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Factors affecting housing prices in
Izmir, Turkey: a quantile
regression approach
Quantile
regression
approach
Onur Özsoy and Hasan S ahin
Department of Economics, Faculty of Political Science,
Ankara University, Ankara, Turkey
Received 10 November 2020
Revised 26 December 2020
Accepted 3 January 2021
Abstract
Purpose – The purpose of this paper is to investigate empirically the main factors that affect the house
prices in Izmir, Turkey using the quantile regression and ordinary least square approaches.
Design/methodology/approach – Sample data about the housing market for Izmir collected from the
web pages of various real estate agencies during June 2018. Following this, the quantile regression method is
used to estimate all possible effects of variables on each interested quantile to determine the factors that affect
house prices to guide the potential consumers, house developers, city planners and the policymakers in Izmir,
Turkey.
Findings – Results show that the age of the house, central heating and parking have no significant effect on
prices. The size of the house, the existence of an elevator, fire and security have a positive and significant
effect on prices. The number of rooms has lower values for high-priced houses, while the floor, the number of
balconies, air conditioning, proximity to schools have a higher value for high-priced houses. The number of
toilets, the number of bathrooms and the distance to the hospital have a lower value on the high-priced
housing. The value of the distance from the city center and the shopping center is almost uniform in all
quantiles and lowers the value of the higher-priced houses. With the exception of the value of the houses in the
10th percentile in Balcova district, the value of the houses in Konak, Balcova and Narlidere is lower prices in
Karsiyaka.
Originality/value – This is the first comprehensive research to determine the major factors that affect
house prices in Izmir. The second contribution of this paper is that it includes all possible variables and
accordingly derives adequate policy implications, which could be used both by the public housing authority
and private housing constructing companies in designing and implementing effective housing policies.
Keywords Housing, Real estate, Housing markets, Housing prices, Quantile regression,
Izmir/Turkey
Paper type Research paper
1. Introduction
The main goal of this paper is to empirically estimate and analyze the determinants that
have an impact on the prices of houses in Izmir, Turkey using the quantile regression
method and micro-data belong to all districts of Izmir.
The construction sector, especially the housing sector, has significant importance in the
economy of Turkey [1]. The housing sector together with the construction sector has a
stimulus impact on many other sectors [2] and has a multiplier effect on aggregate demand
in Turkey. This feature of the housing sector makes it to be one of the leading locomotive
sectors of the economy of Turkey. Due to rapid increase in population, high urbanization
rates, increase in income of average households, changes in the need for housing types over
the years, the expansion, ease and increase opportunities of credit channels, availability of
mortgages and low-interest rates on the credits for dwellings, the demand for housing in
International Journal of Housing
Markets and Analysis
© Emerald Publishing Limited
1753-8270
DOI 10.1108/IJHMA-11-2020-0133
IJHMA
Turkey has been increasing in recent years. Consequently, it is important to determine the
factors affecting housing prices to guide the potential consumers, house developers, city
planners and policymakers.
The housing market is quite different from other goods and services markets and
needs to be given special attention in searching for the factors affecting the decisions on
housing demand and housing supply. Additionally, the behaviors of housing market
actors are different from the behaviors of actors in other goods and services markets.
Housing expenditures have a very big share in the budget of average households/
individual consumers, it has the properties of both investment goods and consumption
goods, its location is fixed and the buyers of houses and houses are heterogeneous (Fallis,
1985). These features of housing show that each of the above-mentioned characteristics is
unique and that it is not so easy to determine what factors and to what extent these
factors exactly affect the price of houses with the classical hedonic regression method.
The classical hedonic regression model simply measures the impact of each feature of a
single housing unit on its average price. The studies on factors affecting real estate prices
started in the early 1920s and gained momentum after the 1960s. The pioneering work
affecting real estate prices was done by Hass in 1922 (Colwell and Dilmore, 1999). Using
the hedonic price model, Hass (1922) studied the effects of the variables of distance to the
city center and the size of the agricultural lands on the prices in Blue Earth County,
Minnesota. Wallace (1926) also used the hedonic price model to investigate the factors
affecting farmland prices in Iowa. Later on, factors affecting house prices were
investigated by using the hedonic price model (e.g. Lancaster, 1966; Ridker and Henning,
1967; Ball, 1973; Rosen, 1974; Borukhov et al., 1978; Goodman, 1978; Follain and
Malpezzi, 1981; Brown and Mendelsohn, 1984; Bover et al., 1989; Arimah, 1992; Malpezzi,
1998; Cheshire and Sheppard, 1998; Bover and Velilla, 2002; Mok et al., 1995; Song and
Shon, 2007; Gao et al., 2006; Gao and Asami, 2007). Hedonic price regression studies tend
to produce different results in terms of magnitude and direction of the effect of certain
characteristics of houses, which are heterogeneous in nature. For example, Sirmans et al.
(2005) surveyed more than 125 hedonic price model empirical studies and found out that
most of these studies differ in terms of magnitude and direction of impact of certain
characteristics of houses on their prices (Sirmans et al., 2005). Additionally, the hedonic
price regression model cannot determine which of the certain characteristics of a house
have more impact on the price category of a house (low to high) (Kim et al., 2015). In
contrast, it is possible to clearly determine how much of certain properties of a house
affect the price of the house in which the price is estimated by using the quantile
regression model (Sirmans et al., 2005). Consequently, the results obtained by using the
quantile regression model will be less confusing than the hedonic regression model
results and researchers could derive more robust policy implications based on the
quantile regression model results. Hedonic price models and quantile regression models
generally used cross-sectional data and survey data. This study also uses cross-sectional
data.
