Merge pull request #31 from ZacharyHampton/v0.3

v0.3

.gitignore

@@ -2,4 +2,6 @@
 **/dist/
 **/__pycache__/
 **/.pytest_cache/
-*.pyc
+*.pyc
+/.ipynb_checkpoints/
+*.csv

README.md

@@ -1,35 +1,184 @@
-# HomeHarvest
+<img src="https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/d1a2bf8b-09f5-4c57-b33a-0ada8a34f12d" width="400">
 
-**HomeHarvest** aims to be the top Python real estate scraping library.
+**HomeHarvest** is a simple, yet comprehensive, real estate scraping library that extracts and formats data in the style of MLS listings.
 
-## RoadMap
+[![Try with Replit](https://replit.com/badge?caption=Try%20with%20Replit)](https://replit.com/@ZacharyHampton/HomeHarvestDemo)
 
-- **Supported Sites**: Currently, we support scraping from sites such as `Zillow` and `RedFin`.
-- **Output**: Provides the option to return the scraped data as a Pandas dataframe.
-- **Under Consideration**: We're looking into the possibility of an Excel plugin to cater to a broader audience.
+\
+**Not technical?** Try out the web scraping tool on our site at [tryhomeharvest.com](https://tryhomeharvest.com).
 
-## Site Name Options
+*Looking to build a data-focused software product?* **[Book a call](https://calendly.com/zachary-products/15min)** *to work with us.*
 
-- `zillow`
-- `redfin`
+Check out another project we wrote: ***[JobSpy](https://github.com/cullenwatson/JobSpy)** – a Python package for job scraping*
 
-## Listing Types
+## HomeHarvest Features
 
-- `for_rent`
-- `for_sale`
+- **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.
+- **Usage Modes**:
+  - **CLI**: For users who prefer command-line operations.
+  - **Python**: For those who'd like to integrate scraping into their Python scripts.
 
-### Installation
+[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 --upgrade homeharvest
+pip install homeharvest
+```
+  _Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_ 
+
+## Usage
+
+### CLI 
+
+```
+usage: homeharvest [-l {for_sale,for_rent,sold}] [-o {excel,csv}] [-f FILENAME] [-p PROXY] [-d DAYS] [-r RADIUS] [-m] location
+                                                                                                                             
+Home Harvest Property Scraper                                                                                                 
+                                                                                                                             
+positional arguments:                                                                                                         
+  location              Location to scrape (e.g., San Francisco, CA)                                                          
+                                                                                                                             
+options:                                                                                                                      
+  -l {for_sale,for_rent,sold}, --listing_type {for_sale,for_rent,sold}                                                        
+                        Listing type to scrape                                                                                
+  -o {excel,csv}, --output {excel,csv}                                                                                        
+                        Output format                                                                                         
+  -f FILENAME, --filename FILENAME                                                                                            
+                        Name of the output file (without extension)                                                           
+  -p PROXY, --proxy PROXY                                                                                                     
+                        Proxy to use for scraping                                                                             
+  -d DAYS, --days DAYS  Sold/listed in last _ days filter.                                                                           
+  -r RADIUS, --radius RADIUS                                                                                                  
+                        Get comparable properties within _ (eg. 0.0) miles. Only applicable for individual addresses.        
+  -m, --mls_only        If set, fetches only MLS listings.
+```
+```bash
+> homeharvest "San Francisco, CA" -l for_rent -o excel -f HomeHarvest
 ```
 
-### Example Usage
-```
+### Python
+
+```py
 from homeharvest import scrape_property
+from datetime import datetime
+
+# Generate filename based on current timestamp
+current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+filename = f"output/{current_timestamp}.csv"
 
 properties = scrape_property(
-    location="85281", site_name="zillow", listing_type="for_rent"
+  location="San Diego, CA",
+  listing_type="sold",  # or (for_sale, for_rent)
+  property_younger_than=30,  # sold in last 30 days - listed in last x days if (for_sale, for_rent)
+  mls_only=True,  # only fetch MLS listings
 )
-print(properties)
+print(f"Number of properties: {len(properties)}")
+
+# Export to csv
+properties.to_csv(filename, index=False)
+print(properties.head())
 ```
+
+## Output
+```plaintext
+>>> properties.head()
+    MLS       MLS # Status          Style  ...     COEDate LotSFApx PrcSqft Stories
+0  SDCA   230018348   SOLD         CONDOS  ...  2023-10-03   290110     803       2
+1  SDCA   230016614   SOLD      TOWNHOMES  ...  2023-10-03     None     838       3
+2  SDCA   230016367   SOLD         CONDOS  ...  2023-10-03    30056     649       1
+3  MRCA  NDP2306335   SOLD  SINGLE_FAMILY  ...  2023-10-03     7519     661       2
+4  SDCA   230014532   SOLD         CONDOS  ...  2023-10-03     None     752       1
+[5 rows x 22 columns]
+```
+
+### Parameters for `scrape_property()`
+```
+Required
+├── location (str): The address in various formats - this could be just a zip code, a full address, or city/state, etc.
+└── listing_type (option): Choose the type of listing.
+    - 'for_rent'
+    - 'for_sale'
+    - 'sold'
+
+Optional
+├── radius (decimal): Radius in miles to find comparable properties based on individual addresses.
+│    Example: 5.5 (fetches properties within a 5.5-mile radius if location is set to a specific address; otherwise, ignored)
+│
+├── property_younger_than (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)
+│
+├── mls_only (True/False): If set, fetches only MLS listings (mainly applicable to 'sold' listings)
+│
+└── proxy (string): In format 'http://user:pass@host:port'
+
+```
+### Property Schema
+```plaintext
+Property
+├── Basic Information:
+│ ├── property_url
+│ ├── mls
+│ ├── mls_id
+│ └── status
+
+├── Address Details:
+│ ├── street
+│ ├── unit
+│ ├── city
+│ ├── state
+│ └── zip_code
+
+├── Property Description:
+│ ├── style
+│ ├── beds
+│ ├── full_baths
+│ ├── half_baths
+│ ├── sqft
+│ ├── year_built
+│ ├── stories
+│ └── lot_sqft
+
+├── Property Listing Details:
+│ ├── list_price
+│ ├── list_date
+│ ├── sold_price
+│ ├── last_sold_date
+│ ├── price_per_sqft
+│ └── hoa_fee
+
+├── Location Details:
+│ ├── latitude
+│ ├── longitude
+
+└── Parking Details:
+    └── parking_garage
+```
+
+### Exceptions
+The following exceptions may be raised when using HomeHarvest:
+
+- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
+- `NoResultsFound` - no properties found from your search
+  
+  
+## Frequently Asked Questions
+---
+
+**Q: Encountering issues with your searches?**  
+**A:** Try to broaden the parameters you're using. 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 Realtor.com for sending too many requests. We recommend:
+
+- Waiting a few seconds between requests.
+- Trying a VPN or useing a proxy as a parameter to scrape_property() to change your IP address.
+
+---
+

