1# RECORD 7a87d94cbb2a1bb3af93865c70eaef38f5783daa2# TITLE: Hedonic Modeling for a Growing Housing Market: Valuation of Apartments in Complexes3# YEAR: 2014.0 VENUE: nan DOI: 10.5539/ijef.v6n3p1884# AUTHORS: Ebubekir Ayan; H. C. Erkin5# PAGES: 12 CANDIDATE TABLE PAGES (1-based): [5, 8]6# ABSTRACT: The share of apartment complexes in housing stock of major cities in the developing world has been increasing. They represent a different housing submarket whose growing importance necessitates further research. Using survey data on transaction records of real estate agents, we employ a hedonic pricing model to explore the factors influencing apartment prices in the Metropolitan Izmit area. The site of a major reconstruction effort following a strong earthquake, Izmit has been in the forefront of the changes taking place in the Turkish real estate sector in particular, and in urban housing in general. We aim to get better estimates by focusing the study on apartment complexes in Metropolitan Izmit, the major submarket in the area. The results reveal that structural characteristics of an apartment as well as amenities provided in an apartment complex are important determinants of price. Air quality and proximity to a good public school have a major effect on price as well. The findings suggest that the submarket could be segmented further geographically.789===== PAGE 1 (score 14, numeric-paren 0, table-words 0, var-words 14) =====1011International Journal of Economics and Finance; Vol. 6, No. 3; 2014 12ISSN 1916-971X E-ISSN 1916-9728 13Published by Canadian Center of Science and Education 14188 15 16Hedonic Modeling for a Growing Housing Market: Valuation of 17Apartments in Complexes 18Ebubekir Ayan1 & H. Cenk Erkin2 191 Marmara University, Turkey 202 Kocaeli University, Turkey 21Correspondence: Ebubekir Ayan, Marmara University, Nisantasi Campus, 34365 Istanbul, Turkey. Tel: 22212-233-0447. E-mail: bekir.ayan@marmara.edu.tr 23 24Received: December 7, 2013 Accepted: December 20, 2013 Online Published: February 25, 2014 25doi:10.5539/ijef.v6n3p188 URL: http://dx.doi.org/10.5539/ijef.v6n3p188 26 27Abstract 28The share of apartment complexes in housing stock of major cities in the developing world has been increasing. 29They represent a different housing submarket whose growing importance necessitates further research. Using 30survey data on transaction records of real estate agents, we employ a hedonic pricing model to explore the factors 31influencing apartment prices in the Metropolitan Izmit area. The site of a major reconstruction effort following a 32strong earthquake, Izmit has been in the forefront of the changes taking place in the Turkish real estate sector in 33particular, and in urban housing in general. We aim to get better estimates by focusing the study on apartment 34complexes in Metropolitan Izmit, the major submarket in the area. The results reveal that structural characteristics 35of an apartment as well as amenities provided in an apartment complex are important determinants of price. Air 36quality and proximity to a good public school have a major effect on price as well. The findings suggest that the 37submarket could be segmented further geographically. 38Keywords: real estate, asset pricing, housing prices, hedonic model, Turkey 391. Introduction 40Rapid urbanization has been a major factor for housing demand in developing countries since the middle of the 41last century. Urban population in those countries has increased by more than six fold during this period (Pugh, 422001). A major concern of policymakers has been the shortage of healthy and affordable living space in growing 43cities. City dwellers in better-off developing countries have been luckier in this respect; rising personal incomes 44and relatively well-functioning real estate and credit markets have allowed them to live in modern units with 45higher standards. These units are usually apartments, and increasingly, they are being located in a complex of 46multistoried buildings (Wester et al., 2002; Rabenhorst & Ignatova, 2009; Rosen & Walks, 2013). 47Apartment complexes offer several advantages over single-buildings for both developers and residents. Firstly, 48developers who build many units enjoy economies of scale because of lower input costs, better utilization of 49resources and employment of capital intensive methods. Their efficiency in construction, and thus low unit cost, 50allows them to set apartment prices lower compared to their smaller rivals. Secondly, offering buyers a variety of 51apartment types, layouts and quality becomes less costly as the size of the project increases. Thirdly, limited 52available land in an urban area makes provision of recreational facilities expensive for developers. Due to much 53lower cost per unit, large apartment complexes can have swimming pools, club houses and larger green space 54compared to single-buildings. Lastly, providers of public transportation and infrastructure services are more 55sensitive to the needs of a large population living in a complex probably because of both the larger demand and 56more political power (Ho, 2003). 