import requests # UAE Population Growth Rate (SP.POP.GROW) url = "https://api.worldbank.org/v2/country/AE/indicator/SP.POP.GROW" r = requests.get(url, params={"format": "json", "mrv": 5}) pop_data = r.json()[1] # Last 5 years # UAE GDP Growth (NY.GDP.MKTP.KD.ZG) url2 = "https://api.worldbank.org/v2/country/AE/indicator/NY.GDP.MKTP.KD.ZG" r2 = requests.get(url2, params={"format": "json", "mrv": 1}) gdp = r2.json()[1][0]["value"] # Useful indicators for Dubai RE: # SP.POP.TOTL → Total population # NY.GDP.PCAP.CD → GDP per capita # FP.CPI.TOTL.ZG → Inflation rate # BX.KLT.DINV.CD.WD→ FDI inflows

import requests, os FRED_KEY = os.getenv("FRED_API_KEY") # SOFR (Secured Overnight Financing Rate) r = requests.get( "https://api.stlouisfed.org/fred/series/observations", params={ "series_id": "SOFR", "api_key": FRED_KEY, "file_type": "json", "limit": 1, "sort_order": "desc" } ) sofr = float(r.json()["observations"][0]["value"]) # Other useful series: # FEDFUNDS → US Fed Funds Rate # DCOILWTICO → WTI Crude Oil Price # DTWEXBGS → USD Trade-Weighted Index

import requests, math # Query all Dubai Metro stations overpass_query = """ [out:json]; node["railway"="station"]["network"="Dubai Metro"] (24.7,54.9,25.4,55.6); out body; """ r = requests.post( "https://overpass-api.de/api/interpreter", data=overpass_query ) stations = r.json()["elements"] # → [{lat, lon, tags.name}, ...] def nearest_metro_km(prop_lat, prop_lon, stations): def haversine(lat1, lon1, lat2, lon2): R = 6371 dlat = math.radians(lat2-lat1) dlon = math.radians(lon2-lon1) a = math.sin(dlat/2)**2 + math.cos(math.radians(lat1)) * \ math.cos(math.radians(lat2)) * math.sin(dlon/2)**2 return R * 2 * math.asin(math.sqrt(a)) dists = [haversine(prop_lat, prop_lon, s["lat"], s["lon"]) for s in stations] return min(dists)

# pip install newsapi-python textblob from newsapi import NewsApiClient from textblob import TextBlob NEWS_KEY = os.getenv("NEWS_API_KEY") api = NewsApiClient(api_key=NEWS_KEY) # Search Dubai real estate news articles = api.get_everything( q="Dubai real estate property", language="en", sort_by="publishedAt", page_size=50 ) # NLP Sentiment Scoring scores = [] for article in articles["articles"]: blob = TextBlob(article["title"] + " " + (article["description"] or "")) scores.append(blob.sentiment.polarity) # -1 to +1 sentiment_score = sum(scores) / len(scores) # > 0.2 → Bullish market sentiment # -0.2 to 0.2 → Neutral # < -0.2 → Bearish / negative news cycle

# .env file — never commit this to git DLD_API_KEY=your_dubai_rest_key_here FRED_API_KEY=your_fred_key_here NEWS_API_KEY=your_newsapi_key_here SUPABASE_URL=https://xxxx.supabase.co SUPABASE_KEY=your_supabase_anon_key ────────────────────────────────────────── # Supabase Tables Needed: -- transactions (from DLD) CREATE TABLE transactions ( id UUID PRIMARY KEY, area TEXT, price NUMERIC, sqft NUMERIC, price_per_sqft NUMERIC, beds INT, transaction_date DATE, is_anomaly BOOLEAN, source TEXT DEFAULT 'DLD' ); -- macro_indicators (World Bank + FRED) CREATE TABLE macro_indicators ( id UUID PRIMARY KEY, indicator TEXT, value NUMERIC, date DATE, source TEXT ); -- sentiment_scores (NewsAPI + TextBlob) CREATE TABLE sentiment_scores ( id UUID PRIMARY KEY, score NUMERIC, label TEXT, article_count INT, fetched_at TIMESTAMPTZ ); ────────────────────────────────────────── # Cron schedule (run on server): # DLD transactions → every 6 hours # World Bank → daily # FRED SOFR → daily # NewsAPI sentiment → every 3 hours # OSM Metro data → weekly