- bug fixes

- optimized get extra fields with query clustering

.github/FUNDING.yml

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

.pre-commit-config.yaml

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

README.md

@@ -1,36 +1,23 @@
 <img src="https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/d1a2bf8b-09f5-4c57-b33a-0ada8a34f12d" width="400">
 
-**HomeHarvest** is a simple, yet comprehensive, real estate scraping library 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** is a real estate scraping library that extracts and formats data in the style of MLS listings.
 
 ## 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/JnV7eR2Ve2o) - _updated for release v0.2.7_
+[Video Guide for HomeHarvest](https://youtu.be/J1qgNPgmSLI) - _updated for release v0.3.4_
 
 ![homeharvest](https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/b3d5d727-e67b-4a9f-85d8-1e65fd18620a)
 
 ## Installation
 
 ```bash
-pip install homeharvest
+pip install -U homeharvest
 ```
-  _Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_ 
+  _Python version >= [3.9](https://www.python.org/downloads/release/python-3100/) required_
 
 ## Usage
 
@@ -46,11 +33,14 @@ 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
+  listing_type="sold",  # or (for_sale, for_rent, pending)
+  past_days=30,  # sold in last 30 days - listed in last 30 days if (for_sale, for_rent)
+
+  # 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)}")
 
@@ -59,38 +49,6 @@ 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()
@@ -107,30 +65,61 @@ options:
 ```
 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)
 │
-├── property_younger_than (integer): Number of past days to filter properties. Utilizes 'last_sold_date' for 'sold' listing types, and 'list_date' for others (for_rent, for_sale).
+├── past_days (integer): Number of past days to filter properties. Utilizes 'last_sold_date' for 'sold' listing types, and 'list_date' for others (for_rent, for_sale).
 │    Example: 30 (fetches properties listed/sold in the last 30 days)
-|
-├── pending_or_contingent (True/False): If set, fetches only pending or contingent listings. Only applicable for `for_sale listings` from general area searches.
+│
+├── date_from, date_to (string): Start and end dates to filter properties listed or sold, both dates are required.
+|    (use this to get properties in chunks as there's a 10k result limit)
+│    Format for both must be "YYYY-MM-DD".
+│    Example: "2023-05-01", "2023-05-15" (fetches properties listed/sold between these dates)
 │
 ├── mls_only (True/False): If set, fetches only MLS listings (mainly applicable to 'sold' listings)
 │
-└── proxy (string): In format 'http://user:pass@host:port'
-
+├── foreclosure (True/False): If set, fetches only foreclosures
+│
+├── 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
@@ -150,44 +139,60 @@ 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`
-- `NoResultsFound` - no properties found from your search
-  
-  
-## Frequently Asked Questions
----
-
-**Q: Encountering issues with your searches?**  
-**A:** Try to broaden the parameters you're using. If problems persist, [submit an issue](https://github.com/ZacharyHampton/HomeHarvest/issues).
-
----
-
-**Q: Received a Forbidden 403 response code?**  
-**A:** This indicates that you have been blocked by Realtor.com for sending too many requests. We recommend:
-
-- Waiting a few seconds between requests.
-- Trying a VPN or useing a proxy as a parameter to scrape_property() to change your IP address.
-
----
-
+- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`, `pending`.
+- `InvalidDate` - date_from or date_to is not in the format YYYY-MM-DD.
+- `AuthenticationError` - Realtor.com token request failed.

examples/HomeHarvest_Demo.ipynb

@@ -1,115 +0,0 @@
-{
- "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

@@ -1,20 +0,0 @@
-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

@@ -0,0 +1,104 @@
+"""
+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

@@ -1,50 +1,77 @@
 import warnings
 import pandas as pd
 from .core.scrapers import ScraperInput
-from .utils import process_result, ordered_properties, validate_input
+from .utils import process_result, ordered_properties, validate_input, validate_dates, validate_limit
 from .core.scrapers.realtor import RealtorScraper
-from .core.scrapers.models import ListingType
-from .exceptions import InvalidListingType, NoResultsFound
+from .core.scrapers.models import ListingType, SearchPropertyType, ReturnType, Property
 
 
 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,
-    property_younger_than: int = None,
-    pending_or_contingent: bool = False,
+    past_days: int = None,
     proxy: str = None,
-) -> pd.DataFrame:
+    date_from: str = None,  #: TODO: Switch to one parameter, Date, with date_from and date_to, pydantic validation
+    date_to: str = None,
+    foreclosure: bool = None,
+    extra_property_data: bool = True,
+    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)
+    :param listing_type: Listing Type (for_sale, for_rent, sold, pending)
+    :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=property_younger_than,
-        pending_or_contingent=pending_or_contingent,
+        last_x_days=past_days,
+        date_from=date_from,
+        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()
 
-    properties_dfs = [process_result(result) for result in results]
+    if scraper_input.return_type != ReturnType.pandas:
+        return results
+
+    properties_dfs = [df for result in results if not (df := process_result(result)).empty]