examples/HomeHarvest_Demo.ipynb

@@ -0,0 +1,115 @@
+{
+ "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": [
+    "# check for sale properties\n",
+    "scrape_property(\n",
+    "    location=\"dallas\",\n",
+    "    listing_type=\"for_sale\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaf86093",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# search a specific address\n",
+    "scrape_property(\n",
+    "    location=\"2530 Al Lipscomb Way\",\n",
+    "    listing_type=\"for_sale\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab7b4c21-da1d-4713-9df4-d7425d8ce21e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check rentals\n",
+    "scrape_property(\n",
+    "    location=\"chicago, illinois\",\n",
+    "    listing_type=\"for_rent\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af280cd3",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# check sold properties\n",
+    "scrape_property(\n",
+    "    location=\"90210\",\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
+}

examples/HomeHarvest_Demo.py

@@ -0,0 +1,18 @@
+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"output/{current_timestamp}.csv"
+
+properties = scrape_property(
+    location="San Diego, CA",
+    listing_type="sold", # for_sale, for_rent
+    property_younger_than=30, # sold/listed in last 30 days
+    mls_only=True, # only fetch MLS listings
+)
+print(f"Number of properties: {len(properties)}")
+
+# Export to csv
+properties.to_csv(filename, index=False)
+print(properties.head())

homeharvest/__init__.py

@@ -1,37 +1,50 @@
-from .core.scrapers.redfin import RedfinScraper
-from .core.scrapers.realtor import RealtorScraper
-from .core.scrapers.zillow import ZillowScraper
-from .core.scrapers.models import ListingType, Property, Building
+import warnings
+import pandas as pd
 from .core.scrapers import ScraperInput
-from .exceptions import InvalidSite, InvalidListingType
-from typing import Union
-
-
-_scrapers = {
-    "redfin": RedfinScraper,
-    "realtor.com": RealtorScraper,
-    "zillow": ZillowScraper,
-}
+from .utils import process_result, ordered_properties, validate_input
+from .core.scrapers.realtor import RealtorScraper
+from .core.scrapers.models import ListingType
+from .exceptions import InvalidListingType, NoResultsFound
 
 
 def scrape_property(
     location: str,
-    site_name: str,
-    listing_type: str = "for_sale",  #: for_sale, for_rent, sold
-) -> Union[list[Building], list[Property]]:  #: eventually, return pandas dataframe
-    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."
-        )
+    listing_type: str = "for_sale",
+    radius: float = None,
+    mls_only: bool = False,
+    property_younger_than: int = None,
+    pending_or_contingent: bool = False,
+    proxy: str = None,
+) -> pd.DataFrame:
+    """
+    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 property_younger_than: Get properties sold/listed in last _ days.
+    :param pending_or_contingent: If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.
+    :param proxy: Proxy to use for scraping
+    """
+    validate_input(listing_type)
 
     scraper_input = ScraperInput(
         location=location,
         listing_type=ListingType[listing_type.upper()],
+        proxy=proxy,
+        radius=radius,
+        mls_only=mls_only,
+        last_x_days=property_younger_than,
+        pending_or_contingent=pending_or_contingent,
     )
 
-    site = _scrapers[site_name.lower()](scraper_input)
+    site = RealtorScraper(scraper_input)
+    results = site.search()
 
-    return site.search()
+    properties_dfs = [process_result(result) for result in results]
+    if not properties_dfs:
+        raise NoResultsFound("no results found for the query")
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=FutureWarning)
+        return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties]

homeharvest/cli.py

@@ -0,0 +1,89 @@
+import argparse
+import datetime
+from homeharvest import scrape_property
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Home Harvest Property Scraper")
+    parser.add_argument(
+        "location", type=str, help="Location to scrape (e.g., San Francisco, CA)"
+    )
+
+    parser.add_argument(
+        "-l",
+        "--listing_type",
+        type=str,
+        default="for_sale",
+        choices=["for_sale", "for_rent", "sold"],
+        help="Listing type to scrape",
+    )
+
+    parser.add_argument(
+        "-o",
+        "--output",
+        type=str,
+        default="excel",
+        choices=["excel", "csv"],
+        help="Output format",
+    )
+
+    parser.add_argument(
+        "-f",
+        "--filename",
+        type=str,
+        default=None,
+        help="Name of the output file (without extension)",
+    )
+
+    parser.add_argument(
+        "-p", "--proxy", type=str, default=None, help="Proxy to use for scraping"
+    )
+    parser.add_argument(
+        "-d",
+        "--days",
+        type=int,
+        default=None,
+        help="Sold/listed in last _ days filter.",
+    )
+
+    parser.add_argument(
+        "-r",
+        "--radius",
+        type=float,
+        default=None,
+        help="Get comparable properties within _ (eg. 0.0) miles. Only applicable for individual addresses.",
+    )
+    parser.add_argument(
+        "-m",
+        "--mls_only",
+        action="store_true",
+        help="If set, fetches only MLS listings.",
+    )
+
+    args = parser.parse_args()
+
+    result = scrape_property(
+        args.location,
+        args.listing_type,
+        radius=args.radius,
+        proxy=args.proxy,
+        mls_only=args.mls_only,
+        property_younger_than=args.days,
+    )
+
+    if not args.filename:
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        args.filename = f"HomeHarvest_{timestamp}"
+
+    if args.output == "excel":
+        output_filename = f"{args.filename}.xlsx"
+        result.to_excel(output_filename, index=False)
+        print(f"Excel file saved as {output_filename}")
+    elif args.output == "csv":
+        output_filename = f"{args.filename}.csv"
+        result.to_csv(output_filename, index=False)
+        print(f"CSV file saved as {output_filename}")
+
+
+if __name__ == "__main__":
+    main()

homeharvest/core/scrapers/__init__.py

@@ -1,28 +1,44 @@
 from dataclasses import dataclass
 import requests
-from .models import Property, ListingType
+import tls_client
+from .models import Property, ListingType, SiteName
 
 
 @dataclass
 class ScraperInput:
     location: str
     listing_type: ListingType
-    proxy_url: str | None = None
+    radius: float | None = None
+    mls_only: bool | None = None
+    proxy: str | None = None
+    last_x_days: int | None = None
+    pending_or_contingent: bool | None = None
 
 
 class Scraper:
-    listing_type = ListingType.FOR_SALE
-
-    def __init__(self, scraper_input: ScraperInput):
+    def __init__(
+        self,
+        scraper_input: ScraperInput,
+        session: requests.Session | tls_client.Session = None,
+    ):
         self.location = scraper_input.location
-        self.session = requests.Session()
-        Scraper.listing_type = scraper_input.listing_type
+        self.listing_type = scraper_input.listing_type
 
-        if scraper_input.proxy_url:
-            self.session.proxies = {
-                "http": scraper_input.proxy_url,
-                "https": scraper_input.proxy_url,
-            }
+        if not session:
+            self.session = requests.Session()
+        else:
+            self.session = session
+
+        if scraper_input.proxy:
+            proxy_url = scraper_input.proxy
+            proxies = {"http": proxy_url, "https": proxy_url}
+            self.session.proxies.update(proxies)
+
+        self.listing_type = scraper_input.listing_type
+        self.radius = scraper_input.radius
+        self.last_x_days = scraper_input.last_x_days
+        self.mls_only = scraper_input.mls_only
+        self.pending_or_contingent = scraper_input.pending_or_contingent
 
     def search(self) -> list[Property]:
         ...

