# How to Build a Property Price Heatmap with the Zillow API and Python - Part 1: Setup & Code **Visualize real estate prices on an interactive map - no GIS experience needed.** > **Series:** This is Part 1 of 2. You'll get the environment ready and run the script for the first time. > **Part 2** covers reading the map output, customizing colors and filters, and troubleshooting common issues. > **Time to complete:** 10 minutes > **Python level:** Beginner > **Keywords:** zillow API python heatmap, real estate price visualization, property price map python, RapidAPI real estate --- ## What You'll Build A Python script that pulls live property listings from the Zillow API for any geographic area and generates an interactive HTML heatmap - color-coded by price, clickable for details, ready to open in any browser. By the end of Part 1 you'll have `price_heatmap.html` open on your screen. Part 2 explains how to read it and make it your own. --- ## Why Build a Property Price Heatmap? When you're analyzing a real estate market, raw lists of prices tell you very little. A heatmap immediately answers the question every buyer, investor, and agent actually asks: **where is it expensive, and where is it not?** This tutorial uses the [Zillow Real Estate API on RapidAPI](https://rapidapi.com/letsscrape/api/real-estate-zillow-com) to pull live listings by geographic bounding box, then plots them as a weighted heatmap using [Folium](https://python-visualization.github.io/folium/) - a Python library that generates interactive Leaflet.js maps. The output is a single `.html` file you can open in any browser or publish on a website. --- ## How It Works The key endpoint here is **`GET /v1/search/coords`**, which accepts a geographic bounding box instead of a city name: | Parameter | What it means | Example | |---|---|---| | `type` | Listing type: `sale`, `rent`, or `sold` | `sale` | | `north` | North boundary (latitude) | `40.917577` | | `south` | South boundary (latitude) | `40.477399` | | `east` | East boundary (longitude) | `-73.700272` | | `west` | West boundary (longitude) | `-74.259090` | Each listing in the response includes a `latLong` field with exact GPS coordinates and an `unformattedPrice` field with the price as a plain number - exactly what a heatmap needs. ```json { "address": "232 W Linden Ave, Linden City, NJ 07036", "unformattedPrice": 479000, "beds": 3, "baths": 2, "latLong": { "latitude": 40.62738, "longitude": -74.25229 }, "detailUrl": "https://www.zillow.com/homedetails/..." } ``` --- ## Step 1 - Get Your Free API Key 1. Go to [https://rapidapi.com/letsscrape/api/real-estate-zillow-com](https://rapidapi.com/letsscrape/api/real-estate-zillow-com) 2. Click **Sign Up** and create a free account 3. Your API key appears automatically on the page - copy it > **Free tier:** 65 requests/month. One map = one request. --- ## Step 2 - Install the Only Dependency This script uses one external library: `folium`, which generates interactive maps. ```bash pip install folium ``` Everything else (`http.client`, `json`, `webbrowser`) is built into Python. --- ## Step 3 - Find Your Bounding Box Coordinates The `/v1/search/coords` endpoint needs 4 coordinates that define the rectangle of the area you want to analyze. **The easiest way to find them:** 1. Go to [https://www.google.com/maps](https://www.google.com/maps) 2. Navigate to your target area 3. Right-click on the **top-left corner** of the area - copy the coordinates (latitude, longitude) 4. Right-click on the **bottom-right corner** - copy those coordinates Then map them like this: ``` top-left corner - north (lat), west (lng) bottom-right corner - south (lat), east (lng) ``` **Example - New York City area:** ```python NORTH = 40.917577 # top of the map SOUTH = 40.477399 # bottom EAST = -73.700272 # right side WEST = -74.259090 # left side ``` > **Tip:** A box roughly 0.5° × 0.5° covers about a city-sized area and typically returns 40–100 listings on the free tier. --- ## Step 4 - Configure and Run the Script Save the script below as `price_heatmap.py`. Then open it and update the **settings block** at the top: ```python API_KEY = "YOUR_API_KEY_HERE" # ← paste your key here NORTH = 40.917577 SOUTH = 40.477399 EAST = -73.700272 WEST = -74.259090 LISTING_TYPE = "sale" # "sale", "rent", or "sold" ``` Then run it: ```bash python price_heatmap.py ``` The script will: 1. Fetch listings from the API 2. Print a price summary in the terminal 3. Build the heatmap 4. Save `price_heatmap.html` 5. Open it automatically in your browser --- ## Step 5 - The Full Script ```python """ ===================================================== PROPERTY PRICE HEATMAP Visualize home prices in any area using the Zillow API + Folium ===================================================== BEFORE YOU RUN: 1. Get a free API key: https://rapidapi.com/letsscrape/api/real-estate-zillow-com 2. Replace YOUR_API_KEY_HERE below with your key 3. Install dependencies: pip install folium 4. Run: python price_heatmap.py 5. Open the generated price_heatmap.html in your browser HOW TO FIND YOUR COORDINATES: - Go to https://www.google.com/maps - Right-click any corner of the area you want - Click the coordinates shown to copy them - North/South = latitude, East/West = longitude ===================================================== """ import http.client import json import webbrowser import os import folium from folium.plugins import HeatMap # ===================================================== # SETTINGS - CHANGE THESE # ===================================================== API_KEY = "YOUR_API_KEY_HERE" # <-- paste your RapidAPI key here # Bounding box for the area you want to analyze # Default: New York City area NORTH = 40.917577 SOUTH = 40.477399 EAST = -73.700272 WEST = -74.259090 # What type of listings to show: "sale", "rent", or "sold" LISTING_TYPE = "sale" # Output filename OUTPUT_FILE = "price_heatmap.html" # ===================================================== # DO NOT EDIT BELOW THIS LINE # ===================================================== def check_config(): if API_KEY == "YOUR_API_KEY_HERE": print() print("=" * 52) print(" SETUP REQUIRED") print("=" * 52) print() print(" You haven't added your API key yet.") print() print(" 1. Go to: https://rapidapi.com/letsscrape/") print(" api/real-estate-zillow-com") print(" 2. Sign up for a free account") print(" 3. Copy your API key") print(" 4. Open this file and replace YOUR_API_KEY_HERE") print(" on line 40 with your actual key") print() exit(1) def fetch_listings(): print(f"\nFetching {LISTING_TYPE} listings for the selected area...") print(f" Bounds: N={NORTH}, S={SOUTH}, E={EAST}, W={WEST}") endpoint = ( f"/v1/search/coords" f"?type={LISTING_TYPE}" f"&north={NORTH}" f"&south={SOUTH}" f"&east={EAST}" f"&west={WEST}" ) conn = http.client.HTTPSConnection("real-estate-zillow-com.p.rapidapi.com") headers = { "x-rapidapi-key": API_KEY, "x-rapidapi-host": "real-estate-zillow-com.p.rapidapi.com", "Content-Type": "application/json" } try: conn.request("GET", endpoint, headers=headers) res = conn.getresponse() raw = res.read() data = json.loads(raw.decode("utf-8")) except Exception as e: print(f"\n ERROR: Could not connect to the API.") print(f" Details: {e}") exit(1) if data.get("status") != 200: print(f"\n API ERROR: {data.get('description', 'Unknown error')}") print(f" Check your API key and try again.") exit(1) listings = data["data"].get("listings", []) print(f" Found {len(listings)} listings.") return listings def extract_heatmap_points(listings): points = [] skipped = 0 for home in listings: lat = home.get("latLong", {}).get("latitude") lng = home.get("latLong", {}).get("longitude") price = home.get("unformattedPrice", 0) if not lat or not lng or not price: skipped += 1 continue points.append([lat, lng, price]) if skipped: print(f" Skipped {skipped} listings with missing location or price.") return points def normalize_weights(points): """Scale prices to 0-1 so the heatmap colors show relative prices.""" if not points: return points prices = [p[2] for p in points] min_price = min(prices) max_price = max(prices) price_range = max_price - min_price if price_range == 0: return [[lat, lng, 1.0] for lat, lng, _ in points] normalized = [] for lat, lng, price in points: weight = (price - min_price) / price_range weight = 0.2 + weight * 0.8 # floor at 0.2 so cheap areas still show normalized.append([lat, lng, weight]) return normalized def build_marker_popup(home): address = home.get("address", "Unknown address") price = home.get("price", "N/A") beds = home.get("beds", "?") baths = home.get("baths", "?") area = home.get("area", "?") url = home.get("detailUrl", "#") broker = home.get("brokerName", "") return f"""