     if not properties_dfs:
-        raise NoResultsFound("no results found for the query")
+        return pd.DataFrame()
 
     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

@@ -5,16 +5,14 @@ from homeharvest import scrape_property
 
 def main():
     parser = argparse.ArgumentParser(description="Home Harvest Property Scraper")
-    parser.add_argument(
-        "location", type=str, help="Location to scrape (e.g., San Francisco, CA)"
-    )
+    parser.add_argument("location", type=str, help="Location to scrape (e.g., San Francisco, CA)")
 
     parser.add_argument(
         "-l",
         "--listing_type",
         type=str,
         default="for_sale",
-        choices=["for_sale", "for_rent", "sold"],
+        choices=["for_sale", "for_rent", "sold", "pending"],
         help="Listing type to scrape",
     )
 
@@ -35,9 +33,7 @@ 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",
@@ -60,13 +56,6 @@ 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(
@@ -75,8 +64,7 @@ def main():
         radius=args.radius,
         proxy=args.proxy,
         mls_only=args.mls_only,
-        property_younger_than=args.days,
-        pending_or_contingent=args.pending_or_contingent,
+        past_days=args.days,
     )
 
     if not args.filename:

homeharvest/core/scrapers/__init__.py

@@ -1,33 +1,73 @@
+from __future__ import annotations
 from dataclasses import dataclass
+from typing import Union
+
 import requests
-import tls_client
-from .models import Property, ListingType, SiteName
+from requests.adapters import HTTPAdapter
+from urllib3.util.retry import Retry
+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 = None
+    mls_only: bool | None = False
     proxy: str | None = None
     last_x_days: int | None = None
-    pending_or_contingent: bool | None = None
+    date_from: str | 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 session:
-            self.session = requests.Session()
-        else:
-            self.session = session
+        if not self.session:
+            Scraper.session = requests.Session()
+            retries = Retry(
+                total=3, backoff_factor=4, status_forcelist=[429, 403], allowed_methods=frozenset(["GET", "POST"])
+            )
+
+            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
@@ -38,14 +78,51 @@ class Scraper:
         self.radius = scraper_input.radius
         self.last_x_days = scraper_input.last_x_days
         self.mls_only = scraper_input.mls_only
-        self.pending_or_contingent = scraper_input.pending_or_contingent
+        self.date_from = scraper_input.date_from
+        self.date_to = scraper_input.date_to
+        self.foreclosure = scraper_input.foreclosure
+        self.extra_property_data = scraper_input.extra_property_data
+        self.exclude_pending = scraper_input.exclude_pending
+        self.limit = scraper_input.limit
+        self.return_type = scraper_input.return_type
 
-    def search(self) -> list[Property]:
-        ...
+    def search(self) -> list[Union[Property | dict]]: ...
 
     @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

@@ -1,8 +1,15 @@
+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"
@@ -16,14 +23,60 @@ 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
@@ -33,7 +86,9 @@ class Address:
 
 @dataclass
 class Description:
-    style: str | None = None
+    primary_photo: str | None = None
+    alt_photos: list[str] | None = None
+    style: PropertyType | None = None
     beds: int | None = None
     baths_full: int | None = None
     baths_half: int | None = None
@@ -43,23 +98,97 @@ 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

@@ -4,21 +4,48 @@ homeharvest.realtor.__init__
 
 This module implements the scraper for realtor.com
 """
-from typing import Dict, Union, Optional
+
+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 tenacity import (
+    retry,
+    retry_if_exception_type,
+    wait_exponential,
+    stop_after_attempt,
+)
 
 from .. import Scraper
-from ....exceptions import NoResultsFound
-from ..models import Property, Address, ListingType, Description
+from ..models import (
+    Property,
+    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):
@@ -38,121 +65,10 @@ class RealtorScraper(Scraper):
         result = response_json["autocomplete"]
 
         if not result:
-            raise NoResultsFound("No results found for location: " + self.location)
+            return None
 
         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) {
@@ -186,56 +102,15 @@ class RealtorScraper(Scraper):
         else:
             return property_info["listings"][0]["listing_id"]
 
-    def handle_address(self, property_id: str) -> list[Property]:
-        """
-        Handles a specific address & returns one property
-        """
-        query = """query Property($property_id: ID!) {
-                    property(id: $property_id) {
-                        property_id
-                        details {
-                            date_updated
-                            garage
-                            permalink
-                            year_built
-                            stories
-                        }
-                        address {
-                            street_number
-                            street_name
-                            street_suffix
-                            unit
-                            city
-                            state_code
-                            postal_code
-                            location {
