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70 Commits

Author SHA1 Message Date
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
17 changed files with 899 additions and 1279 deletions

240
README.md
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@@ -1,165 +1,193 @@
<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 simple, yet comprehensive, real estate scraping library that extracts and formats data in the style of MLS listings.
[![Try with Replit](https://replit.com/badge?caption=Try%20with%20Replit)](https://replit.com/@ZacharyHampton/HomeHarvestDemo)
\
**Not technical?** Try out the web scraping tool on our site at [tryhomeharvest.com](https://tryhomeharvest.com).
*Looking to build a data-focused software product?* **[Book a call](https://calendly.com/zachary-products/15min)** *to work with us.*
## Features
- Scrapes properties from **Zillow**, **Realtor.com** & **Redfin** simultaneously
- Aggregates the properties in a Pandas DataFrame
Check out another project we wrote: ***[JobSpy](https://github.com/cullenwatson/JobSpy)** a Python package for job scraping*
[Video Guide for HomeHarvest](https://www.youtube.com/watch?v=HCoHoiJdWQY)
## HomeHarvest Features
- **Source**: Fetches properties directly from **Realtor.com**.
- **Data Format**: Structures data to resemble MLS listings.
- **Export Flexibility**: Options to save as either CSV or Excel.
- **Usage Modes**:
- **Python**: For those who'd like to integrate scraping into their Python scripts.
- **CLI**: For users who prefer command-line operations.
[Video Guide for HomeHarvest](https://youtu.be/JnV7eR2Ve2o) - _updated for release v0.2.7_
![homeharvest](https://github.com/ZacharyHampton/HomeHarvest/assets/78247585/b3d5d727-e67b-4a9f-85d8-1e65fd18620a)
## Installation
```bash
pip install --force-reinstall homeharvest
pip install homeharvest
```
_Python version >= [3.10](https://www.python.org/downloads/release/python-3100/) required_
## Usage
### CLI
```bash
homeharvest "San Francisco, CA" -s zillow realtor.com redfin -l for_rent -o excel -f HomeHarvest
```
This will scrape properties from the specified sites for the given location and listing type, and save the results to an Excel file named `HomeHarvest.xlsx`.
By default:
- If `-s` or `--site_name` is not provided, it will scrape from all available sites.
- If `-l` or `--listing_type` is left blank, the default is `for_sale`. Other options are `for_rent` or `sold`.
- The `-o` or `--output` default format is `excel`. Options are `csv` or `excel`.
- If `-f` or `--filename` is left blank, the default is `HomeHarvest_<current_timestamp>`.
- If `-p` or `--proxy` is not provided, the scraper uses the local IP.
### Python
### 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)
past_days=30, # sold in last 30 days - listed in last x days if (for_sale, for_rent)
# pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
# mls_only=True, # only fetch MLS listings
# proxy="http://user:pass@host:port" # use a proxy to change your IP address
)
print(f"Number of properties: {len(properties)}")
#: 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())
```
### CLI
```
usage: homeharvest [-l {for_sale,for_rent,sold}] [-o {excel,csv}] [-f FILENAME] [-p PROXY] [-d DAYS] [-r RADIUS] [-m] [-c] location
Home Harvest Property Scraper
positional arguments:
location Location to scrape (e.g., San Francisco, CA)
options:
-l {for_sale,for_rent,sold}, --listing_type {for_sale,for_rent,sold}
Listing type to scrape
-o {excel,csv}, --output {excel,csv}
Output format
-f FILENAME, --filename FILENAME
Name of the output file (without extension)
-p PROXY, --proxy PROXY
Proxy to use for scraping
-d DAYS, --days DAYS Sold/listed in last _ days filter.
-r RADIUS, --radius RADIUS
Get comparable properties within _ (e.g., 0.0) miles. Only applicable for individual addresses.
-m, --mls_only If set, fetches only MLS listings.
-c, --pending_or_contingent
If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.
```
```bash
> homeharvest "San Francisco, CA" -l for_rent -o excel -f HomeHarvest
```
## Output
```py
>>> properties.head()
property_url site_name listing_type apt_min_price apt_max_price ...
0 https://www.redfin.com/AZ/Tempe/1003-W-Washing... redfin for_rent 1666.0 2750.0 ...
1 https://www.redfin.com/AZ/Tempe/VELA-at-Town-L... redfin for_rent 1665.0 3763.0 ...
2 https://www.redfin.com/AZ/Tempe/Camden-Tempe/a... redfin for_rent 1939.0 3109.0 ...
3 https://www.redfin.com/AZ/Tempe/Emerson-Park/a... redfin for_rent 1185.0 1817.0 ...
4 https://www.redfin.com/AZ/Tempe/Rio-Paradiso-A... redfin for_rent 1470.0 2235.0 ...
[5 rows x 41 columns]
```
### Parameters for `scrape_properties()`
```plaintext
Required
├── location (str): address in various formats e.g. just zip, full address, city/state, etc.
└── listing_type (enum): for_rent, for_sale, sold
