Real Estate Data Analytics: Property Data Collection and Analysis

Why do I have to use a proxy IP for real estate data?

Recently, a friend who works as an agent complained to me that their company used a crawler to grab data from a certain website, and as a result, the entire office network was blocked the next day. Does this sound familiar? Now all the major real estate platforms have installedIntelligent Risk Control SystemJust like the security guards at the entrance of the neighborhood, when they see suspicious people, they just stop them.

For example, if you use your own broadband to brush the information of a property, the platform can immediately find that the IP address is unusually active. The light is to restrict access, the heavy is directly blocked. This time you have to rely on proxy IP tomasquerading as different users, it's like changing your clothes and wearing a wig every time you look at a house so that the platform doesn't recognize the same person.

What are the doors to look for when choosing a proxy IP?

There are a plethora of agency service providers on the market, but you have to pick the right type to engage in real estate data collection. Here's a solid comparison table for you:

The biggest advantage of dynamic proxies, like the ipipgo dynamic proxy we use, is that theIP pools are automatically updated hourlyIt's a good idea. The last time I helped a customer to catch the chain's listing data, ran for 7 consecutive days 500,000 requests, and did not trigger the anti-climbing mechanism. Their IP survival time is set up in a smart way, unlike some service providers who either change too diligently to waste resources or change too slowly to be easily exposed.

Real-world case study: grabbing home price trends with Python

Here's a snippet of code that works, note the proxy configuration section:

import requests
from time import sleep

proxies = {
    'http': 'http://username:password@gateway.ipipgo.com:9020',
    'https': 'http://username:password@gateway.ipipgo.com:9020'
}

def get_house_data(city).
    url = f'https://fangjia.{city}.com/list'
    try.
        response = requests.get(url, proxies=proxies, timeout=10)
         Remember to add a random delay here, so you don't have to machine-gun it
        sleep(1.5 + random.random())
        return response.text
    except Exception as e.
        print(f'Capture failed: {str(e)}')

Highlight it three times:timeout setting,random delay,Exception handlingThe first thing you need to do is to get your hands on a proxy server and get it to work! A lot of newbies fall head over heels because these three points are not done. ipipgo's proxy server response speed control in 200ms or less, this point is particularly important to maintain the collection of stability.

Top 3 Tips for Washing Your Data Clean

The data captured back often comes in all sorts of odd formats, so I'll share a few tricks for handling them:

1. Harmonization of price units"$15,000 per square foot" and "$15,000" are converted to plain numbers.