- bug fixes

- return type parameter
- optimized get extra fields with query clustering
2025-04-12 18:34:35 -07:00 · 2025-04-12 17:55:52 -07:00 · 2025-02-21 13:33:32 -07:00 · 2025-01-06 05:28:36 -06:00 · 2024-11-05 23:55:59 -06:00 · 2024-11-04 10:22:32 -06:00
22 changed files with 2448 additions and 1497 deletions
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@ -0,0 +1 @@
+github: Bunsly
--- a/.github/workflows/publish-to-pypi.yml
+++ b/.github/workflows/publish-to-pypi.yml
@ -30,4 +30,4 @@ jobs:
      if: startsWith(github.ref, 'refs/tags')
      uses: pypa/gh-action-pypi-publish@release/v1
      with:
-        password: ${{ secrets.PYPI_API_TOKEN }}
+        password: ${{ secrets.PYPI_API_TOKEN }}
--- a/.gitignore
+++ b/.gitignore
@ -3,4 +3,5 @@
 **/__pycache__/
 **/.pytest_cache/
 *.pyc
-/.ipynb_checkpoints/
+/.ipynb_checkpoints/
+*.csv
--- a/.pre-commit-config.yaml
+++ b/.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]
--- a/HomeHarvest_Demo.ipynb
+++ b/HomeHarvest_Demo.ipynb
@ -1,118 +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": [
-    "# scrapes all 3 sites by default\n",
-    "scrape_property(\n",
-    "    location=\"dallas\",\n",
-    "    listing_type=\"for_sale\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaf86093",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# search a specific address\n",
-    "scrape_property(\n",
-    "    location=\"2530 Al Lipscomb Way\",\n",
-    "    site_name=\"zillow\",\n",
-    "    listing_type=\"for_sale\"\n",
-    "),"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ab7b4c21-da1d-4713-9df4-d7425d8ce21e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check rentals\n",
-    "scrape_property(\n",
-    "    location=\"chicago\",\n",
-    "    site_name=[\"redfin\", \"realtor.com\"],\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=\"chicago, illinois\",\n",
-    "    site_name=[\"redfin\"],\n",
-    "    listing_type=\"sold\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--- a/README.md
+++ b/README.md
@ -1,137 +1,198 @@
 <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.
+**HomeHarvest** is a 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)
+## HomeHarvest Features

-*Looking to build a data-focused software product?* **[Book a call](https://calendly.com/zachary-products/15min)** *to work with us.*
-## 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.
+
+[Video Guide for HomeHarvest](https://youtu.be/J1qgNPgmSLI) - _updated for release v0.3.4_

- Scrapes properties from **Zillow**, **Realtor.com** & **Redfin** simultaneously
- Aggregates the properties in a Pandas DataFrame
-  
 ![homeharvest](https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/b3d5d727-e67b-4a9f-85d8-1e65fd18620a)

 ## Installation

 ```bash
-pip install --upgrade 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
+
+### Python
+
 ```py
 from homeharvest import scrape_property
-import pandas as pd
+from datetime import datetime

-properties: pd.DataFrame = scrape_property(
-    site_name=["zillow", "realtor.com", "redfin"],
-    location="85281",
-    listing_type="for_rent" # for_sale / sold
+# Generate filename based on current timestamp
+current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+filename = f"HomeHarvest_{current_timestamp}.csv"
+
+properties = scrape_property(
+  location="San Diego, CA",
+  listing_type="sold",  # or (for_sale, for_rent, pending)
+  past_days=30,  # sold in last 30 days - listed in last 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
 )
+print(f"Number of properties: {len(properties)}")

-#: Note, to export to CSV or Excel, use properties.to_csv() or properties.to_excel().
-print(properties)
+# Export to csv
+properties.to_csv(filename, index=False)
+print(properties.head())
 ```
+
 ## Output
-```py
+```plaintext
 >>> properties.head()
-                           street   city  ... mls_id description
-0                 420 N  Scottsdale Rd  Tempe  ...    NaN         NaN
-1                1255 E  University Dr  Tempe  ...    NaN         NaN
-2              1979 E  Rio Salado Pkwy  Tempe  ...    NaN         NaN
-3                      548 S Wilson St  Tempe  ...   None        None
-4  945 E  Playa Del Norte Dr Unit 4027  Tempe  ...    NaN         NaN
-[5 rows x 23 columns]
+    MLS       MLS # Status          Style  ...     COEDate LotSFApx PrcSqft Stories
+0  SDCA   230018348   SOLD         CONDOS  ...  2023-10-03   290110     803       2
+1  SDCA   230016614   SOLD      TOWNHOMES  ...  2023-10-03     None     838       3
+2  SDCA   230016367   SOLD         CONDOS  ...  2023-10-03    30056     649       1
+3  MRCA  NDP2306335   SOLD  SINGLE_FAMILY  ...  2023-10-03     7519     661       2
+4  SDCA   230014532   SOLD         CONDOS  ...  2023-10-03     None     752       1
+[5 rows x 22 columns]
 ```

-### Parameters for `scrape_properties()`
-```plaintext
+### Parameters for `scrape_property()`
+```
 Required
-├── location (str): address in various formats e.g. just zip, full address, city/state, etc.
-└── listing_type (enum): for_rent, for_sale, sold
+├── location (str): The address in various formats - this could be just a zip code, a full address, or city/state, etc.
+├── listing_type (option): Choose the type of listing.
+    - 'for_rent'
+    - 'for_sale'
+    - 'sold'
+    - 'pending' (for pending/contingent sales)
+
 Optional
-├── site_name (List[enum], default=all three sites): zillow, realtor.com, redfin
+├── property_type (list): Choose the type of properties.
+    - 'single_family'
+    - 'multi_family'
+    - 'condos'
+    - 'condo_townhome_rowhome_coop'
+    - 'condo_townhome'
+    - 'townhomes'
+    - 'duplex_triplex'
+    - 'farm'
+    - 'land'
+    - 'mobile'
+│
+├── return_type (option): Choose the return type.
+│    - 'pandas' (default)
+│    - 'pydantic'
+│    - 'raw' (json)
+│
+├── radius (decimal): Radius in miles to find comparable properties based on individual addresses.
+│    Example: 5.5 (fetches properties within a 5.5-mile radius if location is set to a specific address; otherwise, ignored)
+│
+├── past_days (integer): Number of past days to filter properties. Utilizes 'last_sold_date' for 'sold' listing types, and 'list_date' for others (for_rent, for_sale).
+│    Example: 30 (fetches properties listed/sold in the last 30 days)
+│
+├── date_from, date_to (string): Start and end dates to filter properties listed or sold, both dates are required.
+|    (use this to get properties in chunks as there's a 10k result limit)
+│    Format for both must be "YYYY-MM-DD".
+│    Example: "2023-05-01", "2023-05-15" (fetches properties listed/sold between these dates)
+│
+├── mls_only (True/False): If set, fetches only MLS listings (mainly applicable to 'sold' listings)
+│
+├── foreclosure (True/False): If set, fetches only foreclosures
+│
+├── proxy (string): In format 'http://user:pass@host:port'
+│
+├── 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 (str)
-│   ├── site_name (enum): zillow, redfin, realtor.com
-│   ├── listing_type (enum: ListingType)
-│   └── property_type (enum): house, apartment, condo, townhouse, single_family, multi_family, building
+│ ├── property_url
+│ ├── property_id
+│ ├── listing_id
+│ ├── mls
+│ ├── mls_id
+│ └── status

 ├── Address Details:
-│   ├── street_address (str)
-│   ├── city (str)
-│   ├── state (str)
-│   ├── zip_code (str)
-│   ├── unit (str)
-│   └── country (str)
+│ ├── street
+│ ├── unit
+│ ├── city
+│ ├── state
+│ └── zip_code

-├── Property Features:
-│   ├── price (int)
-│   ├── tax_assessed_value (int)
-│   ├── currency (str)
-│   ├── square_feet (int)
-│   ├── beds (int)
-│   ├── baths (float)
-│   ├── lot_area_value (float)
-│   ├── lot_area_unit (str)
-│   ├── stories (int)
-│   └── year_built (int)
+├── Property Description:
+│ ├── style
+│ ├── beds
+│ ├── full_baths
+│ ├── half_baths
+│ ├── sqft
+│ ├── year_built
+│ ├── stories
+│ ├── garage
+│ └── lot_sqft

-├── Miscellaneous Details:
-│   ├── price_per_sqft (int)
-│   ├── mls_id (str)
-│   ├── agent_name (str)
-│   ├── img_src (str)
-│   ├── description (str)
-│   ├── status_text (str)
-│   ├── latitude (float)
-│   ├── longitude (float)
-│   └── posted_time (str) [Only for Zillow]
+├── 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

-├── Building Details (for property_type: building):
-│   ├── bldg_name (str)
-│   ├── bldg_unit_count (int)
-│   ├── bldg_min_beds (int)
-│   ├── bldg_min_baths (float)
-│   └── bldg_min_area (int)
+├── 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

-└── Apartment Details (for property type: apartment):
-    └── apt_min_price (int)
 ```
-## Supported Countries for Property Scraping
-
-* **Zillow**: contains listings in the **US** & **Canada** 
-* **Realtor.com**: mainly from the **US** but also has international listings
-* **Redfin**: listings mainly in the **US**, **Canada**, & has expanded to some areas in **Mexico**

 ### Exceptions
 The following exceptions may be raised when using HomeHarvest:

- `InvalidSite` - valid options: `zillow`, `redfin`, `realtor.com`
- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
- `NoResultsFound` - no properties found from your input
- `GeoCoordsNotFound` - if Zillow scraper is not able to create geo-coordinates from the location you input
-
-## Frequently Asked Questions
-
---
-
-**Q: Encountering issues with your queries?**  
-**A:** Try a single site and/or broaden the location. If problems persist, [submit an issue](https://github.com/ZacharyHampton/HomeHarvest/issues).
-
---
-
-**Q: Received a Forbidden 403 response code?**  
-**A:** This indicates that you have been blocked by the real estate site for sending too many requests. Currently, **Zillow** is particularly aggressive with blocking. We recommend:
-
- Waiting a few seconds between requests.
- Trying a VPN to change your IP address.
-
---
-
+- `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.
--- a/examples/price_of_land.py
+++ b/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)
--- a/homeharvest/init.py
+++ b/homeharvest/init.py
@ -1,179 +1,77 @@
+import warnings
 import pandas as pd
-from typing import Union
-import concurrent.futures
-from concurrent.futures import ThreadPoolExecutor
-
 from .core.scrapers import ScraperInput
-from .core.scrapers.redfin import RedfinScraper
+from .utils import process_result, ordered_properties, validate_input, validate_dates, validate_limit
 from .core.scrapers.realtor import RealtorScraper
-from .core.scrapers.zillow import ZillowScraper
-from .core.scrapers.models import ListingType, Property, SiteName
-from .exceptions import InvalidSite, InvalidListingType
-
-
-_scrapers = {
-    "redfin": RedfinScraper,
-    "realtor.com": RealtorScraper,
-    "zillow": ZillowScraper,
-}
-
-
-def validate_input(site_name: str, listing_type: str) -> None:
-    if site_name.lower() not in _scrapers:
-        raise InvalidSite(f"Provided site, '{site_name}', does not exist.")
-
-    if listing_type.upper() not in ListingType.__members__:
-        raise InvalidListingType(
-            f"Provided listing type, '{listing_type}', does not exist."
