105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
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"""
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This script scrapes sold and pending sold land listings in past year for a list of zip codes and saves the data to individual Excel files.
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It adds two columns to the data: 'lot_acres' and 'ppa' (price per acre) for user to analyze average price of land in a zip code.
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"""
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import os
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import pandas as pd
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from homeharvest import scrape_property
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def get_property_details(zip: str, listing_type):
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properties = scrape_property(location=zip, listing_type=listing_type, property_type=["land"], past_days=365)
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if not properties.empty:
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properties["lot_acres"] = properties["lot_sqft"].apply(lambda x: x / 43560 if pd.notnull(x) else None)
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properties = properties[properties["sqft"].isnull()]
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properties["ppa"] = properties.apply(
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lambda row: (
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int(
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(
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row["sold_price"]
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if (pd.notnull(row["sold_price"]) and row["status"] == "SOLD")
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else row["list_price"]
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)
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/ row["lot_acres"]
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)
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if pd.notnull(row["lot_acres"])
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and row["lot_acres"] > 0
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and (pd.notnull(row["sold_price"]) or pd.notnull(row["list_price"]))
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else None
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),
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axis=1,
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)
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properties["ppa"] = properties["ppa"].astype("Int64")
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selected_columns = [
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"property_url",
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"property_id",
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"style",
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"status",
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"street",
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"city",
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"state",
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"zip_code",
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"county",
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"list_date",
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"last_sold_date",
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"list_price",
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"sold_price",
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"lot_sqft",
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"lot_acres",
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"ppa",
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]
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properties = properties[selected_columns]
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return properties
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def output_to_excel(zip_code, sold_df, pending_df):
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root_folder = os.getcwd()
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zip_folder = os.path.join(root_folder, "zips", zip_code)
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# Create zip code folder if it doesn't exist
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os.makedirs(zip_folder, exist_ok=True)
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# Define file paths
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sold_file = os.path.join(zip_folder, f"{zip_code}_sold.xlsx")
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pending_file = os.path.join(zip_folder, f"{zip_code}_pending.xlsx")
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# Save individual sold and pending files
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sold_df.to_excel(sold_file, index=False)
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pending_df.to_excel(pending_file, index=False)
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zip_codes = map(
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str,
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[
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22920,
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77024,
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78028,
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24553,
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22967,
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22971,
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22922,
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22958,
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22969,
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22949,
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22938,
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24599,
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24562,
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22976,
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24464,
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22964,
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24581,
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],
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)
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combined_df = pd.DataFrame()
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for zip in zip_codes:
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sold_df = get_property_details(zip, "sold")
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pending_df = get_property_details(zip, "pending")
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combined_df = pd.concat([combined_df, sold_df, pending_df], ignore_index=True)
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output_to_excel(zip, sold_df, pending_df)
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combined_file = os.path.join(os.getcwd(), "zips", "combined.xlsx")
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combined_df.to_excel(combined_file, index=False)
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