Predicting owner-occupied housing values using machine learning: an empirical investigation of California census tracts data

2021 ◽  
pp. 1-32
Author(s):  
Prodosh E. Simlai
Author(s):  
Miguel Couceiro ◽  
Nicolas Hug ◽  
Henri Prade ◽  
Gilles Richard

Training set extension is an important issue in machine learning. Indeed when the examples at hand are in a limited quantity, the performances of standard classifiers may significantly decrease and it can be helpful to build additional examples. In this paper, we consider the use of analogical reasoning, and more particularly of analogical proportions for extending training sets. Here the ground truth labels are considered to be given by a (partially known) function. We examine the conditions that are required for such functions to ensure an error-free extension in a Boolean setting. To this end, we introduce the notion of Analogy Preserving (AP) functions, and we prove that their class is the class of affine Boolean functions. This noteworthy theoretical result is complemented with an empirical investigation of approximate AP functions, which suggests that they remain suitable for training set extension.


2019 ◽  
Vol 8 (3) ◽  
pp. 5892-5896

In belonging to other supports duel beside researchers of image spam detections, unsolicited mail have newly developed the image based spam dodge to construct the investigation of e-mails’ content of text unsuccessful. To avoid signature based recognition, it involves in implanting the unsolicited text or message into an appendage image, which is frequently arbitrarily customized. Identifying image based spam emails tries out to be an motivating illustration of the problem text embedded in images were subjected to noise such as background pattern, color, font variations and imperfections in a font size so as to eliminate the chances of being identified as unsolicited e-mail by classification techniques. In this research paper we spring a exhaustive review and categorization of machine learning and classification systems suggested so far in contradiction of image based spam email, and make an empirical investigation and correlation of few of them on real, widely accessible data sets.


Urban Studies ◽  
2019 ◽  
Vol 57 (1) ◽  
pp. 134-151 ◽  
Author(s):  
Elizabeth Delmelle ◽  
Isabelle Nilsson

This article tests the hypothesis that low-income residents disproportionately move out of neighbourhoods in close proximity to new rail transit stations. This transit-induced gentrification scenario posits that the development of rail transit will place an upward pressure on land and housing values and that higher-income residents will outbid low-income residents for this new amenity. The most transit-dependent population may therefore be displaced from the most accessible locations, forming a paradox in the investment in new transit systems. We test this hypothesis using the Panel Study on Income Dynamics (PSID) dataset to trace the out-migration of residents across the United States from census tracts within five years of the opening of a new station, between 1970 and 2014. We find that low-income individuals are more likely to move, regardless of their neighbourhood. However, we do not find significant evidence that low-income individuals are more likely to move out of transit neighbourhoods, after controlling for both individual and other neighbourhood characteristics. The odds of moving out of a transit neighbourhood for low-income residents is statistically insignificant. In other words, they do not have a heightened probability of leaving new transit neighbourhoods compared with other residents. Our results are robust across decades, when examining renters alone, for different time spans and for varying definitions of transit neighbourhoods. We further find that those living in transit neighbourhoods are not more likely to live in a crowded dwelling. Our results therefore suggest that, on average, across the nation, low-income residents do not disproportionately exit new transit neighbourhoods.


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