-                                coordinate {
-                                    lat
-                                    lon
-                                }
-                            }
-                        }
-                        basic {
-                            baths
-                            beds
-                            price
-                            sqft
-                            lot_sqft
-                            type
-                            sold_price
-                        }
-                        public_record {
-                            lot_size
-                            sqft
-                            stories
-                            units
-                            year_built
-                        }
-                    }
+    def handle_home(self, property_id: str) -> list[Property]:
+        query = (
+            """query Home($property_id: ID!) {
+                    home(property_id: $property_id) %s
                 }"""
+            % HOMES_DATA
+        )
 
         variables = {"property_id": property_id}
-
         payload = {
             "query": query,
             "variables": variables,
@@ -244,152 +119,218 @@ class RealtorScraper(Scraper):
         response = self.session.post(self.SEARCH_GQL_URL, json=payload)
         response_json = response.json()
 
-        property_info = response_json["data"]["property"]
+        property_info = response_json["data"]["home"]
 
-        return [
-            Property(
-                mls_id=property_id,
-                property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
-                address=self._parse_address(
-                    property_info, search_type="handle_address"
-                ),
-                description=self._parse_description(property_info),
+        return [self.process_property(property_info)]
+
+    @staticmethod
+    def process_advertisers(advertisers: list[dict] | None) -> Advertisers | None:
+        if not advertisers:
+            return None
+
+        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"),
                 )
-        ]
 
-    def general_search(
-        self, variables: dict, search_type: str
-    ) -> Dict[str, Union[int, list[Property]]]:
+                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"],
+            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]]]]:
         """
         Handles a location area & returns a list of properties
         """
-        results_query = """{
-                            count
-                            total
-                            results {
-                                property_id
-                                list_date
-                                status
-                                last_sold_price
-                                last_sold_date
-                                list_price
-                                price_per_sqft
-                                description {
-                                    sqft
-                                    beds
-                                    baths_full
-                                    baths_half
-                                    lot_sqft
-                                    sold_price
-                                    year_built
-                                    garage
-                                    sold_price
-                                    type
-                                    name
-                                    stories
-                                }
-                                source {
-                                    id
-                                    listing_id
-                                }
-                                hoa {
-                                    fee
-                                }
-                                location {
-                                    address {
-                                        street_number
-                                        street_name
-                                        street_suffix
-                                        unit
-                                        city
-                                        state_code
-                                        postal_code
-                                        coordinate {
-                                            lon
-                                            lat
-                                        }
-                                    }
-                                    neighborhoods {
-                                        name
-                                    }
-                                }
-                            }
-                        }
-                    }"""
 
-        date_param = (
-            'sold_date: { min: "$today-%sD" }' % self.last_x_days
-            if self.listing_type == ListingType.SOLD and self.last_x_days
-            else (
-                'list_date: { min: "$today-%sD" }' % self.last_x_days
-                if self.last_x_days
-                else ""
-            )
-        )
+        date_param = ""
+        if self.listing_type == ListingType.SOLD:
+            if self.date_from and self.date_to:
+                date_param = f'sold_date: {{ min: "{self.date_from}", max: "{self.date_to}" }}'
+            elif self.last_x_days:
+                date_param = f'sold_date: {{ min: "$today-{self.last_x_days}D" }}'
+        else:
+            if self.date_from and self.date_to:
