.github/FUNDING.yml

@@ -1 +0,0 @@
-github: Bunsly

.pre-commit-config.yaml

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

README.md

@@ -1,23 +1,36 @@
 <img src="https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/d1a2bf8b-09f5-4c57-b33a-0ada8a34f12d" width="400">
 
-**HomeHarvest** is a real estate scraping library that extracts and formats data in the style of MLS listings.
+**HomeHarvest** is a simple, yet comprehensive, real estate scraping library that extracts and formats data in the style of MLS listings.
+
+[![Try with Replit](https://replit.com/badge?caption=Try%20with%20Replit)](https://replit.com/@ZacharyHampton/HomeHarvestDemo)
+
+\
+**Not technical?** Try out the web scraping tool on our site at [tryhomeharvest.com](https://tryhomeharvest.com).
+
+*Looking to build a data-focused software product?* **[Book a call](https://calendly.com/zachary-products/15min)** *to work with us.*
+
+Check out another project we wrote: ***[JobSpy](https://github.com/cullenwatson/JobSpy)** – a Python package for job scraping*
 
 ## HomeHarvest Features
 
 - **Source**: Fetches properties directly from **Realtor.com**.
 - **Data Format**: Structures data to resemble MLS listings.
 - **Export Flexibility**: Options to save as either CSV or Excel.
+- **Usage Modes**:
+  - **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/J1qgNPgmSLI) - _updated for release v0.3.4_
+
+[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 -U homeharvest
+pip install homeharvest
 ```
-  _Python version >= [3.9](https://www.python.org/downloads/release/python-3100/) required_
+  _Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_ 
 
 ## Usage
 
@@ -33,14 +46,11 @@ filename = f"HomeHarvest_{current_timestamp}.csv"
 
 properties = scrape_property(
   location="San Diego, CA",
-  listing_type="sold",  # or (for_sale, for_rent, pending)
+  listing_type="sold",  # or (for_sale, for_rent)
-  past_days=30,  # sold in last 30 days - listed in last 30 days if (for_sale, for_rent)
+  property_younger_than=30,  # sold in last 30 days - listed in last x days if (for_sale, for_rent)
-
+  # pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
-  # property_type=['single_family','multi_family'],
-  # date_from="2023-05-01", # alternative to past_days
-  # date_to="2023-05-28",
-  # foreclosure=True
   # 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)}")
 
@@ -49,6 +59,38 @@ 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] [-c] 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 _ (e.g., 0.0) miles. Only applicable for individual addresses.        
+  -m, --mls_only        If set, fetches only MLS listings.                                                                    
+  -c, --pending_or_contingent
+                        If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.
+
+```
+```bash
+> homeharvest "San Francisco, CA" -l for_rent -o excel -f HomeHarvest
+```
+
+
 ## Output
 ```plaintext
 >>> properties.head()
@@ -65,61 +107,30 @@ print(properties.head())
 ```
 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.
+└── listing_type (option): Choose the type of listing.
     - 'for_rent'
     - 'for_sale'
     - 'sold'
-    - 'pending' (for pending/contingent sales)
 
 Optional
-├── property_type (list): Choose the type of properties.
-    - 'single_family'
-    - 'multi_family'
-    - 'condos'
-    - 'condo_townhome_rowhome_coop'
-    - 'condo_townhome'
-    - 'townhomes'
-    - 'duplex_triplex'
-    - 'farm'
-    - 'land'
-    - 'mobile'
-│
-├── return_type (option): Choose the return type.
-│    - 'pandas' (default)
-│    - 'pydantic'
-│    - 'raw' (json)
-│
 ├── radius (decimal): Radius in miles to find comparable properties based on individual addresses.
 │    Example: 5.5 (fetches properties within a 5.5-mile radius if location is set to a specific address; otherwise, ignored)
 │
-├── past_days (integer): Number of past days to filter properties. Utilizes 'last_sold_date' for 'sold' listing types, and 'list_date' for others (for_rent, for_sale).
+├── 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)
-│
+|
-├── date_from, date_to (string): Start and end dates to filter properties listed or sold, both dates are required.
+├── pending_or_contingent (True/False): If set, fetches only pending or contingent listings. Only applicable for `for_sale listings` from general area searches.
-|    (use this to get properties in chunks as there's a 10k result limit)
-│    Format for both must be "YYYY-MM-DD".
-│    Example: "2023-05-01", "2023-05-15" (fetches properties listed/sold between these dates)
 │
 ├── mls_only (True/False): If set, fetches only MLS listings (mainly applicable to 'sold' listings)
 │
-├── foreclosure (True/False): If set, fetches only foreclosures
+└── proxy (string): In format 'http://user:pass@host:port'
-│
-├── proxy (string): In format 'http://user:pass@host:port'
-│
-├── extra_property_data (True/False): Increases requests by O(n). If set, this fetches additional property data for general searches (e.g. schools, tax appraisals etc.)
-│
-├── exclude_pending (True/False): If set, excludes 'pending' properties from the 'for_sale' results unless listing_type is 'pending'
-│
-└── limit (integer): Limit the number of properties to fetch. Max & default is 10000.
-```
 
+```
 ### Property Schema
 ```plaintext
 Property
 ├── Basic Information:
 │ ├── property_url
-│ ├── property_id
-│ ├── listing_id
 │ ├── mls
 │ ├── mls_id
 │ └── status
@@ -139,60 +150,44 @@ Property
 │ ├── sqft
 │ ├── year_built
 │ ├── stories
-│ ├── garage
 │ └── lot_sqft
 
 ├── Property Listing Details:
-│ ├── days_on_mls
 │ ├── list_price
-│ ├── list_price_min
-│ ├── list_price_max
 │ ├── list_date
-│ ├── pending_date
 │ ├── sold_price
 │ ├── last_sold_date
 │ ├── price_per_sqft
-│ ├── new_construction
 │ └── hoa_fee
 
-├── Tax Information:
-│  ├── year
-│  ├── tax
-│  ├── assessment
-│  │   ├── building
-│  │   ├── land
-│  │   └── total
-
 ├── Location Details:
 │ ├── latitude
 │ ├── longitude
-│ ├── nearby_schools
-
-├── Agent Info:
-│ ├── agent_id
-│ ├── agent_name
-│ ├── agent_email
-│ └── agent_phone
-
-├── Broker Info:
-│ ├── broker_id
-│ └── broker_name
-
-├── Builder Info:
-│ ├── builder_id
-│ └── builder_name
-
-├── Office Info:
-│ ├── office_id
-│ ├── office_name
-│ ├── office_phones
-│ └── office_email
 
+└── Parking Details:
+    └── parking_garage
 ```
 
 ### Exceptions
 The following exceptions may be raised when using HomeHarvest:
 
-- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`, `pending`.
+- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
-- `InvalidDate` - date_from or date_to is not in the format YYYY-MM-DD.
+- `NoResultsFound` - no properties found from your search
-- `AuthenticationError` - Realtor.com token request failed.
+  
+  
+## 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 +1,20 @@
+from homeharvest import scrape_property
+from datetime import datetime
+
+# Generate filename based on current timestamp
+current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+filename = f"HomeHarvest_{current_timestamp}.csv"
+
+properties = scrape_property(
+    location="San Diego, CA",
+    listing_type="sold",  # or (for_sale, for_rent)
+    property_younger_than=30,  # sold in last 30 days - listed in last x days if (for_sale, for_rent)
+    # pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
+    # mls_only=True,  # only fetch MLS listings
+    # proxy="http://user:pass@host:port"  # use a proxy to change your IP address
+)
+print(f"Number of properties: {len(properties)}")
+
+# Export to csv
+properties.to_csv(filename, index=False)
+print(properties.head())

examples/price_of_land.py

@@ -1,104 +0,0 @@
-"""
-This script scrapes sold and pending sold land listings in past year for a list of zip codes and saves the data to individual Excel files.
-It adds two columns to the data: 'lot_acres' and 'ppa' (price per acre) for user to analyze average price of land in a zip code.
