# RECORD 933204aac69df35d44733b61b90a1210852e7b68 # TITLE: A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line # YEAR: 2017.0 VENUE: nan DOI: 10.24294/jipd.v1i1.22 # AUTHORS: M. Diao; Yi Fan; Tien Foo Sing # PAGES: 26 CANDIDATE TABLE PAGES (1-based): [9, 12, 17, 19, 21, 22] # ABSTRACT: This study uses the opening of a new Mass Rapid Transit (MRT) line, known as the Circle Line (CL), between 2010 and 2012 in Singapore as the exogenous event to empirically test the impact of the new CL on housing wealth of households. Using a differences-in-differences (DID) approach, and with the non-landed private housing transaction data covering the period from 2009 to 2013, we find that the average housing prices in our sample increase by 1.6% in the post-opening of the CL. We also find significant capitalization of the new CL into housing prices, especially households living within a 400 meter radius (the treatment zone) from the closest MRT stations on the CL. The treatment effects that are measured by the marginal willingness to pay for houses located within the treatment zone is 13.2% relative to houses located outside the treatment zone. The new CL opening creates an estimated of S$1.23 billion housing effects for households living in the close proximity to the CL MRT stations. However, we do not find significant “anticipative” effects on house prices in the 6-month window prior to the opening of CL. The strongest treatment effect is found after the opening of the phase 1 of CL, and the intensity of treatment effects declines in phases 2 and 3 of the CL opening. The results show strong evidence of the causality from the accessibility to MRT stations on housing prices. ===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) ===== 64 Journal of Infrastructure, Policy and Development (2017) Volume 1 Issue 1, pp.64-89. DOI: 10.24294/jipd.v1i1.22 Original Article A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line Mi Diao 1, Yi Fan 1 and Tien Foo Sing 2* 1 Department of Real Estate, National University of Singapore 2 Department of Real Estate/Institute of Real Estate Studies (IRES), National University of Singapore ABSTRACT This study uses the opening of the new Mass Rapid Transit (MRT) in stages between 2010 and 2012 in Singapore as the exogenous event to empirically test the impact of the new Circle Line (CL) on transaction data covering the period from 2009 to 2013, we find that the average housing prices into housing prices, especially households living within a 400-meter radius (the treatment zone) willingness to pay” for houses located within the treatment zone is 13.2% relative to houses located outside the treatment zone. The new CL opening creates an estimated S$1.23 billion housing wealth intensity declines in phases 2 and 3 of the CL opening. Keywords marginal willingness-to-pay; housing wealth 1. Introduction Investments in public infrastructure projects, such as new MRT lines, highways etc., are highly risky and have a long-gestation period. It is difficult to attract sufficient private capitals into public infrastructure projects, based solely on economic considerations. Governments invariably take on the key roles and responsibilities in developing infrastructure projects. Public infrastructure investments constitute a large portion of government’s fiscal expenditures in many countries. and policymakers exercise due restraints to avoid over-committing on public infrastructure project spending. Public infrastructure investments boost economic growth and create new employment in productive sectors, and they also generate positive spillovers that improve social welfare and environmental quality. At the urban level, new MRT line investments generate significant economic activities, which increase prices of lands surrounding MRT stations. New MRT lines radiating out of the central business district (CBD) ARTICLE INFO Received: January 17, 2017 Accepted: March 8, 2017 Available online: March 14, 2017 *CORRESPONDING AUTHOR Tien Foo Sing, Department of Real Estate/Institute of Real Estate Studies (IRES), National University of Singapore, 4 Architecture Drive, Singapore 117566; rststf@nus.edu.sg CITATION Diao M, Fan Y and Sing TF (2017). “A New Mass Rapid Transit (MRT) Line Construction and Housing Wealth: Evidence from the Circle Line”. Journal of Infrastructure, Policy and Development, 1(1): 64-89. http://doi.org/10.24294/jipd.v1i1.22 COPYRIGHT Copyright © 2017 by author(s) and EnPress Publisher LLC. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). http://creativecommons.org/licenses/ by/4.0/ , Singapore , Singapore ===== PAGE 9 (score 84, numeric-paren 60, table-words 3, var-words 5) ===== A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line 72 Table 1. Descriptive statistics Variable Symbol distmrt<=4000 after==0 after ==1 dumMRT ==0 dumMRT==1 Observation 81,316.00 33,739.00 47,577.00 74,752.00 6,564.00 Sale price (in SGD) price $1,546,549.00 $1,629,331.00 $1,487,845.00 $1,557,934.00 $1,416,896.00 ($1,259,147.00) ($1,278,837.00) ($1,241,668.00) ($1,289,634.00) ($826,063.00) ln-sale price lnprice 14.069 14.120 14.034 14.071 14.052 (0.557) (0.568) (0.547) (0.566) (0.446) Floor level floor 9.514 10.689 8.680 9.543 9.182 (8.445) (9.461) (7.533) (8.543) (7.229) Unit size (sqm) area 110.715 117.371 