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zillow

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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This dataset is comprised of seven different configurations of data covering different aspects of the housing market in the United States. All data is provided by Zillow. The seven configurations are: home_values_forecasts, new_construction, for_sale_listings, rentals, sales, home_values, and days_on_market. Each configuration has a different set of features and target variables. The data is provided in JSONL format."""
import json
import os
import datasets
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Housing Data},
author={Zillow},
year={2024}
}
"""
_DESCRIPTION = """\
This dataset is comprised of seven different configurations of data covering different aspects of the housing market in the United States. All data is provided by Zillow. The seven configurations are: home_values_forecasts, new_construction, for_sale_listings, rentals, sales, home_values, and days_on_market. Each configuration has a different set of features and target variables. The data is provided in JSONL format.
"""
_HOMEPAGE = "https://www.zillow.com/research/data/"
_LICENSE = "other"
HOME_TYPES = [
"multifamily",
"condo/co-op",
"SFR",
"all homes",
"all homes plus multifamily",
]
REGION_TYPES = [
"zip",
"city",
"county",
"msa",
"state",
"country",
]
class Zillow(datasets.GeneratorBasedBuilder):
"""Housing data in the United States provided by Zillow"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="days_on_market",
version=VERSION,
description="This dataset covers days-on-market data for the United States",
),
datasets.BuilderConfig(
name="for_sale_listings",
version=VERSION,
description="This dataset covers for sale listings for the United States",
),
datasets.BuilderConfig(
name="home_values",
version=VERSION,
description="This dataset covers home values for the United States",
),
datasets.BuilderConfig(
name="home_values_forecasts",
version=VERSION,
description="This data covers home value forecasts for the United States",
),
datasets.BuilderConfig(
name="new_construction",
version=VERSION,
description="This dataset covers new construction data for the United States",
),
datasets.BuilderConfig(
name="rentals",
version=VERSION,
description="This dataset covers rental data for the United States",
),
datasets.BuilderConfig(
name="sales",
version=VERSION,
description="This dataset covers sales data for the United States",
),
]
DEFAULT_CONFIG_NAME = ""
def _info(self):
if self.config.name == "home_values_forecasts":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.ClassLabel(
num_classes=len(REGION_TYPES), names=REGION_TYPES
),
"State": datasets.Value(dtype="string", id="State"),
"City": datasets.Value(dtype="string", id="City"),
"Metro": datasets.Value(dtype="string", id="Metro"),
"County": datasets.Value(dtype="string", id="County"),
"Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
"Month Over Month % (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Month Over Month % (Smoothed) (Seasonally Adjusted)",
),
"Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)",
),
"Year Over Year % (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Year Over Year % (Smoothed) (Seasonally Adjusted)",
),
"Month Over Month %": datasets.Value(
dtype="float32", id="Month Over Month %"
),
"Quarter Over Quarter %": datasets.Value(
dtype="float32", id="Quarter Over Quarter %"
),
"Year Over Year %": datasets.Value(
dtype="float32", id="Year Over Year %"
),
}
)
elif self.config.name == "new_construction":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.ClassLabel(
num_classes=len(REGION_TYPES), names=REGION_TYPES
),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.ClassLabel(
num_classes=len(HOME_TYPES), names=HOME_TYPES
),
"Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
"Median Sale Price": datasets.Value(
dtype="float32", id="Median Sale Price"
),
"Median Sale Price per Sqft": datasets.Value(
dtype="float32", id="Sale Price per Sqft"
),
"Sales Count": datasets.Value(dtype="int32", id="Sales Count"),
}
)
elif self.config.name == "for_sale_listings":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.ClassLabel(
num_classes=len(REGION_TYPES), names=REGION_TYPES
),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.ClassLabel(
num_classes=len(HOME_TYPES), names=HOME_TYPES
),
"Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
"Median Listing Price": datasets.Value(
