List of content Page
Summary of the Research 1
Introduction
Introduction and Motivation 2
Research Question 3
Conceptual Framework 3
Contribution Literature
Relevant literature review 3
Empirical 4
Methodology and Data
Methodology 4
Data Collection 5
Econometric Specification 5
Hypothesis 6
Main Result and Analysis 7
Implication of Finding 9
Conclusion 10
Limitations 10 Future Work 11
List of Reference 11
Appendix 12
Summary of the research The lifestyle of human being is steadily changed all the time. Obviously, the crucial living place was significantly different between rural and urban. Private house with traditional style is enormous set in the rural area. In contrast, with the rush hour in a city, Condominium is more popular and appropriated for urban living. Especially, the world busiest road in Bangkok, townspeople wish for accessibility of rapid transportation called BTS which is exactly punctual. Looking back to the history of Green light BTS line, it was established (2011) upon Bangna road. To build the rapid transit, the government spent a huge amount of budget to develop each zone, but it was a tradeoff with more comfortable lifestyle and other factors. Non-life objects also receive benefits by building it, such as price of land, location change, migration, standard of living and prosperity in economy. Think more deeply, it leads with a change of many economic indicators such as income per capita, job creation, and purchasing power and so on. In 2011, the Bangkok metropolitan opened five new stations which are Bang Chak, Punnawithi, Udom Suk, Bang Na and Bearing Station. The transportation mode is changed the routine of people in those areas. People have more choices to chose the mode of transportation and facilitate lifestyles. As a result, the BTS significantly make change to the area. On factor of the change is the living style. Condominium is one of the modern living places which are the most popular in all civilized cities around the world. Thus, we meet the main study which is condominium price. Furthermore, the study involved the relationship between BTS factors and Condominium price. In the real world, the residential condominium separates into two types, first is a sale price, and second is a rental price. Researchers will prove whether sale market and rental market have a same trend or not.
Looking closer, “How rapid transit affects Condominium price in Bangna?” The question can be determined when factors such classification of condominium, price for sale, rental price budget of the project come along with economic factors such distant to the BTS, Time, and economic circumstance are analyzed together. It is undeniable that Bangna is one of the new growing areas of Bangkok. Researcher is interesting in the popular area in the year 2008-2013. The finding from this research should make a clearer picture between real estate and mega-project as rapid transit.
Introduction
Introduction and Motivation
Prior research on the impact of Transit-oriented development prices in San Diego (Michael Duncan,2010) suggests that Transit-oriented development is a primary key of condominium market expansion. Interaction between station distance and pedestrian orientation are concerned. The result of the model shows it is a significant relationship between proximity distance and price of Condominium. He concludes that the living place in California with auto-oriented has no power to sale in high price. Unfortunately, the research ignores other environment factors such as river-oriented, near to facilities which can also affect the price. Other argument is United States has many comfortable types of transit, the effect of subway would actually different than Bangkok; the cruel traffic jam city. Second, The Influence of rapid transit system on condominium price in expansion of Silom BTS, Bangkok (Thamrongsrisook, 2011) uses Chau et. al. (2001) and So et. al. (1996) (as cited in Chau & Chin, 2002) called Hedonic Price Function which we argue that the factor in the function lacks of external aspect leads to a high standard of error. Another point, Silom line is more expansion to Thonburi side, the result is not about price premium in a short run, but in the long run evidence buys many cheap condominiums around Bangwa station.
Research Question
Researchers are interesting in price premium of condominium, especially in Bangna. As we mentioned, to be specific, we choose an area around 5 BTS expanded stations of Sukhumvit line to study relationship with green light rapid transit. Thus, ours research question is “How accessibility of rapid-transit affects price premium of each Bangna condominium classification?”
Conceptual framework and Theoretical Foundation
We have mentioned on Hedonic Price Function by Chau et. al. (2001) and So et. al. (1996) (as cited in Chau & Chin, 2002) model is provided as
Which P= Price of Property (Sale Price of condominium of studio room, Rental price)
L= Location attribute (Distance to the closest station, Distance to the closest CBD)
S= Structure attribute ( Land price, total floor)
N= Neighborhood attribute (garden, swimming pool, fitness club, population density)
Relevant literatures
Gatzlaff&Smith(1993) study about the impact of Metrorail how affects with living place near Miami station by using Hedonic Price Function. The result comes up with a significant relationship between the station and housing. Forrest, Glen, and Ward (1996) study about the impact of light train and house price by using dummy in distance. The result is an inverse relationship between house price and light train. Then, Tse, Ganesan(1997) estimate transport on house price. They used Box-Cox Function Model. The result is unpredictable because one coefficient shows unexpected sign. Gibbons and Machin (2005) study house price in term of transportation development using quasi-experimental theory. Conclusion is transportation is a key drive of house price. Celik and Yakaya (2006) study of transit investment in variety of areas such as poverty valued area in developing country based on Turkey. The result is longer distance from a station, property price is cheaper.
