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🙂 Which neighborhoods in Brooklyn had the highest year-over-year change in rental prices as of 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, NYCHousingPriceIndices, 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 specific neighborhoods in Brooklyn and their rental price changes over a one-year period, with a focus on September 2018. In the story, it is asking for detailed, localized information about real estate trends, specifically rental prices, in Brooklyn neighborhoods. This requires granular data that goes beyond city-wide or borough-wide indices, focusing instead on neighborhood-level metrics and recent, specific time periods.

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 NYCHousingPriceIndices is partitioned into 15 tables
  table_names:
  - stg_priceIndex_condos_co_ops_homes_All_Downtown_2018_09
  - stg_priceIndex_condos_co_ops_homes_All_Midtown_2018_09
  - stg_priceIndex_condos_co_ops_homes_All_Upper_East_Side_2018_09
  - stg_priceIndex_condos_co_ops_homes_All_Upper_Manhattan_2018_09
  - stg_priceIndex_condos_co_ops_homes_All_Upper_West_Side_2018_09
  - stg_priceIndex_condos_co_ops_homes_Central_Queens_2018_09
  - stg_priceIndex_condos_co_ops_homes_East_Brooklyn_2018_09
  - stg_priceIndex_condos_co_ops_homes_Northeast_Queens_2018_09
  - stg_priceIndex_condos_co_ops_homes_Northwest_Brooklyn_2018_09
  - stg_priceIndex_condos_co_ops_homes_Northwest_Queens_2018_09
  - stg_priceIndex_condos_co_ops_homes_North_Brooklyn_2018_09
  - stg_priceIndex_condos_co_ops_homes_Prospect_Park_2018_09
  - stg_priceIndex_condos_co_ops_homes_South_Brooklyn_2018_09
  - stg_priceIndex_condos_co_ops_homes_South_Queens_2018_09
  - stg_priceIndex_condos_co_ops_homes_The_Rockaways_2018_09
  table_desc: The table shows price indices for housing in NYC boroughs. Each index
    uses a repeat-sales method of comparing the sales prices of the same properties.
    It includes data for Downtown, Midtown, Upper East Side, and other neighborhoods.
    Each partition has columns for date, sales price, month-over-month change, and
    year-over-year change. The data spans from 1995 to at least 2018. It tracks housing
    market trends across various NYC areas.
  attributes:
  - date_
  - average_sales_price
  - month_over_month_change
  - year_over_year_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_rentIndex_condos_co_ops_homes_Brooklyn_2018_09, stg_priceIndex_condos_co_ops_homes_East_Brooklyn_2018_09, stg_priceIndex_condos_co_ops_homes_Northwest_Brooklyn_2018_09, stg_priceIndex_condos_co_ops_homes_North_Brooklyn_2018_09, stg_priceIndex_condos_co_ops_homes_Prospect_Park_2018_09, stg_priceIndex_condos_co_ops_homes_South_Brooklyn_2018_09

🧠 Explanation: The available data appears to be sufficient to answer the business question "Which neighborhoods in Brooklyn had the highest year-over-year change in rental prices as of September 2018?". The most relevant table for this analysis is the "stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09" from the BrooklynRealEstateIndices partition. This table contains rent indices for Brooklyn, including year-over-year changes, which directly addresses our question. Additionally, the NYCHousingPriceIndices partition contains tables for specific Brooklyn neighborhoods (East_Brooklyn, Northwest_Brooklyn, North_Brooklyn, Prospect_Park, South_Brooklyn), which can provide more granular data if needed. The approach would involve selecting the relevant Brooklyn neighborhood data, focusing on the September 2018 timeframe, and comparing the year-over-year changes in rental prices across these neighborhoods.
💡 Instruction to write SQL: 1. Start with the "stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09" table from BrooklynRealEstateIndices. 2. Select the date_ column and all columns related to year-over-year changes (ending with '_yoy'). 3. Filter for the row where date_ is '2018-09-01' (September 2018). 4. If more granular neighborhood data is needed, join this with relevant tables from NYCHousingPriceIndices (e.g., East_Brooklyn, Northwest_Brooklyn, etc.). 5. For the neighborhood tables, select the date_, area (derived from table name), and year_over_year_change columns. 6. Again, filter for September 2018 data. 7. Combine the results, ordering by the year-over-year change in descending order. 8. Limit the results to show only the top neighborhoods with the highest changes.