105.995 111.298 104.078 (57.682) (59.804) (55.649) (58.569) (45.881) Building age age 4.318 4.260 4.359 4.339 4.079 (7.552) (7.556) (7.549) (7.589) (7.114) Freehold tenure dumfh 0.616 0.661 0.584 0.640 0.349 (0.486) (0.473) (0.493) (0.480) (0.477) Condominium dummy dumcond 0.588 0.625 0.561 0.587 0.599 (0.492) (0.484) (0.496) (0.492) (0.490) Apartment dummy dumapt 0.412 0.375 0.439 0.413 0.401 (0.492) (0.484) (0.496) (0.492) (0.490) Resale dummy (secondary market) dumresale 0.410 0.400 0.417 0.416 0.338 (0.492) (0.490) (0.493) (0.493) (0.473) Newsale dummy (sales by developers) dumnews 0.472 0.446 0.491 0.467 0.531 (0.499) (0.497) (0.500) (0.499) (0.499) Sub-sale dummy (sale by individuals) dumsubs 0.118 0.154 0.092 0.116 0.131 (0.322) (0.361) (0.289) (0.321) (0.337) HDB buyer dummy hdbbuyer 0.343 0.300 0.374 0.344 0.335 (0.475) (0.458) (0.484) (0.475) (0.472) Distance to the nearest MRT station (in meter) distmrt 1536.357 1509.933 1555.096 1646.854 278.000 (903.328) (836.038) (947.718) (857.723) (89.980) Note: The table shows the means (first row for each variable) and standard deviations (in parentheses) for the dependent variable, which is the natural logarithm of housing prices (lnprice), and other control variables (the descriptions and symbols are given in the first and the second columns). The full sample includes 81,360 non-landed private housing sales in a 4000 meter boundary from the MRT stations on the CL. Based on the time of the transactions, the sample is further sorted into a sub-sample of sales before the opening of CL (after =0) (Column 4) and a sub-sample of sales after the opening of the CL (after=1) (Column5). Based on the cutoff distance of 400 meters from the closest MRT station, the sample is also sorted into the treatment group ( 400 meters) (the treatment group) (Column 6) and the control group (>400 meters) (Column 7). ===== PAGE 10 (score 9, numeric-paren 0, table-words 1, var-words 7) ===== Mi Diao, Yi Fan and Tien Foo Sing 73 apartment units in buildings that are below six-storey in height to foreigners. The housing sample is made up of 58.8% of condominiums and 41.2% of apartments. More condominiums (62.5%) were sold in the pre-MRT opening period compared to 56.1% of condominiums sold in the post-MRT opening period. By sale type, we divide the sale samples into the primary market (new and uncompleted units) (“dumresale” =0) and the secondary market (“dumresale” =1), which consist mainly of completed units sold by individual owners. The primary market consists mainly of housing units sold by developers in new developments (“dumnews” 47.2%); some of the new units in projects under construction are sold by individual buyers to other buyers before temporary occupation permits (TOPs) 9 are granted, and these sales are classified as “subsales” (“dumsubs” 11.8%). The resales make up about 41.0% of the sample. The resale units take up 40.0% and 41.7% of the full samples in the “before” and “after” periods, respectively. By the linear distance (in meter) to the closest CL MRT stations, the average distance is estimated at 1,536.357 meters. For the “before” and “after” the CL opening subsamples, the average distances to the closest CL MRT stations are estimated at 1,509.933 meters and 1,555.096 meters, respectively. Figure 2 shows a scatter plot of log-sale price (“lnprice”) against distance to the closest MRT station (“distmrt”). The sale distributions appear random within the 4-km bounded area, though we observe a marginal downward trend in housing price over the MRT distance range. We conduct further tests in the next section to see if the price gradient is monotonically decreasing. 3.2. Cut-off boundary (discontinuity) to the closest MRT stations In our empirical tests for MRT proximity effects on housing prices, the cut-off distance— that shows discontinuity in housing price changes with respects to distance (accessibility) to the nearest MRT station—is important. As an identification strategy, we need to households’ “marginal willingness to pay” (MWTP) for being close to a MRT station; or in other words, we need to identify how far (maximum distance) a typical buyer is willing to “walk” to the nearest MRT station so as to take a MRT ride to his/her destination. Instead of having an arbitrarily selected distance, 10 we use the pre-CL opening sales of non-landed housing that are located within 2-km boundary of the six MRT stations on the existing lines (operational stations) (“MRTold” =1) to test the MWTP for MRT accessibility effects. We create discrete distance dummies starting with the first dummy: “dist2 200 meters”, and an increment of 100-meter in length for subsequent dummies (i.e., “dist3 = 201 to 300 meters”, “dist4 = 301 to 400 meters” ... to “dist15 = 1,401 to 1,500 meters”). We run OLS regressions with two sets of distance “treatment” dummies: the first set includes houses located within the first 1-km boundary from the closest MRT stations, and the second set extends the boundary area to 1.5 km. The models control for heterogeneity in housing and spatial attributes, and sale type. The results are summarized in Table 2. Models 1 and 2 do not have the year of sale fixed effects (“saleyear”); whereas Models 3 and 4 include the year of sale fixed effects. The estimated coefficients on the control variables are consistent across the models, and have the 9. When a building is completed, a developer can apply to the Commissioner of Building Control for a Temporary Occupation Permit (TOP) before giving keys to flats in a project and allowing buyers / owners to move into the building. 