-        )
-
-
-def get_ordered_properties(result: Property) -> list[str]:
-    return [
-        "property_url",
-        "site_name",
-        "listing_type",
-        "property_type",
-        "status_text",
-        "currency",
-        "price",
-        "apt_min_price",
-        "tax_assessed_value",
-        "square_feet",
-        "price_per_sqft",
-        "beds",
-        "baths",
-        "lot_area_value",
-        "lot_area_unit",
-        "street_address",
-        "unit",
-        "city",
-        "state",
-        "zip_code",
-        "country",
-        "posted_time",
-        "bldg_min_beds",
-        "bldg_min_baths",
-        "bldg_min_area",
-        "bldg_unit_count",
-        "bldg_name",
-        "stories",
-        "year_built",
-        "agent_name",
-        "mls_id",
-        "description",
-        "img_src",
-        "latitude",
-        "longitude",
-    ]
-
-
-def process_result(result: Property) -> pd.DataFrame:
-    prop_data = result.__dict__
-
-    prop_data["site_name"] = prop_data["site_name"].value
-    prop_data["listing_type"] = prop_data["listing_type"].value.lower()
-    if "property_type" in prop_data and prop_data["property_type"] is not None:
-        prop_data["property_type"] = prop_data["property_type"].value.lower()
-    else:
-        prop_data["property_type"] = None
-    if "address" in prop_data:
-        address_data = prop_data["address"]
-        prop_data["street_address"] = address_data.street_address
-        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_code
-        prop_data["country"] = address_data.country
-
-        del prop_data["address"]
-
-    properties_df = pd.DataFrame([prop_data])
-    properties_df = properties_df[get_ordered_properties(result)]
-
-    return properties_df
-
-
-def _scrape_single_site(
-    location: str, site_name: str, listing_type: str
-) -> pd.DataFrame:
-    """
-    Helper function to scrape a single site.
-    """
-    validate_input(site_name, listing_type)
-
-    scraper_input = ScraperInput(
-        location=location,
-        listing_type=ListingType[listing_type.upper()],
-        site_name=SiteName.get_by_value(site_name.lower()),
-    )
-
-    site = _scrapers[site_name.lower()](scraper_input)
-    results = site.search()
-
-    properties_dfs = [process_result(result) for result in results]
-    properties_dfs = [
-        df.dropna(axis=1, how="all") for df in properties_dfs if not df.empty
-    ]
-    if not properties_dfs:
-        return pd.DataFrame()
-
-    return pd.concat(properties_dfs, ignore_index=True)
+from .core.scrapers.models import ListingType, SearchPropertyType, ReturnType, Property


 def scrape_property(
    location: str,
-    site_name: Union[str, list[str]] = None,
    listing_type: str = "for_sale",
-) -> pd.DataFrame:
+    return_type: str = "pandas",
+    property_type: list[str] | None = None,
+    radius: float = None,
+    mls_only: bool = False,
+    past_days: int = None,
+    proxy: str = None,
+    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 property from various sites from a given location and listing type.
-
-    :returns: pd.DataFrame
-    :param location: US Location (e.g. 'San Francisco, CA', 'Cook County, IL', '85281', '2530 Al Lipscomb Way')
-    :param site_name: Site name or list of site names (e.g. ['realtor.com', 'zillow'], 'redfin')
-    :param listing_type: Listing type (e.g. 'for_sale', 'for_rent', 'sold')
-    :return: pd.DataFrame containing properties
+    Scrape properties from Realtor.com based on a given location and listing type.
+    :param location: Location to search (e.g. "Dallas, TX", "85281", "2530 Al Lipscomb Way")
+    :param listing_type: Listing Type (for_sale, for_rent, sold, pending)
+    :param 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 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.
    """
-    if site_name is None:
-        site_name = list(_scrapers.keys())
+    validate_input(listing_type)
+    validate_dates(date_from, date_to)
+    validate_limit(limit)

-    if not isinstance(site_name, list):
-        site_name = [site_name]
+    scraper_input = ScraperInput(
+        location=location,
+        listing_type=ListingType(listing_type.upper()),
+        return_type=ReturnType(return_type.lower()),
+        property_type=[SearchPropertyType[prop.upper()] for prop in property_type] if property_type else None,
+        proxy=proxy,
+        radius=radius,
+        mls_only=mls_only,
+        last_x_days=past_days,
+        date_from=date_from,
+        date_to=date_to,
+        foreclosure=foreclosure,
+        extra_property_data=extra_property_data,
+        exclude_pending=exclude_pending,
+        limit=limit,
+    )

-    results = []
+    site = RealtorScraper(scraper_input)
+    results = site.search()

-    if len(site_name) == 1:
-        final_df = _scrape_single_site(location, site_name[0], listing_type)
-        results.append(final_df)
-    else:
-        with ThreadPoolExecutor() as executor:
-            futures = {
-                executor.submit(
-                    _scrape_single_site, location, s_name, listing_type
-                ): s_name
-                for s_name in site_name
-            }
+    if scraper_input.return_type != ReturnType.pandas:
+        return results

-            for future in concurrent.futures.as_completed(futures):
-                result = future.result()
-                results.append(result)
-
-    results = [df for df in results if not df.empty and not df.isna().all().all()]
-
-    if not results:
+    properties_dfs = [df for result in results if not (df := process_result(result)).empty]
+    if not properties_dfs:
        return pd.DataFrame()

-    final_df = pd.concat(results, ignore_index=True)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=FutureWarning)

-    columns_to_track = ["street_address", "city", "unit"]
-
-    #: validate they exist, otherwise create them
-    for col in columns_to_track:
-        if col not in final_df.columns:
-            final_df[col] = None
-
-    final_df = final_df.drop_duplicates(
-        subset=["street_address", "city", "unit"], keep="first"
-    )
-    return final_df
+        return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties].replace(
+            {"None": pd.NA, None: pd.NA, "": pd.NA}
+        )
--- a/homeharvest/cli.py
+++ b/homeharvest/cli.py
@ -0,0 +1,85 @@
+import argparse
+import datetime
+from homeharvest import scrape_property
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Home Harvest Property Scraper")
+    parser.add_argument("location", type=str, help="Location to scrape (e.g., San Francisco, CA)")
+
+    parser.add_argument(
+        "-l",
+        "--listing_type",
+        type=str,
+        default="for_sale",
+        choices=["for_sale", "for_rent", "sold", "pending"],
+        help="Listing type to scrape",
+    )
+
+    parser.add_argument(
+        "-o",
+        "--output",
+        type=str,
+        default="excel",
+        choices=["excel", "csv"],
+        help="Output format",
+    )
+
+    parser.add_argument(
+        "-f",
+        "--filename",
+        type=str,
+        default=None,
+        help="Name of the output file (without extension)",
+    )
+
+    parser.add_argument("-p", "--proxy", type=str, default=None, help="Proxy to use for scraping")
+    parser.add_argument(
+        "-d",
+        "--days",
+        type=int,
+        default=None,
+        help="Sold/listed in last _ days filter.",
+    )
+
+    parser.add_argument(
+        "-r",
+        "--radius",
+        type=float,
+        default=None,
+        help="Get comparable properties within _ (eg. 0.0) miles. Only applicable for individual addresses.",
+    )
+    parser.add_argument(
+        "-m",
+        "--mls_only",
+        action="store_true",
+        help="If set, fetches only MLS listings.",
+    )
+
+    args = parser.parse_args()
+
+    result = scrape_property(
+        args.location,
+        args.listing_type,
+        radius=args.radius,
+        proxy=args.proxy,
+        mls_only=args.mls_only,
+        past_days=args.days,
+    )
+
+    if not args.filename:
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+        args.filename = f"HomeHarvest_{timestamp}"
+
+    if args.output == "excel":
+        output_filename = f"{args.filename}.xlsx"
+        result.to_excel(output_filename, index=False)
+        print(f"Excel file saved as {output_filename}")
+    elif args.output == "csv":
+        output_filename = f"{args.filename}.csv"
+        result.to_csv(output_filename, index=False)
+        print(f"CSV file saved as {output_filename}")
+
+
+if __name__ == "__main__":
+    main()
--- a/homeharvest/core/scrapers/init.py
+++ b/homeharvest/core/scrapers/init.py
@ -1,37 +1,128 @@
+from __future__ import annotations
 from dataclasses import dataclass
+from typing import Union
+
 import requests
-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
-    site_name: SiteName
-    proxy_url: str | None = None
+    property_type: list[SearchPropertyType] | None = None
+    radius: float | None = None
+    mls_only: bool | None = False
+    proxy: str | None = None
+    last_x_days: int | 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:
-    def __init__(self, scraper_input: ScraperInput):
+    session = None
+
+    def __init__(
+        self,
+        scraper_input: ScraperInput,
+    ):
        self.location = scraper_input.location
        self.listing_type = scraper_input.listing_type
+        self.property_type = scraper_input.property_type
+
+        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
+            proxies = {"http": proxy_url, "https": proxy_url}
+            self.session.proxies.update(proxies)

-        self.session = requests.Session()
        self.listing_type = scraper_input.listing_type
-        self.site_name = scraper_input.site_name
+        self.radius = scraper_input.radius
+        self.last_x_days = scraper_input.last_x_days
+        self.mls_only = scraper_input.mls_only
+        self.date_from = scraper_input.date_from
+        self.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

-        if scraper_input.proxy_url:
-            self.session.proxies = {
-                "http": scraper_input.proxy_url,
-                "https": scraper_input.proxy_url,
-            }
-
-    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
--- a/homeharvest/core/scrapers/models.py
+++ b/homeharvest/core/scrapers/models.py
@ -1,5 +1,13 @@
+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):
@ -15,91 +23,172 @@ 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):
-    HOUSE = "HOUSE"
-    BUILDING = "BUILDING"
-    CONDO = "CONDO"
-    TOWNHOUSE = "TOWNHOUSE"
-    SINGLE_FAMILY = "SINGLE_FAMILY"
-    MULTI_FAMILY = "MULTI_FAMILY"
-    MANUFACTURED = "MANUFACTURED"
-    NEW_CONSTRUCTION = "NEW_CONSTRUCTION"
    APARTMENT = "APARTMENT"
-    APARTMENTS = "APARTMENTS"
+    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"
-    LOT = "LOT"
+    MOBILE = "MOBILE"
+    MULTI_FAMILY = "MULTI_FAMILY"
+    RENTAL = "RENTAL"
+    SINGLE_FAMILY = "SINGLE_FAMILY"
+    TOWNHOMES = "TOWNHOMES"
    OTHER = "OTHER"

-    BLANK = "BLANK"
-
-    @classmethod
-    def from_int_code(cls, code):
-        mapping = {
-            1: cls.HOUSE,
-            2: cls.CONDO,
-            3: cls.TOWNHOUSE,
-            4: cls.MULTI_FAMILY,
-            5: cls.LAND,
-            6: cls.OTHER,
-            8: cls.SINGLE_FAMILY,
-            13: cls.SINGLE_FAMILY,
-        }
-
-        return mapping.get(code, cls.BLANK)
-

@dataclass
 class Address:
-    street_address: str
-    city: str
-    state: str
-    zip_code: str
+    full_line: str | None = None
+    street: str | None = None
    unit: str | None = None
-    country: str | None = None
+    city: str | None = None
+    state: str | None = None
+    zip: str | None = None
+
+
+@dataclass
+class Description:
+    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
+    sqft: int | None = None
+    lot_sqft: int | None = None
+    sold_price: int | None = None
+    year_built: int | None = None
+    garage: float | None = None
+    stories: int | None = None
+    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
-    site_name: SiteName
-    listing_type: ListingType
-    address: Address
-    property_type: PropertyType | None = None

-    # house for sale
-    price: int | None = None
-    tax_assessed_value: int | None = None
-    currency: str | None = None
-    square_feet: int | None = None
-    beds: int | None = None
-    baths: float | None = None
-    lot_area_value: float | None = None
-    lot_area_unit: str | None = None
-    stories: int | None = None
-    year_built: int | None = None
-    price_per_sqft: int | None = None
+    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

-    agent_name: str | None = None
-    img_src: str | None = None
-    description: str | None = None
-    status_text: str | None = None
    latitude: float | None = None