Empirical Strategies
Independence variable is price of studio room per square meters and rental price of the room per month while dependent variable is Distance to the closest station, Distance to the closest community, CPI, Land price per square meters, Brand perception in the year 2008-2013 Ours concern is to compare the trend between sale price and rental price including land price when the BTS built pass though the district. Simply says, the researcher will run three times of regression then explain the result at last.
Methodology
This Thesis applied quantitative method to determine the research to receive a significant in term of quantitative relationship among price and other involved factor. The benefit of using method is the result is easy understandable. However, Bryman and Bell (2007) argued that this method sometimes is not significant because it always conflict with logic while people affirm that they have a right number leads to distortion of the result by individual opinion. Moreover, the quantitative data is also depends on how precise of raw data and research process (Collis & Hussey, 2009).
Planned estimation or analysis
The research question is set in a related to the content, references literatures review. The main research question is “How accessibility of rapid-transit affects price premium of each Bangna condominium classification?”
The thesis estimates a relevant of how rapid transit system affects the price and types of the condominiums in Bangna, Bangkok. The research is carried throughout a popular model named hedonic price model referred at literature review. The area has chosen in specific area between On-Nut to Bearing station. The Well-known central business district (CBD) with the completed transportation system made a lot of condominiums established there. Detailed information and all relevant factors such as distance, size of the room, types of condo and period of time will carefully discuss in based on hedonic price model regression function.
Data Collection Researchers divide data into two parts. First, the data will be collected via from secondary resources such as reliable authority internet of popular company paper. Moreover, the data from other relevant literatures are used in ours research. Specifically, average condominium price based on the top 30 around the 5 new expanded stations derived yearly between 2008 and 2013. Second method of collection is lunching questionnaire asking personal income to define an exactly relation with condominium price in that area 120 people.
Econometric specification * price_unit: Price of a stereo room announced in established year per square meters * rental_unit: Rental price of a studio room provided in the year in a room per month * cpi: Consumer price index based 2008 * price_land: The minimum announced land price per square meters nearby the condominium (in the same Soi.) * distance_station: distance between a condominium to the closest station * distance_community: distance from a condominium to the closest standard department store ex. Lotus, Big C * floor: total floors of condominium * pop_density: Population density per square meters in each district * well_known: the brand image classified by real estate department AU * well_finance: the brand with good financial statement * well_customer: the brand with honestly customer * well_landnego: the brand with powerful land price negotiation (All are dummy variables)
Hypotheses
* Rental price per month(rental_unit) has positive impact to the close distance of community and BTS * CPI(cpi) has positive impact to the price of condominium in both sale and rental market * Price of a land per Sq. M.(price_land) has positive impact to the closer distance of community and BTS * Distance to the closest station (distance_station) has positive relationship to the price of condominium in both markets. * Distance to the closest Community (distance_community) has positive relationship to the price of condominium in both markets. * Total Floor levels(floor) has positive relationship to the price of condominium in both markets. * Brand perception(well_known, well_finance, well_customer, well_landnego) has positive relationship to the price of condominium in both markets.