10. Diao, Leonard and Sing (2016) propose a local-polynomial-regression-based approach to identify the housing price discontinuity boundary to the MRT stations in Singapore. ===== PAGE 12 (score 166, numeric-paren 141, table-words 3, var-words 4) ===== Mi Diao, Yi Fan and Tien Foo Sing 75 Table 2. Boundary discontinuities in housing prices   (1) (2) (3) (4) Distance<200m dist2 0.090* 0.090* 0.172*** 0.173*** (0.052) (0.049) (0.051) (0.048) 201 to 300m dist3 0.118*** 0.125*** 0.191*** 0.193*** (0.024) (0.023) (0.023) (0.022) 301 to 400m dist4 -0.029 0.009 0.054** 0.085*** (0.022) (0.021) (0.022) (0.021) 401 to 500m dist5 -0.063** -0.040* 0.014 0.034 (0.025) (0.023) (0.024) (0.023) 501 to 600m dist6 0.069*** 0.088*** 0.140*** 0.158*** (0.026) (0.025) (0.026) (0.025) 601 to 700m dist7 -0.035* -0.053*** 0.030 0.008 (0.019) (0.018) (0.019) (0.018) 701 to 800m dist8 -0.156*** -0.161*** -0.087*** -0.096*** (0.019) (0.018) (0.019) (0.018) 801 to 900m dist9 -0.026 -0.052*** 0.062*** 0.033** (0.017) (0.016) (0.017) (0.016) 901 to 1000m dist10 0.030* 0.087*** 0.113*** 0.162*** (0.017) (0.016) (0.017) (0.016) 1001 to 1100m dist11 0.197*** 0.166*** (0.013) (0.012) 1101 to 1200m dist12 0.279*** 0.229*** (0.018) (0.018) 1201 to 1300m dist13 0.208*** 0.236*** (0.022) (0.021) 1301 to 1400m dist14 0.150*** 0.181*** (0.018) (0.017) 1401 to 1500m dist15 0.055** 0.097*** (0.023) (0.022) Floor level floor 0.013*** 0.013*** 0.013*** 0.013*** (0.001) (0.001) (0.001) (0.001) Unit size area 0.006*** 0.006*** 0.006*** 0.006*** (0.000) (0.000) (0.000) (0.000) Condominium dummy dumcond 0.226*** 0.219*** 0.231*** 0.235*** (0.009) (0.008) (0.009) (0.008) Freehold dummy dumfh 0.251*** 0.259*** 0.223*** 0.235*** (0.011) (0.011) (0.011) (0.011) Age age -0.011*** -0.012*** -0.010*** -0.011*** (0.001) (0.001) (0.001) (0.001) Resale dummy dumresale -0.069*** -0.091*** -0.072*** -0.093*** (0.011) (0.011) (0.011) (0.011) Constant Constant 13.038*** 12.969*** 12.968*** 12.905*** (0.016) (0.015) (0.016) (0.015) R-squared 0.674 0.706 0.692 0.722 Note: This table summarizes the OLS regression results with the ln-housing price (lnprice) as the dependent variables. The models are estimated using only 7,679 housing transactions located within 2,000 meters from the 6 existing MRT stations (Buona Vista, Paya Lebar (East-West line), Bishan, Dhoby Ghaut inter-change (North-South line), Serangoon and Harbour Front (North-East line) before the opening of the CL. The “dist2” and “dist_k”, where k=(3…15) are distance dummies with a 100-meter interval. Column (4) includes the planning sector fixed effects (FE), which is defined by the first 2 digits of the postal code. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 correct signs. The models show that housing attributes, such as floor level, unit size, freehold tenure, and condominium dummy all have positive effects on housing prices; whereas, the age variable has a negative effect on housing prices. Housing prices in the resale market are lower than prices for comparable houses in the developers’ sale market. In Models 1 and 2, where housing sales in the 1-km to 2-km boundary are used as the control sample, we find that houses located in the first 200 meters and between 201 and 300 meters have positive price premiums relative to the control sample of houses located outside the 1-km boundary from the nearest CL MRT stations. The proximity premiums ranging between 9.0% and 12.5% are ===== PAGE 13 (score 11, numeric-paren 0, table-words 3, var-words 5) ===== A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line 76 statistically and economically significant. However, the premiums disappear for houses in the 301- m to 400-m region, and then become negative for houses located in the 401-m to 500-m region. The premium is positive again in the 501-m to 600-m region, and then becomes negative beyond the 601-m radius range. The results are consistent in the two models, and we observe a reasonable clear discontinuity in housing price at the 301-m radius, though we believe the 601-m radius could be caused by the trade-off against other negative externalities associated with being close to MRT stations, such as noise, traffic congestion, etc. (Bollinger and Ihlanfeldt, 1997; and Bowes and Ihlanfeldt 2001) In Models 3 and 4, where we include 5 more discrete distance dummies ranging from 1,001 to 1,500 meters into the treatment group, and use housing sales between 1,600 meters and 2,000 meters as the control sample. The two models are consistent and the coefficients do not vary significantly in Model 4, where the year of sale fixed effects are added. We observe a similar pattern of price increases for houses located near the MRT stations, and the positive premiums are economically and statistically significant and positive in the first 400 m range. The coefficient on “dist5” (401- to 500-meter range) is positive but insignificant, and the coefficient on “dist6” (501- to 600-meter range) is again positive and significant. Figure 