homeharvest/core/scrapers/models.py

@@ -1,52 +1,65 @@
 from dataclasses import dataclass
 from enum import Enum
+from typing import Optional
+
+
+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"
+    FOR_SALE = "FOR_SALE"
+    FOR_RENT = "FOR_RENT"
+    SOLD = "SOLD"
 
 
 @dataclass
 class Address:
-    address_one: str
-    city: str
-    state: str
-    zip_code: str
+    street: str | None = None
+    unit: str | None = None
+    city: str | None = None
+    state: str | None = None
+    zip: str | None = None
 
-    address_two: str | None = None
+
+@dataclass
+class Description:
+    style: str | 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
 
 
 @dataclass
 class Property:
-    address: Address
-    url: str
-
-    beds: int | None = None
-    baths: float | None = None
-    stories: int | None = None
-    agent_name: str | None = None
-    year_built: int | None = None
-    square_feet: int | None = None
-    price_per_square_foot: int | None = None
-    year_built: int | None = None
-    price: int | None = None
+    property_url: str
+    mls: str | None = None
     mls_id: str | None = None
+    status: str | None = None
+    address: Address | None = None
 
-    listing_type: ListingType | None = None
-    lot_size: int | None = None
-    description: str | None = None
+    list_price: int | None = None
+    list_date: str | None = None
+    last_sold_date: str | None = None
+    prc_sqft: int | None = None
+    hoa_fee: int | None = None
+    description: Description | None = None
 
-
-@dataclass
-class Building:
-    address: Address
-    url: str
-
-    num_units: int | None = None
-    min_unit_price: int | None = None
-    max_unit_price: int | None = None
-    avg_unit_price: int | None = None
-
-    listing_type: str | None = None
+    latitude: float | None = None
+    longitude: float | None = None
+    neighborhoods: Optional[str] = None

homeharvest/core/scrapers/realtor/__init__.py

@@ -1,51 +1,575 @@
-import json
-from ..models import Property, Address
+"""
+homeharvest.realtor.__init__
+~~~~~~~~~~~~
+
+This module implements the scraper for realtor.com
+"""
+from typing import Dict, Union, Optional
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
 from .. import Scraper
-from typing import Any
+from ....exceptions import NoResultsFound
+from ..models import Property, Address, ListingType, Description
 
 
 class RealtorScraper(Scraper):
+    SEARCH_GQL_URL = "https://www.realtor.com/api/v1/rdc_search_srp?client_id=rdc-search-new-communities&schema=vesta"
+    PROPERTY_URL = "https://www.realtor.com/realestateandhomes-detail/"
+    ADDRESS_AUTOCOMPLETE_URL = "https://parser-external.geo.moveaws.com/suggest"
+
     def __init__(self, scraper_input):
+        self.counter = 1
         super().__init__(scraper_input)
 
     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": "for-sale",
+            "client_id": self.listing_type.value.lower().replace("_", "-"),
             "limit": "1",
             "area_types": "city,state,county,postal_code,address,street,neighborhood,school,school_district,university,park",
         }
 
         response = self.session.get(
-            "https://parser-external.geo.moveaws.com/suggest",
+            self.ADDRESS_AUTOCOMPLETE_URL,
             params=params,
-            headers=headers,
         )
         response_json = response.json()
 
