer the business question "Which borough had the highest total inventory in June 2018?". The relevant information can be found in the NYCRealEstateMarketMetrics dataset, specifically in the table stg_A1_totalInventory_All. This table contains total inventory data for all property types across different areas and boroughs in New York City, with monthly data from 2010 to 2018. To answer the question, we need to: 1. Focus on the stg_A1_totalInventory_All table 2. Filter the data for June 2018 (column date_2018_06) 3. Group the data by Borough 4. Calculate the total inventory for each borough 5. Identify the borough with the highest total inventory This can be achieved using SQL with selection, grouping, and aggregation operations. No joins are necessary as all required data is in a single table.
💡 Instruction to write SQL: 1. Select data from the stg_A1_totalInventory_All table 2. Group the data by the Borough column 3. Sum the values in the date_2018_06 column for each borough 4. Order the results by the summed inventory in descending order 5. Limit the result to the top 1 to get the borough with the highest inventory This approach will give us the borough with the highest total inventory in June 2018.

GenAI

😎 RAG from Cocoon: Looking at how tables connect, thanks to Cocoon's key info...

stg_A1_totalInventory_All:
  foreign_keys: null
  primary_key: null
  time_keys: null

🤓 We've planned the join: To execute the SQL, no joins are needed. The query can be performed entirely on the stg_A1_totalInventory_All table without joining to any other tables.

GenAI

😎 RAG from Cocoon: Diving into column details Cocoon prepared... getting a bit technical here!

stg_A1_totalInventory_All:
- name: Borough
  description: Borough in New York City
  domain: '[''Queens'', ''Brooklyn'', ''Bronx'', ''Manhattan'', ''Staten Island'']'
- name: date_2018_06
  description: Metric value for June 2018

🤔 Reasoning for column selection: Based on the SQL approach and join description provided, we only need to use the stg_A1_totalInventory_All table. The necessary columns are: 1. Borough: This column is needed for grouping the data as specified in step 2 of the approach. 2. date_2018_06: This column is required for summing the inventory values for June 2018 as mentioned in step 3. No other columns are necessary for joins, filtering, or additional calculations. The query focuses solely on grouping by Borough and summing the inventory for a specific month (June 2018), then ordering and limiting the results to find the borough with the highest inventory.

GenAI

😊 We have written the SQL:

SELECT Borough, SUM(date_2018_06) AS total_inventory_june_2018
FROM stg_A1_totalInventory_All
GROUP BY Borough
ORDER BY total_inventory_june_2018 DESC
LIMIT 1

GenAI

🤓 RAG from Cocoon: Beware! The following are unaddressed data quality issues that may compromise the results:

❓ stg_A1_totalInventory_All[Borough] has Missing Value: Not applicable for areas not within a specific borough.