3 plots the coefficients on the “dist_i”, where [i= (2, 3, ….., 15]; and we could see the clear discontinuity in the price premiums for houses located up to 400 meters from the closest MRT stations on the CL. The positive premiums for houses located in the range from 1,000-m to 1,500-m range relative to the control sample (1,501-m to 2,000-m range) could be due to factors other than being in close proximity to the CL MRT stations. Figure 3. Estimated coefficients of the distance to the closest CL MRT stations Note: The graph shows the estimated ln-housing price regression against different dummies on the distance to the closest MRT stations (“dist2” and “dist_k”, where k=(3, 4, …15), where the sample includes houses that are located within 2,000 meters from the nearest CL MRT stations. We control for other variables in the models as shown in Table 2 (Column 4). Therefore, based on the above results, we set the treatment boundary at 400 meters to capture the proximity (accessibility) effects; we use a dummy variable, “dumMRT”, to sort houses into the treatment group if they are located within 400 meters from the nearest CL MRT station, and ===== PAGE 17 (score 81, numeric-paren 60, table-words 5, var-words 5) ===== A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line 80 Table 3. Treatment effects of the opening of new CL MRT stations Sample criteria: Distance to the MRT stations (“distMRT”) (1) (2) (3) <=4000m <=4000m <=2000m Floor level floor 0.015*** 0.007*** 0.007*** (0.000) (0.000) (0.000) Unit size area 0.007*** 0.007*** 0.007*** (0.000) (0.000) (0.000) Condominium dummy dumcond 0.133*** 0.165*** 0.154*** (0.003) (0.002) (0.002) Freehold dummy dumfh 0.149*** 0.099*** 0.086*** (0.003) (0.002) (0.003) Age age -0.011*** -0.009*** -0.008*** (0.000) (0.000) (0.000) Resale dummy dumresale 0.017*** -0.052*** -0.051*** (0.003) (0.002) (0.003) After the opening of MRT line after 0.051*** 0.016*** 0.015*** (0.002) (0.002) (0.003) Proximity to MRT (≤ 400m) dumMRT 0.126*** 0.042*** 0.049*** (0.007) (0.005) (0.005) After × Proximity to MRT aftdumMRT -0.078*** 0.132*** 0.122*** (0.009) (0.006) (0.006) Constant Constant 12.959*** 13.532*** 13.539*** (0.004) (0.008) (0.009) Observations 81,316 81,316 57,417 R-squared 0.669 0.865 0.859 Planning Sector FE No Yes Yes Year of Sale FE No Yes Yes Note: This table summarizes the OLS regression results with the ln-housing price (lnprice) as the dependent variables. Models in Columns (1) and (2) are estimated using a sample of 81,316 transactions found within a 4,000-meter boundary from the closest CL MRT stations, and Column (3) is estimated using a sub-sample that includes only 57,417 transactions within a 2000 meter boundary. The models include a vector of control variables that include floor level (“floor”), unit size (“area”), condominium dummy (“dumcond”), freehold dummy (“dumfh”), age, and resale dummy (“dumresale”). “After” is the treatment dummy that have a value of 1, if a sale occurs after the CL opening; and otherwise 0 for a sale occurs before the CL opening. “dumMRT” is the proximity dummy that has a value of 1, if a house is located within 400 meters from the closest CL MRT station; and 0 otherwise; and “aftdumMRT” is the interactive term of the two treatment variables. The planning sector fixed effects (FE) is defined by the first 2 digits of the postal code, and the year of sale fixed effect is also included in the model. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 5. Empirical Analysis and Results 5.1. Housing wealth effects and MRT line opening We estimate the baseline OLS model as in Equation 1 without the year of sale and the planning sector (spatial) fixed effects, and use only the housing sales that fall within 4,000 meters from the closest MRT station so as to minimize boundary discontinuity effects. The results are summarized in Column 1 of Table 3. The adjusted R 2 of the model is estimated at 0.669. The estimated coefficients on the vector of hedonic attributes are all statistically significant and have the correct signs. Variables such as floor level, unit size, condominium dummy, freehold dummy and resale dummy are positively correlated with housing prices; whereas, housing age is negatively correlated with housing prices. The results on the key treatment variables in the model shows that the CL opening has positive impact on housing prices, and houses located within 400-meter radius from the CL MRT stations are sold for higher prices than other houses located outside the vicinity. The results seem to be consistent with the expected treatment outcomes. However, when we look at the interactive term, it ===== PAGE 18 (score 17, numeric-paren 0, table-words 6, var-words 5) ===== Mi Diao, Yi Fan and Tien Foo Sing 81 has an incorrect sign, which implies that housing prices in the treatment area decrease more than the house prices in the control area after the CL opening. The results could be influenced by unobserved spatial heterogeneity and time variations that are not controlled for in the model. In Column 2, we include the planning sector and the year of sales fixed effects to the model, and the goodness of fit of the model increases significantly to 0.865. While most of the estimated coefficients on the hedonic variables are significant and have