-"""
-
-import os
-import pandas as pd
-from homeharvest import scrape_property
-
-
-def get_property_details(zip: str, listing_type):
-    properties = scrape_property(location=zip, listing_type=listing_type, property_type=["land"], past_days=365)
-    if not properties.empty:
-        properties["lot_acres"] = properties["lot_sqft"].apply(lambda x: x / 43560 if pd.notnull(x) else None)
-
-        properties = properties[properties["sqft"].isnull()]
-        properties["ppa"] = properties.apply(
-            lambda row: (
-                int(
-                    (
-                        row["sold_price"]
-                        if (pd.notnull(row["sold_price"]) and row["status"] == "SOLD")
-                        else row["list_price"]
-                    )
-                    / row["lot_acres"]
-                )
-                if pd.notnull(row["lot_acres"])
-                and row["lot_acres"] > 0
-                and (pd.notnull(row["sold_price"]) or pd.notnull(row["list_price"]))
-                else None
-            ),
-            axis=1,
-        )
-        properties["ppa"] = properties["ppa"].astype("Int64")
-        selected_columns = [
-            "property_url",
-            "property_id",
-            "style",
-            "status",
-            "street",
-            "city",
-            "state",
-            "zip_code",
-            "county",
-            "list_date",
-            "last_sold_date",
-            "list_price",
-            "sold_price",
-            "lot_sqft",
-            "lot_acres",
-            "ppa",
-        ]
-        properties = properties[selected_columns]
-    return properties
-
-
-def output_to_excel(zip_code, sold_df, pending_df):
-    root_folder = os.getcwd()
-    zip_folder = os.path.join(root_folder, "zips", zip_code)
-
-    # Create zip code folder if it doesn't exist
-    os.makedirs(zip_folder, exist_ok=True)
-
-    # Define file paths
-    sold_file = os.path.join(zip_folder, f"{zip_code}_sold.xlsx")
-    pending_file = os.path.join(zip_folder, f"{zip_code}_pending.xlsx")
-
-    # Save individual sold and pending files
-    sold_df.to_excel(sold_file, index=False)
-    pending_df.to_excel(pending_file, index=False)
-
-
-zip_codes = map(
-    str,
-    [
-        22920,
-        77024,
-        78028,
-        24553,
-        22967,
-        22971,
-        22922,
-        22958,
-        22969,
-        22949,
-        22938,
-        24599,
-        24562,
-        22976,
-        24464,
-        22964,
-        24581,
-    ],
-)
-
-combined_df = pd.DataFrame()
-for zip in zip_codes:
-    sold_df = get_property_details(zip, "sold")
-    pending_df = get_property_details(zip, "pending")
-    combined_df = pd.concat([combined_df, sold_df, pending_df], ignore_index=True)
-    output_to_excel(zip, sold_df, pending_df)
-
-combined_file = os.path.join(os.getcwd(), "zips", "combined.xlsx")
-combined_df.to_excel(combined_file, index=False)

homeharvest/__init__.py

@@ -1,77 +1,50 @@
 import warnings
 import pandas as pd
 from .core.scrapers import ScraperInput
-from .utils import process_result, ordered_properties, validate_input, validate_dates, validate_limit
+from .utils import process_result, ordered_properties, validate_input
 from .core.scrapers.realtor import RealtorScraper
-from .core.scrapers.models import ListingType, SearchPropertyType, ReturnType, Property
+from .core.scrapers.models import ListingType
+from .exceptions import InvalidListingType, NoResultsFound
 
 
 def scrape_property(
     location: str,
     listing_type: str = "for_sale",
-    return_type: str = "pandas",
-    property_type: list[str] | None = None,
     radius: float = None,
     mls_only: bool = False,
-    past_days: int = None,
+    property_younger_than: int = None,
+    pending_or_contingent: bool = False,
     proxy: str = None,
-    date_from: str = None,  #: TODO: Switch to one parameter, Date, with date_from and date_to, pydantic validation
+) -> pd.DataFrame:
-    date_to: str = None,
-    foreclosure: bool = None,
-    extra_property_data: bool = True,
-    exclude_pending: bool = False,
-    limit: int = 10000
-) -> pd.DataFrame | list[dict] | list[Property]:
     """
     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, pending)
+    :param listing_type: Listing Type (for_sale, for_rent, sold)
-    :param return_type: Return type (pandas, pydantic, raw)
-    :param property_type: Property Type (single_family, multi_family, condos, condo_townhome_rowhome_coop, condo_townhome, townhomes, duplex_triplex, farm, land, mobile)
     :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
-    :param past_days: Get properties sold or listed (dependent on your listing_type) in the last _ days.
-    :param date_from, date_to: Get properties sold or listed (dependent on your listing_type) between these dates. format: 2021-01-28
-    :param foreclosure: If set, fetches only foreclosure listings.
-    :param extra_property_data: Increases requests by O(n). If set, this fetches additional property data (e.g. agent, broker, property evaluations etc.)
-    :param exclude_pending: If true, this excludes pending or contingent properties from the results, unless listing type is pending.
-    :param limit: Limit the number of results returned. Maximum is 10,000.
     """
     validate_input(listing_type)
-    validate_dates(date_from, date_to)
-    validate_limit(limit)
 
     scraper_input = ScraperInput(
         location=location,
-        listing_type=ListingType(listing_type.upper()),
+        listing_type=ListingType[listing_type.upper()],
-        return_type=ReturnType(return_type.lower()),
-        property_type=[SearchPropertyType[prop.upper()] for prop in property_type] if property_type else None,
         proxy=proxy,
         radius=radius,
         mls_only=mls_only,
-        last_x_days=past_days,
+        last_x_days=property_younger_than,
-        date_from=date_from,
+        pending_or_contingent=pending_or_contingent,
-        date_to=date_to,
-        foreclosure=foreclosure,
-        extra_property_data=extra_property_data,
-        exclude_pending=exclude_pending,
-        limit=limit,
     )
 
     site = RealtorScraper(scraper_input)
     results = site.search()
 
-    if scraper_input.return_type != ReturnType.pandas:
+    properties_dfs = [process_result(result) for result in results]
-        return results
-
-    properties_dfs = [df for result in results if not (df := process_result(result)).empty]
     if not properties_dfs:
-        return pd.DataFrame()
+        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]
-        return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties].replace(
-            {"None": pd.NA, None: pd.NA, "": pd.NA}
-        )

homeharvest/cli.py

@@ -5,14 +5,16 @@ 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(
+        "location", type=str, help="Location to scrape (e.g., San Francisco, CA)"
+    )
 
     parser.add_argument(
         "-l",
         "--listing_type",
         type=str,
         default="for_sale",
-        choices=["for_sale", "for_rent", "sold", "pending"],
+        choices=["for_sale", "for_rent", "sold"],
         help="Listing type to scrape",
     )
 
@@ -33,7 +35,9 @@ def main():
         help="Name of the output file (without extension)",
     )
 
-    parser.add_argument("-p", "--proxy", type=str, default=None, help="Proxy to use for scraping")
+    parser.add_argument(
+        "-p", "--proxy", type=str, default=None, help="Proxy to use for scraping"
+    )
     parser.add_argument(
         "-d",
         "--days",
@@ -56,6 +60,13 @@ def main():
         help="If set, fetches only MLS listings.",
     )
 
+    parser.add_argument(
+        "-c",
+        "--pending_or_contingent",
+        action="store_true",
+        help="If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.",
+    )
+
     args = parser.parse_args()
 
     result = scrape_property(
@@ -64,7 +75,8 @@ def main():
         radius=args.radius,
         proxy=args.proxy,
         mls_only=args.mls_only,
-        past_days=args.days,
+        property_younger_than=args.days,
+        pending_or_contingent=args.pending_or_contingent,
     )
 
     if not args.filename:

homeharvest/core/scrapers/__init__.py

@@ -1,73 +1,33 @@
-from __future__ import annotations
 from dataclasses import dataclass
-from typing import Union
-
 import requests
-from requests.adapters import HTTPAdapter
+import tls_client
-from urllib3.util.retry import Retry
+from .models import Property, ListingType, SiteName
-import uuid
-from ...exceptions import AuthenticationError
-from .models import Property, ListingType, SiteName, SearchPropertyType, ReturnType
-import json
 
 
 @dataclass
 class ScraperInput:
     location: str
     listing_type: ListingType
-    property_type: list[SearchPropertyType] | None = None
     radius: float | None = None
-    mls_only: bool | None = False
+    mls_only: bool | None = None
     proxy: str | None = None
     last_x_days: int | None = None
-    date_from: str | None = None
+    pending_or_contingent: bool | None = None
-    date_to: str | None = None
-    foreclosure: bool | None = False
-    extra_property_data: bool | None = True
-    exclude_pending: bool | None = False
-    limit: int = 10000
-    return_type: ReturnType = ReturnType.pandas
 
 
 class Scraper:
-    session = None
-
     def __init__(
         self,
         scraper_input: ScraperInput,
+        session: requests.Session | tls_client.Session = None,
     ):
         self.location = scraper_input.location
         self.listing_type = scraper_input.listing_type
-        self.property_type = scraper_input.property_type
 
-        if not self.session:
+        if not session:
-            Scraper.session = requests.Session()
+            self.session = requests.Session()
-            retries = Retry(
+        else:
-                total=3, backoff_factor=4, status_forcelist=[429, 403], allowed_methods=frozenset(["GET", "POST"])
+            self.session = session
-            )
-
-            adapter = HTTPAdapter(max_retries=retries)
-            Scraper.session.mount("http://", adapter)
-            Scraper.session.mount("https://", adapter)
-            Scraper.session.headers.update(
-                {
-                    "accept": "application/json, text/javascript",
-                    "accept-language": "en-US,en;q=0.9",
-                    "cache-control": "no-cache",
-                    "content-type": "application/json",
-                    "origin": "https://www.realtor.com",
-                    "pragma": "no-cache",
-                    "priority": "u=1, i",
-                    "rdc-ab-tests": "commute_travel_time_variation:v1",
-                    "sec-ch-ua": '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
-                    "sec-ch-ua-mobile": "?0",
-                    "sec-ch-ua-platform": '"Windows"',
-                    "sec-fetch-dest": "empty",
-                    "sec-fetch-mode": "cors",
-                    "sec-fetch-site": "same-origin",
-                    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36",
-                }
-            )
 
         if scraper_input.proxy:
             proxy_url = scraper_input.proxy
@@ -78,51 +38,14 @@ class Scraper:
         self.radius = scraper_input.radius
         self.last_x_days = scraper_input.last_x_days
         self.mls_only = scraper_input.mls_only
-        self.date_from = scraper_input.date_from
+        self.pending_or_contingent = scraper_input.pending_or_contingent
-        self.date_to = scraper_input.date_to
-        self.foreclosure = scraper_input.foreclosure
-        self.extra_property_data = scraper_input.extra_property_data
-        self.exclude_pending = scraper_input.exclude_pending
-        self.limit = scraper_input.limit
-        self.return_type = scraper_input.return_type
 
-    def search(self) -> list[Union[Property | dict]]: ...
