1# RECORD 602e1fb4b1e820fd13056357b14a38759c9354ab2# TITLE: Metro station inauguration, housing prices, and transportation accessibility: Tehran case study3# YEAR: 2021.0 VENUE: Journal of Transport and Land Use DOI: 10.5198/jtlu.2021.16224# AUTHORS: Yekta Yazdanifard; Hosein Joshaghani; Masoud Talebian5# PAGES: 25 CANDIDATE TABLE PAGES (1-based): [5, 12, 13, 15, 18, 19, 20, 21, 22, 23]6# ABSTRACT: The opening of a new metro station, as a mode of the transportation corridor, potentially could have different effects on housing prices. We have investigated its effect on the value of residential properties around those stations, using data from large expansions of the metro network in Tehran, Iran. In the period of our study (April 2010 to December 2018), forty-five metro stations were inaugurated in Tehran. We use a difference-in-difference regression method to identify the causal effect of interest, where adjacent properties are used as the treatment group and similar but distant properties as the control group. The results indicate that, on average, the adjacent properties are affected by a 3.7 percent increase in price relative to distant properties. We also extend our study by categorizing new metro stations according to the extent of ex-ante access to other modes of public transportation such as bus rapid transit (BRT). We find 2 to 11 percent positive effect of new metro stations in regions with lower public transport, while in regions with ex-ante extensive public transportation system, we find less than 2 percent positive effect.789===== PAGE 1 (score 7, numeric-paren 0, table-words 0, var-words 7) =====101112http://jtlu.org13. 14 . 1 [2021] pp. 537–56114Metro station inauguration, housing prices, and transportation15accessibility: Tehran case study16Yekta Yazdanifard17Sharif University of Technology18Hosein Joshaghani19Khatam University20Masoud Talebian21Sharif University of Technology22Abstract: e opening of a new metro station, as a mode of the transporta-23tion corridor, potentially could have different effects on housing prices. We24have investigated its effect on the value of residential properties around those25stations, using data from large expansions of the metro network in Tehran,26Iran. In the period of our study (April 2010 to December 2018), forty-five27metro stations were inaugurated in Tehran. We use a difference-in-difference28regression method to identify the causal effect of interest, where adjacent29properties are used as the treatment group and similar but distant properties30as the control group. e results indicate that, on average, the adjacent prop-31erties are affected by a 3.7 percent increase in price relative to distant proper-32ties. We also extend our study by categorizing new metro stations according33totheextentofex-anteaccesstoothermodesofpublictransportationsuchas34bus rapid transit (BRT). We find 2 to 11 percent positive effect of new metro35stations in regions with lower public transport, while in regions with ex-ante36extensive public transportation system, we find less than 2 percent positive37effect.38Article History:39Received: June5,201940Accepted: May 27,41202042Availableonline: April4326, 202144145Introduction46Public transportation in large cities and metropolitan regions is of paramount importance, and there-47fore are policies regarding its creation and expansion. While the transportation corridors can bring48some disadvantages to residents adjacent to them, e.g., crowding and pollution, they are mainly per-49ceived as advantageous because they make the region more accessible and easier for daily commutes.50erefore, itisexpectedthatnearbyhousingpricestoincreaseduetomoreandfasteraccesstootherre-51gions of the city. In this study, we not only measure this effect but also examine factors that determine52its magnitude.53OuruniqueadministrativedatacomesfromTehran,andweconsiderthetotalnumberof1,336,26654transactions. Tehran is the capital and biggest city of Iran and home to about 10 million population.55Copyright 2021 Yekta Yazdanifard, Hosein Joshaghani, Masoud Talebian.56doi: 10.5198/jtlu.2021.162257ISSN: 1938-7849 | Licensed under the Creative Commons Attribution – NonCommercial License 4.0.58e Journal of Transport and Land Use is the official journal of the World Society for Transport and Land Use59(WSTLUR) and is published and sponsored by the University of Minnesota Center for Transportation Studies.606162===== PAGE 5 (score 18, numeric-paren 0, table-words 4, var-words 8) =====6364Metro station inauguration, housing prices, and transportation