enh: add agent (#68)

- alt photos bug fix (#57)

.github/workflows/publish-to-pypi.yml

@@ -30,4 +30,4 @@ jobs:
       if: startsWith(github.ref, 'refs/tags')
       uses: pypa/gh-action-pypi-publish@release/v1
       with:
-        password: ${{ secrets.PYPI_API_TOKEN }}
+        password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -3,4 +3,5 @@
 **/__pycache__/
 **/.pytest_cache/
 *.pyc
-/.ipynb_checkpoints/
+/.ipynb_checkpoints/
+*.csv

.pre-commit-config.yaml

@@ -0,0 +1,21 @@
+---
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.2.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-added-large-files
+      - id: check-yaml
+  - repo: https://github.com/adrienverge/yamllint
+    rev: v1.29.0
+    hooks:
+      - id: yamllint
+        verbose: true # create awareness of linter findings
+        args: ["-d", "{extends: relaxed, rules: {line-length: {max: 120}}}"]
+  - repo: https://github.com/psf/black
+    rev: 24.2.0
+    hooks:
+    - id: black
+      language_version: python
+      args: [--line-length=120, --quiet]

HomeHarvest_Demo.ipynb

@@ -1,73 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cb48903e-5021-49fe-9688-45cd0bc05d0f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from homeharvest import scrape_property\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "156488ce-0d5f-43c5-87f4-c33e9c427860",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option('display.max_columns', None)  # Show all columns\n",
-    "pd.set_option('display.max_rows', None)     # Show all rows\n",
-    "pd.set_option('display.width', None)        # Auto-adjust display width to fit console\n",
-    "pd.set_option('display.max_colwidth', 50)   # Limit max column width to 50 characters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c8b9744-8606-4e9b-8add-b90371a249a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scrape_property(\n",
-    "    location=\"dallas\", site_name=\"zillow\", listing_type=\"for_sale\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ab7b4c21-da1d-4713-9df4-d7425d8ce21e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scrape_property(\n",
-    "    location=\"dallas\", site_name=\"redfin\", listing_type=\"for_sale\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

README.md

@@ -1,33 +1,148 @@
-# HomeHarvest
+<img src="https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/d1a2bf8b-09f5-4c57-b33a-0ada8a34f12d" width="400">
 
-**HomeHarvest** aims to be the top Python real estate scraping library.
+**HomeHarvest** is a real estate scraping library that extracts and formats data in the style of MLS listings.
 
-_**Under Consideration**: We're looking into the possibility of an Excel plugin to cater to a broader audience._
+**Not technical?** Try out the web scraping tool on our site at [tryhomeharvest.com](https://tryhomeharvest.com).
+
+*Looking to build a data-focused software product?* **[Book a call](https://bunsly.com)** *to work with us.*
+
+## HomeHarvest Features
+
+- **Source**: Fetches properties directly from **Realtor.com**.
+- **Data Format**: Structures data to resemble MLS listings.
+- **Export Flexibility**: Options to save as either CSV or Excel.
+
+[Video Guide for HomeHarvest](https://youtu.be/J1qgNPgmSLI) - _updated for release v0.3.4_
+
+![homeharvest](https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/b3d5d727-e67b-4a9f-85d8-1e65fd18620a)
 
 ## Installation
 
 ```bash
-pip install --upgrade homeharvest
+pip install -U homeharvest
 ```
+  _Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_
 
-## Example Usage
-```
+## Usage
+
+### Python
+
+```py
 from homeharvest import scrape_property
+from datetime import datetime
+
+# Generate filename based on current timestamp
+current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+filename = f"HomeHarvest_{current_timestamp}.csv"
 
 properties = scrape_property(
-    location="85281", site_name="zillow", listing_type="for_rent"
+  location="San Diego, CA",
+  listing_type="sold",  # or (for_sale, for_rent, pending)
+  past_days=30,  # sold in last 30 days - listed in last 30 days if (for_sale, for_rent)
+
+  # date_from="2023-05-01", # alternative to past_days
+  # date_to="2023-05-28",
+  # foreclosure=True
+
+  # mls_only=True,  # only fetch MLS listings
 )
-print(properties)
+print(f"Number of properties: {len(properties)}")
+
+# Export to csv
+properties.to_csv(filename, index=False)
+print(properties.head())
 ```
 
-### Site Name Options
+## Output
+```plaintext
+>>> properties.head()
+    MLS       MLS # Status          Style  ...     COEDate LotSFApx PrcSqft Stories
+0  SDCA   230018348   SOLD         CONDOS  ...  2023-10-03   290110     803       2
+1  SDCA   230016614   SOLD      TOWNHOMES  ...  2023-10-03     None     838       3
+2  SDCA   230016367   SOLD         CONDOS  ...  2023-10-03    30056     649       1
+3  MRCA  NDP2306335   SOLD  SINGLE_FAMILY  ...  2023-10-03     7519     661       2
+4  SDCA   230014532   SOLD         CONDOS  ...  2023-10-03     None     752       1
+[5 rows x 22 columns]
+```
 
-- `zillow`
-- `redfin`
-- `realtor.com`
+### Parameters for `scrape_property()`
+```
+Required
+├── location (str): The address in various formats - this could be just a zip code, a full address, or city/state, etc.
+└── listing_type (option): Choose the type of listing.
+    - 'for_rent'
+    - 'for_sale'
+    - 'sold'
+    - 'pending'
 
-### Listing Types
+Optional
+├── radius (decimal): Radius in miles to find comparable properties based on individual addresses.
+│    Example: 5.5 (fetches properties within a 5.5-mile radius if location is set to a specific address; otherwise, ignored)
+│
+├── past_days (integer): Number of past days to filter properties. Utilizes 'last_sold_date' for 'sold' listing types, and 'list_date' for others (for_rent, for_sale).
+│    Example: 30 (fetches properties listed/sold in the last 30 days)
+│
+├── date_from, date_to (string): Start and end dates to filter properties listed or sold, both dates are required.
+|    (use this to get properties in chunks as there's a 10k result limit)
+│    Format for both must be "YYYY-MM-DD".
+│    Example: "2023-05-01", "2023-05-15" (fetches properties listed/sold between these dates)
+│
+├── mls_only (True/False): If set, fetches only MLS listings (mainly applicable to 'sold' listings)
+│
+├── foreclosure (True/False): If set, fetches only foreclosures
+│
+└── proxy (string): In format 'http://user:pass@host:port'
+```
 
-- `for_rent`
-- `for_sale`
-- `sold`
+### Property Schema
+```plaintext
+Property
+├── Basic Information:
+│ ├── property_url
+│ ├── mls
+│ ├── mls_id
+│ └── status
+
+├── Address Details:
+│ ├── street
+│ ├── unit
+│ ├── city
+│ ├── state
+│ └── zip_code
+
+├── Property Description:
+│ ├── style
+│ ├── beds
+│ ├── full_baths
+│ ├── half_baths
+│ ├── sqft
+│ ├── year_built
+│ ├── stories
+│ └── lot_sqft
+
+├── Property Listing Details:
+│ ├── days_on_mls
+│ ├── list_price
+│ ├── list_date
+│ ├── pending_date
+│ ├── sold_price
+│ ├── last_sold_date
+│ ├── price_per_sqft
+│ ├── parking_garage
+│ └── hoa_fee
+
+├── Location Details:
+│ ├── latitude
+│ ├── longitude
+
+├── Agent Info:
+│ ├── agent
+│ ├── broker
+│ └── broker_phone
+```
+
+### Exceptions
+The following exceptions may be raised when using HomeHarvest:
+
+- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
+- `InvalidDate` - date_from or date_to is not in the format YYYY-MM-DD

examples/HomeHarvest_Demo.ipynb

@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb48903e-5021-49fe-9688-45cd0bc05d0f",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "from homeharvest import scrape_property\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "156488ce-0d5f-43c5-87f4-c33e9c427860",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_columns', None)  # Show all columns\n",
+    "pd.set_option('display.max_rows', None)     # Show all rows\n",
+    "pd.set_option('display.width', None)        # Auto-adjust display width to fit console\n",
+    "pd.set_option('display.max_colwidth', 50)   # Limit max column width to 50 characters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c8b9744-8606-4e9b-8add-b90371a249a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check for sale properties\n",
+    "scrape_property(\n",
+    "    location=\"dallas\",\n",
+    "    listing_type=\"for_sale\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaf86093",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# search a specific address\n",
+    "scrape_property(\n",
+    "    location=\"2530 Al Lipscomb Way\",\n",
+    "    listing_type=\"for_sale\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab7b4c21-da1d-4713-9df4-d7425d8ce21e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check rentals\n",
+    "scrape_property(\n",
+    "    location=\"chicago, illinois\",\n",
+    "    listing_type=\"for_rent\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af280cd3",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# check sold properties\n",
+    "properties = scrape_property(\n",
+    "    location=\"90210\",\n",
+    "    listing_type=\"sold\",\n",
+    "    past_days=10\n",
+    ")\n",
+    "display(properties)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "628c1ce2",
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# display clickable URLs\n",
+    "from IPython.display import display, HTML\n",
+    "properties['property_url'] = '<a href=\"' + properties['property_url'] + '\" target=\"_blank\">' + properties['property_url'] + '</a>'\n",
+    "\n",
+    "html = properties.to_html(escape=False)\n",
+    "truncate_width = f'<style>.dataframe td {{ max-width: 200px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}</style>{html}'\n",
+    "display(HTML(truncate_width))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

examples/HomeHarvest_Demo.py

@@ -0,0 +1,20 @@
+from homeharvest import scrape_property
+from datetime import datetime
+
+# Generate filename based on current timestamp
+current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+filename = f"HomeHarvest_{current_timestamp}.csv"
+
+properties = scrape_property(
+    location="San Diego, CA",
+    listing_type="sold",  # or (for_sale, for_rent)
+    past_days=30,  # sold in last 30 days - listed in last x days if (for_sale, for_rent)
+    # pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
+    # mls_only=True,  # only fetch MLS listings
+    # proxy="http://user:pass@host:port"  # use a proxy to change your IP address
+)
+print(f"Number of properties: {len(properties)}")
+
+# Export to csv
+properties.to_csv(filename, index=False)
+print(properties.head())

homeharvest/__init__.py

@@ -1,117 +1,54 @@
-from .core.scrapers.redfin import RedfinScraper
-from .core.scrapers.realtor import RealtorScraper
-from .core.scrapers.zillow import ZillowScraper
-from .core.scrapers.models import ListingType, Property, Building, SiteName
-from .core.scrapers import ScraperInput
-from .exceptions import InvalidSite, InvalidListingType
-from typing import Union
+import warnings
 import pandas as pd
-
-
-_scrapers = {
-    "redfin": RedfinScraper,
-    "realtor.com": RealtorScraper,
-    "zillow": ZillowScraper,
-}
-
-
-def validate_input(site_name: str, listing_type: str) -> None:
-    if site_name.lower() not in _scrapers:
-        raise InvalidSite(f"Provided site, '{site_name}', does not exist.")
