scholarly journals International Real Estate Review

2012 ◽  
Vol 15 (3) ◽  
pp. 325-346
Author(s):  
Chu-Chia Lin ◽  
◽  
Chien-Liang Chen ◽  
Ya-Chien Twu ◽  
◽  
...  
Keyword(s):  
Real Estate ◽  
Negative Impact ◽  
Daily Life ◽  
Housing Prices ◽  
Chinese Society ◽  
Feng Shui ◽  
Data Set ◽  
Body Of Knowledge ◽  
The Impact ◽  
Housing Units

Feng-shui is an old and traditional body of knowledge in Chinese society. Feng-shui has a significant influence on many aspects in daily life for most Chinese, including choosing locations for dwelling units, offices, burial sites, and so on. However, there have been few studies on the impact of feng-shui on housing prices. By applying a housing hedonic equation and a data set of 77,624 observations in Taiwan, we have attempted to estimate the impact of feng-shui on housing prices. We find that all six types of bad feng-shui have a significantly negative impact on housing prices. Moreover, by applying a quantile regression, we find that most of the bad feng-shui has a stronger negative impact on expensive dwelling units. Our findings confirm that people who buy expensive housing units care about feng-shui more than those who buy less expensive housing units.

Driving forces for the US residential housing price: a predictive analysis

2019 ◽  
Vol 9 (4) ◽  
pp. 515-529
Author(s):  
Amirhosein Jafari ◽  
Reza Akhavian
Keyword(s):  
Real Estate ◽  
Predictive Models ◽  
Driving Forces ◽  
Housing Prices ◽  
Housing Price ◽  
Data Set ◽  
Content Type ◽  
The Usa ◽  
Decision Making Processes ◽  
The Impact

Purpose The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes. Design/methodology/approach A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity. Findings Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics. Research limitations/implications An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation. Practical implications The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors. Social implications Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices. Originality/value Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.

scholarly journals International Real Estate Review

2007 ◽  
Vol 10 (2) ◽  
pp. 113-130
Author(s):  
Benoit Julien ◽  
◽  
Paul Lanoie ◽  
Keyword(s):  
Real Estate ◽  
Negative Impact ◽  
House Prices ◽  
Real Estate Market ◽  
Data Set ◽  
Long Run ◽  
Before And After ◽  
Short Run ◽  
Noise Barrier ◽  
The Impact

This paper provides the first study on the impact of noise barriers on the price of adjacent houses based on a repeat sale analysis (RSA). RSA allows us to empirically examine the differential between the prices of houses sold before and after an event that may have affected their value, and after other relevant variables such as the evolution of the real estate market and major renovations performed on the house are controlled. This paper focuses on the neighborhood of Laval, a suburb of Montreal, where a large noise barrier was built in 1990 along a highway. The data set contains transaction information on 134 houses that were sold at least twice from 1980–2000. The empirical result will show that the noise barrier induced a decrease of 6% in the house prices in our sample in the short run, while it had a stronger negative impact of 11% in the long run.

scholarly journals Empirical Testing of the Impact of Gender and Marital Status on the Price and Trend of Urban Real Estate – Evidence from Provincial Panel Data of China

2018 ◽  
Vol 10 (7) ◽  
pp. 38
Author(s):  
Min Tan ◽  
Yajie Bai
Keyword(s):  
Panel Data ◽  
Real Estate ◽  
Marital Status ◽  
Negative Impact ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Empirical Results ◽  
Real Estate Price ◽  
The Impact

This paper investigates the impact of demographic structure, especially gender and marital status, on the price of regional real estate. This paper utilizes controlled-heteroskedasticity fixed-effect model for the empirical tests based on a panel data set of 30 Chinese provinces from 2011 to 2015. Empirical results show that the gender ratio in the provincial panel data does have a significant negative impact on the regional real estate prices, which implies that when the number of women in a region increases, the real estate price in this region tends to rise. The impact of marital status on the real estate price is not significant according to empirical results.

Has tourism driven house prices in Germany? Time-varying evidence since 1870

2021 ◽  
pp. 135481662110088
Author(s):  
Sefa Awaworyi Churchill ◽  
John Inekwe ◽  
Kris Ivanovski
Keyword(s):  
World War Ii ◽  
Historical Data ◽  
House Prices ◽  
Housing Prices ◽  
Time Varying ◽  
Data Set ◽  
Time Series Modelling ◽  
The Impact ◽  
Tourism Flows ◽  
Non Parametric

Using a historical data set and recent advances in non-parametric time series modelling, we investigate the nexus between tourism flows and house prices in Germany over nearly 150 years. We use time-varying non-parametric techniques given that historical data tend to exhibit abrupt changes and other forms of non-linearities. Our findings show evidence of a time-varying effect of tourism flows on house prices, although with mixed effects. The pre-World War II time-varying estimates of tourism show both positive and negative effects on house prices. While changes in tourism flows contribute to increasing housing prices over the post-1950 period, this is short-lived, and the effect declines until the mid-1990s. However, we find a positive and significant relationship after 2000, where the impact of tourism on house prices becomes more pronounced in recent years.

