- full street line (data quality improvement)

- updated docs
- classified exception

.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

@@ -4,4 +4,4 @@
 **/.pytest_cache/
 *.pyc
 /.ipynb_checkpoints/
-*.csv
+*.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]

README.md

@@ -1,165 +1,152 @@
 <img src="https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/d1a2bf8b-09f5-4c57-b33a-0ada8a34f12d" width="400">
 
-**HomeHarvest** is a simple, yet comprehensive, real estate scraping library.
+**HomeHarvest** is a real estate scraping library that extracts and formats data in the style of MLS listings.
 
-[![Try with Replit](https://replit.com/badge?caption=Try%20with%20Replit)](https://replit.com/@ZacharyHampton/HomeHarvestDemo)
+**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://calendly.com/zachary-products/15min)** *to work with us.*
-## Features
+*Looking to build a data-focused software product?* **[Book a call](https://bunsly.com)** *to work with us.*
 
-- Scrapes properties from **Zillow**, **Realtor.com** & **Redfin** simultaneously
-- Aggregates the properties in a Pandas DataFrame
+## HomeHarvest Features
 
-[Video Guide for HomeHarvest](https://www.youtube.com/watch?v=HCoHoiJdWQY)
+- **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 --force-reinstall homeharvest
+pip install -U homeharvest
 ```
-  _Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_ 
+  _Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_
 
 ## Usage
 
-### CLI 
-
-```bash
-homeharvest "San Francisco, CA" -s zillow realtor.com redfin -l for_rent -o excel -f HomeHarvest
-```
-
-This will scrape properties from the specified sites for the given location and listing type, and save the results to an Excel file named `HomeHarvest.xlsx`.
-
-By default:
-- If `-s` or `--site_name` is not provided, it will scrape from all available sites.
-- If `-l` or `--listing_type` is left blank, the default is `for_sale`. Other options are `for_rent` or `sold`.
-- The `-o` or `--output` default format is `excel`. Options are `csv` or `excel`.
-- If `-f` or `--filename` is left blank, the default is `HomeHarvest_<current_timestamp>`.
-- If `-p` or `--proxy` is not provided, the scraper uses the local IP.
-### Python 
+### Python
 
 ```py
 from homeharvest import scrape_property
-import pandas as pd
+from datetime import datetime
 
-properties: pd.DataFrame = scrape_property(
-    site_name=["zillow", "realtor.com", "redfin"],
-    location="85281",
-    listing_type="for_rent" # for_sale / sold
+# 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, 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(f"Number of properties: {len(properties)}")
 
-#: Note, to export to CSV or Excel, use properties.to_csv() or properties.to_excel().
-print(properties)
+# Export to csv
+properties.to_csv(filename, index=False)
+print(properties.head())
 ```
 
 ## Output
-```py
+```plaintext
 >>> properties.head()
-                                        property_url site_name listing_type  apt_min_price  apt_max_price   ...  
-0  https://www.redfin.com/AZ/Tempe/1003-W-Washing...    redfin     for_rent         1666.0         2750.0   ... 
-1  https://www.redfin.com/AZ/Tempe/VELA-at-Town-L...    redfin     for_rent         1665.0         3763.0   ...  
-2  https://www.redfin.com/AZ/Tempe/Camden-Tempe/a...    redfin     for_rent         1939.0         3109.0   ...  
-3  https://www.redfin.com/AZ/Tempe/Emerson-Park/a...    redfin     for_rent         1185.0         1817.0   ... 
-4  https://www.redfin.com/AZ/Tempe/Rio-Paradiso-A...    redfin     for_rent         1470.0         2235.0   ...   
-[5 rows x 41 columns]
+    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]
 ```
 
-### Parameters for `scrape_properties()`
-```plaintext
+### Parameters for `scrape_property()`
+```
 Required
-├── location (str): address in various formats e.g. just zip, full address, city/state, etc.
-└── listing_type (enum): for_rent, for_sale, sold
+├── 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'
+
 Optional
-├── site_name (List[enum], default=all three sites): zillow, realtor.com, redfin
-├── proxy (str): in format 'http://user:pass@host:port' or [https, socks]
+├── 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'
+│
+└── extra_property_data (bool): Increases requests by O(n). If set, this fetches additional property data (e.g. agent, broker, property evaluations etc.)
 ```
 
 ### Property Schema
 ```plaintext
 Property
 ├── Basic Information:
-│   ├── property_url (str)
-│   ├── site_name (enum): zillow, redfin, realtor.com
-│   ├── listing_type (enum: ListingType)
-│   └── property_type (enum): house, apartment, condo, townhouse, single_family, multi_family, building
+│ ├── property_url
+│ ├── mls
+│ ├── mls_id
+│ └── status
 
 ├── Address Details:
-│   ├── street_address (str)
-│   ├── city (str)
-│   ├── state (str)
-│   ├── zip_code (str)
-│   ├── unit (str)
-│   └── country (str)
+│ ├── street
+│ ├── unit
+│ ├── city
+│ ├── state
+│ └── zip_code
 
-├── Property Features:
-│   ├── price (int)
-│   ├── tax_assessed_value (int)
-│   ├── currency (str)
-│   ├── square_feet (int)
-│   ├── beds (int)
-│   ├── baths (float)
-│   ├── lot_area_value (float)
-│   ├── lot_area_unit (str)
-│   ├── stories (int)
-│   └── year_built (int)
+├── Property Description:
+│ ├── style
+│ ├── beds
+│ ├── full_baths
+│ ├── half_baths
+│ ├── sqft
+│ ├── year_built
+│ ├── stories
+│ └── lot_sqft
 
-├── Miscellaneous Details:
-│   ├── price_per_sqft (int)
-│   ├── mls_id (str)
-│   ├── agent_name (str)
-│   ├── img_src (str)
-│   ├── description (str)
-│   ├── status_text (str)
-│   ├── latitude (float)
-│   ├── longitude (float)
-│   └── posted_time (str) [Only for Zillow]
+├── Property Listing Details:
+│ ├── days_on_mls
+│ ├── list_price
+│ ├── list_date
+│ ├── pending_date
+│ ├── sold_price
+│ ├── last_sold_date
+│ ├── price_per_sqft
+│ ├── parking_garage
+│ └── hoa_fee
 
-├── Building Details (for property_type: building):
-│   ├── bldg_name (str)
-│   ├── bldg_unit_count (int)
-│   ├── bldg_min_beds (int)
-│   ├── bldg_min_baths (float)
-│   └── bldg_min_area (int)
+├── Location Details:
+│ ├── latitude
+│ ├── longitude
+│ ├── nearby_schools
 
