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

This repository contains supplementary materials for the article: <strong>Determinants of Airbnb prices in European cities: A spatial econometrics approach</strong> <strong>(</strong>DOI<strong>: </strong>https://doi.org/10.1016/j.tourman.2021.104319) The materials include the used datasets and Python scripts for spatial regression models. <strong>Datasets</strong> 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 <strong>Programming Scripts</strong> 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., &amp; 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 table本仓库包含论文《欧洲城市Airbnb定价影响因素：空间计量经济学方法》（Determinants of Airbnb prices in European cities: A spatial econometrics approach）的补充材料，论文DOI：https://doi.org/10.1016/j.tourman.2021.104319。材料涵盖本次研究使用的数据集与空间回归模型的Python脚本。 ### 数据集 针对每个城市，我们提供两类文件：工作日与周末的房源定价数据。各字段说明如下： - realSum：双人入住两晚的住宿全额价格，单位为欧元（EUR） - room_type：住宿房型 - room_shared：共享房间虚拟变量（dummy variable） - room_private：私人房间虚拟变量 - person_capacity：房源最大容纳客人数 - host_is_superhost：房东为超级房东（superhost）的虚拟变量 - multi：该房源属于拥有2-4套房源的房东的虚拟变量 - biz：该房源属于拥有4套以上房源的房东的虚拟变量 - cleanliness_rating：清洁度评分 - guest_satisfaction_overall：房源整体满意度评分 - bedrooms：卧室数量（单间公寓设为0） - dist：房源至城市中心的距离，单位为公里（km） - metro_dist：房源至最近地铁站的距离，单位为公里 - attr_index：房源所在区位的景点指数 - attr_index_norm：标准化景点指数（取值范围0-100） - rest_index：房源所在区位的餐饮指数 - rest_index_norm：标准化餐饮指数（取值范围0-100） - lng：房源位置的经度 - lat：房源位置的纬度 ### 编程脚本 本仓库包含基于PySAL库实现空间回归分析的Python脚本（models_robust.py），代码覆盖以下回归模型：普通最小二乘回归（OLS）、滞后解释变量模型（SLX，lagged_x）、滞后被解释变量模型（SAR，lagged_y）、空间杜宾模型（SDM，lagged_x_y）、空间误差模型（SEM，lagged_e）、空间误差交互模型（SDEM，lagged_e_x）。 主要参数说明： 1. `cities`：待纳入分析的数据集内城市列表 2. `Robust=False`：基于10近邻权重矩阵（W）计算OLS、SLX、SAR与SDM回归 3. `Robust=True`：基于不同权重矩阵规格计算全部回归模型 4. `direct_indirect=True`：计算直接效应与间接效应（参考Golgher, A. B., & Voss, P. R. (2016). 《如何解读空间模型系数：溢出效应、直接效应与间接效应》，《空间人口学》（第4卷），https://doi.org/10.1007/s40980-015-0016-y） 核心函数说明： - `create_weights`：定义权重矩阵（W）的规格 - `write_stats`：计算莫兰指数（Moran's I）与吉尔里C统计量（Geary's C） - `direct`：计算变量的直接效应 - `indirect`：计算变量的间接效应 - `coord`：设置适配分析城市的坐标参考系统（CRS） - `total_results`：执行回归分析。代码会将坐标从GPS坐标系（epsg:4326）投影至局部坐标系（km_lat, km_lon）。所有回归结果将保存为格式化文本表格，也可导出为CSV表格。