There are two major contributions of this paper to the housing economics literature. The
first contribution of this paper is that it is the first comprehensive research which is
conducted to determine the major factors that influence housing prices by using a quantile
regression model for Izmir, Turkey. The second contribution of this paper is that it considers
all possible variables and uses a quantile regression approach to determine the impact of
each factor separately to come up with adequate policy proposals, which could be used both
by the public housing authority and private housing constructing companies in designing
and implementing effective housing policies and develop better strategies for home buyers.
The organization of the remaining part of this paper is as follows: Section 2 provides an
overview of the housing markets in Izmir and Turkey. Section 3 contains the literature
review of quantile regression models used in housing economics. Section 4 includes the
econometric model and method. Section 5 explains the estimation procedure and Section 6
describes the data sources and evaluates the estimated results. Finally, Section 7 concludes
and derives policy implications for the major actors of the housing market specifically for
Izmir and for Turkey in general.
2. An overview of the housing market in Izmir and Turkey
Estimation of the main determinants of house prices in Izmir, which is the third-largest city
in Turkey with approximately 5 million inhabitants will help us to derive some policy
implications. These may be used by local and national public housing authorities and
private home construction companies in designing and implementing more effective
housing policies. Additionally, the outcome of this study may guide private home buyers in
making better decisions when purchasing a house.
Due to its nice climate, location and social structure, Izmir is included in the retirement
plans of many people and attracts the attention of a significant number of people including
foreigners with its very dynamic housing market in recent years. This situation increases the
demand for housing in Izmir causing an increase in housing prices in recent years. Although
recently, a number of luxury housing projects have been constructed in Izmir, the housing
price index figures show that the housing prices in Izmir are increasing faster than the other
large cities in Turkey. According to the data of the hedonic house price index (2017 = 100) of
the Central Bank of the Republic of Turkey (CBRT), the annual real price increase in housing
in Izmir in 2017 and 2018 is observed to be around 18.70% and 5.85%, respectively. The
reason why there was a decline in the increase in house prices between 2017 and 2018 is
mostly due to the decreasing economic growth rate of Turkey and increasing unemployment
rates, which has been much higher than the national average in Izmir in recent years.
Consequently, these features of the Izmir housing market itself make it worth evaluating in
detail. When the developments in the hedonic house price indexes of the three major cities in
Turkey are evaluated, in October of 2017, it is observed that the increase in house prices were
0.93, 0.82 and 2.61% in Istanbul, Ankara and Izmir, respectively. If the index values were
compared to the same month of the previous year, it is seen that the house prices were
increased by 6.29, 7.23 and 18.66% in Istanbul, Ankara and Izmir, respectively (CBRT, 2018).
Figure 1 indicates that the total number of house sales decreased between 2014 and 2019
(except for the years between 2016 and 2017), in three major cities and in Turkey. The main
reason for the declining number of house sales is the slowdown of the economy during the
mentioned time period. As illustrated in Figure 2, GDP growth rates have declined from
5.17% to 2.6% between 2014 and 2018. Furthermore, the unemployment rate has increased
slightly in Turkey. However, the unemployment rate remained at approximately 14% in
Izmir (TUIK, 2019), which was above the national average. Consequently, the number of
houses sold in Turkey has decreased between 2014 and 2018 and in Izmir.
Although there has been a decline in the number of houses sold on average, many
housing construction companies kept building new houses and investing in the housing
sector not only in major cities but also all over Turkey. Consequently, the housing sector in
Turkey in general and in Izmir, in particular, deserves to be studied in depth.
3. Literature review
There have been many studies conducted using the Quantile regression model to determine
the factors affecting housing prices. These studies were conducted in different cities in
Quantile
regression
approach
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different countries around the world. Unfortunately, the number of studies using the
quantile regression method conducted to determine the factors affecting housing prices for
the cities in Turkey is limited. To the best of our knowledge, there is one study for only
Istanbul and none for Izmir and other cities. Thus, this will be the first study conducted to
analyze the factors that affect the housing prices in Izmir using quantile regression.
As emphasized before, the hedonic regression model is a commonly used model in
studies on the factors affecting house prices. However, the hedonic regression model can
estimate the magnitude and direction of the coefficients of the same factors differently due to
the mainly locational and time differences and preference differences of buyers and sellers of
the houses. Nevertheless, the results of comprehensive literature review studies using the
hedonic regression model also show that different results are obtained for the same factors
in studies conducted in different regions and different cities and in different time periods. In
other words, in studies using the hedonic regression model, the results are specific to the
location of the house and the time periods covered and cannot be generalized (Sirmans et al.,
2005). Sirmans et al. (2005), in a detailed literature review study covering 125 articles, was
determined that the direction and size of the estimated coefficients of some important
residential properties were not as expected and/or statistically insignificant. For example,
while an inverse relationship is expected between the age of the house and its price, the age
of the building was included in the study in 78 of these 125 studies, 63 of them were found to
be negative, 7 of them were positive and 8 of them were statistically insignificant. In
1,600,000.00
1,400,000.00
1,200,000.00
Figure 1.
