scholarly journals Sensitivity Analysis of Machine Learning Models for the Mass Appraisal of Real Estate. Case Study of Residential Units in Nicosia, Cyprus

2019 ◽  
Vol 11 (24) ◽  
pp. 3047 ◽  
Author(s):  
Thomas Dimopoulos ◽  
Nikolaos Bakas
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Real Estate ◽  
Daily Practice ◽  
Learning Approaches ◽  
Residential Units ◽  
The Cost ◽  
Automated Valuation ◽  
Further Development

A recent study of property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on Mass Appraisals rather than on the further development of the existing valuation methods. Researchers are using a variety of mathematical models used within the field of Machine Learning, which are applied to real estate valuations with high accuracy. On the other hand, it appears that professional valuers do not use these sophisticated models during daily practice, rather they operate using the traditional five methods. The Department of Lands and Surveys in Cyprus recently published the property values (General Valuation) for taxation purposes which were calculated by applying a hybrid model based on the Cost approach with the use of regression analysis in order to quantify the specific parameters of each property. In this paper, the authors propose a number of algorithms based on Artificial Intelligence and Machine Learning approaches that improve the accuracy of these results significantly. The aim of this work is to investigate the capabilities of such models and how they can be used for the mass appraisal of properties, to highlight the importance of sensitivity analysis in such models and also to increase the transparency so that automated valuation models (AVM) can be used for the day-to-day work of the valuer.

scholarly journals LEASING SERVICES MARKET IN UKRAINE: STATUS AND TRENDS

2019 ◽  
pp. 85-97
Author(s):  
O.Yu. Miroshnychenko ◽  
N.O. Mykhailova ◽  
K.O. Yaschuk
Keyword(s):  
Real Estate ◽  
Dynamic Structure ◽  
Financial Leasing ◽  
Existing Problems ◽  
Effective Functioning ◽  
Status And Trends ◽  
The Status ◽  
The Cost ◽  
Further Development ◽  
Legal Entities

The status, dynamic, structure of sources of financing of leasing operations and tendencies of the development of the leasing services market in Ukraine in 2014-2018 have been considered. Today, financial leasing services are predominantly provided by financial companies, unlike in 2014, its share in the leasing market is 77.03%. The dynamics of the value and quantity of financial leasing agreements concluded by legal entities and financial companies have been considered. During the period under review, the largest value and number of financial leasing contracts were concluded by the legal entities and their value has increased in three times. The structure of the cost of financial leasing agreements by industry has been analyzed. The transport, agriculture, construction and services sectors are found to be the largest users of financial leasing services. The distribution of the value of the financial leasing contracts by its terms has been characterized and the agreements with the term of validity from 2 to 5 years are established. The structure of sources of financing of leasing operations has been investigated. It is established that their structure is dominated by equity of companies. The perspective directions of use of leasing in Ukraine have been considered. Industries such as healthcare, real estate, tourism and IT have been found to be the most promising for the leasing market. The problems that hinder the effective functioning of the leasing market in Ukraine and hinder its further development have been identified. The measures that should be taken to overcome existing problems and ensure the further development of leasing activities in Ukraine have been suggested.

scholarly journals Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach

2019 ◽  
Vol 11 (18) ◽  
pp. 4896 ◽  
Author(s):  
Morano ◽  
Rosato ◽  
Tajani ◽  
Manganelli ◽  
Liddo
Keyword(s):  
Urban Planning ◽  
Functional Form ◽  
Property Market ◽  
Market Models ◽  
Italian Study ◽  
The Public ◽  
Valuation Model ◽  
Residential Units ◽  
Automated Valuation

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016–2017. The ability to generate a “unique” functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies.

scholarly journals Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches

2020 ◽  
Vol 28 (4) ◽  
pp. 532-551
Author(s):  
Blake Miller ◽  
Fridolin Linder ◽  
Walter R. Mebane
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Random Sampling ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Simulation Studies ◽  
Text Data ◽  
Passive Learning ◽  
Machine Learning Model ◽  
The Cost