-        return response_json["autocomplete"][0]
+        result = response_json["autocomplete"]
+
+        if not result:
+            raise NoResultsFound("No results found for location: " + self.location)
+
+        return result[0]
+
+    def handle_listing(self, listing_id: str) -> list[Property]:
+        query = """query Listing($listing_id: ID!) {
+                    listing(id: $listing_id) {
+                        source {
+                            id
+                            listing_id
+                        }
+                        address {
+                            street_number
+                            street_name
+                            street_suffix
+                            unit
+                            city
+                            state_code
+                            postal_code
+                            location {
+                                coordinate {
+                                    lat
+                                    lon
+                                }
+                            }
+                        }
+                        basic {
+                            sqft
+                            beds
+                            baths_full
+                            baths_half
+                            lot_sqft
+                            sold_price
+                            sold_price
+                            type
+                            price
+                            status
+                            sold_date
+                            list_date
+                        }
+                        details {
+                            year_built
+                            stories
+                            garage
+                            permalink
+                        }
+                    }
+                }"""
+
+        variables = {"listing_id": listing_id}
+        payload = {
+            "query": query,
+            "variables": variables,
+        }
+
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
+        response_json = response.json()
+
+        property_info = response_json["data"]["listing"]
+
+        mls = (
+            property_info["source"].get("id")
+            if "source" in property_info and isinstance(property_info["source"], dict)
+            else None
+        )
+
+        able_to_get_lat_long = (
+                property_info
+                and property_info.get("address")
+                and property_info["address"].get("location")
+                and property_info["address"]["location"].get("coordinate")
+        )
+
+        listing = Property(
+            mls=mls,
+            mls_id=property_info["source"].get("listing_id")
+            if "source" in property_info and isinstance(property_info["source"], dict)
+            else None,
+            property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
+            status=property_info["basic"]["status"].upper(),
+            list_price=property_info["basic"]["price"],
+            list_date=property_info["basic"]["list_date"].split("T")[0]
+            if property_info["basic"].get("list_date")
+            else None,
+            prc_sqft=property_info["basic"].get("price") / property_info["basic"].get("sqft")
+            if property_info["basic"].get("price") and property_info["basic"].get("sqft")
+            else None,
+            last_sold_date=property_info["basic"]["sold_date"].split("T")[0]
+            if property_info["basic"].get("sold_date")
+            else None,
+            latitude=property_info["address"]["location"]["coordinate"].get("lat")
+            if able_to_get_lat_long
+            else None,
+            longitude=property_info["address"]["location"]["coordinate"].get("lon")
+            if able_to_get_lat_long
+            else None,
+            address=self._parse_address(property_info, search_type="handle_listing"),
+            description=Description(
+                style=property_info["basic"].get("type", "").upper(),
+                beds=property_info["basic"].get("beds"),
+                baths_full=property_info["basic"].get("baths_full"),
+                baths_half=property_info["basic"].get("baths_half"),
+                sqft=property_info["basic"].get("sqft"),
+                lot_sqft=property_info["basic"].get("lot_sqft"),
+                sold_price=property_info["basic"].get("sold_price"),
+                year_built=property_info["details"].get("year_built"),
+                garage=property_info["details"].get("garage"),
+                stories=property_info["details"].get("stories"),
+            )
+        )
+
+        return [listing]
+
+    def get_latest_listing_id(self, property_id: str) -> str | None:
+        query = """query Property($property_id: ID!) {
+                    property(id: $property_id) {
+                        listings {
+                            listing_id
+                            primary
+                        }
+                    }
+                }
+                """
+
+        variables = {"property_id": property_id}
+        payload = {
+            "query": query,
+            "variables": variables,
+        }
+
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
+        response_json = response.json()
+
+        property_info = response_json["data"]["property"]
+        if property_info["listings"] is None:
+            return None
+
+        primary_listing = next((listing for listing in property_info["listings"] if listing["primary"]), None)
+        if primary_listing:
+            return primary_listing["listing_id"]
+        else:
+            return property_info["listings"][0]["listing_id"]
+
+    def handle_address(self, property_id: str) -> list[Property]:
+        """
+        Handles a specific address & returns one property
+        """
+        query = """query Property($property_id: ID!) {
+                    property(id: $property_id) {
+                        property_id
+                        details {
+                            date_updated
+                            garage
+                            permalink
+                            year_built
+                            stories
+                        }
+                        address {
+                            street_number
+                            street_name
+                            street_suffix
+                            unit
+                            city
+                            state_code
+                            postal_code
+                            location {
+                                coordinate {
+                                    lat
+                                    lon
+                                }
+                            }
+                        }
+                        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_GQL_URL, json=payload)
+        response_json = response.json()
+
+        property_info = response_json["data"]["property"]
+
+        return [
+            Property(
+                mls_id=property_id,
+                property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
+                address=self._parse_address(
+                    property_info, search_type="handle_address"
+                ),
+                description=self._parse_description(property_info),
+            )
+        ]
+
+    def general_search(
+        self, variables: dict, search_type: str
+    ) -> Dict[str, Union[int, list[Property]]]:
+        """
+        Handles a location area & returns a list of properties
+        """
+        results_query = """{
+                            count
+                            total
+                            results {
+                                property_id
+                                list_date
+                                status
+                                last_sold_price
+                                last_sold_date
+                                list_price
+                                price_per_sqft
+                                description {
+                                    sqft
+                                    beds
+                                    baths_full
+                                    baths_half
+                                    lot_sqft
+                                    sold_price
+                                    year_built
+                                    garage
+                                    sold_price
+                                    type
+                                    name
+                                    stories
+                                }
+                                source {
+                                    id
+                                    listing_id
+                                }
+                                hoa {
+                                    fee
+                                }
+                                location {
+                                    address {
+                                        street_number
+                                        street_name
+                                        street_suffix
+                                        unit
+                                        city
+                                        state_code
+                                        postal_code
+                                        coordinate {
+                                            lon
+                                            lat
+                                        }
+                                    }
+                                    neighborhoods {
+                                        name
+                                    }
+                                }
+                            }
+                        }
+                    }"""
+
+        date_param = (
+            'sold_date: { min: "$today-%sD" }' % self.last_x_days
+            if self.listing_type == ListingType.SOLD and self.last_x_days
+            else (
+                'list_date: { min: "$today-%sD" }' % self.last_x_days
+                if self.last_x_days
+                else ""
+            )
+        )
+
+        sort_param = (
+            "sort: [{ field: sold_date, direction: desc }]"
+            if self.listing_type == ListingType.SOLD
+            else "sort: [{ field: list_date, direction: desc }]"
+        )
+
+        pending_or_contingent_param = "or_filters: { contingent: true, pending: true }" if self.pending_or_contingent else ""
+
+        if search_type == "comps":  #: comps search, came from an address
+            query = """query Property_search(
+                    $coordinates: [Float]!
+                    $radius: String!
+                    $offset: Int!,
+                    ) {
+                        property_search(
+                            query: { 
+                                nearby: {
+                                    coordinates: $coordinates
+                                    radius: $radius 
+                                }
+                                status: %s
+                                %s
+                            }
+                            %s
+                            limit: 200
+                            offset: $offset
+                    ) %s""" % (
+                self.listing_type.value.lower(),
+                date_param,
+                sort_param,
+                results_query,
+            )
+        elif search_type == "area":  #: general search, came from a general location
+            query = """query Home_search(
+                                $city: String,
+                                $county: [String],
+                                $state_code: String,
+                                $postal_code: String
+                                $offset: Int,
+                            ) {
+                                home_search(
+                                    query: {
+                                        city: $city
+                                        county: $county
+                                        postal_code: $postal_code
+                                        state_code: $state_code
+                                        status: %s
+                                        %s
+                                        %s
+                                    }
+                                    %s
+                                    limit: 200
+                                    offset: $offset
+                                ) %s""" % (
+                self.listing_type.value.lower(),
+                date_param,
+                pending_or_contingent_param,
+                sort_param,
+                results_query,
+            )
+        else:  #: general search, came from an address
+            query = (
+                    """query Property_search(
+                        $property_id: [ID]!
+                        $offset: Int!,
+                    ) {
+                        property_search(
+                            query: {
+                                property_id: $property_id
+                            }
+                            limit: 1
+                            offset: $offset
+                        ) %s""" % results_query)
+
+        payload = {
+            "query": query,
+            "variables": variables,
+        }
+
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
+        response.raise_for_status()
+        response_json = response.json()
+        search_key = "home_search" if search_type == "area" else "property_search"
+
+        properties: list[Property] = []
+
+        if (
+            response_json is None
+            or "data" not in response_json
+            or response_json["data"] is None
+            or search_key not in response_json["data"]
+            or response_json["data"][search_key] is None
+            or "results" not in response_json["data"][search_key]
+        ):
+            return {"total": 0, "properties": []}
+
+        for result in response_json["data"][search_key]["results"]:
+            self.counter += 1
+            mls = (
+                result["source"].get("id")
+                if "source" in result and isinstance(result["source"], dict)
+                else None
+            )
+
+            if not mls and self.mls_only:
+                continue
+
+            able_to_get_lat_long = (
+                result
+                and result.get("location")
+                and result["location"].get("address")
+                and result["location"]["address"].get("coordinate")
+            )
+
+            realty_property = Property(
+                mls=mls,
+                mls_id=result["source"].get("listing_id")
+                if "source" in result and isinstance(result["source"], dict)
+                else None,
+                property_url=f"{self.PROPERTY_URL}{result['property_id']}",
+                status=result["status"].upper(),
+                list_price=result["list_price"],
+                list_date=result["list_date"].split("T")[0]
+                if result.get("list_date")
+                else None,
+                prc_sqft=result.get("price_per_sqft"),
+                last_sold_date=result.get("last_sold_date"),
+                hoa_fee=result["hoa"]["fee"]
+                if result.get("hoa") and isinstance(result["hoa"], dict)
+                else None,
+                latitude=result["location"]["address"]["coordinate"].get("lat")
+                if able_to_get_lat_long
+                else None,
+                longitude=result["location"]["address"]["coordinate"].get("lon")
+                if able_to_get_lat_long
+                else None,
+                address=self._parse_address(result, search_type="general_search"),
+                #: neighborhoods=self._parse_neighborhoods(result),
+                description=self._parse_description(result),
+            )
+            properties.append(realty_property)
+
+        return {
+            "total": response_json["data"][search_key]["total"],
+            "properties": properties,
+        }
 
     def search(self):
         location_info = self.handle_location()
         location_type = location_info["area_type"]
 