The Psychological and Financial Impact of Cybercrime Victimization: A Novel Application of the Shattered Assumptions Theory

2021 ◽  
pp. 089443932098382
Author(s):  
Jildau Borwell ◽  
Jurjen Jansen ◽  
Wouter Stol
Keyword(s):  
Negative Impact ◽  
Secondary Analysis ◽  
Well Being ◽  
Policy Implications ◽  
Financial Impact ◽  
Financial Loss ◽  
Data Set ◽  
Loss Compensation ◽  
Shattered Assumptions ◽  
The Impact

While criminality is digitizing, a theory-based understanding of the impact of cybercrime on victims is lacking. Therefore, this study addresses the psychological and financial impact of cybercrime on victims, applying the shattered assumptions theory (SAT) to predict that impact. A secondary analysis was performed on a representative data set of Dutch citizens ( N = 33,702), exploring the psychological and financial impact for different groups of cybercrime victims. The results showed a higher negative impact on emotional well-being for victims of person-centered cybercrime, victims for whom the offender was an acquaintance, and victims whose financial loss was not compensated and a lower negative impact on emotional well-being for victims with a higher income. The study led to novel scientific insights and showed the applicability of the SAT for developing hypotheses about cybercrime victimization impact. In this study, most hypotheses had to be rejected, leading to the conclusion that more work has to be done to test the applicability of the SAT in the field of cybercrime. Furthermore, policy implications were identified considering the prioritization of and approach to specific cybercrimes, treatment of victims, and financial loss compensation.

scholarly journals Impact of Working Capital Management on Corporate Profitability- Empirical Evidence from Pharmaceutical Industry of Bangladesh

2018 ◽  
Vol 10 (9) ◽  
pp. 136
Author(s):  
Rakibul Islam ◽  
Mohammad Emdad Hossain ◽  
Mohammad Nazmul Hoq ◽  
Md. Morshedul Alam
Keyword(s):  
Regression Analysis ◽  
Correlation Matrix ◽  
Negative Impact ◽  
Pearson Correlation ◽  
Capital Management ◽  
Working Capital ◽  
Data Set ◽  
Working Capital Management ◽  
Pharmaceutical Firms ◽  
The Impact

Working capital management plays centric role in enhancing operational efficiency and their ultimate profitability. Globally financial managers have been searching the proper way on how to utilize working capital components which prolong profitability. The purpose of this study is to assess the impact of working capital components on profitability indicators of selected pharmaceutical firms in Bangladesh. The paper used financial data of 9 pharmaceutical firms listed in Dhaka stock exchange (DSE) covered 2011-2015. Two methods were used in this study for analysis data set. Firstly, to measure the relationship between selected variables Pearson Correlation matrix was used. Secondly, multiple regression analysis was used to investigate the impact working capital components on profitability of selected pharmaceutical firms. The study also conducted Durbin Watson test to assess autocorrelation of selected variables. In this study the correlation matrix identified a negative correlation between working capital components and profitability, whereas regression analysis found number of days account receivable (AR) had significant positive and current ratio (CR) and debt ratio (DR) had appeared a significant negative impact on profitability.

The Impacts of Traffic Noise on House Renting and Selling Prices Concerning Residential Areas Case of Sulaimaniyah City Center

2021 ◽  
Author(s):  
Ayoub Smaqaey ◽  
◽  
Mohammed AbdulKareem ◽  
Meryem Komşu ◽  
◽  
...  
Keyword(s):  
Real Estate ◽  
High Efficiency ◽  
Negative Impact ◽  
Traffic Noise ◽  
Noise Pollution ◽  
Real Estate Market ◽  
Residential Areas ◽  
City Center ◽  
The Government ◽  
The Impact

The purposes of this research are to examine the impact of traffic noise on the sale and rent prices of the housing real estate in the Sulaimaniyah city center. Besides, highlight the concept of traffic noise pollution in general and in particular in the Sulaimaniyah city center. Thus, people have the right to choose the nature of the acoustic environment, as others should not impose it, the problem of traffic noise considered as one of the main problems that have imposed on the people in Sulaimaniyah city center. Which began to take severe economic and social dimensions, affects the decision-making process in the real estate market. Moreover, consequently, this research analyzes the impact of traffic noise pollution in the sale and rent prices of residential property in Sulaimaniyah city center, the results of the research have confirmed a clear and negative impact the traffic noise on residential real estate prices in Sulaimaniyah city center. Finally, the research indorsed range of important recommendations, such as necessity control the noise pollution at the level of governments and companies, either at the companies’ level by choosing vehicles that release less sound and the use of sound control devices of high efficiency. Either at the government level to determine the volume level or prevent annoying noises (painful), through legislation and laws of environmental protection and impose fees and raise awareness.