Optional
├── site_name (List[enum], default=all three sites): zillow, realtor.com, redfin
├── proxy (str): in format 'http://user:pass@host:port' or [https, socks]
>>> properties.head()
MLS MLS # Status Style ... COEDate LotSFApx PrcSqft Stories
0 SDCA 230018348 SOLD CONDOS ... 2023-10-03 290110 803 2
1 SDCA 230016614 SOLD TOWNHOMES ... 2023-10-03 None 838 3
2 SDCA 230016367 SOLD CONDOS ... 2023-10-03 30056 649 1
3 MRCA NDP2306335 SOLD SINGLE_FAMILY ... 2023-10-03 7519 661 2
4 SDCA 230014532 SOLD CONDOS ... 2023-10-03 None 752 1
[5 rows x 22 columns]
```
### Parameters for `scrape_property()`
```
Required
├── location (str): The address in various formats - this could be just a zip code, a full address, or city/state, etc.
└── listing_type (option): Choose the type of listing.
- 'for_rent'
- 'for_sale'
- 'sold'
Optional
├── radius (decimal): Radius in miles to find comparable properties based on individual addresses.
│ Example: 5.5 (fetches properties within a 5.5-mile radius if location is set to a specific address; otherwise, ignored)
├── property_younger_than (integer): Number of past days to filter properties. Utilizes 'last_sold_date' for 'sold' listing types, and 'list_date' for others (for_rent, for_sale).
│ Example: 30 (fetches properties listed/sold in the last 30 days)
|
├── pending_or_contingent (True/False): If set, fetches only pending or contingent listings. Only applicable for `for_sale listings` from general area searches.
├── mls_only (True/False): If set, fetches only MLS listings (mainly applicable to 'sold' listings)
└── proxy (string): In format 'http://user:pass@host:port'
```
### Property Schema
```plaintext
Property
├── Basic Information:
├── property_url (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
│ ├── 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
── 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:
│ ├── list_price
│ ├── list_date
│ ├── sold_price
│ ├── last_sold_date
│ ├── price_per_sqft
└── 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)
├── Location Details:
│ ├── latitude
│ ├── longitude
└── Apartment Details (for property type: apartment):
── apt_min_beds: int
├── apt_max_beds: int
├── apt_min_baths: float
├── apt_max_baths: float
├── apt_min_price: int
├── apt_max_price: int
├── apt_min_sqft: int
├── apt_max_sqft: int
└── Parking Details:
── parking_garage
```
## 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
- `NoResultsFound` - no properties found from your search
## 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: Encountering issues with your searches?**
**A:** Try to broaden the parameters you're using. If problems persist, [submit an issue](https://github.com/ZacharyHampton/HomeHarvest/issues).
---
**Q: Received a Forbidden 403 response code?**
**A:** This indicates that you have been blocked by the real estate site for sending too many requests. Currently, **Zillow** is particularly aggressive with blocking. We recommend:
**A:** This indicates that you have been blocked by Realtor.com for sending too many requests. We recommend:
- Waiting a few seconds between requests.
- Trying a VPN to change your IP address.
- Trying a VPN or useing a proxy as a parameter to scrape_property() to change your IP address.
---

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@@ -31,7 +31,7 @@
"metadata": {},
"outputs": [],
"source": [
"# scrapes all 3 sites by default\n",
"# check for sale properties\n",
"scrape_property(\n",
" location=\"dallas\",\n",
" listing_type=\"for_sale\"\n",
@@ -53,7 +53,6 @@
"# search a specific address\n",
"scrape_property(\n",
" location=\"2530 Al Lipscomb Way\",\n",
" site_name=\"zillow\",\n",
" listing_type=\"for_sale\"\n",
")"
]
@@ -68,7 +67,6 @@
"# check rentals\n",
"scrape_property(\n",
" location=\"chicago, illinois\",\n",
" site_name=[\"redfin\", \"zillow\"],\n",
" listing_type=\"for_rent\"\n",
")"
]
@@ -88,7 +86,6 @@
"# check sold properties\n",
"scrape_property(\n",
" location=\"90210\",\n",
" site_name=[\"redfin\"],\n",
" listing_type=\"sold\"\n",
")"
]

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@@ -0,0 +1,20 @@
from homeharvest import scrape_property
from datetime import datetime
# Generate filename based on current timestamp
current_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"HomeHarvest_{current_timestamp}.csv"
properties = scrape_property(
location="San Diego, CA",
listing_type="sold", # or (for_sale, for_rent)
past_days=30, # sold in last 30 days - listed in last x days if (for_sale, for_rent)
# pending_or_contingent=True # use on for_sale listings to find pending / contingent listings
# mls_only=True, # only fetch MLS listings
# proxy="http://user:pass@host:port" # use a proxy to change your IP address
)
print(f"Number of properties: {len(properties)}")
# Export to csv
properties.to_csv(filename, index=False)
