API Use Cases & Project Ideas

""" Investment property screener — ranks ZIP codes by investment attractiveness. Requires: pip install requests pandas Usage: RAPIDAPI_KEY=your_key python screener.py """ import requests, os, time, csv, sys from dataclasses import dataclass, asdict API_KEY = os.environ.get("RAPIDAPI_KEY") if not API_KEY: sys.exit("Set RAPIDAPI_KEY environment variable first.") HOST = "real-estate-market-data.p.rapidapi.com" BASE = f"https://{HOST}" HEADERS = {"X-RapidAPI-Key": API_KEY, "X-RapidAPI-Host": HOST} # ── Your target ZIP codes ───────────────────────────────────────────────────── TARGET_ZIPS = [ "30301", # Atlanta, GA "37201", # Nashville, TN "35203", # Birmingham, AL "28201", # Charlotte, NC "45201", # Cincinnati, OH "43201", # Columbus, OH "32801", # Orlando, FL "70112", # New Orleans, LA "55401", # Minneapolis, MN "64101", # Kansas City, MO ] TEMP_SCORE = { "Hot 🔥": 5, "Warm": 4, "Balanced": 3, "Cool": 2, "Buyer's Market": 1 } def get(endpoint, params): """GET with simple retry on 429.""" for attempt in range(3): r = requests.get(f"{BASE}/{endpoint}", params=params, headers=HEADERS, timeout=10) if r.status_code == 429: time.sleep(2 ** attempt) continue r.raise_for_status() return r.json() raise RuntimeError(f"Failed after retries: {endpoint} {params}") rows = [] for zip_code in TARGET_ZIPS: print(f" Fetching {zip_code}...", end=" ", flush=True) try: est = get("property-estimate", {"zip_code": zip_code}) market = get("market-stats", {"zip_code": zip_code}) rent = get("rental-yield", {"zip_code": zip_code}) m = est["investment_metrics"] # Composite score (normalised to 0–100) cap_score = min(m["cap_rate_pct"] / 8 * 40, 40) # 8% cap = full score coc_score = min(m["cash_on_cash_return_pct"] / 10 * 40, 40) # 10% CoC = full score temp_score = TEMP_SCORE.get(market["market_temperature"], 3) / 5 * 20 composite = round(cap_score + coc_score + temp_score, 1) rows.append({ "zip_code": zip_code, "price": est["estimated_value"], "monthly_rent": m["estimated_monthly_rent"], "gross_yield_pct": est["gross_yield_pct"], "cap_rate_pct": m["cap_rate_pct"], "cash_flow_monthly": m["monthly_cash_flow"], "coc_return_pct": m["cash_on_cash_return_pct"], "investment_rating": m["investment_rating"], "down_payment": m["down_payment_20pct"], "market_temp": market["market_temperature"], "dom": market["median_days_on_market"], "yoy_change_pct": round(market["yoy_price_change"] * 100, 1), "inventory_months": market["inventory_months"], "composite_score": composite, "data_source": est["data_source"], }) print(f"cap {m['cap_rate_pct']}% coc {m['cash_on_cash_return_pct']}% score {composite}") except Exception as e: print(f"ERROR: {e}") time.sleep(0.5) # stay within rate limits # Sort by composite score descending rows.sort(key=lambda r: r["composite_score"], reverse=True) # Print ranked table print() print(f"{'Rank':<5} {'ZIP':<8} {'Price':>10} {'Cap%':>7} {'CoC%':>7} " f"{'CF/mo':>8} {'Score':>6} {'Rating'}") print("─" * 68) for i, r in enumerate(rows, 1): print( f"{i:<5} {r['zip_code']:<8} ${r['price']:>9,} " f"{r['cap_rate_pct']:>6.1f}% {r['coc_return_pct']:>6.1f}% " f"${r['cash_flow_monthly']:>+7,} {r['composite_score']:>6.1f} " f"{r['investment_rating']}" ) # Export CSV with open("screener_results.csv", "w", newline="") as f: writer = csv.DictWriter(f, fieldnames=rows[0].keys()) writer.writeheader() writer.writerows(rows) print(f"\nExported {len(rows)} results to screener_results.csv")