-        """
-        property types:
-        apartment + building + commercial + condo_townhome + condo_townhome_rowhome_coop + condos + coop + duplex_triplex + farm + investment + land + mobile + multi_family + rental + single_family + townhomes
-        """
-        print("a")
+        search_variables = {
+            "offset": 0,
+        }
+
+        search_type = "comps" if self.radius and location_type == "address" else "address" if location_type == "address" and not self.radius else "area"
+        if location_type == "address":
+            if not self.radius:  #: single address search, non comps
+                property_id = location_info["mpr_id"]
+                search_variables |= {"property_id": property_id}
+
+                gql_results = self.general_search(search_variables, search_type=search_type)
+                if gql_results["total"] == 0:
+                    listing_id = self.get_latest_listing_id(property_id)
+                    if listing_id is None:
+                        return self.handle_address(property_id)
+                    else:
+                        return self.handle_listing(listing_id)
+                else:
+                    return gql_results["properties"]
+
+            else:  #: general search, comps (radius)
+                coordinates = list(location_info["centroid"].values())
+                search_variables |= {
+                    "coordinates": coordinates,
+                    "radius": "{}mi".format(self.radius),
+                }
+
+        else:  #: general search, location
+            search_variables |= {
+                "city": location_info.get("city"),
+                "county": location_info.get("county"),
+                "state_code": location_info.get("state_code"),
+                "postal_code": location_info.get("postal_code"),
+            }
+
+        result = self.general_search(search_variables, search_type=search_type)
+        total = result["total"]
+        homes = result["properties"]
+
+        with ThreadPoolExecutor(max_workers=10) as executor:
+            futures = [
+                executor.submit(
+                    self.general_search,
+                    variables=search_variables | {"offset": i},
+                    search_type=search_type,
+                )
+                for i in range(200, min(total, 10000), 200)
+            ]
+
+            for future in as_completed(futures):
+                homes.extend(future.result()["properties"])
+
+        return homes
+
+    @staticmethod
+    def _parse_neighborhoods(result: dict) -> Optional[str]:
+        neighborhoods_list = []
+        neighborhoods = result["location"].get("neighborhoods", [])
+
+        if neighborhoods:
+            for neighborhood in neighborhoods:
+                name = neighborhood.get("name")
+                if name:
+                    neighborhoods_list.append(name)
+
+        return ", ".join(neighborhoods_list) if neighborhoods_list else None
+
+    @staticmethod
+    def _parse_address(result: dict, search_type):
+        if search_type == "general_search":
+            return Address(
+                street=f"{result['location']['address']['street_number']} {result['location']['address']['street_name']} {result['location']['address']['street_suffix']}",
+                unit=result["location"]["address"]["unit"],
+                city=result["location"]["address"]["city"],
+                state=result["location"]["address"]["state_code"],
+                zip=result["location"]["address"]["postal_code"],
+            )
+        return Address(
+            street=f"{result['address']['street_number']} {result['address']['street_name']} {result['address']['street_suffix']}",
+            unit=result["address"]["unit"],
+            city=result["address"]["city"],
+            state=result["address"]["state_code"],
+            zip=result["address"]["postal_code"],
+        )
+
+    @staticmethod
+    def _parse_description(result: dict) -> Description:
+        description_data = result.get("description", {})
+        return Description(
+            style=description_data.get("type", "").upper(),
+            beds=description_data.get("beds"),
+            baths_full=description_data.get("baths_full"),
+            baths_half=description_data.get("baths_half"),
+            sqft=description_data.get("sqft"),
+            lot_sqft=description_data.get("lot_sqft"),
+            sold_price=description_data.get("sold_price"),
+            year_built=description_data.get("year_built"),
+            garage=description_data.get("garage"),
+            stories=description_data.get("stories"),
+        )

homeharvest/core/scrapers/redfin/__init__.py

@@ -1,114 +0,0 @@
-import json
-from ..models import Property, Address
-from .. import Scraper
-from typing import Any
-
-
-class RedfinScraper(Scraper):
-    def __init__(self, scraper_input):
-        super().__init__(scraper_input)
-
-    def _handle_location(self):
-        url = "https://www.redfin.com/stingray/do/location-autocomplete?v=2&al=1&location={}".format(
-            self.location
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        def get_region_type(match_type: str):
-            if match_type == "4":
-                return "2"  #: zip
-            elif match_type == "2":
-                return "6"  #: city
-            elif match_type == "1":
-                return "address"  #: address, needs to be handled differently
-
-        if response_json["payload"]["exactMatch"] is not None:
-            target = response_json["payload"]["exactMatch"]
-        else:
-            target = response_json["payload"]["sections"][0]["rows"][0]
-
-        return target["id"].split("_")[1], get_region_type(target["type"])
-
-    @staticmethod
-    def _parse_home(home: dict, single_search: bool = False) -> Property:
-        def get_value(key: str) -> Any | None:
-            if key in home and "value" in home[key]:
-                return home[key]["value"]
-
-        if not single_search:
-            address = Address(
-                address_one=get_value("streetLine"),
-                city=home["city"],
-                state=home["state"],
-                zip_code=home["zip"],
-            )
-        else:
-            address_info = home["streetAddress"]
-
-            address = Address(
-                address_one=address_info["assembledAddress"],
-                city=home["city"],
-                state=home["state"],
-                zip_code=home["zip"],
-            )
-
-        url = "https://www.redfin.com{}".format(home["url"])
-
-        return Property(
-            address=address,
-            url=url,
-            beds=home["beds"] if "beds" in home else None,
-            baths=home["baths"] if "baths" in home else None,
-            stories=home["stories"] if "stories" in home else None,
-            agent_name=get_value("listingAgent"),
-            description=home["listingRemarks"] if "listingRemarks" in home else None,
-            year_built=get_value("yearBuilt")
-            if not single_search
-            else home["yearBuilt"],
-            square_feet=get_value("sqFt"),
-            price_per_square_foot=get_value("pricePerSqFt"),
-            price=get_value("price"),
-            mls_id=get_value("mlsId"),
-        )
-
-    def handle_address(self, home_id: str):
-        """
-        EPs:
-        https://www.redfin.com/stingray/api/home/details/initialInfo?al=1&path=/TX/Austin/70-Rainey-St-78701/unit-1608/home/147337694
-        https://www.redfin.com/stingray/api/home/details/mainHouseInfoPanelInfo?propertyId=147337694&accessLevel=3
-        https://www.redfin.com/stingray/api/home/details/aboveTheFold?propertyId=147337694&accessLevel=3
-        https://www.redfin.com/stingray/api/home/details/belowTheFold?propertyId=147337694&accessLevel=3
-        """
-
-        url = "https://www.redfin.com/stingray/api/home/details/aboveTheFold?propertyId={}&accessLevel=3".format(
-            home_id
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        parsed_home = self._parse_home(
-            response_json["payload"]["addressSectionInfo"], single_search=True
-        )
-        return [parsed_home]
-
-    def search(self):
-        region_id, region_type = self._handle_location()
-
-        if region_type == "address":
-            home_id = region_id
-            return self.handle_address(home_id)
-
-        url = "https://www.redfin.com/stingray/api/gis?al=1&region_id={}&region_type={}".format(
-            region_id, region_type
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        homes = [
-            self._parse_home(home) for home in response_json["payload"]["homes"]
-        ]  #: support buildings
-        return homes