print(properties.head())

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@@ -1,188 +1,50 @@
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
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",
"apt_max_price",
"apt_min_sqft",
"apt_max_sqft",
"apt_min_beds",
"apt_max_beds",
"apt_min_baths",
"apt_max_baths",
"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",
"img_src",
"latitude",
"longitude",
"description",
]
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, proxy: str = None
) -> 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()),
proxy=proxy,
)
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
from .exceptions import InvalidListingType, NoResultsFound
def scrape_property(
location: str,
site_name: Union[str, list[str]] = None,
listing_type: str = "for_sale",
radius: float = None,
mls_only: bool = False,
past_days: int = None,
pending_or_contingent: bool = False,
proxy: str = None,
) -> pd.DataFrame:
"""
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)
: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 past_days: Get properties sold or listed (dependent on your listing_type) in the last _ days.
:param pending_or_contingent: If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.
:param proxy: Proxy to use for scraping
"""
if site_name is None:
site_name = list(_scrapers.keys())
validate_input(listing_type)
if not isinstance(site_name, list):
site_name = [site_name]
results = []
if len(site_name) == 1:
final_df = _scrape_single_site(location, site_name[0], listing_type, proxy)
results.append(final_df)
else:
with ThreadPoolExecutor() as executor:
futures = {
executor.submit(
_scrape_single_site, location, s_name, listing_type, proxy
): s_name
for s_name in site_name
}
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:
return pd.DataFrame()
final_df = pd.concat(results, ignore_index=True)
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"
scraper_input = ScraperInput(
location=location,
listing_type=ListingType[listing_type.upper()],
proxy=proxy,
radius=radius,
mls_only=mls_only,
last_x_days=past_days,
pending_or_contingent=pending_or_contingent,
)
return final_df
site = RealtorScraper(scraper_input)
results = site.search()
properties_dfs = [process_result(result) for result in results]
if not properties_dfs:
raise NoResultsFound("no results found for the query")
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=FutureWarning)
return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties]

View File

@@ -9,15 +9,6 @@ def main():
"location", type=str, help="Location to scrape (e.g., San Francisco, CA)"
)
parser.add_argument(
"-s",
"--site_name",
type=str,
nargs="*",
default=None,
help="Site name(s) to scrape from (e.g., realtor, zillow)",
)
parser.add_argument(
"-l",
"--listing_type",
@@ -47,11 +38,45 @@ def main():
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.",
)
parser.add_argument(
"-c",
"--pending_or_contingent",
action="store_true",
help="If set, fetches only pending or contingent listings. Only applicable for for_sale listings from general area searches.",
)
args = parser.parse_args()
result = scrape_property(
args.location, args.site_name, args.listing_type, proxy=args.proxy
args.location,
args.listing_type,
radius=args.radius,
proxy=args.proxy,
mls_only=args.mls_only,
past_days=args.days,
pending_or_contingent=args.pending_or_contingent,
)
if not args.filename:

View File

@@ -1,5 +1,6 @@
from dataclasses import dataclass
import requests
import tls_client
from .models import Property, ListingType, SiteName
@@ -7,18 +8,37 @@ from .models import Property, ListingType, SiteName
class ScraperInput:
location: str
listing_type: ListingType
site_name: SiteName
radius: float | None = None
mls_only: bool | None = None
proxy: str | None = None
last_x_days: int | None = None
pending_or_contingent: bool | None = None
class Scraper:
def __init__(self, scraper_input: ScraperInput):
def __init__(
self,
scraper_input: ScraperInput,
session: requests.Session | tls_client.Session = None,
):
self.location = scraper_input.location
self.listing_type = scraper_input.listing_type
self.session = requests.Session(proxies=scraper_input.proxy)
if not session:
self.session = requests.Session()
else:
self.session = session
if scraper_input.proxy:
proxy_url = scraper_input.proxy
proxies = {"http": proxy_url, "https": proxy_url}
self.session.proxies.update(proxies)
self.listing_type = scraper_input.listing_type
self.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.pending_or_contingent = scraper_input.pending_or_contingent
def search(self) -> list[Property]:
...

View File

@@ -1,5 +1,6 @@
from dataclasses import dataclass
from enum import Enum
from typing import Optional
class SiteName(Enum):
@@ -21,92 +22,44 @@ class ListingType(Enum):
SOLD = "SOLD"
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"