+                date_param = f'list_date: {{ min: "{self.date_from}", max: "{self.date_to}" }}'
+            elif self.last_x_days:
+                date_param = f'list_date: {{ min: "$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 }]"
+            else ""  #: "sort: [{ field: list_date, direction: desc }]"  #: prioritize normal fractal sort from realtor
         )
 
         pending_or_contingent_param = (
-            "or_filters: { contingent: true, pending: true }"
-            if self.pending_or_contingent
-            else ""
+            "or_filters: { contingent: true, pending: true }" if self.listing_type == ListingType.PENDING else ""
         )
 
+        listing_type = ListingType.FOR_SALE if self.listing_type == ListingType.PENDING else self.listing_type
+        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!,
                     ) {
-                        property_search(
+                        home_search(
                             query: {
+                                %s
                                 nearby: {
                                     coordinates: $coordinates
                                     radius: $radius
                                 }
                                 status: %s
                                 %s
+                                %s
+                                %s
                             }
                             %s
                             limit: 200
                             offset: $offset
-                    ) %s""" % (
-                self.listing_type.value.lower(),
+                    ) %s
+                }""" % (
+                is_foreclosure,
+                listing_type.value.lower(),
                 date_param,
+                property_type_param,
+                pending_or_contingent_param,
                 sort_param,
-                results_query,
+                GENERAL_RESULTS_QUERY,
             )
         elif search_type == "area":  #: general search, came from a general location
             query = """query Home_search(
-                                $city: String,
-                                $county: [String],
-                                $state_code: String,
-                                $postal_code: String
+                                $location: String!,
                                 $offset: Int,
                             ) {
                                 home_search(
                                     query: {
-                                        city: $city
-                                        county: $county
-                                        postal_code: $postal_code
-                                        state_code: $state_code
+                                        %s
+                                        search_location: {location: $location}
                                         status: %s
+                                        unique: true
+                                        %s
                                         %s
                                         %s
                                     }
+                                    bucket: { sort: "fractal_v1.1.3_fr" }
                                     %s
                                     limit: 200
                                     offset: $offset
-                                ) %s""" % (
-                self.listing_type.value.lower(),
+                                ) %s
+                            }""" % (
+                is_foreclosure,
+                listing_type.value.lower(),
                 date_param,
+                property_type_param,
                 pending_or_contingent_param,
                 sort_param,
-                results_query,
+                GENERAL_RESULTS_QUERY,
             )
         else:  #: general search, came from an address
             query = (
@@ -397,14 +338,15 @@ class RealtorScraper(Scraper):
                         $property_id: [ID]!
                         $offset: Int!,
                     ) {
-                        property_search(
+                        home_search(
                             query: {
                                 property_id: $property_id
                             }
                             limit: 1
                             offset: $offset
-                        ) %s"""
-                % results_query
+                        ) %s
+                    }"""
+                % GENERAL_RESULTS_QUERY
             )
 
         payload = {
@@ -413,11 +355,10 @@ class RealtorScraper(Scraper):
         }
 
         response = self.session.post(self.SEARCH_GQL_URL, json=payload)
-        response.raise_for_status()
         response_json = response.json()
-        search_key = "home_search" if search_type == "area" else "property_search"
+        search_key = "home_search" if "home_search" in query else "property_search"
 
-        properties: list[Property] = []
+        properties: list[Union[Property, dict]] = []
 
         if (
             response_json is None
@@ -429,59 +370,42 @@ class RealtorScraper(Scraper):
         ):
             return {"total": 0, "properties": []}
 
-        for result in response_json["data"][search_key]["results"]:
-            self.counter += 1
-            mls = (
-                result["source"].get("id")
-                if "source" in result and isinstance(result["source"], dict)
-                else None
-            )