-└── Apartment Details (for property type: apartment):
-    ├── apt_min_beds: int
-    ├── apt_max_beds: int
-    ├── apt_min_baths: float
-    ├── apt_max_baths: float
-    ├── apt_min_price: int
-    ├── apt_max_price: int
-    ├── apt_min_sqft: int
-    ├── apt_max_sqft: int
+
+├── Agent Info:
+│ ├── agent
+│ ├── agent_email
+│ └── agent_phone
 ```
-## Supported Countries for Property Scraping
-
-* **Zillow**: contains listings in the **US** & **Canada** 
-* **Realtor.com**: mainly from the **US** but also has international listings
-* **Redfin**: listings mainly in the **US**, **Canada**, & has expanded to some areas in **Mexico**
 
 ### Exceptions
 The following exceptions may be raised when using HomeHarvest:
 
-- `InvalidSite` - valid options: `zillow`, `redfin`, `realtor.com`
 - `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
-- `NoResultsFound` - no properties found from your input
-- `GeoCoordsNotFound` - if Zillow scraper is not able to create geo-coordinates from the location you input
-
-## Frequently Asked Questions
-
----
-
-**Q: Encountering issues with your queries?**  
-**A:** Try a single site and/or broaden the location. If problems persist, [submit an issue](https://github.com/ZacharyHampton/HomeHarvest/issues).
-
----
-
-**Q: Received a Forbidden 403 response code?**  
-**A:** This indicates that you have been blocked by the real estate site for sending too many requests. Currently, **Zillow** is particularly aggressive with blocking. We recommend:
-
-- Waiting a few seconds between requests.
-- Trying a VPN to change your IP address.
-
----
-
+- `InvalidDate` - date_from or date_to is not in the format YYYY-MM-DD.
+- `AuthenticationError` - Realtor.com token request failed.

examples/HomeHarvest_Demo.ipynb

@@ -4,7 +4,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "cb48903e-5021-49fe-9688-45cd0bc05d0f",
-   "metadata": {},
+   "metadata": {
+    "is_executing": true
+   },
    "outputs": [],
    "source": [
     "from homeharvest import scrape_property\n",
@@ -31,7 +33,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# scrapes all 3 sites by default\n",
+    "# check for sale properties\n",
     "scrape_property(\n",
     "    location=\"dallas\",\n",
     "    listing_type=\"for_sale\"\n",
@@ -53,7 +55,6 @@
     "# search a specific address\n",
     "scrape_property(\n",
     "    location=\"2530 Al Lipscomb Way\",\n",
-    "    site_name=\"zillow\",\n",
     "    listing_type=\"for_sale\"\n",
     ")"
    ]
@@ -68,7 +69,6 @@
     "# check rentals\n",
     "scrape_property(\n",
     "    location=\"chicago, illinois\",\n",
-    "    site_name=[\"redfin\", \"zillow\"],\n",
     "    listing_type=\"for_rent\"\n",
     ")"
    ]
@@ -86,11 +86,34 @@
    "outputs": [],
    "source": [
     "# check sold properties\n",
-    "scrape_property(\n",
+    "properties = scrape_property(\n",
     "    location=\"90210\",\n",
-    "    site_name=[\"redfin\"],\n",
-    "    listing_type=\"sold\"\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))"
    ]
   }
  ],

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

@@ -1,188 +1,59 @@
+import warnings
 import pandas as pd
-from typing import Union
-import concurrent.futures
-from concurrent.futures import ThreadPoolExecutor
-
 from .core.scrapers import ScraperInput
-from .core.scrapers.redfin import RedfinScraper
+from .utils import process_result, ordered_properties, validate_input, validate_dates
 from .core.scrapers.realtor import RealtorScraper
-from .core.scrapers.zillow import ZillowScraper
-from .core.scrapers.models import ListingType, Property, SiteName
-from .exceptions import InvalidSite, InvalidListingType
-
-
-_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: Property) -> list[str]:
-    return [
-        "property_url",
-        "site_name",
-        "listing_type",
-        "property_type",
-        "status_text",
-        "currency",
-        "price",
-        "apt_min_price",
-        "apt_max_price",
-        "apt_min_sqft",
-        "apt_max_sqft",
-        "apt_min_beds",
-        "apt_max_beds",
-        "apt_min_baths",
-        "apt_max_baths",
-        "tax_assessed_value",
-        "square_feet",
-        "price_per_sqft",
-        "beds",
-        "baths",
-        "lot_area_value",
-        "lot_area_unit",
-        "street_address",
-        "unit",
-        "city",
-        "state",
-        "zip_code",
-        "country",
-        "posted_time",
-        "bldg_min_beds",
-        "bldg_min_baths",
-        "bldg_min_area",
-        "bldg_unit_count",
-        "bldg_name",
-        "stories",
-        "year_built",
-        "agent_name",
-        "mls_id",
-        "img_src",
-        "latitude",
-        "longitude",
-        "description",
-    ]
-
-
-def _process_result(result: Property) -> pd.DataFrame:
-    prop_data = result.__dict__
-
-    prop_data["site_name"] = prop_data["site_name"].value
-    prop_data["listing_type"] = prop_data["listing_type"].value.lower()
-    if "property_type" in prop_data and prop_data["property_type"] is not None:
-        prop_data["property_type"] = prop_data["property_type"].value.lower()
-    else:
-        prop_data["property_type"] = None
-    if "address" in prop_data:
-        address_data = prop_data["address"]
-        prop_data["street_address"] = address_data.street_address
-        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_code
-        prop_data["country"] = address_data.country
-
-        del prop_data["address"]
-
-    properties_df = pd.DataFrame([prop_data])
-    properties_df = properties_df[_get_ordered_properties(result)]
-
-    return properties_df
-
-
-def _scrape_single_site(
-    location: str, site_name: str, listing_type: str, proxy: str = None
-) -> pd.DataFrame:
-    """
-    Helper function to scrape a single site.
-    """
-    _validate_input(site_name, listing_type)
-
-    scraper_input = ScraperInput(
-        location=location,
-        listing_type=ListingType[listing_type.upper()],
-        site_name=SiteName.get_by_value(site_name.lower()),
-        proxy=proxy,
-    )
-
-    site = _scrapers[site_name.lower()](scraper_input)
-    results = site.search()
-
-    properties_dfs = [_process_result(result) for result in results]
-    properties_dfs = [
-        df.dropna(axis=1, how="all") for df in properties_dfs if not df.empty
-    ]
-    if not properties_dfs:
-        return pd.DataFrame()
-
-    return pd.concat(properties_dfs, ignore_index=True)
+from .core.scrapers.models import ListingType
 
 
 def scrape_property(
     location: str,
-    site_name: Union[str, list[str]] = None,
     listing_type: str = "for_sale",
+    radius: float = None,
+    mls_only: bool = False,
+    past_days: int = None,
     proxy: str = None,
+    date_from: str = None,  #: TODO: Switch to one parameter, Date, with date_from and date_to, pydantic validation
+    date_to: str = None,
+    foreclosure: bool = None,
+    extra_property_data: bool = True,
 ) -> 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 or list of site names (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 proxy: Proxy to use for scraping
