1# RECORD 5bed224cb10ce261eda55857ae903598b9f966972# TITLE: Investigation of the impact of railways on housing values; the case of Istanbul, Turkey3# YEAR: 2019.0 VENUE: International Journal of Transport Development and Integration DOI: 10.2495/tdi-v3-n4-295-3054# AUTHORS: E. Cengiz; H. Çelik5# PAGES: 11 CANDIDATE TABLE PAGES (1-based): [6]6# ABSTRACT: Housing units located near transportation facilities tend to have a higher market value than those that are at a greater distance to transport facilities but have similar characteristics. Provision of public infrastructure has a great impact on urban areas, especially on urban development patterns, spatial distribution of urban land use and real estate values. Mass transit systems are one of the most important public infrastructures in cities. Mass transit operations have a significant effect on property values, since they increase accessibility in urban areas. Railway projects are long-lasting investments and by their nature are extremely expensive when compared to other land transportation modes. It is not always possible or reasonable for many countries to fund these high-price projects only with their national budget generated by taxes collected from all citizens. Literature suggests that property values tend to rise in parallel to land values after any kind of public infrastructure investment. Nowadays transportation, especially railway system investments, is regarded as the most important public infrastructure in urban areas impacting land values maximally. Railway investments are becoming widespread in Turkey, especially in Istanbul as Istanbul is the most important and most populated city in country. As due to their high operation costs urban public transport systems, especially railway systems, rarely make a profit, private investors are not willing to get involved in these projects. Furthermore, it is not possible to finance all rail system investments from public funds. This article aims to prove the impact of railway systems on housing values and investigate the possibility of internalizing this increased value for financing railway investments. The main objective of this article is to calculate the impact of distance to a railway station on the value of properties by using the hedonic price model on an existing railway corridor and applying this anticipated impact on a proposed railway corridor. Finally, the calculated value is used as a preliminary basis as a funding model for railway projects in developing countries like Turkey.789===== PAGE 1 (score 13, numeric-paren 0, table-words 0, var-words 13) =====1011© 2019 WIT Press, www.witpress.com12ISSN: 2058-8305 (paper format), ISSN: 2058-8313 (online), http://www.witpress.com/journals13DOI: 10.2495/TDI-V3-N4-295-30514 Elif Can Cengiz & Hüseyin Murat Çelik, Int. J. Transp. Dev. Integr., Vol. 3, No. 4 (2019), 295–30515INVESTIGATION OF THE IMPACT OF RAILWAYS ON 16HOUSING VALUES; THE CASE OF ISTANBUL, TURKEY17ELİF CAN CENGİZ1, & HÜSEYİN MURAT ÇELİK1181Istanbul Technical University, Turkey19Abstract20Housing units located near transportation facilities tend to have a higher market value than those that 21are at a greater distance to transport facilities but have similar characteristics. Provision of public infra-22structure has a great impact on urban areas, especially on urban development patterns, spatial distribu-23tion of urban land use and real estate values. Mass transit systems are one of the most important public 24infrastructures in cities. Mass transit operations have a significant effect on property values, since they 25increase accessibility in urban areas. Railway projects are long-lasting investments and by their nature 26are extremely expensive when compared to other land transportation modes. It is not always possible or 27reasonable for many countries to fund these high-price projects only with their national budget gener-28ated by taxes collected from all citizens. Literature suggests that property values tend to rise in parallel 29to land values after any kind of public infrastructure investment. Nowadays transportation, especially 30railway system investments, is regarded as the most important public infrastructure in urban areas 31impacting land values maximally. Railway investments are becoming widespread in Turkey, especially 32in Istanbul as Istanbul is the most important and most populated city in country. As due to their high 33operation costs urban public transport systems, especially railway systems, rarely make a profit, private 34investors are not willing to get involved in these projects. Furthermore, it is not possible to finance all 35rail system investments from public funds. This article aims to prove the impact of railway systems on 36housing values and investigate the possibility of internalizing this increased value for financing railway 37investments. The main objective of this article is to calculate the impact of distance to a railway station 38on the value of properties by using the hedonic price model on an existing railway corridor and applying 39this anticipated impact on a proposed railway corridor. Finally, the calculated value is used as a prelimi-40nary basis as a funding model for railway projects in developing countries like Turkey.41Keywords: hedonic price model, housing values, metro investment, public transport, railway funding. 