GenAI

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

? '[stg_priceIndex_condos_co_ops_homes_East_Brooklyn_2018_09, stg_priceIndex_condos_co_ops_homes_North_Brooklyn_2018_09,
  stg_priceIndex_condos_co_ops_homes_Northwest_Brooklyn_2018_09, stg_priceIndex_condos_co_ops_homes_Prospect_Park_2018_09,
  stg_priceIndex_condos_co_ops_homes_South_Brooklyn_2018_09]'
: foreign_keys: null
  primary_key: null
  time_keys:
  - date_
stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09:
  foreign_keys: null
  primary_key: null
  time_keys:
  - date_

🤓 We've planned the join: To execute the SQL, five joins are needed. For each join, the join keys are the 'date_' column, and they should be LEFT OUTER joins. The main "stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09" table should be joined with each of the following tables: 1. stg_priceIndex_condos_co_ops_homes_East_Brooklyn_2018_09 2. stg_priceIndex_condos_co_ops_homes_North_Brooklyn_2018_09 3. stg_priceIndex_condos_co_ops_homes_Northwest_Brooklyn_2018_09 4. stg_priceIndex_condos_co_ops_homes_Prospect_Park_2018_09 5. stg_priceIndex_condos_co_ops_homes_South_Brooklyn_2018_09

GenAI

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

stg_priceIndex_condos_co_ops_homes_East_Brooklyn_2018_09:
- name: date_
  description: Date of the housing price data
- name: average_sales_price
  description: Sales price of housing in the area
- name: year_over_year_change
  description: Year-over-year change in housing prices
stg_priceIndex_condos_co_ops_homes_North_Brooklyn_2018_09:
- name: date_
  description: Date of the housing price data
- name: average_sales_price
  description: Sales price of housing in the area
- name: year_over_year_change
  description: Year-over-year change in housing prices
stg_priceIndex_condos_co_ops_homes_Northwest_Brooklyn_2018_09:
- name: date_
  description: Date of the housing price data
- name: average_sales_price
  description: Sales price of housing in the area
- name: year_over_year_change
  description: Year-over-year change in housing prices
stg_priceIndex_condos_co_ops_homes_Prospect_Park_2018_09:
- name: date_
  description: Date of the housing price data
- name: average_sales_price
  description: Sales price of housing in the area
- name: year_over_year_change
  description: Year-over-year change in housing prices
stg_priceIndex_condos_co_ops_homes_South_Brooklyn_2018_09:
- name: date_
  description: Date of the housing price data
- name: average_sales_price
  description: Sales price of housing in the area
- name: year_over_year_change
  description: Year-over-year change in housing prices
stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09:
- name: date_
  description: Date of the recorded data
- name: brooklyn_price_index_yoy
  description: Year-over-year change in overall Brooklyn price index
- name: brooklyn_q1_price_index_yoy
  description: Year-over-year change in Brooklyn's 1st quintile price index
- name: brooklyn_q2_price_index_yoy
  description: Year-over-year change in Brooklyn's 2nd quintile price index
- name: brooklyn_q3_price_index_yoy
  description: Year-over-year change in Brooklyn's 3rd quintile price index
- name: brooklyn_q4_price_index_yoy
  description: Year-over-year change in Brooklyn's 4th quintile price index
- name: brooklyn_q5_price_index_yoy
  description: Year-over-year change in Brooklyn's 5th quintile price index

🤔 Reasoning for column selection: Based on the SQL approach and join description, we need to select specific columns from each table to fulfill the requirements. The main table (stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09) requires the date_ column for joining and filtering, as well as all columns ending with '_yoy' for year-over-year changes. For the neighborhood tables, we need the date_ column for joining and filtering, the year_over_year_change column for comparison, and we'll derive the area from the table name. The average_sales_price column might be useful for additional context, so we'll include it as well.

