- version bump

- .get

.gitignore

@@ -1,2 +1,7 @@
 /.idea
-dist
+**/dist/
+**/__pycache__/
+**/.pytest_cache/
+*.pyc
+/.ipynb_checkpoints/
+*.csv

HomeHarvest_Demo.ipynb

@@ -0,0 +1,118 @@
+{
+ "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": [
+    "# scrapes all 3 sites by default\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",
+    "    site_name=\"zillow\",\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",
+    "    site_name=[\"redfin\", \"zillow\"],\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",
+    "scrape_property(\n",
+    "    location=\"90210\",\n",
+    "    site_name=[\"redfin\"],\n",
+    "    listing_type=\"sold\"\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
+}

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Zachary Hampton
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1 +1,166 @@
-# HomeHarvest
+<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.
+
+[![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.*
+
+Check out another project we wrote: ***[JobSpy](https://github.com/cullenwatson/JobSpy)** – a Python package for job scraping*
+
+## Features
+
+- Scrapes properties from **Zillow**, **Realtor.com** & **Redfin** simultaneously
+- Aggregates the properties in a Pandas DataFrame
+
+[Video Guide for HomeHarvest](https://youtu.be/JnV7eR2Ve2o) - _updated for release v0.2.7_
+
+![homeharvest](https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/b3d5d727-e67b-4a9f-85d8-1e65fd18620a)
+
+## Installation
+
+```bash
+pip install homeharvest
+```
+  _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.
+- Use `-k` or `--keep_duplicates` to keep duplicate properties based on address. If not provided, duplicates will be removed.
+### Python 
+
+```py
+from homeharvest import scrape_property
+import pandas as pd
+
+properties: pd.DataFrame = scrape_property(
+    site_name=["zillow", "realtor.com", "redfin"],
+    location="85281",
+    listing_type="for_rent" # for_sale / sold
+)
+
+#: Note, to export to CSV or Excel, use properties.to_csv() or properties.to_excel().
+print(properties)
+```
+
+## Output
+```py
+>>> 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]
+```
+
+### Parameters for `scrape_properties()`
+```plaintext
+Required
+├── location (str): address in various formats e.g. just zip, full address, city/state, etc.
+└── listing_type (enum): for_rent, for_sale, sold
+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]
+└── keep_duplicates (bool, default=False): whether to keep or remove duplicate properties based on address
+```
+
+### Property Schema
+```plaintext
+Property
+├── Basic Information:
+│   ├── property_url (str)
+│   ├── site_name (enum): zillow, redfin, realtor.com
+│   ├── listing_type (enum): for_sale, for_rent, sold
+│   └── property_type (enum): house, apartment, condo, townhouse, single_family, multi_family, building
+
+├── Address Details:
+│   ├── street_address (str)
+│   ├── city (str)
+│   ├── state (str)
+│   ├── zip_code (str)
+│   ├── unit (str)
+│   └── country (str)
+
+├── House for Sale Features:
+│   ├── tax_assessed_value (int)
+│   ├── lot_area_value (float)
+│   ├── lot_area_unit (str)
+│   ├── stories (int)
+│   ├── year_built (int)
+│   └── price_per_sqft (int)
+
+├── Building for Sale and Apartment Details:
+│   ├── bldg_name (str)
+│   ├── beds_min (int)
+│   ├── beds_max (int)
+│   ├── baths_min (float)
+│   ├── baths_max (float)
+│   ├── sqft_min (int)
+│   ├── sqft_max (int)
+│   ├── price_min (int)
+│   ├── price_max (int)
+│   ├── area_min (int)
+│   └── unit_count (int)
+
+├── Miscellaneous Details:
+│   ├── mls_id (str)
+│   ├── agent_name (str)
+│   ├── img_src (str)
+│   ├── description (str)
+│   ├── status_text (str)
+│   └── posted_time (str)
+
+└── Location Details:
+    ├── latitude (float)
+    └── longitude (float)
+```
+## 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 derive 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.
+
+---
+

homeharvest/__init__.py

@@ -1,30 +1,171 @@
-from .core.scrapers.redfin import RedfinScraper
-from .core.scrapers.types import ListingType, Home
+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 .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 scrape_property(
-        location: str,
-        listing_type: str = "for_sale",  #: for_sale, for_rent, sold
-        site_name: str = "redfin",
-) -> list[Home]:  #: eventually, return pandas dataframe
+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",
+        "baths_min",
+        "baths_max",
+        "beds_min",
+        "beds_max",
+        "sqft_min",
+        "sqft_max",
+        "price_min",
+        "price_max",
+        "unit_count",
+        "tax_assessed_value",
+        "price_per_sqft",
+        "lot_area_value",
+        "lot_area_unit",
+        "address_one",
+        "address_two",
+        "city",
+        "state",
+        "zip_code",
+        "posted_time",
+        "area_min",
+        "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["address_one"] = address_data.address_one
+        prop_data["address_two"] = address_data.address_two
+        prop_data["city"] = address_data.city
+        prop_data["state"] = address_data.state
+        prop_data["zip_code"] = address_data.zip_code
+
+        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()
 