{price}
{address}

{beds} bed {baths} bath {area} sqft

{f'{broker}
' if broker else ''} View on Zillow -

""" def create_heatmap(listings, points_normalized): center_lat = (NORTH + SOUTH) / 2 center_lng = (EAST + WEST) / 2 lat_span = NORTH - SOUTH zoom = 11 if lat_span > 0.3 else 13 if lat_span > 0.1 else 15 m = folium.Map( location=[center_lat, center_lng], zoom_start=zoom, tiles="CartoDB positron" ) HeatMap( points_normalized, radius=25, blur=20, min_opacity=0.3, gradient={ "0.0": "#313695", "0.3": "#74add1", "0.5": "#fee090", "0.7": "#f46d43", "1.0": "#a50026" } ).add_to(m) for home in listings: lat = home.get("latLong", {}).get("latitude") lng = home.get("latLong", {}).get("longitude") price = home.get("unformattedPrice", 0) if not lat or not lng or not price: continue folium.CircleMarker( location=[lat, lng], radius=5, color="white", weight=1, fill=True, fill_color="#333", fill_opacity=0.6, popup=folium.Popup(build_marker_popup(home), max_width=280) ).add_to(m) prices = [h.get("unformattedPrice", 0) for h in listings if h.get("unformattedPrice")] if prices: min_p = min(prices) max_p = max(prices) mid_p = (min_p + max_p) // 2 legend_html = f"""

Price Heatmap
{LISTING_TYPE.capitalize()} listings · {len(listings)} properties

High ${max_p:,.0f}
Mid ${mid_p:,.0f}
Low ${min_p:,.0f}

Click any dot for details

""" m.get_root().html.add_child(folium.Element(legend_html)) return m def print_summary(listings): prices = sorted([h.get("unformattedPrice", 0) for h in listings if h.get("unformattedPrice")]) if not prices: return median = prices[len(prices) // 2] print() print("=" * 52) print(f" PRICE SUMMARY ({LISTING_TYPE.upper()})") print("=" * 52) print(f" Properties mapped : {len(prices)}") print(f" Lowest price : ${min(prices):>12,.0f}") print(f" Median price : ${median:>12,.0f}") print(f" Highest price : ${max(prices):>12,.0f}") print(f" Average price : ${sum(prices)//len(prices):>12,.0f}") print("=" * 52) print() if __name__ == "__main__": check_config() listings = fetch_listings() points = extract_heatmap_points(listings) points_normalized = normalize_weights(points) print_summary(listings) print(" Building map...") m = create_heatmap(listings, points_normalized) m.save(OUTPUT_FILE) print(f" Map saved: {OUTPUT_FILE}") print(f" Opening in your browser...") print() webbrowser.open(f"file://{os.path.abspath(OUTPUT_FILE)}") ``` --- ## Coming Up in Part 2 Once the map is open in your browser, you'll want to know: - **What exactly am I looking at?** - how to read the color gradient and what the dot markers tell you - **Why are all the dots blue?** - the normalization logic explained, and when it matters - **How do I switch to rentals or sold listings?** - one-line changes - **How do I zoom into a single neighborhood?** - tightening the bounding box - **Something went wrong** - the full troubleshooting table - Continue to [Part 2: Reading the Map, Customization & Troubleshooting](https://rapidapi.com/letsscrape/api/real-estate-zillow-com/tutorials/how-to-build-a-property-price-heatmap-with-the-zillow-api-and-python---part-2%3A-reading-the-map%2C-customization-%26-troubleshooting)