The impact of Seoul's subway Line 5 on residential property values

Transport Policy 10 (2003) 85–94 www.elsevier.com/locate/tranpol

The impact of Seoul’s subway Line 5 on residential property values Chang-Hee Christine Baea,*, Myung-Jin Junb, Hyeon Parkc a

Department of Urban Design and Planning, University of Washington, 410 Gould Box 355740, Seattle, WA 98198-5740, USA b Department of Urban and Regional Planning, Chung-Ang University, Ansung, Kyunggi-do, South Korea c Korea Development Institute, Seoul, South Korea Received 1 September 2001; revised 1 March 2002; accepted 1 September 2002

Abstract This research investigates the impact of the construction of a new subway line (Line 5) in Seoul on nearby residential property values. A hedonic pricing model shows that distance from a Line 5 subway station had a statistically significant effect on residential prices only prior to the line’s opening. This is consistent with the anticipatory effect observed in other studies. Moreover, accessibility to transit had, in general, less of an impact on house prices than other variables such as the size of the unit, the quality of the school district, proximity to the high-status Kangnam subcenter, and possibly accessibility to recreational resources. q 2003 Elsevier Science Ltd. All rights reserved. Keywords: Residential property values; Subway stations; Hedonic pricing; Accessibility

1. Introduction The city of Seoul had a 2000 population of 9.9 million, down from its 1990 peak of 10.6 million. However, the Seoul Metropolitan Area has continued to grow, reaching 21.35 million in the year 2000, equivalent to 46% of the national population. Of course, the transportation system needed to cope with these huge numbers must be very extensive, and Korea has developed a multimodal system of subway lines, suburban rail, an extensive bus system, and a highway network for private autos and taxis. This paper reports on some of the residential pricing impacts associated with the construction of Subway Line 5, a 52.3 km (32.7 mile) East-West line that was opened in January 1997. Line 5 is the longest and newest of Seoul’s 8 subway lines. It has 51 stations (10 of them transfer stations) including Kimpo Airport (now the domestic airport), 608 railcars (in 76 8-car trains) and a terminus to terminus speed (including stops) of 37 km/h. The total subway system had more than 5.4 million daily riders in 2000, and Line 5’s current daily ridership is 795,000. The trains run every * Corresponding author. E-mail addresses: [email protected] (C.-H.C. Bae), mjjun1@ dreamwiz.com (M.J. Jun), [email protected] (M.-J. Jun), hpark@kdi. re.kr (H. Park).

2 –3 min during rush hour and 4– 6 min in off-peak hours, and service is provided from 5:30 am to midnight at a fare of 600 – 700 Won (US 50 –60 cents), depending on zonal distance. This paper analyzes the impact of the new subway (Line 5) on house prices via a hedonic pricing regression analysis. These price impacts are for 4 years (1989, 1995, 1997 and 2000), corresponding to the announcement of the subway, a year during construction, the completion date, and 3 years after its opening. The homes sampled in this survey are condominiums, the typical new housing type in Seoul (as of 2000, 51.3% of all the housing stock in Seoul consisted of condominiums). These are usually built in multiblock complexes that are typically 15– 25 stories high. The units are either owner-occupied or leased by tenants under a tenure system that is unique to Korea called chonsei (a typical chonsei arrangement might be to advance the owner up to 80% of the purchase price for a 2-year lease; at the end of the lease, the tenant gets all the money back but the owner has free use of the tenant’s money during the period of the lease). It is not unusual for an owner with children to lease out a unit he owns in a less desirable school district and lease a chonsei unit in a better school district. This common practice partially explains why this paper uses school district as a key neighborhood variable.

0967-070X/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 7 - 0 7 0 X ( 0 2 ) 0 0 0 4 8 - 3

