Hedonic pricing models based on Machine Learning

我的博士使用新的数据形式来了解西班牙首都住房子市场(房价相似的地区)之间的空间结构和差异。到目前为止，博士学位由两篇研究论文组成(如下所示)。最后一篇论文将使用第二篇论文中确定的子市场来探索房价驱动因素的变化。博士与房地产列表门户网站IDealista合作，后者为论文提供了部分数据。金融危机后西班牙马德里的城市内房价：空间不平等的探索在本文中，我们探索了西班牙首都马德里房价空间不平等的时间动态。空间不平等是全球城市地区的一个令人担忧的特征。有人认为，在城市内部，随着时间的推移，房价在地理上的不平等正在变得更加不平等，特别是在2008年房地产市场崩盘之后。然而，需要在城市内部层面上获得更多证据，才能了解大城市地区的邻里房价差异。使用一家主要住房列表门户网站的数据，分析了房地产市场低迷(2010-2015)和繁荣(2016-2019年)关键时期的变化。对房价分布的细粒度时空分析支持邻里层面的空间不平等和两极分化加剧。确定了高价位住房和低价位住房的两个空间差异住房子市场。房价空间不平等的持续和扩大对城市贫富差距和贫富分化具有重要的社会影响。从卫星图像中提取特征以了解马德里住房次级市场的规模和规模。本文提出了一种新的机器学习方法来分割城市住房市场。我们使用一种名为MOSAIKS的无监督机器学习模型从全球可用的卫星图像中提取特征，并将k-均值聚类算法应用于提取的特征，以西班牙马德里为例识别多个城市内部尺度的子市场。为了系统地探索产生的星系团的尺度效应，对不同大小的卫星图像斑块重复进行分析。我们使用几个内部集群评估指标跨规模评估结果集群。此外，我们使用在线列表门户网站IDealista的数据来衡量集群内房价的同质性，以了解通过图像特征可以很好地区分子市场。本文评估了确定城市住房次级市场的方法的优缺点，这是一项对规划者和政策制定者来说很重要的任务，通常受到数据缺乏的限制。我们的结论是，该方法似乎有助于根据不同的属性和规模划分大型城市住房市场。

My PhD uses new forms of data to understand the spatial structure and differences between housing sub-markets (areas of similar housing prices) in the capital city of Spain. The PhD is so far composed of two research papers (listed below). The final paper will explore variations in the drivers in housing prices using the sub-markets identified from the second paper. The PhD is partnered with real estate listings portal Idealista, who provided some of the data for the thesis.Intra-Urban House Prices In Madrid (Spain) Following The Financial Crisis: An Exploration Of Spatial InequalityIn this paper we explore the temporal dynamics of spatial inequality in housing prices for Madrid, the capital city of Spain. Spatial inequalities are a concerning feature of urban areas across the globe. It has been suggested that within cities, housing prices are becoming more geographically unequal over time, particularly since the 2008 housing market crash. However, more evidence is needed at the intra-urban level to understand neighbourhood house price differences in large urban areas. Changes are analysed during a key period of housing market bust (2010-2015) and boom (2016-2019), using data from a major housing listing portal. Fine grain space-time analysis of the distribution of housing prices supports an increase in spatial inequality and polarisation at the neighbourhood level. Two spatially differentiated housing sub-markets of high- and low-priced housing are identified. The persistence and growth of spatial house price inequality has important societal implications for the wealth gap and segregation of rich and poor in cities.Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in MadridThe following paper proposes a novel machine learning approach to the segmentation of urban housing markets. We extract features from globally available satellite imagery using an unsupervised machine learning model called MOSAIKS, and apply a k-means clustering algorithm to the extracted features to identify sub-markets at multiple intra-urban scales within a case study of Madrid (Spain). To systematically explore scale effects on the resulting clusters, the analysis is repeated with varying sizes of satellite image patches. We assess the resulting clusters across scales using several internal cluster-evaluation metrics. Additionally, we use data from online listings portal Idealista to measure the homogeneity of housing prices within the clusters, to understand how well sub-markets can be differentiated by the image features. This paper evaluates the strengths and weakness of the method to identify urban housing sub-markets, a task which is important for planners and policy makers and is often limited by a lack of data. We conclude that the approach seems useful to divide large urban housing markets according to different attributes and scales.