""" Relocation affordability comparison. Usage: RAPIDAPI_KEY=your_key python relocate.py """ import requests, os API_KEY = os.environ["RAPIDAPI_KEY"] HEADERS = { "X-RapidAPI-Key": API_KEY, "X-RapidAPI-Host": "real-estate-market-data.p.rapidapi.com" } YOUR_INCOME = 95_000 # ← change to your household income METROS = [ "Austin", "Denver", "Nashville", "Raleigh", "Phoenix", "Charlotte", "Tampa", "Columbus", ] results = [] for metro in METROS: resp = requests.get( "https://real-estate-market-data.p.rapidapi.com/affordability", params={"metro": metro, "income": YOUR_INCOME}, headers=HEADERS, timeout=10 ) d = resp.json() p = d.get("personalized", {}) results.append({ "metro": d["metro"], "index": d["affordability_index"], "label": d["interpretation"], "home_price": d["median_home_price"], "pti": d["price_to_income_ratio"], "down_payment": d["down_payment_20pct"], "yrs_to_save": d["years_to_save_down_payment"], "monthly_pmt": d["monthly_payment_estimate"], "dti": p.get("debt_to_income_pct", 0), "verdict": p.get("affordability_verdict", "N/A"), "max_price": p.get("max_home_price_28dti", 0), }) # Sort by affordability index descending (most affordable first) results.sort(key=lambda r: r["index"], reverse=True) print(f"\nAffordability comparison for ${YOUR_INCOME:,} household income\n") print(f"{'Metro':<12} {'Index':>6} {'Home Price':>11} {'DTI':>5} {'Verdict':<12} {'Yrs to Down':>11}") print("─" * 66) for r in results: print( f"{r['metro']:<12} {r['index']:>6.0f} ${r['home_price']:>10,} " f"{r['dti']:>4.0f}% {r['verdict']:<12} {r['yrs_to_save']:>10.1f}y" ) # Highlight the most affordable choice best = results[0] print(f"\nBest value for your income: {best['metro']}") print(f" Affordability index: {best['index']:.0f} ({best['label']})") print(f" Monthly payment: ${best['monthly_pmt']:,}") print(f" Max home at 28% DTI: ${best['max_price']:,}") print(f" Down payment needed: ${best['down_payment']:,} " f"(~{best['yrs_to_save']:.1f} years to save at 15% savings rate)")

""" Weekly real estate market report. Schedule: cron 0 8 * * 1 (every Monday at 8am) Requires: pip install requests Env vars: RAPIDAPI_KEY, SMTP_HOST, SMTP_PORT, SMTP_USER, SMTP_PASS, REPORT_TO """ import requests, os, smtplib, time from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText from datetime import datetime API_KEY = os.environ["RAPIDAPI_KEY"] HEADERS = { "X-RapidAPI-Key": API_KEY, "X-RapidAPI-Host": "real-estate-market-data.p.rapidapi.com" } # ── Markets to monitor ──────────────────────────────────────────────────────── WATCHLIST = [ {"zip": "78701", "label": "Austin, TX (Downtown)"}, {"zip": "30301", "label": "Atlanta, GA"}, {"zip": "37201", "label": "Nashville, TN"}, {"zip": "85001", "label": "Phoenix, AZ"}, ] def fetch_market(zip_code): r = requests.get( "https://real-estate-market-data.p.rapidapi.com/market-stats", params={"zip_code": zip_code}, headers=HEADERS, timeout=10 ) r.raise_for_status() return r.json() def trend_arrow(value): return "▲" if value > 0 else ("▼" if value < 0 else "—") def build_html_report(markets): rows = "" for m in markets: d = m["data"] yoy = d["yoy_price_change"] * 100 arrow = trend_arrow(yoy) rows += f""" {m['label']}