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2. Literature review There have been a few studies examining the impact of subway lines in Seoul (Lee, 1997; Won and Son, 1993; Kim, 1995; Han, 1991). Kim (1995) researched how much land prices were affected by Subway Lines 3 and 4. He found that the land value changes were higher more as a result of land use changes than by proximity to the station. For example, when a property changed its land use designation from residential to commercial, or from open space to residential zoning, land prices were higher regardless of whether the property was located within a subway impact area or not. However, he reported that (i) land prices were 9– 10 times higher within a major impact area (, 200 m; Ssangmoon and Nokbun); (ii) 9– 11 times higher in a secondary impact area (200 – 500 m.; Ssangmoon and Mia Sam-georie); and (iii) 9 –28 times higher in the indirect impact area (500 – 1000 m; Ssangmoon and Goopabal). These large ratios reflect land use conversion rather than an accessibility impact on house prices. Han’s (1991) case study was limited to Sadang Station, a major transfer station between Lines 2 and 4, the only crossing point from the South to the North of the Han River. He examined property value changes from 1978 to 1991. Initially, he observed that commercial property values were three to four times higher than residential values before the opening of Line 4, but he could not observe any price differentiation among the major, secondary and indirect areas. However, he reported that after Lines 2 and 4 opened in 1984 and 1986, respectively, property values escalated rapidly around the station; 17 times higher in the major and secondary impact areas and 14 times higher in the indirect areas in 1991 compared with 1978. He interpreted these changes as the result of higher accessibility. However, because this was a case study of only one station, we do not know whether it was a representative case. Won and Son (1993) used constant elasticity of substitution (CES) production function to analyze land values relative to distance from the CBD, population density and other locational characteristics. The model was applied to properties along Subway Lines 3 and 4 connecting the Northwest and the Northeast with the South of Han River. They chose reference areas that had similar characteristics to the impact study areas but that were located away from the influence of the subway lines, such as Dogsan, Myunmok, Dunchon, and Susaeg. They found that (i) accessibility to CBD had greater explanatory power than proximity to individual subway stations; and (ii) nevertheless, the influence of subway location on land values was greater than in the comparison areas (R 2 ¼ 0.80 vs. 0.57). The latest study (Lee, 1997) calculated accessibility to subway stations to examine how accessibility changed spatial structure. The author chose an individual station (Kundae-Ipku) as a case study for detailed analysis. Land values rose rapidly immediately before and after the opening of the subway lines, and land uses became more

dense, both along the subway lines as well as on nearby arterials. More general land use-transportation issues in Seoul have been examined by Park (2000). There are also several relevant studies from other countries. Bajic (1983) combined two models (modal choice and a hedonic pricing model) to examine the impact of the Spadina Corridor in Toronto between 1971 and 1978. He found that increased accessibility and shorter commuting time were capitalized in higher housing prices. Earlier, also in Toronto, Dewees (1976) found that land value changes are steeper around the stations than along the subway lines themselves. In Philadelphia, Voith (1991) estimated a price impact of 6.4% associated with accessibility to rail service. A study of Helsinki found a 6% price premium limited to within a kilometer from stations (Laakso, 1992), while an analysis of Hong Kong (So et al., 1998) found impacts geographically restricted to within 10 min walking distance. In a 1995 study of five rail systems (BART, CalTrain, Sacramento Light Rail, San Diego Trolley, and the Santa Clara light rail), Landis found that residential property values increased as distance from rail transit declined (NHCRP, 1996). There have also been several studies on other than price impacts, such as that of Cervero et al. (1995) on the land use effects of the BART system in San Francisco and that of Bollinger and Ihlanfeldt (1997) on the population and employment effects of Atlanta’s MARTA rail transit system. These types of impact are beyond the scope of this paper. Three recent papers (McDonald and Osuji, 1995; Henneberry, 1998; and Knaap, Ding and Hopkins, 2001) are more directly relevant to the results reported here (although the anticipatory effect argument was outlined much earlier in studies of the Washington Metro (Damm et al., 1980) and of the Vancouver light rail system (Ferguson et al., 1988). McDonald and Osuji (1995) found that residential land values anticipated the construction of the Southwest Rapid Transit Line (also known as the Orange Line or the Midway Line) from downtown Chicago to Midway Airport. The paper used a ‘before’ and ‘after’ comparison with the Census years of 1980 and 1990 counting as the ‘before’ and ‘after’ years. Although the decision to build was announced in 1979, the alignment and route were not confirmed until 1984. Construction was underway in 1990, the stations were under construction, and the completion date had been announced. The key finding of the research was that the 1990 land values anticipated the opening of the line with a 17% increase within one-half mile of the station sites. This was an important study in publicizing the anticipatory effect, but its results were modest from a statistical perspective. Even in 1990, the distance to a Midway station coefficient was barely significant (91%), much less than other accessibility variables (to downtown Chicago, shopping center, arterial streets, and industrial sites). The sample size was small (79 observations).

