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13 Commits

Author SHA1 Message Date
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
10 changed files with 762 additions and 536 deletions

View File

@@ -21,7 +21,7 @@
```bash
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
@@ -90,9 +90,13 @@ Optional
├── foreclosure (True/False): If set, fetches only foreclosures
── proxy (string): In format 'http://user:pass@host:port'
── proxy (string): In format 'http://user:pass@host:port'
── extra_property_data (bool): Increases requests by O(n). If set, this fetches additional property data (e.g. agent, broker, property evaluations etc.)
── 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 results unless listing_type is 'pending'
└── limit (integer): Limit the number of properties to fetch. Max & default is 10000.
```
### Property Schema
@@ -119,17 +123,20 @@ Property
│ ├── sqft
│ ├── year_built
│ ├── stories
│ ├── garage
│ └── lot_sqft
├── Property Listing Details:
│ ├── days_on_mls
│ ├── list_price
│ ├── list_price_min
│ ├── list_price_max
│ ├── list_date
│ ├── pending_date
│ ├── sold_price
│ ├── last_sold_date
│ ├── price_per_sqft
│ ├── parking_garage
│ ├── new_construction
│ └── hoa_fee
├── Location Details:
@@ -137,16 +144,31 @@ Property
│ ├── longitude
│ ├── nearby_schools
├── Agent Info:
│ ├── agent
│ ├── 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
```
### Exceptions
The following exceptions may be raised when using HomeHarvest:
- `InvalidListingType` - valid options: `for_sale`, `for_rent`, `sold`
- `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.

View File

@@ -1,7 +1,7 @@
import warnings
import pandas as pd
from .core.scrapers import ScraperInput
from .utils import process_result, ordered_properties, validate_input, validate_dates
from .utils import process_result, ordered_properties, validate_input, validate_dates, validate_limit
from .core.scrapers.realtor import RealtorScraper
from .core.scrapers.models import ListingType
@@ -17,11 +17,13 @@ def scrape_property(
date_to: str = None,
foreclosure: bool = None,
extra_property_data: bool = True,
exclude_pending: bool = False,
limit: int = 10000,
) -> pd.DataFrame:
"""
Scrape properties from Realtor.com based on a given location and listing type.
:param location: Location to search (e.g. "Dallas, TX", "85281", "2530 Al Lipscomb Way")
:param listing_type: Listing Type (for_sale, for_rent, sold)
:param listing_type: Listing Type (for_sale, for_rent, sold, pending)
:param 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
@@ -29,9 +31,12 @@ def scrape_property(
:param date_from, date_to: Get properties sold or listed (dependent on your listing_type) between these dates. format: 2021-01-28
:param foreclosure: If set, fetches only foreclosure listings.
:param extra_property_data: Increases requests by O(n). If set, this fetches additional property data (e.g. agent, broker, property evaluations etc.)
:param exclude_pending: If true, this excludes pending or contingent properties from the results, unless listing type is pending.
:param limit: Limit the number of results returned. Maximum is 10,000.
"""
validate_input(listing_type)
validate_dates(date_from, date_to)
validate_limit(limit)
scraper_input = ScraperInput(
location=location,
@@ -44,16 +49,18 @@ def scrape_property(
date_to=date_to,
foreclosure=foreclosure,
extra_property_data=extra_property_data,
exclude_pending=exclude_pending,
limit=limit,
)
site = RealtorScraper(scraper_input)
results = site.search()
properties_dfs = [process_result(result) for result in results]
properties_dfs = [df for result in results if not (df := process_result(result)).empty]
if not properties_dfs:
return pd.DataFrame()
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=FutureWarning)
return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties].replace({"None": "", None: ""})
return pd.concat(properties_dfs, ignore_index=True, axis=0)[ordered_properties].replace({"None": pd.NA, None: pd.NA, "": pd.NA})

View File

@@ -1,3 +1,4 @@
from __future__ import annotations
from dataclasses import dataclass
import requests
from requests.adapters import HTTPAdapter
@@ -5,6 +6,7 @@ from urllib3.util.retry import Retry
import uuid
from ...exceptions import AuthenticationError
from .models import Property, ListingType, SiteName
import json
@dataclass
@@ -19,6 +21,8 @@ class ScraperInput:
date_to: str | None = None
foreclosure: bool | None = False
extra_property_data: bool | None = True
exclude_pending: bool | None = False
limit: int = 10000
class Scraper:
@@ -60,6 +64,8 @@ class Scraper:
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
def search(self) -> list[Property]: ...
