[{"data":1,"prerenderedAt":32},["ShallowReactive",2],{"changelog-in-house-neighbourhood-scoring":3},{"id":4,"title":5,"body":6,"date":20,"description":12,"extension":21,"meta":22,"module":23,"navigation":24,"path":25,"prs":26,"seo":27,"stem":28,"summary":29,"url":30,"__hash__":31},"changelog/changelog/in-house-neighbourhood-scoring.md","Our in-house models turn evidence into neighbourhood scores",{"type":7,"value":8,"toc":16},"minimark",[9,13],[10,11,12],"p",{},"We built our scoring framework in-house to turn local measurements into interpretable metric and neighbourhood profiles. It defines comparison baselines, metric direction, weights, and how small-area evidence rolls up to neighbourhoods. The same computed evidence supports reports, similarity, and recommendations.",[10,14,15],{},"Read the scoring methodology to follow the current framework, then inspect a neighbourhood’s lens breakdown. Input percentiles and blended scores have different meanings. Census is context, and the current headline decision score uses Value, Safety, Access, and Climate. AI explains these computed results.",{"title":17,"searchDepth":18,"depth":18,"links":19},"",2,[],"2026-03-29","md",{},"data",true,"/changelog/in-house-neighbourhood-scoring",[],{"title":5,"description":12},"changelog/in-house-neighbourhood-scoring","Our scoring framework combines local metrics, neighbourhood aggregation, and preference-based comparisons.","https://www.pickyourplace.app/methodology/scoring","dgF8-ioblSEDXyAFm93pEi4nJ8z0IgZiyITgbXQkRk8",1790805351629]