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As a hedonic pricing model, the neighborhood variables were limited (population density, minority population share, and household income), and only the minority population share was clearly significant. A similar study by Henneberry (1998) explored the impact of the Supertram, a light-rail line in Sheffield, England. This system cost 240 million pounds sterling and was completed in 1995. Data for 1988 showed a statistically significant 4% increase in property values close to the future route. By 1993, however, this had dissipated; prices increased slightly with distance, but the significance level was marginal (less than 7%). By 1996, the results were even worse, negligible Beta values and no statistical significance. The conclusion of the study is: ‘The difference between the results for 1988 and 1993 suggest that anticipation of the construction of Supertram acted to reduce house prices: possibly because of disruption during the building of the system. On completion of Supertram, the negative impact has disappeared. However, the analysis of prices was undertaken only 4 months after the full opening of the system. It may take much longer for the benefits of Supertram to be fully appreciated by homeowners and, therefore, for its impact on house prices to become evident’ (Henneberry, 1998, p. 156). The most recent study (Knaap, Ding and Hopkins, 2001) examined the light rail MAX system in Portland, Oregon. It looked at vacant land values prior to and during the opening of the Western section of the line in 1998. Once again, the data support the argument that rail transit announcements increase nearby land values much more than an ex-post response, although the analysis did not examine the post1998 changes in land values (after the line was opened). Instead, it looked at pre- and post-announcement changes in land values between January 1992 and August 1996 (the route was announced in late July 1993). Pre-announcement distance had no impact on land prices, but post announcement land prices were 31% higher within a half-mile of a station and 10% higher within one mile. The sample sizes within these distances, however, were very small (25 within a half-mile and 142 within one mile). Finally, an earlier study on the Miami Metrorail (Gatzlaff and Smith, 1993) found that its announcement had only a ‘weak’ and usually (depending on neighborhood) a statistically insignificant effect on residential property values. Clearly, more case studies are needed.

the housing stock built after 1989 is not covered in the analysis. The other data include the following housing characteristics (size, number of rooms and bathrooms [eventually dropped from the equations], heating system, number of households, and parking; these are available on a Website (http://www.allapt.co.kr). Network distances to Line 5, the CBD, the major subcenters, the Greenbelt and the Han River were estimated using the 1999 GIS maps derived from the network data file for Seoul. Population and employment density data are from the City of Seoul’s database. School District data were provided by the Department of Education in Seoul (Gyoyuk Chong ).

3. Data

Line 5 impact area ,1000 m

The housing price data used in this study are from the Budongsan Bank (Real Estate Bank) for the years 1989, 1995, 1997 and 2000; the data are not the prices of individual properties but the average price of the same models (condominiums with the same floor space, heating type, number of bathrooms, and other characteristics) sold within a particular complex in each of the 4 years. Thus,

4. Price trends Table 1 shows what happened to residential sales prices at different distances from a Subway Line 5 station both before and after construction and in the rest of Seoul (the data here differ from those in the subsequent analysis because they cover new as well as existing units). Prices increased more rapidly closer to the stations, especially within the range of less than 200 m. Apparently, there are no negative externalities associated with close proximity to a subway station; this contrasts with the findings of a recent study on rail lines in Oslo that the doubling of distance from a railroad within a range of 100 m increased residential Table 1 Line 5 residential unit price indices, 1989–2000 Distance from the station

Year

Index

Midrange sales price (million won)

,200 m

1989 1992 1995 1997 2000 1989 1992 1995 1997 2000 1989 1992 1995 1997 2000 1989 1992 1995 1997 2000 1989 1992 1995 1997 2000

100 161.2 157.8 204.2 207.5 100.0 151.9 153.8 192.2 185.2 100.0 120.0 126.4 155.5 164.5 100.0 152.7 155.3 193.9 190.7 100.0 154.8 152.3 179.5 175.5

103.6 167.0 163.5 211.5 215.0 167.0 253.7 256.8 320.9 309.3 163.0 195.5 206.0 253.3 268.1 153.8 234.8 238.9 298.3 293.2 133.9 207.3 204.0 240.5 235.0

200 –500 m

500 –1000 m

Rest of Seoul

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property prices by 10% (Strand and Vagnes, 2001). The explanation of the sharply different results is obvious: proximity to surface tracks is much more environmentally damaging than proximity to subway lines and stations. After the line was opened, however, prices increased only modestly, and in some distance ranges fell slightly. However, interpreting this as further evidence of anticipatory effects is unclear because property markets as a whole were depressed because of the repercussions of the post1997 economic crisis. More telling are the differences in price trends between the impact area as a whole and the rest of Seoul. The increase over the period as a whole was somewhat higher (by a margin of 8.7%) in the impact area than in the rest of Seoul. This result needs qualification because of two considerations: the inclusion of new units in Table 1; and the fact that Table 1 reflects only raw data rather the standardized impacts adjusted for the influence of other variables revealed by hedonic (and other) regression models.