-
-    if listing_type.upper() not in ListingType.__members__:
-        raise InvalidListingType(
-            f"Provided listing type, '{listing_type}', does not exist."
-        )
-
-
-def get_ordered_properties(result: Union[Building, Property]) -> list[str]:
-    if isinstance(result, Property):
-        return [
-            "listing_type",
-            "address_one",
-            "city",
-            "state",
-            "zip_code",
-            "address_two",
-            "url",
-            "property_type",
-            "price",
-            "beds",
-            "baths",
-            "square_feet",
-            "price_per_square_foot",
-            "lot_size",
-            "stories",
-            "year_built",
-            "agent_name",
-            "mls_id",
-            "description",
-        ]
-    elif isinstance(result, Building):
-        return [
-            "address_one",
-            "city",
-            "state",
-            "zip_code",
-            "address_two",
-            "url",
-            "num_units",
-            "min_unit_price",
-            "max_unit_price",
-            "avg_unit_price",
-            "listing_type",
-        ]
-    return []
-
-
-def process_result(result: Union[Building, Property]) -> pd.DataFrame:
-    prop_data = result.__dict__
-
-    address_data = prop_data["address"]
-    prop_data["site_name"] = prop_data["site_name"]
-    prop_data["listing_type"] = prop_data["listing_type"].value
-    prop_data["property_type"] = prop_data["property_type"].value.lower() if prop_data.get("property_type") else None
-    prop_data["address_one"] = address_data.address_one
-    prop_data["city"] = address_data.city
-    prop_data["state"] = address_data.state
-    prop_data["zip_code"] = address_data.zip_code
-    prop_data["address_two"] = address_data.address_two
-
-    del prop_data["address"]
-
-    properties_df = pd.DataFrame([prop_data])
-    properties_df = properties_df[get_ordered_properties(result)]
-
-    return properties_df
+from .core.scrapers import ScraperInput
+from .utils import process_result, ordered_properties, validate_input, validate_dates
+from .core.scrapers.realtor import RealtorScraper
+from .core.scrapers.models import ListingType
 
 
 def scrape_property(
     location: str,
-    site_name: str,
-    listing_type: str = "for_sale",  #: for_sale, for_rent, sold
+    listing_type: str = "for_sale",
+    radius: float = None,
+    mls_only: bool = False,
+    past_days: int = None,
+    proxy: str = None,
+    date_from: str = None,
+    date_to: str = None,
+    foreclosure: bool = None,
 ) -> pd.DataFrame:
     """
-    Scrape property from various sites from a given location and listing type.
-
-    :returns: pd.DataFrame
-    :param location: US Location (e.g. 'San Francisco, CA', 'Cook County, IL', '85281', '2530 Al Lipscomb Way')
-    :param site_name: Site name (e.g. 'realtor.com', 'zillow', 'redfin')
-    :param listing_type: Listing type (e.g. 'for_sale', 'for_rent', 'sold')
-    :return: pd.DataFrame containing properties
+    Scrape properties from Realtor.com based on a given location and listing type.
+    :param location: Location to search (e.g. "Dallas, TX", "85281", "2530 Al Lipscomb Way")
+    :param listing_type: Listing Type (for_sale, for_rent, sold)
+    :param radius: Get properties within _ (e.g. 1.0) miles. Only applicable for individual addresses.
+    :param mls_only: If set, fetches only listings with MLS IDs.
+    :param past_days: Get properties sold or listed (dependent on your listing_type) in the last _ days.
+    :param date_from, date_to: Get properties sold or listed (dependent on your listing_type) between these dates. format: 2021-01-28
+    :param proxy: Proxy to use for scraping
     """
-
-    validate_input(site_name, listing_type)
+    validate_input(listing_type)
+    validate_dates(date_from, date_to)
 
     scraper_input = ScraperInput(
         location=location,
         listing_type=ListingType[listing_type.upper()],
-        site_name=site_name.lower(),
+        proxy=proxy,
+        radius=radius,
+        mls_only=mls_only,
+        last_x_days=past_days,
+        date_from=date_from,
+        date_to=date_to,
+        foreclosure=foreclosure,
     )
 
-    site = _scrapers[site_name.lower()](scraper_input)
+    site = RealtorScraper(scraper_input)
     results = site.search()
 
     properties_dfs = [process_result(result) for result in results]
+    if not properties_dfs:
+        return pd.DataFrame()
 
-    return pd.concat(properties_dfs, ignore_index=True)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=FutureWarning)
+        return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties]

homeharvest/cli.py

@@ -0,0 +1,85 @@
+import argparse
+import datetime
+from homeharvest import scrape_property
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Home Harvest Property Scraper")
+    parser.add_argument("location", type=str, help="Location to scrape (e.g., San Francisco, CA)")
+
+    parser.add_argument(
+        "-l",
+        "--listing_type",
+        type=str,
+        default="for_sale",
+        choices=["for_sale", "for_rent", "sold", "pending"],
+        help="Listing type to scrape",
+    )
+
+    parser.add_argument(
+        "-o",
+        "--output",
+        type=str,
+        default="excel",
+        choices=["excel", "csv"],
+        help="Output format",
+    )
+
+    parser.add_argument(
+        "-f",
+        "--filename",
+        type=str,
+        default=None,
+        help="Name of the output file (without extension)",
+    )
+
+    parser.add_argument("-p", "--proxy", type=str, default=None, help="Proxy to use for scraping")
+    parser.add_argument(
+        "-d",
+        "--days",
+        type=int,
+        default=None,
+        help="Sold/listed in last _ days filter.",
+    )
+
+    parser.add_argument(
+        "-r",
+        "--radius",
+        type=float,
+        default=None,
+        help="Get comparable properties within _ (eg. 0.0) miles. Only applicable for individual addresses.",
+    )
+    parser.add_argument(
+        "-m",
+        "--mls_only",
+        action="store_true",
+        help="If set, fetches only MLS listings.",
+    )
+
+    args = parser.parse_args()
+
+    result = scrape_property(
+        args.location,
+        args.listing_type,
+        radius=args.radius,
+        proxy=args.proxy,
+        mls_only=args.mls_only,
+        past_days=args.days,
+    )
+
+    if not args.filename:
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        args.filename = f"HomeHarvest_{timestamp}"
+
+    if args.output == "excel":
+        output_filename = f"{args.filename}.xlsx"
+        result.to_excel(output_filename, index=False)
+        print(f"Excel file saved as {output_filename}")
+    elif args.output == "csv":
+        output_filename = f"{args.filename}.csv"
+        result.to_csv(output_filename, index=False)
+        print(f"CSV file saved as {output_filename}")
+
+
+if __name__ == "__main__":
+    main()

homeharvest/core/scrapers/__init__.py

@@ -1,5 +1,6 @@
 from dataclasses import dataclass
 import requests
+import uuid
 from .models import Property, ListingType, SiteName
 
 
@@ -7,31 +8,68 @@ from .models import Property, ListingType, SiteName
 class ScraperInput:
     location: str
     listing_type: ListingType
-    site_name: str
-    proxy_url: str | None = None
+    radius: float | None = None
+    mls_only: bool | None = None
+    proxy: str | None = None
+    last_x_days: int | None = None
+    date_from: str | None = None
+    date_to: str | None = None
+    foreclosure: bool | None = None
 
 
 class Scraper:
-    def __init__(self, scraper_input: ScraperInput):
+    def __init__(
+        self,
+        scraper_input: ScraperInput,
+        session: requests.Session = None,
+    ):
         self.location = scraper_input.location
         self.listing_type = scraper_input.listing_type
 
-        self.session = requests.Session()
+        if not session:
+            self.session = requests.Session()
+            self.session.headers.update(
+                {
+                    "auth": f"Bearer {self.get_access_token()}",
+                    "apollographql-client-name": "com.move.Realtor-apollo-ios",
+                }
+            )
+        else:
+            self.session = session
+
+        if scraper_input.proxy:
+            proxy_url = scraper_input.proxy
+            proxies = {"http": proxy_url, "https": proxy_url}
+            self.session.proxies.update(proxies)
+
         self.listing_type = scraper_input.listing_type
-        self.site_name = scraper_input.site_name
+        self.radius = scraper_input.radius
+        self.last_x_days = scraper_input.last_x_days
+        self.mls_only = scraper_input.mls_only
+        self.date_from = scraper_input.date_from
+        self.date_to = scraper_input.date_to
+        self.foreclosure = scraper_input.foreclosure
 
-        if scraper_input.proxy_url:
-            self.session.proxies = {
-                "http": scraper_input.proxy_url,
-                "https": scraper_input.proxy_url,
-            }
-
-    def search(self) -> list[Property]:
-        ...
+    def search(self) -> list[Property]: ...
 
     @staticmethod
-    def _parse_home(home) -> Property:
-        ...