accessibility: Tehran case study65663.167Housing Prices68Data on housing prices are according to the recorded transactions, obtained from the Office of Plan-69ning and Housing Economy (OPHE) of the Ministry of Roads and Urban Development in Iran. is70administrative information includes complete data on all individual housing transactions from April711, 2010, to December 20, 2018 (nine years) in Iran. We focus on part of data which is for the resi-72dential housing transaction in Tehran, which consists of almost 1,260,000 housing sale transactions.73All real estate transactions in Iran are done by real estate agencies. ese transactions must be fully74integrated into a system connected with the Ministry of Roads and Urban Development. e system75has been in operation throughout the country since 2010. e information recorded within the sys-76tem is provided by real estate managers. OPHE record all the housing transaction information which77are officially registered in real estate agencies since 2010². We refer to Appendix 2 for data cleaning78steps. For each transaction, the OPHE data has the following variables, and the summary statistics is79reported in Table (1). e description of the time categories is presented in the appendix.801. Transaction code,812. e transaction dates,823. Total transaction price,834. e price per square meter,845. e surface area of the house,856. Age of the building,867. Using type of the building,878. Frame type of the building skeleton,889. e first six digits of the ten digits postal code of the housing traded,8910. Percentage of the property traded.90e using type of the building in our administrative data is divided in 4 categories: i) Residen-91tial Buildings ii) Commercial Buildings iii) Institutions iv) Office Buildings. More than 99% of these92transactions are residential houses. In this research, we calculate the impact of metro station inaugu-93ration on residential houses and omit the other types of building which are less than 1%.94Table 1: Summary statistics of residential properties’ valuables95Total96Treatment97Control98mean99sd100mean101sd102mean103sd104Area10584.9310638.3010776.5810833.4110984.8911040.56111Price (Mill. Tomans)1124.241133.481143.661152.741164.251173.49118Price (Dollars)1191000.95120613.69121881.19122501.851231007.79124628.66125Age1268.821278.5912810.121299.371309.081319.02132Observations1331,267,46613466,324135766,958136Comments: USD/IRR changes in different years. In 2010 each Dollar was averagely1371100 Tomans and in 2019 it grows up to 12000 Tomans in the market. Prices are138nominal and are not adjusted for inflation. Treatment and control groups are intro-139duced later in Section 4.3 of the paper.140As shown in the Table (2), the skeleton is mostly made of concrete, metal or both. Only less than1411% of skeleton properties are different.142Residential properties in Iran and Tehran are one or more units in one building, each unit having143one household and owner. In this situation, several families may live in one apartment. ere are, of144course, single-unit duplex houses that are very few. Each residential property in Iran is divided into 6145² e housing information used in this article is available in the following link: https://bit.ly/2IteOEz146147148===== PAGE 6 (score 6, numeric-paren 0, table-words 1, var-words 4) =====149150151.152Table 2: Summary statistics by frame type of residential properties153Count154Col %155Cum %156Frame Type157Concrete158609,14115948.116048.1161Steel162584,52016346.116494.2165Steel and Concrete16669,3961675.516899.7169Without Frame1702,7381710.217299.9173Brick or Cement Block1741,0911750.1176100.0177Wood1783171790.0180100.0181Adobe or Clay1822621830.0184100.0185Total1861,267,466187100.0188Comments : More than 99 percent of frame types are concrete or/and189metal.190shares, where any real or legal person can own one or more of these shares, but generally one person191owns all 6 shares. At the time of trading, any of these shares can be traded. As a result, 16% ,33% ,50%192,67% ,84% and 100% of a property may be traded on each transaction. In the administrative OPHE193data, the number of transactions that is traded less than 100% of the house is only 1.7%, so they are194omitted from our study.1953.2196Metro Stations197e metro stations data has been obtained from Tehran Metro Operations Company. is informa-198tion includes:1991. Time of the inauguration of the metro station,2002. Geographic coordinates of the metro station,2013. Name of the metro station,2024. e number of metro lines which passes through the station.203e