Supervised machine learning methods are increasingly employed in political science. Such models require costly manual labeling of documents. In this paper, we introduce active learning, a framework in which data to be labeled by human coders are not chosen at random but rather targeted in such a way that the required amount of data to train a machine learning model can be minimized. We study the benefits of active learning using text data examples. We perform simulation studies that illustrate conditions where active learning can reduce the cost of labeling text data. We perform these simulations on three corpora that vary in size, document length, and domain. We find that in cases where the document class of interest is not balanced, researchers can label a fraction of the documents one would need using random sampling (or “passive” learning) to achieve equally performing classifiers. We further investigate how varying levels of intercoder reliability affect the active learning procedures and find that even with low reliability, active learning performs more efficiently than does random sampling.

scholarly journals Deep Learning for Recommender Systems: A Netflix Case Study

AI Magazine ◽  
2022 ◽  
Vol 42 (3) ◽  
pp. 7-18
Author(s):  
Harald Steck ◽  
Linas Baltrunas ◽  
Ehtsham Elahi ◽  
Dawen Liang ◽  
Yves Raimond ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recommender Systems ◽  
Input Data ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Methods ◽  
Recommendation Algorithms

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks. Even though many deep-learning models can be understood as extensions of existing (simple) recommendation algorithms, we initially did not observe significant improvements in performance over well-tuned non-deep-learning approaches. Only when we added numerous features of heterogeneous types to the input data, deep-learning models did start to shine in our setting. We also observed that deep-learning methods can exacerbate the problem of offline–online metric (mis-)alignment. After addressing these challenges, deep learning has ultimately resulted in large improvements to our recommendations as measured by both offline and online metrics. On the practical side, integrating deep-learning toolboxes in our system has made it faster and easier to implement and experiment with both deep-learning and non-deep-learning approaches for various recommendation tasks. We conclude this article by summarizing our take-aways that may generalize to other applications beyond Netflix.

scholarly journals Studying the Effect of Taking Statins before Infection in the Severity Reduction of COVID-19 with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Alireza Davoudi ◽  
Mohsen Ahmadi ◽  
Abbas Sharifi ◽  
Roshina Hassantabar ◽  
Narges Najafi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diastolic Pressure ◽  
Systolic Pressure ◽  
Illness Severity ◽  
Support Vector ◽  
Learning Approaches ◽  
Angiotensin Converting Enzyme 2 ◽  
Decision Tree Method ◽  
The Impact

Statins can help COVID-19 patients’ treatment because of their involvement in angiotensin-converting enzyme-2. The main objective of this study is to evaluate the impact of statins on COVID-19 severity for people who have been taking statins before COVID-19 infection. The examined research patients include people that had taken three types of statins consisting of Atorvastatin, Simvastatin, and Rosuvastatin. The case study includes 561 patients admitted to the Razi Hospital in Ghaemshahr, Iran, during February and March 2020. The illness severity was encoded based on the respiratory rate, oxygen saturation, systolic pressure, and diastolic pressure in five categories: mild, medium, severe, critical, and death. Since 69.23% of participants were in mild severity condition, the results showed the positive effect of Simvastatin on COVID-19 severity for people that take Simvastatin before being infected by the COVID-19 virus. Also, systolic pressure for this case study is 137.31, which is higher than that of the total patients. Another result of this study is that Simvastatin takers have an average of 95.77 mmHg O2Sat; however, the O2Sat is 92.42, which is medium severity for evaluating the entire case study. In the rest of this paper, we used machine learning approaches to diagnose COVID-19 patients’ severity based on clinical features. Results indicated that the decision tree method could predict patients’ illness severity with 87.9% accuracy. Other methods, including the K -nearest neighbors (KNN) algorithm, support vector machine (SVM), Naïve Bayes classifier, and discriminant analysis, showed accuracy levels of 80%, 68.8%, 61.1%, and 85.1%, respectively.

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