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Want more housing market stories from Lance Lamberts ResiClub in your inbox? Subscribe to the ResiClub newsletter. Among the nations 100 largest metro area housing markets, no major market saw greater home price appreciation during the Pandemic Housing Boom than Austin, TXwhere home prices surged a staggering 72.5% between March 2020 and June 2022. Since the boom fizzled out three years ago, Austin has also experienced the largest home price correction (-26.0%) among those same 100 major markets. Austin being among the hardest-hit markets isnt surprising. Back in May 2022, I wrote an article for Fortune outlining Austins heightened downside risk this cycle, driven in part by the fact that the market had significantly overheated and become markedly overvalued relative to underlying fundamentals, including local incomes. Put simply: The bigger the local boom, the greater the potential for a local bust. Three years on, that rule of thumb has proven to be a useful guide for the post-Pandemic Housing Boom period. For todays article, ResiClub analyzed how much home prices rose during the Pandemic Housing Boom between March 2020 and June 2022 and examined how they correlate with the shift in home prices since the 2022 peak. We looked at just the nations 100 largest metro areas by population. The finding? Theres a moderate statistical correlation (R = 0.37) between home price shift between March 2020 and June 2022 and the change in home prices from their 2022 peak through the end of October 2025. If the New Orleans metrothe largest outlieris excluded, that correlation strengthens slightly (R = 0.44). window.addEventListener("message",function(a){if(void 0!==a.data["datawrapper-height"]){var e=document.querySelectorAll("iframe");for(var t in a.data["datawrapper-height"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data["datawrapper-height"][t]+"px";r.style.height=d}}}); Our statistical analysis suggests that the U.S. housing market, to a degree, is experiencing a classic partial mean-reversion cycle. The markets that overshot the fundamentals the most during the 20202022 frenzy generally gave back more ground afterward. It makes sense: Housing markets where prices rose too high too fastand became increasingly detached from local incomes and income growthwere more likely to experience a sharper demand shock once the boom ended. That was especially true in markets where the run-up had been fueled by an influx of higher-income out-of-state buyers during the Pandemic Housing Boom, a source of demand that could also roll over. window.addEventListener("message",function(a){if(void 0!==a.data["datawrapper-height"]){var e=document.querySelectorAll("iframe");for(var t in a.data["datawrapper-height"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data["datawrapper-height"][t]+"px";r.style.height=d}}}); In statistical terms, an R of 0.37 means that about 37% of the variation in how local home prices have performed since 2022 can be explained by how hot they ran during the pandemic frenzy. (When we ran a similar analysis in August using Q2 2022 overvaluation scores, we arrived at a similar result: R = 0.27.) Of course, other factors are at play. This coming weekend, ResiClub PRO members will receive a more in-depth research article exploring the other variables driving todays regional home price variation across the country.
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E-Commerce
In the race to deploy large language models and generative AI across global markets, many companies assume that English model translate it is sufficient. But if youre an American executive preparing for expansion into Asia, Europe, the Middle East, or Africa, that assumption could be your biggest blind spot. In those regions, language isnt just a packaging detail: its culture, norms, values, and business logic all wrapped into one. If your AI doesnt code-switch, it wont just underperform; it may misinterpret, misalign, or mis-serve your new market. The multilingual and cultural gap in LLMs Most of the major models are still trained predominantly on English-language corpora, and that creates a double disadvantage when deployed in other languages. For example, a study found that non-English and morphologically complex languages often incur 35X more tokens (and hence cost and compute) per unit of text compared to English. Another research paper places around 1.5 billion people speaking low-resource languages at higher cost and worse performance when using mainstream English-centric models. The result: a model that works well for American users may stumble in India, the Gulf, or Southeast Asia, not because the business problem is harder, but because the system lacks the cultural-linguistic infrastructure to handle it. A regional example worth noting Take Mistral Saba, launched by French company Mistral AI as a 24B-parameter model tailored for Arabic and South Asian languages (Tamil, Malayalam, etc.) Mistral touts that Saba provides more accurate and relevant responses than models five times its size when used in those regions. But it also underperforms in English benchmarks. Thats the point: context matters more than volume. A model may be smaller but far smarter for its locale. For a U.S. company entering the MENA region (Middle East & North Africa) or the South-Asia market, that means your global AI strategy isnt global unless it respects local languages, idioms, regulation, and context. Token costs, language bias, and global ROI From a business perspective, the technical detail of tokenization matters. A recent article points out that inference costs for Chinese may be 2X English, while for languages like Shan or Burmese, token inflation can be 15X. That means if your model uses English-based encoding and you deploy in non-English markets, your usage cost skyrockets, or your quality drops because you cut back tokens. And because your training corpus was heavily English-centric, your underlying model may lack semantic depth in other languages. Add culture and normative differences into the mix: tone, references, business practices, cultural assumptions, etc., and you arrive at a very different competitive set: not were we accurate but were