Default Prediction of Commercial Real Estate Properties Using Machine Learning Techniques

2019 ◽  
Vol 45 (7) ◽  
pp. 55-67
Author(s):  
Chad Cowden ◽  
Frank J. Fabozzi ◽  
Abdolreza Nazemi
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Commercial Real Estate ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Default Prediction

scholarly journals Estimating Warehouse Rental Price using Machine Learning Techniques

2018 ◽  
Vol 13 (2) ◽  
pp. 235-250 ◽  
Author(s):  
Yixuan Ma ◽  
Zhenji Zhang ◽  
Alexander Ihler ◽  
Baoxiang Pan
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Estate ◽  
Rapid Development ◽  
Supply And Demand ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Logistics Industry ◽  
Real Estate Price ◽  
Learning Techniques

Boosted by the growing logistics industry and digital transformation, the sharing warehouse market is undergoing a rapid development. Both supply and demand sides in the warehouse rental business are faced with market perturbations brought by unprecedented peer competitions and information transparency. A key question faced by the participants is how to price warehouses in the open market. To understand the pricing mechanism, we built a real world warehouse dataset using data collected from the classified advertisements websites. Based on the dataset, we applied machine learning techniques to relate warehouse price with its relevant features, such as warehouse size, location and nearby real estate price. Four candidate models are used here: Linear Regression, Regression Tree, Random Forest Regression and Gradient Boosting Regression Trees. The case study in the Beijing area shows that warehouse rent is closely related to its location and land price. Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation. Additionally, tree models have better performance than the linear model, with the best model (Random Forest) achieving correlation coefficient of 0.57 in the test set. Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size.

scholarly journals Machine Learning Centric Product Endorsement on Flipkart Database

2019 ◽  
Vol 8 (6) ◽  
pp. 2750-2753
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Product Reviews ◽  
Customer Information ◽  
Learning Techniques

The growing need for individual mining information from text leads to analyzing sentiment and viewpoints. Retailers, E-commerce companies, product companies, media houses, real estate firms, and whom all have recognized that sentiment analysis is the key to success. They perform sentiment analysis necessary to get customer information related to feelings; attitudes, reactions, and opinions of existing and potential buyers towards their product or services. In this context, evaluating an individual's viewpoint or humor from a piece of text is challenging. In recent years the need for this analysis has increased due to the benefits obtained from it. In this paper, we conduct sentiment analysis on Flipkart product reviews using machine learning techniques to address the above challenge.

MACHINE LEARNING FOR PROPERTY PRICE PREDICTION AND PRICE VALUATION: A SYSTEMATIC LITERATURE REVIEW

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Nur Shahirah Ja’afar ◽  
Junainah Mohamad ◽  
Suriatini Ismail
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Statistical Tool ◽  
The Real Estate ◽  
Price Prediction ◽  
Learning Techniques ◽  
Property Price ◽  
Future Data

Machine learning is a branch of artificial intelligence that allows software applications to be more accurate in its data predicting, as well as to predict current performance and improve for future data. This study reviews published articles with the application of machine learning techniques for price prediction and valuation. Authors seek to explore optimal solutions in predicting the property price indices, that will be beneficial to the policymakers in assessing the overall economic situation. This study also looks into the use of machine learning in property valuation towards identifying the best model in predicting property values based on its characteristics such as location, land size, number of rooms and others. A systematic review was conducted to review previous studies that imposed machine learning as statistical tool in predicting and valuing property prices. Articles on real estate price prediction and price valuation using machine learning techniques were observed using electronics database. The finding shows the increasing use of this method in the real estate field. The most successful machine learning algorithms used is the Random Forest that has better compatibility to the data situation.

scholarly journals Machine learning predictivity applied to consumer creditworthiness

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Maisa Cardoso Aniceto ◽  
Flavio Barboza ◽  
Herbert Kimura
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Credit Risk ◽  
Performance Metrics ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Default Prediction

AbstractCredit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower’s classification models using a Brazilian bank’s loan database, and exploring machine learning techniques. We develop Support Vector Machine, Decision Trees, Bagging, AdaBoost and Random Forest models, and compare their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are analyzed based on usual classification performance metrics. Our results show that Random Forest and Adaboost perform better when compared to other models. Moreover, Support Vector Machine models show poor performance using both linear and nonlinear kernels. Our findings suggest that there are value creating opportunities for banks to improve default prediction models by exploring machine learning techniques.

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