    longitude: float | None = None
-    posted_time: str | 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

-    # building for sale
-    bldg_name: str | None = None
-    bldg_unit_count: int | None = None
-    bldg_min_beds: int | None = None
-    bldg_min_baths: float | None = None
-    bldg_min_area: int | None = None
-
-    # apt
-    apt_min_price: int | None = None
+    advertisers: Advertisers | None = None
--- a/homeharvest/core/scrapers/realtor/init.py
+++ b/homeharvest/core/scrapers/realtor/init.py
@ -1,33 +1,54 @@
+"""
+homeharvest.realtor.__init__
+~~~~~~~~~~~~
+
+This module implements the scraper for realtor.com
+"""
+
+from __future__ import annotations
+
 import json
-from ..models import Property, Address
-from .. import Scraper
-from typing import Any, Generator
-from ....exceptions import NoResultsFound
-from ....utils import parse_address_two, parse_unit
 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 ..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):
        super().__init__(scraper_input)
-        self.search_url = "https://www.realtor.com/api/v1/rdc_search_srp?client_id=rdc-search-new-communities&schema=vesta"

    def handle_location(self):
-        headers = {
-            "authority": "parser-external.geo.moveaws.com",
-            "accept": "*/*",
-            "accept-language": "en-US,en;q=0.9",
-            "origin": "https://www.realtor.com",
-            "referer": "https://www.realtor.com/",
-            "sec-ch-ua": '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"Windows"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "cross-site",
-            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-        }
-
        params = {
            "input": self.location,
            "client_id": self.listing_type.value.lower().replace("_", "-"),
@ -36,283 +57,612 @@ class RealtorScraper(Scraper):
        }

        response = self.session.get(
-            "https://parser-external.geo.moveaws.com/suggest",
+            self.ADDRESS_AUTOCOMPLETE_URL,
            params=params,
-            headers=headers,
        )
        response_json = response.json()

        result = response_json["autocomplete"]

        if not result:
-            raise NoResultsFound("No results found for location: " + self.location)
+            return None

        return result[0]

-    def handle_address(self, property_id: str) -> list[Property]:
+    def get_latest_listing_id(self, property_id: str) -> str | None:
        query = """query Property($property_id: ID!) {
                    property(id: $property_id) {
-                        property_id
-                        details {
-                            date_updated
-                            garage
-                            permalink
-                            year_built
-                            stories
-                        }
-                        address {
-                            address_validation_code
-                            city
-                            country
-                            county
-                            line
-                            postal_code
-                            state_code
-                            street_direction
-                            street_name
-                            street_number
-                            street_suffix
-                            street_post_direction
-                            unit_value
-                            unit
-                            unit_descriptor
-                            zip
-                        }
-                        basic {
-                            baths
-                            beds
-                            price
-                            sqft
-                            lot_sqft
-                            type
-                            sold_price
-                        }
-                        public_record {
-                            lot_size
-                            sqft
-                            stories
-                            units
-                            year_built
+                        listings {
+                            listing_id
+                            primary
                        }
                    }
-                }"""
+                }
+                """

        variables = {"property_id": property_id}
-
        payload = {
            "query": query,
            "variables": variables,
        }

-        response = self.session.post(self.search_url, json=payload)
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
        response_json = response.json()

        property_info = response_json["data"]["property"]
-        street_address, unit = parse_address_two(property_info["address"]["line"])
+        if property_info["listings"] is None:
+            return None

-        return [
-            Property(
-                site_name=self.site_name,
-                address=Address(
-                    street_address=street_address,
-                    city=property_info["address"]["city"],
-                    state=property_info["address"]["state_code"],
-                    zip_code=property_info["address"]["postal_code"],
-                    unit=unit,
-                    country="USA",
-                ),
-                property_url="https://www.realtor.com/realestateandhomes-detail/"
-                + property_info["details"]["permalink"],
-                beds=property_info["basic"]["beds"],
-                baths=property_info["basic"]["baths"],
-                stories=property_info["details"]["stories"],
-                year_built=property_info["details"]["year_built"],
-                square_feet=property_info["basic"]["sqft"],
-                price_per_sqft=property_info["basic"]["price"]
-                // property_info["basic"]["sqft"]
-                if property_info["basic"]["sqft"] is not None
-                and property_info["basic"]["price"] is not None
-                else None,
-                price=property_info["basic"]["price"],
-                mls_id=property_id,
-                listing_type=self.listing_type,
-                lot_area_value=property_info["public_record"]["lot_size"]
-                if property_info["public_record"] is not None
-                else None,
-            )
-        ]
-
-    def handle_area(
-        self, variables: dict, return_total: bool = False
-    ) -> list[Property] | int:
-        query = (
-            """query Home_search(
-                            $city: String,
-                            $county: [String],
-                            $state_code: String,
-                            $postal_code: String
-                            $offset: Int,
-                        ) {
-                            home_search(
-                                query: {
-                                    city: $city
-                                    county: $county
-                                    postal_code: $postal_code
-                                    state_code: $state_code
-                                    status: %s
-                                }
-                                limit: 200
-                                offset: $offset
-                            ) {
-                                count
-                                total
-                                results {
-                                    property_id
-                                    description {
-                                        baths
-                                        beds
-                                        lot_sqft
-                                        sqft
-                                        text
-                                        sold_price
-                                        stories
-                                        year_built
-                                        garage
-                                        unit_number
-                                        floor_number
-                                    }
-                                    location {
-                                        address {
-                                            city
-                                            country
-                                            line
-                                            postal_code
-                                            state_code
-                                            state
-                                            street_direction
-                                            street_name
-                                            street_number
-                                            street_post_direction
-                                            street_suffix
-                                            unit
-                                            coordinate {
-                                                lon
-                                                lat
-                                            }
-                                        }
-                                    }
-                                    list_price
-                                    price_per_sqft
-                                    source {
-                                        id
-                                    }
-                                }
-                            }
-                        }"""
-            % self.listing_type.value.lower()
+        primary_listing = next(
+            (listing for listing in property_info["listings"] if listing["primary"]),
+            None,
        )
+        if primary_listing:
+            return primary_listing["listing_id"]
+        else:
+            return property_info["listings"][0]["listing_id"]
+
+    def handle_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,
+        }
+
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
+        response_json = response.json()
+
+        property_info = response_json["data"]["home"]
+
+        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"),
+                )
+
+                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
+        """
+
+        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 }]"  #: prioritize normal fractal sort from realtor
+        )
+
+        pending_or_contingent_param = (
+            "or_filters: { contingent: true, pending: true }" if self.listing_type == ListingType.PENDING else ""
+        )
+
+        listing_type = ListingType.FOR_SALE if self.listing_type == ListingType.PENDING else self.listing_type
+        is_foreclosure = ""
+
+        if variables.get("foreclosure") is True:
+            is_foreclosure = "foreclosure: true"
+        elif variables.get("foreclosure") is False:
+            is_foreclosure = "foreclosure: false"
+
+        if search_type == "comps":  #: comps search, came from an address
+            query = """query Property_search(
+                    $coordinates: [Float]!
+                    $radius: String!
+                    $offset: Int!,
+                    ) {
+                        home_search(
+                            query: {
+                                %s
+                                nearby: {
+                                    coordinates: $coordinates
+                                    radius: $radius
+                                }
+                                status: %s
+                                %s
+                                %s
+                                %s
+                            }
+                            %s
+                            limit: 200
+                            offset: $offset
+                    ) %s
+                }""" % (
+                is_foreclosure,
+                listing_type.value.lower(),
+                date_param,
+                property_type_param,
+                pending_or_contingent_param,
+                sort_param,
+                GENERAL_RESULTS_QUERY,
+            )
+        elif search_type == "area":  #: general search, came from a general location
+            query = """query Home_search(
+                                $location: String!,
+                                $offset: Int,
+                            ) {
+                                home_search(
+                                    query: {
+                                        %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
+                            }""" % (
+                is_foreclosure,
+                listing_type.value.lower(),
+                date_param,
+                property_type_param,
+                pending_or_contingent_param,
+                sort_param,
+                GENERAL_RESULTS_QUERY,
+            )
+        else:  #: general search, came from an address
+            query = (
+                """query Property_search(
+                        $property_id: [ID]!
