Purpose

The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.

Design/methodology/approach

The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.

Findings

The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

Practical implications

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.

Originality/value

Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.

中文翻译：

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用于 Airbnb 租赁价格建模的集成机器学习框架，无需使用设施驱动的功能

目的

Airbnb 房源价格的预测主要使用一组设施驱动的特征。从数千个可用的便利性驱动的特征中选择一组合适的特征使得预测任务变得困难。本文旨在提出一个可扩展、稳健的框架来预测 Airbnb 单位的挂牌价格，而无需使用设施驱动的功能。

设计/方法论/途径

作者提出了一种基于人工智能 (AI) 的框架来预测 Airbnb 的房源价格。作者考虑了来自美国五个城市的 7.5 万个 Airbnb 房源，并进行了超过 190 万次观察。所提出的框架集成了（i）特征筛选，（ii）结合了梯度增强、装袋、随机森林的堆叠，（iii）粒子群优化和（iv）可解释的人工智能来完成研究目标。

发现

主要发现包括三个方面——预测准确性、同质性以及最佳和最不可预测城市的识别。所提出的框架产生了最高精确度的预测。不同城市的挂牌价格的可预测性差异很大。波士顿的挂牌价格最容易预测，而芝加哥的挂牌价格最难预测。

实际影响

东道主可以利用研究的框架和结果来确定租金价格，并通过强调关键功能来增强服务内容。

原创性/价值

尽管各个组成部分是已知的，但将它们集成到拟议框架中以得出 Airbnb 房源价格高质量预测的方式是独特的。它是可扩展的。Airbnb 挂牌价格建模文献很少见到这样的框架。