186
Ö. Akay et al.
Nunes et al. (2009) studied the profitability determinants of Portuguese service
industries based on various panel models.
Davis et al. (2017) stated that households holding an FHA mortgage increased the
value of the housing they purchased by approximately 2.5% using the financing of
Fannie Mae and Freddie Mac. After converting the premium discount to an equivalent
decrease in the mortgage rate, conditional on purchasing a home with roughly 3.4
estimates means semi-elasticity of the value of the housing purchased on the mortgage
rate.
Nguyen and Nordman (2018) shaded light on the links between households’ and
entrepreneurs’ social networks and business performance by using a unique panel of
household businesses for Vietnam.
Do et al. (2019) examined the determinants of livestock assets with panel data
from Vietnam. They suggested that authorising rural households to better cope with
shocks contributes to reducing rural poverty and to developing livestock. Ahmad et al.
(2018) investigated the determinants of housing demand in urban areas of Pakistan.
Empirical analysis was performed using the 2004–05 and 2010–11 Pakistan Social
and Standard of Living Measurement (PSLM) survey. The hedonic price model was
used to estimate housing prices. Heckman’s two-stage selection procedure is used
to control the bias of selectivity between duty term selection and the amount of
housing services requested. Empirical analysis shows that house prices and revenues
(temporary and permanent) play an important role in determining the demand for
housing units.
Huarng et al. (2019) analyzed housing demand by using Google Trends’ big data as
a proxy. They use to estimate a qualitative method (fuzzy set/ Qualitative Comparative
Analysis, fsQCA) instead of a quantitative method. According to empirical results,
although the size of the data set is small, fsQCA successfully predicts seasonal time
series.
Zheng et al. (2018) estimated the income elasticity of demand for private rental
housing using micro data between 1996 and 2011 from four waves of four Hong Kong
census data. In order to isolate permanent and temporary income at the household
level they adopted a permanent income model. They used the Heckman two-step
procedure to correct selection bias and used the quantile regression (QR) approach
to investigate the heterogeneity of demand elasticities between different levels of
housing expenditure. Empirical results show that permanent income elasticities fall
within the range of 0.536–0.698 and that temporary income shock has a positive and
significant impact on rental housing demand.
Liu (2019) examined the theoretical relationship between income and home prices
by using the user cost equilibrium condition. Empirically, the short-term and longterm dynamics of this relationship studied from 1991 to 2015 for 25 years in the state
of New South Wales, Australia, using data for 144 Local Government Areas (LGAs).
He estimated to be 1.07 the income elasticity of housing prices for the government
with multi-factor panel data models and cointegration analysis.
Çelik and Kiral (2018a) applied balanced and unbalanced panel data analysis
and clustering analysis methods to factors that affect housing sales in provinces of
Turkey and examined significant variables by hierarchical clustering method. They
Ö. Akay et al.
Nunes et al. (2009) studied the profitability determinants of Portuguese service
industries based on various panel models.
Davis et al. (2017) stated that households holding an FHA mortgage increased the
value of the housing they purchased by approximately 2.5% using the financing of
Fannie Mae and Freddie Mac. After converting the premium discount to an equivalent
decrease in the mortgage rate, conditional on purchasing a home with roughly 3.4
estimates means semi-elasticity of the value of the housing purchased on the mortgage
rate.
Nguyen and Nordman (2018) shaded light on the links between households’ and
entrepreneurs’ social networks and business performance by using a unique panel of
household businesses for Vietnam.
Do et al. (2019) examined the determinants of livestock assets with panel data
from Vietnam. They suggested that authorising rural households to better cope with
shocks contributes to reducing rural poverty and to developing livestock. Ahmad et al.
(2018) investigated the determinants of housing demand in urban areas of Pakistan.
Empirical analysis was performed using the 2004–05 and 2010–11 Pakistan Social
and Standard of Living Measurement (PSLM) survey. The hedonic price model was
used to estimate housing prices. Heckman’s two-stage selection procedure is used
to control the bias of selectivity between duty term selection and the amount of
housing services requested. Empirical analysis shows that house prices and revenues
(temporary and permanent) play an important role in determining the demand for
housing units.
Huarng et al. (2019) analyzed housing demand by using Google Trends’ big data as
a proxy. They use to estimate a qualitative method (fuzzy set/ Qualitative Comparative
Analysis, fsQCA) instead of a quantitative method. According to empirical results,
although the size of the data set is small, fsQCA successfully predicts seasonal time
series.
Zheng et al. (2018) estimated the income elasticity of demand for private rental
housing using micro data between 1996 and 2011 from four waves of four Hong Kong
census data. In order to isolate permanent and temporary income at the household
level they adopted a permanent income model. They used the Heckman two-step
procedure to correct selection bias and used the quantile regression (QR) approach
to investigate the heterogeneity of demand elasticities between different levels of
housing expenditure. Empirical results show that permanent income elasticities fall
within the range of 0.536–0.698 and that temporary income shock has a positive and
significant impact on rental housing demand.
Liu (2019) examined the theoretical relationship between income and home prices
by using the user cost equilibrium condition. Empirically, the short-term and longterm dynamics of this relationship studied from 1991 to 2015 for 25 years in the state
of New South Wales, Australia, using data for 144 Local Government Areas (LGAs).
He estimated to be 1.07 the income elasticity of housing prices for the government
with multi-factor panel data models and cointegration analysis.
Çelik and Kiral (2018a) applied balanced and unbalanced panel data analysis
and clustering analysis methods to factors that affect housing sales in provinces of
Turkey and examined significant variables by hierarchical clustering method. They