5. The model This paper uses a standard hedonic pricing model where house price is a function of structure, neighborhood and accessibility variables: Pi ¼ f ðXsi ; Xnj ; Xai Þ

ð1Þ

where i refers to the individual property, Xsi are a set of structure variables, Xnj refer to neighborhood variables at location j, and Xai are accessibility variables for the individual property. A hedonic pricing equation is estimated on the following lines: Pi ¼ a 0 þ S k a k X k

ð2Þ

where k is the number of hedonic attributes. We recognize that hedonic pricing models have non-negligible conceptual problems, primarily because of inadequate specification of demand and supply functions. However, the hedonic pricing approach has attained a wide degree of acceptance as a serviceable reduced-form model, despite its underlying theoretical weaknesses. This study provides a strong case for the anticipatory effects argument. The sample size is larger than in McDonald and Osuji and Knaap et al. (241 compared with 79 and 142, respectively). Our sample size is still smaller than desirable, but the problem was that we needed properties that were sold back in 1989; as we go back in time, the potential sample size in the vicinity of Line 5 stations shrinks dramatically and the Budongsan Bank’s data base is limited because it only started gathering house price data in 1988. The number of years is greater than in McDonald and Osuji (1995) (1989, 1997 and 2000) corresponding to announcement, a year during construction, the completion date, and 3 years after opening. The study refers to built structures not vacant residential land (as in

Knaap et al., 2001). The Henneberry (1998) study used asking prices in 3 years (1988, 1993 and 1996), although the sample size was larger (1431 – 1767). The neighborhood variables included in the Henneberry study were minimal, merely dummy variables for four neighborhoods. This model contains five structure variables, four accessibility variables and seven neighborhood variables (especially school districts, a very important consideration in residential location decisions in Seoul). Also, although they are intended to measure amenity values, two of the neighborhood variables (distance to the Greenbelt and to the Han River) are also accessibility measures. Floor space (expressed in pyeong, the conventional unit in Korea equal to 3.3 m2 or 32.5 ft2) is the main structure variable; the numbers of rooms and bathrooms, although available, were not used because of multicollinearity problems. Other structure variables included age, the size of the complex, parking and type of heating. The neighborhood variables fall into three categories: area densities (both population and employment), school districts (as pointed out above, access to good education continues to be a key locational variable in Seoul), and amenity variables (distance to the Greenbelt and to the Han River) both are important recreational resources in this megacity with its very limited open spaces. The accessibility variables, in addition to distance from the Line 5 subway station, include distance from downtown (the CBD) and distances from the other two major subcenters, Kangnam and Yeongdungpo. All these variables are shown in Table 2.

6. Spatial autocorrelation, spatial multicollinearity and heteroscedasticity In a study of this kind, it is desirable to address the issues of spatial autocorrelation and spatial multicollinearity. One view is that we should not worry about them too much. For example: “Multicollinearity is God’s will, not a problem with OLS or statistical techniques in general. …One should not…expect miracles; multicollinearity is likely to prevent the data from speaking loudly on some issues” (Blanchard, 1987, p. 449). In any event, there are standard techniques to deal with these problems, such as principal components analysis, ridge regression techniques to deal with spatial multicollinearity and mixed regressive-spatial autoregressive models to deal with spatial autocorrelation. In view of possible concerns, we considered the spatial distribution of the data from an autocorrelation and multicollinearity perspective. Spatial correlation is unlikely to be a problem because the observations are not clustered but spread along the 52-km non-linear subway line. As for spatial multicollinearity among the different distance variables (see Heikkila (1988), for a detailed theoretical analysis), there are two issues: possible collinear distances

C.-H.C. Bae et al. / Transport Policy 10 (2003) 85–94 Table 2 Description of variables Variable

Description

PRICE_LN

Dependent variable, natural log of sales price Floor space of apartment in ‘pyeong’ (3.3 m2) The year in which the apartment was built Total households in the apartment complex Parking space per household Type of heating (1 for gas, 0 for oil or other) Distance from the Greenbelt Distance from the Han river Kangseo school district (1 if it locates within Kangseo, 0 if not) Nambu school district (same as above) Kangdong school district (same as above) Population density of the Dong where the apartment is located Job density of the Dong where the apartment is located Distance from a subway Line 5 station in meters Distance from the CBD Distance from subcenter Kangnam Distance from subcenter Yeongdungpo Sales in 1995 Sales in 1997 Sales in 2000 Constant