+        properties_list = response_json["data"][search_key]["results"]
+        total_properties = response_json["data"][search_key]["total"]
+        offset = variables.get("offset", 0)
 
-            if not mls and self.mls_only:
-                continue
+        #: limit the number of properties to be processed
+        #: example, if your offset is 200, and your limit is 250, return 50
+        properties_list: list[dict] = properties_list[: self.limit - offset]
 
-            able_to_get_lat_long = (
-                result
-                and result.get("location")
-                and result["location"].get("address")
-                and result["location"]["address"].get("coordinate")
-            )
+        if self.extra_property_data:
+            property_ids = [data["property_id"] for data in properties_list]
+            extra_property_details = self.get_bulk_prop_details(property_ids) or {}
 
-            realty_property = Property(
-                mls=mls,
-                mls_id=result["source"].get("listing_id")
-                if "source" in result and isinstance(result["source"], dict)
-                else None,
-                property_url=f"{self.PROPERTY_URL}{result['property_id']}",
-                status=result["status"].upper(),
-                list_price=result["list_price"],
-                list_date=result["list_date"].split("T")[0]
-                if result.get("list_date")
-                else None,
-                prc_sqft=result.get("price_per_sqft"),
-                last_sold_date=result.get("last_sold_date"),
-                hoa_fee=result["hoa"]["fee"]
-                if result.get("hoa") and isinstance(result["hoa"], dict)
-                else None,
-                latitude=result["location"]["address"]["coordinate"].get("lat")
-                if able_to_get_lat_long
-                else None,
-                longitude=result["location"]["address"]["coordinate"].get("lon")
-                if able_to_get_lat_long
-                else None,
-                address=self._parse_address(result, search_type="general_search"),
-                #: neighborhoods=self._parse_neighborhoods(result),
-                description=self._parse_description(result),
-            )
-            properties.append(realty_property)
+            for result in properties_list:
+                result.update(extra_property_details.get(result["property_id"], {}))
+
+        if self.return_type != ReturnType.raw:
+            with ThreadPoolExecutor(max_workers=self.NUM_PROPERTY_WORKERS) as executor:
+                futures = [executor.submit(self.process_property, result) for result in properties_list]
+
+                for future in as_completed(futures):
+                    result = future.result()
+                    if result:
+                        properties.append(result)
+        else:
+            properties = properties_list
 
         return {
-            "total": response_json["data"][search_key]["total"],
+            "total": total_properties,
             "properties": properties,
         }
 
     def search(self):
         location_info = self.handle_location()
+        if not location_info:
+            return []
+
         location_type = location_info["area_type"]
 
         search_variables = {
@@ -491,54 +415,52 @@ 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"]
-                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"]
+                return self.handle_home(property_id)
 
             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),
                 }
 
-        else:  #: general search, location
+        elif location_type == "postal_code":
             search_variables |= {
-                "city": location_info.get("city"),
-                "county": location_info.get("county"),
-                "state_code": location_info.get("state_code"),
                 "postal_code": location_info.get("postal_code"),
             }
 
+        else:  #: general search, location
+            search_variables |= {
+                "location": self.location,
+            }
+
+        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(max_workers=10) as executor:
+        with ThreadPoolExecutor() as executor:
             futures = [
                 executor.submit(
                     self.general_search,
                     variables=search_variables | {"offset": i},
                     search_type=search_type,
                 )
-                for i in range(200, min(total, 10000), 200)
+                for i in range(
+                    self.DEFAULT_PAGE_SIZE,
+                    min(total, self.limit),
+                    self.DEFAULT_PAGE_SIZE,
+                )
             ]
 
             for future in as_completed(futures):
@@ -546,6 +468,90 @@ 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 = []
@@ -559,45 +565,104 @@ 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(
-                street=f"{result['location']['address']['street_number']} {result['location']['address']['street_name']} {result['location']['address']['street_suffix']}",
-                unit=result["location"]["address"]["unit"],
-                city=result["location"]["address"]["city"],
-                state=result["location"]["address"]["state_code"],
-                zip=result["location"]["address"]["postal_code"],
-            )
-        return Address(
-            street=f"{result['address']['street_number']} {result['address']['street_name']} {result['address']['street_suffix']}",
