Non-parametric machine learning models, such as random forests and gradient
boosted trees, are frequently used to estimate house prices due to their
predictive accuracy, but such methods are often limited in their ability to
quantify prediction uncertainty. Conformal Prediction (CP) is a model-agnostic
framework for constructing confidence sets around machine learning prediction
models with minimal assumptions. However, due to the spatial dependencies
observed in house prices, direct application of CP leads to confidence sets
that are not calibrated everywhere, i.e., too large of confidence sets in
certain geographical regions and too small in others. We survey various
approaches to adjust the CP confidence set to account for this and demonstrate
their performance on a data set from the housing market in Oslo, Norway. Our
findings indicate that calibrating the confidence sets on a \textit{locally
weighted} version of the non-conformity scores makes the coverage more
consistently calibrated in different geographical regions. We also perform a
simulation study on synthetically generated sale prices to empirically explore
the performance of CP on housing market data under idealized conditions with
known data-generating mechanisms.

非参数机器学习模型与符合预测是房地产市场数据中准确预测房屋价格的方法，但由于地理空间上的依赖，直接应用符合预测在不同地理区域的置信区间不完全校准。本研究调查了各种方法来调整符合预测的置信区间以解决此问题，并在挪威奥斯陆房地产市场数据集上验证其性能。研究结果表明，在非一致性分数的	extit {局部加权} 版本上校准置信区间可以在不同地理区域中一致地进行校准。我们还在合成生成的销售价格上进行了模拟研究，从而在已知数据生成机制的理想条件下实证探索符合预测在房地产市场数据上的性能。