House price index (HPI) and Covid-19 pandemic shocks: evidence from Turkey and Kazakhstan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yeşim Aliefendioğlu ◽  
Harun Tanrivermis ◽  
Monsurat Ayojimi Salami
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Housing Prices ◽  
House Price ◽  
Monthly Basis ◽  
Data Set ◽  
House Price Index ◽  
Content Type ◽  
Asymmetric Pricing ◽  
Long Run

Purpose This paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan. Design/methodology/approach Monthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis. Findings The findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets. Research limitations/implications The main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices; however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study. Practical implications Despite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision. Originality/value This is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.

The Impact of Social Networking Websites on the Education of Youth

2011 ◽  
pp. 1880-1892
Author(s):  
Sunitha Kuppuswamy ◽  
P. B. Shankar Narayan
Keyword(s):  
Young People ◽  
Social Networking ◽  
Social Networking Sites ◽  
Negative Impact ◽  
Daily Life ◽  
Social Networking Websites ◽  
The Impact ◽  
Pedagogical Principles

Social networking websites like Orkut, Facebook, Myspace and Youtube are becoming more and more popular and has become part of daily life for an increasing number of people. Because of their features, young people are attracted to social networking sites. In this paper, the authors explore the impact of social networking sites on the education of youth. The study argues that these social networking websites distract students from their studies, but these websites can be useful for education based on sound pedagogical principles and proper supervision by the teachers. Moreover, the research concludes that social networking websites have both positive as well as negative impact on the education of youth, depending on one’s interest to use it in a positive manner for his or her education and vice versa.

scholarly journals Tourism under the Early Phase of COVID-19 in Four APEC Economies: An Estimation with Special Focus on SARS Experiences

2020 ◽  
Vol 17 (20) ◽  
pp. 7543
Author(s):  
Bao-Linh Tran ◽  
Chi-Chung Chen ◽  
Wei-Chun Tseng ◽  
Shu-Yi Liao
Keyword(s):  
Hong Kong ◽  
New Zealand ◽  
Fixed Effects ◽  
Negative Impact ◽  
Asia Pacific ◽  
International Tourism ◽  
Special Focus ◽  
Tourism Demand ◽  
Data Set ◽  
The Impact

This study examines how experience of severe acute respiratory syndrome (SARS) influences the impact of coronavirus disease (COVID-19) on international tourism demand for four Asia-Pacific Economic Cooperation (APEC) economies, Taiwan, Hong Kong, Thailand, and New Zealand, over the 1 January–30 April 2020 period. To proceed, panel regression models are first applied with a time-lag effect to estimate the general effects of COVID-19 on daily tourist arrivals. In turn, the data set is decomposed into two nation groups and fixed effects models are employed for addressing the comparison of the pandemic-tourism relationship between economies with and without experiences of the SARS epidemic. Specifically, Taiwan and Hong Kong are grouped as economies with SARS experiences, while Thailand and New Zealand are grouped as countries without experiences of SARS. The estimation result indicates that the number of confirmed COVID-19 cases has a significant negative impact on tourism demand, in which a 1% COVID-19 case increase causes a 0.075% decline in tourist arrivals, which is a decline of approximately 110 arrivals for every additional person infected by the coronavirus. The negative impact of COVID-19 on tourist arrivals for Thailand and New Zealand is found much stronger than for Taiwan and Hong Kong. In particular, the number of tourist arrivals to Taiwan and Hong Kong decreased by 0.034% in response to a 1% increase in COVID-19 confirmed cases, while in Thailand and New Zealand, a 1% national confirmed cases increase caused a 0.103% reduction in tourism demand. Moreover, the effect of the number of domestic cases on international tourism is found lower than the effect caused by global COVID-19 mortality for the economies with SARS experiences. In contrast, tourist arrivals are majorly affected by the number of confirmed COVID-19 cases in Thailand and New Zealand. Finally, travel restriction in all cases is found to be the most influencing factor for the number of tourist arrivals. Besides contributing to the existing literature focusing on the knowledge regarding the nexus between tourism and COVID-19, the paper’s findings also highlight the importance of risk perception and the need of transmission prevention and control of the epidemic for the tourism sector.

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