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Cambridge Hospitality and Housing Analytics
Infrastructure & Utilities
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About
Analysing the short-term rental market in Cambridge, Massachusetts, provides a detailed view of how hosts and guests utilised the platform throughout 2022. This collection of metrics offers a snapshot of listing activity, property types, and pricing structures in a major American academic and commercial hub. By examining the distribution of listings across various neighbourhoods, such as those near MIT, users can identify patterns in traveler preferences and host professionalisation within the local hospitality sector.
Columns
- id: A unique numerical identifier for each specific property listing.
- name: The descriptive title of the listing, often highlighting key features like proximity to transport, gyms, or dining.
- host_id: A unique identifier assigned to the individual or entity managing the property.
- host_name: The public name of the host, ranging from individual residents to large-scale management firms like Blueground.
- neighbourhood_group: A secondary regional classification (this field is currently empty for this specific location).
- neighbourhood: The specific local area within Cambridge where the listing is situated, such as Area 2/MIT or East Cambridge.
- latitude: The exact geographic north-south coordinate of the accommodation.
- longitude: The exact geographic east-west coordinate of the accommodation.
- room_type: The category of stay offered, including entire homes or apartments, private rooms, and other shared arrangements.
- price: The nightly cost of the rental listed in US dollars.
Distribution
The data is delivered in a CSV format titled
listings.csv with a file size of 508.91 kB. The collection contains 3,089 valid records, maintaining high integrity with no missing values for primary fields like coordinates, names, or prices. This is a static resource with no further updates expected.Usage
This resource is ideal for conducting exploratory data analysis on the hospitality industry or building predictive models for rental pricing based on location and room type. Analysts can use it to identify which neighbourhoods experience the highest traffic or to study the dominance of various hosts within the city. It also serves as a robust foundation for geospatial visualisations and urban housing studies.
Coverage
The geographic scope is focused exclusively on Cambridge, Massachusetts, in the United States. Temporally, the records reflect the state of the market specifically during the year 2022. The data represents a wide variety of hosts, with professional property managers accounting for a notable portion of the listings in areas like East Cambridge and the MIT vicinity.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
Urban researchers and policy makers can leverage these figures to understand the impact of short-term rentals on local housing availability. Business analysts may find the pricing and host data useful for benchmarking competitive accommodation rates. Additionally, data science students can use the structured records to practice data cleaning, statistical analysis, and mapping techniques.
Dataset Name Suggestions
- Cambridge Airbnb Market Metrics 2022
- Short-Term Rental Inventory: Cambridge, Massachusetts
- Cambridge Hospitality and Housing Analytics
- Airbnb Open Data - Cambridge Listing Registry 2022
- Massachusetts Urban Rental and Host Performance Data
Attributes
Original Data Source: Cambridge Hospitality and Housing Analytics
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