+                        $offset: Int!,
+                    ) {
+                        home_search(
+                            query: {
+                                property_id: $property_id
+                            }
+                            limit: 1
+                            offset: $offset
+                        ) %s
+                    }"""
+                % GENERAL_RESULTS_QUERY
+            )

        payload = {
            "query": query,
            "variables": variables,
        }

-        response = self.session.post(self.search_url, json=payload)
-        response.raise_for_status()
+        response = self.session.post(self.SEARCH_GQL_URL, json=payload)
        response_json = response.json()
+        search_key = "home_search" if "home_search" in query else "property_search"

-        if return_total:
-            return response_json["data"]["home_search"]["total"]
-
-        properties: list[Property] = []
+        properties: list[Union[Property, dict]] = []

        if (
            response_json is None
            or "data" not in response_json
            or response_json["data"] is None
-            or "home_search" not in response_json["data"]
-            or response_json["data"]["home_search"] is None
-            or "results" not in response_json["data"]["home_search"]
+            or search_key not in response_json["data"]
+            or response_json["data"][search_key] is None
+            or "results" not in response_json["data"][search_key]
        ):
-            return []
+            return {"total": 0, "properties": []}

-        for result in response_json["data"]["home_search"]["results"]:
-            street_address, unit = parse_address_two(
-                result["location"]["address"]["line"]
-            )
-            realty_property = Property(
-                address=Address(
-                    street_address=street_address,
-                    city=result["location"]["address"]["city"],
-                    state=result["location"]["address"]["state_code"],
-                    zip_code=result["location"]["address"]["postal_code"],
-                    unit=parse_unit(result["location"]["address"]["unit"]),
-                    country="USA",
-                ),
-                latitude=result["location"]["address"]["coordinate"]["lat"]
-                if result
-                and result.get("location")
-                and result["location"].get("address")
-                and result["location"]["address"].get("coordinate")
-                and "lat" in result["location"]["address"]["coordinate"]
-                else None,
-                longitude=result["location"]["address"]["coordinate"]["lon"]
-                if result
-                and result.get("location")
-                and result["location"].get("address")
-                and result["location"]["address"].get("coordinate")
-                and "lon" in result["location"]["address"]["coordinate"]
-                else None,
-                site_name=self.site_name,
-                property_url="https://www.realtor.com/realestateandhomes-detail/"
-                + result["property_id"],
-                beds=result["description"]["beds"],
-                baths=result["description"]["baths"],
-                stories=result["description"]["stories"],
-                year_built=result["description"]["year_built"],
-                square_feet=result["description"]["sqft"],
-                price_per_sqft=result["price_per_sqft"],
-                price=result["list_price"],
-                mls_id=result["property_id"],
-                listing_type=self.listing_type,
-                lot_area_value=result["description"]["lot_sqft"],
-            )
+        properties_list = response_json["data"][search_key]["results"]
+        total_properties = response_json["data"][search_key]["total"]
+        offset = variables.get("offset", 0)

-            properties.append(realty_property)
+        #: 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]

-        return properties
+        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 {}
+
+            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": total_properties,
+            "properties": properties,
+        }

    def search(self):
        location_info = self.handle_location()
+        if not location_info:
+            return []
+
        location_type = location_info["area_type"]

-        if location_type == "address":
-            property_id = location_info["mpr_id"]
-            return self.handle_address(property_id)
-
-        offset = 0
        search_variables = {
-            "city": location_info.get("city"),
-            "county": location_info.get("county"),
-            "state_code": location_info.get("state_code"),
-            "postal_code": location_info.get("postal_code"),
-            "offset": offset,
+            "offset": 0,
        }

-        total = self.handle_area(search_variables, return_total=True)
+        search_type = (
+            "comps"
+            if self.radius and location_type == "address"
+            else "address" if location_type == "address" and not self.radius else "area"
+        )
+        if location_type == "address":
+            if not self.radius:  #: single address search, non comps
+                property_id = location_info["mpr_id"]
+                return self.handle_home(property_id)

-        homes = []
-        with ThreadPoolExecutor(max_workers=10) as executor:
+            else:  #: general search, comps (radius)
+                if not location_info.get("centroid"):
+                    return []
+
+                coordinates = list(location_info["centroid"].values())
+                search_variables |= {
+                    "coordinates": coordinates,
+                    "radius": "{}mi".format(self.radius),
+                }
+
+        elif location_type == "postal_code":
+            search_variables |= {
+                "postal_code": location_info.get("postal_code"),
+            }
+
+        else:  #: general search, location
+            search_variables |= {
+                "location": self.location,
+            }
+
+        if self.foreclosure:
+            search_variables["foreclosure"] = self.foreclosure
+
+        result = self.general_search(search_variables, search_type=search_type)
+        total = result["total"]
+        homes = result["properties"]
+
+        with ThreadPoolExecutor() as executor:
            futures = [
                executor.submit(
-                    self.handle_area,
+                    self.general_search,
                    variables=search_variables | {"offset": i},
-                    return_total=False,
+                    search_type=search_type,
+                )
+                for i in range(
+                    self.DEFAULT_PAGE_SIZE,
+                    min(total, self.limit),
+                    self.DEFAULT_PAGE_SIZE,
                )
-                for i in range(0, total, 200)
            ]

            for future in as_completed(futures):
-                homes.extend(future.result())
+                homes.extend(future.result()["properties"])

        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 = []
+        neighborhoods = result["location"].get("neighborhoods", [])
+
+        if neighborhoods:
+            for neighborhood in neighborhoods:
+                name = neighborhood.get("name")
+                if name:
+                    neighborhoods_list.append(name)
+
+        return ", ".join(neighborhoods_list) if neighborhoods_list else None
+
+    @staticmethod
+    def handle_none_safely(address_part):
+        if address_part is None:
+            return ""
+
+        return address_part
+
+    @staticmethod
+    def _parse_address(result: dict, search_type):
+        if search_type == "general_search":
+            address = result["location"]["address"]
+        else:
+            address = result["address"]
+
+        return Address(
+            full_line=address.get("line"),
+            street=" ".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 | None:
+        if not result:
+            return None
+
+        description_data = result.get("description", {})
+
+        if description_data is None or not isinstance(description_data, dict):
+            description_data = {}
+
+        style = description_data.get("type", "")
+        if style is not None:
+            style = style.upper()
+
+        primary_photo = ""
+        if (primary_photo_info := result.get("primary_photo")) and (
+            primary_photo_href := primary_photo_info.get("href")
+        ):
+            primary_photo = primary_photo_href.replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
+
+        return Description(
+            primary_photo=primary_photo,
+            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=(
+                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")
+        ]
--- a/homeharvest/core/scrapers/realtor/queries.py
+++ b/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
--- a/homeharvest/core/scrapers/redfin/init.py
+++ b/homeharvest/core/scrapers/redfin/init.py
@ -1,185 +0,0 @@
-import json
-from typing import Any
-from .. import Scraper
-from ....utils import parse_address_two, parse_unit
-from ..models import Property, Address, PropertyType
-from ....exceptions import NoResultsFound
-
-
-class RedfinScraper(Scraper):
-    def __init__(self, scraper_input):
-        super().__init__(scraper_input)
-        self.listing_type = scraper_input.listing_type
-
-    def _handle_location(self):
-        url = "https://www.redfin.com/stingray/do/location-autocomplete?v=2&al=1&location={}".format(
-            self.location
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        def get_region_type(match_type: str):
-            if match_type == "4":
-                return "2"  #: zip
-            elif match_type == "2":
-                return "6"  #: city
-            elif match_type == "1":
-                return "address"  #: address, needs to be handled differently
-
-        if "exactMatch" not in response_json["payload"]:
-            raise NoResultsFound(
-                "No results found for location: {}".format(self.location)
-            )
-
-        if response_json["payload"]["exactMatch"] is not None:
-            target = response_json["payload"]["exactMatch"]
-        else:
-            target = response_json["payload"]["sections"][0]["rows"][0]
-
-        return target["id"].split("_")[1], get_region_type(target["type"])
-
-    def _parse_home(self, home: dict, single_search: bool = False) -> Property:
-        def get_value(key: str) -> Any | None:
-            if key in home and "value" in home[key]:
-                return home[key]["value"]
-
-        if not single_search:
-            street_address, unit = parse_address_two(get_value("streetLine"))
-            unit = parse_unit(get_value("streetLine"))
-            address = Address(
-                street_address=street_address,
-                city=home["city"],
-                state=home["state"],
-                zip_code=home["zip"],
-                unit=unit,
-                country="USA",
-            )
-        else:
-            address_info = home["streetAddress"]
-            street_address, unit = parse_address_two(address_info["assembledAddress"])
-
-            address = Address(
-                street_address=street_address,
-                city=home["city"],
-                state=home["state"],
-                zip_code=home["zip"],
-                unit=unit,
-                country="USA",
-            )
-
-        url = "https://www.redfin.com{}".format(home["url"])
-        #: property_type = home["propertyType"] if "propertyType" in home else None
-        lot_size_data = home.get("lotSize")
-
-        if not isinstance(lot_size_data, int):
-            lot_size = (
-                lot_size_data.get("value", None)
-                if isinstance(lot_size_data, dict)
-                else None
-            )
-        else:
-            lot_size = lot_size_data
-
-        return Property(
-            site_name=self.site_name,
-            listing_type=self.listing_type,
-            address=address,
-            property_url=url,
-            beds=home["beds"] if "beds" in home else None,
-            baths=home["baths"] if "baths" in home else None,
-            stories=home["stories"] if "stories" in home else None,
-            agent_name=get_value("listingAgent"),
-            description=home["listingRemarks"] if "listingRemarks" in home else None,
-            year_built=get_value("yearBuilt")
-            if not single_search
-            else home["yearBuilt"],
-            square_feet=get_value("sqFt"),
-            lot_area_value=lot_size,
-            property_type=PropertyType.from_int_code(home.get("propertyType")),
-            price_per_sqft=get_value("pricePerSqFt"),
-            price=get_value("price"),
-            mls_id=get_value("mlsId"),
-            latitude=home["latLong"]["latitude"]
-            if "latLong" in home and "latitude" in home["latLong"]
-            else None,
-            longitude=home["latLong"]["longitude"]
-            if "latLong" in home and "longitude" in home["latLong"]
-            else None,
-        )
-
-    def _parse_building(self, building: dict) -> Property:
-        street_address = " ".join(
-            [
-                building["address"]["streetNumber"],
-                building["address"]["directionalPrefix"],
-                building["address"]["streetName"],
-                building["address"]["streetType"],
-            ]
-        )
-        street_address, unit = parse_address_two(street_address)
-        return Property(
-            site_name=self.site_name,
-            property_type=PropertyType("BUILDING"),
-            address=Address(
-                street_address=street_address,
-                city=building["address"]["city"],
-                state=building["address"]["stateOrProvinceCode"],
-                zip_code=building["address"]["postalCode"],
-                unit=parse_unit(
-                    " ".join(
-                        [
-                            building["address"]["unitType"],
-                            building["address"]["unitValue"],
-                        ]
-                    )
-                ),
-            ),
-            property_url="https://www.redfin.com{}".format(building["url"]),
-            listing_type=self.listing_type,
-            bldg_unit_count=building["numUnitsForSale"],
-        )
-
-    def handle_address(self, home_id: str):
-        """
-        EPs:
-        https://www.redfin.com/stingray/api/home/details/initialInfo?al=1&path=/TX/Austin/70-Rainey-St-78701/unit-1608/home/147337694
-        https://www.redfin.com/stingray/api/home/details/mainHouseInfoPanelInfo?propertyId=147337694&accessLevel=3
-        https://www.redfin.com/stingray/api/home/details/aboveTheFold?propertyId=147337694&accessLevel=3
-        https://www.redfin.com/stingray/api/home/details/belowTheFold?propertyId=147337694&accessLevel=3
-        """
-
-        url = "https://www.redfin.com/stingray/api/home/details/aboveTheFold?propertyId={}&accessLevel=3".format(
-            home_id
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        parsed_home = self._parse_home(
-            response_json["payload"]["addressSectionInfo"], single_search=True
-        )
-        return [parsed_home]
-
-    def search(self):
-        region_id, region_type = self._handle_location()
-
-        if region_type == "address":
-            home_id = region_id
-            return self.handle_address(home_id)
-
-        url = "https://www.redfin.com/stingray/api/gis?al=1&region_id={}&region_type={}".format(
-            region_id, region_type
-        )
-
-        response = self.session.get(url)
-        response_json = json.loads(response.text.replace("{}&&", ""))
-
-        homes = [
-            self._parse_home(home) for home in response_json["payload"]["homes"]
-        ] + [
-            self._parse_building(building)
-            for building in response_json["payload"]["buildings"].values()
-        ]
-
-        return homes
--- a/homeharvest/core/scrapers/zillow/init.py
+++ b/homeharvest/core/scrapers/zillow/init.py
@ -1,329 +0,0 @@
-import re
-import json
-import string
-from .. import Scraper
-from ....utils import parse_address_two, parse_unit
-from ....exceptions import GeoCoordsNotFound, NoResultsFound
-from ..models import Property, Address, ListingType, PropertyType
-
-
-class ZillowScraper(Scraper):
-    def __init__(self, scraper_input):
-        super().__init__(scraper_input)
-        self.listing_type = scraper_input.listing_type
-        if not self.is_plausible_location(self.location):
-            raise NoResultsFound("Invalid location input: {}".format(self.location))
-        if self.listing_type == ListingType.FOR_SALE:
-            self.url = f"https://www.zillow.com/homes/for_sale/{self.location}_rb/"
-        elif self.listing_type == ListingType.FOR_RENT:
-            self.url = f"https://www.zillow.com/homes/for_rent/{self.location}_rb/"
-        else:
-            self.url = f"https://www.zillow.com/homes/recently_sold/{self.location}_rb/"
-
-    @staticmethod
-    def is_plausible_location(location: str) -> bool:
-        blocks = location.split()
-        for block in blocks:
-            if (
-                any(char.isdigit() for char in block)
-                and any(char.isalpha() for char in block)
-                and len(block) > 6
-            ):
-                return False
-        return True
-
-    def search(self):
-        resp = self.session.get(self.url, headers=self._get_headers())
-        resp.raise_for_status()
-        content = resp.text
-
-        match = re.search(
-            r'<script id="__NEXT_DATA__" type="application/json">(.*?)</script>',
-            content,
-            re.DOTALL,
-        )
-        if not match:
-            raise NoResultsFound(
-                "No results were found for Zillow with the given Location."
