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Zillow Research offers datasets and reports on real estate market trends, home values, rental prices, housing affordability and mortgage rates in the United States.
Zillow Research data is available as CSV downloads from zillow.com/research/data. Engineers use `pandas.read_csv()` on these files, then `pd.melt()` to convert wide-format date columns to long-format time-series. Geographic joins with Census FIPS codes enable spatial analysis.
Zillow's home value indices are key features for AI real estate valuation models. Train gradient boosting or LSTM models on ZHVI time-series to predict future price movements, or build a RAG system on Zillow's market reports so AI real estate assistants can answer 'Is Austin still a seller's market?' with data.
# pip install pandas
import pandas as pd
# Zillow Home Value Index (ZHVI) — All Homes, Metro & US
url = "https://files.zillowstatic.com/research/public_csvs/zhvi/Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv"
df = pd.read_csv(url)
# Melt to long format
id_cols = ["RegionID", "RegionName", "StateName"]
df_long = df.melt(id_vars=id_cols, var_name="date", value_name="zhvi")
df_long["date"] = pd.to_datetime(df_long["date"])
print(df_long[df_long["RegionName"] == "New York, NY"].tail(12))Official dataset source
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