@@ -70,18 +76,25 @@ class Scraper:
@staticmethod
def get_access_token():
url = "https://graph.realtor.com/auth/token"
device_id = str(uuid.uuid4()).upper()
payload = f'{{"client_app_id":"rdc_mobile_native,24.20.4.149916,iphone","device_id":"{str(uuid.uuid4()).upper()}","grant_type":"device_mobile"}}'
headers = {
"Host": "graph.realtor.com",
"x-client-version": "24.20.4.149916",
"accept": "*/*",
"content-type": "Application/json",
"user-agent": "Realtor.com/24.20.4.149916 CFNetwork/1410.0.3 Darwin/22.6.0",
"accept-language": "en-US,en;q=0.9",
}
response = requests.post(url, headers=headers, data=payload)
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()

View File

@@ -1,3 +1,4 @@
from __future__ import annotations
from dataclasses import dataclass
from enum import Enum
from typing import Optional
@@ -33,9 +34,12 @@ class PropertyType(Enum):
APARTMENT = "APARTMENT"
BUILDING = "BUILDING"
COMMERCIAL = "COMMERCIAL"
GOVERNMENT = "GOVERNMENT"
INDUSTRIAL = "INDUSTRIAL"
CONDO_TOWNHOME = "CONDO_TOWNHOME"
CONDO_TOWNHOME_ROWHOME_COOP = "CONDO_TOWNHOME_ROWHOME_COOP"
CONDO = "CONDO"
CONDOP = "CONDOP"
CONDOS = "CONDOS"
COOP = "COOP"
DUPLEX_TRIPLEX = "DUPLEX_TRIPLEX"
@@ -86,18 +90,43 @@ class AgentPhone: #: For documentation purposes only (at the moment)
@dataclass
class Agent:
name: str | None = None
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 Broker:
name: str | None = None
phone: str | None = None
website: str | None = None
class Office(Entity):
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
@@ -109,10 +138,14 @@ class Property:
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
@@ -122,8 +155,8 @@ class Property:
neighborhoods: Optional[str] = None
county: Optional[str] = None
fips_code: Optional[str] = None
agents: list[Agent] | None = None
brokers: list[Broker] | None = None
nearby_schools: list[str] = None
assessed_value: int | None = None
estimated_value: int | None = None
advertisers: Advertisers | None = None

View File

@@ -5,12 +5,14 @@ homeharvest.realtor.__init__
This module implements the scraper for realtor.com
"""
from __future__ import annotations
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
from typing import Dict, Union, Optional
from .. import Scraper
from ..models import Property, Address, ListingType, Description, PropertyType, Agent, Broker
from ..models import Property, Address, ListingType, Description, PropertyType, Agent, Broker, Builder, Advertisers, Office
from .queries import GENERAL_RESULTS_QUERY, SEARCH_HOMES_DATA, HOMES_DATA
class RealtorScraper(Scraper):
@@ -19,6 +21,7 @@ class RealtorScraper(Scraper):
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)
@@ -44,151 +47,6 @@ 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 {
line
street_direction
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
}
media {
photos {
href
}
}
}
}"""
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")
)
list_date_str = (
property_info["basic"]["list_date"].split("T")[0] if property_info["basic"].get("list_date") else None
)
last_sold_date_str = (
property_info["basic"]["sold_date"].split("T")[0] if property_info["basic"].get("sold_date") else None
)
pending_date_str = property_info["pending_date"].split("T")[0] if property_info.get("pending_date") else None
list_date = datetime.strptime(list_date_str, "%Y-%m-%d") if list_date_str else None
last_sold_date = datetime.strptime(last_sold_date_str, "%Y-%m-%d") if last_sold_date_str else None
pending_date = datetime.strptime(pending_date_str, "%Y-%m-%d") if pending_date_str else None
today = datetime.now()
days_on_mls = None
status = property_info["basic"]["status"].lower()
if list_date:
if status == "sold" and last_sold_date:
days_on_mls = (last_sold_date - list_date).days
elif status in ("for_sale", "for_rent"):
days_on_mls = (today - list_date).days
if days_on_mls and days_on_mls < 0:
days_on_mls = None
property_id = property_info["details"]["permalink"]
prop_details = self.get_prop_details(property_id)
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_id}",
status=property_info["basic"]["status"].upper(),
list_price=property_info["basic"]["price"],
list_date=list_date,
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=last_sold_date,
pending_date=pending_date,
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(
