# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** [https://zenodo.org/record/4446043#.ZEV8d-zMI-R](https://zenodo.org/record/4446043#.ZEV8d-zMI-R)
- **Paper:** [https://www.sciencedirect.com/science/article/pii/S0261517721000388](https://www.sciencedirect.com/science/article/pii/S0261517721000388)

### Dataset Summary
This dataset contains accommodation offers from the [AirBnb](https://airbnb.com/) platform from 10 European cities.  
It has been copied from [https://zenodo.org/record/4446043#.ZEV8d-zMI-R](https://zenodo.org/record/4446043#.ZEV8d-zMI-R) to make it available as a Huggingface Dataset.  
It was originally published as supplementary material for the article:
**Determinants of Airbnb prices in European cities:  A spatial econometrics approach**  
(DOI: https://doi.org/10.1016/j.tourman.2021.104319)

## Dataset Structure

### Data Fields

The data fields contain all fields from the source dataset
along with additional `city` field denoting the city of the offer.
`all` split contains an additional field `day_type` denoting whether the offer is for
`weekdays` or `weekends`.

- city: the city of the offer,
- realSum: the full price of accommodation for two people and two nights in EUR,
- room_type: the type of the accommodation,
- room_shared: dummy variable for shared rooms,
- room_private: dummy variable for private rooms,
- person_capacity: the maximum number of guests,
- host_is_superhost: dummy variable for superhost status,
- multi: dummy variable if the listing belongs to hosts with 2-4 offers,
- biz: dummy variable if the listing belongs to hosts with more than 4 offers,
- cleanliness_rating: cleanliness rating,
- guest_satisfaction_overall: overall rating of the listing,
- bedrooms: number of bedrooms (0 for studios),
- dist: distance from city centre in km,
- metro_dist: distance from nearest metro station in km,
- attr_index: attraction index of the listing location,
- attr_index_norm: normalised attraction index (0-100),
- rest_index: restaurant index of the listing location,
- attr_index_norm: normalised restaurant index (0-100),
- lng: longitude of the listing location,
- lat: latitude of the listing location,

`all` config contains additionally:
- day_type: either `weekdays` or `weekends`

### Data Splits
| name       |   train |
|------------|--------:|
| weekdays | 25500 |
| weekends | 26207 |
| all | 51707 |

## Additional Information

### Licensing Information
The data is released under the licensing scheme from the original authors - CC-BY-4.0 ([source](https://zenodo.org/record/4446043#.ZEV8d-zMI-R)).

### Citation Information

```
@dataset{gyodi_kristof_2021_4446043,
  author       = {Gyódi, Kristóf and
                  Nawaro, Łukasz},
  title        = {{Determinants of Airbnb prices in European cities: 
                   A spatial econometrics approach (Supplementary
                   Material)}},
  month        = jan,
  year         = 2021,
  note         = {{This research was supported by National Science 
                   Centre, Poland: Project number 2017/27/N/HS4/00951}},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.4446043},
  url          = {https://doi.org/10.5281/zenodo.4446043}
}
```