-    return 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)
+
+
+def scrape_property(
+    location: str,
+    site_name: Union[str, list[str]] = None,
+    listing_type: str = "for_sale",
+    proxy: str = None,
+    keep_duplicates: bool = False
+) -> 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
+    """
+    if site_name is None:
+        site_name = list(_scrapers.keys())
+
+    if not isinstance(site_name, list):
+        site_name = [site_name]
+
+    results = []
+
+    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:
+        return pd.DataFrame()
+
+    final_df = pd.concat(results, ignore_index=True)
+
+    columns_to_track = ["address_one", "address_two", "city"]
+
+    #: validate they exist, otherwise create them
+    for col in columns_to_track:
+        if col not in final_df.columns:
+            final_df[col] = None
+
+    if not keep_duplicates:
+        final_df = final_df.drop_duplicates(subset=columns_to_track, keep="first")
+    return final_df

homeharvest/cli.py

@@ -0,0 +1,73 @@
+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(
+        "-s",
+        "--site_name",
+        type=str,
+        nargs="*",
+        default=None,
+        help="Site name(s) to scrape from (e.g., realtor, zillow)",
+    )
+
+    parser.add_argument(
+        "-l",
+        "--listing_type",
+        type=str,
+        default="for_sale",
+        choices=["for_sale", "for_rent", "sold"],
+        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(
+        "-k",
+        "--keep_duplicates",
+        action="store_true",
+        help="Keep duplicate properties based on address"
+    )
+
+    parser.add_argument("-p", "--proxy", type=str, default=None, help="Proxy to use for scraping")
+
+    args = parser.parse_args()
+
+    result = scrape_property(args.location, args.site_name, args.listing_type, proxy=args.proxy, keep_duplicates=args.keep_duplicates)
+
+    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

@@ -1,27 +1,35 @@
 from dataclasses import dataclass
 import requests
-from .types import Home, ListingType
+from .models import Property, ListingType, SiteName
 
 
 @dataclass
 class ScraperInput:
     location: str
     listing_type: ListingType
-    proxy_url: str | None = None
+    site_name: SiteName
+    proxy: str | None = None
 
 
 class Scraper:
     def __init__(self, scraper_input: ScraperInput):
         self.location = scraper_input.location
+        self.listing_type = scraper_input.listing_type
+
         self.session = requests.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
 
-        if scraper_input.proxy_url:
-            self.session.proxies = {
-                "http": scraper_input.proxy_url,
-                "https": scraper_input.proxy_url,
-            }
-
-    def search(self) -> list[Home]: ...
+    def search(self) -> list[Property]:
+        ...
 
     @staticmethod
-    def parse_home(home) -> Home: ...
+    def _parse_home(home) -> Property:
+        ...
+
+    def handle_location(self):
+        ...

homeharvest/core/scrapers/models.py

@@ -0,0 +1,109 @@
+from dataclasses import dataclass
+from enum import Enum
+from typing import Tuple
+
+
+class SiteName(Enum):
+    ZILLOW = "zillow"
+    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"
+
+
+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"
+    LAND = "LAND"
+    LOT = "LOT"
+    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:
+    address_one: str | None = None
+    address_two: str | None = "#"
+    city: str | None = None
+    state: str | None = None
+    zip_code: 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
+    tax_assessed_value: int | 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_id: str | None = None
+
+    agent_name: str | None = None
+    img_src: str | None = None
+    description: str | None = None
+    status_text: str | None = None
+    posted_time: str | None = None
+
+    # building for sale
+    bldg_name: str | None = None
+    area_min: int | None = None
+
+    beds_min: int | None = None
+    beds_max: int | None = None
+
+    baths_min: float | None = None
+    baths_max: float | None = None
+
+    sqft_min: int | None = None
+    sqft_max: int | None = None
+
+    price_min: int | None = None
+    price_max: int | None = None
+
+    unit_count: int | None = None
+
+    latitude: float | None = None
+    longitude: float | None = None