+    def search(self) -> list[Property]:
+        ...
 
     @staticmethod
-    def _parse_home(home) -> Property: ...
+    def _parse_home(home) -> Property:
+        ...
 
-    def handle_location(self): ...
+    def handle_location(self):
-
+        ...
-    @staticmethod
-    def get_access_token():
-        device_id = str(uuid.uuid4()).upper()
-
-        response = requests.post(
-            "https://graph.realtor.com/auth/token",
-            headers={
-                "Host": "graph.realtor.com",
-                "Accept": "*/*",
-                "Content-Type": "Application/json",
-                "X-Client-ID": "rdc_mobile_native,iphone",
-                "X-Visitor-ID": device_id,
-                "X-Client-Version": "24.21.23.679885",
-                "Accept-Language": "en-US,en;q=0.9",
-                "User-Agent": "Realtor.com/24.21.23.679885 CFNetwork/1494.0.7 Darwin/23.4.0",
-            },
-            data=json.dumps(
-                {
-                    "grant_type": "device_mobile",
-                    "device_id": device_id,
-                    "client_app_id": "rdc_mobile_native,24.21.23.679885,iphone",
-                }
-            ),
-        )
-
-        data = response.json()
-
-        if not (access_token := data.get("access_token")):
-            raise AuthenticationError(
-                "Failed to get access token, use a proxy/vpn or wait a moment and try again.", response=response
-            )
-
-        return access_token

homeharvest/core/scrapers/models.py

@@ -1,15 +1,8 @@
-from __future__ import annotations
 from dataclasses import dataclass
 from enum import Enum
 from typing import Optional
 
 
-class ReturnType(Enum):
-    pydantic = "pydantic"
-    pandas = "pandas"
-    raw = "raw"
-
-
 class SiteName(Enum):
     ZILLOW = "zillow"
     REDFIN = "redfin"
@@ -23,60 +16,14 @@ class SiteName(Enum):
         raise ValueError(f"{value} not found in {cls}")
 
 
-class SearchPropertyType(Enum):
-    SINGLE_FAMILY = "single_family"
-    APARTMENT = "apartment"
-    CONDOS = "condos"
-    CONDO_TOWNHOME_ROWHOME_COOP = "condo_townhome_rowhome_coop"
-    CONDO_TOWNHOME = "condo_townhome"
-    TOWNHOMES = "townhomes"
-    DUPLEX_TRIPLEX = "duplex_triplex"
-    FARM = "farm"
-    LAND = "land"
-    MULTI_FAMILY = "multi_family"
-    MOBILE = "mobile"
-
-
 class ListingType(Enum):
     FOR_SALE = "FOR_SALE"
     FOR_RENT = "FOR_RENT"
-    PENDING = "PENDING"
     SOLD = "SOLD"
 
 
-@dataclass
-class Agent:
-    name: str | None = None
-    phone: str | None = None
-
-
-class PropertyType(Enum):
-    APARTMENT = "APARTMENT"
-    BUILDING = "BUILDING"
-    COMMERCIAL = "COMMERCIAL"
-    GOVERNMENT = "GOVERNMENT"
-    INDUSTRIAL = "INDUSTRIAL"
-    CONDO_TOWNHOME = "CONDO_TOWNHOME"
-    CONDO_TOWNHOME_ROWHOME_COOP = "CONDO_TOWNHOME_ROWHOME_COOP"
-    CONDO = "CONDO"
-    CONDOP = "CONDOP"
-    CONDOS = "CONDOS"
-    COOP = "COOP"
-    DUPLEX_TRIPLEX = "DUPLEX_TRIPLEX"
-    FARM = "FARM"
-    INVESTMENT = "INVESTMENT"
-    LAND = "LAND"
-    MOBILE = "MOBILE"
-    MULTI_FAMILY = "MULTI_FAMILY"
-    RENTAL = "RENTAL"
-    SINGLE_FAMILY = "SINGLE_FAMILY"
-    TOWNHOMES = "TOWNHOMES"
-    OTHER = "OTHER"
-
-
 @dataclass
 class Address:
-    full_line: str | None = None
     street: str | None = None
     unit: str | None = None
     city: str | None = None
@@ -86,9 +33,7 @@ class Address:
 
 @dataclass
 class Description:
-    primary_photo: str | None = None
+    style: str | None = None
-    alt_photos: list[str] | None = None
-    style: PropertyType | None = None
     beds: int | None = None
     baths_full: int | None = None
     baths_half: int | None = None
@@ -98,97 +43,23 @@ class Description:
     year_built: int | None = None
     garage: float | None = None
     stories: int | None = None
-    text: str | None = None
-
-
-@dataclass
-class AgentPhone:  #: For documentation purposes only (at the moment)
-    number: str | None = None
-    type: str | None = None
-    primary: bool | None = None
-    ext: str | None = None
-
-
-@dataclass
-class Entity:
-    name: str
-    uuid: str | None = None
-
-
-@dataclass
-class Agent(Entity):
-    mls_set: str | None = None
-    nrds_id: str | None = None
-    phones: list[dict] | AgentPhone | None = None
-    email: str | None = None
-    href: str | None = None
-
-
-@dataclass
-class Office(Entity):
-    mls_set: str | None = None
-    email: str | None = None
-    href: str | None = None
-    phones: list[dict] | AgentPhone | None = None
-
-
-@dataclass
-class Broker(Entity):
-    pass
-
-
-@dataclass
-class Builder(Entity):
-    pass
-
-
-@dataclass
-class Advertisers:
-    agent: Agent | None = None
-    broker: Broker | None = None
-    builder: Builder | None = None
-    office: Office | None = None
 
 
 @dataclass
 class Property:
     property_url: str
-
-    property_id: str
-    #: allows_cats: bool
-    #: allows_dogs: bool
-
-    listing_id: str | None = None
-
     mls: str | None = None
     mls_id: str | None = None
     status: str | None = None
     address: Address | None = None
 
     list_price: int | None = None
-    list_price_min: int | None = None
-    list_price_max: int | None = None
-
     list_date: str | None = None
-    pending_date: str | None = None
     last_sold_date: str | None = None
     prc_sqft: int | None = None
-    new_construction: bool | None = None
     hoa_fee: int | None = None
-    days_on_mls: int | None = None
     description: Description | None = None
-    tags: list[str] | None = None
-    details: list[dict] | None = None
 
     latitude: float | None = None
     longitude: float | None = None
     neighborhoods: Optional[str] = None
-    county: Optional[str] = None
-    fips_code: Optional[str] = None
-    nearby_schools: list[str] = None
-    assessed_value: int | None = None
-    estimated_value: int | None = None
-    tax: int | None = None
-    tax_history: list[dict] | None = None
-
-    advertisers: Advertisers | None = None

homeharvest/core/scrapers/realtor/__init__.py

@@ -4,48 +4,21 @@ homeharvest.realtor.__init__
 
 This module implements the scraper for realtor.com
 """
-
-from __future__ import annotations
-
-import json
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from datetime import datetime
-from json import JSONDecodeError
 from typing import Dict, Union, Optional
-
+from concurrent.futures import ThreadPoolExecutor, as_completed
-from tenacity import (
-    retry,
-    retry_if_exception_type,
-    wait_exponential,
-    stop_after_attempt,
-)
 
 from .. import Scraper
-from ..models import (
+from ....exceptions import NoResultsFound
-    Property,
+from ..models import Property, Address, ListingType, Description
-    Address,
-    ListingType,
-    Description,
-    PropertyType,
-    Agent,
-    Broker,
-    Builder,
-    Advertisers,
-    Office,
-    ReturnType
-)
-from .queries import GENERAL_RESULTS_QUERY, SEARCH_HOMES_DATA, HOMES_DATA, HOME_FRAGMENT
 
 
 class RealtorScraper(Scraper):
     SEARCH_GQL_URL = "https://www.realtor.com/api/v1/rdc_search_srp?client_id=rdc-search-new-communities&schema=vesta"
     PROPERTY_URL = "https://www.realtor.com/realestateandhomes-detail/"
-    PROPERTY_GQL = "https://graph.realtor.com/graphql"
     ADDRESS_AUTOCOMPLETE_URL = "https://parser-external.geo.moveaws.com/suggest"
-    NUM_PROPERTY_WORKERS = 20
-    DEFAULT_PAGE_SIZE = 200
 
     def __init__(self, scraper_input):
+        self.counter = 1
         super().__init__(scraper_input)
 
     def handle_location(self):
@@ -65,10 +38,121 @@ class RealtorScraper(Scraper):
         result = response_json["autocomplete"]
 
         if not result:
-            return None
+            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) {
@@ -102,15 +186,56 @@ class RealtorScraper(Scraper):
         else:
             return property_info["listings"][0]["listing_id"]
 
-    def handle_home(self, property_id: str) -> list[Property]:
+    def handle_address(self, property_id: str) -> list[Property]:
-        query = (
+        """
-            """query Home($property_id: ID!) {
+        Handles a specific address & returns one property
-                    home(property_id: $property_id) %s
+        """
+        query = """query Property($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
+                        }
+                    }
                 }"""
-            % HOMES_DATA
-        )
 
         variables = {"property_id": property_id}
+