dtype="float32", id="Median Listing Price"
),
"Median Listing Price (Smoothed)": datasets.Value(
dtype="float32", id="Median Listing Price (Smoothed)"
),
"New Listings": datasets.Value(dtype="int32", id="New Listings"),
"New Listings (Smoothed)": datasets.Value(
dtype="int32", id="New Listings (Smoothed)"
),
"New Pending (Smoothed)": datasets.Value(
dtype="int32", id="New Pending (Smoothed)"
),
"New Pending": datasets.Value(dtype="int32", id="New Pending"),
}
)
elif self.config.name == "rentals":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.ClassLabel(
num_classes=len(REGION_TYPES), names=REGION_TYPES
),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.ClassLabel(
num_classes=len(HOME_TYPES), names=HOME_TYPES
),
"Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
"Rent (Smoothed)": datasets.Value(
dtype="float32", id="Rent (Smoothed)"
),
"Rent (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32", id="Rent (Smoothed) (Seasonally Adjusted)"
),
}
)
elif self.config.name == "sales":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.ClassLabel(
num_classes=len(REGION_TYPES), names=REGION_TYPES
),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.ClassLabel(
num_classes=len(HOME_TYPES), names=HOME_TYPES
),
"Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
"Mean Sale to List Ratio (Smoothed)": datasets.Value(
dtype="float32", id="Mean Sale to List Ratio (Smoothed)"
),
"Median Sale to List Ratio": datasets.Value(
dtype="float32", id="Median Sale to List Ratio"
),
"Median Sale Price": datasets.Value(
dtype="float32", id="Median Sale Price"
),
"Median Sale Price (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Median Sale Price (Smoothed) (Seasonally Adjusted)",
),
"Median Sale Price (Smoothed)": datasets.Value(
dtype="float32", id="Median Sale Price (Smoothed)"
),
"Median Sale to List Ratio (Smoothed)": datasets.Value(
dtype="float32", id="Median Sale to List Ratio (Smoothed)"
),
"% Sold Below List": datasets.Value(
dtype="float32", id="% Sold Below List"
),
"% Sold Below List (Smoothed)": datasets.Value(
dtype="float32", id="% Sold Below List (Smoothed)"
),
"% Sold Above List": datasets.Value(
dtype="float32", id="% Sold Above List"
),
"% Sold Above List (Smoothed)": datasets.Value(
dtype="float32", id="% Sold Above List (Smoothed)"
),
"Mean Sale to List Ratio": datasets.Value(
dtype="float32", id="Mean Sale to List Ratio"
),
}
)
elif self.config.name == "home_values":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
# this is the problem
"Region Type": datasets.ClassLabel(
num_classes=len(REGION_TYPES), names=REGION_TYPES
),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.ClassLabel(
num_classes=len(HOME_TYPES), names=HOME_TYPES
),
"Bedroom Count": datasets.ClassLabel(
num_classes=6,
names=[
"1-Bedroom",
"2-Bedrooms",
"3-Bedrooms",
"4-Bedrooms",
"5+-Bedrooms",
"All Bedrooms",
],
),
"Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
"Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)",
),
"Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)",
),
"Top Tier ZHVI (Smoothed) (Seasonally Adjusted)": datasets.Value(
dtype="float32",
id="Top Tier ZHVI (Smoothed) (Seasonally Adjusted)",
),
}
)
elif self.config.name == "days_on_market":
features = datasets.Features(
{
"Region ID": datasets.Value(dtype="string", id="Region ID"),
"Size Rank": datasets.Value(dtype="int32", id="Size Rank"),
"Region": datasets.Value(dtype="string", id="Region"),
"Region Type": datasets.ClassLabel(
num_classes=len(REGION_TYPES), names=REGION_TYPES
),
"State": datasets.Value(dtype="string", id="State"),
"Home Type": datasets.ClassLabel(
num_classes=len(HOME_TYPES), names=HOME_TYPES
),
"Date": datasets.Value(dtype="timestamp[ms]", id="Date"),
"Mean Listings Price Cut Amount (Smoothed)": datasets.Value(
dtype="float32", id="Mean Listings Price Cut Amount (Smoothed)"
),
"Percent Listings Price Cut": datasets.Value(
dtype="float32", id="Percent Listings Price Cut"
),
"Mean Listings Price Cut Amount": datasets.Value(
dtype="float32", id="Mean Listings Price Cut Amount"
),
"Percent Listings Price Cut (Smoothed)": datasets.Value(
dtype="float32", id="Percent Listings Price Cut (Smoothed)"
),
"Median Days on Pending (Smoothed)": datasets.Value(
dtype="float32", id="Median Days on Pending (Smoothed)"
),
"Median Days on Pending": datasets.Value(
dtype="float32", id="Median Days on Pending"
),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# Each config file is a JSONL file produced by the config's corresponding python script in the `processors` directory.
