Big Data and Real Estate: Market Trend Analysis Report

When Crawler Meets Real Estate: The Pitfalls of Data Collection

Recently, I helped a friend to analyze the price of a second-hand house and wrote a crawler script in Python. The result is that the target website blocked our IP just after two days of running. At this time, I remembered that I have to use proxy IP, but the service providers in the market are either too expensive or the IP pool is too small. Until I used ipipgo's dynamic residential proxy, I finally caught all the house price data of 30 cities in China.

import requests
from itertools import cycle

proxies = [
    "http://user:pass@gateway.ipipgo.com:30001",
    "http://user:pass@gateway.ipipgo.com:30002"
]

proxy_pool = cycle(proxies)

for page in range(1,100)::
    try: proxy = next(proxy_pool)
        proxy = next(proxy_pool)
        response = requests.get(
            f "https://fangjia.com/list?page={page}",
            proxies={"http": proxy}, timeout=10
            timeout=10
        )
         Data parsing logic...
    except Exception as e.
        print(f "Failed to capture page {page}, switching IPs automatically.")

The Secret Weapon of House Price Prediction: Dynamic IP Networks

The biggest headache of doing market trend analysis isIncomplete dataThe reason for this is that many of the intermediary platforms have very cheap anti-climbing mechanisms. Many intermediary platforms have a very sneaky anti-crawl mechanism that ordinary proxy IPs can't handle. ipipgo's unique feature is theirResidential-grade dynamic IP poolsThe IPs of real home broadband can be randomly switched for each request, which is much more reliable than those server room IPs.

Here is a practical tip: when collecting data from different cities, remember to match the local IP segments. For example, if you want to catch the price of Shenzhen, choose the export node in Guangdong. ipipgo's background can precisely select the location of the base station, which is particularly important for analyzing regional price differences.

A data collection solution that even a novice can handle

I recently had a real estate agent friend who wanted to monitor competing quotes himself, and I gave him a tip:

1. Buy a pay-as-you-go package from the ipipgo website (newbies are advised to go for the 10GB traffic package)
2. Download their client to generate API call addresses in one click!
3. Use an off-the-shelf crawler tool like Octoparse and fill in the proxy address into the settings

Here's the point! Remember to setRandomized visit intervalsIt is best to mimic the rhythm of a real person's operation. Don't let the program crawl data in the middle of the night, it is easy to be targeted by the wind control. ipipgo's intelligent scheduling system automatically adjusts the frequency of requests, this point is particularly friendly to the little white.

Case Study: Monitoring Price Fluctuations in School District Housing

Last year, when I was helping educational institutions to do school district analysis, I found an interesting phenomenon: many platforms will put the school district informationIntentionally incomplete displayThis is where proxy IPs are needed to simulate multi-location user access. This is where proxy IPs are needed to simulate multi-location user access and piece together the complete data.

We used ipipgo'sCity-level positioningThe function simultaneously collects listing information from three districts in Beijing: Xicheng, Haidian and Dongcheng. By comparing the listing prices of the same neighborhood in different districts, it successfully predicts the price fluctuations caused by the adjustment of school district policies.

Frequently Asked Questions QA

Q: Why use a paid proxy? Isn't free more cost effective?
A: The free agent's availability is less than 10%, real estate data does not move to continuous collection for several months, professional things still have to be professional tools. ipipgo new users have a three-day trial period, their own experience to know the gap.

Q: How do you verify the authenticity of the collected data?
A: It is recommended to collect the same listing with 3-4 export IPs at the same time and compare the median values. ipipgo'sData Validation APIYou can directly return the geographic location of the IP to avoid being fooled by fake data.

Q: What should I do if I encounter a CAPTCHA?
A: Don't tough it out, set the number of failed retries. ipipgo'sHigh Stash AgentsIt reduces the probability of triggering a CAPTCHA, and really experiencing a large number of CAPTCHAs indicates that it's time to change IP segments.

Getting into real estate data analysis is, in the end, awar of attrition. Choosing the right proxy tool is equivalent to having a pair of good running shoes, and ipipgo's flexible billing model is particularly suitable for this kind of long-term project. Recently, I saw that they are engaged in activities, enterprise users to send data cleaning services, do batch analysis can go.