The determinants of Airbnb prices in New York City: a spatial quantile regression approach

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-02-08 DOI:10.1093/jrsssc/qlad001

M. Bernardi, M. Guidolin

引用次数: 0

Abstract

In this paper, we study the price determinants of Airbnb rentals, for the case of New York City, by developing a new dataset, which combines attributes of the property and of the related service, with other information available as open data. This dataset is employed within a spatial quantile semiparametric regression model, able to handle the intrinsic heterogeneity of house prices. The results confirm that property and service attributes play a significant role in determining rental prices, while some variables exert a different impact on prices in magnitude and sign, depending on the quantile considered.

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纽约市Airbnb价格的决定因素:空间分位数回归方法

在本文中，我们以纽约市为例，通过开发一个新的数据集来研究Airbnb租金的价格决定因素，该数据集结合了房产和相关服务的属性以及其他作为开放数据的信息。该数据集采用空间分位数半参数回归模型，能够处理房价的内在异质性。结果证实，物业和服务属性在决定租金价格方面发挥着重要作用，而一些变量对价格的影响程度和影响程度不同，取决于所考虑的分位数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.