Chatting with US Metro Area Housing Inventory Last 13 months

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US Metro Area Housing Inventory Last 13 months Source: realtor.com

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month_date_yyyymm cbsa_title median_listing_price active_listing_count median_days_on_market new_listing_count price_increased_count price_reduced_count pending_listing_count median_listing_price_per_square_foot median_square_feet average_listing_price total_listing_count pending_ratio
0 202311 New York-Newark-Jersey City, NY-NJ-PA 728000 34209 58 11312 292 5550 14550.0 497 1567 1667908 48422 0.4253
1 202311 Los Angeles-Long Beach-Anaheim, CA 1150000 11424 47 6462 264 2804 6157.0 661 1808 2552219 17545 0.5389
2 202311 Chicago-Naperville-Elgin, IL-IN-WI 360000 15247 41 7402 140 5018 9861.0 201 1779 548387 25093 0.6467
3 202311 Dallas-Fort Worth-Arlington, TX 445000 18953 50 7578 414 8724 8156.0 205 2209 637365 27112 0.4303
4 202311 Houston-The Woodlands-Sugar Land, TX 365000 22095 50 7676 744 7780 8629.0 174 2099 509986 30736 0.3905

Started at Aug. 20, 2023, 11:18 a.m.


Top 10 most expensive metro last month
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df = data.copy()
df = df[df["month_date_yyyymm"] == df["month_date_yyyymm"].max()]
df = df[["cbsa_title", "median_listing_price"]].sort_values(by="median_listing_price", ascending=False).head(10)
df.reset_index(drop=True, inplace=True)
print(df)
cbsa_title median_listing_price
Vineyard Haven, MA 2795000
Santa Maria-Santa Barbara, CA 1996000
Hailey, ID 1962500
Glenwood Springs, CO 1785000
Napa, CA 1697000
Jackson, WY-ID 1695000
Edwards, CO 1690000
Kapaa, HI 1617500
Heber, UT 1595000
San Jose-Sunnyvale-Santa Clara, CA 1498000

10 rows × 2 columns

Least expensive 10 metros
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df = data.copy()
df = df[df["month_date_yyyymm"] == df["month_date_yyyymm"].max()]
df = df[["cbsa_title", "median_listing_price"]].sort_values(by="median_listing_price").head(10)
df.reset_index(drop=True, inplace=True)
print(df)
cbsa_title median_listing_price
Macomb, IL 75750
Selma, AL 79900
Parsons, KS 85000
Danville, IL 94900
Johnstown, PA 95000
Marion, IN 99900
St. Marys, PA 100000
Taylorville, IL 103450
Vernon, TX 105000
Hope, AR 109900

10 rows × 2 columns

Create a chart to visualize the difference between top 10 and bottom 10
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df_top = data.copy()
df_top = df_top[df_top["month_date_yyyymm"] == df_top["month_date_yyyymm"].max()]
df_top = df_top[["cbsa_title", "median_listing_price"]].sort_values(by="median_listing_price", ascending=False).head(10)

df_bottom = data.copy()
df_bottom = df_bottom[df_bottom["month_date_yyyymm"] == df_bottom["month_date_yyyymm"].max()]
df_bottom = df_bottom[["cbsa_title", "median_listing_price"]].sort_values(by="median_listing_price").head(10)

df_final = pd.concat([df_top, df_bottom])
df_final.reset_index(drop=True, inplace=True)

fig = px.bar(df_final, x='cbsa_title', y='median_listing_price', color='median_listing_price',
             labels={'cbsa_title':'Metro', 'median_listing_price':'Median Price'},
             title="Comparison between top 10 and bottom 10 metros", template='plotly_dark')
fig.show()
Explain the data set
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This dataset contains information about the US Metro Area Housing Inventory for the last 13 months. The data is sourced from realtor.com.

The columns in the dataset are as follows:

  • month_date_yyyymm: The year and month of the record.
  • cbsa_title: The title of the metro area.
  • median_listing_price: The median price at which houses in the area are listed.
  • active_listing_count: The number of active listings in the area.
  • median_days_on_market: The median number of days a listing is on the market before it is sold.
  • new_listing_count: The number of new listings in the area.
  • price_increased_count: The number of listings where the price increased.
  • price_reduced_count: The number of listings where the price reduced.
  • pending_listing_count: The number of listings that are pending sale.
  • median_listing_price_per_square_foot: The median listing price per square foot of the houses.
  • median_square_feet: The median square footage of the houses.
  • average_listing_price: The average price at which houses in the area are listed.
  • total_listing_count: The total number of listings in the area.
  • pending_ratio: The ratio of pending listings to total listings.

The data can be used to understand the housing market in different metro areas, including how prices are changing, how long houses stay on the market, and how many new listings there are.

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