size_categories:10K<n<100K license:cc-by-4.0 geospatial hotels housing region:us
{"license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "tags": ["geospatial", "hotels", "housing"], "dataset_info": [{"config_name": "weekdays", "features": [{"name": "_id", "dtype": "string"}, {"name": "city", "dtype": "string"}, {"name": "realSum", "dtype": "float64"}, {"name": "room_type", "dtype": "string"}, {"name": "room_shared", "dtype": "bool"}, {"name": "room_private", "dtype": "bool"}, {"name": "person_capacity", "dtype": "float64"}, {"name": "host_is_superhost", "dtype": "bool"}, {"name": "multi", "dtype": "int64"}, {"name": "biz", "dtype": "int64"}, {"name": "cleanliness_rating", "dtype": "float64"}, {"name": "guest_satisfaction_overall", "dtype": "float64"}, {"name": "bedrooms", "dtype": "int64"}, {"name": "dist", "dtype": "float64"}, {"name": "metro_dist", "dtype": "float64"}, {"name": "attr_index", "dtype": "float64"}, {"name": "attr_index_norm", "dtype": "float64"}, {"name": "rest_index", "dtype": "float64"}, {"name": "rest_index_norm", "dtype": "float64"}, {"name": "lng", "dtype": "float64"}, {"name": "lat", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 3998764, "num_examples": 25500}], "download_size": 5303928, "dataset_size": 3998764}, {"config_name": "weekends", "features": [{"name": "_id", "dtype": "string"}, {"name": "city", "dtype": "string"}, {"name": "realSum", "dtype": "float64"}, {"name": "room_type", "dtype": "string"}, {"name": "room_shared", "dtype": "bool"}, {"name": "room_private", "dtype": "bool"}, {"name": "person_capacity", "dtype": "float64"}, {"name": "host_is_superhost", "dtype": "bool"}, {"name": "multi", "dtype": "int64"}, {"name": "biz", "dtype": "int64"}, {"name": "cleanliness_rating", "dtype": "float64"}, {"name": "guest_satisfaction_overall", "dtype": "float64"}, {"name": "bedrooms", "dtype": "int64"}, {"name": "dist", "dtype": "float64"}, {"name": "metro_dist", "dtype": "float64"}, {"name": "attr_index", "dtype": "float64"}, {"name": "attr_index_norm", "dtype": "float64"}, {"name": "rest_index", "dtype": "float64"}, {"name": "rest_index_norm", "dtype": "float64"}, {"name": "lng", "dtype": "float64"}, {"name": "lat", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 4108612, "num_examples": 26207}], "download_size": 5451150, "dataset_size": 4108612}, {"config_name": "all", "features": [{"name": "_id", "dtype": "string"}, {"name": "city", "dtype": "string"}, {"name": "realSum", "dtype": "float64"}, {"name": "room_type", "dtype": "string"}, {"name": "room_shared", "dtype": "bool"}, {"name": "room_private", "dtype": "bool"}, {"name": "person_capacity", "dtype": "float64"}, {"name": "host_is_superhost", "dtype": "bool"}, {"name": "multi", "dtype": "int64"}, {"name": "biz", "dtype": "int64"}, {"name": "cleanliness_rating", "dtype": "float64"}, {"name": "guest_satisfaction_overall", "dtype": "float64"}, {"name": "bedrooms", "dtype": "int64"}, {"name": "dist", "dtype": "float64"}, {"name": "metro_dist", "dtype": "float64"}, {"name": "attr_index", "dtype": "float64"}, {"name": "attr_index_norm", "dtype": "float64"}, {"name": "rest_index", "dtype": "float64"}, {"name": "rest_index_norm", "dtype": "float64"}, {"name": "lng", "dtype": "float64"}, {"name": "lat", "dtype": "float64"}, {"name": "day_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8738970, "num_examples": 51707}], "download_size": 10755078, "dataset_size": 8738970}]}
TAGS
#size_categories-10K<n<100K #license-cc-by-4.0 #geospatial #hotels #housing #region-us 

Dataset Card for Dataset Name
=============================


Dataset Description
-------------------


* Homepage: URL
* Paper: URL


### Dataset Summary


This dataset contains accommodation offers from the AirBnb platform from 10 European cities.  

It has been copied from URL to make it available as a Huggingface Dataset.  

It was originally published as supplementary material for the article:
Determinants of Airbnb prices in European cities: A spatial econometrics approach  

(DOI: URL


Dataset Structure
-----------------


### Data Fields


The data fields contain all fields from the source dataset
along with additional 'city' field denoting the city of the offer.
'all' split contains an additional field 'day\_type' denoting whether the offer is for
'weekdays' or 'weekends'.


* city: the city of the offer,
* realSum: the full price of accommodation for two people and two nights in EUR,
* room\_type: the type of the accommodation,
* room\_shared: dummy variable for shared rooms,
* room\_private: dummy variable for private rooms,
* person\_capacity: the maximum number of guests,
* host\_is\_superhost: dummy variable for superhost status,
* multi: dummy variable if the listing belongs to hosts with 2-4 offers,
* biz: dummy variable if the listing belongs to hosts with more than 4 offers,
* cleanliness\_rating: cleanliness rating,
* guest\_satisfaction\_overall: overall rating of the listing,
* bedrooms: number of bedrooms (0 for studios),
* dist: distance from city centre in km,
* metro\_dist: distance from nearest metro station in km,
* attr\_index: attraction index of the listing location,
* attr\_index\_norm: normalised attraction index (0-100),
* rest\_index: restaurant index of the listing location,
* attr\_index\_norm: normalised restaurant index (0-100),
* lng: longitude of the listing location,
* lat: latitude of the listing location,


'all' config contains additionally:


* day\_type: either 'weekdays' or 'weekends'


### Data Splits



Additional Information
----------------------


### Licensing Information


The data is released under the licensing scheme from the original authors - CC-BY-4.0 (source).