we relevant. Why it matters for executives expanding abroad If youre leading a U.S. corporation or scaling startup into international markets, here are three implications: Model selection isnt one-size-fits-all: you may need a regional model or a specialized fine-tuning layer, not just the largest English model you can license. Cost structure will vary by language and region: token inflation and encoding inefficiencies mean your unit cost in non-English markets will likely be higher, unless you plan for it. Brand risk and user experience are cultural: A chatbot that misunderstands basic local context (e.g., religious calendar, locale idioms, regulatory norms) will erode trust faster than a slower response. How to build a culturally aware multilingual AI strategy For executives ready to sell, serve, and operate in global markets, here are practical steps: Map languages and markets as first-class features. Before you pick your largest model, list your markets, languages, local norms, and business priorities. If Arabic, Hindi, Malay, or Thai matter, treat them not as translations but as first-class us-cases. Consider regional models or joint-deployment. A model like Mistral Saba may handle Arabic content more cheaply, more accurately, and more natively than a generic English model fine-tuned. Plan for token-cost inflation. Use pricing comparison tools. A model may have a U.S. cost of $X per 1 M tokens, but if your deployment is Turkish or Thai, the effective cost may be 2X or more. Fine-tune not just for language, but for culture and business logic. Local datasets shouldnt just include language, they should capture regional context: regulations, business customs, idioms, risk frameworks. Design for active switching and evaluation. Dont assume your global model will behave locally. Deploy pilot tests, evaluate on local benchmarks, test user-acceptance, and include local governance in your rollout. The bigger ethical and strategic lens When AI models privilege English and Anglophone norms, we risk reinforcing cultural hegemony. The technical inefficiencies (token cost, performance gap) are symptoms of a deeper bias: which voices, languages, economies are considered core versus edge. As executives, its tempting to think well translate later. But translation alone fails to address token inflation, semantic mismatch, cultural irrelevance. The real challenge is making AI locally grounded and globally scaled. If youre betting on generative AI to power your expansion into new markets, dont treat language as a footnote. Language is infrastructure. Cultural fluency is a competitive advantage. Token costs and performance disparities are not just technical: they are strategic. In the AI world, English was the path of least resistance. But your next growth frontier? It might require language, culture, and cost structures that act more like differentiators than obstacles. Choose your model, languages, rollout strategy not on the size of the parameter count, but on how well it understands your market. If you dont, you wont just fall behind in performance: youll fall behind in credibility and relevance.
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E-Commerce
The city of San Francisco filed a lawsuit against some of the nation’s top food manufacturers on Tuesday, arguing that ultraprocessed food from the likes of Coca-Cola and Nestle are responsible for a public health crisis.City Attorney David Chiu named 10 companies in the lawsuit, including the makers of such popular foods as Oreo cookies, Sour Patch Kids, Kit Kat, Cheerios and Lunchables. The lawsuit argues that ultraprocessed foods are linked to diseases such as Type 2 diabetes, fatty liver disease and cancer.“They took food and made it unrecognizable and harmful to the human body,” Chiu said in a news release. “These companies engineered a public health crisis, they profited handsomely, and now they need to take responsibility for the harm they have caused.”Ultraprocessed foods include candy, chips, processed meats, sodas, energy drinks, breakfast cereals and other foods that are designed to “stimulate cravings and encourage overconsumption,” Chiu’s office said in the release. Such foods are “formulations of often chemically manipulated cheap ingredients with little if any whole food added,” Chiu wrote in the lawsuit.The other companies named in the lawsuit are PepsiCo; Kraft Heinz Company; Post Holdings; Mondelez International; General Mills; Kellogg; Mars Incorporated; and ConAgra Brands.None of the companies named in the suit immediately responded to emailed requests for comment.U.S. Health Secretary Robert F. Kennedy Jr. has been vocal about the negative impact of ultraprocessed foods and their links to chronic disease and has targeted them in his Make America Healthy Again campaign. Kennedy has pushed to ban such foods from the Supplemental Nutrition Assistance Program for low-income families.An August report by the U.S. Centers for Disease Control and Prevention found that most Americans get more than half their calories from ultraprocessed foods.In October, California Gov. Gavin Newsom signed a first-in-the-nation law to phase out certain ultraprocessed foods from school meals over the next decade.San Francisco’s lawsuit cites several scientific studies on the negative impact of ultraprocessed foods on human health.“Mounting research now links these products to serious diseasesincluding Type 2 diabetes, fatty liver disease, heart disease, colorectal cancer, and even depression at younger ages,” University of California, San Francisco, professor Kim Newell-Green said in the news release.The lawsuit argues that by producing and promoting ultraprocessed foods, the companies violate California’s Unfair Competition Law and public nuisance statute. It seeks a court order preventing the companies from “deceptive marketing” and requiring them to take actions such as consumer education on the health risks of ultraprocessed foods and limiting advertising and marketing of ultraprocessed foods to children.It also asks for financial penalties to help local governments with health care costs caused by the consumption of ultraprocessed foods. Jaimie Ding, Associated Press
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