LAND = "LAND"
LOT = "LOT"
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: str | None = None
unit: str | None = None
city: str | None = None
state: str | None = None
zip: str | None = None
@dataclass
class Address:
street_address: str
city: str
state: str
zip_code: str
unit: str | None = None
country: str | None = None
class Description:
style: str | None = None
beds: int | None = None
baths_full: int | None = None
baths_half: int | None = None
sqft: int | None = None
lot_sqft: int | None = None
sold_price: int | None = None
year_built: int | None = None
garage: float | None = None
stories: int | None = None
@dataclass
class Property:
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
mls: str | None = None
mls_id: str | None = None
status: str | None = None
address: Address | None = None
list_price: int | None = None
list_date: str | None = None
last_sold_date: str | None = None
prc_sqft: int | None = None
hoa_fee: int | None = None
description: Description | None = None
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
# 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_beds: int | None = None
apt_max_beds: int | None = None
apt_min_baths: float | None = None
apt_max_baths: float | None = None
apt_min_price: int | None = None
apt_max_price: int | None = None
apt_min_sqft: int | None = None
apt_max_sqft: int | None = None
neighborhoods: Optional[str] = None

View File

@@ -1,33 +1,27 @@
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
"""
homeharvest.realtor.__init__
~~~~~~~~~~~~
This module implements the scraper for realtor.com
"""
from typing import Dict, Union, Optional
from concurrent.futures import ThreadPoolExecutor, as_completed
from .. import Scraper
from ....exceptions import NoResultsFound
from ..models import Property, Address, ListingType, Description
class RealtorScraper(Scraper):
SEARCH_GQL_URL = "https://www.realtor.com/api/v1/rdc_search_srp?client_id=rdc-search-new-communities&schema=vesta"
PROPERTY_URL = "https://www.realtor.com/realestateandhomes-detail/"
ADDRESS_AUTOCOMPLETE_URL = "https://parser-external.geo.moveaws.com/suggest"
def __init__(self, scraper_input):
self.counter = 1
super().__init__(scraper_input)
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("_", "-"),
@@ -36,9 +30,8 @@ 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()
@@ -49,7 +42,154 @@ class RealtorScraper(Scraper):
return result[0]
def handle_listing(self, listing_id: str) -> list[Property]:
query = """query Listing($listing_id: ID!) {
listing(id: $listing_id) {
source {
id
listing_id
}
address {
street_number
street_name
street_suffix
unit
city
state_code
postal_code
location {
coordinate {
lat
lon
}
}
}
basic {
sqft
beds
baths_full
baths_half
lot_sqft
sold_price
sold_price
type
price
status
sold_date
list_date
}
details {
year_built
stories
garage
permalink
}
}
}"""
variables = {"listing_id": listing_id}
payload = {
"query": query,
"variables": variables,
}
response = self.session.post(self.SEARCH_GQL_URL, json=payload)
response_json = response.json()
property_info = response_json["data"]["listing"]
mls = (
property_info["source"].get("id")
if "source" in property_info and isinstance(property_info["source"], dict)
else None
)
able_to_get_lat_long = (
property_info
and property_info.get("address")
and property_info["address"].get("location")
and property_info["address"]["location"].get("coordinate")
)
listing = Property(
mls=mls,
mls_id=property_info["source"].get("listing_id")
if "source" in property_info and isinstance(property_info["source"], dict)
else None,
property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
status=property_info["basic"]["status"].upper(),
list_price=property_info["basic"]["price"],
list_date=property_info["basic"]["list_date"].split("T")[0]
if property_info["basic"].get("list_date")
else None,
prc_sqft=property_info["basic"].get("price")
/ property_info["basic"].get("sqft")
if property_info["basic"].get("price")
and property_info["basic"].get("sqft")
else None,
last_sold_date=property_info["basic"]["sold_date"].split("T")[0]
if property_info["basic"].get("sold_date")
else None,
latitude=property_info["address"]["location"]["coordinate"].get("lat")
if able_to_get_lat_long
else None,
longitude=property_info["address"]["location"]["coordinate"].get("lon")
if able_to_get_lat_long
else None,
address=self._parse_address(property_info, search_type="handle_listing"),
description=Description(
style=property_info["basic"].get("type", "").upper(),
beds=property_info["basic"].get("beds"),
baths_full=property_info["basic"].get("baths_full"),
baths_half=property_info["basic"].get("baths_half"),
sqft=property_info["basic"].get("sqft"),
lot_sqft=property_info["basic"].get("lot_sqft"),
sold_price=property_info["basic"].get("sold_price"),
year_built=property_info["details"].get("year_built"),
