Merge branch 'master' of https://github.com/ZacharyHampton/HomeHarvest

[enh] days_on_mls attr

README.md

@@ -4,7 +4,6 @@
 
 [![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.*
@@ -17,10 +16,11 @@ Check out another project we wrote: ***[JobSpy](https://github.com/cullenwatson/
 - **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.
+  - **CLI**: For users who prefer command-line operations.
 
-[Video Guide for HomeHarvest](https://youtu.be/JnV7eR2Ve2o) - _updated for release v0.2.7_
+
+[Video Guide for HomeHarvest](https://youtu.be/J1qgNPgmSLI) - _updated for release v0.3.4_
 
 ![homeharvest](https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/b3d5d727-e67b-4a9f-85d8-1e65fd18620a)
 
@@ -33,10 +33,34 @@ pip install homeharvest
 
 ## Usage
 
+### Python
+
+```py
+from homeharvest import scrape_property
+from datetime import datetime
+
+# Generate filename based on current timestamp
+current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+filename = f"HomeHarvest_{current_timestamp}.csv"
+
+properties = scrape_property(
+  location="San Diego, CA",
+  listing_type="sold",  # or (for_sale, for_rent, pending)
+  past_days=30,  # sold in last 30 days - listed in last x days if (for_sale, for_rent)
+  # mls_only=True,  # only fetch MLS listings
+  # proxy="http://user:pass@host:port"  # use a proxy to change your IP address
+)
+print(f"Number of properties: {len(properties)}")
+
+# Export to csv
+properties.to_csv(filename, index=False)
+print(properties.head())
+```
+
 ### CLI 
 
 ```
-usage: homeharvest [-l {for_sale,for_rent,sold}] [-o {excel,csv}] [-f FILENAME] [-p PROXY] [-d DAYS] [-r RADIUS] [-m] location
+usage: homeharvest [-l {for_sale,for_rent,sold}] [-o {excel,csv}] [-f FILENAME] [-p PROXY] [-d DAYS] [-r RADIUS] [-m] [-c] location
                                                                                                                              
 Home Harvest Property Scraper                                                                                                 
                                                                                                                              
@@ -44,7 +68,7 @@ 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}                                                        
+  -l {for_sale,for_rent,sold,pending}, --listing_type {for_sale,for_rent,sold,pending}                                                        
                         Listing type to scrape                                                                                
   -o {excel,csv}, --output {excel,csv}                                                                                        
                         Output format                                                                                         
@@ -54,35 +78,13 @@ options:
                         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.        
+                        Get comparable properties within _ (e.g., 0.0) miles. Only applicable for individual addresses.        
-  -m, --mls_only        If set, fetches only MLS listings.
+  -m, --mls_only        If set, fetches only MLS listings.                                                                    
 ```
 ```bash
-> homeharvest "San Francisco, CA" -l for_rent -o excel -f HomeHarvest
+homeharvest "San Francisco, CA" -l for_rent -o excel -f HomeHarvest
 ```
 
-### 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="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(f"Number of properties: {len(properties)}")
-
-# Export to csv
-properties.to_csv(filename, index=False)
-print(properties.head())
-```
 