-            )
-
-        json_str = match.group(1)
-        data = json.loads(json_str)
-
-        if "searchPageState" in data["props"]["pageProps"]:
-            pattern = r'window\.mapBounds = \{\s*"west":\s*(-?\d+\.\d+),\s*"east":\s*(-?\d+\.\d+),\s*"south":\s*(-?\d+\.\d+),\s*"north":\s*(-?\d+\.\d+)\s*\};'
-
-            match = re.search(pattern, content)
-
-            if match:
-                coords = [float(coord) for coord in match.groups()]
-                return self._fetch_properties_backend(coords)
-
-            else:
-                raise GeoCoordsNotFound("Box bounds could not be located.")
-
-        elif "gdpClientCache" in data["props"]["pageProps"]:
-            gdp_client_cache = json.loads(data["props"]["pageProps"]["gdpClientCache"])
-            main_key = list(gdp_client_cache.keys())[0]
-
-            property_data = gdp_client_cache[main_key]["property"]
-            property = self._get_single_property_page(property_data)
-
-            return [property]
-        raise NoResultsFound("Specific property data not found in the response.")
-
-    def _fetch_properties_backend(self, coords):
-        url = "https://www.zillow.com/async-create-search-page-state"
-
-        filter_state_for_sale = {
-            "sortSelection": {
-                # "value": "globalrelevanceex"
-                "value": "days"
-            },
-            "isAllHomes": {"value": True},
-        }
-
-        filter_state_for_rent = {
-            "isForRent": {"value": True},
-            "isForSaleByAgent": {"value": False},
-            "isForSaleByOwner": {"value": False},
-            "isNewConstruction": {"value": False},
-            "isComingSoon": {"value": False},
-            "isAuction": {"value": False},
-            "isForSaleForeclosure": {"value": False},
-            "isAllHomes": {"value": True},
-        }
-
-        filter_state_sold = {
-            "isRecentlySold": {"value": True},
-            "isForSaleByAgent": {"value": False},
-            "isForSaleByOwner": {"value": False},
-            "isNewConstruction": {"value": False},
-            "isComingSoon": {"value": False},
-            "isAuction": {"value": False},
-            "isForSaleForeclosure": {"value": False},
-            "isAllHomes": {"value": True},
-        }
-
-        selected_filter = (
-            filter_state_for_rent
-            if self.listing_type == ListingType.FOR_RENT
-            else filter_state_for_sale
-            if self.listing_type == ListingType.FOR_SALE
-            else filter_state_sold
-        )
-
-        payload = {
-            "searchQueryState": {
-                "pagination": {},
-                "isMapVisible": True,
-                "mapBounds": {
-                    "west": coords[0],
-                    "east": coords[1],
-                    "south": coords[2],
-                    "north": coords[3],
-                },
-                "filterState": selected_filter,
-                "isListVisible": True,
-                "mapZoom": 11,
-            },
-            "wants": {"cat1": ["mapResults"]},
-            "isDebugRequest": False,
-        }
-        resp = self.session.put(url, headers=self._get_headers(), json=payload)
-        resp.raise_for_status()
-        a = resp.json()
-        return self._parse_properties(resp.json())
-
-    def _parse_properties(self, property_data: dict):
-        mapresults = property_data["cat1"]["searchResults"]["mapResults"]
-
-        properties_list = []
-
-        for result in mapresults:
-            if "hdpData" in result:
-                home_info = result["hdpData"]["homeInfo"]
-                address_data = {
-                    "street_address": parse_address_two(home_info["streetAddress"])[0],
-                    "unit": parse_unit(home_info["unit"])
-                    if "unit" in home_info
-                    else None,
-                    "city": home_info["city"],
-                    "state": home_info["state"],
-                    "zip_code": home_info["zipcode"],
-                    "country": home_info["country"],
-                }
-                property_data = {
-                    "site_name": self.site_name,
-                    "address": Address(**address_data),
-                    "property_url": f"https://www.zillow.com{result['detailUrl']}",
-                    "beds": int(home_info["bedrooms"])
-                    if "bedrooms" in home_info
-                    else None,
-                    "baths": home_info.get("bathrooms"),
-                    "square_feet": int(home_info["livingArea"])
-                    if "livingArea" in home_info
-                    else None,
-                    "currency": home_info["currency"],
-                    "price": home_info.get("price"),
-                    "tax_assessed_value": int(home_info["taxAssessedValue"])
-                    if "taxAssessedValue" in home_info
-                    else None,
-                    "property_type": PropertyType(home_info["homeType"]),
-                    "listing_type": ListingType(
-                        home_info["statusType"]
-                        if "statusType" in home_info
-                        else self.listing_type
-                    ),
-                    "lot_area_value": round(home_info["lotAreaValue"], 2)
-                    if "lotAreaValue" in home_info
-                    else None,
-                    "lot_area_unit": home_info.get("lotAreaUnit"),
-                    "latitude": result["latLong"]["latitude"],
-                    "longitude": result["latLong"]["longitude"],
-                    "status_text": result.get("statusText"),
-                    "posted_time": result["variableData"]["text"]
-                    if "variableData" in result
-                    and "text" in result["variableData"]
-                    and result["variableData"]["type"] == "TIME_ON_INFO"
-                    else None,
-                    "img_src": result.get("imgSrc"),
-                    "price_per_sqft": int(home_info["price"] // home_info["livingArea"])
-                    if "livingArea" in home_info and "price" in home_info
-                    else None,
-                }
-                property_obj = Property(**property_data)
-                properties_list.append(property_obj)
-
-            elif "isBuilding" in result:
-                price = result["price"]
-                building_data = {
-                    "property_url": f"https://www.zillow.com{result['detailUrl']}",
-                    "site_name": self.site_name,
-                    "property_type": PropertyType("BUILDING"),
-                    "listing_type": ListingType(result["statusType"]),
-                    "img_src": result["imgSrc"],
-                    "price": int(price.replace("From $", "").replace(",", ""))
-                    if "From $" in price
-                    else None,
-                    "apt_min_price": int(
-                        price.replace("$", "").replace(",", "").replace("+/mo", "")
-                    )
-                    if "+/mo" in price
-                    else None,
-                    "address": self._extract_address(result["address"]),
-                    "bldg_min_beds": result["minBeds"],
-                    "currency": "USD",
-                    "bldg_min_baths": result["minBaths"],
-                    "bldg_min_area": result.get("minArea"),
-                    "bldg_unit_count": result["unitCount"],
-                    "bldg_name": result.get("communityName"),
-                    "status_text": result["statusText"],
-                    "latitude": result["latLong"]["latitude"],
-                    "longitude": result["latLong"]["longitude"],
-                }
-                building_obj = Property(**building_data)
-                properties_list.append(building_obj)
-
-        return properties_list
-
-    def _get_single_property_page(self, property_data: dict):
-        """
-        This method is used when a user enters the exact location & zillow returns just one property
-        """
-        url = (
-            f"https://www.zillow.com{property_data['hdpUrl']}"
-            if "zillow.com" not in property_data["hdpUrl"]
-            else property_data["hdpUrl"]
-        )
-        address_data = property_data["address"]
-        street_address, unit = parse_address_two(address_data["streetAddress"])
-        address = Address(
-            street_address=street_address,
-            unit=unit,
-            city=address_data["city"],
-            state=address_data["state"],
-            zip_code=address_data["zipcode"],
-            country=property_data.get("country"),
-        )
-        property_type = property_data.get("homeType", None)
-        return Property(
-            site_name=self.site_name,
-            address=address,
-            property_url=url,
-            beds=property_data.get("bedrooms", None),
-            baths=property_data.get("bathrooms", None),
-            year_built=property_data.get("yearBuilt", None),
-            price=property_data.get("price", None),
-            tax_assessed_value=property_data.get("taxAssessedValue", None),
-            latitude=property_data.get("latitude"),
-            longitude=property_data.get("longitude"),
-            img_src=property_data.get("streetViewTileImageUrlMediumAddress"),
-            currency=property_data.get("currency", None),
-            lot_area_value=property_data.get("lotAreaValue"),
-            lot_area_unit=property_data["lotAreaUnits"].lower()
-            if "lotAreaUnits" in property_data
-            else None,
-            agent_name=property_data.get("attributionInfo", {}).get("agentName", None),
-            stories=property_data.get("resoFacts", {}).get("stories", None),
-            description=property_data.get("description", None),
-            mls_id=property_data.get("attributionInfo", {}).get("mlsId", None),
-            price_per_sqft=property_data.get("resoFacts", {}).get(
-                "pricePerSquareFoot", None
-            ),
-            square_feet=property_data.get("livingArea", None),
-            property_type=PropertyType(property_type),
-            listing_type=self.listing_type,
-        )
-
-    def _extract_address(self, address_str):
-        """
-        Extract address components from a string formatted like '555 Wedglea Dr, Dallas, TX',
-        and return an Address object.