alt_photos=self.process_alt_photos(property_info.get("media", {}).get("photos", [])),
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"),
text=property_info.get("description", {}).get("text"),
),
days_on_mls=days_on_mls,
agents=prop_details.get("agents"),
brokers=prop_details.get("brokers"),
nearby_schools=prop_details.get("schools"),
assessed_value=prop_details.get("assessed_value"),
estimated_value=prop_details.get("estimated_value"),
)
return [listing]
def get_latest_listing_id(self, property_id: str) -> str | None:
query = """query Property($property_id: ID!) {
property(id: $property_id) {
@@ -222,65 +80,12 @@ class RealtorScraper(Scraper):
else:
return property_info["listings"][0]["listing_id"]
def handle_address(self, property_id: str) -> list[Property]:
"""
Handles a specific address & returns one property
"""
query = """query Property($property_id: ID!) {
property(id: $property_id) {
property_id
details {
date_updated
garage
permalink
year_built
stories
}
address {
line
street_direction
street_number
street_name
street_suffix
unit
city
state_code
postal_code
location {
coordinate {
lat
lon
}
}
}
basic {
baths
beds
price
sqft
lot_sqft
type
sold_price
}
public_record {
lot_size
sqft
stories
units
year_built
}
primary_photo {
href
}
photos {
href
}
}
}"""
def handle_home(self, property_id: str) -> list[Property]:
query = """query Home($property_id: ID!) {
home(property_id: $property_id) %s
}""" % HOMES_DATA
variables = {"property_id": property_id}
prop_details = self.get_prop_details(property_id)
payload = {
"query": query,
"variables": variables,
@@ -289,101 +94,125 @@ class RealtorScraper(Scraper):
response = self.session.post(self.SEARCH_GQL_URL, json=payload)
response_json = response.json()
property_info = response_json["data"]["property"]
property_info = response_json["data"]["home"]
return [
Property(
mls_id=property_id,
property_url=f"{self.PROPERTY_URL}{property_info['details']['permalink']}",
address=self._parse_address(property_info, search_type="handle_address"),
description=self._parse_description(property_info),
agents=prop_details.get("agents"),
brokers=prop_details.get("brokers"),
nearby_schools=prop_details.get("schools"),
assessed_value=prop_details.get("assessed_value"),
estimated_value=prop_details.get("estimated_value"),
)
self.process_property(property_info, "home")
]
@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")) or 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, query_name: str) -> 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") or result["flags"].get("is_contingent")
if is_pending and (self.exclude_pending and self.listing_type != ListingType.PENDING):
return
property_id = result["property_id"]
prop_details = self.get_prop_details(property_id) if self.extra_property_data and query_name != "home" else {}
if not prop_details:
prop_details = self.process_extra_property_details(result)
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=(
f"{self.PROPERTY_URL}{property_id}"
if self.listing_type != ListingType.FOR_RENT
else f"{self.PROPERTY_URL}M{property_id}?listing_status=rental"
),
status="PENDING" if is_pending 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,
)
return realty_property
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 {
pending_date
property_id
list_date
status
last_sold_price
last_sold_date
list_price
price_per_sqft
flags {
is_contingent
is_pending
}
description {
type
sqft
beds
baths_full
baths_half
lot_sqft
sold_price
year_built
garage
sold_price
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 {
href
}
photos {
href
}
}
}
}"""
date_param = ""
if self.listing_type == ListingType.SOLD:
@@ -435,13 +264,14 @@ class RealtorScraper(Scraper):
%s
limit: 200
offset: $offset
) %s""" % (
) %s
}""" % (
is_foreclosure,
listing_type.value.lower(),
date_param,
pending_or_contingent_param,
sort_param,
results_query,
GENERAL_RESULTS_QUERY,
)
elif search_type == "area": #: general search, came from a general location
query = """query Home_search(
@@ -465,28 +295,30 @@ class RealtorScraper(Scraper):
%s
limit: 200
offset: $offset
) %s""" % (
) %s
}""" % (
is_foreclosure,
listing_type.value.lower(),
date_param,
pending_or_contingent_param,
sort_param,
results_query,
GENERAL_RESULTS_QUERY,
)
else: #: general search, came from an address
query = (
"""query Property_search(
"""query Property_search(
$property_id: [ID]!