 ## Output
 ```plaintext
@@ -104,12 +106,13 @@ Required
     - 'for_rent'
     - 'for_sale'
     - 'sold'
+    - 'pending'
 
 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).
+├── past_days (integer): Number of past days to filter properties. Utilizes 'last_sold_date' for 'sold' listing types, and 'list_date' for others (for_rent, for_sale).
 │    Example: 30 (fetches properties listed/sold in the last 30 days)
 │
 ├── mls_only (True/False): If set, fetches only MLS listings (mainly applicable to 'sold' listings)
@@ -144,6 +147,7 @@ Property
 │ └── lot_sqft
 
 ├── Property Listing Details:
+│ ├── days_on_mls
 │ ├── list_price
 │ ├── list_date
 │ ├── sold_price

examples/HomeHarvest_Demo.ipynb

@@ -4,7 +4,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "cb48903e-5021-49fe-9688-45cd0bc05d0f",
-   "metadata": {},
+   "metadata": {
+    "is_executing": true
+   },
    "outputs": [],
    "source": [
     "from homeharvest import scrape_property\n",
@@ -84,10 +86,34 @@
    "outputs": [],
    "source": [
     "# check sold properties\n",
-    "scrape_property(\n",
+    "properties = scrape_property(\n",
     "    location=\"90210\",\n",
-    "    listing_type=\"sold\"\n",
+    "    listing_type=\"sold\",\n",
-    ")"
+    "    past_days=10\n",
+    ")\n",
+    "display(properties)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "628c1ce2",
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# display clickable URLs\n",
+    "from IPython.display import display, HTML\n",
+    "properties['property_url'] = '<a href=\"' + properties['property_url'] + '\" target=\"_blank\">' + properties['property_url'] + '</a>'\n",
+    "\n",
+    "html = properties.to_html(escape=False)\n",
+    "truncate_width = f'<style>.dataframe td {{ max-width: 200px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }}</style>{html}'\n",
+    "display(HTML(truncate_width))"
    ]
   }
  ],

examples/HomeHarvest_Demo.py

@@ -3,16 +3,18 @@ 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"
+filename = f"HomeHarvest_{current_timestamp}.csv"
 
 properties = scrape_property(
     location="San Diego, CA",
-    listing_type="sold", # for_sale, for_rent
+    listing_type="sold",  # or (for_sale, for_rent)
-    property_younger_than=30, # sold/listed in last 30 days
+    past_days=30,  # sold in last 30 days - listed in last x days if (for_sale, for_rent)
-    mls_only=True, # only fetch MLS listings
+    # pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
+    # mls_only=True,  # only fetch MLS listings
+    # proxy="http://user:pass@host:port"  # use a proxy to change your IP address
 )
 print(f"Number of properties: {len(properties)}")
 
 # Export to csv
 properties.to_csv(filename, index=False)
-print(properties.head())
+print(properties.head())

homeharvest/__init__.py

@@ -12,8 +12,7 @@ def scrape_property(
     listing_type: str = "for_sale",
     radius: float = None,
     mls_only: bool = False,
-    property_younger_than: int = None,
+    past_days: int = None,
-    pending_or_contingent: bool = False,
     proxy: str = None,
 ) -> pd.DataFrame:
     """
@@ -22,8 +21,7 @@ def scrape_property(
     :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 past_days: Get properties sold or listed (dependent on your listing_type) in the 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)
@@ -34,8 +32,7 @@ def scrape_property(
         proxy=proxy,
         radius=radius,
         mls_only=mls_only,
-        last_x_days=property_younger_than,
+        last_x_days=past_days,
-        pending_or_contingent=pending_or_contingent,
     )
 
     site = RealtorScraper(scraper_input)

homeharvest/cli.py

@@ -14,7 +14,7 @@ def main():
         "--listing_type",
         type=str,
         default="for_sale",
-        choices=["for_sale", "for_rent", "sold"],
+        choices=["for_sale", "for_rent", "sold", "pending"],
         help="Listing type to scrape",
     )
 
@@ -68,7 +68,7 @@ def main():
         radius=args.radius,
         proxy=args.proxy,
         mls_only=args.mls_only,
-        property_younger_than=args.days,
+        past_days=args.days,
     )
 
     if not args.filename:

homeharvest/core/scrapers/__init__.py

@@ -1,6 +1,5 @@
 from dataclasses import dataclass
 import requests
-import tls_client
 from .models import Property, ListingType, SiteName
 
 
@@ -12,14 +11,13 @@ class ScraperInput:
     mls_only: bool | None = None
     proxy: str | None = None
     last_x_days: int | None = None
-    pending_or_contingent: bool | None = None
 
 
 class Scraper:
     def __init__(
         self,
         scraper_input: ScraperInput,
-        session: requests.Session | tls_client.Session = None,
+        session: requests.Session = None,
     ):
         self.location = scraper_input.location
         self.listing_type = scraper_input.listing_type
@@ -38,7 +36,6 @@ class Scraper:
