# RECORD c15ba8a0a6017224eaf4694459bf962179074662 # TITLE: A Study of the Metro and Housing Value Interaction with Hedonic Price Model: Comparison of Ankara Batıkent and Koru Metro Stations # YEAR: 2023.0 VENUE: Mimarlık Bilimleri ve Uygulamaları Dergisi (MBUD) DOI: 10.30785/mbud.1284843 # AUTHORS: Fatma Erdoğanaras; Kübra Cihangir Çamur; Gülsel Satoğlu # PAGES: 17 CANDIDATE TABLE PAGES (1-based): [10, 11, 12] # ABSTRACT: The effects of urban rail systems on the real estate market were studied by analysing housing prices around Ankara Koru and Batıkent Metro Stations using the Hedonic Price Model. Quantitative data on residential sales were used. The findings reveal that sale values of residences are more elastic in term of metro effect in Batıkent, where middle-lower income groups live, compared to Koru Metro Station, where middle-upper income groups live. As the distance to the metro station decreases in Batıkent, prices increase. These results suggest that subways can help reduce polarization between income groups in cities while increasing accessibility. Providing access to neighbourhoods with both low and high-income individuals by metro increases housing prices more in favour of low-income groups and reduces income polarization between income groups. ===== PAGE 1 (score 10, numeric-paren 0, table-words 0, var-words 10) ===== Journal of Architectural Sciences and Applications JASA 2023, 8 (1), 161-177. Research article e-ISSN: 2548-0170 https://dergipark.org.tr/en/pub/mbud Citation: Erdoğanaras, F., Cihangir Çamur, K. & Satoğlu, G. (2023). A study of the metro and housing value interaction with Hedonic Price Model: Comparison of Ankara Batıkent and Koru Metro Stations. Journal of Architectural Sciences and Applications, 8 (1), 161-177. DOI: https://doi.org/10.30785/mbud.1284843 Received: 19/04/2023 – Accepted: 26/05/2023 161 A Study of the Metro and Housing Value Interaction with Hedonic Price Model: Comparison of Ankara Batıkent and Koru Metro Stations Fatma ERDOĞANARAS 1* , Kübra CİHANGİR ÇAMUR 2 Gülsel SATOĞLU 3 ORCID 1: 0000-0002-0892-8431 ORCID 2: 0000-0003-0343-3306 ORCID 3: 0000-0003-4601-2034 1,2 Gazi University, Faculty of Architecture, Department of City and Regional Planning, 06570, Ankara, Türkiye. 3 Master's Degree, Gazi University, Faculty of Architecture, Department of City and Regional Planning, 06570, Ankara, Türkiye. * e-mail: fatmaerdoganaras1862@gmail.com Abstract The effects of urban rail systems on the real estate market were studied by analysing housing prices around Ankara Koru and Batıkent Metro Stations using the Hedonic Price Model. Quantitative data on residential sales were used. The findings reveal that sale values of residences are more elastic in term of metro effect in Batıkent, where middle-lower income groups live, compared to Koru Metro Station, where middle-upper income groups live. As the distance to the metro station decreases in Batıkent, prices increase. These results suggest that subways can help reduce polarization between income groups in cities while increasing accessibility. Providing access to neighbourhoods with both low and high-income individuals by metro increases housing prices more in favour of low-income groups and reduces income polarization between income groups. Keywords: Metro stations, housing prices, hedonic price model, accessibility, transportation planning, urban planning. Metro ve Konut Değeri Etkileşiminin Hedonik Fiyat Modeli ile İncelenmesi: Ankara Batıkent ve Koru Metro İstasyonlarının Karşılaştırılması Öz Kentsel raylı sistemlerin emlak piyasası üzerindeki etkileri, Hedonik Fiyat Modeli kullanılarak Ankara Koru ve Batıkent Metro istasyonları çevresindeki konut fiyatları analiz edilerek incelenmiştir. Konut satış değerlerine ilişkin nicel veriler kullanılmıştır. Bulgular, orta-alt gelir grubunun yaşadığı Batıkent'teki konutların satış değerlerinin, orta-üst gelir grubunun yaşadığı Koru Metro İstasyonu'na kıyasla metro etkisi açısından daha esnek olduğunu ortaya koymaktadır. Batıkent'te, metro istasyonuna yaklaştıkça fiyatlar artmaktadır. Bu sonuçlar, metroların erişilebilirliği artırırken şehirlerdeki gelir grupları arasındaki kutuplaşmayı azaltmaya yardımcı olabileceğini göstermektedir. Hem düşük hem de yüksek gelirli bireylerin yaşadığı mahallelere metro ile erişim sağlanması, konut fiyatlarını düşük gelirli gruplar lehine artırmakta ve gelir grupları arasındaki kutuplaşmayı azaltmaktadır. Anahtar kelimeler: Metro istasyonları, konut fiyatları, hedonik fiyat modeli, erişilebilirlik, ulaşım planlaması. ===== PAGE 10 (score 27, numeric-paren 10, table-words 4, var-words 8) ===== Journal of Architectural Sciences and Applications, 2023, 8 (1), 161-177. 