$offset: Int!,
) {
property_search(
home_search(
query: {
property_id: $property_id
}
limit: 1
offset: $offset
) %s"""
% results_query
) %s
}"""
% GENERAL_RESULTS_QUERY
)
payload = {
@@ -501,73 +333,26 @@ class RealtorScraper(Scraper):
properties: list[Property] = []
if (
response_json is None
or "data" not in response_json
or response_json["data"] is None
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]
response_json is None
or "data" not in response_json
or response_json["data"] is None
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 {"total": 0, "properties": []}
def process_property(result: dict) -> Property | None:
mls = result["source"].get("id") if "source" in result and isinstance(result["source"], dict) else None
properties_list = response_json["data"][search_key]["results"]
total_properties = response_json["data"][search_key]["total"]
offset = variables.get("offset", 0)
if not mls and self.mls_only:
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") or result["flags"].get("is_contingent")
if is_pending and self.listing_type != ListingType.PENDING:
return
property_id = result["property_id"]
prop_details = self.get_prop_details(property_id)
realty_property = Property(
mls=mls,
mls_id=(
result["source"].get("listing_id")
if "source" in result and isinstance(result["source"], dict)
else None
),
property_url=(
f"{self.PROPERTY_URL}{property_id}"
if self.listing_type != ListingType.FOR_RENT
else f"{self.PROPERTY_URL}M{property_id}?listing_status=rental"
),
status="PENDING" if is_pending else result["status"].upper(),
list_price=result["list_price"],
list_date=result["list_date"].split("T")[0] if result.get("list_date") else None,
prc_sqft=result.get("price_per_sqft"),
last_sold_date=result.get("last_sold_date"),
hoa_fee=result["hoa"]["fee"] if result.get("hoa") and isinstance(result["hoa"], dict) else None,
latitude=result["location"]["address"]["coordinate"].get("lat") if able_to_get_lat_long else None,
longitude=result["location"]["address"]["coordinate"].get("lon") if able_to_get_lat_long else None,
address=self._parse_address(result, search_type="general_search"),
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),
agents=prop_details.get("agents"),
brokers=prop_details.get("brokers"),
nearby_schools=prop_details.get("schools"),
assessed_value=prop_details.get("assessed_value"),
estimated_value=prop_details.get("estimated_value"),
)
return 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 = properties_list[:self.limit - offset]
with ThreadPoolExecutor(max_workers=self.NUM_PROPERTY_WORKERS) as executor:
futures = [
executor.submit(process_property, result) for result in response_json["data"][search_key]["results"]
executor.submit(self.process_property, result, search_key) for result in properties_list
]
for future in as_completed(futures):
@@ -576,7 +361,7 @@ class RealtorScraper(Scraper):
properties.append(result)
return {
"total": response_json["data"][search_key]["total"],
"total": total_properties,
"properties": properties,
}
@@ -599,17 +384,7 @@ class RealtorScraper(Scraper):
if location_type == "address":
if not self.radius: #: single address search, non comps
property_id = location_info["mpr_id"]
search_variables |= {"property_id": property_id}
gql_results = self.general_search(search_variables, search_type=search_type)
if gql_results["total"] == 0:
listing_id = self.get_latest_listing_id(property_id)
if listing_id is None:
return self.handle_address(property_id)
else:
return self.handle_listing(listing_id)
else:
return gql_results["properties"]
return self.handle_home(property_id)
else: #: general search, comps (radius)
if not location_info.get("centroid"):
@@ -641,14 +416,14 @@ class RealtorScraper(Scraper):
total = result["total"]
homes = result["properties"]
with ThreadPoolExecutor(max_workers=10) as executor:
with ThreadPoolExecutor() as executor:
futures = [
executor.submit(
self.general_search,
variables=search_variables | {"offset": i},
search_type=search_type,
)
for i in range(200, min(total, 10000), 200)
for i in range(self.DEFAULT_PAGE_SIZE, min(total, self.limit), self.DEFAULT_PAGE_SIZE)