homeharvest/core/scrapers/realtor/__init__.py

@@ -0,0 +1,330 @@
+"""
+homeharvest.realtor.__init__
+~~~~~~~~~~~~
+
+This module implements the scraper for relator.com
+"""
+from ..models import Property, Address
+from .. import Scraper
+from ....exceptions import NoResultsFound
+from ....utils import parse_address_one, parse_address_two
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+
+class RealtorScraper(Scraper):
+    def __init__(self, scraper_input):
+        self.counter = 1
+        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.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",
+            params=params,
+            headers=headers,
+        )
+        response_json = response.json()
+
+        result = response_json["autocomplete"]
+
+        if not result:
+            raise NoResultsFound("No results found for location: " + self.location)
+
+        return result[0]
+
+    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
+                        details {
+                            date_updated
+                            garage
+                            permalink
+                            year_built
+                            stories
+                        }
+                        address {
+                            address_validation_code
+                            city
+                            country
+                            county
+                            line
+                            postal_code
+                            state_code
+                            street_direction
+                            street_name
+                            street_number
+                            street_suffix
+                            street_post_direction
+                            unit_value
+                            unit
+                            unit_descriptor
+                            zip
+                        }
+                        basic {
+                            baths
+                            beds
+                            price
+                            sqft
+                            lot_sqft
+                            type
+                            sold_price
+                        }
+                        public_record {
+                            lot_size
+                            sqft
+                            stories
+                            units
+                            year_built
+                        }
+                    }
+                }"""
+
+        variables = {"property_id": property_id}
+
+        payload = {
+            "query": query,
+            "variables": variables,
+        }
+
+        response = self.session.post(self.search_url, json=payload)
+        response_json = response.json()
+
+        property_info = response_json["data"]["property"]
+        address_one, address_two = parse_address_one(property_info["address"]["line"])
+
+        return [
+            Property(
+                site_name=self.site_name,
+                address=Address(
+                    address_one=address_one,
+                    address_two=address_two,
+                    city=property_info["address"]["city"],
+                    state=property_info["address"]["state_code"],
+                    zip_code=property_info["address"]["postal_code"],
+                ),
+                property_url="https://www.realtor.com/realestateandhomes-detail/"
+                + property_info["details"]["permalink"],
+                stories=property_info["details"]["stories"],
+                year_built=property_info["details"]["year_built"],
+                price_per_sqft=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,
+                mls_id=property_id,
+                listing_type=self.listing_type,
+                lot_area_value=property_info["public_record"]["lot_size"]
+                if property_info["public_record"] is not None
+                else None,
+                beds_min=property_info["basic"]["beds"],
+                beds_max=property_info["basic"]["beds"],
+                baths_min=property_info["basic"]["baths"],
+                baths_max=property_info["basic"]["baths"],
+                sqft_min=property_info["basic"]["sqft"],
+                sqft_max=property_info["basic"]["sqft"],
+                price_min=property_info["basic"]["price"],
+                price_max=property_info["basic"]["price"],
+            )
+        ]
+
+    def handle_area(self, variables: dict, return_total: bool = False) -> list[Property] | int:
+        """
+        Handles a location area & returns a list of properties
+        """
+        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
+                                }
+                                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
+                                            coordinate {
+                                                lon
+                                                lat
+                                            }
+                                        }
+                                    }
+                                    list_price
+                                    price_per_sqft
+                                    source {
+                                        id
+                                    }
+                                }
+                            }
+                        }"""
+            % self.listing_type.value.lower()
+        )
+
+        payload = {
+            "query": query,
+            "variables": variables,
+        }
+
+        response = self.session.post(self.search_url, json=payload)
+        response.raise_for_status()
+        response_json = response.json()
+
+        if return_total:
+            return response_json["data"]["home_search"]["total"]
+
+        properties: list[Property] = []
+
+        if (
+            response_json is None
+            or "data" not in response_json
+            or response_json["data"] is None
+            or "home_search" not in response_json["data"]
+            or response_json["data"]["home_search"] is None
+            or "results" not in response_json["data"]["home_search"]
+        ):
+            return []
+
+        for result in response_json["data"]["home_search"]["results"]:
+            self.counter += 1
+            address_one, _ = parse_address_one(result["location"]["address"]["line"])
+            realty_property = Property(
+                address=Address(
+                    address_one=address_one,
+                    city=result["location"]["address"]["city"],
+                    state=result["location"]["address"]["state_code"],
+                    zip_code=result["location"]["address"]["postal_code"],
+                    address_two=parse_address_two(result["location"]["address"]["unit"]),
+                ),
+                latitude=result["location"]["address"]["coordinate"]["lat"]
+                if result
+                and result.get("location")
+                and result["location"].get("address")
+                and result["location"]["address"].get("coordinate")
+                and "lat" in result["location"]["address"]["coordinate"]
+                else None,
+                longitude=result["location"]["address"]["coordinate"]["lon"]
+                if result
+                and result.get("location")
+                and result["location"].get("address")
+                and result["location"]["address"].get("coordinate")
+                and "lon" in result["location"]["address"]["coordinate"]
+                else None,
+                site_name=self.site_name,
+                property_url="https://www.realtor.com/realestateandhomes-detail/" + result["property_id"],
+                stories=result["description"]["stories"],
+                year_built=result["description"]["year_built"],
+                price_per_sqft=result["price_per_sqft"],
+                mls_id=result["property_id"],
+                listing_type=self.listing_type,
+                lot_area_value=result["description"]["lot_sqft"],
+                beds_min=result["description"]["beds"],
+                beds_max=result["description"]["beds"],
+                baths_min=result["description"]["baths"],
+                baths_max=result["description"]["baths"],
+                sqft_min=result["description"]["sqft"],
+                sqft_max=result["description"]["sqft"],
+                price_min=result["list_price"],
+                price_max=result["list_price"],
+            )
+            properties.append(realty_property)
+
+        return properties
+
+    def search(self):
+        location_info = self.handle_location()
+        location_type = location_info["area_type"]
+
+        if location_type == "address":
+            property_id = location_info["mpr_id"]
+            return self.handle_address(property_id)
+
+        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:
+            futures = [
+                executor.submit(
+                    self.handle_area,
+                    variables=search_variables | {"offset": i},
+                    return_total=False,
+                )
+                for i in range(0, total, 200)
+            ]
+
+            for future in as_completed(futures):
+                homes.extend(future.result())
+
+        return homes

