Determinants of Airbnb prices in European cities: A spatial econometrics approach (Supplementary Material)

This repository contains supplementary materials for the article: Determinants of Airbnb prices in European cities: A spatial econometrics approach (DOI: https://doi.org/10.1016/j.tourman.2021.104319) The materials include the used datasets and Python scripts for spatial regression models. Datasets For each city two files are provided: data for weekday and weekend offers The columns are as following: 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 Programming Scripts In this repository you will find a script for spatial regressions in Python using PySAL (models_robust.py). The codes cover the following regression models: OLS SLX (lagged_x) SAR (lagged_y) SDM (lagged_x_y) SEM (lagged_e) SDEM (lagged_e_x) Main parameters: cities - list of cities from the dataset to be included in the analysis Robust=False: calculate the OLS, SLX, SAR and SDM regressions with W (weight matrix) based on 10 closest neighbours Robust=True: calculate all regression models with different specifications of W direct_indirect=True: calculate the direct and indirect effects (based on Golgher, A. B., & Voss, P. R. (2016). How to Interpret the Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects. Spatial Demography (Vol. 4). https://doi.org/10.1007/s40980-015-0016-y) Key functions: create_weights - defines the W specification write_stats - calculates's Moran's I and Geary's C direct - calculates the direct effect of the variable indirect - calculates the indirect effect coord - sets the coordinate refence system (CRS) appropriate to the analysed city total_results calculates the regressions the coordinates are projected from GPS (epsg:4326) to the local CRS (km_lat, km_lon) all regressions are saved as formatted txt table the results can be also saved as csv tableThis research was supported by National Science Centre, Poland: Project number 2017/27/N/HS4/0095