Total number of
house sales in the
three largest cities in
Turkey
1,000,000.00
Turkey
800,000.00
Istanbul
600,000.00
Ankara
400,000.00
Izmir
200,000.00
0.00
2014
2015
2016
2017
2018
2019
Source: Central Bank of Turkey
12.00
10.00
%
8.00
GDP Growth Rate
6.00
Unemployment Rate
4.00
2.00
Figure 2.
GDP growth rates
and unemployment
rates
0.00
2014
2015
2016
2017
2018
Source: Central Bank of Turkey and TUIK
addition, in 40 of these 125 studies, the number of rooms was used as a variable. In 21 of
these studies, the result was positive, as theoretically expected, while 9 of them were
negative and 10 of them were statistically insignificant. Although there are many reasons
why the magnitudes and directions of the estimated coefficients are different, the most
important of these is the heterogeneity of the housing market and home buyers. Different
home buyers in different markets value different factors differently (Liao and Wang, 2012).
This makes it difficult for estimated results to always come out in line with theoretical
expectations. For this reason, using the quantile regression model, which is one of the best
methods available to partially reveal these differences, will provide a more effective
estimation of the coefficients of the factors affecting house prices. In the quantile regression
model, it is possible to determine exactly to what extent which factor affects the price of the
house by revealing the differences between quantiles (from small to large). Thus, it will be
plausible to determine the effects of different features of the house on its price more precisely
and to produce more effective housing policies.
To the best of our knowledge and results of our literature survey, Gyourko and Tracy
(1999) were the first to make use of the quantile regression model in studies on the factors
affecting the price of housing in housing markets. In this study, constant-quality quantile
specific price indices were generated for house prices using the quantile regression model.
The authors compared the results that they obtained from this study with the housing price
index generated using ordinary least square (OLS) in the study conducted by Gyourko and
Linnerman (1993). According to the result of this study, which used the quantile regression
model, constant-quality house prices increased more during the 1980s in comparison to that
estimated by Gyourko and Linnerman’s (1993) study, which used the OLS model.
In later periods, similarly, using hedonic and quantile regression models, price indices
were created to determine reasons for variations in house prices. Coulson and McMillen
(2007) determined that the different physical properties of the dwelling show significant
variations between quantities. Similarly, Deng et al. (2012), McMillen (2014) and Zhang and
Yi (2017) generated a house price index using the quantile regression model and found that
different physical features of the house have a different effect on the price of the house. Deng
et al. (2012) used a quantile-specific dummy and generated a house price index for Singapore
between January 1995 and May 2010. They figured out that private residential house sales
prices increased faster for higher-priced houses compared to lower-priced houses in the
period of 1995–2010, this increase was particularly evident for the boom period of 1996 and
2005–2007 and the variance of the private residential house sales price increased
significantly in the boom period. McMillen (2014) used locally weighted quantile regressions
and analyzed house prices in Cook County, IL. He found out that the growth of lower-priced
homes was faster during the boom years between 2000 and 2001 and slower during the
years between 2006 and 2011 compared to the higher-priced homes. He pointed out that
using standard approaches to estimate the house prices oversimplify the fact that change in
house prices is a rich set of spatial and temporal variation. Similarly, Zhang and Yi (2017)
developed a housing price index using OLS and quantile regression models for micro-level
data belong to newly-built residential dwellings in Beijing between 2013 and 2015. They
determined that housing prices have increased significantly, many housing properties have
a different effect on the price distribution of the house and the magnitude of the change in
housing prices is different between the quantiles despite the fact that the house price indices
have a similar trend.
On the other hand, in some studies, using the hedonic regression model together with the
quantile regression model, cross-sectional changes in the price of the house were determined
(Zietz et al., 2008; Mak et al., 2010; Liao and Wang, 2012). Zietz et al. (2008) using spatial
Quantile
regression
approach
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quantile regressions across price categories use multiple-listing-service data belong to
Orem-Provo, UT, USA figured out that housing prices are affected by standard housing
attributes and some specific regional, geographic and neighborhood characteristics. Their
findings indicate that higher-priced homebuyers value certain attributes of houses such as
size and the number of bathrooms different from the lower-priced house purchasers.