+    :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 foreclosure: If set, fetches only foreclosure listings.
+    :param extra_property_data: Increases requests by O(n). If set, this fetches additional property data (e.g. agent, broker, property evaluations etc.)
     """
-    if site_name is None:
-        site_name = list(_scrapers.keys())
+    validate_input(listing_type)
+    validate_dates(date_from, date_to)
 
-    if not isinstance(site_name, list):
-        site_name = [site_name]
+    scraper_input = ScraperInput(
+        location=location,
+        listing_type=ListingType[listing_type.upper()],
+        proxy=proxy,
+        radius=radius,
+        mls_only=mls_only,
+        last_x_days=past_days,
+        date_from=date_from,
+        date_to=date_to,
+        foreclosure=foreclosure,
+        extra_property_data=extra_property_data,
+    )
 
-    results = []
+    site = RealtorScraper(scraper_input)
+    results = site.search()
 
-    if len(site_name) == 1:
-        final_df = _scrape_single_site(location, site_name[0], listing_type, proxy)
-        results.append(final_df)
-    else:
-        with ThreadPoolExecutor() as executor:
-            futures = {
-                executor.submit(
-                    _scrape_single_site, location, s_name, listing_type, proxy
-                ): s_name
-                for s_name in site_name
-            }
-
-            for future in concurrent.futures.as_completed(futures):
-                result = future.result()
-                results.append(result)
-
-    results = [df for df in results if not df.empty and not df.isna().all().all()]
-
-    if not results:
+    properties_dfs = [process_result(result) for result in results]
+    if not properties_dfs:
         return pd.DataFrame()
 
-    final_df = pd.concat(results, ignore_index=True)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=FutureWarning)
 
-    columns_to_track = ["street_address", "city", "unit"]
-
-    #: validate they exist, otherwise create them
-    for col in columns_to_track:
-        if col not in final_df.columns:
-            final_df[col] = None
-
-    final_df = final_df.drop_duplicates(
-        subset=["street_address", "city", "unit"], keep="first"
-    )
-    return final_df
+        return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties].replace({"None": "", None: ""})

homeharvest/cli.py

@@ -5,25 +5,14 @@ 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(
-        "-s",
-        "--site_name",
-        type=str,
-        nargs="*",
-        default=None,
-        help="Site name(s) to scrape from (e.g., realtor, zillow)",
-    )
+    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"],
+        choices=["for_sale", "for_rent", "sold", "pending"],
         help="Listing type to scrape",
     )
 
@@ -44,14 +33,38 @@ def main():
         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(
-        "-p", "--proxy", type=str, default=None, help="Proxy to use for scraping"
+        "-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.site_name, args.listing_type, proxy=args.proxy
+        args.location,
+        args.listing_type,
+        radius=args.radius,
+        proxy=args.proxy,
+        mls_only=args.mls_only,
+        past_days=args.days,
     )
 
     if not args.filename:

homeharvest/core/scrapers/__init__.py

@@ -1,5 +1,9 @@
 from dataclasses import dataclass
 import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+import uuid
+from ...exceptions import AuthenticationError
 from .models import Property, ListingType, SiteName
 
 
@@ -7,25 +11,84 @@ from .models import Property, ListingType, SiteName
 class ScraperInput:
     location: str
     listing_type: ListingType
-    site_name: SiteName
+    radius: float | None = None
+    mls_only: bool | None = False
     proxy: str | None = None
+    last_x_days: int | None = None
+    date_from: str | None = None
+    date_to: str | None = None
+    foreclosure: bool | None = False
+    extra_property_data: bool | None = True
 
 
 class Scraper:
-    def __init__(self, scraper_input: ScraperInput):
+    session = None
+
+    def __init__(
+        self,
+        scraper_input: ScraperInput,
+    ):
         self.location = scraper_input.location
         self.listing_type = scraper_input.listing_type
 
-        self.session = requests.Session(proxies=scraper_input.proxy)
-        self.listing_type = scraper_input.listing_type
-        self.site_name = scraper_input.site_name
+        if not self.session:
+            Scraper.session = requests.Session()
+            retries = Retry(
+                total=3, backoff_factor=3, status_forcelist=[429, 403], allowed_methods=frozenset(["GET", "POST"])
+            )
 
-    def search(self) -> list[Property]:
-        ...
+            adapter = HTTPAdapter(max_retries=retries)
+            Scraper.session.mount("http://", adapter)
+            Scraper.session.mount("https://", adapter)
+            Scraper.session.headers.update(
+                {
+                    "auth": f"Bearer {self.get_access_token()}",
+                    "apollographql-client-name": "com.move.Realtor-apollo-ios",
+                }
+            )
+
+        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.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
+        self.extra_property_data = scraper_input.extra_property_data
+
+    def search(self) -> list[Property]: ...
 
     @staticmethod
-    def _parse_home(home) -> Property:
-        ...
+    def _parse_home(home) -> Property: ...
 
-    def handle_location(self):
-        ...
+    def handle_location(self): ...
+
+    @staticmethod
+    def get_access_token():
+        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()
+
+        if not (access_token := data.get("access_token")):
+            raise AuthenticationError(
+                "Failed to get access token, use a proxy/vpn or wait a moment and try again.",
+                response=response
+            )
+
+        return 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):
@@ -18,95 +19,111 @@ class SiteName(Enum):
 class ListingType(Enum):
     FOR_SALE = "FOR_SALE"
     FOR_RENT = "FOR_RENT"
+    PENDING = "PENDING"
     SOLD = "SOLD"
 
 
+@dataclass
+class Agent:
+    name: str | None = None
+    phone: str | None = None
+
+
 class PropertyType(Enum):
-    HOUSE = "HOUSE"
-    BUILDING = "BUILDING"
-    CONDO = "CONDO"
-    TOWNHOUSE = "TOWNHOUSE"
-    SINGLE_FAMILY = "SINGLE_FAMILY"
-    MULTI_FAMILY = "MULTI_FAMILY"
-    MANUFACTURED = "MANUFACTURED"
-    NEW_CONSTRUCTION = "NEW_CONSTRUCTION"
     APARTMENT = "APARTMENT"
-    APARTMENTS = "APARTMENTS"
+    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"
-    LOT = "LOT"
+    MOBILE = "MOBILE"
+    MULTI_FAMILY = "MULTI_FAMILY"
+    RENTAL = "RENTAL"
+    SINGLE_FAMILY = "SINGLE_FAMILY"
+    TOWNHOMES = "TOWNHOMES"
     OTHER = "OTHER"
 
-    BLANK = "BLANK"
-
-    @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.BLANK)
-
 
 @dataclass
 class Address:
-    street_address: str
-    city: str
-    state: str
-    zip_code: str