garage=property_info["details"].get("garage"),
stories=property_info["details"].get("stories"),
),
)
return [listing]
def get_latest_listing_id(self, property_id: str) -> str | None:
query = """query Property($property_id: ID!) {
property(id: $property_id) {
listings {
listing_id
primary
}
}
}
"""
variables = {"property_id": property_id}
payload = {
"query": query,
"variables": variables,
}
response = self.session.post(self.SEARCH_GQL_URL, json=payload)
response_json = response.json()
property_info = response_json["data"]["property"]
if property_info["listings"] is None:
return None
primary_listing = next(
(listing for listing in property_info["listings"] if listing["primary"]),
None,
)
if primary_listing:
return primary_listing["listing_id"]
else:
return property_info["listings"][0]["listing_id"]
def handle_address(self, property_id: str) -> list[Property]:
"""
Handles a specific address & returns one property
"""
query = """query Property($property_id: ID!) {
property(id: $property_id) {
property_id
@@ -61,22 +201,19 @@ class RealtorScraper(Scraper):
stories
}
address {
address_validation_code
city
country
county
line
postal_code
state_code
street_direction
street_name
street_number
street_name
street_suffix
street_post_direction
unit_value
unit
unit_descriptor
zip
city
state_code
postal_code
location {
coordinate {
lat
lon
}
}
}
basic {
baths
@@ -104,125 +241,181 @@ class RealtorScraper(Scraper):
"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"])
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,
property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
address=self._parse_address(
property_info, search_type="handle_address"
),
description=self._parse_description(property_info),
)
]
def 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
def general_search(
self, variables: dict, search_type: str
) -> Dict[str, Union[int, list[Property]]]:
"""
Handles a location area & returns a list of properties
"""
results_query = """{
count
total
results {
property_id
list_date
status
last_sold_price
last_sold_date
list_price
price_per_sqft
description {
sqft
beds
baths_full
baths_half
lot_sqft
sold_price
year_built
garage
sold_price
type
name
stories
}
limit: 200
offset: $offset
) {
count
total
results {
property_id
description {
baths
beds
lot_sqft
sqft
text
sold_price
stories
year_built
garage
unit_number
floor_number
}
location {
address {
city
country
line
postal_code
state_code
state
street_direction
street_name
street_number
street_post_direction
street_suffix
unit
coordinate {
lon
lat
}
source {
id
listing_id
}
hoa {
fee
}
location {
address {
street_number
street_name
street_suffix
unit
city
state_code
postal_code
coordinate {
lon
lat
}
}
list_price
price_per_sqft
source {
id
neighborhoods {
name
}
}
}
}"""
% self.listing_type.value.lower()
}
}"""
date_param = (
'sold_date: { min: "$today-%sD" }' % self.last_x_days
if self.listing_type == ListingType.SOLD and self.last_x_days
else (
'list_date: { min: "$today-%sD" }' % self.last_x_days
if self.last_x_days
else ""
)
)
sort_param = (
"sort: [{ field: sold_date, direction: desc }]"
if self.listing_type == ListingType.SOLD
else "sort: [{ field: list_date, direction: desc }]"
)
pending_or_contingent_param = (
"or_filters: { contingent: true, pending: true }"
if self.pending_or_contingent
else ""
)
if search_type == "comps": #: comps search, came from an address
query = """query Property_search(
$coordinates: [Float]!
$radius: String!
$offset: Int!,
) {
property_search(
query: {
nearby: {
coordinates: $coordinates
radius: $radius
}
status: %s
%s
}
%s
limit: 200
offset: $offset
) %s""" % (
self.listing_type.value.lower(),
date_param,
sort_param,
results_query,
)
elif search_type == "area": #: general search, came from a general location
query = """query Home_search(
$city: String,
$county: [String],
$state_code: String,
$postal_code: String
$offset: Int,
) {
home_search(
query: {
city: $city
county: $county
postal_code: $postal_code
state_code: $state_code
status: %s
%s
%s
}
%s
limit: 200
offset: $offset
) %s""" % (
self.listing_type.value.lower(),
date_param,
pending_or_contingent_param,
sort_param,
results_query,
)
else: #: general search, came from an address
query = (
"""query Property_search(
$property_id: [ID]!
$offset: Int!,
) {
property_search(
query: {
property_id: $property_id
}
limit: 1
offset: $offset
) %s"""
% results_query
)
payload = {
"query": query,
"variables": variables,
}
response = self.session.post(self.search_url, json=payload)
response = self.session.post(self.SEARCH_GQL_URL, json=payload)
response.raise_for_status()
response_json = response.json()
if return_total:
return response_json["data"]["home_search"]["total"]
search_key = "home_search" if search_type == "area" else "property_search"
properties: list[Property] = []
