The expansion of short-term rental platforms, such as Airbnb, has significantly disrupted local housing markets, often leading to increased rental prices and housing affordability issues. Accurately forecasting regional Airbnb market trends can thus offer critical insights for policymakers and urban planners aiming to mitigate these impacts. This study proposes a novel time-series forecasting framework to predict three key Airbnb indicators -- Revenue, Reservation Days, and Number of Reservations -- at the regional level. Using a sliding-window approach, the model forecasts trends 1 to 3 months ahead. Unlike prior studies that focus on individual listings at fixed time points, our approach constructs regional representations by integrating listing features with external contextual factors such as urban accessibility and human mobility. We convert structured tabular data into prompt-based inputs for a Large Language Model (LLM), producing comprehensive regional embeddings. These embeddings are then fed into advanced time-series models (RNN, LSTM, Transformer) to better capture complex spatio-temporal dynamics. Experiments on Seoul's Airbnb dataset show that our method reduces both average RMSE and MAE by approximately 48% compared to conventional baselines, including traditional statistical and machine learning models. Our framework not only improves forecasting accuracy but also offers practical insights for detecting oversupplied regions and supporting data-driven urban policy decisions.

A hallmark of intelligence is the ability to flesh out underspecified situations using "common sense." We propose to extract that common sense from large language models (LLMs), in a form that can feed into probabilistic inference. We focus our investigation on $\textit{guesstimation}$ questions such as "How much are Airbnb listings in Newark, NJ?" Formulating a sensible answer without access to data requires drawing on, and integrating, bits of common knowledge about how $\texttt{Price}$ and $\texttt{Location}$ may relate to other variables, such as $\texttt{Property Type}$. Our framework answers such a question by synthesizing an $\textit{ad hoc}$ probabilistic model. First we prompt an LLM to propose a set of random variables relevant to the question, followed by moment constraints on their joint distribution. We then optimize the joint distribution $p$ within a log-linear family to maximize the overall constraint satisfaction. Our experiments show that LLMs can successfully be prompted to propose reasonable variables, and while the proposed numerical constraints can be noisy, jointly optimizing for their satisfaction reconciles them. When evaluated on probabilistic questions derived from three real-world tabular datasets, we find that our framework performs comparably to a direct prompting baseline in terms of total variation distance from the dataset distribution, and is similarly robust to noise.

Our research group wanted to take on the difficult task of predicting prices in a dynamic market. And short term rentals such as Airbnb listings seemed to be the perfect proving ground to do such a thing. Airbnb has revolutionized the travel industry by providing a platform for homeowners to rent out their properties to travelers. The pricing of Airbnb rentals is prone to high fluctuations, with prices changing frequently based on demand, seasonality, and other factors. Accurate prediction of Airbnb rental prices is crucial for hosts to optimize their revenue and for travelers to make informed booking decisions. In this project, we aim to predict the prices of Airbnb rentals using a machine learning modeling approach. Our project expands on earlier research in the area of analyzing Airbnb rental prices by taking a methodical machine learning approach as well as incorporating sentiment analysis into our feature engineering. We intend to gain a deeper understanding on periodic changes of Airbnb rental prices. The primary objective of this study is to construct an accurate machine learning model for predicting Airbnb rental prices specifically in Austin, Texas. Our project's secondary objective is to identify the key factors that drive Airbnb rental prices and to investigate how these factors vary across different locations and property types.

You can find the files for this project at my GitHub and the slides here. The final project is accessible here (interactive web app).] I recently designed a new approach to automatic pricing for Airbnb listings using the Inside Airbnb dataset. I used linear regression to establish a base price and time series analysis to forecast price fluctuations due to the date. I used unsupervised learning to build a recommender system so hosts could compare their listing to other similar popular listings.

As part of the IBM Data Science Professional Certificate, we get to have a go at our very own Data Science Capstone, where we get a taste of what is like to solve problems and answer questions like a data scientist. For my assignment, I decided to do yet another project that looks into the relationship between Airbnb prices and its determinants. Yes, there are several very cool ones like Laura Lewis's here. I would not have been able to do mine without reading and understanding hers (and her code), so kudos! However, being that I'm all about transportation research, I added a little touch of geospatial analysis by looking into locational features as possible predictors. This post explains a bit of the project background, data collection, cleaning and pre-processing, modeling, and a quick wrap up. For the complete notebook with all the code, you can check out the repo on my Github.

I previously worked for a year and a half at an Airbnb property management company, as head of the team responsible for pricing, revenue and analysis. One thing I find particularly interesting is how to figure out what price to charge for a listing on the site. Although'it's a two bedroom in Manchester' will get you reasonably far, there are actually a huge number of factors that can influence a listing's price. As part of a bigger project on using deep learning to predict Airbnb prices, I found myself thrown back into the murky world of property data. Geospatial data can be very complex and messy -- and user-entered geospatial data doubly so.

There's a four-bedroom Edinburgh unit with "original wood floors," listed by Christine. And there's a two-bathroom apartment in Gainesville with a double sofa bed and open kitchen plan, listed by Michel. A "beautiful apartment" in Berlin has a "floral feeling." A three-bedroom in Rome includes "utilities and toiletries." There's just one problem with these Airbnb listings: they don't exist.

Airbnb's growth has been phenomenal over the years and they are one of hottest unicorn startups in the present days. In fact, they have become profitable and hopefully, looking at IPO in the near future. According to popular media outlets, currently Airbnb has more than 2 million listings in 192 countries and operating in 65,000 cities. Considering the amount of data that Airbnb hosts, it'd be interesting to perform analyses and uncover insights related to vacation rental space in the sharing economy. In this study we'll be using the property listings data extracted for Texas, United States. Let's first try to visualize the property density across cities of Texas and find out which one has maximum number of listings.