-        """
-        parts = address_str.split(", ")
-
-        if len(parts) != 3:
-            raise ValueError(f"Unexpected address format: {address_str}")
-
-        street_address = parts[0].strip()
-        city = parts[1].strip()
-        state_zip = parts[2].split(" ")
-
-        if len(state_zip) == 1:
-            state = state_zip[0].strip()
-            zip_code = None
-        elif len(state_zip) == 2:
-            state = state_zip[0].strip()
-            zip_code = state_zip[1].strip()
-        else:
-            raise ValueError(f"Unexpected state/zip format in address: {address_str}")
-
-        street_address, unit = parse_address_two(street_address)
-        return Address(
-            street_address=street_address,
-            city=city,
-            unit=unit,
-            state=state,
-            zip_code=zip_code,
-            country="USA",
-        )
-
-    @staticmethod
-    def _get_headers():
-        return {
-            "authority": "www.zillow.com",
-            "accept": "*/*",
-            "accept-language": "en-US,en;q=0.9",
-            "content-type": "application/json",
-            "cookie": 'zjs_user_id=null; zg_anonymous_id=%220976ab81-2950-4013-98f0-108b15a554d2%22; zguid=24|%246b1bc625-3955-4d1e-a723-e59602e4ed08; g_state={"i_p":1693611172520,"i_l":1}; zgsession=1|d48820e2-1659-4d2f-b7d2-99a8127dd4f3; zjs_anonymous_id=%226b1bc625-3955-4d1e-a723-e59602e4ed08%22; JSESSIONID=82E8274D3DC8AF3AB9C8E613B38CF861; search=6|1697585860120%7Crb%3DDallas%252C-TX%26rect%3D33.016646%252C-96.555516%252C32.618763%252C-96.999347%26disp%3Dmap%26mdm%3Dauto%26sort%3Ddays%26listPriceActive%3D1%26fs%3D1%26fr%3D0%26mmm%3D0%26rs%3D0%26ah%3D0%26singlestory%3D0%26abo%3D0%26garage%3D0%26pool%3D0%26ac%3D0%26waterfront%3D0%26finished%3D0%26unfinished%3D0%26cityview%3D0%26mountainview%3D0%26parkview%3D0%26waterview%3D0%26hoadata%3D1%263dhome%3D0%26commuteMode%3Ddriving%26commuteTimeOfDay%3Dnow%09%0938128%09%7B%22isList%22%3Atrue%2C%22isMap%22%3Atrue%7D%09%09%09%09%09; AWSALB=gAlFj5Ngnd4bWP8k7CME/+YlTtX9bHK4yEkdPHa3VhL6K523oGyysFxBEpE1HNuuyL+GaRPvt2i/CSseAb+zEPpO4SNjnbLAJzJOOO01ipnWN3ZgPaa5qdv+fAki; AWSALBCORS=gAlFj5Ngnd4bWP8k7CME/+YlTtX9bHK4yEkdPHa3VhL6K523oGyysFxBEpE1HNuuyL+GaRPvt2i/CSseAb+zEPpO4SNjnbLAJzJOOO01ipnWN3ZgPaa5qdv+fAki; search=6|1697587741808%7Crect%3D33.37188814545521%2C-96.34484483007813%2C32.260490641365685%2C-97.21001816992188%26disp%3Dmap%26mdm%3Dauto%26p%3D1%26sort%3Ddays%26z%3D1%26listPriceActive%3D1%26fs%3D1%26fr%3D0%26mmm%3D0%26rs%3D0%26ah%3D0%26singlestory%3D0%26housing-connector%3D0%26abo%3D0%26garage%3D0%26pool%3D0%26ac%3D0%26waterfront%3D0%26finished%3D0%26unfinished%3D0%26cityview%3D0%26mountainview%3D0%26parkview%3D0%26waterview%3D0%26hoadata%3D1%26zillow-owned%3D0%263dhome%3D0%26featuredMultiFamilyBuilding%3D0%26commuteMode%3Ddriving%26commuteTimeOfDay%3Dnow%09%09%09%7B%22isList%22%3Atrue%2C%22isMap%22%3Atrue%7D%09%09%09%09%09',
-            "origin": "https://www.zillow.com",
-            "referer": "https://www.zillow.com",
-            "sec-ch-ua": '"Chromium";v="116", "Not)A;Brand";v="24", "Google Chrome";v="116"',
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"Windows"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-        }
--- a/homeharvest/exceptions.py
+++ b/homeharvest/exceptions.py
@ -1,14 +1,14 @@
-class InvalidSite(Exception):
-    """Raised when a provided site is does not exist."""
-
-
 class InvalidListingType(Exception):
    """Raised when a provided listing type is does not exist."""


-class NoResultsFound(Exception):
-    """Raised when no results are found for the given location"""
+class 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 GeoCoordsNotFound(Exception):
-    """Raised when no property is found for the given address"""
+class AuthenticationError(Exception):
+    """Raised when there is an issue with the authentication process."""
+    def __init__(self, *args, response):
+        super().__init__(*args)
+
+        self.response = response
--- a/homeharvest/utils.py
+++ b/homeharvest/utils.py
@ -1,48 +1,166 @@
-import re
+from __future__ import annotations
+import pandas as pd
+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",
+    "state",
+    "zip_code",
+    "beds",
+    "full_baths",
+    "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",
+]


-def parse_address_two(street_address: str) -> tuple:
-    if not street_address:
-        return street_address, None
+def process_result(result: Property) -> pd.DataFrame:
+    prop_data = {prop: None for prop in ordered_properties}
+    prop_data.update(result.__dict__)

-    apt_match = re.search(
-        r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+|SUITE\s*[\dA-Z]+)$",
-        street_address,
-        re.I,
-    )
+    if "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 apt_match:
-        apt_str = apt_match.group().strip()
-        cleaned_apt_str = re.sub(
-            r"(APT\s*|UNIT\s*|LOT\s*|SUITE\s*)", "#", apt_str, flags=re.I
+    if "advertisers" in prop_data and prop_data.get("advertisers"):
+        advertiser_data: Advertisers | None = prop_data["advertisers"]
+        if advertiser_data.agent:
+            agent_data = advertiser_data.agent
+            prop_data["agent_id"] = agent_data.uuid
+            prop_data["agent_name"] = agent_data.name
+            prop_data["agent_email"] = agent_data.email
+            prop_data["agent_phones"] = agent_data.phones
+            prop_data["agent_mls_set"] = agent_data.mls_set
+            prop_data["agent_nrds_id"] = agent_data.nrds_id
+
+        if advertiser_data.broker:
+            broker_data = advertiser_data.broker
+            prop_data["broker_id"] = broker_data.uuid
+            prop_data["broker_name"] = broker_data.name
+
+        if advertiser_data.builder:
+            builder_data = advertiser_data.builder
+            prop_data["builder_id"] = builder_data.uuid
+            prop_data["builder_name"] = builder_data.name
+
+        if advertiser_data.office:
+            office_data = advertiser_data.office
+            prop_data["office_id"] = office_data.uuid
+            prop_data["office_name"] = office_data.name
+            prop_data["office_email"] = office_data.email
+            prop_data["office_phones"] = office_data.phones
+            prop_data["office_mls_set"] = office_data.mls_set
+
+    prop_data["price_per_sqft"] = prop_data["prc_sqft"]
+    prop_data["nearby_schools"] = filter(None, prop_data["nearby_schools"]) if prop_data["nearby_schools"] else None
+    prop_data["nearby_schools"] = ", ".join(set(prop_data["nearby_schools"])) if prop_data["nearby_schools"] else None
+
+    description = result.description
+    if description:
+        prop_data["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
+        prop_data["sqft"] = description.sqft
+        prop_data["lot_sqft"] = description.lot_sqft
+        prop_data["sold_price"] = description.sold_price
+        prop_data["year_built"] = description.year_built
+        prop_data["parking_garage"] = description.garage
+        prop_data["stories"] = description.stories
+        prop_data["text"] = description.text

-        main_address = street_address.replace(apt_str, "").strip()
-        return main_address, cleaned_apt_str
-    else:
-        return street_address, None
+    properties_df = pd.DataFrame([prop_data])
+    properties_df = properties_df.reindex(columns=ordered_properties)
+
+    return properties_df[ordered_properties]


-def parse_unit(street_address: str):
-    if not street_address:
-        return None
-    apt_match = re.search(
-        r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+)$",
-        street_address,
-        re.I,
-    )
-
-    if apt_match:
-        apt_str = apt_match.group().strip()
-        apt_str = re.sub(r"(APT\s*|UNIT\s*|LOT\s*)", "#", apt_str, flags=re.I)
-        return apt_str
-    else:
-        return None
+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.")


-if __name__ == "__main__":
-    print(parse_address_two("4303 E Cactus Rd Apt 126"))
-    print(parse_address_two("1234 Elm Street apt 2B"))
-    print(parse_address_two("1234 Elm Street UNIT 3A"))
-    print(parse_address_two("1234 Elm Street unit 3A"))
-    print(parse_address_two("1234 Elm Street SuIte 3A"))
+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.")
--- a/poetry.lock
+++ b/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 = [
-    {file = "charset-normalizer-3.2.0.tar.gz", hash = "sha256:3bb3d25a8e6c0aedd251753a79ae98a093c7e7b471faa3aa9a93a81431987ace"},
-    {file = "charset_normalizer-3.2.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0b87549028f680ca955556e3bd57013ab47474c3124dc069faa0b6545b6c9710"},
-    {file = "charset_normalizer-3.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7c70087bfee18a42b4040bb9ec1ca15a08242cf5867c58726530bdf3945672ed"},
-    {file = "charset_normalizer-3.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a103b3a7069b62f5d4890ae1b8f0597618f628b286b03d4bc9195230b154bfa9"},
-    {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:94aea8eff76ee6d1cdacb07dd2123a68283cb5569e0250feab1240058f53b623"},
-    {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:db901e2ac34c931d73054d9797383d0f8009991e723dab15109740a63e7f902a"},
-    {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b0dac0ff919ba34d4df1b6131f59ce95b08b9065233446be7e459f95554c0dc8"},
-    {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:193cbc708ea3aca45e7221ae58f0fd63f933753a9bfb498a3b474878f12caaad"},
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+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
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 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
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 xml = ["lxml (>=4.8.0)"]

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+
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 version = "1.3.0"
@ -304,6 +384,134 @@ files = [
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 testing = ["pytest", "pytest-benchmark"]

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+
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+
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+    {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
+    {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
+    {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
+    {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
+]
+
 [[package]]
 name = "requests"
 version = "2.31.0"
@ -372,6 +640,22 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]

+[[package]]
+name = "setuptools"
+version = "69.5.1"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"},
+    {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"},
+]
+
+[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"
@ -383,6 +667,21 @@ files = [
    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]

+[[package]]
+name = "tenacity"
+version = "9.0.0"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {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"
@ -394,6 +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"
@ -407,13 +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]
@ -422,7 +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 = "eede625d6d45085e143b0af246cb2ce00cff8579c667be3b63387c8594a5570d"
+python-versions = ">=3.9,<3.13"
+content-hash = "cefc11b1bf5ad99d628f6d08f6f03003522cc1b6e48b519230d99d716a5c165c"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,19 +1,25 @@
 [tool.poetry]
 name = "homeharvest"
-version = "0.2.0"
-description = "Real estate scraping library supporting Zillow, Realtor.com & Redfin."
