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Max Klein
Max Klein

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Building a Real Estate Price Tracker with Python

#python #beginners #tutorial #webdev

In today's fast-paced real estate market, staying ahead of price trends can be the difference between a smart investment and a costly mistake. This tutorial guides you through building a real estate price tracker using Python, combining web scraping, data analysis, and visualization.

Prerequisites 

pip install requests beautifulsoup4 pandas matplotlib seaborn schedule plotly
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Step 1: Scraping Real Estate Listings 

import requests
from bs4 import BeautifulSoup
import pandas as pd

def scrape_real_estate_data(url):
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
    }
    response = requests.get(url, headers=headers)
    if response.status_code != 200:
        return pd.DataFrame()

    soup = BeautifulSoup(response.text, "html.parser")
    properties = []
    for item in soup.select(".property-listing .item"):
        title = item.select_one(".title").text.strip()
        price = item.select_one(".price").text.strip()
        properties.append({"Title": title, "Price": price})

    return pd.DataFrame(properties)
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Tips: Use headers to mimic browser requests. Respect rate limits. Test selectors with browser dev tools.

Step 2: Data Cleaning 

def clean_real_estate_data(df):
    df.dropna(subset=["Price"], inplace=True)
    df["Price"] = df["Price"].str.replace("[^0-9]", "", regex=True)
    df["Price"] = pd.to_numeric(df["Price"], errors="coerce")
    df.dropna(subset=["Price"], inplace=True)
    return df
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Step 3: Visualization 

import plotly.express as px

fig = px.scatter(cleaned_data, x="Location", y="Price", 
                 size="Price", color="Price",
                 hover_name="Title", 
                 title="Real Estate Prices by Location")
fig.show()
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Step 4: Automation 

import schedule
import time

def job():
    print("Updating real estate data...")
    data = scrape_real_estate_data(url)
    cleaned = clean_real_estate_data(data)
    # Save, analyze, alert...

schedule.every().day.at("08:00").do(job)

while True:
    schedule.run_pending()
    time.sleep(1)
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Step 5: Deploy as a Web App 

from flask import Flask, render_template
import pandas as pd

app = Flask(__name__)

@app.route("/")
def index():
    df = pd.read_csv("real_estate_data.csv")
    return render_template("index.html", data=df.to_dict())

if __name__ == "__main__":
    app.run(debug=True)
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Conclusion

You've built a real estate price tracker that scrapes, cleans, analyzes, and visualizes data. Extend it with ML predictions, database storage, or mobile deployment.

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