ZIP {m['zip']} ${d['median_sale_price']:,} {d['median_days_on_market']} days {arrow} {abs(yoy):.1f}% {d['market_temperature']} """ return f""" """ def send_report(html_body): msg = MIMEMultipart("alternative") msg["Subject"] = f"Weekly Market Report — {datetime.now().strftime('%b %-d')}" msg["From"] = os.environ["SMTP_USER"] msg["To"] = os.environ["REPORT_TO"] msg.attach(MIMEText(html_body, "html")) with smtplib.SMTP_SSL(os.environ["SMTP_HOST"], int(os.environ["SMTP_PORT"])) as server: server.login(os.environ["SMTP_USER"], os.environ["SMTP_PASS"]) server.send_message(msg) # ── Main ────────────────────────────────────────────────────────────────────── markets = [] for item in WATCHLIST: print(f"Fetching {item['label']}...") item["data"] = fetch_market(item["zip"]) markets.append(item) time.sleep(0.5) html = build_html_report(markets) send_report(html) print(f"Report sent to {os.environ['REPORT_TO']}")

""" Rental portfolio yield tracker. Define your properties below and run to see market yield vs your actual yield. """ import requests, os, time API_KEY = os.environ["RAPIDAPI_KEY"] HEADERS = { "X-RapidAPI-Key": API_KEY, "X-RapidAPI-Host": "real-estate-market-data.p.rapidapi.com" } # ── Your portfolio ───────────────────────────────────────────────────────────── PORTFOLIO = [ {"address": "123 Main St, Austin TX", "zip": "78701", "bedrooms": 2, "purchase_price": 520000, "actual_monthly_rent": 2400}, {"address": "456 Oak Ave, Nashville TN", "zip": "37201", "bedrooms": 3, "purchase_price": 340000, "actual_monthly_rent": 1950}, {"address": "789 Pine Rd, Atlanta GA", "zip": "30301", "bedrooms": 2, "purchase_price": 275000, "actual_monthly_rent": 1700}, ] def fetch_yield(zip_code, bedrooms): r = requests.get( "https://real-estate-market-data.p.rapidapi.com/rental-yield", params={"zip_code": zip_code, "bedrooms": bedrooms}, headers=HEADERS, timeout=10 ) r.raise_for_status() return r.json() print(f"\n{'Property':<35} {'Purchase':>10} {'Mkt Value':>10} " f"{'Your Rent':>10} {'Mkt Rent':>9} {'Your Yield':>11} {'Mkt Yield':>10} {'Signal'}") print("─" * 110) for prop in PORTFOLIO: d = fetch_yield(prop["zip"], prop["bedrooms"]) market_rent = d["estimated_monthly_rent"] market_val = d["median_sale_price"] # Your actual yield based on purchase price your_yield = round(prop["actual_monthly_rent"] * 12 / prop["purchase_price"] * 100, 2) # Market yield at current prices market_yld = d["gross_yield_pct"] # Under/over market rent rent_delta = prop["actual_monthly_rent"] - market_rent rent_signal = ( "RAISE RENT" if rent_delta < -100 else "AT MARKET" if abs(rent_delta) <= 100 else "ABOVE MKT" ) print( f"{prop['address']:<35} ${prop['purchase_price']:>9,} ${market_val:>9,} " f"${prop['actual_monthly_rent']:>9,} ${market_rent:>8,} " f"{your_yield:>10.1f}% {market_yld:>9.1f}% {rent_signal}" ) time.sleep(0.5) print("\nGross yields only — deduct ~35-45% operating expenses for net yield.")