Similarly, Mak et al. (2010) used the hedonic and quantile regression model together and
determined that buyers of higher-priced houses value the houses different from buyers of
lower-priced houses in Hong Kong. Moreover, they indicated that quantile effects were
observed even for a single housing unit. Additionally, Liao and Wang (2012) used a spatial
quantile regression model to investigate how implicit prices of housing attributes may vary
across the conditional distribution of house prices. They determined that there exist
significant price differences among the quantiles. In addition to this, they showed that
spatial dependence is strong in the upper and lower portion of the response distribution and
weak in the medium part. In another study, Nicodemo and Raya (2012) used a quantile
regression model to predict house prices between 2004 and 2007 in several Spanish cities. In
this study, it was concluded that the differences in housing prices were greater in low and
high percentiles, the coefficients of all independent variables best explained the reasons for
price differences and that huge price differences were observed between the cities of Spain,
Madrid, Valencia and Bilbao. In a study conducted for Istanbul using the survey data using
the quantile regression model, the age of the house, the presence of cable TV, security
presence, parking place, type of heating system, the size of the kitchen, the number of rooms
and bathrooms increased the price of the house; the facade of the house has been found to
negatively affect the price (Ebru and Eban, 2009). Furthermore, Baroni et al. (2013) analyzed
the market heterogeneity and the factors that affected Paris apartment prices between 2000
and 2006 with 159,000 house sales by using a quantile regression model. In this study,
variables such as the type of the house, the size of the house, the number of rooms, the
environmental characteristics and location dummy variables are used. According to
the findings, although the differences vary greatly according to the characteristics, the
relative hedonic price of many properties of the house shows significant differences between
the deciles. The floors, age and number of rooms where the house is located are among the
important factors affecting the price and their effects are determined differently. In another
study, which included three regions of Seoul, the capital of South Korea, Kangnam, Songpa
and Nowon, the quantile regression model used to determine the factors affecting housing
prices using auction data between 2006 and 2012, while the size of the house in all three
regions affected the house price positively, it has been found that the effect is greater in the
lower price range. In addition, it has been determined that the proximity of the house to the
metro and the landscape has a positive effect on the price of the house. It has also been found
that the landscape has a greater impact on medium- and high-priced quantities (Kim et al.,
2015). On the other hand, in the study conducted for Alicante, Spain, using hedonic price and
quantile regression model to determine the factors affecting the price in the second-hand
housing market, it was found that high-priced housing sellers value different housing
features than low-price housing sellers. In addition, in this study, the size and floor of the
house and the number of university graduates among neighbors affect the price positively
as price increases; It was determined that as the price increased, variables such as garage
spot and elevator negatively affected the house price. Additionally, it is determined that the
effect of variables such as the number of bathrooms and whether there is a swimming pool
on the sales price of the house remains constant compared to other variables in different
price ranges (Mora-Garcia et al., 2019). Furthermore, In the study conducted for Polish
counties using two-stage quantile spatial regression and two-stage least-squares regression
methods, it was determined that economic, demographic, environmental and local factors
and exploratory variables positively and significantly affect the prices of houses (Tomal,
2019). In another study, using a spatial quantile regression approach for the Chinese city of
Hangzhou, it is shown that proximity to primary and secondary schools significantly
increase housing prices across the entire conditional distribution, while kindergartens affect
housing prices in lower and higher-priced range houses and it is found out that higher priced
home buyers were willing to pay more for homes near high schools and universities (Wen
et al., 2019).
As can be understood from the literature review, there are many factors affecting house
prices and the effect of these factors on the price of the house varies according to the
location, time and characteristics of the buyers and sellers. However, according to the
findings obtained from the above studies, the factors affecting house prices can be divided
into five main categories: The characteristics of the houses such as the type of residence, size
of the house, number of rooms, number of bathrooms, the existence of balcony and
warehouse. Attributes of the building such as the floor of the apartment, garage, elevator,
sports facility. Environmental characteristics such as neighbors’ educational status, age of
the house. Neighbor and neighborhood characteristics such as the number of children the
neighbors have. Market characteristics such as prices of the house (Mora-Garcia et al., 2019;
Chica-Olmo et al., 2019). As a result of this, within the above context, we will analyze the
main factors that affect the house prices in Izmir by including as many variables as possible
using a quantile regression model.
4. The econometric model and method
The quantile regression is a model that is used to make an empirical analysis of different
subjects in many areas and is more flexible than the OLS model. One of the widely used
areas of the quantile regression model is the housing market. The regression model is used
to investigate the relationship between dependent and independent variables. However,
regression analysis gives the mean effect of independent variables on the dependent
variable; in effect, regression is a conditional mean of the dependent variable, y, given
independent variables, xi. Unlike the OLS, quantile regression is a method for estimating
functional relations between variables for all portions of a probability distribution not only
mean. With the quantile regression method, it is possible to estimate the effect of variables
on each interested quantile. Furthermore, quantile regression is a more robust estimation
method than OLS; outliers do not affect the estimates as in regression. Therefore, quantile
regression provides a clearer picture of the effects of independent variables on the dependent
variable and results can be used to better explain factors affecting the prices of houses. As a
result of this, the quantile regression method has been used in our estimation. The quantile
regression method is proposed and developed by Koenker and Bassett (1978). After the
initial publication of the seminal paper of Koenker and Bassett (1978), theoretical and
especially empirical studies on the subject are grown rapidly. Koenker and Machado (1999),
Adrover et al. (2004) and Bassett and Koenker (1986) are among theoretical studies.
Probably, the most commonly used statistical method in empirical studies without any
doubt is ordinary regression or just simply regression. However, some of the problems of
regression require other estimation methods and quantile regression is one of the methods
that are better than regression. Regression is used to investigate the relation between
dependent and independent variables. However, regression gives the mean effect of those
variables on dependent variables; in effect, regression is a conditional mean of the dependent
variable, y, given independent variables, xi. Unlike regression, quantile regression is a
method for estimating functional relations between variables for all portions of a probability
Quantile
regression
approach
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distribution not only mean. With the quantile regression method, it is possible to estimate
the effect of variables on each interested quantile.
However, quantile regression is not a problem-free model. It has some disadvantages.