-            unit=result["address"]["unit"],
-            city=result["address"]["city"],
-            state=result["address"]["state_code"],
-            zip=result["address"]["postal_code"],
+            full_line=address.get("line"),
+            street=" ".join(
+                part
+                for part in [
+                    address.get("street_number"),
+                    address.get("street_direction"),
+                    address.get("street_name"),
+                    address.get("street_suffix"),
+                ]
+                if part is not None
+            ).strip(),
+            unit=address["unit"],
+            city=address["city"],
+            state=address["state_code"],
+            zip=address["postal_code"],
         )
 
     @staticmethod
-    def _parse_description(result: dict) -> Description:
+    def _parse_description(result: dict) -> Description | None:
+        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(
-            style=style,
+            primary_photo=primary_photo,
+            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=description_data.get("sold_price"),
+            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

@@ -0,0 +1,242 @@
+_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

@@ -2,5 +2,13 @@ class InvalidListingType(Exception):
     """Raised when a provided listing type is does not exist."""
 
 
-class NoResultsFound(Exception):
-    """Raised when no results are found for the given location"""
+class InvalidDate(Exception):
+    """Raised when only one of date_from or date_to is provided or not in the correct format. ex: 2023-10-23"""
+
+
+class 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

@@ -1,13 +1,19 @@
-from .core.scrapers.models import Property, ListingType
+from __future__ import annotations
 import pandas as pd
-from .exceptions import InvalidListingType
+from datetime import datetime
+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",
@@ -18,17 +24,46 @@ 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",
 ]
 
 
@@ -38,16 +73,55 @@ 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
-    prop_data["style"] = description.style
+    if description:
+        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
@@ -57,6 +131,7 @@ 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)
@@ -66,6 +141,26 @@ 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

@@ -1,4 +1,15 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 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"
@@ -11,88 +22,114 @@ files = [
     {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"},
 ]
 
+[[package]]
+name = "cfgv"
+version = "3.4.0"
+description = "Validate configuration and produce human readable error messages."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"},
+    {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"},
+]
+
 [[package]]
 name = "charset-normalizer"
-version = "3.2.0"
+version = "3.3.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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-description = "An implementation of lxml.xmlfile for the standard library"
+name = "distlib"
+version = "0.3.8"
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-python-versions = ">=3.6"
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 [[package]]
@@ -131,6 +168,36 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
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+version = "3.13.4"
+description = "A platform independent file lock."
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+python-versions = ">=3.8"
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+
+[[package]]
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+version = "2.5.35"
+description = "File identification library for Python"
+optional = false
+python-versions = ">=3.8"
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+[package.extras]
+license = ["ukkonen"]
+
 [[package]]
 name = "idna"
 version = "3.4"
@@ -154,39 +221,19 @@ files = [
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-description = "Fundamental package for array computing in Python"
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+description = "Node.js virtual environment builder"
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-python-versions = ">=3.9"
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+[package.dependencies]
+setuptools = "*"
+
 [[package]]
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 version = "1.26.0"
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 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
 numpy = [
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
-    {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -314,6 +354,21 @@ sql-other = ["SQLAlchemy (>=1.4.36)"]
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.8.0)"]
 
+[[package]]
+name = "platformdirs"
+version = "4.2.0"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+optional = false
+python-versions = ">=3.8"
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+
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+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+
 [[package]]
 name = "pluggy"
 version = "1.3.0"
@@ -329,6 +384,134 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
 
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+pyyaml = ">=5.1"
+virtualenv = ">=20.10.0"
+
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+description = "Data validation using Python type hints"
+optional = false