the correct signs, but the values of the coefficients are different. For the resale dummy, we find that the coefficient becomes negative after controlling for the spatial and temporal variations in the housing price model. We show that the treatment effects are more consistent with the expected outcomes. The results show that housing prices in the post-CL opening period increase at a more moderate rate of 1.6% compared to 5.1% estimated in the baseline model. We find that houses located within 400 meters from the closest CL MRT station sell for a price of 4.2% higher than comparable houses located outside the 400-m MRT station region. The interactive term (“aftdumMRT”) is also statistically significant and has a positive sign, which implies that houses with the accessibility convenience to the new CL (“dumMRT” = 1) sell for 13.2% more expensive than other houses without the accessibility convenience to the new CL. The results confirm that the opening of CL generates positive housing wealth effects to households who live and own houses in close proximity to MRT stations along the CL. We conduct a robustness test by reducing the sample coverage area from 4,000 meters to 2,000 meters, which cut the sample size from 81,316 observations to 57,417 observations. The adjusted R 2 of the model as shown in Column 3 of Table 3 decreases only marginally to 0.859. The estimated coefficients for the Model (3) are consistent with the estimated coefficients of the early Model (2). We find significant treatment effects, which show that the opening of the new CL increases prices for houses located within 400 meters from the closest CL MRT station by 12.2% in the post-opening period relative to price changes of other houses that are located outside the 400-m range. The results again support the story that the new CL opening generates positive economic effects on houses that are within an accessibility distance to the MRT stations along the line. 5.2. Effects on existing MRT stations The CL is connected to other major MRT lines via six existing MRT stations, which are denoted by the dummy “MRTold”. Except for the two terminating stations on the CL (Harbour Front in the west and Dhoby Ghaut in the centre), four of the existing stations serve as the interchanges that connect the CL to other major MRT lines. These four interchanges are Buona Vista station and Paya Lebar station on the East-West line, Bishan station on the North-South line, and Serangoon station on the North-East line. Commuters living in the city fringe areas could travel to the outskirts of the island and the CBD with MRT via these four interchanges. We first use the six existing stations to establish the cut-off distance for the MWTP in Table 2; and as additional robustness tests, we next test how the opening of new stations on the CL affects housing prices around these existing stations relative to other stations. The results of the panel regressions with the spatial and time fixed effects are summarized in Table 4. Column 1 shows the results when all the six existing stations as identified by the dummy “MRTold” are included in the triple-interactive term, (“after×dumMRT×MRTold”); whereas Column 2 replaces the “MRTold” with the “interchg” dummy to capture the incremental effects of ===== PAGE 19 (score 60, numeric-paren 40, table-words 5, var-words 6) ===== A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line 82 the four interchanges on the CL line (Buona Vista, Bishan, Serangoon and Paya Lebar stations). The estimated coefficients on the control variables are significant, and the overall results are consistent with those reported for Model 2 in Table 3. The results show an average growth in housing prices of 2.6% after the CL opening, and houses located within the 400-m radius from the CL stations enjoy significant price premiums of about 13.0% relative to other houses located outside the 400-m range. We find that the coefficients on the two triple-interactive terms—“after×dumMRT×MRTold” (Model 1) and “after×dumMRT×interchg” (Model 2)—are significant, but have negative values of -0.093 and -0.096, respectively. The results imply that while housing prices near the CL MRT stations in general increase following the opening of CL, housing prices increase at a faster rate in other CL stations than housing prices in the existing stations (both “MRTold” and “interchg”) (“dumMRT|MRTold=0” and “dumMRT|interchg=0”). The new CL generates incrementally higher housing wealth effects to households, who were previously not served by the CL line, relative to households who have already had the access to the existing lines via the six stations. Table 4. Treatment effects in existing MRT stations and inter-changes (1) (2) Floor level floor 0.007*** 0.007*** (0.000) (0.000) Unit size area 0.007*** 0.007*** (0.000) (0.000) Condominium dummy dumcond 0.163*** 0.163*** (0.002) (0.002) Freehold dummy dumfh 0.094*** 0.094*** (0.002) (0.002) Age age -0.009*** -0.009*** (0.000) (0.000) Resale dummy dumresale -0.050*** -0.050*** (0.002) (0.002) After the opening of MRT line after 0.026*** 0.026*** (0.002) (0.002) Proximity to MRT (≤ 400m) dumMRT 0.129*** 0.130*** (0.003) (0.003) After × Proximity × Existing MRT station aftdumMRTold -0.093*** (0.009) After × Proximity ×Interchange aftdumMRTinterchg -0.096*** (0.009) Constant Constant 13.509*** 13.511*** (0.008) (0.008) Observations 81,316 81,316 R-squared 0.864 0.864 Planning Sector FE Yes Yes Year of Sale FE Yes Yes Note: This table summarizes the OLS regression results with the ln-housing price (lnprice) as the dependent variables. The Models are estimated using a sample of 81,316 transactions found within a 4,000-meter boundary from the closest CL MRT stations. The models include a vector of control variables that include floor level (“floor”), unit size (“area”), condominium dummy (“dumcond”), freehold dummy (“dumfh”), age, and resale dummy (“dumresale”). “After” is the treatment dummy that have a value of 1, if a sale occurs after the CL opening; and otherwise 0 for a sale occurs before the CL opening. “dumMRT” is the proximity dummy that has a value of 1, if a house is located within 400 meters from the closest CL MRT station; and 0 otherwise; and “aftdumMRT” is the interactive term of the two treatment variables. The triple-interactive terms “aftdumMRTold” and “aftdumMRTinterchg” interact “aftdumMRT” with the two station-related variables, where “MRTold” is used to identify six of the existing MRT stations ((Buona Vista, Paya Lebar (East-West line), Bishan, Dhoby Ghaut interchange (North-South line), Serangoon and Harbour Front (North-East line)), and “interchg” identifies four of them that are used as the inter-changes on the CL line (Buona Vista, Paya Lebar (East-West line), Bishan (North-South line), Serangoon (North-East line). The planning sector fixed effects (FE) is defined by the first 2 digits of the postal code, and the year of sale fixed effect is also included in the model. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ===== PAGE 20 (score 9, numeric-paren 0, table-words 2, var-words 5) ===== Mi Diao, Yi Fan and Tien Foo Sing 83 5.3. Heterogeneity Tests We run some heterogeneity tests to further identify if the differential economic spillovers effects of the new CL between new sales (consist mainly of pre-completion sales by developers) and resales (completed), and private buyers and public housing upgraders. The results are summarized in Table 5. Column 1 shows the incremental effects of the new CL line on the resale housing market vis-à- vis the new (pre-completion) sale markets. We find that the new CL have larger incremental on the treatment group of houses in the new sale markets than those in the resale market. The differential price effects associated with the accessibility benefits of the CL are about 6.4% with the developers’ new houses enjoy the greater price increases relative to houses sold in the secondary market. There is anecdotal evidence showing that private developers capitalize on the opening of the CL in the launch of new projects, such as Sky Habitat 11 that was labelled as the most expensive suburban residential project when it was launched in April 2012 by CapitaLand, one of the largest listed developers in Singapore. 12 By buyer type, we separately identify new private housing buyers, who previously live in HDB flats identified with public housing addresses, and other housing buyers with private housing addresses. We denote the “public housing upgraders” group by a new dummy variable, “HDBbuyer”; and they make up about 34.3% of the sample buyers. We observe a significant increase in the number of upgraders from 30.0% to 37.4% in the two periods “before” and “after” opening of the CL. In Column 2, the triple-interactive term shows that HDB upgraders pay 4.3% more than other private housing buyers for houses located within 400 meters from the closest MRT stations after the CL opening, ceteris paribus. Public housing buyers, who are more likely to use MRT to commute on daily basic, place higher MWTP premiums for accessibility benefits to the CL MRT stations. 5.4. Temporal variations of the MRT line opening As the CL opened in stages over a 2.5-year period from May 2009 to Jan 2012, we test the temporal variations in the treatments associated with the CL opening events. Based on the gradual information diffusion argument, we expect the earliest phase of CL opening (the stretch from Bartley station to Marymount station) to have larger incremental price impact than the last phase of the CL line extension from Promenade station to Marina Bay station. We represent each of the four phases of CL opening with a dummy “Ph_k”, where k = (1, 2, 3, 4), and replace the treatment term “after” in Equation (1) with the four new interactive terms: “aftPh_k” and “aftPh_k×dumMRT”. The regression results are reported in Column 1 of Table 6. The results show significant negative price changes after the phase 1 (“aftPh_1”: May 2009), phase 3 (“aftPh_3”: October 2011) and phase 4 (“aftPh_4”: January 2012); and the only period that show positive price growth is after the phase 2 of the CL opening (“aftPh_2”: April 2010). The first period decline could be caused by the negative sentiment from the aftermath of the Subprime Crisis in 2007–2008; whereas the negative growth in October 2011 and January 2012 could be caused by the impact of cooling measures introduced by the government during the periods. We next examine 11. “CapitaLand sells 125 units at Sky Habitat”, by Kalpana Rashiwala, The Business Times, 16th Apr 2012. 