57Living in apartment complexes has been gaining popularity in major Turkish cities. This trend has gained 58momentum as modernization of the urban housing stock has become a policy priority after the 7.4 magnitude 59earthquake in 1999. The disaster, in which more than 30 thousand buildings became unusable, revealed 60weaknesses of the aging housing stock. Government responded with the initiation of large housing projects. 61These early public initiatives, which were mainly oriented towards the victims of the earthquake, gained a higher 62momentum by the establishment of the Housing Development Administration of Turkey (TOKI) in 2002. TOKI, 63with its mission of providing modern and affordable dwellings has become a big player in the supply side of the 646566===== PAGE 5 (score 39, numeric-paren 15, table-words 1, var-words 22) =====6768www.ccsenet.org/ijef 69International Journal of Economics and Finance 70Vol. 6, No. 3; 2014 71192 72Existence of additional bathrooms is consistently found to increase the value of a unit. In their survey article, 73Sirmans et al. (2005) report that in 40 studies where a variable for multiple bathrooms appeared, it had the 74expected sign in 39 of them, and was significant in 34. The binary variable BATHROOM takes value of 1 if an 75apartment has more than one bathroom. In the dataset, 56 apartments have two bathrooms and only one has 76three. 77 78Table 1. Descriptive statistics of data set (N = 405) 79Variable 80Description 81Mean (Std. Dev.) 82PRICE 83The sale price of the apartment (TL) 84107216 (44847) 85AREA 86The floor area of the apartment (m2) 87111.22 (27.55) 88AREA2 89The square of the floor area of the apartment (m2) 9013128 (7420) 91CNSTR_LQ 92Binary: the construction quality is below average 930.16 (0.37) 94AGE 95The age of the apartment building (years) 967.14 (5.21) 97BATHROOM98Binary: there is more than 1 bathroom 990.14 (0.35) 100BLW_GRND101Binary: the apartment is located below the ground floor 1020.02 (0.13) 103GROUND 104Binary: the apartment is located on the ground floor 1050.25 (0.43) 106NRTH_WST 107Binary: the apartment faces north and/or west 1080.49 (0.50) 109POOL 110Binary: there is a swimming pool in the apartment complex 1110.06 (0.24) 112SECURITY 113Binary: security guards are present in the apartment complex 1140.15 (0.36) 115DISTANCE 116Binary: the distance from the city center (kilometers) 1175.77 (3.56) 118YHYKPTN 119Binary: the apartment is in Yahyakaptan apartment complex 1200.11 (0.31) 121SCHOOL 122Binary: there is a high quality public school within 1 kilometer1230.25 (0.43) 124AIR 125Binary: air is clean in the neighborhood 1260.32 (0.47) 127KARTEPE 128Binary: the apartment building is located in Kartepe 1290.18 (0.38) 130BASISKELE131Binary: the apartment building is located in Basiskele 1320.07 (0.25) 133 134There is considerable variation in construction quality, especially for apartment interiors, between the complexes 135in our data set. Even though the buildings are considered to be safe in the sense that they can withstand to a high 136magnitude earthquake similar to the one in 1999, the quality of materials used, such as roofing materials, heat 137and sound insulation, paint, floor coverings, kitchen countertops, wall tiles and toilet in bathroom, as well as the 138workmanship might differ significantly. Most of the apartments with poor quality interiors are located in 139complexes built by private contractors commissioned by the state enterprise TOKI. We designated a binary 140variable (CNSTR_LQ) that takes value of 0 if real estate agents consider interior construction quality of an 141apartment to be low. 142Metropolitan Izmit is located on a plain and the hills around it. Some apartments in a building located on a hill 143could be below the ground level. Such apartments are sold at a major discount compared to those above the 144ground level in the same building because they have low sunlight and no scenery. Ground level apartments also 145carry a price penalty, although not as much as those below them, because of limited view and noise created by 146people coming in and going out of the building. Binary variables BLW_GRND and GROUND are included to 147measure these price impacts. 148An apartment facing only north has lower sunlight and requires more energy to heat during colder months. 