homeharvest/core/scrapers/zillow/__init__.py

@@ -1,205 +0,0 @@
-import re
-import json
-from ..models import Property, Address, Building, ListingType
-from ....exceptions import NoResultsFound, PropertyNotFound
-from .. import Scraper
-
-
-class ZillowScraper(Scraper):
-    listing_type: ListingType.FOR_SALE
-
-    def __init__(self, scraper_input):
-        super().__init__(scraper_input)
-        if self.listing_type == ListingType.FOR_SALE:
-            self.url = f"https://www.zillow.com/homes/for_sale/{self.location}_rb/"
-        elif self.listing_type == ListingType.FOR_RENT:
-            self.url = f"https://www.zillow.com/homes/for_rent/{self.location}_rb/"
-
-    def search(self):
-        resp = self.session.get(self.url, headers=self._get_headers())
-        resp.raise_for_status()
-        content = resp.text
-
-        match = re.search(
-            r'<script id="__NEXT_DATA__" type="application/json">(.*?)</script>',
-            content,
-            re.DOTALL,
-        )
-        if not match:
-            raise NoResultsFound(
-                "No results were found for Zillow with the given Location."
-            )
-
-        json_str = match.group(1)
-        data = json.loads(json_str)
-
-        if "searchPageState" in data["props"]["pageProps"]:
-            houses = data["props"]["pageProps"]["searchPageState"]["cat1"][
-                "searchResults"
-            ]["listResults"]
-            return [self._parse_home(house) for house in houses]
-        elif "gdpClientCache" in data["props"]["pageProps"]:
-            gdp_client_cache = json.loads(data["props"]["pageProps"]["gdpClientCache"])
-            main_key = list(gdp_client_cache.keys())[0]
-
-            property_data = gdp_client_cache[main_key]["property"]
-            property = self._get_single_property_page(property_data)
-
-            return [property]
-        raise PropertyNotFound("Specific property data not found in the response.")
-
-    @classmethod
-    def _parse_home(cls, home: dict):
-        """
-        This method is used when a user enters a generic location & zillow returns more than one property
-        """
-        url = (
-            f"https://www.zillow.com{home['detailUrl']}"
-            if "zillow.com" not in home["detailUrl"]
-            else home["detailUrl"]
-        )
-
-        if "hdpData" in home and "homeInfo" in home["hdpData"]:
-            price_data = cls._extract_price(home)
-            address = cls._extract_address(home)
-            agent_name = cls._extract_agent_name(home)
-            beds = home["hdpData"]["homeInfo"]["bedrooms"]
-            baths = home["hdpData"]["homeInfo"]["bathrooms"]
-            listing_type = home["hdpData"]["homeInfo"].get("homeType")
-
-            return Property(
-                address=address,
-                agent_name=agent_name,
-                url=url,
-                beds=beds,
-                baths=baths,
-                listing_type=listing_type,
-                **price_data,
-            )
-        else:
-            keys = ("addressStreet", "addressCity", "addressState", "addressZipcode")
-            address_one, city, state, zip_code = (home[key] for key in keys)
-            address_one, address_two = cls._parse_address_two(address_one)
-            address = Address(address_one, city, state, zip_code, address_two)
-
-            building_info = cls._extract_building_info(home)
-            return Building(address=address, url=url, **building_info)
-
-    @classmethod
-    def _get_single_property_page(cls, 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 = cls._parse_address_two(address_data["streetAddress"])
-        address = Address(
-            address_one=address_one,
-            address_two=address_two,
-            city=address_data["city"],
-            state=address_data["state"],
-            zip_code=address_data["zipcode"],
-        )
-
-        return Property(
-            address=address,
-            url=url,
-            beds=property_data.get("bedrooms", None),
-            baths=property_data.get("bathrooms", None),
-            year_built=property_data.get("yearBuilt", None),
-            price=property_data.get("price", None),
-            lot_size=property_data.get("lotSize", None),
-            agent_name=property_data.get("attributionInfo", {}).get("agentName", None),
-            stories=property_data.get("resoFacts", {}).get("stories", None),
-            description=property_data.get("description", None),
-            mls_id=property_data.get("attributionInfo", {}).get("mlsId", None),
-            price_per_square_foot=property_data.get("resoFacts", {}).get(
-                "pricePerSquareFoot", None
-            ),
-            square_feet=property_data.get("livingArea", None),
-            listing_type=property_data.get("homeType", None),
-        )
-
-    @classmethod
-    def _extract_building_info(cls, home: dict) -> dict:
-        num_units = len(home["units"])
-        prices = [
-            int(unit["price"].replace("$", "").replace(",", "").split("+")[0])
-            for unit in home["units"]
-        ]
-        return {
-            "listing_type": cls.listing_type,
-            "num_units": len(home["units"]),
-            "min_unit_price": min(
-                (
-                    int(unit["price"].replace("$", "").replace(",", "").split("+")[0])
-                    for unit in home["units"]
-                )
-            ),
-            "max_unit_price": max(
-                (
-                    int(unit["price"].replace("$", "").replace(",", "").split("+")[0])
-                    for unit in home["units"]
-                )
-            ),
-            "avg_unit_price": sum(prices) // len(prices) if num_units else None,
-        }
-
-    @staticmethod
-    def _extract_price(home: dict) -> dict:
-        price = int(home["hdpData"]["homeInfo"]["priceForHDP"])
-        square_feet = home["hdpData"]["homeInfo"].get("livingArea")
-
-        lot_size = home["hdpData"]["homeInfo"].get("lotAreaValue")
-        price_per_square_foot = price // square_feet if square_feet and price else None
-
-        return {
-            k: v
-            for k, v in locals().items()
-            if k in ["price", "square_feet", "lot_size", "price_per_square_foot"]
-        }
-
-    @staticmethod
-    def _extract_agent_name(home: dict) -> str | None:
-        broker_str = home.get("brokerName", "")
-        match = re.search(r"Listing by: (.+)", broker_str)
-        return match.group(1) if match else None
-
-    @staticmethod
-    def _parse_address_two(address_one: str):
-        apt_match = re.search(r"(APT\s*.+|#[\s\S]+)$", address_one, re.I)
-        address_two = apt_match.group().strip() if apt_match else None
-        address_one = (
-            address_one.replace(address_two, "").strip() if address_two else address_one
-        )
-        return address_one, address_two
-
-    @staticmethod
-    def _extract_address(home: dict) -> Address:
-        keys = ("streetAddress", "city", "state", "zipcode")
-        address_one, city, state, zip_code = (
-            home["hdpData"]["homeInfo"][key] for key in keys
-        )
-        address_one, address_two = ZillowScraper._parse_address_two(address_one)
-        return Address(address_one, city, state, zip_code, address_two=address_two)
-
-    @staticmethod
-    def _get_headers():
-        return {
-            "authority": "parser-external.geo.moveaws.com",
-            "accept": "*/*",
-            "accept-language": "en-US,en;q=0.9",
-            "origin": "https://www.zillow.com",
-            "referer": "https://www.zillow.com/",
-            "sec-ch-ua": '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"Windows"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "cross-site",
-            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-        }

homeharvest/exceptions.py

@@ -1,14 +1,6 @@
-class InvalidSite(Exception):
-    """Raised when a provided site is does not exist."""
-
-
 class InvalidListingType(Exception):
     """Raised when a provided listing type is does not exist."""
 