         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

@@ -19,6 +19,7 @@ class SiteName(Enum):
 class ListingType(Enum):
     FOR_SALE = "FOR_SALE"
     FOR_RENT = "FOR_RENT"
+    PENDING = "PENDING"
     SOLD = "SOLD"
 
 
@@ -58,6 +59,7 @@ class Property:
     last_sold_date: str | None = None
     prc_sqft: int | None = None
     hoa_fee: int | None = None
+    days_on_mls: int | None = None
     description: Description | None = None
 
     latitude: float | None = None

homeharvest/core/scrapers/realtor/__init__.py

@@ -4,6 +4,7 @@ homeharvest.realtor.__init__
 
 This module implements the scraper for realtor.com
 """
+from datetime import datetime
 from typing import Dict, Union, Optional
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
@@ -18,7 +19,6 @@ class RealtorScraper(Scraper):
     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):
@@ -110,6 +110,24 @@ class RealtorScraper(Scraper):
                 and property_info["address"].get("location")
                 and property_info["address"]["location"].get("coordinate")
         )
+        list_date_str = property_info["basic"]["list_date"].split("T")[0] if property_info["basic"].get(
+            "list_date") else None
+        last_sold_date_str = property_info["basic"]["sold_date"].split("T")[0] if property_info["basic"].get(
+            "sold_date") else None
+
+        list_date = datetime.strptime(list_date_str, "%Y-%m-%d") if list_date_str else None
+        last_sold_date = datetime.strptime(last_sold_date_str, "%Y-%m-%d") if last_sold_date_str else None
+        today = datetime.now()
+
+        days_on_mls = None
+        status = property_info["basic"]["status"].lower()
+        if list_date:
+            if status == "sold" and last_sold_date:
+                days_on_mls = (last_sold_date - list_date).days
+            elif status in ('for_sale', 'for_rent'):
+                days_on_mls = (today - list_date).days
+            if days_on_mls and days_on_mls < 0:
+                days_on_mls = None
 
         listing = Property(
             mls=mls,
@@ -119,15 +137,13 @@ class RealtorScraper(Scraper):
             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]
+            list_date=list_date,
-            if property_info["basic"].get("list_date")
+            prc_sqft=property_info["basic"].get("price")
-            else None,
+                     / property_info["basic"].get("sqft")
-            prc_sqft=property_info["basic"].get("price") / property_info["basic"].get("sqft")
+            if property_info["basic"].get("price")
-            if property_info["basic"].get("price") and property_info["basic"].get("sqft")
+               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,
+            last_sold_date=last_sold_date,
             latitude=property_info["address"]["location"]["coordinate"].get("lat")
             if able_to_get_lat_long
             else None,
@@ -146,7 +162,8 @@ class RealtorScraper(Scraper):
                 year_built=property_info["details"].get("year_built"),
                 garage=property_info["details"].get("garage"),
                 stories=property_info["details"].get("stories"),
-            )
+            ),
+            days_on_mls=days_on_mls
         )
 
         return [listing]
@@ -175,7 +192,10 @@ class RealtorScraper(Scraper):
         if property_info["listings"] is None:
             return None
 
-        primary_listing = next((listing for listing in property_info["listings"] if listing["primary"]), None)
+        primary_listing = next(
+            (listing for listing in property_info["listings"] if listing["primary"]),
+            None,
+        )
         if primary_listing:
             return primary_listing["listing_id"]
         else:
@@ -269,6 +289,10 @@ class RealtorScraper(Scraper):
                                 last_sold_date
                                 list_price
                                 price_per_sqft
+                                flags {
+                                    is_contingent
+                                    is_pending
+                                }
                                 description {
                                     sqft
                                     beds
@@ -328,7 +352,13 @@ class RealtorScraper(Scraper):
             else "sort: [{ field: list_date, direction: desc }]"
         )
 
-        pending_or_contingent_param = "or_filters: { contingent: true, pending: true }" if self.pending_or_contingent else ""
+        pending_or_contingent_param = (
+            "or_filters: { contingent: true, pending: true }"
+            if self.listing_type == ListingType.PENDING
+            else ""
+        )
+
+        listing_type = ListingType.FOR_SALE if self.listing_type == ListingType.PENDING else self.listing_type
 
