Open Airbnb datasets for Florence, Italy

Florence, Italy

Total listings: 12,166

Last Updated: 2025-07-26

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How many Airbnbs are there in Florence?

There are 10,076 entire homes listed across Airbnb in Florence and the surrounding area, with 1,810 private bedrooms and 86 serviced apartments. The neighbourhood with the most listings is , with 7,691 entire homes costing an average of €5 per night. The second most populated neighbourhood for whole rental properties is with 1,020 listings, followed by with 646 entire homes.

Entire Homes

10,076

Private Rooms

1,810

Serviced Apartments

Shared Rooms

Bed Homes

5,378

Bed Homes

3,383

Bed Homes

957

Bed Homes

168

Bed Homes

Basic Pricing

Average Price per night per Bedrooms

for entire homes and serviced apartments

Average Price per night per Bedrooms

for entire homes and serviced apartments

Average Price per night per Max. Guests

for entire homes and serviced apartments

Average Price per Review Score

Average Price for Other Property Types

Campsite: €5

Farm stay: €5

Barn: €5

Campervan/Motorhome: €5

Airbnbs charging cleaning fee

73.9%

Average Airbnb cleaning fee

€63.69

Airbnbs charging taxes

90.5%

Average Airbnb taxes

€30.43

Average Price for Private Rooms

€5.00

Average Price for Serviced Apartments

€5.00

Airbnbs charging cleaning fee

73.9%

Avg. Airbnb cleaning fee

€63.69

Airbnbs charging taxes

90.5%

Avg. Airbnb taxes

€30.43

Avg. Price for Private Rooms

€5.00

Avg. Price for Serviced Apartments

€5.00

Basic pricing is taken from the date of data collection for 1 guest. This is the average price over the first 5 consecutive days the listing is available for, within the next two months. Where these conditions are not satisfied, no pricing data is recorded. Full weekly pricing and daily availability data is available on request. All prices are in EUR.

Doorstep Analytics offers comprehensive holiday rental datasets, including detailed pricing, availability, and estimated income. You can access the complete dataset directly or request a tailored analytics report to address specific questions and insights.

Reviews

Rating per Airbnb Listing

Number of Reviews on each Listing

Airbnb review scores in Florence (4.77) are slightly above the average Airbnb review score across all locations (4.754) Approximately a third of Airbnb listings use self-check in, with lockboxes, keypads or similar. This implies the property is in regular use as an Airbnb listing, or is run as part of a professional business. 14% of hosts have more than one listing in Florence, also suggesting they are a business.

Hosts

Hosts listed as superhosts

46.5%

Hosts with more than 1 listing

13.9%

35%

of listings welcome guests with lockboxes or other self check-in methods

Hosts with Most Listings

Host Name	No. of Listings	Avg. Review Score
Edoardo	242	4.46
Istay	148	4.51
Mamo	144	4.61
Francesco	115	4.46
Lorenzo	109	4.63
Roberto	78	4.62
Apartments Florence	66	4.56
Irene	62	4.41
Unyca	52	4.7
Florjan	50	4.2
Lorenzo	48	4.63
Federico	46	4.56
Gessjca E Luca	41	4.64
ApartmentsFlorence	39	4.74

35%

of listings welcome guests with lockboxes or other self check-in methods

Neighbourhoods

	Total	All Entire Homes		1-Bed Homes		2-Bed Homes		3-Bed Homes		Private Rooms		Reviews
	Count	Count	Avg. Price	Count	Avg. Price	Count	Avg. Price	Count	Avg. Price	Count	Avg. Price	Avg. Score
Centro Storico	8,997	7,691	€5	4,014	€5	2,571	€5	759	€5	1,086	€5	4.75
Firenze	1,336	1,020	€5	556	€5	343	€5	76	€5	297	€5	4.79
Rifredi	899	646	€5	351	€5	231	€5	39	€5	228	€5	4.80
Novoli - Fiat	220	164	€5	102	€5	43	€5	13	€5	55	€5	4.81
Gavinana	177	137	€5	71	€5	46	€5	17	€5	37	€5	4.78
Soffiano	77	59	€5	32	€5	19	€5	7	€5	18	€5	4.79
Fiesole	73	53	€5	34	€5	9	€5	7	€5	13	€5	4.83
Europa	64	46	€5	22	€5	18	€5	4	-	17	€5	4.83
Galluzzo	52	49	€5	25	€5	16	€5	5	€5	2	€5	4.78
Coverciano	18	14	€5	8	€5	5	€5	0	-	4	€5	4.80

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Neighbourhoods are provided by user input in Airbnb. Where this data is not provided on Airbnb, a kNN machine learning model categorises listings into their most likely neighbourhood. Translated city names and names of large regions are removed where possible.