homeharvest/core/scrapers/redfin/__init__.py

@@ -1,63 +1,233 @@
+"""
+homeharvest.redfin.__init__
+~~~~~~~~~~~~
+
+This module implements the scraper for redfin.com
+"""
 import json
-from ..types import Home, Address
-from .. import Scraper
 from typing import Any
+from .. import Scraper
+from ....utils import parse_address_two, parse_address_one
+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)
+    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('{}&&', ''))
+        response_json = json.loads(response.text.replace("{}&&", ""))
 
-        if response_json['payload']['exactMatch'] is not None:
-            return response_json['payload']['exactMatch']['id'].split('_')[1]
+        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:
-            return response_json['payload']['sections'][0]['rows'][0].split('_')[1]
+            target = response_json["payload"]["sections"][0]["rows"][0]
 
-    @staticmethod
-    def parse_home(home: dict) -> Home:
-        address = Address(
-            address_one=home['streetLine']['value'],
-            city=home['city'],
-            state=home['state'],
-            zip_code=home['zip']
-        )
-
-        url = 'https://www.redfin.com{}'.format(home['url'])
+        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 key in home and "value" in home[key]:
+                return home[key]["value"]
 
-        return Home(
+        if not single_search:
+            address = Address(
+                address_one=parse_address_one(get_value("streetLine"))[0],
+                address_two=parse_address_one(get_value("streetLine"))[1],
+                city=home.get("city"),
+                state=home.get("state"),
+                zip_code=home.get("zip"),
+            )
+        else:
+            address_info = home.get("streetAddress")
+            address_one, address_two = parse_address_one(address_info.get("assembledAddress"))
+
+            address = Address(
+                address_one=address_one,
+                address_two=address_two,
+                city=home.get("city"),
+                state=home.get("state"),
+                zip_code=home.get("zip"),
+            )
+
+        url = "https://www.redfin.com{}".format(home["url"])
+        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'),
-            square_feet=get_value('sqFt'),
-            price_per_square_foot=get_value('pricePerSqFt'),
-            price=get_value('price'),
-            mls_id=get_value('mlsId')
+            property_url=url,
+            beds_min=home["beds"] if "beds" in home else None,
+            beds_max=home["beds"] if "beds" in home else None,
+            baths_min=home["baths"] if "baths" in home else None,
+            baths_max=home["baths"] if "baths" in home else None,
+            price_min=get_value("price"),
+            price_max=get_value("price"),
+            sqft_min=get_value("sqFt"),
+            sqft_max=get_value("sqFt"),
+            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.get("yearBuilt"),
+            lot_area_value=lot_size,
+            property_type=PropertyType.from_int_code(home.get("propertyType")),
+            price_per_sqft=get_value("pricePerSqFt") if type(home.get("pricePerSqFt")) != int else home.get("pricePerSqFt"),
+            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(
+                address_one=parse_address_one(address_info.get("formattedStreetLine"))[0],
+                city=address_info.get("city"),
+                state=address_info.get("state"),
+                zip_code=address_info.get("zip"),
+            )
+
+            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,
+                description=rental_data.get("description"),
+                latitude=centroid.get("latitude"),
+                longitude=centroid.get("longitude"),
+                baths_min=bath_range.get("min"),
+                baths_max=bath_range.get("max"),
+                beds_min=bed_range.get("min"),
+                beds_max=bed_range.get("max"),
+                price_min=price_range.get("min"),
+                price_max=price_range.get("max"),
+                sqft_min=sqft_range.get("min"),
+                sqft_max=sqft_range.get("max"),
+                img_src=home_data.get("staticMapUrl"),
+                posted_time=rental_data.get("lastUpdated"),
+                bldg_name=rental_data.get("propertyName"),
+            )
+
+            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"],
+            ]
+        )
+        return Property(
+            site_name=self.site_name,
+            property_type=PropertyType("BUILDING"),
+            address=Address(
+                address_one=parse_address_one(street_address)[0],
+                city=building["address"]["city"],
+                state=building["address"]["stateOrProvinceCode"],
+                zip_code=building["address"]["postalCode"],
+                address_two=parse_address_two(
+                    " ".join(
+                        [
+                            building["address"]["unitType"],
+                            building["address"]["unitValue"],
+                        ]
+                    )
+                ),
+            ),
+            property_url="https://www.redfin.com{}".format(building["url"]),
+            listing_type=self.listing_type,
+            unit_count=building.get("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 = self.handle_location()
+        region_id, region_type = self._handle_location()
 