SIZE_PYEONG YRBUILT TOTHH PARKING HEAT

DIST_GB DIST_HAN SCHOOL_KANGSEO

SCHOOL_NAMBU SCHOOL_KANGDONG POPDEN

JOBDEN

DIST_LINE5

DIST_CBD SUBCENTER_KANGNAM SUBCENTER_YDP YR_1995 YR_1997 YR_2000 INTERCEPT

to the three centers (downtown, Kangnam, and Yeongdungpo) and collinear distances to each center and to the Han River. The three centers are not close to each other (they are between 8 and 12 km apart from each other), so we would not expect that problem to be severe. However, each center is not far from the Han River. These comments suggest that spatial autocorrelation is not a problem, but that spatial multicollinearity might be. Hence, it is necessary to test for the latter. First, the correlation matrix revealed that most of the coefficients among pairwise distance variables were low (usually in the 0.20 range). Exceptions were negative relationships between distances to centers and distance to the Greenbelt (an expected relationship between core and peripheral locations) and a high correlation coefficient of 0.68 between distance to the CBD and distance to Yeongdungpo (perhaps explained by some clustering of observations between these two centers).

89

Second, a standard collinearity diagnosis (Belsey et al., 1980) generated a condition index of 189, that considered alone is quite high. However, there are mitigating circumstances that substantially alleviate the problem: much of the collinearity observed had nothing to do with space, but rather was explained by relatively higher correlations among the non-distance variables; there were many (17) independent variables; and, as we shall see, high R 2-values were combined with many statistically significant coefficients (as opposed to high R 2-values with many insignificant coefficients). We also tested for heteroscedasticity by applying the Breusch-Pagan (BP) test. The null hypothesis of no heteroscedasticity was rejected for each of the 4 years. Accordingly, we adopted generalized least squares (GLS) estimation method rather than an OLS approach, and this corrected the problem.

7. Results and their interpretation Tables 3 –6 report runs of the identical model for the 4 years (1989, 1995, 1997 and 2000). The overall performance of the model is excellent: R 2s in the 0.95– 0.96 range. Although it is not in the spirit of the research that focuses on changes over time, we also present pooled data results aggregating over the 4 years, thereby quadrupling the sample size (Table 7). Floor space was the most important structure variable. Age was also important; newer developments commanded a premium. The size of the complex made no difference (except in 1989, when units in smaller complexes were more expensive). The heating system had an insignificant impact in 1989 but was significant in the later years, possibly the result of changing price differentials among alternative fuels and well-publicized fears about propane gas explosions in the 1990s. More surprisingly, at least to Americans, is that the provision of parking spaces had no impact on the price. There are several explanations. First, auto ownership remains much lower than in the United States, especially for households living close to transit. Second, parking enforcement is minimal, so car owners can often find offsite parking, frequently illegal (Kim and Choe, 1997, pp. 96 – 97). Third, even when there is onsite parking, it is usually insufficient. Cars are double- or triple-parked everywhere near apartment buildings, gears are left in neutral, so a common sight in the early morning is to find people pushing cars out of the way so they can access their own vehicles. Fourth, underground parking (although sometimes provided in high-end buildings) remains unpopular. The most important result is that distance from the Line 5 subway station is significant in 1989, 1995 and 1997 but not in the year 2000. The line had already been announced in 1989 and opened in 1997; the anticipatory price effects

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Table 3 Hedonic pricing analysis, 1989 Variable

B

SE B

T

Sig T

INTERCEPT SIZE_PYEONG YRBUILT TOTHH PARKING HEAT DIST_CBD SUBCENTER_KANGNAM SUBCENTER_YDP SCHOOL_KANGSEO SCHOOL_NAMBU SCHOOL_KANGDONG POPDEN JOBDEN DIST_LINE5 DIST_GB DIST_HAN

7.73805 0.04240 0.02983 20.00003 20.02402 20.00729 0.00768 20.12326 0.07061 1.73681 1.36075 20.32790 23 £ 1026 1 £ 1025 23 £ 1025 22 £ 1026 27 £ 1027 N ¼ 241, R 2 ¼ 0.9553