         if search_type == "comps":  #: comps search, came from an address
             query = """query Property_search(
@@ -336,7 +366,7 @@ class RealtorScraper(Scraper):
                     $radius: String!
                     $offset: Int!,
                     ) {
-                        property_search(
+                        home_search(
                             query: { 
                                 nearby: {
                                     coordinates: $coordinates
@@ -344,13 +374,15 @@ class RealtorScraper(Scraper):
                                 }
                                 status: %s
                                 %s
+                                %s
                             }
                             %s
                             limit: 200
                             offset: $offset
                     ) %s""" % (
-                self.listing_type.value.lower(),
+                listing_type.value.lower(),
                 date_param,
+                pending_or_contingent_param,
                 sort_param,
                 results_query,
             )
@@ -376,7 +408,7 @@ class RealtorScraper(Scraper):
                                     limit: 200
                                     offset: $offset
                                 ) %s""" % (
-                self.listing_type.value.lower(),
+                listing_type.value.lower(),
                 date_param,
                 pending_or_contingent_param,
                 sort_param,
@@ -384,7 +416,7 @@ class RealtorScraper(Scraper):
             )
         else:  #: general search, came from an address
             query = (
-                    """query Property_search(
+                """query Property_search(
                         $property_id: [ID]!
                         $offset: Int!,
                     ) {
@@ -394,7 +426,9 @@ class RealtorScraper(Scraper):
                             }
                             limit: 1
                             offset: $offset
-                        ) %s""" % results_query)
+                        ) %s"""
+                % results_query
+            )
 
         payload = {
             "query": query,
@@ -404,7 +438,7 @@ class RealtorScraper(Scraper):
         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"
+        search_key = "home_search" if "home_search" in query else "property_search"
 
         properties: list[Property] = []
 
@@ -419,7 +453,6 @@ class RealtorScraper(Scraper):
             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)
@@ -436,13 +469,15 @@ class RealtorScraper(Scraper):
                 and result["location"]["address"].get("coordinate")
             )
 
+            is_pending = result["flags"].get("is_pending") or result["flags"].get("is_contingent")
+
             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(),
+                status="PENDING" if is_pending else result["status"].upper(),
                 list_price=result["list_price"],
                 list_date=result["list_date"].split("T")[0]
                 if result.get("list_date")
@@ -459,8 +494,8 @@ class RealtorScraper(Scraper):
                 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),
+                days_on_mls=self.calculate_days_on_mls(result)
             )
             properties.append(realty_property)
 
@@ -477,13 +512,21 @@ class RealtorScraper(Scraper):
             "offset": 0,
         }
 
-        search_type = "comps" if self.radius and location_type == "address" else "address" if location_type == "address" and not self.radius else "area"
+        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)
+                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:
@@ -561,8 +604,16 @@ class RealtorScraper(Scraper):
     @staticmethod
     def _parse_description(result: dict) -> Description:
         description_data = result.get("description", {})
+
+        if description_data is None or not isinstance(description_data, dict):
+            description_data = {}
+
+        style = description_data.get("type", "")
+        if style is not None:
+            style = style.upper()
+
         return Description(
-            style=description_data.get("type", "").upper(),
+            style=style,
             beds=description_data.get("beds"),
             baths_full=description_data.get("baths_full"),
             baths_half=description_data.get("baths_half"),
@@ -573,3 +624,22 @@ class RealtorScraper(Scraper):
             garage=description_data.get("garage"),
             stories=description_data.get("stories"),
         )
+
+    @staticmethod
+    def calculate_days_on_mls(result: dict) -> Optional[int]:
+        list_date_str = result.get("list_date")
+        list_date = datetime.strptime(list_date_str.split("T")[0], "%Y-%m-%d") if list_date_str else None