-        url = 'https://www.redfin.com/stingray/api/gis?al=1&region_id={}&region_type=2'.format(region_id)
+        if region_type == "address":
+            home_id = region_id
+            return self.handle_address(home_id)
 
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace('{}&&', ''))
-
-        homes = [self.parse_home(home) for home in response_json['payload']['homes']]
-        return homes
+        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("{}&&", ""))
 
+            if "payload" in response_json:
+                homes_list = response_json["payload"].get("homes", [])
+                buildings_list = response_json["payload"].get("buildings", {}).values()
 
+                homes = [self._parse_home(home) for home in homes_list] + [
+                    self._parse_building(building) for building in buildings_list
+                ]
+                return homes
+            else:
+                return []

homeharvest/core/scrapers/types.py

@@ -1,35 +0,0 @@
-from dataclasses import dataclass
-from enum import Enum
-
-
-class ListingType(Enum):
-    FOR_SALE = "for_sale"
-    FOR_RENT = "for_rent"
-    SOLD = "sold"
-
-
-@dataclass
-class Address:
-    address_one: str
-    city: str
-    state: str
-    zip_code: str
-
-    address_two: str | None = None
-
-
-@dataclass
-class Home:
-    address: Address
-    url: str
-
-    beds: int | None = None
-    baths: int | None = None
-    stories: int | None = None
-    agent_name: str | None = None
-    description: str | None = None
-    year_built: int | None = None
-    square_feet: int | None = None
-    price_per_square_foot: int | None = None
-    price: int | None = None
-    mls_id: str | None = None

homeharvest/core/scrapers/zillow/__init__.py

@@ -0,0 +1,317 @@
+"""
+homeharvest.zillow.__init__
+~~~~~~~~~~~~
+
+This module implements the scraper for zillow.com
+"""
+import re
+import json
+from .. import Scraper
+from ....utils import parse_address_one, parse_address_two
+from ....exceptions import GeoCoordsNotFound, NoResultsFound
+from ..models import Property, Address, ListingType, PropertyType
+
+
+class ZillowScraper(Scraper):
+    def __init__(self, scraper_input):
+        super().__init__(scraper_input)
+        self.cookies = None
+
+        if not self.is_plausible_location(self.location):
+            raise NoResultsFound("Invalid location input: {}".format(self.location))
+
+        listing_type_to_url_path = {
+            ListingType.FOR_SALE: "for_sale",
+            ListingType.FOR_RENT: "for_rent",
+            ListingType.SOLD: "recently_sold",
+        }
+
+        self.url = f"https://www.zillow.com/homes/{listing_type_to_url_path[self.listing_type]}/{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()
+        self.cookies = resp.cookies
+        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 = {
+                    "address_one": parse_address_one(home_info.get("streetAddress"))[0],
+                    "address_two": parse_address_two(home_info["unit"]) if "unit" in home_info else "#",
+                    "city": home_info.get("city"),
+                    "state": home_info.get("state"),
+                    "zip_code": home_info.get("zipcode"),
+                }
+                property_obj = Property(
+                    site_name=self.site_name,
+                    address=Address(**address_data),
+                    property_url=f"https://www.zillow.com{result['detailUrl']}",
+                    tax_assessed_value=int(home_info["taxAssessedValue"]) if "taxAssessedValue" in home_info else None,
+                    property_type=PropertyType(home_info.get("homeType")),
+                    listing_type=ListingType(
+                        home_info["statusType"] if "statusType" in home_info else self.listing_type
+                    ),
+                    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,
+                    price_min=home_info.get("price"),
+                    price_max=home_info.get("price"),
+                    beds_min=int(home_info["bedrooms"]) if "bedrooms" in home_info else None,
+                    beds_max=int(home_info["bedrooms"]) if "bedrooms" in home_info else None,
+                    baths_min=home_info.get("bathrooms"),
+                    baths_max=home_info.get("bathrooms"),
+                    sqft_min=int(home_info["livingArea"]) if "livingArea" in home_info else None,
+                    sqft_max=int(home_info["livingArea"]) if "livingArea" in home_info else None,
+                    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,
+                    latitude=result["latLong"]["latitude"],
+                    longitude=result["latLong"]["longitude"],
+                    lot_area_value=round(home_info["lotAreaValue"], 2) if "lotAreaValue" in home_info else None,
+                    lot_area_unit=home_info.get("lotAreaUnit"),
+                    img_src=result.get("imgSrc"),
+                )
+
+                properties_list.append(property_obj)
+
+            elif "isBuilding" in result:
+                price_string = result["price"].replace("$", "").replace(",", "").replace("+/mo", "")
+
+                match = re.search(r"(\d+)", price_string)
+                price_value = int(match.group(1)) if match else None
+                building_obj = Property(
+                    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.get("imgSrc"),
+                    address=self._extract_address(result["address"]),
+                    baths_min=result.get("minBaths"),
+                    area_min=result.get("minArea"),
+                    bldg_name=result.get("communityName"),
+                    status_text=result.get("statusText"),
+                    price_min=price_value if "+/mo" in result.get("price") else None,
+                    price_max=price_value if "+/mo" in result.get("price") else None,
+                    latitude=result.get("latLong", {}).get("latitude"),
+                    longitude=result.get("latLong", {}).get("longitude"),
+                    unit_count=result.get("unitCount"),
+                )
+
+                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"]
+        address_one, address_two = parse_address_one(address_data["streetAddress"])
+        address = Address(
+            address_one=address_one,
+            address_two=address_two if address_two else "#",
+            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,
+            property_url=url,
+            property_type=PropertyType(property_type) if property_type in PropertyType.__members__ else None,
+            listing_type=self.listing_type,
+            address=address,
+            year_built=property_data.get("yearBuilt"),
+            tax_assessed_value=property_data.get("taxAssessedValue"),
+            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"),
+            stories=property_data.get("resoFacts", {}).get("stories"),
+            mls_id=property_data.get("attributionInfo", {}).get("mlsId"),
+            beds_min=property_data.get("bedrooms"),
+            beds_max=property_data.get("bedrooms"),
+            baths_min=property_data.get("bathrooms"),
+            baths_max=property_data.get("bathrooms"),
+            price_min=property_data.get("price"),
+            price_max=property_data.get("price"),
+            sqft_min=property_data.get("livingArea"),
+            sqft_max=property_data.get("livingArea"),
+            price_per_sqft=property_data.get("resoFacts", {}).get("pricePerSquareFoot"),
+            latitude=property_data.get("latitude"),
+            longitude=property_data.get("longitude"),
+            img_src=property_data.get("streetViewTileImageUrlMediumAddress"),
+            description=property_data.get("description"),
+        )
+
+    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}")
+
+        address_one = 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}")
+
+        address_one, address_two = parse_address_one(address_one)
+        return Address(
+            address_one=address_one,
+            address_two=address_two if address_two else "#",
+            city=city,
+            state=state,
+            zip_code=zip_code,
+        )
+
+    def _get_headers(self):
+        headers = {
+            "authority": "www.zillow.com",
+            "accept": "*/*",
+            "accept-language": "en-US,en;q=0.9",
+            "content-type": "application/json",
+            "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",
+        }
+        if self.cookies:
+            headers['Cookie'] = self.cookies
+        return headers