total number of stations operated in Tehran is 105, which is planned to increase up to 150 stations204by 2022. e number of stations opened during the period considered in this study is presented in205Figure (1). e red lines specify the stations opened within our research period.206As will be discussed further, we use difference-in-difference method to investigate the causal effect207of a metro station inauguration on residential housing prices. To implement this method, it is neces-208sary to have access to the data before and aer the inauguration of the metro station. Based on the209availability of housing prices from April 1, 2010, only those stations inaugurated aer this date have210been investigated. erefore, 45 stations are investigated, most of which are related to stations of lines211C and D.212It is important to mention that four of these stations (A1, A8, B9, D1) have been inaugurated two213times because they are the junctions of two different metro lines. ese stations opened for the first214time before the period under investigation, and for the second time within our research period. Since215the second opening of metro station can have a different impact from the first opening, we produce216two different outputs: with and without these stations. Location of these 45 stations investigated is217depicted on Figure (2).2183.3219Postal Codes and Geographic Coordinates220In Iran, each property has a unique 10-digit postal code that not only identifies the city and neigh-221borhood of the property but also its exact geographic coordinates. Postal code in Tehran begins with222numbers 11 to 19 (excluding 12). According to the first three digits of the postal code, the city of223224225===== PAGE 12 (score 47, numeric-paren 30, table-words 4, var-words 3) =====226227228.229It can be argued that the increase in housing prices is a combined effect of metro station inaugura-230tion and change in usage of houses around metro station. We believe this is not the case in the city of231Tehran as the stations opened in the period of our analysis are located in regions that have a persistent232structure in type of usage of buildings and this concern cannot significantly affect our results.233Table 4: Regression results for average effect of metro station234(1)235(2)236(3)237(4)238(5)239T=12400.5941∗∗∗241-0.0123∗∗∗242-0.0172∗∗∗243-0.0202∗∗∗244-0.0248∗∗∗245(0.0015)246(0.0013)247(0.0012)248(0.0012)249(0.0015)250M=1251-0.0457∗∗∗2520.0122∗∗∗2530.0180∗∗∗2540.0228∗∗∗2550.0139∗∗∗256(0.0037)257(0.0022)258(0.0021)259(0.0021)260(0.0021)261T=1 * M=12620.0105∗2630.0236∗∗∗2640.0247∗∗∗2650.0226∗∗∗2660.0371∗∗∗267(0.0053)268(0.0031)269(0.0030)270(0.0029)271(0.0030)272Age (centuries)273-1.1762∗∗∗274-2.5029∗∗∗275-2.5031∗∗∗276(0.0046)277(0.0125)278(0.0125)279Area (Hectares)28016.9128∗∗∗28110.2480∗∗∗28210.7196∗∗∗283(0.1210)284(0.3469)285(0.3442)286Age2 (centuries)2874.1766∗∗∗2884.1717∗∗∗289(0.0354)290(0.0352)291Area2 (Hectares)2921.279 × 102∗∗∗2931.143 × 102∗∗∗294(12.3798)295(12.2858)296Constant2970.8337∗∗∗298-0.0478299-0.02613000.0407301-0.0441302(0.0012)303(0.0421)304(0.0401)305(0.0417)306(0.2509)307Region308No309Yes310Yes311Yes312Yes313uarter314No315Yes316Yes317Yes318Yes319Frame320No321No322No323Yes324Yes325N326833,282327833,282328833,282329833,281330833,281331R-squared332.1683333.7155334.742335.7466336.7524337Standard errors in parentheses338∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001339Comments: Regression number (1) has no control variable and regression number (5) uses all340existing control variables and fixed effects. As specified in the table, by adding control variables,341the value of r 2 goes to 75.2%. e post, treat variables are defined as the time and place dummy342variables.3435.2344Inauguration of the Metro Station for the Second Time345To test the second hypothesis, those stations that have been opened for the second time during the346study are examined separately. e last column of Table 7 considers only those four stations and shows347that the coefficient is not statistically different from zero. ese results indicate that those stations348349350===== PAGE 13 (score 41, numeric-paren 24, table-words 4, var-words 4) =====351352Metro station inauguration, housing prices, and transportation accessibility: Tehran case study353354opened for the second time due to connection of a new line, do not have a significant impact on the355prices of the properties near them. In other words, these stations have had their impact on the housing356prices aer their initial opening.357We also estimate the primary