Idea Details

```text
## Cursor/Windsurf Coding Prompt: Dynamic Pricing Engine & Market Simulation

**Objective:** Implement a dynamic pricing engine that uses real-time market data, competitor analysis, and property features to suggest optimal pricing strategies. Include a market simulation tool that allows users to forecast potential ROI based on different pricing scenarios.

**Tech Stack:** Python, Flask, Docker

**Files Changes:**

1.  **Create:** `pricing_engine/models.py` - Defines data models for properties, market data, competitor data, and pricing recommendations.
2.  **Create:** `pricing_engine/pricing_logic.py` - Contains the core pricing logic and market simulation algorithms.
3.  **Create:** `pricing_engine/api.py` - Flask API endpoints for the dynamic pricing engine and market simulation.
4.  **Modify:** `app.py` (or equivalent main application file) - Integrate the pricing engine API endpoints.
5.  **Create:** `pricing_engine/market_data_fetcher.py` - Module responsible for fetching and processing market data.

**Code Logic:**

**1. `pricing_engine/models.py`:**

```python
# pricing_engine/models.py
from dataclasses import dataclass

@dataclass
class Property:
    property_id: int
    property_type: str  # e.g., Apartment, House
    location: str
    bedrooms: int
    bathrooms: float
    square_footage: float
    features: dict # e.g., {'balcony': True, 'parking': True}
    market_value: float # Last known market value. This is a "seed" value.

@dataclass
class MarketData:
    location: str
    avg_price_per_sqft: float
    median_days_on_market: int
    interest_rates: float
    housing_inventory: int

@dataclass
class CompetitorData:
    property_id: int
    location: str
    price: float
    days_on_market: int
    features: dict

@dataclass
class PricingRecommendation:
    property_id: int
    suggested_price: float
    confidence_level: float
    rationale: str
```

**2. `pricing_engine/pricing_logic.py`:**

```python
# pricing_engine/pricing_logic.py
import numpy as np
from pricing_engine.models import Property, MarketData, CompetitorData, PricingRecommendation
from typing import List


def calculate_suggested_price(property: Property, market_data: MarketData, competitors: List[CompetitorData]) -> PricingRecommendation:
    """
    Calculates a suggested price based on property features, market data, and competitor analysis.
    """

    # Base price based on market value and square footage.
    base_price = property.market_value * (1 + (market_data.avg_price_per_sqft * property.square_footage) / property.market_value -1)

    # Adjust for property features (simple example).
    feature_adjustment = 0
    if property.features.get('balcony'):
        feature_adjustment += 0.05  # 5% increase for balcony
    if property.features.get('parking'):
        feature_adjustment += 0.03  # 3% increase for parking

    adjusted_price = base_price * (1 + feature_adjustment)

    # Competitor analysis (simple average).
    competitor_prices = [c.price for c in competitors if c.location == property.location]
    if competitor_prices:
        avg_competitor_price = np.mean(competitor_prices)
        adjusted_price = (adjusted_price + avg_competitor_price) / 2 # Average with competitor price

    # Adjust for market conditions.  If market is HOT prices go UP.
    market_adjustment = (market_data.median_days_on_market / 60) #Arbitrary scaling
    adjusted_price = adjusted_price * (1 - market_adjustment) # If days on market are high, reduce price


    return PricingRecommendation(
        property_id=property.property_id,
        suggested_price=round(adjusted_price, 2),
        confidence_level=0.75, #Arbitrary Value
        rationale="Based on market data, property features, and competitor analysis."
    )



def simulate_roi(property: Property, initial_price: float, market_growth: float, holding_period: int) -> float:
    """
    Simulates potential ROI based on different pricing scenarios and market conditions.
    """
    future_value = initial_price * (1 + market_growth)**holding_period
    roi = (future_value - initial_price) / initial_price
    return round(roi, 2)
```

**3. `pricing_engine/api.py`:**

```python
# pricing_engine/api.py
from flask import Flask, request, jsonify
from pricing_engine.pricing_logic import calculate_suggested_price, simulate_roi
from pricing_engine.models import Property, MarketData, CompetitorData
import json

app = Flask(__name__)

# Mock Data - Replace with database integration later
mock_market_data = {
    "location": "ExampleCity",
    "avg_price_per_sqft": 500.0,
    "median_days_on_market": 45,
    "interest_rates": 0.06,
    "housing_inventory": 1000
}

mock_competitor_data = [
    {
        "property_id": 101,
        "location": "ExampleCity",
        "price": 550000.0,
        "days_on_market": 30,
        "features": {"balcony": False, "parking": True}
    },
    {
        "property_id": 102,
        "location": "ExampleCity",
        "price": 600000.0,
        "days_on_market": 60,
        "features": {"balcony": True, "parking": True}
    }
]