+        last_sold_date_str = result.get("last_sold_date")
+        last_sold_date = datetime.strptime(last_sold_date_str, "%Y-%m-%d") if last_sold_date_str else None
+        today = datetime.now()
+
+        if list_date:
+            if result["status"] == 'sold':
+                if last_sold_date:
+                    days = (last_sold_date - list_date).days
+                    if days >= 0:
+                        return days
+            elif result["status"] in ('for_sale', 'for_rent'):
+                days = (today - list_date).days
+                if days >= 0:
+                    return days

homeharvest/utils.py

@@ -18,6 +18,7 @@ ordered_properties = [
     "half_baths",
     "sqft",
     "year_built",
+    "days_on_mls",
     "list_price",
     "list_date",
     "sold_price",

poetry.lock

@@ -13,86 +13,101 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.2.0"
+version = "3.3.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "charset-normalizer-3.2.0.tar.gz", hash = "sha256:3bb3d25a8e6c0aedd251753a79ae98a093c7e7b471faa3aa9a93a81431987ace"},
+    {file = "charset-normalizer-3.3.0.tar.gz", hash = "sha256:63563193aec44bce707e0c5ca64ff69fa72ed7cf34ce6e11d5127555756fd2f6"},
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-    {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"
@@ -244,47 +225,54 @@ et-xmlfile = "*"
 
 [[package]]
 name = "packaging"
-version = "23.1"
+version = "23.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "packaging-23.1-py3-none-any.whl", hash = "sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61"},
+    {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
-    {file = "packaging-23.1.tar.gz", hash = "sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"},
+    {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
 ]
 
 [[package]]
 name = "pandas"
-version = "2.1.0"
+version = "2.1.1"
 description = "Powerful data structures for data analysis, time series, and statistics"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "pandas-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:58d997dbee0d4b64f3cb881a24f918b5f25dd64ddf31f467bb9b67ae4c63a1e4"},
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+    {file = "pandas-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffa8f0966de2c22de408d0e322db2faed6f6e74265aa0856f3824813cf124363"},
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+    {file = "pandas-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c1f84c144dee086fe4f04a472b5cd51e680f061adf75c1ae4fc3a9275560f8f4"},
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+    {file = "pandas-2.1.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:75ce97667d06d69396d72be074f0556698c7f662029322027c226fd7a26965cb"},
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+    {file = "pandas-2.1.1-cp310-cp310-win_amd64.whl", hash = "sha256:4c3f32fd7c4dccd035f71734df39231ac1a6ff95e8bdab8d891167197b7018d2"},
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+    {file = "pandas-2.1.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:9e2959720b70e106bb1d8b6eadd8ecd7c8e99ccdbe03ee03260877184bb2877d"},
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-    {file = "pandas-2.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8c58b1113892e0c8078f006a167cc210a92bdae23322bb4614f2f0b7a4b510f"},
+    {file = "pandas-2.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b8bd1685556f3374520466998929bade3076aeae77c3e67ada5ed2b90b4de7f0"},
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+    {file = "pandas-2.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dc3657869c7902810f32bd072f0740487f9e030c1a3ab03e0af093db35a9d14e"},
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+    {file = "pandas-2.1.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:05674536bd477af36aa2effd4ec8f71b92234ce0cc174de34fd21e2ee99adbc2"},
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+    {file = "pandas-2.1.1-cp311-cp311-win_amd64.whl", hash = "sha256:b407381258a667df49d58a1b637be33e514b07f9285feb27769cedb3ab3d0b3a"},
-    {file = "pandas-2.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:86f100b3876b8c6d1a2c66207288ead435dc71041ee4aea789e55ef0e06408cb"},
+    {file = "pandas-2.1.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c747793c4e9dcece7bb20156179529898abf505fe32cb40c4052107a3c620b49"},
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+    {file = "pandas-2.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f5ec7740f9ccb90aec64edd71434711f58ee0ea7f5ed4ac48be11cfa9abf7317"},
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+    {file = "pandas-2.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:29deb61de5a8a93bdd033df328441a79fcf8dd3c12d5ed0b41a395eef9cd76f0"},