0.35060 0.00085 0.00391 0.00001 0.01920 0.02860 0.01710 0.01820 0.01430 0.26050 0.24620 0.20400 9 £ 1027 4 £ 1026 1 £ 1025 2 £ 1025 2 £ 1025

22.07 49.75 7.63 22.45 21.25 20.25 0.45 26.78 4.93 6.67 5.53 21.61 22.91 2.82 22.67 20.13 20.05

0.0001 0.0001 0.0001 0.0149 0.2114 0.7993 0.6544 0.0001 0.0001 0.0001 0.0001 0.1094 0.0040 0.0052 0.0083 0.9005 0.9615

(noted by McDonald and Osuji (1995)) were reflected up to the year of opening, but had evaporated by 2000. Other than at peripheral locations, Seoul has a dense subway system (and many other types of transit). As a result, locations do not differ widely in terms of access to transit. This may explain the modest price premium in a city with a relatively high transit share (71.8% of motorized modes (Hwang et al., 1998); or 52.5% of all trips, reflecting the fact that walking accounts for a substantial share of non-work trips [Seoul Development Institute data]). Transit is much more important than in the United States, and many central locations share a high degree of accessibility to it. Among the other accessibility variables, the major finding is that distance to the CBD is insignificant while

distance to the subcenter, Kangnam, is highly significant. This parallels the ‘Westside effect’ noted by Heikkila et al. (1989); (see also Richardson et al. (1990)) in their hedonic pricing study of Los Angeles. Kangnam is Seoul’s major subcenter, located south of the Han River. It is not only a major office center, but also a magnet for shopping, restaurants, and nightlife. Proximity to Kangnam carries a huge price premium. On the other hand, proximity to the other subcenter (Yeongdungpo), an industrial suburb, results in a heavy price discount that is consistently statistically significant. As for the neighborhood variables, both population and employment density were both significant (marginally so in the case of job density in 1989; a possible explanation is

Table 4 Hedonic pricing analysis, 1995 Variable

B

SE B

T

Sig T

INTERCEPT SIZE_PYEONG YRBUILT TOTHH PARKING HEAT DIST_CBD SUBCENTER_KANGNAM SUBCENTER_YDP SCHOOL_KANGSEO SCHOOL_NAMBU SCHOOL_KANGDONG POPDEN JOBDEN DIST_LINE5 DIST_GB DIST_HAN

9.90522 0.03552 0.01355 0.00002 20.00163 20.10670 0.00187 20.12836 0.08629 1.89043 1.45784 20.41334 25 £ 1026 1 £ 1025 23 £ 1025 24 £ 1025 24 £ 1026 N ¼ 241, R 2 ¼ 0.9682

0.31460 0.00064 0.00332 0.00001 0.01180 0.02660 0.01600 0.01660 0.01020 0.18060 0.16630 0.20580 8 £ 1027 3 £ 1026 1 £ 1025 2 £ 1025 2 £ 1025

31.49 55.47 4.08 1.92 20.14 24.01 0.12 27.73 8.49 10.47 8.77 22.01 26.25 4.64 23.04 22.37 22.81

0.0001 0.0001 0.0001 0.0561 0.8903 0.0001 0.9075 0.0001 0.0001 0.0001 0.0001 0.0458 0.0001 0.0001 0.0026 0.0188 0.0053

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Table 5 Hedonic pricing analysis, 1997 Variable

B

SE B

T

Sig T

INTERCEPT SIZE_PYEONG YRBUILT TOTHH PARKING HEAT DIST_CBD SUBCENTER_KANGNAM SUBCENTER_YDP SCHOOL_KANGSEO SCHOOL_NAMBU SCHOOL_KANGDONG POPDEN JOBDEN DIST_LINE5 DIST_GB DIST_HAN

9.90984 0.03573 0.01733 0.00000 0.01317 20.10474 20.01328 20.16991 0.11394 2.72762 2.03247 20.37771 26 £ 1026 1 £ 1025 23 £ 1025 25 £ 1025 24 £ 1025 N ¼ 241, R 2 ¼ 0.9591

0.35530 0.00078 0.00391 0.00001 0.01540 0.02840 0.01780 0.01880 0.01250 0.22760 0.20620 0.22840 8 £ 1027 3 £ 1026 1 £ 1025 2 £ 1025 2 £ 1025