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+
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 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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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"
@@ -409,16 +668,20 @@ files = [
 ]
 
 [[package]]
-name = "tls-client"
-version = "0.2.2"
-description = "Advanced Python HTTP Client."
+name = "tenacity"
+version = "9.0.0"
+description = "Retry code until it succeeds"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "tls_client-0.2.2-py3-none-any.whl", hash = "sha256:30934871397cdad6862e00b5634f382666314a452ddd3d774e18323a0ad9b765"},
-    {file = "tls_client-0.2.2.tar.gz", hash = "sha256:78bc0e291e3aadc6c5e903b62bb26c01374577691f2a9e5e17899900a5927a13"},
+    {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
+    {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
 ]
 
+[package.extras]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -430,6 +693,17 @@ 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"
@@ -443,13 +717,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.0.4"
+version = "2.0.6"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
-    {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
+    {file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
+    {file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
 ]
 
 [package.extras]
@@ -458,7 +732,27 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "virtualenv"
+version = "20.25.1"
+description = "Virtual Python Environment builder"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"},
+    {file = "virtualenv-20.25.1.tar.gz", hash = "sha256:e08e13ecdca7a0bd53798f356d5831434afa5b07b93f0abdf0797b7a06ffe197"},
+]
+
+[package.dependencies]
+distlib = ">=0.3.7,<1"
+filelock = ">=3.12.2,<4"
+platformdirs = ">=3.9.1,<5"
+
+[package.extras]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
+
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.10"
-content-hash = "9b77e1a09fcf2cf5e7e6be53f304cd21a6a51ea51680d661a178afe5e5343670"
+python-versions = ">=3.9,<3.13"
+content-hash = "cefc11b1bf5ad99d628f6d08f6f03003522cc1b6e48b519230d99d716a5c165c"

pyproject.toml

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

tests/test_realtor.py

@@ -1,35 +1,60 @@
-from homeharvest import scrape_property
-from homeharvest.exceptions import (
-    InvalidListingType,
-    NoResultsFound,
-)
+from homeharvest import scrape_property, Property
+import pandas as pd
 
 
 def test_realtor_pending_or_contingent():
-    pending_or_contingent_result = scrape_property(
-        location="Surprise, AZ",
-        pending_or_contingent=True,
-    )
+    pending_or_contingent_result = scrape_property(location="Surprise, AZ", listing_type="pending")
 
-    regular_result = scrape_property(
-        location="Surprise, AZ",
-        pending_or_contingent=False,
-    )
+    regular_result = scrape_property(location="Surprise, AZ", listing_type="for_sale", exclude_pending=True)
 
-    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,
-        property_younger_than=180,
+        past_days=180,
         listing_type="sold",
     )
 
@@ -37,17 +62,27 @@ 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", property_younger_than=30
+        location="Dallas, TX", listing_type="sold", date_from="2023-05-01", date_to="2023-05-28"
     )
 
-    days_result_10 = scrape_property(
-        location="Dallas, TX", listing_type="sold", property_younger_than=10
+    days_result_60 = scrape_property(
+        location="Dallas, TX", listing_type="sold", date_from="2023-04-01", date_to="2023-06-10"
     )
 
-    assert all(
-        [result is not None for result in [days_result_30, days_result_10]]
-    ) and len(days_result_30) != len(days_result_10)
+    assert all([result is not None for result in [days_result_30, days_result_60]]) and len(days_result_30) < len(
+        days_result_60
+    )
 
 
 def test_realtor_single_property():
@@ -72,25 +107,196 @@ def test_realtor():
             listing_type="for_sale",
         ),
         scrape_property(
-            location="Phoenix, AZ", listing_type="for_rent"
+            location="Phoenix, AZ", listing_type="for_rent", limit=1000
         ),  #: does not support "city, state, USA" format
         scrape_property(
-            location="Dallas, TX", listing_type="sold"
+            location="Dallas, TX", listing_type="sold", limit=1000
         ),  #: does not support "city, state, USA" format
         scrape_property(location="85281"),
     ]
 
     assert all([result is not None for result in results])
 
-    bad_results = []
-    try:
-        bad_results += [
-            scrape_property(
+
+def test_realtor_city():
+    results = scrape_property(location="Atlanta, GA", listing_type="for_sale", limit=1000)
+
+    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",
     )
-        ]
-    except (InvalidListingType, NoResultsFound):
+
+    if len(bad_results) == 0:
         assert True
 
-    assert all([result is None for result in bad_results])
+
+def test_realtor_foreclosed():
+    foreclosed = scrape_property(location="Dallas, TX", listing_type="for_sale", past_days=100, foreclosure=True)
+
+    not_foreclosed = scrape_property(location="Dallas, TX", listing_type="for_sale", past_days=100, foreclosure=False)
+
+    assert len(foreclosed) != len(not_foreclosed)
+
+
+def test_realtor_agent():
+    scraped = scrape_property(location="Detroit, MI", listing_type="for_sale", 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)