         payload = {
             "query": query,
             "variables": variables,
@@ -119,218 +244,152 @@ class RealtorScraper(Scraper):
         response = self.session.post(self.SEARCH_GQL_URL, json=payload)
         response_json = response.json()
 
-        property_info = response_json["data"]["home"]
+        property_info = response_json["data"]["property"]
 
-        return [self.process_property(property_info)]
+        return [
-
+            Property(
-    @staticmethod
+                mls_id=property_id,
-    def process_advertisers(advertisers: list[dict] | None) -> Advertisers | None:
+                property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
-        if not advertisers:
+                address=self._parse_address(
-            return None
+                    property_info, search_type="handle_address"
-
-        def _parse_fulfillment_id(fulfillment_id: str | None) -> str | None:
-            return fulfillment_id if fulfillment_id and fulfillment_id != "0" else None
-
-        processed_advertisers = Advertisers()
-
-        for advertiser in advertisers:
-            advertiser_type = advertiser.get("type")
-            if advertiser_type == "seller":  #: agent
-                processed_advertisers.agent = Agent(
-                    uuid=_parse_fulfillment_id(advertiser.get("fulfillment_id")),
-                    nrds_id=advertiser.get("nrds_id"),
-                    mls_set=advertiser.get("mls_set"),
-                    name=advertiser.get("name"),
-                    email=advertiser.get("email"),
-                    phones=advertiser.get("phones"),
-                )
-
-                if advertiser.get("broker") and advertiser["broker"].get("name"):  #: has a broker
-                    processed_advertisers.broker = Broker(
-                        uuid=_parse_fulfillment_id(advertiser["broker"].get("fulfillment_id")),
-                        name=advertiser["broker"].get("name"),
-                    )
-
-                if advertiser.get("office"):  #: has an office
-                    processed_advertisers.office = Office(
-                        uuid=_parse_fulfillment_id(advertiser["office"].get("fulfillment_id")),
-                        mls_set=advertiser["office"].get("mls_set"),
-                        name=advertiser["office"].get("name"),
-                        email=advertiser["office"].get("email"),
-                        phones=advertiser["office"].get("phones"),
-                    )
-
-            if advertiser_type == "community":  #: could be builder
-                if advertiser.get("builder"):
-                    processed_advertisers.builder = Builder(
-                        uuid=_parse_fulfillment_id(advertiser["builder"].get("fulfillment_id")),
-                        name=advertiser["builder"].get("name"),
-                    )
-
-        return processed_advertisers
-
-    def process_property(self, result: dict) -> Property | None:
-        mls = result["source"].get("id") if "source" in result and isinstance(result["source"], dict) else None
-
-        if not mls and self.mls_only:
-            return
-
-        able_to_get_lat_long = (
-            result
-            and result.get("location")
-            and result["location"].get("address")
-            and result["location"]["address"].get("coordinate")
-        )
-
-        is_pending = result["flags"].get("is_pending")
-        is_contingent = result["flags"].get("is_contingent")
-
-        if (is_pending or is_contingent) and (self.exclude_pending and self.listing_type != ListingType.PENDING):
-            return
-
-        property_id = result["property_id"]
-        prop_details = self.process_extra_property_details(result) if self.extra_property_data else {}
-
-        property_estimates_root = result.get("current_estimates") or result.get("estimates", {}).get("currentValues")
-        estimated_value = self.get_key(property_estimates_root, [0, "estimate"])
-
-        advertisers = self.process_advertisers(result.get("advertisers"))
-
-        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=result["href"],
+                description=self._parse_description(property_info),
-            property_id=property_id,
-            listing_id=result.get("listing_id"),
-            status=("PENDING" if is_pending else "CONTINGENT" if is_contingent else result["status"].upper()),
-            list_price=result["list_price"],
-            list_price_min=result["list_price_min"],
-            list_price_max=result["list_price_max"],
-            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"),
-            new_construction=result["flags"].get("is_new_construction") is True,
-            hoa_fee=(result["hoa"]["fee"] if result.get("hoa") and isinstance(result["hoa"], dict) else None),
-            latitude=(result["location"]["address"]["coordinate"].get("lat") if able_to_get_lat_long else None),
-            longitude=(result["location"]["address"]["coordinate"].get("lon") if able_to_get_lat_long else None),
-            address=self._parse_address(result, search_type="general_search"),
-            description=self._parse_description(result),
-            neighborhoods=self._parse_neighborhoods(result),
-            county=(result["location"]["county"].get("name") if result["location"]["county"] else None),
-            fips_code=(result["location"]["county"].get("fips_code") if result["location"]["county"] else None),
-            days_on_mls=self.calculate_days_on_mls(result),
-            nearby_schools=prop_details.get("schools"),
-            assessed_value=prop_details.get("assessed_value"),
-            estimated_value=estimated_value if estimated_value else None,
-            advertisers=advertisers,
-            tax=prop_details.get("tax"),
-            tax_history=prop_details.get("tax_history"),
             )
-        return realty_property
+        ]
 
-    def general_search(self, variables: dict, search_type: str) -> Dict[str, Union[int, Union[list[Property], list[dict]]]]:
+    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 = ""
+        date_param = (
-        if self.listing_type == ListingType.SOLD:
+            'sold_date: { min: "$today-%sD" }' % self.last_x_days
-            if self.date_from and self.date_to:
+            if self.listing_type == ListingType.SOLD and self.last_x_days
-                date_param = f'sold_date: {{ min: "{self.date_from}", max: "{self.date_to}" }}'
+            else (
-            elif self.last_x_days:
+                'list_date: { min: "$today-%sD" }' % self.last_x_days
-                date_param = f'sold_date: {{ min: "$today-{self.last_x_days}D" }}'
+                if self.last_x_days
-        else:
+                else ""
-            if self.date_from and self.date_to:
+            )
-                date_param = f'list_date: {{ min: "{self.date_from}", max: "{self.date_to}" }}'
+        )
-            elif self.last_x_days:
-                date_param = f'list_date: {{ min: "$today-{self.last_x_days}D" }}'
-
-        property_type_param = ""
-        if self.property_type:
-            property_types = [pt.value for pt in self.property_type]
-            property_type_param = f"type: {json.dumps(property_types)}"
 
         sort_param = (
             "sort: [{ field: sold_date, direction: desc }]"
             if self.listing_type == ListingType.SOLD
-            else ""  #: "sort: [{ field: list_date, direction: desc }]"  #: prioritize normal fractal sort from realtor
+            else "sort: [{ field: list_date, direction: desc }]"
         )
 
         pending_or_contingent_param = (
-            "or_filters: { contingent: true, pending: true }" if self.listing_type == ListingType.PENDING else ""
+            "or_filters: { contingent: true, pending: true }"
+            if self.pending_or_contingent
+            else ""
         )
 
-        listing_type = ListingType.FOR_SALE if self.listing_type == ListingType.PENDING else self.listing_type
-        is_foreclosure = ""
-
-        if variables.get("foreclosure") is True:
-            is_foreclosure = "foreclosure: true"
-        elif variables.get("foreclosure") is False:
-            is_foreclosure = "foreclosure: false"
-
         if search_type == "comps":  #: comps search, came from an address
             query = """query Property_search(
                     $coordinates: [Float]!
                     $radius: String!