file_path = os.path.join("processed", self.config.name + ".jsonl")
file_train = dl_manager.download(file_path)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": file_train,
"split": "train",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
if self.config.name == "home_values_forecasts":
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"City": data["City"],
"Metro": data["Metro"],
"County": data["County"],
"Date": data["Date"],
"Month Over Month % (Smoothed) (Seasonally Adjusted)": data[
"Month Over Month % (Smoothed) (Seasonally Adjusted)"
],
"Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)": data[
"Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)"
],
"Year Over Year % (Smoothed) (Seasonally Adjusted)": data[
"Year Over Year % (Smoothed) (Seasonally Adjusted)"
],
"Month Over Month %": data["Month Over Month %"],
"Quarter Over Quarter %": data["Quarter Over Quarter %"],
"Year Over Year %": data["Year Over Year %"],
}
elif self.config.name == "new_construction":
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Date": data["Date"],
"Median Sale Price": data["Median Sale Price"],
"Median Sale Price per Sqft": data[
"Median Sale Price per Sqft"
],
"Sales Count": data["Sales Count"],
}
elif self.config.name == "for_sale_listings":
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Date": data["Date"],
"Median Listing Price": data["Median Listing Price"],
"Median Listing Price (Smoothed)": data[
"Median Listing Price (Smoothed)"
],
"New Listings": data["New Listings"],
"New Listings (Smoothed)": data["New Listings (Smoothed)"],
"New Pending (Smoothed)": data["New Pending (Smoothed)"],
"New Pending": data["New Pending"],
}
elif self.config.name == "rentals":
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Date": data["Date"],
"Rent (Smoothed)": data["Rent (Smoothed)"],
"Rent (Smoothed) (Seasonally Adjusted)": data[
"Rent (Smoothed) (Seasonally Adjusted)"
],
}
elif self.config.name == "sales":
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Date": data["Date"],
"Mean Sale to List Ratio (Smoothed)": data[
"Mean Sale to List Ratio (Smoothed)"
],
"Median Sale to List Ratio": data["Median Sale to List Ratio"],
"Median Sale Price": data["Median Sale Price"],
"Median Sale Price (Smoothed) (Seasonally Adjusted)": data[
"Median Sale Price (Smoothed) (Seasonally Adjusted)"
],
"Median Sale Price (Smoothed)": data[
"Median Sale Price (Smoothed)"
],
"Median Sale to List Ratio (Smoothed)": data[
"Median Sale to List Ratio (Smoothed)"
],
"% Sold Below List": data["% Sold Below List"],
"% Sold Below List (Smoothed)": data[
"% Sold Below List (Smoothed)"
],
"% Sold Above List": data["% Sold Above List"],
"% Sold Above List (Smoothed)": data[
"% Sold Above List (Smoothed)"
],
"Mean Sale to List Ratio": data["Mean Sale to List Ratio"],
}
elif self.config.name == "home_values":
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Bedroom Count": data["Bedroom Count"],
"Date": data["Date"],
"Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
"Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)"
],
"Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
"Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)"
],
"Top Tier ZHVI (Smoothed) (Seasonally Adjusted)": data[
"Top Tier ZHVI (Smoothed) (Seasonally Adjusted)"
],
}
elif self.config.name == "days_on_market":
yield key, {
"Region ID": data["Region ID"],
"Size Rank": data["Size Rank"],
"Region": data["Region"],
"Region Type": data["Region Type"],
"State": data["State"],
"Home Type": data["Home Type"],
"Date": data["Date"],
"Mean Listings Price Cut Amount (Smoothed)": data[
"Mean Listings Price Cut Amount (Smoothed)"
],
"Percent Listings Price Cut": data[
"Percent Listings Price Cut"
],
"Mean Listings Price Cut Amount": data[
"Mean Listings Price Cut Amount"
],
"Percent Listings Price Cut (Smoothed)": data[
"Percent Listings Price Cut (Smoothed)"
],
"Median Days on Pending (Smoothed)": data[
"Median Days on Pending (Smoothed)"
],
"Median Days on Pending": data["Median Days on Pending"],
}