The Smart City Evolution in South Korea: Findings from Big Data Analytics

Choi, Choongik; Choi, Jong-Soo; KIM, Chulmin; LEE, Dongkwan

doi:10.13106/jafeb.2019.vol6.no4.179

Cited by 16 publications

(15 citation statements)

References 49 publications

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“…The results show that BEDROOMS positively affects PRICE. This is consistent with the findings of Kain and Quigley (1970), Cebula (2009), Selim (2009), Baek et al (2019). Accordingly, houses with more bedrooms suit families with many members.…”

Section: -The Impact Of the Number Of Bedrooms (Bedrooms) On House Price (Price)supporting

confidence: 92%

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The Hedonic Pricing Model Applied to the Housing Market

Nguyen¹

2020

IJEBA

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Purpose:The article applies the hedonic pricing model to estimate house price in the housing market of Vietnam, which is a country with a fledgeling housing market, so the study is expected to bring interesting findings. Design/Methodology/Approach: By applying the hedonic pricing model, most of the previous studies have reported that house price is significantly influenced by the characteristics of the house itself, its location and surrounding facilities. Based on this, the article adopts the Ordinary Least Squares (OLS) regression in combination with robustness statistics in the model estimation, so the estimated results on house price are reliable and able to be widely applied. Data are collected through the survey into housing projects in Ho Chi Minh and Ha Noi city, which are the two largest cities in Vietnam. Findings: The findings reveal that the hedonic pricing model can be applied to estimate house price in Vietnam's housing market. This can be said to be a big success in giving first empirical evidence in Vietnam on this matter. Specifically, house price is negatively affected by its proximity to the city center. Also, factors including house size, house type, house structure, number of bedrooms, amenities around the house exert a positive influence on the price. Practical Implications: The estimated results are typical and reliable, which can be applied universally. Originality/Value: The study confirms the hedonic pricing model can be well-applied to accurately estimate the price of houses in Vietnam. More than that, the findings are also essential for other countries, especially those with a nascent housing market like Vietnam.

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Section: -The Impact Of the Number Of Bedrooms (Bedrooms) On House Price (Price)supporting

confidence: 92%

“…Therefore, the number of bedrooms positively affects the house's value. This result is also in line with what have been found by Kain and Quigley (1970), Cebula (2009), Selim (2009), Baek et al (2019). Hence, the research hypothesis is suggested as follows:…”

Section: The Impact Of the Number Of Bedrooms On House Pricesupporting

confidence: 91%

The Hedonic Pricing Model Applied to the Housing Market

Nguyen¹

2020

IJEBA

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“…Model 1 has a statistical significance of 1%, in line with the initial expectations of the study. The more toilets and bedrooms the apartment has, the higher the price of that apartment in the same project (applicable to all segments from mainstream to high-end) according to Melichar et al (2009) and Baek et al (2019). This is one of the essential factors or one of the criteria when choosing an apartment, along with other elements like kitchen, balcony, and laundry.…”

Section: Variable "Wcbr" (Number Of Toilets and Bedrooms)mentioning

confidence: 99%

The Hedonic Method in Evaluating Apartment Price: A Case of Ho Chi Minh City, Vietnam

Nguyen¹,

Phan²,

Tran³

et al. 2020

JAFEB

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The study examines factors affecting apartment prices in the real estate market of Ho Chi Minh City, Vietnam. The study uses primary data based on surveys of customers who have traded successfully, and collects transaction data from real estate trading companies that are the top investors in Ho Chi Minh City real estate market. The collected data include 384 observations in a total of 24 districts, detailing that each district surveyed on a minimum of four projects, each project carried out a survey on a minimum of four apartments. The survey collected 339 valid questionnaires for analysis and model testing. This study employs multivariate regression with the data of 339 observations. The research results reveal that five significant factors affect positively the price of apartments in Ho Chi Minh City-apartment area, toilet and bedroom, apartment floor, reference price, and apartment interior. Besides, there are three significant factors affecting negatively the price of apartments-next price trend, distance to city center, and potential building. From the results, the research proposes solutions in the pricing of apartments in the real estate market in Ho Chi Minh City-better information system, a real estate transaction index, and stricter management of small brokerage activities.

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“…As diverse predictive techniques improve the accuracy of the prediction rate, the use of predictive techniques through data mining has been on the rise in various fields with different perspectives. Data mining is the process of extracting data from a huge volume of data set from various different sources and transforming it into a recognizable patterns and useful information (Choi, Choi, Kim, & Lee, 2019;Park & Alenezi, 2018). Accordingly, the number of studies involved is increasing sharply, in particular, in the field of Business, Medical science, Politics, and Computer science as well (Abramowitz, 1988;Amigobulls, 2015;Fishbein, Azjen, & Hinkle, 1980;Franch, 2013;Lewis-Beck & Rice, 1984;Nadeau, Lewis-Beck, & Belanger, 2009;Park & Alenezi, 2018;Park, 2019;Whiteley, 2005;Whiteley, Sanders, Steward, & Clarke, 2011).…”

Section: Predictive Data Mining With Social Mediamentioning

confidence: 99%

Insights Discovery through Hidden Sentiment in Big Data: Evidence from Saudi Arabia’s Financial Sector

Park¹,

Javed²

2020

JAFEB

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This study aims to recognize customers' real sentiment and then discover the data-driven insights for strategic decision-making in the financial sector of Saudi Arabia. The data was collected from the social media (Facebook and Twitter) from start till October 2018 in financial companies (NCB, Al Rajhi, and Bupa) selected in the Kingdom of Saudi Arabia according to criteria. Then, it was analyzed using a sentiment analysis, one of data mining techniques. All three companies have similar likes and followers as they serve customers as B2B and B2C companies. In addition, for Al Rajhi no negative sentiment was detected in English posts, while it can be seen that Internet penetration of both banks are higher than BUPA, rarely mentioned in few hours. This study helps to predict the overall popularity as well as the perception or real mood of people by identifying the positive and negative feelings or emotions behind customers' social media posts or messages. This research presents meaningful insights in data-driven approaches using a specific data mining technique as a tool for corporate decision-making and forecasting. Understanding what the key issues are from customers' perspective, it becomes possible to develop a better data-based global strategies to create a sustainable competitive advantage.

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The Smart City Evolution in South Korea: Findings from Big Data Analytics

Cited by 16 publications

References 49 publications

The Hedonic Pricing Model Applied to the Housing Market

The Hedonic Pricing Model Applied to the Housing Market

The Hedonic Method in Evaluating Apartment Price: A Case of Ho Chi Minh City, Vietnam

Insights Discovery through Hidden Sentiment in Big Data: Evidence from Saudi Arabia’s Financial Sector

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