1,720,967 research outputs found
Who performs better? AVMs vs hedonic models
Full text linkPurpose: In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis. Design/methodology/approach: All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other. Findings: Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities. Practical implications: AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical. Originality/value: According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained
Automated models for value prediction: A critical review of the debate
No full textMass appraisal techniques are used in the valuation of large groups of real estate assets. Their use involves the use of common real estate data, a single evaluation protocol and result verification tests. Given the vast amount of information they have to process, they are entrusted to automatic value prediction models. If initially these models were based on the theory of implicit marginal prices, identified through regression analysis, now they can take radically different forms thanks to the novelties brought by statistical self-learning algorithms. The algorithms of automatic learning – known as machine learning models – autonomously learn the information contained in a dataset. They are able to acquire the existing relations between the characteristics of the assets and the values of price of the goods, even when these have forms well distant from the more traditional linear relation. Each model is first trained with the data of known cases, and then tested in its ability to predict unknown values. The scientific literature has followed the evolution of the machine learning models for the prediction of the value, investigating them under more analysis profiles. The most frequently found research theme concerns the comparison of several evaluation models on the same dataset of real estate data, compared in terms of accuracy in the prediction. The research provides a critical review of the debate in all publications in which the effectiveness of new value prediction models has been empirically investigated. The models prove to be effective in their predictive capacity, less effective in their inferential capacity, i.e. to evaluate the dependence of the price phenomenon on the causes explained by the variables. The debate confirms a higher accuracy of prediction of the new models with respect to the traditional regression analysis. However, it is not possible to rank the models in order of accuracy, as the effectiveness of each model depends on the data available to it. In the face of this undeniable advantage, these models present a limit in their characteristic of black box: the valuer cannot know with certainty what values and forms the variables assume in the learning processes. This makes the models ineffective for understanding the dynamics of formation and variation of value in relation to the characteristics of the good and external agents
The architectures of failure. Strategies and techniques for reusing unfinished projects
No full textThe increase in the rate of insolvency of mortgage loans, due to the context of global crisis, has increased the number of unfinished buildings. These areas are financially deficits and sources of negative externalities from an urban point of view. The solution can be offered by reuse projects that transform the forms and functions of previous projects, which have proved incapable of generating utility and profitability. The research investigates whether from some project experiences in Italy it is possible to find valid strategies for the category. Two guidelines for intervention are outlined: Reuse or demolition. The choice also exploits the potential of urban planning tools that, conceived for the expansion phase of cities, offer new possibilities even in the regeneration phase
The Cross Validation in Automated Valuation Models: A Proposal for Use
No full textThe appraisal of large amounts of properties is often entrusted to Automated Valuation Models (AVM). At one time, only econometric models were used for this purpose. More recently, also machine learning models are used in mass appraisal techniques. The literature has devoted much attention to assessing the performance capabilities of these models. Verification tests first train a model on a training set, then measure the prediction error of the model on a set of data not met before: the testing set. The prediction error is measured with an accuracy indicator. However, verification on the testing set alone may be insufficient to describe the model’s performance. In addition, it may not detect the existence of model bias such as overfitting. This research proposes the use of cross validation to provide a more complete and effective evaluation of models. Ten-fold cross validation is used within 5 models (linear regression, regression tree, random forest, nearest neighbors, multilayer perception) in the assessment of 1,400 properties in the city of Turin. The results obtained during validation provide additional information for the evaluation of the models. This information cannot be provided by the accuracy measurement when considered alone
Evaluating avms performance. beyond the accuracy
No full textAutomated Valuation Models (AVMs) are regularly used in mass appraisal techniques. Thanks to developments in artificial intelligence, machine learning algorithms are increasingly being used alongside traditional econometric models. The final phase in the definition of the models consists in the verification phase of the results elaborated by Avm. The predictive effectiveness tests evaluate the models trained on part of the dataset (the training set) and then measure their ability to predict the remaining values of the dataset (testing set). This verification methodology provides as final output the accuracy parameter, i.e. the difference between predicted prices and actual prices. According to many authors this parameter, if considered alone, is insufficient. The research consists in an accuracy test of 5 Avm in the ability to predict the values of 1038 properties in the city of Padua. To the accuracy results of the test are added the results of cross-validation and the use of different statistical indicators for the measurement of predictive effectiveness. The results provide useful information that broadens the framework of model knowledge. They can be used in the analysis and description of automated evaluation models
Occurrence and etiology of brown apical necrosis on Persian (English) walnut fruit
No full textIn 1998, a severe fruit drop was observed in Italy, principally on cv. Lara Persian (English) walnut (Juglans regia). Dropped fruit showed a brown patch at the blossom end and blackening and rot of inner tissues. The disease, called brown apical necrosis (BAN), was investigated on fruit collected in Italy and France in 1999. In 2000, studies were carried out in three walnut orchards located in Italy and in France to substantiate the etiology of BAN. Isolations performed from inner diseased fruit tissues yielded several fungi, in decreasing frequency of isolation: species of Fusarium and Alternaria, and one species each of Cladosporium, Colletotrichum, and Phomopsis. However, only Fusarium spp. were recovered from stigmas of BAN-affected fruit. The fungi associated with BAN-diseased fruit and species composition differed among locations and over time, confirming results obtained in previous investigations. The species of Fusarium used in pathogenicity tests reproduced BAN-disease symptoms when inoculated on fruit, whereas an Alternaria alternata isolate caused only limited necrosis of the style. However, the role of the other fungi commonly isolated from BAN-diseased fruit remains to be defined. The walnut blight pathogen, Xanthomonas arboricola pv. juglandis, occasionally was isolated from BAN-diseased fruit. No correlation was found between the extent of external brown patches and the size of inner lesions. Repeated isolations from and inoculations of fruit demonstrated that BAN can be considered a complex disease, and the inner infections originate from the style of the fruit
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