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HomeHarvest
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reactor(redfin)

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Cullen Watson 2023-09-18 14:36:18 -05:00
parent 471e53118e 10c01f373e
commit ffd3ce6aed
9 changed files with 391 additions and 126 deletions
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51
README.md
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@ -2,34 +2,41 @@
**HomeHarvest** aims to be the top Python real estate scraping library.
## RoadMap
_**Under Consideration**: We're looking into the possibility of an Excel plugin to cater to a broader audience._
- **Supported Sites**: Currently, we support scraping from sites such as `Zillow` and `RedFin`.
- **Output**: Provides the option to return the scraped data as a Pandas dataframe.
- **Under Consideration**: We're looking into the possibility of an Excel plugin to cater to a broader audience.
[![Try with Replit](https://replit.com/badge?caption=Try%20with%20Replit)](https://replit.com/@ZacharyHampton/HomeHarvestDemo)
## Site Name Options
- `zillow`
- `redfin`
## Listing Types
- `for_rent`
- `for_sale`
### Installation
## Installation
```bash
pip install --upgrade homeharvest
```
### Example Usage
```
from homeharvest import scrape_property
## Example Usage
```py
>>> from homeharvest import scrape_property
... properties = scrape_property(
... location="85281", site_name="zillow", listing_type="for_rent"
... )
properties = scrape_property(
location="85281", site_name="zillow", listing_type="for_rent"
)
print(properties)
>>> properties.head()
address_one 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]
```
### Site Name Options
- `zillow`
- `redfin`
- `realtor.com`
### Listing Types
- `for_rent`
- `for_sale`
- `sold`

12
homeharvest/__init__.py
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@ -92,7 +92,17 @@ def scrape_property(
location: str,
site_name: str,
listing_type: str = "for_sale", #: for_sale, for_rent, sold
) -> list[Property]:
) -> 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 (e.g. 'realtor.com', 'zillow', 'redfin')
:param listing_type: Listing type (e.g. 'for_sale', 'for_rent', 'sold')
:return: pd.DataFrame containing properties
"""
validate_input(site_name, listing_type)
scraper_input = ScraperInput(

2
homeharvest/core/scrapers/__init__.py
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@ -14,6 +14,8 @@ class ScraperInput:
class Scraper:
def __init__(self, scraper_input: ScraperInput):
self.location = scraper_input.location
self.listing_type = scraper_input.listing_type
self.session = requests.Session()
self.listing_type = scraper_input.listing_type
self.site_name = scraper_input.site_name

1
homeharvest/core/scrapers/models.py
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@ -57,6 +57,7 @@ class Address:
country: str | None = None
@dataclass
class Property:
property_url: str

233
homeharvest/core/scrapers/realtor/__init__.py
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@ -1,12 +1,15 @@
import json
from ..models import Property, Address
from .. import Scraper
from typing import Any
from typing import Any, Generator
from ....exceptions import NoResultsFound
from concurrent.futures import ThreadPoolExecutor, as_completed
class RealtorScraper(Scraper):
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 = {
@ -26,7 +29,7 @@ class RealtorScraper(Scraper):
params = {
"input": self.location,
"client_id": "for-sale",
"client_id": self.listing_type.value.replace('_', '-'),
"limit": "1",
"area_types": "city,state,county,postal_code,address,street,neighborhood,school,school_district,university,park",
}
@ -38,14 +41,228 @@ class RealtorScraper(Scraper):
)
response_json = response.json()
return response_json["autocomplete"][0]
result = response_json["autocomplete"]
if result is None:
raise NoResultsFound("No results found for location: " + self.location)
return result[0]
def handle_address(self, property_id: str) -> list[Property]:
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
}
}
}"""
variables = {
'property_id': property_id
}
payload = {
'query': query,
'variables': variables,
}
response = self.session.post(self.search_url, json=payload)
response_json = response.json()
property_info = response_json['data']['property']
return [Property(
site_name=self.site_name,
address=Address(
address_one=property_info['address']['line'],
city=property_info['address']['city'],
state=property_info['address']['state_code'],
zip_code=property_info['address']['postal_code'],
),