+    def _parse_home(home) -> Property: ...
 
-    def handle_location(self):
-        ...
+    def handle_location(self): ...
+
+    def get_access_token(self):
+        url = "https://graph.realtor.com/auth/token"
+
+        payload = f'{{"client_app_id":"rdc_mobile_native,24.20.4.149916,iphone","device_id":"{str(uuid.uuid4()).upper()}","grant_type":"device_mobile"}}'
+        headers = {
+            "Host": "graph.realtor.com",
+            "x-client-version": "24.20.4.149916",
+            "accept": "*/*",
+            "content-type": "Application/json",
+            "user-agent": "Realtor.com/24.20.4.149916 CFNetwork/1410.0.3 Darwin/22.6.0",
+            "accept-language": "en-US,en;q=0.9",
+        }
+        response = requests.post(url, headers=headers, data=payload)
+
+        data = response.json()
+        return data["access_token"]

homeharvest/core/scrapers/models.py

@@ -1,5 +1,6 @@
 from dataclasses import dataclass
 from enum import Enum
+from typing import Optional
 
 
 class SiteName(Enum):
@@ -7,78 +8,92 @@ class SiteName(Enum):
     REDFIN = "redfin"
     REALTOR = "realtor.com"
 
+    @classmethod
+    def get_by_value(cls, value):
+        for item in cls:
+            if item.value == value:
+                return item
+        raise ValueError(f"{value} not found in {cls}")
+
 
 class ListingType(Enum):
-    FOR_SALE = "for_sale"
-    FOR_RENT = "for_rent"
-    SOLD = "sold"
+    FOR_SALE = "FOR_SALE"
+    FOR_RENT = "FOR_RENT"
+    PENDING = "PENDING"
+    SOLD = "SOLD"
 
 
 class PropertyType(Enum):
-    HOUSE = "HOUSE"
-    CONDO = "CONDO"
-    TOWNHOUSE = "TOWNHOUSE"
-    SINGLE_FAMILY = "SINGLE_FAMILY"
-    MULTI_FAMILY = "MULTI_FAMILY"
-    MANUFACTURED = "MANUFACTURED"
     APARTMENT = "APARTMENT"
+    BUILDING = "BUILDING"
+    COMMERCIAL = "COMMERCIAL"
+    CONDO_TOWNHOME = "CONDO_TOWNHOME"
+    CONDO_TOWNHOME_ROWHOME_COOP = "CONDO_TOWNHOME_ROWHOME_COOP"
+    CONDO = "CONDO"
+    CONDOS = "CONDOS"
+    COOP = "COOP"
+    DUPLEX_TRIPLEX = "DUPLEX_TRIPLEX"
+    FARM = "FARM"
+    INVESTMENT = "INVESTMENT"
     LAND = "LAND"
+    MOBILE = "MOBILE"
+    MULTI_FAMILY = "MULTI_FAMILY"
+    RENTAL = "RENTAL"
+    SINGLE_FAMILY = "SINGLE_FAMILY"
+    TOWNHOMES = "TOWNHOMES"
     OTHER = "OTHER"
 
-    @classmethod
-    def from_int_code(cls, code):
-        mapping = {
-            1: cls.HOUSE,
-            2: cls.CONDO,
-            3: cls.TOWNHOUSE,
-            4: cls.MULTI_FAMILY,
-            5: cls.LAND,
-            6: cls.OTHER,
-            8: cls.SINGLE_FAMILY,
-            13: cls.SINGLE_FAMILY,
-        }
-
-        return mapping.get(code, cls.OTHER)
-
 
 @dataclass
 class Address:
-    address_one: str
-    city: str
-    state: str
-    zip_code: str
-
-    address_two: str | None = None
-
-
-@dataclass()
-class Realty:
-    site_name: str
-    address: Address
-    url: str
-    listing_type: ListingType | None = None
+    street: str | None = None
+    unit: str | None = None
+    city: str | None = None
+    state: str | None = None
+    zip: str | None = None
 
 
 @dataclass
-class Property(Realty):
-    price: int | None = None
+class Description:
+    primary_photo: str | None = None
+    alt_photos: list[str] | None = None
+    style: PropertyType | None = None
     beds: int | None = None
-    baths: float | None = None
-    stories: int | None = None
+    baths_full: int | None = None
+    baths_half: int | None = None
+    sqft: int | None = None
+    lot_sqft: int | None = None
+    sold_price: int | None = None
     year_built: int | None = None
-    square_feet: int | None = None
-    price_per_square_foot: int | None = None
-    mls_id: str | None = None
-
-    agent_name: str | None = None
-    property_type: PropertyType | None = None
-    lot_size: int | None = None
-    description: str | None = None
+    garage: float | None = None
+    stories: int | None = None
 
 
 @dataclass
-class Building(Realty):
-    num_units: int | None = None
-    min_unit_price: int | None = None
-    max_unit_price: int | None = None
-    avg_unit_price: int | None = None
+class Agent:
+    name: str | None = None
+    phone: str | None = None
+
+
+@dataclass
+class Property:
+    property_url: str
+    mls: str | None = None
+    mls_id: str | None = None
+    status: str | None = None
+    address: Address | None = None
+
+    list_price: int | None = None
+    list_date: str | None = None
+    pending_date: str | None = None
+    last_sold_date: str | None = None
+    prc_sqft: int | None = None
+    hoa_fee: int | None = None
+    days_on_mls: int | None = None
+    description: Description | None = None
+
+    latitude: float | None = None
+    longitude: float | None = None
+    neighborhoods: Optional[str] = None
+
+    agents: list[Agent] = None

homeharvest/core/scrapers/realtor/__init__.py

@@ -1,54 +1,225 @@
-import json
-from ..models import Property, Address
-from .. import Scraper
-from typing import Any, Generator
-from ....exceptions import NoResultsFound
+"""
+homeharvest.realtor.__init__
+~~~~~~~~~~~~
+
+This module implements the scraper for realtor.com
+"""
+
+from datetime import datetime
+from typing import Dict, Union, Optional
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
+from .. import Scraper
+from ..models import Property, Address, ListingType, Description, PropertyType, Agent
+
 
 class RealtorScraper(Scraper):
+    SEARCH_GQL_URL = "https://www.realtor.com/api/v1/rdc_search_srp?client_id=rdc-search-new-communities&schema=vesta"
+    PROPERTY_URL = "https://www.realtor.com/realestateandhomes-detail/"
+    PROPERTY_GQL = "https://graph.realtor.com/graphql"
+    ADDRESS_AUTOCOMPLETE_URL = "https://parser-external.geo.moveaws.com/suggest"
+    NUM_PROPERTY_WORKERS = 20
+
     def __init__(self, scraper_input):
         super().__init__(scraper_input)
-        self.search_url = "https://www.realtor.com/api/v1/rdc_search_srp?client_id=rdc-search-new-communities&schema=vesta"
 
     def handle_location(self):
-        headers = {
-            "authority": "parser-external.geo.moveaws.com",
-            "accept": "*/*",
-            "accept-language": "en-US,en;q=0.9",
-            "origin": "https://www.realtor.com",
-            "referer": "https://www.realtor.com/",
-            "sec-ch-ua": '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"Windows"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "cross-site",
-            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-        }
-
         params = {
             "input": self.location,
-            "client_id": self.listing_type.value.replace('_', '-'),
+            "client_id": self.listing_type.value.lower().replace("_", "-"),
             "limit": "1",
             "area_types": "city,state,county,postal_code,address,street,neighborhood,school,school_district,university,park",
         }
 
         response = self.session.get(
-            "https://parser-external.geo.moveaws.com/suggest",
+            self.ADDRESS_AUTOCOMPLETE_URL,
             params=params,
-            headers=headers,
         )
         response_json = response.json()
 
         result = response_json["autocomplete"]
 
-        if result is None:
-            raise NoResultsFound("No results found for location: " + self.location)
+        if not result:
+            return None
 
         return result[0]
 
+    def handle_listing(self, listing_id: str) -> list[Property]:
+        query = """query Listing($listing_id: ID!) {
+                    listing(id: $listing_id) {
+                        source {
+                            id
+                            listing_id
+                        }
+                        address {
+                            street_direction
+                            street_number
+                            street_name
+                            street_suffix
+                            unit
+                            city
+                            state_code
+                            postal_code
+                            location {
+                                coordinate {
+                                    lat
+                                    lon
+                                }
+                            }
+                        }
+                        basic {
+                            sqft
+                            beds
+                            baths_full
+                            baths_half
+                            lot_sqft
+                            sold_price
+                            sold_price
+                            type
+                            price
+                            status
+                            sold_date
+                            list_date
+                        }
+                        details {
+                            year_built
+                            stories
+                            garage
+                            permalink
+                        }
+                        media {
+                            photos {
+                                href
+                            }
+                        }
+                    }
+                }"""
+
+        variables = {"listing_id": listing_id}
+        payload = {
+            "query": query,
+            "variables": variables,
+        }
+
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
+        response_json = response.json()
+
+        property_info = response_json["data"]["listing"]
+
+        mls = (
+            property_info["source"].get("id")
+            if "source" in property_info and isinstance(property_info["source"], dict)
+            else None
+        )
+
+        able_to_get_lat_long = (
+            property_info
+            and property_info.get("address")
+            and property_info["address"].get("location")
+            and property_info["address"]["location"].get("coordinate")
+        )
+        list_date_str = (
+            property_info["basic"]["list_date"].split("T")[0] if property_info["basic"].get("list_date") else None
+        )
+        last_sold_date_str = (
+            property_info["basic"]["sold_date"].split("T")[0] if property_info["basic"].get("sold_date") else None
+        )
+        pending_date_str = property_info["pending_date"].split("T")[0] if property_info.get("pending_date") else None
+
+        list_date = datetime.strptime(list_date_str, "%Y-%m-%d") if list_date_str else None
+        last_sold_date = datetime.strptime(last_sold_date_str, "%Y-%m-%d") if last_sold_date_str else None
+        pending_date = datetime.strptime(pending_date_str, "%Y-%m-%d") if pending_date_str else None
+        today = datetime.now()
+
+        days_on_mls = None
+        status = property_info["basic"]["status"].lower()
+        if list_date:
+            if status == "sold" and last_sold_date:
+                days_on_mls = (last_sold_date - list_date).days
+            elif status in ("for_sale", "for_rent"):
+                days_on_mls = (today - list_date).days
+            if days_on_mls and days_on_mls < 0:
+                days_on_mls = None
+
+        property_id = property_info["details"]["permalink"]
+        agents = self.get_agents(property_id)
+        listing = Property(
+            mls=mls,
+            mls_id=(
+                property_info["source"].get("listing_id")
+                if "source" in property_info and isinstance(property_info["source"], dict)
+                else None
+            ),
+            property_url=f"{self.PROPERTY_URL}{property_id}",
+            status=property_info["basic"]["status"].upper(),
+            list_price=property_info["basic"]["price"],
+            list_date=list_date,
+            prc_sqft=(
+                property_info["basic"].get("price") / property_info["basic"].get("sqft")
+                if property_info["basic"].get("price") and property_info["basic"].get("sqft")
+                else None
+            ),
+            last_sold_date=last_sold_date,
+            pending_date=pending_date,
+            latitude=property_info["address"]["location"]["coordinate"].get("lat") if able_to_get_lat_long else None,
+            longitude=property_info["address"]["location"]["coordinate"].get("lon") if able_to_get_lat_long else None,
+            address=self._parse_address(property_info, search_type="handle_listing"),
+            description=Description(
+                alt_photos=self.process_alt_photos(property_info.get("media", {}).get("photos", [])),
+                style=property_info["basic"].get("type", "").upper(),
+                beds=property_info["basic"].get("beds"),
+                baths_full=property_info["basic"].get("baths_full"),
+                baths_half=property_info["basic"].get("baths_half"),
+                sqft=property_info["basic"].get("sqft"),
+                lot_sqft=property_info["basic"].get("lot_sqft"),
+                sold_price=property_info["basic"].get("sold_price"),
