19.7 Airbnb posterior group-level analysis

• This section looks at neighborhood-level trends, focusing on two neighborhoods with mean log price but vastly different walk ability

## # A tibble: 2 × 4
##   neighborhood walk_score mean_log_price n_listings
##   <fct>             <int>          <dbl>      <int>
## 1 Edgewater            89           4.47         35
## 2 Pullman              49           4.47          5

• Compare the group-level intercepts for models with (closed circle) and without (open circle) the walk_score group level indicator:

• The model with the walk_score predictor has pulled Pullman’s intercept (\(\gamma_0 + \gamma_1 U_j\)) down, closer to the trend

• Note the small sample size for Pullman. Using group level predictors helps us to pool information across groups, improving understanding of small sample size groups.