Main result and analysis We run OLS regression on the data collected between 2008 and 2013. There are two main results concerns, first is to compare the trend between sale market and rental market which is two markets of real condominium trade including the expected involved factor such as land price. Second, we also focus to the year 2011 which is the year that BTS was built and the effect to the sale market and rental market the focused year is 2008 to 2013. Firstly, we prepared the data sets which are year of building each condominium, sale price of each condominium, rental price per room, CPI, population density, distance to the closest station, distance to the community and the total floor. Then, we will turn those factors to logarithm in order to see clearly result analysis in term of percentage. Then, we are ready to run the first regression. The first regression is to analyze the relationship between sale price with the closest distance to the station, distance to the closest community, population density, total floor and brand perceptions. The result show in the Table1 in appendix, we can see that the more one more percent year condominium will decrease the sale price .218 percent. The distance to the closest BTS station also affect to the sale price. The result shown, one further one percent kilometer from the closest station, sale price will decrease by .083 percent. As the same way as the distance to the closest community, the further one percent kilometer far from the department store, the sale price will decrease .232 percent. Total floor also affect to the sale price. One more percent of floor will increase the sale price about .138 percent. Other important factor is brand perception, as the data collected from the Department of Real Estate Assumption University shown that the better known name one percent, the higher sale price 19.9 percent. On the other hands, we move to look at the rental price whether it has the same trend as the sale price or not. The result has posted in Table 1 appendix; the result shows the same trend as the sale market. The rental price will decrease .259 percent when one year older one percent. Moreover, the distance to the closest station has the same trend as sale market. One further one percent of kilometer from the station will decrease the rental price .192 percent. As the same way as distance to the closest community, when the condominium set one further one percent kilometer from the department store, the rental price will reduce by .20 percent. Total floor is also affects the rental price. The higher floor one percent will make rental price can charge more .147 percent. However, the only thing different from the sale market is brand perception is not significant effect to the rental price. The researcher wonders whether land price has the same effect or not. Thus, we expand our study to have one more regression. The given result, we found that the result is conflict with the fact of the rule of land price which always increase by the time. We found that, one more percent of age for the condominium will makes the land price decrease 1.48 percent while the another factor effect to the land price is distance to the community. Saying, one further one percent kilometer from the closest apartment store, the land price will decrease .267 percent. Second study, the researcher wants to study whether the price for both markets did get an effect from the coming of BTS in the year 2011 or not. The method is, we compare the price of both market before and after the year of BTS on service (2011).The result is clearly shown in the Table 1 in appendix. The result proves that the price in both markets before and after 2011 did not change much because the investor might knows the news before the year 2008. Thus, both sale market and rental market of condominium does not much difference between 2008 and 2013 from the opening of BTS green line. To make a supportive result, the researcher launched 120 online-questionnaires. Specifically spread to the people who have a condominium in the study area. From the questionnaire 65 percent has a condominium and 42 people have not. Forty-five people strongly support the statement that BTS is a key factor before choosing condominium. The strongest effect to the renting price of condominium 66 percent response is distance between condominium and BTS. While the weakest factor affects to the renting price is total floor; 38 percent from total responses. Sixty-seventh percent of people has working place or school not further from 2 kilometers from the BTS station. The reason of purchasing condominium is 30 percent for investment, 69 percent for self-living and 1 percent for other purpose. The most 38 percent of our sample has the Entry Level (price below than THB60, 000 per square meter). For other results please see in appendix.
Implication of Finding and Conclusion
Implication of Finding * Sale market and rental market almost got the same trend of relationship from provided variables which are ages of condominium, distance to the closest station, distance to the closest community and total floor level except brand perception has a positive relationship with only sale market. * When the BTS built the price of the land increase 239 percent while distance to the community has positive effect to the land price. * Both sale price and rental condominium price compare to ”BTS on service” before and after the year 2011 did not significant difference.
Conclusion
In this research paper, we have two main focuses. First, we want to compare between sale market and rental market in the specific area which is the 6 stations in green line rapid transit. Second, we want to compare the price of both markets before and after the BTS on service 2011.
In conclusion, from On-Nut to Bearing Station (Green line rapid transit) between 2008 and 2013, the age of condominium has the negative effect to sale price, rental price and also land price while distance to the closest station has positive effect to both sale market and rental market. As the same result as distance to the closest community, it has a positive effect to sale market, rental market and also the land price. Furthermore, total floor has a positive relation to both markets, while brand perception has a positive affect only sale market. Other finding we found that the price of both markets before and after the year BTS on service 2011 did not significant difference due to the reason that people know the news before 2008. Thus, could make the price upsurge before 2008.
Limitations
The limitation is the length of the year is too limited because Thailand has not provided a statistic data before the period of the 2008. Thus, it is very difficult to find the sources and make it continuous period for running regression. The result of limited data set might reduce a clearly effect and relationship between dependent variables and independent variables Another limitation is a biased answer from online-questionnaires distributed. The evidence can be seen in the questionnaire result that there are many people answer “they do not have a condominium” even though we expected to distribute to all sample who has condominium in that area.
Future work Availability of Data must be reconsidered in order to extent the scope of the study. We might collect the data from other line of rapid transit in order to compare the result between each line. We can compare to other modes of transportation such as the road or the rail both in Thailand and other countries. More variable should be added in order to see more effectiveness result such as the facility nearby the condominium or more economic indicator in real estate to collect precise information
Expand more target group to cover the entire population and use more up-to-date information as possible in order to show the change in perspective of individuals though the price of condominium over time.