+                year_built=property_info["details"].get("year_built"),
+                garage=property_info["details"].get("garage"),
+                stories=property_info["details"].get("stories"),
+            ),
+            days_on_mls=days_on_mls,
+            agents=agents,
+        )
+
+        return [listing]
+
+    def get_latest_listing_id(self, property_id: str) -> str | None:
+        query = """query Property($property_id: ID!) {
+                    property(id: $property_id) {
+                        listings {
+                            listing_id
+                            primary
+                        }
+                    }
+                }
+                """
+
+        variables = {"property_id": property_id}
+        payload = {
+            "query": query,
+            "variables": variables,
+        }
+
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
+        response_json = response.json()
+
+        property_info = response_json["data"]["property"]
+        if property_info["listings"] is None:
+            return None
+
+        primary_listing = next(
+            (listing for listing in property_info["listings"] if listing["primary"]),
+            None,
+        )
+        if primary_listing:
+            return primary_listing["listing_id"]
+        else:
+            return property_info["listings"][0]["listing_id"]
+
     def handle_address(self, property_id: str) -> list[Property]:
+        """
+        Handles a specific address & returns one property
+        """
         query = """query Property($property_id: ID!) {
                     property(id: $property_id) {
                         property_id
@@ -60,22 +231,20 @@ class RealtorScraper(Scraper):
                             stories
                         }
                         address {
-                            address_validation_code
-                            city
-                            country
-                            county
-                            line
-                            postal_code
-                            state_code
                             street_direction
-                            street_name
                             street_number
+                            street_name
                             street_suffix
-                            street_post_direction
-                            unit_value
                             unit
-                            unit_descriptor
-                            zip
+                            city
+                            state_code
+                            postal_code
+                            location {
+                                coordinate {
+                                    lat
+                                    lon
+                                }
+                            }
                         }
                         basic {
                             baths
@@ -93,176 +262,489 @@ class RealtorScraper(Scraper):
                             units
                             year_built
                         }
+                        primary_photo {
+                            href
+                        }
+                        photos {
+                            href
+                        }
                     }
                 }"""
 
-        variables = {
-            'property_id': property_id
-        }
+        variables = {"property_id": property_id}
+        agents = self.get_agents(property_id)
 
         payload = {
-            'query': query,
-            'variables': variables,
+            "query": query,
+            "variables": variables,
         }
 
-        response = self.session.post(self.search_url, json=payload)
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
         response_json = response.json()
 
-        property_info = response_json['data']['property']
+        property_info = response_json["data"]["property"]
 
-        return [Property(
-            site_name=self.site_name,
-            address=Address(
-                address_one=property_info['address']['line'],
-                city=property_info['address']['city'],
-                state=property_info['address']['state_code'],
-                zip_code=property_info['address']['postal_code'],
-            ),
-            url="https://www.realtor.com/realestateandhomes-detail/" + property_info['details']['permalink'],
-            beds=property_info['basic']['beds'],
-            baths=property_info['basic']['baths'],
-            stories=property_info['details']['stories'],
-            year_built=property_info['details']['year_built'],
-            square_feet=property_info['basic']['sqft'],
-            price_per_square_foot=property_info['basic']['price'] / property_info['basic']['sqft']
-            if property_info['basic']['sqft'] is not None and
-            property_info['basic']['price'] is not None
-            else None,
-            price=property_info['basic']['price'],
-            mls_id=property_id,
-            listing_type=self.listing_type,
-            lot_size=property_info['public_record']['lot_size'] if property_info['public_record'] is not None else None,
-        )]
+        return [
+            Property(
+                mls_id=property_id,
+                property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
+                address=self._parse_address(property_info, search_type="handle_address"),
+                description=self._parse_description(property_info),
+                agents=agents,
+            )
+        ]
 
-    def handle_area(self, variables: dict, return_total: bool = False) -> list[Property] | int:
-        query = """query Home_search(
-                            $city: String,
-                            $county: [String],
-                            $state_code: String,
-                            $postal_code: String
-                            $offset: Int,
-                        ) {
-                            home_search(
-                                query: {
-                                    city: $city
-                                    county: $county
-                                    postal_code: $postal_code
-                                    state_code: $state_code
-                                    status: %s
+    def general_search(self, variables: dict, search_type: str) -> Dict[str, Union[int, list[Property]]]:
+        """
+        Handles a location area & returns a list of properties
+        """
+        results_query = """{
+                            count
+                            total
+                            results {
+                                pending_date
+                                property_id
+                                list_date
+                                status
+                                last_sold_price
+                                last_sold_date
+                                list_price
+                                price_per_sqft
+                                flags {
+                                    is_contingent
+                                    is_pending
                                 }
-                                limit: 200
-                                offset: $offset
-                            ) {
-                                count
-                                total
-                                results {
-                                    property_id
-                                    description {
-                                        baths
-                                        beds
-                                        lot_sqft
-                                        sqft
-                                        text
-                                        sold_price
-                                        stories
-                                        year_built
-                                        garage
-                                        unit_number
-                                        floor_number
-                                    }
-                                    location {
-                                        address {
-                                            city
-                                            country
-                                            line
-                                            postal_code
-                                            state_code
-                                            state
-                                            street_direction
-                                            street_name
-                                            street_number
-                                            street_post_direction
-                                            street_suffix
-                                            unit
+                                description {
+                                    type
+                                    sqft
+                                    beds
+                                    baths_full
+                                    baths_half
+                                    lot_sqft
+                                    sold_price
+                                    year_built
+                                    garage
+                                    sold_price
+                                    type
+                                    name
+                                    stories
+                                }
+                                source {
+                                    id
+                                    listing_id
+                                }
+                                hoa {
+                                    fee
+                                }
+                                location {
+                                    address {
+                                        street_direction
+                                        street_number
+                                        street_name
+                                        street_suffix
+                                        unit
+                                        city
+                                        state_code
+                                        postal_code
+                                        coordinate {
+                                            lon
+                                            lat
                                         }
                                     }
-                                    list_price
-                                    price_per_sqft
-                                    source {
-                                        id
+                                    neighborhoods {
+                                        name
                                     }
                                 }
+                                primary_photo {
+                                    href
+                                }
+                                photos {
+                                    href
+                                }
                             }
-                        }""" % self.listing_type.value
+                        }
+                    }"""
+
+        date_param = ""
+        if self.listing_type == ListingType.SOLD:
+            if self.date_from and self.date_to:
+                date_param = f'sold_date: {{ min: "{self.date_from}", max: "{self.date_to}" }}'
+            elif self.last_x_days:
+                date_param = f'sold_date: {{ min: "$today-{self.last_x_days}D" }}'
+        else:
+            if self.date_from and self.date_to:
+                date_param = f'list_date: {{ min: "{self.date_from}", max: "{self.date_to}" }}'
+            elif self.last_x_days:
+                date_param = f'list_date: {{ min: "$today-{self.last_x_days}D" }}'
+
+        sort_param = (
+            "sort: [{ field: sold_date, direction: desc }]"
+            if self.listing_type == ListingType.SOLD
+            else "sort: [{ field: list_date, direction: desc }]"
+        )
+
+        pending_or_contingent_param = (
+            "or_filters: { contingent: true, pending: true }" if self.listing_type == ListingType.PENDING else ""
+        )
+
+        listing_type = ListingType.FOR_SALE if self.listing_type == ListingType.PENDING else self.listing_type
+        is_foreclosure = ""
+
+        if variables.get("foreclosure") is True:
+            is_foreclosure = "foreclosure: true"
+        elif variables.get("foreclosure") is False:
+            is_foreclosure = "foreclosure: false"
+
+        if search_type == "comps":  #: comps search, came from an address
+            query = """query Property_search(
+                    $coordinates: [Float]!
+                    $radius: String!