]
for future in as_completed(futures):
@@ -656,91 +431,49 @@ class RealtorScraper(Scraper):
return homes
@staticmethod
def get_key(data: dict, keys: list):
try:
value = data
for key in keys:
value = value[key]
return value or {}
except (KeyError, TypeError, IndexError):
return {}
def process_extra_property_details(self, result: dict) -> dict:
schools = self.get_key(result, ["nearbySchools", "schools"])
assessed_value = self.get_key(result, ["taxHistory", 0, "assessment", "total"])
schools = [school["district"]["name"] for school in schools if school["district"].get("name")]
return {
"schools": schools if schools else None,
"assessed_value": assessed_value if assessed_value else None,
}
def get_prop_details(self, property_id: str) -> dict:
if not self.extra_property_data:
return {}
#: TODO: migrate "advertisers" and "estimates" to general query
query = """query GetHome($property_id: ID!) {
home(property_id: $property_id) {
__typename
advertisers {
__typename
type
name
email
phones { number type ext primary }
nearbySchools: nearby_schools(radius: 5.0, limit_per_level: 3) {
__typename schools { district { __typename id name } }
}
consumer_advertisers {
name
phone
href
type
}
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 } }
estimates {
__typename
currentValues: current_values {
__typename
source { __typename type name }
estimate
estimateHigh: estimate_high
estimateLow: estimate_low
date
isBestHomeValue: isbest_homevalue
}
}
}
}"""
variables = {"property_id": property_id}
response = self.session.post(self.PROPERTY_GQL, json={"query": query, "variables": variables})
data = response.json()
property_details = data["data"]["home"]
def get_key(keys: list):
try:
value = data
for key in keys:
value = value[key]
return value or {}
except (KeyError, TypeError, IndexError):
return {}
agents = get_key(["data", "home", "advertisers"])
advertisers = get_key(["data", "home", "consumer_advertisers"])
schools = get_key(["data", "home", "nearbySchools", "schools"])
assessed_value = get_key(["data", "home", "taxHistory", 0, "assessment", "total"])
estimated_value = get_key(["data", "home", "estimates", "currentValues", 0, "estimate"])
agents = [Agent(
name=ad["name"],
email=ad["email"],
phones=ad["phones"]
) for ad in agents]
brokers = [Broker(
name=ad["name"],
phone=ad["phone"],
website=ad["href"]
) for ad in advertisers if ad.get("type") != "Agent"]
schools = [school["district"]["name"] for school in schools if school['district'].get('name')]
return {
"agents": agents if agents else None,
"brokers": brokers if brokers else None,
"schools": schools if schools else None,
"assessed_value": assessed_value if assessed_value else None,
"estimated_value": estimated_value if estimated_value else None,
}
return self.process_extra_property_details(property_details)
@staticmethod
def _parse_neighborhoods(result: dict) -> Optional[str]:
@@ -772,12 +505,14 @@ class RealtorScraper(Scraper):
return Address(
full_line=address.get("line"),
street=" ".join(
part for part in [
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
]
if part is not None
).strip(),
unit=address["unit"],
city=address["city"],
@@ -786,7 +521,10 @@ class RealtorScraper(Scraper):
)
@staticmethod
def _parse_description(result: dict) -> Description:
def _parse_description(result: dict) -> Description | None:
if not result:
return None
description_data = result.get("description", {})
if description_data is None or not isinstance(description_data, dict):
@@ -797,22 +535,23 @@ class RealtorScraper(Scraper):
style = style.upper()
primary_photo = ""
if result and "primary_photo" in result:
primary_photo_info = result["primary_photo"]
if primary_photo_info and "href" in primary_photo_info:
primary_photo_href = primary_photo_info["href"]
primary_photo = primary_photo_href.replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
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(style) if style else None,
alt_photos=RealtorScraper.process_alt_photos(result.get("photos", [])),
style=PropertyType.__getitem__(style) if style and style in PropertyType.__members__ else None,