@@ -230,89 +423,181 @@ class RealtorScraper(Scraper):
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"]
for result in response_json["data"][search_key]["results"]:
self.counter += 1
mls = (
result["source"].get("id")
if "source" in result and isinstance(result["source"], dict)
else None
)
if not mls and self.mls_only:
continue
able_to_get_lat_long = (
result
and result.get("location")
and result["location"].get("address")
and result["location"]["address"].get("coordinate")
)
realty_property = Property(
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"]
mls=mls,
mls_id=result["source"].get("listing_id")
if "source" in result and isinstance(result["source"], dict)
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"]
property_url=f"{self.PROPERTY_URL}{result['property_id']}",
status=result["status"].upper(),
list_price=result["list_price"],
list_date=result["list_date"].split("T")[0]
if result.get("list_date")
else None,
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"],
prc_sqft=result.get("price_per_sqft"),
last_sold_date=result.get("last_sold_date"),
hoa_fee=result["hoa"]["fee"]
if result.get("hoa") and isinstance(result["hoa"], dict)
else None,
latitude=result["location"]["address"]["coordinate"].get("lat")
if able_to_get_lat_long
else None,
longitude=result["location"]["address"]["coordinate"].get("lon")
if able_to_get_lat_long
else None,
address=self._parse_address(result, search_type="general_search"),
#: neighborhoods=self._parse_neighborhoods(result),
description=self._parse_description(result),
)
properties.append(realty_property)
return properties
return {
"total": response_json["data"][search_key]["total"],
"properties": properties,
}
def search(self):
location_info = self.handle_location()
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"]
search_variables |= {"property_id": property_id}
gql_results = self.general_search(
search_variables, search_type=search_type
)
if gql_results["total"] == 0:
listing_id = self.get_latest_listing_id(property_id)
if listing_id is None:
return self.handle_address(property_id)
else:
return self.handle_listing(listing_id)
else:
return gql_results["properties"]
else: #: general search, comps (radius)
coordinates = list(location_info["centroid"].values())
search_variables |= {
"coordinates": coordinates,
"radius": "{}mi".format(self.radius),
}
else: #: general search, location
search_variables |= {
"city": location_info.get("city"),
"county": location_info.get("county"),
"state_code": location_info.get("state_code"),
"postal_code": location_info.get("postal_code"),
}
result = self.general_search(search_variables, search_type=search_type)
total = result["total"]
homes = result["properties"]
homes = []
with ThreadPoolExecutor(max_workers=10) 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(0, total, 200)
for i in range(200, min(total, 10000), 200)
]
for future in as_completed(futures):
homes.extend(future.result())
homes.extend(future.result()["properties"])
return homes
@staticmethod
def _parse_neighborhoods(result: dict) -> Optional[str]:
neighborhoods_list = []
neighborhoods = result["location"].get("neighborhoods", [])
if neighborhoods:
for neighborhood in neighborhoods:
name = neighborhood.get("name")
if name:
neighborhoods_list.append(name)
return ", ".join(neighborhoods_list) if neighborhoods_list else None
@staticmethod
def _parse_address(result: dict, search_type):
if search_type == "general_search":
return Address(
street=f"{result['location']['address']['street_number']} {result['location']['address']['street_name']} {result['location']['address']['street_suffix']}",
unit=result["location"]["address"]["unit"],
city=result["location"]["address"]["city"],
state=result["location"]["address"]["state_code"],
zip=result["location"]["address"]["postal_code"],
)
return Address(
street=f"{result['address']['street_number']} {result['address']['street_name']} {result['address']['street_suffix']}",
unit=result["address"]["unit"],
city=result["address"]["city"],
state=result["address"]["state_code"],
zip=result["address"]["postal_code"],
)
@staticmethod
def _parse_description(result: dict) -> Description:
description_data = result.get("description", {})
if description_data is None or not isinstance(description_data, dict):
print("Warning: description_data is invalid!")
description_data = {}
style = description_data.get("type", "")
if style is not None:
style = style.upper()
return Description(
style=style,
beds=description_data.get("beds"),
baths_full=description_data.get("baths_full"),
baths_half=description_data.get("baths_half"),
sqft=description_data.get("sqft"),
lot_sqft=description_data.get("lot_sqft"),
sold_price=description_data.get("sold_price"),
year_built=description_data.get("year_built"),
garage=description_data.get("garage"),
stories=description_data.get("stories"),
)