homeharvest/exceptions.py

@@ -1,8 +1,14 @@
 class InvalidSite(Exception):
     """Raised when a provided site is does not exist."""
-    pass
 
 
 class InvalidListingType(Exception):
     """Raised when a provided listing type is does not exist."""
-    pass
+
+
+class NoResultsFound(Exception):
+    """Raised when no results are found for the given location"""
+
+
+class GeoCoordsNotFound(Exception):
+    """Raised when no property is found for the given address"""

homeharvest/utils.py

@@ -0,0 +1,38 @@
+import re
+
+
+def parse_address_one(street_address: str) -> tuple:
+    if not street_address:
+        return street_address, "#"
+
+    apt_match = re.search(
+        r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+|SUITE\s*[\dA-Z]+)$",
+        street_address,
+        re.I,
+    )
+
+    if apt_match:
+        apt_str = apt_match.group().strip()
+        cleaned_apt_str = re.sub(r"(APT\s*|UNIT\s*|LOT\s*|SUITE\s*)", "#", apt_str, flags=re.I)
+
+        main_address = street_address.replace(apt_str, "").strip()
+        return main_address, cleaned_apt_str
+    else:
+        return street_address, "#"
+
+
+def parse_address_two(street_address: str):
+    if not street_address:
+        return "#"
+    apt_match = re.search(
+        r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+|SUITE\s*[\dA-Z]+)$",
+        street_address,
+        re.I,
+    )
+
+    if apt_match:
+        apt_str = apt_match.group().strip()
+        apt_str = re.sub(r"(APT\s*|UNIT\s*|LOT\s*|SUITE\s*)", "#", apt_str, flags=re.I)
+        return apt_str
+    else:
+        return "#"