// Server-side proxy — keeps RAPIDAPI_KEY secret export default async function handler(req, res) { const { zip_code } = req.query; if (!/^\d{5}$/.test(zip_code)) { return res.status(400).json({ error: "Invalid ZIP code" }); } const url = new URL( "https://real-estate-market-data.p.rapidapi.com/market-stats" ); url.searchParams.set("zip_code", zip_code); const response = await fetch(url, { headers: { "X-RapidAPI-Key": process.env.RAPIDAPI_KEY, // server-side env only "X-RapidAPI-Host": "real-estate-market-data.p.rapidapi.com" } }); const data = await response.json(); // Cache for 24 hours (data updates daily) res.setHeader("Cache-Control", "s-maxage=86400, stale-while-revalidate"); res.status(response.status).json(data); }

import { useState } from "react"; const TEMP_COLOR = { "Hot 🔥": "#c83800", "Warm": "#e07a00", "Balanced": "#15602f", "Cool": "#1a3a7a", "Buyer's Market": "#1a3a7a" }; function Stat({ label, value }) { return ( <div style={{ textAlign: "center", padding: "12px 16px" }}> <div style={{ fontSize: 11, fontFamily: "monospace", color: "#82796f", textTransform: "uppercase", letterSpacing: 1, marginBottom: 4 }}> {label} </div> <div style={{ fontSize: 20, fontWeight: 700, fontFamily: "serif" }}> {value} </div> </div> ); } export default function MarketStatsWidget() { const [zip, setZip] = useState(""); const [data, setData] = useState(null); const [loading, setLoading] = useState(false); const [error, setError] = useState(null); async function lookup() { if (!/^\d{5}$/.test(zip)) { setError("Enter a valid 5-digit ZIP code."); return; } setLoading(true); setError(null); try { const res = await fetch(`/api/market-stats?zip_code=${zip}`); if (!res.ok) throw new Error(`HTTP ${res.status}`); setData(await res.json()); } catch (e) { setError("Could not fetch data — please try again."); } finally { setLoading(false); } } return ( <div style={{ fontFamily: "sans-serif", maxWidth: 480, border: "1px solid #e0d9cf", borderRadius: 12, overflow: "hidden", background: "#fff" }}> {/* Search bar */} <div style={{ padding: "16px 20px", background: "#111009", display: "flex", gap: 10 }}> <input value={zip} onChange={e => setZip(e.target.value)} onKeyDown={e => e.key === "Enter" && lookup()} placeholder="Enter ZIP code" maxLength={5} style={{ flex: 1, padding: "8px 14px", borderRadius: 7, border: "1px solid #3a3832", background: "#1e1c18", color: "#e2dfd8", fontFamily: "monospace", fontSize: 14 }} /> <button onClick={lookup} disabled={loading} style={{ padding: "8px 18px", borderRadius: 7, border: "none", background: "#c83800", color: "#fff", cursor: "pointer", fontFamily: "monospace", fontSize: 12 }}> {loading ? "..." : "Look up →"} </button> </div> {/* Error */} {error && ( <div style={{ padding: "12px 20px", color: "#c83800", fontFamily: "monospace", fontSize: 12 }}>{error}</div> )} {/* Results */} {data && ( <> <div style={{ padding: "14px 20px", borderBottom: "1px solid #e0d9cf", display: "flex", justifyContent: "space-between", alignItems: "center" }}> <span style={{ fontFamily: "monospace", fontSize: 11, color: "#82796f" }}> ZIP {data.zip_code} · {data.data_date} </span> <span style={{ fontSize: 11, fontFamily: "monospace", fontWeight: 600, color: TEMP_COLOR[data.market_temperature] || "#111009" }}> {data.market_temperature} </span> </div> <div style={{ display: "grid", gridTemplateColumns: "repeat(3, 1fr)", borderBottom: "1px solid #e0d9cf" }}> <Stat label="Median Price" value={`$${(data.median_sale_price / 1000).toFixed(0)}k`} /> <Stat label="Days on Mkt" value={data.median_days_on_market} /> <Stat label="YoY Change" value={`${(data.yoy_price_change * 100).toFixed(1)}%`} /> </div> <div style={{ display: "grid", gridTemplateColumns: "repeat(3, 1fr)" }}> <Stat label="Inventory" value={`${data.inventory_months}mo`} /> <Stat label="Listings" value={data.active_listings} /> <Stat label="Reduced" value={`${(data.price_reduced_pct * 100).toFixed(0)}%`} /> </div> </> )} </div> ); }