+                    $offset: Int!,
+                    ) {
+                        home_search(
+                            query: {
+                                %s
+                                nearby: {
+                                    coordinates: $coordinates
+                                    radius: $radius
+                                }
+                                status: %s
+                                %s
+                                %s
+                            }
+                            %s
+                            limit: 200
+                            offset: $offset
+                    ) %s""" % (
+                is_foreclosure,
+                listing_type.value.lower(),
+                date_param,
+                pending_or_contingent_param,
+                sort_param,
+                results_query,
+            )
+        elif search_type == "area":  #: general search, came from a general location
+            query = """query Home_search(
+                                $city: String,
+                                $county: [String],
+                                $state_code: String,
+                                $postal_code: String
+                                $offset: Int,
+                            ) {
+                                home_search(
+                                    query: {
+                                        %s
+                                        city: $city
+                                        county: $county
+                                        postal_code: $postal_code
+                                        state_code: $state_code
+                                        status: %s
+                                        %s
+                                        %s
+                                    }
+                                    %s
+                                    limit: 200
+                                    offset: $offset
+                                ) %s""" % (
+                is_foreclosure,
+                listing_type.value.lower(),
+                date_param,
+                pending_or_contingent_param,
+                sort_param,
+                results_query,
+            )
+        else:  #: general search, came from an address
+            query = (
+                """query Property_search(
+                        $property_id: [ID]!
+                        $offset: Int!,
+                    ) {
+                        property_search(
+                            query: {
+                                property_id: $property_id
+                            }
+                            limit: 1
+                            offset: $offset
+                        ) %s"""
+                % results_query
+            )
 
         payload = {
-            'query': query,
-            'variables': variables,
+            "query": query,
+            "variables": variables,
         }
 
-        response = self.session.post(self.search_url, json=payload)
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
+        response.raise_for_status()
         response_json = response.json()
-
-        if return_total:
-            return response_json['data']['home_search']['total']
+        search_key = "home_search" if "home_search" in query else "property_search"
 
         properties: list[Property] = []
 
-        for result in response_json['data']['home_search']['results']:
-            realty_property = Property(
-                address=Address(
-                    address_one=result['location']['address']['line'],
-                    city=result['location']['address']['city'],
-                    state=result['location']['address']['state_code'],
-                    zip_code=result['location']['address']['postal_code'],
-                    address_two=result['location']['address']['unit'],
-                ),
-                site_name=self.site_name,
-                url="https://www.realtor.com/realestateandhomes-detail/" + result['property_id'],
-                beds=result['description']['beds'],
-                baths=result['description']['baths'],
-                stories=result['description']['stories'],
-                year_built=result['description']['year_built'],
-                square_feet=result['description']['sqft'],
-                price_per_square_foot=result['price_per_sqft'],
-                price=result['list_price'],
-                mls_id=result['property_id'],
-                listing_type=self.listing_type,
-                lot_size=result['description']['lot_sqft'],
+        if (
+            response_json is None
+            or "data" not in response_json
+            or response_json["data"] is None
+            or search_key not in response_json["data"]
+            or response_json["data"][search_key] is None
+            or "results" not in response_json["data"][search_key]
+        ):
+            return {"total": 0, "properties": []}
+
+        def process_property(result: dict) -> Property | None:
+            mls = result["source"].get("id") if "source" in result and isinstance(result["source"], dict) else None
+
+            if not mls and self.mls_only:
+                return
+
+            able_to_get_lat_long = (
+                result
+                and result.get("location")
+                and result["location"].get("address")
+                and result["location"]["address"].get("coordinate")
             )
 
-            properties.append(realty_property)
+            is_pending = result["flags"].get("is_pending") or result["flags"].get("is_contingent")
 
-        return properties
+            if is_pending and self.listing_type != ListingType.PENDING:
+                return
 
-    def search(self):
-        location_info = self.handle_location()
-        location_type = location_info["area_type"]
+            property_id = result["property_id"]
+            agents = self.get_agents(property_id)
 
-        if location_type == 'address':
-            property_id = location_info['mpr_id']
-            return self.handle_address(property_id)
+            realty_property = Property(
+                mls=mls,
+                mls_id=(
+                    result["source"].get("listing_id")
+                    if "source" in result and isinstance(result["source"], dict)
+                    else None
+                ),
+                property_url=(
+                    f"{self.PROPERTY_URL}{property_id}"
+                    if self.listing_type != ListingType.FOR_RENT
+                    else f"{self.PROPERTY_URL}M{property_id}?listing_status=rental"
+                ),
+                status="PENDING" if is_pending else result["status"].upper(),
+                list_price=result["list_price"],
+                list_date=result["list_date"].split("T")[0] if result.get("list_date") else None,
+                prc_sqft=result.get("price_per_sqft"),
+                last_sold_date=result.get("last_sold_date"),
+                hoa_fee=result["hoa"]["fee"] if result.get("hoa") and isinstance(result["hoa"], dict) else None,
+                latitude=result["location"]["address"]["coordinate"].get("lat") if able_to_get_lat_long else None,
+                longitude=result["location"]["address"]["coordinate"].get("lon") if able_to_get_lat_long else None,
+                address=self._parse_address(result, search_type="general_search"),
+                description=self._parse_description(result),
+                days_on_mls=self.calculate_days_on_mls(result),
+                agents=agents,
+            )
+            return realty_property
 
-        offset = 0
-        search_variables = {
-            'city': location_info.get('city'),
-            'county': location_info.get('county'),
-            'state_code': location_info.get('state_code'),
-            'postal_code': location_info.get('postal_code'),
-            'offset': offset,
-        }
-
-        total = self.handle_area(search_variables, return_total=True)
-
-        homes = []
-        with ThreadPoolExecutor(max_workers=10) as executor:
+        with ThreadPoolExecutor(max_workers=self.NUM_PROPERTY_WORKERS) as executor:
             futures = [
-                executor.submit(
-                    self.handle_area, variables=search_variables | {'offset': i}, return_total=False
-                ) for i in range(0, total, 200)
+                executor.submit(process_property, result) for result in response_json["data"][search_key]["results"]
             ]
 
             for future in as_completed(futures):
-                homes.extend(future.result())
+                result = future.result()
+                if result:
+                    properties.append(result)
+
+        return {
+            "total": response_json["data"][search_key]["total"],
+            "properties": properties,
+        }
+
+    def search(self):
+        location_info = self.handle_location()
+        if not location_info:
+            return []
+
+        location_type = location_info["area_type"]
+
+        search_variables = {
+            "offset": 0,
+        }
+
+        search_type = (
+            "comps"
+            if self.radius and location_type == "address"
+            else "address" if location_type == "address" and not self.radius else "area"
+        )
+        if location_type == "address":
+            if not self.radius:  #: single address search, non comps
+                property_id = location_info["mpr_id"]
+                search_variables |= {"property_id": property_id}
+
+                gql_results = self.general_search(search_variables, search_type=search_type)
+                if gql_results["total"] == 0:
+                    listing_id = self.get_latest_listing_id(property_id)
+                    if listing_id is None:
+                        return self.handle_address(property_id)
+                    else:
+                        return self.handle_listing(listing_id)
+                else:
+                    return gql_results["properties"]
+
+            else:  #: general search, comps (radius)
+                if not location_info.get("centroid"):
+                    return []
+
+                coordinates = list(location_info["centroid"].values())
+                search_variables |= {
+                    "coordinates": coordinates,
+                    "radius": "{}mi".format(self.radius),
+                }
+
+        elif location_type == "postal_code":
+            search_variables |= {
+                "postal_code": location_info.get("postal_code"),
+            }
+
+        else:  #: general search, location
+            search_variables |= {
+                "city": location_info.get("city"),
+                "county": location_info.get("county"),
+                "state_code": location_info.get("state_code"),
+                "postal_code": location_info.get("postal_code"),
+            }
+
+        if self.foreclosure:
+            search_variables["foreclosure"] = self.foreclosure
+
+        result = self.general_search(search_variables, search_type=search_type)
+        total = result["total"]
+        homes = result["properties"]
+
+        with ThreadPoolExecutor(max_workers=10) as executor:
+            futures = [
+                executor.submit(
+                    self.general_search,
+                    variables=search_variables | {"offset": i},
+                    search_type=search_type,
+                )
+                for i in range(200, min(total, 10000), 200)
+            ]
+
+            for future in as_completed(futures):
+                homes.extend(future.result()["properties"])
 
         return homes
+
+    def get_agents(self, property_id: str) -> list[Agent]:
+        payload = f'{{"query":"query GetHome($property_id: ID!) {{\\n  home(property_id: $property_id) {{\\n    __typename\\n\\n    consumerAdvertisers: consumer_advertisers {{\\n      __typename\\n      type\\n      advertiserId: advertiser_id\\n      name\\n      phone\\n      type\\n      href\\n      slogan\\n      photo {{\\n        __typename\\n        href\\n      }}\\n      showRealtorLogo: show_realtor_logo\\n      hours\\n    }}\\n\\n\\n  }}\\n}}\\n","variables":{{"property_id":"{property_id}"}}}}'
+        response = self.session.post(self.PROPERTY_GQL, data=payload)
+