+    full_line: str | None = None
+    street: str | None = None
     unit: str | None = None
-    country: str | None = None
+    city: str | None = None
+    state: str | None = None
+    zip: str | None = None
+
+
+@dataclass
+class Description:
+    primary_photo: str | None = None
+    alt_photos: list[str] | None = None
+    style: PropertyType | None = None
+    beds: 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
+    garage: float | None = None
+    stories: int | None = None
+    text: str | None = None
+
+
+@dataclass
+class AgentPhone:  #: For documentation purposes only (at the moment)
+    number: str | None = None
+    type: str | None = None
+    primary: bool | None = None
+    ext: str | None = None
+
+
+@dataclass
+class Agent:
+    name: str | None = None
+    phones: list[dict] | AgentPhone | None = None
+    email: str | None = None
+    href: str | None = None
+
+
+@dataclass
+class Broker:
+    name: str | None = None
+    phone: str | None = None
+    website: str | None = None
 
 
 @dataclass
 class Property:
     property_url: str
-    site_name: SiteName
-    listing_type: ListingType
-    address: Address
-    property_type: PropertyType | None = None
-
-    # house for sale
-    price: int | None = None
-    tax_assessed_value: int | None = None
-    currency: str | None = None
-    square_feet: int | None = None
-    beds: int | None = None
-    baths: float | None = None
-    lot_area_value: float | None = None
-    lot_area_unit: str | None = None
-    stories: int | None = None
-    year_built: int | None = None
-    price_per_sqft: int | None = None
+    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
 
-    agent_name: str | None = None
-    img_src: str | None = None
-    description: str | None = None
-    status_text: str | None = None
     latitude: float | None = None
     longitude: float | None = None
-    posted_time: str | None = None
-
-    # building for sale
-    bldg_name: str | None = None
-    bldg_unit_count: int | None = None
-    bldg_min_beds: int | None = None
-    bldg_min_baths: float | None = None
-    bldg_min_area: int | None = None
-
-    # apt
-    apt_min_beds: int | None = None
-    apt_max_beds: int | None = None
-    apt_min_baths: float | None = None
-    apt_max_baths: float | None = None
-    apt_min_price: int | None = None
-    apt_max_price: int | None = None
-    apt_min_sqft: int | None = None
-    apt_max_sqft: int | None = None
+    neighborhoods: Optional[str] = None
+    county: Optional[str] = None
+    fips_code: Optional[str] = None
+    agents: list[Agent] | None = None
+    brokers: list[Broker] | None = None
+    nearby_schools: list[str] = None
+    assessed_value: int | None = None
+    estimated_value: int | None = None

homeharvest/core/scrapers/redfin/__init__.py

@@ -1,244 +0,0 @@
-import json
-from typing import Any
-from .. import Scraper
-from ....utils import parse_address_two, parse_unit
-from ..models import Property, Address, PropertyType, ListingType, SiteName
-from ....exceptions import NoResultsFound
-
-
-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 "exactMatch" not in response_json["payload"]:
-            raise NoResultsFound(
-                "No results found for location: {}".format(self.location)
-            )
-
-        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:
-            street_address, unit = parse_address_two(get_value("streetLine"))
-            unit = parse_unit(get_value("streetLine"))
-            address = Address(
-                street_address=street_address,
-                city=home["city"],
-                state=home["state"],
-                zip_code=home["zip"],
-                unit=unit,
-                country="USA",
-            )
-        else:
-            address_info = home["streetAddress"]
-            street_address, unit = parse_address_two(address_info["assembledAddress"])
-
-            address = Address(
-                street_address=street_address,
-                city=home["city"],
-                state=home["state"],
-                zip_code=home["zip"],
-                unit=unit,
-                country="USA",
-            )
-
-        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,
-            property_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_area_value=lot_size,
-            property_type=PropertyType.from_int_code(home.get("propertyType")),
-            price_per_sqft=get_value("pricePerSqFt"),
-            price=get_value("price"),
-            mls_id=get_value("mlsId"),
-            latitude=home["latLong"]["latitude"]
-            if "latLong" in home and "latitude" in home["latLong"]
-            else None,
-            longitude=home["latLong"]["longitude"]
-            if "latLong" in home and "longitude" in home["latLong"]
-            else None,
-        )
-
-    def _handle_rentals(self, region_id, region_type):
-        url = f"https://www.redfin.com/stingray/api/v1/search/rentals?al=1&isRentals=true&region_id={region_id}&region_type={region_type}&num_homes=100000"
-
-        response = self.session.get(url)
-        response.raise_for_status()
-        homes = response.json()
-
-        properties_list = []
-
-        for home in homes["homes"]:
-            home_data = home["homeData"]
-            rental_data = home["rentalExtension"]
-
-            property_url = f"https://www.redfin.com{home_data.get('url', '')}"
-            address_info = home_data.get("addressInfo", {})
-            centroid = address_info.get("centroid", {}).get("centroid", {})
-            address = Address(
-                street_address=address_info.get("formattedStreetLine", None),
-                city=address_info.get("city", None),
-                state=address_info.get("state", None),
-                zip_code=address_info.get("zip", None),
-                unit=None,
-                country="US" if address_info.get("countryCode", None) == 1 else None,
-            )
-
-            price_range = rental_data.get("rentPriceRange", {"min": None, "max": None})
-            bed_range = rental_data.get("bedRange", {"min": None, "max": None})
-            bath_range = rental_data.get("bathRange", {"min": None, "max": None})
-            sqft_range = rental_data.get("sqftRange", {"min": None, "max": None})
-
-            property_ = Property(
-                property_url=property_url,
-                site_name=SiteName.REDFIN,
-                listing_type=ListingType.FOR_RENT,
-                address=address,
-                apt_min_beds=bed_range.get("min", None),
-                apt_min_baths=bath_range.get("min", None),
-                apt_max_beds=bed_range.get("max", None),
-                apt_max_baths=bath_range.get("max", None),
-                description=rental_data.get("description", None),
-                latitude=centroid.get("latitude", None),
-                longitude=centroid.get("longitude", None),
-                apt_min_price=price_range.get("min", None),
-                apt_max_price=price_range.get("max", None),
-                apt_min_sqft=sqft_range.get("min", None),
