A multilevel model of the housing market for San José Metropolitan Region (Costa Rica) was developed, including spatial effects. The model is used to explore two main questions: the extent to which contextual (of the surroundings) and compositional (of the property itself) effects explain variation of housing prices and how does the relation between price and key covariates change with the introduction of multilevel effects. Hierarchical relations (lower level units nested into higher level) were modeled by specifying multilevel models with random intercepts and a conditional autoregressive term to include spatial effects from neighboring units at the higher level (districts). The random intercepts and conditional autoregressive models presented the best fit to the data. Variation at the higher level accounted for 16% of variance in the random intercepts model and 28% in the conditional autoregressive model. The sign and magnitude of regression coefficients proved remarkably stable across model specifications. Travel time to the city center, which presented a non-linear relation to price, was found to be the most important determinant. Multilevel and conditional autoregressive models constituted important improvements in modeling housing price, despite most of the variation still occurring at the lower level, by improving the overall model fit. They were capable of representing the regional structure and of reducing sampling bias in the data. However, the conditional autoregressive specification only represented a limited advance over the random intercepts formulation.

开发了圣何塞大都会区（哥斯达黎加）住房市场的多层次模型，包括空间效应。该模型用于探讨两个主要问题：环境（周围环境）和组合（物业本身）影响在多大程度上解释了房价的变化，以及价格与关键协变量之间的关系如何随着多层次的引入而发生变化。效果。通过指定具有随机截距和条件自回归项的多级模型来对层次关系（低级单元嵌套到更高级别）进行建模，以包括来自更高级别（区域）的相邻单元的空间效应。随机截距和条件自回归模型最适合数据。较高水平的变异在随机截距模型中占方差的 16%，在条件自回归模型中占 28%。回归系数的符号和大小证明在模型规格中非常稳定。到市中心的旅行时间与价格呈非线性关系，被发现是最重要的决定因素。多水平和条件自回归模型通过改善整体模型拟合，构成了对房价建模的重要改进，尽管大部分变化仍发生在较低水平。它们能够代表区域结构并减少数据中的抽样偏差。然而，条件自回归规范仅代表了随机截距公式的有限进步。