### Dataset Summary


This dataset contains accommodation offers from the AirBnb platform from 10 European cities.  

It has been copied from URL to make it available as a Huggingface Dataset.  

It was originally published as supplementary material for the article:
Determinants of Airbnb prices in European cities: A spatial econometrics approach  

(DOI: URL


Dataset Structure
----------------- ### Data Fields


The data fields contain all fields from the source dataset
along with additional 'city' field denoting the city of the offer.
'all' split contains an additional field 'day\_type' denoting whether the offer is for
'weekdays' or 'weekends'.


* city: the city of the offer,
* realSum: the full price of accommodation for two people and two nights in EUR,
* room\_type: the type of the accommodation,
* room\_shared: dummy variable for shared rooms,
* room\_private: dummy variable for private rooms,
* person\_capacity: the maximum number of guests,
* host\_is\_superhost: dummy variable for superhost status,
* multi: dummy variable if the listing belongs to hosts with 2-4 offers,
* biz: dummy variable if the listing belongs to hosts with more than 4 offers,
* cleanliness\_rating: cleanliness rating,
* guest\_satisfaction\_overall: overall rating of the listing,
* bedrooms: number of bedrooms (0 for studios),
* dist: distance from city centre in km,
* metro\_dist: distance from nearest metro station in km,
* attr\_index: attraction index of the listing location,
* attr\_index\_norm: normalised attraction index (0-100),
* rest\_index: restaurant index of the listing location,
* attr\_index\_norm: normalised restaurant index (0-100),
* lng: longitude of the listing location,
* lat: latitude of the listing location,


'all' config contains additionally:


* day\_type: either 'weekdays' or 'weekends' ### Data Splits



Additional Information
---------------------- ### Licensing Information


The data is released under the licensing scheme from the original authors - CC-BY-4.0 (source).
TAGS
#size_categories-10K<n<100K #license-cc-by-4.0 #geospatial #hotels #housing #region-us 
 ### Dataset Summary


This dataset contains accommodation offers from the AirBnb platform from 10 European cities.  

It has been copied from URL to make it available as a Huggingface Dataset.  

It was originally published as supplementary material for the article:
Determinants of Airbnb prices in European cities: A spatial econometrics approach  

(DOI: URL


Dataset Structure
----------------- ### Data Fields


The data fields contain all fields from the source dataset
along with additional 'city' field denoting the city of the offer.
'all' split contains an additional field 'day\_type' denoting whether the offer is for
'weekdays' or 'weekends'.


* city: the city of the offer,
* realSum: the full price of accommodation for two people and two nights in EUR,
* room\_type: the type of the accommodation,
* room\_shared: dummy variable for shared rooms,
* room\_private: dummy variable for private rooms,
* person\_capacity: the maximum number of guests,
* host\_is\_superhost: dummy variable for superhost status,
* multi: dummy variable if the listing belongs to hosts with 2-4 offers,
* biz: dummy variable if the listing belongs to hosts with more than 4 offers,
* cleanliness\_rating: cleanliness rating,
* guest\_satisfaction\_overall: overall rating of the listing,
* bedrooms: number of bedrooms (0 for studios),
* dist: distance from city centre in km,
* metro\_dist: distance from nearest metro station in km,
* attr\_index: attraction index of the listing location,
* attr\_index\_norm: normalised attraction index (0-100),
* rest\_index: restaurant index of the listing location,
* attr\_index\_norm: normalised restaurant index (0-100),
* lng: longitude of the listing location,
* lat: latitude of the listing location,


'all' config contains additionally:


* day\_type: either 'weekdays' or 'weekends' ### Data Splits



Additional Information
---------------------- ### Licensing Information


The data is released under the licensing scheme from the original authors - CC-BY-4.0 (source).
passage: TAGS
#size_categories-10K<n<100K #license-cc-by-4.0 #geospatial #hotels #housing #region-us 
### Dataset Summary


This dataset contains accommodation offers from the AirBnb platform from 10 European cities.  

It has been copied from URL to make it available as a Huggingface Dataset.  

It was originally published as supplementary material for the article:
Determinants of Airbnb prices in European cities: A spatial econometrics approach  

(DOI: URL


Dataset Structure
-----------------