GenAI

😊 We have written the SQL:

WITH brooklyn_data AS (
    SELECT date_, 
           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
    FROM stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09
    WHERE date_ = '2018-09-01'
),
neighborhood_data AS (
    SELECT date_, 'East Brooklyn' AS neighborhood, year_over_year_change
    FROM stg_priceIndex_condos_co_ops_homes_East_Brooklyn_2018_09
    WHERE date_ = '2018-09-01'
    UNION ALL
    SELECT date_, 'North Brooklyn' AS neighborhood, year_over_year_change
    FROM stg_priceIndex_condos_co_ops_homes_North_Brooklyn_2018_09
    WHERE date_ = '2018-09-01'
    UNION ALL
    SELECT date_, 'Northwest Brooklyn' AS neighborhood, year_over_year_change
    FROM stg_priceIndex_condos_co_ops_homes_Northwest_Brooklyn_2018_09
    WHERE date_ = '2018-09-01'
    UNION ALL
    SELECT date_, 'Prospect Park' AS neighborhood, year_over_year_change
    FROM stg_priceIndex_condos_co_ops_homes_Prospect_Park_2018_09
    WHERE date_ = '2018-09-01'
    UNION ALL
    SELECT date_, 'South Brooklyn' AS neighborhood, year_over_year_change
    FROM stg_priceIndex_condos_co_ops_homes_South_Brooklyn_2018_09
    WHERE date_ = '2018-09-01'
)
SELECT 
    nd.neighborhood,
    nd.year_over_year_change AS neighborhood_yoy_change,
    bd.brooklyn_price_index_yoy AS overall_brooklyn_yoy_change,
    bd.brooklyn_q1_price_index_yoy,
    bd.brooklyn_q2_price_index_yoy,
    bd.brooklyn_q3_price_index_yoy,
    bd.brooklyn_q4_price_index_yoy,
    bd.brooklyn_q5_price_index_yoy
FROM brooklyn_data bd
LEFT OUTER JOIN neighborhood_data nd ON bd.date_ = nd.date_
ORDER BY nd.year_over_year_change DESC

GenAI

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

❓ stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09[brooklyn_price_index_yoy] has Missing Value: Unknown
❓ stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09[brooklyn_q1_price_index_yoy] has Missing Value: Unknown
❓ stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09[brooklyn_q2_price_index_yoy] has Missing Value: Unknown
❓ stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09[brooklyn_q3_price_index_yoy] has Missing Value: Unknown
❓ stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09[brooklyn_q4_price_index_yoy] has Missing Value: Unknown
❓ stg_rentIndex_condos_co_ops_homes_Brooklyn_2018_09[brooklyn_q5_price_index_yoy] has Missing Value: Unknown
❓ stg_priceIndex_condos_co_ops_homes_East_Brooklyn_2018_09[year_over_year_change] has Missing Value: No previous year data for first year's entries.
❓ stg_priceIndex_condos_co_ops_homes_North_Brooklyn_2018_09[year_over_year_change] has Missing Value: No previous year data for first year's entries.
❓ stg_priceIndex_condos_co_ops_homes_Northwest_Brooklyn_2018_09[year_over_year_change] has Missing Value: No previous year data for first year's entries.
❓ stg_priceIndex_condos_co_ops_homes_Prospect_Park_2018_09[year_over_year_change] has Missing Value: No previous year data for first year's entries.
❓ stg_priceIndex_condos_co_ops_homes_South_Brooklyn_2018_09[year_over_year_change] has Missing Value: No previous year data for first year's entries.