-                apt_max_sqft=sqft_range.get("max", None),
-                img_src=home_data.get("staticMapUrl", None),
-                posted_time=rental_data.get("lastUpdated", None),
-                bldg_name=rental_data.get("propertyName", None),
-            )
-
-            properties_list.append(property_)
-
-        if not properties_list:
-            raise NoResultsFound("No rentals found for the given location.")
-
-        return properties_list
-
-    def _parse_building(self, building: dict) -> Property:
-        street_address = " ".join(
-            [
-                building["address"]["streetNumber"],
-                building["address"]["directionalPrefix"],
-                building["address"]["streetName"],
-                building["address"]["streetType"],
-            ]
-        )
-        street_address, unit = parse_address_two(street_address)
-        return Property(
-            site_name=self.site_name,
-            property_type=PropertyType("BUILDING"),
-            address=Address(
-                street_address=street_address,
-                city=building["address"]["city"],
-                state=building["address"]["stateOrProvinceCode"],
-                zip_code=building["address"]["postalCode"],
-                unit=parse_unit(
-                    " ".join(
-                        [
-                            building["address"]["unitType"],
-                            building["address"]["unitValue"],
-                        ]
-                    )
-                ),
-            ),
-            property_url="https://www.redfin.com{}".format(building["url"]),
-            listing_type=self.listing_type,
-            bldg_unit_count=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)
-
-        if self.listing_type == ListingType.FOR_RENT:
-            return self._handle_rentals(region_id, region_type)
-        else:
-            if self.listing_type == ListingType.FOR_SALE:
-                url = f"https://www.redfin.com/stingray/api/gis?al=1&region_id={region_id}&region_type={region_type}&num_homes=100000"
-            else:
-                url = f"https://www.redfin.com/stingray/api/gis?al=1&region_id={region_id}&region_type={region_type}&sold_within_days=30&num_homes=100000"
-            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

@@ -1,333 +0,0 @@
-import re
-import json
-from .. import Scraper
-from ....utils import parse_address_two, parse_unit
-from ....exceptions import GeoCoordsNotFound, NoResultsFound
-from ..models import Property, Address, ListingType, PropertyType
-
-
-class ZillowScraper(Scraper):
-    def __init__(self, scraper_input):
-        super().__init__(scraper_input)
-
-        if not self.is_plausible_location(self.location):
-            raise NoResultsFound("Invalid location input: {}".format(self.location))
-
-        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/"
-        else:
-            self.url = f"https://www.zillow.com/homes/recently_sold/{self.location}_rb/"
-
-    def is_plausible_location(self, location: str) -> bool:
-        url = (
-            "https://www.zillowstatic.com/autocomplete/v3/suggestions?q={"
-            "}&abKey=6666272a-4b99-474c-b857-110ec438732b&clientId=homepage-render"
-        ).format(location)
-
-        response = self.session.get(url)
-
-        return response.json()["results"] != []
-
-    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"]:
-            pattern = r'window\.mapBounds = \{\s*"west":\s*(-?\d+\.\d+),\s*"east":\s*(-?\d+\.\d+),\s*"south":\s*(-?\d+\.\d+),\s*"north":\s*(-?\d+\.\d+)\s*\};'
-
-            match = re.search(pattern, content)
-
-            if match:
-                coords = [float(coord) for coord in match.groups()]
-                return self._fetch_properties_backend(coords)
-
-            else:
-                raise GeoCoordsNotFound("Box bounds could not be located.")
-
-        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 NoResultsFound("Specific property data not found in the response.")
-
-    def _fetch_properties_backend(self, coords):
-        url = "https://www.zillow.com/async-create-search-page-state"
-
-        filter_state_for_sale = {
-            "sortSelection": {
-                # "value": "globalrelevanceex"
-                "value": "days"
-            },
-            "isAllHomes": {"value": True},
-        }
-
-        filter_state_for_rent = {
-            "isForRent": {"value": True},
-            "isForSaleByAgent": {"value": False},
-            "isForSaleByOwner": {"value": False},
-            "isNewConstruction": {"value": False},
-            "isComingSoon": {"value": False},
-            "isAuction": {"value": False},
-            "isForSaleForeclosure": {"value": False},
-            "isAllHomes": {"value": True},
-        }
-
-        filter_state_sold = {
-            "isRecentlySold": {"value": True},
-            "isForSaleByAgent": {"value": False},
-            "isForSaleByOwner": {"value": False},
-            "isNewConstruction": {"value": False},
-            "isComingSoon": {"value": False},
-            "isAuction": {"value": False},
-            "isForSaleForeclosure": {"value": False},
-            "isAllHomes": {"value": True},
-        }
-
-        selected_filter = (
-            filter_state_for_rent
-            if self.listing_type == ListingType.FOR_RENT
-            else filter_state_for_sale
-            if self.listing_type == ListingType.FOR_SALE
-            else filter_state_sold
-        )
-
-        payload = {
-            "searchQueryState": {
-                "pagination": {},
-                "isMapVisible": True,
-                "mapBounds": {
-                    "west": coords[0],
-                    "east": coords[1],
-                    "south": coords[2],
-                    "north": coords[3],
-                },
-                "filterState": selected_filter,
-                "isListVisible": True,
-                "mapZoom": 11,
-            },
-            "wants": {"cat1": ["mapResults"]},
-            "isDebugRequest": False,
-        }
-        resp = self.session.put(
-            url, headers=self._get_headers(), json=payload
-        )
-        resp.raise_for_status()
-        a = resp.json()
-        return self._parse_properties(resp.json())
-
-    def _parse_properties(self, property_data: dict):
-        mapresults = property_data["cat1"]["searchResults"]["mapResults"]
-
-        properties_list = []
-
-        for result in mapresults:
-            if "hdpData" in result:
-                home_info = result["hdpData"]["homeInfo"]
-                address_data = {
-                    "street_address": parse_address_two(home_info["streetAddress"])[0],
-                    "unit": parse_unit(home_info["unit"])
-                    if "unit" in home_info
-                    else None,
-                    "city": home_info["city"],
-                    "state": home_info["state"],
-                    "zip_code": home_info["zipcode"],
-                    "country": home_info["country"],
-                }