-authors = ["Zachary Hampton <zachary@zacharysproducts.com>", "Cullen Watson <cullen@cullen.ai>"]
-homepage = "https://github.com/ZacharyHampton/HomeHarvest"
+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"
+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"]
--- a/tests/test_realtor.py
+++ b/tests/test_realtor.py
@ -1,40 +1,302 @@
-from homeharvest import scrape_property
-from homeharvest.exceptions import (
-    InvalidSite,
-    InvalidListingType,
-    NoResultsFound,
-    GeoCoordsNotFound,
-)
+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", listing_type="pending")
+
+    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 len(pending_or_contingent_result) != len(regular_result)
+
+
+def test_realtor_pending_comps():
+    pending_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="pending",
+    )
+
+    for_sale_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="for_sale",
+    )
+
+    sold_comps = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=5,
+        past_days=180,
+        listing_type="sold",
+    )
+
+    results = [pending_comps, for_sale_comps, sold_comps]
+    assert all([result is not None for result in results])
+
+    #: assert all lengths are different
+    assert len(set([len(result) for result in results])) == len(results)
+
+
+def test_realtor_sold_past():
+    result = scrape_property(
+        location="San Diego, CA",
+        past_days=30,
+        listing_type="sold",
+    )
+
+    assert result is not None and len(result) > 0
+
+
+def test_realtor_comps():
+    result = scrape_property(
+        location="2530 Al Lipscomb Way",
+        radius=0.5,
+        past_days=180,
+        listing_type="sold",
+    )
+
+    assert result is not None and len(result) > 0
+
+
+def test_realtor_last_x_days_sold():
+    days_result_30 = scrape_property(location="Dallas, TX", listing_type="sold", past_days=30)
+
+    days_result_10 = scrape_property(location="Dallas, TX", listing_type="sold", past_days=10)
+
+    assert all([result is not None for result in [days_result_30, days_result_10]]) and len(days_result_30) != len(
+        days_result_10
+    )
+
+
+def test_realtor_date_range_sold():
+    days_result_30 = scrape_property(
+        location="Dallas, TX", listing_type="sold", date_from="2023-05-01", date_to="2023-05-28"
+    )
+
+    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_60]]) and len(days_result_30) < len(
+        days_result_60
+    )
+
+
+def test_realtor_single_property():
+    results = [
+        scrape_property(
+            location="15509 N 172nd Dr, Surprise, AZ 85388",
+            listing_type="for_sale",
+        ),
+        scrape_property(
+            location="2530 Al Lipscomb Way",
+            listing_type="for_sale",
+        ),
+    ]
+
+    assert all([result is not None for result in results])


 def test_realtor():
    results = [
        scrape_property(
            location="2530 Al Lipscomb Way",
-            site_name="realtor.com",
            listing_type="for_sale",
        ),
        scrape_property(
-            location="Phoenix, AZ", site_name=["realtor.com"], listing_type="for_rent"
+            location="Phoenix, AZ", listing_type="for_rent", limit=1000
        ),  #: does not support "city, state, USA" format
        scrape_property(
-            location="Dallas, TX", site_name="realtor.com", listing_type="sold"
+            location="Dallas, TX", listing_type="sold", limit=1000
        ),  #: does not support "city, state, USA" format
-        scrape_property(location="85281", site_name="realtor.com"),
+        scrape_property(location="85281"),
    ]

    assert all([result is not None for result in results])

-    bad_results = []
-    try:
-        bad_results += [
-            scrape_property(
-                location="abceefg ju098ot498hh9",
-                site_name="realtor.com",
-                listing_type="for_sale",
-            )
-        ]
-    except (InvalidSite, InvalidListingType, NoResultsFound, GeoCoordsNotFound):
+
+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",
+    )
+
+    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)
--- a/tests/test_redfin.py
+++ b/tests/test_redfin.py
@ -1,38 +0,0 @@
-from homeharvest import scrape_property
-from homeharvest.exceptions import (
-    InvalidSite,
-    InvalidListingType,
-    NoResultsFound,
-    GeoCoordsNotFound,
-)
-
-
-def test_redfin():
-    results = [
-        scrape_property(
-            location="2530 Al Lipscomb Way", site_name="redfin", listing_type="for_sale"
-        ),
-        scrape_property(
-            location="Phoenix, AZ, USA", site_name=["redfin"], listing_type="for_rent"
-        ),
-        scrape_property(
-            location="Dallas, TX, USA", site_name="redfin", listing_type="sold"
-        ),
-        scrape_property(location="85281", site_name="redfin"),
-    ]
-
-    assert all([result is not None for result in results])
-
-    bad_results = []
-    try:
-        bad_results += [
-            scrape_property(
-                location="abceefg ju098ot498hh9",
-                site_name="redfin",
-                listing_type="for_sale",
-            )
-        ]
-    except (InvalidSite, InvalidListingType, NoResultsFound, GeoCoordsNotFound):
-        assert True
-
-    assert all([result is None for result in bad_results])
--- a/tests/test_zillow.py
+++ b/tests/test_zillow.py
@ -1,38 +0,0 @@
-from homeharvest import scrape_property
-from homeharvest.exceptions import (
-    InvalidSite,
-    InvalidListingType,
-    NoResultsFound,
-    GeoCoordsNotFound,
-)
-
-
-def test_zillow():
-    results = [
-        scrape_property(
-            location="2530 Al Lipscomb Way", site_name="zillow", listing_type="for_sale"
-        ),
-        scrape_property(
-            location="Phoenix, AZ, USA", site_name=["zillow"], listing_type="for_rent"
-        ),
-        scrape_property(
-            location="Dallas, TX, USA", site_name="zillow", listing_type="sold"
-        ),
-        scrape_property(location="85281", site_name="zillow"),
-    ]
-
-    assert all([result is not None for result in results])
-
-    bad_results = []
-    try:
-        bad_results += [
-            scrape_property(
-                location="abceefg ju098ot498hh9",
-                site_name="zillow",
-                listing_type="for_sale",
-            )
-        ]
-    except (InvalidSite, InvalidListingType, NoResultsFound, GeoCoordsNotFound):
-        assert True
-
-    assert all([result is None for result in bad_results])
Author	SHA1	Message	Date
zachary	e378feeefe	- bug fixes	2025-04-12 18:34:35 -07:00
zachary	8a5683fe79	- return type parameter - optimized get extra fields with query clustering	2025-04-12 17:55:52 -07:00
Zachary Hampton	65f799a27d	Update README.md	2025-02-21 13:33:32 -07:00
Cullen Watson	0de916e590	enh:tax history	2025-01-06 05:28:36 -06:00
Cullen Watson	6a3f7df087	chore:yml	2024-11-05 23:55:59 -06:00
Cullen Watson	a75bcc2aa0	docs:readme	2024-11-04 10:22:32 -06:00
Cullen Watson	1082b86fa1	docs:readme	2024-11-03 17:23:58 -06:00
Cullen Watson	8e04f6b117	enh: property type (#102 )	2024-11-03 17:23:07 -06:00
Zachary Hampton	1f717bd9e3	- switch eps - new hrefs - property_id, listing_id data points	2024-09-06 15:49:07 -07:00
Zachary Hampton	8cfe056f79	- office mls set	2024-08-23 10:54:43 -07:00
Zachary Hampton	1010c743b6	- agent mls set and nrds id	2024-08-23 10:47:45 -07:00
Zachary Hampton	32fdc281e3	- rewrote & optimized flow - new_construction data point - renamed "agent" & "broker" to "agent_name" & "broker_name" - added builder & office data - added entity uuids	2024-08-20 05:19:15 -07:00
Zachary Hampton	6d14b8df5a	- fix limit parameter - fix specific for_rent apartment listing prices	2024-08-13 10:44:11 -07:00
Zachary Hampton	3f44744d61	- primary photo bug fix - limit parameter	2024-07-15 07:19:57 -07:00
Zachary Hampton	ac0cad62a7	- optimizations	2024-06-14 21:50:23 -07:00
Cullen Watson	beb885cc8d	fix: govt type (#82 )	2024-06-12 17:34:34 -05:00
Zachary Hampton	011680f7d8	- style error bug fix	2024-06-06 15:24:12 -07:00
Zachary Hampton	93e6778a48	- exclude_pending parameter	2024-05-31 22:17:29 -07:00
Zachary Hampton	ec036bb989	- optimizations & updated realtor headers	2024-05-20 12:13:30 -07:00
Zachary Hampton	aacd168545	- alt photos bug fix	2024-05-18 17:47:55 -07:00
Zachary Hampton	0d70007000	- alt photos bug fix	2024-05-16 23:04:07 -07:00
Zachary Hampton	018d3fbac4	- Python 3.9 support (tested) (could potentially work for lower versions, but I have not validated such)	2024-05-14 19:13:04 -07:00
Zachary Hampton	803fd618e9	- data cleaning & CONDOP bug fixes	2024-05-12 21:12:12 -07:00
Zachary Hampton	b23b55ca80	- full street line (data quality improvement)	2024-05-12 18:49:44 -07:00
Zachary Hampton	3458a08383	- broker data	2024-05-11 21:35:29 -07:00
Zachary Hampton	c3e24a4ce0	- extra_property_details parameter - updated docs - classified exception	2024-05-02 09:04:49 -07:00
Zachary Hampton	46985dcee4	- various data quality fixes (including #70 )	2024-05-02 08:48:53 -07:00
Cullen Watson	04ae968716	enh: assessed/estimated value (#77 )	2024-04-30 15:29:54 -05:00
Cullen	c5b15e9be5	chore: version	2024-04-20 17:45:29 -05:00
joecryptotoo	7a525caeb8	added county, fips, and text desciption fields (#72 )	2024-04-20 17:44:28 -05:00
Cullen Watson	7246703999	Schools (#69 )	2024-04-16 20:01:20 -05:00
Cullen Watson	6076b0f961	enh: add agent (#68 )	2024-04-16 15:09:32 -05:00
Cullen Watson	cdc6f2a2a8	docs: readme	2024-04-16 14:59:50 -05:00
Cullen Watson	0bdf56568e	enh: add agent name/phone (#66 )	2024-04-16 14:55:44 -05:00
Cullen Watson	1f47fc3b7e	fix: use enum value (#65 )	2024-04-12 01:41:15 -05:00
Zachary Hampton	5c2498c62b	- pending date, property type fields (#45 ) - alt photos bug fix (#57)	2024-03-13 19:17:17 -07:00
Zachary Hampton	d775540afd	- location bug fix	2024-03-06 16:31:06 -07:00
Cullen Watson	5ea9a6f6b6	docs: readme	2024-03-03 11:49:27 -06:00
robertomr100	ab6a0e3b6e	Add foreclosure parameter (#55 )	2024-03-03 11:45:28 -06:00
Zachary Hampton	03198428de	Merge pull request #48 from Bunsly/for_rent_url fix: rent url	2024-01-09 13:12:30 -07:00
Cullen Watson	70fa071318	fix: rent url	2024-01-08 12:46:31 -06:00
Cullen Watson	f7e74cf535	Merge pull request #44 from Bunsly/fix_postal_search fix postal search to search just by zip	2023-12-02 00:40:13 -06:00