12. “Sky Habitat effect’ lifts sales in other estates”, by Esther Teo, The Straits Times, 16th Jun 2012. ===== PAGE 21 (score 57, numeric-paren 40, table-words 4, var-words 5) ===== A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line 84 the treatment effects shown by the triple “diff-in-diff” variables in Model 1, and find that stronger price increases when the phase 1 CL opened in May 2009, which creates positive price effects of 19.2% for houses located within 400 meters from the stations along the Bartley and Marymount stretch. The incremental price effects are still positive in the phases 2 and 3 of the CL opening, but with a weaker magnitude of 12.5% and 3.4%, respectively. There is no significant price effect shown in the phase 4 extension of the CL to the Marina Bay in January 2012. The results are consistent with the information diffusion hypothesis, which predicts strong price effects when the CL opening event was first revealed in the market. Table 5. Effects on developers’ sales and public housing upgraders (1) (2) Floor level floor 0.007*** 0.007*** (0.000) (0.000) Unit size area 0.007*** 0.007*** (0.000) (0.000) Condominium dummy dumcond 0.164*** 0.163*** (0.002) (0.002) Freehold dummy dumfh 0.096*** 0.095*** (0.002) (0.002) Age age -0.009*** -0.009*** (0.000) (0.000) Resale dummy dumresale -0.048*** -0.052*** (0.002) (0.002) After the opening of MRT line after 0.026*** 0.024*** (0.002) (0.002) Proximity to MRT (≤ 400m) dumMRT 0.134*** 0.111*** (0.003) (0.003) After × Proximity × Resale aftdumMRTresale -0.064*** (0.006) After × Proximity × HDB buyer aftdumMRThdbbuyer 0.043*** (0.006) Constant 13.507*** 13.514*** (0.008) (0.008) Observations 81,316 81,316 R-squared 0.864 0.864 Planning Sector FE Yes Yes Year of Sale FE Yes Yes Note: This table summarizes the OLS regression results with the ln-housing price (lnprice) as the dependent variables. The Models are estimated using a sample of 81,316 transactions found within a 4000 meters boundary from the closest CL MRT stations. The models include a vector of control variables that include floor level (“floor”), unit size (“area”), condominium dummy (“dumcond”), freehold dummy (“dumfh”), age, and resale dummy (“dumresale”). “After” is the treatment dummy that have a value of 1, if a sale occurs after the CL opening; and otherwise 0 for a sale occurs before the CL opening. “dumMRT” is the proximity dummy that has a value of 1, if a house is located within a 400meter from the closest CL MRT station; and 0 otherwise; and “aftdumMRT” is the interactive term of the two treatment variables. The triple-interactive terms “aftdumMRTresale” and “aftdumMRThdbbuyer” interact “aftdumMRT” with the two new dummies, which include “dumresale” that indicates non-developers’ sales, and “hdbbuyer” identifies public housing upgraders. The planning sector fixed effects (FE) is defined by the first 2 digits of the postal code, and the year of sale fixed effect is also included in the model. . Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Like McDonald and Osuji (1995) and McMillen and McDonald (2004), we test if “anticipative” effects occur around the CL opening event. We use a 6-month pre-opening dummy, “bef6mth”, which has a value of 1 if a house were sold in less than 180 days (6 months) before the opening of the closest CL MRT stations, to capture the “anticipative” effects, alongside the post-opening effects in the Model 2. Our results show no “anticipative” effects associated with the CL opening, but prices of houses located close to the MRT stations were 4.4% lower than prices of other houses located outside the 400m cut-off boundary in the pre-opening of CL. However, houses located in ===== PAGE 22 (score 114, numeric-paren 89, table-words 6, var-words 4) ===== Mi Diao, Yi Fan and Tien Foo Sing 85 the treatment zone increase by 14.3% more than prices of comparable houses outside the treatment zone in the post-CL opening period. We next replace the “after” dummy by “Ph_k”, which captures different opening event time effects, and estimate the regression model as shown in Column 3 of Table 6. The results are consistent with the earlier findings, where we observe negative price changes in the pre-opening period, and the information impact is positive in the next three phases of the CL opening but at a declining rate. The news of the extension of the CL (phase 4) has no incremental impact on housing prices in the treatment zone. Table 6. Temporal effects of the opening of CL (1) (2) (3) Floor level floor 0.007*** 0.007*** 0.007*** (0.000) (0.000) (0.000) Unit size area 0.007*** 0.007*** 0.007*** (0.000) (0.000) (0.000) Condominium dummy dumcond 0.168*** 0.165*** 0.168*** (0.002) (0.002) (0.002) Freehold dummy dumfh 0.099*** 0.098*** 0.098*** (0.002) (0.002) (0.002) Age age -0.009*** -0.009*** -0.009*** (0.000) (0.000) (0.000) Resale dummy dumresale -0.052*** -0.053*** -0.053*** (0.002) (0.002) (0.002) Proximity to MRT (≤ 400m) dumMRT 0.038*** 0.031*** 0.028*** (0.005) (0.005) (0.005) 6 months before MRT opening bef6mth -0.007** -0.017*** (0.003) (0.003) 6 months before MRT opening x Proximity to MRT bef6mthdumMRT -0.044*** -0.037*** (0.010) (0.010) After the opening of MRT line after 0.021*** (0.003) After×Proximity to MRT aftdumMRT 0.143*** (0.007) AfterxPhase 1 aftph1 -0.041*** -0.028*** (0.005) (0.006) AfterxPhase 2 aftph2 0.046*** 0.058*** (0.003) (0.003) AfterxPhase 3 aftph3 -0.007* -0.001 (0.004) (0.004) AfterxPhase4 aftph4 -0.022*** -0.020** (0.008) (0.008) After×Phase1×Proximity to MRT aftph1dumMRT 0.192*** 0.202*** (0.008) (0.008) After×Phase2×Proximity to MRT aftph2dumMRT 0.125*** 0.134*** (0.008) (0.008) After×Phase3×Proximity to MRT aftph3dumMRT 0.034*** 0.044*** (0.011) (0.011) After×Phase3×Proximity to MRT aftph4dumMRT -0.080 -0.070 (0.054) (0.054) Constant 13.545*** 13.535*** 13.549*** (0.008) (0.008) (0.008) Observations 81,316 81,316 81,316 R-squared 0.866 0.865 0.866 Planning Sector FE Yes Yes Yes Year of Sale FE Yes Yes Yes Note: The table summarizes the regression with the ln-housing price (lnprice) as the dependent variable. The Models are estimated using a sample of 81,316 transactions found within a 4,000-meter boundary from the closest CL MRT stations. “Phase 1” = 28 May, 2009: (Bartley–Marymount); “Phase 2” = 17 April, 2010: (Dhoby Ghaut–Bartley) (eastern stretch); “Phase 3” = 8 October, 2011: (Marymount–Harbour Front) (western stretch); “Phase 4”= 14 January, 2012: CL Extension (CLe) (Promenade–Marina Bay). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 ===== PAGE 23 (score 9, numeric-paren 0, table-words 0, var-words 9) ===== A new Mass Rapid Transit (MRT) line construction and housing wealth: Evidence from the Circle Line 86 6. Conclusion The bid-rent theory of Alonso (1964), Muth (1969), and Mills (1972) has shaped urban growth of monocentric cities for decades. Household preference for bigger space and tranquillity of living environment in suburban areas induces them to live further away from central areas. The trade- off between commuting costs and housing consumption results in a negative bid-rent curve, which predicts that housing price declines with respect to distance from the CBD. However, with large public investments in transit and MRT networks that improve connectivity and accessibility to suburban areas, the Alonso-Muth-Mill’s bid-rent theory has become less relevant in the urban structure in today’s modern cities. There are two competing hypotheses that explain possible positive rent gradient from the CBD: First, Richardson (1977) argues that less congested and low- density living environment in suburbs creates externality rents for high income households, who pay higher housing prices in areas located further away from the CBD. Second, the decentralisation trend also reduces the CBD effect. The emergence of multiple regional centres changes a monocentric city structure to a poly-centric one. Richardson et al. (1990) show that the distance- to-CBD effects on bid rent is still statistically significant but weak in 1970, and the distance effects disappeared in 1980 in some US cities. This study empirically tests housing wealth effects associated with the opening of new CL MRT Line in Singapore. Using non-landed private housing transactions for the period from January 2009 to May 2013, we find that the opening of the new CL MRT line increases housing prices by 1.6%, on average. Houses in the treatment zone, which is within 400 meters from the CL MRT stations, sell for 4.2% higher in price than comparable houses located outside the 400-m MRT zone. Housing price increases in the MRT treatment region are 13.2% more than housing price increases of houses located outside the treatment regions after the CL opening. Based on 6,564 houses sold for an average price of S$1,416,896 in the post-CL opening period, which is translated into an aggregate value of S$9.30 billion, we estimate the housing wealth of S$1.23 billion accrued to households living in 400-m radius from the closest MRT stations after the opening of the CL. We also find that the accessibility premiums are larger in developers’ market, and for public housing buyers (HDB upgraders) who pay relatively higher accessibility premiums relative to private housing buyers. When we test temporal variations in the treatment effects, we find no significant “anticipative” effects in the six-month window prior to the opening of the new CL. Housing price in the treatment zone increases by more than 14.3% relative to housing prices outside the treatment zone after the CL opening. We find significant positive treatment effects in the first three phases of the CL opening, but the treatment intensity diminishes after phase 1. The results are consistent with the information diffusion hypothesis, and the results find no sentiment-driven momentum effects. Governments of rapidly expanding cities invest heavily in public transit projects to meet increasing transportation demand. In countries including Singapore, new public transit lines are seen as a key driver for economic and employment growth.13 There are also other non-pecuniary benefits associated with public transit lines, which include reductions in road congestion and greenhouse 13. Mr Ngiam Tong Dow, the then permanent secretary of the Ministry of Communications, used the economic motivations to persuade the government to commit on the MRT project in Singapore. Sources: “Success and Failure of Public Policies: The Singapore Experience, 1960-2000”. In A Mandarin and the Making of Public Policy: Reflections by Ngiam Tong Dow. Singapore: NUS Press, 2006, pp 150.