regression without the four re-opened stations in Table 7. e co-358efficients of the case of considering all 45 stations together and the case of removing of four stations359openedforthesecondtimecanbecompared. Asitcanbeseenincluding/excludingthefourre-opened360stations does not affect the statistical significance of the coefficients. ⁴361Table 5: Regression results to test the effect of opening the station for the362second time363(1)364(2)365(3)36645 Stations36741 Stations3684 Stations369T=1370-0.0248∗∗∗371-0.0233∗∗∗372-0.0243∗∗∗373(0.0015)374(0.0017)375(0.0072)376M=13770.0139∗∗∗3780.0164∗∗∗379-0.0178∗∗380(0.0021)381(0.0023)382(0.0058)383T=1 * M=13840.0371∗∗∗3850.0388∗∗∗386-0.0005387(0.0030)388(0.0032)389(0.0083)390Age (centuries)391-2.5031∗∗∗392-2.5244∗∗∗393-2.5746∗∗∗394(0.0125)395(0.0135)396(0.0314)397Area (Hectares)39810.7196∗∗∗39913.8140∗∗∗400-17.5511∗∗∗401(0.3442)402(0.3653)403(1.1811)404Age2 (centuries)4054.1717∗∗∗4064.2409∗∗∗4074.1220∗∗∗408(0.0352)409(0.0381)410(0.0908)411Area2 (Hectares)4121.143 × 102∗∗∗4133.212 × 10∗4149.903 × 102∗∗∗415(12.2858)416(12.8276)417(48.9493)418Constant419-0.0441420-0.05644210.3082422(0.2509)423(0.2533)424(0.1760)425N426833,281427725,519428107,762429R-squared430.7524431.7542432.7396433Standard errors in parentheses434∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001435Comments: In regression number (1), all stations are considered and436in regression number (2) 4 stations that have been opened for the437second time have been removed. e (post × treat) factor in these438two situations is roughly equal, indicating no appreciable effect on439housing prices at the time of the second opening of the station440⁴ Notice that the diff-in-diffcoefficient (Pos t ×T reat) in the two cases (3.71% and 3.88%) are economically close, but441statistically different, so we make no claim about the equivalence of columns 1 and 2 in Table (5).442443444===== PAGE 14 (score 14, numeric-paren 0, table-words 1, var-words 12) =====445446447.4485.3449Clustering Stations450In this section, we divide stations based on their urban differences, and test the third hypothesis. We451will explain each of these cases below.452- North (B1-B3 and C1-C2): Stations located in the North of Tehran are in this category. One453of the characteristics of this category is that household income in these regions is higher than454the average of the community and less public transport than the city center.455- South (B19-B23): Stations located in the south of Tehran are in this category. Regarding the456weak economic conditions in the south of Tehran, it is expected that the property prices near457these metro stations will improve.458- West (A1-A4): e common characteristic of these stations is that they are all on the western459margins of Tehran, and the metro is one of the most important vehicles for transportation of460the residents of this region. erefore, it is anticipated that the opening of the metro station in461this region will have a positive impact on the price of housing around it.462- Sayyad (B4-B10): Due to the lack of a BRT line and the stations of other metro lines, it is463expected that the opening of metro stations in this region will have a positive impact on the464property prices around it.465- Azadi–Enqelab(A5-A14): Again,regardingthepresenceoftheBRTline,whichwasoperated466before2010, itisexpectedthattheopeningofmetrostationsonthesestreetswillnothavemuch467impact on the property prices around it.468- Vali Asr (B11-B18): Regarding the existence of the BRT line, which was implemented before4692010, it is expected that the opening of metro stations on this street will not have much effect470on the property prices around it.471- Navvab (D1-D4): Regarding the presence of the BRT line in this street, which was operated472before 2010, it is predictable that the opening of metro stations on these streets will not have a473positive effect regarding public transport access for the properties around it. It should be noted474that these stations belong to line 7 of the Tehran Metro, which have recently been operated475in 2018. Based on the literature reviewed earlier, we conjecture that the negative effects, e.g.,476disturbance, noise, crime rates, and pollution have contributed to the negative coefficient.477Results of the regression analysis for each of these categories are reported in Table (6) which indi-478cates that those stations opened in regions with access to bus rapid transit (BRT) lines did not have a479significant effect on the prices of the properties around it. We interpret this finding