Cullen Watson	e17b976923	fix postal search to search just by zip	2023-12-02 00:39:28 -06:00
Zachary Hampton	ad13b55ea6	Update README.md	2023-11-30 11:48:48 -07:00
Cullen Watson	19f23c95c4	Merge pull request #43 from Bunsly/add_photos Add photos	2023-11-24 21:40:34 -06:00
Cullen	4676ec9839	chore: remove test file	2023-11-24 13:42:52 -06:00
Cullen	6dd0b058d3	chore: version	2023-11-24 13:41:46 -06:00
Cullen	a74c1a9950	enh: add photos	2023-11-24 13:40:57 -06:00
Cullen Watson	fa507dbc72	docs: typo	2023-11-20 01:05:10 -06:00
Cullen Watson	5b6a9943cc	Merge pull request #42 from Bunsly/street_dirction fix: add street direction	2023-11-08 16:53:29 -06:00
Cullen Watson	9816defaf3	chore: version	2023-11-08 16:53:05 -06:00
Cullen Watson	f692b438b2	fix: add street direction	2023-11-08 16:52:06 -06:00
Zachary Hampton	30f48f54c8	Update README.md	2023-11-06 22:13:01 -07:00
Cullen Watson	7f86f69610	docs: readme	2023-11-03 18:53:46 -05:00
Cullen Watson	cc64dacdb0	docs: readme - date_from, date_to	2023-11-03 18:52:22 -05:00
Cullen Watson	d3268d8e5a	Merge pull request #40 from Bunsly/date_range Add date_to and date_from params	2023-11-03 18:42:13 -05:00
Cullen Watson	4edad901c5	[enh] date_to and date_from	2023-11-03 18:40:34 -05:00
Zachary Hampton	c597a78191	- None address bug fix	2023-10-18 16:32:43 -07:00
Zachary Hampton	11a7d854f0	- remove pending listings from for_sale	2023-10-18 14:41:41 -07:00
Zachary Hampton	f726548cc6	Update pyproject.toml	2023-10-18 09:35:48 -07:00
Zachary Hampton	fad7d670eb	Update README.md	2023-10-18 08:37:42 -07:00
Zachary Hampton	89a6f93c9f	Update pyproject.toml	2023-10-18 08:37:26 -07:00
Zachary Hampton	e1090b06e4	Update README.md	2023-10-17 20:22:25 -07:00
Cullen Watson	5036e74b60	Merge branch 'master' of https://github.com/ZacharyHampton/HomeHarvest	2023-10-09 11:30:17 -05:00
Cullen Watson	2cb544bc8d	[chore] display clickable URLs in jupyter	2023-10-09 11:28:56 -05:00
Zachary Hampton	68cb365e03	Merge pull request #34 from ZacharyHampton/days_on_mls [enh] days_on_mls attr	2023-10-09 09:04:59 -07:00
Cullen Watson	23876d5725	[chore] function types	2023-10-09 11:02:51 -05:00
Cullen Watson	b59d55f6b5	[enh] days_on_mls attr	2023-10-09 11:00:36 -05:00
Cullen Watson	3c3adb5f29	[docs] update video	2023-10-05 20:24:23 -05:00
Zachary Hampton	6ede8622cc	- pending listing support - removal of pending_or_contingent param	2023-10-05 11:43:00 -07:00
Cullen Watson	9f50d33bdb	[chore] remove unused dependency	2023-10-05 10:11:45 -05:00
Cullen Watson	735ec021f7	[docs] README	2023-10-05 10:03:21 -05:00
Zachary Hampton	00537329cf	- version bump	2023-10-04 21:35:21 -07:00
Zachary Hampton	a9225b532f	- rename days variable	2023-10-04 21:35:14 -07:00
Zachary Hampton	ba7ad069c9	Merge pull request #32 from ZacharyHampton/key_error [fix] keyerror on style	2023-10-04 20:35:05 -07:00
Cullen Watson	22bda972b0	[chore] version number	2023-10-04 22:34:52 -05:00
Cullen Watson	6f5bbf79a4	[fix] keyerror on style	2023-10-04 22:33:21 -05:00
Cullen Watson	608cceba34	[docs] reorder	2023-10-04 22:12:16 -05:00
Cullen Watson	3609586995	[docs]: add contingent to example	2023-10-04 22:11:38 -05:00
Cullen Watson	68c7e411e4	[docs] pending / contingent searches	2023-10-04 22:07:51 -05:00
Cullen Watson	5e825601a7	[docs] update example	2023-10-04 21:50:54 -05:00
Cullen Watson	ce3f94d0af	[docs] update example	2023-10-04 21:50:16 -05:00
Zachary Hampton	4a1116440d	Merge pull request #31 from ZacharyHampton/v0.3 v0.3	2023-10-04 19:26:44 -07:00
Cullen Watson	2d092c595f	[docs]: Update README.md	2023-10-04 21:24:24 -05:00
Cullen Watson	4dbb064fe9	[docs]: Update README.md	2023-10-04 21:21:45 -05:00
Cullen Watson	4e78248032	Update README.md	2023-10-04 21:17:49 -05:00
Zachary Hampton	37e20f4469	- remove neighborhoods - rename data	2023-10-04 18:44:47 -07:00
Zachary Hampton	8a5f0dc2c9	- pending or contingent support	2023-10-04 18:25:01 -07:00
Zachary Hampton	de692faae2	- rename last_x_days - docstrings for scrape_property	2023-10-04 18:06:06 -07:00
Zachary Hampton	6bb68766fc	- realtor tests	2023-10-04 12:04:05 -07:00
Zachary Hampton	446d5488b8	- single address support again	2023-10-04 10:07:32 -07:00
Cullen Watson	68e15ce696	[docs] clarify example	2023-10-04 10:14:11 -05:00
Cullen Watson	c4870677c2	[enh]: make last_x_days generic add mls_only make radius generic	2023-10-04 10:11:53 -05:00
Cullen Watson	51bde20c3c	[chore]: clean up	2023-10-04 08:58:55 -05:00
Zachary Hampton	f8c0dd766d	- realtor support	2023-10-03 23:33:53 -07:00
Zachary Hampton	f06a01678c	- cli readme update	2023-10-03 22:31:23 -07:00
Zachary Hampton	d2879734e6	- cli update	2023-10-03 22:25:29 -07:00
Zachary Hampton	bf81ef413f	- version bump	2023-10-03 22:22:09 -07:00
Zachary Hampton	29664e4eee	- cullen merge	2023-10-03 22:21:16 -07:00
Zachary Hampton	088088ae51	- last x days param	2023-10-03 15:05:17 -07:00
Zachary Hampton	40bbf76db1	- realtor radius	2023-10-02 13:58:47 -07:00
Zachary Hampton	1f1ca8068f	- realtor.com default	2023-10-02 10:28:13 -07:00
Zachary Hampton	8388d47f73	- version bump	2023-10-01 09:13:37 -07:00
Zachary Hampton	ba503b0ca3	Merge pull request #27 from ddxv/zillow-ua-header Zillow Request Header: Match observed behaivor in FireFox of not sending sec-ch-ua headers	2023-10-01 09:12:58 -07:00
james	8962d619e1	Match observed behaivor in FireFox of not sending ua-ch headers in request to prevent recent 403	2023-10-01 11:31:51 +08:00
Zachary Hampton	3b7c17b7b5	- zillow proxy support	2023-09-28 18:40:16 -07:00
Zachary Hampton	59317fd6fc	Merge pull request #25 from ZacharyHampton/fix/recent-issues Fix/recent issues	2023-09-28 18:27:04 -07:00
Zachary Hampton	928b431d1f	- bump version	2023-09-28 18:25:53 -07:00
Zachary Hampton	896f862137	- zillow flow update	2023-09-28 18:25:47 -07:00
Zachary Hampton	3174f5076c	Merge pull request #23 from ZacharyHampton/fix/recent-issues Fixes & Changes for recent issues	2023-09-28 18:07:55 -07:00
Zachary Hampton	2abbb913a8	- convert posted_time to datetime - zillow location bug fix	2023-09-28 18:07:42 -07:00
Cullen Watson	73b6d5b33f	[fix] zilow tls client	2023-09-28 19:34:01 -05:00
Zachary Hampton	da39c989d9	- version bump	2023-09-28 15:27:36 -07:00
Zachary Hampton	01c53f9399	- redfin bug fix - add recent features for issues	2023-09-28 15:19:43 -07:00
Zachary Hampton	9200c17df2	- version bump	2023-09-23 10:55:50 -07:00
Zachary Hampton	9e262bf214	Merge remote-tracking branch 'origin/master'	2023-09-23 10:55:29 -07:00
Zachary Hampton	82f78fb578	- zillow bug fix	2023-09-23 10:55:14 -07:00
Cullen Watson	b0e40df00a	Update pyproject.toml	2023-09-22 09:51:24 -05:00
Cullen Watson	2fc40e0dad	fix: cookie	2023-09-22 09:47:37 -05:00
Zachary Hampton	254f3a68a1	- redfin bug fix	2023-09-21 18:54:03 -07:00
Zachary Hampton	05713c76b0	- redfin bug fix - .get	2023-09-21 11:27:12 -07:00
Cullen Watson	9120cc9bfe	fix: remove line	2023-09-21 13:10:14 -05:00
Cullen Watson	eee4b19515	Merge branch 'master' of https://github.com/ZacharyHampton/HomeHarvest	2023-09-21 13:06:15 -05:00
Cullen Watson	c25961eded	fix: KeyEror : [minBaths]	2023-09-21 13:06:06 -05:00
Zachary Hampton	0884c3d163	Update README.md	2023-09-21 09:55:29 -07:00
Cullen Watson	8f37bfdeb8	chore: version number	2023-09-21 11:19:23 -05:00
Cullen Watson	48c2338276	fix: keyerror	2023-09-21 11:18:37 -05:00
Cullen Watson	f58a1f4a74	docs: tryhomeharvest.com	2023-09-21 10:57:11 -05:00
Zachary Hampton	4cef926d7d	Merge pull request #14 from ZacharyHampton/keep_duplicates_flag Keep duplicates flag	2023-09-20 20:27:08 -07:00
Cullen Watson	e82eeaa59f	docs: add keep duplicates flag	2023-09-20 20:25:50 -05:00
Cullen Watson	644f16b25b	feat: keep duplicates flag	2023-09-20 20:24:18 -05:00
Cullen Watson	e9ddc6df92	docs: update tutorial vid for release v0.2.7	2023-09-19 22:18:49 -05:00
Cullen Watson	50fb1c391d	docs: update property schema	2023-09-19 21:35:37 -05:00
Cullen Watson	4f91f9dadb	chore: version number	2023-09-19 21:17:12 -05:00
Zachary Hampton	66e55173b1	Merge pull request #13 from ZacharyHampton/simplify_fields fix: simplify fields	2023-09-19 19:16:18 -07:00
Cullen Watson	f6054e8746	fix: simplify fields	2023-09-19 21:13:20 -05:00
Cullen Watson	e8d9235ee6	chore: update version number	2023-09-19 16:43:59 -05:00
Cullen Watson	043f091158	fix: keyerror on address	2023-09-19 16:43:17 -05:00
Cullen Watson	eae8108978	docs: change cmd	2023-09-19 16:18:01 -05:00
Zachary Hampton	0a39357a07	Merge pull request #12 from ZacharyHampton/proxy_bug fix: proxy add to session correctly	2023-09-19 14:07:25 -07:00
Cullen Watson	8f06d46ddb	chore: version number	2023-09-19 16:07:06 -05:00
Cullen Watson	0dae14ccfc	fix: proxy add to session correctly	2023-09-19 16:05:14 -05:00
Zachary Hampton	9aaabdd5d8	Merge pull request #11 from ZacharyHampton/proxy_support Proxy support	2023-09-19 13:50:14 -07:00
Cullen Watson	cdf41fe9f2	fix: remove self.proxy	2023-09-19 15:49:50 -05:00
Cullen Watson	1f0feb836d	refactor: move proxy to session	2023-09-19 15:48:46 -05:00
Cullen Watson	5f31beda46	chore: version number	2023-09-19 15:44:41 -05:00
Cullen Watson	fd9cdea499	feat: proxy support	2023-09-19 15:43:24 -05:00
Zachary Hampton	93a1cbe17f	Merge pull request #10 from ZacharyHampton/cli_homeharvest add cli	2023-09-19 13:07:27 -07:00
Cullen Watson	49d27943c4	add cli	2023-09-19 15:01:39 -05:00
Zachary Hampton	05fca9b7e6	Update README.md	2023-09-19 11:08:08 -07:00
Zachary Hampton	20ce44fb3a	- redfin limiting bug fix	2023-09-19 10:37:10 -07:00
Zachary Hampton	52017c1bb5	Merge pull request #9 from ZacharyHampton/redfin_rental_support feat(redfin): rental support	2023-09-19 10:28:02 -07:00
Cullen Watson	dba1c03081	feat(redfin): add sold listing_type	2023-09-19 12:27:13 -05:00
Cullen Watson	1fc2d8c549	feat(redfin): rental support	2023-09-19 11:58:20 -05:00
Zachary Hampton	02d112eea0	Merge pull request #8 from ZacharyHampton/fix/zillow-location-validation - zillow location validation	2023-09-19 09:33:33 -07:00
Zachary Hampton	30e510882b	- version bump and excel support	2023-09-19 09:26:52 -07:00
Zachary Hampton	78b56c2cac	- zillow location validation	2023-09-19 09:25:08 -07:00
Cullen Watson	087854a688	Merge branch 'master' of https://github.com/ZacharyHampton/HomeHarvest	2023-09-19 00:04:03 -05:00
Cullen Watson	80586467a8	docs:add guide	2023-09-18 23:53:10 -05:00
Cullen Watson	3494b152b8	docs: change install cmd	2023-09-18 23:32:51 -05:00
Cullen Watson	6c6fef80ed	chore: change version number	2023-09-18 23:16:54 -05:00