149North-side apartments could be less preferred because of health and heating cost concerns. Since apartment 150buildings in complexes are not attached like those in downtown, there is no apartment in the data set that faces 151only north. Therefore, we have created a binary variable (NRTH_WST) that takes a value of 1 if the apartment 152faces north and west. 153Demand for private residential security services in Metropolitan Izmit has been rising with the increasing 154number of gated communities. Although crime rate for the area is not above those of similar-sized cities, 155residential security services, probably signaling prestige and social rank, are seen as a selling point by developers 156157158===== PAGE 6 (score 32, numeric-paren 0, table-words 1, var-words 30) =====159160www.ccsenet.org/ijef 161International Journal of Economics and Finance 162Vol. 6, No. 3; 2014 163193 164and used in their marketing. Another amenity that is featured prominently in developer ads is swimming pools. 165Previous studies found that existence of a swimming pool increases the value of apartments in a complex 166(Sirmans et al., 2005). Therefore, the binary variables SECURITY and POOL are also included in the analysis. 167We take as the central business district the area around Anıtpark in downtown Izmit. It is the most visited site in 168the city due to government agencies, banks, a large number of stores, cafes and restaurants as well as the largest 169weekly one-day market located there. The variable DISTANCE measures how far in kilometers a particular 170apartment complex is from the central business district. 171A good education is seen as the key to a good job, and thus access to quality schools is very competitive in 172Turkey. Students who have completed 8th grade are first allocated to high-schools and then to universities 173according to their scores on nationwide centralized exams. A student's success in the university entrance exam is 174thought to be largely dependent on the high school she graduated from, and, in turn, her allocation to a high 175school to be dependent on the quality of her primary education. Access to public primary school is decided 176according to the location of the family. Parents either have to send their children to a public school in their 177school district or they have to choose a private school. The second option is only available to a minority since 178tuition and other expenses of private primary education could easily add up to half of the mean annual family 179income, about $12000 in 2012. On the other hand, there aren’t many high quality public primary schools given 180their performance at the high school entrance exam, SBS. Of 85 public primary schools in Izmit, only 6 was in 181the top 10% and 24 in the top 100 among 350 primary schools in Kocaeli according to a province-wide SBS trial 182test in 2012. Average test scores of high performing schools were nearly 30% higher than other schools’. 183Families with school aged children are known to move to neighborhoods with quality public schools so that their 184children can be enrolled to these schools. This could drive up the rents and apartment prices around quality 185public primary schools. We have chosen a public primary school’s performance in 2012 SBS trial test as a proxy 186for its educational quality (whether it was among the top 100 schools in Kocaeli). Since we don’t have data 187about school district boundaries, we created a binary variable (SCHOOL) that takes 1 if an apartment is within 1 188km distance from a good public primary school. This definition of a school district is conservative; the implied 189school districts will probably be smaller than real ones. 190The effect of air quality on price is consistently found to be negative, though some studies reported the effect to 191be small (Smith et al., 1995). Later studies with more robust econometric methods found that air pollution had a 192large negative effect on residential unit prices (Zabel & Kiel, 2000; Chay & Greenstone, 2005). As an industrial 193center, Metropolitan Izmit is home to many factories producing a large variety of commodities ranging from 194chemicals, steel, machines, cars to construction materials. As it is also a major transportation hub, one of the 195most heavily used routes in Turkey passes through the city. Therefore, people living in the area have increasingly 196been concerned about the levels and possible effects of air pollution. Lacking an objective factor to measure air 197quality, we use a binary variable, AIR, to reflect real estate agents’ perceptions of air cleanness in a 198neighborhood. Chasco and Gallo (2013) show subjective evaluation measures of air quality can effectively 199explain part of the variation in housing prices. 200Apartments in Yahyakaptan complex, the largest and oldest in Metropolitan Izmit, are priced higher than 201apartments of similar quality in other complexes. The community has a higher proportion of professionals and 202well educated, upper middle class, white color workers (public servants, university employees, engineers, 203doctors and teachers). It has a theater, a shopping center and various cafes and restaurants, and is richer in green 204space and recreational opportunities compared to other large complexes. Although all apartment complexes have 205access to public transportation, Yahyakaptan is better connected to every major community in the area. Omitting 206a variable for the complex could create an upward bias for older complexes, thus we include the binary variable 207YHYKPTN in the estimation. 