as follows: Advan-480tage of having access to the metro station that causes the prices of the region’s properties to be raised481aer the opening of the metro station is more and faster access to the rest of the city. erefore, prop-482erties with good access to BRT lines and already easy access to city center, we find less than 2 percent483effect on their prices. Categories 5, 6, and 7 have the same status.484Unlike the categories described in the previous paragraph, the properties located in Tehran’s pe-485ripheral regions (north, northeast, west, and south) had a growth of prices from 2 to 11% at the time of486opening of these stations. e most significant price increase is identified for the properties around the487stations in the north of Tehran that includes Tajrish and Mahallati stations. ese stations are in more488affluent part of the city and while there is good access to well developed highways, there is relatively489less public transportation in these regions, therefore the opening of the metro station in these regions490cause the price of the surrounding properties to increase. e least increase is found for properties491around the Sayyad highway (No.4).492493494===== PAGE 15 (score 76, numeric-paren 56, table-words 2, var-words 5) =====495496Metro station inauguration, housing prices, and transportation accessibility: Tehran case study497498Table 6: Regression results for 7 defined categories499(1)500(2)501(3)502(4)503(5)504(6)505(7)506North507South508West509Sayyad510Azadi-Enghelab511Valiasr512Navvab513T=15140.0323∗∗∗5150.0248∗516-0.0326∗∗∗5170.0105518-0.0101∗∗5190.0753∗∗∗5200.0996∗∗∗521(0.0079)522(0.0100)523(0.0059)524(0.0082)525(0.0035)526(0.0056)527(0.0129)528M=1529-0.1403∗∗∗530-0.1193∗∗∗531-0.0323∗∗532-0.0819∗∗∗5330.0569∗∗∗5340.0516∗∗∗5350.0071∗536(0.0392)537(0.0094)538(0.0101)539(0.0075)540(0.0062)541(0.0052)542(0.0029)543T=1 * M=15440.1138∗∗5450.0688∗∗∗5460.0337∗∗5470.00105480.0175∗∗5490.0128550-0.0137∗551(0.0415)552(0.0133)553(0.0124)554(0.0122)555(0.0067)556(0.0088)557(0.0064)558Age (centuries)559-3.2597∗∗∗560-2.3733∗∗∗561-3.1817∗∗∗562-2.7542∗∗∗563-2.6335∗∗∗564-2.6704∗∗∗565-2.6744∗∗∗566(0.0485)567(0.0724)568(0.0397)569(0.0420)570(0.0195)571(0.0408)572(0.0364)573Area (Hectares)57424.7873∗∗∗575-1.5277576-30.7669∗∗∗57723.5485∗∗∗57817.7836∗∗∗5797.4146∗∗∗580-8.8337∗∗∗581(1.0445)582(2.8774)583(1.3572)584(1.0859)585(0.7445)586(1.2790)587(1.6689)588Age2 (centuries)5896.8146∗∗∗5904.7623∗∗∗5919.3918∗∗∗5925.1907∗∗∗5934.1099∗∗∗5944.1474∗∗∗5953.9768∗∗∗596(0.1437)597(0.2565)598(0.1322)599(0.1278)600(0.0532)601(0.1043)602(0.1124)603Area2 (Hectares)604-2.19 × 102∗∗∗6052.62 × 10260614.73 × 102∗∗∗607-1.61 × 102∗∗∗608-1.43 × 102∗∗∗609-4.32 × 106102.81 × 10611(29.6552)612(161.2154)613(56.9606)614(36.8792)615(34.1970)616(47.4865)617(97.8459)618Constant6190.29336200.6732∗6210.28306221.0138∗∗∗623-1.2945∗∗∗6240.04756250.6529∗∗626(0.3666)627(0.3161)628(0.1889)629(0.2605)630(0.2191)631(0.2724)632(0.2147)633N63480,42363555,04563688,72063771,263638284,35263987,91364081,984641R-squared642.641643.5066644.7257645.7436646.7222647.7417648.7136649Comments: Columns 1-4 are the categories without initial bus rapid transit (BRT) lines in these regions. On650the other hand, columns number 5-7 are located near BRT lines, initally operational before metro stations are651opened.652653654===== PAGE 16 (score 5, numeric-paren 0, table-words 0, var-words 5) =====655656657.6586659Conclusion660In this paper, using a unique administrative data of transacted housing properties in Tehran, capital of661Iran, we investigate the effect of metro station inauguration on prices of proximate properties consid-662ering the proximity of other public transportation such as Bus Rapid Transit (BRT) to those Metro663stations. In summary, we find that opening of the metro station in Tehran, on average, significantly664has increased the price of housing in the regions around them. Studying the impact of all stations aer665their opening shows a 3.7% increase in property prices at a radius of 400 meters from the metro station666compared with the property at a radius of 400 meters to 3 kilometers. is finding confirms earlier667studies that the advantage of metro station due to ease of access to other city regions overcomes the668possible disadvantages of crowding and pollution, confirming the results of Chen et al. (1998).669Using diversity of various city regions, we find that the lower the initial quality and volume of670public transport near the opened metro station is, the more profound the positive effect on the price of671surrounding properties