 
 class NoResultsFound(Exception):
     """Raised when no results are found for the given location"""
-
-
-class PropertyNotFound(Exception):
-    """Raised when no property is found for the given address"""

homeharvest/utils.py

@@ -0,0 +1,71 @@
+from .core.scrapers.models import Property, ListingType
+import pandas as pd
+from .exceptions import InvalidListingType
+
+ordered_properties = [
+    "property_url",
+    "mls",
+    "mls_id",
+    "status",
+    "style",
+    "street",
+    "unit",
+    "city",
+    "state",
+    "zip_code",
+    "beds",
+    "full_baths",
+    "half_baths",
+    "sqft",
+    "year_built",
+    "list_price",
+    "list_date",
+    "sold_price",
+    "last_sold_date",
+    "lot_sqft",
+    "price_per_sqft",
+    "latitude",
+    "longitude",
+    "stories",
+    "hoa_fee",
+    "parking_garage",
+]
+
+
+def process_result(result: Property) -> pd.DataFrame:
+    prop_data = {prop: None for prop in ordered_properties}
+    prop_data.update(result.__dict__)
+
+    if "address" in prop_data:
+        address_data = prop_data["address"]
+        prop_data["street"] = address_data.street
+        prop_data["unit"] = address_data.unit
+        prop_data["city"] = address_data.city
+        prop_data["state"] = address_data.state
+        prop_data["zip_code"] = address_data.zip
+
+    prop_data["price_per_sqft"] = prop_data["prc_sqft"]
+
+    description = result.description
+    prop_data["style"] = description.style
+    prop_data["beds"] = description.beds
+    prop_data["full_baths"] = description.baths_full
+    prop_data["half_baths"] = description.baths_half
+    prop_data["sqft"] = description.sqft
+    prop_data["lot_sqft"] = description.lot_sqft
+    prop_data["sold_price"] = description.sold_price
+    prop_data["year_built"] = description.year_built
+    prop_data["parking_garage"] = description.garage
+    prop_data["stories"] = description.stories
+
+    properties_df = pd.DataFrame([prop_data])
+    properties_df = properties_df.reindex(columns=ordered_properties)
+
+    return properties_df[ordered_properties]
+
+
+def validate_input(listing_type: str) -> None:
+    if listing_type.upper() not in ListingType.__members__:
+        raise InvalidListingType(
+            f"Provided listing type, '{listing_type}', does not exist."
+        )