First, quantile regression estimators require more computer time (for details see Waldmann,
2018), as, unlike OLS, it does not lead to a closed form for the estimator. Second, if the
regression model satisfies the classical assumptions then OLS provides the best linear
unbiased estimator. So, in that case, the OLS estimator has better precision than the quantile
regression estimator. Last, Inference and interpretation of coefficients can be complicated
comparing to the OLS (Olsen et al., 2012).
Quantile regression is a more robust estimation method; outliers do not affect the
estimates as in regression. Quantile regression provides a clearer picture of the effects of
independent variables. Nevertheless, providing a clearer picture comes at a cost; more
estimation, which is not a problem because of the high-speed computer technology of our
age.
5. Estimation procedure
While in ordinary regression, the sum of squared error is minimized in the estimation:
min
b
n X
yi x i 0 b
2
i
in quantile regression framework the following functional form is minimized:
min
b e Rp
n
X
r t yi x i 0 b
i
where the function r t is the tilted absolute function that yields the t th sample quantile at
the solution (Koenker and Hallock, 2001). The above functional form is solved efficiently by
linear programming methods. There is several commercial software such as STATA,
EVIEWS and non-commercial such as R that can be used in estimation. We used a
MATLAB code written by Paul Elliers for estimation purposes.
Interpreting the quantile regression coefficients is not different from ordinary regression
coefficients. However, it has to be emphasized that the effect of each independent variable is
not on the mean value of a dependent variable but on the quantile.
6. Data and estimation results
Sample data about the housing market of Izmir used in this study are collected from various
sources [3] in June of 2018. Various studies that are conducted on factors affecting housing
prices suggest a variety of different sets of variables to be used in the quantile regression
method. However, our quantile regression model includes only those variables that are
thought to affect housing prices in the Turkish housing market in general and in Izmir
specifically. Definitions of variables and descriptive statistics are given in Appendix
Table A1. Estimation results are provided in Table A2 and on Figure A1 in Appendix. All
through the graphical form is easy to follow the effect of the variables in different quantiles
visually, the table form gives the exact numerical size of the related variables. Therefore, the
empirical analysis and interpretations are based on the estimates of the OLS and selected
quantile regression coefficients that are illustrated in Table A2.
Variables included in our model have different impacts on the price of houses in Izmir. In
fact, one group of variables have no statistical effects on any chosen quantile level, while the
other group of variables demonstrates the partial or full effect on the chosen quantile levels.
As illustrated in Table A2, according to OLS results, age, rooms bath, WC, balcony, parking,
social, security, dist_school, dist_hos, dist_center, centheat, Konak and Balcova variables
have no statistically significant impact on the house prices in Izmir. According to OLS
results, size, floor, storage, elevator, aircon, fire, dist_shop and Narlidere variables have a
significant impact on the house prices in Izmir. As indicated by the OLS results, while the
size, floor, elevator, aircon and fire variables have a positive impact on the prices of houses,
the variables storage, dist_shop and Narlidere have a negative impact. On the other hand,
according to the quantile regression results, the variables age, parking and centheat do not
have a statistically significant effect on the house price in Izmir at all levels quantiles. While
the variables size, elevator and security are significant at all levels of quantiles all other
variables show a significant effect on some quantile levels. According to the quantile
regression results, the variables rooms, floor, bath, WC, balcony, storage, aircon, social,
dist_school, dist_hos, dist_shop, dist_center, Konak, Balcova have a partial significant
impact on the selling prices of houses in Izmir.
Unlike the simple regression coefficients, which provide only the partial picture, quantile
regression coefficients give more information on the effect of any variables. This assertion is
proved true in our study. For example, the OLS coefficient of sizes is roughly 5,622. This
value implies that an additional one square meter will increase the average house price by
5,622 Turkish Lira (TL hereinafter). However, quantile regression results show that the
effect is not uniform. The effect of size on lower-priced and higher-priced houses is different.
While an additional one square meter will increase the 10th percentile house (lower-priced
house) price by 2,639 TL, it increases the 95th percentile house (higher-priced house) price
by 9,875 TL. This means that lower price house buyers value the large size houses less than
the higher price house buyers. This finding is in line with the existing literature (Ebru and
Eban, 2009; Zietz et al., 2008; Mora-Garcia et al., 2019; Kim et al., 2015).
Similar to the result obtained by Ebru and Eban’s (2009) study, the existence of an
elevator has a positive and significant impact on house prices in Izmir. Additionally, there
exists a similar and striking case for the effect of the variable elevator in terms of the results
provided by OLS and quantile regression models. The value of the OLS regression
coefficient for the elevator is 106,627. This indicates that the existence of an elevator will
increase the average house price by 106,627 TL. However, according to the quantile
regression results, the values of the coefficient range from 30,283 of 10th percentile to
216,363 of 95th quantile. This result shows that the elevator has more value for higherpriced homes. Additionally, it is determined that while the impact of the existence of fire
system on the price is positive and significant at each level of quantile including OLS, except
for 95th percentile, it increases from the 10th to 40th percentile and decreases from 50th to
90th percentile and the effect disappears in the 95th percentile. Those homes having a fire
system has an additional value of 40,586 TL for the lower-priced houses (10th percentile)
and an additional value of 89,240 TL for the higher-priced house (95th percentile). This
indicates that the existence of a fire system has more value for approximately the medianpriced houses in Izmir. The values of the coefficient for security variable range between
45,185 and 177,735 for the 10th and 95th percentile respectively. This is an indication that
security has a higher value for higher-priced homes. The house prices in the Narlidere
district are estimated with reference to the house prices in the district of Karsiyaka. As
shown in Table A2, the estimated coefficients for Narlidere range between 60,390 and
255,639. This shows that the value of houses in the Narlidere district decrease for the
Quantile
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higher-priced homes in reference to the house prices in the Karsiyaka district. These results
suggest that increasing the size, adding an elevator, security and a fire system to houses
creates more value for higher-priced houses.