will be. Our study finds the price increase of 2 to 11% of the nearby properties672with less ex ante access to public modes of transportation. However, if these stations open in regions673with an extensive public transportation system such as BRT lines, then there will be no significant674positive impact on housing prices in those regions or even negative impact.675It is worth noting that it usually takes time for the construction of metro station from the date that676the construction is publicized to the actual date of opening. erefore, in practice, prices can adjust to677the news of metro inauguration before the inauguration takes place. It implies that by comparison of678prices before and aer inauguration, we underestimate the effect of metro stations. Hence we interpret679our findings as minimum effect of metro stations on the house prices. Nevertheless, the result of this680research shows that metro operation in Tehran generally results in at least a 3.7% increase in housing681prices. Finally, further research is needed to study dynamics of house price adjustment which was682beyond scope of this research.6837684Appendix6857.1686Calculate Distance and latitude-longitude687e geographic coordinate system is a system through which the location of each point on the earth688can be determined using several components. In the coordinates, one component represents the verti-689cal location or the height of the desired point, and the second component determines the horizontal690location. An ordinary coordinate system has three components, height (elevation), latitude, and lon-691gitude. Latitude (ϕ) of each point is the northern or southern angle between the equatorial plane and692the line passing through the point and the center of the earth. Points with the same latitude constitute693the circuits, the circles parallel to the Equator that their radiuses vary from the largest (the Equator) to694the smallest (in poles). e longitude (λ) of a point represents the eastern or western angle between695the meridian on which the point is located and the prime meridian, whose longitude is zero degree.696is angle is at most 180◦E (East) or W (West).697e city of Tehran expands between 51◦24′E and 51◦36′E longitude with an approximate length698of50kmandbetween35◦34′E and35◦50′E latitudewithapproximately30kmwidth. egeographic699coordinates of each point as degrees, minutes, and seconds can be converted to the decimal degrees by700the following formula:701Decimal Degrees = Degrees + ( Minutes /60) + ( Seconds /3600)702For example:70335◦40′20′′ = 35 +7044070560 +706207073600 = 35.672222708709710===== PAGE 18 (score 47, numeric-paren 30, table-words 4, var-words 3) =====711712713.714Table 7: Regression results in different ranges of treatment group715(1)716(2)717(3)718(4)719(5)720200m721400m722600m723800m7241000m725T=1 * M1=17260.0484∗∗∗727(0.0062)728Age (centuries)729-2.4984∗∗∗730-2.5031∗∗∗731-2.5052∗∗∗732-2.5119∗∗∗733-2.5083∗∗∗734(0.0125)735(0.0125)736(0.0125)737(0.0125)738(0.0125)739Area (Hectar2)74010.8345∗∗∗74110.7197∗∗∗74210.6780∗∗∗74310.6476∗∗∗74410.8049∗∗∗745(0.3444)746(0.3443)747(0.3442)748(0.3441)749(0.3441)750Age2 (centuries)7514.1609∗∗∗7524.1717∗∗∗7534.1743∗∗∗7544.1881∗∗∗7554.1844∗∗∗756(0.0352)757(0.0352)758(0.0352)759(0.0351)760(0.0352)761Area2 (Hectar2)762110.7304∗∗∗763114.3933∗∗∗764116.0682∗∗∗765116.9601∗∗∗766113.1338∗∗∗767(12.2888)768(12.2858)769(12.2818)770(12.2776)771(12.2798)772T=1 * M2=17730.0371∗∗∗774(0.0030)775T=1 * M3=17760.0271∗∗∗777(0.0022)778T=1 * M4=17790.0136∗∗∗780(0.0019)781T=1 * M5=17820.0111∗∗∗783(0.0019)784Constant785-0.0388786-0.0441787-0.0575788-0.0693789-0.0659790(0.2510)791(0.2509)792(0.2509)793(0.2508)794(0.2508)795N796833,281797833,281798833,281799833,281800833,281801R-squared802.7522803.7524804.7526805.7527806.7526807Standard errors in parentheses808∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001809Comments: As shown in the table, the post * treat coefficient is increased by shrinking the810range.811price appears more significantly. More specifically, if the range under control is considered up to 2km,812the effect of the metro opening will be 4.1%, while if we increase the range up to 6km, the impact of813the metro opening will be approximately 3%. As said before, the basic case considered in this research814is up to 3km.8157.5816Robustness check - Stepwise-Linear-Quadratic Model817Table(9)reportstheresultsofmodelsweconsiderhere. Intheprimarymodel,thedistancewasdivided818into two categories coded by 0 (400m to 3km) and 1 (up to 400m). Here, for robustness check