+        data = response.json()
+        try:
+            ads = data["data"]["home"]["consumerAdvertisers"]
+        except (KeyError, TypeError):
+            return []
+
+        agents = [Agent(name=ad["name"], phone=ad["phone"]) for ad in ads]
+        return agents
+
+    @staticmethod
+    def _parse_neighborhoods(result: dict) -> Optional[str]:
+        neighborhoods_list = []
+        neighborhoods = result["location"].get("neighborhoods", [])
+
+        if neighborhoods:
+            for neighborhood in neighborhoods:
+                name = neighborhood.get("name")
+                if name:
+                    neighborhoods_list.append(name)
+
+        return ", ".join(neighborhoods_list) if neighborhoods_list else None
+
+    @staticmethod
+    def handle_none_safely(address_part):
+        if address_part is None:
+            return ""
+
+        return address_part
+
+    def _parse_address(self, result: dict, search_type):
+        if search_type == "general_search":
+            address = result["location"]["address"]
+        else:
+            address = result["address"]
+
+        return Address(
+            street=" ".join(
+                [
+                    self.handle_none_safely(address.get("street_number")),
+                    self.handle_none_safely(address.get("street_direction")),
+                    self.handle_none_safely(address.get("street_name")),
+                    self.handle_none_safely(address.get("street_suffix")),
+                ]
+            ).strip(),
+            unit=address["unit"],
+            city=address["city"],
+            state=address["state_code"],
+            zip=address["postal_code"],
+        )
+
+    @staticmethod
+    def _parse_description(result: dict) -> Description:
+        description_data = result.get("description", {})
+
+        if description_data is None or not isinstance(description_data, dict):
+            description_data = {}
+
+        style = description_data.get("type", "")
+        if style is not None:
+            style = style.upper()
+
+        primary_photo = ""
+        if result and "primary_photo" in result:
+            primary_photo_info = result["primary_photo"]
+            if primary_photo_info and "href" in primary_photo_info:
+                primary_photo_href = primary_photo_info["href"]
+                primary_photo = primary_photo_href.replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
+
+        return Description(
+            primary_photo=primary_photo,
+            alt_photos=RealtorScraper.process_alt_photos(result.get("photos")),
+            style=PropertyType(style) if style else None,
+            beds=description_data.get("beds"),
+            baths_full=description_data.get("baths_full"),
+            baths_half=description_data.get("baths_half"),
+            sqft=description_data.get("sqft"),
+            lot_sqft=description_data.get("lot_sqft"),
+            sold_price=description_data.get("sold_price"),
+            year_built=description_data.get("year_built"),
+            garage=description_data.get("garage"),
+            stories=description_data.get("stories"),
+        )
+
+    @staticmethod
+    def calculate_days_on_mls(result: dict) -> Optional[int]:
+        list_date_str = result.get("list_date")
+        list_date = datetime.strptime(list_date_str.split("T")[0], "%Y-%m-%d") if list_date_str else None
+        last_sold_date_str = result.get("last_sold_date")
+        last_sold_date = datetime.strptime(last_sold_date_str, "%Y-%m-%d") if last_sold_date_str else None
+        today = datetime.now()
+
+        if list_date:
+            if result["status"] == "sold":
+                if last_sold_date:
+                    days = (last_sold_date - list_date).days
+                    if days >= 0:
+                        return days
+            elif result["status"] in ("for_sale", "for_rent"):
+                days = (today - list_date).days
+                if days >= 0:
+                    return days
+
+    @staticmethod
+    def process_alt_photos(photos_info):
+        try:
+            alt_photos = []
+            if photos_info:
+                for photo_info in photos_info:
+                    href = photo_info.get("href", "")
+                    alt_photo_href = href.replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
+                    alt_photos.append(alt_photo_href)
+            return alt_photos
+        except Exception:
+            pass

homeharvest/core/scrapers/redfin/__init__.py

@@ -1,158 +0,0 @@
-import json
-from ..models import Property, Address, PropertyType, Building
-from .. import Scraper
-from typing import Any
-
-
-class RedfinScraper(Scraper):
-    def __init__(self, scraper_input):
-        super().__init__(scraper_input)
-        self.listing_type = scraper_input.listing_type
-
-    def _handle_location(self):
-        url = "https://www.redfin.com/stingray/do/location-autocomplete?v=2&al=1&location={}".format(
-            self.location
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        def get_region_type(match_type: str):
-            if match_type == "4":
-                return "2"  #: zip
-            elif match_type == "2":
-                return "6"  #: city
-            elif match_type == "1":
-                return "address"  #: address, needs to be handled differently
-
-        if response_json["payload"]["exactMatch"] is not None:
-            target = response_json["payload"]["exactMatch"]
-        else:
-            target = response_json["payload"]["sections"][0]["rows"][0]
-
-        return target["id"].split("_")[1], get_region_type(target["type"])
-
-    def _parse_home(self, home: dict, single_search: bool = False) -> Property:
-        def get_value(key: str) -> Any | None:
-            if key in home and "value" in home[key]:
-                return home[key]["value"]
-
-        if not single_search:
-            address = Address(
-                address_one=get_value("streetLine"),
-                city=home["city"],
-                state=home["state"],
-                zip_code=home["zip"],
-            )
-        else:
-            address_info = home["streetAddress"]
-
-            address = Address(
-                address_one=address_info["assembledAddress"],
-                city=home["city"],
-                state=home["state"],
-                zip_code=home["zip"],
-            )
-        url = "https://www.redfin.com{}".format(home["url"])
-        property_type = home["propertyType"] if "propertyType" in home else None
-        lot_size_data = home.get("lotSize")
-        if not isinstance(lot_size_data, int):
-            lot_size = (
-                lot_size_data.get("value", None)
-                if isinstance(lot_size_data, dict)
-                else None
-            )
-        else:
-            lot_size = lot_size_data
-
-        return Property(
-            site_name=self.site_name,
-            listing_type=self.listing_type,
-            address=address,
-            url=url,
-            beds=home["beds"] if "beds" in home else None,
-            baths=home["baths"] if "baths" in home else None,
-            stories=home["stories"] if "stories" in home else None,
-            agent_name=get_value("listingAgent"),
-            description=home["listingRemarks"] if "listingRemarks" in home else None,
-            year_built=get_value("yearBuilt")
-            if not single_search
-            else home["yearBuilt"],
-            square_feet=get_value("sqFt"),
-            lot_size=lot_size,
-            property_type=PropertyType.from_int_code(home.get("propertyType")),
-            price_per_square_foot=get_value("pricePerSqFt"),
-            price=get_value("price"),
-            mls_id=get_value("mlsId"),
-        )
-
-    def _parse_building(self, building: dict) -> Building:
-        return Building(
-            address=Address(
-                address_one=" ".join(
-                    [
-                        building['address']['streetNumber'],
-                        building['address']['directionalPrefix'],
-                        building['address']['streetName'],
-                        building['address']['streetType'],
-                    ]
-                ),
-                city=building['address']['city'],
-                state=building['address']['stateOrProvinceCode'],
-                zip_code=building['address']['postalCode'],
-                address_two=" ".join(
-                    [
-                        building['address']['unitType'],
-                        building['address']['unitValue'],
-                    ]
-                )
-            ),
-            site_name=self.site_name,
-            url="https://www.redfin.com{}".format(building["url"]),
-            listing_type=self.listing_type,
-            num_units=building["numUnitsForSale"],
-        )
-
-
-    def handle_address(self, home_id: str):
-        """
-        EPs:
-        https://www.redfin.com/stingray/api/home/details/initialInfo?al=1&path=/TX/Austin/70-Rainey-St-78701/unit-1608/home/147337694
-        https://www.redfin.com/stingray/api/home/details/mainHouseInfoPanelInfo?propertyId=147337694&accessLevel=3
-        https://www.redfin.com/stingray/api/home/details/aboveTheFold?propertyId=147337694&accessLevel=3
-        https://www.redfin.com/stingray/api/home/details/belowTheFold?propertyId=147337694&accessLevel=3
-        """
-
-        url = "https://www.redfin.com/stingray/api/home/details/aboveTheFold?propertyId={}&accessLevel=3".format(
-            home_id
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        parsed_home = self._parse_home(
-            response_json["payload"]["addressSectionInfo"], single_search=True
-        )
-        return [parsed_home]
-
-    def search(self):
-        region_id, region_type = self._handle_location()
-
-        if region_type == "address":
-            home_id = region_id
-            return self.handle_address(home_id)
-
-        url = "https://www.redfin.com/stingray/api/gis?al=1&region_id={}&region_type={}".format(
-            region_id, region_type
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        homes = [
-            self._parse_home(home) for home in response_json["payload"]["homes"]
-        ] + [
-            self._parse_building(building) for building in response_json["payload"]["buildings"].values()
-        ]
-
-        return homes

homeharvest/core/scrapers/zillow/__init__.py

@@ -1,210 +0,0 @@
-import re
-import json
-from ..models import Property, Address, Building, ListingType, PropertyType
-from ....exceptions import NoResultsFound, PropertyNotFound
-from .. import Scraper
-
-
-class ZillowScraper(Scraper):
-    def __init__(self, scraper_input):
-        super().__init__(scraper_input)
-        self.listing_type = scraper_input.listing_type
-        if self.listing_type == ListingType.FOR_SALE:
-            self.url = f"https://www.zillow.com/homes/for_sale/{self.location}_rb/"
-        elif self.listing_type == ListingType.FOR_RENT:
-            self.url = f"https://www.zillow.com/homes/for_rent/{self.location}_rb/"
-
-    def search(self):
-        resp = self.session.get(self.url, headers=self._get_headers())
-        resp.raise_for_status()
-        content = resp.text
-
-        match = re.search(
-            r'<script id="__NEXT_DATA__" type="application/json">(.*?)</script>',
-            content,
-            re.DOTALL,
-        )
-        if not match:
-            raise NoResultsFound(
-                "No results were found for Zillow with the given Location."
-            )
-
-        json_str = match.group(1)
-        data = json.loads(json_str)
-
-        if "searchPageState" in data["props"]["pageProps"]:
-            houses = data["props"]["pageProps"]["searchPageState"]["cat1"][
-                "searchResults"
-            ]["listResults"]
-            return [self._parse_home(house) for house in houses]
-        elif "gdpClientCache" in data["props"]["pageProps"]:
-            gdp_client_cache = json.loads(data["props"]["pageProps"]["gdpClientCache"])
-            main_key = list(gdp_client_cache.keys())[0]
-
-            property_data = gdp_client_cache[main_key]["property"]
-            property = self._get_single_property_page(property_data)
-
-            return [property]
-        raise PropertyNotFound("Specific property data not found in the response.")