The statistically significant effect of the room variable varies between the 50th and 80th
percentiles, negative and ranges between 43,882 and 69,980. Rooms variable has a low
value for higher-priced houses. The storage variable has a negative and significant impact
for only the houses in the 10th percentile. Similarly, the variables of bath and dist_hos have a
low value for high-priced homes. In addition, in line with the findings of Liao and Wang
(2012), Jim and Chen (2006) and Jim and Chen (2009), the variable floor has a positive and
significant impact on the average price of the house. This impact is also positive and
significant from the 10th to 70th percentile, while the 90th and 95th percentiles are
insignificant. This is an indication that the floor has more value for the higher-priced houses.
The variable WC decreases the value of the house significantly for the lower-priced houses
(for only 10th and 20th percentile) and increases it for the 90th percentile all other quantiles
are insignificant. Balcony lowers the value of the house significantly for only the 70th and
90th percentile. The variable aircon significantly adds to the value of the house for the
higher-priced houses. Although the variable social has no significant impact on the
price on average, it lowers the value of the house significantly for the 50th and 60th
percentile. The remaining quantiles are insignificant. Moreover, dist_school are of high
value for high-priced homes. This result is in line with Wen et al. (2019), who found out
that people are willing to pay more for the houses which are close to schools. Also, the
effect of dist_shop and dist_center variables on the price for almost all quantiles are
significantly negative and uniform from 20th to 80th percentile and from 10th to 70th
percentile, respectively. Houses have lower values for almost all ranges of quantiles in
Konak district in reference to the house prices in Karsiyaka district. The 10th percentile
for the estimated coefficient of the value of the Balcova district is positive and
significant, while estimated coefficients for the rest of the percentiles are insignificant.
This is an indication that the estimated coefficient value of only the houses in the 10th
percentile in the Balcova district is higher than the house prices in the Karsiyaka
district. Consequently, Among the most favorable districts in Izmir, the Karsiyaka
district is preferred over the Konak, Balcova and Narlidere districts with the exception
of 10th percentile priced houses in the Balcova district.
7. Conclusions and policy implications
In this article, the factors affecting house prices in Izmir are examined by using OLS and
quantile regression model. The data used in the study were collected from the websites of
different real estate companies in June 2018. According to the findings obtained from the
empirical study, some variables have no effect on the price of the house, some variables have
an effect on the price of both OLS and in all quantiles and some variables have a partial
effect on the price of the house. It has been determined that the age of the house, central
heating system and the parking lot variables do not have a statistically significant effect on
the price of the house, on the other hand, the size of the house, whether there is an elevator,
fire system and security have a positive and statistically significant effect on the price of the
house. This result is provided for both OLS and for all quantiles. The remaining variables
were found to have a partial effect on the value of the house. It has been determined that the
number of rooms has lower values for high-priced houses, while the floor of the house, the
number of balconies, air conditioning, proximity to schools have a higher value for highpriced houses. On the other hand, the number of toilets, the number of bathrooms and the
distance to the hospital have a lower value on the high-priced housing. In addition, the value
of the distance from the city center and the shopping center is more or less the same in all
quantiles and lowers the value of the higher-priced houses. When the prices of the houses in
Karsiyaka district are taken as a reference, with the exception of the value of the houses in
the 10th percentile in Balcova district, the value of the houses in Konak, Balcova and
Narlıdere districts is lower for all price ranges than the house prices in Karsiyaka.
Based on the findings obtained from this study, it is possible to make very important
inferences for the actors operating in the housing market. As stated in the introduction of
the study, the important actors in the housing market are consumers with the potential to
purchase housing on the demand side, construction companies on the supply side and
local municipalities, especially the relevant units of the central government on the policy
side. Policies and regulations for the housing market in Turkey are being developed by
the Ministry of Environment and Urban Planning in coordination with the Presidency
and it is enacted by the Parliament and implemented by local authorities and other
relevant public institutions. According to the results we have obtained, policymakers,
regulatory agencies, local governments and city planners should consider the fact that
households have priority in the housing market, especially in terms of the size of the
house, the number of rooms, the floor of the house and number of bathrooms, fire,
security and elevator systems, distance to schools, when developing policies to improve
the housing conditions not only in Izmir but also all over Turkey. Additionally, it will be
beneficial in terms of the profitability of their businesses, if housing producers/
developers build new houses by taking these features into account. Finally, it is
recommended that households demanding housing should consider that housing prices
are higher in Karsıyaka district compare to the other favorable districts in Izmir,
probably due to the fact that the houses built are new and have many favorable features
that consumers are looking for and they should set their budgets in accordance with this
result.
Notes
1. The construction sector accounts for approximately 30% of GDP in Turkey (Statistical Institute
of Turkey).
2. It is estimated that housing sector either directly or indirectly affects approximately 250 sectors
and positively contributes to economic growth and employment in Turkey (Emlak Konut, 2018).