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_square_foot=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_size=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
}
}
list_price
price_per_sqft
source {
id
}
}
}
}""" % self.listing_type.value
payload = {
'query': query,
'variables': variables,
}
response = self.session.post(self.search_url, json=payload)
response_json = response.json()
if return_total:
return response_json['data']['home_search']['total']
properties: list[Property] = []
for result in response_json['data']['home_search']['results']:
realty_property = Property(
address=Address(
address_one=result['location']['address']['line'],
city=result['location']['address']['city'],
state=result['location']['address']['state_code'],
zip_code=result['location']['address']['postal_code'],
address_two=result['location']['address']['unit'],
),
site_name=self.site_name,
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_square_foot=result['price_per_sqft'],
price=result['list_price'],
mls_id=result['property_id'],
listing_type=self.listing_type,
lot_size=result['description']['lot_sqft'],
)
properties.append(realty_property)
return properties
def search(self):
location_info = self.handle_location()
location_type = location_info["area_type"]
"""
property types:
apartment + building + commercial + condo_townhome + condo_townhome_rowhome_coop + condos + coop + duplex_triplex + farm + investment + land + mobile + multi_family + rental + single_family + townhomes
"""
print("a")
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,
}
total = self.handle_area(search_variables, return_total=True)
homes = []
with ThreadPoolExecutor(max_workers=10) as executor:
futures = [
executor.submit(
self.handle_area, variables=search_variables | {'offset': i}, return_total=False
) for i in range(0, total, 200)
]
for future in as_completed(futures):
homes.extend(future.result())
return homes

34
homeharvest/core/scrapers/redfin/__init__.py
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@ -93,6 +93,35 @@ class RedfinScraper(Scraper):
mls_id=get_value("mlsId"),
)
def _parse_building(self, building: dict) -> Property:
return Property(
site_name=self.site_name,
property_type=PropertyType("BUILDING"),
address=Address(
street_address=" ".join(
[
building['address']['streetNumber'],
building['address']['directionalPrefix'],
building['address']['streetName'],
building['address']['streetType'],
]
),
city=building['address']['city'],
state=building['address']['stateOrProvinceCode'],
zip_code=building['address']['postalCode'],
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:
@ -130,5 +159,8 @@ class RedfinScraper(Scraper):
homes = [
self._parse_home(home) for home in response_json["payload"]["homes"]
] #: support buildings
] + [
self._parse_building(building) for building in response_json["payload"]["buildings"].values()
]
return homes

9
homeharvest/core/scrapers/zillow/__init__.py
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@ -117,11 +117,10 @@ class ZillowScraper(Scraper):
"isDebugRequest": False,
}
)
print(payload)
resp = self.session.put(url, headers=self._get_headers(), data=payload)
resp.raise_for_status()
a = resp.json()
return parse_properties(resp.json())
return self._parse_properties(resp.json())
def _parse_properties(self, property_data: dict):
mapresults = property_data["cat1"]["searchResults"]["mapResults"]
@ -129,7 +128,6 @@ class ZillowScraper(Scraper):
properties_list = []
for result in mapresults:
try:
if "hdpData" in result:
home_info = result["hdpData"]["homeInfo"]
address_data = {
@ -217,11 +215,6 @@ class ZillowScraper(Scraper):
building_obj = Property(**building_data)
properties_list.append(building_obj)
except Exception as e:
print(home_info)
traceback.print_exc()
sys.exit()
return properties_list
def _extract_units(self, result: dict):

2
pyproject.toml
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@ -1,6 +1,6 @@
[tool.poetry]
name = "homeharvest"
version = "0.1.2"
version = "0.1.3"
description = "Real estate scraping library"
authors = ["Zachary Hampton <zachary@zacharysproducts.com>", "Cullen Watson <cullen@cullen.ai>"]
homepage = "https://github.com/ZacharyHampton/HomeHarvest"

3
tests/test_realtor.py
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@ -3,6 +3,9 @@ from homeharvest import scrape_property
def test_realtor():
results = [
scrape_property(location="2530 Al Lipscomb Way", site_name="realtor.com"),
scrape_property(location="Phoenix, AZ", site_name="realtor.com"), #: does not support "city, state, USA" format
scrape_property(location="Dallas, TX", site_name="realtor.com"), #: does not support "city, state, USA" format
scrape_property(location="85281", site_name="realtor.com"),
]