poetry.lock

@@ -106,6 +106,17 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "et-xmlfile"
+version = "1.1.0"
+description = "An implementation of lxml.xmlfile for the standard library"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "et_xmlfile-1.1.0-py3-none-any.whl", hash = "sha256:a2ba85d1d6a74ef63837eed693bcb89c3f752169b0e3e7ae5b16ca5e1b3deada"},
+    {file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"},
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.1.3"
@@ -142,6 +153,95 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "numpy"
+version = "1.25.2"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "numpy-1.25.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:db3ccc4e37a6873045580d413fe79b68e47a681af8db2e046f1dacfa11f86eb3"},
+    {file = "numpy-1.25.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:90319e4f002795ccfc9050110bbbaa16c944b1c37c0baeea43c5fb881693ae1f"},
+    {file = "numpy-1.25.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dfe4a913e29b418d096e696ddd422d8a5d13ffba4ea91f9f60440a3b759b0187"},
+    {file = "numpy-1.25.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f08f2e037bba04e707eebf4bc934f1972a315c883a9e0ebfa8a7756eabf9e357"},
+    {file = "numpy-1.25.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bec1e7213c7cb00d67093247f8c4db156fd03075f49876957dca4711306d39c9"},
+    {file = "numpy-1.25.2-cp310-cp310-win32.whl", hash = "sha256:7dc869c0c75988e1c693d0e2d5b26034644399dd929bc049db55395b1379e044"},
+    {file = "numpy-1.25.2-cp310-cp310-win_amd64.whl", hash = "sha256:834b386f2b8210dca38c71a6e0f4fd6922f7d3fcff935dbe3a570945acb1b545"},
+    {file = "numpy-1.25.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c5462d19336db4560041517dbb7759c21d181a67cb01b36ca109b2ae37d32418"},
+    {file = "numpy-1.25.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c5652ea24d33585ea39eb6a6a15dac87a1206a692719ff45d53c5282e66d4a8f"},
+    {file = "numpy-1.25.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d60fbae8e0019865fc4784745814cff1c421df5afee233db6d88ab4f14655a2"},
+    {file = "numpy-1.25.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:60e7f0f7f6d0eee8364b9a6304c2845b9c491ac706048c7e8cf47b83123b8dbf"},
+    {file = "numpy-1.25.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:bb33d5a1cf360304754913a350edda36d5b8c5331a8237268c48f91253c3a364"},
+    {file = "numpy-1.25.2-cp311-cp311-win32.whl", hash = "sha256:5883c06bb92f2e6c8181df7b39971a5fb436288db58b5a1c3967702d4278691d"},
+    {file = "numpy-1.25.2-cp311-cp311-win_amd64.whl", hash = "sha256:5c97325a0ba6f9d041feb9390924614b60b99209a71a69c876f71052521d42a4"},
+    {file = "numpy-1.25.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b79e513d7aac42ae918db3ad1341a015488530d0bb2a6abcbdd10a3a829ccfd3"},
+    {file = "numpy-1.25.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:eb942bfb6f84df5ce05dbf4b46673ffed0d3da59f13635ea9b926af3deb76926"},
+    {file = "numpy-1.25.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e0746410e73384e70d286f93abf2520035250aad8c5714240b0492a7302fdca"},
+    {file = "numpy-1.25.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d7806500e4f5bdd04095e849265e55de20d8cc4b661b038957354327f6d9b295"},
+    {file = "numpy-1.25.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8b77775f4b7df768967a7c8b3567e309f617dd5e99aeb886fa14dc1a0791141f"},
+    {file = "numpy-1.25.2-cp39-cp39-win32.whl", hash = "sha256:2792d23d62ec51e50ce4d4b7d73de8f67a2fd3ea710dcbc8563a51a03fb07b01"},
+    {file = "numpy-1.25.2-cp39-cp39-win_amd64.whl", hash = "sha256:76b4115d42a7dfc5d485d358728cdd8719be33cc5ec6ec08632a5d6fca2ed380"},
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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"
+files = [
+    {file = "openpyxl-3.1.2-py2.py3-none-any.whl", hash = "sha256:f91456ead12ab3c6c2e9491cf33ba6d08357d802192379bb482f1033ade496f5"},
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+]
+
+[package.dependencies]
+et-xmlfile = "*"
+
 [[package]]
 name = "packaging"
 version = "23.1"
@@ -153,6 +253,67 @@ files = [
     {file = "packaging-23.1.tar.gz", hash = "sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"},
 ]
 