List of Reference * Bryman,A.,& Bell,E. (2207). Business Research Method (2ed.). Oxford: Oxford University * CB Richard Ellis. (2011). Bangkok overall market view, Q4 2010. Bangkok: CB Richard Ellis. * CB Richard Ellis. (2011). Market View: Bangkok residential sales. Bangkok: CB Richard Ellis. * Colliers International Thailand. (2011). Bangkok condominium market report, Q1 2011 Bangkok: Colliers International Thailand. * Colliers International Thailand. (2011). Bangkok condominium market report, Q4 2010 Bangkok: Colliers International Thailand. * Forrest, D., Glen, J., & Smith, M. T.(1993). The impact of a light rail system on the structure of house prices. Journal of transport Economics and Policy, 30 (1), 15-29
Appendix
| Sale Price | Rental Price | Land Price | Age of Condominium | -0.218 | -0.259 | -1.484 | | (2.41)* | (2.88)** | (4.24)** | Distance to the closest station | -0.083 | -0.192 | -0.096 | | (2.38)* | (5.11)** | -1.65 | Distance to the closest community | -0.232 | -0.204 | -0.267 | | (5.43)** | (4.55)** | (3.78)** | Population Density | -0.018 | 0.14 | 0.364 | | -0.07 | -0.52 | -0.86 | Total Floor levels | 0.138 | 0.147 | 0.052 | | (2.37)* | (2.45)* | -0.54 | Brand perception | 0.199 | 0.131 | 0.146 | | (2.19)* | -1.35 | -0.96 | Customer perception (Loyalty) | -0.182 | -0.085 | 0.054 | | -1.77 | -0.77 | -0.32 | Land negotiation power of entrepreneur | -0.165 | -0.056 | -0.068 | | -1.63 | -0.51 | -0.41 | Built in 2008 (comparing the price to 2011) | -0.443 | -0.029 | | | -1.4 | -0.27 | | Built in 2009 (comparing the price to 2011) | -0.328 | -0.098 | | | -1.03 | -0.86 | | Built in 2010 (comparing the price to 2011) | -0.266 | 0 | | | -0.83 | (.) | | Built in 2012 (comparing the price to 2011) | -0.05 | -0.042 | | | -0.6 | -0.51 | | Built in 2013 (comparing the price to 2011) | 0 | 0 | | | (.) | (.) | | BTS distance and year of BTS built | -0.057 | 0.005 | -0.184 | | -1.2 | -0.1 | (2.34)* | BTS built | | -0.183 | 2.39 | | | -0.54 | (4.13)** | CPI | | | -27.057 | | | | (3.97)** | Constant | 13.449 | 10.121 | 136.541 | | (5.41)** | (3.99)** | (4.39)** | Observations | 120 | 131 | 120 | R-squared | 0.52 | 0.55 | 0.53 | Absolute value of t statistics in parentheses | | | | * significant at 5%; ** significant at 1% | | | |
Table 1: the regression result of comparison between sale market and rental market from
On-Nut station to Bearing station
Questionnaire with 120 samples
Result from the questionnaire
Chart1
Do you or your parent have a condominium?
Chart2
Do you think accessibility to BTS is a key factor before choosing condominium?
๕ 5 is strongly agree – 1 is strongly disagree
Chart3
What is the factor that you think it is the strongest effect to the renting price of condominium?
Chart4
What is the factor that you think it is the weakest effect to the renting price of condominium?
Chart5
Does your working place, school or residence close to BTS Station? (not further than 2 Km)
Chart6
The reason of purchasing condominium is
What is the type of your condominium?
Terms Explanation: Super Luxury (THB 180,000 per square meter)
Luxury (THB 130,000-179,999 per square meter)
High End (THB100,000-129,999 per square meter)
Upper-Mid-range (THB 80,000-99,999 per square meter)
Mid-range (THB60,000-79,000 per square meter)
Entry level (Below THB 60,000 per square meter)
Chart7
Sample Ages
Chart8
Gender
Chart9
Nationality
Chart10
Occupation
Chart11
Income
Terms Explanation: Less than 10,000THB
10,000-20,000THB
20,001-50,000THB
More than 50,000THB
.do file cd "C:\Documents and Settings\xp\Desktop" use condo.dta, clear gen age=2014-built
gen ln_age=log(age) gen ln_price_unit=log(price_unit) gen ln_price_land=log(price_land) gen ln_rental_unit=log(rental_unit) gen ln_cpi=log(cpi) gen ln_pop_density=log(pop_density) gen ln_distance_station=log(distance_station) gen ln_distance_community=log(distance_community) gen ln_floor=log(floor) gen bts_built=0 replace bts_built=1 if built>=2011 & built!=. gen bts_distance=ln_distance_station*bts_built tab built, gen(year)