-
-    def _parse_home(self, home: dict):
-        """
-        This method is used when a user enters a generic location & zillow returns more than one property
-        """
-        url = (
-            f"https://www.zillow.com{home['detailUrl']}"
-            if "zillow.com" not in home["detailUrl"]
-            else home["detailUrl"]
-        )
-
-        if "hdpData" in home and "homeInfo" in home["hdpData"]:
-            price_data = self._extract_price(home)
-            address = self._extract_address(home)
-            agent_name = self._extract_agent_name(home)
-            beds = home["hdpData"]["homeInfo"]["bedrooms"]
-            baths = home["hdpData"]["homeInfo"]["bathrooms"]
-            property_type = home["hdpData"]["homeInfo"].get("homeType")
-
-            return Property(
-                site_name=self.site_name,
-                address=address,
-                agent_name=agent_name,
-                url=url,
-                beds=beds,
-                baths=baths,
-                listing_type=self.listing_type,
-                property_type=PropertyType(property_type),
-                **price_data,
-            )
-        else:
-            keys = ("addressStreet", "addressCity", "addressState", "addressZipcode")
-            address_one, city, state, zip_code = (home[key] for key in keys)
-            address_one, address_two = self._parse_address_two(address_one)
-            address = Address(address_one, city, state, zip_code, address_two)
-
-            building_info = self._extract_building_info(home)
-            return Building(
-                site_name=self.site_name, address=address, url=url, **building_info
-            )
-
-    def _get_single_property_page(self, property_data: dict):
-        """
-        This method is used when a user enters the exact location & zillow returns just one property
-        """
-        url = (
-            f"https://www.zillow.com{property_data['hdpUrl']}"
-            if "zillow.com" not in property_data["hdpUrl"]
-            else property_data["hdpUrl"]
-        )
-        address_data = property_data["address"]
-        address_one, address_two = self._parse_address_two(
-            address_data["streetAddress"]
-        )
-        address = Address(
-            address_one=address_one,
-            address_two=address_two,
-            city=address_data["city"],
-            state=address_data["state"],
-            zip_code=address_data["zipcode"],
-        )
-        property_type = property_data.get("homeType", None)
-
-        return Property(
-            site_name=self.site_name,
-            address=address,
-            url=url,
-            beds=property_data.get("bedrooms", None),
-            baths=property_data.get("bathrooms", None),
-            year_built=property_data.get("yearBuilt", None),
-            price=property_data.get("price", None),
-            lot_size=property_data.get("lotSize", None),
-            agent_name=property_data.get("attributionInfo", {}).get("agentName", None),
-            stories=property_data.get("resoFacts", {}).get("stories", None),
-            description=property_data.get("description", None),
-            mls_id=property_data.get("attributionInfo", {}).get("mlsId", None),
-            price_per_square_foot=property_data.get("resoFacts", {}).get(
-                "pricePerSquareFoot", None
-            ),
-            square_feet=property_data.get("livingArea", None),
-            property_type=PropertyType(property_type),
-            listing_type=self.listing_type,
-        )
-
-    def _extract_building_info(self, home: dict) -> dict:
-        num_units = len(home["units"])
-        prices = [
-            int(unit["price"].replace("$", "").replace(",", "").split("+")[0])
-            for unit in home["units"]
-        ]
-        return {
-            "listing_type": self.listing_type,
-            "num_units": len(home["units"]),
-            "min_unit_price": min(
-                (
-                    int(unit["price"].replace("$", "").replace(",", "").split("+")[0])
-                    for unit in home["units"]
-                )
-            ),
-            "max_unit_price": max(
-                (
-                    int(unit["price"].replace("$", "").replace(",", "").split("+")[0])
-                    for unit in home["units"]
-                )
-            ),
-            "avg_unit_price": sum(prices) // len(prices) if num_units else None,
-        }
-
-    @staticmethod
-    def _extract_price(home: dict) -> dict:
-        price = int(home["hdpData"]["homeInfo"]["priceForHDP"])
-        square_feet = home["hdpData"]["homeInfo"].get("livingArea")
-
-        lot_size = home["hdpData"]["homeInfo"].get("lotAreaValue")
-        price_per_square_foot = price // square_feet if square_feet and price else None
-
-        return {
-            k: v
-            for k, v in locals().items()
-            if k in ["price", "square_feet", "lot_size", "price_per_square_foot"]
-        }
-
-    @staticmethod
-    def _extract_agent_name(home: dict) -> str | None:
-        broker_str = home.get("brokerName", "")
-        match = re.search(r"Listing by: (.+)", broker_str)
-        return match.group(1) if match else None
-
-    @staticmethod
-    def _parse_address_two(address_one: str):
-        apt_match = re.search(r"(APT\s*.+|#[\s\S]+)$", address_one, re.I)
-        address_two = apt_match.group().strip() if apt_match else None
-        address_one = (
-            address_one.replace(address_two, "").strip() if address_two else address_one
-        )
-        return address_one, address_two
-
-    @staticmethod
-    def _extract_address(home: dict) -> Address:
-        keys = ("streetAddress", "city", "state", "zipcode")
-        address_one, city, state, zip_code = (
-            home["hdpData"]["homeInfo"][key] for key in keys
-        )
-        address_one, address_two = ZillowScraper._parse_address_two(address_one)
-        return Address(address_one, city, state, zip_code, address_two=address_two)
-
-    @staticmethod
-    def _get_headers():
-        return {
-            "authority": "parser-external.geo.moveaws.com",
-            "accept": "*/*",
-            "accept-language": "en-US,en;q=0.9",
-            "origin": "https://www.zillow.com",
-            "referer": "https://www.zillow.com/",
-            "sec-ch-ua": '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"Windows"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "cross-site",
-            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-        }

homeharvest/exceptions.py

@@ -1,14 +1,6 @@
-class InvalidSite(Exception):
-    """Raised when a provided site is does not exist."""
-
-
 class InvalidListingType(Exception):
     """Raised when a provided listing type is does not exist."""
 
 
-class NoResultsFound(Exception):
-    """Raised when no results are found for the given location"""
-
-
-class PropertyNotFound(Exception):
-    """Raised when no property is found for the given address"""
+class InvalidDate(Exception):
+    """Raised when only one of date_from or date_to is provided or not in the correct format. ex: 2023-10-23"""

homeharvest/utils.py

@@ -0,0 +1,101 @@
+import pandas as pd
+from datetime import datetime
+from .core.scrapers.models import Property, ListingType
+from .exceptions import InvalidListingType, InvalidDate
+
+ordered_properties = [
+    "property_url",
+    "mls",
+    "mls_id",
+    "status",
+    "style",
+    "street",
+    "unit",
+    "city",
+    "state",
+    "zip_code",
+    "beds",
+    "full_baths",
+    "half_baths",
+    "sqft",
+    "year_built",
+    "days_on_mls",
+    "list_price",
+    "list_date",
+    "sold_price",
+    "last_sold_date",
+    "lot_sqft",
+    "price_per_sqft",
+    "latitude",
+    "longitude",
+    "stories",
+    "hoa_fee",
+    "parking_garage",
+    "agent",
+    "broker",
+    "broker_phone",
+    "primary_photo",
+    "alt_photos",
+]
+
+
+def process_result(result: Property) -> pd.DataFrame:
+    prop_data = {prop: None for prop in ordered_properties}
+    prop_data.update(result.__dict__)
+
+    if "address" in prop_data:
+        address_data = prop_data["address"]
+        prop_data["street"] = address_data.street
+        prop_data["unit"] = address_data.unit
+        prop_data["city"] = address_data.city
+        prop_data["state"] = address_data.state
+        prop_data["zip_code"] = address_data.zip
+
+    if "agents" in prop_data:
+        agents = prop_data["agents"]
+        if agents:
+            prop_data["agent"] = agents[0].name
+            if len(agents) > 1:
+                prop_data["broker"] = agents[1].name
+                prop_data["broker_phone"] = agents[1].phone
+
+    prop_data["price_per_sqft"] = prop_data["prc_sqft"]
+
+    description = result.description
+    prop_data["primary_photo"] = description.primary_photo
+    prop_data["alt_photos"] = ", ".join(description.alt_photos)
+    prop_data["style"] = description.style.value
+    prop_data["beds"] = description.beds
+    prop_data["full_baths"] = description.baths_full
+    prop_data["half_baths"] = description.baths_half
+    prop_data["sqft"] = description.sqft
+    prop_data["lot_sqft"] = description.lot_sqft
+    prop_data["sold_price"] = description.sold_price
+    prop_data["year_built"] = description.year_built
+    prop_data["parking_garage"] = description.garage
+    prop_data["stories"] = description.stories
+
+    properties_df = pd.DataFrame([prop_data])
+    properties_df = properties_df.reindex(columns=ordered_properties)
+
+    return properties_df[ordered_properties]
+
+
+def validate_input(listing_type: str) -> None:
+    if listing_type.upper() not in ListingType.__members__:
+        raise InvalidListingType(f"Provided listing type, '{listing_type}', does not exist.")
+
+
+def validate_dates(date_from: str | None, date_to: str | None) -> None:
+    if (date_from is not None and date_to is None) or (date_from is None and date_to is not None):
+        raise InvalidDate("Both date_from and date_to must be provided.")
+
+    if date_from and date_to:
+        try:
+            date_from_obj = datetime.strptime(date_from, "%Y-%m-%d")
+            date_to_obj = datetime.strptime(date_to, "%Y-%m-%d")
+
+            if date_to_obj < date_from_obj:
+                raise InvalidDate("date_to must be after date_from.")
