ABSTRACT Property valuation studies often use classical statistics techniques. Among these techniques, the Artificial Neural Networks are the most applied, overcoming the inflexibility and the linearity of the hedonic models. Other researchers have used Geostatistics techniques, specifically the Kriging Method, for interpreting spatial-temporal variability and to predict housing unit prices. The innovation of this study is to highlight how the Kriging Method can help to better understand the urban environment, improving the results obtained by classical statistics. This study presents two different methods that share the general objective of extracting information regarding a city’s housing from datasets. The procedures applied are Ordinary Kriging (Geostatistics) and Multi-Layer Perceptron algorithm (Artificial Neural Networks). These methods were used to predict housing unit prices in the municipality of Pozuelo de Alarcon (Madrid). The implementation of both methods provides us with the urban characteristics of the study area and the most significant variables related to price. The main conclusion is that the Ordinary Kriging models and the Neural Networks models, applied to predicting housing unit prices are necessary methodologies to improve the information obtained in classical statistical techniques. Abbreviations: ANN: Artificial Neural Networks; OK: ordinary Kriging; MLP: multi-layer perceptron

中文翻译：

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用于城市住宅区房屋估价的人工神经网络和地统计模型

摘要 房地产估价研究通常使用经典的统计技术。在这些技术中，人工神经网络是应用最多的，克服了享乐模型的不灵活性和线性。其他研究人员使用地质统计学技术，特别是克里金法来解释时空变异性和预测住房单价。本研究的创新之处在于突出克里金法如何帮助更好地了解城市环境，改善经典统计所获得的结果。本研究提出了两种不同的方法，它们的共同目标是从数据集中提取有关城市住房的信息。应用的程序是普通克里金法（地统计学）和多层感知器算法（人工神经网络）。这些方法用于预测 Pozuelo de Alarcon（马德里）市的住房单价。两种方法的实施为我们提供了研究区域的城市特征和与价格相关的最重要变量。主要结论是普通克里金模型和神经网络模型应用于预测住房单位价格是改进经典统计技术中获得的信息的必要方法。缩写：ANN：人工神经网络；OK：普通克里金法；MLP：多层感知器 主要结论是普通克里金模型和神经网络模型应用于预测住房单位价格是改进经典统计技术中获得的信息的必要方法。缩写：ANN：人工神经网络；OK：普通克里金法；MLP：多层感知器 主要结论是普通克里金模型和神经网络模型应用于预测住房单位价格是改进经典统计技术中获得的信息的必要方法。缩写：ANN：人工神经网络；OK：普通克里金法；MLP：多层感知器