View File

@@ -1,244 +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, ListingType, SiteName
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 _handle_rentals(self, region_id, region_type):
url = f"https://www.redfin.com/stingray/api/v1/search/rentals?al=1&isRentals=true&region_id={region_id}&region_type={region_type}&num_homes=100000"
response = self.session.get(url)
response.raise_for_status()
homes = response.json()
properties_list = []
for home in homes["homes"]:
home_data = home["homeData"]
rental_data = home["rentalExtension"]
property_url = f"https://www.redfin.com{home_data.get('url', '')}"
address_info = home_data.get("addressInfo", {})
centroid = address_info.get("centroid", {}).get("centroid", {})
address = Address(
street_address=address_info.get("formattedStreetLine", None),
city=address_info.get("city", None),
state=address_info.get("state", None),
zip_code=address_info.get("zip", None),
unit=None,
country="US" if address_info.get("countryCode", None) == 1 else None,
)
price_range = rental_data.get("rentPriceRange", {"min": None, "max": None})
bed_range = rental_data.get("bedRange", {"min": None, "max": None})
bath_range = rental_data.get("bathRange", {"min": None, "max": None})
sqft_range = rental_data.get("sqftRange", {"min": None, "max": None})
property_ = Property(
property_url=property_url,
site_name=SiteName.REDFIN,
listing_type=ListingType.FOR_RENT,
address=address,
apt_min_beds=bed_range.get("min", None),
apt_min_baths=bath_range.get("min", None),
apt_max_beds=bed_range.get("max", None),
apt_max_baths=bath_range.get("max", None),
description=rental_data.get("description", None),
latitude=centroid.get("latitude", None),
longitude=centroid.get("longitude", None),
apt_min_price=price_range.get("min", None),
apt_max_price=price_range.get("max", None),
apt_min_sqft=sqft_range.get("min", None),
apt_max_sqft=sqft_range.get("max", None),
img_src=home_data.get("staticMapUrl", None),
posted_time=rental_data.get("lastUpdated", None),
bldg_name=rental_data.get("propertyName", None),
)
properties_list.append(property_)
if not properties_list:
raise NoResultsFound("No rentals found for the given location.")
return properties_list
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)
if self.listing_type == ListingType.FOR_RENT:
return self._handle_rentals(region_id, region_type)
else:
if self.listing_type == ListingType.FOR_SALE:
url = f"https://www.redfin.com/stingray/api/gis?al=1&region_id={region_id}&region_type={region_type}&num_homes=100000"
else:
url = f"https://www.redfin.com/stingray/api/gis?al=1&region_id={region_id}&region_type={region_type}&sold_within_days=30&num_homes=100000"
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

View File

@@ -1,333 +0,0 @@
import re
import json
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)
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/"
def is_plausible_location(self, location: str) -> bool:
url = (
"https://www.zillowstatic.com/autocomplete/v3/suggestions?q={"
"}&abKey=6666272a-4b99-474c-b857-110ec438732b&clientId=homepage-render"
).format(location)
response = self.session.get(url)
return response.json()["results"] != []
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 home_info["livingArea"] != 0
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",
}