-                property_data = {
-                    "site_name": self.site_name,
-                    "address": Address(**address_data),
-                    "property_url": f"https://www.zillow.com{result['detailUrl']}",
-                    "beds": int(home_info["bedrooms"])
-                    if "bedrooms" in home_info
-                    else None,
-                    "baths": home_info.get("bathrooms"),
-                    "square_feet": int(home_info["livingArea"])
-                    if "livingArea" in home_info
-                    else None,
-                    "currency": home_info["currency"],
-                    "price": home_info.get("price"),
-                    "tax_assessed_value": int(home_info["taxAssessedValue"])
-                    if "taxAssessedValue" in home_info
-                    else None,
-                    "property_type": PropertyType(home_info["homeType"]),
-                    "listing_type": ListingType(
-                        home_info["statusType"]
-                        if "statusType" in home_info
-                        else self.listing_type
-                    ),
-                    "lot_area_value": round(home_info["lotAreaValue"], 2)
-                    if "lotAreaValue" in home_info
-                    else None,
-                    "lot_area_unit": home_info.get("lotAreaUnit"),
-                    "latitude": result["latLong"]["latitude"],
-                    "longitude": result["latLong"]["longitude"],
-                    "status_text": result.get("statusText"),
-                    "posted_time": result["variableData"]["text"]
-                    if "variableData" in result
-                    and "text" in result["variableData"]
-                    and result["variableData"]["type"] == "TIME_ON_INFO"
-                    else None,
-                    "img_src": result.get("imgSrc"),
-                    "price_per_sqft": int(home_info["price"] // home_info["livingArea"])
-                    if "livingArea" in home_info
-                    and home_info["livingArea"] != 0
-                    and "price" in home_info
-                    else None,
-                }
-                property_obj = Property(**property_data)
-                properties_list.append(property_obj)
-
-            elif "isBuilding" in result:
-                price = result["price"]
-                building_data = {
-                    "property_url": f"https://www.zillow.com{result['detailUrl']}",
-                    "site_name": self.site_name,
-                    "property_type": PropertyType("BUILDING"),
-                    "listing_type": ListingType(result["statusType"]),
-                    "img_src": result["imgSrc"],
-                    "price": int(price.replace("From $", "").replace(",", ""))
-                    if "From $" in price
-                    else None,
-                    "apt_min_price": int(
-                        price.replace("$", "").replace(",", "").replace("+/mo", "")
-                    )
-                    if "+/mo" in price
-                    else None,
-                    "address": self._extract_address(result["address"]),
-                    "bldg_min_beds": result["minBeds"],
-                    "currency": "USD",
-                    "bldg_min_baths": result["minBaths"],
-                    "bldg_min_area": result.get("minArea"),
-                    "bldg_unit_count": result["unitCount"],
-                    "bldg_name": result.get("communityName"),
-                    "status_text": result["statusText"],
-                    "latitude": result["latLong"]["latitude"],
-                    "longitude": result["latLong"]["longitude"],
-                }
-                building_obj = Property(**building_data)
-                properties_list.append(building_obj)
-
-        return properties_list
-
-    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"]
-        street_address, unit = parse_address_two(address_data["streetAddress"])
-        address = Address(
-            street_address=street_address,
-            unit=unit,
-            city=address_data["city"],
-            state=address_data["state"],
-            zip_code=address_data["zipcode"],
-            country=property_data.get("country"),
-        )
-        property_type = property_data.get("homeType", None)
-        return Property(
-            site_name=self.site_name,
-            address=address,
-            property_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),
-            tax_assessed_value=property_data.get("taxAssessedValue", None),
-            latitude=property_data.get("latitude"),
-            longitude=property_data.get("longitude"),
-            img_src=property_data.get("streetViewTileImageUrlMediumAddress"),
-            currency=property_data.get("currency", None),
-            lot_area_value=property_data.get("lotAreaValue"),
-            lot_area_unit=property_data["lotAreaUnits"].lower()
-            if "lotAreaUnits" in property_data
-            else 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_sqft=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_address(self, address_str):
-        """
-        Extract address components from a string formatted like '555 Wedglea Dr, Dallas, TX',
-        and return an Address object.
-        """
-        parts = address_str.split(", ")
-
-        if len(parts) != 3:
-            raise ValueError(f"Unexpected address format: {address_str}")
-
-        street_address = parts[0].strip()
-        city = parts[1].strip()
-        state_zip = parts[2].split(" ")
-
-        if len(state_zip) == 1:
-            state = state_zip[0].strip()
-            zip_code = None
-        elif len(state_zip) == 2:
-            state = state_zip[0].strip()
-            zip_code = state_zip[1].strip()
-        else:
-            raise ValueError(f"Unexpected state/zip format in address: {address_str}")
-
-        street_address, unit = parse_address_two(street_address)
-        return Address(
-            street_address=street_address,
-            city=city,
-            unit=unit,
-            state=state,
-            zip_code=zip_code,
-            country="USA",
-        )
-
-    @staticmethod
-    def _get_headers():
-        return {
-            "authority": "www.zillow.com",
-            "accept": "*/*",
-            "accept-language": "en-US,en;q=0.9",
-            "content-type": "application/json",
