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.

Metropolis The Housing Market Is Already Terrible. A.I. Is Making It Even Worse. While digital staging is nothing new to real estate, bot-made listings are forcing homebuyers and professionals to ask themselves if this is a straight-up deceptive practice. DeAnn Wiley was on the hunt for a new rental in Detroit earlier this month when she had the displeasure of arriving at a property that looked nothing like what was advertised online. "The photos made the home look brand new, only to get there and see the usual wear and tear and the old'landlord special,' " she told Slate.

Things to Do in L.A. Tap to enable a layout that focuses on the article. How bad is California's housing shortage? It depends on who's doing the counting This is read by an automated voice. Please report any issues or inconsistencies here . Imagine you've finally taken your car to the mechanic to investigate that mysterious warning light that's been flashing on your dashboard for the past week and a half.

The housing market in Ghana has been facing significant challenges, with the rental sector being particularly affected by issues such as the advance rent system, asymmetrical perceptions between landlords and tenants, and the lack of an institutional framework for regulating the market [2]. These challenges create a highly dynamic and often opaque rental environment, where both tenants and landlords face difficulties in determining fair rental prices. This issue is further exacerbated by the absence of comprehensive and up-to-date data on rental trends, making it challenging for stakeholders to make informed decisions. In recent years, the use of machine learning in real estate has gained traction globally as a means to address such challenges. Machine learning (ML) models can analyse large datasets, uncover hidden patterns, and make accurate predictions, thereby providing valuable insights for various stakeholders in the housing market.

Stock portfolios are often exposed to rare consequential events (e.g., 2007 global financial crisis, 2020 COVID-19 stock market crash), as they do not have enough historical information to learn from. Large Language Models (LLMs) now present a possible tool to tackle this problem, as they can generalize across their large corpus of training data and perform zero-shot reasoning on new events, allowing them to detect possible portfolio crash events without requiring specific training data. However, detecting portfolio crashes is a complex problem that requires more than basic reasoning abilities. Investors need to dynamically process the impact of each new information found in the news articles, analyze the the relational network of impacts across news events and portfolio stocks, as well as understand the temporal context between impacts across time-steps, in order to obtain the overall aggregated effect on the target portfolio. In this work, we propose an algorithmic framework named Temporal Relational Reasoning (TRR). It seeks to emulate the spectrum of human cognitive capabilities used for complex problem-solving, which include brainstorming, memory, attention and reasoning. Through extensive experiments, we show that TRR is able to outperform state-of-the-art solutions on detecting stock portfolio crashes, and demonstrate how each of the proposed components help to contribute to its performance through an ablation study. Additionally, we further explore the possible applications of TRR by extending it to other related complex problems, such as the detection of possible global crisis events in Macroeconomics.

This paper aims to enrich the capabilities of existing deep learning-based automated valuation models through an efficient graph representation of peer dependencies, thus capturing intricate spatial relationships. In particular, we develop two novel graph neural network models that effectively identify sequences of neighboring houses with similar features, employing different message passing algorithms. The first strategy consider standard spatial graph convolutions, while the second one utilizes transformer graph convolutions. This approach confers scalability to the modeling process. The experimental evaluation is conducted using a proprietary dataset comprising approximately 200,000 houses located in Santiago, Chile. We show that employing tailored graph neural networks significantly improves the accuracy of house price prediction, especially when utilizing transformer convolutional message passing layers.

Public availability of Artificial Intelligence generated information can change the markets forever, and its factoring into economical dynamics may take economists by surprise, out-dating models and schools of thought. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years, coinciding with prominence of public estimation information from Zillow, a successful Mass Real Estate Estimator (MREE), could not escape unobserved. What we model is a repetitive theoretical game between the MREE and the home owners, where each player has secret information and expertise. If the intention is to keep housing affordable and maintain old American lifestyle with broad home-ownership, new challenges are defined. Simulations show that a simple restriction of MREE-style price estimation availability to opt-in properties may help partially reduce feedback loop by acting on its likely causes, as suggested by experimental simulation models. The conjecture that the MREE pressure on real estate inflation rate is correlated with the absolute MREE estimation errors, which is logically explainable, is then validated in simulations.

Mortgages account for the largest portion of household debt in the United States, totaling around \$12 trillion nationwide. In times of financial hardship, alleviating mortgage burdens is essential for supporting affected households. The mortgage servicing industry plays a vital role in offering this assistance, yet there has been limited research modelling the complex relationship between households and servicers. To bridge this gap, we developed an agent-based model that explores household behavior and the effectiveness of relief measures during financial distress. Our model represents households as adaptive learning agents with realistic financial attributes. These households experience exogenous income shocks, which may influence their ability to make mortgage payments. Mortgage servicers provide relief options to these households, who then choose the most suitable relief based on their unique financial circumstances and individual preferences. We analyze the impact of various external shocks and the success of different mortgage relief strategies on specific borrower subgroups. Through this analysis, we show that our model can not only replicate real-world mortgage studies but also act as a tool for conducting a broad range of what-if scenario analyses. Our approach offers fine-grained insights that can inform the development of more effective and inclusive mortgage relief solutions.

You can find the code and data for this article at this link. It's all hosted on Deepnote, a new kind of data notebook designed for collaboration. Deepnote has become my home in the cloud for all of my data science work. Thank you to Deepnote for sponsoring this week's

In this contributed article, Anupam Datta, Co-Founder, President, and Chief Scientist of TruEra, discusses Zillow and what went wrong with the AI models. For AI and ML models to perform for profitable outcomes, especially for high stakes models like Zillow’s, it is crucial to have serious AI governance supported by tools for monitoring and debugging, which includes having qualified humans-in-the-loop to adjust to major market shifts that can arise during unexpected events.