Airbnb rental price modeling based on Latent Dirichlet Allocation and MESF-XGBoost composite model

An ensemble machine learning framework for Airbnb rental price modeling without using amenity-driven features

“…The findings identified key amenities that can be leveraged to increase rental prices. Islam et al (2022) combined sentiment analysis and ensemble ML to predict Airbnb rental prices by incorporating textual information, spatial features and amenities as explanatory variables. Thakur et al (2022) performed predictive modeling of Airbnb units in Rio de Janeiro using a deep neural network to identify the influential features.…”

“…These studies focus on testing research hypotheses for evaluating the influence of respective features without focusing on the predictive modeling of the listing prices. On the other hand, authors have predicted Airbnb listing prices using amenities-driven features (Kalehbasti et al , 2021; Islam et al , 2022). Thus, no focus has been given to Airbnb listing price modeling without using amenity-driven features.…”

Spatio-temporal variation of land use and land cover changes and their impact on land surface temperature: A case of Kutupalong Refugee Camp, Bangladesh

“…(2021) focused on determining how LST varies with respect to vegetation change and stated that aside from forest lands, ground biomass and carbon stock suffered significant losses throughout the study period. Also, most of the studies focused on vegetation change over the year due to the Rohingya crisis, however they neglected the impact of rapid expansion of human settlement and its impact on the surrounding thermal environment ( Islam et al., 2019 ; Islam et al., 2022a , Islam et al., 2022b ). Where they ignored the spatio-temporal changes in the environment and ecosystem and its impact on the surrounding thermal environment in the Kutupalong Mega Camp area.…”

Towards Virtual 3D Asset Price Prediction Based on Machine Learning

“…The literature on pricing and the identification of price determinants for virtual 3D assets is sparse. However, previous work emphasizes approaches to examine price determinants based on ML for other fields of application, ranging from accommodation [49][50][51] and the stock market [52,53], to e-commerce [54,55], cryptocurrencies [56][57][58], and energy prices [59]. Apart from dynamic pricing approaches based on reinforcement learning [60,61], most publications have focused on the application of supervised learning algorithms to identify price determinants.…”

“…First, the datasets are described and pre-processed by removing irrelevant features and missing values and transforming the data into an appropriate format for the ML process, such as by converting the data into a consistent file format or normalizing screwed data based on log-transformation [49,52]. Second, preliminary analyses are applied to the dataset to enable clustering of the data and the first insights into correlations between the predictors and the target variable.…”