-            "cookie": 'zjs_user_id=null; zg_anonymous_id=%220976ab81-2950-4013-98f0-108b15a554d2%22; zguid=24|%246b1bc625-3955-4d1e-a723-e59602e4ed08; g_state={"i_p":1693611172520,"i_l":1}; zgsession=1|d48820e2-1659-4d2f-b7d2-99a8127dd4f3; zjs_anonymous_id=%226b1bc625-3955-4d1e-a723-e59602e4ed08%22; JSESSIONID=82E8274D3DC8AF3AB9C8E613B38CF861; search=6|1697585860120%7Crb%3DDallas%252C-TX%26rect%3D33.016646%252C-96.555516%252C32.618763%252C-96.999347%26disp%3Dmap%26mdm%3Dauto%26sort%3Ddays%26listPriceActive%3D1%26fs%3D1%26fr%3D0%26mmm%3D0%26rs%3D0%26ah%3D0%26singlestory%3D0%26abo%3D0%26garage%3D0%26pool%3D0%26ac%3D0%26waterfront%3D0%26finished%3D0%26unfinished%3D0%26cityview%3D0%26mountainview%3D0%26parkview%3D0%26waterview%3D0%26hoadata%3D1%263dhome%3D0%26commuteMode%3Ddriving%26commuteTimeOfDay%3Dnow%09%0938128%09%7B%22isList%22%3Atrue%2C%22isMap%22%3Atrue%7D%09%09%09%09%09; AWSALB=gAlFj5Ngnd4bWP8k7CME/+YlTtX9bHK4yEkdPHa3VhL6K523oGyysFxBEpE1HNuuyL+GaRPvt2i/CSseAb+zEPpO4SNjnbLAJzJOOO01ipnWN3ZgPaa5qdv+fAki; AWSALBCORS=gAlFj5Ngnd4bWP8k7CME/+YlTtX9bHK4yEkdPHa3VhL6K523oGyysFxBEpE1HNuuyL+GaRPvt2i/CSseAb+zEPpO4SNjnbLAJzJOOO01ipnWN3ZgPaa5qdv+fAki; search=6|1697587741808%7Crect%3D33.37188814545521%2C-96.34484483007813%2C32.260490641365685%2C-97.21001816992188%26disp%3Dmap%26mdm%3Dauto%26p%3D1%26sort%3Ddays%26z%3D1%26listPriceActive%3D1%26fs%3D1%26fr%3D0%26mmm%3D0%26rs%3D0%26ah%3D0%26singlestory%3D0%26housing-connector%3D0%26abo%3D0%26garage%3D0%26pool%3D0%26ac%3D0%26waterfront%3D0%26finished%3D0%26unfinished%3D0%26cityview%3D0%26mountainview%3D0%26parkview%3D0%26waterview%3D0%26hoadata%3D1%26zillow-owned%3D0%263dhome%3D0%26featuredMultiFamilyBuilding%3D0%26commuteMode%3Ddriving%26commuteTimeOfDay%3Dnow%09%09%09%7B%22isList%22%3Atrue%2C%22isMap%22%3Atrue%7D%09%09%09%09%09',
-            "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": "same-origin",
-            "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 +1,14 @@
-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 InvalidDate(Exception):
+    """Raised when only one of date_from or date_to is provided or not in the correct format. ex: 2023-10-23"""
 
 
-class GeoCoordsNotFound(Exception):
-    """Raised when no property is found for the given address"""
+class AuthenticationError(Exception):
+    """Raised when there is an issue with the authentication process."""
+    def __init__(self, *args, response):
+        super().__init__(*args)
+
+        self.response = response

homeharvest/utils.py

@@ -1,48 +1,122 @@
-import re
+import pandas as pd
+from datetime import datetime
+from .core.scrapers.models import Property, ListingType, Agent
+from .exceptions import InvalidListingType, InvalidDate
+
+ordered_properties = [
+    "property_url",
+    "mls",
+    "mls_id",
+    "status",
+    "text",
+    "style",
+    "full_street_line",
+    "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",
+    "assessed_value",
+    "estimated_value",
+    "lot_sqft",
+    "price_per_sqft",
+    "latitude",
+    "longitude",
+    "neighborhoods",
+    "county",
+    "fips_code",
+    "stories",
+    "hoa_fee",
+    "parking_garage",
+    "agent",
+    "agent_email",
+    "agent_phones",
+    "broker",
+    "broker_phone",
+    "broker_website",
+    "nearby_schools",
+    "primary_photo",
+    "alt_photos",
+]
 
 
-def parse_address_two(street_address: str) -> tuple:
-    if not street_address:
-        return street_address, None
+def process_result(result: Property) -> pd.DataFrame:
+    prop_data = {prop: None for prop in ordered_properties}
+    prop_data.update(result.__dict__)
 
-    apt_match = re.search(
-        r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+|SUITE\s*[\dA-Z]+)$",
-        street_address,
-        re.I,
-    )
+    if "address" in prop_data:
+        address_data = prop_data["address"]
+        prop_data["full_street_line"] = address_data.full_line
+        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 apt_match:
-        apt_str = apt_match.group().strip()
-        cleaned_apt_str = re.sub(
-            r"(APT\s*|UNIT\s*|LOT\s*|SUITE\s*)", "#", apt_str, flags=re.I
-        )
+    if "agents" in prop_data:
+        agents: list[Agent] | None = prop_data["agents"]
+        if agents:
+            prop_data["agent"] = agents[0].name
+            prop_data["agent_email"] = agents[0].email
+            prop_data["agent_phones"] = agents[0].phones
 
-        main_address = street_address.replace(apt_str, "").strip()
-        return main_address, cleaned_apt_str
-    else:
-        return street_address, None
+    if "brokers" in prop_data:
+        brokers = prop_data["brokers"]
+        if brokers:
+            prop_data["broker"] = brokers[0].name
+            prop_data["broker_phone"] = brokers[0].phone
+            prop_data["broker_website"] = brokers[0].website
+
+    prop_data["price_per_sqft"] = prop_data["prc_sqft"]
+    prop_data["nearby_schools"] = filter(None, prop_data["nearby_schools"]) if prop_data["nearby_schools"] else None
+    prop_data["nearby_schools"] = ", ".join(set(prop_data["nearby_schools"])) if prop_data["nearby_schools"] else None
+
+    description = result.description
+    prop_data["primary_photo"] = description.primary_photo
+    prop_data["alt_photos"] = ", ".join(description.alt_photos)
+    prop_data["style"] = description.style if type(description.style) == str else 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
+    prop_data["text"] = description.text
+
+    properties_df = pd.DataFrame([prop_data])
+    properties_df = properties_df.reindex(columns=ordered_properties)
+
+    return properties_df[ordered_properties]
 
 
-def parse_unit(street_address: str):
-    if not street_address:
-        return None
-    apt_match = re.search(
-        r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+)$",
-        street_address,
-        re.I,
-    )
-
-    if apt_match:
-        apt_str = apt_match.group().strip()
-        apt_str = re.sub(r"(APT\s*|UNIT\s*|LOT\s*)", "#", apt_str, flags=re.I)
-        return apt_str
-    else:
-        return None
+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.")
 
 
-if __name__ == "__main__":
-    print(parse_address_two("4303 E Cactus Rd Apt 126"))
-    print(parse_address_two("1234 Elm Street apt 2B"))
-    print(parse_address_two("1234 Elm Street UNIT 3A"))
-    print(parse_address_two("1234 Elm Street unit 3A"))
-    print(parse_address_two("1234 Elm Street SuIte 3A"))
+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:
+            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"
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-description = "An implementation of lxml.xmlfile for the standard library"
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@@ -131,6 +157,36 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
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+description = "A platform independent file lock."
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+typing = ["typing-extensions (>=4.8)"]
+
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+version = "2.5.35"
+description = "File identification library for Python"
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+python-versions = ">=3.8"
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+
+[package.extras]
+license = ["ukkonen"]
+
 [[package]]
 name = "idna"
 version = "3.4"
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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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 ]
 
+[package.dependencies]
+setuptools = "*"
+
 [[package]]
 name = "numpy"
 version = "1.26.0"
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 ]
 
-[[package]]
-name = "openpyxl"
-version = "3.1.2"
