Standard empirical asset-pricing models impose a common mapping from firm characteristics to expected returns. We develop the Lasso Fama–MacBeth Clustering Model, which jointly estimates characteristic-defined regions of the cross section and locally sparse average slopes within each region. In U.S. equities from 1980 to 2024, a pervasive core of short-term reversal and earnings surprise coexists with a broader periphery of characteristics retained only in particular regions. Cluster-based value-weighted portfolios deliver an out-of-sample Sharpe ratio of 2.04 and monthly alphas above 1.6% unspanned by standard factor models. They outperform pooled benchmarks and remain robust after transaction costs. The set of selected characteristics changes over the business cycle. State-specific fits for expansions and recessions produce distinct partitions and a broader set of selected characteristics in recessions.