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"},
+    {file = "numpy-1.25.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:1a1329e26f46230bf77b02cc19e900db9b52f398d6722ca853349a782d4cff55"},
+    {file = "numpy-1.25.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c3abc71e8b6edba80a01a52e66d83c5d14433cbcd26a40c329ec7ed09f37901"},
+    {file = "numpy-1.25.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:1b9735c27cea5d995496f46a8b1cd7b408b3f34b6d50459d9ac8fe3a20cc17bf"},
+    {file = "numpy-1.25.2.tar.gz", hash = "sha256:fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760"},
+]
+
+[[package]]
+name = "numpy"
+version = "1.26.0"
+description = "Fundamental package for array computing in Python"
+optional = false
+python-versions = "<3.13,>=3.9"
+files = [
+    {file = "numpy-1.26.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f8db2f125746e44dce707dd44d4f4efeea8d7e2b43aace3f8d1f235cfa2733dd"},
+    {file = "numpy-1.26.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0621f7daf973d34d18b4e4bafb210bbaf1ef5e0100b5fa750bd9cde84c7ac292"},
+    {file = "numpy-1.26.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:51be5f8c349fdd1a5568e72713a21f518e7d6707bcf8503b528b88d33b57dc68"},
+    {file = "numpy-1.26.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:767254ad364991ccfc4d81b8152912e53e103ec192d1bb4ea6b1f5a7117040be"},
+    {file = "numpy-1.26.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:436c8e9a4bdeeee84e3e59614d38c3dbd3235838a877af8c211cfcac8a80b8d3"},
+    {file = "numpy-1.26.0-cp310-cp310-win32.whl", hash = "sha256:c2e698cb0c6dda9372ea98a0344245ee65bdc1c9dd939cceed6bb91256837896"},
+    {file = "numpy-1.26.0-cp310-cp310-win_amd64.whl", hash = "sha256:09aaee96c2cbdea95de76ecb8a586cb687d281c881f5f17bfc0fb7f5890f6b91"},
+    {file = "numpy-1.26.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:637c58b468a69869258b8ae26f4a4c6ff8abffd4a8334c830ffb63e0feefe99a"},
+    {file = "numpy-1.26.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:306545e234503a24fe9ae95ebf84d25cba1fdc27db971aa2d9f1ab6bba19a9dd"},
+    {file = "numpy-1.26.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8c6adc33561bd1d46f81131d5352348350fc23df4d742bb246cdfca606ea1208"},
+    {file = "numpy-1.26.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e062aa24638bb5018b7841977c360d2f5917268d125c833a686b7cbabbec496c"},
+    {file = "numpy-1.26.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:546b7dd7e22f3c6861463bebb000646fa730e55df5ee4a0224408b5694cc6148"},
+    {file = "numpy-1.26.0-cp311-cp311-win32.whl", hash = "sha256:c0b45c8b65b79337dee5134d038346d30e109e9e2e9d43464a2970e5c0e93229"},
+    {file = "numpy-1.26.0-cp311-cp311-win_amd64.whl", hash = "sha256:eae430ecf5794cb7ae7fa3808740b015aa80747e5266153128ef055975a72b99"},
+    {file = "numpy-1.26.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:166b36197e9debc4e384e9c652ba60c0bacc216d0fc89e78f973a9760b503388"},
+    {file = "numpy-1.26.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f042f66d0b4ae6d48e70e28d487376204d3cbf43b84c03bac57e28dac6151581"},
+    {file = "numpy-1.26.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5e18e5b14a7560d8acf1c596688f4dfd19b4f2945b245a71e5af4ddb7422feb"},
+    {file = "numpy-1.26.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7f6bad22a791226d0a5c7c27a80a20e11cfe09ad5ef9084d4d3fc4a299cca505"},
+    {file = "numpy-1.26.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4acc65dd65da28060e206c8f27a573455ed724e6179941edb19f97e58161bb69"},
+    {file = "numpy-1.26.0-cp312-cp312-win32.whl", hash = "sha256:bb0d9a1aaf5f1cb7967320e80690a1d7ff69f1d47ebc5a9bea013e3a21faec95"},
+    {file = "numpy-1.26.0-cp312-cp312-win_amd64.whl", hash = "sha256:ee84ca3c58fe48b8ddafdeb1db87388dce2c3c3f701bf447b05e4cfcc3679112"},
+    {file = "numpy-1.26.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4a873a8180479bc829313e8d9798d5234dfacfc2e8a7ac188418189bb8eafbd2"},
+    {file = "numpy-1.26.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:914b28d3215e0c721dc75db3ad6d62f51f630cb0c277e6b3bcb39519bed10bd8"},
+    {file = "numpy-1.26.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c78a22e95182fb2e7874712433eaa610478a3caf86f28c621708d35fa4fd6e7f"},
+    {file = "numpy-1.26.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86f737708b366c36b76e953c46ba5827d8c27b7a8c9d0f471810728e5a2fe57c"},
+    {file = "numpy-1.26.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:b44e6a09afc12952a7d2a58ca0a2429ee0d49a4f89d83a0a11052da696440e49"},
+    {file = "numpy-1.26.0-cp39-cp39-win32.whl", hash = "sha256:5671338034b820c8d58c81ad1dafc0ed5a00771a82fccc71d6438df00302094b"},
+    {file = "numpy-1.26.0-cp39-cp39-win_amd64.whl", hash = "sha256:020cdbee66ed46b671429c7265cf00d8ac91c046901c55684954c3958525dab2"},
+    {file = "numpy-1.26.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0792824ce2f7ea0c82ed2e4fecc29bb86bee0567a080dacaf2e0a01fe7654369"},
+    {file = "numpy-1.26.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7d484292eaeb3e84a51432a94f53578689ffdea3f90e10c8b203a99be5af57d8"},
+    {file = "numpy-1.26.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:186ba67fad3c60dbe8a3abff3b67a91351100f2661c8e2a80364ae6279720299"},
+    {file = "numpy-1.26.0.tar.gz", hash = "sha256:f93fc78fe8bf15afe2b8d6b6499f1c73953169fad1e9a8dd086cdff3190e7fdf"},
+]
+
+[[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"},
+    {file = "openpyxl-3.1.2.tar.gz", hash = "sha256:a6f5977418eff3b2d5500d54d9db50c8277a368436f4e4f8ddb1be3422870184"},
+]
+
+[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 = [
+    {file = "pandas-2.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:40dd20439ff94f1b2ed55b393ecee9cb6f3b08104c2c40b0cb7186a2f0046242"},
+    {file = "pandas-2.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d4f38e4fedeba580285eaac7ede4f686c6701a9e618d8a857b138a126d067f2f"},
+    {file = "pandas-2.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6e6a0fe052cf27ceb29be9429428b4918f3740e37ff185658f40d8702f0b3e09"},
+    {file = "pandas-2.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9d81e1813191070440d4c7a413cb673052b3b4a984ffd86b8dd468c45742d3cc"},
+    {file = "pandas-2.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:eb20252720b1cc1b7d0b2879ffc7e0542dd568f24d7c4b2347cb035206936421"},
+    {file = "pandas-2.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:38f74ef7ebc0ffb43b3d633e23d74882bce7e27bfa09607f3c5d3e03ffd9a4a5"},
+    {file = "pandas-2.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:cda72cc8c4761c8f1d97b169661f23a86b16fdb240bdc341173aee17e4d6cedd"},
+    {file = "pandas-2.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d97daeac0db8c993420b10da4f5f5b39b01fc9ca689a17844e07c0a35ac96b4b"},
+    {file = "pandas-2.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8c58b1113892e0c8078f006a167cc210a92bdae23322bb4614f2f0b7a4b510f"},
+    {file = "pandas-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:629124923bcf798965b054a540f9ccdfd60f71361255c81fa1ecd94a904b9dd3"},
+    {file = "pandas-2.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:70cf866af3ab346a10debba8ea78077cf3a8cd14bd5e4bed3d41555a3280041c"},
+    {file = "pandas-2.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:d53c8c1001f6a192ff1de1efe03b31a423d0eee2e9e855e69d004308e046e694"},
+    {file = "pandas-2.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:86f100b3876b8c6d1a2c66207288ead435dc71041ee4aea789e55ef0e06408cb"},
+    {file = "pandas-2.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:28f330845ad21c11db51e02d8d69acc9035edfd1116926ff7245c7215db57957"},
+    {file = "pandas-2.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9a6ccf0963db88f9b12df6720e55f337447aea217f426a22d71f4213a3099a6"},
+    {file = "pandas-2.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d99e678180bc59b0c9443314297bddce4ad35727a1a2656dbe585fd78710b3b9"},
+    {file = "pandas-2.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:b31da36d376d50a1a492efb18097b9101bdbd8b3fbb3f49006e02d4495d4c644"},
+    {file = "pandas-2.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:0164b85937707ec7f70b34a6c3a578dbf0f50787f910f21ca3b26a7fd3363437"},
+    {file = "pandas-2.1.0.tar.gz", hash = "sha256:62c24c7fc59e42b775ce0679cfa7b14a5f9bfb7643cfbe708c960699e05fb918"},
+]
+
+[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 +397,28 @@ 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 = "tls-client"
+version = "0.2.2"
+description = "Advanced Python HTTP Client."
+optional = false
+python-versions = "*"
+files = [
+    {file = "tls_client-0.2.2-py3-none-any.whl", hash = "sha256:30934871397cdad6862e00b5634f382666314a452ddd3d774e18323a0ad9b765"},
+    {file = "tls_client-0.2.2.tar.gz", hash = "sha256:78bc0e291e3aadc6c5e903b62bb26c01374577691f2a9e5e17899900a5927a13"},
+]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -222,6 +430,17 @@ 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 +461,4 @@ zstd = ["zstandard (>=0.18.0)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "bc3567f9501f9e18bf9f53d8b4efe1e7e3fc2d750ceda2fbab165bfa22d49c64"
+content-hash = "9b77e1a09fcf2cf5e7e6be53f304cd21a6a51ea51680d661a178afe5e5343670"

pyproject.toml

@@ -1,14 +1,20 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.1.2"
-description = "Real estate scraping library"
+version = "0.3.0"
+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"
+tls-client = "^0.2.2"
 
 
 [tool.poetry.group.dev.dependencies]

tests/test_realtor.py

@@ -1,9 +1,91 @@
 from homeharvest import scrape_property
+from homeharvest.exceptions import (
+    InvalidListingType,
+    NoResultsFound,
+)
+
+
+def test_realtor_pending_or_contingent():
+    pending_or_contingent_result = scrape_property(
+        location="Surprise, AZ",
+        pending_or_contingent=True,
+    )
+
+    regular_result = scrape_property(
+        location="Surprise, AZ",
+        pending_or_contingent=False,
+    )
+
+    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_comps():
+    result = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=0.5,
+        property_younger_than=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", property_younger_than=30
+    )
+
+    days_result_10 = scrape_property(
+        location="Dallas, TX", listing_type="sold", property_younger_than=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_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="85281", site_name="realtor.com"),
+        scrape_property(
+            location="2530 Al Lipscomb Way",
+            listing_type="for_sale",
+        ),
+        scrape_property(
+            location="Phoenix, AZ", listing_type="for_rent"
+        ),  #: does not support "city, state, USA" format
+        scrape_property(
+            location="Dallas, TX", listing_type="sold"
+        ),  #: does not support "city, state, USA" format
+        scrape_property(location="85281"),
     ]
 
     assert all([result is not None for result in results])
+
+    bad_results = []
+    try:
+        bad_results += [
+            scrape_property(
+                location="abceefg ju098ot498hh9",
+                listing_type="for_sale",
+            )
+        ]
+    except (InvalidListingType, NoResultsFound):
+        assert True
+
+    assert all([result is None for result in bad_results])

tests/test_redfin.py

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

tests/test_zillow.py

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