-description = "A Python library to read/write Excel 2010 xlsx/xlsm files"
-optional = false
-python-versions = ">=3.6"
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-]
-
-[package.dependencies]
-et-xmlfile = "*"
-
 [[package]]
 name = "packaging"
-version = "23.1"
+version = "23.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
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 name = "pandas"
-version = "2.1.0"
+version = "2.1.1"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
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+    {file = "pandas-2.1.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:0489b0e6aa3d907e909aef92975edae89b1ee1654db5eafb9be633b0124abe97"},
+    {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"
@@ -314,6 +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 = [
+    {file = "platformdirs-4.2.0-py3-none-any.whl", hash = "sha256:0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068"},
+    {file = "platformdirs-4.2.0.tar.gz", hash = "sha256:ef0cc731df711022c174543cb70a9b5bd22e5a9337c8624ef2c2ceb8ddad8768"},
+]
+
+[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"
@@ -329,6 +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 = [
+    {file = "pre_commit-3.7.0-py2.py3-none-any.whl", hash = "sha256:5eae9e10c2b5ac51577c3452ec0a490455c45a0533f7960f993a0d01e59decab"},
+    {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"
@@ -376,6 +438,66 @@ files = [
     {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
 ]
 
+[[package]]
+name = "pyyaml"
+version = "6.0.1"
+description = "YAML parser and emitter for Python"
+optional = false
+python-versions = ">=3.6"
+files = [
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+    {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
+    {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
+]
+
 [[package]]
 name = "requests"
 version = "2.31.0"
@@ -397,6 +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"
@@ -432,13 +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]
@@ -447,7 +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 = "3647d568f5623dd762f19029230626a62e68309fa2ef8be49a36382c19264a5f"
+python-versions = ">=3.10,<3.13"
+content-hash = "371781da268d5f61d6e798c023777f337b620e9b07a48c316825d7b998b63f02"

pyproject.toml

@@ -1,24 +1,24 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.2.4"
-description = "Real estate scraping library supporting Zillow, Realtor.com & Redfin."
-authors = ["Zachary Hampton <zachary@zacharysproducts.com>", "Cullen Watson <cullen@cullen.ai>"]
-homepage = "https://github.com/ZacharyHampton/HomeHarvest"
+version = "0.3.23"
+description = "Real estate scraping library"
+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"
-openpyxl = "^3.1.2"
+pandas = "^2.1.1"
 
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.2"
+pre-commit = "^3.7.0"
 
 [build-system]
 requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+build-backend = "poetry.core.masonry.api"

tests/test_realtor.py

@@ -1,40 +1,167 @@
 from homeharvest import scrape_property
-from homeharvest.exceptions import (
-    InvalidSite,
-    InvalidListingType,
-    NoResultsFound,
-    GeoCoordsNotFound,
-)
+
+
+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",
             listing_type="for_sale",
         ),
-        scrape_property(
-            location="Phoenix, AZ", site_name=["realtor.com"], listing_type="for_rent"
-        ),  #: does not support "city, state, USA" format
-        scrape_property(
-            location="Dallas, TX", site_name="realtor.com", listing_type="sold"
-        ),  #: does not support "city, state, USA" format
-        scrape_property(location="85281", site_name="realtor.com"),
+        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])
 
-    bad_results = []
-    try:
-        bad_results += [
-            scrape_property(
-                location="abceefg ju098ot498hh9",
-                site_name="realtor.com",
-                listing_type="for_sale",
-            )
-        ]
-    except (InvalidSite, InvalidListingType, NoResultsFound, GeoCoordsNotFound):
+
+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
 
-    assert all([result is None for result in bad_results])
+
+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
+
+
+def test_realtor_without_extra_details():
+    results = [
+        scrape_property(
+            location="15509 N 172nd Dr, Surprise, AZ 85388",
+            extra_property_data=False,
+        ),
+        scrape_property(
+            location="15509 N 172nd Dr, Surprise, AZ 85388",
+        ),
+    ]
+
+    assert results[0] != results[1]
+
+
+def test_pr_zip_code():
+    results = scrape_property(
+        location="00741",
+        listing_type="for_sale",
+    )
+
+    assert results is not None and len(results) > 0

tests/test_redfin.py

@@ -1,38 +0,0 @@
-from homeharvest import scrape_property
-from homeharvest.exceptions import (
-    InvalidSite,
-    InvalidListingType,
-    NoResultsFound,
-    GeoCoordsNotFound,
-)
-
-
-def test_redfin():
-    results = [
-        scrape_property(
-            location="2530 Al Lipscomb Way", site_name="redfin", listing_type="for_sale"
-        ),
-        scrape_property(
-            location="Phoenix, AZ, USA", site_name=["redfin"], listing_type="for_rent"
-        ),
-        scrape_property(
-            location="Dallas, TX, USA", site_name="redfin", listing_type="sold"
-        ),
-        scrape_property(location="85281", site_name="redfin"),
-    ]
-
-    assert all([result is not None for result in results])
-
-    bad_results = []
-    try:
-        bad_results += [
-            scrape_property(
-                location="abceefg ju098ot498hh9",
-                site_name="redfin",
-                listing_type="for_sale",
-            )
-        ]
-    except (InvalidSite, InvalidListingType, NoResultsFound, GeoCoordsNotFound):
-        assert True
-
-    assert all([result is None for result in bad_results])

tests/test_zillow.py

@@ -1,38 +0,0 @@
-from homeharvest import scrape_property
-from homeharvest.exceptions import (
-    InvalidSite,
-    InvalidListingType,
-    NoResultsFound,
-    GeoCoordsNotFound,
-)
-
-
-def test_zillow():
-    results = [
-        scrape_property(
-            location="2530 Al Lipscomb Way", site_name="zillow", listing_type="for_sale"
-        ),
-        scrape_property(
-            location="Phoenix, AZ, USA", site_name=["zillow"], listing_type="for_rent"
-        ),
-        scrape_property(
-            location="Dallas, TX, USA", site_name="zillow", listing_type="sold"
-        ),
-        scrape_property(location="85281", site_name="zillow"),
-    ]
-
-    assert all([result is not None for result in results])
-
-    bad_results = []
-    try:
-        bad_results += [
-            scrape_property(
-                location="abceefg ju098ot498hh9",
-                site_name="zillow",
-                listing_type="for_sale",
-            )
-        ]
-    except (InvalidSite, InvalidListingType, NoResultsFound, GeoCoordsNotFound):
-        assert True
-
-    assert all([result is None for result in bad_results])