Relative Price Comparison Normalize cost of living data across cities: Convert all prices to USD. Use Numbeo or LivingCost.io as cross-references for validation. Create a price index (e.g. NYC = 100, others relative to that)
To create a relative price comparison using cost-of-living data across cities, you'll want to normalize, validate, and index your figures. Here’s how you can structure it:
-
Collect Raw Cost Data
- From sources like Nomad List, scrape or extract the monthly cost of living per city (ensure it's per individual, and includes rent).
- Example raw data:
{ "New York City": 4500, "Bangkok": 1600, "Berlin": 2800, "Buenos Aires": 1200 }
-
Convert to a Common Currency
- If your sources provide prices in various currencies, use ExchangeRate-API or an up-to-date source to convert everything to USD.
- Use consistent exchange rates (e.g., pulled daily or weekly) and round to two decimals.
-
Cross-Validate Prices
- Pull the same city costs from Numbeo or LivingCost.io.
- Average the values or flag outliers. You could assign confidence weights if a city’s cost differs across sources.
-
Normalize via Indexing
- Pick a baseline city (e.g. NYC = 100).
- For each city: [ \text{Index} = \left( \frac{\text{City Cost}}{\text{NYC Cost}} \right) \times 100 ]
- Example:
NYC: $4500 → 100 Berlin: $2800 → 62.2 Buenos Aires: $1200 → 26.7
-
Store & Visualize
- Save normalized data in JSON or CSV:
{ "New York City": 100, "Bangkok": 35.6, "Berlin": 62.2, "Buenos Aires": 26.7 } - Plot this on a choropleth map, bar chart, or scatter plot using Mapbox, D3.js, or Plotly.
- Save normalized data in JSON or CSV:
Let me know if you’d like a ready-made Python script to do this—or want to combine this with map annotations and interactive filters. We can totally build a city explorer together.
A price index—like the Consumer Price Index (CPI) or a city-specific cost-of-living index—acts as a lens through which live rent data can be interpreted, normalized, and forecasted. Here’s how it plays a role in rent estimation:
Price indexes allow you to compare rents across cities or time periods by adjusting for inflation or regional cost differences. For example:
- If NYC has a price index of 100 and Austin has 70, a $2,000 rent in NYC would be roughly equivalent to $1,400 in Austin in terms of relative affordability.
Indexes like the CPI Rent Index or Zillow Rent Index help track how rents are changing over time:
- CPI Rent Index reflects long-term rent trends across all leases.
- New Tenant Rent Index focuses on newly signed leases, offering a more real-time view.
This helps you distinguish between market shifts and seasonal noise.
When pulling live rent data (e.g. from Zillow, RentCast, or Apartment List), price indexes help:
- Smooth out anomalies (e.g. unusually high or low listings).
- Adjust for inflation to compare historical vs. current rents.
- Weight cities by cost-of-living to build fair comparisons.
Investors and analysts use price indexes to:
- Predict future rent increases based on macroeconomic trends.
- Identify undervalued or overvalued rental markets.
- Adjust rent estimates for lease renewals or new developments.
If you're building a rent estimator or dashboard, integrating a price index layer can make your insights far more accurate and actionable. Want help wiring that into your data pipeline?
make an estimation system for nightly airbnb cost using historical airbnb data
Building a nightly Airbnb cost estimation system using historical data is a powerful way to predict pricing trends and optimize revenue. Here's a high-level blueprint to get you started:
Use platforms that offer historical Airbnb data:
- AirDNA – Daily-level data on occupancy, ADR (average daily rate), and revenue.
- Airbtics – Offers neighborhood-level insights and user demographics.
- Mashvisor / AllTheRooms – Monthly-level data with investment metrics.
- Location (city, neighborhood, coordinates)
- Property type (entire home, private room, etc.)
- Bedrooms, bathrooms, amenities
- Seasonality (month, holidays, events)
- Historical occupancy and ADR
def estimate_nightly_rate(location, bedrooms, season, data):
base_rate = data[location]['base_rate']
occupancy = data[location]['occupancy'][season]
multiplier = 1 + (0.1 * (bedrooms - 1)) # Adjust for size
seasonal_factor = data[location]['seasonal_factor'][season]
return round(base_rate * multiplier * seasonal_factor * occupancy, 2)- Linear regression for simple rate prediction.
- Random Forest or XGBoost for more nuanced models using multiple features.
- Time series forecasting (e.g., Prophet) for seasonal trends.
- Use Mapbox or Leaflet.js to visualize estimated rates by location.
- Color-code by affordability or occupancy rate.
- Cross-check with live listings or APIs (e.g., Zillow, RentCast).
- Compare with Numbeo or LivingCost indexes for price normalization.
Would you like a working prototype in Python or a dashboard mockup to visualize this? I can help you build it step by step.
Awesome! Let’s build a nightly Airbnb cost estimator using historical data in Python. This first version will use a simplified dataset, and we can level it up with more features like seasonality, neighborhood trends, or events.
