Scraping Real estate website (100$)
Budget: $30 – $250 AUD
I need to extract information from a real-estate website and pack them into an excel sheet or a database.
The site name is realestate.com.au
The extracted information should include the following :
(1) suburb profiles: https://www.realestate.com.au/neighbourhoods/doncaster-3108-vic?cid=pdp
a. median house prices (2br, 3br, 4br)
b. median house rent (2br,3br, 4br)
c. median unit price (1br, 2br, 3br)
d. median unit rent (1br, 2br, 3br)
e. monthly price trend
d. annual price trend
f. visits per property per suburb
g. visits per property per state
h. life style detailed stats (couples and families, older couples and families, ... )
(2) all the properties listed on the site for rent or for sale and the details of each property including the following:
a. address
1. suburb
2. city
3. street
4. number
b. property type (house, unit, apartment, townhouse, retirement living, land, rural, acreage, villa, block of units)
c. price
d. bedrooms
e. bathroom
f. car spaces
g. land size
g. building size
h. is_new
i. is_established
j. having swimming pool
k. having balcony
l. having ensuite
m. having solar panels
n. Having air condition
o. Having ducted heating
p. Having pergola / patio
q. repayment per month
r. savings
s. stamp duty
t. government fees
u. available deposit
v. LMI estimate
w. primary schools
x. secondary schools
and any other information in any property page such as the page below:
https://www.realestate.com.au/property-apartment-vic-doncaster-134099014?sourcePage=rea%3Abuy%3Asrp-map&sourceElement=listing-tile
The deliverable will include the following:
1. The suburb data in an excel sheet (or the database in case the data was too much)
2. The sale property data in an excel sheet (or the database in case the data was too much)
3. The rent property data in an excel sheet (or the database in case the data was too much)
4. the code for scraping the data and storing them locally in python
The code should be smart enough to avoid being blocked by the site servers, this might require adding lags between every request and the other, and may require o hide the identity of the request issuer by hiding the IP or using a proxy chain
The site name is realestate.com.au
The extracted information should include the following :
(1) suburb profiles: https://www.realestate.com.au/neighbourhoods/doncaster-3108-vic?cid=pdp
a. median house prices (2br, 3br, 4br)
b. median house rent (2br,3br, 4br)
c. median unit price (1br, 2br, 3br)
d. median unit rent (1br, 2br, 3br)
e. monthly price trend
d. annual price trend
f. visits per property per suburb
g. visits per property per state
h. life style detailed stats (couples and families, older couples and families, ... )
(2) all the properties listed on the site for rent or for sale and the details of each property including the following:
a. address
1. suburb
2. city
3. street
4. number
b. property type (house, unit, apartment, townhouse, retirement living, land, rural, acreage, villa, block of units)
c. price
d. bedrooms
e. bathroom
f. car spaces
g. land size
g. building size
h. is_new
i. is_established
j. having swimming pool
k. having balcony
l. having ensuite
m. having solar panels
n. Having air condition
o. Having ducted heating
p. Having pergola / patio
q. repayment per month
r. savings
s. stamp duty
t. government fees
u. available deposit
v. LMI estimate
w. primary schools
x. secondary schools
and any other information in any property page such as the page below:
https://www.realestate.com.au/property-apartment-vic-doncaster-134099014?sourcePage=rea%3Abuy%3Asrp-map&sourceElement=listing-tile
The deliverable will include the following:
1. The suburb data in an excel sheet (or the database in case the data was too much)
2. The sale property data in an excel sheet (or the database in case the data was too much)
3. The rent property data in an excel sheet (or the database in case the data was too much)
4. the code for scraping the data and storing them locally in python
The code should be smart enough to avoid being blocked by the site servers, this might require adding lags between every request and the other, and may require o hide the identity of the request issuer by hiding the IP or using a proxy chain