Spavik
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Predict a house’s sale price

Your listings platform already records completed sales with their final price. Spavik learns from them and estimates a sale price for each new listing from square footage, bedrooms, neighborhood and year built.

Returns
An estimated sale price, in dollars
Language
Python
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In your app

Listing #218

$1,285,000

estimated price

Show the estimated price on the property listing.

The data you need

One row per past case. Spavik learns how the inputs relate to the column you want to predict.

Completed San Francisco sales, with square footage, bedrooms, neighborhood, year built and final sale price in dollars.

ColumnRoleMeaning
sqftInputLiving area in square feet
bedroomsInputNumber of bedrooms
neighborhoodInputNeighborhood name
year_builtInputYear of construction
pricePredictedFinal sale price in dollars

The code

A complete, commented script. Set SPAVIK_API_KEY in your server environment, never in browser code.

house-price-prediction.py Python 
from spavik import Spavik
 
# Set SPAVIK_API_KEY in your server environment.
spavik = Spavik()
 
# sf_sales.csv: sqft, bedrooms, neighborhood,
# year_built, price (final sale price in USD).
history = "sf_sales.csv"
report = spavik.evaluate(history, target="price")
if report["assessment"]["status"] != "better_than_baseline":
    raise ValueError(report["assessment"]["message"])
 
# Create the model from known sale prices.
model = spavik.train(history, target="price")
 
# Listing #218: provide the same input columns.
# The sale price is unknown, so do not include it.
listing = {
    "sqft": 1450,
    "bedrooms": 3,
    "neighborhood": "Noe Valley",
    "year_built": 1925,
}
result = model.predict(listing)
 
# Show the estimated price on the listing.
price = result.lignes[0]["prediction"]
print(f"Estimated price: ${price:,.0f}")

How it works

  1. 1

    Check your data

    evaluate() tells you, for free, whether your history does better than a simple guess.

  2. 2

    Learn from your history

    train() creates the model once, for free. Keep it and reuse it for every new case.

  3. 3

    Ask for predictions

    predict() returns a value for each new row, from your server. One credit per row.

  4. 4

    Keep it accurate

    Report what actually happened with submit_outcomes(), check performance(), then refresh().

Questions

How much history do I need?

At least 10 rows, with at least 2 different values in the column you predict. Use completed sales only, with the price actually paid. evaluate() tells you for free whether your history does better than a simple guess before you train anything.

What does it cost?

Checking your data with evaluate() and creating the model with train() are free. Each predicted row costs one credit on the default engine. You train once and keep the model, so you pay only for the predictions you request.

Try it on your own data

Get an API key. Checking your data is free.

Get API key Read the docs