@app.route('/pricing/recommend', methods=['POST'])
def get_pricing_recommendation():
    """
    Endpoint to get a pricing recommendation for a property.
    """
    try:
        data = request.get_json()
        property_data = data.get('property')
        if not property_data:
            return jsonify({"error": "Property data is required"}), 400

        # Convert JSON to Property Object
        property = Property(**property_data)
        market_data = MarketData(**mock_market_data)

        # Convert Mock Competitor Data to CompetitorData Objects
        competitor_data = [CompetitorData(**c) for c in mock_competitor_data]

        recommendation = calculate_suggested_price(property, market_data, competitor_data)
        return jsonify({"recommendation": recommendation.__dict__}), 200

    except Exception as e:
        return jsonify({"error": str(e)}), 500


@app.route('/pricing/simulate', methods=['POST'])
def simulate_roi_endpoint():
    """
    Endpoint to simulate ROI based on different pricing scenarios.
    """
    try:
        data = request.get_json()
        property_data = data.get('property')
        initial_price = data.get('initial_price')
        market_growth = data.get('market_growth')
        holding_period = data.get('holding_period')

        if not all([property_data, initial_price, market_growth, holding_period]):
            return jsonify({"error": "Missing required parameters"}), 400

        # Convert JSON to Property Object
        property = Property(**property_data)


        roi = simulate_roi(property, initial_price, market_growth, holding_period)
        return jsonify({"roi": roi}), 200

    except Exception as e:
        return jsonify({"error": str(e)}), 500



if __name__ == '__main__':
    app.run(debug=True, port=5001)
```

**4. `app.py` (or equivalent):**

```python
# app.py  (Example - Adjust based on your main app structure)
from flask import Flask
# Existing imports
# ...

# Import the pricing engine API
from pricing_engine.api import app as pricing_app

app = Flask(__name__)

# Register the pricing engine blueprint (if using blueprints) or mount the app directly.
app.register_blueprint(pricing_app, url_prefix='/pricing') #Mount at the /pricing namespace

# OR if you just created a flask app:
# app = pricing_app

# Existing routes and logic for your main app
# ...

if __name__ == '__main__':
    app.run(debug=True)
```

**5. `pricing_engine/market_data_fetcher.py`:**

```python
# pricing_engine/market_data_fetcher.py
# This is a stub. Implement actual market data fetching from external APIs or databases.
import random
from pricing_engine.models import MarketData

def fetch_market_data(location: str) -> MarketData:
    """
    Fetches market data for a given location.  Replace with actual API calls.
    """

    # Replace this with API calls to Zillow, Redfin, etc.
    # This is DUMMY data.
    avg_price_per_sqft = random.uniform(400, 600)
    median_days_on_market = random.randint(30, 90)
    interest_rates = random.uniform(0.05, 0.08)
    housing_inventory = random.randint(500, 1500)

    return MarketData(
        location=location,
        avg_price_per_sqft=avg_price_per_sqft,
        median_days_on_market=median_days_on_market,
        interest_rates=interest_rates,
        housing_inventory=housing_inventory
    )

```

**Integration:**

*   The `pricing_engine/api.py` provides Flask API endpoints:
    *   `/pricing/recommend` (POST): Takes property data as input and returns a pricing recommendation.  Expects a JSON payload with the `property` key.
    *   `/pricing/simulate` (POST): Takes property data, initial price, market growth, and holding period as input and returns the simulated ROI. Expects a JSON payload with `property`, `initial_price`, `market_growth` and `holding_period` keys.
*   These API endpoints are mounted in the main application (`app.py`) under the `/pricing` prefix.
*   The `fetch_market_data` in `pricing_engine/market_data_fetcher.py` should be replaced with actual calls to external APIs or a database to retrieve real-time market data.  The current implementation uses mock data.

**Next Steps:**

1.  Implement real market data fetching in `pricing_engine/market_data_fetcher.py`.
2.  Implement data persistence (database integration) for properties, market data, and competitor data.
3.  Improve the pricing logic in `pricing_engine/pricing_logic.py` with more sophisticated algorithms and feature weighting.
4.  Add error handling and input validation to the API endpoints.
5.  Implement unit tests for all components.
6.  Containerize the application using Docker (as per the technical constraints).

**Important Considerations:**

*   **Security:** Protect your API endpoints with authentication and authorization.
*   **Data Sources:** Choose reliable and accurate market data sources.
*   **Scalability:** Design the application to handle a large number of properties and requests.
*   **Monitoring:** Implement monitoring and logging to track performance and identify issues.
*   **Dockerization**: Create a Dockerfile for the application to ensure consistent deployment across different environments.  This includes specifying the base image, installing dependencies, copying source code, and defining the startup command.

This prompt provides a solid foundation for building the dynamic pricing engine and market simulation feature.  Remember to adapt it to your specific needs and context.
```