                     $offset: Int!,
                     ) {
-                        home_search(
+                        property_search(
                             query: { 
-                                %s
                                 nearby: {
                                     coordinates: $coordinates
                                     radius: $radius 
                                 }
                                 status: %s
                                 %s
-                                %s
-                                %s
                             }
                             %s
                             limit: 200
                             offset: $offset
-                    ) %s
+                    ) %s""" % (
-                }""" % (
+                self.listing_type.value.lower(),
-                is_foreclosure,
-                listing_type.value.lower(),
                 date_param,
-                property_type_param,
-                pending_or_contingent_param,
                 sort_param,
-                GENERAL_RESULTS_QUERY,
+                results_query,
             )
         elif search_type == "area":  #: general search, came from a general location
             query = """query Home_search(
-                                $location: String!,
+                                $city: String,
+                                $county: [String],
+                                $state_code: String,
+                                $postal_code: String
                                 $offset: Int,
                             ) {
                                 home_search(
                                     query: {
-                                        %s
+                                        city: $city
-                                        search_location: {location: $location}
+                                        county: $county
+                                        postal_code: $postal_code
+                                        state_code: $state_code
                                         status: %s
-                                        unique: true
-                                        %s
                                         %s
                                         %s
                                     }
-                                    bucket: { sort: "fractal_v1.1.3_fr" }
                                     %s
                                     limit: 200
                                     offset: $offset
-                                ) %s
+                                ) %s""" % (
-                            }""" % (
+                self.listing_type.value.lower(),
-                is_foreclosure,
-                listing_type.value.lower(),
                 date_param,
-                property_type_param,
                 pending_or_contingent_param,
                 sort_param,
-                GENERAL_RESULTS_QUERY,
+                results_query,
             )
         else:  #: general search, came from an address
             query = (
@@ -338,15 +397,14 @@ class RealtorScraper(Scraper):
                         $property_id: [ID]!
                         $offset: Int!,
                     ) {
-                        home_search(
+                        property_search(
                             query: {
                                 property_id: $property_id
                             }
                             limit: 1
                             offset: $offset
-                        ) %s
+                        ) %s"""
-                    }"""
+                % results_query
-                % GENERAL_RESULTS_QUERY
             )
 
         payload = {
@@ -355,10 +413,11 @@ 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 "home_search" in query else "property_search"
+        search_key = "home_search" if search_type == "area" else "property_search"
 
-        properties: list[Union[Property, dict]] = []
+        properties: list[Property] = []
 
         if (
             response_json is None
@@ -370,42 +429,59 @@ class RealtorScraper(Scraper):
         ):
             return {"total": 0, "properties": []}
 
-        properties_list = response_json["data"][search_key]["results"]
+        for result in response_json["data"][search_key]["results"]:
-        total_properties = response_json["data"][search_key]["total"]
+            self.counter += 1
-        offset = variables.get("offset", 0)
+            mls = (
+                result["source"].get("id")
+                if "source" in result and isinstance(result["source"], dict)
+                else None
+            )
 
-        #: limit the number of properties to be processed
+            if not mls and self.mls_only:
-        #: example, if your offset is 200, and your limit is 250, return 50
+                continue
-        properties_list: list[dict] = properties_list[: self.limit - offset]
 
-        if self.extra_property_data:
+            able_to_get_lat_long = (
-            property_ids = [data["property_id"] for data in properties_list]
+                result
-            extra_property_details = self.get_bulk_prop_details(property_ids) or {}
+                and result.get("location")
+                and result["location"].get("address")
+                and result["location"]["address"].get("coordinate")
+            )
 
-            for result in properties_list:
+            realty_property = Property(
-                result.update(extra_property_details.get(result["property_id"], {}))
+                mls=mls,
-
+                mls_id=result["source"].get("listing_id")
-        if self.return_type != ReturnType.raw:
+                if "source" in result and isinstance(result["source"], dict)
-            with ThreadPoolExecutor(max_workers=self.NUM_PROPERTY_WORKERS) as executor:
+                else None,
-                futures = [executor.submit(self.process_property, result) for result in properties_list]
+                property_url=f"{self.PROPERTY_URL}{result['property_id']}",
-
+                status=result["status"].upper(),
-                for future in as_completed(futures):
+                list_price=result["list_price"],
-                    result = future.result()
+                list_date=result["list_date"].split("T")[0]
-                    if result:
+                if result.get("list_date")
-                        properties.append(result)
+                else None,
-        else:
+                prc_sqft=result.get("price_per_sqft"),
-            properties = properties_list
+                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": total_properties,
+            "total": response_json["data"][search_key]["total"],
             "properties": properties,
         }
 
     def search(self):
         location_info = self.handle_location()
-        if not location_info:
-            return []
-
         location_type = location_info["area_type"]
 
         search_variables = {
@@ -415,52 +491,54 @@ class RealtorScraper(Scraper):
         search_type = (
             "comps"
             if self.radius and location_type == "address"
-            else "address" if location_type == "address" and not self.radius else "area"
+            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"]
-                return self.handle_home(property_id)
+                search_variables |= {"property_id": property_id}
+
+                gql_results = self.general_search(
+                    search_variables, search_type=search_type
+                )
+                if gql_results["total"] == 0:
+                    listing_id = self.get_latest_listing_id(property_id)
+                    if listing_id is None:
+                        return self.handle_address(property_id)
+                    else:
+                        return self.handle_listing(listing_id)
+                else:
+                    return gql_results["properties"]
 
             else:  #: general search, comps (radius)
-                if not location_info.get("centroid"):
-                    return []
-
                 coordinates = list(location_info["centroid"].values())
                 search_variables |= {
                     "coordinates": coordinates,
                     "radius": "{}mi".format(self.radius),
                 }
 
-        elif location_type == "postal_code":
-            search_variables |= {
-                "postal_code": location_info.get("postal_code"),
-            }
-
         else:  #: general search, location
             search_variables |= {
-                "location": self.location,
+                "city": location_info.get("city"),
+                "county": location_info.get("county"),
+                "state_code": location_info.get("state_code"),
+                "postal_code": location_info.get("postal_code"),
             }
 
-        if self.foreclosure:
-            search_variables["foreclosure"] = self.foreclosure
-
         result = self.general_search(search_variables, search_type=search_type)
         total = result["total"]
         homes = result["properties"]
 
-        with ThreadPoolExecutor() as executor:
+        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(
+                for i in range(200, min(total, 10000), 200)
-                    self.DEFAULT_PAGE_SIZE,
-                    min(total, self.limit),
-                    self.DEFAULT_PAGE_SIZE,
-                )
             ]
 
             for future in as_completed(futures):
@@ -468,90 +546,6 @@ class RealtorScraper(Scraper):
 
         return homes
 
-    @staticmethod
-    def get_key(data: dict, keys: list):
-        try:
-            value = data
-            for key in keys:
-                value = value[key]
-
-            return value or {}
-        except (KeyError, TypeError, IndexError):
-            return {}
-
-    def process_extra_property_details(self, result: dict) -> dict:
-        schools = self.get_key(result, ["nearbySchools", "schools"])
-        assessed_value = self.get_key(result, ["taxHistory", 0, "assessment", "total"])
-        tax_history = self.get_key(result, ["taxHistory"])
-
-        schools = [school["district"]["name"] for school in schools if school["district"].get("name")]
-
-        # Process tax history
-        latest_tax = None
-        processed_tax_history = None
-        if tax_history and isinstance(tax_history, list):
-            tax_history = sorted(tax_history, key=lambda x: x.get("year", 0), reverse=True)
-
-            if tax_history and "tax" in tax_history[0]:
-                latest_tax = tax_history[0]["tax"]
-
-            processed_tax_history = []
-            for entry in tax_history:
-                if "year" in entry and "tax" in entry:
-                    processed_entry = {
-                        "year": entry["year"],
-                        "tax": entry["tax"],
-                    }
-                    if "assessment" in entry and isinstance(entry["assessment"], dict):
-                        processed_entry["assessment"] = {
-                            "building": entry["assessment"].get("building"),
-                            "land": entry["assessment"].get("land"),
-                            "total": entry["assessment"].get("total"),
-                        }
-                    processed_tax_history.append(processed_entry)
-
-        return {
-            "schools": schools if schools else None,
-            "assessed_value": assessed_value if assessed_value else None,
-            "tax": latest_tax,
-            "tax_history": processed_tax_history,
-        }
-
-    @retry(
-        retry=retry_if_exception_type(JSONDecodeError),
-        wait=wait_exponential(min=4, max=10),
-        stop=stop_after_attempt(3),
-    )
-    def get_bulk_prop_details(self, property_ids: list[str]) -> dict:
-        """
-        Fetch extra property details for multiple properties in a single GraphQL query.
-        Returns a map of property_id to its details.