208Kartepe and Basiskele differ from Izmit in several aspects. Both municipalities have lower population density 209and limited service sector activity compared to Izmit. Until recently, agriculture has been the main economic 210activity but is being replaced with service jobs in Izmit and industrial jobs in the two municipalities. As a result, 211there is abundance of land available for development, especially in Kartepe, which is reflected in lower land 212values. In order to account for the probability that Metropolitan Izmit area has geographical submarkets as well, 213we include the binary variables KARTEPE and BASISKELE in the analysis. We prefer this method instead of 214dividing data into subsamples and performing F tests for coefficient equality because of the small size of our 215dataset. The variables could also proxy for some of location specific missing variables (those highly correlated 216with the municipality variables) in the model. 217We expect the coefficients for AREA, BATHROOM, POOL, SECURITY, YHYKPTN, SCHOOL, AIR to be 218219220===== PAGE 8 (score 119, numeric-paren 101, table-words 5, var-words 8) =====221222www.ccsenet.org/ijef 223International Journal of Economics and Finance 224Vol. 6, No. 3; 2014 225195 226All variables in models (1)–(4) have the expected sign and most are highly significant. Successive improvement 227from the base to the full model is observed with t values remaining significant, R2 increasing and both Akaike 228(AIC) and Bayesian (BIC) information criterion values consistently dropping (Note 3). 229The floor area is the variable with the most explanatory power; regressing the log of price on the floor area and 230its square alone explains 61% of the variation around the mean. Both AREA and AREA2 are consistently 231significant through the models with little change in their coefficients. Cost of construction is highly correlated 232with floor area; the availability of land for development especially in Kartepe and Basiskele could be a factor in 233keeping apartment prices close to construction costs. The relationship between the log price and the floor area is 234nonlinear as might be expected: % increase in price drops as area increases. In the full model, an additional m2 235increases price by 0.84% at 80 m2 but only by 0.26% at 200 m2. When calculated at respective mean prices, the 236elasticity of price with respect to floor area is relatively constant around 0.7 up to 150 m2 and then drops to 0.5 237around 200 m2. 238Apartments with poor build quality are penalized on average by a 10% drop in price. A similar finding, albeit 239conceptualized in the opposite way, is reported for Izmir: an above-average build quality increases the price by 24015.8% (Yankaya & Celik, 2005). An apartment on the ground floor is discounted by 8.4% compared to 241apartments on upper floors in the same building. The discount reaches up to 23% for apartments below the 242ground level. Apartments facing northwest might be slightly less preferred, even though the effect is marginally 243significant only in the full model. 244 245Table 2. Hedonic price estimates. dependent variable: log (PRICE) 246 247(1) 248(2) 249(3) 250(4) 251CONSTANT 25210.563*** (0.079) 25310.599*** (0.081) 25410.692*** (0.074) 25510.610*** (0.072) 256AREA 2570.012*** (0.001) 2580.012*** (0.001) 2590.012*** (0.001) 2600.012*** (0.001) 261AREA2 262-2.39E-05*** (0.47E-05) 263-2.45E-05*** (0.49E-05) 264-2.10E-05*** (0.38E-05)265-2.42E-05*** (0.37E-05)266CNSTR_LQ 267-0.140*** (0.021) 268-0.104*** (0.023) 269-0.104*** (0.025) 270-0.102*** (0.021) 271AGE 272-0.001 (0.001) 273-0.009*** (0.002) 274-0.018*** (0.002) 275-0.018*** (0.002) 276BATHROOM 2770.135*** (0.030) 2780.144*** (0.030) 2790.123*** (0.033) 2800.151*** (0.030) 281BLW_GRND 282-0.205*** (0.050) 283-0.191*** (0.045) 284-0.237*** (0.057) 285-0.266*** (0.051) 286GROUND 287-0.075*** (0.016) 288-0.078*** (0.016) 289-0.086*** (0.015) 290-0.088*** (0.014) 291NRTH_WST 292-0.011 (0.014) 293-0.013 (0.014) 294-0.014 (0.012) 295-0.019* (0.011) 296POOL 2970.233*** (0.045) 2980.234*** (0.046) 2990.217*** (0.039) 3000.190*** (0.041) 301SECURITY 3020.196*** (0.023) 3030.174*** (0.024) 3040.134*** (0.024) 3050.148*** (0.024) 306DISTANCE 307-0.018*** (0.003) 308-0.018*** (0.003) 309-0.030*** (0.003) 310-0.010** (0.005) 311YHYKPTN 312 3130.158*** (0.029) 3140.100*** (0.025) 3150.134*** (0.024) 316SCHOOL 317 318 3190.200*** (0.029) 3200.151*** (0.028) 321AIR 322 323 3240.141*** (0.020) 3250.091*** (0.022) 326KARTEPE 327 328 329 330-0.185*** (0.032) 331BASISKELE 332 333 334 335-0.041 (0.072) 336R2 3370.851 3380.861 3390.894 3400.908 341N 342405 343405 344405 345405 346Note: White-Eicker heteroscedasticity robust standard error estimates in parentheses. Significant at 10% (*), at 5% (**), at 1% (***). 