+[[package]]
+name = "pandas"
+version = "2.1.0"
+description = "Powerful data structures for data analysis, time series, and statistics"
+optional = false
+python-versions = ">=3.9"
+files = [
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+]
+
+[package.dependencies]
+numpy = [
+    {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+]
+python-dateutil = ">=2.8.2"
+pytz = ">=2020.1"
+tzdata = ">=2022.1"
+
+[package.extras]
+all = ["PyQt5 (>=5.15.6)", "SQLAlchemy (>=1.4.36)", "beautifulsoup4 (>=4.11.1)", "bottleneck (>=1.3.4)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=0.8.1)", "fsspec (>=2022.05.0)", "gcsfs (>=2022.05.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.8.0)", "matplotlib (>=3.6.1)", "numba (>=0.55.2)", "numexpr (>=2.8.0)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pandas-gbq (>=0.17.5)", "psycopg2 (>=2.9.3)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.5)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "pyxlsb (>=1.0.9)", "qtpy (>=2.2.0)", "s3fs (>=2022.05.0)", "scipy (>=1.8.1)", "tables (>=3.7.0)", "tabulate (>=0.8.10)", "xarray (>=2022.03.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)", "zstandard (>=0.17.0)"]
+aws = ["s3fs (>=2022.05.0)"]
+clipboard = ["PyQt5 (>=5.15.6)", "qtpy (>=2.2.0)"]
+compression = ["zstandard (>=0.17.0)"]
+computation = ["scipy (>=1.8.1)", "xarray (>=2022.03.0)"]
+consortium-standard = ["dataframe-api-compat (>=0.1.7)"]
+excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pyxlsb (>=1.0.9)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)"]
+feather = ["pyarrow (>=7.0.0)"]
+fss = ["fsspec (>=2022.05.0)"]
+gcp = ["gcsfs (>=2022.05.0)", "pandas-gbq (>=0.17.5)"]
+hdf5 = ["tables (>=3.7.0)"]
+html = ["beautifulsoup4 (>=4.11.1)", "html5lib (>=1.1)", "lxml (>=4.8.0)"]
+mysql = ["SQLAlchemy (>=1.4.36)", "pymysql (>=1.0.2)"]
+output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.8.10)"]
+parquet = ["pyarrow (>=7.0.0)"]
+performance = ["bottleneck (>=1.3.4)", "numba (>=0.55.2)", "numexpr (>=2.8.0)"]
+plot = ["matplotlib (>=3.6.1)"]
+postgresql = ["SQLAlchemy (>=1.4.36)", "psycopg2 (>=2.9.3)"]
+spss = ["pyreadstat (>=1.1.5)"]
+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 = "pluggy"
 version = "1.3.0"
@@ -190,6 +351,31 @@ tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 [package.extras]
 testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
+[[package]]
+name = "python-dateutil"
+version = "2.8.2"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+files = [
+    {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
+    {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "pytz"
+version = "2023.3.post1"
+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
+    {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
+]
+
 [[package]]
 name = "requests"
 version = "2.31.0"
@@ -211,6 +397,17 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -222,6 +419,17 @@ files = [
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
 
+[[package]]
+name = "tzdata"
+version = "2023.3"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+files = [
+    {file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
+    {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
+]
+
 [[package]]
 name = "urllib3"
 version = "2.0.4"
@@ -242,4 +450,4 @@ zstd = ["zstandard (>=0.18.0)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "bc3567f9501f9e18bf9f53d8b4efe1e7e3fc2d750ceda2fbab165bfa22d49c64"
+content-hash = "3647d568f5623dd762f19029230626a62e68309fa2ef8be49a36382c19264a5f"

pyproject.toml

@@ -1,14 +1,19 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.1.1"
-description = "Real estate scraping library"
-authors = ["Zachary Hampton <zachary@zacharysproducts.com>"]
+version = "0.2.15"
+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"
 readme = "README.md"
 
+[tool.poetry.scripts]
+homeharvest = "homeharvest.cli:main"
+
 [tool.poetry.dependencies]
 python = "^3.10"
 requests = "^2.31.0"
+pandas = "^2.1.0"
+openpyxl = "^3.1.2"
 
 
 [tool.poetry.group.dev.dependencies]

tests/test_realtor.py

@@ -0,0 +1,40 @@
+from homeharvest import scrape_property
+from homeharvest.exceptions import (
+    InvalidSite,
+    InvalidListingType,
+    NoResultsFound,
+    GeoCoordsNotFound,
+)
+
+
+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"),
+    ]
+
+    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):
+        assert True
+
+    assert all([result is None for result in bad_results])

tests/test_redfin.py

@@ -1,9 +1,32 @@
 from homeharvest import scrape_property
+from homeharvest.exceptions import (
+    InvalidSite,
+    InvalidListingType,
+    NoResultsFound,
+    GeoCoordsNotFound,
+)
 
 
 def test_redfin():
-    result = scrape_property(
-        location="85281"
-    )
+    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 result is not None
+    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_utils.py

@@ -0,0 +1,24 @@
+from homeharvest.utils import parse_address_one, parse_address_two
+
+
+def test_parse_address_one():
+    test_data = [
+        ("4303 E Cactus Rd Apt 126", ("4303 E Cactus Rd", "#126")),
+        ("1234 Elm Street apt 2B", ("1234 Elm Street", "#2B")),
+        ("1234 Elm Street UNIT 3A", ("1234 Elm Street", "#3A")),
+        ("1234 Elm Street unit 3A", ("1234 Elm Street", "#3A")),
+        ("1234 Elm Street SuIte 3A", ("1234 Elm Street", "#3A")),
+    ]
+
+    for input_data, (exp_addr_one, exp_addr_two) in test_data:
+        address_one, address_two = parse_address_one(input_data)
+        assert address_one == exp_addr_one
+        assert address_two == exp_addr_two
+
+
+def test_parse_address_two():
+    test_data = [("Apt 126", "#126"), ("apt 2B", "#2B"), ("UNIT 3A", "#3A"), ("unit 3A", "#3A"), ("SuIte 3A", "#3A")]
+
+    for input_data, expected in test_data:
+        output = parse_address_two(input_data)
+        assert output == expected

tests/test_zillow.py

@@ -0,0 +1,33 @@
+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"),
+        scrape_property(location="3268 88th st s, Lakewood", site_name="zillow", listing_type="for_rent"),
+    ]
+
+    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])