3. The data were collected from the following WEB pages: www.emlak.milliyet.com.tr, www.
hurriyetemlak.com, www.emlakilan.com, www.emlak.mynet.com, www.turyap.com, www.yapi.
com.tr, www.tuik.gov.tr and remax.com.tr
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Quantile
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Corresponding author
Onur Özsoy can be contacted at: ozsoyonur@gmail.com
Appendix
Quantile
regression
approach
Figure A1.
Effect of various
factors on the prices
of houses in Izmir
IJHMA
Figure A1.
Quantile
regression
approach
Figure A1.
IJHMA
Table A1.
Variables,
explanation and
descriptive statistics
Size (m2)
m2
137.346
47.744
40
340
Rooms
Bath
Wc
Storage
Balcony
Floor
Parking
Elevator
Aircon
Social
Age
Fire
Dist_school
Dist_hos
Dist_shop
Dist_center
Security
Side
North
South
East
Centheat
Kombi
Karsiyaka (a popular district in Izmir)
Konak (a popular district in Izmir)
Balcova (a popular district in Izmir)
Narlidere (a popular district in Izmir)
number of rooms
number of baths
number of toilets
=1 if exists 0 otherwise
number of balconies
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
in years
=1 if exists 0 otherwise
in km
in km
in km
in km
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise (reference)
=1 if exists 0 otherwise
=1 if exists 0 otherwise
=1 if exists 0 otherwise
2.899
1.489
1.406
0.091
1.944
3.313
0.559
0.644
0.319
0.206
15.262
0.157
0.403
0.834
1.236
9.925
0.248
0.273
0.295
0.238
0.124
0.998
0.002
0.390
0.429
0.099
0.082
0.764
0.571
0.505
0.288
0.243
2.708
0.497
0.479
0.466
0.405
13.076
0.364
0.607
0.545
1.446
4.281
0.432
0.446
0.456
0.426
0.330
0.045
0.045
0.488
0.495
0.299
0.275
1
1
1
0
1
0
0
0
0
0
0
0
0.08
0.1
0.1
1.5
0
0
0
0
0
0
0
0
0
0
0
5
4
4
1
3
19
1
1
1
1
48
1
5.1
3
10
18
1
1
1
1
1
1
1
1
1
1
1
OLS
5,622.5***
(11.14)
Age
1,653.1
(1.24)
Rooms
30,692.5
(0.97)
Floor
13,600.6**
(2.60)
Bath
613.0
(0.01)
Wc
17,200.8
(0.35)
Balcony 90,349.1
(1.53)
Storage
78,198.6þ
(1.73)
Parking 50,209.6
(1.54)
Elevator 106,627.4**
(3.19)
Aircon
100,866.5***
(3.55)
Social
45,136.6
(1.08)
Fire
223,823.7***
(5.47)
Security 43,590.7
(1.03)
Dist_school39.44
(1.64)
Dist_hos 40.40
(1.56)
Dist_shop 26.89**
Size
Price
0.20
0.30
2,639.7*** 2,828.1*** 3,349.7***
(15.50)
(9.92)
(10.23)
336.9
629.7
476.7
(0.75)
(0.84)
(0.55)
3,420.5
1,298.5
15,854.8
(0.32)
(0.07)
(0.77)
8,059.9*** 6,163.9* 11,634.7***
(4.57)
(2.09)
(3.43)
75,207.3*** 72,834.4** 55,989.9þ
(4.97)
(2.88)
(1.93)
72,010.0***63,121.6*47,097.4
(4.31)
(2.26)
(1.47)
12,718.4 10,139.8 3,230.1
(0.64)
(0.30)
(0.08)
46,159.3** 25,650.5 3,914.7
(3.02)
(1.00)
(0.13)
4,044.3
4,814.1 4,870.1
(0.37)
(0.26)
(0.23)
30,283.6** 31,296.2þ 36,520.0þ
(2.69)
(1.66)
(1.68)
31,055.8** 26,876.7þ 27,037.8
(3.24)
(1.68)
(1.47)
18,821.9
2,413.6
26,391.4
(1.33)
(0.10)
(0.97)
40,586.1** 61,667.3** 112,982.3***
(2.94)
(2.67)
(4.26)
45,185.3** 47,589.1* 63,745.1*
(3.16)
(1.99)
(2.32)
31.71***
18.83
16.03
(3.91)
(1.39)
(1.03)
27.37**
32.73*
50.06**
(3.12)
(2.23)
(2.97)
4.723
17.49** 26.36***
0.10
3,971.6***
(11.67)
22.95
(0.03)
31,630.7
(1.49)
11,373.9**
(3.23)
59,066.7þ
(1.95)
30,697.6
(0.92)
11,878.9
(0.30)
7,357.0
(0.24)
2,827.9
(0.13)
50,737.2*
(2.25)
29,658.3
(1.55)
34,999.7
(1.24)
150,798.7***
(5.46)
68,788.1*
(2.41)
23.62
(1.46)