beds=description_data.get("beds"),
baths_full=description_data.get("baths_full"),
baths_half=description_data.get("baths_half"),
sqft=description_data.get("sqft"),
lot_sqft=description_data.get("lot_sqft"),
sold_price=description_data.get("sold_price") 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
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"),
@@ -839,14 +578,8 @@ class RealtorScraper(Scraper):
return days
@staticmethod
def process_alt_photos(photos_info):
try:
alt_photos = []
if photos_info:
for photo_info in photos_info:
href = photo_info.get("href", "")
alt_photo_href = href.replace("s.jpg", "od-w480_h360_x2.webp?w=1080&q=75")
alt_photos.append(alt_photo_href)
return alt_photos
except Exception:
pass
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")]

View File

@@ -0,0 +1,162 @@
_SEARCH_HOMES_DATA_BASE = """{
pending_date
listing_id
property_id
list_date
status
last_sold_price
last_sold_date
list_price
list_price_max
list_price_min
price_per_sqft
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 {
href
}
photos {
href
}
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
fulfillment_id
}
}
"""
HOMES_DATA = """%s
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 } }
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

View File

@@ -1,6 +1,7 @@
from __future__ import annotations
import pandas as pd
from datetime import datetime
from .core.scrapers.models import Property, ListingType, Agent
from .core.scrapers.models import Property, ListingType, Advertisers
from .exceptions import InvalidListingType, InvalidDate
ordered_properties = [
@@ -23,11 +24,14 @@ ordered_properties = [
"year_built",
"days_on_mls",
"list_price",
"list_price_min",
"list_price_max",
"list_date",
"sold_price",
"last_sold_date",
"assessed_value",
"estimated_value",
"new_construction",
"lot_sqft",
"price_per_sqft",
"latitude",
@@ -38,12 +42,20 @@ ordered_properties = [
"stories",
"hoa_fee",
"parking_garage",
"agent",
"agent_id",
"agent_name",
"agent_email",
"agent_phones",
"broker",
"broker_phone",
"broker_website",
"agent_mls_set",
"agent_nrds_id",
"broker_id",
"broker_name",
"builder_id",
"builder_name",
"office_id",
"office_name",
"office_email",
"office_phones",
"nearby_schools",
"primary_photo",
"alt_photos",
@@ -63,38 +75,54 @@ def process_result(result: Property) -> pd.DataFrame:
prop_data["state"] = address_data.state
prop_data["zip_code"] = address_data.zip
if "agents" in prop_data:
agents: list[Agent] | None = prop_data["agents"]
if agents:
prop_data["agent"] = agents[0].name
prop_data["agent_email"] = agents[0].email
prop_data["agent_phones"] = agents[0].phones
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 "brokers" in prop_data:
brokers = prop_data["brokers"]
if brokers:
prop_data["broker"] = brokers[0].name
prop_data["broker_phone"] = brokers[0].phone
prop_data["broker_website"] = brokers[0].website
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["price_per_sqft"] = prop_data["prc_sqft"]
prop_data["nearby_schools"] = filter(None, prop_data["nearby_schools"]) if prop_data["nearby_schools"] else None
prop_data["nearby_schools"] = ", ".join(set(prop_data["nearby_schools"])) if prop_data["nearby_schools"] else None
description = result.description
prop_data["primary_photo"] = description.primary_photo
prop_data["alt_photos"] = ", ".join(description.alt_photos)
prop_data["style"] = description.style if type(description.style) == str else description.style.value
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
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
properties_df = pd.DataFrame([prop_data])
properties_df = properties_df.reindex(columns=ordered_properties)
@@ -108,7 +136,7 @@ def validate_input(listing_type: str) -> None:
def validate_dates(date_from: str | None, date_to: str | None) -> None:
if (date_from is not None and date_to is None) or (date_from is None and date_to is not 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:
@@ -120,3 +148,10 @@ def validate_dates(date_from: str | None, date_to: str | None) -> None:
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.")