View File

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

View File

@@ -1,48 +1,71 @@
import re
from .core.scrapers.models import Property, ListingType
import pandas as pd
from .exceptions import InvalidListingType
ordered_properties = [
"property_url",
"mls",
"mls_id",
"status",
"style",
"street",
"unit",
"city",
"state",
"zip_code",
"beds",
"full_baths",
"half_baths",
"sqft",
"year_built",
"list_price",
"list_date",
"sold_price",
"last_sold_date",
"lot_sqft",
"price_per_sqft",
"latitude",
"longitude",
"stories",
"hoa_fee",
"parking_garage",
]
def 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]+)$",
street_address,
re.I,
)
if "address" in prop_data:
address_data = prop_data["address"]
prop_data["street"] = address_data.street
prop_data["unit"] = address_data.unit
prop_data["city"] = address_data.city
prop_data["state"] = address_data.state
prop_data["zip_code"] = address_data.zip
if apt_match:
apt_str = apt_match.group().strip()
cleaned_apt_str = re.sub(
r"(APT\s*|UNIT\s*|LOT\s*|SUITE\s*)", "#", apt_str, flags=re.I
prop_data["price_per_sqft"] = prop_data["prc_sqft"]
description = result.description
prop_data["style"] = description.style
prop_data["beds"] = description.beds
prop_data["full_baths"] = description.baths_full
prop_data["half_baths"] = description.baths_half
prop_data["sqft"] = description.sqft
prop_data["lot_sqft"] = description.lot_sqft
prop_data["sold_price"] = description.sold_price
prop_data["year_built"] = description.year_built
prop_data["parking_garage"] = description.garage
prop_data["stories"] = description.stories
properties_df = pd.DataFrame([prop_data])
properties_df = properties_df.reindex(columns=ordered_properties)
return properties_df[ordered_properties]
def validate_input(listing_type: str) -> None:
if listing_type.upper() not in ListingType.__members__:
raise InvalidListingType(
f"Provided listing type, '{listing_type}', does not exist."
)
main_address = street_address.replace(apt_str, "").strip()
return main_address, cleaned_apt_str
else:
return street_address, None
def parse_unit(street_address: str):
if not street_address:
return None
apt_match = re.search(
r"(APT\s*[\dA-Z]+|#[\dA-Z]+|UNIT\s*[\dA-Z]+|LOT\s*[\dA-Z]+)$",
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
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"))

13
poetry.lock generated
View File

@@ -408,6 +408,17 @@ files = [
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
]
[[package]]
name = "tls-client"
version = "0.2.2"
description = "Advanced Python HTTP Client."
optional = false
python-versions = "*"
files = [
{file = "tls_client-0.2.2-py3-none-any.whl", hash = "sha256:30934871397cdad6862e00b5634f382666314a452ddd3d774e18323a0ad9b765"},
{file = "tls_client-0.2.2.tar.gz", hash = "sha256:78bc0e291e3aadc6c5e903b62bb26c01374577691f2a9e5e17899900a5927a13"},
]
[[package]]
name = "tomli"
version = "2.0.1"
@@ -450,4 +461,4 @@ zstd = ["zstandard (>=0.18.0)"]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "3647d568f5623dd762f19029230626a62e68309fa2ef8be49a36382c19264a5f"
content-hash = "9b77e1a09fcf2cf5e7e6be53f304cd21a6a51ea51680d661a178afe5e5343670"

View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "homeharvest"
version = "0.2.4"
version = "0.3.2"
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"
@@ -14,6 +14,7 @@ python = "^3.10"
requests = "^2.31.0"
pandas = "^2.1.0"
openpyxl = "^3.1.2"
tls-client = "^0.2.2"
[tool.poetry.group.dev.dependencies]
@@ -21,4 +22,4 @@ pytest = "^7.4.2"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
build-backend = "poetry.core.masonry.api"

View File

@@ -1,26 +1,83 @@
from homeharvest import scrape_property
from homeharvest.exceptions import (
InvalidSite,
InvalidListingType,
NoResultsFound,
GeoCoordsNotFound,
)
def test_realtor_pending_or_contingent():
pending_or_contingent_result = scrape_property(
location="Surprise, AZ",
pending_or_contingent=True,
)
regular_result = scrape_property(
location="Surprise, AZ",
pending_or_contingent=False,
)
assert all(
[
result is not None
for result in [pending_or_contingent_result, regular_result]
]
)
assert len(pending_or_contingent_result) != len(regular_result)
def test_realtor_comps():
result = scrape_property(
location="2530 Al Lipscomb Way",
radius=0.5,
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_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"
), #: 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"
), #: 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])
@@ -30,11 +87,10 @@ def test_realtor():
bad_results += [
scrape_property(
location="abceefg ju098ot498hh9",
site_name="realtor.com",
listing_type="for_sale",
)
]
except (InvalidSite, InvalidListingType, NoResultsFound, GeoCoordsNotFound):
except (InvalidListingType, NoResultsFound):
assert True
assert all([result is None for result in bad_results])

View File

@@ -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])

View File

@@ -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])