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.

Former Arizona gubernatorial candidate Kari Lake weighs in as Democratic Gov. Katie Hobbs vetoes legislation limiting Chinese land ownership near U.S. military bases and strategic assets and warns how the move puts national security at risk. Texas has become the latest state to cement a ban on land and property purchases by individuals or entities from adversarial nations. Republican Gov. Greg Abbott signed Senate Bill 17 into law over the weekend, prohibiting countries identified as security threats in the intelligence community's 2025 Annual Threat Assessment, from acquiring "real property" in the state. The countries include China, Russia, Iran and North Korea, and the bill identified "real property" as agricultural land, commercial or industrial properties, residential properties and land used for mining or water use. Amid heightened global tensions, there has been an increased appetite for protecting foreign asset acquisitions in the United States.

In recent years, machine learning (ML) techniques have become a powerful tool for improving the accuracy of predictions and decision-making. Machine learning technologies have begun to penetrate all areas, including the real estate sector. Correct forecasting of real estate value plays an important role in the buyer-seller chain, because it ensures reasonableness of price expectations based on the offers available in the market and helps to avoid financial risks for both parties of the transaction. Accurate forecasting is also important for real estate investors to make an informed decision on a specific property. This study helps to gain a deeper understanding of how effective and accurate ensemble machine learning methods are in predicting real estate values. The results obtained in the work are quite accurate, as can be seen from the coefficient of determination (R^2), root mean square error (RMSE) and mean absolute error (MAE) calculated for each model. The Gradient Boosting Regressor model provides the highest accuracy, the Extra Trees Regressor, Hist Gradient Boosting Regressor and Random Forest Regressor models give good results. In general, ensemble machine learning techniques can be effectively used to solve real estate valuation. This work forms ideas for future research, which consist in the preliminary processing of the data set by searching and extracting anomalous values, as well as the practical implementation of the obtained results.

Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the recent advances of large language models (LLMs), there is growing potential to leverage high-frequency online text data for improved CPI prediction, an area still largely unexplored. This paper proposes LLM-CPI, an LLM-based approach for CPI prediction inference incorporating online text time series. We collect a large set of high-frequency online texts from a popularly used Chinese social network site and employ LLMs such as ChatGPT and the trained BERT models to construct continuous inflation labels for posts that are related to inflation. Online text embeddings are extracted via LDA and BERT. We develop a joint time series framework that combines monthly CPI data with LLM-generated daily CPI surrogates. The monthly model employs an ARX structure combining observed CPI data with text embeddings and macroeconomic variables, while the daily model uses a VARX structure built on LLM-generated CPI surrogates and text embeddings. We establish the asymptotic properties of the method and provide two forms of constructed prediction intervals. The finite-sample performance and practical advantages of LLM-CPI are demonstrated through both simulation and real data examples.

The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we propose FADAML, a novel end-to-end machine learning system to detect and filter out fake online advertisements. Our system combines techniques in multimodal machine learning and automated machine learning to achieve a high detection rate. As a case study, we apply FADAML to detect fake advertisements on popular Vietnamese real estate websites. Our experiments show that we can achieve 91.5% detection accuracy, which significantly outperforms three different state-of-the-art fake news detection systems.

Even in a booming seller's market, real estate remains an outstanding investment, even for those who are new to it. But you want to make sure you're making the right decisions, and that's where a tool like Mashvisor comes in handy. Mashvisor helps you perform smarter property analysis with accurate, real-time real estate market data sourced from the MLS, Zillow, Rentometer, Airbnb.com, and the US Census Bureau. Mashvisor's AI and machine learning algorithms turn the raw data from these sources into actionable analytics, helping you find markets and properties in your budget with high investment return potential. Mashvisor covers 95% of US markets, allowing you to cast a wide net in your investment search.

The real estate market has slowed a little, but property remains one of the best investments you can make. With Mashvisor, it's even easier to make smart investments and get a big discount now through January 1. Mashvisor offers comprehensive coverage of 95% of US markets with up-to-date real estate data sourced from some of the best sources in the country. With data from the MLS, Zillow, Rentometer, Airbnb, and the US Census Bureau and powerful machine learning algorithms, Mashvisor assesses properties and neighborhoods to identify short- and long-term potential. You can search properties depending on your preferred market, property type, size, budget, and more to start building a real estate portfolio from scratch. Find out why Mashvisor has earned an excellent Trustpilot rating.

The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.

If you're worried artificial intelligence is going to take your job, you're not alone -- and it's not unfounded. By 2030, reports estimate 375 million jobs worldwide are at risk, with financial advisors and brokers, insurers and data processors at the top of the list. A new AI calculator estimates how much of your job today's AI is equipped to handle. Simply answer six questions about your job and you'll get back a percentage. The creators recommend really reflecting on your specific role and the way you do it, not only your title and task list.

Chintan Soni is the co-founder and CEO of Ecolibrium, a decarbonisation platform for commercial and industrial real estate that uses machine learning and internet-connected sensors to provide insights on infrastructure energy use. London-based Ecolibrium's core product, called SmartSense, collects data from thousands of internet-of-things (IoT) sensors placed on a building's energy infrastructure. These sensors feed back real-time insights to help businesses reduce energy consumption. The company relocated its headquarters to the UK from India in 2022. It is backed by QPR Football Club Chairman Amit Bhatia's Swordfish Investments and venture capital firm Unbound.