421 Introduction43The concern of economists about urban rent values dates back to 18th century. Today’s urban 44land value theories are derived from Von Thünen’s location differential rent theory [1]. As a 45successor of Von Thünen; Alonso developed the bid rent theory which states that rents tend 46to increase close to the city center as individuals wish to minimize daily transportation costs 47[2]. Nowadays, unlike their historical counterparts, cities have more than one center. These 48centers are formed by transportation networks. In the past, cities had one market place where 49all economic activities took place. Thus being close to the central business district (CBD) was 50an important determinant for housing prices. After transportation became easily accessible 51throughout the city, transportation access points which connect the CBD with locations at 52greater distances became new attraction points. Thus, property prices increased around such 53transportation access points as these meant less mobility time and cost [3].54Housing units at closer distance to public transportation networks mostly tend to demand 55higher market values than those with similar characteristics but at a further distance. Such 56differentiation is due to the savings in transportation costs [4]. 57Provision of public infrastructure has a deep influence on urban areas, especially on urban 58development patterns and spatial distribution of urban land use and real estate values [5]. 59Mass transit systems are one of the most important public infrastructures in cities. Mass 60transit operations have a significant impact on property values as they increase accessibility 616263===== PAGE 6 (score 30, numeric-paren 0, table-words 8, var-words 7) =====6465300 66Elif Can Cengiz & Hüseyin Murat Çelik, Int. J. Transp. Dev. Integr., Vol. 3, No. 4 (2019)67In order to calculate the value impact of proximity to railway stations, a questionnaire 68survey is conducted with rail estate agents working with 4–5 students on the site around the 69stations. On-site survey was started on 1 April 2017 and lasted for 1 week. Detailed informa-70tion on housing prices and their attributes are collected in the control case which has five 71stations. A number of 665 questionnaires are collected in total around these five stations. 72After the model is developed, the impact of being 1 m closer to a station is calculated, and the 73obtained coefficient is used for the main case study [16]. 74In this part of the study, the hedonic price model is run for the control case. The impact 75of each and every attribute on the housing price is calculated using the data gathered via 76questionnaires. The study continues with detailed discussions on the first model. The Google 77Earth program is used to obtain the distance between housing units and stations. The distance 78used in the model is air distance; the reason for using air distance instead of walking distance 79is the calculation methodology for the anticipated value increase on the main case.803 MODEL RESULTS81After questionnaires digitized to excel sheet, model is run with R-Studio program by defining 82every variable in order to obtain an equation. Price of the apartment is the dependent variable 83to the other features of it. Table 2 shows the model results obtained from the related program.84Table 2: Model results.85Price_new86Coef.87Std. Err.88t89P>ItI90[95% Conf. Interval]91air distance92–53.847919319.80832 –2.72940.00795–92.7435996–14.9522297age98–3,856.09599241.4267 –15.971000.000101–4,330.161102–3,382.029103size104612.0502105158.61171063.861070.0001088,559.484109923.5007110rooms11118,641.781125,134.5871133.631140.0001158,559.484011628,724.07117baths11823,672.191196,665.661203.551210.00012210,583.4712336,760.9124alaturka12514,625.761264,799.8351273.051280.0021295,200.78613024,050.73131corner132–25,102.6613310,815.1134–2.321350.021136–46,339.24137–3,866.091138south139–22,210.6314010,416.01141–2.131420.033143–42,663.55144–1,757.709145west146–20,810.514711,364.87148–1.831490.068150–43,126.591511,505.596152north153–19,716.215414,065.37155–1.401560.161157–47,335.011587,902.614159elevator16022,008.681614,815.6311624.571630.00016412,552.6916531,464.67166_cons167167,93816814,69716911.431700.000171139,078.8172196,797.1173X1: air distance, X2: age, X3: size, X4: rooms X5: bathrooms, X6: alaturka, X7: corner, X8: south, 174X9: west, X10: north, X11: elevator.175Total1763.2397e+121776641784.8791e+09179Root MSE180=18146008182Adj R-squared183=1840.5662185Residual1861.3822e+121876531882.1168e+09189R-squared190=1910.5733192Model1931.8575e+12194111951.6886e+11196Prob > F197=1980.0000199F(11, 653)200=20179.77202Source203SS204df205MS206Number of obs207=208665209.regress price_new airdistance age size rooms baths alaturka corner south west north elevator210211212===== PAGE 7 (score 15, numeric-paren 1, table-words 3, var-words 8) =====213214 215Elif Can Cengiz & Hüseyin Murat Çelik, Int. J. Transp. Dev. Integr., Vol. 3, No. 4 (2019) 301216To begin with, according to the calculated t-test values asserted above, almost all the 217exploratory variables exhibit statistically significant coefficients, except for X9 (west or not) 218and X10 (north or not). Second, according to the calculated F-test, the model is statistically 219significant. In other words, the theoretical investigation which refers to the hedonic pricing 220model of the related housing units seems statistically meaningful. Third, the calculation of 221the coefficient of determination (R^2), which is equal to 0.5733 and is a kind of acceptable 222value for such an exploratory investigation, asserts that the stated independent variables in 223modeling housing prices are able to statistically explain the related price changes around 22456.62 percentage level [16]. 225Besides the t-test values, the coefficients for variables seem significant. As distance and 226housing value have an inverse ratio, the coefficient for air distance carries a negative value. 227That means, as the distance from the station increases, the price of apartments decrease. Sim-228ilar to distance, age also carries negative value. As apartments get older their price decreases. 229As the size of the apartments increases, the price also increases; in other words, bigger apart-230ments are more expensive. Number of bathrooms, existence of an alaturka toilette and eleva-231tor also increase the price. But it is surprising that the facade of the apartment does not have 232a positive impact on the price, especially as according to literature corner apartments are 233always more expensive than others since they have a dual facade. But in this data set, it has 234a negative impact. This can only be explained by the cultural characteristics of the neigh-235borhood. Most of the buildings in the area are old and very close to each other. This may 236influence users’ preferences as for privacy reasons they may not wish to be too close to other 237buildings. Finally, the equation for an apartment depending on all its attributes is as follows; 238P=167,938–53.84×X1-3,856.09×X2+612.05×X3+18,641×X4+23,672×X5+14,625.72396×X6–25,102.66×X7–22,210.63×X8–-20,810.5×X9–19,716.2*X10+22,008.68×X11240In the second part of the study, the calculated coefficient for distance in the control case is 241used to calculate the total value increase in the main case. The methodology for this is obtain-242ing the total number of single housing units served by each station.2434 DISCUSSION244The positive impact of railway investments on the housing market is demonstrated with the 245help of literature and the use of two case studies in Istanbul. As a further research, a new 246model will be developed to collect some part of this increased value from property owners 247for the financing of the railway. Since his kinds of urban infrastructure projects are mainly 248financed by governmental funds, some parts should turn to national budget in order to main-249tain the further investment funds. In the “Conclusion” section, the betterment tax collection 250methodology will be discussed as a new financial instrument. 2515 CONCLUSION252Based on the model, being located 1 m closer to the railway station increases the property 253value by 53.84 TL. The model is developed for the properties within a 500-m radius from 254the stations. Thus, this area is divided into zones of 100-m range see Fig. 5. The number of 255individual housing units is established within these zones. The average distance for each zone 256is accepted as follows: 2571–100 m----------50 m258100–200 m------150 m259200–300 m------250 m260300–400 m------350 m261400–500 m------450 m262