27.89 45.56 4.43 0.06 0.86 23.69 20.75 29.04 9.14 11.98 9.86 21.65 27.51 3.68 22.94 23.12 22.29

0.0001 0.0001 0.0001 0.9525 0.3923 0.0003 0.4557 0.0001 0.0001 0.0001 0.0001 0.0996 0.0001 0.0003 0.0036 0.0021 0.0232

the expansion of jobs close to the route in the 1990s once construction was underway). As expected, prices are negatively associated with population density but positively associated with employment density; people like to live in low-density environments but close to their jobs. The school district data show that the Kangseo and Nambu school districts are much more attractive to households than the Kangdong school district (which has a negative but statistically insignificant sign). Accessibility to amenities (both the Greenbelt and the Han River) is consistently important from 1995 onwards. The most interesting and complicated question is the difference in results between the earlier and later years,

especially the anticipatory impact of proximity to the subway and the insignificance of the amenity variables in 1989 compared with the reverse effects in 1995, 1997 and 2000. Perhaps these reversals are related to each other. Prior to completion of Line 5, home purchasers were aware of the advantage of being close to a new subway station but did not appreciate the more general accessibility advantages. Incomes were increasing dramatically in the 1990s and the value of environmental amenities was rising. Also, there may have been a ‘learning effect’ as people became more aware that the opening of Line 5 increased their accessibility to recreational resources. Similarly, in more objective terms, there was a ‘network effect;’ the opening of Line 5

Table 6 Hedonic pricing analysis, 2000 Variable

B

SE B

T

Sig T

INTERCEPT SIZE_PYEONG YRBUILT TOTHH PARKING HEAT DIST_CBD SUBCENTER_KANGNAM SUBCENTER_YDP SCHOOL_KANGSEO SCHOOL_NAMBU SCHOOL_KANGDONG POPDEN JOBDEN DIST_LINE5 DIST_GB DIST_HAN

10.45932 0.03438 0.00958 0.00002 20.01800 20.09015 20.02574 20.14307 0.09868 2.72588 1.86897 20.00350 26 £ 1026 1 £ 1025 22 £ 1025 25 £ 1025 24 £ 1025 N ¼ 241, R 2 ¼ 0.9585

0.40670 0.00089 0.00420 0.00001 0.01560 0.03490 0.02040 0.02070 0.01290 0.23480 0.20420 0.26600 9 £ 1027 3 £ 1026 1 £ 1025 2 £ 1025 2 £ 1025

25.72 38.82 2.28 1.84 21.16 22.58 21.26 26.91 7.67 11.61 9.15 20.01 26.71 4.2 21.56 22.43 22.44

0.0001 0.0001 0.0234 0.0668 0.2491 0.0104 0.2095 0.0001 0.0001 0.0001 0.0001 0.9895 0.0001 0.0001 0.1195 0.0157 0.0156

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Table 7 GLS results with pooled data Variable

B

SE B

T

Sig T

INTERCEPT SIZE_PYEONG YRBUILT TOTHH PARKING HEAT DIST_CBD SUBCENTER_KANGNAM SUBCENTER_YDP SCHOOL_KANGSEO SCHOOL_NAMBU SCHOOL_KANGDONG POPDEN JOBDEN DIST_LINE5 DIST_GB DIST_HAN YR_1995a YR_1997a YR_2000a

9.27250 0.03533 0.01617 0.00000 20.00628 20.07227 20.01255 20.14091 0.09742 2.27965 1.71938 20.28932 25.47 £ 1026 0.000011 20.00003 20.00004 20.00003 0.46197 0.66149 0.65153 N ¼ 956, R 2 ¼ 0.9542

0.19840 0.00040 0.00216 0.00001 0.00921 0.01590 0.00953 0.01110 0.00855 0.14630 0.14290 0.12700 5.44 £ 1026 2.14 £ 1026 6.98 £ 1026 9.99 £ 1026 9.35 £ 1026 0.01400 0.01460 0.01560

46.73 87.63 7.48 20.15 20.68 24.54 21.32 212.68 11.39 15.59 12.03 22.28 210.05 5.06 24.31 24.11 23.30 33.03 45.30 41.72

0.0001 0.0001 0.0001 0.8813 0.496 0.0001 0.1881 0.0001 0.0001 0.0001 0.0001 0.0229 0.0001 0.0001 0.0001 0.0001 0.001 0.0001 0.0001 0.0001

a

Reference year for dummy variables is 1989.

resulted in a significant improvement in system wide accessibility, much more powerful than the percentage addition in system mileage. The pooled data results shown in Table 7 obviously largely reflect the averages of the annual results. The R 2 remains very high (0.954), but the other results remain more or less the same. Distance both to the Greenbelt and to Line 5 are highly significant and the annual dummies show that prices are significantly different from year to year. All other variables are very significant, except for complex size, the availability of parking, and distance from the CBD.