170 Table 2. Correlation relationships of Batıkent Metro Station for housing sale prices Distance to Metro Station (m) Site /Apartment Price (TL) Square Meter (m2) Number of Rooms Floor Level Buildin g Age (year) Distance to Bus Stop (m) Distance to School (m) Distance to Metro Station (m) 1 .142 -.162 -.161 -.330 .020 .133 .532* * -.160 Site/Apartment .142 1 .296 -.108 -.046 .226 .274 .156 .041 Price (TL) -.162 .296 1 .609** .421* .007 -.345 .173 -.103 Square meter(m2) -.161 -.108 .609** 1 .689** -.307 -.582** -.111 -.274 Number of Rooms -.330 -.046 .421* .689** 1 -.067 -.188 -.260 -.080 Floor Level .020 .226 .007 -.307 -.067 1 .363 -.233 .251 Building Age (year) .133 .274 -.345 -.582** -.188 .363 1 .134 -.194 Distance to Bus Stop (m) .532** .156 .173 -.111 -.260 -.233 .134 1 -.239 Distance to School (m) -.160 .041 -.103 -.274 -.080 .251 -.194 -.239 1 According to correlation matrix (Table 2) there is a very strong positive relation between the variables price and square meter (0.609), a strong positive relation between price and the number of rooms/sections (0.421), positive relation between price and whether it is located in a site or an apartment building (0.296), price and the distance to the bus stop (0.173) and a negative relation between the variables price and the age of the building (0.345), price and the distance to the metro station (0.162), price and the distance to the school (0.103). According to the results of the Hedonic Regression Model, the variables of distance to the school, floor level, and square meter size, location in a site or apartment building, distance to the metro stop, distance to the bus stop, age, and number of rooms explain 53.5% of the variation in the housing price. For the One-Way Analysis of Variance (ANOVA) to determine whether multiple independent variables are significant or not in the created model, the significant value of the F-test is 0.005, which is less than 0.05. Therefore, a statistically significant relationship between the variables in the regression model has been determined. The significant value (B) of the T-test is greater than 0.05 for variables such as location in a site/apartment building, housing size, number of rooms, floor level, and housing age, indicating that there is no statistically significant relationship between these variables and the housing price. However, the significant value (B) of the T-test is less than 0.05 for the variables of distance to the metro station and distance to the bus stop, indicating that there is a statistically significant relationship between these variables and the housing price. The table shows the correlation coefficients between the price and different variables of residential properties located near the Batıkent Metro Station. The variables include square meter size, number of rooms, building age, distance to the bus stop, distance to the metro station, distance to the school, floor level, and whether the property is located in a site or apartment building. The correlation coefficients range from -1 to 1, where a value of -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation. The table shows that the variables with the strongest positive correlation to price are the square meter size (0.609), number of rooms (0.421), and whether the property is located in a site or apartment building (0.296). The variables with the strongest negative correlation to price are age (-0.345) and distance to the metro station (-0.162). ===== PAGE 11 (score 23, numeric-paren 7, table-words 4, var-words 6) ===== Journal of Architectural Sciences and Applications, 2023, 8 (1), 161-177. 171 Table 3. Independent variable relationships of Batıkent Metro Station for housing sale prices Coefficients Model Unstandardized Coefficients Standardized Coefficients T Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 13.205 .394 33.482 .000 Distance to Metro Station (m) .000 .000 -.379 -2.152 .047 .651 1.537 Site/ Apartment .140 .072 .297 1.945 .070 .867 1.153 Square Meter (m2) .008 .004 .556 2.099 .052 .288 3.467 Number of Rooms .018 .099 .042 .184 .856 .389 2.571 Floor .021 .010 .338 2.015 .061 .720 1.390 Building Age (years ) -.050 .043 -.232 -1.153 .266 .501 1.996 Distance to Bus Stop (m) .002 .001 .520 2.854 .011 .609 1.643 4.2. The Effect of Koru Metro Station on Housing Sale Prices The distance of the house for sale to the metro station is an independent variable that affects the housing price in fourth place. As the distance increases, there is an increase in the sale price of the house, so the relationship between distance and price is proportional. Table 4. Correlation relationship for Koru Station for housing sale prices Correlations Pearson Correlation Coefficient DISTANCE to METRO STATION(m) SITE/ APARTMENT PRICE (TL) SQUARE METER (m2) NUMBER of ROOMS FLO OR BUILDING AGE (yıl) DISTANCE to BUS STOP (m) DISTANCE to SCHOOL(m) Distance to Metro Station (m) 1 .274 .351 .298 .146 .263 -.368 .284 -.077 Site/ Apartment .274 1 -.037 .023 .258 .066 .000 -.218 -.281 Price (Tl) .351 -.037 1 .937** .695** - .027 -.563* .242 -.143 Square Meter .298 .023 .937* * 1 .684** - .077 -.536* .381 -.140 Number of Rooms .146 .258 .695* * .684** 1 - .280 -.409 -.072 -.165 Floor .263 .066 -.027 -.077 -.280 1 -.440 .168 .046 Building Age (Years) -.368 .000 - .563* -.536* -.409 - .440 1 -.319 .268 Distance to Bus Stop (M) .284 -.218 .242 .381 -.072 .168 -.319 1 -.174 Distance to School (m) -.077 -.281 -.143 -.140 -.165 .046 .268 -.174 1 According to correlation matrix (Table 4) there is a very strong positive relation between the variables price and square meter (0.937), price and the number of rooms/sections (0.695), positive relation between price and distance to metro stop (0.351) and distance to bus stop (0.242) and a negative relation between price and building age (0.563), whether it is located in a site or an apartment building (0.037) and the floor (0.027). According to the summary of the Hedonic Regression Model, variables such as distance from the school, the floor it is located on, square meter size, whether it is located in a site/apartment, distance to the metro station, distance to the bus stop, age, and number of rooms explain 93.2% of the price variable of the houses for sale in Korukent. For one-way analysis of variance (ANOVA) to determine whether a model with multiple independent variables is significant or not, the null (H0) and alternative (H1) hypotheses are tested. Since the F-test's significant value is less than 0.05, it has been determined that the regression model is significant and there is a statistically significant ===== PAGE 12 (score 23, numeric-paren 2, table-words 3, var-words 13) ===== Journal of Architectural Sciences and Applications, 2023, 8 (1), 161-177. 172 relationship between the variables. The significant value (B) of the t-test is greater than 0.05, indicating that there is no statistically significant relationship between the distance of the house to the metro, whether it is located in a site/apartment, the age of the house, the distance to the bus stop or school, and the price of the house. However, since the significant value (B) of the t-test is less than 0.05, there is a statistically significant relationship between the square meter, the number of rooms, and the floor number of the house and the price of the house. When the square meter value of the house increases by one unit, the price of the house increases by 0.004 units, when the number of rooms increases by one unit, the price of the house increases by 0.344 units, and when the floor number of the house increases by one unit, there is a change of 0.028 units in the price. The Table 5 shows the correlation coefficients between the price and different variables of residential properties located near the Koru Metro Station. The variables include square meter size, number of rooms, building age, distance to the bus stop, distance to the metro station, distance to the school, floor level, and whether the property is located in a site or apartment building. The correlation coefficients of the model range from -1 to 1, where a value of -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation. The table shows that the variables with the strongest positive correlation to price are the square meter size (0.677), number of rooms (0.414), and the floor (0,291). The variable distance school has a negative correlation (-0.120). Table 5. Independent variable relationships of Koru Metro Station for housing sale prices Coefficients Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta VIF Constant 13.629 .380 35.882 .000 Distance to Metro Station(M) -1.345E-5 .000 -.007 -.090 .930 1.439 Site/ Apartment -.168 .164 -.084 -1.027 .331 1.646 Square Meter (M2) .004 .001 .677 6.015 .000 3.155 Number of Rooms .344 .099 .414 3.484 .007 3.508 Floor .028 .009 .291 3.105 .013 2.179 Building Age (Years) .029 .055 .057 .526 .612 2.963 Distance to Bus Stop (M) .000 .000 .027 .319 .757 1.777 Distance to School(M) -.001 .000 -.120 -1.613 .141 1.374 4.3 Evaluation of the Housing Sale Prices around the Batıkent and Koru Metro Stations Compared to the Cases The residential areas around the Batıkent Metro Station hosts both developing and established housing areas subject to planning implementations related to middle and low-income households. On the other hand, the residential areas around the Koru Metro Station is characterized by low-rise housing units as well as high-rise apartments for upper-middle and high-income households. The hedonic regression analysis of the housing sale prices presents the differences and similarities of the both locations, Batıkent and Koru, in terms of variables affecting the housing sale prices. The most important variables affecting the housing sale prices in Batıkent case are the size of the house, the distance to bus stop, the number of the rooms, the age of the building, whether it is located in a site or an apartment building and distance to metro station. On the other hand it is seen for the Koru case that only the variables related to housing characteristics are affecting the housing sale prices are; the size of the house, the number of the rooms, the floor number and distance to school. Some of the findings of the two cases investigated in this article are parallel to the cases found in the literature. In a middle-low-income residential area (Batıkent), the distance to the bus stop and metro station affects the housing sale prices, whereas in a high-income residential area (Koru), there is no such effect. Studies in the literature indicate that rail system investments affect housing prices, and the accessibility to metro stations also contributes positively to the increase in housing prices, just as the proximity to the city center does (Bajic, 1983; Cervero, 2003; Pan & Zhang, 2008). ===== PAGE 13 (score 14, numeric-paren 0, table-words 0, var-words 14) ===== Journal of Architectural Sciences and Applications, 2023, 8 (1), 161-177. 