136
poetry.lock generated
View File

@@ -1,5 +1,16 @@
# This file is automatically @generated by Poetry 1.8.2 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"
version = "2023.7.22"
@@ -391,6 +402,116 @@ nodeenv = ">=0.11.1"
pyyaml = ">=5.1"
virtualenv = ">=20.10.0"
[[package]]
name = "pydantic"
version = "2.7.4"
description = "Data validation using Python type hints"
optional = false
python-versions = ">=3.8"
files = [
{file = "pydantic-2.7.4-py3-none-any.whl", hash = "sha256:ee8538d41ccb9c0a9ad3e0e5f07bf15ed8015b481ced539a1759d8cc89ae90d0"},
{file = "pydantic-2.7.4.tar.gz", hash = "sha256:0c84efd9548d545f63ac0060c1e4d39bb9b14db8b3c0652338aecc07b5adec52"},
]
[package.dependencies]
annotated-types = ">=0.4.0"
pydantic-core = "2.18.4"
typing-extensions = ">=4.6.1"
[package.extras]
email = ["email-validator (>=2.0.0)"]
[[package]]
name = "pydantic-core"
version = "2.18.4"
description = "Core functionality for Pydantic validation and serialization"
optional = false
python-versions = ">=3.8"
files = [
{file = "pydantic_core-2.18.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:f76d0ad001edd426b92233d45c746fd08f467d56100fd8f30e9ace4b005266e4"},
{file = "pydantic_core-2.18.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:59ff3e89f4eaf14050c8022011862df275b552caef8082e37b542b066ce1ff26"},
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]
[package.dependencies]
typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
[[package]]
name = "pytest"
version = "7.4.2"
@@ -557,6 +678,17 @@ 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"
@@ -607,5 +739,5 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.13"
content-hash = "371781da268d5f61d6e798c023777f337b620e9b07a48c316825d7b998b63f02"
python-versions = ">=3.9,<3.13"
content-hash = "21ef9cfb35c446a375a2b74c37691d7031afb1e4f66a8b63cb7c1669470689d2"

View File

@@ -1,6 +1,6 @@
[tool.poetry]
name = "homeharvest"
version = "0.3.23"
version = "0.4.1"
description = "Real estate scraping library"
authors = ["Zachary Hampton <zachary@bunsly.com>", "Cullen Watson <cullen@bunsly.com>"]
homepage = "https://github.com/Bunsly/HomeHarvest"
@@ -10,9 +10,10 @@ readme = "README.md"
homeharvest = "homeharvest.cli:main"
[tool.poetry.dependencies]
python = ">=3.10,<3.13"
python = ">=3.9,<3.13"
requests = "^2.31.0"
pandas = "^2.1.1"
pydantic = "^2.7.4"
[tool.poetry.group.dev.dependencies]

View File

@@ -4,7 +4,7 @@ from homeharvest import scrape_property
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")
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)
@@ -105,8 +105,8 @@ def test_realtor():
location="2530 Al Lipscomb Way",
listing_type="for_sale",
),
scrape_property(location="Phoenix, AZ", listing_type="for_rent"), #: does not support "city, state, USA" format
scrape_property(location="Dallas, TX", listing_type="sold"), #: does not support "city, state, USA" format
scrape_property(location="Phoenix, AZ", listing_type="for_rent", limit=1000), #: does not support "city, state, USA" format
scrape_property(location="Dallas, TX", listing_type="sold", limit=1000), #: does not support "city, state, USA" format
scrape_property(location="85281"),
]
@@ -117,6 +117,7 @@ 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
@@ -140,22 +141,27 @@ def test_realtor_foreclosed():
def test_realtor_agent():
scraped = scrape_property(location="Detroit, MI", listing_type="for_sale")
assert scraped["agent"].nunique() > 1
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="15509 N 172nd Dr, Surprise, AZ 85388",
location="00741",
listing_type="sold",
limit=10,
extra_property_data=False,
),
scrape_property(
location="15509 N 172nd Dr, Surprise, AZ 85388",
location="00741",
listing_type="sold",
limit=10,
extra_property_data=True,
),
]
assert results[0] != results[1]
assert not results[0].equals(results[1])
def test_pr_zip_code():
@@ -165,3 +171,85 @@ def test_pr_zip_code():
)
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