Fake video walk-throughs, a magically expanding loft, and stair hallucinations are just some of the new AI-generated features house hunters are coming across. As you're hunting through real estate listings for a new home in Franklin, Tennessee, you come across a vertical video showing off expansive rooms featuring a four-poster bed, a fully stocked wine cellar, and a soaking tub. It looks perfect--maybe a little too perfect. Everything in the video is AI-generated . The real property is completely empty, and the luxury furniture is a product of virtual staging.

Large language models (LLMs) are increasingly deployed in decision-support systems for high-stakes domains such as hiring and university admissions, where choices often involve selecting among competing alternatives. While prior work has noted position order biases in LLM-driven comparisons, these biases have not been systematically analyzed or linked to underlying preference structures. We present the first comprehensive study of position biases across multiple LLMs and two distinct domains: resume comparisons, representing a realistic high-stakes context, and color selection, which isolates position effects by removing confounding factors. We find strong and consistent order effects, including a quality-dependent shift: when all options are high quality, models favor the first option, but when quality is lower, they favor later options. We also identify two previously undocumented biases in both human and machine decision-making: a centrality bias (favoring the middle position in triplewise comparisons) and a name bias, where certain names are favored despite controlling for demographic signals. To separate superficial tie-breaking from genuine distortions of judgment, we extend the rational choice framework to classify pairwise preferences as robust, fragile, or indifferent. Using this framework, we show that order effects can lead models to select strictly inferior options, and that position biases are typically stronger than gender biases. These results indicate that LLMs exhibit distinct failure modes not documented in human decision-making. We also propose targeted mitigation strategies, including a novel use of the temperature parameter, to recover underlying preferences when order effects distort model behavior.

The development of large language models (LLMs) has greatly promoted the progress of chatbot in multiple fields. There is an urgent need to evaluate whether LLMs can play the role of agent in housing transactions and services as well as humans. We present Real Estate Agent Large Language Model Evaluation (REAL), the first evaluation suite designed to assess the abilities of LLMs in the field of housing transactions and services. REAL comprises 5,316 high-quality evaluation entries across 4 topics: memory, comprehension, reasoning and hallucination. All these entries are organized as 14 categories to assess whether LLMs have the knowledge and ability in housing transactions and services scenario. Additionally, the REAL is used to evaluate the performance of most advanced LLMs. The experiment results indicate that LLMs still have significant room for improvement to be applied in the real estate field.

Southern California's real estate market is as cold as the snow currently adorning the peaks of its mountains. And deals are few and far between. In slow markets, the agents at the top -- those with experience, connections and plenty of clients -- typically maintain a modest but steady stream of business. It's the agents at the bottom -- those just getting into the industry who've only managed to close a handful of sales -- who starve. As those agents have grown more desperate for leads, they're trying alternative ways of finding them.

Current datasets for unwanted social bias auditing are limited to studying protected demographic features such as race and gender. In this work, we introduce a comprehensive benchmark that is meant to capture the amplification of social bias, via stigmas, in generative language models. Taking inspiration from social science research, we start with a documented list of 93 US-centric stigmas and curate a question-answering (QA) dataset which involves simple social situations. Our benchmark, SocialStigmaQA, contains roughly 10K prompts, with a variety of prompt styles, carefully constructed to systematically test for both social bias and model robustness. We present results for SocialStigmaQA with two open source generative language models and we find that the proportion of socially biased output ranges from 45% to 59% across a variety of decoding strategies and prompting styles. We demonstrate that the deliberate design of the templates in our benchmark (e.g., adding biasing text to the prompt or using different verbs that change the answer that indicates bias) impacts the model tendencies to generate socially biased output. Additionally, through manual evaluation, we discover problematic patterns in the generated chain-of-thought output that range from subtle bias to lack of reasoning. Warning: This paper contains examples of text which are toxic, biased, and potentially harmful.

"The ad said this pool was lagoon-like. That's the sarcastic comment of two clients in an iconic scene from American Beauty right before Carolyn, the movie co-protagonist working as a real estate agent, has a meltdown. Despite the typical cinematic overdramatisation, it's actually true that few industries in the world are driven by the "human factor", including interpersonal interactions between sellers and buyers, as much as real estate, and this seems to result in a certain conservatism and low propensity for change. Just a few years ago, indeed, this sector was ranked as the second least digitised by the Morgan Stanley Digitalisation Index. However, things are evolving faster, as relying on technology to compete in a saturated, unpredictable market like real estate can certainly come in handy.

Post-COVID, almost every industry has become data-driven and has adopted AI to beat the heat of the increasing competition. But, the real estate industry has been one of the slowest to go into digitization. One of the vital reasons for this could be the varying disparities in the data of this industry. It's one thing to enlist house types by rooms and another thing when users search with specific instructions. Furthermore, the urge of property dealers to cross-verify everything in person is another crucial factor that refrains real estate agents (and brokers) to invest in digital marketing and stick to non-digitized, traditional marketing mediums like print media and television ads. But even this industry could not escape the aftermath of the pandemic, which forced its stakeholders to turn towards digital solutions and adopt the bleeding-edge technologies of Artificial Intelligence and Machine Learning.

As a humanist who writes about the impact of digital technology on our lives, I am often mistaken for a futurist. The people most interested in hiring me for my opinions about technology are usually less concerned with building tools that help people live better lives in the present than they are in identifying the Next Big Thing through which to dominate them in the future. I don't usually respond to their inquiries. Why help these guys ruin what's left of the internet, much less civilisation? Still, sometimes a combination of morbid curiosity and cold hard cash is enough to get me on a stage in front of the tech elite, where I try to talk some sense into them about how their businesses are affecting our lives out here in the real world. That's how I found myself accepting an invitation to address a group mysteriously described as "ultra-wealthy stakeholders", out in the middle of the desert. A limo was waiting for me at the airport.

Technology allows us to take new approaches to old problems, and these approaches are faster, easier, and yield better results. This is as true in the real estate industry as any other – in a realm where buying and selling homes used to require lots of hard work, innovations like the internet, artificial intelligence, and mobile computing have made the process much less strenuous than it was in the past. One of the newest technological breakthroughs, both in the real estate sector and beyond, is the use of artificial intelligence. AI has absolutely revolutionized many processes associated with buying and selling homes, and as a result, first-time home buyers are reaping the benefits in a major way. Let's take a look at how AI potentially affects first-time home buyers for the better.

In addition, the real estate field, like many others, is transitioning to a "data-driven" world and developing artificial intelligence use cases. From consumer exchange of goods and services strategies to investing in large commercial projects, A.I. is making inroads into real estate. On the other hand, the real estate field is still in the early stages of implementing A.I. services in real estate will become more visible as technology advances. This article will look at the buzz around A.I. in the real estate industry. Artificial intelligence is a word that refers to a type of technology that can draw logical conclusions on its own.