-    {file = "pandas-2.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:b31da36d376d50a1a492efb18097b9101bdbd8b3fbb3f49006e02d4495d4c644"},
+    {file = "pandas-2.1.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4f99bebf19b7e03cf80a4e770a3e65eee9dd4e2679039f542d7c1ace7b7b1daa"},
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+    {file = "pandas-2.1.1-cp312-cp312-win_amd64.whl", hash = "sha256:84e7e910096416adec68075dc87b986ff202920fb8704e6d9c8c9897fe7332d6"},
-    {file = "pandas-2.1.0.tar.gz", hash = "sha256:62c24c7fc59e42b775ce0679cfa7b14a5f9bfb7643cfbe708c960699e05fb918"},
+    {file = "pandas-2.1.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:366da7b0e540d1b908886d4feb3d951f2f1e572e655c1160f5fde28ad4abb750"},
+    {file = "pandas-2.1.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9e50e72b667415a816ac27dfcfe686dc5a0b02202e06196b943d54c4f9c7693e"},
+    {file = "pandas-2.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cc1ab6a25da197f03ebe6d8fa17273126120874386b4ac11c1d687df288542dd"},
+    {file = "pandas-2.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a0dbfea0dd3901ad4ce2306575c54348d98499c95be01b8d885a2737fe4d7a98"},
+    {file = "pandas-2.1.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:0489b0e6aa3d907e909aef92975edae89b1ee1654db5eafb9be633b0124abe97"},
+    {file = "pandas-2.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:4cdb0fab0400c2cb46dafcf1a0fe084c8bb2480a1fa8d81e19d15e12e6d4ded2"},
+    {file = "pandas-2.1.1.tar.gz", hash = "sha256:fecb198dc389429be557cde50a2d46da8434a17fe37d7d41ff102e3987fd947b"},
 ]
 
 [package.dependencies]
 numpy = [
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -408,17 +396,6 @@ files = [
     {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"
@@ -443,13 +420,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.0.4"
+version = "2.0.6"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
+    {file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
-    {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
+    {file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
 ]
 
 [package.extras]
@@ -460,5 +437,5 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.10"
+python-versions = ">=3.10,<3.13"
-content-hash = "9b77e1a09fcf2cf5e7e6be53f304cd21a6a51ea51680d661a178afe5e5343670"
+content-hash = "09ad811d74a42363ff4c3ccd012d8f73c89d7d978e5a6445b0f3d2e231922f1b"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.3.0"
+version = "0.3.5"
 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"
@@ -10,11 +10,10 @@ readme = "README.md"
 homeharvest = "homeharvest.cli:main"
 
 [tool.poetry.dependencies]
-python = "^3.10"
+python = ">=3.10,<3.13"
 requests = "^2.31.0"
-pandas = "^2.1.0"
+pandas = "^2.1.1"
 openpyxl = "^3.1.2"
-tls-client = "^0.2.2"
 
 
 [tool.poetry.group.dev.dependencies]

tests/test_realtor.py

@@ -7,24 +7,54 @@ from homeharvest.exceptions import (
 
 def test_realtor_pending_or_contingent():
     pending_or_contingent_result = scrape_property(
-        location="Surprise, AZ",
+        location="Surprise, AZ", listing_type="pending"
-        pending_or_contingent=True,
     )
 
-    regular_result = scrape_property(
+    regular_result = scrape_property(location="Surprise, AZ", listing_type="for_sale")
-        location="Surprise, AZ",
-        pending_or_contingent=False,
-    )
 
-    assert all([result is not None for result in [pending_or_contingent_result, regular_result]])
+    assert all(
+        [
+            result is not None
+            for result in [pending_or_contingent_result, regular_result]
+        ]
+    )
     assert len(pending_or_contingent_result) != len(regular_result)
 
 
+def test_realtor_pending_comps():
+    pending_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="pending",
+    )
+
+    for_sale_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="for_sale",
+    )
+
+    sold_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="sold",
+    )
+
+    results = [pending_comps, for_sale_comps, sold_comps]
+    assert all([result is not None for result in results])
+
+    #: assert all lengths are different
+    assert len(set([len(result) for result in results])) == len(results)
+
+
 def test_realtor_comps():
     result = scrape_property(
         location="2530 Al Lipscomb Way",
         radius=0.5,
-        property_younger_than=180,
+        past_days=180,
         listing_type="sold",
     )
 
@@ -33,11 +63,11 @@ def test_realtor_comps():
 
 def test_realtor_last_x_days_sold():
     days_result_30 = scrape_property(
-        location="Dallas, TX", listing_type="sold", property_younger_than=30
+        location="Dallas, TX", listing_type="sold", past_days=30
     )
 
     days_result_10 = scrape_property(
-        location="Dallas, TX", listing_type="sold", property_younger_than=10
+        location="Dallas, TX", listing_type="sold", past_days=10
     )
 
     assert all(