41.25*
(2.36)
24.57***
0.40
4,754.3***
(13.46)
57.05
(0.06)
43,882.9*
(1.98)
14,205.9***
(3.88)
40,614.1
(1.29)
16,483.6
(0.48)
40,456.5
(0.98)
9,066.4
(0.29)
6,383.8
(0.28)
59,294.1*
(2.53)
19,893.5
(1.00)
52,847.2þ
(1.80)
169,025.3***
(5.90)
104,590.3***
(3.53)
28.96þ
(1.72)
39.91*
(2.20)
25.57***
0.50
5,898.1***
(17.16)
16.19
(0.02)
75,262.5***
(3.50)
13,472.0***
(3.79)
27,541.3
(0.90)
518.8
(0.02)
57,066.7
(1.42)
13,074.6
(0.42)
14,426.9
(0.65)
92,670.7***
(4.07)
36,254.4þ
(1.87)
48,372.2þ
(1.70)
160,373.1***
(5.75)
118,965.6***
(4.12)
24.54
(1.50)
37.80*
(2.14)
23.89***
0.60
6,156.0***
(17.06)
48.56
(0.05)
75,211.1***
(3.33)
16,317.9***
(4.37)
5,512.0
(0.17)
49,253.5
(1.39)
72,260.1þ
(1.71)
34,004.6
(1.05)
3,465.5
(0.15)
91,876.6***
(3.84)
68,809.0***
(3.39)
39,889.1
(1.33)
114,366.4***
(3.91)
132,302.0***
(4.37)
38.88*
(2.26)
25.51
(1.37)
24.63***
0.70
7,488.5***
(14.97)
567.1
(0.43)
69,980.0*
(2.24)
7,325.6
(1.42)
7,938.2
(0.18)
30,234.9
(0.62)
91,375.5
(1.56)
72,398.5
(1.61)
9,389.0
(0.29)
127,577.6***
(3.85)
78,376.4**
(2.78)
59,853.4
(1.44)
132,840.1**
(3.28)
115,265.3**
(2.75)
48.70*
(2.04)
23.05
(0.90)
25.15*
0.80
0.95
(continued)
8,622.9*** 9,875.3***
(9.58)
(9.97)
583.2
3,603.9
(0.25)
(1.38)
63,939.9 79,491.1
(1.14)
(1.28)
4,714.9
7,027.8
(0.51)
(0.69)
78,470.3 113,660.6
(0.98)
(1.29)
88,770.2
189,731.8þ
(1.01)
(1.95)
181,045.9þ159,608.7
(1.72)
(1.38)
3,401.5
93,274.2
(0.04)
(1.05)
15,958.0
9,237.0
(0.27)
(0.14)
190,000.2** 216,363.1**
(3.19)
(3.30)
117,675.6* 91,557.2
(2.32)
(1.64)
42,920.4 112,233.3
(0.57)
(1.37)
142,619.6þ 89,240.8
(1.95)
(1.11)
173,501.1* 177,535.7*
(2.30)
(2.14)
39.57
36.67
(0.92)
(0.78)
17.03
40.15
(0.79)
(0.37)
21.15
21.97
0.90
Quantile
regression
approach
Table A2.
Estimation results of
regression and
quantiles regression
at selected quantile
Table A2.
OLS
0.10
0.30
0.40
0.50
0.60
0.70
0.80
0.90
0.95
(3.07)
(4.03)
(3.62)
(3.62)
(3.48)
(3.42)
(2.52)
(1.18)
(1.11)
8.033* 10.11**
7.191þ
8.753*
8.545*
7.875þ
2.892
3.678
6.888
(2.44)
(2.67)
(1.83)
(2.15)
(2.15)
(1.89)
(0.50)
(0.35)
(0.60)
85,373.0 91,587.7
60,866.3 57,592.5
177,568.3 166,796.3 171,724.7 132,680.5 62,075.3
(0.33)
(0.30)
(0.95)
(0.85)
(0.63)
(0.27)
(0.12)
(0.55)
(0.51)
81,365.8*123,036.0***100,942.4**114,228.7** 99,223.8* 92,510.6* 67,926.3
24,268.4 120,548.2
(2.55)
(3.36)
(2.65)
(2.89)
(2.58)
(2.29)
(1.21)
(0.24)
(1.09)
3,316.6
23,809.5
24,566.0 19,065.9
16,789.1
21,165.1
57,971.4
66,661.7 105,451.4
(0.12)
(0.74)
(0.73)
(0.55)
(0.49)
(0.59)
(1.17)
(0.75)
(1.08)
50,046.6 50,480.3
95,956.4* 146,933.6***141,901.2***174,537.4***225,129.3***181,432.9þ255,639.5*
(1.60)
(1.41)
(2.57)
(3.79)
(3.76)
(4.41)
(4.11)
(1.84)
(2.35)
153,187.4 181,825.2
99,610.3
144,057.3
252,091.3
232,268.0
165,645.9
203,317.6
98,156.6
(0.91)
(0.94)
(0.50)
(0.69)
(1.24)
(1.09)
(0.56)
(0.38)
(0.17)
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
0.20
Note: t statistics are in parentheses; þ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
(2.67)
(1.39)
Dist_center 7.082
7.885***
(1.22)
(4.01)
Centheat 128802.6 11,3240.1
(0.47)
(1.22)
Konak
32,208.7 48,878.2*
(0.57)
(2.56)
Balcova 55,306.5
40,891.2*
(1.11)
(2.43)
Narlidere 150,465.8**60,390.2**
(2.72)
(3.23)
_cons
54,219.8
137,266.0
(0.18)
(1.37)
n
1,000
1,000
Price
IJHMA