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NYC Airbnb Price Drivers & Geo Analysis In Minutes

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How it works

Multiple linear regression with geo-spatial visualization to identify price drivers across NYC Airbnb listings, with borough and room-type segmentation

Use when you have Airbnb listing data with price, location, and listing attributes and want to understand price drivers and geographic distribution

Do not use if your data lacks location coordinates or price information

What data do you need?

Dataset with 12 columns

listing_id (identifier)	neighbourhood_group (categorical)	neighbourhood (categorical)	room_type (categorical)	price (numeric)	minimum_nights (numeric)	number_of_reviews (numeric)	availability_365 (numeric)	latitude (numeric)	longitude (numeric)	reviews_per_month (numeric)	host_listings_count (numeric)

Minimum 100 rows

What's in the report?

Cornerstone #17 — regression + geo visualization on NYC Airbnb (3,106 votes)

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Median Price by Borough

Median nightly price comparison across the five NYC boroughs

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Price Distribution by Room Type

Price distribution by room type showing entire home premium

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Top Neighbourhoods by Median Price

Top neighbourhoods ranked by median nightly price

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Numeric Feature Correlations

Correlation matrix of numeric listing features with price

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Price Regression Coefficients

OLS regression coefficients showing independent effect of each predictor on price

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Geo Price Distribution Across NYC

Geo-spatial scatter of listing prices across NYC geography

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Borough Availability Profile

Average listing availability per year by borough

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Borough Market Summary

Borough-level summary of key market metrics

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AI Insights

Plain-English interpretation — what the numbers mean, what's significant, and what to do next.

The Question This Answers

See description

Questions?

See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.

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