We'll start with a sample dictionary of historical averages:
historical_data = {
"New York City": {
"base_rate": 250,
"seasonal_factor": {
"Jan": 0.9, "Jul": 1.2, "Dec": 1.4
},
"occupancy_rate": {
"Jan": 0.65, "Jul": 0.85, "Dec": 0.95
}
},
"Lisbon": {
"base_rate": 120,
"seasonal_factor": {
"Jan": 0.7, "Jul": 1.5, "Dec": 1.3
},
"occupancy_rate": {
"Jan": 0.5, "Jul": 0.9, "Dec": 0.8
}
}
}def estimate_airbnb_rate(city, month, bedrooms=1, data=historical_data):
info = data.get(city)
if not info:
return f"No data available for {city}"
base = info["base_rate"]
season_multiplier = info["seasonal_factor"].get(month, 1)
occupancy = info["occupancy_rate"].get(month, 0.7)
bedroom_factor = 1 + 0.15 * (bedrooms - 1) # Adds 15% per extra bedroom
estimated_rate = base * season_multiplier * bedroom_factor
adjusted_rate = estimated_rate * occupancy
return round(adjusted_rate, 2)print(estimate_airbnb_rate("Lisbon", "Jul", bedrooms=2))
# Output: e.g., $145.8 depending on data- 🗂 Import real historical data from AirDNA, Airbtics, or InsideAirbnb.
- 📅 Add support for local events or day-of-week multipliers.
- 📊 Build a dashboard with Streamlit, Plotly Dash, or Tableau Public.
- 🧠 Optionally train a model (e.g. XGBoost) on a CSV dataset to handle more variables.
Would you like me to scaffold a Streamlit app or help set up real data pipelines next?
- cookbook.openai.com/examples/agentkit/agentkit_walkthrough
- datacamp.com/tutorial/openai-agentkit-tutorial
- https://github.com/MingSheng92/open_ai_agents
- https://docs.langdb.ai/guides/building-agents/building-travel-agent-with-openai-agents-sdk
- https://dev.to/jackyxbb/how-to-build-a-travel-agent-with-openai-api-2hoj
- https://blog.dataopslabs.com/building-a-travel-agent-with-open-ai-swarm-multi-agent-orchestrator
Below is a complete Python example showing how to integrate SerpAPI Places Search with LangChain to provide travel restaurant suggestions. This example uses OpenAI as the LLM and properly demonstrates retrieving Place data and generating recommendations.
# Language: Python
# Necessary imports
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
import requests
# ----------------------------
# SERPAPI SETTINGS
# ----------------------------
SERPAPI_API_KEY = "YOUR_SERPAPI_API_KEY"
GOOGLE_PLACES_ENDPOINT = "https://serpapi.com/search.json"
# Function to search restaurants with SerpAPI Places
def search_restaurants(location: str, keyword: str = "restaurant", num_results: int = 5):
"""
Queries SerpAPI for restaurants in a given location.
"""
params = {
"engine": "google_maps",
"q": keyword,
"type": "search",
"location": location,
"api_key": SERPAPI_API_KEY
}
response = requests.get(GOOGLE_PLACES_ENDPOINT, params=params)
data = response.json()
restaurants = []
for place in data.get("local_results", [])[:num_results]:
restaurants.append({
"name": place.get("title"),
"address": place.get("address"),
"rating": place.get("rating")
})
return restaurants
# ----------------------------
# LANGCHAIN SETTINGS
# ----------------------------
# Initialize the LLM
llm = OpenAI(temperature=0.7)
# Define a prompt template for restaurant recommendations
prompt_template = """
You are a travel assistant. Based on the following list of restaurants, generate a short, friendly travel recommendation:
{restaurants}
"""
prompt = PromptTemplate(
input_variables=["restaurants"],
template=prompt_template
)
# Setup LangChain LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
# ----------------------------
# WORKFLOW EXAMPLE
# ----------------------------
if __name__ == "__main__":
location = "Chicago, IL" # Example location
restaurants = search_restaurants(location, keyword="restaurant", num_results=5)
if restaurants:
restaurant_text = "
".join([f"{r['name']} (Rating: {r.get('rating', 'N/A')}) - {r['address']}" for r in restaurants])
# Generate travel-friendly recommendation using LangChain
recommendation = chain.run(restaurants=restaurant_text)
print("Travel Restaurant Suggestions:
")
print(recommendation)
else:
print("No restaurants found for this location.")MyMap = ({ geoJsonData }) => {
return (
<MapContainer center={[45.0, -112.0]} zoom={13} style={{ height: "100vh", width: "100%" }}>
<TileLayer url="https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png" />
<GeoJSON data={geoJsonData} />
</MapContainer>
);
- SerpAPI is used here with the
google_mapsengine to fetch restaurant information. Make sure you have a valid API key and enable Places results. - The
search_restaurantsfunction formats the response into a list of restaurant dictionaries. - LangChain is used to turn raw data into user-friendly travel suggestions using an LLM prompt.
PromptTemplateensures we control how the data is presented to the model for recommendation generation.- Modify
num_resultsorkeywordto refine your search for specific cuisine types or other preferences.
Travel Restaurant Suggestions:
Chicago has some amazing dining options! You should check out Giordano's (Rating: 4.5) at 223 W Jackson Blvd, famous for its deep-dish pizza. Next, d’Agostino’s (Rating: 4.3) at 123 Main St offers great Italian flavors. For sushi lovers, visit Sushi Samba (Rating: 4.4) at 456 Lake Shore Dr. Don't miss out on Lou Malnati's (Rating: 4.6) for authentic Chicago pizza, or Shake Shack (Rating: 4.2) at 789 Michigan Ave for a delicious burger experience!
This workflow efficiently combines live restaurant data from SerpAPI with LangChain’s natural language capabilities to provide personalized travel suggestions.
Source(s):
- https://python.langchain.com/docs/integrations/providers/serpapi/
- https://serpapi.com/blog/how-to-make-a-travel-guide-using-serp-data-and-python/
- https://github.com/arvind1606/LangChain_SERP_API_example
- https://plainenglish.io/blog/enhance-your-chatbots-web-search-capabilities-with-langchain-and-serpapi