8. Qualifications A competing interpretation of the Greenbelt accessibility result is to suggest that this variable may measure the composite locational amenities of other nearby areas (e.g. Kangseo in the Southwest and Kangdong in the Southeast) rather than access to the recreational resources. To test this argument, we partitioned the pooled sample into two-sub-samples, one excluding Kangseo and Kangdong (744 observations), the other for Kangseo and Kangdong observations only (212 observations). The first sub-sample yields almost the same results as the overall sample; the only noticeable difference is that the size of the condominium complex becomes significant at the 3% level. The second subsample generated some different, and in some senses unexpected, results. A plausible hypothesis is that distance to Line 5 would be more important at

the urban fringes where other subway lines are not close. In fact, the coefficient was very insignificant (with a t-value of only 0.78). One possibility is that commuters in these outlying districts have reasonable alternative transportation modes with well-functioning exclusive buslanes, frequent express bus services to the CBD and to Kangnam, the possibility that Kangseo residents living close to Kimpo Airport can even take the affordable airport bus to the major centers, and proximity to the 88 Expressway that makes commuting by automobile relatively attractive (usually a shorter travel time). The reliance on automobiles may also explain the continued significance of the Greenbelt accessibility variable (tvalue ¼ 2.63), although accessibility to the other close-by recreational resource (the Han River) is statistically insignificant (t-value ¼ 1.07). In any event, the potential influence of the composite locational amenities of (especially) Kangseo and Kangdong is partially taken into account by inclusion of their school district variables. This study has focused only on units that were built before the construction of the Line 5 Subway was announced (i.e. pre-1989). Since then, there has been major construction of condominium complexes along and close to the route. The impact of the opening of the line on new construction is a separate research study that we hope to address in the future. Also, although we included price data for the rest of Seoul in Table 1, we have not explicitly studied the systemic benefits to Seoul residents as a whole of the opening up of a new line. Moreover, Subway Line 5 had other impacts that are not examined

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here. Subway ridership increased among residents within the 500-m impact area from 17.9% in 1996 to 25.7% of all trips in the year 2000 (Jun et al., 2000). There was also significant expansion of retail trade, personal services and neighborhood activities, resulting in denser development around the stations. On the other hand, manufacturing, traditional markets and single-family housing declined along the route. Employment also declined, but these were years of financial crisis, and the employment losses were proportionately smaller than elsewhere in Seoul.

9. Policy implications The policy implications of this research are subtle but important. The key point is that the construction of Subway Line 5 added more than 14.7% of riders to the overall Seoul subway system. This represents a significant commitment to public policy that is paying off in a city that lacks an efficient region wide highway system. A second important policy finding is that many of the benefits were to riders throughout the whole system (in terms of systemic access to major subcenters and recreational resources) rather than to Line 5 riders alone. This finding is reinforced by the modest gains to residential property owners living close to Line 5. An important public policy objective is to spread the benefits of public policy investments around, rather than limiting them to a narrow section of the population. The investment in Line 5 appears to have paid off in this respect, although the result was probably largely accidental. A third important policy issue, not addressed in detail in this paper but being pursued in parallel research, is the impacts on land use and employment generation in the areas surrounding subway stations. Some changes in land uses are to be expected, especially near the peripherally located stations. The most important source of the debate, however, is whether the jobs generated near the new subway stations were net jobs or employment redistributed from elsewhere in the metropolitan region. We suspect primarily the latter, but the question needs further exploration.

10. Conclusions This research has demonstrated an impact of the construction of a new subway line (Line 5) in Seoul. However, the hedonic pricing model showed that distance from a Line 5 subway station had a statistically significant effect on residential prices only prior to the Line’s opening. This is consistent with the anticipatory effect observed in other studies. Also, it is possible that system wide accessibility is more important than proximity to Line 5 stations; if so, this would be an artifact of the dense subway

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system in Seoul. Moreover, accessibility to Line 5 had, in general, less of an impact on house prices than other variables such as the size of the unit, the quality of the school district, proximity to the high-status Kangnam subcenter, and even accessibility to the Han River.

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