+        except ValueError as e:
+            raise InvalidDate(f"Invalid date format or range")

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "certifi"
@@ -11,88 +11,114 @@ files = [
     {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"},
 ]
 
+[[package]]
+name = "cfgv"
+version = "3.4.0"
+description = "Validate configuration and produce human readable error messages."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"},
+    {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
+]
+
 [[package]]
 name = "charset-normalizer"
-version = "3.2.0"
+version = "3.3.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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+description = "Distribution utilities"
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+python-versions = "*"
+files = [
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 version = "1.1.3"
@@ -120,6 +157,36 @@ files = [
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 test = ["pytest (>=6)"]
 
+[[package]]
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+version = "3.13.4"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.8"
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+typing = ["typing-extensions (>=4.8)"]
+
+[[package]]
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+version = "2.5.35"
+description = "File identification library for Python"
+optional = false
+python-versions = ">=3.8"
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+
+[package.extras]
+license = ["ukkonen"]
+
 [[package]]
 name = "idna"
 version = "3.4"
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 [[package]]
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-description = "Fundamental package for array computing in Python"
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+description = "Node.js virtual environment builder"
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-python-versions = ">=3.9"
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+[package.dependencies]
+setuptools = "*"
+
 [[package]]
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 version = "1.26.0"
@@ -219,47 +266,54 @@ files = [
 
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 python-versions = ">=3.9"
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+    {file = "pandas-2.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:4cdb0fab0400c2cb46dafcf1a0fe084c8bb2480a1fa8d81e19d15e12e6d4ded2"},
+    {file = "pandas-2.1.1.tar.gz", hash = "sha256:fecb198dc389429be557cde50a2d46da8434a17fe37d7d41ff102e3987fd947b"},
 ]
 
 [package.dependencies]
 numpy = [
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -289,6 +343,21 @@ sql-other = ["SQLAlchemy (>=1.4.36)"]
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.8.0)"]
 
+[[package]]
+name = "platformdirs"
+version = "4.2.0"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+
 [[package]]
 name = "pluggy"
 version = "1.3.0"
@@ -304,6 +373,24 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
+[[package]]
+name = "pre-commit"
+version = "3.7.0"
+description = "A framework for managing and maintaining multi-language pre-commit hooks."
+optional = false
+python-versions = ">=3.9"
+files = [
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+    {file = "pre_commit-3.7.0.tar.gz", hash = "sha256:e209d61b8acdcf742404408531f0c37d49d2c734fd7cff2d6076083d191cb060"},
+]
+
+[package.dependencies]
+cfgv = ">=2.0.0"
+identify = ">=1.0.0"
+nodeenv = ">=0.11.1"
+pyyaml = ">=5.1"
+virtualenv = ">=20.10.0"
+
 [[package]]
 name = "pytest"
 version = "7.4.2"
@@ -351,6 +438,66 @@ files = [
     {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
 ]
 
+[[package]]
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+version = "6.0.1"
+description = "YAML parser and emitter for Python"
+optional = false
+python-versions = ">=3.6"
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+    {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
+]
+
 [[package]]
 name = "requests"
 version = "2.31.0"
@@ -372,6 +519,22 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "setuptools"
+version = "69.5.1"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"},
+    {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"},
+]
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -407,13 +570,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.0.4"
+version = "2.0.6"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
-    {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
+    {file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
+    {file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
 ]
 
 [package.extras]
@@ -422,7 +585,27 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "virtualenv"
+version = "20.25.1"
+description = "Virtual Python Environment builder"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"},
+    {file = "virtualenv-20.25.1.tar.gz", hash = "sha256:e08e13ecdca7a0bd53798f356d5831434afa5b07b93f0abdf0797b7a06ffe197"},
+]
+
+[package.dependencies]
+distlib = ">=0.3.7,<1"
+filelock = ">=3.12.2,<4"
+platformdirs = ">=3.9.1,<5"
+
+[package.extras]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
+
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.10"
-content-hash = "eede625d6d45085e143b0af246cb2ce00cff8579c667be3b63387c8594a5570d"
+python-versions = ">=3.10,<3.13"
+content-hash = "371781da268d5f61d6e798c023777f337b620e9b07a48c316825d7b998b63f02"

pyproject.toml

@@ -1,19 +1,23 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.1.3"
+version = "0.3.17"
 description = "Real estate scraping library"
-authors = ["Zachary Hampton <zachary@zacharysproducts.com>", "Cullen Watson <cullen@cullen.ai>"]
-homepage = "https://github.com/ZacharyHampton/HomeHarvest"
+authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
+homepage = "https://github.com/Bunsly/HomeHarvest"
 readme = "README.md"
 
+[tool.poetry.scripts]
+homeharvest = "homeharvest.cli:main"
+
 [tool.poetry.dependencies]
-python = "^3.10"
+python = ">=3.10,<3.13"
 requests = "^2.31.0"
-pandas = "^2.1.0"
+pandas = "^2.1.1"
 
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.2"
+pre-commit = "^3.7.0"
 
 [build-system]
 requires = ["poetry-core"]

tests/test_realtor.py

@@ -1,12 +1,144 @@
 from homeharvest import scrape_property
 
 
+def test_realtor_pending_or_contingent():
+    pending_or_contingent_result = scrape_property(location="Surprise, AZ", listing_type="pending")
+
+    regular_result = scrape_property(location="Surprise, AZ", listing_type="for_sale")
+
+    assert all([result is not None for result in [pending_or_contingent_result, regular_result]])
+    assert len(pending_or_contingent_result) != len(regular_result)
+
+
+def test_realtor_pending_comps():
+    pending_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="pending",
+    )
+
+    for_sale_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="for_sale",
+    )
+
+    sold_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="sold",
+    )
+
+    results = [pending_comps, for_sale_comps, sold_comps]
+    assert all([result is not None for result in results])
+
+    #: assert all lengths are different
+    assert len(set([len(result) for result in results])) == len(results)
+
+
+def test_realtor_sold_past():
+    result = scrape_property(
+        location="San Diego, CA",
+        past_days=30,
+        listing_type="sold",
+    )
+
+    assert result is not None and len(result) > 0
+
+
+def test_realtor_comps():
+    result = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=0.5,
+        past_days=180,
+        listing_type="sold",
+    )
+
+    assert result is not None and len(result) > 0
+
+
+def test_realtor_last_x_days_sold():
+    days_result_30 = scrape_property(location="Dallas, TX", listing_type="sold", past_days=30)
+
+    days_result_10 = scrape_property(location="Dallas, TX", listing_type="sold", past_days=10)
+
+    assert all([result is not None for result in [days_result_30, days_result_10]]) and len(days_result_30) != len(
+        days_result_10
+    )
+
+
+def test_realtor_date_range_sold():
+    days_result_30 = scrape_property(
+        location="Dallas, TX", listing_type="sold", date_from="2023-05-01", date_to="2023-05-28"
+    )
+
+    days_result_60 = scrape_property(
+        location="Dallas, TX", listing_type="sold", date_from="2023-04-01", date_to="2023-06-10"
+    )
+
+    assert all([result is not None for result in [days_result_30, days_result_60]]) and len(days_result_30) < len(
+        days_result_60
+    )
+
+
+def test_realtor_single_property():
+    results = [
+        scrape_property(
+            location="15509 N 172nd Dr, Surprise, AZ 85388",
+            listing_type="for_sale",
+        ),
+        scrape_property(
+            location="2530 Al Lipscomb Way",
+            listing_type="for_sale",
+        ),
+    ]
+
+    assert all([result is not None for result in results])
+
+
 def test_realtor():
     results = [
-        scrape_property(location="2530 Al Lipscomb Way", site_name="realtor.com"),
-        scrape_property(location="Phoenix, AZ", site_name="realtor.com"),  #: does not support "city, state, USA" format
-        scrape_property(location="Dallas, TX", site_name="realtor.com"),  #: does not support "city, state, USA" format
-        scrape_property(location="85281", site_name="realtor.com"),
+        scrape_property(
+            location="2530 Al Lipscomb Way",
+            listing_type="for_sale",
+        ),
+        scrape_property(location="Phoenix, AZ", listing_type="for_rent"),  #: does not support "city, state, USA" format
+        scrape_property(location="Dallas, TX", listing_type="sold"),  #: does not support "city, state, USA" format
+        scrape_property(location="85281"),
     ]
 
     assert all([result is not None for result in results])
+
+
+def test_realtor_city():
+    results = scrape_property(
+        location="Atlanta, GA",
+        listing_type="for_sale",
+    )
+
+    assert results is not None and len(results) > 0
+
+
+def test_realtor_bad_address():
+    bad_results = scrape_property(
+        location="abceefg ju098ot498hh9",
+        listing_type="for_sale",
+    )
+    if len(bad_results) == 0:
+        assert True
+
+
+def test_realtor_foreclosed():
+    foreclosed = scrape_property(location="Dallas, TX", listing_type="for_sale", past_days=100, foreclosure=True)
+
+    not_foreclosed = scrape_property(location="Dallas, TX", listing_type="for_sale", past_days=100, foreclosure=False)
+
+    assert len(foreclosed) != len(not_foreclosed)
+
+
+def test_realtor_agent():
+    scraped = scrape_property(location="Detroit, MI", listing_type="for_sale")
+    assert scraped["agent"].nunique() > 1

tests/test_redfin.py

@@ -1,12 +0,0 @@
-from homeharvest import scrape_property
-
-
-def test_redfin():
-    results = [
-        scrape_property(location="2530 Al Lipscomb Way", site_name="redfin"),
-        scrape_property(location="Phoenix, AZ, USA", site_name="redfin"),
-        scrape_property(location="Dallas, TX, USA", site_name="redfin"),
-        scrape_property(location="85281", site_name="redfin"),
-    ]
-
-    assert all([result is not None for result in results])

tests/test_zillow.py

@@ -1,12 +0,0 @@
-from homeharvest import scrape_property
-
-
-def test_zillow():
-    results = [
-        scrape_property(location="2530 Al Lipscomb Way", site_name="zillow"),
-        scrape_property(location="Phoenix, AZ, USA", site_name="zillow"),
-        scrape_property(location="Dallas, TX, USA", site_name="zillow"),
-        scrape_property(location="85281", site_name="zillow"),
-    ]
-
-    assert all([result is not None for result in results])