-        """
-        if not self.extra_property_data or not property_ids:
-            return {}
-
-        property_ids = list(set(property_ids))
-
-        # Construct the bulk query
-        fragments = "\n".join(
-            f'home_{property_id}: home(property_id: {property_id}) {{ ...HomeData }}'
-            for property_id in property_ids
-        )
-        query = f"""{HOME_FRAGMENT}
-        
-        query GetHomes {{
-            {fragments}
-        }}"""
-
-        response = self.session.post(self.SEARCH_GQL_URL, json={"query": query})
-        data = response.json()
-
-        if "data" not in data:
-            return {}
-
-        properties = data["data"]
-        return {data.replace('home_', ''): properties[data] for data in properties if properties[data]}
-
     @staticmethod
     def _parse_neighborhoods(result: dict) -> Optional[str]:
         neighborhoods_list = []
@@ -565,104 +559,45 @@ class RealtorScraper(Scraper):
 
         return ", ".join(neighborhoods_list) if neighborhoods_list else None
 
-    @staticmethod
-    def handle_none_safely(address_part):
-        if address_part is None:
-            return ""
-
-        return address_part
-
     @staticmethod
     def _parse_address(result: dict, search_type):
         if search_type == "general_search":
-            address = result["location"]["address"]
-        else:
-            address = result["address"]
-
             return Address(
-            full_line=address.get("line"),
+                street=f"{result['location']['address']['street_number']} {result['location']['address']['street_name']} {result['location']['address']['street_suffix']}",
-            street=" ".join(
+                unit=result["location"]["address"]["unit"],
-                part
+                city=result["location"]["address"]["city"],
-                for part in [
+                state=result["location"]["address"]["state_code"],
-                    address.get("street_number"),
+                zip=result["location"]["address"]["postal_code"],
-                    address.get("street_direction"),
+            )
-                    address.get("street_name"),
+        return Address(
-                    address.get("street_suffix"),
+            street=f"{result['address']['street_number']} {result['address']['street_name']} {result['address']['street_suffix']}",
-                ]
+            unit=result["address"]["unit"],
-                if part is not None
+            city=result["address"]["city"],
-            ).strip(),
+            state=result["address"]["state_code"],
-            unit=address["unit"],
+            zip=result["address"]["postal_code"],
-            city=address["city"],
-            state=address["state_code"],
-            zip=address["postal_code"],
         )
 
     @staticmethod
-    def _parse_description(result: dict) -> Description | None:
+    def _parse_description(result: dict) -> Description:
-        if not result:
-            return None
-
         description_data = result.get("description", {})
 
         if description_data is None or not isinstance(description_data, dict):
+            print("Warning: description_data is invalid!")
             description_data = {}
 
         style = description_data.get("type", "")
         if style is not None:
             style = style.upper()
 
-        primary_photo = ""
-        if (primary_photo_info := result.get("primary_photo")) and (
-            primary_photo_href := primary_photo_info.get("href")
-        ):
-            primary_photo = primary_photo_href.replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
-
         return Description(
-            primary_photo=primary_photo,
+            style=style,
-            alt_photos=RealtorScraper.process_alt_photos(result.get("photos", [])),
-            style=(PropertyType.__getitem__(style) if style and style in PropertyType.__members__ else None),
             beds=description_data.get("beds"),
             baths_full=description_data.get("baths_full"),
             baths_half=description_data.get("baths_half"),
             sqft=description_data.get("sqft"),
             lot_sqft=description_data.get("lot_sqft"),
-            sold_price=(
+            sold_price=description_data.get("sold_price"),
-                result.get("last_sold_price") or description_data.get("sold_price")
-                if result.get("last_sold_date") or result["list_price"] != description_data.get("sold_price")
-                else None
-            ),  #: has a sold date or list and sold price are different
             year_built=description_data.get("year_built"),
             garage=description_data.get("garage"),
             stories=description_data.get("stories"),
-            text=description_data.get("text"),
         )
-
-    @staticmethod
-    def calculate_days_on_mls(result: dict) -> Optional[int]:
-        list_date_str = result.get("list_date")
-        list_date = datetime.strptime(list_date_str.split("T")[0], "%Y-%m-%d") if list_date_str else None
-        last_sold_date_str = result.get("last_sold_date")
-        last_sold_date = datetime.strptime(last_sold_date_str, "%Y-%m-%d") if last_sold_date_str else None
-        today = datetime.now()
-
-        if list_date:
-            if result["status"] == "sold":
-                if last_sold_date:
-                    days = (last_sold_date - list_date).days
-                    if days >= 0:
-                        return days
-            elif result["status"] in ("for_sale", "for_rent"):
-                days = (today - list_date).days
-                if days >= 0:
-                    return days
-
-    @staticmethod
-    def process_alt_photos(photos_info: list[dict]) -> list[str] | None:
-        if not photos_info:
-            return None
-
-        return [
-            photo_info["href"].replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
-            for photo_info in photos_info
-            if photo_info.get("href")
-        ]

homeharvest/core/scrapers/realtor/queries.py

@@ -1,242 +0,0 @@
-_SEARCH_HOMES_DATA_BASE = """{
-    pending_date
-    listing_id
-    property_id
-    href
-    list_date
-    status
-    last_sold_price
-    last_sold_date
-    list_price
-    list_price_max
-    list_price_min
-    price_per_sqft
-    tags
-    details {
-        category
-        text
-        parent_category
-    }
-    pet_policy {
-        cats
-        dogs
-        dogs_small
-        dogs_large
-        __typename
-    }
-    units {
-        availability {
-          date
-          __typename
-        }
-        description {
-          baths_consolidated
-          baths
-          beds
-          sqft
-          __typename
-        }
-        list_price
-        __typename
-    }
-    flags {
-        is_contingent
-        is_pending
-        is_new_construction
-    }
-    description {
-        type
-        sqft
-        beds
-        baths_full
-        baths_half
-        lot_sqft
-        year_built
-        garage
-        type
-        name
-        stories
-        text
-    }
-    source {
-        id
-        listing_id
-    }
-    hoa {
-        fee
-    }
-    location {
-        address {
-            street_direction
-            street_number
-            street_name
-            street_suffix
-            line
-            unit
-            city
-            state_code
-            postal_code
-            coordinate {
-                lon
-                lat
-            }
-        }
-        county {
-            name
-            fips_code
-        }
-        neighborhoods {
-            name
-        }
-    }
-    tax_record {
-        public_record_id
-    }
-    primary_photo(https: true) {
-        href
-    }
-    photos(https: true) {
-        href
-        tags {
-            label
-        }
-    }
-    advertisers {
-        email
-        broker {
-            name
-            fulfillment_id
-        }
-        type
-        name
-        fulfillment_id
-        builder {
-            name
-            fulfillment_id
-        }
-        phones {
-            ext
-            primary
-            type
-            number
-        }
-        office {
-            name
-            email
-            fulfillment_id
-            href
-            phones {
-                number
-                type
-                primary
-                ext
-            }
-            mls_set
-        }
-        corporation {
-            specialties
-            name
-            bio
-            href
-            fulfillment_id
-        }
-        mls_set
-        nrds_id
-        rental_corporation {
-            fulfillment_id
-        }
-        rental_management {
-            name
-            href
-            fulfillment_id
-        }
-    }
-    """
-
-
-HOME_FRAGMENT = """
-fragment HomeData on Home {
-    property_id
-    nearbySchools: nearby_schools(radius: 5.0, limit_per_level: 3) {
-        __typename schools { district { __typename id name } }
-    }
-    taxHistory: tax_history { __typename tax year assessment { __typename building land total } }
-    monthly_fees {
-        description
-        display_amount
-    }
-    one_time_fees {
-        description
-        display_amount
-    }
-    parking {
-        unassigned_space_rent
-        assigned_spaces_available
-        description
-        assigned_space_rent
-    }
-    terms {
-        text
-        category
-    }
-}
-"""
-
-HOMES_DATA = """%s
-                nearbySchools: nearby_schools(radius: 5.0, limit_per_level: 3) {
-                            __typename schools { district { __typename id name } }
-                        }
-                monthly_fees {
-                    description
-                    display_amount
-                }
-                one_time_fees {
-                    description
-                    display_amount
-                }
-                parking {
-                    unassigned_space_rent
-                    assigned_spaces_available
-                    description
-                    assigned_space_rent
-                }
-                terms {
-                    text
-                    category
-                }
-                taxHistory: tax_history { __typename tax year assessment { __typename building land total } }
-                estimates {
-                    __typename
-                    currentValues: current_values {
-                        __typename
-                        source { __typename type name }
-                        estimate
-                        estimateHigh: estimate_high
-                        estimateLow: estimate_low
-                        date
-                        isBestHomeValue: isbest_homevalue
-                    }
-                }
-}""" % _SEARCH_HOMES_DATA_BASE
-
-SEARCH_HOMES_DATA = """%s
-current_estimates {
-    __typename
-    source {
-        __typename
-        type
-        name
-    }
-    estimate
-    estimateHigh: estimate_high
-    estimateLow: estimate_low
-    date
-    isBestHomeValue: isbest_homevalue
-}
-}""" % _SEARCH_HOMES_DATA_BASE
-
-GENERAL_RESULTS_QUERY = """{
-                            count
-                            total
-                            results %s
-                        }""" % SEARCH_HOMES_DATA

homeharvest/exceptions.py

@@ -2,13 +2,5 @@ class InvalidListingType(Exception):
     """Raised when a provided listing type is does not exist."""
 
 
-class InvalidDate(Exception):
+class NoResultsFound(Exception):
-    """Raised when only one of date_from or date_to is provided or not in the correct format. ex: 2023-10-23"""
+    """Raised when no results are found for the given location"""
-
-
-class AuthenticationError(Exception):
-    """Raised when there is an issue with the authentication process."""