173 Research conducted in Istanbul shows that being close to a metro line has a positive effect on the surrounding housing prices (Şahin, 2019). Similarly, another study examining the effect of Istanbul metro stations on housing prices found that the closer the distance to the station, the higher the housing prices (Cengiz, 2020). In a thesis study that examined the effects of metro stations and metro lines on the housing sub- market, it was found that there is no significant difference in the prices of the housing around the station areas and metro lines within 250 meters. However, it was determined that the values of the for-sale and rental housing around the station areas were higher than those in areas with similar proximity to the metro line (Alas, 2021). Another study found that proximity to transportation points such as public transportation stations positively affect housing prices, in addition to variables such as size, age, floor number, garden view, sea view, etc. In this study, it was found that housing prices are high around public transportation stations (Choy et al., 2007). Another study conducted in Istanbul states that the housing prices around the station areas increase in areas where housing prices are already high and similar results were obtained for rental values (Alkay, 2011). However, there are also studies that demonstrate the opposite of this general trend, such as the effect of metro stations decreasing housing prices in areas where high-income groups live (Nelson, 1999). 5. Conclusion and Suggestions In the relevant literature, it is emphasized that metro stations within walking distance cause an increase in housing prices and accessibility is an important criterion in people's transportation mode choices. The results of the Hedonic Price Model applied in this study reveal that the impact of Batıkent and Koru Metro Stations, with different socio-economic characteristics, on housing sales prices varies depending on accessibility within the 1500-meter radius defined as a walkable distance. Batıkent with a mixed-use land pattern and is inhabited by the middle-lower income group, as the distance of the housing to the station decreases, the sale prices of the houses increase. In this case, it can be said that users' lower income levels make them more sensitive to travel costs and less expected to own a car. On the other hand, in Koru, where high-income group lives, the increase in housing sales prices within the 1500-meter radius of the Koru Metro Station as one moves away from the station indicates that housing-related features such as the size, number of rooms, and number of floors are more important in determining housing prices. This situation is also supported by the fact that high-income individuals use private cars more when compared to public transportation and their time is more valuable. In other words, the relationship between accessibility and housing sales values is lower near Koru Metro Station, where users with a high socio-economic level, while it is higher near Batıkent Metro Station, where users with a low socio-economic level. This situation shows that metro transportation systems are effective in reducing polarization between different socio-economic groups in the city. In the future, conducting similar studies in high and lower socio-economic housing areas using the same method and applying this model at regular intervals will contribute to monitoring socio-economic problems such as polarization in the city and overcoming these problems with transportation policies. Additionally, this study can be expanded to include rental housing prices around metro stations and compare sales and rental values. Similarly, sales and rental values in areas located along the metro line and those far away from it can be compared, taking into account socio-economic differences, to obtain more general results. Acknowledgements and Information Note We thank the anonymous reviewers and our colleagues for their comments and guidance on the development of the article. The article complies with national and international research and publication ethics. Ethics Committee approval was not required for the study. Author Contribution and Conflict of Interest Declaration Information All authors have contributed equally. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.