""" Price trend alert system. Sends notifications when market conditions change. Run daily: 0 9 * * * python trend_alerts.py State is stored in alerts_state.json to avoid duplicate notifications. """ import requests, os, json, smtplib, time from email.mime.text import MIMEText from pathlib import Path from datetime import datetime API_KEY = os.environ["RAPIDAPI_KEY"] HEADERS = { "X-RapidAPI-Key": API_KEY, "X-RapidAPI-Host": "real-estate-market-data.p.rapidapi.com" } STATE_FILE = Path("alerts_state.json") # ── Alert thresholds ───────────────────────────────────────────────────────── WATCHLIST = [ { "zip": "78701", "label": "Austin TX Downtown", "alerts": [ {"field": "yoy_price_change", "op": "<", "threshold": 0.03, "message": "YoY appreciation dropped below 3% — possible entry signal"}, {"field": "median_days_on_market", "op": ">", "threshold": 45, "message": "DOM exceeded 45 days — market cooling"}, {"field": "inventory_months", "op": ">", "threshold": 4.0, "message": "Inventory exceeded 4 months — buyer's market forming"}, ] }, { "zip": "30301", "label": "Atlanta GA", "alerts": [ {"field": "yoy_price_change", "op": ">", "threshold": 0.10, "message": "YoY appreciation exceeded 10% — strong appreciation"}, {"field": "median_days_on_market", "op": "<", "threshold": 15, "message": "DOM under 15 days — hot market, act fast"}, ] }, ] def check(value, op, threshold): if op == "<": return value < threshold if op == ">": return value > threshold return False def load_state(): if STATE_FILE.exists(): return json.loads(STATE_FILE.read_text()) return {} def save_state(state): STATE_FILE.write_text(json.dumps(state, indent=2)) def send_alert(subject, body): msg = MIMEText(body) msg["Subject"] = subject msg["From"] = os.environ["SMTP_USER"] msg["To"] = os.environ["ALERT_EMAIL"] with smtplib.SMTP_SSL(os.environ["SMTP_HOST"], int(os.environ["SMTP_PORT"])) as s: s.login(os.environ["SMTP_USER"], os.environ["SMTP_PASS"]) s.send_message(msg) state = load_state() alerts_sent = [] for market in WATCHLIST: r = requests.get( "https://real-estate-market-data.p.rapidapi.com/market-stats", params={"zip_code": market["zip"]}, headers=HEADERS, timeout=10 ) data = r.json() key = market["zip"] for alert in market["alerts"]: field = alert["field"] value = data.get(field, 0) triggered = check(value, alert["op"], alert["threshold"]) alert_key = f"{key}_{field}" was_triggered = state.get(alert_key, False) if triggered and not was_triggered: # New trigger — send alert msg = ( f"ALERT: {market['label']} (ZIP {market['zip']})\n" f"Condition: {alert['message']}\n" f"Current value: {field} = {value}\n" f"Threshold: {alert['op']} {alert['threshold']}\n" f"Checked: {datetime.now().strftime('%Y-%m-%d %H:%M')}\n" ) alerts_sent.append(msg) print(f" ALERT: {market['label']} — {alert['message']}") state[alert_key] = triggered time.sleep(0.5) save_state(state) if alerts_sent: body = "\n\n---\n\n".join(alerts_sent) send_alert(f"Market Alert — {len(alerts_sent)} condition(s) triggered", body) print(f"Sent {len(alerts_sent)} alert(s).") else: print("No new alerts. State saved.")