-    def __init__(self, *args, response):
-        super().__init__(*args)
-
-        self.response = response

homeharvest/utils.py

@@ -1,19 +1,13 @@
-from __future__ import annotations
+from .core.scrapers.models import Property, ListingType
 import pandas as pd
-from datetime import datetime
+from .exceptions import InvalidListingType
-from .core.scrapers.models import Property, ListingType, Advertisers
-from .exceptions import InvalidListingType, InvalidDate
 
 ordered_properties = [
     "property_url",
-    "property_id",
-    "listing_id",
     "mls",
     "mls_id",
     "status",
-    "text",
     "style",
-    "full_street_line",
     "street",
     "unit",
     "city",
@@ -24,46 +18,17 @@ ordered_properties = [
     "half_baths",
     "sqft",
     "year_built",
-    "days_on_mls",
     "list_price",
-    "list_price_min",
-    "list_price_max",
     "list_date",
     "sold_price",
     "last_sold_date",
-    "assessed_value",
-    "estimated_value",
-    "tax",
-    "tax_history",
-    "new_construction",
     "lot_sqft",
     "price_per_sqft",
     "latitude",
     "longitude",
-    "neighborhoods",
-    "county",
-    "fips_code",
     "stories",
     "hoa_fee",
     "parking_garage",
-    "agent_id",
-    "agent_name",
-    "agent_email",
-    "agent_phones",
-    "agent_mls_set",
-    "agent_nrds_id",
-    "broker_id",
-    "broker_name",
-    "builder_id",
-    "builder_name",
-    "office_id",
-    "office_mls_set",
-    "office_name",
-    "office_email",
-    "office_phones",
-    "nearby_schools",
-    "primary_photo",
-    "alt_photos",
 ]
 
 
@@ -73,55 +38,16 @@ def process_result(result: Property) -> pd.DataFrame:
 
     if "address" in prop_data:
         address_data = prop_data["address"]
-        prop_data["full_street_line"] = address_data.full_line
         prop_data["street"] = address_data.street
         prop_data["unit"] = address_data.unit
         prop_data["city"] = address_data.city
         prop_data["state"] = address_data.state
         prop_data["zip_code"] = address_data.zip
 
-    if "advertisers" in prop_data and prop_data.get("advertisers"):
-        advertiser_data: Advertisers | None = prop_data["advertisers"]
-        if advertiser_data.agent:
-            agent_data = advertiser_data.agent
-            prop_data["agent_id"] = agent_data.uuid
-            prop_data["agent_name"] = agent_data.name
-            prop_data["agent_email"] = agent_data.email
-            prop_data["agent_phones"] = agent_data.phones
-            prop_data["agent_mls_set"] = agent_data.mls_set
-            prop_data["agent_nrds_id"] = agent_data.nrds_id
-
-        if advertiser_data.broker:
-            broker_data = advertiser_data.broker
-            prop_data["broker_id"] = broker_data.uuid
-            prop_data["broker_name"] = broker_data.name
-
-        if advertiser_data.builder:
-            builder_data = advertiser_data.builder
-            prop_data["builder_id"] = builder_data.uuid
-            prop_data["builder_name"] = builder_data.name
-
-        if advertiser_data.office:
-            office_data = advertiser_data.office
-            prop_data["office_id"] = office_data.uuid
-            prop_data["office_name"] = office_data.name
-            prop_data["office_email"] = office_data.email
-            prop_data["office_phones"] = office_data.phones
-            prop_data["office_mls_set"] = office_data.mls_set
-
     prop_data["price_per_sqft"] = prop_data["prc_sqft"]
-    prop_data["nearby_schools"] = filter(None, prop_data["nearby_schools"]) if prop_data["nearby_schools"] else None
-    prop_data["nearby_schools"] = ", ".join(set(prop_data["nearby_schools"])) if prop_data["nearby_schools"] else None
 
     description = result.description
-    if description:
+    prop_data["style"] = description.style
-        prop_data["primary_photo"] = description.primary_photo
-        prop_data["alt_photos"] = ", ".join(description.alt_photos) if description.alt_photos else None
-        prop_data["style"] = (
-            description.style
-            if isinstance(description.style, str)
-            else description.style.value if description.style else None
-        )
     prop_data["beds"] = description.beds
     prop_data["full_baths"] = description.baths_full
     prop_data["half_baths"] = description.baths_half
@@ -131,7 +57,6 @@ def process_result(result: Property) -> pd.DataFrame:
     prop_data["year_built"] = description.year_built
     prop_data["parking_garage"] = description.garage
     prop_data["stories"] = description.stories
-        prop_data["text"] = description.text
 
     properties_df = pd.DataFrame([prop_data])
     properties_df = properties_df.reindex(columns=ordered_properties)
@@ -141,26 +66,6 @@ def process_result(result: Property) -> pd.DataFrame:
 
 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.")
+        raise InvalidListingType(
-
+            f"Provided listing type, '{listing_type}', does not exist."
-
+        )
-def validate_dates(date_from: str | None, date_to: str | None) -> None:
-    if isinstance(date_from, str) != isinstance(date_to, str):
-        raise InvalidDate("Both date_from and date_to must be provided.")
-
-    if date_from and date_to:
-        try:
-            date_from_obj = datetime.strptime(date_from, "%Y-%m-%d")
-            date_to_obj = datetime.strptime(date_to, "%Y-%m-%d")
-
-            if date_to_obj < date_from_obj:
-                raise InvalidDate("date_to must be after date_from.")
-        except ValueError:
-            raise InvalidDate(f"Invalid date format or range")
-
-
-def validate_limit(limit: int) -> None:
-    #: 1 -> 10000 limit
-
-    if limit is not None and (limit < 1 or limit > 10000):
-        raise ValueError("Property limit must be between 1 and 10,000.")

poetry.lock

@@ -1,15 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
-
-[[package]]
-name = "annotated-types"
-version = "0.7.0"
-description = "Reusable constraint types to use with typing.Annotated"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
-    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
-]
 
 [[package]]
 name = "certifi"
@@ -22,114 +11,88 @@ files = [
     {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"},
 ]
 
-[[package]]
-name = "cfgv"
-version = "3.4.0"
-description = "Validate configuration and produce human readable error messages."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"},
-    {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
-]
-
 [[package]]
 name = "charset-normalizer"
-version = "3.3.0"
+version = "3.2.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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+    {file = "charset-normalizer-3.2.0.tar.gz", hash = "sha256:3bb3d25a8e6c0aedd251753a79ae98a093c7e7b471faa3aa9a93a81431987ace"},
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 [[package]]
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 version = "3.4"
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 [package.dependencies]
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 [[package]]
 name = "pluggy"
 version = "1.3.0"
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 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
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-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -668,20 +409,16 @@ files = [
 ]
 
 [[package]]
-name = "tenacity"
+name = "tls-client"
-version = "9.0.0"
+version = "0.2.2"
-description = "Retry code until it succeeds"
+description = "Advanced Python HTTP Client."
 optional = false
-python-versions = ">=3.8"
+python-versions = "*"
 files = [
-    {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
+    {file = "tls_client-0.2.2-py3-none-any.whl", hash = "sha256:30934871397cdad6862e00b5634f382666314a452ddd3d774e18323a0ad9b765"},
-    {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
+    {file = "tls_client-0.2.2.tar.gz", hash = "sha256:78bc0e291e3aadc6c5e903b62bb26c01374577691f2a9e5e17899900a5927a13"},
 ]
 
-[package.extras]
-doc = ["reno", "sphinx"]
-test = ["pytest", "tornado (>=4.5)", "typeguard"]
-
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -693,17 +430,6 @@ files = [
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
 
-[[package]]
-name = "typing-extensions"
-version = "4.12.2"
-description = "Backported and Experimental Type Hints for Python 3.8+"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
-    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
-]
-
 [[package]]
 name = "tzdata"
 version = "2023.3"
@@ -717,13 +443,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.0.6"
+version = "2.0.4"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
+    {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
-    {file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
+    {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
 ]
 
 [package.extras]
@@ -732,27 +458,7 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
-[[package]]
-name = "virtualenv"
-version = "20.25.1"
-description = "Virtual Python Environment builder"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"},
-    {file = "virtualenv-20.25.1.tar.gz", hash = "sha256:e08e13ecdca7a0bd53798f356d5831434afa5b07b93f0abdf0797b7a06ffe197"},
-]
-
-[package.dependencies]
-distlib = ">=0.3.7,<1"
-filelock = ">=3.12.2,<4"
-platformdirs = ">=3.9.1,<5"
-
-[package.extras]
-docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
-test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
-
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.9,<3.13"
+python-versions = "^3.10"
-content-hash = "cefc11b1bf5ad99d628f6d08f6f03003522cc1b6e48b519230d99d716a5c165c"
+content-hash = "9b77e1a09fcf2cf5e7e6be53f304cd21a6a51ea51680d661a178afe5e5343670"

pyproject.toml

@@ -1,25 +1,24 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.4.7"
+version = "0.3.1"
-description = "Real estate scraping library"
+description = "Real estate scraping library supporting Zillow, Realtor.com & Redfin."
-authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
+authors = ["Zachary Hampton <zachary@zacharysproducts.com>", "Cullen Watson <cullen@cullen.ai>"]
-homepage = "https://github.com/Bunsly/HomeHarvest"
+homepage = "https://github.com/ZacharyHampton/HomeHarvest"
 readme = "README.md"
 
 [tool.poetry.scripts]
 homeharvest = "homeharvest.cli:main"
 
 [tool.poetry.dependencies]
-python = ">=3.9,<3.13"
+python = "^3.10"
 requests = "^2.31.0"
-pandas = "^2.1.1"
+pandas = "^2.1.0"
-pydantic = "^2.7.4"
+openpyxl = "^3.1.2"
-tenacity = "^9.0.0"
+tls-client = "^0.2.2"
 
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.2"
-pre-commit = "^3.7.0"
 
 [build-system]
 requires = ["poetry-core"]

tests/test_realtor.py

@@ -1,60 +1,35 @@
-from homeharvest import scrape_property, Property
+from homeharvest import scrape_property
-import pandas as pd
+from homeharvest.exceptions import (
+    InvalidListingType,
+    NoResultsFound,
+)
 
 
 def test_realtor_pending_or_contingent():
-    pending_or_contingent_result = scrape_property(location="Surprise, AZ", listing_type="pending")
+    pending_or_contingent_result = scrape_property(
+        location="Surprise, AZ",
+        pending_or_contingent=True,
+    )
 
-    regular_result = scrape_property(location="Surprise, AZ", listing_type="for_sale", exclude_pending=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 all(
+        [
+            result is not None
+            for result in [pending_or_contingent_result, regular_result]
+        ]
+    )
     assert len(pending_or_contingent_result) != len(regular_result)
 
 
-def test_realtor_pending_comps():
-    pending_comps = scrape_property(
-        location="2530 Al Lipscomb Way",
-        radius=5,
-        past_days=180,
-        listing_type="pending",
-    )
-
-    for_sale_comps = scrape_property(
-        location="2530 Al Lipscomb Way",
-        radius=5,
-        past_days=180,
-        listing_type="for_sale",
-    )
-
-    sold_comps = scrape_property(
-        location="2530 Al Lipscomb Way",
-        radius=5,
-        past_days=180,
-        listing_type="sold",
-    )
-
-    results = [pending_comps, for_sale_comps, sold_comps]
-    assert all([result is not None for result in results])
-
-    #: assert all lengths are different
-    assert len(set([len(result) for result in results])) == len(results)
-
-
-def test_realtor_sold_past():
-    result = scrape_property(
-        location="San Diego, CA",
-        past_days=30,
-        listing_type="sold",
-    )
-
-    assert result is not None and len(result) > 0
-
-
 def test_realtor_comps():
     result = scrape_property(
         location="2530 Al Lipscomb Way",
         radius=0.5,
-        past_days=180,
+        property_younger_than=180,
         listing_type="sold",
     )
 
@@ -62,27 +37,17 @@ def test_realtor_comps():
 
 
 def test_realtor_last_x_days_sold():
-    days_result_30 = scrape_property(location="Dallas, TX", listing_type="sold", past_days=30)
-
-    days_result_10 = scrape_property(location="Dallas, TX", listing_type="sold", past_days=10)
-
-    assert all([result is not None for result in [days_result_30, days_result_10]]) and len(days_result_30) != len(
-        days_result_10
-    )
-
-
-def test_realtor_date_range_sold():
     days_result_30 = scrape_property(
-        location="Dallas, TX", listing_type="sold", date_from="2023-05-01", date_to="2023-05-28"
+        location="Dallas, TX", listing_type="sold", property_younger_than=30
     )
 
-    days_result_60 = scrape_property(
+    days_result_10 = scrape_property(
-        location="Dallas, TX", listing_type="sold", date_from="2023-04-01", date_to="2023-06-10"
+        location="Dallas, TX", listing_type="sold", property_younger_than=10
     )
 
-    assert all([result is not None for result in [days_result_30, days_result_60]]) and len(days_result_30) < len(
+    assert all(
-        days_result_60
+        [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():
@@ -107,196 +72,25 @@ def test_realtor():
             listing_type="for_sale",
         ),
         scrape_property(
-            location="Phoenix, AZ", listing_type="for_rent", limit=1000
+            location="Phoenix, AZ", listing_type="for_rent"
         ),  #: does not support "city, state, USA" format
         scrape_property(
-            location="Dallas, TX", listing_type="sold", limit=1000
+            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 = []
-def test_realtor_city():
+    try:
-    results = scrape_property(location="Atlanta, GA", listing_type="for_sale", limit=1000)
+        bad_results += [
-
+            scrape_property(
-    assert results is not None and len(results) > 0
-
-
-def test_realtor_land():
-    results = scrape_property(location="Atlanta, GA", listing_type="for_sale", property_type=["land"], limit=1000)
-
-    assert results is not None and len(results) > 0
-
-
-def test_realtor_bad_address():
-    bad_results = scrape_property(
                 location="abceefg ju098ot498hh9",
                 listing_type="for_sale",
             )
-
+        ]
-    if len(bad_results) == 0:
+    except (InvalidListingType, NoResultsFound):
         assert True
 
-
+    assert all([result is None for result in bad_results])
-def test_realtor_foreclosed():
-    foreclosed = scrape_property(location="Dallas, TX", listing_type="for_sale", past_days=100, foreclosure=True)
-
-    not_foreclosed = scrape_property(location="Dallas, TX", listing_type="for_sale", past_days=100, foreclosure=False)
-
-    assert len(foreclosed) != len(not_foreclosed)
-
-
-def test_realtor_agent():
-    scraped = scrape_property(location="Detroit, MI", listing_type="for_sale", limit=1000, extra_property_data=False)
-    assert scraped["agent_name"].nunique() > 1
-
-
-def test_realtor_without_extra_details():
-    results = [
-        scrape_property(
-            location="00741",
-            listing_type="sold",
-            limit=10,
-            extra_property_data=False,
-        ),
-        scrape_property(
-            location="00741",
-            listing_type="sold",
-            limit=10,
-            extra_property_data=True,
-        ),
-    ]
-
-    assert not results[0].equals(results[1])
-
-
-def test_pr_zip_code():
-    results = scrape_property(
-        location="00741",
-        listing_type="for_sale",
-    )
-
-    assert results is not None and len(results) > 0
-
-
-def test_exclude_pending():
-    results = scrape_property(
-        location="33567",
-        listing_type="pending",
-        exclude_pending=True,
-    )
-
-    assert results is not None and len(results) > 0
-
-
-def test_style_value_error():
-    results = scrape_property(
-        location="Alaska, AK",
-        listing_type="sold",
-        extra_property_data=False,
-        limit=1000,
-    )
-
-    assert results is not None and len(results) > 0
-
-
-def test_primary_image_error():
-    results = scrape_property(
-        location="Spokane, PA",
-        listing_type="for_rent",  # or (for_sale, for_rent, pending)
-        past_days=360,
-        radius=3,
-        extra_property_data=False,
-    )
-
-    assert results is not None and len(results) > 0
-
-
-def test_limit():
-    over_limit = 876
-    extra_params = {"limit": over_limit}
-
-    over_results = scrape_property(
-        location="Waddell, AZ",
-        listing_type="for_sale",
-        **extra_params,
-    )
-
-    assert over_results is not None and len(over_results) <= over_limit
-
-    under_limit = 1
-    under_results = scrape_property(
-        location="Waddell, AZ",
-        listing_type="for_sale",
-        limit=under_limit,
-    )
-
-    assert under_results is not None and len(under_results) == under_limit
-
-
-def test_apartment_list_price():
-    results = scrape_property(
-        location="Spokane, WA",
-        listing_type="for_rent",  # or (for_sale, for_rent, pending)
-        extra_property_data=False,
-    )
-
-    assert results is not None
-
-    results = results[results["style"] == "APARTMENT"]
-
-    #: get percentage of results with atleast 1 of any column not none, list_price, list_price_min, list_price_max
-    assert (
-        len(results[results[["list_price", "list_price_min", "list_price_max"]].notnull().any(axis=1)]) / len(results)
-        > 0.5
-    )
-
-
-def test_builder_exists():
-    listing = scrape_property(
-        location="18149 W Poston Dr, Surprise, AZ 85387",
-        extra_property_data=False,
-    )
-
-    assert listing is not None
-    assert listing["builder_name"].nunique() > 0
-
-
-def test_phone_number_matching():
-    searches = [
-        scrape_property(
-            location="Phoenix, AZ",
-            listing_type="for_sale",
-            limit=100,
-        ),
-        scrape_property(
-            location="Phoenix, AZ",
-            listing_type="for_sale",
-            limit=100,
-        ),
-    ]
-
-    assert all([search is not None for search in searches])
-
-    #: random row
-    row = searches[0][searches[0]["agent_phones"].notnull()].sample()
-
-    #: find matching row
-    matching_row = searches[1].loc[searches[1]["property_url"] == row["property_url"].values[0]]
-
-    #: assert phone numbers are the same
-    assert row["agent_phones"].values[0] == matching_row["agent_phones"].values[0]
-
-
-def test_return_type():
-    results = {
-        "pandas": scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100),
-        "pydantic": scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100, return_type="pydantic"),
-        "raw": scrape_property(location="Surprise, AZ", listing_type="for_rent", limit=100, return_type="raw"),
-    }
-
-    assert isinstance(results["pandas"], pd.DataFrame)
-    assert isinstance(results["pydantic"][0], Property)
-    assert isinstance(results["raw"][0], dict)