347 348Apartments having a second bathroom sell at a large premium, 16.3%; the effect is close to Tekel and 349Akbarishahabi (2013)’s estimate for apartments near Botanik Park in Ankara, 17.9%. Similarly, security on site 350and having access to a swimming pool increases the price on average by 16% and 20.9%, respectively. Selim’s 351(2008) estimate for swimming pool is more than 3 times larger, 54%, probably due to the heterogeneity in her 352nationwide sample where shanty houses are grouped with luxury apartments. Caglayan and Arikan (2011) find 353354355===== PAGE 9 (score 18, numeric-paren 0, table-words 3, var-words 12) =====356357www.ccsenet.org/ijef 358International Journal of Economics and Finance 359Vol. 6, No. 3; 2014 360196 361for Istanbul that security adds on price a premium of 16–22% but this might be an overestimate since pool is not 362included in their estimation. Even though the estimates reported here are lower, there could still be 363overestimation to some extent. These features are most common to more luxurious apartment complexes with 364high build quality and upper social class environment, both of which are not captured in our data. 365Distance to the central business district has a significant negative effect on price. The effect decreases 366significantly with the inclusion of municipality variables because apartments in Kartepe and Basiskele are on 367average 11.0 and 10.9 kms away from the center while those in Izmit are only 4.1 kms away. Nevertheless, the 368penalty for being away from the central business district in the full model is not marginal; a 5-km distance 369decreases the price by 5%. 370Proximity to a good public primary school and thus the option to enroll one’s child in there has a large impact on 371price, 16.3%. Our definition of proximity to being within 1 km distance from the school might slightly 372overestimate the average effect for the whole school district. Apartments farther away from the school but still in 373the same district could be priced less because parents living in those apartments would incur additional 374transportation costs in sending their children to the school. Haurin and Brasington (1996) and Downes and Zabel 375(2002) find the elasticity of house price with respect to school test score between 0.5 and 1 for US: a 10% increase 376in the average test score of a school raises house prices in the neighborhood by 5 to 10%. Zheng and Kahn (2008) 377report for Beijing that cutting distance to a core high school by half increases home prices by 7%. 378 379Table 3. Transformed regression coefficients (percentage change in price) 380AREA (at 100 m2) 3810.007382CNSTR_LQ 383-0,097384AGE 385-0,018386BATHROOM 3870,163388BLW_GRND 389-0,234390GROUND 391-0,084392NRTH_WST 393-0,019394POOL 3950,209396SECURITY 3970,160398DISTANCE 399-0,010400YHYKPTN 4010,143402SCHOOL 4030,163404AIR 4050,095406KARTEPE 407-0,169408BASISKELE 409-0,041410 411Air quality is another neighborhood factor capitalized in apartment prices; apartments in areas perceived to have 412clean air sell at a premium of 9.5%. The stronger effect in (3) is reduced with the inclusion of Kartepe and 413especially Basiskele, the municipalities where air quality is generally superior. 414AGE has the expected negative sign but is statistically significant only after the effect of Yahyakaptan complex 415is controlled in models (2)–(4). Yahyakaptan buildings are the oldest in the data, but the apartments there sell at a 416large premium, around 14%, thanks to a unique set of favorable factors explained above. The increase in the 417adverse impact of age on price from 0.9 % in model 2 to 1.8 % in models 3 and 4 could be largely due to 418addition AIR to the specification because older projects, due to their location on the hills around the downtown, 419tend to have cleaner air. 420The results point to the existence of possible geographical submarkets in Metropolitan Izmit area. Kartepe has a 421large negative coefficient which is highly significant. Apartments in Kartepe are valued on average 16.9% less 422than those in Izmit. The coefficient for Basiskele is also negative but the hypothesis that it is not different from 0 423cannot be rejected even at 10% level. A similar apartment can be built and sold for less in Kartepe compared to 424one in Izmit because the availability of large tracts of undeveloped agricultural land in Kartepe results in lower 425land prices and lower construction costs, which must be reflected in apartment prices there. Another factor 426pulling down the prices in the suburbs could be the capitalization of transportation costs: many people living in 427Kartepe and Basiskele have to commute daily to their jobs in Izmit. 428To our knowledge, this set of results is the first to be reported for Izmit. Our modeling strategy, focusing on the 429