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🙂 Which quintile of properties in Queens showed the highest month-over-month price index change in September 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 quintile of properties in Queens that showed the highest month-over-month price index change in September 2018. In the story, it is asking for detailed, specific information about real estate price changes in a particular borough (Queens), for a specific time period (September 2018), and across different property quintiles. This level of granularity suggests we need to look at borough-specific data and detailed metrics that break down the market into different segments or price ranges.

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_priceIndex_condos_co_ops_homes_Queens_2018_09

🧠 Explanation: The available data is sufficient to answer the business question "Which quintile of properties in Queens showed the highest month-over-month price index change in September 2018?". The QueensRealEstateIndices table, specifically the stg_priceIndex_condos_co_ops_homes_Queens_2018_09 partition, contains the necessary information. This table includes month-over-month changes for each quintile of properties in Queens, and it covers the date we're interested in (September 2018). To answer this question, we would need to: 1. Select the row for September 2018 from the Queens price index table. 2. Compare the month-over-month change values for each quintile (q1 through q5). 3. Determine which quintile has the highest value. This can be achieved using a single SELECT statement with a WHERE clause to filter for the specific date, and then using a combination of comparison functions to find the highest value among the quintiles.
💡 Instruction to write SQL: 1. Select from the stg_priceIndex_condos_co_ops_homes_Queens_2018_09 table. 2. Filter the data to get only the row for September 2018 using the date_ column. 3. Select the month-over-month change columns for each quintile (queens_q1_mom_change through queens_q5_mom_change). 4. Use a CASE statement or similar to compare these values and determine which is the highest. 5. Return the quintile number (1-5) that corresponds to the highest month-over-month change.

GenAI

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

stg_priceIndex_condos_co_ops_homes_Queens_2018_09:
  foreign_keys: null
  primary_key: null
  time_keys:
  - date_

🤓 We've planned the join: To execute the SQL, no joins are needed. The query can be performed using a single table (stg_priceIndex_condos_co_ops_homes_Queens_2018_09) without any joins.

GenAI

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

stg_priceIndex_condos_co_ops_homes_Queens_2018_09:
- name: date_
  description: Date of the recorded data
- name: queens_q1_mom_change
  description: Month-over-month change for Queens 1st quintile
- name: queens_q2_mom_change
  description: Month-over-month change for Queens 2nd quintile
- name: queens_q3_mom_change
  description: Month-over-month change for Queens 3rd quintile
- name: queens_q4_mom_change
  description: Month-over-month change for Queens 4th quintile
- name: queens_q5_mom_change
  description: Month-over-month change for Queens 5th quintile

🤔 Reasoning for column selection: Based on the SQL approach and join description, we only need to use the stg_priceIndex_condos_co_ops_homes_Queens_2018_09 table. The necessary columns are: 1. date_: This is needed for filtering the data to get only the row for September 2018. 2. queens_q1_mom_change through queens_q5_mom_change: These are required to compare the month-over-month changes for each quintile and determine which is the highest. We don't need any columns for joins as the query can be performed using a single table without any joins. The other columns (index and yoy_change) are not necessary for this specific query based on the given SQL approach.

GenAI

😊 We have written the SQL:

WITH september_2018_data AS (
    SELECT
        queens_q1_mom_change,
        queens_q2_mom_change,
        queens_q3_mom_change,
        queens_q4_mom_change,
        queens_q5_mom_change
    FROM stg_priceIndex_condos_co_ops_homes_Queens_2018_09
    WHERE date_ = '2018-09-01'
)
SELECT
    CASE
        WHEN queens_q1_mom_change = greatest(queens_q1_mom_change, queens_q2_mom_change, queens_q3_mom_change, queens_q4_mom_change, queens_q5_mom_change) THEN 1
        WHEN queens_q2_mom_change = greatest(queens_q1_mom_change, queens_q2_mom_change, queens_q3_mom_change, queens_q4_mom_change, queens_q5_mom_change) THEN 2
        WHEN queens_q3_mom_change = greatest(queens_q1_mom_change, queens_q2_mom_change, queens_q3_mom_change, queens_q4_mom_change, queens_q5_mom_change) THEN 3
        WHEN queens_q4_mom_change = greatest(queens_q1_mom_change, queens_q2_mom_change, queens_q3_mom_change, queens_q4_mom_change, queens_q5_mom_change) THEN 4
        WHEN queens_q5_mom_change = greatest(queens_q1_mom_change, queens_q2_mom_change, queens_q3_mom_change, queens_q4_mom_change, queens_q5_mom_change) THEN 5
    END AS highest_change_quintile
FROM september_2018_data

GenAI

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

❓ stg_priceIndex_condos_co_ops_homes_Queens_2018_09[queens_q1_mom_change] has Missing Value: Not applicable for first month in dataset
❓ stg_priceIndex_condos_co_ops_homes_Queens_2018_09[queens_q2_mom_change] has Missing Value: Not applicable for first month in dataset
❓ stg_priceIndex_condos_co_ops_homes_Queens_2018_09[queens_q3_mom_change] has Missing Value: Not applicable for first month in dataset
❓ stg_priceIndex_condos_co_ops_homes_Queens_2018_09[queens_q4_mom_change] has Missing Value: Not applicable for first month in dataset
❓ stg_priceIndex_condos_co_ops_homes_Queens_2018_09[queens_q5_mom_change] has Missing Value: Not applicable for first month in dataset