we819consider the distance as a linear and a quadratic function, and only transactions within the range of8201kmofthemetrostationareconsidered, thenumberof transactionsis 305,468 inthis case. Also, a case821822823===== PAGE 19 (score 49, numeric-paren 30, table-words 4, var-words 5) =====824825Metro station inauguration, housing prices, and transportation accessibility: Tehran case study826827Table 8: Regression results in different ranges of control group828(1)829(2)830(3)831(4)832(5)8332km8343km8354km8365km8376km838T=1 * M1=18390.0413∗∗∗840(0.0030)841Age (centuries)842-2.5158∗∗∗843-2.5031∗∗∗844-2.4259∗∗∗845-2.3996∗∗∗846-2.3846∗∗∗847(0.0143)848(0.0125)849(0.0117)850(0.0112)851(0.0108)852Area (Hectares)8537.7151∗∗∗85410.7196∗∗∗85512.3332∗∗∗85612.2424∗∗∗85712.4509∗∗∗858(0.3964)859(0.3442)860(0.3236)861(0.3108)862(0.3007)863Age2 (centuries)8644.1837∗∗∗8654.1717∗∗∗8664.0274∗∗∗8674.0404∗∗∗8684.0831∗∗∗869(0.0402)870(0.0352)871(0.0333)872(0.0321)873(0.0312)874Area2 (Hectares)8752.21 × 102∗∗∗87611.14 × 102∗∗∗8776.84 × 10∗∗∗8789 × 10∗∗∗8791.028 × 102∗∗∗880(14.2462)881(12.2858)882(11.6416)883(11.2525)884(10.9528)885T=1 * M2=18860.0371∗∗∗887(0.0030)888T=1 * M3=18890.0349∗∗∗890(0.0030)891T=1 * M4=18920.0342∗∗∗893(0.0030)894T=1 * M5=18950.0307∗∗∗896(0.0030)897Constant898-0.0341899-0.0441900-0.1212901-0.1341902-0.1510903(0.2495)904(0.2509)905(0.2539)906(0.2534)907(0.2528)908N909628,782910833,281911977,0489121,077,5069131,169,264914R-squared915.7538916.7524917.7478918.7481919.7478920Standard errors in parentheses921∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001922Comments: As shown in the table, the post * treat coefficient is increased by shrinking the range.923with a stepwise function is considered. In this case, we consider distances to four categories 0 to 150m,924150 to 400m and 400m to 1km and 1 to 3km. In the case with stepwise function, the coefficient of925the variable of the difference-in-difference in the first category, which includes properties in the range926of up to 150 meters from the metro station, is 4.6 percent, which is smaller for the other categories,927so that it is negative for the last category (400m to 1 kilometers). In the linear and quadratic case, the928coefficient of the difference-in-difference variable of degree one is -4.4%, meaning that, up to 1km, for929every 100 meters away from the metro station, 0.44% of the price of the property is reduced due to the930opening of the metro station. As summarized in the Table (9), the coefficient for the term of degree931one is equal to -14%.932933934===== PAGE 20 (score 29, numeric-paren 11, table-words 6, var-words 4) =====935936937.938Table 9: Comparison of linear, quadratic and stepwise models939(1)940(2)941(3)942(4)943Basic944Stepwise945Linear946uadratic947T=1 * M=19480.0371∗∗∗9490.0460∗∗∗950(0.0030)951(0.0096)952T=1 * M=29530.0362∗∗∗954(0.0032)955T=1 * M=39560.0056∗∗957(0.0020)958T=1 * M959-0.0442∗∗∗960-0.1434∗∗∗961(0.0055)962(0.0276)963T=1 * M29640.0895∗∗∗965(0.0230)966Constant967-0.0441968-0.06679690.2523∗∗∗9700.1934∗∗971(0.2509)972(0.2508)973(0.0679)974(0.0681)975N976833,281977833,281978305,468979305,468980R-squared981.7524982.7527983.7365984.7367985Standard errors in parentheses986∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001987Comments: Stepwise, linear and quadratic modes are compared988with the initial state. In stepwise mode, the treatment group is989divided into 3 groups of less than 150 meters, between 150 and990500 meters and 1000 meters. In linear and quadratic mode, the991range is 1 km.9927.6993Robustness check - ln(Age), ln(Area) and categorized age and area994In Table (10) logarithm of variables Age and Area are used instead of their levels. Moreover, in Table995(11) we used 10 decile fixed effects for variables Age and Area. As can be seen, controlled for fixed996effects, the effect of metro station inauguration changes slightly with model specification, but this997change is negligible regarding its amount. erefore, we conclude that our results are robust to model998specification.99910001001===== PAGE 21 (score 41, numeric-paren 28, table-words 2, var-words 4) =====10021003Metro station inauguration, housing prices, and transportation accessibility: Tehran case study10041005Table 10: Regression results for log form of variables1006(1)1007(2)1008(3)1009(4)1010(5)1011T=110120.5941∗∗∗10130.6266∗∗∗10140.0505∗∗∗1015-0.0194∗∗∗1016-0.0265∗∗∗1017(0.0015)1018(0.0014)1019(0.0014)1020(0.0013)1021(0.0016)1022M=11023-0.0457∗∗∗10240.0513∗∗∗1025-0.0413∗∗∗10260.0211∗∗∗10270.0127∗∗∗1028(0.0037)1029(0.0032)1030(0.0027)1031(0.0022)1032(0.0022)1033T=1 * M=110340.0105∗10350.0157∗∗∗1036-0.006310370.0222∗∗∗10380.0371∗∗∗1039(0.0053)1040(0.0045)1041(0.0039)1042(0.0031)1043(0.0032)1044lnAge1045-0.1259∗∗∗1046-0.0785∗∗∗1047-0.1069∗∗∗1048-0.1049∗∗∗1049(0.0006)1050(0.0005)1051(0.0004)1052(0.0005)1053lnArea10540.0883∗∗∗10550.5667∗∗∗10560.1081∗∗∗10570.1076∗∗∗1058(0.0019)1059(0.0013)1060(0.0013)1061(0.0013)1062Constant10630.8337∗∗∗10640.3309∗∗∗1065-2.0560∗∗∗1066-0.2625∗∗∗1067-0.40281068(0.0012)1069(0.0119)1070(0.0507)1071(0.0408)1072(0.2553)1073Region1074No1075Yes1076No1077Yes1078Yes1079uarter1080No1081No1082Yes1083Yes1084Yes1085Region*uarter1086No1087No1088No1089No1090Yes1091Frame1092No1093No1094No1095No1096Yes1097N1098833,2821099759,0571100759,0571101759,0571102759,0561103R-squared1104.16831105.44351106.57081107.72961108.7361109Standard errors in parentheses1110∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.0011111lnArea and lnAge are exerted instead of Area and Age.111211131114===== PAGE 22 (score 29, numeric-paren 20, table-words 2, var-words 1) =====111511161117.1118Table 11: Regression results for categorized form of variables1119(1)1120(2)1121(3)1122(4)1123(5)1124T=111250.5941∗∗∗11260.5921∗∗∗11270.0522∗∗∗1128-0.0198∗∗∗1129-0.0244∗∗∗1130(0.0015)1131(0.0014)1132(0.0013)1133(0.0012)1134(0.0015)1135M=11136-0.0457∗∗∗11370.0504∗∗∗1138-0.0419∗∗∗11390.0227∗∗∗11400.0141∗∗∗1141(0.0037)1142(0.0030)1143(0.0026)1144(0.0021)1145(0.0021)1146T=1 * M=111470.0105∗11480.0210∗∗∗1149-0.005111500.0238∗∗∗11510.0380∗∗∗1152(0.0053)1153(0.0042)1154(0.0037)1155(0.0029)1156(0.0030)1157Age Deciles FE1158No1159Yes1160Yes1161Yes1162Yes1163Area Deciles FE1164No1165Yes1166Yes1167Yes1168Yes1169Constant11700.8337∗∗∗11710.8589∗∗∗11720.1502∗∗11730.1756∗∗∗11740.03861175(0.0012)1176(0.0086)1177(0.0503)1178(0.0398)1179(0.2510)1180Region1181No1182Yes1183No1184Yes1185Yes1186uarter1187No1188No1189Yes1190Yes1191Yes1192Region*uarter1193No1194No1195No1196No1197Yes1198Frame1199No1200No1201No1202No1203Yes1204N1205833,2821206833,2821207833,2821208833,2821209833,2811210R-squared1211.16831212.46671213.57591214.74441215.75041216Standard errors in parentheses1217∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.0011218Values for Age and Area are categorized in 10 deciles. First column report re-1219gression results for the whole sample without any control and any fixed effect. In1220columns 2-5, deciles of age and area are-fixed effects are used to control any nonlin-1221ear effect of these variables.122212231224===== PAGE 23 (score 17, numeric-paren 0, table-words 3, var-words 4) =====12251226Metro station inauguration, housing prices, and transportation accessibility: Tehran case study12271228Table 12: Summary statistics of variables comparison12292010123020111231201212322013123320141234mean1235sd1236mean1237sd1238mean1239sd1240mean1241sd1242mean1243sd1244Area124584.24124638.28124784.82124838.66124983.22125038.20125183.18125238.66125382.31125437.541255Price (Mill. Tomans)12561.621257.8612581.871259.8912602.5712611.2512623.7312632.0312644.0112652.201266Price (Dollars)12671480.461268785.5712691041.091270499.021271704.501272343.9312731036.441274566.4012751135.681276620.191277Age127811.4112798.19128010.9712818.5112829.5912838.3212848.0512858.3112867.1912878.191288Observations128985964129013316712911680971292111369129314329212942015129520161296201712972018129820191299mean1300sd1301mean1302sd1303mean1304sd1305mean1306sd1307mean1308sd1309Area131083.56131137.59131286.04131337.07131488.22131537.35131687.67131740.48131885.94131940.091320Price (Mill. Tomans)13214.0713222.2913234.3113242.2413254.8513262.4613277.1913284.25132912.3213306.761331Price (Dollars)13321131.911333637.8313341151.651335599.1813361143.081337579.341338719.561339425.541340147.39134180.971342Age13437.1513448.1713457.7413468.4013478.5713488.8113499.2213509.0713519.6413529.011353Observations1354131341135514692713561624451357131727135853137135913601361===== PAGE 24 (score 12, numeric-paren 0, table-words 0, var-words 12) =====136213631364.1365References1366Badoe, D. A. and E. J. Miller. 2000. Transportation–land-use interaction: Empirical findings in North1367America, and their implications for modeling. Transportation Research Part D: Transport and En-1368vironment, 5(4):235–263.1369Bajic, V. 1983. e effects of a new subway line on housing prices in metropolitan Toronto. Urban1370Studies, 20(2):147–158.1371Baker, C. 1983. Tracking Washington’s Metro. American Demographics, 5(11):30–35.1372Billings, S. B. 2011. Estimating the value of a new transit option. 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