Things to Do in L.A. Tap to enable a layout that focuses on the article. Television City, at Beverly Boulevard and Fairfax Avenue, has served as a stage for some of TV's most legendary moments. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

The 2024 Department of Justice antitrust complaint against RealPage, Inc. named five major residential REITs for coordinating algorithmic rent pricing across hundreds of thousands of apartment units in major US metropolitan areas. This paper studies whether census-tract-level corporate landlord concentration (CLC), measured from SEC EDGAR 10-K property filings geocoded to census tracts, the first such application in the literature, is associated with rent growth 2019-2023, and whether that association is larger in majority-minority neighborhoods. Rent outcomes are measured using the Zillow Observed Rent Index (ZORI). To account for the possibility that corporate landlords preferentially locate in neighborhoods already seeing rent appreciation, all regressions control for a fully novel Algorithmic Housing Burden Index (AHBI), a composite of pre-existing rent burden and market tightness from ACS data. Across 665 census tracts in ten US metropolitan areas, doubling REIT concentration is associated with 2.8 percentage points higher rent growth (p = 0.086, p = 0.030, HC1 robust). This association is significantly stronger in majority-minority tracts. Within the same metro, high-CLC majority-minority tracts are associated with 5.9 percentage points higher rent growth than comparable white tracts (p = 0.039). An XGBoost model predicts 44 percent of out-of-sample rent growth variance, with SHAP analysis independently confirming that CLC's contribution is positive in minority tracts and negative in white tracts. Taken all together, these findings provide the first tract-level evidence consistent with corporate landlord concentration being associated with disproportionately higher rent growth in communities of color.

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We introduce the zero-one censored transformed normal (ZOC-TN) model for proportional responses with potential probability mass at the boundaries 0 and 1. The model combines a censored Gaussian variable with a two-parameter affine-logit transformation on the interior (0,1). We characterize the transformation parameters, establish large-sample properties, and relate the affine-logit specification to broader classes of interior distributions. Theoretical and experimental results demonstrate that the proposed model can capture a wider range of qualitative density shapes than several benchmark models while remaining parsimonious, computationally efficient, and numerically stable. Furthermore, the ZOC-TN model can be extended (i) to account for nonlinearities and interactions in a tree-boosting machine learning framework and (ii) to explicitly model residual spatio-temporal variability. We apply the ZOC-TN model to loss given default (LGD) modeling for a large dataset of U.S. residential mortgages and compare it to multiple benchmark models. We find that a tree-boosted ZOC-TN model with a spatio-temporal frailty Gaussian process delivers the strongest out-of-sample performance, indicating that mortgage losses are shaped by nonlinear covariate effects and by unaccounted-for space-time variation.

Tech has been encroaching on the family domicile for years--but actor, writer, and satirist Jill Kargman is all in on analog. My husband Harry works in tech, and every January he makes his yearly pilgrimage to Consumer Electronics Show (CES) in Las Vegas, where some 4,100 exhibitors are spread across 2.6 million square feet. The dominant concept at this year's edition was that, very soon, anything you put in your house will be compatible with voice-activated AI services like Siri, Alexa, or HomePod. Your newest home automation systems will come equipped with sensors and jazzy master controls on an iPad. The problem for me is that a tiny photoelectric cell you frantically wave to--rather than a switch to flick or press--rarely acknowledges me, because somehow I'm not human temperature.

The'painted ladies' in San Francisco on 20 August 2024. The'painted ladies' in San Francisco on 20 August 2024. Home prices in the San Francisco Bay Area's already expensive market are skyrocketing as employees at leading artificial intelligence companies come into gargantuan sums of money thanks to a boom in initial public offerings . With San Francisco's OpenAI and Anthropic, as well as SpaceX, which operates a major facility in the Los Angeles area, eyeing debuts on the stock market, the hot housing market may not abate soon. If their initial public offering (IPO) is well-received, the companies' multibillion-dollar valuations are poised to produce massive wealth for employees and executives holding shares, which experts say could trigger an uptick in demand for the Bay Area's limited housing stock.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Why L.A. condo sales have slumped to a 20-year low In January and February, fewer than 2,000 condominiums were sold in Los Angeles County, according to Attom data. Above, the three towers that make up the Marina City Club condo complex in Marina del Rey. This is read by an automated voice. Please report any issues or inconsistencies here .

Florida students boo graduation speaker who called AI'next Industrial Revolution' Real estate executive got an unexpected earful when she spoke of'living in a time of profound change' Though college graduations usually consist of a speaker giving advice to students, one recent ceremony featured students giving the speaker their opinions - loudly. The University of Central Florida's 2026 graduating class booed as a real estate development executive spoke about how "the rise of artificial intelligence is the next Industrial Revolution" and about "living in a time of profound change". US university's commencement speaker reveals he will pay off students' final-year loans The crowd of students was so loud that Gloria Caulfield paused, turned away from the podium and threw her hands up in the air. As the crowd calmed down, Caulfield proceeded. "Only a few years ago, AI was not a factor in our lives."

This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter task, for instance, the proposed system takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D translations and rotations, akin to solving the jigsaw puzzle of room layouts. A surprising discovery of the paper is that the simple use of a Diffusion Model effectively solves these challenging spatial puzzle tasks as a conditional generation process. To enable learning of an end-to-end neural system, the paper introduces new datasets with ground-truth arrangements: 1) 2DVoronoi jigsaw dataset, a synthetic one where pieces are generated by Voronoi diagram of 2D pointset; and 2) MagicPlan dataset, a real one offered by MagicPlan from its production pipeline, where pieces are room layouts constructed by augmented reality App by real-estate consumers. The qualitative and quantitative evaluations demonstrate that our approach outperforms the competing methods by significant margins in all the tasks. We have provided code and data here.